From e5bd4b5285dee9e6c877edb01eab2dee5c2c9a45 Mon Sep 17 00:00:00 2001 From: Lauren M Ostrowski Date: Sat, 17 Aug 2024 12:22:23 -0700 Subject: [PATCH] Updates to be compatible with spikeinterface==0.101.0 --- .../0-reference_files-checkpoint.ipynb | 15 +- ...ess_acoustics-zebra_finch-checkpoint.ipynb | 101 +- ...s-zebra_finch-one_session-checkpoint.ipynb | 377 ++- ...ch-one_session-off-and-on-checkpoint.ipynb | 859 +++++ .../2-curate_acoustics-checkpoint.ipynb | 2831 ++++++++++------- .../3-sort_spikes-checkpoint.ipynb | 423 --- .../3-sort_spikes_v0.100.8-checkpoint.ipynb | 583 ++++ .../3-sort_spikes_v0.100.8-checkpoint.py | 236 ++ .../3-sort_spikes_v0.101-checkpoint.ipynb | 534 ++++ .../3-sort_spikes_v0.101-checkpoint.py | 218 ++ ...3.1-sort_spikes_concatenate-checkpoint.py} | 7 +- .../4-curate_spikes-checkpoint.ipynb | 821 +++-- .../4-new_curate_spikes-checkpoint.ipynb | 707 ++++ 0-reference_files.ipynb | 15 +- 1-preprocess_acoustics-zebra_finch.ipynb | 152 +- ...ss_acoustics-zebra_finch-one_session.ipynb | 377 ++- ...s-zebra_finch-one_session-off-and-on.ipynb | 859 +++++ 2-curate_acoustics.ipynb | 2827 +++++++++------- 2.log | 20 + ...ikes.ipynb => 3-sort_spikes_v0.100.8.ipynb | 5 +- 3-sort_spikes_v0.100.8.py | 236 ++ 3-sort_spikes_v0.101.ipynb | 534 ++++ 3-sort_spikes_v0.101.py | 218 ++ 3.1-sort_spikes_concatenate.ipynb | 3 +- ...pikes.py => 3.1-sort_spikes_concatenate.py | 7 +- 4-curate_spikes.ipynb | 867 +++-- 4-new_curate_spikes.ipynb | 707 ++++ archive/4-curate_spikes.ipynb | 11 + spec-file.txt | 4 + 29 files changed, 11150 insertions(+), 3404 deletions(-) create mode 100644 .ipynb_checkpoints/1.2-preprocess_acoustics-zebra_finch-one_session-off-and-on-checkpoint.ipynb delete mode 100755 .ipynb_checkpoints/3-sort_spikes-checkpoint.ipynb create mode 100755 .ipynb_checkpoints/3-sort_spikes_v0.100.8-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/3-sort_spikes_v0.100.8-checkpoint.py create mode 100644 .ipynb_checkpoints/3-sort_spikes_v0.101-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/3-sort_spikes_v0.101-checkpoint.py rename .ipynb_checkpoints/{3-sort_spikes-checkpoint.py => 3.1-sort_spikes_concatenate-checkpoint.py} (98%) create mode 100644 .ipynb_checkpoints/4-new_curate_spikes-checkpoint.ipynb create mode 100644 1.2-preprocess_acoustics-zebra_finch-one_session-off-and-on.ipynb create mode 100644 2.log rename 3-sort_spikes.ipynb => 3-sort_spikes_v0.100.8.ipynb (99%) create mode 100644 3-sort_spikes_v0.100.8.py create mode 100644 3-sort_spikes_v0.101.ipynb create mode 100644 3-sort_spikes_v0.101.py rename 3-sort_spikes.py => 3.1-sort_spikes_concatenate.py (98%) create mode 100644 4-new_curate_spikes.ipynb create mode 100644 spec-file.txt diff --git a/.ipynb_checkpoints/0-reference_files-checkpoint.ipynb b/.ipynb_checkpoints/0-reference_files-checkpoint.ipynb index 69cd7ae..0e0437b 100755 --- a/.ipynb_checkpoints/0-reference_files-checkpoint.ipynb +++ b/.ipynb_checkpoints/0-reference_files-checkpoint.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "id": "ecd1bc70-88ed-447f-a19f-7f4f3d9530d6", "metadata": {}, "outputs": [], @@ -25,8 +25,8 @@ "from ceciestunepipe.file import bcistructure as et\n", "\n", "sess_par = {\n", - " 'bird': 'z_p5y10_23',\n", - " 'sess': '2024-05-17'\n", + " 'bird': 'z_r5r13_24',\n", + " 'sess': '2024-08-08'\n", "}\n", "\n", "rig_dict_path = et.get_exp_struct(sess_par['bird'],sess_par['sess'])['files']['rig']" @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "id": "c130bc8c-eb91-4e28-99b5-29ac6269e435", "metadata": {}, "outputs": [], @@ -50,7 +50,8 @@ " 'microphone_M': 'adc-00',\n", " 'microphone_F': 'adc-05',\n", " 'wav_stim': 'adc-01',\n", - " 'wav_syn': 'adc-02'},\n", + " 'wav_syn': 'adc-02'\n", + " },\n", " 'port': {\n", " 'probe_0': 'A-'\n", " }\n", @@ -58,7 +59,7 @@ " 'probe': {\n", " 'probe_0': { # will always be probe_0 unless multiple recordings from different probes on the same day\n", " 'model': 'NP2013',\n", - " 'serial': '22420012794', # update every time\n", + " 'serial': '22420013432', # update every time\n", " 'headstage': '23280347'\n", " }\n", " }\n", @@ -67,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "id": "8a27aec7-32d6-4523-912f-c0eaaebed071", "metadata": {}, "outputs": [], diff --git a/.ipynb_checkpoints/1-preprocess_acoustics-zebra_finch-checkpoint.ipynb b/.ipynb_checkpoints/1-preprocess_acoustics-zebra_finch-checkpoint.ipynb index 77c9f57..53c6cd2 100755 --- a/.ipynb_checkpoints/1-preprocess_acoustics-zebra_finch-checkpoint.ipynb +++ b/.ipynb_checkpoints/1-preprocess_acoustics-zebra_finch-checkpoint.ipynb @@ -26,7 +26,7 @@ "text": [ "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/spikeextractors/__init__.py:21: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n", " if StrictVersion(h5py.__version__) > '2.10.0':\n", - "2024-05-14 12:05:55,248 root INFO Running on pakhi.ucsd.edu\n" + "2024-08-14 19:52:19,864 root INFO Running on pakhi.ucsd.edu\n" ] }, { @@ -210,7 +210,7 @@ "# 'on_signal':1, # sglx, whether signal on is hi or lo\n", "# 'sort':'sort_0', # sort info\n", "# 'ephys_software':'sglx' # sglx or oe\n", - "# }]\n", + "# }],\n", "# 'z_g9y18_23':[\n", "# {'sess_par_list':['2024-04-17','2024-04-18','2024-04-19'], # sessions with this configuration\n", "# 'stim_sess_list':[], # sessions where stimuli were presented\n", @@ -224,8 +224,21 @@ "# 'sort':'sort_0', # sort info\n", "# 'ephys_software':'sglx' # sglx or oe\n", "# }],\n", - " 'test':[\n", - " {'sess_par_list':['2024-05-14'], # sessions with this configuration\n", + " # 'z_p5y10_23':[\n", + " # {'sess_par_list':['2024-05-16','2024-05-17'], # sessions with this configuration\n", + " # 'stim_sess_list':[], # sessions where stimuli were presented\n", + " # 'mic_list':['microphone_M','microphone_F'], # list of mics of interest, by signal name in rig.json\n", + " # 'adc_list':[], # list of adc channels of interest\n", + " # 'stim_list':['wav_stim'], # list of adc chans with the stimulus\n", + " # 'nidq_ttl_list':[], # list of TTL signals form the nidq digital inputs to extract (besides the 'sync')\n", + " # 'ref_stream':'ap_0', # what to synchronize everything to (sglx only, oe already synced)\n", + " # 'trial_tag_chan':2, # sglx, what was the tag channel in the stimulus wave (this should come from meta et. al)\n", + " # 'on_signal':1, # sglx, whether signal on is hi or lo\n", + " # 'sort':'sort_0', # sort info\n", + " # 'ephys_software':'sglx' # sglx or oe\n", + " # }]\n", + " 'z_r5r13_24':[\n", + " {'sess_par_list':['2024-08-05','2024-08-05_reduced_chans','2024-08-06','2024-08-07','2024-08-08'], # sessions with this configuration\n", " 'stim_sess_list':[], # sessions where stimuli were presented\n", " 'mic_list':['microphone_M','microphone_F'], # list of mics of interest, by signal name in rig.json\n", " 'adc_list':[], # list of adc channels of interest\n", @@ -252,72 +265,35 @@ "execution_count": 4, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-05-14 12:05:55,305 ceciestunepipe.util.sglxutil WARNING no file found for: ['lf_0']\n", - "2024-05-14 12:05:55,305 ceciestunepipe.util.sglxutil WARNING no file found for: ['lf_0']\n", - "2024-05-14 12:05:55,305 ceciestunepipe.util.sglxutil WARNING no file found for: ['lf_0']\n", - "/mnt/cube/lo/envs/ceciestunepipe/ceciestunepipe/util/spikeextractors/extractors/spikeglxrecordingextractor/readSGLX.py:226: RuntimeWarning: divide by zero encountered in double_scalars\n", - " conv[i] = fI2V / APgain[k]\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "test 2024-05-14 no log file found -- running preprocessing\n", - "test 2024-05-14 sglx preprocessing session..\n", + "z_r5r13_24 2024-08-05 failed previously -- skipping preprocessing\n", + "z_r5r13_24 2024-08-05_reduced_chans failed previously -- skipping preprocessing\n", + "z_r5r13_24 2024-08-06 failed previously -- skipping preprocessing\n", + "z_r5r13_24 2024-08-07 no log file found -- running preprocessing\n", + "z_r5r13_24 2024-08-07 sglx preprocessing session..\n", "derived data folder exists..\n", - "preprocessing..\n", - "test 2024-05-14 deriving bout information..\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "08ad7865dca644eea70d5d41a8c4e151", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/1 [00:00:62\u001b[0m\n", + "File \u001b[0;32m/mnt/cube/lo/envs/ceciestunepipe/ceciestunepipe/mods/preproc_sglx.py:42\u001b[0m, in \u001b[0;36mpreprocess_session\u001b[0;34m(sess_par, force_redo, skip_wav)\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 41\u001b[0m exp_struct \u001b[38;5;241m=\u001b[39m et\u001b[38;5;241m.\u001b[39msgl_struct(sess_par,epoch)\n\u001b[0;32m---> 42\u001b[0m one_epoch_dict \u001b[38;5;241m=\u001b[39m \u001b[43mpre\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpreprocess_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43msess_par\u001b[49m\u001b[43m,\u001b[49m\u001b[43mexp_struct\u001b[49m\u001b[43m,\u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43mskip_wav\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskip_wav\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 43\u001b[0m epoch_dict_list\u001b[38;5;241m.\u001b[39mappend(one_epoch_dict)\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n", + "File \u001b[0;32m/mnt/cube/lo/envs/ceciestunepipe/ceciestunepipe/util/spikeextractors/preprocess.py:213\u001b[0m, in \u001b[0;36mpreprocess_run\u001b[0;34m(sess_par, exp_struct, epoch, do_sync_to_stream, skip_wav)\u001b[0m\n\u001b[1;32m 211\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGetting microphone channel(s) \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(mic_list))\n\u001b[1;32m 212\u001b[0m \u001b[38;5;66;03m# if multiple mics, will save as multiple channels within wav file\u001b[39;00m\n\u001b[0;32m--> 213\u001b[0m mic_stream \u001b[38;5;241m=\u001b[39m \u001b[43mextract_nidq_channels\u001b[49m\u001b[43m(\u001b[49m\u001b[43msess_par\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_recs_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrig_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmic_list\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchan_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43madc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 214\u001b[0m mic_file_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(sgl_exp_struct[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfolders\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mderived\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwav_mic.wav\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 215\u001b[0m wav_s_f \u001b[38;5;241m=\u001b[39m wu\u001b[38;5;241m.\u001b[39msave_wav(mic_stream, nidq_s_f, mic_file_path, skip_wav\u001b[38;5;241m=\u001b[39mskip_wav)\n", + "File \u001b[0;32m/mnt/cube/lo/envs/ceciestunepipe/ceciestunepipe/util/spikeextractors/preprocess.py:48\u001b[0m, in \u001b[0;36mextract_nidq_channels\u001b[0;34m(sess_par, run_recs_dict, rig_dict, chan_name_list, chan_type)\u001b[0m\n\u001b[1;32m 45\u001b[0m chan_n_list \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mint\u001b[39m(ru\u001b[38;5;241m.\u001b[39mlookup_signal(rig_dict, n)[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]) \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m chan_name_list]\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chan_type\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madc\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m---> 48\u001b[0m stream \u001b[38;5;241m=\u001b[39m \u001b[43mrun_recs_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnidq\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_traces\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchannel_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchan_n_list\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m chan_type\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mttl\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 50\u001b[0m stream \u001b[38;5;241m=\u001b[39m run_recs_dict[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnidq\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mget_ttl_traces()[chan_n_list, :]\n", + "File \u001b[0;32m/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/spikeextractors/extraction_tools.py:815\u001b[0m, in \u001b[0;36mcheck_get_traces_args..corrected_args\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 812\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mend_frame\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m end_frame\n\u001b[1;32m 814\u001b[0m \u001b[38;5;66;03m# pass recording as arg and rest as kwargs\u001b[39;00m\n\u001b[0;32m--> 815\u001b[0m get_traces_correct_arg \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 817\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m get_traces_correct_arg\n", + "File \u001b[0;32m/mnt/cube/lo/envs/ceciestunepipe/ceciestunepipe/util/spikeextractors/extractors/spikeglxrecordingextractor/spikeglxrecordingextractor.py:159\u001b[0m, in \u001b[0;36mSpikeGLXRecordingExtractor.get_traces\u001b[0;34m(self, channel_ids, start_frame, end_frame, dtype)\u001b[0m\n\u001b[1;32m 156\u001b[0m recordings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeseries[channel_idxs[\u001b[38;5;241m0\u001b[39m] :channel_idxs[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m+\u001b[39m\u001b[38;5;28mlen\u001b[39m(channel_idxs), start_frame:end_frame]\n\u001b[1;32m 157\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 158\u001b[0m \u001b[38;5;66;03m# This block of the execution will return the data as an array, not a memmap\u001b[39;00m\n\u001b[0;32m--> 159\u001b[0m recordings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_timeseries\u001b[49m\u001b[43m[\u001b[49m\u001b[43mchannel_idxs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 160\u001b[0m \u001b[43m \u001b[49m\u001b[43mstart_frame\u001b[49m\u001b[43m:\u001b[49m\u001b[43mend_frame\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 162\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m dtype \u001b[38;5;129;01min\u001b[39;00m [\n\u001b[1;32m 163\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mint16\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfloat\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m can be either \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mint16\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m or \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfloat\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", + "File \u001b[0;32m/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/numpy/core/memmap.py:334\u001b[0m, in \u001b[0;36mmemmap.__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m 333\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, index):\n\u001b[0;32m--> 334\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getitem__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 335\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(res) \u001b[38;5;129;01mis\u001b[39;00m memmap \u001b[38;5;129;01mand\u001b[39;00m res\u001b[38;5;241m.\u001b[39m_mmap \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 336\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39mndarray)\n", + "File \u001b[0;32m/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/numpy/core/memmap.py:288\u001b[0m, in \u001b[0;36mmemmap.__array_finalize__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 284\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilename \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 286\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[0;32m--> 288\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__array_finalize__\u001b[39m(\u001b[38;5;28mself\u001b[39m, obj):\n\u001b[1;32m 289\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(obj, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_mmap\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m np\u001b[38;5;241m.\u001b[39mmay_share_memory(\u001b[38;5;28mself\u001b[39m, obj):\n\u001b[1;32m 290\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mmap \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_mmap\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], @@ -606,6 +582,7 @@ " error_tracker[this_epoch] = f\"Error: {str(e)}\"\n", " \n", " if not np.any(epoch_fail): # if preprocessing complete without error..\n", + " print(sess_par['bird']+' '+sess_par['sess']+' preprocessing complete without error')\n", " \n", " # concatenate list of synced bout data frames from each epoch and save\n", " bout_syn_pd_all_cat = pd.concat(bout_syn_pd_all)\n", diff --git a/.ipynb_checkpoints/1.1-preprocess_acoustics-zebra_finch-one_session-checkpoint.ipynb b/.ipynb_checkpoints/1.1-preprocess_acoustics-zebra_finch-one_session-checkpoint.ipynb index 98fc6b1..35a448e 100755 --- a/.ipynb_checkpoints/1.1-preprocess_acoustics-zebra_finch-one_session-checkpoint.ipynb +++ b/.ipynb_checkpoints/1.1-preprocess_acoustics-zebra_finch-one_session-checkpoint.ipynb @@ -12,7 +12,7 @@ "\n", "Common errors include:\n", "- data streams cannot be synched (ex. neural and audio data streams are of different lengths)\n", - "- TTL events were skipped (i.e., the machine clock malfunctioned)\n", + "- TTL events were skipped (i.e., the machine clock malfunctioned or SpikeGLX crashed and the data streams terminated at different moments)\n", "\n", "Use the environment **songproc** to run this notebook\n", "\n", @@ -30,7 +30,7 @@ "text": [ "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/spikeextractors/__init__.py:21: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n", " if StrictVersion(h5py.__version__) > '2.10.0':\n", - "2024-04-29 09:39:13,742 root INFO Running on pakhi.ucsd.edu\n" + "2024-08-14 14:51:59,372 root INFO Running on pakhi.ucsd.edu\n" ] }, { @@ -144,8 +144,8 @@ "source": [ "# single session params\n", "sess_par = {\n", - " 'bird':'z_g9y18_23',\n", - " 'sess':'2024-04-19',\n", + " 'bird':'z_r5r13_24',\n", + " 'sess':'2024-08-07',\n", " 'stim_sess':[], # sessions where stimuli were presented\n", " 'mic_list':['microphone_M','microphone_F'], # list of mics of interest, by signal name in rig.json\n", " 'adc_list':[], # list of adc channels of interest\n", @@ -181,36 +181,16 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "derived data folder exists..\n", - "preprocessing..\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in double_scalars\n", - " ret = ret.dtype.type(ret / rcount)\n", - "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in double_scalars\n", - " ret = ret.dtype.type(ret / rcount)\n", - "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in double_scalars\n", - " ret = ret.dtype.type(ret / rcount)\n", - "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in double_scalars\n", - " ret = ret.dtype.type(ret / rcount)\n" + "preprocessing..\n", + "done.\n" ] } ], @@ -219,7 +199,8 @@ "if sess_par['ephys_software'] == 'sglx':\n", " preproc_sglx.preprocess_session(sess_par,force_redo=True)\n", "elif sess_par['ephys_software'] == 'oe':\n", - " preproc_oe.preprocess_session(sess_par,force_redo=True)" + " preproc_oe.preprocess_session(sess_par,force_redo=True)\n", + "print('done.')" ] }, { @@ -230,12 +211,82 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a9ab6f0e54654dce828b8f7d0d2f515a", + "model_id": "6bbefeb74d6a4f868ff3c340c824cc54", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/8 [00:00 27\u001b[0m \u001b[43msy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msync_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43mall_syn_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43msess_par\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mref_stream\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mforce\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;66;03m# load bouts\u001b[39;00m\n\u001b[1;32m 30\u001b[0m hparams, bout_pd \u001b[38;5;241m=\u001b[39m sb\u001b[38;5;241m.\u001b[39mload_bouts(sess_par[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbird\u001b[39m\u001b[38;5;124m'\u001b[39m],sess_par[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msess\u001b[39m\u001b[38;5;124m'\u001b[39m],\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m, derived_folder\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbouts_sglx\u001b[39m\u001b[38;5;124m'\u001b[39m,bout_file_key\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbout_auto_file\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m/mnt/cube/lo/envs/ceciestunepipe/ceciestunepipe/util/sglxsync.py:93\u001b[0m, in \u001b[0;36msync_all\u001b[0;34m(all_syn_dict, ref_stream, force)\u001b[0m\n\u001b[1;32m 86\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m t_prime file \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m not found or forced computation, getting the events\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(t_p_path))\n\u001b[1;32m 88\u001b[0m \u001b[38;5;66;03m# check if it had skipped beats\u001b[39;00m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;66;03m# skipped_beat = check_skipped(one_syn_dict)\u001b[39;00m\n\u001b[1;32m 90\u001b[0m \u001b[38;5;66;03m# if skipped_beat:\u001b[39;00m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;66;03m# raise RuntimeError('Events array for {} had skipped heartbeats'.format(one_stream))\u001b[39;00m\n\u001b[0;32m---> 93\u001b[0m t_prime \u001b[38;5;241m=\u001b[39m \u001b[43msu\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msync_to_pattern\u001b[49m\u001b[43m(\u001b[49m\u001b[43mone_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mevt_arr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mone_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt_0\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 94\u001b[0m \u001b[43m \u001b[49m\u001b[43mref_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mevt_arr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mref_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt_0\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 95\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m saving t_prime array to \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m t_p_path) \n\u001b[1;32m 96\u001b[0m np\u001b[38;5;241m.\u001b[39msave(t_p_path, t_prime)\n", - "File \u001b[0;32m/mnt/cube/lo/envs/ceciestunepipe/ceciestunepipe/util/syncutil.py:51\u001b[0m, in \u001b[0;36msync_to_pattern\u001b[0;34m(x_ttl, t, x_0_ttl, t_0)\u001b[0m\n\u001b[1;32m 49\u001b[0m n_edges \u001b[38;5;241m=\u001b[39m x_ttl\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 50\u001b[0m n_edges_0 \u001b[38;5;241m=\u001b[39m x_0_ttl\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m---> 51\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mx_ttl\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m!=\u001b[39m x_0_ttl[\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m]:\n\u001b[1;32m 52\u001b[0m \u001b[38;5;66;03m# If the signals don't have the same number of edges there may be an error, better stop and debug\u001b[39;00m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 54\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSign of first edge transition of pattern and target dont match\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_edges \u001b[38;5;241m!=\u001b[39m n_edges_0:\n\u001b[1;32m 56\u001b[0m \u001b[38;5;66;03m# If the signals don't have the same number of edges there may be an error, better stop and debug\u001b[39;00m\n", - "File \u001b[0;32m/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/numpy/core/memmap.py:334\u001b[0m, in \u001b[0;36mmemmap.__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m 333\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, index):\n\u001b[0;32m--> 334\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getitem__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 335\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(res) \u001b[38;5;129;01mis\u001b[39;00m memmap \u001b[38;5;129;01mand\u001b[39;00m res\u001b[38;5;241m.\u001b[39m_mmap \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 336\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39mndarray)\n", - "\u001b[0;31mIndexError\u001b[0m: index 0 is out of bounds for axis 1 with size 0" + "name": "stdout", + "output_type": "stream", + "text": [ + "Checking for skipped heartbeats in nidq stream:\n", + "Event array has 883 events\n", + "No skipped heartbeats\n", + "\n", + "Checking for skipped heartbeats in ap_0 stream:\n", + "Event array has 883 events\n", + "No skipped heartbeats\n", + "\n", + "Checking for skipped heartbeats in wav stream:\n", + "Event array has 883 events\n", + "No skipped heartbeats\n", + "\n" ] } ], "source": [ - "mismatched_streams = False\n", - "short_stream = 'nidq'\n", + "## debugging: check if streams have skipped heartbeats\n", + "def check_skipped(one_syn_dict: dict, round_ms=10):\n", + " no_skips = True\n", + " evt_arr = one_syn_dict['evt_arr']\n", + " evt_t = one_syn_dict['t_0'][evt_arr[0]]\n", + " print('Event array has {} events'.format(evt_arr.size//2))\n", + " \n", + " # get the unique periods, rounded at round_ms (default 50 ms)\n", + " evt_period_ms = np.unique(np.round((np.unique(np.diff(evt_t))*1000)/round_ms)*round_ms).astype(int)\n", + " if evt_period_ms.size > 1:\n", + " evt_diff = (np.round(np.diff(evt_t)*1000/round_ms)*round_ms).astype(int)\n", + " no_skips = False\n", + " bad_periods = np.where(evt_diff != np.argmax(np.bincount(evt_diff)))[0]\n", + " print('More than 1 different periods detected: {}'.format(evt_period_ms))\n", + " print('Most periods equal to {} -- bad periods:'.format(np.argmax(np.bincount(evt_diff))))\n", + " for bp in bad_periods:\n", + " print('evt_diff['+str(bp)+']='+str(evt_diff[bp]))\n", + " \n", + " # check that the diff between every other edge is zero\n", + " period_diff = np.hstack([np.diff(evt_arr[1][1:][::2]), np.diff(evt_arr[1][::2])])\n", + " if not (all(period_diff==0)):\n", + " no_skips = False\n", + " print('Difference between corresponding periodic edges is not zero: {}'.format(np.unique(period_diff)))\n", + " \n", + " if no_skips: print('No skipped heartbeats')\n", "\n", + "for stream in all_syn_dict.keys():\n", + " print('Checking for skipped heartbeats in',stream,'stream:')\n", + " check_skipped(all_syn_dict[stream]);print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Record epochs with mismatched streams and which stream is shortest" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "mismatched_streams = {\n", + " '0949_g0': (True, 'nidq'),\n", + " '1226_g0': (True, 'nidq'),\n", + " '1227_g0': (True, 'nidq'),\n", + " '1233_g0': (True, 'nidq'),\n", + " '1235_g0': (True, 'nidq'),\n", + " '1244_g0': (True, 'nidq'),\n", + " '1245_g0': (True, 'nidq'),\n", + " '2355_g0': (False,),\n", + " '2631_g0': (False,)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "z_r5r13_24 2024-08-07 0949_g0 syncing..\n", + "z_r5r13_24 2024-08-07 1226_g0 syncing..\n", + "z_r5r13_24 2024-08-07 1227_g0 syncing..\n", + "z_r5r13_24 2024-08-07 1233_g0 syncing..\n", + "z_r5r13_24 2024-08-07 1235_g0 syncing..\n", + "z_r5r13_24 2024-08-07 1245_g0 syncing..\n", + "z_r5r13_24 2024-08-07 2355_g0 syncing..\n", + "z_r5r13_24 2024-08-07 2631_g0 syncing..\n", + "done.\n" + ] + } + ], + "source": [ "# loop through epochs:\n", "epoch_list = sess_epochs # process all epochs\n", + "\n", "for this_epoch in epoch_list:\n", " \n", " sess_par['epoch'] = this_epoch\n", @@ -380,8 +552,8 @@ " # get sync pattern\n", " all_syn_dict = {k:sy.get_syn_pattern(run_recordings,epoch_struct,k,force=False) for k in all_streams}\n", " # run sync\n", - " if mismatched_streams:\n", - " syd.sync_all_mismatched_streams(all_syn_dict,sess_par['ref_stream'],short_stream,force=False)\n", + " if mismatched_streams[this_epoch][0]:\n", + " syd.sync_all_mismatched_streams(all_syn_dict,sess_par['ref_stream'],mismatched_streams[this_epoch][1],force=False)\n", " else:\n", " sy.sync_all(all_syn_dict,sess_par['ref_stream'],force=False)\n", "\n", @@ -393,7 +565,7 @@ " bout_pd.drop(bout_pd[drop_condition].index, inplace=True)\n", " bout_pd.reset_index(drop=True, inplace=True)\n", " # sync bouts to spike time base\n", - " if mismatched_streams:\n", + " if mismatched_streams[this_epoch][0]:\n", " bout_dict, bout_syn_pd = syd.bout_dict_from_pd_mismatched_streams(bout_pd,all_syn_dict,s_f_key='wav')\n", " else:\n", " bout_dict, bout_syn_pd = sy.bout_dict_from_pd(bout_pd,all_syn_dict,s_f_key='wav')\n", @@ -558,7 +730,9 @@ " with open(stim_dict_path,'wb') as handle:\n", " pickle.dump(trial_dict,handle)\n", " trial_syn_pd.to_pickle(stim_pd_path)\n", - " logger.info('saved syncronized stim dict and pandas dataframe to {}, {}'.format(stim_dict_path, stim_pd_path))" + " logger.info('saved syncronized stim dict and pandas dataframe to {}, {}'.format(stim_dict_path, stim_pd_path))\n", + "\n", + "print('done.')" ] }, { @@ -570,7 +744,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -603,16 +777,23 @@ "outputs": [], "source": [] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Debugging: check that all streams are the same length (address ttl events error)" + "#### To look up lengths of recordings to stitch them together in 2-curate_acoustics" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 40, "metadata": { "tags": [] }, @@ -621,90 +802,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "nidq recording ends at 45:04\n", - "lf_0 recording ends at 45:04\n", - "ap_0 recording ends at 45:04\n", - "wav recording ends at 45:04\n" + "n samples: 14681207\n" ] } ], "source": [ - "sess_par['epoch'] = '1340_g0' # problematic epoch\n", + "sess_par['epoch'] = '1235_g0'\n", "epoch_struct = et.sgl_struct(sess_par,sess_par['epoch'],ephys_software=sess_par['ephys_software'])\n", - "\n", - "# get epoch files\n", - "sgl_folders, sgl_files = sglu.sgl_file_struct(epoch_struct['folders']['sglx'])\n", - "run_meta_files = {k:v[0] for k,v in sgl_files.items()}\n", - "run_recordings = {k:sglex.SpikeGLXRecordingExtractor(sglu.get_data_meta_path(v)[0]) for k,v in run_meta_files.items()}\n", - "\n", - "# get streams, from raw recording extractors and preprocessed data\n", - "all_streams = list(run_recordings.keys()) + ['wav'] ### might want to just remove this\n", - "# get sync pattern\n", "all_syn_dict = {k:sy.get_syn_pattern(run_recordings,epoch_struct,k,force=False) for k in all_streams}\n", - "\n", - "for stream in all_syn_dict.keys():\n", - " time_end = np.shape(all_syn_dict[stream]['t_0'])[0]/all_syn_dict[stream]['s_f']/60\n", - " print(stream+' recording ends at '+str(int(np.floor(time_end)))+':'+f\"{round((time_end % 1)*60):02d}\")" + "print('n samples:',np.shape(all_syn_dict['ap_0']['t_0'])[0])" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Checking for skipped heartbeats in nidq stream:\n", - "Event array has 5407 events\n", - "No skipped heartbeats\n", - "\n", - "Checking for skipped heartbeats in lf_0 stream:\n", - "Event array has 5408 events\n", - "No skipped heartbeats\n", - "\n", - "Checking for skipped heartbeats in ap_0 stream:\n", - "Event array has 5408 events\n", - "No skipped heartbeats\n", - "\n", - "Checking for skipped heartbeats in wav stream:\n", - "Event array has 5407 events\n", - "No skipped heartbeats\n", - "\n" + "sampling rate: 29999.844262295082\n" ] } ], "source": [ - "## debugging: check if streams have skipped heartbeats\n", - "def check_skipped(one_syn_dict: dict, round_ms=10):\n", - " no_skips = True\n", - " evt_arr = one_syn_dict['evt_arr']\n", - " evt_t = one_syn_dict['t_0'][evt_arr[0]]\n", - " print('Event array has {} events'.format(evt_arr.size//2))\n", - " \n", - " # get the unique periods, rounded at round_ms (default 50 ms)\n", - " evt_period_ms = np.unique(np.round((np.unique(np.diff(evt_t))*1000)/round_ms)*round_ms).astype(int)\n", - " if evt_period_ms.size > 1:\n", - " evt_diff = (np.round(np.diff(evt_t)*1000/round_ms)*round_ms).astype(int)\n", - " no_skips = False\n", - " bad_periods = np.where(evt_diff != np.argmax(np.bincount(evt_diff)))[0]\n", - " print('More than 1 different periods detected: {}'.format(evt_period_ms))\n", - " print('Most periods equal to {} -- bad periods:'.format(np.argmax(np.bincount(evt_diff))))\n", - " for bp in bad_periods:\n", - " print('evt_diff['+str(bp)+']='+str(evt_diff[bp]))\n", - " \n", - " # check that the diff between every other edge is zero\n", - " period_diff = np.hstack([np.diff(evt_arr[1][1:][::2]), np.diff(evt_arr[1][::2])])\n", - " if not (all(period_diff==0)):\n", - " no_skips = False\n", - " print('Difference between corresponding periodic edges is not zero: {}'.format(np.unique(period_diff)))\n", - " \n", - " if no_skips: print('No skipped heartbeats')\n", - "\n", - "for stream in all_syn_dict.keys():\n", - " print('Checking for skipped heartbeats in',stream,'stream:')\n", - " check_skipped(all_syn_dict[stream]);print()" + "print('sampling rate:',all_syn_dict['ap_0']['s_f'])" ] }, { diff --git a/.ipynb_checkpoints/1.2-preprocess_acoustics-zebra_finch-one_session-off-and-on-checkpoint.ipynb b/.ipynb_checkpoints/1.2-preprocess_acoustics-zebra_finch-one_session-off-and-on-checkpoint.ipynb new file mode 100644 index 0000000..1919ad8 --- /dev/null +++ b/.ipynb_checkpoints/1.2-preprocess_acoustics-zebra_finch-one_session-off-and-on-checkpoint.ipynb @@ -0,0 +1,859 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Preprocess acoustic data and sync to neural data, one session at a time\n", + "\n", + "This notebook is a modified version of the *1-preprocess_acoustics* in the chronic ephys processing pipeline\n", + "\n", + "If *1-preprocess_acoustics* exits with errors, this notebook allows you to make manual adjustments\n", + "\n", + "Common errors include:\n", + "- data streams cannot be synched (ex. neural and audio data streams are of different lengths)\n", + "- TTL events were skipped (i.e., the machine clock malfunctioned or SpikeGLX crashed and the data streams terminated at different moments)\n", + "\n", + "Use the environment **songproc** to run this notebook\n", + "\n", + "(currently using environment spikesort)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/spikeextractors/__init__.py:21: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n", + " if StrictVersion(h5py.__version__) > '2.10.0':\n", + "2024-08-14 12:19:41,818 root INFO Running on pakhi.ucsd.edu\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "h5py version > 2.10.0. Some extractors might not work properly. It is recommended to downgrade to version 2.10.0: \n", + ">>> pip install h5py==2.10.0\n" + ] + } + ], + "source": [ + "%matplotlib widget\n", + "import numpy as np\n", + "import pandas as pd\n", + "import os\n", + "import pickle\n", + "from scipy.io import wavfile\n", + "import traceback\n", + "\n", + "import sys\n", + "sys.path.append('/mnt/cube/lo/envs/ceciestunepipe')\n", + "from ceciestunepipe.file import bcistructure as et\n", + "from ceciestunepipe.util.sound import boutsearch as bs\n", + "from ceciestunepipe.pipeline import searchbout as sb\n", + "from ceciestunepipe.util import stimutil as su\n", + "from ceciestunepipe.util import sglxutil as sglu\n", + "from ceciestunepipe.util import sglxsync as sy\n", + "from ceciestunepipe.mods import sglxsync_debug as syd\n", + "from ceciestunepipe.util.spikeextractors.extractors.spikeglxrecordingextractor import spikeglxrecordingextractor as sglex\n", + "from ceciestunepipe.util import oeutil as oeu\n", + "from ceciestunepipe.mods import preproc_sglx, preproc_oe\n", + "\n", + "import logging\n", + "logger = logging.getLogger()\n", + "handler = logging.StreamHandler()\n", + "formatter = logging.Formatter(\n", + " '%(asctime)s %(name)-12s %(levelname)-8s %(message)s')\n", + "handler.setFormatter(formatter)\n", + "logger.addHandler(handler)\n", + "logger.setLevel(logging.WARNING) # set to logging.INFO if you'd like to see the full readout" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "## default bout detection parameters that work well for zebra finches\n", + "hparams = {\n", + " # spectrogram\n", + " 'num_freq':1024, # how many channels to use in a spectrogram\n", + " 'preemphasis':0.97,\n", + " 'frame_shift_ms':5, # step size for fft\n", + " 'frame_length_ms':10, # frame length for fft FRAME SAMPLES < NUM_FREQ!!!\n", + " 'min_level_db':-55, # minimum threshold db for computing spectrogram\n", + " 'ref_level_db':110, # reference db for computing spectrogram\n", + " 'sample_rate':None, # sample rate of your data\n", + " \n", + " # mel filter\n", + " 'mel_filter':False, # should a mel filter be used?\n", + " 'num_mels':1024, # how many channels to use in the mel-spectrogram\n", + " 'fmin':300, # low frequency cutoff for mel filter\n", + " 'fmax':12000, # high frequency cutoff for mel filter\n", + " \n", + " # spectrogram inversion\n", + " 'max_iters':200,\n", + " 'griffin_lim_iters':20,\n", + " 'power':1.5,\n", + " \n", + " # bout searching\n", + " 'bout_auto_file':'bout_auto.pickle', # extension for saving the auto found files\n", + " 'bout_sync_file':'bout_sync.pickle', # extension for saving the synchronized auto bouts\n", + " 'stim_sync_file':'stim_sync.pickle', # extension for saving the synchronized stim if stim session\n", + " 'bout_curated_file':'bout_curated.pickle', # extension for manually curated files\n", + " \n", + " # if using deep_bout_search = False, the following parameters will apply for automatic bout detection:\n", + " 'read_wav_fun':bs.read_npy_chan, # function for loading the wav_like_stream (returns fs, ndarray)\n", + " 'file_order_fun':bs.sess_file_id, # function for extracting the file ID within the session\n", + " 'min_segment':20, # minimum length of supra_threshold to consider a 'syllable' (ms)\n", + " 'min_silence':3000, # minmum distance between groups of syllables to consider separate bouts (ms)\n", + " 'min_bout':500, # min bout duration (ms)\n", + " 'peak_thresh_rms':0.55, # threshold (rms) for peak acceptance,\n", + " 'thresh_rms':0.25, # threshold for detection of syllables\n", + " 'mean_syl_rms_thresh':0.3, # threshold for acceptance of mean rms across the syllable (relative to rms of the file)\n", + " 'max_bout':180000, # exclude bouts too long (ms)\n", + " 'l_p_r_thresh':100, # threshold for n of len_ms/peaks (typycally about 2-3 syllable spans)\n", + " 'waveform_edges':1000, # get number of ms before and after the edges of the bout for the waveform sample\n", + "}\n", + "\n", + "## other processing parameters\n", + "n_jobs = 1 # n_jobs for deriving bout info (errors when increased)\n", + "mic_file_ext = 'npy' # npy method more efficient than wav\n", + "force_preprocess = False # skip preprocessing for previously failed epochs\n", + "deep_bout_search = True # detect bouts using deep search -- see ceciestunepipe.mods.bout_detection_mf" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# single session params\n", + "sess_par = {\n", + " 'bird':'z_r5r13_24',\n", + " 'sess':'2024-08-08',\n", + " 'stim_sess':[], # sessions where stimuli were presented\n", + " 'mic_list':['microphone_M','microphone_F'], # list of mics of interest, by signal name in rig.json\n", + " 'adc_list':[], # list of adc channels of interest\n", + " 'stim_list':['wav_stim'], # list of adc chans with the stimulus\n", + " 'nidq_ttl_list':[], # list of TTL signals form the nidq digital inputs to extract (besides the 'sync')\n", + " 'ref_stream':'ap_0', # what to synchronize everything to (sglx only, oe already synced)\n", + " 'trial_tag_chan':2, # sglx, what was the tag channel in the stimulus wave (this should come from meta et. al)\n", + " 'on_signal':1, # sglx, whether signal on is hi or lo\n", + " 'sort':'sort_0', # sort index\n", + " 'ephys_software':'sglx'\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Preprocess and synchronize recordings" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# get experiment structure\n", + "exp_struct = et.get_exp_struct(sess_par['bird'],sess_par['sess'],sort=sess_par['sort'],ephys_software=sess_par['ephys_software'])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "preprocessing..\n", + "done.\n" + ] + } + ], + "source": [ + "##### preprocess acoustics #####\n", + "if sess_par['ephys_software'] == 'sglx':\n", + " preproc_sglx.preprocess_session(sess_par,force_redo=True)\n", + "elif sess_par['ephys_software'] == 'oe':\n", + " preproc_oe.preprocess_session(sess_par,force_redo=True)\n", + "print('done.')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "042c7905a373496688d27d7ffc3bdc9e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/9 [00:00 0:\n", + " trial_syn_pd_all = []" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['0700_g0', '1000_g0', '2355_g0', '2705_g0']\n" + ] + } + ], + "source": [ + "# get epochs\n", + "sess_epochs = et.list_ephys_epochs(sess_par)\n", + "print(sess_epochs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Debugging: check that all streams are the same length (address ttl events error)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nidq recording ends at 175:20\n", + "ap_0 recording ends at 175:17\n", + "wav recording ends at 175:19\n" + ] + } + ], + "source": [ + "sess_par['epoch'] = '1000_g0' # problematic epoch\n", + "epoch_struct = et.sgl_struct(sess_par,sess_par['epoch'],ephys_software=sess_par['ephys_software'])\n", + "\n", + "# get epoch files\n", + "sgl_folders, sgl_files = sglu.sgl_file_struct(epoch_struct['folders']['sglx'])\n", + "run_meta_files = {k:v[0] for k,v in sgl_files.items()}\n", + "run_recordings = {k:sglex.SpikeGLXRecordingExtractor(sglu.get_data_meta_path(v)[0]) for k,v in run_meta_files.items()}\n", + "\n", + "# get streams, from raw recording extractors and preprocessed data\n", + "all_streams = list(run_recordings.keys()) + ['wav'] ### might want to just remove this\n", + "# get sync pattern\n", + "all_syn_dict = {k:sy.get_syn_pattern(run_recordings,epoch_struct,k,force=False) for k in all_streams}\n", + "\n", + "for stream in all_syn_dict.keys():\n", + " time_end = np.shape(all_syn_dict[stream]['t_0'])[0]/all_syn_dict[stream]['s_f']/60\n", + " print(stream+' recording ends at '+str(int(np.floor(time_end)))+':'+f\"{round((time_end % 1)*60):02d}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Checking for skipped heartbeats in nidq stream:\n", + "Event array has 21040 events\n", + "More than 1 different periods detected: [130 440 500]\n", + "Most periods equal to 500 -- bad periods:\n", + "evt_diff[13797]=440\n", + "evt_diff[13798]=130\n", + "\n", + "Checking for skipped heartbeats in ap_0 stream:\n", + "Event array has 21034 events\n", + "More than 1 different periods detected: [140 500]\n", + "Most periods equal to 500 -- bad periods:\n", + "evt_diff[13792]=140\n", + "\n", + "Checking for skipped heartbeats in wav stream:\n", + "Event array has 21040 events\n", + "More than 1 different periods detected: [130 440 500]\n", + "Most periods equal to 500 -- bad periods:\n", + "evt_diff[13797]=440\n", + "evt_diff[13798]=130\n", + "\n" + ] + } + ], + "source": [ + "## debugging: check if streams have skipped heartbeats\n", + "def check_skipped(one_syn_dict: dict, round_ms=10):\n", + " no_skips = True\n", + " evt_arr = one_syn_dict['evt_arr']\n", + " evt_t = one_syn_dict['t_0'][evt_arr[0]]\n", + " print('Event array has {} events'.format(evt_arr.size//2))\n", + " \n", + " # get the unique periods, rounded at round_ms (default 50 ms)\n", + " evt_period_ms = np.unique(np.round((np.unique(np.diff(evt_t))*1000)/round_ms)*round_ms).astype(int)\n", + " if evt_period_ms.size > 1:\n", + " evt_diff = (np.round(np.diff(evt_t)*1000/round_ms)*round_ms).astype(int)\n", + " no_skips = False\n", + " bad_periods = np.where(evt_diff != np.argmax(np.bincount(evt_diff)))[0]\n", + " print('More than 1 different periods detected: {}'.format(evt_period_ms))\n", + " print('Most periods equal to {} -- bad periods:'.format(np.argmax(np.bincount(evt_diff))))\n", + " for bp in bad_periods:\n", + " print('evt_diff['+str(bp)+']='+str(evt_diff[bp]))\n", + " \n", + " # check that the diff between every other edge is zero\n", + " period_diff = np.hstack([np.diff(evt_arr[1][1:][::2]), np.diff(evt_arr[1][::2])])\n", + " if not (all(period_diff==0)):\n", + " no_skips = False\n", + " print('Difference between corresponding periodic edges is not zero: {}'.format(np.unique(period_diff)))\n", + " \n", + " if no_skips: print('No skipped heartbeats')\n", + "\n", + "for stream in all_syn_dict.keys():\n", + " print('Checking for skipped heartbeats in',stream,'stream:')\n", + " check_skipped(all_syn_dict[stream]);print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Need to\n", + "- cut all streams at evt_t = 13971\n", + "- resume streams at evt_t = [total length of stream] - 7240\n", + " - 13794 for ap_0\n", + " - 13800 for nidq\n", + " - 13800 for wav" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from ceciestunepipe.util import syncutil as scu\n", + "\n", + "def sync_all_mismatched_streams(all_syn_dict: dict, ref_stream: str, short_stream: str, force=False) -> dict:\n", + " ref_syn_dict = all_syn_dict[ref_stream]\n", + "\n", + " first_len_evt_arr = 13971 # number of events captured before streams were cut off\n", + " first_len = first_len_evt_arr/2 # time when streams got cut off (s)\n", + "\n", + " last_len_evt_arr = 7240 # number of events captured after streams resumed\n", + " last_len = last_len_evt_arr/2 # time after streams were resumed (s)\n", + " \n", + " # new_len = np.shape(all_syn_dict[short_stream]['t_0'])[0]/all_syn_dict[short_stream]['s_f'] # length of short stream (s)\n", + " ref_end = int(first_len * all_syn_dict[ref_stream]['s_f']) # where to truncate ref_stream (samples)\n", + " ref_restart = int((np.shape(all_syn_dict[ref_stream]['t_0'])[0] - last_len) * all_syn_dict[ref_stream]['s_f']) # where to restart ref_stream (samples)\n", + " ref_len_evt_arr = np.shape(all_syn_dict[short_stream]['evt_arr'])[1] # number of events captured in ref stream\n", + " \n", + " for one_stream, one_syn_dict in all_syn_dict.items():\n", + " if one_stream==ref_stream:\n", + " continue\n", + " \n", + " print(' sync {}...'.format(one_stream))\n", + " \n", + " t_0_folder = os.path.split(one_syn_dict['t_0_path'])[0]\n", + " t_p_path = os.path.join(t_0_folder, '{}-tp.npy'.format(one_stream))\n", + " \n", + " if not(os.path.exists(t_p_path) and (force is False)):\n", + " print(' t_prime file {} not found or forced computation, getting the events'.format(t_p_path))\n", + " \n", + " # Edit one_stream to length of short_stream:\n", + " one_end = int(first_len * all_syn_dict[one_stream]['s_f']) # where to truncate one_stream (samples)\n", + " one_restart = int((np.shape(all_syn_dict[one_stream]['t_0'])[0] - last_len) * all_syn_dict[one_stream]['s_f']) # where to resume one_stream (samples)\n", + " \n", + " # Sync to ref stream\n", + " one_len_evt_arr = np.shape(one_syn_dict['evt_arr'])[1]\n", + " t_prime = scu.sync_to_pattern(np.concatenate((one_syn_dict['evt_arr'][:,:first_len_evt_arr],\n", + " one_syn_dict['evt_arr'][:,one_len_evt_arr-last_len_evt_arr:]), axis=1), \n", + " np.concatenate((one_syn_dict['t_0'][:one_end],\n", + " one_syn_dict['t_0'][one_restart:])),\n", + " np.concatenate((ref_syn_dict['evt_arr'][:,:first_len_evt_arr],\n", + " ref_syn_dict['evt_arr'][:,ref_len_evt_arr-last_len_evt_arr:]), axis=1),\n", + " np.concatenate((ref_syn_dict['t_0'][:ref_end],\n", + " ref_syn_dict['t_0'][ref_restart:]))\n", + " )\n", + " print(' saving t_prime array to ' + t_p_path)\n", + " np.save(t_p_path, t_prime)\n", + " \n", + " # clear the memory, then load as memmap\n", + " del t_prime\n", + " \n", + " one_syn_dict['t_p_path'] = t_p_path\n", + " # save the dict with the path to the sync it\n", + " print(' saving synced dict to {}'.format(one_syn_dict['path']))\n", + " with open(one_syn_dict['path'], 'wb') as fp:\n", + " pickle.dump(one_syn_dict, fp)\n", + " \n", + " one_syn_dict['t_p'] = np.load(t_p_path, mmap_mode='r')\n", + " print('Done with sync_all')\n", + " return\n", + " \n", + "\n", + "def bout_dict_from_pd_mismatched_streams(bout_pd: pd.DataFrame, all_syn_dict: dict, s_f_key: str='wav') -> dict:\n", + " s_f = all_syn_dict[s_f_key]['s_f']\n", + "\n", + " start_ms = bout_pd['start_ms'].values\n", + " len_ms = bout_pd['len_ms'].values\n", + " \n", + " bout_dict = {\n", + " 's_f': s_f, # s_f used to get the spectrogram\n", + " 's_f_nidq': all_syn_dict['nidq']['s_f'],\n", + " 's_f_ap_0': all_syn_dict['ap_0']['s_f'],\n", + " 'start_ms': start_ms,\n", + " 'len_ms': len_ms,\n", + " 'start_sample_naive': ( start_ms * s_f * 0.001).astype(np.int64),\n", + " 'start_sample_nidq': np.array([np.where(all_syn_dict['nidq']['t_0'] > start)[0][0] for start in start_ms*0.001]),\n", + " 'start_sample_wav': np.array([np.where(all_syn_dict['wav']['t_0'] > start)[0][0] for start in start_ms*0.001])\n", + " }\n", + " \n", + " # Edit to remove bout starts > length of ap_0 recording\n", + " keep = bout_dict['start_sample_wav'] <= len(all_syn_dict['wav']['t_p'])\n", + " bout_dict['start_ms'] = bout_dict['start_ms'][keep]\n", + " bout_dict['len_ms'] = bout_dict['len_ms'][keep]\n", + " bout_dict['start_sample_naive'] = bout_dict['start_sample_naive'][keep]\n", + " bout_dict['start_sample_nidq'] = bout_dict['start_sample_nidq'][keep]\n", + " bout_dict['start_sample_wav'] = bout_dict['start_sample_wav'][keep]\n", + " \n", + " start_ms_ap_0 = all_syn_dict['wav']['t_p'][bout_dict['start_sample_wav']]*1000\n", + " \n", + " bout_dict['start_ms_ap_0'] = start_ms_ap_0\n", + " bout_dict['start_sample_ap_0'] = np.array([np.where(all_syn_dict['ap_0']['t_0'] > start)[0][0] for start in start_ms_ap_0*0.001])\n", + " bout_dict['start_sample_ap_0'] = (bout_dict['start_sample_ap_0']).astype(np.int64)\n", + " bout_dict['end_sample_ap_0'] = bout_dict['start_sample_ap_0'] + (bout_dict['len_ms'] * bout_dict['s_f_ap_0'] * 0.001).astype(np.int64)\n", + " \n", + " ## update the bout pandas dataframe with the synced columns\n", + " bout_pd = bout_pd.head(keep.sum()) # trim bout_pd to bouts within ap_0 recording\n", + " for k in ['start_ms_ap_0', 'start_sample_ap_0', 'len_ms', 'start_ms', 'start_sample_naive']:\n", + " warnings.simplefilter(action='ignore', category=SettingWithCopyWarning)\n", + " bout_pd[k] = bout_dict[k]\n", + " warnings.resetwarnings()\n", + "\n", + " return bout_dict, bout_pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Record epochs with mismatched streams and which stream is shortest" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "mismatched_streams = {\n", + " '0700_g0': (False,),\n", + " '1000_g0': (True, 'ap_0'),\n", + " '2355_g0': (False,),\n", + " '2705_g0': (False,)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "z_r5r13_24 2024-08-08 1000_g0 syncing..\n", + " sync nidq...\n", + " t_prime file /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-08/sglx/1000_g0/nidq-tp.npy not found or forced computation, getting the events\n", + "(2, 13971)\n", + "(2, 7240)\n" + ] + }, + { + "ename": "IndexError", + "evalue": "index 209570288 is out of bounds for axis 0 with size 209557048", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[23], line 24\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m# run sync\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mismatched_streams[this_epoch][\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m---> 24\u001b[0m \u001b[43msync_all_mismatched_streams\u001b[49m\u001b[43m(\u001b[49m\u001b[43mall_syn_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43msess_par\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mref_stream\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mmismatched_streams\u001b[49m\u001b[43m[\u001b[49m\u001b[43mthis_epoch\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mforce\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 26\u001b[0m sy\u001b[38;5;241m.\u001b[39msync_all(all_syn_dict,sess_par[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mref_stream\u001b[39m\u001b[38;5;124m'\u001b[39m],force\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", + "Cell \u001b[0;32mIn[22], line 38\u001b[0m, in \u001b[0;36msync_all_mismatched_streams\u001b[0;34m(all_syn_dict, ref_stream, short_stream, force)\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28mprint\u001b[39m(one_syn_dict[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mevt_arr\u001b[39m\u001b[38;5;124m'\u001b[39m][:,:first_len_evt_arr]\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28mprint\u001b[39m(one_syn_dict[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mevt_arr\u001b[39m\u001b[38;5;124m'\u001b[39m][:,one_len_evt_arr\u001b[38;5;241m-\u001b[39mlast_len_evt_arr:]\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m---> 38\u001b[0m t_prime \u001b[38;5;241m=\u001b[39m \u001b[43mscu\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msync_to_pattern\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mone_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mevt_arr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m:\u001b[49m\u001b[43mfirst_len_evt_arr\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 39\u001b[0m \u001b[43m \u001b[49m\u001b[43mone_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mevt_arr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43mone_len_evt_arr\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mlast_len_evt_arr\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 40\u001b[0m \u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mone_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt_0\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43mone_end\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 41\u001b[0m \u001b[43m \u001b[49m\u001b[43mone_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt_0\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mone_restart\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 42\u001b[0m \u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mref_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mevt_arr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m:\u001b[49m\u001b[43mfirst_len_evt_arr\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 43\u001b[0m \u001b[43m \u001b[49m\u001b[43mref_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mevt_arr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43mref_len_evt_arr\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mlast_len_evt_arr\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 44\u001b[0m \u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mref_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt_0\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43mref_end\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 45\u001b[0m \u001b[43m \u001b[49m\u001b[43mref_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt_0\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mref_restart\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 46\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m saving t_prime array to \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m t_p_path)\n\u001b[1;32m 48\u001b[0m np\u001b[38;5;241m.\u001b[39msave(t_p_path, t_prime)\n", + "File \u001b[0;32m/mnt/cube/lo/envs/ceciestunepipe/ceciestunepipe/util/syncutil.py:61\u001b[0m, in \u001b[0;36msync_to_pattern\u001b[0;34m(x_ttl, t, x_0_ttl, t_0)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 58\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNumber of edges in the syn ttl events of pattern and target dont match\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 60\u001b[0m \u001b[38;5;66;03m# if all checks out, do the deed\u001b[39;00m\n\u001b[0;32m---> 61\u001b[0m t_0_edge \u001b[38;5;241m=\u001b[39m \u001b[43mt_0\u001b[49m\u001b[43m[\u001b[49m\u001b[43mx_0_ttl\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 62\u001b[0m sample_edge \u001b[38;5;241m=\u001b[39m x_ttl[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 63\u001b[0m \u001b[38;5;66;03m# the interpolation function. fill_value='extrapolate' allows extrapolation from zero and until the last time stamp\u001b[39;00m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;66;03m# careful, this could lead to negative time, but it is the correct way to do it.\u001b[39;00m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;66;03m# interpolation function interpolates time as a target, t_0=f(sample) with true values at the edges\u001b[39;00m\n", + "\u001b[0;31mIndexError\u001b[0m: index 209570288 is out of bounds for axis 0 with size 209557048" + ] + } + ], + "source": [ + "# loop through epochs:\n", + "# epoch_list = ['0700_g0', '1000_g0', '2355_g0','2705_g0']\n", + "epoch_list = ['1000_g0']\n", + "\n", + "for this_epoch in epoch_list:\n", + " \n", + " sess_par['epoch'] = this_epoch\n", + " epoch_struct = et.sgl_struct(sess_par,sess_par['epoch'],ephys_software=sess_par['ephys_software'])\n", + "\n", + " ##### synchronization - sglx #####\n", + " print(sess_par['bird'],sess_par['sess'],this_epoch,'syncing..')\n", + " if sess_par['ephys_software'] == 'sglx':\n", + " # get epoch files\n", + " sgl_folders, sgl_files = sglu.sgl_file_struct(epoch_struct['folders']['sglx'])\n", + " run_meta_files = {k:v[0] for k,v in sgl_files.items()}\n", + " run_recordings = {k:sglex.SpikeGLXRecordingExtractor(sglu.get_data_meta_path(v)[0]) for k,v in run_meta_files.items()}\n", + "\n", + " # get streams, from raw recording extractors and preprocessed data\n", + " all_streams = list(run_recordings.keys()) + ['wav'] ### might want to just remove this\n", + " # get sync pattern\n", + " all_syn_dict = {k:sy.get_syn_pattern(run_recordings,epoch_struct,k,force=False) for k in all_streams}\n", + " # run sync\n", + " if mismatched_streams[this_epoch][0]:\n", + " sync_all_mismatched_streams(all_syn_dict,sess_par['ref_stream'],mismatched_streams[this_epoch][1],force=False)\n", + " else:\n", + " sy.sync_all(all_syn_dict,sess_par['ref_stream'],force=False)\n", + "\n", + " # load bouts\n", + " hparams, bout_pd = sb.load_bouts(sess_par['bird'],sess_par['sess'],'', derived_folder='bouts_sglx',bout_file_key='bout_auto_file')\n", + " # keep only epoch bouts\n", + " logger.info('bouts from this epoch {}'.format(sess_par['epoch']))\n", + " drop_condition = ~bout_pd['file'].str.contains(sess_par['epoch'])\n", + " bout_pd.drop(bout_pd[drop_condition].index, inplace=True)\n", + " bout_pd.reset_index(drop=True, inplace=True)\n", + " # sync bouts to spike time base\n", + " bout_dict, bout_syn_pd = sy.bout_dict_from_pd(bout_pd,all_syn_dict,s_f_key='wav')\n", + " # if mismatched_streams[this_epoch][0]:\n", + " # bout_dict, bout_syn_pd = bout_dict_from_pd_mismatched_streams(bout_pd,all_syn_dict,s_f_key='wav')\n", + " # else:\n", + " # bout_dict, bout_syn_pd = sy.bout_dict_from_pd(bout_pd,all_syn_dict,s_f_key='wav')\n", + " # store epoch synced bout info\n", + " bout_syn_pd['bird'] = sess_par['bird']\n", + " bout_syn_pd['sess'] = sess_par['sess']\n", + " bout_syn_pd['epoch'] = sess_par['epoch']\n", + " bout_syn_pd_all.append(bout_syn_pd)\n", + " # save synced bouts\n", + " bout_dict_path = os.path.join(epoch_struct['folders']['derived'],'bout_dict_ap0.pkl')\n", + " with open(bout_dict_path, 'wb') as handle:\n", + " pickle.dump(bout_dict, handle)\n", + " bout_pd_path = os.path.join(epoch_struct['folders']['derived'],'bout_pd_ap0.pkl')\n", + " bout_pd.to_pickle(bout_pd_path)\n", + " logger.info('saved syncronized bout dict and pandas dataframe to {}, {}'.format(bout_dict_path, bout_pd_path))\n", + "\n", + " if len(sess_par['stim_sess']) > 0:\n", + " # syn_ttl comes from the digital pin, syn_sine_ttl from the sine\n", + " event_name = 'wav_stim'\n", + " ttl_ev_name = event_name + '_sync_sine_ttl' \n", + " # get the events npy file\n", + " npy_stim_path = os.path.join(epoch_struct['folders']['derived'],ttl_ev_name + '_evt.npy')\n", + " stream_stim_path = os.path.join(epoch_struct['folders']['derived'],event_name + '.npy')\n", + " trial_ttl = np.load(npy_stim_path)\n", + " # epoch may not have trials - if so ttl file will be empty\n", + " if len(trial_ttl) > 0:\n", + " trial_stream = np.load(stream_stim_path,mmap_mode='r')\n", + " # get sampling frequency\n", + " stim_s_f = int(all_syn_dict['nidq']['s_f'])\n", + " # load the stimulus name - frequency tag dictionary\n", + " stim_tags_dict = preproc_sglx.load_stim_tags_dict(sess_par['stim_sess'],sess_par['bird'])\n", + " # get trial tagged dataframe\n", + " trial_tagged_pd = su.get_trials_pd(trial_ttl, trial_stream, stim_s_f,on_signal=sess_par['on_signal'],\n", + " tag_chan=sess_par['trial_tag_chan'],stim_tags_dict=stim_tags_dict,\n", + " trial_is_onof=True)\n", + " # sync stim\n", + " trial_dict, trial_syn_pd = sy.trial_syn_from_pd(trial_tagged_pd,all_syn_dict,s_f_key='nidq')\n", + " # store epoch synced stim info\n", + " trial_syn_pd['bird'] = sess_par['bird']\n", + " trial_syn_pd['sess'] = sess_par['sess']\n", + " trial_syn_pd['epoch'] = this_epoch\n", + " trial_syn_pd_all.append(trial_syn_pd)\n", + " # save synced stim\n", + " stim_dict_path = os.path.join(epoch_struct['folders']['derived'],'stim_dict_ap0.pkl')\n", + " stim_pd_path = os.path.join(epoch_struct['folders']['derived'],'stim_pd_ap0.pkl')\n", + " with open(stim_dict_path,'wb') as handle:\n", + " pickle.dump(trial_dict,handle)\n", + " trial_syn_pd.to_pickle(stim_pd_path)\n", + " logger.info('saved syncronized stim dict and pandas dataframe to {}, {}'.format(stim_dict_path, stim_pd_path))\n", + "\n", + " ###### synchronization - oe #####\n", + " elif sess_par['ephys_software'] == 'oe':\n", + " # get epoch files\n", + " run_recordings = {'oeb':preproc_oe.get_oe_cont_recording(exp_struct,this_epoch)}\n", + "\n", + " # make an all_syn_dict\n", + " mic_file_name = os.path.join(exp_struct['folders']['derived'],this_epoch,'wav_mic-npy_meta.pickle')\n", + " with open(mic_file_name, 'rb') as handle:\n", + " wav_mic_meta = pickle.load(handle)\n", + " all_syn_dict = {'wav': {'s_f':wav_mic_meta['s_f']}, \n", + " 'ap_0': {'s_f':run_recordings['oeb'].get_sampling_frequency()},\n", + " 'nidq': {'s_f':run_recordings['oeb'].get_sampling_frequency()}}\n", + " # make bouts pandas file for this session - match sglx format, streams already synced\n", + " bout_oe_struct = et.get_exp_struct(sess_par['bird'],sess_par['sess'],sort=sess_par['sort'],ephys_software='bouts_oe')\n", + " bout_pd_path = os.path.join(bout_oe_struct['folders']['derived'], 'bout_auto.pickle')\n", + " bout_syn_pd = pd.read_pickle(bout_pd_path)\n", + " bout_dict = preproc_oe.bout_dict_from_pd(bout_syn_pd,all_syn_dict)\n", + " # store epoch synced bout info\n", + " bout_syn_pd['bird'] = sess_par['bird']\n", + " bout_syn_pd['sess'] = sess_par['sess']\n", + " bout_syn_pd['epoch'] = this_epoch\n", + " bout_syn_pd_all.append(bout_syn_pd)\n", + " # save synced bouts\n", + " bout_dict_path = os.path.join(epoch_struct['folders']['derived'],'bout_dict_oe.pkl')\n", + " bout_pd_path = os.path.join(epoch_struct['folders']['derived'],'bout_pd_oe.pkl')\n", + " with open(bout_dict_path,'wb') as handle:\n", + " pickle.dump(bout_dict,handle)\n", + " bout_syn_pd.to_pickle(bout_pd_path)\n", + "\n", + " if len(sess_par['stim_sess']) > 0:\n", + " # this epoch name - get recording events path\n", + " raw_folder = exp_struct['folders']['oe']\n", + " epoch_path = os.path.join(raw_folder,this_epoch)\n", + " node_path = preproc_oe.get_default_node(exp_struct,this_epoch)\n", + " rec_path = preproc_oe.get_default_recording(node_path)\n", + " events_path = os.path.join(rec_path,'events/Network_Events-102.0/TEXT_group_1/')\n", + " # load stim lables / onsets\n", + " stim_labels = np.load(os.path.join(events_path,'text.npy'))\n", + " stim_onsets = np.load(os.path.join(events_path,'timestamps.npy'))\n", + "\n", + " # get stim onsets and offsets\n", + " stim_on_all = []; stim_off_all = []; \n", + " stim_proc_path_all = []; stim_exp_path_all = [];\n", + " stim_map_dir_all = []; stim_id_all = [];\n", + " # loop through stim\n", + " for stim_i in range(len(stim_labels)):\n", + " this_stim_label = stim_labels[stim_i].astype('str')\n", + " this_stim_onset = stim_onsets[stim_i]\n", + " if this_stim_label[:4] == 'stim':\n", + " stim_exp_file = this_stim_label[5:]\n", + " # get stim preprocessing directory\n", + " stim_file_split = stim_exp_file.split('/')\n", + " stim_map_i = np.where([stim_file_split[i] in list(stim_map_dict.keys()) for i in range(len(stim_file_split))])[0][0]\n", + " stim_map_dir = stim_map_dict[stim_file_split[stim_map_i]]\n", + " # get remaining stim file path - identical for experiment and preprocessing\n", + " remaining_stim_file = '/'.join(stim_file_split[stim_map_i+1:])\n", + " # processing file location\n", + " stim_file = os.path.join(stim_map_dir,remaining_stim_file)\n", + " # load stim and get length\n", + " sf,this_wav = wavfile.read(stim_file,mmap=True)\n", + " stim_len = this_wav.shape[0]/sf\n", + " # get length of stim in samples - round up\n", + " stim_samp_len = int(np.ceil(stim_len * bout_dict['s_f']))\n", + " # get stim on / off\n", + " stim_on_all.append(this_stim_onset)\n", + " stim_off_all.append(this_stim_onset+stim_samp_len) \n", + " stim_proc_path_all.append(stim_file)\n", + " stim_exp_path_all.append(stim_exp_file)\n", + " stim_map_dir_all.append(stim_map_dir)\n", + " stim_id_all.append(remaining_stim_file)\n", + "\n", + " # make into a pd - oe already synced\n", + " stim_on_all_np = np.array(stim_on_all).astype('int')\n", + " stim_off_all_np = np.array(stim_off_all).astype('int')\n", + " stim_on_all_np_ms = 1000*(stim_on_all_np/bout_dict['s_f'])\n", + " stim_off_all_np_ms = 1000*(stim_off_all_np/bout_dict['s_f'])\n", + " trial_syn_pd = pd.DataFrame(np.vstack([stim_on_all_np,\n", + " stim_off_all_np,\n", + " stim_on_all_np_ms,\n", + " stim_off_all_np_ms,\n", + " stim_off_all_np_ms-stim_on_all_np_ms,\n", + " stim_proc_path_all,\n", + " stim_exp_path_all,\n", + " stim_map_dir_all,\n", + " stim_id_all]).T,\n", + " columns=['start_sample','end_sample','start_ms','end_ms','len_ms',\n", + " 'proc_file','exp_file','map_dir','stim_id'])\n", + " trial_syn_pd['start_sample'] = trial_syn_pd['start_sample'].astype('int')\n", + " trial_syn_pd['end_sample'] = trial_syn_pd['end_sample'].astype('int')\n", + " trial_syn_pd['start_ms'] = trial_syn_pd['start_ms'].astype('float')\n", + " trial_syn_pd['len_ms'] = trial_syn_pd['len_ms'].astype('float')\n", + " # store epoch synced stim info\n", + " trial_syn_pd['bird'] = sess_par['bird']\n", + " trial_syn_pd['sess'] = sess_par['sess']\n", + " trial_syn_pd['epoch'] = this_epoch\n", + " trial_syn_pd_all.append(trial_syn_pd)\n", + " trial_dict = {\n", + " 's_f': all_syn_dict['wav']['s_f'],\n", + " 'ap_0':all_syn_dict['ap_0']['s_f'],\n", + " 'nidq':all_syn_dict['nidq']['s_f'],\n", + " 'start_ms':trial_syn_pd['start_ms'],\n", + " 'len_ms':trial_syn_pd['len_ms'],\n", + " 'start_sample':trial_syn_pd['start_sample'],\n", + " 'end_sample':trial_syn_pd['end_sample'],\n", + " 'proc_file':trial_syn_pd['proc_file'],\n", + " 'exp_file':trial_syn_pd['exp_file'],\n", + " 'map_dir':trial_syn_pd['map_dir'],\n", + " 'stim_id':trial_syn_pd['stim_id']}\n", + " # save synced stim\n", + " stim_dict_path = os.path.join(epoch_struct['folders']['derived'],'stim_dict_ap0.pkl')\n", + " stim_pd_path = os.path.join(epoch_struct['folders']['derived'],'stim_pd_ap0.pkl')\n", + " with open(stim_dict_path,'wb') as handle:\n", + " pickle.dump(trial_dict,handle)\n", + " trial_syn_pd.to_pickle(stim_pd_path)\n", + " print('saved syncronized stim dict and pandas dataframe to {}, {}'.format(stim_dict_path, stim_pd_path))\n", + " \n", + "print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### After handling all errors, save outputs and log preprocessing complete" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# concatenate list of synced bout data frames from each epoch and save\n", + "bout_syn_pd_all_cat = pd.concat(bout_syn_pd_all)\n", + "sb.save_auto_bouts(bout_syn_pd_all_cat,sess_par,hparams,software=sess_par['ephys_software'],bout_file_key='bout_sync_file')\n", + "\n", + "# stim sess save the all sync epoch stim data frame as well\n", + "if len(sess_par['stim_sess']) > 0:\n", + " trial_syn_pd_all_cat = pd.concat(trial_syn_pd_all)\n", + " sb.save_auto_bouts(trial_syn_pd_all_cat,sess_par,hparams,software=sess_par['ephys_software'],bout_file_key='stim_sync_file')\n", + "\n", + "# # log preprocessing complete without error\n", + "# log_dir = os.path.join('/mnt/cube/chronic_ephys/log', sess_par['bird'], sess_par['sess'])\n", + "# with open(os.path.join(log_dir,'preprocessing.log'), 'w') as f:\n", + "# f.write(sess_par['bird']+' '+sess_par['sess']+' preprocessing complete without error\\nEpochs '+', '.join(sess_epochs)+' processed\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "songproc", + "language": "python", + "name": "songproc" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/2-curate_acoustics-checkpoint.ipynb b/.ipynb_checkpoints/2-curate_acoustics-checkpoint.ipynb index 3797c18..abd18e1 100755 --- a/.ipynb_checkpoints/2-curate_acoustics-checkpoint.ipynb +++ b/.ipynb_checkpoints/2-curate_acoustics-checkpoint.ipynb @@ -65,16 +65,17 @@ "source": [ "# session parameters\n", "sess_par = {\n", - " 'bird':'z_p5y10_23', # bird ID\n", - " 'sess':'2024-05-16', # session date\n", + " 'bird':'z_r5r13_24', # bird ID\n", + " 'sess':'2024-08-08', # session date\n", " 'ephys_software':'sglx', # recording software, sglx or oe\n", - " 'stim_sess':False, # if song stimulus was played during the session, ignore detected bouts\n", + " 'stim_sess':True, # if song stimulus was played during the session\n", + " 'stim_epoch':['2355_g0'], # mark all detections as song for these overnight epochs\n", " 'trim_bouts':True, # manually trim bouts after curation\n", " 'sort':'sort_0', # sort index\n", "}\n", "\n", "# set type of ALSA bout dataframe to load, depending on how far it's been previously processed\n", - "bout_df_type = 'checked' # options are 'auto' (not checked), 'checked' (checked not trimmed), and 'curated' (checked and trimmed)" + "bout_df_type = 'auto' # options are 'auto' (not checked), 'checked' (checked not trimmed), and 'curated' (checked and trimmed)" ] }, { @@ -97,7 +98,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Bouts: 420\n" + "All bouts: 1273 | Post stim removal: 603\n", + "Bouts post stim removal begin at index 0 and end at index 602\n" ] } ], @@ -111,11 +113,18 @@ "fs, bout_dicts_all = cb.epoch_bout_dict_sample_rate_check(bout_df, sess_par)\n", "\n", "# if stim session, remove stim that overlap with bouts\n", + "bout_df_updated = bout_df.copy()\n", + "bout_df_updated = bout_df_updated.assign(bout_check=False, confusing=False, is_call=False)\n", + "# bout_df_updated = bout_df_updated.sort_values(by=['epoch', 'start_sample'])\n", "if sess_par['stim_sess']:\n", - " bout_df_updated = cb.remove_stim_bouts(bout_df, sess_par)\n", - " print('All bouts:',len(bout_df), ' | Post stim removal:',len(bout_df_updated)) \n", + " bout_df_updated.loc[bout_df_updated['epoch'].isin(sess_par['stim_epoch']), 'bout_check'] = True\n", + " len_bouts = len(bout_df_updated[bout_df_updated['bout_check']==False])\n", + " print('All bouts:',len(bout_df), '| Post stim removal:',len_bouts)\n", + " first_bout = bout_df_updated[~bout_df_updated['epoch'].isin(sess_par['stim_epoch'])].index[0]\n", + " last_bout = bout_df_updated[~bout_df_updated['epoch'].isin(sess_par['stim_epoch'])].index[-1]\n", + " print('Bouts post stim removal begin at index',first_bout,'and end at index',last_bout)\n", "else:\n", - " bout_df_updated = bout_df.copy()\n", + " len_bouts = len(bout_df)\n", " print('Bouts:',len(bout_df))" ] }, @@ -129,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "90c0ab08", "metadata": { "scrolled": true @@ -157,23 +166,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "34ba1212-7b8b-421b-a299-864a300dde3c", - "metadata": {}, - "outputs": [], - "source": [ - "bout_df_updated = bout_df_updated.assign(bout_check=True, confusing=False, is_call=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, "id": "8cc5bd8a-634b-4b5b-b2a5-d8a59d9e25f6", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8e0284d6faa04fcdb33aa35dae5fc1bd", + "model_id": "f7767881c42242399d7eecc6db1a881e", "version_major": 2, "version_minor": 0 }, @@ -187,18 +186,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ea70286a41a0463f967dcffa40c07e27", + "model_id": "5ecdb1e4a5524ff4b59a020c8071926c", "version_major": 2, "version_minor": 0 }, - "image/png": 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6zuHDh3n33XepqanB4XBEj5HYY+XMM8+M+/y2bdvQNK3bvu2677u+9tprr3HNNdfwu9/9jmnTpqU94tlf7030maamJi677LLoNvif//kfLrnkkpTXPdFrXf9vtVppbm5m1KhRtLe3c+DAAYYNG0Z7ezvBYDDlsqe7ntn4N93PBINBvvOd76DrOi+99BIAo0ePZu/evdHlRSJ5brjhBmbNmoXJZEo6Qprua7l4/65du6irqwPgwQcf5Pzzz8fj8WA0GqPRFl3rVa7+v3//fhYuXEgupTpri67rtLa2EgwGKSoqwmw2R0N9I6OMidYh1XXN9G/J3veHP/yBe+65B+icuvQrX/lKXLucqLuUymuZfu5UXP5AKmui1y644ILo/51OJ8899xwVFRVxEZNdIygNBgNmszkuGsPn81FfX88XvvAFvve97xEMBrsdU6dMB10IkT5d1xN2DL73ve/xP//zP3Hv27t3L7/+9a/ZsGFDt/c//fTTXHTRRTkt66nG7Xbz7LPPcuWVV3b726ZNm5g0aRLjxo0DEnfeuvL5fL0+By9OPYnqRnFxMa2trUDiiw4hxOknlfNIT6699lomTJjAt7/97SyVSIiBJ5JDo6/HU2+kgy6ESKiuri6amDA2a2uiRunEiRMMGjSoP4t3yki0Pbs2y6mcCKQpPz11rRuRHAEvvvgiM2bMOC0T7AkhusukQyE3foXID+mgCyHSkkqHUqQukq04Vrod9JEjRyZNciNObV3rhhyLQohEfv3rX3P99den9RlpT4TIj8Kb50AIIU4jXaebef3111P+bGSqwB07dmS1TEIIIU4t//iP/xidylIIUdikgy6ESMt3vvOduN/TmRtZ9C7VpF+XXHIJjY2N6LqOqqo5LpUYCIYOHZrvIgghCtjvf//7bq/V19fnoSRCiJ5IB10IkZaKioq43x955JE8leT0Jp0xIYQQ6SgrK8Ptdse99vOf/zxPpRFCJCMddCFEWs4444y4388666w8leTUlGoin3/913/NcUnEQBNJECeEEMlEpn6LGDx4cML3ff3rX++P4gghEpAOuhAiY3feeWe+i3BKGD58ePT/yS6WevqMEACrVq3KdxGEEAPAjTfeCMDPfvazPJdECJGIdNCFEBkbNWpUvotwSoh9BtDhcOSxJGIg++lPf5rvIgghBoB169Zx/PjxaCRWokfVQqFQfxdLCPEZ6aALIdISG4ItU7Bkh8lkyncRxCnAbDbnuwhCiAEiNp/MFVdc0e3vP/jBD/qzOEKIGNJBF0JkTJ55zY6u86D3RqbKEUIIkUvjx4/PdxGEOG1JB10IkZbYEXTpoGfHunXrOPPMM7n++utTev+wYcNyXCIx0MhUe0KIbCotLc13EYQ4bUlcpRAiLdJBz76SkhI++eSTlN+faqZ3cfqQOiGEyJY//vGPOJ3OfBdDiNOWjKALITJ2zjnn5LsIQgghhOijGTNmAHDxxRdz5ZVX5rk0QpzeZARdCJGxkSNH5rsIpyWJXBBdyQi6EKIv/v73v/Pxxx8zduzYfBdFiNOedNCFEGKAMRgk+EkIIUR2TZgwId9FEEIgIe5CiDTJSF3+SQdddCXHpRBCCHFqkKs8IURapCOQfzL/vIiwWq0A1NbW5rkkQgghhMgG6aALIUSBu+666/JdBFGgtmzZwrXXXsuDDz6Y76IIIYQQIgvkGXQhRFpkBL3/yeioSOacc87h97//fb6LIYQQQogskRF0IYQYYCTEXQghhBDi1CQddCGEEEIIIYQQogBIB10IkRYJcc8/GUEXQgghhDg1SQddCJEW6aD3v67bXDroQgghhBCnJumgCyHEACMddCGEEEKIU5N00IUQaZERdCGEEEIIIXJDplkTQqTla1/7GmPGjKGmpibfRTltzZw5M99FEEIIIYQQOSAddCFEWlRV5dNPP5WR9Dy65JJL8l0EIYQQQgiRA9JBF0KkzWCQp2PySW6OCCGEEEKcmuQqWwghCpx0yIUQQgghTg/SQRdCCCGEEEIIIQqAdNCFEKLAyQi6EEIIIcTpQTroQghR4GTecyGEEEKI04N00IUQQgghhBBCiAIgHXQhhChwEuIuhBBCCHF6kA66EEIIIYQQQghRAKSDLoQQA0h1dXW+iyCEEEIIIXJEOuhCCFHgYkPcX3vttTyWRAghhBBC5JJ00IUQosAZjcbo/zVNy2NJhBBCCCFELpnyXQAhhBA9u/TSS6mrq+NLX/pSvosihBBCCCFySNFlgl0hhBBCCCGEECLvJMRdCCGEEEIIIYQoANJBF0IIIYQQQgghCoB00IUQQgghhBBCiAIgHXQhhBBCCCGEEKIASAddCCGEEEIIIYQoANJBF0IIIYQQQgghCoB00IUQQgghhBBCiAIgHXQhhBBCCCGEEKIASAddCCGEEEIIIYQoAKZ8FyAbwuEwhw8fxul0oihKvosjhBBCCCGEEEL0SNd12traGDp0KAZD59j5KdFBP3z4MCNGjMh3MYQQQgghhBBCiLQcOHCA4cOHA6dIB93pdH72PwOQfAT9i7ZFvNXxQL+U6VRSrc6nyuDEqCg0B31s7rgv30UCoEa9iq3e/43+fo52BR96Hs1oWdXqfLZ5H8q4LH39fDbV2a7m7Y4H415bWl6PwwSuANzXdBtftC1ibqUNb8jAxy4dbyjMXz13AvDvY5czytFGe8BMs8/K00fAbDAy1m7g5uO3cevZCxjiaOPZg0N5rdlNuVHlGfcdfS53tTqfEGHe9/6xT+vak/PUb6S1/Fy6vKieL1f6+IcLXmPnJ+MYVNKC5nTzPy9N5aZjt8Wt2/XD67nszE8Yc/4OfK0Otrw3keNuO6+f1Lin8baclK+3bfUVdTkBwrzivScn39/V+eo3eC/L+65rGwJ9ryP9fZ7Z/g9fwF7URvPxcv598/ns8ruytk7V6nwMKN2WV8hq1KsIEEppfb9kW8wbHff3Q6lyr7d9fI52BWrYyjsdG/uxVLnXH216X64tsq23siRq02KtraynSg0TBr4xcRtmS4C3P5nAR61FnOxQmFTmYUxJI+8cH8Jg1ctLx5w4THC0AzpCOseDnrhzbrU6H5tuwWGwcFJvZ5v3ISbZFmLCmLNj66vaClrDHSmf+y9Ul6Z1nppjX4nNYKA56I9+bqrtGoyKodtyptquocho6fEaaLa2grawP6vb40u2xTgMFsK6zt+8d6X12d7qCGS3zkeuj+tsVxNSQmz1/m9KZeirqbZrctpf+Yq6vNu2/6q2goAe5iXv3WkuTQfCMf3ZU+QZ9M/D2pWkP1PV5bzV8SCgME1d0eN7q7WFPf493Z+J9qui/0/03XXaNWl/53R1ZdK/9bZ+qf5EyrTN+zDPuO/kqfY72NxxP5c41vVpG2TrZ6v3objfP/T8KaPlTFdXss37cMK/1WhXR/8/R1tDrbY44fuSfT4bP7XaYuq0a1J+/9sdG5miLmOWtjr62j2Nt7On3ch9TbcDCrrBgIKNQRYTrUGdEMboe5t8dp45VMWajzbxo9330Kz4GG5TcZqs3DRhFcMdIQy6nQsqOhhtLaXIZOOHQ69ldcW1cdsr9vt7W7/INnzf+0jS98XW68hn3u7YmNa26Wn5qfzErl+m+zJyLFRYrXzaVoRNV/nKNS9w7vQPsYVVBlvNrK28lhKDI/q57858g3PP3Y/FW0ygsYphDj9vnSyl1W9mUem1XFnUkPQ7E+2Ham0h09WV3bZpZP2qtYW9bqu/ee/mFe+9GW+TKeqyhK8na9ve8z7CZHVp3Gtz7Wup1hYmrQPJviPys9X7EFPV5d3qSKLPxX73LG11t89F6sdbHQ8m/Fuy+hBZbirHS622mDnamrjXyp1Bhow/zpixJ1CNVhTFzIKSa6N/n2df221fxpZvmrqCmVp9wrZtm/fhuHa2WlvIDHVVt3WOLVMq9SHZ9pkdsw267uvefiarS5miLmOr96Ho+iYqyxxtTXT/vtHxQErLnp1iW5aoPqRzbo8tbyrbMbae9na8fuj5E+90bEr699hyxp6vz7V/M+XtH1vmVM75E+1Xsaj0WmZq9UxXV8bVi5lafUp1JtF6d/3uWWnUq0TfE3tt0dP+nGdfG21T0zkv9fYT+52RsiRqoy52rOt2XdR1W9x8/HZ2tKp0BFUOHD6DP229gBKLAZffxmi7iUlDGhk1yM3cMYc4o8TLP4xw87bLz1i7lafa7+Dtjo1x23Gb92He6HiAv3ruwqxYAYV3OjalfGwl++npOvavnrtQFTVpPYys8zz7Wi5xrOMV773R8kbqaLJrOVA4rnuwGSyUGLXoa5s77ucV773d3ltktOMJE607XevXVHU5z3vu6tP2SFQnI9v8b967u7XJier4TK2eufa1wOfXznPta6PbY7q6klptcfQ9ia6nL3Gs6/WcCkq38kSuj9/u2Bj97q3eh3rcB5E60FPbO0Vd1q08sdtqc8f9ST/b9bt7Ol67HkO12mKmqyv5m/duLu7SH/qr5y5e8t6T9j6uVq8CiHtM+5TooAshhBBCCCGEEAOdouu6nu9C9JXL5aK4uBiio4B9V6stwqqb2Zx2mELvpqnLefWzsIg6bTFvezrDXqarK3jZe2ePn+36non2+Wx3pxdaXastwqKbeD3N0NQ52mrKLGb2+doxYsCjdLDV03u4XCZl7A9z7WswKgptIX/cNq3TFhMiFF232H3Um5naKjr0QK/btlpbwDbPprjfjRjZ4ukeGpvp9pujreY5z60ATFaXUG5QCeo6z3lu5dtVDQzTgnzcauLOxg1AZ91yGi0YFYWvDQlgUnSa/GZePBamVe/gTe+9XOJYyxPtN1OnLWbZcBt3H+xghMnJ7mAzZ1nK2dSyoddy1WqLMOlGrJh42XsnddpiTLox6Tar1RZh16287L2TWm0RVQYnJ0MebJh5ydtzaH2ttijhNs2HWVo9AC94Pg9Jv7qkgZ9MeZ9RX/gIk+bj3ecvYOpLf2FZeQOfeNzR9sf7cweKoqOofgD2vPBFnv34HP58CIpMZh5x3dinskXaOx09uh+61tFsSbZP5trX8Iz7lujv09UVuBRPwjJMV1fQYmjPWrsyS6vHo/sT1sE6bTFW3cyr3ruYpi7Hqpji9mEyse18b75ZvJ6HWxPvw9jjf7K6BCMGfEogekycqD+Tkqm70dsU7rhxKXvdVn518EZWlDdgN8Pvj3Y/JudoqwkQ5kXP7dH1d+FN2M7N0uoJ6CFe9t5JtbaAYt1Os6ENk25kq2cjc7TV+PRQr8diRE/1aoq6lFKDjac/qwfJ6socbTVtui+tc9g0dTlnaBrbvS1s8TyQUrs6Q12JSTHwgue26HkTiH5vjbaQrZ6N0X8T6e380dNnoXObBJVQyuegvoqtt7O0+pTqOvS+Hl3NUFfSbGhjhFKGUVF4vO0moLOOj7M68YTCPPbZa3XaYgy6gTe991KnLSZAMOW2qVpbAIBVt/Cm914AZmv1qEZT9DsjUrkGg87t0q77ossDuLJoPa5ggOc9t0W3Rey+T3f7ZMtUdRmbvXczWV3Cm957aRjcwPuuDuZVmTmjqI0hzha2HBvKJ20W6srcfHH0biqGHMNgCrL/0zE8/Ml4Lqg8yYfNpVy/+5Yev6vrtdN0dQUBQj0epzXaQsLoGZ1remtjp6rLcCiW6HUQdLYp5dhp1/1x1/exbU3k2vCp9puB5NdgM9SVBAix2Xs3s7R6rIqBxrA3rl5EzLWvwaeHom1urBptITbdmrS/kUq9nGdfQ3s4kPR9FzvWRtcn0XpYFAPPp3CsT1GX9rg/+/p36DxmDShs9WyMa4PSuRZPRW/XOF2P2dg6Ml1dgVfx41P8fb4OqdUWoesBtnofpLW1laKiIkBG0IUQQgghhBBCiIIgHXQhhBBCCCGEEKIAnHIh7nXaNdEQiGnqcsLoaYepT7TPZ7BeTCPt3UKSYkMiarSFGDFSaXDQFvLjI5gwtCXZdxRi2DckDiucoa5MOYQxmUJe51RD204VM9SVVBfb0EkcAvvTkesosYTY1W7m90c3ME1dzpxKG0NVH0M0N0e9Gse8VsIo/H97b0ornHcgmqwuIayEsxZeNV1dwZl2lRa/zrkl8L2v/wVLcTtNu4ez/qnJTCwxsLddiT4y0PELO6ZqlfDeZt575Ct8d/Nw/uW8Ng60FbHXbePpxmbMujnl9qcQpNIeFNIjCpC/MNWeNDWMpmjaYULHdK7+t3p2BZsYbSxlYolCS0BJeHynKhIe29t7gJw8DpaOyLl5iroUp2KNC2uNqNUWMdTg5IkkoZ79LTaUM5si4cy5ks4jgLk6ZhItN9XvStT2pHN9Ms++JvoYRtcw2a5tVj7PjZFw7mKziYdbb2SKupQap4NhWohhWgdjSpq4++MRXFDp4b1mjRmDWyiydGC3dmC3deBwuHlv71jePFnClqYgQ1Vz3KNXiczUViUM4y4kqYTVd33kqque9muyv2XjOjoVtdoigGg9TCWsPFv68shCIYm0B9kOq09OB0IS4i6EEEIIIYQQQhQa6aALIYQQQgghhBAF4JQLcY/N4p6NMK9shWflKpSt63fkIqykL9sglZCe2NCyifb5OMIa7QZPj+FmhRb6GjFZXQKQtN5NV1egoKQV5jRbq08pu2Zvum7nOcUVGBX49aHEIbAryhs43BHkGfctLCptoLYswN+PG1k61sUQZwvH2otp8ln5qFVjb7ve50zi2ZLr8E7o3H5FYXvGoYuztXqmDTIx3N7BmKIWhlccp7ishXaXk/u31vBRq4FGvz+634+tmED57D0Q1ml5ZQS/evwiHOYwVoOOO2jgqcbWgjweTiW5bnMShdhOU5fjVfw9fu/JtWMprt5LsEnj2v9eyh0nNzBdXYFNMSUM805XOpm8I6apy/EpgT6HBub7kYJM2pJMtlcuZOORsv4Mjc2GbB6jM7VVhHU96bk69norEgYbGw4b+3jI1SUNADwYM8tJKo+PRCQ7DlI5PhKF6F7uXIfdZGBahY/Jw/az+eAoHj+k4NEDzB1s45DHQP25O2lqdzL3rUe4omgdxwMdVJpttAdDPbYrs7V6TtCW9xDn3upCT9s/MrOMgpLwPb1d19ZoCynFkbAdSPe4rNUWoRPOWTuYqH7ku91NplCv+7NDQtyFEEIIIYQQQoiCdEqPoIu+q9MWU4qW0UjMFHUpYfSkIxC5GvHPhv5LDNE/ZmqrKDNZoqPcs7R6pg8y4QoY+NAVjEuGMkVdyhmqg46QHp2beUV5Ayf9YSaWKFSXtvJ+SzEAr58MRhPQdJXNu7Cp3nWu0xZThJryCFa262C1tiBuvt1YE+3zUcM23vTey8WOtZxTZOQ3hzfwwMRrMCg6I4ubaO3QeP1YBdtblLiIhI3nL+ayK5/AcpYf/w4Lf39yNu+cGMTzx4O86Lk9YUKbVOpwOqM42ZJKvRiod8ojiUP72nYk23eJkg81rh+DWtFM06cjeHrrJO7aS/Q92dqO09TlqIopGtExUPdPb2Zr9dHksIU6ihSrRltIOY6sRFhB/Ij5qbqPe9LXdZ6sLuFczckJXyguEeGVRespNit85GnPekRCotHcns5rV5c0MGNwBx0hI1OH7edYWzEP7B6EzQgfedq4aqiNiWWNtPhsfNrmxKjoPH80nLU6NlCc6olvRaGREXQhhBBCCCGEEKIgSQddCCGEEEIIIYQoABLiniP5CN9OJzxrjraadt2f97lr+2teyFNRbNj3DHUlrYq713k9Q/rnoWrXDW1gmBrgL0eIe4ThYsdazioyYjbo/MfBzjDr6eoKznOqjHMGmFjWSGVxCx1+C+8eG8r6j/u2/wr5UYdUpRJOPl1dwcveO+Neq9YWsHRIEV8etQeDorO3sYL7dpdyItBBu+JFwcAWzwN89PVpOIra2fbJeIaWNNHsdrD1RCVDNS8ftzp4pLERR1hL63hOVOZsJJbqKpvHeLLy5SIcOV+PuaS7D07Un4l9+HF8TUX86pFLeKnRg10xM8RmwRUI81jbTTksbd/0R0LHU1222s/+rO912mLC6HHXK3XaYsZZOh+d2tSyganqMkoM1uh844VuhrqSIqM5LrS9EP105DpKrUHOKW3EHzKhmv389wdDOBRysWq4ytjiFpxWL3ZbB03tTv7r/SqeaL+Zix1reSrL6zZTW0WHHsCvBPvlcYqJ9vmYdGPWzhXphsF3bdtnafW04Elr3dM5P+TifJ6r7+nPhJQz1JV4FF+BPcYqIe5CCCGEEEIIIURBkg66EEIIIYQQQghRACTEvQD0Fp4ZG7oemU8dIIw+4EODM3E6ZpfNhth6Vq0tYFZxOQc9OrsC8XNo11c0METVcQcVfns4fo707w9pYIga5L0WEzs6Wlk6TOU7OztD5hPN/9tfYUuR2QY8eqAgM6/O1Fbxouf2uNdi6/EDE6/BZgxy5bsbma6uoMJswxsKRcM793xjEhbVh83pJhwy8umOM9h+fAivn1R5392GGWNBrndEf4Xa5dJkdQlWTNgUEz49VDCP5pyoPxNtyEnaDlTx/T99haCuM1xTUE067zWHCz7kVh5zEpH2YaBnzo5t53pr8+bZ19AY9vbrIx7T1RVcOcyE3RTEYQ4woqiZT5sH0eSz8INdt/KLMWsZ52xneFEzpUVtfHp0CJdv+d/oeTybx2rXRypOhUfdBpLYGVx66oMM5OvtRI/tpPooT/+3RRLiLoQQQgghhBBCFKTTcgT9VBjNKXSxd1oTJceKdTrsj3yt40T7fCr04oTzgs/W6qmyWbivKX6UvE5bzNfKi9jTpnDI1xF3x3x1RQNVqo7dFGKo2sGDe2149VC30WHRs4sdaxmqGjnWEcakwMLRLkptXva6Snhgv4G5g02c8BmjEQyvTZ/HmDH78HdYMVkC7N8/nKf2jmFHK2gmhbsbN/TyjZ0SRTmI/DvX/g0+cP8xo88euLqaoqqTnNw7jD9vr+b7n3YmfLymrIF9Xm+07c10hGqauhyv4i+4UZS+jrjlqk3OVUTAqXienKIu5Ry7A1dA56DfjRFD3KhVYSZz6l3XhGqxkWR12mJU3dLjNVEuXTe0ga9UnWRs1RH2HR/MK8cqcJrCvNmo8KjrJq4oWseVI90EwwYqVA9PHaxgu8tLhdnGI64bE65TPmXaDtRqizCgDLi6lUi+Eppmw0xtFe10DIjy5yIZ7edkBF0IIYQQQgghhChI0kEXQgghhBBCCCEKwGkZ4l5oshm6Jok20hdJgnEqhhCmomsSkJWDGqgp82E1hHn6sJVHXTcxQ13J1HIrJeYwYz5LInPIVcJTh0rwh8AT6nm+5XTmO56mLieMntac3j3pr1C8GepKBpmtHAt4CRFO+p2RY/S7QxrwBGGn28dFVUauOHc7wZCJx3ecTTCs8LHLyL1NG6jTFvOjM3W2NRXz9TF78AdN7G4ppzVgxmYI80mbLS6Z3zR1OWbFSCvuHIZjDWwztVWYMfCc59Z+/+6J9vkAWWtr9l1VQ9now3hOlPDn16fyXrPGBy4/msGIoijsDTdm9F3V2gJGGEqiSebm2dcMmDmp86VaW0CVUswJvb3gHgnoT6m299PU5Zzv1GgL0u1Rq4EqUbhxrbaIoQZnvyRsnKXV48KbNGT4X0as54RPYfaQFo57VT5otfK1oY38w9ZN3HBmPUXmALfuUVg9RueTNo2jXoU7TvbfvolNXpZPs7V6ni/Ax8GyeZ3a2+On2da1fzJQ+ivZLmfiZJIS4i6EEEIIIYQQQhQkGUHvRTojf13lIqFAf9/x6i9ztNUZjWb1dGerWltAsW6XKXx6MEVdikOxxN0pvm5oA5MHteIOmFn10V1A5/6pLTPREVI4r8TDhn0d/POZBlr8Fl47rnHMF4pLiiN6Nkurx2YwMkIzst8dZNEYLxec+TGKEubZD87n0zYbbzR/nuBrzzcmMejM/RiMYY59NIaH360lpCv8eM/NXFm0Pm5KtlNJjbYQTbdldbqTU60NPbhoImVnHKBlzzBWPDINX0zSxsj5q6+jLgN5qp2BphAiufprfy8pa2CwDdxBuPH4qTGC3tUMdSXlZgvtwVBOI3amqstQFXOvSUD/bdQ6xhe1YzWGmHTmx+hhhcfeq+FvR018sVxnrNNNzcg9eDtsNLsd/J9t5WldQ/UlYVmu610qy7+6pIGgrvNQ6409vi8TddpiTLqxIJLrZWqifT6OsBYX5TBQRsJTket1iV3+ZHUJXkOHjKALIYQQQgghhBCFLO8d9J07d7Jw4UKGDx+OpmmcddZZ/OxnP8Pj8eS7aEIIIYQQQgghRL/Jawf9wIEDTJ48mddff51vfetb/Pd//zdTp07lpz/9KVdffXU+iwb0LbwdSBreXq0tYKJ9PtXagrSXeSqFZsaKhH7N0uqZpi6Pvh5JqJRMT6Eo2zybCiq8fbK6JG/fXa0t6FbfZmqrqHY6GKZa4v72SnMbB90aroCZGm0hAIMsZtoCCv9zZAN2U5A7LjhOWIff7/MC4DQZWV3RkPC767TFOVqrgasFD8NUI2YDfGUwfGnsTorKmzl0bDCbT9o42gFlJmv0/VXVOzFaAvznvfOx2Hy802Tix3tuZqa2ikdcN9IS9jFNXZ5Rm5IrtdqilN43W6tP+retno29hrdPVpekdWwVQhs6Q12ZteNCLW5HDxkJ+CwUmYycV2TlyqL1zC9eT7lBZbZWj0E39NqW9iTX4c6Zli3VOjaQ5Du8HXK/v6Fzn3tDOgEdmvzxf5trX9PjZxPt96nqsmwWL2te8t7Bo66bMgpvT+e42Oy9mxNKa/ScncxQ1YdB0VHQKR93gEFn7meE3c0wzcAzx/xsa3by908nYDKG8AXNeBQfV5c0cLlzXUrleNtzf8bHZa7rXSrLNxlgrLPz8YtsM+gGKkxa1pfbX2ZqqzDr5m5J/E6V8HZIfV1i+ymZLv9N7709tvemjL4hS+69915aWlp45ZVXOPfccwFYs2YN4XCYe+65h+bmZkpLS/NZRCGEEEIIIYQQol/kdQTd5XIBMHjw4LjXhwwZgsFgwGKx5KNYQgghhBBCCCFEv8trB33mzJkArFq1infffZcDBw6wadMmbrzxRr7zne9gt9vzVraJ9vl9Cm/vyTbPJra7H+oWSlFI4an9pWso1Aue2+JCWgsh3K+vImFquapPqdjm2dStvoX0MAc8Ie5u3IA5JphmVrkDX9jAW43m6GMaLYEgo+1B7j1vCU6zH6MhzFBnK1dXFRPQ4YTfzzvuloTfnWlG13yr1hYwXV2Rk2Vv8TxAMAyTytxMH74fm9pBW1MxvqCZuxs3MEyLn1zDWOLGcn6In1x3GwcPDcVs6Hx0oKbYSq22iA7Fx6veu9IKNZton8/VJQ05e/QilXDCam1Bn+eafdN7b78cW9ncTi9578jacWErc2EZ3kzFObuZOdjP7vYQZoPCOCeYDAbOKrIw0VYebUtrtIUFc66Zqa1iprYKNWzL6POSWT77+qtubHc/xBG/l44QGBXiQrOfcd/CsvKGpKHuASUAdF4/REJNN3vvjv6/WlsQrVupyPWjEpmG3293P8Rc+5peQ90j+2y7+6FeZw5SFB2j0nl+8TU70YNGKu0uxjoCrBkXpNIapEpzY7X6GORwUYRKkQXGOVPvLmT7uJzd5dHHiFptUa8h/YlMV1fEteeXOtdxZdF6pqhLqbCBatTZ6W1Pe7lT1KU97us3vffyeNtNaS83U9mu1y96bu/zvp2qLiuY809fOAyWXh/FicikjkKeQ9wvuugi/u///b/88pe/5M9//nP09Z/85Cf8/Oc/T/o5n8+Hz+eL/h4ZiRdCCCGEEEIIIQaqvGdxHz16NDNmzOCWW27hj3/8IytXruSXv/wlv//975N+5t///d8pLi6O/owYMaLbe/qSEAd6H7mdoa5khrqyT9/RVSVFORux62812sKkSZBit9sWzwMZ310qVF3vWhZqFMDL3jt5qv1mLneuixvNMyhQZfMxvijM+srORClHdRc720wsef9ehpQ0AdDmUwmGFUbadcrMZoYanCmPWMSaaJ/PxY612VmpLDJizGlCsTsbN7C7XeOeHWMZueldntj+BdwBC1cWrafRp2A2KNH3ht1WdE1FDxjpCFj4apWbhUM0fGH40Rkwt6yUOdrqlL+7RlvIUErZ4W/CmKfTwOXOdVRSlJfvjkjnTn6+ImB6K6NiDqJoBggrhD97rUqFCmuQCwbpfOjyc1/T53NMB5VQWpEWk9UlzOohkV+knc8kac6Lntt50XN7XqOLCtFUdVmfr2Ey1Z8JnyLRcu5gmOHGkmgissud6/jY084z7lvi3h/ZJpFz6hbPA7Qr3m7L2+bZFK1bycQeV7mOxOiaVCuZRKOvz7hvSXoNca79G0Dq+2yufQ1VqodRpSeZNP5jDuwazSsvXsiGD0bxw1238pvdfsYXt/LFcz+gcsxBihztfH2IwnklPsqtoZS+o6tEkUdT1KUJ3zsvwYjkTG0Vz3eJrIzY4nmgx4iBZNegDoMZM8bodz7edhOPuG7EppjRdXAHDfiVYNxnpqsrem2LX/fe021fp3J9m2nCsSnq0h7P+/0dYZRKn2iz9+5u9TVfbV1fPOO+pVv7lExvUS3J5HUEfePGjaxZs4ZPPvmE4cOHA3DllVcSDof50Y9+xNVXX015eXm3z/34xz/muuuui/7ucrkSdtKFEEIIIYQQQoiBIq8j6Bs2bKCmpibaOY+47LLL8Hg8bN26NeHnrFYrRUVFcT9CCCGEEEIIIcRApui6rvf+ttyYMGECpaWlvP7663Gv/+///i8LFizg6aef5qKLLup1OS6Xi+LiYsAIKL29PStmafXYjaZuCR+mqsuwK+a4xEfV2oKch41Fwoh6ChWcqi5LOdQqF1LdDhPt8ws2LDwTNdpCirH3GG7Xn6apyxliUVEAs0HhgeYNcX//ZvF6AmGdeUP97HVb+dXBGwGor2jg/BI/V37xDYymEEcPV3GwaRCXbvlfLnWuw2E08GDLhgTfKLqaa1/DM+5buP3s5Uwavo+TrmKe3D+UnW0hLAYDk8rD7Gk3ctuJzu3ZuH4MFqcH9eyT+Pc4+fCVOgJBEx0BC1uOD+aQ18zfWpoyDqXKlkTHbq22KGmoXSrtVl/0R9ubL5Fwy9f/+UnM55mguY3/+flKnjyiM6nUgmbsDHj/88mWPtWL6eqKXh/1mKwuie7DufY1eMKBfplvvhD3byGdv2q0hYTR87qNpqnLE4YnR1xT1oDNCLed2MA8+xpKLaZu56R8qdMW5yTJaY22EAVDxiHIlzjWEtD1lEJsY49NgOe/9A+YjCGCISODS5ppaXfw6J5RvNLcxpvee7n97OWEdAVf2MD44mbu2lnFhOIwx7wGbjye+/0yRV3K6957elyHvpqprerxeiz2WrlGW5j382qmCrF9zIfe9uF0dQXtijfP+1kHQrS2tkYHnfM6gj5+/Hi2bt3KJ598Evf6gw8+iMFg4Pzzz89TyYQQQgghhBBCiP6V1xH0l156iVmzZlFeXs63vvUtysvLeeKJJ3j66aepr6/n1ltvTWk5vY2g93a3LFPT1OWE0fM6Kt1VoruP6crlqNbVJQ0EdZ19/nZJDNRPqrUFVOBMOJ3VHG01rXpHt33xu/GrGGFvp7FDZdVHdzFHW81XqxQ+ajXx46nvUjnyMCcODOGR98/nnSYDdpOCKxDmUVf3KUTSGVHqabS1UMyzr+GE7k44spLq3fbLnev4Qil8ZfghrKYAJc42GluLOdJWwkG3nUBYYWebiVs/G0F/c+bXGDn6AMVnHCDosvOXZ75KSFewGYM8c6icve4gT7tv6fXY7e/RvVRGX0XqEo3oeX9WhPk8CO/3cPuNS/mw1cbvjnaORo62mzAawBeCW09soFZbxFCDE4CTIS/mHCdCzKVU2ooZ6kp09AG7jtmSq5HgbFhQ0oArEMSnh3nhs3PUHG01z3lSu/7Lhon2+QynlKdTTPqUDb1FFuTK01/8BqMrj+L2qljNATTNyws7zmH1R3fxWO1VjBl0HFXzcvh4BftdpQTDBv5y2M6mDKPjCimiJFZP5ZqurmCCQ+WAJ9hrlEJ/rl+hbst8maGu5CXvHSm9t2sURiRKIlf9w4jUoxi6j6DnNUncjBkzeO211/i3f/s3NmzYQGNjI2PGjOEXv/gF119/fT6LJoQQQgghhBBC9Ku8dtABJk+ezFNPPZXvYgghhBBCCCGEEHmV93nQsy3RnIMvem5POL8kJJ+PMRWveu8qqPB26JyHses83L3pOgfhm957+xx+Xq0tSDhnZFMgyEG/J2/h7bXaoqzPX59tNdpCpqhLmWifn9Ec8bXaori5R7d5NvG85zauKFrH3Jh5RqepyzEpCiMtjrjPXze0gfHFzZSqbj5o1Zit1ePRA2xrMmExwKGTFTz90oU8tP18zIYwR/xe7m7cgC+c+GmZSEhW17k+p6srur03G+HtM9SVCedeTVfkuOh6fDztviVpuGiqSUYsBoVKW4DnDwznrSPD2XNsCHtayjnk0fiw1Yw7aMAU87SO12+haOQRQm4bH7zxBdwBMy8dK+aXOyyMtIfw6p1z1BYrNsJKOOF3JguPmx0zx3Um9a0np3tocTbEnqMS1btguwpuL3pYYXuLDX/4szl7S40c69D5/dEN3HpiA9PVFRhQeKL9Zp5ov5nXvffwsvfOtOaCz1SNtjDr35NKW/GS9464ebJPZT0du4Ua3g5w0OfhqO6KhrcDePQAV5c0pLyMdOtW1zZ9u/uhHsPb+zpP8/zi9VziWBv3Wjrh7V33ba22KOMyHXM7+OMH5/HrLeM56SrmwLHB+ENGLnasZWhxM0XFLg4eG8xJj5Mzyk6wz61ywu+nYXAD9RWf75NUr6MyDcmerC5hVsy5qa8S7fNkjIqBj9u9KSXh68+Q81S/qz/a9EL47lTD2wGCSih6HFVrC6J9t0h4+0xtVdrfn6hf2XX9t3k2ZXw9esp10IUQQgghhBBCiIFIOuhCCCGEEEIIIUQByGsW92zJxzzo/ZVNsaf5H3vK2J4sc2BsuSP/n6Yup9RoxRMO4dUDCcP2q7UFFOmahKwWuCnqUjTFEhcumMw3i9czzgnHvQp3Nn6eofUP41dx3qBjeAJWPmwq4we7bmV+8Xr8YZ1J5RAIK7gCCqpRx2rQefqEO+NHFiKZNLN1PM1QV2JQFFx4+xQun+3jO1l2929XNfAPow/hD5nY0VzGvYfdfNFRwggtRJPfyG8Pd+6Xv19wCW8dG8KMEfuorDjJrgMjeO9kJVuazOz0tvc4c8M0dTmaYsajB3jVexfT1OUUG6081X5z9D2ZtjPpmKIuxaFYaMKT1r6ZrC4hrIQLIkw3Er6WzbllE2WiTWf+2pNrx+KccADf4XLW3nYlD7Zs4Oej1+Iwh/io1cLNXeYunmifj1k3Y9KNvOm9l+nqCmyKKadZs2dqq7AqRg7TnLPz5kCZ8zd2juW+mqIuJaiECuLYgM5s8T7Fn3Qfz9LqCerhaH2foi5llNWBUYGPfE1JHxGaoi7FbfBmXHf6+vlkumbHj62DvdXHfM1Y8rvxqzin7CRWU4D9rWWMLmmkxWvn4b2VLBu/nzafysF2J3XD9rPrZCV/P1ZCRwgCYXi/o4lKpSil0O+ezFBX0qZ4ku7v2PNvbzOjTFGXYsGUUrhzJsdejbYQs24mqIQKfoaZVGZrSHde93TeP0VdyhCzxu5gc7TuD6TM8309Jnv7fM/7p8DmQRdCCCGEEEIIIUQnGUH/zEC6y5NIpPyprke2RsW6KoRRjGR3sfI172ihmqIuZWqJg6NeeDBmjtMfDm1AUWBXm44/HOaJmJFWgKfqvsmI8hP4AmZe3DeGH+7qHHlLZ07KRNK9s9uX71EwJL3TGZmDN5sjXcn859g1VNp8eENGHj9k5rJhAT5ps+EOws3HN0SPp8drr+L8M3ZisfnweW389f3zafGbeb/FzC5PBxbFgMVgpDnUQZHBynHdlXBb1mqLsOpmAkqQECHC6EmP1zptMWfZirmvKbX5b6u1BWi6LavbrL/b5UJov9J16JrzsJe34Do6iP/8+wX8/ugG1lY2UG7V+ahV53igQ9q9LMjl+aO/2r5M5eJ6IbI9I+eNc+3foDxcTLvSQTl2mvHkPDKgp8ihU9XPR69l+tBDeAJWfEETe9qK2Os282JrIz8aZ6ZSa+fJ/UMZogbxhxVaA0YOeeC4z8/zKUTmZdssrT6liMBsipz7UxmRFt1lu73INPLBqlvSLkcmbXF2zg0ygi6EEEIIIYQQQhQk6aALIYQQQgghhBAF4LTqoNdpi5P+ra9hlH2ZlzITXeeUjpQ/1fXIRXg7pJ88KRfbLFno8qveu7I+z3NXsXMWF5Kp6rJuc4qeY3dwwgf7fZ7ofpiqLuOQB0osIYZpCq0hf7flnDH0IGUVjbS4HVgMnz8hk2l4e+S7+yvE06ybe0zk0YyH6eqKaPK6XPlWVQNtQSPjy49z6aS3+J/Z7zKh7CS/PbyBJp/OFHVp9Hi6dMv/0thYhq4rfLhnLJ+0qbx43MgwTeeKYUYsBiNPtd/MZu/dPOO+Jem23OJ5gM3eu3nbcz9bPRt7PF7D6NzXtCHlbbDNs6lbGNpkdQlztNUJj4ve5kvtWl9rtIU5P77MmLKynP48F7S1FNF2vJx9B4dRZO48Hm8+voEdrWBUlIShd3O01TlvCwtd7PVAKnP35vIxgUIOb4fO64VM60uttijh617FT622iAAhAD5w/5FKi5VhxiLadX/Cz3TV0zVdKnoKb080x3Gsam0BU9SlOZ33ubcyJDPXvqbbNWLEDpeRmz8aSZNXBaDU4me0PcA0Zzk2Y5B9rhIuHNzMuKI2WgNG2gPwQPOGtMLbp6hLu839HDsHdbK6lOj12PD2RHVpnn1NyuVKVeQ81lt4+2R1SUbzZ3dVoy2MrvtMbVVGx1p/nnN6k+3+RSaPzW31bEy7HJPVJT22xcmO9VydG06rDroQQgghhBBCCFGoToskcZkmGpprX4M3HMRPMGcjzn0xR1vNUaWlT6P/s7R6XHh7vVMYm6RhsroEM8a07hqlk+RhICZqgs67oEElFN0fPU25MEdbTZvu6/d6FdkP09UVvOy9k28Wr6ctGIpOm3KJYy1TBnW+V1HgxWNhTtIWvatYqy3iyW++i1rq4sS+Yfzy5UlxU7SlKtkxOVldgtfQkVKd/mbxetzBEE/3ccqX3vQ1gVOiz1/sWIvdaODS4W6a/BbGOV0MKWni4+NVPHukCM0I+zwhDoVb2ObZxIsXXMr4cbsxmoI88+aXKLL4cPmtmAxhXjtRxO+Pdt8HU9SlKCjR4y6VKUQmq0sYbnbwiOvGjNY13bY2m1Oo9dTGpJu0Jvb959q/wQfuP/a5fD3py/QuH1w8neETdtPR4uDGF77Cv+27iZnaKs6wWym36jzVfHJAJ0DNtq7beq59TVrTRtVoC7Hp1pwnkEymWluAQ1dPicR/NdpCKpUiWsMd0eMtH0nBsqlaW0AFTo4prXk77qaqyxin2mnxh7oleYXORLATij14gya+s/M2fjpyHf9n/038beplFGtumtqd7G4tZZSzlbdOVHDCZ+R4l2SyqcgkWVg6x2PXa8UZ6kpUg6nP08ClK5XzXp22mBChlK4lBnLywv6YFrVaW0A5zn5pJ/pvX0iSOCGEEEIIIYQQoiBJB10IIYQQQgghhCgAp3SI+xR1KV7FNyDDpbNtsrqEIsVKS0w4e0+h5D2F9eYy5KOn8LbeQt/7EiaaLf09b3NfzC9eT5WqcMijx4U0V2sLuLS8jHJrEF9Y4fmjYWwGI4qiMNZh4IczX8Vm93Ds4BBW/n3UgAjF6u/5TKeoS3EbvD0mpFtQ0sDXh7XjCZpwBUycU9rMWyfK+T/7b+Jy5zqMihLdLx9fOpWq8XvZ895ZdPgtlDjbaHYV8bvtY3mgOf1HDBKJ3Ub5npc5nTZmllbPiRTCSQtxTtup6jLKjbaEYaip+uSyKZRWNnLySCX3v3c+Pz9wI6srOudB/7QNHm7N7HGFiDptMSbdWJCPefWXfB8PyQyk800y84vX0xHWebztpuic6HO01TznuTWlzw/UR+L6S9cw82nqclaOBosxhK4rmAxh/na0iPYALB53gnNH7yYQMHOyuZSPGyt55IDKKLuBXW1BnnbfwpVF6zEofW9XejJTW8WLnttztvxsK8RzS6xCL19PIm1CT7LRJ8mkjY88KpodEuIuhBBCCCGEEEIUJOmgCyGEEEIIIYQQBeCUDnHvD9kMMctGGEouws8HQtbYgZ75NZ9ma/Vxc5wuKm0AYOZgL7vabfzHwc9D2a4pa+D6ug9xONp5YvsX+KDVwl53EFfYR4jwgAh3T0edtpgAwZyEUF7uXMfXhgQ4p/wEBiXMvtYynjzkwB8OU2kz0OTTeeizMMLXvzyXMWP3og1uomn3cA4cHgLAP75TgVUxUWG2RMNEUzVdXYFRMaQVSphKuJnoX/uuqqFk2HFaD1fw3y9fwG8Pb+C/z6zHaQpyxGvlz8c8VBq1PoXRQ2d9fSyN+tWTrufN6eoKAoTiQnEzDV3uz7DvyHzFhRj+nk2RuZ5zEXZ8dUkDqgnuOLmBifb5nGkclPEsEv0t07qW6HPT1OVx11i5fJTwD+NXce6g41iMQfa2lLO7zc6rJ3QuHx7kaIeF0XYvRzuslJiDhHSFaz+5Pa5M6TyCIApTZE7vbF/bxNbtU+ERnIiuj4rUaYspQaUJT5YerZUQdyGEEEIIIYQQoiBJB72Put4dmmtfE72rnq5sJHGIveNapy3u8/Kg8w5bNkfPa7VFXOJYS622KPpaOmWdrC7p9toLntuid/kL3XR1BdPU5Tn/nhnqSmaoKwG41LmOy53rmGifH/372soGrhvaQInZHPe5Hb4WJpUFABiiBuLKbVTA6WzD49GwGMJcNfYQ0yoMbPbenfBuf+z3DSRT1WXMUFfytuf+nCUgeqztJu482MGrR4bwf96tIqwrOM3wqOsmXH5oD4ai7916dBhHDg7l6I6xNDWV8tbRYexvLeM/a0+y7owOdIiOnk9WlzDPvoYp6tIev9+r+NEMpuid9N7UaAuzOnoee/ynKtWynmqqtQVJzysmSwBrVSOlYw5hjAkgK7H6+PvxMBeWOvv8/fPsa2gLBdP+XOzxH/v/7e6H4n5/2XtntyR0scddpC6nUmeSjdj0te5Uawu6nae2ejb2OHpepy1mqrosre/J5Ljoi1TORS96bs9Z0q4HWzZwx8nORJfb3Q/1OHqeq/Nmb21lMsnq2iytPu3Pdb3GytXo+feHNPBJm5V3jlXxwcnBHPaonFfawiXDQgx3tPHloYcZ7myl3Opnv8fKtZ/czrerGlhQ0sCb3nuZa1+DRw/0/kUx0j0G8m2gXEumItl5Y5tnU06ubba7H2KWVs9c+5qCGz2v0xZHr4nTMUurx6v44l5723M/z3tuQyec8DOJ+inpkg66EEIIIYQQQghRAKSDLoQQQgghhBBCFABJElcAcpFIIZUkHoWSwKEQytHfZZimLieMzmbv3f32nXPta2gOe6Ohc/82ah0OU4hnjhBXV75V1YAvBKMdIZp9Rn5z+PN5tmdr9Ty08jmsZa24D1fw9OapbG9x8OtDfZuLu1pbgAElb8mWqrUFqLq1z3M9p1OP/nFYA2aDztwRByhztvHox2ezt93AIW+Q9rA/LuTxo69P4+PDw3FaOyhSPdz90Zkc8+oUWxRmVLaz9IO+z1Gdy6RE2VKrLWKsuRh3MMTT7lt6ff/pMEfy0eVnU3rubvyNRdyw6Up+sudm/nXkeqpsfmzGEM8f0XiwJfPjc0FJAzYj3N3YfRmp1veBPA9vf+rvY3CquizuHFSnLcaBrV/moC6kueVz2U503cbZ0JekuCsHNdAW0DmnWEc1hRnnbOeg286WJjPrz91Dkd3N87vG816zhYklAU74TPzywI2sHNQQfRQhm2aoKzEphoJK8lunLaYItaDK1JtCuI4+nczS6mnpQ4K4Wm1Rl89KkjghhBBCCCGEEKIgSQddCCGEEEIIIYQoANJB72eJMrTmIiwllTkqCyUcZrv7obxnZ+7vbfGq965ew956ywSbjkWlDUwpN/C1QY5oRlWrQWeQ1c+Xyk1xGZX3ucNMHuRhjMONyRD/BEy5xYKl1IXB3gHAnw/aaYxJbplKNv4abWG3TJrbPJsoxZHy+qSTzTeVTPLbPJt6DG9P9fvSqUdllhDjnR6CYSMAV0z4iEllPmpKjUywa8y1r4m+V7N7+OrcF6ip3Uarx86coSepH3+M+nN2UWTx8a2qBmbH1Jfp6ooeMxMnyjDan6G1mWb33+J5gIdbb4wLb5+sLkm6vEIJb89l+6aHFQJNTtoOVXLEa2Smtor/u/9G/nzIyB8PWHmwZUPGM4sAHPP7Eoa3Q+r1/VQOb4+c07ORYTyXx2DXOlijLcTQ5ZHAtz3390t4O8TPHR85b9Rpi7nYsTbr3zW7l3NpKu1EtbYgo2zv2Q5vn6Yu71Po9R0nN2AzKoxxeKmrOA7AuWUnKbHAtuNDeHv/GD5xWbhkeBNNfhN/aWzlF2PW0uhLnK26L2q1RbQq7n4PJe+tPX7bcz/tuo9p6nKmqEt7zPzdl7Y1m3J1DZtoW2VrhqhClcr6teChQom/Zq3RFlKtLUgpg3sqofHSQRdCCCGEEEIIIQqAdNB7kY257GJlmlCgEMzRVqc9P2Sqd5xzOdLVX6Pzs7R6Lneuy/jzXcuZzbvKbcEwRgM0+hQ2e++mVluEO2jgkzaNJr8Svftaoy1EMxpoD5gJhg0Um+Pvmpdbof1gJd5DFXzw4VlcObINa0wrkspI2VbPxm7zaU9XV2AzGFNen65zxvYkG3eWe/u+Om1xWqPCM7VVvHRc56Y9On89NJRjLaXsO1nJX49aOOJV+Njt4ZmYUeIOrw3XwcH89vF5jB12kMNuB/vbi/j5O6PZ3+7k90c38HxMfYmdVzpR/c93Mrhs3u3P97r0pkZbmLX2LdFoTShoRDGFMKs+hmtBWnHz05Hr+PrQMFeM8PEvI9Yzzlya8fzaVsXY6whkoUi2jqmueybnisg5PZ02KR+61sGtno0FUeYZ6kre9tzPFUXrOF8rJpSDvMXPZ+Fc2luUFXReIyW65snm/O3Z2GeDbXC8w4rD5mVG7TtUFrfwu6MbGGpv53iHldUTd1DpcLGnXWHREDs/2XMzZsPn0RbpnOtir6G7jkRv8TyQtG1MdMxOVpek1Y5NtM+PK2vk+E6lPX7Tey+veu/ide893a5XImq0hQSVUMrlyWQO7r7qax+mHGe3Ov225/6sXVf3dm7JR3RtKtexJWjRa7TIiPtWz0YMKFm7JpEOuhBCCCGEEEIIUQCkgy6EEEIIIYQQQhSAU6qDfo52BdPVFVldZjqhChPt8zMOI8zEFHUpVxatZ7ZW3y/f+5zn1l4TyNRpi+NCUvo6r3Q29FeiqBc8t/FY201xr6UTXpStcsaGdH2zeD1XFq2nzGJgb7uBT9sDQGdo2V9PevjlgRvZcOzzBFBbPRvxh8N8/9NbefWEg13t8WHnhzxhdN2AwRxkSPlJ3m9xcuPxDdEQvmThSFPUpXHHZtewppe9d/JU+819Wm/ofMwgkgSvP73tub/XsO3YY/RFz+087b6F1733cOHgE5TY23H5rZgNCmYDDLdqcevxwYFRGAxhfnT1I4TCRipVL1ZDmLlDg3xn523868j1SR+vKJREaRHZTjBTrS1Iuu1TbRdzWWd6muu56/mqtxDIRMsK+CwEXHY8LU4+aDGx1bOR0Q4vo5wuXjmu8fMDN/Jw640ZP14V0sO49M5MkDXaQmZr9QWTGCnWRPv8pOsYIsRMbVWv9aHQjpX+vJ5IRarhpnPta1J+byR82KgoaCawGHJzWZqoPNncvrO1ejr0YMJrHpfBnfCYqdYWpLydsvWYyfrKBuymMO6ggREjDmEwhzjYNIi59jWMH3KI5V95kf1Ng3jjyDDqyv2c9Jn46ch1hD978mC6uiKtR5Rir6Ej+zqVdU50LL/pvTfu9Yn2+T2eT7a7H4ora+T47tre99Se9XQNt9WzkQq9mEtSTGyYLFQ+oq/h3InWI9U+TLLt+ILntrg6Xa0tYIa6ste2cqq6LOl5NfY81/Xxk67HZLbb5HQT1MbukynqUuq0xdRpi+MeQY0Nie/pfA/Jt3O12r1cp1QHXQghhBBCCCGEGKgUXc9BRo5+5nK5KC4uBozQZdqQdNRqi/KexG2aujztJCB12uIekxrMUFf2eucu3e8Lo+PQbVldbi5NtM/vc2KqyeqSgk9IFSu2PtdoCxlnLqUtGIomtqjVFlGGhtNk4lFX/Mj/xY61WA0KgbDOE+03M1Vdxmbv3aytbOC/Gu5HMYU4+cE4fvSXaezuaOd17z1MUZcWRMREb6arK3jZe2deyzBNXY7L4MYeVvnx2R2U2Dw4VS+HW8rY2VrC9z+9Ne64feQLC/nKjFcx2fyc3DWcjVvqaAsaGOPwcsRr4//be1Mv3xivRlvISGMJ7lAQtx7oNhVQrtrCWVo9zbT3epc5FdXagoIb8cyGmdqqtKa62nHJBTiK2mg6Wc6d28+hNQBTKzwUmf3saXdw/7HmgplSM9KODES9nWd7k+n5Y7ZWn5UkZwPBFUXrMCoKD7feyExtFR7dP6DOubkyW6vHqwf7nCBuhrqSK4YZKbX4OaPsBMOHHMVoCvH01lrealRpqN7BoIpGAI4cGcwxVwm72orY1WbBaoDfHE483WJ/y2XbX60tYKypNO6aaJ59TdzUnolk6/owG9eqqcj3dVB/97eyee6p0RZm5Romng6EaG1tpaioCJARdCGEEEIIIYQQoiBIB10IIYQQQgghhCgAp2QHPZIEYKJ9fkpJF6arK/oUblGtLeg1yVCqSXUiIUzz7GtS/v7ewu4iYbKZJkSJLXtkztItngeyHt7e1/kae/p8OiFDddpiLo5J+jFFXcpsrT5p+FI+Evn0luhisrokrj5v9WzkiN9Lc9gb/fwWzwM877mNR103xSWgmWifz1PtN/Oo6yae+CxxWyQ0yGkCyxg35nMMlJ2xn+mVHdGw9myEt6ebwCOTz+Q7vB06j/MvqhV8udzOa8fL+fvh4ew8MRibKUBYh+8PacBPMPr+MYOOY7Z7MTm8KAadMc52yixBDrpt7GkzpL0NHLrK3lAzz3tuSxj2lavQsxc8t2UtNCybSRXTaW9zJdLOphPeDmA2B7GoPhRFZ7gWIKTDe80ae9odfNRq4uulg1I6//RUhyba5yec3zlVM9SVzNFWD9jwduhMNNcXZoxJz1E9XafkKrw9H/MLR8yzr+ESx1quKWtgfWUDDYMbAGgOBvB9lo3sRc/t3c65U9VleZlLOtd6a7+f99yWVnh7snr2kvcODnjMPHvEzpZjQ2lvc9DcWEqp1ccRb4iHPp7AVX/6Aq99cC4HWsqxmgJ8b+dtjLYHCOhwZdH6Hr83m8k2L3euS5p8LdW2P5PriXKcBMI6S8oaWFDSWS+fdt+SsN7FJqnL1qMY/fU4Um/XQV3nkE+kLwlfs3WN0Vu/YaJ9PtPVFXiUjqx8H3ReT09XV+T8uuGU7KALIYQQQgghhBADjXTQhRBCCCGEEEKIAnBKdtC3ux+iRlvIdvdD3UJhEoUjDzJbGWcuzvj7tnk29Rq6l05Y5xxtda8ZIzORaUhJbNmzEdaeLCSla4jQZHUJFzvWphRCfrFjLWEl3OeyQecjA7Fzchsx0II37j2xoT0m3RidBxziw6qShXz1FF5Yoy3sNSS1tzCoROFWr3rvir5eEnYAnVlz11Y2UG6xMFVdxlz7Gr6oVnBpzJzasWFmVWoAnBqhISOwDG3FYQ4k/P46bXF0Haepy3tcn8g2mqGuTCu8K1KP+iMkrKf9lUoo2Cytvtt7Kmw61aVtDNf8bD4R5niHysG2YuymEPvcOg7FEn2vL2Dm5ee/zMtPfJWiiiaGOFo51mHGF1a4s3EDpWFnSusx0T6fmdoqXvbemVGIeOx2qNUWUact7nHfRo6TTEIN+4tZN6fc3k5Tl2d9HveITEP/NWc7jhHHGDz8CEc7zBiA/e4w3qCBecNa+dilp7Ts2OMo0hbMta9hurqCsYZySg22jMpXqy3iJe8dPOe5NaPPp2qufU1Ow58TbcPYut9bvYhtf2u0hUy0z2equoxp6nIMfZh9JlPbPJuyGpIMqT/K97T7Fp5ov5n7mjZwtCPMq22NzLWvobbESpnl88vSmdqquM9t9t6dt5ljEl2HxLaHM7VVXF3SEPd4XESdtjj6+URtYU/nsGTbdKa2Klrfu9Z7n+KPuyaJtcMVYvmZR5g55lN2Ha/iulfO4KDbzj69iSMehW+NC1HlcFFi8/Bhczkryhto9psot4Z5xHVjwmVWawuo0xZn9RGWgK5Takn+WEisGerKuH0x67NH9hJt1651foq6NLpPIiH8rlCAfd4O9vvc0TrYtd5Fsq3HPmKaq3NDMpF17un6ZLK6hOnqipSWF7k+uLJoPVV6Sa/XVn2Z1SIituyZPHbT9Vq36zIMugGn0ZL1rP8KCof11rjXsr3/T8kOuhBCCCGEEEIIMdCccvOgT1WXJ72LN1urp133ZyWZ1UCbm7RGW4gRY1p3vGZp9ZxQWgtmDt1EYuc27Os8tbkwWV2CGWNaCV4id+Ei6zJZXUKRYu2xvsXOC5psjsbY+TXn2ddwhtNEsx+a/KG4iAHojEioshl5x9MYd+fxmrIG7tz4GLrFiv8ZF88+PZv7dpfiD4fxhcN06EFcioewEsYR1nq9ox4p04KSBgzAQZ83oyRuA2F+5WptAQ5d5VXvXXHlXVbewNnFQcyKzqTBR/EFzGzaU8UdJzdE51b9+wWXMGH8LrRBzbz3ei33fjKaecMauX93GdWlYTwhAztaSTrCETEQtlM21WqL0Amz1bORifb5mHVzt0iiaerylI7PQmxfAE6uHYvzzIMEGov4zQPf5NM2A3XlfoZpHlr8Flr9Zl47Yeq1biRTrS1gpLEUgMfbbur29xnqyl5HNWu1RZh0Y7/PaV2tLcCAklZ0Qq22CLtuxaV4eh11yc18uP2vtzmc+2Nu5lptETNLStB1+K8j8fNt12gLGWYoJqTr+PQwLwyga69cmKwuQVMs+PRgwva8p3o5v3g9y844idkQQrP4sFn8fHR8CEs/6LwuvnHCSqaN2k1RsYt9h4byYWMlr56wcV/ThqzVg4n2+ahhW9bag1Tb8FTVaYspRct51E8i2WpTIpEH6WzjSNtXCIl0U9XbNc2VRes5EeiIW6ds179smGpbzOaOu2UedCGEEEIIIYQQotBIB10IIYQQQgghhCgAp1yIOyjd5jTvyxznieQrrG2qugwzRiA7ydp6M8++pmDDyWL3aSRcJaiEsOgm/EowZ/M4D1Qz1JWoBhNH9Va2eTZxZdF6JpWHcQcNPH+ynZEWB2EdhmoKe9tDTKuARp+R3xzuDDW82LGWUZqRkfYQ1/3vm/irzsT28Wb+9s91/O6jwXSEQmmFg82zryGo69HPzFBXcobdxk538hD3gRBOmkoYdORxhEWlDXSEdMqtCree2MBjtVdRYvPw1rEhfNhqBuAt7wm2ux9iy1e/ygt7xzJr9G6Ki9rweFT2nhjMQbeD9R/fEQ0znqouw6cEutX/2Vo9pWYzD7VmFuZcqCLtQCQBU+x6ZzvsMRXT1OV4lI64ejpZXYIVU1bDBmPDypu/NQpt7GE6DlRyyU1fQ0Hh+nPaKLe34QuaOeF28onLyU/23NzLUhObo61mhGbmfXdbt5DA6eqKlNZrurqCYqOFJ9ozK0OsmdqqbnPFT1aXEFRCbPE8kHIY7iytviDPbfkS+5hUIonqdiomq0swoEQfLazWFqDq1ujvM9SVdBDgwlInZzo76AgZueNIS7d92FvbOkdbjUcPZPWYn6Iupdhg45kcJO3Nl29XNVBi0bmm+l08XpXHdo5nnNPNLbtM/OAsD5rZz/nn7ADA26bxyLu1nPCZOOlTeNfdilk3oaNjxsjL3ju71ZtUj6tabRFW3RwNT+7L+b1OW4xVN/d530+0z2e0oZxBFgMHvYG8hLjHynbfJd9ycQ1XrS1gqFLMUd0VXXbX7+ntEZ50H/272LGWo+G2Pu+b+HLpQEhC3IUQQgghhBBCiEJzSo6gR0xVlxFQgmkn9untTnJ/SXS3qVDKFmu6uoIg4YR3oPJ1BzAfo2eQeRKunj6X7l3HRHVknn0NI+0m9ruD0SmlvjukgVJLmGa/gUMeHbtJ4e7GDfxm3GrOKmlB1xX+fnQQvzm8gWptAd8ZpTKqqIUL7nIRHHEhlp1/YXNDFddt0xKWL9G+n2ifT0nY0W3UbbZWT4XFwn6fh1e9d/V6xzMThZbk6+qSBkY5dD526VwzthXN7KfJq/LisSK8Ifigoym6XV+bPo9z695DHXcU3Wvihf+9hA+ayrCbQuxss/HbzyIdLnGsxWY08HDrjdRoCwkqoZST+szS6vGkkESzPxJGZaKQRxtSSaSWqinqUkZZHbiDIZ5ov5nmb43CVtWI+0Aly++fw8mQl386y8fw0kae2TMOf0hhv9tAkz/MYwmSvPWkRlvIaFMJj7q6fy7dY3SquowSgzUnU4j2h7n2NTkZSZ2mLser+PtUd6u1BYSVcMbHZS6iPNJ1RdE6vjbEjy9s4IGDvl7rVtfjvVpbQCVFhNBTjoyYoi6NjgSno05bjE/xp7y952irCRAmpIfznoBrrn0N3xzpp8LmxWHxMXLwUfYcGcpTB6tYV7uN8qoTtDcXUTr8GK6jg3j1o3Nx+S2812Jjw7ENzFBXYlCUblEs8Pm1yhR1aVaSMWeip2vkGm0hxdgJ6WG8ij/ueiBS5osda7EZOscuuyYXi5hon49BN2TlWry3a/oritbRFgx2SxI8W6unBW9Ormmyeb7qb7kue+S8t6y8gaMdwT6fEy51ruNgqIWtngeREXQhhBBCCCGEEKIASQddCCGEEEIIIYQoAKd0iPtA1Fv46GR1CeUGlZawLxoS3fUz+QiDn6ouw6N0FET4fWySklRCrQo1ZDdTyUJ9V5Q3YDLArSc2sLqigS+WewnrncdLR9jA93Z2brN/G7WO8UXtGJUwB912frDrVmq0hSwe4sQXNvDd5w8SKhuP9s7DmGcf4JqyzmRnD/chAVmdtpizbMV4gnpG8zVnM6Su6/GTziMGE+3zMenGXt9fpy3GrJvY7L2bWVo9/1p9nMrSZlrbHFzw8tPML17PiYA/GkZ44Opq7t48hfXznsXvVnnsjSkYDWF2tal82gYjNIW97jCVNgPuINzXtCFanlzX7Vw8OjBHW81J2vo9KWDkcR2rYiKs63gUX8rrVqMtJIzeb21gZLufWHMGReP303GknP/54+U8dczDujEKQx0unDYvO04O5r1me7e5pVMxU1tFOx287bmf2Vp9NMwyk2Q/hfh4VjoudqzFHw51CzXNhllaPQbAaTIRCOtZSaaXbdkOqe36iMSPhq9nnKMDV8DED3flPjlXJvVxjrY6mjhsnn1N3OMac+1r8IaDAyI0+BLHWs4vNTDO4SUMnFnayNvHqphUeYzRQw+hGHRe/uhcDOjsddt55bjO1AoDW5ug3Gqg0RdO+NhLrNjzQrW2ADOm6O9T1WWUG21p1/O+JhnrKanlXPsaAnqYZtqZW1aKSYFXGv0JQ/lPJbHXCPl8DLBaW4BDV9N6NDWf1+7fH9KAosBfWxqzdF6TJHFCCCGEEEIIIURBMvX2hlAoxBtvvMGWLVs4duwYzc3NlJaWMnjwYCZNmsTkyZMxGo39UVYhhBBCCCGEEOKUlTTE/ZVXXuEPf/gDTz75JG63O/q6rusoyudh5A6Hg69//etce+21TJs2LfclTqAQQtwn2ufjCGspZ/DumgW0TlvMaHMRLYFA2iF0saGHhWCKulTmIi8wc7TVDFXNfOBt5W3P/XyzeD1fG9KB2RBmxYd38y8j1vNY08lofYxklY9kxIyElv1u/CrW3vUaYdWJ4eV3+H//Vc+PY+ZXziQErVZbRK29hFAY9nh9WQspSyeEMTbkvL/9Ysxa9rQb+devbMZgDLPpzcndQjy9PyvCfI4Obi8f3n8hR5rLOOx2Mszhwh8y8ZdDFexzh/GGgvj18IAIszwdTFOXY1aMuPDmrD08dM15FA0/RvvhCp7fMolbdnVm476mrIHzS4L4wwq7243ccTI+xD2V8MBqbQEVOPt8fqnVFlGESivufn9sIZv6IwQ0nTa0vx4b6Mt6pxqGekXROs5wGrAadH5+IPPHpbKhpzL3FCadTCafSVW6+6ZWW8QUZwkj7UG2NZlQTfCVqjb+dtTJV6ra6AgZObv8OLubB9EaMLO7zcpIe4B7DrclnWc6XZnMspPpzC6ztXosBiPHw+1Jt9MMdSUBQtHz/7erGnCadbY2hRLOOpHPLPUDTV9nmMi2dPZdT+3rd4c00OKHuxvTf3Qsse4h7t1G0F9++WW+//3vs3XrVnRdx2AwMHHiRM4991zKy8spKiqitbWVxsZG3n//fT788EM2btzIpk2bqK2t5be//S3Tp0/PUoGFEEIIIYQQQojTQ9wI+sKFC3nooYcwmUxcfPHFLF++nFmzZuF0OpMuwOVy8de//pW77rqLv/zlLwSDQa666ioefPDBflmBSBnyPYIekWw0O5t3uuu0xQQIDtiEO4nuJtdqizCg5HWe6mzOvV2jLaRKKcrbnL+93eGeqa3ii6VWxjk6MBrC/O8+czT5TWQu7V2BZoYZiuMSudw0YSWrrr8bpdSO900rf35iLovfvy/hd9Rqi7DopujdythRiRptIZpu41XvXQMuSV82y1ujLeTqqiK+MOgEFUWtNLU72d5YEU3YF9HxCzumCQaCH4c5tm08r3x4Lovfv48HJl7DEY/KfUc6RzdSbWe6tlP9MRLXXwlosjW60TWxUcQljrU5TeLV03aaqa3qNgq9+8o6ykcdoaPFwbNvTeb23RYuHWKg3OrnkNfG6ycVDJBwDvRqbQFW3ZK03ZuhruRcpw2LEba1xke3pLOd52irGaGZOeAJRNuZXOrpGB3oyeoyEYmI6otkyUd7kkpbeWXRegDKrAolZjjggU0t2RqVKhw12kKCSgg1bEt7jvds+q8zVnOsw8SFg09iNIQxKmHeOlGJZgzz5kkz55aEOOAxMnWQmz3tKuOL2vmo1Zm1qIZMIz6zcd7teu0Ze1xE2rNFpQ0MVaElAO+1tyXdVzO1VTiNZh5P0K72dq6bqa2Ka0u7/n4q62v7Wwjt9+XOdRwLerIcRdFLkrhHH32UhoYG9u/fz6OPPsrll1/eY+ccoKioiCuuuILHHnuM/fv3s27dOh599NG0irVlyxYuu+wyysrK0DSN8847jxtuuCG9dRNCCCGEEEIIIQawuBD3jz/+mNGjR2e8sMGDB/OHP/yBH/7whyl/5tlnn+XSSy+lpqaGf/3Xf8XhcLBr1y4OHjyYcTmEEEIIIYQQQoiBJm4EvS+d81hjxoxJ6X0ul4ulS5fy9a9/nddee43vf//7rF69ml/96lf853/+Z1bKkqlp6nJqtUVpf+55z21MtM/v9no2QzLe9tzf5+VVawuyVJr0BQh1e22L54Gchb9OUZdG/z9dXZH0fdkKbwfQdFu/hrfXaAu7ff9kdUn094n2+XHvOdth5aAbPnbZ+LhViws79YXDKMBIYwmtIT+ztPro31oCJkInzegn2vE1OzneoXKpcx0z1JXdyhQihFfxRX/f7n4o+r6tno3RJDHb3Q8xv3h9NNSx0GUrvL2+ooFpRWW802Tia28+yvYjw7n942E8dDDY7b2NH42h5bkK3AcrefWjczmn6jBPTJrPou33UWIJcNmgEuYXr2eoUhy3v5LpGmKYbnsyVV2W1vuBfglvr9UWZS3sbJtnEwHi90WdtrjX8Pap6rKMtk9EZDslOo94dH+3R1e8HSr+No0TRwbT4rdQU2zFbNAxG0LsajPyeNtNnAx2JPyubZ5NPbZ7doOJG49v4INWP+c4rXF/S2c7n6SNtoBOu+5P+PdIW9W1HcvELK2+x2M0k3PnFHUpc+1rMirPdHVFwn3ZnzZ7787oeibWFs8D1GmL0/pMb23lNHU5ZVaFYrNCKAyqSWe0I2He4j7JRr2aqa3K+Dvm2ddQjoPt7ofijrdZWj0ztVXM0uqZrdUzXV3BbK0+pwl2h2puvjl+JztaSvAGzPiCZoaqHbiDBr421M0oh4fR9hAl1g7GF7Xzv/sc/KXRFXc90ReZJpzMxnk3SDj6/4n2+XGPfbzuvYcZ6koeaN7Aiy2tdITApyRurwBacUfD27seW72d67qGs2crvD3d47M/zbWvYa59Tbf2N9G1Y09GGEpYXdHAJY612SxeymZp9TzWdlO/JAnM6zzoDzzwAMeOHeMXv/gFBoMBt9tNOBzu/YNCCCGEEEIIIcQppscO+tixYxk/fjxPPvlkjwv5x3/8R8aNG5f2lz///PMUFRVx6NAhJkyYgMPhoKioiPXr19PRkfiOvxBCCCGEEEIIcSpKOg86gMHQ2X83mUz87ne/Y+3axCEFK1as4J577iEU6h663JPq6mo+/fRTAFatWsXMmTN58cUX+d3vfsfChQuTZoL3+Xz4fJ+HzrpcLkaMGEGyLO7ZyGCajjnaao4qLQMqM3U2zNRW4dODOd3Wc7TVBAifNhkv+yqSzX2yuoRJTiftQbi3aQPfqmpg6iAXZaqXLScH8fCx1mjobK22iLHmYoyKQoUNfn/084y6t569nGVr70Nx6rRvG8pNj1/Mm40Kj7q6ZzItZH2dxzVWJhl3p6srGKupBMJw+YgWQrqBRdsTZ8MH+NvUy5hwxi5OHqvAFzBzoq0YRdE56nbw+kk729ra0RQLL3huY5q6HJ8SAPonrHwgyzSjcES6GeNTPRelk/H+/YtmYLX5OHy8gi+/9gTQmanZZgxx0wE32zyb+lTfZ2qrmFhkZa87nDBjcarLGGS24AvrGS8jHdmeGeKKonW0BYO91pVcZt8+1czRVuM0mXjE9Xl28Mud6yizGDjg9ffpuOxqurqCFkN7v12Tdc0WPtE+H7NujtaNfM5c8ljtVRxxO/CHDRSZA3zlvPdobCxj08fjmTXkGBVFrZhNQUJhA/6AmQ+PD+Gkz8IbJ0081Jrb+el7On4y2WaJMn7P0Vb3OJPEj4avZ4gaoNln4rDXwDvulmiZEs30s6i0gUBYz/m2GWiz3eRSopmgMpHNjPB9X1YvWdwTOe+887DZbDQ0NPCTn/ykD1/eXXt7Ox6Ph6VLl3LDDTdw5ZVXcsMNN7B27Vo2btzIzp07E37u3//93ykuLo7+dHbOhRBCCCGEEEKIgavXDnpdXR0vvfQSgwcP5le/+hVLliwhGOyexCgTqqoCcPXVV8e9vmhRZ8KFzZs3J/zcj3/8Y1pbW6M/Bw4c6PF7NnvvZpq6vO8FTsEsrZ4Qetx8z+nIdzKZviQ3etFze84jFZ7z3Eqj4iroZBiFIJIEMDJq9qb3Xj5o6+Bd3wkAjArYTCG8ATMVVj+XDCqNJoEx6UbMisI4p04gJiXENWUNPLLfQthnQndDy6FK3ms2cjyQ+eMofUlclG5ykVjZGj2fZ19DheJI+3Mve+/k7sYNPNC8AW/QjEHpDGSKJHhbVNoAfN4eTL3kr1TO/pizF7zMoLImnjtUyX98UMIte6DJpzPUbMeFlzptMa967+Jtz/2UK3auLFofl9xoprYqrbYwn8kk+0NfR+liR8/rtMVxCSkjYtv0doMnpWRL6UQ+BIImLBY/quXzhEb73WYafWauqChlSVkDw40lKS+vqxc9t3O8A3oItktpGQ+33tgvo+fQt4RSlzvXdXvtUddN2I2mBO/uHF2fZ1/DVHVZWqPnfTnXZ+O4rNYW9Ol831fPeW6NGz1fX9nAMM3AviyPnkNne+sIa31eTm/bfaJ9PrO1+ujoXuT6b7v7obi6kWn9TNS+xH53KgzAuYOOcU7ZSS4442M8bo2TbUXUlLl4+ehg9jRW0thWRDBkBOC/9nbww123sicQf93V2/f1VNZkkh0/ddpitrsf6rbMWm1Rj9/TdUTzEsdajiot3d53uXNddH12tcH3dt7Gey2w1x2IK1OihJoPNG/I+eg59FxnspEEsT9Uawui1yN9acMSjZ5PtM/vlsizVlvU4/d0rR/ptMlztNU9LitVPdXflJLEfeELX2Dz5s1MmDCBBx54gIsuugiXy5VRYWINHToU6JyeLVZlZSUAzc3NCT9ntVopKiqK+xFCCCGEEEIIIQaylLO4jxo1itdee40LL7yQF154genTp3P48OE+ffmkSZMAOHToUNzrkeVWVFT0aflCCCGEEEIIIcRAkThmK4mSkhKee+45li1bxqZNm5gyZUqvGd57ctVVV/GrX/2K22+/nVmzZkVfv+222zCZTMycOTPjZUNnuGgjbRTpWlYSCvSmWlvAC11Cs3oKpZ1rX8Nx3RX3nnwmgajWFrDZEx+iHpvUKN0kCHXaYsoVO8/0cT7wrgmOYrdRrbaIEjTadV/K85jXaYvxKf6sbes6bTEBglmd676raepywugpP0LQtSyT1SW0Km6sugWAUkuYE14VHXjpmA27uTN8K7K/LyxtYE+bwoMtnyeIu69pA98f0kDYbSPstrH70HAebNkQDTWv1RZh0o1pzScf+CyZWbom2ufzkvuOjD6bLTO1VXj1UMYJC2eoK6krtXHu4J0MKmvivaqZtHsPsb91Me826XzL2hBN0Gca7gOnxolHR1A2+jAjtwUAKxdUetnaBL880D3ELqCHCekGXvTczkxtFa24URUjPr3zEaVU6m06dTqbCVeS6WsSrllaPQE9hFvxRZeTrcReYfRuCeO6Jvbprc2Zoa7kJW98vU6UmCiWL2BGK2mjStFZVt6AJ6jTGoD2oJF33C1MVEt6DS3v6TuuLmnAEwr3Ov97rsVuy4n2+VToxRhRaNf9BJRg1hIiPtZ2E3PtawjpYcJ0zj1fZlQ5FGpL+P5MEmTWaAsp1x1Y00gGGCtynPXlmNvm2dRjeGW6CRDTUactphSNCquZ4z4/7bofTwjGOMLYjBZe8HS+b5ZWn9a5vSfZePxum2dT3CMqRYoVi8HIAb2J7e6H2O5+CENMSG0pDqarK/ARTGsdkiUE62l/OMIaM9SVtCmehNedkcSTjR0qVlOAF49UYTg8hLpBTXzUUkKjz4iiQFhXuH3HCK6r3YFB0Vkz3MER73qMio4rYGCkaT3uUIhn3LcwTV2OAYUAISyY4tqu1733MEur73ZNnKoabSEKBlTdQkgPxy3zhNIKwBZ36u12jbaQJ9pv7hbGPFldwmMx7ePDrTcyWV2CxWDAbMj8sZ7+tNWzkanqMkwYMurrTFGXUmqw0RzuyOn83rFtVTbasFjb3Q+xvcvy0j2vp9MnCKFzsWMtT6V4XuzpmO68Bul+Hkirgw5gsVh48MEHGTFiBL/5zW+YMWMGo0aNSncxANTU1LBy5UruuOMOgsEgX/7yl3nxxRd56KGH+PGPfxwNgRdCCCGEEEIIIU51vU6ztnz5cu64I/FI1R/+8Ae++93vRpPHpDvNGkAgEOCXv/wld955J4cPH2bUqFFce+21fO9730t5GS6Xi+LiYpJNs5ZtXafMyLcp6lKCSijhnfhsjmplc2qq/tL1rlUuR/kG0vb5VlUDFgO0+OFTd0fcne9Lnev4UrmOUYF3m41sihlF//cxa/nOoodRjCH2vD2RJ3eczfW7u0dInC7TDfU2utnT52aUOjEadBoueB1F0fG4NaxWH09s/wIbD4TwKYHoMR18wIbvizMxNR3GdauXA3tGYrX6+fjwMN5vKeH/2xs/indF0TqGqAb2ukMp3+EVA9PfL7iE0cMP4XFrNLYWs/1EFX86aOIZ9y2sr2ygNdCZyCjfZmqrsjo9ZmSEt6/tbn9EfuTLVHUZIcJZGYHOhWptAePNZTzUeiNXFK2jymbgWIfO3mArFYqjz9F3sWq1RZ2jsYRzNkrYU13M1tRQ0HmOdoeCvOC5La36W60t4BuVpThNIYbb3RRbvQA0dWj8bqeZn1U3U6R6MJuCvLxvLE5TEH/YwNEOK+6gwn8c7B6pNV1dgVfx99uUntPU5bzqvSvh32q1RQSUQK8jodXaAspx4tUDCSMrLnWuwx8OZ7X+QWeEVAeBfjkep6sraFe8WbsmPZXbyb7obfq7ifb5GHRDj9tusroEox5mc8fdcdOs9TiCvmzZMi688MKkf7/22msZPnw41113XcYZXs1mMz/96U/56U9/mtHnhRBCCCGEEEKIU0GPHfQ77+z9bt/ll1/O5ZdfnrUCCSGEEEIIIYQQp6OUs7iLz3Wd0zJW7DyR2ZizMlay+fxe996TNLwomyEp/RW+HbuefZ0XPjb0ZLq6Iun2qNUWpTW3etd9O9E+P2lilkLQdb7xjhBoRh2DAhMctri/Pd52Ey1+I21BAwd9nri/vdtsxH20nIBbxWgIMdzu4cqi9d2+byCEt0+0z89oLs7YOhkJVUt3OW9672VXe5gic5hQ0EjpmEOUDT5JS2sxmjEUncs8KhDE+tGbGHd8iG1QCy6vxqdHh7CnzcknLgPXDW1girqU6eoKoDNx1ZttrdHw9kT1MN05kPt6LGZT1/oMnQmlkpUx3bKnOx9qsv2fzpzzmXJ1aHy4Zyx//3QCj+wezcvHbThNRv7fuNWMtIcwKLC2siFaN9Ixz76GS53rMvpsV4nC2yfa5zNNXZ5R3YqEKff1vJTqOXK6uoJLE8yRnopM5oTOhs3eu7MeTtvbvkqlLYwcv2ElTEugM2nlo66buPH4Bh5x3cgWzwN9Ci+erC6JS+YGneekV713JQ1vz0Yd3+rZ2G0uZoAlZQ2EyV7Csb3hxmjytXSu8SpwUmn1YzGEafFb2Osq4ZaPq/jDTgtDLSq6rqCpHXxwZBjf/uR23m6y4wqYOOrt3lWYaJ/Ppc51mBVj9FyVaD7unupDJuelZOHt0LmPewo3nmtfwyytnm2eTbzguS1p4sDBNgPjHCauKMrseI8Ve7y85L2jT8djqtcZ1doCXvbemdVr9rASTvh6Ltu26eqKhOf6bMjWtXlvj1Nsdz/U7Rjtuh+NGNjccV+3z0oHXQghhBBCCCGEKABxIe5GozHjBSmKQjAY7HOBhBBCCCGEEEKI01FcFneDIfMBdUVRMsring19yeKeTrbpOm0xZt2Ulfk0symb2UF7y0gYK3aO9GxLNwt4T5k9I2Zp9VgVA0/3IXRuon0+Vt2SUsbSTDN8Z9tsrZ7nu8xFurqigS+U+vCGDPxw163dMnR+u6qBUfYA7UEjzx3viG7b7w9p4NtT38Re3Ma+PaP4674xHPYaOegJd5sPeLq6gipLZ/j8Qb+n4I6bdPQlU3TXulyjLWSYoZgn2m/mn4av50ynl6sXPIrlnCDuN4p49OmvsfzD+G3l+40Fgz1IyGUh2OLg1ednUOZoo9ntwGgIc9cnw7i3aQPV2gJKcfCi5/ZoyGaytmG2Vo9RMURDSQulvqYiMq97ZJ9UawsIK+GUZ9eYo63GpCh9agsiemp/Z2qrKDWZM5orO1V//MLV1IzdiaLoHDleydtHh1GpeqnQ2lHNfhrdTt44Uc7PD3TPwJyKq0saAHiwJf+Z4EV+pXKeTcc3i9dTZFZ4393GeXYnmgk8Qbjj5MCpa7XaIsqxM8hipsQCNx6PL/sMdSUvee/g6pKGhMdQptmxZ2qrCOihtPfHM1+8ksriFg42DaLFb8VsCPOpy4EvbMCo6NQNamTUoON8eGQYJoPOO40ljHd6OOix8S97b2aWVs84uwV/GO5uzHw/JTvfpHMN2pNk15CJrociarSFlOLAZjAyuVyh1W/gv450X8eu1wNdyzxNXY6qmJJ+T0SkbhS6ifb5lIQd+Agm3GfJtmmNtpBBOHnOc2vWyxRbf3LZF8m2yGMg3a8ndSCUPIv73/72t4QL1HWdWbNmcdFFF/GjH/0o6wUWQgghhBBCCCFOdz3Ogx6rtznR8ylb86BXawswY+p1dLROW5yTOR/7cucwMifsQJfqHfrInKbZvJsfkcpdzUzm+c7WXb5I0oy+JGK7omgdlw33YlR0Hj9o56HWz0fXJtrnY9KNjLeUxc2BDnCJYy13Lvor4YCRbR+cw8tHq/i/+zMbmesPuR4VTnVkPdGxfbFjLVeM8HHVRc+hjjlK2Gsm7LVywz0L+Kcuc8s3f2cE9jMO4ztcRvOeYdzzxpe4fMIOikta2bFnDP/fdidGDIxVNQwK7PF0ZP3OfKJ5rPPR7qQ64pRs39RqiyhBiyZZyrbZWj0naIsrY7W2gGGGEo6G21I+bhO1F4nW/fHaq9jVVsSXR+1G1xXu/GACo+0BALY2mym3wtaWvteHbM9jPhD0dY71ZGZp9QnrX7ZGD2Nle/7iROe+2Ha2t3Nz5L112mKKUKPb4ZqyBs4rCXK8w8RvD2d3BL2/5nC+smg9TrPSbWT5W1UNtAVgu7cFyG8S1fvPu4bB9nb2uUr4y2EVzaRQaYNfH9rAzn/4EoOGHyXgtfLxjjM53FbM+MqjbDs8gldO2NnibsGAwjhLMTv9LdH1SHZt09NodSr6ut8yueb64dAGzitx4w0Z2eGy8UZLe9w5rqcy1WmLCRA85ecLr9EWEkbHqlvirq8i7Vcu2rFCM1VdRrvBk8X17D6CLknihBBCCCGEEEKIAiAddCGEEEIIIYQQogBIBz3GNs8m3vbc3+u8e7kIb4fe59PrSS7DTHM1D2Gi5b7qvSul+Qkjc5pmqxyztPro76mEgmYSopatJBZW3YxJNzJVXdbrvJixf5+t1UfnhlVQUICwruAwKSwpa4jO+bnd/RAKBja1bIj7/DR1OeOcRixON47hx6kqaWZHa/pzgKdS1mzpGt6eaJ7Wvkg1/DXRsT1lkMKedpXGfUN45LaFPLfpMra9MJXHjvi6vddS3E77jhHs2XIuuw+MYJTDw4dHhrFz3yh+9X45lwxWURUTJ31B7m7cEFeHq7UFPc7xm+rxnSi8udhg42LH2pQ+n4lEdWKbZ1NK82Yn2zdbPA8kDS/Ohuc9t3ULcdzm2cQBvSmtdmOz9+5u9TVR6GQY+EbdWwwZfoRmt4Nd7SF+sOtWXj9pYpwjTFsArErmM7QAXOpc1y/h7ZnMTVujLWSaurzP56mu82ZD3+dYj4itx1cWrcej+/u8zIn2+b22Z1PUpVkPt01Uh2Pb2d7OzZH3WnUzVVYLsz87927vaOR4hwm7qfOpy7n2NSwqbaBOW9xj+9XTeWO6uoIZ6spu2yBRPZuprUq6nETf2fV75xevx6h0T5w2174GdwA8QZ0tngfyGt4OEEbhuMfBS8c1asvCfGWwm5F2H1cWrceg6AT9ZrxtdrwBC2PKTvLJ8SqeOWyn0tZZ/grFzhFfR1x4u5nu7csljrUYlcy7GNPVFRnX3VptEZPVJRldcx3rgB0uDZsxhN2kd7u27qlMb3vuT7vMidqdTGVyDku1zY1931bPRrZ5NnW7vopc5/TUl8nFdV4+bPbenXafraf98yVb9/0gHXQhhBBCCCGEEKIAnHJJ4ibar8p4JDo2+Vu2ksPM0uqxGYw81X5zt7/1V+KSfCnURBFT1WV4lI6Utn3k7ma2Eo31xzZJlMwpcvczUr+XlTcwe0gbHSEjqz+6C/g8Qd+CkgYmlwfwhw3sajdy24nOEYHIMeH6YRVG1ceBrWdzormUWz4axb1NhTEtTp22GAe2gk9mNde+hq8PDTNE8zCipJFxE3axbdt5jBx8lH/+ew2lFoVbT3y+TYMPOwkPHY5h16e8e+eXOdFWjNPqxWbxEwiaeOvIcO481M5Wz0Yudqzt1t5c6lzH423xU33FJkMcSFOsJdLbcZWN9ry3KWNylVCsN3eds4wr5j5POGDi5qfm0ugz0RECkwK728MMVQ180JZZkrgp6lKGmDUedd2UUWLMWPmsY9PVFZSZrBwJuqMJyiLRcqmsU6rtduR9c7TVtOodWV3fZMlpY9dhmrqcEOF+S9w4z76Gw3prn65jIpEPw4xFVFgN3HFyQ9z2nqOtzto0TVPUpfgUP1s9GxNuz3SO4a51Inb6zK4WlTZgUOC+DM+T2bxu+OnIdZRag+xrt9ARgo4Q1Jb7sBrCTCg7QVV5I2VDTuBpcfK7V6ZSXdaGxRDCFbCw+qO7mK6uwEcQr6Ej59cylzvX8dhn560p6lL8SjCl4zWSqFBTzLTr/uhIeiS5WWx9naOtRjMaORRqw6qbKTVaqStXON5hYEebP61kojXaQoJKiKGUUmQyciLgz8u1SC7a2mSJLVM1VV1GkcEandY1kenqCgKECCqhpJHKuUrS3VV/JMCdpi7HiIFik4XDgWbe6bhfksQJIYQQQgghhBCFJm4e9LFjxyZ9o6IoPPzww7z44otJ/75r166sFk4IIYQQQgghhDhdxIW4GwyZD6grikIoFMpKodKVzjzoqYQKTVOXA+Rkju3TTapz5861ryGs630KZctXiGnEXPsafHqoIMKrq7UFGFBQMCQMCbuiaB2XD/cC8F6LnSMeeLBlA9PU5WiKmTlVncfRW43GuDnSfzi0gX//P50hZ+3bRvD8y9P4+7ESdrj8fZrvNFv6GobbXy5xrOXCSh2nKcSkIQepLG9k5KVbCbfBI7ctZOF797NyUAN3nOwMifS/OBbfmV/CcngH/ocOs31zLQ/vGsMRD9SUBbl+9y1c6lyHw2igPRTuFs4eETt352ytHrce6DWRTuTRh0SmqytwKZ4eQ1ynqEtxG7x9CoccCI8D1WmLqVDsPN1DCF/ELK2eE0prVkJE/33MWhZ98U1Up5uPPhrPxk9Hcl6Jj0rVi8tv4YlDNhQUHnHd2PvCBojYxzMyka9w+0zmZU5VOiHA2ZRp+HWikNlLnesoNhu4r2kDtdoiRpmK2Btsyet5vatp6nLaFW/C9miOtpoA4W7XACsHNVBp03m50ZuV68p59jUptTOJ3HBmPaopiFnRcQdNtAWNhHQFzRhm4QWvUl73MXrQgOvD0QR8Fpoby9hzrIpjHjt/PmiLhpz3ZJq6HI/SkdF+q9UWYdKNOTs+L3asxW40RK9rutbDyO+LShsI6bCppffHEhLN916nLUbTrX1qp7IlW9fGvZ2HEz1C0N9SWddUw+RrtIXYdCshwjk/X5yj/QMfeh6OC3GPG0G/8847c1oAIYQQQgghhBBCJBbXQV+2bFm+yiGEEEIIIYQQQpzWspYkrrGxMVuLyqlUQrFe9d7VpzCk2Dm1Tzdd5zhMJdy7VlvEM+5b+pypNdthcOnOKfmM+5ak65vN+bdTme93m2cTWz0bk4Y7GhUFg6JjMwUZbfczqTwAdNb9M51mrt99C08cCcaFtwPsag+jDCnDP+OrOC4LUmz18vujnSFg84vXJy3PVLX7zb9abVHc3LOJjpt05qaFzOan70m25sbuKgy0+I0UWfxUljcyYs4W9KBO6/tjGF9xlN+MW405pnU2NJ3E9vFr6M9+wr73JnDBy09zzdkfU3/WIcwGncnqkmhYe7HZkLTcm713U6EXc2XRep733BYNt+1tPZPNQ/yy985oOFudtjjhcl733tPnUO5CD2+HzhkSUg07fcFzW9YyIB/wGNm84xy2f3A2bx8bwlvtrZzwmfGHDGxt1jgQbMtLeHuu5rudrC7hJe8d1GmLmWtfk9Ey3vTe2+Pc2rmSq/D2WVo9r3vvYYvngYzmko9I9NnezjeZ1OOJ9vm46HzEKvbcMFQ1METt/P8WzwM86rqpoMLbofMcmaw9es5za8JrAM0E3pBCkcHS5++fo63OOLwdoNTqwxs00RY08X6LFYCf7LmZD1vNGIwhXn7gEprePRPboBbe/ehszn3qZT51FfNus5ZSeDt0bqOtno0Jz/u9KceeMJx4iro0K8eszWCIu66JDW+v1RZFf9/ha0kpvP1ix1oqLJZu13hve+4viPB2+PzauK9tclgJ99geWHULmm7r03f0VW/txTz7GsKkNHkZWz0b2ey9O2F9nGifn7XrwxptIWbd1O31HjvoP/jBD1JaeGtrK1/72tcyK5kQQgghhBBCCCF6ngfdYDDwhz/8gfXrk4+MeTwe5syZw+uvv16QSeL6K7lQNpPORJKu9GW+v5naKsK6npc7eNPU5RQbrQnnfi8UiZIM9df8ir3pj0RntdoiFg+x4w8beOm4ztnFJn57uPNucaTuDDJbaQsG4yIb5mirefL5V/CN+gK2bX/l99+5nO9/2vc5amdqqyg1mTka8KY0ytSXJDTJdJ33MtuJpGKTrV3qXEcwHMZhMnLFyDYunvUitqomXJ8O56nNU9nh0nihsT36/W3/VEHYZ+H4nuG0upxsPzqM0cXNWE0BDrlKuWWnHZOi4NPDCecqTVSnLnWuI6TrtIX8vOztW/6RKepSjBgksWYe/Gj4eqpL2wiGO++3L/3gHi51rmOsw0CpJcy/7Utt1AsGRjK+ri53rsNiULpF+8Saoi7FgqlgRrRSlU5yp2wnSc3lPMDT1RW87L2T+cXreaj1Rmq0hYwzl/KVwX7agkb+aXfmI8SJ5PPcPlNbxRdLrbQH4I32FqB7pFdskrGeknL2xfzi9ZRYFNxB+PowFyHdgGoK8vC+Eipt0OyHS4a38qUzdzDmj+/wm3GrAdjnNqMDJzo6E6Z9s3g9D7feyGR1CUYM0fP1DHUlAG2KB023YVVMPc6bPVldQlgJx+2XbM73nsh0dQUlJivHgu64c3uiJG+pHk+pJqzs6/E0S6snoId42XvnZyOu5l7nos/19pyjrU4Y+Tq/eD3eUJgn+tAHmGifjyOs9Xo92Nd1zMU2mq3VE4Ye63/3c60OhFKfB338+PF85zvf4cknn0z4d7/fz+WXX87mzZu5/PLL010HIYQQQgghhBBCfKbHDvpTTz1FaWkpCxcuZOvWrXF/C4VCXHXVVfz1r39lzpw5bNo0sO66CyGEEEIIIYQQhaTHEHeA119/na9+9auUlJTw+uuvM2LECHRdZ/HixWzcuJELL7yQZ555BlVV+6vM3SQLca/WFqDq1ri5QXMxV3YulpmtMOdchqkVmmnqclwGd49hMdPU5UlDrwotvLNreeba1/BML8lhegrlm6wuAYiGdn2zeD1jHApmg857zZ+HI01TlzNvsJUic4jtLRZuPbGBix1rGecwUqWG+OEDrxCoOgPrri288q81fGXznzNep0JXpy3Gqpt7DTmcqi7Do3T0uG512mKGGJ083nYTlzrXcYbDwFinjy8NPcC4s3cS7LBw7NAQPj1exZXvbuRix9roYyIdv7BjLPPStn0kXpeDcMjItk/Gs6WxnCqbj+MdFv5lb3rhZItKG7AZ4SO3O2fJq2Llcg7oTCQKbZuqLmOYRaMpEOgxPC1TddpizLopq9th0/mLmDD4CDarj5MtJWw9PoRvf9KZqCrVMEHoDP9sMbRToRfTgierj9lc7FiLAfoU8pjITG0VQ61WHmjuPZkT9Hw+rNYWYMYU137O1FZFk35lcp7P9DGZam0B5ThTqoO5uP6ILUeq7XXsuedc+zf4wP3HhO+LhARPtM+nNOwE4CXvHfxo+HrOKvIQCBtYs6P7IzdT1WWYMPT5cZyupqhLsSnmlBLa9ibZ+Xe2Vo9XDyY8j0Su9fqjfZyqLqPcaGPJ2DbMhjA6CoMdrXx4cjAdISN73Rau/dLbFFU0Eeyw8Ob75xEMG7nxEztnF1nwh+Hm4xtYUNLAMb+PFz23M1ldQkAJpF0HF5Q00OQP9Dk5cF/0dJxFQvZz8VhMNh9jyHUYe6ayWZ+nqcvxKYGCeAS1L3ruj6UZ4g4wZcoU7rnnHo4ePcrFF19Ma2sra9asYePGjdTW1vLkk0/mtXMuhBBCCCGEEEKcCrrndU/gG9/4Bv/xH//B9ddfz1lnncWxY8c4++yzeeaZZ3A6nbkuoxBCCCGEEEIIccrrNcQ91rXXXsuNN97I2LFjeeWVV6iqqspl2VLWUxb3fMlVJs7TTaGERFdrCxhhKKE55MvLfs1GfYp9bGKGuhKTYoiGdl1d0sCDMXN+/uvI9fjD8E5TIJrd9NtVDexqD/HnjX9E1+x0/KWDwx+NZfnz47uFMqUb2jlHW43NaIzO5X0qu6asgfuaNvCtqgamDnKxcZ+DP1z8OkVVJ3EdHcSfttTxVqOFe5s690e1toB3bvwjlBbR/leNkN+Mu6mY2zdPwWbU6Qgp2Iw6L58ATzjIUKsVVyDULZy4WluAEWOfQpcn2uczWC/ulvFW5M8Tk+ZzwZfewteucf0TM5kyyMfGA+GMQoGTPVpVpy1G1S1xy5yursCr+Ad82GEykdksWhV3QZyDCt1c+xpsBgP7Qy0Jw51jz+WRsNzYMOIZ6kq+XGElEIZjXoVPPd6sh7N3NUNdSYhwwVyrpfJoYyQDfiamqsuYN1iluqwJb9DM2LIT2Cx+Xts3lnUf38H2uV/mz5+cxbyxn6LaOnhjzxkc8dooMgcB2Ou28lFr/Lklk8cx+2OmmlRMU5ejKeZuYfbT1RVMcKi0+vUeZ4eIlWidUnksMdtyEfKe7WWmM6tCfz4al9962T3EPW4EfeXKlT1+PBQKYTabGT16NP/8z/8c9zdFUbj99r4/wyOEEEIIIYQQQpyO4kbQDYZeH0lPviBFKch50Ls6nZKmZWKKupQyo5pWQp9UtmmyO1ORO3P9MVKe7E5cX+5IZ6Ms2b47OUurTym50FR1GYNMKmUWAx952pKOeE9Rl1JssHFcdzG3rJSfrrkf8/gAocNBXnzwUn73UQUnQ96kdSBf0SSp7td8RWnML15PYyDArEojF43dxegzd9N4eDBPfHgen7SZ2e8O8vRnd9+9P3dgHubFv8/Bm8/O4JyzP+aVd2rZ0+bEGzLQ6DPyoStIlc3E3Y0bmK3VYzEYear95qzdFc7l3eX+bJfzOSdytsTW2b9NvYxhFcex2ny0thTz5M4JvHhcx6wotGZhjvvezFBXYlEMWYmqyGU9yDRhW08iiUnNujnjY2MgX5OkUvZZWj02gxF/OBRXR+q0xYw0FbE32BrddmsrG4DORGSnmlTqX9dErrlwy1kreLNR5YAnyOLRHZRaOzjodvDCUSs/u/A9Kkcexu9WWf3IdH76xd0cc5Xw0N4KwjrRqK6BbJZWjwHw62EChBJeE87W6jm/xMLOtnBKUX09XXMlmys8VcmSQNZoC7Hp1rwlXs12u5Wt83J/Xc/3NLKf2TV9LyPod97Zv50UIYQQQgghhBBCdIrroC9btixf5RBCCCGEEEIIIU5raSWJK1TJQtyzFTIxWV1CsWJLK0wll3OTDlTJQmT7M8Q4WVhKpmHmuQibzIZE23SGupJys4VHXZ0hW/UVDZSYYZ9bZ7im8F9HOkOjn/fcxhVF6ygxG+gIEU0eFwlV3z73yziL2mhvc7Dpw3OxGcO8clyPhmMnEpn79lRRoy0kqIQyqjM12kKMGHnbcz/fqmpgb3uIZePamHb+NhRF5/8+MxOnWeeNJn90bt6d//Al9h+tYsayx/F8Opjn/zqTqRPf45NdY/nFu1U04uYsawkPNG+IJqGDz5O6uXRfQdbTvirEdrZWW4RVN9Oh+FIuW+S46yqV9uVvUy9j0P/P3pnHt1He+f89o3NGh+/EOclBIBCMSZqahDQhTZPSUFIKjXHqkNtJbP1Kd7vbbrfbg2637bZ7dbulDuGmBQqFlrawsCyUUigbIIRwH7lvO/Ft65Zm5veHkZBsyZZk+Qg879crr1csaWaeee6Z5/N8vu4uQhELbT1unjxZTmcEiqzwf+1+bJIZHSNelxYo65GQMpZGZioZzNdWnXR5kS9yNW5KN37lWgfnqbUAY8IsayjkKuG91t1AZzQSlwavdGzDp0eZrto5FRhafOwlymZ6JH/acpmvrkU25Kz7xLFgWjvULWPPXLaK8UXtBIJ27njrfCYoGoWWKI+eNHHj/KOUFXdgtkT44TML+cK0JsrcXfx+/3mEdYk/tfr79RuJ7X6Bsh4dg6ik5VyvR7NPj8m2N5d6OBPs3bKb6VbPTFiqbon3w9e6GzgdCYzo9r+xOF4ON8MxR69wVFNhLQPgvo702z4yfQZd6diGToQnfLdmFwddIBAIBAKBQCAQCAQCwfCTtIL+V3/1V3znO9+hpKQk5xO2tLTwT//0T/zXf/1XXhKYCSMVZi3bFfmRDA8wXJzNBjbZkO2b8cQ3kbmaZyW+TYXeN312LHlZaa5UayjCmXT+hcoGIlKUkBTGZlhZUljAnAI/3RELz50xx1cz5qtr+fw4J0FN5vvHPwgxskjZiCKZue2zr2C2RPB7HTy3fzZ72lUsMvy06ew3kEkk32Yjif3BPLUWh2HDJMlsnBZl3uSjlJWfobOtiAv++3mucdejG/D79w1qfN8uwvYxA3Qd7VCQpx9YxautpVxY1MGBrgIKrRF+d8JOVzRMqcVGgUXiLX8PISmM21D73cc8tZbzrYWcDAWHXdkwXKZssfo8EoZvFY5qbIZ1VMzlrnRu57EBVnH+fNlVXFT5Frpm4ukXqninq/ftuzci82+nelUxElLOK5L5UL9c6dxOidXEO8GuUTXoi7XBbMfmxDzIZkxcpGzEIpmS+uFU6RlLDDQW5tvQ9ArHNtoMH2WSI0mBVVfm4R2vH6dspVsPjVgepbq/lY5tA6rDBiOfq5arCxroikSHpC544fIrKHJ38+rR6QSiFja+3Zu317jrmeaQubDQj6bLXPWx3WhRM6eax+ML2Xm7vZQv709WtjSM8+Aww2udQ1M8xBgLisSvTvRQZNPwRWUCUYkD3syM4gS5MZp94AJlPT45kLJPG0yp8lcTPBzx6vE5WrrjM3m2WKhsIIifvYFfpV9B//nPf8706dP5xje+wf79+we7tyTee+89vva1rzFz5kx27MgsbqBAIBAIBAKBQCAQCASCXpJM4nbv3s0NN9zAj3/8Y/7lX/6FhQsX8qlPfYqFCxdywQUXUFJSgtPpxOv10tbWxttvv82uXbt48skneemllzAMg0WLFvGzn/1stO5HIBAIBAKBQCAQCASCs5KUJnEPPfQQP/nJT9i1axeSNLBkPHb4ZZddxle+8hW+8IUvDE9KB2AgifsSZTPjrTZOhUfGjCEfMWGHU1Z+NsYAHkxWN9T4zB9244x0EpvvTatnT1uvjDqxzi1RNrOk1IYkwfOtkaT4nouVTTz+149gn91JtNnMnx76LHfsG4fLImE3wTGflpGpSoWjmiLdlTd59VBkuMNt/LPSsY2Qocfzca66hlJc2GSZucUyxdYon73wDdzFXex+vYJnT5fSGYbDvnC8H+n48hSsLj/hHpX24xOwKQGOHJuCYUiYTRrf2zOVcXYzneH0kqtr3Q38tvsDddMSZTMGBs8F7mSxsokI2qD9Tr7y6mxvcyNpFhUrm6ikpey7/3zZVVxwwT783U5ueeFSAN7qNJhbDEe9Mne25WfryVDi+dYUejBJAxvqjBXSjTeJkseY+WKm43yFoxoAs2HKqN73lfouV+twmc0c0NryKjGPMVrtcZlaxxy3lbYQTHUYqGaDoCbxUluEsKFjluS08aVzYYmymSCRrGXU2Uj7E3+b+P/hNj9MZJWrnq5oOOWYuPuTKygu7CIctnD49ASO9bg56LVz0g9fvvggB9vKOBNUWDT5KG6nl5OtZfSE7BzucfNks4xmGDzhu4Vlah3jrVZMMhwIeM/arZCJbW2hsoGLnA7e8HpxSTa8RphdgbuZr64lQnTUDQKzYaS2Cwxn3zFUQ8TBmKfW4jTsA84d56tr0dCS7nGeWss8RyHH/dFBDUfnqmuQkOPPKPPUWgpRU/Rrg8RBj7F69WpWr17Nq6++yu9+9zuefvpp9u7di8/ni//G4XAwb948PvnJT/L5z3+eSy65ZMBECgQCgUAgEAgEAoFAIEhPygf0GJdccgmXXHIJ3/3udwHw+/10dXVRWFiIoigjkT6BQCAQCAQCgUAgEAg+EmQVZk1VVSZMmHBWPZw/G7iD5nAwY5lETIo2FE5LXYNeo1KtYaGygSplXb/vhyoVGugeMpW3VynrWKbWsUjZOOjvFiubWKxsYr66NptkApnld6JcrEpZxwJlfb/jKtWapL8XKhsyTkNU0lKmfYGyPv7/XO5tKMxX18bj5MbItW6mk2U1B2SWjteoLmhIqnNFZivfP76DXa0RCs2WfsdZSrrRO6D1tVmc8LqI7ZF5oacz45ihb/gezKt7eOxcqdpTOmJ1Zrhlax16kIARif+9138/T/pvxWE2ccIncWFRG5Js8H+vVtITsfKvJxvZ6+tKkkPufHQl7YcmY3X5GXf+YXq63JzsLsRs6o3Vep7bzN1tjcjv7/BZomzmi4UetpZ54udIlLcD9Eh+IvQe/1zgTl4I/IJlah1Xu+rT3ks+8mq+upbJpsJ4/sfaWd/6HvvtSFJT6GGpumXA3yxT60ZE6rhE2czVrvp42aTqu69ybkeWDEJ+BUOXCGsSPzy+gykOmSfPBJnu0vmHKQ2sULdmff0Fyvp42Vxf7MFl7v8+v8JRnVFfeyzkQ5Z67ykTMmnHc9U1GZ0rWxKlzLFrLFDWJ80h3vA9mJVc+Q3fg7zhezClFLTv2AX0k6Z2GyEe7r6ZMqOAKxzbMr5uJlzl3J7TcZVqDUvVLRmXaarjAc4EoTkUJqRJlNtDlFijRDGYqtgJGdGkYwabjwzGs4E7kvK2SllHlbKO5Wod89RaFiobWKxsSjpmibI5I3l7rP+K/Xa+upY3fA9SqdbkVd6+VN3CImUjlWpNyroDYJMlznXYU/ZlhiFhd/hp6yrALGvMKmpjhjPIx4ojeIMKBbYQ45UA+1vH8dzB83j21AR+f6yElpCZx7w7UU0yNYUeSiwWeqIa97Q3Dou8/Urn9n7p79svzFNrucZdnzR2LFfrWOnYxnK1jiud2/v1Tysd25LqbGJ92BW4m1tbGql0OakotDDJqrJE2czL/nuT+vyh1sOhME+tZam6JWls7FsPVqhbeSnwy6zmQ7mSD3l7qrEfGPZtya/47xtwHrpI2UgRar97dKPgixCXtw80Fu3135+0BVcxrP3k7ZVqDZVK//m9iIMuEAgEAoFAIBAIBALBGCClSdzZRl+TuETTsEzfXOZqdNDXACBT+sbATiRV/OXBzN1SmZjMU2sx0Efc/GWxsglVtgxqnjBUBiuzoZrHwdiIy5mOpeoWugkk3WOlWoMJ04D3HYuRPq/QhlU2sMjwaocRj/W5qcSDSYbXvT0p7z30rzYMXeLAn6p47cRU1r55z5DvZTRNw/Id23cgVjq2ETF0nvLfRk2hh4husGJCiMkOL2HdxOMniyixGfypzZuU96F/sxJqLqb7VBkFk8/wxq55PH18Kg6zTlvITEtI4ogvyhTVjG7AHa0Dm3FVKeuYbHH2W1UfblLldWLftkytoxP/kNvtUMi031jp2EaL4Rs2081MDT3/uOBqAP50ciIhXWJPRxgTEovHmXirUyaiG0Mq5yplHRc5XFhk2Hmmf72qcFQzkaIB+/tr3Q10RpMNJ+eptcy0FPButJU3fA/G832JshmHbB5S7Ol8c7WrHosscSTSjWJY+43PZxvL1Dra6ImvCi5WNuGTQgPW+9jKcuK95zrGLlE245dCvOy/l4ZxHiQJTvp1znPLHPEaPNi1g6td9UxxyBzyajzm3ZlXY8ZU400+5guZXCdG3/ad6VxjlasewzB41Lsz67HrKud2PLPb0HQZt93Pja+O51yHjSIrlNmiuCxRim1BzgQV5o5r4hN/eYyrnNtZPkFjb7sV1QR7ej6YF6xQt2I3mZLihA9WTgN9X+GoRjZkFMOW8ar8Klc9Zgke7s48VvkKdSs9Rih+jZhxl0WSscoyxVaZuwcx1xyKMe1wcoVjGy2GN16X56trsRjmrGKND6dR9WgwHGVVW+TBJsOrgfaM567pxvQrHNt4wreTviZxYgVdIBAIBAKBQCAQCASCMYB4QBcIBAKBQCAQCAQCgWAM8KGUuCcSM1IY7tjflWpNxrKcTGRJidL8fMp/l6pb6MB7VsVzTMXZHkd5LJBObjNPrWWBqxBflJQyr9C/WTGdq6Id8LP7d8v56Rvn8GDX0KTSwx1Xer66FpthQUNHQsImmdNuMRlOlqpbmOWwYTPBc91tVDlKuLWlkfsvXss5hW00dReyr9vN3x9KlvdG71fQJ05CevMQB/57IaUTznBg30zCUTNnfG7+cLwQgI6wxhTVRHNQ5+Hum/tJWWPEyn4osa3HGun61eGsW/nshxYqG7KSIcZ49GPVTCpqwzAk9reM56GjBcwuAMOA5qDEbS0DSzUr1RpUwz7gtb9Y6CFiGDw0hHaeautWX4Yz7u0CZT0uyZZRfe9bZ650bkc1yRyMdOAy1DEpbc2UbLZt9e0/hkv6eq27AVmCh7p2sEytS9oKkUmdyKSNZ1L/hovE9M1X1+JGwWuEst4+t9KxLb71I9MtMH351pQGHGadqQ4fbmuYsG7iSI+TlpCZb1T/AdkSRbZE8bcU0dFciqaZeO34NP7YVMBBXzhlXPqRknsvU+uwyybO6N74vWdrwFelrGOC2cFxrXvALQ2rXPW4zDLvhAaXMOdaFn3bYiZbFEeKgbbhDoVcJPd9jx/K81yFoxqnruZ8fegtJ5thjZfdYPOAgZ75FiobcEpWnvTfgpC4CwQCgUAgEAgEAoFAMAZJWkF/9tlnKS8v57zzzhvNNGVN3xX0lY5tNBvdGZtzxMh1BSPfpDIrWaCsJyxFkZFSpn00zcwGM1fJdQVrsHtKfCPe9+34YMqJCkc1ZUYB3QSGXV2RjkXKRmSkrN7qD3XFLnb81a56xtlljvgiLCg183RLgOcDd7FcraPcbmWcHUpsGk0BEzc1N7LSsQ2H2USRVeJnX70Doia6Dkymo6WEnXsu4SdNA6/SjTXS1cnFyiYiaCNikFJX5qHcbqAD15x7AIs5SluPm+eaJvDdo8mGN5HbTWiXXIL59VdpfvJC2ltLkCSDU+0laIaML2LlnkNudGCyItMZhl91NlJd0MAsNwSiEorZIKxJHPL2mobFQoNEJW1IJnnDYa6ULcOtwMiGofTFuRgW3jJ7EyFd5kzQyiyXj/Vv/YK6Mg8lNoM3OnUeez/0Ya5miEuUzYSJDtgmEldW+66AxqhUayjBlfK74aLvaslQV/pG0lAyE+aqayjCOWieLlDWY5cs6IYx6P33vceBVs0TV3RTHTtYfi9WNjHbqWCRwWUxaA5IcdVWqmPznf/L1TrChh6/TiZ92VDG4Hz0U9lcP9X9fHWih4XjOvjL6SLcFp3/a9WYrFgI6XD7V+7BPN8OTW18+RvbuXJyKy57gBeaJ3DYa6YlaNAaCaUs0+FQvww2J7/aVc8hvS3rOnGNu57WSCjl3GuxsolzVQVJGtxsda66hglyQbyPhbExHqYim7ZTpawjIAcz/n1fFcNCZQOKZEmp3huMXNpXvp5/FiobCEmRUSo/gwFX0JcuXcqPfvSj+N/Lli3jX/7lX0Y0iQKBQCAQCAQCgUAgEHwUMff9IHFL+jPPPMO0adNGMj0CgUAgEAgEAoFAIBB8JElaQXe5XDQ1NY1WWvJGxNCRBtheH5LCLFE29/s8n/L2WMzQTJin1ib9nUpe8ULgF7zivy+tHDsf8o5KtSan4/qmd7laF5fPApgwZXW+WNkMdE8LlPVJ8qS+UqWX/fcOKF1/w/cgT/tvS2uStkjZmFWacyUXeXuVsi7r62wu9fDViR4mmwoB6NIinO+OsG56iBM+iWKznQXKep7y30ZzMMwbnRE6QiYmq1FqCj087ruFh7p2cGtLI0RNmIoDuKc3oTr8jFei8S0FQyGxziT+P/GzBcr6+N+LlU0sVDbkdK10sqvnAnci9TGazDexNN/W0sj3j+/gudYgvpCdps5i7j0widc6eqWjifjfHoe861WMgEFrSyn+kI0bnjuXMwEHuiFx0q/ysRL4WDH0ROFYyM+3pzZwSbGObsAbXWGO+SQ0A05HAgBMkAuYIBfEZWyZ1qu+v+sV6acnscxSsUjZmHM5xhhpeXuq+gm9fV9ADuZ8Xqeuskyty+qYlpCVpdMPcN0Fb+O0hKlUa2gL6RzqgXL7B31vrtLgZwN3DLrlI/H7dHLr1/wP5EXens049YbvwaRxfSjy9gXKesqNQirVGpZnUEaD9YkVjuqk31Q4quP/z7Q9lOJCZ3Cf3xcCv+AZ/+0Z3b/NsCbNSdKV/Vx1DRFDT2oLiXVsmVpHmOiA1wpIYW5taaTxdCM/PrEDi9wrwa4uaMBpsjBXXUOVso756lpWueopNwqTjk/Ms3Skmutd8X7/+pT/NvxSKP55OmlrYptMJ79NzLN05ZdJP1WlrEvbv0D6+VTfeST0v58rHNtoC8FbnQV8amILbWGZiwos+DWwyOBvKkFqaUeyylw38wRPNZXyfNNECixR7CY4zw3jrLZ+11msbIrL2xco67nCsS1lejIldv995+R92/7ve27mDd+DA+ZXKh7uvjk+9+o7npkkGZMMLkuvOWQqFiobqFLWsdd/f5K8HdLXoeEiMU8qHNUp6zt80DYHyqsKRzWVag0vBX6Z1XjxlP+2pDq/K3A3T/tv4zX/A/E+LtM+LV37WqRsTDuXyMfzz1x1DbsCd8fLr8JRndNcOxNSjQ0L7df3+yxpBf3iiy/m6aef5jvf+Q7nnnsuAAcOHOAXv8hsP+b69QNPxAQCgUAgEAgEAoFAIBCkJukB/e/+7u9YvXo1P/jBD+KfPf/88zz//PMZnUw8oAsEAoFAIBAIBAKBQJAb/eKgv/baa/zud7/j6NGj3HXXXZx77rksWrQoo5PdeefoxJgcKA56KpapdbRIXRlLOBYpG+mWfWPKvXUkWOnYRsTQM44xOVwOt7k4n+bDSXO4Yr6OBguU9Vgxo6HzfOAuaos8fLwkQlCT2d0m89vu5PjG1xd7+PQEL+1hK3+9v7f8r3Bso9Bi5q6v/gKTM0jLy7P531c+xu+OK/y+5+ZUl82aTMp6NCMW5ItVrnqmqDKqCdxWnWWTjzFp/GlOt5ZysK2M3xxzJZXJM5etoqLyTZTx7fzv7z+DWdZRLGHOnXqMgF9h16FZ7G5z0BTojX/uGe+h8XQjC5T1TLQ4OBztiEvHYm0jncPycLjQDiVCRjp34Ez6m6HGTB0NFikbccpWzvSJRJKuXG6cWs8XZr+Lyaxx8ysVzHBGeOwUaeN9Z1q+q1z1BDUNgDZ8FKKmlKhncr6l6paMHMTHKvPVtUy3uHlwCHHgs6FSrcGCOaO6O5YiGAyF7eM8THVotAZNZ0VUkL75vspVj9Mk86vO5LQP5m6ejWt1X9f8hcoGSs0KFknqN4an42pXPV1ahFUTZOaPb+LBQ1PZ3xNlot3MtjmHqFz6AhGfwv69c1DsIZ7cfz7j7EHOBO283GZFAn7Z3nuPFY5qLrSUcTTkJSpp/errQmUDESma9Hnf++3bRw81Us1QmKuuoVxyU2jpXbv0a3re5jZ9GQtjU6VaQ7lUQMjQ4jHPsxn/M52LzVfXMtXsJqBpSfU3nyxRNmOW5BGJEjJPraVcdnHcaM/jM09/F/d+JnGVlZVUVlYCcNddd/GJT3yCO+44OwdVgUAgEAgEAoFAIBAIzhbSO6kBN954I5///OdHKCm9/OAHP0CSJC666KK8nXOeWhs3F5ivruVp/21ZvfV4PnDXmF09z9XYDVKbpySarjzuuwXFZOYadz0r1K1UKesGNHzKJI/mqmuyNvTIZXUgHyuAo7V6nlim+TBfAwhIIfxSiOcDd7FAWc99HY280m7hjU4TYV1nqbol/tul6hYuKoyiGxJhTWauuobVBQ084buFplAQ2R7G0CUMXaItZKVcSd2NZGuwMU+tjZf1QPU629XzVEZCmZgLZcN8dW3K+02XB7NcMjOdEf7S4eU8dw9TJjax79g52CwRdCRMfYRAcy99hd8+czmHX7qYT1TtZlrZaaaWN9PVWUA4bKUzbKUz3PvG/ysTelfPFykbeSHwC45Fu5lpKeJL5R42l3p4xX8fy9U6zlHsVBc0JF1nsbIJeRhM8oZiwJlu9Wmw/maRspHIICZVY5HnA3fxhO+WfqtI6fo0i2zw3JGZnGwt5aopzUiSwZP+W1nlqs/IOApSGwc90nMzT/pv5Un/rTgNe9qVicH62gpHNV6C8dXzbAxUR4NUY5xq2Hiwa0fO4222hlmv+R/IeHXtbF09X6hsYKGygSXKZpYom4no0BEyER3c8y7v/XcqBpun9M33R3puxmGBDSUeoLfvzyQ2eDarxY/7bkkaq3cF7sYmS5yK+DI6fpWrns9OCvGV2T3MH9/E3pZypjsirCg3MMnwSvMkgq2FRLwKij2E2RSlLWThL2fcnAlaeC3Uwi/bG5nj+ALz1bUU6S4e6GxkaYmDSSZXv+v1XT1Pdb+DfT+S7PXfz+O+WzjXbXBxkYY8jH6xsfvOpS4nHjOUtvCa/wGe8N0SXz2H7ObOmc7FXvbfy2+7d+DVI1mnMVOeDdwRH6OqlHX9+uqY2WRfcjGCc6NQYjUxTS4ZVlPpfivoidx4443DctF0nDhxgh/+8Ic4HI4Rva5AIBAIBAKBQCAQCASjzYAP6CPNV7/6VRYsWICmabS2to52cgQCgUAgEAgEAoFAIBgx+pnEjRbPPvssy5YtY+/evdxwww20trby5ptvZnRstiZx6UhnOrRcrUtrlLZAWY8JeVAZU67MU2uJSBFkQ8ZtqBnHzc7VSKvCUY3NsKaU1/U1tRiK8dNYYLhM7VKRT+OtXExUKhzVzDaXYjNJ3NPeyFJ1Cxc4bZzjjLKnzRQ3P1rp2MYk1UxIA29UZ5pD5idNjawuaEAxSdhN0PijnUguOz3PlvLaKxfzblsZDx2XBzQTHKiuLFPr0DGSZFaDsVjZhFO29DMc6ZvPMcMtgGajK2M5aDppYl9joGzLosJRzUSKOM9l5mfNjXx1oofLxrVz0dQjTLjwIP7TxTy1++PcdUhJMvmK/tJC9MKLkXQdnnuTo898DF2XOdlaRmdQ5dHjRTgt8LPmRpapdayZqmGSDHxRM692WFHN4DQbnPD3lj/0SjF1A94KduIwbEy0KfRENdq1ACEpPKpSw2zI1SRrrrqGAnrVWtnUvZEgm3r1oxnbuHR8M61+J384XkihFZ7tbhswT5Yom7FJprRGcmcDy9U62vGPWNzhdKaKmZKqnuZzDF2m1tGBd8jtNlPzqqGOabG5BoBsyEyyODjfLfFet0FPNEqhxQLA6XAwad4zkuN2tmwu9aCa4abm3j52dUEDEuTNXHChsgFFsgxqhDVQHv3yonWUKn7+7/Q4ZAwqizs5HVD5xTGNL83UWHrJqxRMaabpnZn891sVlCsB7KYoZwIqdx7V2RW4O260/AlHGQVWg2M+iYDWa1AKsELdiixJPDEEQ7C56hpKcGZsUpwJy9+PZ594zr71vbbIwyfL/aimKC+1ufnpKBkWLlE2U2S20qNF8RqhUTHGTVePRtPIL1tSPQvNU2vTmp4ORKr7vsKxbUj1fL66FjcKT/tvpa9J3IB70EcKTdO44YYbqKuro6KiYrSTIxAIBAKBQCAQCAQCwYgzJlbQf/7zn/PNb36T/fv3U1ZWxtKlSwdcQQ+FQoRCofjf3d3dTJkyhVQr6OneAC1WNmW8Gg3p357nK8RJpVqDatgzfpue7r4WKhtwy7YhvdHJ5vrDEZIpHVXKOkpNKhFdj6/8fFhCzORKpqsdi5VN2CUzXiOMgZFkgPetKQ18//iOuCnOXv/9LFW3UD0ZgprM705FOVdVuGycH7tJY83/uxdpghvtgJ8///qz3H9oAm/7vMNuqjcWwpIkklj3M2kHVzm3czQhLMc/TGnAYdb5eNkZnmkq56ppR3A7fNz+2kX9wgxF/1BMsHIptoN70HafZvcfPoU3bOfcSScIBuz88dAs/ths5qTWTYVSSHtYZ6Ii0xzQmeKQuam5VwVRrkh0huGe9ka+WOjhdDic9i3yEmUzHXJPVqtVVco6LJgISZERLavFyibKLHZ8mjbsfV+mLFW34DfCWa98ZKt+umX2JmYVtRGIWHn61Dj+7VRj2raSTX+ZrxVZyP+qZz7HnWzyJJeVowXKeuySpZ9KY6m6JWvlRib3PU+tRTGshKQIGlpW6V2kbESVLCOirFiqbkGRTFhlOR7KapWrnmKrzBuBTj7uLEQ34E2v76xQ6q1Qt2KWpLiqq1KtocpRQncEHugcO2Hjbr1gI4GomX09VkIa2E3QHDD4zMQAnzj3PcbPOI61wEuo08UvH7+CEluQsG7i1Q4Hx31GUh87V12D3bDhlf392ncmBnlDZa66hglyAT1aOOP5/EJlAyEpwiv++/qFrVugrOfaCXbK7CFO+u0c8ZnY7UutRurbb6wuaOC9aGvG/dwCZT0S0rDU7aGoys6WlfEPH/3DrI36CnpbWxvf+c53+Pa3v01ZWVlGx/zzP/8zBQUF8X+9D+cCgUAgEAgEAoFAIBCcvYz6A/q3vvUtiouLueGGGzI+5hvf+AZdXV3xf8ePHx/GFAoEAoFAIBAIBAKBQDD8jKrEff/+/cyePZv//M//ZNWqVfHP16xZQ0dHB0888QRut5vi4uIBz5NoEneFY/uwSBwXKRvxS8ExIf8YLpOUSrUGt6HSKXtH1IQlE6OcVa56WqL+UYtNno5cZIqQXoKUadlWKes4x+rM2XymNx59MTOcGi+0wnG9Myk9DeM8OC3wVpfGY96dXOXczvIJGoYh8aWv3Y5klWj/y0zuf3YJX95/W1ZytnlqLTMsBbRGwkl5V6nWoEv6mDUASqSv/LtKWUdICqeVlS1T6/pJyf9hSgPLJjbR5HNhkgymuDvYe2YCL7Raua+jj8T9t26MZi8HH1uA6vBz9MQkHPYgsqxjs4a5742L+adjvXVh+zgPER1OBqLxvvAq53a6tQgzVTvtYR27SSao6XFpaTqGKnlbrGwiip6yfY+EBDKRlY5tRAydp/y35Wyi2ZeRNMpcrGzCJMlJbabCUc268UV8YtJxJMngWGcxpwMqX95/W/yYbLZyQbLE/irndkK6Tis98XqwUNlAoWyj2ejOuG7MU2txGLas05IJsTJYoW6lDV9W0veR3KKVjpHYujPYNWJb+CrVGopwDjim9T1XNvORVH18XZkHtwWaA2CT4XAg1O/6ifV4LJRZX2JtZpWrnoAWzcnYLJO+tsJRzSxTKcei3TnVmSplHbWTbcx0dRPWTbzeXsDbXRIHI11UKoV879PP4y5vJdTtQIuY8fc4efrdCwlrJlRzlJ6IhVc7rNzRmjw+LVfrMEnyiG0vGq65QnVBA4vHRTgTtHDcL2GR4XVvDyEpjM2wph0zchlPlql1dOZocrlY2TTs8/RsZfL5GlPTnVuVrHTgHVNbWleoW5PGxqExxiTuJ0+eRNd1vvzlLzN9+vT4vxdffJF9+/Yxffp0vve9741mEgUCgUAgEAgEAoFAIBgRRjUO+kUXXcTDDz/c7/Nvfetb9PT08NOf/pSZM2eOQsoEAoFAIBAIBAKBQCAYWcaEi3tfBnNx70tM4v5JZQthJLxSABkppewgnSRxhbqVYquFd8MDx4+Nkan8I19y9HTXW6hsICiFhiSxWKRspFv25ZTOmGtzLlLVRInrUGRroyl5u8Zdj12WORLK3mm2Uq2hXCrISRaWS71aoKyPbxFYptbxqfEmgpocl0bHqFLWYUJmV+DueHu5xl3P1ZMDdEYseG64E7lIxvfKOP7tN5/rd3y2zFNrKcGB1wizK3D3gG2rwlGN2TANy1aTxOte6dxOk96V1XXmqmvQMTKWYK0uaKB2egfzZu1DlnX2Hz2HP52ciF+TOOz9IKYsQPRuMxQ48f/FTtfJ8TQ3j8Op+nG6vXS2F/Krty/kT63+uDvt30z0ADBFjdATNaEbYJENmgKmeIzeRcpGdAx2Be5OWZ+GW3q7Qt064nG4U8nqY/HQ8xELPRbbOZN8W6FupcsIDlkW+PPztrD03H0cPTOeJp+TzrCFZ8+YsJtkOiO92xwy2U6QuA0j1qcuUNbjkmw86b815TaNdMTkusvUungM33xuaUjXDwwk6U91/XlqLcCg40cmWxkqHNWcIxXToYWSrpPvSCOJ/XgifSXSC5UNOCQLT/lvY7GyiW7JP2A6FiobUrpx58JytW5QmXcsfvBytQ6LJKOYTExUJYIa7Pa1DTo2ppKEV6o1lODKqJ7mc6vgfHUtK0vdyBj8uTUS70vmq2vRMXjFf188venqw0rHNnTod8+J/dOGEg+dYZ1DehtlRgFeI0RU0vrV34Hkxj8/bwuTnT387mgpVaVBGt67g1Wueh7puZl9n1vApMr3OLLnIpxOLy2tJbx44hxUc5RX2h38rLmRujIPt7V8IHFP7MdXOrbh06PxiEexuprpHO0adz2KSeZEMNgvatJVzu24LSYOB31J9TS2vQwYtJ2tULdiN5l4pM/Wrljkg+cDd7G51MNsd5TmoJkmP3g1HdUk0x6OcIburNtyhaMah64QlTRCUrhfnRuuLauZsEBZz0SLg992DzyHS6yzH4bISanmNum2mMxTa3GjEDKiSWPA1a56dOCRnptZrtbRnUOs+sTISWNO4i4QCAQCgUAgEAgEAoGglzG5gp4tiSZxfeOg50K6Nynz1bUohnXIRje5vm35sJPJW/e+XOnczmPencOUov4MZbV+ibKZIJG8l3umbzT7xmX+qwkeymwa73ab6InomCSJpoifXYG7qSvzMNOpcSZo4idNjXx1ooeLCn0o5gjXbHsAyWbgfW0Kjz6zhGdPu3jP2/+Nd6YsUTYz2W5PaxAUY5laRzeBjFd10600DSeZrjLGVvp2nL+ZiC6zbOY+/nxoFi5LhPuPKjzm3cnVrvq4iVvkVhlJNfPmXYsIhGz85+szqJ3RitsWxGaO8MO9U/lYcW/ft69bZk6hRoktwv4eOyYJ/vVkIw3jPLSHe2Py1hZ50Iz+8XnTrXIOtIIYexs92uZNo7kSAR8YbQ1GYl5e5dzOo0Psv743rZ7PTDtEobub9q4CuvwO7tpfzq86G4d8/mVqHbOcVmTgBW97RoqSVP1RlbIOt2SjLU9x1VORSXtfqGwAyErtFKvf2SpK5ji+wFu+32T8+xgD1aPYCnEbPRmvYg13uxiK0mZzqYeeSO8U9MpJftzWMD1hK/99UkUzIGIY/VY7MyEXc8TBGGyc/f607TgtGnef7K3jVco6JpgdlNhkzgS1AdthpuqSK53bscvyoCue6aguaGDDua1MLWlBlnXePDWFjpCNroiZYz4zHyv28+lL9jJ+0ZuEjpWyf89F7D01he6IBd2AgCbTFjJxOginQiHOd9o45ovyuO+WfjHFY6T7PF36gLj5bWI5frHQQ9QwOB72ZTyup1MSDJSm7eM86AaU2Q0O90j8KkUM+4XKBiJSNKne52u+MRSztcSxJRvFU0zN8mEjG0VbttQUevBFB27XAzFXXYPTUBL6KbGCLhAIBAKBQCAQCAQCwZhEPKALBAKBQCAQCAQCgUAwBvhQS9xHwsxgMKOvvrLP4TReqlLWpTQMSUWidGg45GBjkVwk9Imkq09DKdOh1tG+8sXB5IyrCxpQTBI2U+/fXWGjXyz1ujIPc4tCaIbEs2csPNS1gy+Ve7DK0BGCn99wL5JZ48XHPskt70zhwgKN/2sl660GsbQm5l+Fo5rJFGUsiRsrVKo1QLJJTar6tlytY5rDSqEFpjtDvNlp4zx3hHWf+iPOGacInSni9Rfm8eypybzQKsXlnTGTuIO3X8A9r13C5o+/hMUWxtfjJBC08197L+CO1kZqCj3oRm+Zri5oIKIb/L7nZv5moodAFHacaWSpuoVn/LezrthDsQ1+2tQr4YuVR6zPWq7WxSXJqfqIa9z1mCWpX/0Zaj7G8nCBsh67ZBnQvC2W5sHi0I9lhmr0+feTGyi2arzbbWblpE4O9zjZ025GNcEBfyCp3LLt61e56rHJEqfDwSGNEQuVDUy2qrRFImlll/nYItG3L+7791J1C5qhZ30vsbZ0UuvhZf+9eZlbjPS4m6nUPWYoV2A4Mt62tFjZRAQtY4nvYmUTqmxhtttMmU0jokv4NZmZziDNQSvfPXozi5SNOGVrv7lVKnlx3/KYq65hqqkwvkVoMJYom+mR/EPafvGVCR46w9ATNWh/v54nGnEOdO1M87muzINFgkO+KLphZGWyGZM/P3Xp53ns+ESunXEEw5A41VMAQM3r97FryWeoWPQyp9+dAYDJHOVXL3+cd7tN3N3WyOqCBh5K6O//YUoDE5QwR702XukM87T/NpardQA85b+NmkIP+8KZbY2BXqm/W7amHP8XKOvRMXgp8Esq1RoKDAcTbHZ0w+Bo2Ju1LDyW77Ey8ktBXvM/wFcnerCbDN7rhgORDmZaijgdDgLk3F6zkZtDclvNtN0mtoFMTCHPZvIZc32o42+m/Xi6rdMfICTuAoFAIBAIBAKBQCAQjEnEA7pAIBAIBAKBQCAQCARjgA+1xH0gYhIZm2TKORZvomRhOJxS8xWXdLGyCa8USJJXpJPorVC3EjSig0o2spH4DfbbmINplbIOVbLSTaCf1HGo8scKRzVAxnmZrjwHl6mkJ1MpfN9rD3TNBcp6JlhUjka7B82fueoayiV3XD62+n3H1If6SJQbxnmoLApyJmTlO0c+kAh+e2oDhZYoX2q4G8mq4X13Cs+9UMXvjpZyZ1sjFY5qLIYlno5MYgfH2FDi4XggPKgMLCZtqnBU49TVjM6frZR0OF2P56m1XF5QSHsY7m5r5OuTG1gxqYnKirewF3cT6nLw5htz+M7esiTZY/ROE7hU9GY/+x5dREFRFyZzlEOHz8EfttEVUrhlv4OPFZtRzQZhTaI5KDFZ1Tnmk7mjtde5fZrToDkgIUswQTE45pM47A9ilWTssolm3cvL/ntZrGzCLpl50n9rXBIPvbLAMNF+UtaBJHy5tt2Vjm04zSZaIuG8xCgfKoPV5+HavpR43kXKRiySKSk/6so8THNonPSbGGfXcVs0/vbgrVzl3I5JkuIy33SO7ontI9bXzFXXMNlUSI8WyVveZzqezVXXME7qlfgNl7NwNn1TjMR6nG5Mm6fWYjZMRKTIsLnVjwSJbX44mK+upUxy4LaYKbODLwIhHQwDzDKU2kCS4MUOf7/6nknap9htdEZ0WqJ+wlIUq2GO91nL1Tp8RiTr8k9kmVqHTZKTpNi1RR5mOA1aQhImCY77dcrtMu96A1lLo1e56jFL0BwJJKVza5mHoAa/bO/dlpTYT2dCdUEDW89vZt7c19DCFt5+93wCESuBqIU3OgqouegNpl7yLtZpHQQPluA/Xcx3/vtTHPdrzHKZOOk34vOFRcpGrp1ooSNs5q0uIz4HiW13CEnhrKTtXinQr00lRjKB1E7pVzm349WjyEiDzh/SRf2Zp9biMGyYJJnl48xEdInj/t71yzta+7u4J1LhqEY25EHnuH4p2C8/EvuRKmUd022urJ3BY9tsu/Rgv7zJVb6drjxSkavcPNW8IJO5wgp1a07PbLk6ume61U7R7dix8Gzgjqy3QPX+/n6ExF0gEAgEAoFAIBAIBIIxyIduBb1KWY8Nc1ZvLK90bkc1yf1WEscay9Q6ZHrNNxYpGykw2TBJUk5xQkeD4Y6TnMkKViYr4MOxgjqUlfdULFPr0DGQkWiRugZN7wJlPRc5nfgiENR13BYZhxnaQlBsBcUMJ/1wNORlrsvJee4Ix/0W/uNU79vjlY5tXFBgZleHj2f/85dITjPdz0/kyb9cxiMnCrmnvTFrI5S+XOHYRonVzH0dg7+xfsP3ICvUrXTgz+iNaC4xSisc1Th0Ja+x1L9Y6KE1HCFkaFxcYMdtMZhb3MU4Rzcl7m4iUTPH2ku5+pVfJx3n+3YRtvO8tD09g6KPHaD95fOIhKxIko7f66Cz283Tx6bx94d6V3S+NslDsVXjrS4Tmg5uK7QEDY5EuyjBwZP+W6kr89AVNpAliXF2+FlzI9e465GQkuLsxozC+vaPmbw5X6CsxycH8tKeRjvOel8q1RqchpIyfvF8dS0lkiPlqkau10r3Rv6rEz2U2DQWTjiFW/HT0l3Aa22ltITM7OkIM8luJaAZ/crvCsc2/Hok7VgZ67Ni+T7aceaht86NM6n4dS3jvibT1Yxr3PU0Rfw5l1c+4iCPlTqer5jOfYmV36PenSxVtzDeaqMtHGa6w0qxzaDQotMVkfnRiR1c7apnqkPmZ80DjweJXOXcTrcWwSsF85aPmdaf5Wod3UYo3icuVbdQWWDDKkNnGM4E9YxN69Kx0rGNEpuZe95fQb/aVU97NJTUhgdrp7s/uYLJU04imzRaT4/j6QPn8WanjVmuCLWX/R8lFx+g+72puM8/RtNLc/jfNyrpDFuwyAbH/RaKrRrPtRg84buFujIP5XaDPe0aEUPP2IQ307jviWSieslkdfUadz3Hoz0p5w1z1TV8wl2MAdzUp97lW1WyWNlEQArH0zGUtp9Lfn6YmK+uBRg2A+7hIrnMhUmcQCAQCAQCgUAgEAgEYxLxgC4QCAQCgUAgEAgEAsEY4EP3gP5S4JdZG3IYhsGxsDcv15+n1qb9brGyiaXqlpzP/bT/triE6PnAXTzm3XnWyNuBnOU7C5UN/T5bqm7pl9cRogPmP5BWZh4zkQNw6ApXObcPeq4YsRjY0CuRyua62ZCYxqf9t/GM/3ae9t+Wkez0hcAvcFtgusugORLg7rZGnvO28EBnI3/xtRDUQDMMplgd7DjTSHPQTLk9yhJlMwCP+25hujPE1RNsSC4r+vSZuC89wayy08x2a2wq8TBNtQ7p/ibazXRHtAF/M19dG7/fJ/239ouFO9D9D0aVso7FyiagdwvBG74H8yZPjuG2gstsxisFOerT+eHxHexqKQSg/JwTFBZ08YN3bP3OYVJDRC+ai/LPlwHw51fmokVN2JwBJsw+jMUc5ajXDPTKHvd167SFTLgtEDEMwhocjnZQqRQyUbFQV+bBYQanWWJfuJ2TfoNvT21gsionydsBuqLheBzYRDIxhnkh8IusZNGLlI1py3Gw/iOxfWTzfbo2CwzYX9sMKyEp0u/zRcpG3Cg84bsl5/rTNw8GkthOUiM8c8agM6DS7nVxyufi7w7dwltdvTLwo4Fgyu1bT/huwWWypr3HWJ9VLrtY6diWk7y9SlnHcrWOq5zbB8znTHkp8Ese9e7MaitNqrxLlZaHu29OKq9FysaMzl+lrKNSrclLXzEW5O2QWX+ZCfPVtUn1K1Z+AM/4b+dUKMAlhVZubWkkrEmYJAPz+z6/FYUSQa1X1j1Q357IKb2HMNG85mOq+hMbFxMJGzrTbS5qCj0AlFqsnAnA651RfFGY5Rr6dPtx3y1xeTvA73tu7jfftRlWrnbVA/37kfnqWoJhKy+9eREHD8ygx6diGBJv9wT4zakwkmRgRE0UXX4EJAPV7SWiy1xY1MFJv4WWIHzryE4mKb1jzeveHr5/fAehNPL2VH3uQmXDoHLsueoaliibk+Z+mZj6ZWIediTaSRFq/O/FyiaWvR+7fa//fg57NQ72RLnCsY0rndvjv0snb1+hbmXV+/kdI1V97VtnngvcmTR/GazOLlDWx+cnfdExuL7YQ5WybsBzfFh52X/viMrbE+tlrO70pe/zwwJlfb/fvOK/b8A5xofuAV0gEAgEAoFAIBAIBIKzkQ+dSVwszNpAJifz1bU4scffiMXCJEw1FQGkXJVeomyOhz1apGzEhIwsSfiNcL+VpMHMLBYpG1Ekc8o3jqlCoq1Qt2KWpKSQHpBsPJZrqIO+58kXi5SNaOhDehOfWIaJeTpc4YyGQrrwHdkwWJ6lKqcrHNuwynJGSop5ai2TTO74b69ybmeWy4TVZPBWpx5f2WgY56Gq1IcEvNzu4Kb3zcOWlUcoswdZ/d1HQJYJ7ZF44cklPHliIm0hif2+UEahThK50rmdYBaGT4nMV9dShJpxyI3BTFj65m+q/B5KO1uu1vGU/za+P207L7TCF6f5KLCFmDXxBHYliGzS6Wwv5CvPzerXN0SePofQzI9hO7iH4P8EaT8ykT+8Oo9l5+7jZGsp7UGVXS0FHPHqdGkRKgt6V+H9UeiOgNsCNhO0h+Bo0E8EjeWlTnQDfnQi2RCuSfvAQGehsoHZqoPWsJ62ji1UNuCUrPiNyIChWTIxGYuFqbJjwSGb6dRD7ArcTaVag2rYB11FycYsp2996Bu6b766FothxoIpKeQdfNA35dIXVTiqmSIV06OFU6q9UtXTxcomougp7/+W2ZuY6PBy3OtimruLp0+NpzUILSGNCwpMnPQTN16cp9ZiNcyEpDBOQ6Fb8g+4Ol+p1jDDXIRdlvlVZ+ZmXalYoKwnKmnIhoyMlDIM01BCiy5SNiIj4ZUCSMjYDAsARSY7AGcSQghmq7Jbqm6hC19SevueZ766lkKUjI2yEsk2LM9ADDT3iRldZTteDaXfG+je1hV7mF2gYZIMmgJmiqw6AE0BGYsMFgl+0tSYk2ldX9PSxHuYp9ZSjJpUVjGDub5tfZ5ai82wEJGiSW29b1392iQPJbYoFsngbw/eyoYSDxEdgprBnEI46ZfY42/jNf8DzFfXIhtyUp6myqdc8v1adwPt0fShKX8xZz0OS4Q9bYX88PgOvj65gUM9MK9YZ9X571Bc1oYWNXPyVDl2axjDkGjuKmLl7t+wRNnMVMVOe1ij0GIioOlMVmW8EfBr0BmJDhgasVKtwWZYsWHuN88djCXKZkos1n6h51KxWNmETTLFy3eeWstE2YVFlvBGtaQ5Q9/wk1e76pnlkmkLweFAiDKLFc3oVaIFNa3ffCPTsSkVsbEzds7hmItDdsbHsfo+lDCE0BvGN6h9MK9M1+/mo+/LpE9fqm7BgpxUflXKOkJSOOn6lWoNJbjihtwDXbPAZB0wJF524TyFSZxAIBAIBAKBQCAQCARjEvGALhAIBAKBQCAQCAQCwRjgQyVxX6JsxmlSAOjQgnFpQaI8fSzQN/5hLgy3zLuvJGYoErdcSCV7GWvl2Jd8y5MWK5vQMTKW7A4mp1ld0IBuEDcBW6hsoMRkx2U28avOxiTJ3tcnN6CYDN7s7I1/vULdytLxMu92mbjjnofQyyYhv/IaZ567kLueX8QJn4kdZ/pLYJcom5lgs/NACnlsLlLTK53bk9p2Pliu1hE2dJ4N3DFkuVW64xco61ElK7OcVi4oCHNBYTszJp1EdfoI+lR0Q8Ku9Bqxvfre+fz6SCm/bG9kibKZUouN+/70EpHZVyOf+j+sT/8vd/3neirGNeFy+AmHLcx96mkaxnkYr+g8fSbMs4E7WKJs5jynnVKbwdNt3qT2+8VCDzNdBlED3us2aIr4MSHTKffwlu83/dI/Em2vr4y175aPKxzbBpRPVinr0CU9SZ6/K3B3SnlfrD/Ltc3GDGBiUvRKtYaZ5uIkg73his3a934erKzlkwtewGSL8PifLue50252nGnkbyZ6CEShJWRgkSSOhfxZx8qtcFSz1FXGX7rbUQ17zrF2r3Ju54zmH7YxpMJRjcWwDKvJWqp6FJPVZxOHOhXz1FoM9Jzr4kiby81Ta4lIkZy2IiSyUNlAuUWhJxrlKf9tVBc0MFmViBowSY3SFDDzSmcg63EiFxYrm7BIpkG3W61Qt9KBv1+7vvei6yl39vBueynvdtv4WXNjXD5dXdCAapa4uy15HExXVxLHkVzjb89X1xKSwvHzx661uqCB+tlNmE0a/rCNE14XTzbZiRoGP176KlPmv41k0tBDFnqOj+fNd2bT7HXx6Al3v20uq1z1tET9/bYfzFXXMM1ciDeq0Sx1xtMwT61lkbsQbwTubEu9ZWa5WoeElPHWtXQsU+tokbr65e+17gZkCR7q2pFyS1R1QQOlNonJDo0jXhO3tjQyX13LFLOLh7tvjt9frK0uUNZjwTQidXQw8jlPz2V+1pd086FM0pmP60P/NpbJmJ9Jm/tioQevln7r3xJlM2Gig27NWaFuxWU2E9RDPOa9RUjcBQKBQCAQCAQCgUAgGGuIB3SBQCAQCAQCgUAgEAjGAB8qiXuii3tfhssdMRUjJTnLziFwcHJxSo2RLzlKOobilDkWGag+rlC38qT/1qzkSpm6V68r9hDQDB7q2sG17gYmqRLPd3dSiBqX9l3l3I5JkphdIHGoB7xRjcd9t/CD6dspt4dY/9MnCU+vwNJylOj/NLHj3moeadJTRjSI0bd+9G0jC5UNmJHT1qGYA3ri8WWSkxbDO2xtbaD2lW1biZ3rKud2Li6SUU06l5S0441YOa+sGafDR2e3m+dPnMME1c8tBxTssinutBx+ZgbBCz+FqeACOPI4+u2v032qDC1iRoua+PL/fpxSq0xzMIrbYuZoyMsLgV9whWMbs91myu0ax3wmTgV0OqJhDAxU2cITvltYptZRZrWyP9w5YF72dUXOhUXKxgGd3gci0351sbJpUHdyyE2KnI4Fynp8cqDf+fJ5jXQ8cHEtn1v9KAB7/vcT7D0zgbAmYwD7eyzsTLH1JFO+MsGDJMExn5EylnqmrCv2UGqDrgjc0To0N/hERiqiR6q+IJ/O60OhSlmHBVPO2w8ypcJRzTlSMa1aIG9x0mNUKetYXuqkxBqlKWDhPHcAxRxlX7eTfzqWe70bDpYom+mSfP3K/tGPVTOpqI22HjfPNZXzfGuUcruVY4EgVUV2TLLBqx3agGPWImUjbtnaL2IP9Mpupys2eqK9bbHCUY1DV+JlkUlfc7WrntNRPzsWnaCwoItIxMIf3qqgM2ymsriLiilHmLb4FczlUYLvFnL89fO59/WLuXLaEY50lnDMp9IRNnHSL9ESigJQbDHj13QihjFoZIDY3KJLiyRJiGPO4ecqDjTjg6gT+WaeWsvlBYU4LQb7uiX2hzs531pIe4L7/CpXPdMcMif9Bj3RKE/6b+VK53Z6tDARNKKSltTnXOHYRrHFnNMWonywTK1DlU10aKFhuX42zzNz1TWYMA3YJ+c6JqaKpJGOxcomJKT4tryrXfX0aFGe9t826NbRRcpG/FJwwOtsLfNwvjtCT8TEPx4bPILSYHyp3EMgGuL21p1C4i4QCAQCgUAgEAgEAsFY4yPxgL7SsQ27YRux673ivy9uIjQYmf4uFbsCdzNXXZP1cQuU9UDvm6JEhvJWPNfV81haBuM1/wP9VjAyPTaRXPJrONjrv59r3PUpv4uZo2S6el7hqB70zeliZRMrHdtoDkbojESoUtZhkyXsJri8oBCHyRz/bZsWZEGpwQQlgt0k8bjvFq51N2CTdWTJQPL7sD7zv7z4tRl0HJrMXc2dFJktA6ZXQmKpuiX+d9/407sCd2OTTFQ4qpOOW+WqZ0OJhza8SZ+/4r+PJ3y3DPhmdyhtCxhQrZHYVpaqW/q1pVTnWqpuQTMMLJJBRXEHrQEVX8TC8Y4S2rsKUGwhPnP+2zx8rIBPl8v0aOH48dL+I9h+cTfyj7+L7Y0XsE9rwTm+DYstTCRiYUGpwTjFoNxu5u1ICy7Jxgp1K5qhU2jReb3DhF+jt1xtdqbYFWa5zGwf5+Fp/22YpN560JdKtYYqZR2Vag0tUlfWediXbtmHzbDmdGwmb/EXKOt5LnBnRiubsbf4lWoNFY5qKtWaAX+/SNmYsv+IqSne8D3IYmVT0ndOXWVdsYfriz0sUTYPmqbY+Va5UvcNqbCbo+gRE8HWAv5yajKTVB/Ftt66U2LLXSC3Qt3KOLtGRaGPBaVRtpZ5cj7XOQ6D8UqUY/7wgL8brB31JZfV81zGgGk2B1+Z4GG5Whf/TDFsXOHYFj/nUPubXHkp8MthX7mbp9byhu9BHvXuTDlPuMKxLet8jeVlTaGHOaqLjpCE1aRTaNV4+LiFv5xxZbx6XqWsSzueDkSs7UNm48ViZRPPBu5I2b8YhsTRtjJ+e2Qi/3jsZqaqVsbZ4WOFdiQJymxRZEBOo/IEeD5wV8rVc4Bn/Ldz0B/Er/XGin/D92BSWWSyKnlc6+aFwC9o97qIambePn4Oc4rb+f7xHextL8AwJNrfnEnPngnoETOFJR3UXPg2e05P5NETbo75zPz4xA7uaW/kCd8tTFXNlNqh2CrTqvmTrrVAWc+Vzu1Jnz3q3cnve26Or55XKeuA3r59V+BuojoUWnsVKzGucddz9fvzgA0lnqR+ukpZxypXffw8fYmVaYWjmmvc9Who/KSpkada/HHFWKEVZrk+mP/MKZBQzQaTVIlZLgsLlPU85t1JZYFCpcuJ2udZQjcMDoZ6MmqDsfF0sN/EWKhsSOpzUvG0/zYe9e7k+cBd/eZPA7FskPPGyiCb1fO9/vuT+uRUfUI6U8SBuMKxjWf8t2esgo6ix/8fy++Y+u+5wJ3xsrrSub3fmKNlYNZ5a0sjiknHahp4fB2srKH33pxmAylFt/CReEAXCAQCgUAgEAgEAoFgrCMe0AUCgUAgEAgEAoFAIBgDfOhN4hLNnIZigjYWGarxXYWjmskUpZVU9SVT07IKRzVu3TGo5Ke6oIHuqDZgbOMPAzGZS9+862tkMZxsLvUw06XRHDDxs+ZGVjq24dUjXOhSsJvgp02NcROwRcpGLi1SMQx4rqsLs2GKG44tKJH59k2/Ri8dj/6nY9x0Ry2zCzt58lQZP23KzdiltqhXOpvOGCbTep4uTnEm0rNsY81m2hZiEi8TJsyGiVKTwqIyKLRGWDj1MAB/Onwu22sfxDyuh8DhcXzr3ms46NWY7Tbxfx29Zm/eb5RiXn0O0dJzsL2zizf/7QKmz9lH+/Fy/vm5KiwyVBQGsco6B70KPzzeKw3995lbcVsjvN2pYjUZSMDrnTp+PcqFLhs2GX7S1Mi3pzbQFJC4raWRSrUGXdKHZG52hWMbqkmmI5psBpSJsWU6468KRzU2w4psyFnHeh0Js7Z8k6k5z+5PrmD2pa8C8M4Lc9ENiV/vn0lbqPf7U4EIPUYop7FvlaueR3puZvs4D74o3NOeWxv/UrkHp9nghF/K+Ry5skjZiCpZ6DKCOccIvtK5nUmKibaQkRTrHnoluE2RD2JBz1XXoGNktM1isHqZD8PZfNX9ueoanIaCTwr1S9NiZROFZlvamMCpzrXXfz/L1DpmOqzYTVCuaEx1+OkOW/ljsxWbLCFJyePCYAZPA5nVJvbZfceFgfJosbKJCNqg7efei66nVPHzTmcxe9ut3N3WyOqCBh7q2sF/zqrDLusc6LHzb6c+uJ9cyiYXc8LE69xQ7uGSIj9VUw9TOr6F003jOdI6jhdaitky/xUslggTLn0Tyaxz5On5vH38HGaUneZ3+2bTEZKZV9JDR8hGS8hCICpxKgA9UZ2gpiXFLu/bLmJptxnWpHaYaAB7pXM7Mr1S+L6sK/ZQbIOXOz8wY6tS1mHHQo/kTzlHiOXVNe56Sm0yFhn29YRpoSeeh9UFDRyOdFMuO2nWvXy6xE2JNcoJv4Vdnb3j7xcLPf1iwJ/N5MvgciCTzkxNZWMS/nb88X4lsU6MxbH7+mIPFxVGaQ2ak9pzLqwr9iBLcHfbzwFNmMQJBAKBQCAQCAQCgUAw1vjQr6AnEgtflY6+q2JVyjpUyZq0ApSvcG35DpF2NrJcraPcbmVfoCee79mEUuhLNm8FKxzVyIac9vfZhJTqG+4kkaGUc7q6tkTZnPTGeL66Fg2NUlxp63d1QQOXlUWRJCP+dv8n525ldlE73SEbdxx08oTvFq5x1/Nw9818bZKHiUqEUwEL/3qykQ0lHma5NBxmnRu+90v0qVOR3jjAE7d9AW/Eyq6WAn7a1Jjz2866Mg8hDX7ZZ3XtCse2ARUWS9UtdBMYkbCGMYYSUnCxsonLS+3MLenknOIW2nrcjC/s4PzFezC5/bTuOZ/HX/44YV3mlXY7e3ydTJRdVJVK/MO//BJt+nnw57d49J4v0B6yYZZ6u+9yh5f7D41nqsOg0BLl9U4LrwbauMxZwmSHRkSX+O7Rm/lioYc3Iy1YDAuXOgtpCRmU2CSKrAZtIYlbW/KzSrDSsQ2n2URbJNKvHY1UaKy+6YkaxoD9/3CQTZ9UpawjKmlp63K61dQ3rrgcl7uHP7w6j0/Neo/nD53L3naFN3sC2CUzHfhxo2TUn6Vrv57xHk769X7hkVKxSNmIjJTURpYom0dEKTRcjFb6Y0aouSr/Ym0tHytmg82fsiWmaPzn6dvpjMh0hyV2nGmktsjD4aCPjxU4KLIaPNcaHrDOxeZpMtKQw0BmS6Ly6r9m1TFB9XMmoPC7EzIhQ8MrBVngKmRXTzt7/fdTW+TJKIRYuvK6yrmdbi0yaF1MpRZdomwmTJQ1k61Md3Uzo+w0zxw+l0dPmrhuahhZMqgoP8nF1/4JuVwlul/jxUeWUeDwcbClHAmD1qDCtnfvxDPeQ7HVoDMiYZXhjc4IBRYzTeFAWnXDAmU9dsmCGSlptRR65zo2w0qF00lrSOfh7tRKjOqCBmymZBVONuPxAmU98wucHPJqPObdySpXPV3RMAVma1z9EZsD1ZV5uO398fAad2/otXe7o3HFaaw/nquuYZzkHlUlaJWyjoAcHLaV5nlqLREpMuTzZ/r8dIVjG1NVMycD2qCh+3JhqGrqDSUe3Bb4WXNv/Vim1iFDUijgTOfCH8yHDMQKukAgEAgEAoFAIBAIBGMQ8YAuEAgEAoFAIBAIBALBGOBDK3FfpGwkJEXiUspMzVZylYJlakYFH8grliibcZoswyLhyIV8GNLMVddQgjNJ6pEpmRpvxVikbMSEnLX0cIW6lQ78Wclsl6pbaJO6h0VCtFDZQFAKxaU/FY5qxhsFg+ZhqvyqUtYRkSL9ZERXu+pZWKbTHTHxUluEgBHla7MDFCp+Hj82GQl4r9vg4e6bqS5oYLwiYZLAbTH4p2M7+MoED10RmOrQ+eY/3AJTSuFoKzf+cDvdEYk3u0PohsGzgTvS5tVSdQtegv3yfYGynrAUTVn3FijrsWJOWcYVjmoKdWdc3lbhqEbR7TkbQY0E89RaZlgK+OL0Ts4bf4oun4M3WsqZW36S6TOO0NlazJ/2XYBmSLSELHz3aK/sLvgDB6ZLi5GCAfzPmHn35YuZMvUE0YiFYMDOW0en8V/vudAxcMpmCq0m7mlvZK66BhMmilAxSxLnuc30ROCAL8gEm50HOhvjBmCazogZ4aTqazLpQ3PZQpFtvwL5leEvVbfkVX67VN1CB974OHWkeh5mS4SOtmJ6/CoWc5SnjswgpEu83GakNFzKlnXFHs53a3zrSO+5YnJZnxzoVx59t+BAr9ncNIfMa13Bs0bqnlhHawo9uCzEJa+pSDQ2ypZ09XqFuhW/EUkyxXJLNgJGNOfY55nU7VgM5F2Bu5PmRNe46zmgteVlHFyhbmWaw4Jq7l0pOuE3mOKQsMoGE5UIDnOU/T0KPzqRWSx0GJr5VWJ5pztPYnn0LbPvT9vOK+0y5YpE4+kP6sn1xR7eCLZRgoun/bcNSV57rbuBEpvEfl+on+y/SlmHDTPPBe7sV29irCv2ENQMrpnaw7xzDqGoAbq73BxqGc+Uoja6/A4u+dhruD5+EsOnc+jxSzGbNdo6CtnTNJmQLvNym5WQZvBg1w6+PrkBf1Tixa6evIy7C5T1FJuUlHPi2iIPvqhOazQYv6/lah2TFCthLbOxa4mymXMUO5rxgfngUnVL/HuXycKlJQZm2eB/m3XKrFZ8UQ2LLPFw981c5dxOUNdStvPE7Q5XOrdjGEY/A+bFyibGW+34NZ3jRvuwSdIT62Yu499YY5GyER0jr9uCE8trpWMbbXog43yqK/NwcWGY7oiJd7tNSMCZUDSrbQ6J89UV6lYKLDoPde0UEneBQCAQCAQCgUAgEAjGGuIBXSAQCAQCgUAgEAgEgjHAh1biHmO45B0x58hKtYZJciGPeXdSqdZQLhWgZ+kWHHMMH454f+lkowPJimPEpD9+I4wJGQ19wLxc6dhGs9E9oEtjlbIOE3I/qUpfmXdf+krOMi3XRcpGptpUQrpBUNcJp5Anpcv3XGSuV7vqCer6sDh6LlQ2UCjbMo5bn8jWMg/nuyMENZlvHdnJXHUNf3+ujCyB1RTlmNfFDft65T5fKvcgAW2hD2RgDeM8zHRFePq0zK9v+A22qe1Emty88vRlvHZmAn9stsbjA6dz6qxwVHOOVEyrFugn8ZuvriVCFCCltLDvOfPtKDxSxOrUnRduYMH0AzicPrq73PiDdtp8LgoVH8c6izHLBnvbCvn++/HMw8/MIHjeIqxNb8NTb3H0L3Pp8TlwO728dnQG3oiFkGaiyBakI2Sn/r1eGfHXJnkosGgc8proifTKEmsKPTzQ2ci3pjRQao/QETLji8q82hlOahsLlQ14Zf+wyPDSyTzzESUjXeSEWEz6TM4/T61FRurX/oeyDWi4nMBPXn8RamEPZjXIwb0XcqK9lHsPllFohagBJ/zRnPqMSrUGt6GiyhYudJvRgb2duUnUN5d64vUvRjaRMhKZr67FZlhylnhnQ2ycX6puodRi5aGuzOXWA5HJWJ+PWMB9x83hjqCQSTqXqlvQDD3JeXueWksxKhJSUr/+pXIPNzV/IF3uez+x2OyZuHgvUjYy3qLQFPGnlcpm2v+kqru3X7CRCQ4vL54p5R+PfeBCvkBZz/JSB6926DltN5mrrmGSXMCj3p2sLmhAMUn9op3EWOnYRpHVTEsoknJ8XKpuYXGpFV9UYsn4NqwmjSdPjmPFpDMc7C5gwaRjTJp8ihPHJzFn0R4CrQWsvWc5n5uk8f/23c7mUg9tIZ1yRWbnmQ/SsK7Y0y9N6frKgbYhxLZwpJKQDyR/zzRazkJlA27ZxhO+W7i+2MNroRbe8D2YVG+/PrkBTZf4t1ONrHLVAzBZkSmxGZwKSLzt8yaNXVc5t6NDUrp6JctmeqIabYaPl/33Drq1YShbHwaqt5m0ySud2wGGvN12ibIZm2TiDN2DbjVJ5ww/0L3kK/pVYp6ky/fYM8ZCZQMhKdKvLv/z9O0ENYk/t6aObLJMraONnn75kMpxvzc9v0a4uAsEAoFAIBAIBAKBQDAG+dCvoGdCpVpDEU4ihpb1W/l8vNGpVGviBiL5ZKjqgWyM7zIh1Zu8+epaFMOac1zpTFihbqWVnrzEr8+FocTNHirL1Tq8RpiAFGJZQQneKNza0shKxzbOdZkZb9dwWjQKLRFuPyzzXOBOvjbJw2y3n8NehSM+ifawRpcWom66wZxxTZQUdXKmrZhxJe0cODGZF1vG0ROR2det8/ue1PFL80k+zAxTUaWso0CyD/vK/DXues5zSxzxSmw9/xT3HpjI1xe+iqIGOHZyIr86cA6z3WHe6bKy4/1VitDuuTC3nnC4DeXh73LgV/P50/7ZzJ94nPHjWjh9pow/Hp1OgSXKC612Xg/0xt29odzDNEeEzoiZtzrht907qCvzMEnRkSRwmjUeazKQgekOK0d8kZTmQrlSqdYwzVQ07PViuOrE2UDzxgsovOAwXe+eg724mzMHp/LPf/kYqhlO+nV6otGczctuKPfQGgSrCTrDubfvq131lNllTiXEte27klap1uA0lJR1LxYn2YqZDrln2MyVEhls5Wmuuga7YcurcVFfch2DU6V9OBR6fclFAbOpxENXxOC33TvYUOJhgmIQ1iXe6uo1XbrCsS2uSMu1ncfuPR/x4NPxwMW1TCnooDPg4KlT49jXrfGodyebSjzc2dZ/xXueWovVMGe1arpcrSNs6Ngk06DjVLp7jcVsvmaywYyCTkySzimvm3Gqj4vPe48f/nExP6j+b6wFXrSQhYOvXsi3X5hFs+7FiZ3JdltSHPIYnvEe3u0J52Uem83cNZt6XVPoQTVBa1jnkZ6b2VDiQQbubGtMWkldrGzikgKFw97eMlyu1mGXTSkVEKsLGpAgSR30YSab/M6HIq4vmfSJi5VNBKTwgGqhZWodHXhzSt8XCz2U2uGgd2hx2pP7MxEHXSAQCAQCgUAgEAgEgjGJeEAXCAQCgUAgEAgEAoFgDPCReUCvcFSn/e41/wM84789KynZImUjQF7kba/5H0An/zsN7FhyOi6WV5nmxwp1a/z/i5SNLFfrUv4ulSzmZf+9Q5Z/z1fXDvj9k/5b08pY5qm1SX8vUTZztaueSrUm5e+XqXUsVbewQFmfcfrS3d8CZX3avMqFhcoGrnRup0pZR4WjmkXKRp7y38YLgV8wy1JMexhOBjQAWgwfL3f5+O2ZHo75rBzoUZmhKqxy1WOSDF5uUymyRhln7zUOeT5wF4e8Ki+emoJNCTBl8kkOn5rIm+0lOEwa/3qykWKrzFXvm41kwxWObfH/p8v3WHubq65JkjgmxjBNZL66tl/ZDoZTsg1Z3h4zIhsIb1RDMRl8cnyQYNTC9z79PO6iLoIBO1HdRHcEfneSuLwdQO5ph703ozz6A3yvlPCTly5h0TmHcKp+enpc+EJ2ZOCIz0ahFc61FAGwryfKU80ST7b4OBLtYoW6lVMBjX88djN/PBPi16eCABRaLNza0siT/ltzktQuUTbH8zuxr33N/wC/77k5o3wZCpnIXrOtD/kgXX1OZIW6ldUFDawuaBi0L0tFNGzGVBqhZNkhfGeKaW4p5ZKiMBYJzndLhA2dKmVdLsnHIsNFRRr+qEGhNffpgt0kIwFBXYt/1ld++5r/gbR1b6//fl4I/IJnA3fEx5F0bT9fDCbj3Ou/P+vxP5P6kEhifixX6zJuR4lpX6Js7vfZUFigrE+b93v99w86Nibew5XO7dzZ1sjpSACA7ojOgR54ozPCeS4zC5T1SYarg7XzdG08du/DJW8HeKfLzVut43m7o4hnOjs5pfcAcCIQ6TfOVziqKUbN2hTsKf9tPBu4I6NxKt29Pu2/jdluK6X2AIolTLGzh4ghceu+UszWKP98/cN0nCrj8J453P/oSjp9Th717sSNgiqbORjwscpVH69X0LsVpjsCOgYLlQ1p63km86b56tqU8vZr3Q2sTJgrQK+U2WJkPs8ttML5BVGmqDKVag3j7KCYe+cgLwR+Ea+bxWYbUR1Cug5AJ4G0Bn8Pde1gX6SdhcoGYOBnjUSGc0ycq65hc6knbnIH2fc96cimHxmObaWZzE+eC9w5qBlmGz1I7z8CDzQ3SJVvp8NhXu0KDNlUb7D+7CPzgC4QCAQCgUAgEAgEAsFYRjygCwQCgUAgEAgEAoFAMAYQLu6DMJLuwMvUOgrNlng86ZFklauegJab2+9cdQ0uQ806Pm4uMVkrHNXYDGtOsVwzcQbN1ZU/0dkyU9f2fLjqLlY2EUUfNF51dUEDdpNEc/CD+Kh1ZR50AwosvfGS3+r+wIF1obKBi10OusPwq84PpNa1RR7+9bPPori9hLwqR45M5TcHZvSLoV2p1mAzrGnzu6/D7LXuBsJ6bnFih4sV6lY0jIxdabN161+kbGTleBshXSaiw4Kydg50u3mr08I7/uRYq9G7zeBSCbxkw4ia2PN8FZ/c9QcerKxlckE7b5yZwM3HQiwtLKQj1OtKe6Vz+6ASrIXKBopM9oylWn3b7Dy1lnPMbvyaniRFzZXENhr7/3C7Ty9X62jL0c11IDIdO+aqa9Ax4u0hWwfuQ9fOZ/ycg/hOlbH39YvwRmzsbi3kf9u7mGUt5HQ4hJdgvNyqlHWYkDPq5/5qQq/L8f4ejZN6F3v996eM4zoYw+mePRz0dR+ucFRjNkz96sgiZSMaes7xi4ebWGSKoBEd1kgifcs3m5jO64o9HA0EeTZwRzzWtcMkp3TFHmgMT+XunNhfDTWqTSZc626gzC7FY4TXFnm4ryN1zPJUZNNOKtUaFMOWVd2br67l0yVuLJLBNGcAf9QMQHfEzJOnNR796kPoETOn3joXf0DBG1D4xF8e4z9n1WGSDA702NAM8EfhjtZGFiob0NDRJZ0yycHjOY4Bi5VNuExWJikmjvmjKceS+epaQlI457GgUq1hibuE7gjc3dY4YP+8rthDR7jXwT1VzPtErnBswyJJPOrdOez9XMz5PyCFcrpOuntertZhkeRBy2+Bsj7ttfMVozxf5KO9X+XcnnJOOl9dixN7yvjnMbKvC8LFXSAQCAQCgUAgEAgEgjGJWEEfIRJXQBLfzqd667RE2Zz1anQ+GGurHBWOaiyGJa8KhnzeY6Zv6DJdAUx8279C3YpFlmnXAoSkcF5W95ardbjMZh7u7o1lfI27nhKbzFSHTlCT+OHxHfGVoiuKi9AMiX892fv2f4GyntUTbTjNGpef+x4AT+y7gOmubpp8Tv582k5LOJyTAiNfKxuVag26pI9IjOShsq7Yw6cn9lBkCyJLBpous6etmE9PPcqfT0zl0eZQfDVouVrHo9/8FaaFpRAO0/WwQqDbhRY1oWsy7Z2FvNk8iRN+O+OVEE0BO985cjMVjmquKCjDJBucCUh0RQw0w2CcXaYp0Ls6sFTdgstkoSsaHrTPySWm6Ty1Fg0t4za3UNnAOLOCL4WaJ5tVueEmX/Fd81H337pyMS5XD6eax9PSU4BmSHSE7Bz3Kzzf8oGyIZf8W1fsodQGJ/zGgHF+E/vVkYi3nYqhrODE1C+LlI34pWC/sk3Mu0q1hjJc8RWnKxzbiBh6XuI/D4UKRzVOXY3nwVDraF+1TIWjGkW3p62vuZb7Fws9lNjhpa4e5rpcuC3gsuhYJIOgJvFWl8RD79e9dO1lhbo1yTjtSud2TJLEIz03Z5WWwVQvVco6bJj7qREWKhsoNSuc1Lp5xX9fUn262KXynjc/8cGh1xxxoJW7gZirruGvptm4eMIJdENib9NkHOYoTX6Fvz14Kz1/Nx7ZFuZ/HvkM73S6KbFF6AxbmKQGePi4yrVTfTT5Ffb1mLmtpZHVBQ1YJImoYXA07M2oL5un1uJGSXkPS9UtTLHb+GWKOOvpGGyFO9X1L7QXcjjgT6m4mKMUYJHhtpZGago9PJCgIIyVa2J/N1gc9Eq1hhJc2CS515y3jwItIkXGxJwlk/jiuZBrvzBXXUNU0kZtLJlkVeP9znCzXK3jKf+tiBV0gUAgEAgEAoFAIBAIxiDiAV0gEAgEAoFAIBAIBIIxwIda4j5fXYvFMA+LccFcdQ1FOPtJa+ara5ENGQumJLnIYPLCvuYL2ZjTLVI20i378hrnNB9S0mvdDUQMY1CZWTpZYb6ISWxyMaXryxzHF3jL95tBfzdUuWUUPePjl70fYzVTmdcSZTM2yUQHflTDhoFBhVuh8XSvlOtK53Y+VizTHZH4aVPvZ1c4tuEwmbCbJDac28zs6Yd5+/AM/vv4eN5OMJdLZ6oxGKtc9XGZdazu95X8hwwtq60fqUzbBpJbZWvylivz1Fommdx8qjxKU8DC4vGtlBd0UFzYyQv7Z/N6h4szQYkDvmD8fr3fKMV+Xgfhwy70iJm3d83jV++dy3nuIA3v3cGVzu1YJIlJqswLPZ2cY3ZTbpfZcabXDOdSZyH7vWGiGJiRCBjReP+0QFnPBIsa3/rQl0q1BtWwD6sBTK59zmDbgUbCGGqorC5oQDegPRruJ/vMRB74v1XXUKj68IXs7D5djssSpeG9O/j+tO2cDpr4WXPmctG+pDNHg94+ziv7034fY4GyHh2DElkhahgZxXAei8QkoIuUjRSZbPF+Ljbm56ueDSRhztfWipFgsDFwvrqWctlJUNcIGzrnOuy4LPDTpkY2lXg4163xp9MabXhxGSpA2ra+SNmIjjGsfVSlWkO5VDCoGeZ8dS3AkOcafelrJjkYsT61SlmHLulEiFKEk1UTZCpKWnHZA7zZUk4gamaSw8ejx4v4x+X/R8Hk07z8fBXTJ57keHM5e1vKOa+gkzJXF/e8O4ufNjWyylXPNIc8pL4lXZrtkiVnCf9AzFNrGS85Mcty2jnpFws9zC2OEjEknmiOxI0Lw1IUA33E2l4u5tT5mN9my0Djdi73kOqYKmUdESky4v3eKlc9TVpPUp6m2iKbuB01lznMPLWWRe5CTodC/Lpjp5C4CwQCgUAgEAgEAoFAMNb4UK+gx1iu1uE1wmPGYAhSv4nJ5u144tvpvgYpgrOTWPkPl8lSlbIOGYkXAr+Iv21drtZxcaGVYz4jbogxX13Lp4rdlFg1Xu80cywQ5GOFdiarEd7ptvCDq/4IQMDr4I2D5/KTd93oGGiGPqQV6L4mR7E0x1amlqt1dBuhjFaqFiob8EvBrA0Br3Bsy0u4sHTUFnkotsF73REmKhYO+4NsnRnhslnv0dZRyInOYq59tX8fEPieGy1gpf3QZHa/dwH/9p6V717Uw++OlnOeO8ILrRamOmCqI0QgaqI5aCasw3Gfzu/fXy2oLmjgwa4dLFI2co5dTQr/s6HEQ0tISxlubbjMY1KR6SrAfHUtOkbeDCRT9cfDtfqeycpChaOac6TijNQoO87fzKzCdg50FuONmjDLBvu6rUR00oYsyoRlah2d+HnFf19Sn5RJ/9Q3P2Orzsf1zmE1Ik1VZvPVtYyTnfRo4Yz7p5E2uluhbqXAYuZE2DfoPCVxnLAYllE3mco1rxJXeGNlNk+tZZ6jkNtaeldpp6gyjacbh6RuStWnZGoWm6upbOy4rWUeZAl0A25tyd9q8xWObYQMbdCV5lQqhvsvXotuSER1mSJbkELFx5ut42l47w583yzhnV1zmVX5Du3Hyvnm05dSM72VEtXL3jMT6I6Y2ddtShorEvNomVqHrU+4rnR9eqVagwXziK76Vqo1TJQK0oYTi4WjPRDwca7i4PVgW8ryT9WHDzZ/X6bW0SJ15bWtLlE2U2qx0R2N5GTQm8hg7TiXFfGBwrKlun6uIZRzJZUCIFOlTL6IGSPqRoh7xQq6QCAQCAQCgUAgEAgEYw/xgC4QCAQCgUAgEAgEAsEY4CMhcU8kZliTTrpYpawD4KXALweVfAzFCCwVi5VNFJisHDXas5aazFNrKZOcQ5JlLFI24pataeU/6egr7RmOeOrDaYCRi0Qvm+0Ii5VNuExWwrqWsQwp32ZAVzq3M8NpIqj1xvdMZIW6lXMclvjnS9UtXDHejC8q8/3jO1iqbmFeoY2LC324rGE+/ZmnMNnDBE4X09NaxOOvX0LDe4Obt8WkjH3rR6zuApyigzd8Dyb9Jmaa1De2bbb1LFGqndjO+/7GKwWGVH9T9Quxe7/KuZ05hTJBTaLIqlNmi+AwR3nilINfdTby7akN/NOx3q0GKx3beNx3C/PUWl789/uRCqyE99lo3zcVR3E3+988nw6fE4ctyDtt47CbNDpCNv50+oNY99e6GzgR8RKSwpTgYobDymRVxxeVOeGDnqiGSZJwmGUKLPBmTyCllDSWloHoW2fnqmuQ3n8HnImkO7ENLlDWY8E0oKw1X/1MNgZ1qbZOpJPCpztvvuXTX5/cwGXj2nBYwix/8XdAr1SzXJGQJdjV2UNU0vqVQar0pYop7BnvYVdPO3v99zNXXYMJU9bbC0YrNnoiQzU/TdUnD6UOxoxm2+hhHO6Mt6kNNk6nMq27wrGNQosZzTByju2bzzEpZqz0mHcnG0o8RHTwRXUmKjJRA17ytXHd+EI0Q6IjJHPI+8FWnXywSNlIQArnbYtMOr49tYFpjgDPt6jc0dpr2HmO2R3vn+eqa7AZ1ni9nK+uxWZYBjT9rXBUM0MuoTUazHrr0WJlEzde3MH5Mw4T8CmEw1aOt5Xxh6Pj2XGmkUijjHHxucitZwjtkXj7L/P59b5ZtAZhfkmIkwErL7VFaMePjES57ORR706udtUTMQwe8+5M284Gqz/z1bWEpPCgppPpyCQe+krHNqyyjF/T0prO9sakvi2eppf99w7YzjMxKEzMk5WObf3ioWfCYmUTFslEKMHgFXrHH6dkyyoWfF/ytY0tX/P0ueoaJskFOMwmmkJBbJIpp228A21TG+oWtsXKJrol/4D9/yJlI6VmOxZZojXS3wQ21v4nWx10RYM86btVSNwFAoFAIBAIBAKBQCAYa4gHdIFAIBAIBAKBQCAQCMYAHymJ+0JlA4pkGZIUZCS4xl3PmUj28qV8MlSpynx1LRGi2AxrkowkUSqUiyvkh5m56hqchpKzW206GWmVso4rypwUW6Ps67aw40yvQ25nNMRzgTtZ5arnkZ6b4065tUUeznPreCMy/3aqV/Z+taueqlKD1qCJ7214ENvEdlp3z+a9AzN5tWU8XzkwtCgC17p73VMTncWzoUpZR0AO9pNJuyQbZ+geVufobGWutUUeTBIU26DEpnPMJ7NiQicua5i2oMK+bicnfBJ3tn2QF5FbZZg+CX3vGTrfnsbpkxMoGdfKyRMT2X1qCr88HsEpWVk9RaczYmZPm4kDkQ6mmQs53y1xOiDh18BugrvbGtla5uGgL9zPcTfTiBCJ8rBUUrGzKV7zSJFJn5rLtqlr3PVcPj6KwxylzB6gye/guN/GC20RnvbfxtWu+pzkwUvVLXgJ8pkSN3aTzv+10s/lf766FsWwZtVn5cMdf4myOR6jeCiy9aGMQbFjq5R1uCXbkF2UM2E4to+NJH3ze5WrntluGYdZRzMkNAN+eHwHdWUeznNF6YyY+OHxHcMWUSEbMpFQ9+0/vz21AZus88Tp3ggCC5T1SEhYMKWN6T4Q64o9FFoZUvzx+yqup8geoNTZza/3zeJz045ypKsYTZdZs+5BzOfoaKd0ut6Zxs+e/BQFliiPNulcO0lCMyTaQma+f7x3m8TWMg8uC/zH+/OEBcp6CmT7iDlg50qlWsM0U1HKfnFdsYeZLh3DgHe7ZR7ozI/7fj7GxHRbMxYo65luc/KrDNJaqdZgM6zYseRUB1MxX10b3+7Ql4Ha7nx1LRragPkSm9fm63mhwlGNW3cM+flqsbKJUott0Ge1TMb95WodZ4x2Xg/8euxI3Hfv3s2XvvQl5syZg8PhYOrUqVx33XXs27dvNJMlEAgEAoFAIBAIBALBiDOqK+irV6/m+eefp7q6mosvvpjm5mZuuukmvF4vL7zwAhdddFFG58nGJK4vA70Vjb2tvtpVT0c0POjbpsXKJjplb8pVzL6rm9mY5uTTYGcob+BXqFsxMAZdKZin1mKgZ/S2sFKtQTFsIxKjfihv4AYrg0q1hgLDkfUbycS3qvPUWmZaCuiIZBbTcoW6lQj6oLFQodeYZFGZRE9U5sn2rqR8uMq5nUuKZFpCEkd8vXGTvzbJwwkflNjhpuZGrnU3MFGVONgT5f76R5AtUfbvvpjf7juPCUqYh09II7KClCn5XmlKVf7z1bWohi1lmWf6tnxDiYdrprZx7vgm/EE7vz0wk4M98GDXjqRzBP9ZxfSxIqRwiMgrYToPTOa9AzPZc7qcY34LLnNvN/5Ua28c5Zjpy2JlE3bJTNCIxlc5Vzq2UWQ1A3BfR2P8DfdQ3vDHjPwgeRU40XAn09X5oTJaq/cVjmqKdNegfcBgq4FXOrenjEefikXKRsZbFFwWiXf8Pcx1uZhbFOTPp+0ZraYMxjK1jimKlbAGb4eTYwIPVJ6LlI34pWDacliu1tFthNLmQ7r2m2rFPDEueCYrIxWOaoCcxtSYyiixjmWysjoUEldgBhrD5qtrcaPE03Klc3tWsd9zTZvZMOU8fq8r9jCvOEJIl3j2TK+l5KPenUn9Sa7tOdN5Uybnn6fWIiMRksJpz3mFYxtP+G5hpWMbEUPHLpuY6jDRFoIHOhv7raTlEtu9tsiDTYYTgUhGfenqgoZ+5lTfmtKA06JxQWEn45zdHO4oBeCdLhd/8/lH6W4uJeBXePvEVA71uDjqszCnIMizZ+wcD/aaiC5T65hkt1JohbAOO8/k1teMRuzrweYGS5TNzHHZ0cnuvhLrLHxQ/4aq8kkk3dgRG+/nqmuwG7aMVVgxJVLi+U3IScen+iwVfe9zibIZp8kSH8uWqXVxg7vEup/Y/kbaTDST6y1UNuCQLP3mt7G8y5+6xwC0pBV0cx7OmjN/8zd/w3333YfVao1/VlNTQ0VFBT/60Y+45557RjF1AoFAIBAIBAKBQCAQjByj+oB+2WWX9fts1qxZzJkzh3feeWcUUiQQCAQCgUAgEAgEAsHoMOZM4gzDYMqUKcyZM4cnnngio2MGk7inM96Jfb5Y2YRJkgeUCidKNQcjU0lWOrlapjKPkZaDZGLokAt95WyJcUHzzSJlIyEpMqKSqmwN97Kpa5nypXIP5XaNVztkHurqNd0pkOw0S50U6k6eC9zJFY5taIbOU/7b+PeZWymzh3j2jJNXfV1J6e/5u/HoETMn3pvBI+9egN2k81q7lYP+YM6mI0vVLQSNSM7lPhZMhPqSTkoX629CRpRdgbt5sLKWUtXL8e5CTgXstIXM/OvJZGldu2cauiYT6Hbxhz3zsZo0ZhR08KeTE5nuDPDQMSsmScJlNnEk5Evq72L9RKKsrFKtoQhnRtsj0pEuz0fCyGqhsgEZKa9Gmrn2p4nHDWUbzXK1jjKrNSdp+nfPqWeaI0BTwM5vTnu5WC3gjtbe81Qp6whJYYCU5ZKJ5L5HCzNNUTgTiiYZQFUp69AlPeP+bSA5eD62JmRShvPUWjS0pLwYrNyWqlsYb7WxL9yOatgzqndXOLbh1yM5S8wzlatWqjXISHkbl3Npv9nW+0XKRkzIyJLEDNXGfl+vdHqhsoEJFpXfducWq30gcpGUZ0tNoYemUPI4GJO+p2KlYxtA2pj2g5FJWaVqV/8+cysOs8aedjutIZ22SJiLC+x857NPoY5rp+3gFCTJ4N6XLqWiuB2rKcqDhyfgjYBf0ymzy7QEddqjITplLxMpIqBHBxz/awo92E3wjr8nqb9ZomwGiB8b206QyxxtibI5ZczsvvLzvvHNY3kU20JQZIN93VpK07NUJMYRX6JspkfyD9gelyibkSUp5fhbqdYwy1JM5yBbHYcyzs5X1zJOdma8lWqpugUZKW3fndj+r3Jux6tH4/e2SNlIt+yj3ChM2mqXikzGgIXKBgplG2ZZRjXJHA59UJ8yieeer7nifHUtFsOMXwrGyyEm869UayjBldHWp7nqGsolN0E9xJ8Ct48dk7hU3HvvvZw8eZKampq0vwmFQnR3dyf9EwgEAoFAIBAIBAKB4GxmTK2gv/vuu1x66aXMmTOH5557DpPJlPJ33/3ud/nHf/zHFN9kbxKXCZmugGbzRivdm6IqZR0RKZL2LVLftMxV1+Ay1LyFS8gn6VYzUplmxO5rnlqLw7Dl7U33cIRyG27lwkJlA07JypP+W/O2Gllb5GGqwyCgSfy0aeAVunXFHhaUhmgLW/jOkeQwJPPUWp7xPI19ciuhUyW0HpzM64dncv+h0pxNqa50biesawTeNxBJxUirRTIlVflkUmbL1Dqihs6zgTu4r+J6Lhh/CkkyaO0uYPeZ8bzUJmGWJB7s6l1Jat54Ae6pTXQdmUhXeyGabqKj28U7beN4p0vl5Y4gF7nt2E0Q1OA5bwtv+B5kkbIRi2TCKZt51LszqT1UFzTgskgc90eQJYkuPZjULueptUSkyID5/mELlTiYoU/flY+BjNIyGTf6mvTkwgp1Kx8vMbO/GyqLdNrDJv7jVOOAoYQyPe94u4V72vMTZmi4yLRvyKdZ01jhbA9nOF9di46B07AzXbUzzg4dYbitpTFt37JAWQ8wrGWZ63izRNmMQzbzuO8WFiubKLfaiRoGD3fn1gZTsUBZj12ypFx9TVcfEtUDnvG9q8Q/aWpkc6mHvf52XIZKl+Tj8S+8iUUJ8dxL82kNKiw97x3CYStt3W7eaRvHX1rstIc1CswmWsMRJisWdOCA35+xmilT07GBxpbYKmqL4ct6pf1K53agf8hI6O3zpjks3NrSyIYSD/6oER+D+xILMWmXzCNifppIX0UAZJ6vQ2UoIZiz6YMHa4OZqqVS1aFr3PX0RKM5KVXnqmv4mKOYV31dRIjymv+BpDzJ3TS0v0ncmFlBb25u5rOf/SwFBQU89NBDaR/OAb7xjW/Q1dUV/3f8+PERTKlAIBAIBAKBQCAQCAT5Z1RN4mJ0dXWxcuVKOjs7ee6555g4ceKAv7fZbNhsthFKnUAgEAgEAoFAIBAIBMPPqEvcg8Egn/70p9mzZw9PPfUUCxcuzPocmcZBH245WD7MWhJNDq5ybsdtMaEZ0BmJpjUaySRdw23aNBa41t1AQNM4bXixGuZhk8BlKvGpUtbhlGyDyl3mq2sB+p0zVbnlKi9aptYhA0/5bxvUJKO6oIF3o604dRUNvd9ve/5uPGa3j2MvVnD0dDlvtZfyeoeFO9tyl8IuUTajoWdt+jWYoV4ustbB4nVXqjVYMCeVwzXuesySxImwP2uJ2c3nb2aSw4vZpPHbI+NRzeCPwq0JMs/mjRfgmnSGnpPj+N2uhTx7xk65AismneaRY+NpCfZK8b5U7uH/ujspl11YJCkub56rruE8azEPZLENIV/xnbMxZZmrrhnQhGs0ZcrL1TpMkswTvluocFTHzRVTkYlZTb5YomzGMytEIGrm6WYHAc3gYKQDALthw4KJDrmHQt1Jp+zNSbp7tasekyTRE40mtY1M+qO56hqqnMVEdOgI63mV+8bItI4NZNg1XgfQ8QAAHjlJREFUEKtc9chAUNezPj7T8Tex37nGXc+RaGfGc4lU93+FY1u/bSvDyRWObTQbXRnPNfpKVFe56rmwQOK4T+K+jg/6qcHybyxss5mrrqEAx5BMN2MsVbfgMllojQbYFbg77bw1th3QJ4WS7n+wvufGqfXMdPl5o9PJwR4jyZDv5WXL6QmolLq76PGrHOksIarLOCwRHj9ZhFWGxtP9y2aw9jcSBn2ZjA2J5qgDbS/aWuahNZR5XzXSW/BSbXOIbRcZrC1k+zww0O9zfbaYr67FbJjSllesTVeqNSiGLel3+XqeGYpUH2BDiYdXgi1UWMvi/VVsPtr3/mL1I109ucq5nUe9NzOm4qBrmkZNTQ27du3i97//fU4P5wKBQCAQCAQCgUAgEHwYGNUH9L/927/lD3/4A6tWraK9vZ177rkn6fvrr79+lFImEAgEAoFAIBAIBALByDKqEvelS5fy5z//Oe33mSYtJnG/SLmONwO/HVKaRlMOnq/4fBWOaoABJTe5yv0TZUQxmY1m6PikEAb6gOdcpGwkIIWzlqNlIkVJTNc17vpBpUl9y7lKWUepSeWM7h3wWpmkJRfJXd/zzlfX4kbJm8T4Yy4XkgSyBHu7/GjoOUkfVxc0cPvmP2B2+uk4OJm39s/imNeNRTI44bfj12SO+ySOBj6IBTtYvV6kbGSiVclYHl7hqEbR7YO2lVxkdVXKOgJyMCe52nx1LRpavzaQrk+5vtiDbsCishDjlAAvthTyameYIouFMrvEa90fOOOe2XIe0bCFx1/+OB+bfBQAh8NPc0sp7QEH7UEFgNNBG20hM6929DqzO0wmZAl8US3rWLsVjmrKjcJ+cv9lah2hARz3ByKfEvVM+rChytiGi1zHmUq1N/xo7NhKtYaVxcWEdYm5xT0c86k0BUwc8Wp49Sguk4VHElzcY9ddomymQ+4ZtJ5/qdyDRYI3uyLYZJkzmp+XAr+kwlHNFKk4ozi6Vco6ZtpcnOs2aA1KHPBG+tWphcoGvLI/np6YZLibQLwvXapuwYyUk/PuUJirrmGOvZjpToO3OskpRvcCZT0+ORC/v3Rbc5ardYyzWTkU9BKVtH51N5dxO5WsMtd2mHiuVG1rkbIRm2QedNyqKfTgsvS6tce4yrkdSZKQgd/33ByvA5qhJ/XjuaR9pGTwlWoN43DThg+nYccvhbAZFhTJnFO9vdpVT3s0RIhoxnPD+epaSiQHHXoAXdL7ldGPZmzDMHq3gn7j8E7+ZUZvLPbdbSbmFBp8orwZgO6QncM9Lv724K3cdeEGxju8PHh4PIVWOOI14ttdVqhbcZnNRAyDR3puzrjPzUQWvljZhEmSM946kE2/ulDZwDSbI2X0maXqFkotVh5638F9sGgb89RaClGHNF8bah0d6hbe2Dwt1Xli2yZWOrbRMQLbZpaqW3DKZkK6Hh8rrnbVU2bv9TU/7o/SlqGD/wJlPWEpSjFqVnHlB6qfdWUeZrsjhHSZbx7+YAzsOz4nkm4++sVCD+c4DXzRMD9r3jl2JO7PPPPMaF5eIBAIBAKBQCAQCASCMcOom8Tlg0xN4obKUI1/8rVCnopFykZUyUKz1Dmm4kWP5dizYzW2dr5IVCxsLfMwzanxarucNq5njIHyZXVBA3c2PIRsibLrfy/n795Q4299c1VIfFSpVGuYbS1BBhaWhTnH1c3RHjcPntD6vWk9vel8XFNOE/EpvPHiXDoDDrwRK2tev5eFygbmOB1oOtzZ1jgkQ56PiqEkjP17zcZc7NopGjPcnThsQR45cg7Ptn2gRlmu1tFJAIthztrA8EcztjHV4edQj4M97RIh3YivmmeTf/8yYxtuS5Rjfhs/PJ75CnSqeL+ZkuuKVOIKYGzM/tGMbWiGxKPNwZR5mG1dStXHrnLVc75LpjOSvLKcyEJlw5DjHC9RNuOXQlkrS2IrWF3h9LGhM2VzqYcCC7zd/YH57ZXO7YR1Dats4jHvTpapdQA87b8to3lELmqZvnO64VTcjER/M0+t7bdS2HfeuX2ch5nOKE0BM5eN66RY8dEVVPi/M8V8vLQTi6wzsaCDSNTMqy3lnPBbmayGebvTxo4zjVztqufiIonXO4y4CenVrvr4/9Mx2P2nmh/3re99f7NQ2UBQCg1p5XiVqx7NMGh9Xx0EvfOciN57f+nSvUBZT0AKJX03T60dVoPigViibMYqyUNSGI2EmV8uxMp9hbqVHiM06s8UV7vque4cL51hG/9v38Dj02Dm4YuVTSwoVjjiC/Fg586xGQddIBAIBAKBQCAQCASCjzLiAV0gEAgEAoFAIBAIBIIxgJC4Cwbkwy4D/6iwRNnMHJe9n0FTTL7VV0r2xUIPDkt/qeXmUg8/3fwbLAVe3n2miu+/NJsiq8StaSSZgtTMV9dSLjt51LuTOY4vMN8+nk9P6Casm9j0dn8J64naCgrPaeLkm7N46/g5XPvq/ax0bEMxmfht9454DOX56lrGyc6MzLsEHw6udG5n7TQfBbYQz50uRTEZfPdofmKN33XhBsapPjqCdva0u/mPU7m183+fuZUye4h93Q6+n4XEfTRJNEv61pQGnBaNv5yReHQY29a6Yg9BbegS8uFguVrHeS4rb3YHBzTMyoSVjm0UWc2EdYNjYS8mZNyyjWkOM3u8XRnJzOeqayjFlTSejVWJ7kiz0rENp9mU0ni1ptDDFBWmO0PYTRqlip+7DhTjtshcXBjhgqIOTnhd7GpRWFgW4PFTNqY5ZPZ2hSg2W5mk9s6xX+7yEZGiA8azPptZqm4ByEtse8GHi5WObXxxWpCgZuKeo9KQ+sMrnds5RzVxKhji991C4i4QCAQCgUAgEAgEAsGYY1Rd3PPFByKAs14MMObQjAgiX89+okaYsC4RNaIklmesfKNGOOnziBEirEPfsg/rIbpDOpaggTcaJWKEU/5OMDCaESbyfp5rRoSwHsKvhYnoJlLlZU9EQw7peCNR/FrvcREjjNno/X00fq4Pziv4aBAxwvi1MBYtQkgPI0kG+Sr/gBbGr0XwazIhPZTzeYN6mIAWJqRb8pa24UZLaEchPYxZ14gYEsOZ/rARImIwrNfIld4xpP9YkQsRI0zE0IgYvflsIBM1JMK6lpTvA6EZYaIk/zYfafsw0Ju/ppT5ETFChPTetm2g4dci74/jEkE9il+LENDChHWZgBYmYkiEdImoESZi8P5435vXGlEwUo9ZZzu9eQcfxnsTDI2I0TueBTUT0SGOCb1tT35/3pYcXvxDIXE/ceIEU6ZMGe1kCAQCgUAgEAgEAoFAkBXHjx9n8uTJwIfkAV3XdU6dOoXL5UKSxB70s4Hu7m6mTJnC8ePH4/stBGcHouzOTkS5nb2Isjs7EeV29iLK7uxElNvZy0e57AzDoKenh4kTJyLLvbvPPxQSd1mW428cBGcXbrf7I9cQPyyIsjs7EeV29iLK7uxElNvZiyi7sxNRbmcvH9Wy6zU7/wBhEicQCAQCgUAgEAgEAsEYQDygCwQCgUAgEAgEAoFAMAYQD+iCUcFms3HjjTdis9lGOymCLBFld3Yiyu3sRZTd2Ykot7MXUXZnJ6Lczl5E2SXzoTCJEwgEAoFAIBAIBAKB4GxHrKALBAKBQCAQCAQCgUAwBhAP6AKBQCAQCAQCgUAgEIwBxAO6QCAQCAQCgUAgEAgEYwDxgC7IK6FQiK9//etMnDgRRVG49NJLefLJJwc97rvf/S6SJPX7Z7fbRyDVAq/Xy4033shnPvMZiouLkSSJu+66K+PjOzs72bZtG2VlZTgcDj75yU/yyiuvDF+CBXGGUnZ33XVXynYnSRLNzc3Dm/CPOLt37+ZLX/oSc+bMweFwMHXqVK677jr27duX0fGizY0OQyk30d5Gl7feeovq6mpmzJiBqqqUlpayZMkSHnnkkYyOF21udBhKuYk2N7b4wQ9+gCRJXHTRRRn9/uTJk1x33XUUFhbidru5+uqrOXTo0DCncmxgHu0ECD5cbNy4kYceeoi//uu/ZtasWdx1111ceeWV/OlPf+ITn/jEoMfv2LEDp9MZ/9tkMg1ncgXv09rayve+9z2mTp1KZWUlzzzzTMbH6rrOZz/7WV577TW+9rWvUVpaSmNjI0uXLmXPnj3MmjVr+BIuGFLZxfje977H9OnTkz4rLCzMTwIFKfnxj3/M888/T3V1NRdffDHNzc3cdNNNzJs3jxdeeGHACYxoc6PHUMothmhvo8PRo0fp6elhw4YNTJw4Eb/fz29+8xs+97nPsXPnTrZt25b2WNHmRo+hlFsM0eZGnxMnTvDDH/4Qh8OR0e+9Xi+f/OQn6erq4h/+4R+wWCz85Cc/4fLLL+fVV1+lpKRkmFM8yhgCQZ548cUXDcD413/91/hngUDAmDlzprFw4cIBj73xxhsNwGhpaRnuZApSEAwGjaamJsMwDGP37t0GYNx5550ZHfvAAw8YgPHggw/GPztz5oxRWFhofPGLXxyO5AoSGErZ3XnnnQZg7N69exhTKEjF888/b4RCoaTP9u3bZ9hsNmPt2rUDHiva3OgxlHIT7W3sEY1GjcrKSuP8888f8HeizY0tMi030ebGDjU1NcayZcuMyy+/3JgzZ86gv//xj39sAMZLL70U/+ydd94xTCaT8Y1vfGM4kzomEBJ3Qd546KGHMJlMSW8z7XY7W7ZsYdeuXRw/fnzQcxiGQXd3N4aI/jei2Gw2ysvLczr2oYceYvz48Vx77bXxz8rKyrjuuuv4/e9/TygUylcyBSkYStkl0tPTg6ZpeUiRIBMuu+wyrFZr0mezZs1izpw5vPPOOwMeK9rc6DGUcktEtLexgclkYsqUKXR2dg74O9HmxhaZllsios2NHs8++ywPPfQQ//mf/5nxMQ899BAf//jH+fjHPx7/bPbs2XzqU5/i17/+9TCkcmwhHtAFeWPv3r2cd955uN3upM+rqqoAePXVVwc9x4wZMygoKMDlcnH99ddz+vTp4UiqII/s3buXefPmIcvJ3UlVVRV+vz/jPbWC0eOTn/wkbrcbVVX53Oc+x/79+0c7SR9JDMPg9OnTlJaWDvg70ebGFpmWWwzR3kYXn89Ha2srBw8e5Cc/+QmPP/44n/rUpwY8RrS50SeXcosh2tzooWkaN9xwA3V1dVRUVGR0jK7rvP7668yfP7/fd1VVVRw8eJCenp58J3VMIfagC/JGU1MTEyZM6Pd57LNTp06lPbaoqIgvfelLLFy4EJvNxnPPPcfPf/5zXnrpJV5++eV+D/2CsUNTUxNLlizp93liuWfaKQtGFlVV2bhxY3zysmfPHv7jP/6Dyy67jFdeeYUpU6aMdhI/Utx7772cPHmS733vewP+TrS5sUWm5Sba29jgb//2b9m5cycAsixz7bXXctNNNw14jGhzo08u5Sba3Ohz8803c/ToUZ566qmMj2lvbycUCg36THH++efnLZ1jDfGALsgbgUAAm83W7/OYE3sgEEh77F/91V8l/f2FL3yBqqoq1q5dS2NjI3//93+f38QK8sZQyl0wulx33XVcd9118b8///nPc8UVV7BkyRJ+8IMfcPPNN49i6j5avPvuu/y///f/WLhwIRs2bBjwt6LNjR2yKTfR3sYGf/3Xf83q1as5deoUv/71r9E0jXA4POAxos2NPrmUm2hzo0tbWxvf+c53+Pa3v01ZWVnGx8Xa00e5zQmJuyBvKIqSch9WMBiMf58NtbW1lJeXZ/XWTTDy5LvcBaPLJz7xCS699FLR7kaQ5uZmPvvZz1JQUBD38hgI0ebGBtmWWypEext5Zs+ezfLly1m/fj2PPvooXq+XVatWDeh9I9rc6JNLuaVCtLmR41vf+hbFxcXccMMNWR0Xa08f5TYnHtAFeWPChAk0NTX1+zz22cSJE7M+55QpU2hvbx9y2gTDx3CUu2B0Ee1u5Ojq6mLlypV0dnbyP//zPxm1F9HmRp9cyi0dor2NLqtXr2b37t0D7iMXbW7skUm5pUO0ueFn//793HLLLXz5y1/m1KlTHDlyhCNHjhAMBolEIhw5ciRtGRQXF2Oz2T7SbU48oAvyxiWXXMK+ffvo7u5O+vzFF1+Mf58NhmFw5MiRrGQxgpHnkksu4ZVXXkHX9aTPX3zxRVRV5bzzzhullAly5dChQ6LdjQDBYJBVq1axb98+Hn30US688MKMjhNtbnTJtdzSIdrb6BKTynZ1daX9jWhzY49Myi0dos0NPydPnkTXdb785S8zffr0+L8XX3yRffv2MX369LS+HbIsU1FRwcsvv9zvuxdffJEZM2bgcrmG+xZGFfGALsgbq1evRtM0brnllvhnoVCIO++8k0svvTRuxnHs2DHefffdpGNbWlr6nW/Hjh20tLTwmc98ZngTLsiYpqYm3n33XSKRSPyz1atXc/r0aX7729/GP2ttbeXBBx9k1apVKfcQCUaeVGWXqt099thj7NmzR7S7YUbTNGpqati1axcPPvggCxcuTPk70ebGFkMpN9HeRpczZ870+ywSifCLX/wCRVHiL1pEmxtbDKXcRJsbPS666CIefvjhfv/mzJnD1KlTefjhh9myZQuQ+rkgppBIfEh/7733ePrpp6murh7RexkNJEMEnBbkkeuuu46HH36Yr3zlK5x77rncfffdvPTSS/zxj3+MO6AuXbqUP//5z0n7hlRVpaamhoqKCux2O3/5y1+4//77qays5Pnnn0dV1dG6pY8MN910E52dnZw6dYodO3Zw7bXXMnfuXABuuOEGCgoK2LhxI3fffTeHDx9m2rRpQO+E9ROf+ARvvvkmX/va1ygtLaWxsZFjx46xe/fuD7XL5lgh17KbNWsWc+fOZf78+RQUFPDKK69wxx13MGHCBHbv3s348eNH8a4+3Pz1X/81P/3pT1m1alWSiVGM66+/HkC0uTHGUMpNtLfR5ZprrqG7u5slS5YwadIkmpubuffee3n33Xf593//d/7mb/4GEG1urDGUchNtbuyxdOlSWltbefPNN5M+6/tc0NPTw9y5c+np6eGrX/0qFouF//iP/0DTNF599dUPvwLCEAjySCAQML761a8a5eXlhs1mMz7+8Y8b//M//5P0m8svv9zoW/Xq6uqMCy+80HC5XIbFYjHOPfdc4+tf/7rR3d09ksn/SHPOOecYQMp/hw8fNgzDMDZs2JD0d4z29nZjy5YtRklJiaGqqnH55Zcbu3fvHvmb+IiSa9l985vfNC655BKjoKDAsFgsxtSpU42Ghgajubl5dG7kI0SsH0z3L4Zoc2OLoZSbaG+jy69+9Stj+fLlxvjx4w2z2WwUFRUZy5cvN37/+98n/U60ubHFUMpNtLmxx+WXX27MmTOn32epHkmPHz9urF692nC73YbT6TSuuuoqY//+/SOV1FFFrKALBAKBQCAQCAQCgUAwBhB70AUCgUAgEAgEAoFAIBgDiAd0gUAgEAgEAoFAIBAIxgDiAV0gEAgEAoFAIBAIBIIxgHhAFwgEAoFAIBAIBAKBYAwgHtAFAoFAIBAIBAKBQCAYA4gHdIFAIBAIBAKBQCAQCMYA4gFdIBAIBAKBQCAQCASCMYB4QBcIBAKBQCAQCAQCgWAMIB7QBQKBQCAQCAQCgUAgGAOIB3SBQCAQCMYwR44cQZIkpk2bNtpJGTK6rjN//nzKy8vx+Xw5n+eee+5BkiQaGxvzmDqBQCAQCEYf8YAuEAgEAsEoMm3aNCRJ4siRI6OdlGHn9ttvZ8+ePXz729/G4XDkfJ7a2loqKir49re/TXt7ex5TKBAIBALB6CIe0AUCgUAgGMNMmjSJd955hz/+8Y+jnZQhEQgE+OY3v8nEiRPZtm3bkM4lyzI33ngj7e3tfP/7389TCgUCgUAgGH3EA7pAIBAIBGMYi8XC7NmzmTlz5mgnZUjcc889tLS0sH79eiwWy5DP97nPfY6ysjJuv/12vF5vHlIoEAgEAsHoIx7QBQKBQCAYBe666y4kSeLo0aMATJ8+HUmS4v+eeeYZYOA96LHfQu8DcFVVFU6nk7KyMr74xS9y7NgxAAzD4KabbuKSSy7B4XBQWlrKxo0bOXPmTNr07dv3/9u7u5Co9jWO41/zLc2sTLRAm9RCJTKQxpDELErDdhJpRKWmIZXiTYUgFEShF4F4IemFvVg0dJEZEViJEV2oaQpW2tuJyMrKIgmnfKlsPBcxctwznT277dkznv37wMCwnvV/1rMun7X+///6F3v27CEiIoLp06cza9YsEhMTMZlMv3S/x48fByAnJ8du/OnTp+zatYuwsDC8vb3x8/PDYDCwYcMGampqbM739PRk+/btmM1mzp0790s1iYiIuBq3sbGxMWcXISIi8k/T1NTEyZMnuXjxIoODg6Snp+Pn5zceLy4uJioqip6eHsLCwjAYDDbr1K3NeXFxMWVlZSQmJhIQEMCdO3d4+fIloaGh3Lt3j71793LlyhWSkpLw8fGhubmZ9+/fExMTQ3t7O15eXhPy1tbWkp2dzcjICFFRUURHRzMwMEBbWxuDg4Pk5uZy+vRph+/1+fPnhIeHExISwqtXr2zi3d3drFy5ErPZTGRkJEuWLMHd3Z3e3l66urqIiIjg7t27NuPq6+v57bffSE5OpqGhweF6REREXJWHswsQERH5J0pISCAhIYFbt24xODhIWVnZL+/UfuLECTo6Oli2bBnwY713cnIyTU1NrFq1iqGhIR4/fozBYADgw4cPxMfHc//+fWpra9mxY8d4rq6uLrKysnBzc6Ouro7NmzePx168eMHGjRupqakhKSmJ7Oxsh+q7efMmAPHx8Xbj5eXlmM1mSkpKOHjw4ITY8PAw7e3tdsfFx8fj5uZGU1MTX79+tXnQICIiMtVoiruIiMgUd/To0fHmHMDHx4f9+/cDPxruioqK8eYcIDAwkPz8fACbzedKS0v58uULJSUlE5pzAIPBwKlTpwCoqKhwuL7Ozk4AoqOj7cbfvXsHQGpqqk3Mx8eHxMREu+MCAgKYN2/e+AMIERGRqU4NuoiIyBRnr7FdvHgxAB4eHiQnJ/80/ubNm/FjFouFa9euAbB161a711q+fDl+fn50dnYyMjLiUH3WBnzu3Ll243FxcQDk5+fT0NDgcN7/zGm9hoiIyFSmBl1ERGSKW7Bggc0x63r2+fPn4+Fhu6Jt5syZABOa4f7+fsxmMwChoaETNq2z/qZNm8bnz5+xWCz09/c7VN/AwAAA/v7+duNFRUWsXbuWtrY21q9fj7+/P0ajkQMHDvx0eruVNefHjx8dqkVERMSVaQ26iIjIFDdt2s+ft/+32O9ZLJbx/zt37vzD8729vR3KO3v2bIDx5v/3fH19aWxspL29nevXr9PS0kJLSwsdHR2Ul5dTUFBAZWWl3bHW5n/OnDkO1SIiIuLK1KCLiIgI8GNtuo+PD8PDw5SVlREYGDgpeYOCggD+8I270WjEaDQCMDo6yuXLl8nOzqaqqoqMjAxWr15tM8aaMzg4eFJqFRERcSZNcRcREXEi687jo6OjTq4E3N3dWbduHQAXLlyYtLyxsbEAPHz40OExHh4eZGRkkJKSAmD3M2v9/f309fXh6+v70w3oREREphI16CIiIk4UEhICwIMHD5xcyQ+HDx/Gy8uLoqIizp49O2Hau1V3dzeXLl1yOKf1zfft27ftxquqqnjy5InN8b6+Pjo6OgAm7EJv1dLSAvz4ZJ2np6fD9YiIiLgqNegiIiJOlJ6eDkBmZibp6enk5eWRl5dnt2H9O8TGxmIymQDIycnBYDCQkpJCZmYmqamphIaGsnTp0j/1hj0sLIyYmBhev37No0ePbOLV1dVERUURHh5OWloamZmZpKSkEB4eTm9vL2vWrCEtLc1m3I0bNwDYtGnTr92siIiIi9EadBERESfKz8/n06dPmEwmrl69Or6remZmJpGRkU6pacuWLRiNRioqKmhsbKS5uZnv378THBzMokWLKCwsJCMj40/lLCwsZPfu3Zw5c4Zjx45NiJWWllJfX09rayutra0MDAwQFBTEihUryM3NZdu2bTY70X/79o3z58/j7+9PVlbWX75nERERV+A2NjY25uwiRERE5P/b0NAQCxcuxMPDg56envG197+qrq6OjIwM9u3bR3l5+SRVKSIi4lya4i4iIiL/c76+vpSWlvL27Vuqq6v/Ui6LxcKRI0cICAjg0KFDk1ShiIiI8+kNuoiIiPwtLBYLcXFx9Pb28uzZM2bMmPFLeUwmE1lZWVRWVlJQUDDJVYqIiDiPGnQRERERERERF6Ap7iIiIiIiIiIuQA26iIiIiIiIiAtQgy4iIiIiIiLiAtSgi4iIiIiIiLiAfwN1xP+BucA8ugAAAABJRU5ErkJggg==", + "image/png": 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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", - " \n", + " \n", "
\n", " " ], @@ -235,7 +234,7 @@ "text/html": [ "\n", " \n", " " @@ -250,13 +249,13 @@ } ], "source": [ - "bout_idx = 89\n", + "bout_idx = 405\n", "ipd.Audio(bout_df_updated.iloc[bout_idx]['waveform'],rate=fs)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "95670f50-d805-4258-bfbc-0ac1766ff667", "metadata": { "tags": [] @@ -266,19 +265,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "Out of 420 potential bouts:\n", - "• 0 bouts\n", - "• 1 calls\n", - "• 419 noise\n" + "Out of 603 potential bouts:\n", + "• 45 songs\n", + "• 0 calls\n", + "• 558 noise\n" ] } ], "source": [ "# generate bout summaries\n", - "print(f\"Out of {len(bout_df_updated)} potential bouts:\")\n", - "print(f\"• {len(bout_df_updated[(bout_df_updated['bout_check'] == True) & (bout_df_updated['is_call'] == False)])} bouts\")\n", - "print(f\"• {bout_df_updated['is_call'].sum()} calls\")\n", - "print(f\"• {len(bout_df_updated) - bout_df_updated['bout_check'].sum()} noise\")" + "print(f\"Out of {len(bout_df_updated.head(len_bouts))} potential bouts:\")\n", + "print(f\"• {bout_df_updated['bout_check'].head(len_bouts).sum() - bout_df_updated['is_call'].head(len_bouts).sum()} songs\")\n", + "print(f\"• {bout_df_updated['is_call'].head(len_bouts).sum()} calls\")\n", + "print(f\"• {len(bout_df_updated.head(len_bouts)) - bout_df_updated['bout_check'].head(len_bouts).sum()} noise\")" ] }, { @@ -292,16 +291,48 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "id": "df27a2c0-b9e5-461e-a6e0-d1997bf066e0", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "No bouts to trim...\n" - ] + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7d7bf1940fde41a780dae9ce6c020654", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HBox(children=(Button(button_style='warning', description='Prev', icon='minus', style=ButtonSty…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cf03083b568146f0b32d84125eb661d4", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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8npb4hw0bpgBKSEiIUlpa2uHvnpNmkrhgDPi/4IIL8Pl8bR4bNmwIuI4hQ4YQHR3N5s2b+y3O888/H0VR8Pv9rf5VFIXvvvuuT481efJkNBoNGo2Gzz//nNNOO42EhISW43X0ANps++qrr1AUhbi4OCoqKto9XmhoaMvyQdHR0URGRrJnzx5GjhzJoEGDUKvVGI1GrFYrXq8Xn8/HN99806qOs846C4vFgkajQavVotVqOXjwIN9//z3p6ekkJSXR0NBAfHw8W7ZsIT4+HqPRiNls5qOPPmqpJzIykri4OGJjY/nqq6+A/87UfOmll1JfX09jYyM+nw+v14vX6+XIkSPU19djNptRFIXQ0NA253rRRRe1uce8Xm/LrJTXXHNNy3q1x76ex/7bV9s+++yzNtfd4/EEvK7zJZdcgl6vx+/3d/povl9VKlXLfdX82LBhA1lZWQwaNCigYzbr7u+Knvxu6e9jDKSYHA4Hr732Wpf7jRkzpmU9ZJVK1VJ/d37ubpmuZt7uzOWXX87HH3+My+XqcR2nuhtuuKFX17Sz55YsWXK8T0ecAC677DLee++9YIdxQrriiitaTeR37OeArrb1pMzxrjuQMiaTiYiIiG5NgNidv6/9+ff+RIpjx44dbN26tVsxHG+JiYlMnDix0/ZKZ///5JNPAj7Wsc1xaaAfZy+99BLXX399y/+/+uorJk6ciF6vb9lWWlpKZGQk4eHhwQhRCCGEEEIIIUQ/Gz58ODt27Gi17aRpoAP89NNPjBkzhldeeYUpU6YAsGXLFux2O2eccQYAdXV1REZGAuB2u9HpdC2N+02bNjFmzBgqKytJTk7m//7v/zj77LOJjo7mzjvv5NlnnwXgD3/4A+PHj8doNLZk89RqdcvPXq+XRx55hPfff58XXniBP/7xj8yZM4fbb7+dmJgYFEUhLS2Nv/71r+Tl5XV4PlarlT/+8Y/cf//9pKWlAfD555/z+eefY7FY8Pv9JCQktFmD1mAwkJaWxo4dO3jwwQdblptRqVSo1epWPzudTpYtW8brr7+OoihER0cD8Prrr3P11Ve3vKYAX3/9NZMnT+aVV15hxowZPPbYY+Tn51NUVMTVV1/dcn7NGd7y8nK2bNnCWWed1SojEujDbrfz/fffM3HiRB588EGKi4vR6XQkJiaiVqtJSUnhD3/4Azt27KCkpITzzz+fAwcO4PF4GD58OG63G6fTyZEjR4iKiiIqKgq3280DDzzAL3/5SxITE1EUhZycnJaMdnPsNpuN1157jcsvv5yUlBRsNhvV1dWYzWY0Gg3R0dE4nU62b9/OqlWr+Mtf/kJoaChGo5GamhqsVis6nY7s7GzWrVvHmWeeSWJiIlFRUS1Zeq1Wi8/nw+l0EhkZycaNG1GpVBiNRiZNmsSHH37Y8i3u0ZljrVaLRqOhpKSELVu2cNppp7VcV+g8o9hXz69cuZJPP/0UgJkzZ3LppZei1+v5/vvvO1yG5mhLly5FrVZ3+VAUBY1Gg6Io7fYi+Oabbxg3blyXx2vW3V93Pfn12N/HGGgx+f1+7r777i73e+KJJzAYDO1+y9zRz4Hu19HPP/74I2+++WbA59Js8uTJzJgxgwMHDjBv3rxulxdNHn/88Q57SB27rbv/X7x4cbvrPB8vc+bMYcGCBUE7/oksJCSExsZGNm/ezLJly/j73/8OwM0338zzzz/fozrDw8NJTU3l1ltvZc2aNQEtedaV4cOH9+v65gNJWFgYf/vb34DWCa9j//53ta0nZY533V09b7fbqaurI1Dd+XvZn3+LB8K+3dnf4XDw5z//uVt1d8fpp5/ODz/80Ok+X331FZMnT27VW2LPnj2sW7eORx55hEWLFrUsH9pRz67O/v/KK6/wj3/8o9Uxjxw5Qnx8PE899RS33XYba9eu5cYbb2wbnCKEEH3ouuuuazWu59lnn20ZfyNOTosXL253nJdGoxkQs6X+8Y9/DGg8ZkcTx8ybN6/bYzu3bdvWpp5f/vKXAZUdPHhwq8mjfvzxx24fPxgPv9+vVFRUKAcPHuy3a3m0f//7313GdNVVV7Vb9uhlr7rzOHLkSJu63njjjYDLHy0xMbFbx/7uu+/anZCxvx9vv/220tjY2Oa8x44d26169uzZE9B1tdvtAddZXV3dYT0XXXRRt8+1PXfddVfA938zv98fUJlf//rXit/vV0pLS1vKV1VVdXvViJ4+Opu5Xojj7bvvvlOuuuoqBVBefPHFLj8/HP138thlHxVFafX/uXPnKlVVVYrP51Psdnu7EzQOJNJAF0L0Kb/fr2zcuFFpaGhQampqgh2OOA7amwTzb3/7W7DDauH1erv9AftYffFBv6amJqCyGzZsaFUu0Maky+VSDh8+rHz99dfK6NGjlfT09OPyIR8G5jroGo2m07I2m61PrmtXcTQ/nnjiiVZlAllK7Mcff2z3ePv37z8ujfUHH3yww3Pet29fn7x27Xnvvff6pM6jl1bq6lFSUtJhPT2JpaslDk0mU6ex9/cyet1d+UiIga55ZYzmyax7+vtnIDjxIhZCCDGgHDhwoM2Hv+ZZVweK008/vdMPqyEhIZ2WD3SJNEB54IEHOqxn1KhRXZbv6YzZHTGbzf36QT+YH346iymQ7GBhYWHA55ifn99hPeeff36X5RsaGroVf2fHa3brrbf263WtrKzs8NhWq7Vf75FAssiBcDqdyiOPPKLcfffdyqWXXqoAyp///Odu1VVcXNztWJxOp/L6668r9fX1SmxsbJv9O1rW8WjtrZ9+or9nhTheTuT7/cSLWAghxIDTvBZ0II2KYDi2+9uxj0CySe19qO/JB9+eln/iiSd6VK67S7ydSB/2//a3v7Ubz/nnnx9wHYGe42effdZhHYF8gdPeFy8TJkxQEhISFKfTqTz55JOt9v/hhx+6jD3Q3iE9fXTWqyTQbtw9vUf8fr+i1Wo7rO/uu+/udp1H83g8yrJly5Rf//rXytatW7vcv6ioqMNYuip/bG+NHTt2BBTj0Uvc9uWjuLg4oOMLcSIbCH+jeuqkmiROCCFE8Njt9pYJVQaiBx54oN1JDM8+++yAJ5W6+uqru5x4rqs/q36/v2UJxvae62xVkn379pGSktJm+7///W/OOeecDsslJydTVlbWaVw9dfPNN/Pcc8/1S91d8Xq96HS6ljgWLVrEjh07OPfcc1u2d2Xr1q2MGjUKaFqm691338Xr9eJ0OgkNDUWlUnHhhRfywQcfdHjdoOvVZNq7L/x+Pz6fr1WszfXs27ePpKSkLuN/9913ueKKK7rcrye6upe7s4JOTz5uHn1923uus+vRH5SfJyl85513ePfdd9m9ezePPfYYEyZM6LKszWbj7bff5rLLLgt4lR5FUbq13NfRDh061OEypD6fr8f1CnGiOPr304nW3JUGuhBCiFPG+vXrmThxYqttO3fuZNiwYQHXoSgKKpWKsLAwGhoa2n0+UEc3rg4ePNjhB+qjzZ07l/nz53freF9//TVnn312wHF1R3l5OXFxcf1Sd6Car0lPeTwe6uvriYmJ6XEdu3btYvjw4e0+l5GRQVFRUUD1fPTRR9TW1rZakrUr/bHU7OLFi/nTn/7UJ8eNiIigtra2R3Ho9Xo8Hk/L/y+++GImTZp0yqywsHbtWqZOndqtMsXFxaSmpra5Pl9//TXjx4/HYDD0ZYhCDEiffvopubm5rFixgksuuSTY4XSLNNCFEEKcUvx+P4899hinn346F1xwQY8bNx6PB71e32Z7d/+sNmfNA43j6ON2lTk/Wn804qCp54TJZOqXuk80O3fu5F//+hfnnXceOTk5LdvvvPNOHnvssX47bn9c20DuY7PZjMPh6HK/HTt2dPjlRVdKSkpalpoNtFfByeaf//wnF198ccD7N1+7c845h6+++qrNdiHEwCYNdCGEEKKHjm0Y/elPf2Lx4sVBiqZz/dVAdzqdkpFrx913383//u//AmC1WgkNDe23Y/XltX3xxRf57W9/G9A1/fvf/87vf//7LvfrzT1is9kICQkBwO12Bzx04WTS2bCY9jR/tD+6i/wdd9zRsta5EGJgkwEoQgghRA/t3bu31f8ffvjhIEXStc2bN/dLvdI4b98jjzxCRkYGl156ab82zqEpQ99d7c3HAJCWlhbwNW1uOHemsrKyV/eIxWJh+/bt7Ny585RsnEPPv4BRqVS89dZbTJkyhQceeKCPoxJC9BfJoAshhBC9UFpayvjx41m1ahVXXXVVsMPpVLC6Qp+qujt8oad6MplYR+P2uzOBWEeTFja78soreeutt7oVl2iru9dX3pNCnNikgS6EEEKcInrSUHzggQdaTUp3LPkYMTB099p21EDv7vXs7LiHDh0iMTGxW/WJtqSBLsSpRbq4CyGEEKJDnc2WHRERcdziEJ07ejIwIYQQJy5poAshhBCniPLyct54441uleksQ3q816EWHTvrrLO6XaanM6sHyufz9Wv9p4r+HiIhhBhYpIEuhBBCnCLi4uI47bTTul3uggsuaHe73+/vbUgiiPq74Sf3hxBCdJ800IUQQgjRqY7WWo+JiTnOkYi+dGwDPZBZ2Y91//33d/icZNCPn1deeQWA6667LsiRCCF6SxroQgghxCmkJ1nT9rqyn3nmmbz55pt9EZIIkr///e+t/v/pp592u47Zs2d3+FxYWFi36xPt++yzzzp9fsqUKfh8Pl566aXjFJEQor9ogx2AEEIIIQa2YzOrGRkZfPPNN0GKRvSV8847D4fDgUajwW63Ex4e3u06EhMT+e6775g0aVKb56Kjo/siTAGcf/75Xe7T3aX2hBADk7yThRBCCNGpwsJCzj333GCHIfqB0WhEp9P1qHHe7IwzzsDn8zF37tyWbddee21fhCeEEKccaaALIYQQp5ChQ4cGvO+cOXMAMJvNfPHFF/0VkjgJqNVq5s2bx9dff83dd9/NmjVrgh3SSWfOnDkMHjw42GEIIfqZSlEUJdhBCCGEEOL4CXQc+muvvcY111zTplxGRgZFRUX9Epvoue7MLyAf/05MBw8eZMiQIa22TZgwge+//z5IEQkh+ppk0IUQQgjRwmQytfx8bCOuudvyX/7yl+Mak+hb119/fbBDEH1o0KBBwQ5BCNGHpIEuhBBCnGKuvvrqDp+zWq0tPx/bQF+3bh27du1i1qxZ/Rab6H+//OUvgx2C6KH2ej4YDIYgRCKE6C/SQBdCCCFOMa+88gp79+5tta2goID169ej1f53gZdjGwMajYasrKzjEqPoPr1eH9B+PVlqTwwMZrO5zbaEhIQgRCKE6C/SQBdCCCFOMRqNhrS0tFbbli5dyumnnw7Q0gj/xS9+cdxjEz338ccfk5iY2OV+gTbkxcATFRXFokWLWm2TL1yEOLlIA10IIYQ4xY0ZM6bV/7dt24bVaiUmJiZIEYmeOPfcczl06BC33357p/udeeaZxycg0S+OnQNixIgRQYpECNEftF3vIoQQQoiT2bFrYGu1WkJDQ4MUjeitjjKqX331FeXl5WRkZBzniER/OvYLNiHEiU0a6EIIIcQpTtZWPjWcddZZwQ5B9IMJEyYEOwQhRB+SLu5CCCGEECcRGZN8alGr5eO8ECcTeUcLIYQQp6gHHniAiIgIHnrooWCHIvqQNNCFEOLEJQ10IYQQ4hT14IMPUl1dTXp6erBDEf3siSeeCHYIQgghAiANdCGEEOIUJt1jTz7tZdD/+Mc/BiESIYQQ3aVSFEUJdhBCCCGEEKJvbN++nZEjR7baJh/3Ti5Hfwkj11aIk4s00IUQQgghTjJHjhwhMTERAKPRiMPhCHJEoi9JA12Ik5c00IUQQgghTkLNjTiTyYTdbg9yNKIvSQNdiJOXDDwTQgghhDiJGY3GYIcg+tgnn3wS7BCEEP1EGuhCCCGEECehN998k9TUVN5///1ghyL62C9/+Ut+/PFHDhw4EOxQhBB9TLq4CyGEEEIIIYQQA4Bk0IUQQgghhBBCiAFAGuhCCCGEEEIIIcQAIA10IYQQQgghhBBiAJAGuhBCCCGEEEIIMQBIA10IIYQQQgghhBgApIEuhBBCCCGEEEIMANJAF0IIIYQQQgghBgBpoAshhBBCCCGEEAOANNCFEEIIIYQQQogBQBvsAPqC3+/n0KFDhIaGolKpgh2OEEIIIYQQQgjRKUVRaGhoYNCgQajVTbnzk6KBfujQIYYOHRrsMIQQQgghhBBCiG7Zv38/Q4YMAU6SBnpoaOjPP6kByaALIUSw3D1kFlq1wiNlq4IdSpfmpUxnXunqPqvvniEzWXQg8PO+Z8hMsiOsWHQeUClU280U7Hyxw/3vT57BQ/ueabVtYVou9xav6WnIPJ5xM3fseb7D43l8KhYdWMVfk2a2XNMXRl1DlMmG16+hzBpBg1fXqxj6wsK0XAaZbTS49fxh99oe1XHP0Jks2j/w79uBZl7KdHIia1Gr/Bi0Hg43hjFr+8vdfj+I/nX0e1gIMZAogP+o9iyoFEVRghdQ37BarYSHhwMapIEuhBDB8XhGHiaNj8Jdz3S9swBgYWoBw8LrCTM4OdQYxi3bnjvuMdyfNJuHypZ0ud9zI28hKbyWBqeJKza8chwi656nsmaiBn6/Wxoh3TE/pZAHSpf2uPyitHzuKV7eJ3UJIcSpRwF81NfXExYWBsgkcUIIIYQQQgghxIAgGXQhhBCimx5OKSDR5OKI08CckmV9UufcpEIeLAs8+9jd/Y/1WHoeWrVCnVvLvH2B1bMqOxefoiJ/52oWphZwbx+du+hb9yfNJsHobulNcDyu1bzkwoDvo+5aMmwGWrWfvB1r+qX+nji654AQQvScZNCFEEIIIYQQQogBSTLoQgghTkmBjr3uzOMZeTR4NSf0uNsnM2dxW9FK4PhkWge6xzPySApppKQhBLvvxLy2c5MKSTC5GWJpBMDt13DNpvYnIGyeO8Lq0XJ3H2SEF6QW9FmvkoWpBQw2OzjiMOJTVPihz+oWQoiBQTLoQgghhBBCCCHEgCQNdCGEEKek3mbPAbRqP2E6bx9EE7h5yYV9Vtf9SbMZFlHDO+OuZW3OVExaX4f7PpxS0PLzmhHT+iyGvrAw9b+xrR4xjScyZ/W4rjv2rMDu1XJ+cgnZ4VaezprJ2+Ou5emsmX0R6nGhUYNXURFtaSA5pgKXT9PhvnfsWYFZ6yUpxMby4dNZPWIay4dPZ1FafreOuSC1gHnJhcwpWcby4dN5shfXoFmk3kNxo5m7i5dzb8kyPH7pJdkT9yfNDnYIQohukAa6EEIIIYQQQggxAEgDXQghxCllblJhq4xrb9y6exWhWi+L0/P6pL6uLBk2g5Cfs9wPpxSwoIfn8djP8T5UtoRISyPjR27jwgnrGRtb3mbfuUmFPJk5C4vWzxtjr+fLsy5Do/azNmdqz0+kj4XovKzNmcrjGXnUe3RE6DzMTep5T4O9DRY8Xi3j03fzq+HbCDc6qHTpAip7dE+DYFiQWkBqiI0RUVX4FRVFFYnsa7S0u2/za1Tj1mPz6MjfuZrp258lf+fqbs1Q/mhaPnNKlhFvdPPPib8lPaKGUJ2n1+dSuOuZVjPDz9u3tFe9I04mzddufkrX93lf9BYSQhw/0kAXQgghhBBCCCEGAGmgCyGEEEIIIYQQA4AssyaEEEIE6LH0PO7au6LVtrlJheREWrF5dORuf/a4xfJU1kzUQLVb1+2lwBamFpAR1kCspYG0ofsJS6jC69SzdctIPj84GK1K4b7SpuWsXh97A9HmRow6Nx6vlh3VcRy0G3iwbCnPjbyFW7Y91w9n1z1PZs4iOdSK26dlyuZ13Sq7ILWAFIsNvcbfquyK7Fx0KgWT1oPXr+amrS90WdeSYTPQqJSW1ycYVmTn4vZp+P3uVQHtvzZnKrFmGz5FzYHGUPJ2rOn2MZcPn45F6yHWbMPl1bG3IYzbf166TwghRGdkmTUhhBBCCCGEEGJAkgy6EEKIU9Li9DzuPCYb3pXH0vMwafxtspPzkgsxavzdmlhrVXYuPkWFSqV0O2v5ROYsrB5ttzPnx3p59I3EhzRw/jfv8PnkK/D51fzyu7dbnp+bVIgfFRcNLcPh0XPR92+yILWACJ2XCL0LvcZPhcMUcLa2P63KzkWn9nPQbuLekmUBl5uXXMggkwuPXz0gzuNEc3/SbEZGWIm3NLCzJobZu54JdkinjHnJha0m0WvP/JTCVr8nHk3L5+5u/J4SQvQ3yaALIYQQQgghhBADkmTQhRBCnLJ6kk16KmsmVo+WOd3I0rZnVXYu6ZHVaFR+Km2hXL3ppQ73nZdcSIrFQZ2naamvCJ2HBq+WW3uZ8X1u5C3oNT72WEMwafzoNf42dT6alk9qaCN6jZcrN7zCwtQC4owuZvZgrPKp4tG0fIwaP1aPpmUs/0D1cEoBRo2/zdwKgZqXXIhBrXBvyTKeyJwlY89PAIFk3oUQx4tk0IUQQgghhBBCiAFJMuhCCCFENy1Oz8PhU/dJdvTxjDyMGl+XY3efyJyFGritjzKUK7JzqXbpuad4OU9mzuq03kVp+aSE2KhwmNColAE1VvvobGBX59Hd+npqUVo+ETovGrW/R7Oi94VFafndmhNBCCFEMEgGXQghhBBCCCGEGJCkgS6EEEIIIYQQQgwAQW+gFxUVcf311zNkyBDMZjPDhw9n/vz52O32YIcmhBCim+anFAY7hOPizr0r0PTRiKo79qwIaGmq24tWclvRSh5Ny+/1MRen5xFlcDEp4TAfTbiaSpeu3f0eTilgQWoB9xQvp8JhItLgQqf28+qYG3sdQ1/RqhQeTikAwO1XsTZnKquyc3tcn1nj5/GMvF7FdE/xcgp3PRO07u2PZ+RJ93YhhDhBaYN58P379zNx4kTCw8P5wx/+QFRUFN9++y1z587lxx9/5O233+66EiGEEEIIIYQQ4iQQ1EniHnnkEebMmcPWrVsZOXJky/Zp06bx3HPPUVNTQ2RkZJf1yCRxQgghguHpn5dcu7eXS64FeqwEs50jdnOrSdp6OhnYwtQCxsdWEGays7Mygdztz7a737zkQswaP2NjKtFpfLi9Wr6tiGOo2Ynbrw4o+9/fFqXlE6r19dnkdauyc3H6NMdtMrz+WvZqQWpBr5cDFEII0Z8G2CRxVqsVgPj4+FbbExMTUavV6PX6YIQlhBBCCCGEEEIcd0FtoJ9//vkAzJw5k02bNrF//35efvlllixZwm233YbFYglmeEIIIUSnbF4Nw8LrWT58er8fy6eosHl0OHxNf7qXDJvBO+OuJd7oatlnblLgcwBYtD72N4Szuyoem7f9EW8LUwtQFPAoKg41hlHjsOBT1Jw/+CCRBidOn6Z3J9VH7ilejl7j4+msmX1S38wda9CoFF4dcyOrR0xjcXrvxqR3pa+z581zQQQre35/0uygHFcIIU4GQR2Dfskll/DQQw/xyCOP8I9//KNl+5w5c3j44Yc7LOdyuXC5/vuBpDkTL4QQQgghhBBCnKiCOgYd4IUXXuCFF17gmmuuITo6mvfee4/Vq1fz5JNP8oc//KHdMvPmzePBBx9s5xkZgy6EEOL4eiJzFrEGJ9UuI7cVrQyozP1Js3mobEmfHH9uUiEPlnUvA7swtQCjxs8de1YATdn4QMaSr/h5bPatx2ls9kDweEZey+skhBBC9K22Y9CDmkF/6aWXyM/PZ/fu3QwZMgSAq6++Gr/fz1/+8hduuOEGoqOj25S79957+dOf/tTyf6vVytChQ49b3EIIIYQQQgghRF8Lagb93HPPxefz8fXXX7fa/uabb3L11Vfz8ccfc+GFF3ZZj8ziLoQQQnTf/JRCHijt+9nDRXD012zwgXo0LZ+7Zf11IYTohgE2i3t5eTk+n6/Ndo/HA4DX6z3eIQkhhBBCCCGEEEER1AZ6VlYWGzduZPfu3a22v/jii6jVakaPHh2kyIQQQgghhBBCiOMrqA30P//5z/h8Ps455xweeugh/v73v3PppZfy1ltvMWPGDAYNGhTM8IQQQoiTmnRvb+3hlIJe17EoLZ8nMme1/H9Bau/rDNS8fUuZlxz4Unt9Tbq3CyFE7wV1krhzzz2Xb775hnnz5vH3v/+d6upqUlNTWbBgAXfffXcwQxNCCCGEEEIIIY6roGbQASZOnMj777/P4cOHcbvd7Nq1i7/+9a9otUH97kAIIYQQP5ufErys7PFk0fp7Xcc9xcuJ0Hl4fewNPDfyFizatnPt9KfmSeIWpeW32j43qe01XJye15Lhvz9pdq+uczAz90IIcTIJegNdCCGEEEIIIYQQQV5mra/IMmtCCCFE96zIzkWrUpi+/dlO91szYhoOnwa/osLhU+P0qYnUe/n97lXHKdLja+mwGRTueqbX9TyROQuj2t9lXU9kzsKg9lPn1nFvybJeH3duUiEPli1l6bAZVLl03FfaVOfi9Dzu3Lui1b4PpxQQZ3Tj8qu59SS9nkIIMbANsGXWhBBCCCGEEEII0UQGegshhBA9NC+5EJ+i4qGyJf1+rBXZuRg1Pg7bTX0yW3aF04Dbp2LpsBk4fBru2LOi3f0OO4yMjanC6jKwtS6cwSYXhbue4emsmQMqi97VeQTK7Vfz9rhr2VUX0avX+failQHt5/WriDZ4qHPrenysoz1YtpR3x09BURrRqc0t25uz5/cnzWao2YnVo8WvKOTvXM285EKeypqJTqVw0GE4LvezEEKI9kkGXQghhBBCCCGEGACkgS6EEEIIIYQQQgwA0kAXQggheihc5yM7vIHH0vP6/VjVLj1un6ZVt+unsma2LJMVqPkphSwZNoM5Jcvwo0Kj9mPUtF0KbOHP9SaYnCiKihizjWiDh8Jdz7AqO5da98AaJVe46xnsvt5/rLmtaCVXbniF1NBGnsqayfLh0/v1+t61dwVVTiMJJmev63osPY/nc25mU3UUG2uimLljTZt9Bptc5O9cTaO36V56Kmsm8/Yt5dbdqyjc9QxqTvi5g4UQ4oQmDXQhhBBCCCGEEGIAkGXWhBBCiF54PudmQnVuyhpDuS3AicF64umsmVS6dMzbt7TX9Th9au7cu4L5KYU8UBpYfWtGTCNU7+anmnAeLFvK4xl5vZ6QbSBbMmwGiWYbNo+Om7a+0G/HWZGdy2GHMeDrEIgnMmcFNEndvORC5u1rupYmjY/aXi719mhaPncXL+fJzFl4/Ko2y7oJIYQ4liyzJoQQQgghhBBCDEiSQRdCCCF6aXF6HnqNn1v7edmxJzNnoVf7UYBKl77bWddH0/KxedWE6nw0eDSoVGDW+ANaTuzxjDySQho5YjcTbXRy/U9re3gW/Wtxet4Jlbl9MnMWoToPhxxG5vQiex2ouUmFPFjWdN88nTWTBLMdl0/DjVte4OGUAjx+VcvzPfHy6Btp9OjbHf/eU/cnzcas8fcquy+EEAOTZNCFEEIIIYQQQogBSTLoQgghThlzkwqJMXrw+tUn/Pjph1MKuK/0+GUU56cUMtjkxKLzYHXryd+5+rgdW/SdozPozR7PyMPpU/cqQ/1k5ixijE5u3NJ/4/WFEOLkIxl0IYQQQgghhBBiQJIMuhBCCCFEH7o/aTYPlS0JdhhCCCEGPMmgCyGEEEIIIYQQA5I00IUQQgghhBBCiAFAGuhCCCHEKWh+SiHzkguDHcZJ6aGyJTyfczNLhs3ok/oWp+exOD0v4P2fzJwV0H7zkgtZkFrQ07BaPJ4ReGwng/kp8r4RQvQfaaALIYQQQgghhBADgEwSJ4QQok88mpaPH7ineHmwQzlujudSZ0uGzcCvqPj97lVAU1b1zr2tl4prbwmt9jyekUdSSCN6tY8rNrzS5vmlw2bgU1To1H6GhDRQ6zRy09YXWJszlZu2vtDusQeCR9PyubuX99/TWTPxKSpuK1rZ63gWpBYwp4uly9aNmopO7WdvQwiKourVUmdHe3f8FKxuQ5fLnr0x9npMOg8heieHGsK57qd13T7WwtQCVCoFn6Lq8nxPFo+l56FXK31ynwghTmUySZwQQgghhBBCCDEgSQNdCCFEn7i7ePkplT0HCNd7eXn0jdyfNLtfj3N/0mwGWRqJNLhatnWUwQ5kfKxW7eeaTS9S1hjK+6f/rtU45HnJhdR5tCSY7Vi0Hjx+NamR1Xx33sUccZh4Pudmogxu1uZM7f2J9TGnX81TWTN7VUe1W0eYzhPwOO7OBJJNNms9WHRuciJr+yx7/lTWTC7/8VV2W0PaPLdk2Awez/jvmParN73EocZQDjZEUO829Oh495Ys457i5adM9hzgrr0rsHo0QY3hVBv7L8SpQhroQgghhBBCCCHEACBj0IUQQpwy5qcUEmtwM3vXMywZNoNyp555+7oes92Z50begkHjZb/Nwl39OC77sfQ8tGqF2/thzOu85MIOX4d3x08h2tJAWV0Uhx1mEkwO9jaEDMhsaWfn0V0vjb6JSIODOpeRImvocZtroCP3J83mobIlXe63fPh08neubrVtblIhOrXCfaXLWmaWt3k1WLQ+ogxOLDoPtS4jN299nscz8rhjT+/u4/kphfgV+uxaPJE5C41K4daf51841S0dNoMIgwuHV8t+u4kHSvvmdRZCBIOMQRdCCCGEEEIIIQYkaaALIYQQQgghhBADgDTQhRBCnDKGhVnJjKjhowlXU+PW9UkX3Fu2PYdPURNjcPdBhJ27vWglj2fk8dzIW/qszodTCog3to59QWoBy4dPB2BnXQRn/vtD9jSEEqV3c9huZni4tWUytrlJXU9K15+OnhSur7pUA1z/01ouXv8G1/20Lujd24GAurcDbbq3AzxYtpQwnQ+A2bueYfauZ7hr7wpMGh8xZhs2jw6nT8O85ELu2LOi15MexhncfXYtFqXlY/NqCNF6WZSW3yd1nugUoMJhYvr2Z6V7uxAnIWmgCyGEEEIIIYQQA4A22AEIIYQ48S1KyydK76HOrePuAbrU2rzkQq77qX+yTTdueaFf6j3aILODLyZfjl8pZ2t1LACL0/OwejQ8WLaUJzNn4VNU2LxqVKq2S3wtSssnK8yK1W2g1q1Ho1LQqBQavAp1bl2rycHSQxuxeXQAhOm8fHnWZfiVg5z3zbuszZlKclQlSZEqXjbe2G+vaaBu3b2K53NuxuHVYvVoezRR3+MZeWjV/l5PQvbWaddhdRuodBo6XAbvaE9mziI51Eq9y8gt257rcL+HUwrQqOhwGbbmyfFWj5iGCsjd/myb5zUqT5tyVS49ud+9zfLh0znkMDBv31KWDJvB7F2BZeubPTfyFiqdBs5IPAjAd4f7bvmxo5dulAx6k9m7ngl2CEKIfiQZdCGEEEIIIYQQYgCQBroQQoheUxQV+TtXD9jsOfTt+OS+sGTYjG6N9fUpKswGFwatB6PGx6Np+dy5dwUPljWdl17tJ1LvJs7Y/lj4e4qXs68xlDizjRGR1ahUCj5FRbjOi92nbrW0VoXDRHZMOd+eewlDQ618sn8oYWY7ZdeNZVxyCdUNYeysTGBwWF2vXoO+olP7GRZVyfCIOp7MnMXDKQUBl10ybAZDLDYGm23dKtee3fXhxJjshOq8QFNm/o2x13eY+b2taCU6tZ9Ysw1oynS3577SZR1mzwFCtT7eHnctSWF1RBodvHXadbw7fgrrRk1l3aippIXYqXDq2pRr7m0QbXRy3qBDfHbmb7Bovd06Z4BBIVYuGbYTm8vIhvJB/bbc4D3Fy4M650FH1+dEIL0PhDhxSANdCCGEEEIIIYQYAFSKoijBDqK3rFYr4eHhgAZQBTscIYQ4JT2cUkBOZB11LmObMbCireXDpzMsqhKN2o/Hp+VAQzg3b32+3X2fz7mZSIMTn6Ki0a3noN3MYLOdOreB3/88bnpFdi5JIVYcXh1XbXy5ZVwywMLUAsJ0XiL0Lho8egAiDC521ocyKrKeGpcBh1fLbUUreTQtn6QQG5EGBx6/BpPOTVZqKSHRdTRWR1BeHktYSCM6nYd9hwfx2YHBLVn8YLk/aTZnxFZh8+i47qd1rB4xjendvAdXZOcSa3Sw3xbSq7HoT2bOYmR0JY6fX+cDjaHUunXtZsBfH3sDWfGHcLr1FFXF98tcButGTcWnqNColDb1f3LGVURYGqlsCOfX618HaHXfBOL1sTegV/vYURdBamgju+pD0aqVVmPHhRBCdEQBfNTX1xMWFgZIBl0IIYQQQgghhBgQZBZ3IYQQfSI5xM5+Wwi17rZjXU82T2XNJNrgosJp5PailT2qI3/nauanFBKt9xChd9Ho6fh1c3i1gLFNhv2JzFktP+ftWAM0jTV9eXTr2dXvLVnGmhHTUKnA7VejV/tJi6pkWNxhNh8aipqm8dALUgu4u7gp09sc2+93r2JH9Fn4fWoqq6LZVZGA87CWML2bYfGHOD2mBsp69BL0mYfKlkAZvDPuWj478zeU1ne/jrwda3g4pYDBZmevYrmtaCWrtLkMCbHS6DFQ2MmM23q1jyprOB6fBp+iYkV2LgftxnYz2E9nzcSo8XHEaWgzQ//C1AJMWl/LvfjqmBuZsnkd0HTvhOjcHLBZmJ9SSJjOy+1FK5mXXIhBu59Gp4kqh5nlw6dT7dIToW9/DoOO+BUVTp8Wn6JqOSY03T/9sUb3E5mzevye6ytzkwqJ0PtazdvQkccz8gAC2rc/9df1EEL0PcmgCyGEEEIIIYQQA4A00IUQQgghhBBCiAFAJokTQgjRa4vT87izn5ZW6ktPZs4iTOfhiNOAoqg6XbqqK6uyc4k2OihrDOW249Dldm3OVExaL1dveqnHdTyalo9PUZEa2kiY3oXbp0Gr9nP5j6+22m9eciFjo2rZWR/OPcXL+WDCNRTVRTI8shqD1kOFLYzDdjMjoqqwuoy9iqm3jp7UbM2IaTR4tTR6tD2+tk9lzWSopZGrNr7cp7G1Z0V2LmE6N2oVWHRufqiKadMNuXmCwEaPnut/WtumjvuTZvNQ2RLWjZqKQePD5tFh92pJC6/Fp6jZZw1HAWbveoa5SYU8WLaURWn53FO8nHfGXUu0pQG3T8t3RxIxa/z4oVtdyJvrfDJzVr+9D1Zk5xJlcHHQZjku77VAPJxSQJLFQYXT0Omycquyc7H7NL2aePBoT2TOwqj2U7jrGR7PyOuy6/yjafmYtL4+O74Qoq/JJHFCCCGEEEIIIcSAJBl0IYQQfeLhlALuK+15Rro/zU0qJMHkJtLgQq/24fGrue6ndV0X7MKK7FzSwmvZVh3br5m9xzPyGGS20ejRM3PHGlaPmEa1S99p5q49C1MLiNB7iDfZ2WMN4+7i5TyROYtGj6bNtXtp9E3Emhs50hhKtctInMlBlNFOQkQtPr+aPZUJTNm8jkfT8rk7iEtqLfh5CTmHV9NncTyWnsew8HoaPHoaPToqnPoe39udZdHnpxSSGmID4KDd1O7SZEuGzcDlV3ea1X7q5wnkbF4ttxetbMlqP5U1E0VRtbk3H04pINHkYlBIA5HmpmXWrtjwSo/Ob1FaPlqVwl17V3R7ibZALB8+nUSzDZPOTb3LxH6bJeiTxA0Ej6XnBfT+X5haQIjOKxl0IQYsyaALIYQQQgghhBADkmTQhRBC9Jn5KYWYNb5uZ3ZPZPNTCkkwuqhx69rNgPaFxzPyiDc5cHi1DAppoNZp4sYtL/S63hXZueTtWMOaEdPI3f5sm+eXDpvBYEsjZY2h/H73qpb9BnJvib7UnInuL4+l56FRgV+hz+ZwmJtUiB8VCUY3eo2vZfm99qweMY0jDiP3lizjicxZ2LyaNku4Bao5e96fr5ksFdba3KRCdGrllHgvCnHykgy6EEIIIYQQQggxIGmDHQDAhg0bmDdvHl999RVOp5O0tDTy8/O57bbbgh2aEKKb+mMMojhx6NV+tOoTvmNWt/Qko9c8bjvQ94vLr0JR4Kz03SiKitqyVD6ffAVGnZuD9VFcs+lFoGk8sE6lcOfeFSxMLWgzk/nanKk4fVqijQ5qXUbC9U4+nXQlRbWaNjPxvzPuWvw0EmluZHRGEVeePoqG+j0s908nM/IwL4XdhMOr7dFY+L723MhbSIuswuYyUtYY1mnWuDuaZzwP1fr4fRdjeOenFBKh82LU+MjfubrV9o7uERWQFlZPjdMENI0NjzZ4OOLQt2ShH04pIMbgoXDXMwCdztz9WHoeIyKriDQ3oigqKm2hQNPM3z6/qk2WfmhoPWMSD/Cb7HM4UFnD7vpIAJ7OmolZ62V6O70qOqJVKbw0+ibiLYf4xZDLqXOaOfBzz4u+8kBp/2boTxRPZc1Eq1Lw+L2dzn3xaFo+Fq0Ph08d9PeoECJwQW+g//Of/+SKK67gtNNO4/777yckJIS9e/dy4MCBYIcmhBBCCCGEEEIcN0FtoFutVm655RYuu+wyXnvtNdRq6XEvhBBCCCGEEOLUFNQW8bp16ygvL2fBggWo1WpsNht+vz+YIQkhekm6t5+a1oyYxpOZs7inePkJswTSY+l5zE8pDHj/p7JmsjZnKk9nzWzz3ILUAh7PyOPhlIKA6kowOVmRnUu4zgc0LYX0WHpeh/sPNduJMdnR691oND6MGi+KosLr0xBucLTsp1MpJJodfDThasZEV/PS6JtYkPrfmGLNNoaEWLlq48toVE1DEbw+DYW7niHO5OSpo87tiMNMetwRhiQeoaYmknd+nEB1fThjEw5i0Ho4LbmYnPhD+BUVi3+OfW5SIQtTA3sN+kLz9Wvw6PD51USHWlHTtERcoNaNmsoHE65hRXZuu8+7/WoavJou60kyO5g4aD8jYspbXsdV2blE6j3t3jMARo0fp1eLV1E1TchnduL0aVp14b6vdBkGjY+3TruO5cOn4/F3PBluZng9e+ojOPPfHzL5yw+4csMrLE7PI1znwe1XszC1gLlJTa/ZU1kziQppICK8ns37kwEYamlk+fDp1Hm0AXdvXzNiGt+ffxEXJpdyRuZOEqOrMBtc1LuMfdq9vdkQi7PVPX2qeTwjj1t3r8LlV3Nb0cpOf2/cXbycCL2LMJ0XaFqybsmwGccrVCFEDwW1gf7JJ58QFhbGwYMHGTZsGCEhIYSFhTF79mycTmcwQxNCCCGEEEIIIY6roDbQi4qK8Hq9XHnllVx88cW8/vrrzJgxg6VLlzJ9+vQOy7lcLqxWa6uHEGLgeDQtP9ghiOPo/qTZWD06UkOtLZnD7mSmj6cnM2cBTVmotNAG0kMaAy576+5V2Lw64kyONtlWRYFIvZuQnzPiXbll23Okhddy1pB9vDrmRqINbpJDbB3u7/Zr0Kj9/FSSzuZ9adS6jBxqCGNHdRwX/uctnsicxROZs7hz7wpu3PICG6piufSH17j+p7WYNf6Wc651mrB59Px00flo1X4cXi0Xr3+Dl0bfRJnNjFHz3/jjTHbK6yLx+9TYnEYuHvkTg+IqiI+rJDV1H+YQOyaDC4+iQv1zUvfBsqW4Osnw9rXmydd+v3sVW6viAZiYVIJeHdh1ANCp/ahRGJtwsN0sukal4A3gnA7YjXx7MAmPT0tqqLWl7jMG7cfWQQY+VOdBQUWUwUVaeC3Ttz/b7gRwLp+Gqza+TP7O1dzdzlJ+zRnlcKODSYPL+PKsy1g9Yhr/nPhbLFof07c/S6Tew70lyzBqmnpOKIoKrcaH0eJgfMpeDttCuWrjyxy0G7q11NqgECuJieVkjdzFwfJ4hr/7DVvLB3HLtucCrqM9zZn+Y1ndul7V21PLh0/nnXHX8tmZv+mwt0VHFqYWsCgtv1UPlZ5YkFrQ8h51+9Q8lTWTeo+W53NubrPvwykFPJk5C73Gj8vf9HE/f+dqZu96puX3oBBiYArqGPTGxkbsdjuFhYU8+eSTAFx99dW43W6WLVvG/PnzyczMbFNu4cKFPPjgg8c7XCGEEEIIIYQQot8ENYNuMjUtK3LDDTe02n7jjTcC8O2337Zb7t5776W+vr7lsX///v4NVAgRsPuTZreb4REnL4Paj17tJ8Tg5IxB+3lu5C08ULqUeckDJ4vePAY4NdTKZ2f+hjijg2qXkZu2vtCtevJ3rqbOrSfK4AJoGc+pVSvkbn+2W+PvDzaEU9UYRmbcYSYOLcXp63ic8/Ttz3Lhf95iW10EB2wWnD4NN219AbOmaWzp7UUrWx376OXVnD41z428hUFmG16/mqs3vYTLo+OcnC1cOGE9u644kxCdm9QQG3k71vBYeh5rRkzjyg2vsL0mBp3Bw+hJG7DbzVRUR+N269m3bygelw6Hy0BqiI16z39jP57zUCw+avytUeOjzhZCcWU8V296qdVznZmyeR0Xr3+DL8pSGBV3mHfGXduq7JySZdxX2nVG+b7SZdyxZwVbquIYElVFyTXjOWf4NmptIdh97X/ccvnVTBq2g0k5W4gKaeCLyZezbtRUnsycxarsXOYlF7I4PY9QnZt1o6Z2eE4ROi8fTLgGAL+iwuXVMSGphDCTncJdz7TMC/Bk5iws2qYMrEnrxedXc/DAICyhjSRaGvjkjKsYF1MT0OsGTctqZqcXo9b4+O7706m2h/DqmBupcekDrqMjRy8z98bY6/nnxN/y8ugbuXPvim5l+I8V6DwRx3L6NOyqD+frw4nkxB7h3fFTgKYl7O5Pmt1huRXZuUxOPMjZgw5gUDf1ZunJ78bnc25mcsJh4k12FqQWkBRio8qpI0rv5aDd1Gb/+0qXEWlwsa0ujFuPmQtAr/azesS0bscghDg+gppBHzRoENu2bSM+Pr7V9ri4OABqa2vbLWcwGDAYDP0enxBCCCGEEEIIcbwEtYE+fvx4Pv7445ZJ4podOnQIgNjY2GCFJoTooYfKlgQ7BHGcheh85MQeweYy4ldMZERVsnTYDAp3DZwZ/dPC69j0qwvw+mqot1vIii2nuDqW1SOmsd9uahnLHIi8HWtYM2IaP110Pj5/Ke+H/o69Vh+rsnOxenTtjiFuT/MY3edzbsai9XQ6bnphagFxRhcmrY19jRa0aj/vjp/C5T++wNqcqdi8OsL1Ltx+DTaPDptXg0Xro3DXMySanLj8ahIsDhIiatmRfBbrSxMZPOQQjXXhRMTUkOrTsqmsaSZvvVrB5tXy6aQrGRyzA1NYA/UH40lIPoDfq8UcV0NMg4XPvjqTaHMjtS5D0FZvGGyxs/PyySiKirqGI2w4Mpjf717FE5mz8HZzUZi79q5giXYGEwaXMTiympcsN3HEYeL2opWsys5lv90Y0HneVrSSt0OvJawxFKPOzb6G8A7Lef1qrPVhRMVUk5RaxsF9QzjcGI5F68XjVxNvdONXVC09PTqa3+P3u1fx0uibqLRbGBpWR+qgQ9hsZmrtITyfczNevxO7V4ufpt4Wa3OmkhpZTnxiOeFJR/DajeSodzP4ha28f/rveHf8FNZXRreaTb49ahU4bCbMITYGRVbz44FkZu5Y0ycz+T+cUkC0wcPY+H0kxFaiN7loqAvjq7BLKauPosxm5p4e9Na6r3QZC1MLcPlV3bpvb/u5h8qC1AJ2VsUzPKacH35xIdUNNVy8/o0Oy0UZXFidZg7aQjD8PH480OM+nFLQ0nvj5q3P88GEa0iKqkKv8VHrMnJadC0+RU2MuYEnNbNaYmxm1PgYG1UL+1rXe9ihx8/xmytCCNE9Qe3ifu211wKwalXrrjcrV65Eq9Vy/vnnByEqIYQQQgghhBDi+Osyg+7z+fjPf/7Dhg0bKC8vp7a2lsjISOLj4xk/fjwTJ05Eo+l6fdD2nHbaacyYMYNnnnkGr9fLeeedx+eff86rr77Kvffey6BBg3pUrxBCiONHp1Lw+LSY9S4qbaH88ru3gx1SG3vqI6hymNGp/fj8KsptoaRFVeJwG3o0XjZ3+7MsduVx1ejNJA45zJBD8RyoicFvC+l2XTdvfR7ofPWDEJ2XeLMNk87NmKH7iBt0BKfNzPqwX/HTES35O1fzaFo+6WENeP1qbN6mtc0Bqlx6IvUe9FovJVVxeP0aJqXvpr4mArXaj9boZlBGKTX1YTyR2ZSFWzpsBglRNYRF1lNzOA6TxYEhsgGN3kPFjjTUaj/R5kbO++bdNrEenfXrb5EGBxExtWj0HtSH/YTVxPLBhGtw+6xcueGVbtc3e9czvGO5lsSIGpIjqlERxTfn/Jo6RyM2b+CdDjdURXWZfW4+3sOuAuLK3Bg1PjKiKmnw6CizmdrtjdTZ/B7X/7SWRWn5DApR89ZPY0gKsVHhMBGhd1HpNBBndJH789rmN219gZdH30hIuYPS0iS0Gh8VDeGsvyARraaG6oYw9D/P9t6ZB0qXkh1+Iwdslp/nddCzZNgMZu9a9vPYeS/77cYe9ayKMXgYFlWFRu3nP0XDsbr1nJuxC4Abt3Rv7ohj3VuyrMdrqTePf189YhqRBieVzrbjv49W5zLg9Gla1oRfPnw6+TtXB3Sso99Ha3Om4vO7KKmOIzG0jsGR1SiKinJrBHVOMz6lbUZco/Jj1rl5afRNXP/T2pbtgdybQojg6fCvzVdffcXTTz/Ne++9h83236VfFEVBpfrvL4GQkBAuu+wyfv/733PWWWd1O4ClS5eSlJTE6tWrefPNN0lOTubxxx/n9ttv73ZdQgghhBBCCCHEiapNA/3LL7/kjjvuYOPGjSiKglqtZtSoUYwcOZLo6GjCwsKor6+nurqarVu3sn37dl566SVefvllxo0bx9/+9jfOOeecgAPQ6XTMnTuXuXPn9umJCSGEEEIIIYQQJxKVoigtfZiuv/56Xn31VbRaLZdeeim5ubn84he/IDQ0tMMKrFYrn376KWvWrOHDDz/E6/Vy7bXX8uKLLx6XE2iOITw8HNCATHohhBDH1arsXGrdOpJDbESZbHxfngC0XuprIFicnkedR0uC0d3S3RSalknqzvJoR/vq7EtJSdqPIcRBedkgcj78d7fruD9pdpddgD+YcA1JsRVsOZDEpGE7MIc14miw8NnW0S3dlruyZsQ0DjqMzClZxorsXKIMLkYO2UdYZD1et463NpyO06fm7uLlrBs1ldOSSrCE2LA1WggNtxKZfgB3fQgN5dEcOJTI5C8/YN2oqVS7DDR6tEG53mtzpmLReciIP4zJ5ORQRSw1Dgt6jY9fr3+9x/U+kTmLMTEVmPQuvjs0tMf3R1dWZOdS7jBwX+mylqEBa3Omdmv5v7lJhYTpfNy5t2lywsfS84gzurhl23MsHz4dp0+DSevF61cz++dhD4vS8vH6VYyLqabKYSZE58bt13D9T2t5MnMWbr+Ku/Z2Pdnh3KRCHixrWlJRq1K4r3RZq/fTgtSCXi2J1lw/NC2VqFEpRBhcrbpr90Zv4wvU/JTCbk1E2Z6XRt+Ex69Gr/Zx3U/r+OacX+PxabA6zWjUfi794bU2ZZ7MnIVKpbRZZk0IMZAogI/6+nrCwsKAYzLob775Jv/v//0/7rvvvjZLn3UkLCyM3/72t/z2t7+lvLyc+fPnt5n0TQghhBBCCCGEEJ1rlUEvLS0lJSWl15WWlJSQmpra63oCJRl0IYQInjUjppGTcJDYmGqcDiPfl2RQ7jAGlIE7Xh5Ny293gq3eZM+hKTM2IbaScKMdlUrhzH9/2On+TcvPNWUxuzOZ2utjb2DkkH2ER9WxfXcmO2tiOC+tCKdbz8bDQ8jbsabT8k9nzSTG6KC4IRSVSsHrV3Ff6TJ2Xj4Zg8FFY2MIh2ujuOj7N1vKfHX2pdTaQ9hRF0HBle+hMbr47l9n85+KeC5NL2JXRSKH7WaMGl9LZjYYHk3LJyu8nmhzIzUOCxFGO7UOC1dtfLnX9d5dvJxV2bnYfRocXk2nk7S1V7Yrz428hUEhVmwePU6vlut+Wsf9SbMZanYCUO3Sd9oz4ejeF6+PvYE4ixWtxofLq+Pzg4NRqyDB6CLG6MDp0+JTVFi0Hg7YQrB5NUyIK2doXDlut56K+gh+qorD41cFfJ4LUwta4ntn3LWUNIRh0XrRqBSSw2vZVRPTcr/3lXnJhUFb1q+35iYVEqH3YfOqezyR4qrsXOLNNtQqBadXy476cEZG1OH0advtWXAiv15CnBraZtBbLbPWF41z4Lg2zoUQQgghhBBCiJNBUNdBF0KcmOYmFfbqeXFyUQCj3s13O7PZV57AqMQDxJucnS4bdrzdXbycJzJnsXrEtJZta3OmUusKbOmsxel5rMrObXNvP1C6lCO2EPbWxlBrD2Fecuf3vkXnYf0Fv+K78y4mxuBp2b4oLZ/nc25mzVHxHS1U76SkPJE1X55DqNGBXuNDpVZQFBWJZhsLj1kyan5K6zg8ioo9DaFckFTKL5JKOW/wATZe+AsSM/cRm7UPi8XG3vpInht5C9DUs0Cj9hNqdDAutgK/T42zJoyc7F3M/vVHJGWWkDNoP6OiK9Gr/SwZNiOg17E/3F28nKs2vszumlhykksJt9i4auPLrM2Z2mXZpUfF/dLom1p+PjoD7variTU60Wv8ndb1fM7NfHvuJXxyxlXEGV0t258beQtPZc1st0xRgwWfX02jW891P63j3fFTOC2qFo9fTYjOg0nr6/SYD5Ut4bmRt/DPib9FhcLOmlhCzXaiQ61cP+onLhi8nwtHbCEnqZSUiGqGhNYTZbYxLKKG7Ig6UgcdIi75ELGJFZj1TTE3eANfOtei9fFk5izWjZrKYbuFSYPLuGLyN/z21/8kK72Y5LD6NvdioB5O+e89fX/S7JZ77ETOBj9YtpQ79qzoUfb8/qTZLErLZ+aONRRbw7n0h9fYUR9OmM7LVRtf5vqf1rb7WitK0725fPj0vjgFIcRx0GkDPS0tjaysLN57771OK/nzn/9Menp6nwYmhBBCCCGEEEKcSjptoJeWlrJnzx5++9vfsmxZx9/2VVVVUVpa2texCSEGKE0XfW+aZ90Vpwat2k9JRQLpMRUMjqlif3UsN299PuBxrMfDW6ddh06lMCbxAPuuPY1dV5xJiN5NgsndYdb6aLFGFyPjDnPjmM18edZlvD72hpZsul9R4fBq8SsqjF1kWaONDoqq4rG5jIxLPMDcpEIez8hjeHg9w2LKCdW72y1X6zTh9WmINrj57mASFq2XT4uGUdUYhtVtICOsodX+R88YPS+5EIvWy6/T9hAdWYdW4yMytIFGhwmfS4fHZsLaEEp2dCXJ4TW8O34Kkwbtx+owE2ayE2ay47GZMMXUEzr0CLoQB65GMwerYwgxOnD71TR4AuuJ0NceS89r+TnGZMfj0RIRUc+Oy84i3tLYZflQnZutl5zL3t9OZETCQaApU3n0vTt71zNUOEydzlUwL7mQIaH1xEXXkJFUhkXX1Dvi3fFTGBpWx2Czrd1yD5UtocJhIUzvZm3OVIxaD4lhdWhUClVOI7cXrWR+SmGnPTOijA4cXh1Xb3qJCUP28dbObH7Yn4LbrScq3EpNXQQ+nwaPT4PDo0eFgh8VZp0bm81MeelgKg/HUeewMMRiIyus69etWaVLR7zJzrmjNvO7X3zGyHPWY46rwV0fgrU2nH3W8B7NXn5/0uxWWeb0EDsev7rH2fi+sCC1gKXDZvB01kwW/dw7qDu9xZYMm8HqEdNa9eLpjgidl3uKlzM/pRCbV8M7467FqPajUymsyM7l7XHXMtjk5PGMvFblHixbitOnIVzv4v3Tf9fj4wshjp8uu7jn5ORgNBr5f//v/zFnzpzjEZMQQgghhBBCCHHK6bKBfvrpp/Pvf/+b+Ph4Fi1axM0334zX6z0esQkhhBBCCCGEEKeMgCaJGzt2LN9++y3Dhg1j3bp1XHLJJVit1v6OTQgxQHXWZXFuUiFrRkzrcrIscfLw+tVUO03sq4nl33szsXl1A25ColCDk0tGbiEyoo7a2ghMZgfDBh1gRHQFapXSZfn9dhOTvviIA5VxDI4vJ8bcgEoFL4++kWijg/ydq2n06BlsdnRaz76GMNw+DY1uA9GRdfzhkk+4+Zefctb4DcTFVlHpMLVb7rqf1rG1NrJp+apQK9FGOxekFZE1ZD8T0oto9Oha9j16ci0Ag1ohb8caDtVFoVH7sLsM6PVuUpIOsGvrcPweLR6vlkpbKHVOM2qVwoelqWQMOUBcfAXhoQ1oDW6s++Oxl0c31RliZ9K53xIZZmX2rmeCOpxh068uYNOvLiDCaGfXoSG8s/k0vinOpLg+ssuyDR49VdZwKqqj0Ovd7P3tRC4csh+AFdm5QOtu9B2JNngot4WS/ub37N6XTHbiAdbmTMWidxEZ0oBP6fjjVrTRQZjRzmGHiXCzjTP//SFJoVbCfu4m71ea6l9wzESAzfZYw7Ho3Pzwiwv5pDiDa0Zu5dcTv8dodFJWGcf7xRnUWsPwK2o2Vkfz7ZFBbK+J4ruKeD4vyeDj3dl8V5bK/oYw9BovPn/g8wc/VLaEGLMNvalpgrmNn51JyMIq/vndGWwsS+3xEmvNS8dB0/CUsYPL+GXmLoxqf6+6aC9Ky+/xhIbpoY2MjjvMOSl7uThtD2+ddh0jIqysys5tNcFge1Zl55IWVkesyc707c+ydNiMDicOPNajafmsys4lI7yeL8+6jNQQG7qfJ3A8I/EgZq2XvB1r2FEXgdOn4Y49bZe3zNuxBqtHz6U/vMYhh1EmchWnpGP/Ng5kAf8WTk5O5ptvvuHss8/mX//6F+eccw6HDh3qz9iEEEIIIYQQQohTRrdmdYmIiODjjz9m2rRpvPzyy0yaNKnLGd6FEKeW5gnins6ayfyUwh5NECROLBqVQmZ0BTqtF21dFFdueIXnc24OdlitfHk4EbvbgM2rQ6f2U9sYisenodJu4ZZtz3VZfk5J04RV26pjMWg9ZKaXMPr0zdQeiKe6tilTG2tuxOXVdVYNs3/OKK7KzmX3gaHoDyeSkngYjdbL4fI4Klz6Dsv6gUqXnhiTHaPOzSd7szhryD6qbaFM3/4sC1MLMGl93F7UdlLX18feQKihAbdbT9awPZhiazEk1BKv3Ur198OY8NnHvH/673B4dWypiSRE6+NQRSw6rZfS2hgmaH2ERddiiq7DURlJ1eE4IqJru3zd+ltGmJWoqKY49lfHcuWGV/hi8uW4vDrWV8Z1Wb45w7t8+HQsNTGMHryflCEH+fKsyzjS2DRhn0XrQwU8npHXbnYSmnqR1LkMABxsDMOsdzEpcze19WGM/fizlmx8e3x+NeEWG1eO+okfizP45pxf4/a5sFnDga6XFTt68rqtl5yLyewgYc0Onsic1fLcA6Xw2Zm/waeoGBNVg1qlsNcaTnZkNdGhVrw+DUfqI/EDYXpX+wfqQEriYWorojlSHY3To+fd8VOodGiYvv3ZbtVzrIdTCpgYV0GkpYZ91bEcsoXw68xdFFfG97jOe37u6bE2Zyp1bgO/370q4LJ1LgPXf/kBj2fkccmw7UwYuQ1rbTiNdjP2n699R2buWNPy8+oR0xhkqaPYGhHQcZt7+GyoimJCrMLkzF34FRVlRxL4oTyBu/au4LmRt3DLtvZ7sSwdNgOLzsPNW5tiiNJ7MGl8PJ01s1vnL8SJzuNXBTuEgHV72lW9Xs+LL77I0KFDeeyxxzj33HNJTk7uj9iEEEIIIYQQQohTRuADjY7x6KOP8tRTT9HQ0MCWLVv6MiYhxAlsblIhC1ML+P3uVZI9P0WoVApajQ9FURFraeDLsy7j5q3P80TmrGCH1iJC7+WKDa9w/U9rsXl0bK+OBSDK5Og0u3ms24pW8uXBoewtTqGhPJqEnD2kZxfx7vgpODx6LHpnQPXM3LGGLdXR/PK7t/lw2yhSXt3AlweSOn3PaFQK9xQvJzGsjlFjt/K7Cd/zfkkahxpDAbi3ZFm7S4FlhVsJNzSNjY9OqESt9VK3bxDemhCcpbF4PTreHT+FPfURXLPpRe4rXYbTp6bOYaHebiHGZONwZQy2+lAqdqVgqw9lf2Uchw8m4vVpeW7kLQG/fn3N7tWxtywJRVExbsR2Nv3qAlxeHSadu6XXQyDyd67mpq0vsOXQUEoODGFIwhHiLFZWj5hGcqiVOJMdp6/jj0x37FnRko0vbTRRUhdN+pvfs6MikcfS88g7KoN6rDq3gbEff0bmW/8hTO+iwWlChcLgAJaJa9a87NfeigRSXt3A+gt+xVlD9vHS6JtYlJbPt+dewp66KHQqhfWV0Vz6w2vc+nP2VK/3oFIppMSWs6Mugj3WMOYlF3J/0uyAjl1ZE4nNbsavqKl2mtlSE0WdW89bp13Hy6NvDPgcjuXyqznYGMaEzz7myg2v4FdUfF6SQZjByapuvGfbc9PWF3B3Y6w9/Le3RZ1bw8ayVPbvH4zJ7MCod1NcF91p2cVHzWOgV/sotkZg92oCOu5de1dQ6dKTHdG0lGJDowWX04CiqBgeUce746fg9GlYkZ3LU1kzeSJzVqvx7dFGZ0sM60ZNxebVkLv9WRyd3M9CnIxOpCWAO82gT5s2jbPPPrvD53//+98zZMgQ/vSnP6EoXU+yI4QQQgghhBBCiPaplJOgZW21WgkPDwc0wIkzvkAIIU4Gy4dPx+NXMybuMEmDD2FrtLDr0BCu2vhysENr1/yUQsZFVxNlbsTj03DeN+/2qJ4vJl9OZnoJkcP2oXg1NOxL4P3/TMLu1XZ7bOf8lEISTU4O2Iwdfsu/KjuXMYkHSEgox+PSExJppbYims370piyeV27ZZ7InMXp8YeIiahDpVJwu/VotV4abBb0Og+KosJocPHx7mxGRFWxuSqWO/euYEFqAWfElfOfinhyz/oag8VO+f7BVNWFY3WaCTE4CTE6MOg87DgyiOt+av/4x8MTmbOYmHCQxLgKNFofVVXRVDWE0eA2cs2mF7td1+1FK3l97A2MGFyGWu2nviGU7RWJ5HZjTPXi9Dzu3Nv+ePX2LEwtwKjxU+fW9DjLs3rENLyKiiqngXt/7j2wesQ0pm9/lvkphWSENrKnIYQHSpeyIjsXt0/D8KgqGlxGEsLq8Hi1aNR+dlXHUdpo6lYcC1MLWo65fPh08neu7tE5dKZ5TpMFqQWkWGzst5tbxpQHw6K0fBxeNRlhNm7e+ny/HuvprJmYtV6KG82tZrhflZ2LReeh0mls6RHRbEFqAUPNDvyKigavlii9iwaPvscz6wsh+osC+KivrycsLAzoRRd3IYQQQgghhBBC9B1poAsh+tSitPxWa03KeqsnP5VKISemHKPeTdLLm9h1aAjhxu6N7e5va0ZMY92oqXx51mUkGF0U1UcQE1mLRu3vcg3jjpRZI/l04zj2fTcan1OP3uIkJ/5QQOuqHyvW4OaAzYhJ62/3+VXZuYyKP4Re62XIui18uXMku3Zm4vHqsOjcHdbr9qmxOs14PDp0Og9DMksZMnIP2RM3c7guiti4SqLiqvjdpG8x611ckFLMo2n55ETW8l15PGclHuLggUFUH4onJKSRmIh6Thu+g5RBB/nuYBJH6iIxa73dPt++dHvRSn4sH8T3RcNoaAjFYrb3uC5FaeqFd82mF8l+72uGvfMt3xxMInf7sx2uQ94edTc7891bsox6T1P2/PGMpvHKC1ILmJ8S2O/PecmF6NU+rG4d95Ys493xU1iVncvgECtvnXYdQ80ODBpfyxwH9W4dfqDcFopfUbGvNhq/omJ/fSSHHEbCdL5uxe+HVu/3R9PyWfjz69VXaw8/ULqU+SmFzClZxk1bX+Ce4uXMSw7e35d7ipfzYNnSfs+eA/x+9ypcPg1pIXaWDJvBp5OuBMCk9RKicxOhd/PkMXN+zClZxi3bniN3+7PcunsVN219QbLn4pT1cErBCbUOeqsx6BpNYBNWtEelUuH1BvePtBBCCCGEEEIIcaJq1UA/CYajCyGEEEIIIYQQJ6RWDfTPPvus3Z0UReEXv/gFl1xyCX/5y1+OS2BCiBPTsZP2nEjLWoie8frVJERX43brWTpsBldtHHjdKMP0LqLMNpKHHGT0pA34HAZWfngRl2btRK/1Mi+5kHn7Or5X5yYVttzLzZNVNXp0zN71DM8ptzBBURERXUuoxYZX6bh/81NZMwnXedBrfJQ2Wkiy2LlxywtoVArjYmrwdbD008wda3hJ99+u+Ldsew5o6lYca3R0eLy7i5dDMbw7fgreCjWhhwYzZswWfG4doQYHdTWRDB1ZBH41g5wGamoi8SgqdGo/v8vZQoPNgsujw9oYQtGhIYwbsR2VSsHrUbh196o23WqD5dbdq3hj7PXU1IdxpDEcv0LAE9c9lp7HXUdN6HbsvRBtcPHRhKspa3R1Ws+C1ALmlCxjfkohEfqOr0lHBplcrMrOxeP3MTepELPGh8ffeV/553NuJsZkp8bRSIXTxK/Si9g14ky8viOYaqKaluIavJ/h737DF5MvZ2FqAQkmJ06fj0GWRkoawri9aCUrsnPZX55IjMFNdng90eZGPoq6mgONYczsZIk4aJqIzqxtoMpp4u1x13LYpmJ8XDlDYiu4POs8Ptnb/rCNQDRPPtd8jZLMTUNnQnUeNCqFIw5Pj+sOhqeyZqJVKczuRlfzuUmFhOl8xJoaUaHQaLcwOKaS/TeMYd/+GuxuA0aNr9NlAJsdPZmfEKcavfrESUS3aqCfd955ne6ckJDQ5T5CCCGEEEIIIYTovk7XQRdCCCG64ldU1DeEYjY6GR5d2ZKleTilgPtKB0a2Zl9jKEMjq2loCOXQD/GkpJaRe8HnlJUksbMyAUMX36znRFpZn/4rbC4jDc4aKAXXz9nuW7Y9xzdRv8bpMmC1m9ssd3S0EVFVDImtQK3xE18ez9bqWJ7OmkmcqRGr20CEvv0s7cMpBVjd7lbLVz2cUsABm4q8LjKcADvrIsgMr+eX373NrtgzqayN5Oyv3qcyPwO1zktVURJavZfIiDrmlCxjU8YFlNdEEWpy4PVr0Pj8JMdUsGXnMMx6Fw0uE7CB24pWdnns/rQ4PY+M8HoURYVG5edIYzhxFit1TnNA5Vdl52LSOvh88hXsrI7FrHXh8RtbLZOmUSlY3cYOezc0GxdTxbbsc3C7d1NnCwGaMtyZ0RU43Aa+PDyoZZK2Yz2ekUeIzkFqZBVGvZuPizOwaL1dLlfWPEHZBxOuYWJGEQaDC4PZgd0awoisahRFhdtpYMvF52E2VzDCaSba3Ei4xYbHq8Xr1/Du+Ckoip2RMQ00OE1cvP4NPphwDSadO6AJD6dvf5Z3xl3L8KgqahwWrj77K3QWBz6XDltteJe9ADpzb8kylg6bQUZEJUWjzmB7mYMrN7zCy6NvxObRdfpe6y8LUwvwKKqWa7koLb/L5d4eTikgyeJgcGgVDo+ep7NmUunSddprp9mDZUt5Kmsm12x6kS/PuoyRQ/dhd5gICWtg2IjdVB2MJ7QhFLVKYVV2LkecBuYckyVflJZPZlgDFSdYjwMh+opK9XOPshOEzOIuhBBCCCGEEEIMAJJBF0II0Ss2r4aJn/+TT864ijCTnZzIWh5V5XN38cDIngPcsWcFK3S5Ld9KOz16zHoXEz//J59PvoK9DV1nXE0GF0kpZTgaLbyrmsIhW9NSVAtTCzjSUE+Uz4bb2/mf1eL6SPZbIxg7uIwRw3eR5SmmuiIGlUohbtARivemcn/SbB4qW9KqXHaElSP2/8a4ZsQ0crc3vb5Lhs3ocEzrgtQCEowu/HhweHV8f/5F7D0cxpjhO9kRfhaOehv6UBtavRenw4hO52HrJecSElpPVFQte8uSONAQjqKoqHQamJhwCJPBhcOj59NJV7KlOpbbg5hFH2KxkZNcilrt43BFHJO//ICtl5yLQefhycxZ1Hm0HWatoWl5sEmZO4lIqGKc2YmtIorqihg27E9p2eeQ3cSIyFr0mo5XqpmbVEiooYzQ0AaMIXYSPFredU/Boq8nLaMEv09Do8sIpa3LPZaeR3KIjTB9NamJh4iMq8ZpMzOuIYwqh6XTc384pQCNqinL3Og20NhooaomEp3WS/qIIjwOAyqNn/LD8URG1vHmxvFcnvMTToeR4sp4QvQudtSFtfRyeWfctSSE17I2ZyrflYcEPH/I4xl5DInaS8rwvRjjamksi2f/znRqGkOpc5hxdtHzoDMvj76RzNh9qFQKxQcHMyqlmC+Ml7O50hxwZn5RWj6xBjcmrRdFgZu2vtDjeFZk55IUUsm+hvCWnkKpIY1dlruvdBlvjL2eotpobF4Nd+5dweL0vICPe+vuVTyVNROvvxKXy0B8YjkHDwzC5jISFdKA3W3gmk0vtlt2bc5URiYW4fboqHQMCfiYQpxMju1VMtBJBl0IIYQQQgghhBgAJIMuhBCiV9QqhbdOu46iOguz//NWsMPpUPNY7UVp+fhqYhibcJDdv5lEyWEdfjrPxk3ZvI7HbXmMroskK7WUM07bRGNtGFuSz6O4spbttZGM0vjQqX1dxrAwtYDo6liiY2qITDlI7Ok7Qa3gKY/AdGBQm+w5wDWbXmTdqKksTC1gfGwFceFl7M6YRE19OAfrO54xfE7JMh5Ny8ejqIgyuFCpFJJiKthRlMGkX3yFoqjY/NUE9FovsdHVVFTGYHcbcLr1HK6Lwu3XEKF3kRhRQ1REHRuLM8m0HGbcaZtxNlhweXVdv/D9qN5tYO/BwQxPLSEzu4jShHH8WDSYtJhyJg7az47KhC7K69h/JIGi/UkMia0kKWc3xogGvD4N821Ns/UPj6gj0tyIRt3xbOQpIQ58fjVfbBtFoqWBCZPXc+G17wCguPQ0lsUTG1rfppwKGBxWS3REHSHhDdRVRfHyprH8LmcLmuqYTmNvznyvys6l0aPg9WlISSnD49JjiKkjZHADKr0axaemtiKa34zajMHkIDTCitnsoLo2gvtKm8Z4J5htHLBZ2F0fjtuvJsni7OKVb3J/0mwi9Haio2tQ/Cpqi4bishsZ/c/PeWfctfgUNQnGzme/74zXr2ZHeSIH7WYsWh9un5ZR6XsYEl9ByaFBLFK6Hv999PMLUgt4//Tf4VdUWN0GbtzS/Wx6sTWCCzJ3oigqRkVNodhq5suzLuOcr9/rtNzVm17isfQ89GqFV8fcyE+13fsIHqL1UuswExnSwGcbT+P8sZtQq/3sLxtCUW30zysNhOHzqyk8qkdNjdtAg91MiMlBtDGw6yqECC7JoAshhBBCCCGEEANAq6/v0tLSOtxRpVLx2muv8fnnn3f4/N69e/s0OCGEEEIIIYQQ4lTRqoFeWlra6c6NjY00NrY/GYZK1fNlNIQQQpy4VIBB6yXeZA92KAG5p3g5b4y9Hr+iYmtZCiUNoei6WGYNmiaam+suRK1SSIiqJmZwOaZQOzanEY1KocZpIqyDZdKOdm/JMpbqZ1C9eSwRO4czKLyW2OhqXC4DGw8mtUw+dawbt7zAE5mzMOtdWCx2GhpCmPTFRzyVNbPT491dvJzHM/KodhrJ1HkwmZyMStyBSutDG+Jg1ITN1JQlkPr6j3x51mUoior3izMYE1WD169Co1Lh8Wqpqoli5JB9hEZYsVZGUVMXwY7ayC7Ptz81L0P2meY3JEZXode78fpV7CgfhEHjY/r2Zzstf9feFbC3adiD1W3kSE0UwzP2MijpILFlqcxLLsTtq8Og8/D9geQO6yl3Gpj+zbtAU12Dd6cQWRuG1uBi57bhHG6IaLecw6dm8pcftPz/owlXc2n6Hn4sTcPpC6wL9Mwda1g3aipflKVwucVGYuY+ljx/Lbfe8QyKDWrKY4iKr0KlUnDZjezYlYla5afCFsa3515CfMxP1NWHY9J6iAppwGxyUlKewIrsXGpd+k6XJnqobAmfnfkbyg4OYsumcUQZnOxpCOWTM4YSHVrFkfpI/ErPPh8+lp5HhbPpfdfsqayZhB4cjFbd1N29q+7tx5pTsgxK4JMzrsKkc/N8zs3cvPV5FqQWBDSJVPMwmS+iLycjrZQzkw4xtj6EopLUgI6faHKg1/gpa7SQGWrrVuxpEdWkDj1AQ0MoWrUfxa+iuiqGA3VRXD5+PS6HCWN5HNX2kFbl4owOdtfEMnPHGt4//XfdOqYQIjha/fZfvbrz9TaFEEIIIYQQQgjRP1SKonSdNhjgrFYr4eHhgAa6mOhHCCFE31qcnsede1fw3XkXY9B5WH8gGZVKack2DSSPZ+QRovOQt2MNn5xxFTqNF7vHwD5reKuJlbqyZsQ06jw6Jg8uIz6uEpfTQHl1NCV10ZTazJ0u7XW0h1MKuK90GU9mziJM5yG3k4zv3KRCxsXUEGZwUlQbjV7tZ0TcYaoaw7j0h9cCjjt3+7MsGTaDM5NK+LYslUtyNqM3ufhi81jSoipRFBVF1XHcsu05Hs/I4449K3hn3LVkDjqAyeygtjaC8rpILl7/RofZ/mBYnJ5HRng9QyOrKa2O5epNL3Wr/LzkQubtW8r7p/+OmBArqRkl+H1qnDYzu0pTuOj7NwOua23OVMpsFnRqP3ftXcGTmbNw+1VNGfujLEwtYIjFjlHjY8rmdTyWntdmn0C8O34KGYkHqayNJCrcytARRdQfjMdaH0bmRd+isvjxHAzlzdeuYEhYHSV10SSH11LjsDBicBlqtZ/D1TFU20MwaT24fRqu2vhyQMf+YMI1fFceS0qIg0iDk621ESSH2NGoFHyKCo1K6dFkbMdaOmwGVS4dKhWkhzZi1HjZWB0Z8HJwR3tp9E0MCaulwWViU1UM95Ysa7n+XZmbVMiDZUv59txLiI6ow9oYwuf7Uju8bvcnzcai9XFP8XKeyprJqJgKHB49RXWR3NaNJQo/OeMqNGo/0aFWAGobQxiRsxOvU0/ic9v58qzLiAq3MvL9L5mfUtjyO+iprJkMMttx+TTsaQjBrPH16B4TQvQXBfBRX19PWFgY0IeTxFVXV/dVVUIIIYQQQgghxCmn0wb6nXfeGVAl9fX1XHTRRX0SkBDi5DEvuTDYIYjjQKdW2HjhL0hO3s+goQe5cMQWsiKreDwjL9ihtWH3qTFrvHwx+XIO20JZX57I/oYwDjv03arngN3I7UUr2XB4CP/aOpqtZSnotF5UKiXg7Dn8d6ms24pWdpo9B3iwbClXbniFC779BwftBg47jEz8/J/8p6Lz5biOlrv9WVZl5xJnclBcGc+I6AqK9iexcUc245KLcXl0nPnvD7F5tSxKy28Z/3vQFsK3xZlsK05nb2UC22ujWZWdS6TeE/Cx+9ude1ewoSqKcZ9+2u3sOdCSPb30h9fYWZnA9u3DaKwLR6VSiLQ0djnWv9ncpEKqXUbuLVmGy69mblIhtxWtbDdr6fSpUKNw0G7mqayZaNUKj6blB3ScR9PyW73HjtREMzi+nNjEchzV4YRE15H1269RJ+hQhZtQ67xMSNuDQdd0zb4+nEicxYpfUXOgMg6XR8fg8BoSwmvRazpfLvBopdZwdGqF6dufxaeoua90GYftJlIiqhkeewS1qvcdNZ/MnEWFS48fFWclHmJwaB12r47UkI6XGOzM9T+t5YA1ksGR1fx25Ba+OvtSjJqOl9E7WnPGfnP5IP61awQl1XG4/B1/nH6obEnLWHmzxsdnBwajU/sYbAl8DPrrY28gzGTHoPXQ6DCRPGoXYyZsIjynhNDBFVTPTsWg81BTH8bb464lPaSR+SlNf39v3b2Kn2rCuXHLCzxQulSy50KcADptoD/++OMsWdJ2Pdaj2e12Lr30UjZt2tSXcQkhhBBCCCGEEKeUTqcIzcrK4rbbbiMpKYnLLruszfNut5srr7ySb7/9lquuuqq/YhRCnGCeyppJrVvXrUyiOHEZNT6iomo5eGAQbo+OyDAr533zLqtHTAt2aG0cPVPzE5mzepxNas58HztuPdDsZ281Z/EeTcvn7uLuvc8qnAZm7ljDk5mzqHUZGRpaj0btp6Q8EbtHz8ujb+S6n/57XovS8inctZwV2bl4/GrCDQ6GaT0Bj3s/XgIdQ9yVRWn57LeruGXbuy3zK7w7fgq1bl1A5fUaBdXPWeOj77fm+QaOdvT46cfS80gOsWHRuXlcnddq9vL2HD27+uU/vsqrY25EU+4nSeujwRrK8Ju+hcgofNHxqHwefJureWfbKO7cu4K1OVO5r3QZ70ZP4Zyv32P9Bb9iwmcf8+74KVz+46u8Mfb6gM4VIDnUyuxdr7F8+HS8fhcrsnPx+r1M/vIDvjzrMhJD6gOu61jzkgsZZHJxZnIRZrODsiMJ+PxqttXE8fvdq3pcL0BZowXfoSGE6d2EGJzdnhG++uf7oau5K+anFJIe0ojVoyctoorJadWUlCdyzaYXuzzGwykFJIfYuWbT82y79Bw0ah/RCZUUbRzJsImbcR+ORH9xGMZGK7oiLxM//yfP59yM16/GqP5vj4AHy5ayIjsXRVFh9Wgliy7EANdpBv39998nMjKS66+/no0bN7Z6zufzce211/Lpp5/yq1/9ipdfDmwyESGEEEIIIYQQQrTVaQM9LS2Nf/zjH/j9fi6//HL2798PgKIo3HzzzfzjH//g7LPP5q233kKnC+ybZSHEye/W3aske34K8fjVVFTGEBtTTWSYld2HB/P9+RcRaXC2jIMciG4vWsnDKQV9Wmdna0a3Z2Fq747fnePNTSrk3fFTCNN5gaZx7yE6NxMm/cCkM7/n9NM2c/ZpG4gPaeCdcdcCTWN/w3Ve5qcUkrdjDY1uPfUuU7fGvfe3xzPyeCJzFoNM/12Dvqf33ZOZs7ineHlL5vvOvStYOmwG9S4DsQZ3p2VXZOcCkBXWQKzR2bJ9UVo+z428pcNyb4y9npdH38hde1cQpneyuz6CO/as6Na98fLoG3H71KQml1FfFw6AkpiA44LbUE38E574NA5ty+DqsRv4dNKVxJptvD72Bi7/8dWm/8dUU/fHIUw6/UfKrhuLRdf5uR6twm7h3fFTiDfbGJNUSt6ONVx1xnfU3zGYuKgazAYXj6V3fz6K50bewmVpe7l03A/EJlYQFl3L8Mw9NLoNJIU08ETmrG7XebS7i5dz09YXqHMb2FwVF1CZJzJnsXrENB5Lz0OjUrpcO/3JzFlkhTVg1Pr49ehNDIqpJPu9r4kJsbJ02Iwuj5dkcXDz1udZmzMVt1uHXu9GrfMy5oZ/odZ50V01CH94DP6oWExGJ9+ddzGhOjeDLA34jlp/fnF6HkaND5tXi19RsbgH10MIcfx0OYv7pEmTeO655zhy5AiXXnop9fX15Ofn89JLLzFu3Djee+89TCbT8YhVCCGEEEIIIYQ4aXU6Br3ZNddcw//8z/9w9913M3z4cMrLy8nOzuajjz4iNDS0v2MUQgghhBBCCCFOegE10AHuuusuSkpKWLJkCWlpaXz66adER0f3Z2xCCCFOABqVwqf7UsmxRhATYuXKDa8EO6SAHTtpV1cWp+dh9WgI0/m4sw8mWrr35y6y7U0g1pcWphYQY3Ry+Y+v8mhaPkuGzaDOrSPMUIXfoyV0ZBmKW039tlSy0ot58ZvJADR4NcwpWQk0dSXXa2w0uPWtJjcLtgavhjFRNVQ5zCxKy+ee4uUBD7FZlJZPgtFFnNnGpT+8RpjOw/Lh09GoFGbuWMPC1AKSQqtx+zRUuowd1vNU1kxGxR3g0OkjqSo/RL3NAsDu30zC59tGo93MLZ993KbcY+l5XL1pBcuHT2fLxeeh1lSSGFnDRxFXs7m68+XJHk3LJyPMSojeRY1Thd2n5dChRDJG7EYf3ohqfy1k78CpKcFYuY+4VCvle4eiVfsw6dz4FDWfnfkbDFo3Gq2Pmv0J+PwadpYl46dpaa+d9WGdduN+LD2PkfGlxETVoCgq4oaV4jzHCN59uGrC8Pq02FxGorsYHtAei86DSqXwn+051LgMxBodjB++k0ljNlNXFQUHh3ZZx4LUAkZG1BGid+H2aTnQGEr+ztU8lp7HXXtXMDepkHKHjzijq8u61uZMxeP3YPXo8CsqDGqFFdm5JIfW4/LquKKd33u3Fa3k6ayZZEbUcKQilrjoavbfMIY9pUYKdz3D3KTCTt9Lt2x7jm/O+TVR4cWoVArx2SX4fWrcZeHofp2AOyED7aALcVV8yaDhn7Hl418QZ7Hi8rYednrn3hW8PvYG0sLq2dcQxm1FK7s8XyFE8LRqoM+Y0fl4GJ/Ph06nIyUlhb/+9a+tnlOpVKxa1bsZNYUQQgghhBBCiFOVSlGUlq9o1eouh6R3XJFKhc/n65OgustqtRIeHg5oAFVXuwshhOhD81MKGRNVQ1xIU+amwWmiwmFm5o41wQ6tS09nzcSs9XLIYexywidoWvYpM7SRcIOLy398tcfHXZyeR1Z4PalxR9BqvahUCtbGEDYeHkL+ztVdln959I1YdB58iirgHgvzUwrx+FWck3CE2LB6rHYzYWY7KcP3EjJiP2igcdtg6g7G8dKG0xlsdmDQeIkx27C7DURbGtBqfFQ0hKNR+6lxmNCq/Vz307oevw595fGMPDLD67DoXfgVFVV2S0BxPZqWz/lJpURG1BMS1kBDXRgVNVEU1cRS7jRg0foYamnE5dPg9mu4eevz7dbz6pgbGZ5wEKPJSfSgclRqhYN7kolPOoRlUCU1u5P4cWd2m2u1MLWA84aUEWJyUFIVR7E1nOSQBhJC66mxhwR0j225+Dz0ejdarQ9zaCM+r4bKiliyL/gPmkgH7sPh6BPrcZbG4mk0sXNLNruq48iJP4TNZSRl8EHMYY0YwmzYq8OpKo/loz1ZWLQ+ZnexhNjy4dP59WkbiB1dhCbOhXNXDPt+GkZdYwhDBx0mYnAF+7Zlcrg2iovXv9HluRztnXHXUvJztnd+SiGReg8pIU33oF7jJdTo4OsDyZ0uR7ciO5ec2CNEhDag1XnxeTVYG0Oos1v4riK+pafFU1kzafRoW3q0tOe78y5mUEI5BrODfSXJVNtCCTfaMerdeH0aJn7+z07PZ8mwGaSH11LjNBNucFLlMHd4PzV7MnMWZyeVAPCv0jR+N+5HYjLK2Lc5m4z7a/COm4nf24j6wJeoXvyeT/9xMREmGzUOC2WNoTi8GuKMLqyepox6pMFFoqWBHbXR3NrLZeqEEH1FAXzU19cTFhYGHJNBX7266w8F/W3BggXcd999jBw5kq1btwY7HCGEEEIIIYQQ4rho1UCfNm1asOIA4MCBAzzyyCNYLJagxiGEECJwD5QuhdJgRxGYp7NmkhFei9unZWNNFHUeBXUAyyU1m7evKeP20uib2PDLX3KoLopKhxmPX03+ztUsHTaDwi6yjtA0JvTtcddS1xhC8tCDGMwO9Ho3psqEDsssTC0gI6yBKJMNg7YOg85DrS0koLgXpeUTbXCSHFpPTKgVk9FJ4pDDOBrMeBwGKr4ZQWhCFVqjm9q6CO4tWcYXky8n3GLjQE0MGrWf8J+zs2qVQqXdQnbcYertA+PvdXMW9d3xU3D5tOy3WZiXXNhyvTri9Kv57mASt32+kk/OuIoxY7YSm3SYsN2pbD40FI1KIcbSQLjFxv7q2A7rOWw3s21nNkPNToZVRTN60gayLv8a/OAoabqmGlXbMeVOn4pGl5HSumhu3PICr465kXKHmXq3gcEh1oDOvbohjAhLI+V1kYzMLMIU3kjGkAr8Hi2e0lhMY+vBGIJRU4lncxIxkbX4/GomfPYxq0dMIzOthCP7B3G4NooJEzYQ6a3hro9WsCCAZd6ijU5CouvAr6Lis2z8Pg21DaHsrY3B69dQV5RFZuIhnL6ApzwCmrLNu+o13PXzPA8alYLbp+aKDa/w3MhbGD1oPxX1EZg0nffczNuxhgXOAuaULGNVdi4ZkVUkRFdjNjo5zatr+b2lUyntXp9mj6blU9VYS3lROBFGOyNydpLYYKGuJgKz2UHp4UTmpxS2O/fBorR8HF41s3ct5dG0fCxaH7UuA06fpsvX4bailcx1FTImqo7TYivwebXYjkRjMjrQVB5G9Z+/o9pRTOkHE4hLN5McU8Hof37OquzcVhnyJcNmEG+yo1Yp7LNGMGlwGW+HXHtCzRcixKmke78x+9ldd93FpEmT8Pl8VFVVBTscIYQQQgghhBDiuOn5oPM+9u9//5vXXnuNJ554ItihCCGEOAm9PPpGzhiyj+T4I4xMKeY3mbs4PaYSl7/7fwqv/2kte6viyU7axy9yfuL0wWW8ddp1OHwaHs/IC6iOKze8wtlfvc/X23KIW7WbjXszqXYZOtz/3pJlOLxafvnd25z91ftM+OxjLvr+TR5O6TrTeU/xcg47jGypjmFHeSIf7BzJP76bxM6yFCoOJuDzaHE3mKnbn0BVQxhPZ82kwWXC49USE2LF5tHjculRq/34FRV6jY/i6lhKrBGBvmT97v6k2bh8Wsw6Nx6/usvsOTT1/ritaCULUgu48D9v8eX3p1N1IIEhGaVMztzFmKH70Gp8jProC2wefYf11Li1DDK5mLljDWd/9T7b149h7weTOPjFWFa/+2t+3JlNUX1Em3IPli1lW00MoTo3i9PzmLJ5HbN3PYMKKK6PBJrmDujMlqo4qqzhZAw5gMHsxNVopujHHD5461JCHy3Hs0ePcqQRz+FQnA0W9h4eTFl9JB9MuIaRcYfZuSed0NAGDDoPzgYLepOLtTlTAbq8lysdJux1oRzZNIzDhxJY+fWZ7LdGEml0kJx4iNGpe7E7jBTVh3d+IY7h8qtbZhSan1JItN5DUogNgBHxhyiujOei79+k0dt1Frq5d8zMHWs475t3+WDnSOxOIxmJB3l1zI0sHz6dwl3PcHfx8g7r8CgqLv/xVa7c8Ao/VcWzcdMoVCo/cYOOUG8NZb81osOVA+4pXk6EvinTf3fxcqKNToZHVZFotgf03j09poakqCqGxFYQGlWH02amaH8SyoE6+KkER1EccakH8DSaiImvZEV2LvajsvPzkguxeTWkRFeSFFVFjMnO1vJBmLQeHk3L7/L4Qojjr9Wnkj/+8Y9UV1f3qsLKykpuu+22bpXx+XzceuutzJo1i1GjRvXq+EIIIYQQQgghxImoVQP96aefJjU1lXvvvZeioqJuVbRr1y7+/Oc/k56ezpIlS7pVdunSpezbt4+HHnoooP1dLhdWq7XVQwghhBBCCCGEOJG1GoO+fv16br31Vv7nf/6HRx99lDPPPJNf/vKXnHnmmWRnZxMdHU1ISAiNjY1UV1ezfft2vv32Wz7++GO+//57FEXhrLPO4qmnngo4gOrqah544AHuv/9+YmM7noDlaAsXLuTBBx/s3pkKIYTodx1NlDQQuHxaTv/XJyxILWB4uJVwg4NvK+JIsTi6Xdf8lEKmbF7KZ+bfMCS+nMTBhzGbHPgPJPFTbUTAdTxQupTrf1oLwPa6cO4rbX+yukfT8kkwOalwNnWBX/Rz19Rog5u8HYFNcDfU7GBiSjFqlZ9aaxgqlcKZ//6QV8fcyBnh29mzMxOzwUVceB0Orw63T0NZbQzXbHoRgDfGXk9CaD0unxaT1kNZY+iAWarpsfQ8vIqPSoeJOBQmxJWz3DA9oCXr4L/doK/e9BJrvVOp3DaK68/6mjB9DdFuHXujJlJyyN5heaPGj9Xz349Uk774qGVJvmiDix11ERg1/jblFqXlc0biftw+LUk+LU9lzeTW3auINjrwKSoWp+dx596O30/zUwoZH7+PMIuNiIRKHPUheFx6UobtJaHhCDuHTMbbuJeKremoNX60Og/nXfYJ1btS+GbbSIrqIrmtaCUvjb6JjJhy/D41jgYL6dGVhBtcVDrMnb5uNq+Gbbszuej7N/nkjKtIMLm5ccsLfDH5cv5TNJyLz/83sVn7GJp8gNTQG1rupa7cXrQSaOqePSaqhqzEgwB8arqSBoeCT1GzesQ0pm/veIm1zup+3nAzw1VHOHvMJnxeDVuSz+OTvVkdLtnm9v13Cd/me/5t77XUu4x4/OqW92VHTBofy4dPJzu6Au//Z+/Po+yoysX//11VZ55Pz/M8pTMnBMIMoqAgiiCgTJmT5udXv3hluXQ5EBEWfFi45ANfFiEhEMYrKiriVeEyykxC5qTT3el5ns48n1NVvz86adJ0dwheJdG7X2tlQXefvc8+VXW6u/p59vNoYaQjRelyzOlPXK8k6fT5swkNF1EbdFNSPIiGRPMfzsMfcRBNWVh++g48Z/UQP5CNqskYjil4t7F7E5vqV9M2mo/FkMGiZFh18HFgor2cIAinnik36IsXL+att97it7/9Lb/85S955513ePfdd487wdE26meddRbf/e53ueqqqz7VAn784x+TlZXFt7/97RMe88Mf/pD/+I//mPw4FApRWlr6qZ5XEARBEARBEARBEE4lM1bG+frXv87bb7/Nzp07+clPfsLZZ5+NzWZD1/XJfzabjXPOOYef/vSn7Ny5k7feeutT35y3tbWxefNmvvOd7zAwMEBXVxddXV0kEgnS6TRdXV34fL5p48xmMy6Xa8o/QRAE4eS6u2r9KRs9v7NyAzcdeIL7a9cCcNXu/8SfsHFazjj9ccunnu/o69w+XMBbrQ309xWh6xKRtAmn4fitnz4+x12VG7inav2s0XOYKC7VH7Nya/sWbitr4gcdm8loEilVYWP58YuIHVXmCtAzmofdEaW8vBd/zME7536JXHuYUNCF2ZjG4w3g8QQZidvwWmOY5InX8tLpX6MyZ4TmsXxMskq2LcLy4h7eOudS7jqBdlz/LE/Ou5Edn/s8i3NHMEo6FkXlyt2/YsdoHum/o/gfQGfETkqV+esHp3P/X7/AYF8hmYzCSGzmlnb3Vq/jBx2bubV9y5Rz4TCoxFQDNkMGm6KRUqevx25Q2TFUTJs/G4cpiceU4rUzvwJAscc3Y9QdJgri3V+7lp92bZqImBoyaGkDnvJBCk8/gGRQCfndxBMWYqMecuq7MNviuKv6URMmbJ4QBY4Qy4t7ePPsy2jIHyDLE6CvvwhZ0XBY4wxEHZOR1tl8r30LXSEPzy+5hmDSOvn5tGrgzIaD2KsGMbqjpOJmGor6PvHYH+vPp32db8zfyzlnbKdsyUEqz/uQhQv3EU2Zqc8f+MS1Hc+N+59kIOil5XA1yfjEugOp2QvOWQ3Tz8NA1EFCVQikjJjk6V//Zc06/rD4Wl46/Wu4TCmqPT4UWcNiTNEZyOLqPc+cUEvGnePZ9Ecd3HTgCQZCXvoHClh+5gfsGyihxZfLknkHcC3rRY9qGBxx5uUOkWNJcFtZ02SEvMrtp7Gwn/qiPjQkHjzy+VMlA0YQhKmO22Zt0aJFLFq0iI0bNwIQi8UIBoN4PB6sVuvxhp6Q/v5+NE3jO9/5zoyF5SorK/l//9//V1R2FwRBEARBEARBEP7tfao/L9tsNgoLC/8hN+cA8+bN4/e///20f3PnzqWsrIzf//73rFkj9scIgiD8K8g1p7ivdi0P1K3hzpMYVZ3J0T3GR///ofrVuM1xchwhFmePn1C7o2MdfX1pXaI7aqU/kMX7nTX0x6yEj9P66RfVH7Wtem7RN7mnaj1VzjDf79jMnZUbeKh+9axjf9j5ME/MvYmf9UxE3rPNab7VuhW3UeWuyg3cVtbEHRUbZmzLtblhFXZzgpriPjJpA+mkCYOsMhR2U1fdQXbeGNnZPjRNZnQsm/NrD9Hmz+bynb/mL8uuojx/CKMhg6pLNJR1U9/QRml5HxVlvVxU3smvFlz/qY7fP0qONYZBUWmsa+Omi15jaWkXAKfljpD6hAj6T8pu5rayiWP1i+p1k+e0yhnFomgoskbZkfoEfaN5KNLM0exb27ewdc5KYGK/79GMgjxLkmDKSH/UznfaHsEo69PGqrrEeNJAuTOIjM4ZVW0c8uVQW9SHL+LkW7NEOGudUapcQbbOWUk4aUXXJUYGCsjELJBRcDT04/IGyckdJ500kfK7sOX5QJMw2JLEgw5MhgwWU4r6hjYicSs9Q4WkMwbMtjjFNd1c2HCQ55dcc/wTAMRUha/u/DVzS7pxm1J8cMHFuG1R/H4PqWEPSlYCd9EoNuvse/hnYjMlsdri9LRVYv+5n7ZXz8BePMYFF7+G2xPk5TOuYMuR4/73aA54uOCdF9jRXsOhoeLJ99VMfjBDC7aMLrH+0GN8r33LjNeaqsNI3Mbe8Ry8ljiNdW2UlgxgMaWo8Y5NZvPcVtZ03HZnaU3CKGu8d/4lnLv0QxZe9C7+vnwuXv4+V3/+VZIxC763qvF/WMPI/mqqq7rItYfxmFSiGYVfLbgepyU+8R4vGKWmuI9sS4In5t70dxw1QRA+C8eNoP+z5eTkcMUVV0z7/NGI+UxfEwRBEARBEARBEIR/Ryf1Bl0QBEH417exvImN3Zvoj1tYnOWjL+qYErE+FTw97wZCaRMuYwKAm1seZfuFX8CgqJRkjTESsx93/JY5KzFKOjrQG7OSbUoBE9WdJQlCKTMNuUPkRZ1cuuO3s85zesEAzQ1nE4nZCMVj1KDTEXayqX41OmncR+adybMLrsOXNPCXZVcxGrMzFDfwzPwbiGfSfPfwNgDuq107WQH72HHVOb34Ik68rhCplAmAuvJubK4IJkeM4GAuRnMKb303eRkF/+FSLp6/h47G0+joV7FYE3jzxrmieAhNVTjUXMfLvaX8rGcT751fhEXJfNIp+Kf4cDSHH29/jh2f+zzlVd2UNrTT5VrCh23WWStyH1XliOEypXjAsoZvt27hxWVXssWyklBKJt8aw2bI0Bp044s4Kc4ZxeCbvbZAOGPgzbMvYzzmoCzrMBobKHYOkW9TCCQtvHveF2kbT04bV+/xU1UwgDdvfCLCDdw0v41wfx7Dviw21a+mqeXRyayIo3UL7MY0iqxR4x1DR6J/LJfBqJOyea3I7gSZMQc5S1sY31mHu6of2Zwm7XcSHfXirO7HUz6ImjGQiFswWpPMXbIPNWkkFbOSiptxVfWTZ07RM1Rw3GP4i+p1nFXczVv5l9Lwpz/TcWWKodFcFl30DoorRmbchWSRsTUOEhn1HneuYz0570aynT0c6qxkMOrk3up1BCID7Ht1OUVFgxzoqKY8b5h42nTcee6q3MAPj3wvurd6Hbe2f3RNHN3ff+3eZ3hu0TdPaF3bGlcQzRgIZxQyms691etIazLljui0x0aOZNIszRsmz+snHpmoiO/1BtjeV06+NTb5vRMmMjp+3vNRm+LbK5qodETxmMbJdwWorOvAP5RDPGyn5HO7kBSdVJ8XW8KEYkqjmNPYC8bR0gbqgb2j+fygY+L1vn7W5bz04Wksq+jAk+VHkXRGP6HyvCAIJ88peYP++uuvn+wlCIIgCIIgCIIgCMJn6u8rcSoIgiAIgiAIgiAIwj/UKRlBFwThX9s9Vev5/pGiOsemGAr/noyyzm8WXoemh7h8568BeGb+Deg69MZsMxZY+qxJElS5AhR6ffTMXUQ8ZiUc1WkdKaAt5KTccfwCVnXeMcoKB5EVjcHhPKLJidZsFkXHZcwwlrDgjbjItoe5raxp1oJTVnMSd1aA/PJ+etoqUTWZ0/KjpDIG6iu6+MuexTOO29a4gsG4kcW5Q2Q5w5y2eA+SpJGKWenrLYbmicfd0vbItOcv947jsMUoLBpiZCiPYMxOTUUXJmsSXZMA+LC1nrMX72J4Tx2yrOHMHyc65mFgtJB42oTJksTsitK6u5G2sXyKnQGcRpXHGlfQNv5R8bTjvfZ/hqOt6XYOlBJJWCnNHyYetzAS/+Ritg5jmvllnVxYMsSKrxQQHTmEvaOCc976Mw/UrcEsaziNGXJdQXRdwm2fnsZ81PKiXqrr23HV9qLFzaiaTCRhpbJwgKK5h4mNZNETyJo2TpE0Xjw0l4vUFtxxM0ND+VgtSQoreikvHqAv5AGY1r6wM+wg25zCarCgahLlHh/La1pRk0ZG35sDgKtkGE9dDzv+cj4LTt+NbMzgrBzAMN+GIRZH7tKIxmxoqoyWMIGsIysaRnOKaF8eyYgNq3H2LRcAp+UPYjKmmf/5d0ivkJHczZTZuiAjobbJxMfcyOY0kjk1ea19ktsrmihxDtA1mk972IVNUcm3JugPe9B0iWxvgEVzmnlzz0JU/fhxpnlePwfmnIvdHiUQOMzd+vrJ70eB1MSvwJvqVwNxNjesYv2hx2ac5+6q9dS6wpRn9WNQVAYDWXxp+3M8Oe9GSl0BXDMUwNvYvYlnF1zHhe/+kbsqN5BjSTI3ZxijIcPZVYfZ31eK40hLxolU94emjC+2JihyhHh7qBDZ5+USSSc3Z5x4zEpmzIFin9iuMz6Ui6op5Jf2Exn14qnsx+oNUeYIs7G8CbdRpTeU4Nz6ZjwFo6gpIzW5Q1y959UTOh+CIHz2RARdEARBEARBEARBEE4BUyLof/vb3ygoKKCuru5krUcQhH9hW+aspMAaw6CM87znGoyyxqU7RPT8350MZFmj2ExJnl1wHaou0x2xI0n6KRE9BxhJWGjIG8RoTKNpMjklQ4zvb2AkYeWcogHeGyo87vgWXy7jMQdL61qoqukkFnKw23kh4Vgf8bSJsbidjCaTSJuOG0EeD7s47cmXeefcLzFn8X7qskJIso6WNOLvLCKtS9xfu5bvfKzQ22DcwsLscfKzfDg9Ifo6yghEHUiSzt7RfB5rXMGqg48DTHv+0bCb9wZKafSOU1EwiEFRsecEkBUNTZWJjHpZPn8fyZgVd+Eo0TEPnQfqKKnoRdMlcp1BLK4omioz78L3qQ/Z8PcU4o/b8SctJFSFdc3bZnzuz8po0kROzE5u0ozdHmVh3iCbmCiwNpur9zzDi6YrsbsimFUZV+kwC/J8tOeezuDoINGkBYOi0jZSyIKKDrpGZi+YZlBUkhErwzvmkFPfxaJrXkGLK8huDS0ok+nL59q9z0wb9/ZQPguzAuzpLaf1wFzOLxrg4HARyySNdNpIQp34Ne3uqvWTRc1uaXuEW9u3cH/tWqo84+R4ghwaKGEs6mShksGV58NeMYiSk0YPS2Q0BV9PASZrElvChMMCUiqFJGkseeUVnhi6iYbcIU5//SX2XXI+FmuCd9vquenAEzy/ZO5xj3taNVB/7ocYGyTATmq/zFhzCXva6jj33HeQFY3kuAs1ZcQ/Pj2DYCY/7drENtsK7MY0AElNJseQxm2Jk1EVZFnFP55FJG1izZHrbjYjcRs5ETvxhJksT4Ablr/LN09bzGv7FxDOTMyfa42TVBUUaXobvKOKbXHmlXbh9IQwO2IUxIdgOxQ5QlSV9tLcUTXj+/boOf9h58P8ZuF1jEaduMwJhkMejLLGre1buL2iaVqGBEB/3MKa5ud58+zLCCettI7m44s4mVPVwQu/u4xiV4Bz3vozQyv9dHWWUWROYzMHJ45ZyE6R209XxEmlM4TdmCIet/Dkny/hKzVtGBSVB+rW8O1Z2vgJgnByTYmgX3DBBdx9992TH3/uc5/jnnvu+cwXJQiCIAiCIAiCIAj/20zbg67rH/0F8fXXX6eiouKzXI8gCP/CdF1C1SX8MTtz8iYiUI81rmA0YZ7cky78+7EbVPrDbhYV93BG/SFCQRfR3orJqOqpIM+SoN+fTUvQTak9Sq1vmMrKbgrzRtjfWf2Jkf71hx7j7qr1hPYvoNztp76mndrlu1EsKcLdhRzc34DXEWHQn8XvFn2DK3f/asZ59vuyeah+NTuGZNgFvpiDBTVteCv7MdsSRNKGGWs2WA0ql+74LW+efRnvdtSSVBXm5Q7hdYX5nCvIjp7KycfeXbUes6wTSCn8rGfTZF2AXy24nuGOWjymJOmMgbmX/Q3ZrWHqiBHsKSCrqo9wfx42T5i5S1pIjbkpjlnZ2VJP/mAOVmcMW8UQFleM4N4GFtW00dpdzsUf/P5/cGb+Zx5rXMFwwkyZPYos6Yz4vVijNmIpMyVH9uAebWM1k4Gok0ue2s+Ly65E1WUW1LVQtPgQhRmZzh3z0TWJBWUHOHSgnmRm9rI9mibzwcG52I0p8n1eSqq6cc3tRg9LBA9UEgk6eXreDVy//6nJMXdVbsBhTNMaclFqi7GxexNvlV7KGVVtdAwV0RLwEj/SqusHHZu5q3IDaf2jfdyFthjjMQdp1cC80i48OT5s+T6SfidkFBItboJ9eWQ7Q+w4XM+lX/kL5rkJGAPdlySVMNP+tdNp7w+TlztG+Pv5SMoBxttLybdFeGHJNbQE3cc9/mZDGtmaJLnHxuihSuzeEMmkmXxXgOGOUrLyx0hGbGiazHjYdaKnlZUHH+eeI1kDNe4AVQUDpNNGwjEbPYOFaLqM3Zj+xPO7pnnbZH2GxxpXYFZUipxBLj3zXZIxKwtyvkxfSOH6/U/x7ILrZp3Ha07QMVTEzv0LWOD1s3zJLhJ32pHt/0V63ElFzEp3ePqxOjZKbVEyeKxRFFmjIGscVVV4Yck1XL5z5vUfjaoPRdyUuH1Iks7iBfsB+PzZ75KM2BifX4nBFmBRfTex3jycC3tABpc5jWs0mwpHmCK3n0TKRPNgCecUDLHklVeOPMPrxz8JgiCcNFMi6E6nk8HBwZO1FkEQBEEQBEEQBEH4X2vKn4MXLFjAq6++yk9/+lNqamoAOHz4ME888cQJTXbTTTf941coCMK/jPWHHuMPi6+lIXeIaNLCcMSJpkuYFO2TBwv/sgJpAzmWBKomo2YMSJLOuuZtM+7JPFlUXWIkbuPW9i1sblhFvz+bnJxxgqGJqN6m+uPvVwaIZ2TGkyYIevHvm09k51IuO+9NXA3dnFE+SKQnn2TayPhY/ozjJ55jy+THd2Y28KPOh/nAeTEHOqrJdQZn7XhwS9sjPFi3huZxDbcpicuUwusKo8gqgaiTlQcf5ydlN/PznodIaTIGSZ3cD35P1XryLEnSmoYi6ai6xAd95ZQeKsfkjCIbM2RSRsL9eXjqetAzCsHmClx1PVjGYixfuIeQ38PYUC5Zp7WCBvVfeJd4Xy41qgwf/D1n5H/u7qr15FgCnFN3iHDYwf6hYrzWKGZjGpMhw5JXXuGeqvXHnWMobuGBujVcsn0iyvlk4kbOSZrIKhui+twPkZ1JtLCZmoSJrp1LZ5zjmfk30OnT0JHwR1yMJ2x0j+eS11yH/Ujmhi9uZfRI5f+jftj5MG+dcykmQ4aBoJcn592I2TjEqN/LWNxGhSPM4ZCL2yuacBkzpFR9yvXRGXaQ1iUuLAniC7rpG83D3JYmP2ectuY6lnzlVbzmFL96fzlfrG1Bzyi8+X+/wPwF+zHakkSjdszmJOd+5b8xlidB02n/7TKSSTPDMQdtYTvVn9DdYDzmIHCwAl2XCQTcdPaVcMiXgwSca2ujq70Chy1Gc38pJkU9gbM64faKJk4v6Kc4b4SswhEcVQPIrjTRg0W88eZZnLv8Awa7SkmplZ882RFHazTcXtGE2xalcm4ry+q7WOBz0Tt/IR80z7wHfXPDKhzmMVIZA4qkc/nOX/O65XJyBwoorOhFzRhoGyiZsQL8gpxhmmvPJhB2kszEKSkYwu4Jo6YNhPxupCP73m+vaMIka1MyeY5Wftf0FGMRF0saD+IoHsGQFWHHc19g7um7UeNmLIXjaDELmirT//Iiis7fg+KK0zOST3n2KAVFQ6gZBftwHgZFZVvjCvrjFiodUeIZwyfu4xcE4bM35Qb9+9//Pl//+te58847Jz/39ttv8/bbb5/QZOIGXRAEQRAEQRAEQRD+PlNu0C+//HI++OAD/vCHP9Dd3c22bduorq7m7LPPPlnrEz4jH68iejQSIwifxhNzb8JtCSDLGoG4jVjGiD9lJHJkH6Xw7+mnXZs4cOm5xBNmUikj8aSZ986/hG7/8aNvn5XbK5oYjqsY5Ilo1fpDj/HyGVdgdUYpzx/HMxikf++iT5xHkeF77Vv4Zc26yT2yH+xYQnF7OeXzW1CMKu/1l6HrM/d7Tmky2xpXsPJIJO9HnQ/zYN0a3uyVMSkaqiZzZ+VEVP3j/nza19H0EG1BD1ZDhsG4GWWwiBLvOIm06Ui2wkPcW72OW9un7mmNZmS6olZKbQlkSSfPFuGM7FGGuovJLxnE5Ihh84TobK0mnTRhzwqSddkQYMLjaqfnpdMoqOvClBtg8G8LMFmSOArGkQwqDm+IZxdcN2OF8n82o6RTmj2KJ8dHXmUfOTnjvLBrKZ+rPUQ6beT+2rWE0sfvvX00Ir2xvAmAQCrNjsP1FA7nMxxxs7S2hezKfmRFpTtim3GOUMqE3ZimJ2qjyJqgI2zHYbTgT1ipzxkmmTHgtSQY/Fhv9ofqVxNOBjGmVVqDbpblD+GPOrj4g99z4NJz2ddXBny0F/muyg1Txn+/YzP3Vq9jPOqkL+rg7PIOqpbuRzanqcgLkx5xolhS/D+rJ85NfDCHeXOb0VUFZA1vto900sTI3lrylRYkc5qCui5io16WyBpVEQdjUedxj18gaeHDffOZU96JImt0Bb0sLezD6wniyRtHkjQySTPWkQJ8J9Cb/qifdm1iV83n8OaPYrAlGNnZgN0bxHX2AJct+R3hDwux26OUu30nPOexc/+0C3Zon6e4ZABbrh+DMc14wjLj4xVJJxC3kVANkxHugbCb1/qKKeusxqSos77nY2kzJlOagtxRugeLiEbt7O2spip3mL+01U9mlx09x/dUrcdlzNDU8ujktbnzoovILxwGINRTyOA7hdTUtSMrGliTyO4MSm4MazKIe1EX6BBtK8KoqCTTRgb7C0mkTNTUtdPVXkFfzMKPux7m/tq1mGSR3SYIp6JpFU8WLlzIwoULAdi2bRvnnHMOjz56/LQ/QRAEQRAEQRAEQRD+Z2YvSQrcdtttLF68+LNaiyAIgiAIgiAIgiD8r/WJN+jC/w4eY4YH69bwrSPtQER6u3A8D9atIaXJfPfwlimfL3P7qSzpA8BkyADQM57LQNTxma9R+GylUkZKy3tJJ00EIw4yqkKp28/mhlUzFk/6LBVaE8zPG2T5Gy/yxNybSGoy20fMWA6lKCkcIpkwo5xAqudPuzZxT9V61CPtSL/fsRk6JlopLYraKcgdY453nM+//4cZx8dUGY2J1OvxpIGf9WziW61bJ4/RtsYVlNriM44di9uoyRqlFuiNODFKOh5rDJcjgt0apzDq5Ml5N6Lryckxm+pX4zKliGYSGCWdfHuEirwhIjEbRkOGdMaApGhYisbQ0wYW1PeiBuyko1Z6n66lZMMQUiqNp2CUp5+/jM/VH6Ti4u2gQWbMidGawuSOknMg+qnPyT/C99q38IL7Ghb992u8ftblLDxtN9d98SUio1kcPFzNd9oe4RfV605orqOtup5b9E1KPeMEYnZsxhTvtzRQPpJLtjfAHE9oxrGKrJFSFRrcIdpCTn7c9TCbG1ZNFCaMuFAknc6QG5M8tQjZzUeKEv5u0TfItSQJJS2cuXg340sr6W9XqPCOIZENTKQ+f79jIuX5jooN2A0a3z28hVvbt/Dm2ZdR7B1HknTC/Xlkn95CeshFsKsIkz2OvWQEZB1b+RBq1EKkNx+DJQXAWy2NfOnst+l6exFWWxx34SjhgIu84iFGDzaw3+857nErcQapLBpgT0cNu8a9zPOGCMTs5GT5cJQNoTgSDL4zn2x7mLaA94TOxU/Kbub0nHGslj4OHWgAYDDs4az5e5Hez8fkjpCOWhkaycVqSp3QnDN5v6+cQGc1NkVjUe7wrN+nZiqiFkyZJoswzuaOig10hdLEO41cuftX9F2XwpHrp3ROO3s/WDSt9eg9VetxHklvP5aqyfjHssgvm0jHt3pDKJYkmZgFoyuKFlZQsnUM2SEizcWko1aef+csTivuIRB1kJc9Tm5NL5oqE4rbOKdoALrgO22PsKl+9ac6ZoIgfDbkT36IIAiCIAiCIAiCIAj/bMeNoAv/e9gNGY6NH91RsQFJYsZiRYLgMqVIqdMLvymSRixqYzzopqRwiGjUTkpVqMsaOwmrFD5LO/rLqIk68DrCxFNmIkkLLUE3t7Zv+eTB/2T2I22sOq9aijv/b8jGDDGfm0TUSnNXJUZZRdWO//fq28omioh9v2MT99WunfK1b7duZZthBVnuEA5L/Ei0c/NM0+A0ZFjTvI0H69YA8OS8G7Eb4rx59mV4nZ2MhdzTxtxRsYHRhMZAfylzPQFqPT5KckfIr+hnqLOEsYAHVZe4cf+TAJPt7Y6NxN1fu5bl+YNkF4xSYEuAJpOOmyeinFkpJFMGzEakJVmYfGPYz2wls10j3F3MWH8+Swr7cHpCjH9Qj6t0mGBPAZ7KfhJjHmIp84mfjH+g2yuauHznJm6vaOK9IQ3H/jjFJQMYzSkWzDnEdscXeKnr+Of1zsoNWGSNLHOKtCZjM4YYDnkozR7ltc4aGr3jRBJWIoNWPJaZix6OJMzTflbmWWMEkhYqc0boGM0nxxLHbkxPecym+tUszB9gOKxT5gqQ7ZyI0BtsCfKKhgmMeekNZLOpfjWD8YnXsbG8iR93TY3cqrqMQVExm5P4xrLJMesYS0N0vnYmsZSZ8xf9EclpApMJJRzBnjbQ+eFcNE3mxv1Psj33C1TWtWPN85Pyu8gqHOHQgQaGo07OLBiCrtmPX1pT6BkqoL6oj2VzDzAylEdhySAGcxLZnCa4v5Lx8SySaSPhEywW+vOeh3i58Ao6hop4fySHJTk+sqxRip/az+4vXEhhyQDRoJMOXy7JGX4OnaijGYN3Vm5g9+jMrRFncnfVeuyGxOTHsxV2/HHXR597et4NdPX4qJR1Mikj4zEHt5U1TYnCz/Y9wx91MBp2k1EVjH2FlC9spmtXI5XL9tG3u4Gh8Wzq69pRTGniQQfhsJM8a4zFL7/K1jkrOe3c9zEXjRM9XIw/YcNhSvLisis56M+e0vZREIRTh4igC4IgCIIgCIIgCMIpQETQBWCijcix7TaO/cuvIHxcNG1EnaGtTE/Qi9GQobKih5Dfza/3z+XymsMMBU9s76HwryuaMbB7NB9pLI8aV5BAynxKRM8Brtv3FC8uuxKjOUUyYkPNKHjKB7EEHZQlrAz5sqbt+zzWxvImGtxh8u1h8ixrqHH7uK92LXZDhmjGwC1tj9AZsVIfsZOfM0ZdyDPjPClNxmtOsP+L55FKd/EH+7XMr2hhR3sNbnuU4YCXktwRHqhbQzSjTLZ0AiixR4lnjFy+89d0XrWUgoVtABQZM0ht5YSTH7WwCqUV7j8S5a90hvBYo2S0EdxZAaJBJx57HFNOAHUwG0NWDMluQo+mIJVETveix1TCe8qIBZy4y4ZIdJSxe6gYSdKZf/HbKO4E2dkhzP+RZnPD1/4BZ+jvY5R0NpZ/1CL0xewrKdIH0TIyiagNjztEsTUx6/gn5t6EQY5S5vYhSzop1UBGVfAnrBgUlYuq2hgNuQkmrCypbSWdNs44T0qVeOucSzEZMqjaRDTbaBjhg94Kiqu7ySsaxuyIsX/XvCnjBuMmzrIkWFwwTO9AIX2+HJyOKH99bzlZlolaBJXZI2hjeXjMSe43ryWQhmcXXIcsgdWQ5nDIjSINYjYn8eT6yDqvA4wmsFmYs/AgmZQRyW0Gh5307gQpfx5q3Ex23hh/2n46Lyy5hrGIinswj5LCcWRjhvCol/ln7KIhYqW3veK456A0e5TcwmGcpcN0fLCAORe+j1KQgPIiJJ8Bl9RNmaKyb+88FEk/7lxTj6mBtCaTY0ljUlS89gitX1mO2RJAknVcuT5q/cMse+2/T3hOYEqtnaM8xgwljvBx62X8smYd3z28hTsqNvCDjod5ZflXAfjVguvJtw/ygvcaPhzPmqxlcNRjjStwm5L4kwb2jBRiMaXI9vqpyBkh3xkkx7KGb39sPce6u2o9yUwAjzWKQVHJL+3HVOin/vw26A9RorVSELNgzgkQH8zBZE2S54ySTJomXps5ia+rCONgDo78cS772n+RGPHiH8zjku2/m9ZiVxCEU4OIoAuCIAiCIAiCIAjCKUBE0AUA4qpC7MhfXAXhk/THzTNW+u+M2Kn0jhPwedE1iSvqWln88qvcXbX+JKxS+KzcWbmBUnuI1qCLH3Y+zJ9P+zqlrgC/W/QNOsNOvncKRNLzPX4GBgrp8OVS4AjhGMqnsGiIWNxCT8jDQ/WrJ6tqf9zG7k28c+6XqKru4rSCd0kFHYy/dTbX7XuK3V+4kM3KKtYf2sTX55xPMmnGZpy5srRF1si1h9F1idy8MYrL+ohH7Fz6xVdQbAnq7Qn6353PeNI4JRL3466H+cuyq1hS1U7fovkEfHYGd9cxMp7N3uEiLqhrJpo28fS8G7h+/1P8uOth7qlaT4E1wVDcxlDcxrrmbfRWLKRgySHig9mkg46JSt5G0Avy2f3zOiqqu7AXjWIsCHH4YB0LLnkLNWJFkTW85iRWc5J4fy42wxAAryz/Kj0hjdHEydmD/sPOh3lhyTXco6xHkXQkaYz2jgo8jghGY5qOoSI6I9ZZx5uVDAvLuiie14a5yIdk1ckM20gMZXNg91zeGSjhvNJu6iq7SCeNxGMzz/Wznk18tfYiKurbsdf0I5k0El25fNBbgTXXj/s0Gb0vQFH/1H3OHpPKeNhFImXCYY0TStiQJI2vX/VHJINGbDCbfXvmce3eZybH/GXZVfRFnKRUhUpXirOKe/B6giiySipuJvxhIY6GfiRdw3Z9MUp/D8ldNsw1foyNdvTdcQ58sIiigmG+tPhDenpLiKXMpDNGFEccgyfKK6+dywXZAQD0GTKljqUoKs7SYYxnWalY5SXpWQ+yASk+hmG8A1PyHfb8dj6xlPlTRYQu3fFbHmtcga5LOM1xcnLGaesup7q0l30H5nDa2R9QXDLAG2d9mfPf+dOs8zwz/wbm5A9gt8UwmVNEwm285PkavoSN5oBzSicF7Tiv1SRr7P/iebi97/GtL5cTHu3mRfVKKgo6MJlS2D1hxt49c9o4hzFNTd4QsaSZ5W+8yF/cV9Hnz6a+qA9dl/h269Zpe9Hvq12LzEQmjK5DocdHcckA3vpuDJUqqDIt99dQfb6f+JgbNWUkMjrxM/d328/AasgwL3eIF5ddySXb/5MPPBdTUd1FOmpFMqjYqwZIx828c+6XaPefnA4MgiAcn4igC4IgCIIgCIIgCMIpQNygC4IgCIIgCIIgCMIpQKS4CwAkNRlFPvECLsL/bjOltwMsyRkn2xMgmTQzFPASzxh5rHEFBjn+Ga9Q+CzJgMOY5KLyTt4suoy3BrI5t7iXiuxRUtrf3wbpH+WBujXEEgPE0ya+sfdpfrPwOiR0zPYYjefsoHLES+vBemiZfY7msXw6/dmcVd9MwcI2rv5/nuEamxFt/E3ydw7zRtaXyagxogkLNlNyxjm+37GZFzzXoMgaybSRqobDeOZ2opQZwWRE61JJp43TCk3BRAtDszlJwOelZaiYeaVdLLjwPeanDUQGcqiODvNaV9Xk442yzk0HngBg1+c/x8Bpc3GX9TGwvZFIxEFhRR/um2RSuRdhbt7OnOW7sCyMoufkII0lWbLmNWJ7sgn0FnBwqJjGgn6MxjSRcQ+Oxl7UoIX2QBYDcTPmk/Sz4+l5N6DqaVac/waaqtDdXUokYcXpCqNmDLQGPUjHydAeS1jpGCrC6Qlhi1mwlY5gbJAw1o1xWuEb2F87gz5fDqMtbsyGiRZp99Wu5Za2R6bM88Tcm8jJ2YtszHDwz+dSUtWD98xOVn3+r2ByQ/8AGZ8No2nq1ofvHt7Cb+zXoekSqaiT8YSVeMJKMuCc2H4A2M0fFbl7ct6NqFqCLHMSRdKwGlOc/vpLvH7W5cybfwCjLcnBD+eztGYAcnORwwEwKPg6SsjzhsEfJ9RTw2lX/TfpUTexUS97Rgr50oLdpNNGoh1F2Gv6ufC8t/nw/aVE0ybiGeNk274Zj2HAQ21tlHTxPOREGMvbTzLwZAnRiJ3Cmm6UUona8m46e0v4fsevP9X5rc0apby0n6yaHkwVIYrsLWQ6JTRVBk3GXTFA1kjOcefwJ83sHyom3xbBY4uSlzvGouIhMikj9YP51Ltv4Lp9T7H+0GM8cKT14cfdUbEBuyGB0xXG4oyiphXylhyi8Py9qGETkqKRGMyma4btFIGUiY7RfDRd4idlN+M0dzOnYgizNUE44OLpeTcwnsxwV+UGfnikVdstbY/wi+p1FNsSlDqD9PmzGQ27yektxuWIUDSng+pzP6TrzSXIssb2jhrm5A8SS5qpcQcwyCqpjAFJ0rm9oglN7yQwmkXJkmaSYx6Sfiee+R3UW1Kk98z9VOdEEITPhoigC4IgCIIgCIIgCMIpQETQBQDiGYWkNhFquL2iCUXS+VGnaLUmfHpdQ4WkVAMea5S94zkszh1hLGY/2csS/okkSac77EaSdDy2KBZFQ9Vk/FHHyV4aANGMQn/Ii9cSA6AnYqfQGSDqd5MI21EMKiZj+rhzrGneBsBzhm8yN2kmK38M95wulAoLOQ1+crr/TKrPTcs7S+gNZM84xxNzb8JmCjIUduMwJRndvhTn3kYKc8cAUFWZ5r4yflE9b1phvWDSyqv7FlLh8aHpEiO+LAqCDkzZQazZQfr3LJwyJphW2NywigpXAEX2oxgyRAZycGYFsTpiOMsHkfs0zH296OEkqWAZ+m6J2LCN/MfivLL8q9RVdhEKuqjJGSYrZxzFoKLrEomebAy2JI05w6x/689/72n5HzMpGotq2nCWDqM449iyg2x/ZxmjY9kk08Zpke6P+1brVu6o2MClT+3njbO+TFZLGIs1QV51D7bqYeZd9TpzAhYSQ1lExz0MD+ZjmaFl5FDcQlt3Of37F1CTNUpbcy1FfhdWRxSDJUUmUcXfdi6l0BkAdk8Ze/WeZ3hy3o2UOIMU2sOk0wZa9jSS7Q0wOp6F1fxRNsZ40kxCVQiljCiyToFq4M2zL8NhjRLxeUgNmqhpOEznf5+O6Y0UJmsSWXbT21eE2RbHaE3iLB2i/82F7O2sxqJkyLfGSCVNOD0hJFkn3j1RyG446qAzYqfGFSGamT0LRtVlUu02wq+OYskKMdxVjsmapH8sF6stTnAwl+GRXKymmQsnzubOyg0kM0PYvUEM3gh6WEKPplBydEov2UmyK4uh/bVk1ONn6BzbUu3e6nUofeVIkk61M0RVwSDLqtt43Xk54YSVkbg64xw/7nqYXy24nt2Ha0lkDJgVlZK2cfLzR8mZ244WNRAb9bIwKwA9U8eua97GHRUb+HHXw7x0+tcIJWxoqowk61isCezGNNfvf2rKmNvKmvhe+yY21a+mM+Rhft4gQyEPwyEPRkOGyFA24UOV7OmpoNgVIJw2EU1YsFsSvD9YTKEtjq5Da8jBz3o2UWxdyUJFZWhvLc6sIOmkiQP/dR65eWNkOcPTitQJgnDyiQi6IAiCIAiCIAiCIJwCRARdAJjc+wQT7UQE4e+x15dFqS3GorJudE3iIms77/VWkPiEKIfwr82maJxW3EO/P5tQ3MZXF+ziUE8Fl+747cleGgA/6NjMbxZex+ff/wN7L76AUKwfg6LS01+EQVHp9OdgMxw/gn7UVbv/k2fUGzD3ldMwmE9Wzjj2PB+2+TIGd5RUxsDVe57hzsoN07KQEqrCvvFcbml7hPtq1+IxpmkLeMkOZJFjjZHWFK7Y9Sx3Vm6Y9ryDMdvkPuD7ateS1mT6X7kAqyFNVe4w7WEXv6heNxlF//g+9rfOuRSbOYkk6RgNGYYGChj8fRYea5ShsJsLz3ubt145j46gh5bLs9jdbcfUU4LZmOa9/jKWazKaLmExpTCaUxgsKWxHort3HVnvDz/jrKuWoJOcvhJauiqor+giu7KfufWtdHaW47LFeGLuTZP78Gfz466JNXcGsvHH7Vyx61leWHINnveiOKxx3K4QqZSJUGQiG6RphlZ83+/YDB0Te+Kbx/KxGjIc9uUSyxhoanmUrXNWUuXxcc4s2QY37n+Sl8+4gu6QB4Oi4rDE6egvpjx/iFDEMRnhnC0j4C/LrmL3QCnBtJHGoXGyHGF2tM7BbkyTUBVKHGGe+dt5ZDSJW9u38Mz8OVRljbJ7qJhFBf3U/fE9XlhyDbVFffztcD3LyzrJtUWnRXZnMhxx8tafP0euK4ivuYGCLB97O6s56PeSUg2UZI0RT5nwxz9dFlVak3hzsJCCLh9/efELNHgCeKxRugNZFLuCNNQdpm84j+bxvBOe89aPZaU8ULeGPEucZTWtxGI2mlsbZh0bTRtRJJ1r9z7D3VXruWLXs2wsb+KLAwUEY3bctihjiZnb8FU6orxx1pdp9btZXtZJS285HmsUf8yBxxqb9vij0eyj19pLzq9hM6bIcoTxegOEAy7ebGugPnuEaNKCqkvsHS1AlnQurOhgz2AJqw4+zn21awEYSpixjhbgDGSR7pApdAU4829/5c7KDdgUTUTPBeEUJCLogiAIgiAIgiAIgnAKEDfowjQ/6NjMDzo2n+xlCP+CftCxGYtBZcTvZTzk4vBoAQC+lEjW+XcmSToZVaGxvIvKogF2ddTSHnKf7GVNoUgarV9ZTtXS/Sy74mUWXfwWdXWHGY86uW7fU1yx69lZx24sb5ry8UjCQmfYQd9oLgdba/nvly+k+w9LCDZXkO0NcFflBrJM0yPyvtRH+6JlIK1LfKftEayGDH0RF1/+8DcAM9b/sBsyvHXOpbyy/KuU2qPIkk5jQT+NZd1EExbcxvS0fevHemughEMjhXjcQVJpIxlVoTR7FKMhw5ziXqJjHiryhjijpBuTKcVFZ75HeUk/mi5xbnkHOVk+6hcepHJeK/GwnRf/dg6yrPH8kmsotCY+8+g5TERZL3z3j3SEPGxvbeD1l88nHHBRmDeCJOnU5Qyf8FzhjIGhI7UyLt/5a3YMF7H45Vc52F1JJGYjlTHQG8jmmfk3zDrH9fufwm5ME0kbGUmYybPGuKNiA6G0kfQnZBGlNYUzyjuoqegiL3cMuzlBw5/e4fTXX/rECGdKVXCbkuRZEui6hMsR4QuN+9F1ieVlnVgMaYptMTRdYvuFX2BeYR/7RgrxmJOMht08Mfcmcp1Bekbyuey07eQWjBBOWXh+yTWfeNySqoFY2kTrSAHhpIWxgJuBqIPzS3oozxtmwUuvc3A8j1DK/IlzHavKEeOMvFHm/vlNTLLOWNxGTzCL9ogDizHFXz84nZGoC+PfkfF3W9nE+9kg6XSEnRzoqsIfdpJnSU5+7ePWNG/j6j3PcE/V+snfjzZ2b2I07KY5kMWuoWLWH3psxrGSBCZDhi/M20tB6QCnLdyL3ZIglDLz4XDBJ643kjJTnj9EeVU3akbBbEmyuKiXkYgLuzlBjdvPgtwh5mSP8n5fOasOPs4fFl/LLW2PsKl+NTKg6xPXdU/UwZl/+yvPL7mGH3U+zHcPz/49QxCEk0fcoAuCIAiCIAiCIAjCKUDcoAuC8A+zdc5K+qN2Lnz3j5z/zp+4es8z3Nzy6Kx904V/D9GMwus9FWxvr2VfVyVX7f5Pvn1M9eST7Zn5N1Di8WGxJnjnlXPp/tsSIj35jA7mczjk5rUzv8KT826cdfzR/dx3V60HJrpeSExEPQMJG3t9bkbGs4iH7Yz7PVxY1oV7hqrVmSOdMu6tXke+NYZBmugfLks6aW3ix/F9tWtn3IN+tIp8cc4oiyrb+dJFr1NU2UM47GAo5KE2a/S4x+AHHZtxGifW5HEH0XWJlw7XcWi0gD29FWTSRhRFpah4kFjcRuvBenr6iygv78VqSZBdPoiWNmB0R8hb0MbF57xNUVk/i2raWFTcw4Oz9JD+ZzoaWb655VGu3vMM4bQJTZMJBN0cHCymP5j1iXPcWbmBe6vXYVPUKfvLS+xRbitroi/ixGTIUFY8wPyyTvLt4WkZFQC/qF7HG2d9mRxrlN6YZXK/s8OoEs3IpNSZs4geqFvDc4u+idMcx5vjIxJyUvbsbsaiTrY1rph8zLGR3Y/36/7qzl9Tmz/I55Z+yPyGFhzuMJ6CUc6fu4/5L77BYMSFy5RgWf4QZRU9FJb185Xl73L23P2U54zwxeXv0bhsD2ec/QHeml5cpcMsqW7FKGuTa5hNIGUikLTQEnLhtUYxKip13nGyPQE8WX7Gb67k7PIOcqwx7q1e94nn46j+mJUvbX8OgO+0PUJalzArKpWOKFnuIBcv3UFjcS/KkffQ8dz1sfeTNPE2ZDhh4oedD2M2pMnxBFhU3EOBdfZq81vmrETVpcmPN5Y3EU6buKXtEQIpI3dUTH/f3l+7ljxbhGTGSCDg5vdvnUPLoVpSGQODMRsw0TnneGvvj9mJxa30dpZhMGbwFI9QXtfB0roWOvw5pNSJnuduW5TLl26n/Wunc8WuZ7mnaj1NLY9yWW0Lcwv76btuPt885006rjwN/5H98rNlDAiCcHKJG3RBEARBEARBEARBOAWIG3RBEARBEARBEARBOAWIG3QBmEixOl6alSCciNGkCYuiAvBQ/Wq2zFl5chckfCaMkk6JPcZowjJZbO3Bj6XlnkxmRSU3e5xgwI0/YaXuj++xfediSuo7uOFzr1JX3UFD7tCs45+cdyNb56zkBx2b+WXNRJpuoS1OUjWQVBV+1rOJDl8OmirTsOgAuVl+WkOOafP8uOth3jz7Mm5t34KOxHjSxG1lTVgNaZYU9nHoy2dxUVXbjEXi/nza1wFIJM14CkZB1kiE7XSN5xJNGycf9/S8G3jp9K/x+lmX86elV/NQ/erJr5mUDImEhWDIRW8gmy/WH+KixTu5+Jy36RssQNclkjELg+PZ5Hj9VFb0sOtgIyZTitBQDtZ8H6bqGBhUIuMenOVDFJ61j7LaThbnD/zd5+d/4ujWhHur1xHNGOgfy8VkSnNa9WEk9OP+XHuwbg0/6nyYWneQNc3beKBuDb84kobdF7WzLHecppZHUTUZNaNgMqe46L3np7Wwu7NyA0vzhzj/nT/R4s+mwh4HwG5MsTBnBJOsk1QNbDrmXByVbU5SX9BPrtfPjoONhCIOtl/4BZbUtXBmdSt/Wno1327disek8sz8G7i/di0OQ2bKloxn5t+Ayx1CVlR8o9nkLG3BWd+L0Zzivtq1pFSFPHeAsoIh/vOtcwmMZTE6mE8o6Joo8JgyYq0dwTZviN2vnEWguxB3/jjlecPUf0KhPUXSybbEubi8k/Pf+RNZ7iCKrOHJ8eGp7MdgS/BhXzktAe+0NmfH88POh3l2wXU8t+ibADiNaa7a/Z8cDtkZGMvF6o5QUN5Hzgxtyo66u2o9t5U14TBmuP9Iy7E7KjaQa07zUP1qCiwT6ey+hI14wgJMbJf4uK1zVvL6WZeTbU5wZuEAT867kftq11JqS+C1xNlY3kSlM0K+NTltbLkzREnuCC5rjN39ZSwp6gWgP5BFmSPC6QUD2I78zHxy3o08MfemybF3Vm7gh50PU+EIY7UkKCweRJJ1NFVGknTGx7M4vfIwNYX9OKxxcvNG2dtaTyJppvUry/nq3H10X7MYiyVBPGlGVjQMlhTu/HFcpom1ihZrgnBqEjfogiAIgiAIgiAIgnAKEL2PhEkyn1xsRRCO52j7mZdO/xouaz/BmJ1N9aunFF8S/v2E0gqnOQM05MV5LfsrBBNWrth16hSJaws5qQx4CMVtHAy4AHCa4yjWJNaSUWxhK6PDubOOj6aNXDx3L/6LyknH32CgpxhZ0okmLLzRV8ZD9auxGmLoukTc70JV5WlR1qOqKruJfs6LpPyJPa+eSfhIsaaS0n5kRUNTZbY1rmDlwcenjFMkDUXWMBozPPDnS8g2p7n6rHc4c8FedF3i7b0L2TJnJdfv3wbAY40rcJlSlDrC3FO1nu93bMZkyJBdMIok6STTRtJpA0GfB1nRyMvyYbYkGR3Lprqkl6yKAdS4mRxHCKsjhsUTxpAVwf9mJYmwnXTKhK+1jIH+QsoqenBY4/+AM/XpbG5YRSQt0fqV5YwHelFkjVTGgMsdIhJysD/godoRmXX8WWWd9CxeRDg8SM+1i4hFmznUX8qbZ1/G3wZkvvzhb3hi7k30+RIYFJXokSjrxy3JGePQeC5tV5xBTsnLqGmF+uxLsZmDFJf1Mae+DcWUZv++xinjNpY34bWMMu+vf2Nb4woshgyaLlE7p41MwsSOlgZ6ohOZGEfbYT1Uv5pYxsC3WrdyZ+UGftT5MLKkE4vaCIeclM1rJTnkRVY0ElErdkMGqyHNwaEiimNBvlh/EIOSIaraeKm1lqsW7sZb20uqz42kaDTMb6ZlfwPl5b0U13YiHy4/7jnIscQZT1hZtmAfPQWLsLpHKJnfirnGjxaUiQ1nM7+gnx39ZZ/m1E7MbYtSlD3G3rwLyC3Yhf+8ctoPHua0V18msKyEdMSG2zJ7BN2maFxSdRiLOYmmy+yuuBB/ZJBUxkB32E04PfEr8GDMxlxDBl/YOeM8dmOaQ+O5fK7+IKGIg0XOEPGkmXDCiqrJ3LR0B76Ah5GQZ9pYRdJ57sB85noCBNJGDIpKZUUPxXEzDm8I33AOt779Xzwz/wbK3OMossazC65DlmA8MdGqMZYx8FpbA/NyhpEknVpLAu/Sw8wpGiPWn4sk6Zizg8RHvGTZw/x3Wz2F1hjBlJlsS4KzFuwhv7YbAIMlRbCngLQm8+yC6xhLWPnWKVTQUxCECSKCLgiCIAiCIAiCIAinABFBFwD4aZfYhyT8z91e0cRcTxCrMcTe4SLWH3rsZC9J+Az8rGcTS3KuwWWLUVXaSzjk5Jn5N3DdvqdO9tIAyDanSKaNeOwRalxRAAJxOzveOJMcVxCARMo06/gqtx9vyTC2iiGUfIksqQd1QCPYXEFZWR89PSVkVIW9ndUUuAI4rHHurV43bc/t0/NuwOZ5E9mcRnEkWNb0OhgUMB3ZQ55RSXxoJq85Om0N3WE3UsTFQnmAL1cfZijoxeyKYHJHiI94KXb5SfmzgYnWScF0ZrJ1W7UrzHOLvonV5OPlD07nss+/xpKrX0ZPffQ3ekt3Ph++v5T5c5txVfUj25Okx53Issajb57LdUt2gCbjquvBBWSCdva+cQZt43mMht3kuQLcV7uWW9oe+XtP06eWY4mztK6FvHntVGRF0YIWBj6cg9GcIr+8n7VZAfp6i2cdb1BUrO4I+ac1I7vSYJCo9O8g3FaCxZSiwLKSmw5s48+nfZ35L77BPVXruevIvuBjDUUdfGnhLnIbOzCVR8BsZPlZPWhBHcmgI2WZ0UZSlI1Obfu2sXsT2+wr2HfJ+UhSJ/3jOQRjdkZ7CzEaMxS6Aly5+1cfe80JLMrEnurx5ETLr2/sfRr2TuyVdjgnMgZ0TSK3ppdLbQkOHa6mOGcUd1YA79wOtKiF5M5GohkZX8DNwT98kdLsUYrL+zDa4yiyhpoxkAzZsTuix30v241pltW04qntwVCSBKt54vn9Onpy4rrOzx+lPm77FGd2Qko1kFsyiLOmf2LOlIF5pcPEznNz6M16enw5ZNtmz5BQJB2XM0Lh/FYUVwxkHT1uQo1ZOCNiJTCQx/nln2coGKBjNB+ZiboEH48of2Pv0zy74DpyywapbOxFyjJDOoM6Cv69NeiaREN9F/G/nTFtDcGkmc+VdpNIm7DFbWiajMUZJauuh2h/DuMBDzDRarE7kMX1+5/ixWVXcsn23wET7+XuiMb87HE8jgjebB+yUaXjr2eQX9FPcDgbozmNpsp0tNSw/I0XubtqPfGMkUUF/aQyBgymNJbCcdAl2l45A3/YSYEjRJYzPJE10PqpT40gCP9kIoIuCIIgCIIgCIIgCKcAEUEXBOEfxiRr+JJmRuL5M1bDFf59fXXnr9nWuAJlqJhSV4DIMZXFT7bBuIWCiIv+YQdmReXNsy+jM2BF1yWM0QyBuJ0vf/ibWccHk1YO7JxP96vns6ism7ySQVy1vXhPO4yelvBW9eE7XIrUU0o6Y+DFtnoiGWXaPBaDynBXMYffPZ3K/CFy8kdw1/WiZIWQbAp6WCUVzGZshmhjUpM5raAfkzFNPGlmNG6jr6UKSdIxGDJ8OFyE05CZeB5F55a2R3hy3o3YDBnGExZGkyaGYja+0HCAVNjG2CtLAShobCfpdxL1u8lzBxjqLeLND5dy3unbMZjSWC1JLqlsp/K5D3njrC/z1n99kYsrOigoGMFqTnLTgSfYMmcll+74LRvLP9uq/f6khfHxLNTdChZ7HGfhGLl13RzeMZ9I3Epp0SCSNHttlR29FQQ7ajm/owNVk8nJ9pFd2Y+tcJx6Z4xyfzedjUvp6E/TcvmZ6Pp+XmmdM22e0YQZn88LB6sYeDUfVZNZfOG7mIqCSA4F3Zck2ZeLqk6PiYwlTXSP5WExpAmlLKQ1mV0H5+E2TuxHf2HJNVy+89eTjw+lTNjtKWrcAbSgZ/Lzd1ZuYCQB/7ljGZXOCKfXtODvLkRWVEpyRzCZUpjscfwHqjA7YoQiDpbnj7Lstf/mtTO/gt0eIzTuxRyz4HGF2NNaR2/ESZY5gXycYzgYszNP0Uj5naTDNrp2NdI7nsulO37L1jnXMDdvkOVvvMgbZ5We4Fn96PW4LT1E/W4OPd9Ay3geXnOCPEeI0qJBsrL8hONWdo8WzDpHSpPpHc6nZ6gAsyGNQVEpKhrEme/D6IqSn32YPE3Cvn0e4agdtzPMYFvDtHkerFtDsWsQZI1kbxZquxFzVgglK07O+a0gS6gjCm779MyX6/c/xe8WfYNCV4ByZ5APB0oJx60Y96u47FHOevMvABwOOSh3xHh63g28O+KYrC8wWWW9A94974vkmVOoKSOplIk/vHoBwwkzGU2i0JpkTs4wu79wIal0F5ou4Y86CKUsFPtdmIeykI0Z8ssGiLVWYbMkMJlSeHWJzQ2rRLabIJxiRARdEARBEARBEARBEE4B4gZdEARBEARBEARBEE4Bkq7r//K9tUKhEG63G1AA6WQvRxAE4X+dbY0rGE+aiKsydoOG3ZAhmjFMtoc62Z6edwP59ghvDhZwXtEgpXnDqKrCWMDDYX8OwbTxuAXONjesQpJ0Qikj2eYUc/MH6PLl0lDQj8MRYdzvpbhkgFjYwdutDdx04Ilpc9xe0cRPuzZxX+1a4hmFtC7x+dJuDIrKYMiDSVHxWKO82lvOjz5WiOzY4lV3VW4gqUlU2OP4UyasiorTmOL6/ccvynd/7VpK7BGCKTOrDj5O9zWLefdQIxYlQ4l3nF2DJaxr3sbzS64hkjKRa4sSzxj56s5f86elV/PlD3/DG2d9mfPf+RO7v3AhsqSz4KXXJ9NxT5aN5U0U2xI4jWm85okiZxZjinPe+jMP1K3h28dpI/VA3RpSqsz32rfwqwXXk28PcziQhVHSWXnwcZovO5uiui4yCRMDHWXMf/GNGee5v3Yt3zly/TzWuII5uUMk00aiKQtOc5zxmAODrE3bSnF/7VrmZo8ST5sIJs24TCnMhjQFHj8WS4JAyMXpr78043Me28Ly3up15FsTxDMGYqpCniXBSMKCqknMzx5D1WW+tP05Xj/rcpJH2n1d9N7z3F7RhEnWqHBEybVFqCnrQVY0/mvnUppaHuWBujVE0oZphfGOerBuDfNyhvHH7VTlDrPgpdf5Zc06NB2+176FfZecj80W52B3xZRU/RPxxNybCKaNJFWZtCZjkjWiqsJPuzbxl2VXoUgaF3/w++POcXtFEy5jhlvaHmFT/WryrDFSmsI39j7NK8u/yoJ5B3Ev6EALW+h5fz51f3xv1rn+tPRqHOYEkaSF6oIBdvVU4jEnWNx4EFlR2XOgccb1/LJmHd89vIUXl12J1x4hoypoukQibSKaNvHVTzgud1etx23MUGSPUJY1RkZVeK+/jAJrHLc5jlFRGY46cRhThFJmGgv6aRsppDXkxCxr2A0q83OH8LrCRGNWTMY0b3dX8a3WrWyqX43HnJwoNCgIwkmiAyrBYBCXa6IVrIigC4IgCIIgCIIgCMIp4KQWidu+fTuPP/44r732Gl1dXWRnZ7N8+XLuuOMO6urqTubSBEH4H5qpHZHw78ukqJxf3klGVejw5RJKmU6Z6DmASdHIdob4ojnBf/dUUB+zc9bcA6TTRla9/V/8ZdlVxx1/bBGlTfWr6Q9kcfWeZ7gjuAG7QWNu1hgOnweTKYWmz5zJ9dOuTWyds5JQWkI78vGS7KtRZA2A4agDf8I6YzTaKGs8t+ib7PO7WZg9hiTpVOQNYTYnCYZcjIdd3FO1nu93bAYmIsN5ljidEQf5liQmWWU8CRZDhiK3n7YrzqB/MAuLMlGMLJ0xsK55Gw/WrSGjxajKGuP9wRLyLAkerFvDWDwzuZbRtbVo6gCBsSyemX8Dqh77tKfjH2ai3Risa94GTEQ58z1+MqrC3VXrGUscPw5xbHS9M+wgrcmsa97GPVXrAWjuL6V9qIjqggFUbfa5TEfOIcCqg4/zwpJrCKVMjCYtgAdF0nEYMjOOy3MHcDgiJBIWVFVB1yWGA16UsEYoaZk25idlN+MxZmhq+ej9deuRDIBCt59IyswVu579KLOhfeIxb559GZquk+/x0zWWx0P1q7m5ZaII2bMLrqOquA+zLcH4SA7lriD31a7l263Hb5v3rdat/MZ6HaGUCVvAy9Y5KxlKyPyo82EOXHouNmuEwZG8Tx09BxhJmEmoMj/umng//LJmHT/t2sTT827Aafbhjzk+cY5jW8g2tTzKPVXrMco6v6xZx3A0znBfIWGfG2/RKIqiTXkPHesX1etIqhG+/M4LvLDkGg4PFqPrsGs8Gw420hdxYlHUaeOenHcjFe4BmuvPxpN9CIsnjJ5R6G8vx6HFscSt/GrB9bNGsDfVr8aiJPFa4iyqaUMxZBgbzWEkYSLXMlHAL5420VjQT+dYHvOLegnHbBwIuLApGre2b+GFJdfwRn8pNxR9gNsToKunlG+1buXOyg1YlASh47SYFATh5DipN+j/5//8H95++22uvvpqFixYwNDQEP/f//f/sWTJEt577z3mzZt3MpcnCIIgCIIgCIIgCJ+Zk3qD/h//8R8888wzmEwf/fXu2muvZf78+dx999089dTx99MJgnDqEtHz/12u2/cUT8+7gYUlPdRkj/B2X/nJXtKk+2vXkmsfZt9gCb6UaTKq1lx6NoVl/QzcOJf9Ldqs449GU7/fsZm7q9bT1LKZbY0rACaje0/MvYmClB8Ajzkx61wJVeG7h7dwe8VES7LBmJ2UquA0ptFh1miWUdaozRtkQUUHyYQZtzfI6EgOe3vLKXX7+fz7f2BzwypgYg+0zRDHl7Sg6hKrDj7O5oZVnF7Ui8cZxps7TiphwRdzEEqZ6Y9Z6Y442XfJ+QQiQ4xEXSQzRmpcQXIcIbQjkePNDasYiyXQdQlXdT+SrGPrK2MkbjtpGTPf+VjdgC9/+BueX3INKdXADzo2c2flhhOeS9UheqQ9oEWZuB76og4qnSH8IRfBxPT2d0dFMsqUY5BQDaQ0Bbshgwz0xizoM2RWRDIKvrCT5w7N4dzCQbIcYYIxOx1BL4W26Iz7k3/e89C0z01kVzj5at4gBYbMkT3aIzxgXEOd24+qyxz225lf0E/LcCHBlJm0JnNf7VryLAkiaQO6LmFxh3ElTeQnTXQc08bteNrDDn7QsZnHGlewpnkbv1l4HQCplBF/KJ+eYBZ3V63nBzNEpo/n1vapGTjHZuQYFBXHcd5nMBH1/t7H5ohm5I9alwH3JdayKHeYQNBNMm0kMUOWxJY5K6l0jfL59/8ATFwTOZYEQ/GJbJc79A38uOthHqhbM21stiWO3ZLguQPzmJ/lp9Q7TiJlIqUaSKaNbB/Nnfwecuy6bQYVg6yxrvlR7qrcwNLSLgaG8hkIeQmnTBRbk7hMSQajTmqyRukZz8VuTOELO/HH7XhMGaIZhV/WrGMskUIGNFUGDJPXoa7DyoOPH/cYCoJwcpzUG/Szzjpr2udqa2uZO3cuzc3NJ2FFgiAIgiAIgiAIgnBynHJF4nRdZ3h4mJycnJO9FEEQBOFTGIpbOTBQQipjoNYdONnLmWSUNZJpI7m26GSl9ofqVzPi99LWUkNrexVjcfus42VJx2NKA0xGAdMfi4ZqusR42EU6baTAFZhxnjsrN3A0Tn80ij8YtyBLOl1RG7GMAfMM+1hhYl/zeNhFS28Z/rCTSMhBXv4IVdmjfDhcxC9r1jGaNPGnpVeTa53YE35zy6NUOyNsnbOS9YceYzjsJpE009xSh6bKlGSNUeoKUGyLU+MKIisaTluMq3b/J6mMgSKPD68rRI7XT1pVGE2auGr3fzLcV0hixIujeISzluyi3Bk8qRkzmxtWsWXOSjbVrwYgyxqlOmeYHZ/7PJWO6AnP8+Ouh1F1iY3lTUQzCjARoW8NumkZzyOSMs861mXMkGdJTn4cSpnwJU30xyyMJk1s7N5E+Micx7q1fQvhpJWv1LSRURWCMTsua4wvzN1LfUkP7573xSmR2ftq13J7RRMbyycyMO6vXctzi75Jti3CN+bvxesNYDKlKMkaw5ew4U8ZUWSNcMqEIuls7y/DZshQ6x1nWWEf87LGUCQNVZfoHCgmHbWSVdNDbt4YC3KH+GXNuhM+fjZDmtsrmkhrMpvqV/N+bwUA+fYwbuP0/fcn6rayidd6NGslrU3UuUhlDDy36Juzjvt49Pz2iqYp0XOAW9oeYd9YHqMhNzB1z/pRg3EL+8ZzJz/O6BLxjGFy/qMR8OjHzu9D9asxG9KMhtwsyx2lL+JkKOid/HpPxEWZPT553R677hzLR9kBNa4w42EXiqxR6hlnQWEfpxX3kOsMUpM1itmYpsjjI8cVBMBqSGOUdFRdQpF0YhkDeZYksqLhyA5QV3eYl8+4YlrkXhCEU8cpd4P+9NNP09/fz7XXXjvrY5LJJKFQaMo/QRAEQRAEQRAEQfhXdkrdoB86dIhvfetbnHnmmaxYsWLWx91111243e7Jf6WlpZ/hKgVBEISZfK99C4fDTs5568/T+j2fTDe3PMol23+HP2GdjEbe3PIor/WXcNabf+H1/mKu2/cUPym7ecbxt7ZvmVLF/c7KDSTUiWjZ0TFpXWLPWB4vt9fR5c/hwRn2o/6o82G+3bp1cg1PzL0JTYekJuM1pRlJGFl18HHuqJi+b/qOig28PVhIKGWmJ+hlb285u1sacDkiXNJwgAU5IxRYknz5w9/w5Q9/w8HARC/VeMaAw5gC4Ks7f81/Ha5jNOoklTKRXzgMQLnbh8sS49BACYGog9fPupydY7m831fOyy2NDI7l0BnIptIR5YG6NYyF3Hzw7jJa311MYDQLpyXOy2dcwb3VJx5t/UcqtEU5q7Kdi+Yc4PWzLieeNnFguIhA1IHXEj+hOX5xZO1pXWJj9yaSmjR5br/XvoXRhJnOsGPGcwMwEDez5kgleQB/ysj32rewsXsT+Uci665Zosh7fFkseeUVUqqBXSMFvNtbQXtfKf2jeQyH3aTUj35Vu6XtEdLaxBpvr2jiO22PcNXu/2TvaD4Nf3qH3oFC0hkjb3dVU+Ed44sVHRz05dARdqLpEje3PEpL0M2bg0X0BLJIqQb6onbCaQNdQS+Hmuv45ZPXYHWFqa7qosj2yRkIdoPKg3VraAs58Rgz5NkiFNsjGGUNRdaQJZ2bj/Rr/zS2zlnJE3Nv4ryiQV5Z/lUa8wZpufxMzq1vpiprlMGok6t2/+cJzXVn5QbS2szdFb7dupX9vmzO/Ntfeehj0WyYiKp/9/CWyWyCYMpA+EitgmPZDVOzX0aTJi5673ku2f47do3nkNYlVE1mKOxmKOJiXu4QubboZC/7o+6pWs/Ve56Z7EzQGXbwt4Fidg8Vc/rrL5HKGOgczwPgzL/9lUTKxP6hYg4MFnP+O3/i3ZE8vtW6lbQmkdYkvtW6lTXN2xgdy+bBv1xCLOSgvHDwhI6bIJyK7qrcwG8WXsfvFn2DLXNWTmbZnKijNWBOZafMDfrQ0BCXXXYZbreb3/72tyjK9FSwo374wx8SDAYn//X29n6GKxUEQRAEQRAEQRCEf7yTWiTuqGAwyJe+9CUCgQBvvvkmRUVFx3282WzGbJ59L5ggCIIgCIIgCIIg/Ks56TfoiUSCyy+/nNbWVl5++WUaGxtP9pIEQRCEv9OPTuH2etGMgdMK+tlXeT6RuJWBUJB7lPXI0kRq6kztq47a1riCIkcIjy3KcMjPWMIKgNOg8pOym0mpqckCdMCMKXd3VGxgfpafXPsArQ3LCYSGuOnASzzWuIIa7xiKrHFZ1cW81DXzGn7c9TDPLfomsYyBa/c+w73V63BbYsTTJi7+4PdTHmtRdB6oW8Oqg1unfP5HnQ9zb/U66mNW+kfy2D5cwFca96MYVAI9NgyyiiJrFNviRNNGRhJGXu0r4Wc9EynVBZYkF777R+6tXscl23/H/bVr+U7bI9xbvY7IDEXQPgvRjJHtXVXMK+inpqKLkeE8YmnTZFus2WxrXEGJM4jFmEKW+vhL1lX0RSbS0N1GdUpbr4+35zrWxvImlmb7eLf8izhtMVJpIz2+ido4z8y/gflFXRyoOJeXWmdPWnxy3o3s9dv4UefDbKpfzeff/wN3VGygzB6fVuyswh7nibk3cdOBj9ZjN2R4dsF1mI1jGJQM117y34SHsxkazqPMEcZjjfF6fzEAZkUjx5zi2r3PcF/tWkyKxrdbt/LM/Bs49+3/4oMLLibi86AYVIpnKXh41Kb61ciSxvKyThSDiifLj7NgDINtoh1fbDCHv763/LhzzDZvXdYwDmuckaCHUMrC9pF8KhxRFpd34LTHsPrT3Fm54bjfcx6sW0Otx4fX3oGqyZTZV9IXtWA1aOi6NFnc8HvtW7ijYgM3t8w817bGFaT1NH9ZdhW94RR5RwoxbmtcgQ4UO0L4ElPbvmk6k+tzGlS+dWR7S73HR2nuCImkGWPSMmXMT8pu5vsdU78PWRSN73ds5qXTv8b+L55HfkkHC8veRY2bGKmqY98BEwuKemkZLuTOyg1UOiI8Oe9GIEJ72M5vFl5HJG3EFwnx/7vkJezFY0T7c3hu0TdPeIuAIJwMt5VNL+z4YN0aVF3l6j3PnPA8Pym7ecrP95mKQZ5qTuoNuqqqXHvttbz77rs8//zznHnmmSdzOYIgCIIgCIIgCIJw0ki6rusn68lvueUW/u///b9cfvnlXHPNNdO+fsMNN5zQPKFQCLfbDSjAzEVABEEQhH++zQ2r6I1Z+HnPQ9xRseGUaeXzwpJrqC3qw+EKY7ImMZhTjPUV0DVUOC36/HEP1q1hXs4wJkOGvGwfNmcEkz1OJmEinTQxOpLLaMhNWyCLb7VunXWeB+rWcFpBPwZFxWjIkFcwgtUbQk0akRWNTNJEy6Fazn37v2Ycf1/tWjKaxK1HIqq3lTVhN2icWdjP/rH8KYW4Hqxbg82QIZAyTYkE/2Hxtai6TH1BP9Xn7AQgE7ES7s+ju7uUaNKCImtc+O4fua2siQrHRJG1rogVj0ml1h3AbkryzlDhZOTy7qr1GCR9cl2fpaMRltvKmvCYVM4p7cJhi7Gzu5LWkANJgo3dM0dLtjWuwGVKUps/iMsdwmKPoZjShMe8pFImDvSW0x1x0ugdJ54xcsWuZ2ddx68WXE9jQT/ebD8WewyDLUE6asXiDaGmjAQHc/n9rqVTzsVRz8y/AaOsTUaEnl1wHY2F/ciKRihi5/3BkinZGUf9smbdlPl+t+gbzC3txmBQsTqi2PN8xEa9HGytxWZKkswYCSesDMVtrGveNpn9sLlhFUlNpsAa53DISY0rjMuU4JLtv/vEKOuT827k/Ln7cBeNYs7zgyaR8jvR0gai4x76+4p4f6CUbx/nfTGT5xZ9E4Osss/n5cddD0/5XvLamV/BaY3xTm8F35nhuBy1sbyJM/JGKcsdwWaNYbYlMFqT6KpMOmlicKCAXYMlrGvexjPzb+C6fU8ddz11+QPY7VFsrii6JhEJOQGwOaKEAy7+drh+SjHJe6vXoUjgMKaRYbKI4L3V6yixx7AoGa7Y9SwP1q2Z9n3jtrKmKdfuE3NvIssSp7pgAEnSsTuj2Dwhoj437zTPJZA0Y1ZUVh18nNvKmpjnDTEUt5JUZW5t38LG8iaKrEkumnMAV64PozVB18FaXuuswZcyHDd7SBBOpo3lTRgkfcrvEr+oXkdCk0/pjL1PRwdUgsEgLtdEgdeTGkHfvXs3AC+88AIvvPDCtK+f6A26IAiCIAiCIAiCIPyrO6kR9H8UEUEXBEE4NdxXuxZF0j91xOyf6b7atZNR0M6wi0hawapoXH/uWySiVnoHCmeNWn/cE3NvwqxkKHYFKC0axOYJY7LHScfM9LZXsPjlV9ncsGpKJO2oYyOeW+espD9uIduUZmlhH7Kk0zaWRyhtmrUl1UP1qzHJGmuat3Fn5QYWZvkoyx4lErfSFchmOG6dnP/uqvUA/KBj8+Q+2IfqV+Mypqj0jlOYN4LRnMLqjhAe8xIKuujz5TASszMYt2A3qHyn7RGemX8DdmOKr+78NR9ccDHJtJFdI4VYFJWRhOmUyZA46vkl15BrD9Puz+HG/U9OizLP5Oj5uqNiA/XuMKfVtCDJOvqRtlzjfi8vHK7BouiTe5Y/7t7qdZMZBI81riDfFqWqYID+sVzGYnY85gRjcRvX7/8oSntX5QZ+2DlRVyCtyYwmLHy7dSt/Pu3rjCeslDiDdAe9jCTMfL9j86zrPxoJb7n8TDKqgRcOzeG6ZR8gyTq9fUXEUuYp+/HvqVo/Zb6n591AIGVGlnQUSZ88r5+0v/voXF+oOky/P5s9vizOKezntf4SKuxxihyhT8xO+Xv8ZuF1lHrGGQ67+erOXx/3sbdXNGGSNX5w5PVublhFuTNISe4obk8ASdbp6SnhvYFSVJ1Zs0DurNyAy5ghkjagSDpLckcozhnFYMwQjdoIRB28PVg04/H6RfU6jLKO1ZAh25xA1WVcpgT+pJVv7H36E1/v0etk65yVVLj9mJQMkZQFmzGJpssMhF2kNAWTrHL9/qcmz9sDdWuwKSr5tuhk28vnl1yDDIwnrMzNH6A/kHXczBBBED4L0yPop0ybNUEQBEEQBEEQBEH430xE0AVBEIT/sdvKmpjjCRNNGyf3Wp4qHqhbM2NEv+XyM3F6QmRSRtq6y+kKek9o7XdXrecHHZt5oG4Nlc4Qo3Ebc3KHqKrpJBpw4g94eL6tdsaq33dVbiCtS1OqyD7WuIJVBx8/7j7Y18+6HIcljqbJnP76S7x25lfI8/o5NFCCzZii0ONj32AJN+5/Evhoz6zLHSKVMtE3nE9lSR9Z5QOYvGGSPhdtu+byTm8FC3KHGY44KXYFyMvykUqZ2NNXxvX7n2LLnJXMzRkGQJY1NE3mtb4ynAaVEnuEK3f/CpiIVH7WlXFvr2ii0hGdfM0wca7Pr2jH4w0QDjsZHM/+xGruH7d1zsop18E7534Jlz1KKGpn13DRjHUGts5ZCUzsM/5lzTosiorLmGI0acFpyExmPRwbYX2scQW1WaOMxxwMRh1cWNeMzRFjeDiXXYMlLMwfQNVk3hsoJZxR+GnXJrbOWUlak2lqeZRN9auJZBRK7VHKPD5KS/oxO+JE/S4OdlbhsUZx2mJkVAWXM4LBmCaZMNM/moeqyeR7/CTTRl7uqiKlyRRaE6z72PU/23vnqAfr1hBXZewGlUJbFEnS+erOX7OxvInF2T4ymvIPrxT+QN0a5mWPYjakOezL5aYDTxx3faNJ4+Re7qPX6dH32uaGVZMV6P0hJwfH8qdlv9xXu5Z6j5942sgun5ef9zzEvdXrsCracWtOHHu+/7T0amqL+gBoGyihJeChMcuHzZjkncHiycyMrXNWYlJUPKYkqi4RyxixG1NU5oxgt8foG87j1b4S7AaNlCYxxxOisbgHlzeIbzSb3vFczIY0w1EnZkWlzDtGRlWIpcx0HMnGOLqmE8kuEQThsyAi6IIgCIIgCIIgCIJwShI36IIgCIIgCIIgCIJwChA36IIg/MM9ULeGLUdSPoX/HX7Ws4nOsIORhBmYKIx0V+WGk7yqCeNJI3dUbODOj62n/oV3+fDAXP5r9xKGo05CaeO0sffVruX+2rWTHz9Yt2ay4NS3W7eyz5fFqoOPs3e4CN9wDjZXBJs1jvKxn653VGzgVwuup8YVnpIK/kDdGtKazLbGFbOmt29uWEUqY6B1tIBk2kj/DfOYN/8gLneIhqI+Fs89iMsZRpF0NpY3TbzmhGWieFxfMcGQk76wm96BQgK9BSR9Lsx5fhy2GOeWd6DrEmUeH6GEDV2X0DQZg6zxxNybKHGEMRnTBOJ2BoJeJEnnC+WdzM8Z4crdv+K+I8fms05vB1A1uHH/k9xTtZ67KjdwW1kTZlmjYzSfUNCFxZzEqGROeL7bKyaO3ZrmbVOu3cO+XEaDHlKqYcaU5nur1xHNGEioCgAxVUYCUprCLW2PTKbLOw3qlHGrDj5OMmMk3xnkjNIukkkz/QMFpDMGzqtpwWjIMBZx8d3DW/hp1ya2HEm9V2SNd879ErXecS4o76TM48NqTuIuG8JeMIbZmsBjnUg3bx0uYjTkxmyNY7IkSaVMDEZc+BM2Puyt4M2eShZk+TgtZ4wSR5iXTv8aW+asnLzmP6nY47dat+I1pbm55VEMsoaqydxdtR5dB6OsUZ41ymONK074HJyIaEbhwnf/yDsDJcd93G1lTXyrdeuMbfZaQw4A1h96jJbhIkYDHmIp84zFHUNpAy0BL1fu/hUyOpsbVmGSdVT9+FsqU6rEfbVrubtqPdG0ke7hAtoGSjDKKpXOMA5TAh0J9ZiNpmuat3Hj/idpDngwKSodYTvl2aP4Ik4SSTOFOWMsyx3HpGjkmNNI6Az5shnoL+TtrmoMskpvyENH2I6mS4xFXOwYKqY/7KYha5Qia2LyuUR6uyCcusQNuiAIgiAIgiAIgiCcAk5qH3RBEP793FbWREZTWde6lY3lTTNGL4R/T8e2oPreLO2KTgZZghxzmqZj2pdtql9NU8uj7Pd78ZjSs0avb2l7hLsqN0xGV7/VuomN5U3o+kTWwNHol6pLdAwV4Q25SGUM0yLKP+6aaHs0ljBOtrm6rawJi5LALGsEUiZ+Ub1uxuN2NKr39LwbkCSd7Qfm0hVxMi9rjMLscYb6C3ilo4ZFuSMMxqwA2A1pzvzbX3n9rMspLe9lzpm7MHgiSEYVPSOTGMymZaCEUu84VlOSRNpEWpM50FvOlbt/xS+q16HqErm2KPuHilF1iUDKyOGQi7OK+shxBXl+yTWMxDM8u+A6fEnLrO3h/lk0JP609GrKc1owm5P4gm56A9l0hl2oukwoZSKSOfFfc0yyxsbyJgosKZpaPrqWHcYUA2EX2ixFaG9t38JD9asZTpjYWN6E3ZDBZUoRzximFM/TPjZuW+MKLnrv8Smf+0nZzeRZUsxLm8hxBXFZYsBEJkd/zMBtZU3YlAgjERdVucOYTCmstjiJuIWEz43BlObpt8+m0hkho0kcDLi4oDjKyHAeoyE3l2z/HXdXrSejSZhkne93bJ78Pv3isivpCHppanl0StbIJwmkJjJPVF1CkTVSmozLqGI2pNk5UEpS+/tjQX9YfC1lWWOYjGnSGQOhmI3uYGJKW7vZfLxI491V61GP1EU+9udSKGVC1V1EZ8igAbDI2mQ0y2NSj/w3RXCWx8/2/Md6dsF19AS99MZsM7Yq/H7HZt4qupSr5/cjSTp5BSPY83wkA06iXVXYFJUcSxyjrDEWczASdTAne4SxmJOEqkyZ856q9ZOZENuOZDPcUbHhlGuRKAjCR0QEXRAEQRAEQRAEQRBOAaLNmiAIgvAPc0fFBqqcUYIp02ceUZ3NxvImqp1RSpxBsp0hZFkjEreSURUSaRM7R/P4fsfm47YK21S/mrGkEUWCbHOKtCZP2Y98tFXarxZcTyhlmnEvK0xEBPf6vfy0axO/rFlHoS2GxMSP4Wv3PvOJr+WBujWkVJnvtW/hlzXruLjuEBZzknjcwuHhQpzmBGZDmv6Qh86IgyU5o1iNKUoKhjDbElicUYyuKDveOJPayk4yaSPJhBmTKUU0aufAQAlGWSOhGjDKGlnWKLousWM0D7Osk9QkTssdYekZH2LN95EY8dK6p5EXOqppcIW5fv9T3FbWdNzo4T/KxvImzikYoix/CEnWsVgTGIxpQn43vSP5x22vdntFE2W2OCsPTkSw765aj92gIgOJI8f3zsoNFFiSrGnext1V6ym1xRiMW6dFbo9mRDw570Zu3P8kW+esJMcaI5Iy4UuZJ6OXmxtWTbsubq9ootQWJ6EqhNMGvn+kvsGBS8/FYk7iD7poGSnApExkWaxr3sbmhlXMzxvEaMiQk+0jq3wAoycCwNCuevqH89F1iSx3iO1dVdRlj6BqMpGUhS9tf44tc1YynjSxLG+YeNrEoYAHs6JR4w7QWNmBmlGIJ6yM+L1c+O4fP/E8PFi3BouiMidnGFWTCcTthNMm8mwRLMYUY1EnCdXAN/Y+faKnli1zVlLrHcdjjxCO2fDF7aiajMucoLxwkHDYQftYPgcCruNmaf2yZh2BlMLPejZxf+1aTLJGQpO5pe2Rycf8onodNoNKvjVGX9TBd4752sc91riCXGuMaNpIZ8QxWY8COG7G2LHviccaV3B6RQcmU4pgyMnOgVLWH3qMOyo2YDdo5Fvj2Axpdvu8fLGik7KyPqzeELIykYORjpnpaqvCbEwzEvQwEHGh6xIJVSHbkmA8YQEmsmhiqoF4xsB32h7hibk3EUwbJ6/Ho9+zBEE42USbNUEQBEEQBEEQBEE4JYkbdEEQBOEf4vaKJoptCeIZA9GMcrKXM2lj9yYq3D5qKzvJKxrGm+OjvLyXirJeNF1ifvY4T8678RMrkVc5o7iMGbItCYrtExHLo3vTpSP/3xOxzxo9f7BuDZoucXF550QUNHuUqqxRjLKG05TiL8uu4p1zv8RtZU1Txh1bff7brVsn96l/9/AWojErqiqTzhhwW+IMRFyEk1bKPD7meAJc/MHvCcTtVD73IW/vXUg6biYTsXHml1/BZE0yMJRPTskQWWVDvNxeR3PQxZW7f8V1+54imjbitUfwJ2y4jRkSqswPOjYzGHXi78sHg4qtepCcLB9fq20lmpnYkzvbXu1/tI3dm9g9lkvdH9/j93sX8qedS2nvqMBkSmExpoCJCuszcRkzKLLGk/Nu5NkF15FrTlFgjVNoi1JgjXN/7Vp+1Pkw0YyB+2vXUmaPYTVkMCsf30kO4YzCO+d+CRmdOys3oAFFbj+5tiiNWWNsnbOSB+rWUOEKTBv7065NaLqE25SajJ7fX7uWeMJMLGbFF3Fy/f6nGIlb8SdN3F21HrcpycHRAlpGCmjtLufR57/MgVeXM3agmrzGDuob2qirO4zJlKLUFaAnmMWBsXy+tP05/rD4WlKqwher28h2hrAaU2R0iW+3bmU46qCrrxhvyTDebB8dgSxeP+vyiWNd3jRt7TCxN77eO06F289IxEVx/jChlInxpJnKogEqynpJawpWQ/pTnduUqjAUcfGntjo+GCpi51gWV+7+Ff6Eje3ttZjNKUrcPjym2av0P7fomyQ1iZ/1bGJzwyqcxjSVrgBZphR3VGyYfF1pXcJhTCNLOhZFnTbP3VXreXbBdWydsxKrIYOqSxS7AtS5QgD8+bSvA7DAG5hxHXdXrafQmpr8eNXBx0mljMRjVoIxO6YjHRMcRhWDPHF9hVJm8i0plr/xIrub59DfWklfSxVjXcV0tlZTWDREQXk/CxfuQ0bnpgNPoEg6JlmlyB4h25JAQ0IGxpMTdRjsxjQXVrXxyvKvsv3CL+AwfrpzIginio//jPx3JG7QBUEQBEEQBEEQBOEUIKq4C4LwT/FZ7UMVTh0noxf2ibi3eh1O22FaOqrYO5aHIul8p+0R/rD4WuaWd5FKmZBHc2cd/8ZZX6a+7l3sBWMYHHG0uJnIQA5v2i4jnBzjAdMa7MY4xVaJCrcfOmaeJ61LnLlgL+6qfpZ4oqgRC8lRD/WyjpY2IJtThAdzqPHnTBn3oyPV8e+uWk+lI0Jf1D65RzqcGMKgqITiNj4cycdtSiOjE05YSRypYH75zl+zdc5Krt6zjT8ZrkaWdBY3HsRdMUC1JuGc20Oq30uuOcEtbU9NVsgeT5owGjKcXt/MwFA+b/SVARO9x3fkfR7L/mpkRSOrdAiA7PGJHssyn11pG4M88VxHo88P6GuIJi0UZ4/x4rIrGYvHZxynahJn1zWTVTqEyR2Z/Hx81EtoJIv2vtIj2RETmQOFjhAHx3OJpKf/2vTznof4as3nufSCN7FVDCKZVNSAncSIl0zKyPJcP5mEiaHWiinjjtY8WNO8jQfr1nBv9TrCaQWjnMLliKAoGq5EjD8svpYrdk3sG76naj3+pIWmlkd5oG4NobQJi6xhtSSJRWxEd82h+rL3kHLtZI23UdCey/tvnIkiTRyn0YSVGrcfsymFwZhhMJBFtnkiutsZsbLy4J8YqJ5L9sI2rp/XTrSrkBfTV3LJ9pnf243eceY3NuMsH8JYHEZymikztIBBgWCE5GEnNSO59PuzT/ykwpT6Dsf+LOkMO1B1iaKAB5c1Rp07MOscbnOcm87ey7euyUVW/oimymQiNhIhO7Gwg4vKvkg4MUx70IvTmOKKXc+yqX71tHl+0LGZDy64mOo5bZg9YQyeCFrcTHQgl53ZF+F09jK2tIqew6NTxv2k7GayzWkKrRFCaRMwkQ3zo86HGQl6yHUFUTUZqyFNW8g5rar6T8pu5p6q9fRHU2TZwwAU1XRRdukOKM5BSiTItGSoax3mNwuv4+o929gyZyVGSUeWdCo94wxF3PyseRsAiYxCftkAVafvJdybT48/h/tq12KU9CnHWxBOdT/r2cTtFU14jJlZa0b8pOxmft7z0AnNdyp2HBIRdEEQBEEQBEEQBEE4BYgbdEEQBEEQBEEQBEE4BYgUd0EQ/ilEertwKrijYgOl9jBtI4WkVJlwRsF2pBBUc8DDFbveB2DrnJWzzqHpMromkfQ7QZMxuiN45new/PQ2MMAXADKQGXXQ/cG8Wbd3+JMG0kkTscFsGMzGXjyGtXwY2aKCEbSAEX9PITfuf3La2Hur11HuiBBImUhpE39bPyNvmIIsH7G4haGIixJ7jM6wg3XNv5sy9hfV61jTvIXbypr48oebeKxxBamEGWNhiOwloI/qpMN28u0Rnph7E76Uzqb61SQ0nQFfNomUicO+XJblDfMLaR3fa9+CL+Lkr29W8+Ouh7m7aj3LCwYZiVsBsBumF1L7Z/hlzTpuaZva8uzbrVt5qH41dnMCty1Kd9g949jvtW/hc5WfQxlUcekSJncE2ahirxjEUd1PkbyXczUJZB09aSTen4vnUDVvdlfNOJ+mycjGDGrIhmTKoGRFcRRHkQwSekyDATexI8fnqGO3hDiNaWTJQKk9SpnHRyplYjzkoiOQzaqDj3N7RRMuY4YCa5T+qG3ytR61idU0tTxK9zWLCe0tJzSSRcHiFiRFRdMlImkjd1RsIJ5RGY45MIyr9IY8rDz4OM8uuI4XllxDV2SiaFgk6ETaW4PBMpH6rsizn09Vl7FmBzGVR8BqhmQa3ZdESyjoaQOBzmLSGQOdYdesc3ySY99LR7cyPGhcgyLpk++FmWi6RDppIhV0YLTHkY0ZzDkBrMWjZJnTlAB63MSZQQfxcTdv2i7jg6GZi1saFBVrrh9DThhkMOSlMdUl8V7QB8k06V47oX1zp4z5ec9D3F21nmv3PsN9tWsBOK+oj31152M0DtEyUMKOsewZ03C3zllJNJOeTN+9Pd7EPE+A6piFzLCN2E47liwVNWljx0ApC/MGASbb8NVnjaLqMoOxiWtloihelL6OMvLjZux5Pi448z0i4x4Ods58TQvCqeho6vonbak70fR24JRLbwcRQRcEQRAEQRAEQRCEU4KIoAuCIAj/tn7c9TBPzrsRXZcYiFum/NX9h50Ps7G8CYuisaZ586xz+OJ2Cp84yK8WXI9JVlFkjZG4DYuisrikm5L6DixFYyiuGDnFQ5w3lstdygZ+2PlR0adfVK8j1xKjp7+IWGclsqST3qkQSppJqgYG4hY+X9GBpsncX7t2WuGbW4+0Vts6ZyU/7HyY3y36BsGkxEhvOWZFxaJkGE1YSWpTW5w91riCVQe38MuadXz38MRrX3XwcbbnfwHTuw2MDuVht8dQVZnPv/8HNjes4pa2R7izcgNpTWL7aC7zVQMlziAXvvtH7jrS8m0o6sR0pECbRdG44J0XJqOER6Oc/2wxVWbLnJWsO1IECyYKrw3EJYZ6yjDK+rTCW8d6o7sK90Aa66GJVl0WJcNQzI7NkGF+YR/F5X0Y7XEMtgSSouL1BsgZTkybZ2N5E5reyc73lmI3JwgnrERSZsbiNtY0b+PlM67AYYkTSVinjT2qN2bFZcyg6jKhuI03+sootcVYdfBxNjesIpDSJiMqLtP09liBtIGN5U109A6wf/cSLq4/SPNrZ2A2p9g9louqS6Q1afK62lS/Gn/KyMbyJtpCOjnmNEW2GADtg8V86Y/v8cKSa9jj87IkZ3zWdX9p+3M0553NyDun4Y/bybGHycvyMTyezSt9pVw7bx9v9paT1v/+1nv3VK2fvKYeql/NzS2PnlBRM3/CSvmvd/H0vBsIp00U2yP0RyeKzAFcUNNKXskgZlcEgzlFljtE9scKNB6Vyhg49PZSPhwopdwZxJ+wouoy84t6cXuCSPL0woi3VzTxg45N3FO1nixTgrfOuRS3PYLRmOa9jtqJIowzjNtY3sSa5qkRvZ92beKh+tV0Hq7k7dcv4KsLdtHZUo0iazRkjfFybzl3V63HY8yw/tCj3F+7lpQmcWv7Fp6YexNDcR1fykz3eC7v9FTyrdatbCxv4vyiATLqqdMSUxA+yaeJjP8rExF0QRAEQRAEQRAEQTgFiAi6IAiC8G/txv1P8vS8GyYjZz8puxmbovHDzoeRpYk2SrO5t3odV+2eiF73ROxYFI20JvG9IxFt9sNLvq9hNU7s1y3KG+Wi955nY3nTlHmMsk6ONcb7g8V8r33LZFRP1SUCaQM/6nyYBwxrMB5pAXesY1vA9EQtbKpfjcvsI9sZot+fTTxj5Krdz3J7xfRWMasOPg7Adw9PrPe2siasBo3dQyk6xnO5du8zbKpfTSSj8NyiSq7a/diRqPvD3FGxgWJbgu6wi7yMkR2f+zztY2F+teB6cm1Bkkf2/6bUif/eMku7m38WlzFDuTPIjs99noyqEE1aCCSChFMmVh553cdzdL33/v/bu/P4KKt78eOf2Zckk52EJStLwo6URSw/QKVqVagFhMoatgBVvFq1tVctlsq19lrp9coiyKZoVXC7tlbr2ioohE1AgSCQQMi+TpbZ5/n9McmYmEkIhJAxft+v17xe5nme88yZOfMdOXPO+Z7eizFpfOusK5xaHj6xmb84F5FcFovdo8XjVWHSujlRFU68qfkIuloF7+akoCgQoffQK6SW83Vmat2+kckvS2OJMjj9bRHIQ2eeZVXKEsJ1Ls5YI7g6voDCGgsvDprNrKNbmly7qn4WQ2NalcKjuevZGjKPeJONf54YgNurJkTrppvRQcbX23i6fobD+rQF6NReepptJIZXMGH32+y7biIl1eG8NuwOHG7fe7GvNAq9RuHmfTtbrPcfUzN56xj0NNuYc/QFXhkyE7dXw5HSbjyau55HcwF2tdwILfhTaibdzTYSLJXERX7F1KGjcLm0aLRf8XHUZPKqwwPmamhsxuGXAJh1dDur+yzmq8oIAMwaL3effI4nPYvplZdImd1IN5MNewsjyU/2XszV/9rIhvT5ZB7fwsb+GVQ5db5ZLUd8s2NCtB4qnLom5Rpm66hVCnO/ep4N6fOJrrETZaplQLcCCqwRVDmMzZ6vpfWwXkXFp+eSiDU6eO+rISw7sZl1aQtQAR4vqNWw9MRmHk9ZQplDxdCoSgBMWjcmrYcal5Zal84/++DR3PWQ2+pbKIToJDKCLoQQQgghhBBCBAHpoAshhOjSNqTPJz60ml8MO0jxwn4s/8mHzBq1hw9G38bvctbzeMoSXh4yK2DZhrXf4FtbXevWUO1uOtJ2vsbCmcpoDhZ3Z883/dg6YF6zUbC7Tz7Hzft2oq8fqT1VE4rdq8ZaP3oOUOXUsvTE5mZ16GFy8OKg2QBEGtwsPbGZL0tjcbm1JMUU461fxtqQnb7x625YM/5I4jIA+lhq6R1W02TddqFdz/2nNuLyqtkyYB5lDn39tTXM/3obY3rlMm7oIXr2ymf88IP85Me7ibVUEWu0+d+XzuD0qAkz2oiKqKRXz3wG9T/ONUO+JC2miP/tt5AViUsvfBN8bVxo13Nn9ib/mvV7Tj5Hsc1Moc3ErKPbUVAxPKYk4Ch4pN5FlN6NRu2bqVBmN3LPyedw1ecD0KkVXC1kG39p8GxeGjybx5KX8NCZZ/EqKobG5RNqtBEfasWjNC/30Jmm6+r/lJpJUmgtABlfbyPcYGN59ibu/WYjmce3EG20sS5tgX9mRoFNj1Hjpn+3Avok51Dz2xiS+uQwNP04gxJyiLdUAr7s6U5P62vHHzy9gQdPb6DU7hsJzq8LodJmxu1V89bw6Xw8ZnKr5VsyPLaYq/tkk5pwDr3ehcejxmiyYzA4GJB+gvTYQl4cNJsN6fPbdD+XV+Wva6XLN3n0/lMbOVsTwp3Zm9Br3EQbbZyta54nwO5R88KgOWQe38LK5KVYG0bP6913aiM2j6ZJzgnwrUF/Z8Q0UsOqAcg8vgWvoiLMVIdKpfCzA6+2OGq/qX8GWwbMY0XiUh5LXsLbw6ejU3u579RGVCqFZfXfE8tObKZ/dDG/P7ueX5/ewJ9SM/EC43oUUGo3se+6iZTZjaRFlDM0uoxIo73Z7B4hRPCRDroQQgghhBBCCBEEpIMuhBBCCCGEEEIEAUkSJ4QQokszad0kxhdiDquhvCiGOpuJ0JBahg49SsXIJNz2jzif26t+K7Jvp66uTF5Kd5Mdq1PnTwpn96j4/dmm09fr3Fo8igqrS0O5M7TFJE8Ay+sTNLm8Kioc2ib3cnjVPJbsm5LeeHuwzONbOHD99RyMv44Txb5p5fed2siblhkcrYhgcr8THIq7lkPnnf4yf0rNJCGsjCFxNWwyZmDSVrM+bQED43JRqRQO/eRa9uf5prI31LeXpQK3VwOVUQCEG+wcnHgdqUOPYkoqRh2jA0soVFRhiKim8MPI+oRy21jdZzGO+mnEV4pe4+VIcXfG/Ptd/tJ3EfecfI5/jvo5A/udpEd8EUXFsfSPmMUvDr/YrOxjyUta3YINfEm53PXT1I9UhNM3rIYViUubtf/y7E1s7J9Bz5BaVvdZzNITvs+KUr/04O6TzzWbbv+n1EwsOjdDE06i07roWx3GwzlwqsaMXhODQeMhvUceCXFFvKa7g6mH/grAmn4L6RXqmzJdVBeCSqXg9rpxedVkT76aquowKmq/3Y5sdZ/FJMdm07dHHv+KvhWr3UyY8TyKoiI2pozIlHz0iRUYR7tBrSbqqzO4P/2Rv54mrZfVfRZT6dQ0e92N1bp94z33frORlwbP5t5vNvLJNZMoqwtt9T1uSZXDRG5Bdw6UdPMvodjUP4OFx7byj5FT+dGQI/ToWcD5vB5wvHn5R5OWkhRio8hu4MHTG5osw3A12oqw4fjBsijGdCsmMsAWdr3Mdoz1y0cab9PYWOPvDfAllgMPUSHVhBjt/HPUz7lh7xvUunQUVEZh0LrIuX04tbXfsFU9j7w6Y5PPY/eQGqqdenqHKYQb7JyqtjAoqpR/jvo5R8qM/s/vhvT5ONyVnJ0xDJdTT0l5LoqiIq8qimijnZS0UwyZ8jF4wVUUQfHx5FaT/gkhgoOMoAshhBBCCCGEEEFARtCFEEJ0aZVOPaXlkRzJSaHUbuJcnZHEEBspRZV0jy4jJKQWt0eDSqU0KadXe9GpFFLCqnkkcRl/OLsu4CiiTu3lzuO+rbD+Ur+VVSAb+2dQZDPwcM6z/OHsumbnzRovKpXSbBR6Tb+FREUdRK32ElkV4T/+jdXCDclnsIRbcdgNGLVu/zmjxkthbSg51nBG9DiHyeAgKrYMp92A2VKD26kjsiQO8G0TlRhayzflOpLCK0gIq2JVyhI83nJyy2KJOldB3rE+uFxaLGHV9BxzBE2InTCjjaLSWF4eMou82gu3w+XWMBsBvt0ybW9xN8KMNnrEFxHXrQSHSxew7HdHz7/bHisSl6LgpKo+odijuetZmdx89Bzg6b6L0KlclDsMVLk0rExeyu9y1mPSev3XhOmaJvAL0XpQAI3aQ2S3MnQ6V/12er56fD7uJrQaN+WV4eTXmZuUdXs1mHVOhsTlc74qisTIUjxeNZVWC927F6Ep9nK4LAbwjezu7v5TenYvpE9EDsWF3ThT2g2nV0PesYGMduhRDqvolpJHSGoB6jCF0NAa1qctYOmJzRjUin90vDWN30+nR8O6tAWcs7pIiSi7YNmWFNWGNhn5PlfnS0SXYw0n9HhfwkNqqbY3T+oGkGC2o1EpGNRKs3ON27ohrhu2HHs8wBZ2LW3Z19osDI0KLDoXoSYblnArKpXCyuSlZHy9nkcSlzE17ThejxqzqQ51/ffOmn4L/Qkoi+sc/CghB4PBgd1uJL3nORRFhU7npqQuhFlHtwO+2TUfjL6N8wXxWG1mDpbF8ODpDbw8ZBYp0cXYq0MwnI/E2K8CfUglUfZ8Xhw0m/N1ZlyKqlnCQSFEcJARdCGEEEIIIYQQIghIB10IIUSXtjx7E++fTWbKoZfJPL6FP5xdR4VDz8Q9bzLwnU9J3nGAT8+m+EdhG/i2ZNIx5dDLhGibjoA2rBV/uu8i9OpvR0q/qr2D1QAAHv5JREFUe4/GFEVFH0tNs+Mrk33rk3uabcQZHc3O35m9iWprGC6nvsm2W/ef2ojTrSX3fA/2nenN2ZoQ/7lat4ZzdSaMGg8er5qQkFrUGi+W2HIUrwqXQ4/VaWBT/wwGR5di92jI+HobJ8pjmLjnTVxeFaEGOz0jytm2dzR1Nt/opdOpx1lmQaXxkj7kGLcNOErPsErSIyqv6Ppz8K3j/q6Hc55l9/kE/v31IP55ZCjnrJEtlv9jo/KN14ivSFxKhN6DXu0lSv/trISW1h9rVArRRhuKoiLa4CLe6GB1n8Xo6kdGXxt2BwMiK5qUuTN7E8tObMZaE4qtOgSnU98sd0FuUTwjP34fRfl2zXS1W0N2lQWPV43Xq2Z472wSk8+S2ucMyb1zUOrXVyeE1PLioNm8MmQm+4t6cPibvryzfwRvnexX/3l2cao6hHePDaKwLJqP/j2W8kN98ZSHEJWcj1Hj4bVhd6BRKTyc82yr688fS17CC4PmsLrPYgAKbEYqnTpSIsqa1L2t/piaydRDf/WPEjdoeH+WndjM/9v1d4b88xP2FsUHvIfVpcPlVTdbGw6+eGuI37BGcb0yeWmzrdLA9zlb02+h/+8ViUt5PGUJerXCo0lLWd1nMW9eNaNJmXu/2cjCY1spqYrAbjPicmv9n58/nF2H26Mht6AHKa/tp8zhm1VzZ/YmHjy9AZtbjV7joazagkqt0LN3Lt1S8ujeNxe90UFObQgvD5nFJ9dM4p0R0yioDeNkWTe+qYr0x+AvDr/IieJ4em4/yrnDaZzceTW2Y7Fo9C6iTDZ+fXoDFp2bVSlLWp31I4ToHNJBF0IIIYQQQgghgoBKUZTmC3S+Z6xWK+Hh4YAGuPhfa4UQQnRdKxKXolLRanb11rw1fDoDEnOxRFaiNTrxOHW4nTo8bi0etwaXS0dJWRROj5YJu99u8T6b+mfw//oeJyK23H9M8apQ6zx4XRpqKsP55PgAFh7b2qzsK0Nm0j8+n6raEI6WxrHsxGZeGTKTSKMNs97BkZJ4VMDSE5sDPvfTfRdxd/3o/nsjp9ArtoTI6HLUGi/W8nD2nenDzCNNRyz/9qPbcXvV3HbwFV4ZMpMZh1/if/st5OqeZ4mKqMIcVoNW76KyNIrT53tyY9brF//mtsOfey9Grfo2g7ZvDXfb2nh92gKGd88jJqocY4jNn3/A5dDjcupxOnXY7EYcLh2KoqKwOpwph15u8X5vDZ9O/15nCQ2vRlFUeFxatHoXGq0bp91ISXEMn51N8bdBg5cGzya9WwFOt5aPzyZj1HiJ1DvpZq6lqC6EKpeuWbb/xh5PWcJvzzzL4ylL6GZ08KNeufRIPI9KpVBWGEtuUXyzdvnffguJ0DsJ0bo4URXuHzX+fNxNpPY+g8FSi9elpbo0knP53fmioCf3n2o+Eg3w+rBfMCgxh+ieRWjqM6BrzXY8Th3W/G58/U3vy/a5CJRBvzVbB8xjaI9zREZWojM48Xo0uJ1a1BovDruB2joztXYjlbYQSmxm5rew1hzg7eHTGZh8GoPZDvjiVqVW8Ho0OO0GSsojufpf7wWsw5Duedider4s7k643olZ60Kv8RBhquVcVSQzDr/U4vM2tG+Dk7eNJiYxHwBrYQwlpdEcLOjF4gDfGQ3r68HXtjFRFYRaqjFGVFNbGkFFWRRVtSGcLI9t9bULITqaAnioqqrCYrEAMoIuhBBCCCGEEEIEBcniLoQQokvzouIP9dmxG/bLhraNuK7usxiNqpLT53uiyu+BxVRHpMWKwehAZ3Ci0boxmOxYIiuxVkTw3sgpFNSGBcz8rFYplFeFcywvkbiwKsJDa9Dqvl3jXFYREXD0HOBYlYVuITXER5UxxlxHdtrV1NkKcLp07D6fiFnjIbM+k3wgd598jlUpS4jWu7gxazOfjb2ZsLBqPG4N5VXhzUbPAU5bw0ms33P760rfr/q1bg0jP36fNf0WkmKp4uZ9O3lp8GwK6gJn0+5IOrWCRqX4R/qtzjr+mJrpX/t9XwujvuCbafCCbg6ny2OJNdcQZrRh1DsxGHw5ADQaD2FhNUSoPTgcBhzuwNngATakzyfKVEx+aSz6ikjCzHXodC5UdQpqjZfKKguHC3s2Gz0HyK0JwenpRe/IUm7t59vQW1FUlFVbKLJHXXBdv6N+zbndo2Lhsa3sjvkpn+0fjsurJjGinLwaS7Myy7M3sbrPYs64Q5qsqz9RGseJ0jgsegdlDiN9I8sorLG0OHoOYNS68Xg0FOb28h+rsIah1XjQa93tGj1/YdAcdPX5HWJMtRi057g6bipVDmPAve2/SwG+KYnHWB6DVuMh3FiH2WjH61VjMtoJDa0lNLSWCHsNpspI/tx7ccDPzPq0BUSaCykqjUGj9mI22jGZ7KjVHrR6N3aHgcNFPQKO8Gd8vY1XtDNJCK9gQp8TqNVe6mwmbA4DH+clXjCL+m/PPNskW7ytzsS/Pr0GrdpLt7AqiqvDA46eA/68GY+nLOF0eS1nK6OIMddS5zSQEF0CgN2ll9FzIYKQjKALIYQQQgghhBBBQDroQgghhBBCCCFEEJAkcUIIIbq8t4dPJ85SSVhILSazLymYtcpCQUUUFXZTq4mawLf1U8N047/0XUSY1o0XKLUb/EmcXhg0B41KCThdHGDH0JmcrzNzz8nn+GNqJga1QndzHdnWsBa38Grs0aSljO5WQoSpFq3Gg9lkp6QygoPF8dz7zUY29c+g0qlrdWp3Q+KoTf0zOG8z8ruc9U0SyDXWWlKuhi3KrvTWao09P3AuqZGlRIdXode7UKt9U3prakLJLe3Grft3sCplyQWnEYMv4Zxe45tOXevWNHtdLw+ZRYXDwLIASfgaPht/TM2s32rNSbXLt4KwtbZo8GjSUqINvgRrvS1VGLQuqh1GDldE8ruc9axPW4Ddq251C78GWwbMY/7X29jYP4NSuwEg4NZhLdnUP4NCu4GHzjzb4ueiJU/3XYRJ6/ZPuV7TbyF3Zm9qc/nGnh84l34xRURarL7lAvXLFjRaD16vmprqUAoropi4582Lum/j6eKNl7i8MGgOlU49ywPUd3WfxU22a1uftoBKl5YwrYc7szc1+W4I5JHEZUTo3Ji1HpLCrESaayirDePW/TtYn7YAo8ZDsd3Ar9sQS88PnMvcr55nfdoClp7Y3CSOW7MhfT6Zx7c0SzonhAgGkiROCCGEEEIIIYQISjKCLoQQostb028h0UY7Jq0Lq9NApMFOfHgFADV2EzmVUQETu7WmraOzgfwpNRONSkGBVpNwXeh516UtoNypu6R6NB5NvJjnDFaPJS8hzuSgX2Qpeq2bSlsI31RFBBwVbUlrW/K15f1albIEi859Uc95OTVOgngp/tx7MXavus1t/WTvxXgVVcDR3wuNLLdmU/8MPIoqYOLD9WkLSI8uwebSc67a0mpyxLZamby0TbNYGjSeXXIx2/u113dnNfwpNZNfn95wwS3oLnY2hBDiSpIRdCGEEEIIIYQQIijJNmtCCCG6vEtdC9uSFYlLeejMpY+a2b1qvErgkdoLaTy6GWhNdFupLmLCWbCPngNtng3QmtZGIdty/4fOPMuTvRe3ux6XSlHaN4tQrbq4tm5t9kd7RoBa2m4QfFvkXW4XM3rum0nx7fVXavQcoM7T9F3VqX2TYFv73AL+NfxCiO+HLtFB/3aWvnwBCSGE6HgOr5P2/D/H7nXiVWjXPdrL3s7XIALrzPe1vc99OeveVT9fwdS+ba2LzdM120KIrsEXm41XnXeJNeh5eXkkJCR0djWEEEIIIYQQQoiLcu7cOXr16gV0kQ661+slPz+fsLAwVBczZ08EDavVSkJCAufOnfMnSBDfP9KOXYO0Y9chbdk1SDt2DdKOXYO0Y9cRDG2pKArV1dX06NEDtdq3jKVLTHFXq9X+XxzE95vFYpEvuy5A2rFrkHbsOqQtuwZpx65B2rFrkHbsOjq7LX27kX1LsrgLIYQQQgghhBBBQDroQgghhBBCCCFEEJAOuggKBoOBFStWYDAYOrsqoh2kHbsGaceuQ9qya5B27BqkHbsGaceuI1jbskskiRNCCCGEEEIIIb7vZARdCCGEEEIIIYQIAtJBF0IIIYQQQgghgoB00IUQQgghhBBCiCAgHXTRoRwOB7/5zW/o0aMHJpOJ0aNH8/7771+w3KOPPopKpWr2MBqNV6DW4rtqampYsWIFN910E1FRUahUKrZu3drm8pWVlWRmZhIbG0tISAjXXnstBw4c6LgKi4Da045bt24NGJMqlYrCwsKOrbhoIisri7vuuouBAwcSEhJCYmIi06dPJzs7u03lJR6DQ3vaUeIxeHz11VfcfvvtpKamYjabiYmJYdy4cbz99tttKi/xGBza044Sj8Ft1apVqFQqBg0a1Kbrz58/z/Tp04mIiMBisfCzn/2M06dPd3Atm9Je0WcTPzgZGRns3LmTe+65h759+7J161ZuvvlmPv74Y8aOHXvB8uvWrSM0NNT/t0aj6cjqihaUlpaycuVKEhMTGTp0KJ988kmby3q9Xm655Ra+/PJLHnjgAWJiYli7di0TJkxg//799O3bt+MqLppoTzs2WLlyJSkpKU2ORUREXJ4KijZ54okn2LVrF7fffjtDhgyhsLCQZ555huHDh/PFF1+0+o8Qicfg0Z52bCDx2Plyc3Oprq5m3rx59OjRg7q6Ol577TUmT57Ms88+S2ZmZotlJR6DR3vasYHEY/DJy8vjv/7rvwgJCWnT9TU1NVx77bVUVVXxn//5n+h0OlavXs348eM5dOgQ0dHRHVzjeooQHWTPnj0KoPz3f/+3/5jNZlN69+6tjBkzptWyK1asUAClpKSko6sp2sButysFBQWKoihKVlaWAihbtmxpU9lXXnlFAZQdO3b4jxUXFysRERHKHXfc0RHVFS1oTztu2bJFAZSsrKwOrKFoi127dikOh6PJsezsbMVgMCizZs1qtazEY/BoTztKPAY3t9utDB06VElLS2v1OonH4NbWdpR4DF4zZsxQrrvuOmX8+PHKwIEDL3j9E088oQDK3r17/ceOHTumaDQa5be//W1HVrUJmeIuOszOnTvRaDRNfnU0Go0sXLiQzz//nHPnzl3wHoqiYLVaUWQ3wE5lMBiIj4+/pLI7d+4kLi6OKVOm+I/FxsYyffp03nrrLRwOx+WqpriA9rRjY9XV1Xg8nstQI3EprrnmGvR6fZNjffv2ZeDAgRw7dqzVshKPwaM97diYxGPw0Wg0JCQkUFlZ2ep1Eo/Bra3t2JjEY/D497//zc6dO/nLX/7S5jI7d+5k5MiRjBw50n8sPT2d66+/nldffbUDahmYdNBFhzl48CD9+vXDYrE0OT5q1CgADh06dMF7pKamEh4eTlhYGLNnz6aoqKgjqio60MGDBxk+fDhqddOvm1GjRlFXV9fmdbMiOFx77bVYLBbMZjOTJ0/m5MmTnV0lge/HzKKiImJiYlq9TuIxuLW1HRtIPAaP2tpaSktLOXXqFKtXr+Yf//gH119/fatlJB6Dz6W0YwOJx+Dh8XhYvnw5ixYtYvDgwW0q4/V6OXz4MCNGjGh2btSoUZw6dYrq6urLXdWAZA266DAFBQV079692fGGY/n5+S2WjYyM5K677mLMmDEYDAY+/fRT1qxZw969e9m3b1+zTr8IXgUFBYwbN67Z8cafg7Z+eYrOYzabycjI8P8DZP/+/Tz11FNcc801HDhwgISEhM6u4g/aiy++yPnz51m5cmWr10k8Bre2tqPEY/C57777ePbZZwFQq9VMmTKFZ555ptUyEo/B51LaUeIx+Kxfv57c3Fw++OCDNpcpLy/H4XBcsO+SlpZ22erZEumgiw5js9kwGAzNjjdkYrfZbC2W/Y//+I8mf0+dOpVRo0Yxa9Ys1q5dy4MPPnh5Kys6THs+ByJ4TJ8+nenTp/v/vu2227jxxhsZN24cq1atYv369Z1Yux+248ePc+eddzJmzBjmzZvX6rUSj8HrYtpR4jH43HPPPUybNo38/HxeffVVPB4PTqez1TISj8HnUtpR4jG4lJWV8bvf/Y5HHnmE2NjYNpdriLdgiEmZ4i46jMlkCrh+ym63+89fjJkzZxIfH39Rv4aJzne5PwcieIwdO5bRo0dLTHaiwsJCbrnlFsLDw/15P1oj8RicLrYdA5F47Fzp6elMnDiRuXPn8re//Y2amhomTZrUag4dicfgcyntGIjEY+d5+OGHiYqKYvny5RdVriHegiEmpYMuOkz37t0pKChodrzhWI8ePS76ngkJCZSXl7e7buLK6YjPgQgeEpOdp6qqip/+9KdUVlby7rvvtimWJB6Dz6W0Y0skHoPHtGnTyMrKanUducRj8GtLO7ZE4vHKO3nyJBs2bODuu+8mPz+fnJwccnJysNvtuFwucnJyWmyTqKgoDAZDUMSkdNBFhxk2bBjZ2dlYrdYmx/fs2eM/fzEURSEnJ+eipquIzjds2DAOHDiA1+ttcnzPnj2YzWb69evXSTUTl8Pp06clJjuB3W5n0qRJZGdn87e//Y0BAwa0qZzEY3C51HZsicRj8GiYCltVVdXiNRKPwa8t7dgSiccr7/z583i9Xu6++25SUlL8jz179pCdnU1KSkqLOT7UajWDBw9m3759zc7t2bOH1NRUwsLCOvol+OpyRZ5F/CBNmzYNj8fDhg0b/MccDgdbtmxh9OjR/qQZZ8+e5fjx403KlpSUNLvfunXrKCkp4aabburYiotLVlBQwPHjx3G5XP5j06ZNo6ioiNdff91/rLS0lB07djBp0qSAa31E5wrUjoFi8p133mH//v0Sk1eYx+NhxowZfP755+zYsYMxY8YEvE7iMbi1px0lHoNHcXFxs2Mul4vnn38ek8nk/9FF4jG4tacdJR6Dx6BBg3jjjTeaPQYOHEhiYiJvvPEGCxcuBAL3PxpmTDTupJ84cYKPPvqI22+//Yq9DpUiG0yLDjR9+nTeeOMN7r33Xvr06cO2bdvYu3cvH374oT9z6YQJE/jXv/7VZH2P2WxmxowZDB48GKPRyGeffcbLL7/M0KFD2bVrF2azubNe0g/WM888Q2VlJfn5+axbt44pU6Zw1VVXAbB8+XLCw8PJyMhg27ZtnDlzhuTkZMD3j9CxY8dy9OhRHnjgAWJiYli7di1nz54lKyvrimTDFN+61Hbs27cvV111FSNGjCA8PJwDBw6wefNmunfvTlZWFnFxcZ34qn5Y7rnnHv7nf/6HSZMmNUlM1GD27NkAEo9Brj3tKPEYPH7+859jtVoZN24cPXv2pLCwkBdffJHjx4/z5z//mV/96leAxGOwa087SjwGvwkTJlBaWsrRo0ebHPtu/6O6upqrrrqK6upq7r//fnQ6HU899RQej4dDhw5duRkRihAdyGazKffff78SHx+vGAwGZeTIkcq7777b5Jrx48cr3/0oLlq0SBkwYIASFham6HQ6pU+fPspvfvMbxWq1Xsnqi0aSkpIUIODjzJkziqIoyrx585r83aC8vFxZuHChEh0drZjNZmX8+PFKVlbWlX8R4pLb8aGHHlKGDRumhIeHKzqdTklMTFSWLVumFBYWds4L+QFr+M5s6dFA4jG4tacdJR6Dx1//+ldl4sSJSlxcnKLVapXIyEhl4sSJyltvvdXkOonH4NaedpR4DH7jx49XBg4c2OxYoK7wuXPnlGnTpikWi0UJDQ1Vbr31VuXkyZNXqqqKoiiKjKALIYQQQgghhBBBQNagCyGEEEIIIYQQQUA66EIIIYQQQgghRBCQDroQQgghhBBCCBEEpIMuhBBCCCGEEEIEAemgCyGEEEIIIYQQQUA66EIIIYQQQgghRBCQDroQQgghhBBCCBEEpIMuhBBCCCGEEEIEAemgCyGEEEIIIYQQQUA66EIIIUQQy8nJQaVSkZyc3NlVaTev18uIESOIj4+ntrb2ku+zfft2VCoVa9euvYy1E0IIITqfdNCFEEKITpScnIxKpSInJ6ezq9LhNm3axP79+3nkkUcICQm55PvMnDmTwYMH88gjj1BeXn4ZayiEEEJ0LumgCyGEEEGsZ8+eHDt2jA8//LCzq9IuNpuNhx56iB49epCZmdmue6nValasWEF5eTmPPfbYZaqhEEII0fmkgy6EEEIEMZ1OR3p6Or179+7sqrTL9u3bKSkpYe7cueh0unbfb/LkycTGxrJp0yZqamouQw2FEEKIzicddCGEEKITbN26FZVKRW5uLgApKSmoVCr/45NPPgFaX4PecC34OsCjRo0iNDSU2NhY7rjjDs6ePQuAoig888wzDBs2jJCQEGJiYsjIyKC4uLjF+mVnZ7NkyRJ69+6N0WgkPDyccePGsX379kt6vc888wwAGRkZAc+fPHmSBQsWkJKSgsFgIDQ0lKSkJG655Ra2bNnS7HqdTsfMmTOxWq288MILl1QnIYQQItioFEVROrsSQgghxA/NZ599xnPPPcfOnTupra1l6tSphIaG+s8/+OCDpKenk5OTQ0pKCklJSc3WqTd0zh988EGefPJJxo0bR1RUFHv37uXs2bMkJCTw5ZdfsnTpUv7v//6PCRMmYDKZ2LVrF8XFxQwZMoSsrCz0en2T++7YsYO5c+dit9tJT0+nf//+VFVVsWfPHmpra5k/fz6bN29u82s9c+YMqamp9OrVi3PnzjU7f/ToUX784x9jtVpJS0tj4MCBaDQa8vLyOHLkCL179+bQoUPNyv3973/n1ltv5YYbbuC9995rc32EEEKIYKXt7AoIIYQQP0Rjx45l7NixfPLJJ9TW1vLkk09ecqb2jRs3sm/fPoYOHQr41nvfcMMNfPbZZ4wfP566ujqOHz9OUlISAKWlpYwZM4bDhw+zY8cOZs2a5b/XkSNHmDNnDiqVitdee40pU6b4z+Xm5jJp0iS2bNnChAkTmDt3bpvq99FHHwEwZsyYgOefeuoprFYrjz32GA899FCTczabjaysrIDlxowZg0ql4rPPPsPpdDb7oUEIIYT4vpEp7kIIIcT33MqVK/2dcwCTycSvfvUrwNfhfvrpp/2dc4CYmBiWLVsG0Cz53KpVq3A4HDz22GNNOucASUlJbNq0CYCnn366zfU7ePAgAP379w94vqioCICbb7652TmTycS4ceMClouKiiI+Pt7/A4QQQgjxfScddCGEEOJ7LlDHtm/fvgBotVpuuOGGFs/n5+f7j3m9Xv7xj38AMGPGjIDPNWLECEJDQzl48CB2u71N9WvogEdHRwc8P2rUKACWLVvGe++91+b7Nr5nw3MIIYQQ32fSQRdCCCG+5xITE5sda1jP3r17d7Ta5ivawsLCAJp0hsvKyrBarQAkJCQ0SVrX8FCr1dTU1OD1eikrK2tT/aqqqgCwWCwBzz/wwANMnDiRPXv2cNNNN2GxWBg5ciT33Xdfi9PbGzTcs6Kiok11EUIIIYKZrEEXQgghvufU6pZ/b2/t3Hd5vV7/f8+bN++C1xsMhjbdNyIiAsDf+f8us9nM+++/T1ZWFu+++y67d+9m9+7d7Nu3j6eeeopf/vKXrFmzJmDZhs5/ZGRkm+oihBBCBDPpoAshhBAC8K1NN5lM2Gw2nnzySWJiYi7Lfbt16wZwwRH3kSNHMnLkSADcbjdvvvkmc+fOZe3atUybNo1rr722WZmGe8bFxV2WugohhBCdSaa4CyGEEJ2oIfO42+3u5JqARqPhJz/5CQCvvvrqZbvv8OHDAfj666/bXEar1TJt2jRuvPFGgIDbrJWVlVFYWIjZbG4xAZ0QQgjxfSIddCGEEKIT9erVC4Cvvvqqk2vis2LFCvR6PQ888ADbtm1rMu29wdGjR3n99dfbfM+Gke/PP/884Pm1a9dy4sSJZscLCwvZt28fQJMs9A12794N+Las0+l0ba6PEEIIEaykgy6EEEJ0oqlTpwIwe/Zspk6dyqJFi1i0aFHADuuVMHz4cLZv3w5ARkYGSUlJ3HjjjcyePZubb76ZhIQEBg8efFEj7CkpKQwZMoTz589z7NixZuc3bNhAeno6qampTJ48mdmzZ3PjjTeSmppKXl4e1113HZMnT25W7oMPPgDgtttuu7QXK4QQQgQZWYMuhBBCdKJly5ZRXV3N9u3beeedd/xZ1WfPnk1aWlqn1On2229n5MiRPP3007z//vvs2rULj8dDXFwcffr04a677mLatGkXdc+77rqLzMxMtm7dyhNPPNHk3KpVq/j73//OF198wRdffEFVVRXdunVj9OjRzJ8/nzvuuKNZJnqXy8VLL72ExWJhzpw57X7NQgghRDBQKYqidHYlhBBCCNG11dXVkZycjFarJScnx7/2/lK99tprTJs2jXvvvZennnrqMtVSCCGE6FwyxV0IIYQQHc5sNrNq1SoKCgrYsGFDu+7l9Xr5/e9/T1RUFA8//PBlqqEQQgjR+WQEXQghhBBXhNfrZdSoUeTl5XHq1ClCQkIu6T7bt29nzpw5rFmzhl/+8peXuZZCCCFE55EOuhBCCCGEEEIIEQRkirsQQgghhBBCCBEEpIMuhBBCCCGEEEIEAemgCyGEEEIIIYQQQUA66EIIIYQQQgghRBD4/zgcqwSiOvb1AAAAAElFTkSuQmCC", + "text/html": [ + "\n", + "
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8:37:41 (2.6s long)\n", + "index 413: 8:53:05 - 8:53:12 (6.9s long)\n", + "index 415: 8:53:21 - 8:53:24 (3.4s long)\n", + "index 416: 8:53:31 - 8:53:32 (1.5s long)\n", + "index 450: 9:03:23 - 9:03:32 (9.3s long)\n", + "index 453: 9:04:31 - 9:04:37 (6.2s long)\n", + "index 457: 9:10:35 - 9:10:42 (7.0s long)\n", + "index 470: 9:14:35 - 9:14:40 (5.5s long)\n", + "index 475: 9:16:27 - 9:16:34 (7.0s long)\n", + "index 494: 9:23:01 - 9:23:05 (4.2s long)\n", + "index 508: 9:28:25 - 9:28:30 (4.6s long)\n", + "index 509: 9:28:33 - 9:28:36 (3.4s long)\n", + "index 510: 9:28:55 - 9:28:59 (4.0s long)\n", + "index 511: 9:29:38 - 9:29:41 (3.6s long)\n", + "index 517: 9:32:08 - 9:32:14 (6.5s long)\n", + "index 518: 9:32:20 - 9:32:26 (5.7s long)\n", + "index 519: 9:32:35 - 9:32:38 (3.2s long)\n", + "index 521: 9:32:49 - 9:32:52 (3.1s long)\n", + "index 522: 9:33:09 - 9:33:17 (7.9s long)\n", + "index 523: 9:33:27 - 9:33:31 (3.9s long)\n", + "index 524: 9:34:03 - 9:34:07 (3.7s long)\n", + "index 525: 9:34:11 - 9:34:16 (5.5s long)\n", + "index 537: 9:37:34 - 9:37:38 (4.6s long)\n", + "index 539: 9:38:18 - 9:38:23 (4.8s long)\n", + "index 541: 9:38:51 - 9:38:55 (4.5s long)\n", + "index 543: 9:40:36 - 9:40:40 (3.9s long)\n", + "index 544: 9:41:03 - 9:41:08 (5.4s long)\n", + "index 545: 9:41:29 - 9:41:32 (2.5s long)\n", + "index 547: 9:42:37 - 9:42:40 (3.4s long)\n", + "index 549: 9:43:08 - 9:43:12 (4.2s long)\n", + "index 552: 9:43:55 - 9:43:58 (2.4s long)\n", + "index 556: 9:44:31 - 9:44:34 (2.4s long)\n", + "index 558: 9:44:51 - 9:44:54 (3.3s long)\n", + "index 560: 9:45:29 - 9:45:34 (5.4s long)\n", + "index 581: 9:49:27 - 9:49:31 (3.7s long)\n", + "index 588: 9:50:58 - 9:51:03 (5.1s long)\n", + "index 590: 9:51:21 - 9:51:26 (4.3s long)\n", + "index 591: 9:51:27 - 9:51:31 (3.9s long)\n", + "index 594: 9:55:25 - 9:55:29 (4.0s long)\n", + "index 595: 9:55:49 - 9:55:55 (6.0s long)\n", + "index 597: 9:57:28 - 9:57:29 (1.4s long)\n", + "index 598: 9:57:34 - 9:57:37 (2.6s long)\n" + ] + } + ], "source": [ "for i, r in bout_df_final.iterrows():\n", - " \n", - " if i > 345: # overnight stim\n", - " break\n", + " if i > last_bout: break\n", " \n", " hr_start = int(r['file'][-19:-17])\n", " hr_end = hr_start\n", @@ -517,44 +598,7 @@ }, { "cell_type": "code", - "execution_count": 36, - "id": "85d75a43-4b9c-4cae-adbb-06ce66cb5f76", - "metadata": {}, - "outputs": [], - "source": [ - "with open('/mnt/cube/chronic_ephys/der/z_y19o20_21/2021-10-27/bouts_sglx/bout_curated.pickle', 'rb') as f:\n", - " bout_df_final = pickle.load(f)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "2a776ca6-912a-4aeb-8892-1d380aa5bd42", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['start_ms', 'end_ms', 'start_sample', 'end_sample', 'p_step', 'rms_p',\n", - " 'peak_p', 'bout_check', 'file', 'len_ms', 'syl_in', 'n_syl', 'peaks_p',\n", - " 'n_peaks', 'l_p_ratio', 'waveform', 'confusing', 'valid_waveform',\n", - " 'valid', 'spectrogram', 'start_ms_ap_0', 'start_sample_ap_0',\n", - " 'start_sample_naive', 'bird', 'sess', 'epoch', 'is_call'],\n", - " dtype='object')" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bout_df_final.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, + "execution_count": 67, "id": "40708862-b1b1-4058-b875-9249df6dbf28", "metadata": {}, "outputs": [ @@ -579,437 +623,623 @@ " \n", " \n", " \n", - " start_ms\n", - " end_ms\n", + " file\n", " start_sample\n", " end_sample\n", - " p_step\n", - " rms_p\n", - " peak_p\n", - " bout_check\n", - " file\n", + " start_ms\n", + " end_ms\n", " len_ms\n", + " waveform\n", + " fem_waveform\n", + " spectrogram\n", + " sample_rate\n", " ...\n", - " valid_waveform\n", " valid\n", - " spectrogram\n", " start_ms_ap_0\n", " start_sample_ap_0\n", " start_sample_naive\n", " bird\n", " sess\n", " epoch\n", + " bout_check\n", + " confusing\n", " is_call\n", " \n", " \n", " \n", " \n", - " 8\n", - " 187128\n", - " 189488\n", - " 7485120\n", - " 7579520\n", - " [12.668582973409952, 41.513719119620895, 59.29...\n", - " 6.615156\n", - " 134.106723\n", - " True\n", - " /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1...\n", - " 2360\n", + " 45\n", + " /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08...\n", + " 88356760\n", + " 88673640\n", + " 2208919\n", + " 2216841\n", + " 7922\n", + " [471, 486, 469, 502, 489, 472, 453, 528, 544, ...\n", + " [-255, -238, -251, -283, -266, -251, -247, -24...\n", + " [[0.1069199546870063, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", + " 40000\n", " ...\n", " True\n", + " 2208960\n", + " 66268458\n", + " 88293400\n", + " z_r5r13_24\n", + " 2024-08-07\n", + " 0949_g0\n", " True\n", - " [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", - " 187131\n", - " 5643920\n", - " 7389000\n", - " z_y19o20_21\n", - " 2021-10-27\n", - " 1033_undirected_g0\n", + " False\n", " False\n", " \n", " \n", - " 39\n", - " 1071263\n", - " 1074653\n", - " 42850520\n", - " 42986120\n", - " [5.338206375397029, 3.0978681633708787, 3.9398...\n", - " 6.804745\n", - " 121.659218\n", - " True\n", - " /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1...\n", - " 3390\n", + " 142\n", + " /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08...\n", + " 306126440\n", + " 306319280\n", + " 7653161\n", + " 7657982\n", + " 4821\n", + " [690, 720, 722, 670, 711, 687, 702, 641, 663, ...\n", + " [-202, -205, -234, -205, -210, -204, -202, -22...\n", + " [[0.04617063032697592, 0.0, 0.0, 0.0, 0.0, 0.0...\n", + " 40000\n", " ...\n", " True\n", + " 7653305\n", + " 229597967\n", + " 306070440\n", + " z_r5r13_24\n", + " 2024-08-07\n", + " 0949_g0\n", " True\n", - " [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", - " 1071280\n", - " 32168339\n", - " 42187320\n", - " z_y19o20_21\n", - " 2021-10-27\n", - " 1033_undirected_g0\n", + " False\n", " False\n", " \n", " \n", - " 52\n", - " 1506162\n", - " 1509293\n", - " 60246480\n", - " 60371720\n", - " [11.552444728799006, 40.35527971348254, 57.706...\n", - " 6.804745\n", - " 153.937348\n", - " True\n", - " /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1...\n", - " 3131\n", + " 153\n", + " /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08...\n", + " 319266360\n", + " 319406360\n", + " 7981659\n", + " 7985159\n", + " 3500\n", + " [619, 598, 635, 569, 599, 605, 559, 618, 579, ...\n", + " [-326, -328, -328, -329, -331, -327, -330, -32...\n", + " [[0.021999940761009317, 0.0, 0.0, 0.0, 0.0, 0....\n", + " 40000\n", " ...\n", " True\n", + " 7981810\n", + " 239453041\n", + " 319266360\n", + " z_r5r13_24\n", + " 2024-08-07\n", + " 0949_g0\n", " True\n", - 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" 1782892\n", - " 53516648\n", - " 71354520\n", - " z_y19o20_21\n", - " 2021-10-27\n", - " 1033_undirected_g0\n", + " False\n", " False\n", " \n", " \n", - " 64\n", - " 2002208\n", - " 2007923\n", - " 80088320\n", - " 80316920\n", - " [1.9347568605351688, 7.7485843417036575, 4.873...\n", - " 6.804745\n", - " 141.213069\n", - " True\n", - " /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1...\n", - " 5715\n", + " 156\n", + " /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08...\n", + " 324018040\n", + " 324186040\n", + " 8100451\n", + " 8104651\n", + " 4200\n", + " [367, 315, 366, 313, 327, 350, 351, 298, 368, ...\n", + " [-272, -295, -278, -276, -274, -271, -278, -27...\n", + " [[0.04861262692254681, 0.0, 0.0, 0.0, 0.0, 0.0...\n", + " 40000\n", " ...\n", " True\n", + " 8100604\n", + " 243016851\n", + " 323998040\n", + " z_r5r13_24\n", + " 2024-08-07\n", + " 0949_g0\n", " True\n", - " [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", - " 2002240\n", - " 60097092\n", - " 80128320\n", - " z_y19o20_21\n", - 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12 rows × 27 columns

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18 rows × 21 columns

\n", "" ], "text/plain": [ - " start_ms end_ms start_sample end_sample \\\n", - "8 187128 189488 7485120 7579520 \n", - "39 1071263 1074653 42850520 42986120 \n", - "52 1506162 1509293 60246480 60371720 \n", - "59 1782863 1787293 71314520 71491720 \n", - "64 2002208 2007923 80088320 80316920 \n", - "65 2010033 2014278 80401320 80571120 \n", - "69 2319316 2322816 92772640 92912640 \n", - "79 2622221 2625901 104888840 105036040 \n", - "127 25835 33745 1033400 1349800 \n", - "157 818867 822846 32754680 32913840 \n", - "177 1509479 1513209 60379160 60528360 \n", - "188 2086656 2089610 83466240 83584400 \n", + " file start_sample \\\n", + "45 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 88356760 \n", + "142 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 306126440 \n", + "153 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 319266360 \n", + "155 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 323347800 \n", + "156 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 324018040 \n", + "157 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 324624120 \n", + "158 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 325398080 \n", + "159 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 331901560 \n", + "160 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 332316080 \n", + "161 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 333613360 \n", + "162 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 334314600 \n", + "163 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 335430280 \n", + "164 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 336185240 \n", + "165 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 359295480 \n", + "166 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 360171640 \n", + "193 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 2924520 \n", + "194 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 14719440 \n", + "196 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 12219400 \n", "\n", - " p_step rms_p peak_p \\\n", - "8 [12.668582973409952, 41.513719119620895, 59.29... 6.615156 134.106723 \n", - "39 [5.338206375397029, 3.0978681633708787, 3.9398... 6.804745 121.659218 \n", - "52 [11.552444728799006, 40.35527971348254, 57.706... 6.804745 153.937348 \n", - "59 [19.734600855974794, 55.380104136620304, 88.21... 6.804745 124.455970 \n", - "64 [1.9347568605351688, 7.7485843417036575, 4.873... 6.804745 141.213069 \n", - "65 [4.194721552563428, 34.957344918072536, 81.087... 6.804745 139.213337 \n", - "69 [3.2451614405857594, 3.9892908524229553, 4.173... 4.810822 149.524894 \n", - "79 [2.362219435555598, 3.7773330065235813, 2.1025... 4.810822 120.014404 \n", - "127 [12.678209512221926, 45.38018960916082, 19.775... 6.670950 221.077734 \n", - "157 [1.8657256449298205, 4.105385240576828, 3.9668... 6.670950 150.351373 \n", - "177 [1.8458635010324818, 1.8480902459641393, 2.793... 4.953947 167.735870 \n", - "188 [1.405343200739505, 2.480036543067559, 2.35920... 4.293242 130.600130 \n", + " end_sample start_ms end_ms len_ms \\\n", + "45 88673640 2208919 2216841 7922 \n", + "142 306319280 7653161 7657982 4821 \n", + "153 319406360 7981659 7985159 3500 \n", + "155 323568320 8083695 8089208 5513 \n", + "156 324186040 8100451 8104651 4200 \n", + "157 324808040 8115603 8120201 4598 \n", + "158 325617240 8134952 8140431 5479 \n", + "159 332095280 8297539 8302382 4843 \n", + "160 332481600 8307902 8312040 4138 \n", + "161 333827600 8340334 8345690 5356 \n", + "162 334561880 8357865 8364047 6182 \n", + "163 335612000 8385757 8390300 4543 \n", + "164 336384920 8404631 8409623 4992 \n", + "165 359460160 8982387 8986504 4117 \n", + "166 360456560 9004291 9011414 7123 \n", + "193 3117120 73113 77928 4815 \n", + "194 14939240 367986 373481 5495 \n", + "196 12347520 305485 308688 3203 \n", "\n", - " bout_check file len_ms \\\n", - "8 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 2360 \n", - "39 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3390 \n", - "52 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3131 \n", - "59 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 4430 \n", - "64 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 5715 \n", - "65 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 4245 \n", - "69 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3500 \n", - "79 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3680 \n", - "127 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 7910 \n", - "157 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3979 \n", - "177 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3730 \n", - "188 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 2954 \n", + " waveform \\\n", + "45 [471, 486, 469, 502, 489, 472, 453, 528, 544, ... \n", + "142 [690, 720, 722, 670, 711, 687, 702, 641, 663, ... \n", + "153 [619, 598, 635, 569, 599, 605, 559, 618, 579, ... \n", + "155 [424, 430, 441, 415, 436, 395, 462, 408, 470, ... \n", + "156 [367, 315, 366, 313, 327, 350, 351, 298, 368, ... \n", + "157 [730, 701, 703, 694, 681, 706, 734, 694, 691, ... \n", + "158 [606, 575, 590, 593, 554, 601, 563, 605, 568, ... \n", + "159 [746, 732, 730, 750, 728, 778, 685, 742, 734, ... \n", + "160 [547, 567, 574, 512, 527, 506, 507, 524, 513, ... \n", + "161 [928, 981, 879, 913, 945, 921, 937, 964, 924, ... \n", + "162 [443, 389, 413, 397, 386, 365, 364, 375, 399, ... \n", + "163 [309, 326, 327, 298, 291, 292, 289, 292, 264, ... \n", + "164 [765, 722, 734, 756, 821, 771, 787, 790, 741, ... \n", + "165 [540, 541, 519, 547, 537, 498, 577, 542, 531, ... \n", + "166 [251, 261, 250, 248, 302, 285, 277, 265, 234, ... \n", + "193 [162, 142, 159, 175, 141, 143, 149, 126, 177, ... \n", + "194 [461, 436, 453, 399, 438, 454, 410, 398, 442, ... \n", + "196 [510, 526, 497, 519, 524, 532, 503, 514, 528, ... \n", "\n", - " ... valid_waveform valid \\\n", - "8 ... True True \n", - "39 ... True True \n", - "52 ... True True \n", - "59 ... True True \n", - "64 ... True True \n", - "65 ... True True \n", - "69 ... True True \n", - "79 ... True True \n", - "127 ... True True \n", - "157 ... True True \n", - "177 ... True True \n", - "188 ... True True \n", + " fem_waveform \\\n", + "45 [-255, -238, -251, -283, -266, -251, -247, -24... \n", + "142 [-202, -205, -234, -205, -210, -204, -202, -22... \n", + "153 [-326, -328, -328, -329, -331, -327, -330, -32... \n", + "155 [-212, -208, -209, -216, -216, -183, -213, -20... \n", + "156 [-272, -295, -278, -276, -274, -271, -278, -27... \n", + "157 [-194, -191, -185, -190, -181, -189, -177, -18... \n", + "158 [-271, -288, -273, -275, -283, -281, -267, -28... \n", + "159 [-290, -257, -271, -257, -266, -270, -253, -25... \n", + "160 [-242, -234, -237, -236, -238, -238, -233, -22... \n", + "161 [-194, -196, -190, -194, -204, -183, -201, -21... \n", + "162 [-268, -273, -259, -268, -267, -258, -277, -27... \n", + "163 [-242, -250, -243, -240, -251, -245, -245, -24... \n", + "164 [-239, -234, -249, -245, -224, -253, -272, -22... \n", + "165 [-268, -270, -265, -262, -264, -261, -241, -25... \n", + "166 [-265, -276, -278, -277, -278, -280, -279, -27... \n", + "193 [-229, -224, -215, -221, -223, -217, -220, -23... \n", + "194 [-166, -173, -171, -175, -174, -205, -174, -20... \n", + "196 [-212, -190, -214, -210, -219, -220, -202, -22... \n", "\n", - " spectrogram start_ms_ap_0 \\\n", - "8 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 187131 \n", - "39 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1071280 \n", - "52 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1506186 \n", - "59 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1782892 \n", - "64 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2002240 \n", - "65 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2010065 \n", - "69 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2319353 \n", - "79 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2622263 \n", - "127 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 25835 \n", - "157 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 818880 \n", - "177 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1509503 \n", - "188 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2086690 \n", + " spectrogram sample_rate ... \\\n", + "45 [[0.1069199546870063, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "142 [[0.04617063032697592, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "153 [[0.021999940761009317, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "155 [[0.034754266281730006, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "156 [[0.04861262692254681, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "157 [[0.020798472369377728, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "158 [[0.053783507904516886, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "159 [[0.10646443110827125, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "160 [[0.0417131455845805, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "161 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "162 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "163 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "164 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "165 [[0.07653305031436872, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "166 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "193 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "194 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "196 [[0.06698295276872104, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", "\n", - " start_sample_ap_0 start_sample_naive bird sess \\\n", - "8 5643920 7389000 z_y19o20_21 2021-10-27 \n", - "39 32168339 42187320 z_y19o20_21 2021-10-27 \n", - "52 45215500 60211720 z_y19o20_21 2021-10-27 \n", - "59 53516648 71354520 z_y19o20_21 2021-10-27 \n", - "64 60097092 80128320 z_y19o20_21 2021-10-27 \n", - "65 60331845 80441320 z_y19o20_21 2021-10-27 \n", - "69 69610458 91998240 z_y19o20_21 2021-10-27 \n", - "79 78697746 104812240 z_y19o20_21 2021-10-27 \n", - "127 805057 498600 z_y19o20_21 2021-10-27 \n", - "157 24596361 32651000 z_y19o20_21 2021-10-27 \n", - "177 45315010 59732760 z_y19o20_21 2021-10-27 \n", - "188 62630574 83468760 z_y19o20_21 2021-10-27 \n", + " valid start_ms_ap_0 start_sample_ap_0 start_sample_naive bird \\\n", + "45 True 2208960 66268458 88293400 z_r5r13_24 \n", + "142 True 7653305 229597967 306070440 z_r5r13_24 \n", + "153 True 7981810 239453041 319266360 z_r5r13_24 \n", + "155 True 8083847 242514164 323328320 z_r5r13_24 \n", + "156 True 8100604 243016851 323998040 z_r5r13_24 \n", + "157 True 8115756 243471416 324576680 z_r5r13_24 \n", + "158 True 8135105 244051894 325377240 z_r5r13_24 \n", + "159 True 8297695 248929571 331855280 z_r5r13_24 \n", + "160 True 8308059 249240465 332281600 z_r5r13_24 \n", + "161 True 8340491 250213439 333587600 z_r5r13_24 \n", + "162 True 8358023 250739375 334314600 z_r5r13_24 \n", + "163 True 8385915 251576146 335430280 z_r5r13_24 \n", + "164 True 8404789 252142374 336164920 z_r5r13_24 \n", + "165 True 8982556 269475292 359273120 z_r5r13_24 \n", + "166 True 9004461 270132420 360171640 z_r5r13_24 \n", + "193 True 73114 2193422 2924520 z_r5r13_24 \n", + "194 True 367992 11039732 14719440 z_r5r13_24 \n", + "196 True 305490 9164677 12084080 z_r5r13_24 \n", "\n", - " epoch is_call \n", - "8 1033_undirected_g0 False \n", - "39 1033_undirected_g0 False \n", - "52 1033_undirected_g0 False \n", - "59 1033_undirected_g0 False \n", - "64 1033_undirected_g0 False \n", - "65 1033_undirected_g0 False \n", - "69 1033_undirected_g0 False \n", - "79 1033_undirected_g0 False \n", - "127 1142_directed_g0 False \n", - "157 1142_directed_g0 False \n", - "177 1142_directed_g0 False \n", - "188 1142_directed_g0 False \n", + " sess epoch bout_check confusing is_call \n", + "45 2024-08-07 0949_g0 True False False \n", + "142 2024-08-07 0949_g0 True False False \n", + "153 2024-08-07 0949_g0 True False False \n", + "155 2024-08-07 0949_g0 True False False \n", + "156 2024-08-07 0949_g0 True False False \n", + "157 2024-08-07 0949_g0 True False False \n", + "158 2024-08-07 0949_g0 True False False \n", + "159 2024-08-07 0949_g0 True False False \n", + "160 2024-08-07 0949_g0 True False False \n", + "161 2024-08-07 0949_g0 True False False \n", + "162 2024-08-07 0949_g0 True False False \n", + "163 2024-08-07 0949_g0 True False False \n", + "164 2024-08-07 0949_g0 True False False \n", + "165 2024-08-07 0949_g0 True False False \n", + "166 2024-08-07 0949_g0 True False False \n", + "193 2024-08-07 1233_g0 True False False \n", + "194 2024-08-07 1235_g0 True False False \n", + "196 2024-08-07 1245_g0 True False False \n", "\n", - "[12 rows x 27 columns]" + "[18 rows x 21 columns]" ] }, - "execution_count": 37, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "bout_df_final" + "bout_df_final.head(18)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 34, "id": "4533d2b8-9887-4d49-bae4-bddf0db77ca9", "metadata": {}, "outputs": [], @@ -1021,18 +1251,24 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 35, "id": "f24c3baf-4366-4a9e-8f8c-a950433e55dd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['/mnt/cube/chronic_ephys/der/z_y19o20_21/2021-10-27/sglx/1033_undirected_g0/wav_mic.npy',\n", - " '/mnt/cube/chronic_ephys/der/z_y19o20_21/2021-10-27/sglx/1142_directed_g0/wav_mic.npy']" + "['/mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-07/sglx/0949_g0/wav_mic.npy',\n", + " '/mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-07/sglx/1226_g0/wav_mic.npy',\n", + " '/mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-07/sglx/1227_g0/wav_mic.npy',\n", + " '/mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-07/sglx/1233_g0/wav_mic.npy',\n", + " '/mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-07/sglx/1235_g0/wav_mic.npy',\n", + " '/mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-07/sglx/1245_g0/wav_mic.npy',\n", + " '/mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-07/sglx/2355_g0/wav_mic.npy',\n", + " '/mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-07/sglx/2631_g0/wav_mic.npy']" ] }, - "execution_count": 15, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1043,29 +1279,29 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 59, "id": "e9fb4106-165a-4cc2-9cc2-9038a63f556b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'/mnt/cube/chronic_ephys/der/z_y19o20_21/2021-10-27/sglx/1033_undirected_g0/wav_mic.npy'" + "'/mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-07/sglx/1235_g0/wav_mic.npy'" ] }, - "execution_count": 16, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "file_path = wav_path_list[0]\n", + "file_path = wav_path_list[4]\n", "file_path" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 60, "id": "f55d7abe-8b4f-4dfe-8f92-95146e418a15", "metadata": {}, "outputs": [], @@ -1080,17 +1316,17 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 38, "id": "4fe618b5-debb-47ce-a887-0cea78bc96c5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "166720513" + "376411488" ] }, - "execution_count": 45, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -1102,17 +1338,17 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 39, "id": "4b983bc3-1af1-4d7f-8594-dc49e0c46473", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "4168013" + "9410287" ] }, - "execution_count": 46, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1124,7 +1360,30 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 61, + "id": "3e70150e-7a03-4198-8cb7-ede2513d9540", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "410945921" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## just kept looping through this when I had to stitch 6 recordings together\n", + "# sample_offset = sample_offset + len(x)\n", + "# ms_offset = ms_offset + round(len(x)/s_f*1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, "id": "cab76a1f-1621-49d1-bd1a-2d54a19e9e76", "metadata": {}, "outputs": [], @@ -1134,21 +1393,43 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 63, + "id": "ee5bdf65-c144-4048-8179-222d0a7be0d6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[196]" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "offset_idx = bout_df_new[bout_df_new['epoch']=='1245_g0'].index.tolist()\n", + "offset_idx" + ] + }, + { + "cell_type": "code", + "execution_count": 64, "id": "ef9c47c6-713b-43d3-9bdb-aeae6e5092ec", "metadata": {}, "outputs": [], "source": [ - "for i in [127, 157, 177, 188]:\n", - " bout_df_new.at[i, 'start_sample'] = bout_df_final.at[i, 'start_sample'] + 166720513\n", - " bout_df_new.at[i, 'end_sample'] = bout_df_final.at[i, 'end_sample'] + 166720513\n", - " bout_df_new.at[i, 'start_ms'] = bout_df_final.at[i, 'start_ms'] + 4168013\n", - " bout_df_new.at[i, 'end_ms'] = bout_df_final.at[i, 'end_ms'] + 4168013" + "for i in offset_idx:\n", + " bout_df_new.at[i, 'start_sample'] = bout_df_final.at[i, 'start_sample'] + sample_offset\n", + " bout_df_new.at[i, 'end_sample'] = bout_df_final.at[i, 'end_sample'] + sample_offset\n", + " bout_df_new.at[i, 'start_ms'] = bout_df_final.at[i, 'start_ms'] + ms_offset\n", + " bout_df_new.at[i, 'end_ms'] = bout_df_final.at[i, 'end_ms'] + ms_offset" ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 66, "id": "375ae28c-78bc-4a9d-9ee6-7f52c71fce09", "metadata": {}, "outputs": [ @@ -1173,426 +1454,612 @@ " \n", " \n", " \n", - " start_ms\n", - " end_ms\n", + " file\n", " start_sample\n", " end_sample\n", - " p_step\n", - " rms_p\n", - " peak_p\n", - " bout_check\n", - " file\n", + " start_ms\n", + " end_ms\n", " len_ms\n", + " waveform\n", + " fem_waveform\n", + " spectrogram\n", + " sample_rate\n", " ...\n", - " valid_waveform\n", " valid\n", - " spectrogram\n", " start_ms_ap_0\n", " start_sample_ap_0\n", " start_sample_naive\n", " bird\n", " sess\n", " epoch\n", + " bout_check\n", + " confusing\n", " is_call\n", " \n", " \n", " \n", " \n", - " 8\n", - " 187128\n", - " 189488\n", - " 7485120\n", - " 7579520\n", - " [12.668582973409952, 41.513719119620895, 59.29...\n", - " 6.615156\n", - " 134.106723\n", - " True\n", - " /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1...\n", - " 2360\n", + " 45\n", + " /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08...\n", + " 88356760\n", + " 88673640\n", + " 2208919\n", + " 2216841\n", + " 7922\n", + " [471, 486, 469, 502, 489, 472, 453, 528, 544, ...\n", + " [-255, -238, -251, -283, -266, -251, -247, -24...\n", + " [[0.1069199546870063, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", + " 40000\n", " ...\n", " True\n", + " 2208960\n", + " 66268458\n", + " 88293400\n", + " z_r5r13_24\n", + " 2024-08-07\n", + " 0949_g0\n", " True\n", - " [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", - " 187131\n", - " 5643920\n", - " 7389000\n", - " z_y19o20_21\n", - " 2021-10-27\n", - " 1033_undirected_g0\n", + " False\n", " False\n", " \n", " \n", - " 39\n", - " 1071263\n", - " 1074653\n", - " 42850520\n", - " 42986120\n", - " [5.338206375397029, 3.0978681633708787, 3.9398...\n", - " 6.804745\n", - " 121.659218\n", - " True\n", - " /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1...\n", - " 3390\n", + " 142\n", + " /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08...\n", + " 306126440\n", + " 306319280\n", + " 7653161\n", + " 7657982\n", + " 4821\n", + " [690, 720, 722, 670, 711, 687, 702, 641, 663, ...\n", + " [-202, -205, -234, -205, -210, -204, -202, -22...\n", + " [[0.04617063032697592, 0.0, 0.0, 0.0, 0.0, 0.0...\n", + " 40000\n", " ...\n", " True\n", + " 7653305\n", + " 229597967\n", + " 306070440\n", + " z_r5r13_24\n", + " 2024-08-07\n", + " 0949_g0\n", " True\n", - " [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", - 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18 rows × 21 columns

\n", "" ], "text/plain": [ - " start_ms end_ms start_sample end_sample \\\n", - "8 187128 189488 7485120 7579520 \n", - "39 1071263 1074653 42850520 42986120 \n", - "52 1506162 1509293 60246480 60371720 \n", - "59 1782863 1787293 71314520 71491720 \n", - "64 2002208 2007923 80088320 80316920 \n", - "65 2010033 2014278 80401320 80571120 \n", - "69 2319316 2322816 92772640 92912640 \n", - "79 2622221 2625901 104888840 105036040 \n", - "127 4193848 4201758 167753913 168070313 \n", - "157 4986880 4990859 199475193 199634353 \n", - "177 5677492 5681222 227099673 227248873 \n", - "188 6254669 6257623 250186753 250304913 \n", + " file start_sample \\\n", + "45 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 88356760 \n", + "142 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 306126440 \n", + "153 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 319266360 \n", + "155 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 323347800 \n", + "156 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 324018040 \n", + "157 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 324624120 \n", + "158 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 325398080 \n", + "159 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 331901560 \n", + "160 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 332316080 \n", + "161 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 333613360 \n", + "162 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 334314600 \n", + "163 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 335430280 \n", + "164 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 336185240 \n", + "165 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 359295480 \n", + "166 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 360171640 \n", + "193 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 388693608 \n", + "194 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 406133713 \n", + "196 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 423165321 \n", "\n", - " p_step rms_p peak_p \\\n", - "8 [12.668582973409952, 41.513719119620895, 59.29... 6.615156 134.106723 \n", - "39 [5.338206375397029, 3.0978681633708787, 3.9398... 6.804745 121.659218 \n", - "52 [11.552444728799006, 40.35527971348254, 57.706... 6.804745 153.937348 \n", - "59 [19.734600855974794, 55.380104136620304, 88.21... 6.804745 124.455970 \n", - "64 [1.9347568605351688, 7.7485843417036575, 4.873... 6.804745 141.213069 \n", - "65 [4.194721552563428, 34.957344918072536, 81.087... 6.804745 139.213337 \n", - "69 [3.2451614405857594, 3.9892908524229553, 4.173... 4.810822 149.524894 \n", - "79 [2.362219435555598, 3.7773330065235813, 2.1025... 4.810822 120.014404 \n", - "127 [12.678209512221926, 45.38018960916082, 19.775... 6.670950 221.077734 \n", - "157 [1.8657256449298205, 4.105385240576828, 3.9668... 6.670950 150.351373 \n", - "177 [1.8458635010324818, 1.8480902459641393, 2.793... 4.953947 167.735870 \n", - "188 [1.405343200739505, 2.480036543067559, 2.35920... 4.293242 130.600130 \n", + " end_sample start_ms end_ms len_ms \\\n", + "45 88673640 2208919 2216841 7922 \n", + "142 306319280 7653161 7657982 4821 \n", + "153 319406360 7981659 7985159 3500 \n", + "155 323568320 8083695 8089208 5513 \n", + "156 324186040 8100451 8104651 4200 \n", + "157 324808040 8115603 8120201 4598 \n", + "158 325617240 8134952 8140431 5479 \n", + "159 332095280 8297539 8302382 4843 \n", + "160 332481600 8307902 8312040 4138 \n", + "161 333827600 8340334 8345690 5356 \n", + "162 334561880 8357865 8364047 6182 \n", + "163 335612000 8385757 8390300 4543 \n", + "164 336384920 8404631 8409623 4992 \n", + "165 359460160 8982387 8986504 4117 \n", + "166 360456560 9004291 9011414 7123 \n", + "193 388886208 9717340 9722155 4815 \n", + "194 406353513 10153343 10158838 5495 \n", + "196 423293441 10579133 10582336 3203 \n", "\n", - " bout_check file len_ms \\\n", - "8 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 2360 \n", - "39 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3390 \n", - "52 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3131 \n", - "59 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 4430 \n", - "64 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 5715 \n", - "65 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 4245 \n", - "69 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3500 \n", - "79 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3680 \n", - "127 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 7910 \n", - "157 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3979 \n", - "177 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3730 \n", - "188 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 2954 \n", + " waveform \\\n", + "45 [471, 486, 469, 502, 489, 472, 453, 528, 544, ... \n", + "142 [690, 720, 722, 670, 711, 687, 702, 641, 663, ... \n", + "153 [619, 598, 635, 569, 599, 605, 559, 618, 579, ... \n", + "155 [424, 430, 441, 415, 436, 395, 462, 408, 470, ... \n", + "156 [367, 315, 366, 313, 327, 350, 351, 298, 368, ... \n", + "157 [730, 701, 703, 694, 681, 706, 734, 694, 691, ... \n", + "158 [606, 575, 590, 593, 554, 601, 563, 605, 568, ... \n", + "159 [746, 732, 730, 750, 728, 778, 685, 742, 734, ... \n", + "160 [547, 567, 574, 512, 527, 506, 507, 524, 513, ... \n", + "161 [928, 981, 879, 913, 945, 921, 937, 964, 924, ... \n", + "162 [443, 389, 413, 397, 386, 365, 364, 375, 399, ... \n", + "163 [309, 326, 327, 298, 291, 292, 289, 292, 264, ... \n", + "164 [765, 722, 734, 756, 821, 771, 787, 790, 741, ... \n", + "165 [540, 541, 519, 547, 537, 498, 577, 542, 531, ... \n", + "166 [251, 261, 250, 248, 302, 285, 277, 265, 234, ... \n", + "193 [162, 142, 159, 175, 141, 143, 149, 126, 177, ... \n", + "194 [461, 436, 453, 399, 438, 454, 410, 398, 442, ... \n", + "196 [510, 526, 497, 519, 524, 532, 503, 514, 528, ... \n", "\n", - " ... valid_waveform valid \\\n", - "8 ... True True \n", - "39 ... True True \n", - "52 ... True True \n", - "59 ... True True \n", - "64 ... True True \n", - "65 ... True True \n", - "69 ... True True \n", - "79 ... True True \n", - "127 ... True True \n", - "157 ... True True \n", - "177 ... True True \n", - "188 ... True True \n", + " fem_waveform \\\n", + "45 [-255, -238, -251, -283, -266, -251, -247, -24... \n", + "142 [-202, -205, -234, -205, -210, -204, -202, -22... \n", + "153 [-326, -328, -328, -329, -331, -327, -330, -32... \n", + "155 [-212, -208, -209, -216, -216, -183, -213, -20... \n", + "156 [-272, -295, -278, -276, -274, -271, -278, -27... \n", + "157 [-194, -191, -185, -190, -181, -189, -177, -18... \n", + "158 [-271, -288, -273, -275, -283, -281, -267, -28... \n", + "159 [-290, -257, -271, -257, -266, -270, -253, -25... \n", + "160 [-242, -234, -237, -236, -238, -238, -233, -22... \n", + "161 [-194, -196, -190, -194, -204, -183, -201, -21... \n", + "162 [-268, -273, -259, -268, -267, -258, -277, -27... \n", + "163 [-242, -250, -243, -240, -251, -245, -245, -24... \n", + "164 [-239, -234, -249, -245, -224, -253, -272, -22... \n", + "165 [-268, -270, -265, -262, -264, -261, -241, -25... \n", + "166 [-265, -276, -278, -277, -278, -280, -279, -27... \n", + "193 [-229, -224, -215, -221, -223, -217, -220, -23... \n", + "194 [-166, -173, -171, -175, -174, -205, -174, -20... \n", + "196 [-212, -190, -214, -210, -219, -220, -202, -22... \n", "\n", - " spectrogram start_ms_ap_0 \\\n", - "8 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 187131 \n", - "39 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1071280 \n", - "52 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1506186 \n", - "59 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1782892 \n", - "64 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2002240 \n", - "65 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2010065 \n", - "69 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2319353 \n", - "79 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2622263 \n", - "127 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 25835 \n", - "157 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 818880 \n", - "177 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1509503 \n", - "188 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2086690 \n", + " spectrogram sample_rate ... \\\n", + "45 [[0.1069199546870063, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "142 [[0.04617063032697592, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "153 [[0.021999940761009317, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "155 [[0.034754266281730006, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "156 [[0.04861262692254681, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "157 [[0.020798472369377728, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "158 [[0.053783507904516886, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "159 [[0.10646443110827125, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "160 [[0.0417131455845805, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "161 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "162 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "163 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "164 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "165 [[0.07653305031436872, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "166 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "193 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "194 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "196 [[0.06698295276872104, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", "\n", - " start_sample_ap_0 start_sample_naive bird sess \\\n", - "8 5643920 7389000 z_y19o20_21 2021-10-27 \n", - "39 32168339 42187320 z_y19o20_21 2021-10-27 \n", - "52 45215500 60211720 z_y19o20_21 2021-10-27 \n", - "59 53516648 71354520 z_y19o20_21 2021-10-27 \n", - "64 60097092 80128320 z_y19o20_21 2021-10-27 \n", - "65 60331845 80441320 z_y19o20_21 2021-10-27 \n", - "69 69610458 91998240 z_y19o20_21 2021-10-27 \n", - "79 78697746 104812240 z_y19o20_21 2021-10-27 \n", - "127 805057 498600 z_y19o20_21 2021-10-27 \n", - "157 24596361 32651000 z_y19o20_21 2021-10-27 \n", - "177 45315010 59732760 z_y19o20_21 2021-10-27 \n", - "188 62630574 83468760 z_y19o20_21 2021-10-27 \n", + " valid start_ms_ap_0 start_sample_ap_0 start_sample_naive bird \\\n", + "45 True 2208960 66268458 88293400 z_r5r13_24 \n", + "142 True 7653305 229597967 306070440 z_r5r13_24 \n", + "153 True 7981810 239453041 319266360 z_r5r13_24 \n", + "155 True 8083847 242514164 323328320 z_r5r13_24 \n", + "156 True 8100604 243016851 323998040 z_r5r13_24 \n", + "157 True 8115756 243471416 324576680 z_r5r13_24 \n", + "158 True 8135105 244051894 325377240 z_r5r13_24 \n", + "159 True 8297695 248929571 331855280 z_r5r13_24 \n", + "160 True 8308059 249240465 332281600 z_r5r13_24 \n", + "161 True 8340491 250213439 333587600 z_r5r13_24 \n", + "162 True 8358023 250739375 334314600 z_r5r13_24 \n", + "163 True 8385915 251576146 335430280 z_r5r13_24 \n", + "164 True 8404789 252142374 336164920 z_r5r13_24 \n", + "165 True 8982556 269475292 359273120 z_r5r13_24 \n", + "166 True 9004461 270132420 360171640 z_r5r13_24 \n", + "193 True 73114 2193422 2924520 z_r5r13_24 \n", + "194 True 367992 11039732 14719440 z_r5r13_24 \n", + "196 True 305490 9164677 12084080 z_r5r13_24 \n", "\n", - " epoch is_call \n", - "8 1033_undirected_g0 False \n", - "39 1033_undirected_g0 False \n", - "52 1033_undirected_g0 False \n", - "59 1033_undirected_g0 False \n", - "64 1033_undirected_g0 False \n", - "65 1033_undirected_g0 False \n", - "69 1033_undirected_g0 False \n", - "79 1033_undirected_g0 False \n", - "127 1142_directed_g0 False \n", - "157 1142_directed_g0 False \n", - "177 1142_directed_g0 False \n", - "188 1142_directed_g0 False \n", + " sess epoch bout_check confusing is_call \n", + "45 2024-08-07 0949_g0 True False False \n", + "142 2024-08-07 0949_g0 True False False \n", + "153 2024-08-07 0949_g0 True False False \n", + "155 2024-08-07 0949_g0 True False False \n", + "156 2024-08-07 0949_g0 True False False \n", + "157 2024-08-07 0949_g0 True False False \n", + "158 2024-08-07 0949_g0 True False False \n", + "159 2024-08-07 0949_g0 True False False \n", + "160 2024-08-07 0949_g0 True False False \n", + "161 2024-08-07 0949_g0 True False False \n", + "162 2024-08-07 0949_g0 True False False \n", + "163 2024-08-07 0949_g0 True False False \n", + "164 2024-08-07 0949_g0 True False False \n", + "165 2024-08-07 0949_g0 True False False \n", + "166 2024-08-07 0949_g0 True False False \n", + "193 2024-08-07 1233_g0 True False False \n", + "194 2024-08-07 1235_g0 True False False \n", + "196 2024-08-07 1245_g0 True False False \n", "\n", - "[12 rows x 27 columns]" + "[18 rows x 21 columns]" ] }, - "execution_count": 62, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -1603,37 +2070,37 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 98, "id": "8714a5fd-6d7c-460c-a3fa-057d95fc9e19", "metadata": {}, "outputs": [], "source": [ - "ap_offset = 125042153" + "ap_offset = 282322836" ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 99, "id": "2613932d-2157-4a80-b73d-19a3be70ec90", "metadata": {}, "outputs": [], "source": [ - "ap_sr = 29999.933405327574" + "ap_sr = 29999.842180774747" ] }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 100, "id": "dc223e68-601e-4cc9-8abd-e6dbddaefc58", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "4168081.019066337" + "9410810.70689516" ] }, - "execution_count": 71, + "execution_count": 100, "metadata": {}, "output_type": "execute_result" } @@ -1644,17 +2111,17 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 101, "id": "17a21ad5-f8f1-46c6-8275-7ef392e45220", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "4168081" + "9410811" ] }, - "execution_count": 72, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" } @@ -1666,7 +2133,19 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 115, + "id": "415ff3c7-2188-4b93-ba6a-a79b31ba0213", + "metadata": {}, + "outputs": [], + "source": [ + "## just kept looping through this when I had to stitch 6 recordings together\n", + "# ap_offset = ap_offset + 14681207\n", + "# ap_ms_offset = ap_ms_offset + round(14681207/29999.844262295082*1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 102, "id": "41251ffb-f1bf-469c-ac89-49502bcc0d16", "metadata": {}, "outputs": [], @@ -1676,19 +2155,41 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 117, + "id": "ff7eab70-559c-4a4a-9611-3260f2846db7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[196]" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# offset_idx = bout_df_new[bout_df_new['epoch']=='1245_g0'].index.tolist()\n", + "# offset_idx" + ] + }, + { + "cell_type": "code", + "execution_count": 118, "id": "8d3d2cfc-182e-472b-aedd-56fbe299089c", "metadata": {}, "outputs": [], "source": [ - "for i in [127, 157, 177, 188]:\n", + "for i in offset_idx:\n", " bout_df_concat.at[i, 'start_sample_ap_0'] = bout_df_new.at[i, 'start_sample_ap_0'] + ap_offset\n", " bout_df_concat.at[i, 'start_ms_ap_0'] = bout_df_new.at[i, 'start_ms_ap_0'] + ap_ms_offset" ] }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 120, "id": "5bf75bb0-d890-4dea-86b8-f736920b45ce", "metadata": {}, "outputs": [ @@ -1713,426 +2214,612 @@ " \n", " \n", " \n", - " start_ms\n", - " end_ms\n", + " file\n", " start_sample\n", " end_sample\n", - " p_step\n", - " rms_p\n", - " peak_p\n", - " bout_check\n", - " file\n", + " start_ms\n", + " end_ms\n", " len_ms\n", + " waveform\n", + " fem_waveform\n", + " spectrogram\n", + " sample_rate\n", " ...\n", - " valid_waveform\n", " valid\n", - " spectrogram\n", " start_ms_ap_0\n", " start_sample_ap_0\n", " start_sample_naive\n", " bird\n", " sess\n", " epoch\n", + " bout_check\n", + " confusing\n", " is_call\n", " \n", " \n", " \n", " \n", - " 8\n", - " 187128\n", - " 189488\n", - " 7485120\n", - " 7579520\n", - " [12.668582973409952, 41.513719119620895, 59.29...\n", - " 6.615156\n", - " 134.106723\n", - " True\n", - " /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1...\n", - " 2360\n", + " 45\n", + " /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08...\n", + " 88356760\n", + " 88673640\n", + " 2208919\n", + " 2216841\n", + " 7922\n", + " [471, 486, 469, 502, 489, 472, 453, 528, 544, ...\n", + " [-255, -238, -251, -283, -266, -251, -247, -24...\n", + " [[0.1069199546870063, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", + " 40000\n", " ...\n", " True\n", + " 2208960\n", + " 66268458\n", + " 88293400\n", + " z_r5r13_24\n", + " 2024-08-07\n", + " 0949_g0\n", " True\n", - " [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", - " 187131\n", - " 5643920\n", - " 7389000\n", - " z_y19o20_21\n", - " 2021-10-27\n", - " 1033_undirected_g0\n", + " False\n", " False\n", " \n", " \n", - " 39\n", - " 1071263\n", - " 1074653\n", - " 42850520\n", - " 42986120\n", - " [5.338206375397029, 3.0978681633708787, 3.9398...\n", - " 6.804745\n", - " 121.659218\n", - " True\n", - " /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1...\n", - " 3390\n", + " 142\n", + " /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08...\n", + " 306126440\n", + " 306319280\n", + " 7653161\n", + " 7657982\n", + " 4821\n", + " [690, 720, 722, 670, 711, 687, 702, 641, 663, ...\n", + " [-202, -205, -234, -205, -210, -204, -202, -22...\n", + " [[0.04617063032697592, 0.0, 0.0, 0.0, 0.0, 0.0...\n", + " 40000\n", " ...\n", " True\n", + " 7653305\n", + " 229597967\n", + " 306070440\n", + " z_r5r13_24\n", + " 2024-08-07\n", + " 0949_g0\n", " True\n", - " [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", - " 1071280\n", - " 32168339\n", - " 42187320\n", - " z_y19o20_21\n", - " 2021-10-27\n", - " 1033_undirected_g0\n", + " False\n", " False\n", " \n", " \n", - " 52\n", - " 1506162\n", - " 1509293\n", - " 60246480\n", - " 60371720\n", - " [11.552444728799006, 40.35527971348254, 57.706...\n", - " 6.804745\n", - " 153.937348\n", - " True\n", - " /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1...\n", - " 3131\n", + " 153\n", + " /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08...\n", + " 319266360\n", + " 319406360\n", + " 7981659\n", + " 7985159\n", + " 3500\n", + " [619, 598, 635, 569, 599, 605, 559, 618, 579, ...\n", + " [-326, -328, -328, -329, -331, -327, -330, -32...\n", + " [[0.021999940761009317, 0.0, 0.0, 0.0, 0.0, 0....\n", + " 40000\n", " ...\n", " True\n", + " 7981810\n", + " 239453041\n", + " 319266360\n", + " z_r5r13_24\n", + " 2024-08-07\n", + " 0949_g0\n", " True\n", - " [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", - " 1506186\n", - " 45215500\n", - " 60211720\n", - " z_y19o20_21\n", - " 2021-10-27\n", - " 1033_undirected_g0\n", + " False\n", " False\n", " \n", " \n", - " 59\n", - " 1782863\n", - " 1787293\n", - " 71314520\n", - " 71491720\n", - " [19.734600855974794, 55.380104136620304, 88.21...\n", - " 6.804745\n", - " 124.455970\n", - " True\n", - " /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1...\n", - " 4430\n", + " 155\n", + " /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08...\n", + " 323347800\n", + " 323568320\n", + " 8083695\n", + " 8089208\n", + " 5513\n", + " [424, 430, 441, 415, 436, 395, 462, 408, 470, ...\n", + " [-212, -208, -209, -216, -216, -183, -213, -20...\n", + " [[0.034754266281730006, 0.0, 0.0, 0.0, 0.0, 0....\n", + " 40000\n", " ...\n", " True\n", + " 8083847\n", + " 242514164\n", + " 323328320\n", + " z_r5r13_24\n", + " 2024-08-07\n", + " 0949_g0\n", " True\n", - 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12 rows × 27 columns

\n", + "

18 rows × 21 columns

\n", "" ], "text/plain": [ - " start_ms end_ms start_sample end_sample \\\n", - "8 187128 189488 7485120 7579520 \n", - "39 1071263 1074653 42850520 42986120 \n", - "52 1506162 1509293 60246480 60371720 \n", - "59 1782863 1787293 71314520 71491720 \n", - "64 2002208 2007923 80088320 80316920 \n", - "65 2010033 2014278 80401320 80571120 \n", - "69 2319316 2322816 92772640 92912640 \n", - "79 2622221 2625901 104888840 105036040 \n", - "127 4193848 4201758 167753913 168070313 \n", - "157 4986880 4990859 199475193 199634353 \n", - "177 5677492 5681222 227099673 227248873 \n", - "188 6254669 6257623 250186753 250304913 \n", + " file start_sample \\\n", + "45 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 88356760 \n", + "142 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 306126440 \n", + "153 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 319266360 \n", + "155 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 323347800 \n", + "156 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 324018040 \n", + "157 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 324624120 \n", + "158 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 325398080 \n", + "159 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 331901560 \n", + "160 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 332316080 \n", + "161 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 333613360 \n", + "162 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 334314600 \n", + "163 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 335430280 \n", + "164 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 336185240 \n", + "165 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 359295480 \n", + "166 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 360171640 \n", + "193 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 388693608 \n", + "194 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 406133713 \n", + "196 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 423165321 \n", "\n", - " p_step rms_p peak_p \\\n", - "8 [12.668582973409952, 41.513719119620895, 59.29... 6.615156 134.106723 \n", - "39 [5.338206375397029, 3.0978681633708787, 3.9398... 6.804745 121.659218 \n", - "52 [11.552444728799006, 40.35527971348254, 57.706... 6.804745 153.937348 \n", - "59 [19.734600855974794, 55.380104136620304, 88.21... 6.804745 124.455970 \n", - "64 [1.9347568605351688, 7.7485843417036575, 4.873... 6.804745 141.213069 \n", - "65 [4.194721552563428, 34.957344918072536, 81.087... 6.804745 139.213337 \n", - "69 [3.2451614405857594, 3.9892908524229553, 4.173... 4.810822 149.524894 \n", - "79 [2.362219435555598, 3.7773330065235813, 2.1025... 4.810822 120.014404 \n", - "127 [12.678209512221926, 45.38018960916082, 19.775... 6.670950 221.077734 \n", - "157 [1.8657256449298205, 4.105385240576828, 3.9668... 6.670950 150.351373 \n", - "177 [1.8458635010324818, 1.8480902459641393, 2.793... 4.953947 167.735870 \n", - "188 [1.405343200739505, 2.480036543067559, 2.35920... 4.293242 130.600130 \n", + " end_sample start_ms end_ms len_ms \\\n", + "45 88673640 2208919 2216841 7922 \n", + "142 306319280 7653161 7657982 4821 \n", + "153 319406360 7981659 7985159 3500 \n", + "155 323568320 8083695 8089208 5513 \n", + "156 324186040 8100451 8104651 4200 \n", + "157 324808040 8115603 8120201 4598 \n", + "158 325617240 8134952 8140431 5479 \n", + "159 332095280 8297539 8302382 4843 \n", + "160 332481600 8307902 8312040 4138 \n", + "161 333827600 8340334 8345690 5356 \n", + "162 334561880 8357865 8364047 6182 \n", + "163 335612000 8385757 8390300 4543 \n", + "164 336384920 8404631 8409623 4992 \n", + "165 359460160 8982387 8986504 4117 \n", + "166 360456560 9004291 9011414 7123 \n", + "193 388886208 9717340 9722155 4815 \n", + "194 406353513 10153343 10158838 5495 \n", + "196 423293441 10579133 10582336 3203 \n", "\n", - " bout_check file len_ms \\\n", - "8 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 2360 \n", - "39 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3390 \n", - "52 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3131 \n", - "59 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 4430 \n", - "64 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 5715 \n", - "65 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 4245 \n", - "69 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3500 \n", - "79 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3680 \n", - "127 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 7910 \n", - "157 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3979 \n", - "177 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3730 \n", - "188 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 2954 \n", + " waveform \\\n", + "45 [471, 486, 469, 502, 489, 472, 453, 528, 544, ... \n", + "142 [690, 720, 722, 670, 711, 687, 702, 641, 663, ... \n", + "153 [619, 598, 635, 569, 599, 605, 559, 618, 579, ... \n", + "155 [424, 430, 441, 415, 436, 395, 462, 408, 470, ... \n", + "156 [367, 315, 366, 313, 327, 350, 351, 298, 368, ... \n", + "157 [730, 701, 703, 694, 681, 706, 734, 694, 691, ... \n", + "158 [606, 575, 590, 593, 554, 601, 563, 605, 568, ... \n", + "159 [746, 732, 730, 750, 728, 778, 685, 742, 734, ... \n", + "160 [547, 567, 574, 512, 527, 506, 507, 524, 513, ... \n", + "161 [928, 981, 879, 913, 945, 921, 937, 964, 924, ... \n", + "162 [443, 389, 413, 397, 386, 365, 364, 375, 399, ... \n", + "163 [309, 326, 327, 298, 291, 292, 289, 292, 264, ... \n", + "164 [765, 722, 734, 756, 821, 771, 787, 790, 741, ... \n", + "165 [540, 541, 519, 547, 537, 498, 577, 542, 531, ... \n", + "166 [251, 261, 250, 248, 302, 285, 277, 265, 234, ... \n", + "193 [162, 142, 159, 175, 141, 143, 149, 126, 177, ... \n", + "194 [461, 436, 453, 399, 438, 454, 410, 398, 442, ... \n", + "196 [510, 526, 497, 519, 524, 532, 503, 514, 528, ... \n", "\n", - " ... valid_waveform valid \\\n", - "8 ... True True \n", - "39 ... True True \n", - "52 ... True True \n", - "59 ... True True \n", - "64 ... True True \n", - "65 ... True True \n", - "69 ... True True \n", - "79 ... True True \n", - "127 ... True True \n", - "157 ... True True \n", - "177 ... True True \n", - "188 ... True True \n", + " fem_waveform \\\n", + "45 [-255, -238, -251, -283, -266, -251, -247, -24... \n", + "142 [-202, -205, -234, -205, -210, -204, -202, -22... \n", + "153 [-326, -328, -328, -329, -331, -327, -330, -32... \n", + "155 [-212, -208, -209, -216, -216, -183, -213, -20... \n", + "156 [-272, -295, -278, -276, -274, -271, -278, -27... \n", + "157 [-194, -191, -185, -190, -181, -189, -177, -18... \n", + "158 [-271, -288, -273, -275, -283, -281, -267, -28... \n", + "159 [-290, -257, -271, -257, -266, -270, -253, -25... \n", + "160 [-242, -234, -237, -236, -238, -238, -233, -22... \n", + "161 [-194, -196, -190, -194, -204, -183, -201, -21... \n", + "162 [-268, -273, -259, -268, -267, -258, -277, -27... \n", + "163 [-242, -250, -243, -240, -251, -245, -245, -24... \n", + "164 [-239, -234, -249, -245, -224, -253, -272, -22... \n", + "165 [-268, -270, -265, -262, -264, -261, -241, -25... \n", + "166 [-265, -276, -278, -277, -278, -280, -279, -27... \n", + "193 [-229, -224, -215, -221, -223, -217, -220, -23... \n", + "194 [-166, -173, -171, -175, -174, -205, -174, -20... \n", + "196 [-212, -190, -214, -210, -219, -220, -202, -22... \n", "\n", - " spectrogram start_ms_ap_0 \\\n", - "8 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 187131 \n", - "39 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1071280 \n", - "52 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1506186 \n", - "59 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1782892 \n", - "64 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2002240 \n", - "65 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2010065 \n", - "69 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2319353 \n", - "79 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2622263 \n", - "127 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 4193916 \n", - "157 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 4986961 \n", - "177 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 5677584 \n", - "188 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 6254771 \n", + " spectrogram sample_rate ... \\\n", + "45 [[0.1069199546870063, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "142 [[0.04617063032697592, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "153 [[0.021999940761009317, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "155 [[0.034754266281730006, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "156 [[0.04861262692254681, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "157 [[0.020798472369377728, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "158 [[0.053783507904516886, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "159 [[0.10646443110827125, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "160 [[0.0417131455845805, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "161 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "162 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "163 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "164 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "165 [[0.07653305031436872, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "166 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "193 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "194 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "196 [[0.06698295276872104, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", "\n", - " start_sample_ap_0 start_sample_naive bird sess \\\n", - "8 5643920 7389000 z_y19o20_21 2021-10-27 \n", - "39 32168339 42187320 z_y19o20_21 2021-10-27 \n", - "52 45215500 60211720 z_y19o20_21 2021-10-27 \n", - "59 53516648 71354520 z_y19o20_21 2021-10-27 \n", - "64 60097092 80128320 z_y19o20_21 2021-10-27 \n", - "65 60331845 80441320 z_y19o20_21 2021-10-27 \n", - "69 69610458 91998240 z_y19o20_21 2021-10-27 \n", - "79 78697746 104812240 z_y19o20_21 2021-10-27 \n", - "127 125847210 498600 z_y19o20_21 2021-10-27 \n", - "157 149638514 32651000 z_y19o20_21 2021-10-27 \n", - "177 170357163 59732760 z_y19o20_21 2021-10-27 \n", - "188 187672727 83468760 z_y19o20_21 2021-10-27 \n", + " valid start_ms_ap_0 start_sample_ap_0 start_sample_naive bird \\\n", + "45 True 2208960 66268458 88293400 z_r5r13_24 \n", + "142 True 7653305 229597967 306070440 z_r5r13_24 \n", + "153 True 7981810 239453041 319266360 z_r5r13_24 \n", + "155 True 8083847 242514164 323328320 z_r5r13_24 \n", + "156 True 8100604 243016851 323998040 z_r5r13_24 \n", + "157 True 8115756 243471416 324576680 z_r5r13_24 \n", + "158 True 8135105 244051894 325377240 z_r5r13_24 \n", + "159 True 8297695 248929571 331855280 z_r5r13_24 \n", + "160 True 8308059 249240465 332281600 z_r5r13_24 \n", + "161 True 8340491 250213439 333587600 z_r5r13_24 \n", + "162 True 8358023 250739375 334314600 z_r5r13_24 \n", + "163 True 8385915 251576146 335430280 z_r5r13_24 \n", + "164 True 8404789 252142374 336164920 z_r5r13_24 \n", + "165 True 8982556 269475292 359273120 z_r5r13_24 \n", + "166 True 9004461 270132420 360171640 z_r5r13_24 \n", + "193 True 9718933 291566464 2924520 z_r5r13_24 \n", + "194 True 10156036 304679494 14719440 z_r5r13_24 \n", + "196 True 10582910 317485646 12084080 z_r5r13_24 \n", "\n", - " epoch is_call \n", - "8 1033_undirected_g0 False \n", - "39 1033_undirected_g0 False \n", - "52 1033_undirected_g0 False \n", - "59 1033_undirected_g0 False \n", - "64 1033_undirected_g0 False \n", - "65 1033_undirected_g0 False \n", - "69 1033_undirected_g0 False \n", - "79 1033_undirected_g0 False \n", - "127 1142_directed_g0 False \n", - "157 1142_directed_g0 False \n", - "177 1142_directed_g0 False \n", - "188 1142_directed_g0 False \n", + " sess epoch bout_check confusing is_call \n", + "45 2024-08-07 0949_g0 True False False \n", + "142 2024-08-07 0949_g0 True False False \n", + "153 2024-08-07 0949_g0 True False False \n", + "155 2024-08-07 0949_g0 True False False \n", + "156 2024-08-07 0949_g0 True False False \n", + "157 2024-08-07 0949_g0 True False False \n", + "158 2024-08-07 0949_g0 True False False \n", + "159 2024-08-07 0949_g0 True False False \n", + "160 2024-08-07 0949_g0 True False False \n", + "161 2024-08-07 0949_g0 True False False \n", + "162 2024-08-07 0949_g0 True False False \n", + "163 2024-08-07 0949_g0 True False False \n", + "164 2024-08-07 0949_g0 True False False \n", + "165 2024-08-07 0949_g0 True False False \n", + "166 2024-08-07 0949_g0 True False False \n", + "193 2024-08-07 1233_g0 True False False \n", + "194 2024-08-07 1235_g0 True False False \n", + "196 2024-08-07 1245_g0 True False False \n", "\n", - "[12 rows x 27 columns]" + "[18 rows x 21 columns]" ] }, - "execution_count": 78, + "execution_count": 120, "metadata": {}, "output_type": "execute_result" } @@ -2151,12 +2838,12 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 123, "id": "ab63a138-b2a9-42e6-aa43-9c8ab1c0e274", "metadata": {}, "outputs": [], "source": [ - "bout_df_concat.to_pickle('/mnt/cube/chronic_ephys/der/z_y19o20_21/2021-10-27/sglx/1033_undirected_g0-1142_directed_g0/bout_pd_ap0_curated.pkl')" + "bout_df_concat.to_pickle('/mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-07/sglx/0949_g0-1226_g0-1227_g0-1233_g0-1235_g0-1245_g0/bout_pd_ap0_curated.pkl')" ] }, { diff --git a/.ipynb_checkpoints/3-sort_spikes-checkpoint.ipynb b/.ipynb_checkpoints/3-sort_spikes-checkpoint.ipynb deleted file mode 100755 index 3db17f6..0000000 --- a/.ipynb_checkpoints/3-sort_spikes-checkpoint.ipynb +++ /dev/null @@ -1,423 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Spike sort\n", - "\n", - "Notebook within the chronic ephys processing pipeline\n", - "- 1-preprocess_acoustics\n", - "- 2-curate_acoustics\n", - "- **3-sort_spikes**\n", - "- 4-curate_spikes\n", - "\n", - "Use the environment **spikeproc** to run this notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import os\n", - "import pickle\n", - "os.environ[\"NPY_MATLAB_PATH\"] = '/mnt/cube/chronic_ephys/code/npy-matlab'\n", - "os.environ[\"KILOSORT2_PATH\"] = '/mnt/cube/chronic_ephys/code/Kilosort2'\n", - "os.environ[\"KILOSORT3_PATH\"] = '/mnt/cube/chronic_ephys/code/Kilosort'\n", - "import spikeinterface.full as si\n", - "import sys\n", - "import traceback\n", - "sys.path.append('/mnt/cube/lo/envs/ceciestunepipe/')\n", - "from ceciestunepipe.file import bcistructure as et\n", - "from ceciestunepipe.mods import probe_maps as pm" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'batch_size': 60000,\n", - " 'nblocks': 1,\n", - " 'Th_universal': 9,\n", - " 'Th_learned': 8,\n", - " 'do_CAR': True,\n", - " 'invert_sign': False,\n", - " 'nt': 61,\n", - " 'artifact_threshold': None,\n", - " 'nskip': 25,\n", - " 'whitening_range': 32,\n", - " 'binning_depth': 5,\n", - " 'sig_interp': 20,\n", - " 'nt0min': None,\n", - " 'dmin': None,\n", - " 'dminx': None,\n", - " 'min_template_size': 10,\n", - " 'template_sizes': 5,\n", - " 'nearest_chans': 10,\n", - " 'nearest_templates': 100,\n", - " 'templates_from_data': True,\n", - " 'n_templates': 6,\n", - " 'n_pcs': 6,\n", - " 'Th_single_ch': 6,\n", - " 'acg_threshold': 0.2,\n", - " 'ccg_threshold': 0.25,\n", - " 'cluster_downsampling': 20,\n", - " 'cluster_pcs': 64,\n", - " 'duplicate_spike_bins': 15,\n", - " 'do_correction': True,\n", - " 'keep_good_only': False,\n", - " 'save_extra_kwargs': False,\n", - " 'skip_kilosort_preprocessing': False,\n", - " 'scaleproc': None}" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "si.get_default_sorter_params('kilosort4')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Set `dmin` and `dminx`\n", - "**Setting these appropriately will greatly reduce sort time**\n", - "- The default value for dmin is the median distance between contacts -- if contacts are irregularly spaced, like in a modular Neuropixels 2.0 setup, will need to specify a value\n", - "- The default for dminx is 32um (designed for Neuropixels probes)\n", - "\n", - "Support documentation [here](https://kilosort.readthedocs.io/en/latest/parameters.html#dmin-and-dminx)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# non default spike sorting parameters\n", - "sort_params_dict_ks3 = {'minFR':0.001, 'minfr_goodchannels':0.001} # kilosort 3\n", - "sort_params_dict_ks4_npx = {'nblocks':5, 'Th_universal':8, 'Th_learned':7, 'dmin':15, 'dminx':32} # kilosort 4, neuropixels (set dmin and dminx to true pitch)\n", - "sort_params_dict_ks4_nnx64 = {'nblocks':0, 'nearest_templates':64,\n", - " 'Th_universal':8, 'Th_learned':7} # kilosort 4, neuronexus 64 chan\n", - "\n", - "# waveform extraction parameters\n", - "wave_params_dict = {'ms_before':1, 'ms_after':2, 'max_spikes_per_unit':500,\n", - " 'sparse':True, 'num_spikes_for_sparsity':100, 'method':'radius',\n", - " 'radius_um':40, 'n_components':5, 'mode':'by_channel_local'}\n", - "\n", - "# print stuff\n", - "verbose = True\n", - "\n", - "# errors break sorting\n", - "raise_error = False\n", - "\n", - "# restrict sorting to a specific GPU\n", - "restrict_to_gpu = 1 # 0 1 None\n", - "\n", - "# use specific GPU if specified\n", - "if restrict_to_gpu is not None:\n", - " os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n", - " os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"{}\".format(restrict_to_gpu)\n", - "\n", - "# parallel processing params\n", - "job_kwargs = dict(n_jobs=28,chunk_duration=\"1s\",progress_bar=False)\n", - "si.set_global_job_kwargs(**job_kwargs)\n", - "\n", - "# force processing of previous failed sorts\n", - "skip_failed = False" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "bird_rec_dict = {\n", - " 'z_p5y10_23':[\n", - " {'sess_par_list':['2024-05-16'], # sessions (will process all epochs within)\n", - " 'probe':{'probe_type':'neuropixels-2.0'}, # probe specs\n", - " 'sort':'sort_0', # label for this sort instance\n", - " 'sorter':'kilosort4', # sort method\n", - " 'sort_params':sort_params_dict_ks4_npx, # non-default sort params\n", - " 'wave_params':wave_params_dict, # waveform extraction params\n", - " 'ephys_software':'sglx' # sglx or oe\n", - " },\n", - " ],\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run sorts" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "___________ z_p5y10_23 2024-05-16 1246_g0 ___________\n", - "prep..\n", - "sort..\n", - "========================================\n", - "Loading recording with SpikeInterface...\n", - "number of samples: 368121306\n", - "number of channels: 384\n", - "number of segments: 1\n", - "sampling rate: 30000.0\n", - "dtype: int16\n", - "========================================\n", - "Preprocessing filters computed in 2077.34s; total 2077.34s\n", - "\n", - "computing drift\n", - "Re-computing universal templates from data.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6136/6136 [15:52:26<00:00, 9.31s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "drift computed in 59153.07s; total 61230.41s\n", - "\n", - "Extracting spikes using templates\n", - "Re-computing universal templates from data.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 46%|█████████████████████████████████████████████████████████████████████████▏ | 2824/6136 [7:08:38<9:32:21, 10.37s/it]" - ] - } - ], - "source": [ - "%%time\n", - "\n", - "# store sort summaries\n", - "sort_summary_all = []\n", - "\n", - "# loop through all birds / recordings\n", - "for this_bird in bird_rec_dict.keys():\n", - " # get session configurations\n", - " sess_all = bird_rec_dict[this_bird]\n", - " \n", - " # loop through session configurations\n", - " for this_sess_config in sess_all:\n", - " \n", - " # loop through sessions\n", - " for this_sess in this_sess_config['sess_par_list']:\n", - " log_dir = os.path.join('/mnt/cube/chronic_ephys/log', this_bird, this_sess)\n", - " \n", - " # build session parameter dictionary\n", - " sess_par = {'bird':this_bird,\n", - " 'sess':this_sess,\n", - " 'ephys_software':this_sess_config['ephys_software'],\n", - " 'sorter':this_sess_config['sorter'],\n", - " 'sort':this_sess_config['sort']}\n", - " # get epochs\n", - " sess_epochs = et.list_ephys_epochs(sess_par)\n", - " \n", - " for this_epoch in sess_epochs:\n", - " \n", - " # set output directory\n", - " epoch_struct = et.sgl_struct(sess_par,this_epoch,ephys_software=sess_par['ephys_software'])\n", - " sess_par['epoch'] = this_epoch\n", - " sort_folder = epoch_struct['folders']['derived'] + '/{}/{}/'.format(sess_par['sorter'],sess_par['sort'])\n", - " \n", - " # get spike sort log\n", - " try:\n", - " with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'r') as f:\n", - " log_message=f.readline() # read the first line of the log file\n", - " if log_message[:-1] == sess_par['bird']+' '+sess_par['sess']+' sort complete without error':\n", - " print(sess_par['bird'],sess_par['sess'],'already exists -- skipping sort')\n", - " run_proc = False\n", - " elif log_message[:-1] == sess_par['bird']+' '+sess_par['sess']+' sort failed':\n", - " if skip_failed:\n", - " print(sess_par['bird'],sess_par['sess'],'previously failed -- skipping sort')\n", - " run_proc = False\n", - " else:\n", - " run_proc = True\n", - " else: # uninterpretable log file\n", - " run_proc = True\n", - " except: # no existing log file\n", - " run_proc = True\n", - " \n", - " # run sort\n", - " if run_proc:\n", - " try:\n", - " print('___________',this_bird,this_sess,this_epoch,'___________')\n", - " # prepare recording for sorting\n", - " print('prep..')\n", - " if sess_par['ephys_software'] == 'sglx':\n", - " # load recording\n", - " rec_path = epoch_struct['folders']['sglx']\n", - " this_rec = si.read_spikeglx(folder_path=rec_path,stream_name='imec0.ap')\n", - " # save probe map prior to re-ordering for sorting\n", - " probe_df = this_rec.get_probe().to_dataframe()\n", - " probe_df.to_pickle(os.path.join(epoch_struct['folders']['derived'],'probe_map_df.pickle'))\n", - " # ibl destriping\n", - " this_rec = si.highpass_filter(recording=this_rec)\n", - " this_rec = si.phase_shift(recording=this_rec)\n", - " bad_good_channel_ids = si.detect_bad_channels(recording=this_rec)\n", - " if len(bad_good_channel_ids[0]) > 0:\n", - " this_rec = si.interpolate_bad_channels(recording=this_rec,bad_channel_ids=bad_good_channel_ids[0])\n", - " if this_sess_config['probe']['probe_type'] == 'neuropixels-2.0':\n", - " # highpass by shank\n", - " split_rec = this_rec.split_by(property='group',outputs='list') # split recording by shank\n", - " split_rec = [si.highpass_spatial_filter(recording=r,n_channel_pad=min(r.get_num_channels(),60)) for r in split_rec]\n", - " this_rec_p = si.aggregate_channels(split_rec) # recombine shanks\n", - " # stack shanks\n", - " p,_ = pm.stack_shanks(probe_df) # make new Probe object with shanks stacked\n", - " this_rec_p = this_rec.set_probe(p,group_mode='by_probe') # assign new Probe object to probe\n", - " else:\n", - " this_rec_p = si.highpass_spatial_filter(recording=this_rec)\n", - " elif sess_par['ephys_software'] =='oe':\n", - " # load recording\n", - " rec_path = [f.path for f in os.scandir(epoch_struct['folders']['oe']) if f.is_dir()][0]\n", - " this_rec = si.read_openephys(folder_path=rec_path)\n", - " # add probe\n", - " this_probe = pm.make_probes(this_sess_config['probe']['probe_type'],this_sess_config['probe']['probe_model']) # neuronexus, Buzsaki64\n", - " this_rec_p = this_rec.set_probe(this_probe,group_mode='by_shank')\n", - " # set sort params\n", - " this_rec_p = si.concatenate_recordings([this_rec_p])\n", - " sort_params = si.get_default_sorter_params(this_sess_config['sorter'])\n", - " for this_param in this_sess_config['sort_params'].keys():\n", - " sort_params[this_param] = this_sess_config['sort_params'][this_param]\n", - " # run sort\n", - " print('sort..')\n", - " this_sort = si.run_sorter(sorter_name=this_sess_config['sorter'],recording=this_rec_p,output_folder=sort_folder,\n", - " remove_existing_folder=True,delete_output_folder=False,delete_container_files=False,\n", - " verbose=verbose,raise_error=raise_error,**sort_params)\n", - " # bandpass recording before waveform extraction\n", - " print('bandpass..')\n", - " this_rec_pf = si.bandpass_filter(recording=this_rec_p)\n", - " # extract waveforms\n", - " print('waveform..')\n", - " wave_params = this_sess_config['wave_params']\n", - " wave = si.extract_waveforms(this_rec_pf,this_sort,folder=os.path.join(sort_folder,'waveforms'),\n", - " ms_before=wave_params['ms_before'],ms_after=wave_params['ms_after'],\n", - " max_spikes_per_unit=wave_params['max_spikes_per_unit'],\n", - " sparse=wave_params['sparse'],num_spikes_for_sparsity=wave_params['num_spikes_for_sparsity'],\n", - " method=wave_params['method'],radius_um=wave_params['radius_um'],overwrite=True,**job_kwargs)\n", - " # compute metrics\n", - " print('metrics..')\n", - " loc = si.compute_unit_locations(waveform_extractor=wave)\n", - " cor = si.compute_correlograms(waveform_or_sorting_extractor=wave)\n", - " sim = si.compute_template_similarity(waveform_extractor=wave)\n", - " amp = si.compute_spike_amplitudes(waveform_extractor=wave,**job_kwargs)\n", - " pca = si.compute_principal_components(waveform_extractor=wave,n_components=wave_params['n_components'],\n", - " mode=wave_params['mode'],**job_kwargs)\n", - " qms = si.get_quality_metric_list()\n", - " metric_names = []\n", - " bad_metrics = []\n", - " for qm in qms:\n", - " try:\n", - " si.compute_quality_metrics(waveform_extractor=wave,verbose=False,metric_names=[qm],**job_kwargs)\n", - " metric_names.append(qm)\n", - " except:\n", - " bad_metrics.append(qm)\n", - " met = si.compute_quality_metrics(waveform_extractor=wave,verbose=verbose,metric_names=metric_names,**job_kwargs)\n", - "\n", - " # mark complete\n", - " print('COMPLETE!!')\n", - "\n", - " # log complete sort\n", - " if not os.path.exists(log_dir): os.makedirs(log_dir)\n", - " with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'w') as f:\n", - " f.write(sess_par['bird']+' '+sess_par['sess']+' sort complete without error\\n\\n')\n", - " f.write('Sort method: '+this_sess_config['sorter']+'\\n\\n')\n", - " f.write('Sort params: '+str(sort_params)+'\\n\\n')\n", - " f.write('Computed quality metrics: '+str(metric_names)+'\\n\\n')\n", - " f.write('Failed quality metrics: '+str(bad_metrics)+'\\n')\n", - " sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'COMPLETE']\n", - " \n", - " except Exception as e:\n", - " # mark exception\n", - " print(\"An exception occurred:\", e)\n", - " \n", - " # log failed sort\n", - " if not os.path.exists(log_dir): os.makedirs(log_dir)\n", - " with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'w') as f:\n", - " f.write(sess_par['bird']+' '+sess_par['sess']+' sort failed\\n')\n", - " f.write(traceback.format_exc())\n", - " sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'FAIL']\n", - " else:\n", - " sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'EXISTS']\n", - " \n", - " # report and store sort summary\n", - " print(sort_summary)\n", - " sort_summary_all.append(sort_summary)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "spikeproc", - "language": "python", - "name": "spikeproc" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/.ipynb_checkpoints/3-sort_spikes_v0.100.8-checkpoint.ipynb b/.ipynb_checkpoints/3-sort_spikes_v0.100.8-checkpoint.ipynb new file mode 100755 index 0000000..ebaa7da --- /dev/null +++ b/.ipynb_checkpoints/3-sort_spikes_v0.100.8-checkpoint.ipynb @@ -0,0 +1,583 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Spike sort\n", + "\n", + "Notebook within the chronic ephys processing pipeline\n", + "- 1-preprocess_acoustics\n", + "- 2-curate_acoustics\n", + "- **3-sort_spikes**\n", + "- 4-curate_spikes\n", + "\n", + "Use the environment **spikeproc** to run this notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 7\u001b[0m\n\u001b[1;32m 5\u001b[0m os\u001b[38;5;241m.\u001b[39menviron[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mKILOSORT2_PATH\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/mnt/cube/chronic_ephys/code/Kilosort2\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 6\u001b[0m os\u001b[38;5;241m.\u001b[39menviron[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mKILOSORT3_PATH\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/mnt/cube/chronic_ephys/code/Kilosort\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mspikeinterface\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfull\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msi\u001b[39;00m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtraceback\u001b[39;00m\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc100.8/lib/python3.8/site-packages/spikeinterface/full.py:18\u001b[0m\n\u001b[1;32m 15\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m importlib\u001b[38;5;241m.\u001b[39mmetadata\u001b[38;5;241m.\u001b[39mversion(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspikeinterface\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[0;32m---> 18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mextractors\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msorters\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpreprocessing\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc100.8/lib/python3.8/site-packages/spikeinterface/extractors/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mextractorlist\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtoy_example\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m toy_example\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbids\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m read_bids\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc100.8/lib/python3.8/site-packages/spikeinterface/extractors/extractorlist.py:23\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mspikeinterface\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 7\u001b[0m BaseRecording,\n\u001b[1;32m 8\u001b[0m BaseSorting,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 19\u001b[0m read_npz_sorting,\n\u001b[1;32m 20\u001b[0m )\n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m# sorting/recording/event from neo\u001b[39;00m\n\u001b[0;32m---> 23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mneoextractors\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;66;03m# non-NEO objects implemented in neo folder\u001b[39;00m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mneoextractors\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m NeuroScopeSortingExtractor, MaxwellEventExtractor\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc100.8/lib/python3.8/site-packages/spikeinterface/extractors/neoextractors/__init__.py:4\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01maxona\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AxonaRecordingExtractor, read_axona\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbiocam\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BiocamRecordingExtractor, read_biocam\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mblackrock\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BlackrockRecordingExtractor, BlackrockSortingExtractor, read_blackrock, read_blackrock_sorting\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mced\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CedRecordingExtractor, read_ced\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01medf\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m EDFRecordingExtractor, read_edf\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc100.8/lib/python3.8/site-packages/spikeinterface/extractors/neoextractors/blackrock.py:7\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpackaging\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m version\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Optional\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mneo\u001b[39;00m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mspikeinterface\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore_tools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m define_function_from_class\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mneobaseextractor\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m NeoBaseRecordingExtractor, NeoBaseSortingExtractor\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc100.8/lib/python3.8/site-packages/neo/__init__.py:15\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mlogging\u001b[39;00m\n\u001b[1;32m 13\u001b[0m logging_handler \u001b[38;5;241m=\u001b[39m logging\u001b[38;5;241m.\u001b[39mStreamHandler()\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mneo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mneo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc100.8/lib/python3.8/site-packages/neo/core/__init__.py:35\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124;03m:mod:`neo.core` provides classes for storing common electrophysiological data\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03mtypes. Some of these classes contain raw data, such as spike trains or\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 32\u001b[0m \n\u001b[1;32m 33\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m---> 35\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mneo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mblock\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Block\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mneo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msegment\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Segment\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mneo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manalogsignal\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AnalogSignal\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc100.8/lib/python3.8/site-packages/neo/core/block.py:13\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdatetime\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m datetime\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mneo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcontainer\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Container, unique_objs\n\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mneo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgroup\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Group\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mneo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mobjectlist\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ObjectList\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mneo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msegment\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Segment\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc100.8/lib/python3.8/site-packages/neo/core/group.py:11\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m close\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mneo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcontainer\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Container\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mneo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manalogsignal\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AnalogSignal\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mneo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcontainer\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Container\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mneo\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mobjectlist\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ObjectList\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc100.8/lib/python3.8/site-packages/neo/core/analogsignal.py:23\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mlogging\u001b[39;00m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 23\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msignal\u001b[39;00m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 25\u001b[0m HAVE_SCIPY \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc100.8/lib/python3.8/site-packages/scipy/signal/__init__.py:309\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124;03m=======================================\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03mSignal processing (:mod:`scipy.signal`)\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 307\u001b[0m \n\u001b[1;32m 308\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 309\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _sigtools, windows\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_waveforms\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_max_len_seq\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m max_len_seq\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc100.8/lib/python3.8/site-packages/scipy/signal/windows/__init__.py:42\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124;03mWindow functions (:mod:`scipy.signal.windows`)\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03m==============================================\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 39\u001b[0m \n\u001b[1;32m 40\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m---> 42\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_windows\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;66;03m# Deprecated namespaces, to be removed in v2.0.0\u001b[39;00m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m windows\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc100.8/lib/python3.8/site-packages/scipy/signal/windows/_windows.py:7\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m linalg, special, fft \u001b[38;5;28;01mas\u001b[39;00m sp_fft\n\u001b[1;32m 9\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mboxcar\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtriang\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparzen\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbohman\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblackman\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnuttall\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 10\u001b[0m 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\u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_len_guards\u001b[39m(M):\n", + "File \u001b[0;32m:1039\u001b[0m, in \u001b[0;36m_handle_fromlist\u001b[0;34m(module, fromlist, import_, recursive)\u001b[0m\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc100.8/lib/python3.8/site-packages/scipy/__init__.py:200\u001b[0m, in \u001b[0;36m__getattr__\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getattr__\u001b[39m(name):\n\u001b[1;32m 199\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m submodules:\n\u001b[0;32m--> 200\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_importlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimport_module\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mscipy.\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mname\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 201\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 202\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc100.8/lib/python3.8/importlib/__init__.py:127\u001b[0m, in \u001b[0;36mimport_module\u001b[0;34m(name, package)\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m 126\u001b[0m level \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m--> 127\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_bootstrap\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gcd_import\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpackage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc100.8/lib/python3.8/site-packages/scipy/linalg/__init__.py:197\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124;03m====================================\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03mLinear algebra (:mod:`scipy.linalg`)\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 194\u001b[0m \n\u001b[1;32m 195\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m \u001b[38;5;66;03m# noqa: E501\u001b[39;00m\n\u001b[0;32m--> 197\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_misc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_cythonized_array_utils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 199\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_basic\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc100.8/lib/python3.8/site-packages/scipy/linalg/_misc.py:4\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlinalg\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LinAlgError\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m 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\u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_np\u001b[39;00m\n\u001b[1;32m 823\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mblas\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _get_funcs, _memoize_get_funcs\n\u001b[0;32m--> 824\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlinalg\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _flapack\n\u001b[1;32m 825\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mre\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;28mcompile\u001b[39m \u001b[38;5;28;01mas\u001b[39;00m regex_compile\n\u001b[1;32m 826\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "import numpy as np\n", + "import os\n", + "import pickle\n", + "os.environ[\"NPY_MATLAB_PATH\"] = '/mnt/cube/chronic_ephys/code/npy-matlab'\n", + "os.environ[\"KILOSORT2_PATH\"] = '/mnt/cube/chronic_ephys/code/Kilosort2'\n", + "os.environ[\"KILOSORT3_PATH\"] = '/mnt/cube/chronic_ephys/code/Kilosort'\n", + "import spikeinterface.full as si\n", + "import sys\n", + "import traceback\n", + "import torch\n", + "sys.path.append('/mnt/cube/lo/envs/ceciestunepipe/')\n", + "from ceciestunepipe.file import bcistructure as et\n", + "from ceciestunepipe.mods import probe_maps as pm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'batch_size': 60000,\n", + " 'nblocks': 1,\n", + " 'Th_universal': 9,\n", + " 'Th_learned': 8,\n", + " 'do_CAR': True,\n", + " 'invert_sign': False,\n", + " 'nt': 61,\n", + " 'artifact_threshold': None,\n", + " 'nskip': 25,\n", + " 'whitening_range': 32,\n", + " 'binning_depth': 5,\n", + " 'sig_interp': 20,\n", + " 'nt0min': None,\n", + " 'dmin': None,\n", + " 'dminx': None,\n", + " 'min_template_size': 10,\n", + " 'template_sizes': 5,\n", + " 'nearest_chans': 10,\n", + " 'nearest_templates': 100,\n", + " 'templates_from_data': True,\n", + " 'n_templates': 6,\n", + " 'n_pcs': 6,\n", + " 'Th_single_ch': 6,\n", + " 'acg_threshold': 0.2,\n", + " 'ccg_threshold': 0.25,\n", + " 'cluster_downsampling': 20,\n", + " 'cluster_pcs': 64,\n", + " 'duplicate_spike_bins': 15,\n", + " 'do_correction': True,\n", + " 'keep_good_only': False,\n", + " 'save_extra_kwargs': False,\n", + " 'skip_kilosort_preprocessing': False,\n", + " 'scaleproc': None}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "si.get_default_sorter_params('kilosort4')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Set `dmin` and `dminx`\n", + "**Setting these appropriately will greatly reduce sort time**\n", + "- The default value for dmin is the median distance between contacts -- if contacts are irregularly spaced, like in a modular Neuropixels 2.0 setup, will need to specify a value\n", + "- The default for dminx is 32um (designed for Neuropixels probes)\n", + "\n", + "Support documentation [here](https://kilosort.readthedocs.io/en/latest/parameters.html#dmin-and-dminx)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# non default spike sorting parameters\n", + "sort_params_dict_ks3 = {'minFR':0.001, 'minfr_goodchannels':0.001} # kilosort 3\n", + "sort_params_dict_ks4_npx = {'nblocks':5, 'Th_universal':8, 'Th_learned':7, 'dmin':15, 'dminx':32} # kilosort 4, neuropixels (set dmin and dminx to true pitch)\n", + "sort_params_dict_ks4_nnx64 = {'nblocks':0, 'nearest_templates':64,\n", + " 'Th_universal':8, 'Th_learned':7} # kilosort 4, neuronexus 64 chan\n", + "\n", + "# waveform extraction parameters\n", + "wave_params_dict = {'ms_before':1, 'ms_after':2, 'max_spikes_per_unit':500,\n", + " 'sparse':True, 'num_spikes_for_sparsity':100, 'method':'radius',\n", + " 'radius_um':40, 'n_components':5, 'mode':'by_channel_local'}\n", + "\n", + "# print stuff\n", + "verbose = True\n", + "\n", + "# errors break sorting\n", + "raise_error = False\n", + "\n", + "# restrict sorting to a specific GPU\n", + "restrict_to_gpu = 1 # 0 1 None\n", + "\n", + "# use specific GPU if specified\n", + "if restrict_to_gpu is not None:\n", + " os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n", + " os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"{}\".format(restrict_to_gpu)\n", + "\n", + "# parallel processing params\n", + "job_kwargs = dict(n_jobs=28,chunk_duration=\"1s\",progress_bar=False)\n", + "si.set_global_job_kwargs(**job_kwargs)\n", + "\n", + "# force processing of previous failed sorts\n", + "skip_failed = False" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "bird_rec_dict = {\n", + " 'z_p5y10_23':[\n", + " {'sess_par_list':['2024-05-16'], # sessions (will process all epochs within)\n", + " 'probe':{'probe_type':'neuropixels-2.0'}, # probe specs\n", + " 'sort':'sort_0', # label for this sort instance\n", + " 'sorter':'kilosort4', # sort method\n", + " 'sort_params':sort_params_dict_ks4_npx, # non-default sort params\n", + " 'wave_params':wave_params_dict, # waveform extraction params\n", + " 'ephys_software':'sglx' # sglx or oe\n", + " },\n", + " ],\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run sorts" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "___________ z_p5y10_23 2024-05-16 1246_g0 ___________\n", + "prep..\n", + "sort..\n", + "========================================\n", + "Loading recording with SpikeInterface...\n", + "number of samples: 368121306\n", + "number of channels: 384\n", + "number of segments: 1\n", + "sampling rate: 30000.0\n", + "dtype: int16\n", + "========================================\n", + "Preprocessing filters computed in 2077.34s; total 2077.34s\n", + "\n", + "computing drift\n", + "Re-computing universal templates from data.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6136/6136 [15:52:26<00:00, 9.31s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "drift computed in 59153.07s; total 61230.41s\n", + "\n", + "Extracting spikes using templates\n", + "Re-computing universal templates from data.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6136/6136 [16:23:41<00:00, 9.62s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "19999820 spikes extracted in 61300.19s; total 122530.61s\n", + "\n", + "First clustering\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 107/107 [19:49<00:00, 11.11s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1217 clusters found, in 1197.43s; total 123728.03s\n", + "\n", + "Extracting spikes using cluster waveforms\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6136/6136 [14:52:18<00:00, 8.73s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "37481401 spikes extracted in 53546.84s; total 177274.87s\n", + "\n", + "Final clustering\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 107/107 [26:00<00:00, 14.58s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "829 clusters found, in 1560.73s; total 178835.74s\n", + "\n", + "Merging clusters\n", + "742 units found, in 140.57s; total 178976.31s\n", + "\n", + "Saving to phy and computing refractory periods\n", + "338 units found with good refractory periods\n", + "\n", + "Total runtime: 179044.84s = 49:2984:4 h:m:s\n", + "kilosort4 run time 179047.45s\n", + "bandpass..\n", + "waveform..\n", + "metrics..\n", + "An exception occurred: [Errno 13] Permission denied: '/tmp/spikeinterface_cache/tmpnq7ml3iz'\n", + "['z_p5y10_23', '2024-05-16', 'sglx', '1246_g0', 'FAIL']\n", + "___________ z_p5y10_23 2024-05-16 1611_g0 ___________\n", + "prep..\n", + "sort..\n", + "========================================\n", + "Loading recording with SpikeInterface...\n", + "number of samples: 158273746\n", + "number of channels: 384\n", + "number of segments: 1\n", + "sampling rate: 30000.0\n", + "dtype: int16\n", + "========================================\n", + "Preprocessing filters computed in 878.17s; total 878.28s\n", + "\n", + "computing drift\n", + "Re-computing universal templates from data.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2638/2638 [7:23:47<00:00, 10.09s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "drift computed in 27475.47s; total 28353.88s\n", + "\n", + "Extracting spikes using templates\n", + "Re-computing universal templates from data.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 48%|████████████████████████████████████████████████████████████████████████████▉ | 1276/2638 [3:13:08<3:26:09, 9.08s/it]\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m:93\u001b[0m\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/spikeinterface/sorters/runsorter.py:175\u001b[0m, in \u001b[0;36mrun_sorter\u001b[0;34m(sorter_name, recording, output_folder, remove_existing_folder, delete_output_folder, verbose, raise_error, docker_image, singularity_image, delete_container_files, with_output, **sorter_params)\u001b[0m\n\u001b[1;32m 168\u001b[0m container_image \u001b[38;5;241m=\u001b[39m singularity_image\n\u001b[1;32m 169\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m run_sorter_container(\n\u001b[1;32m 170\u001b[0m container_image\u001b[38;5;241m=\u001b[39mcontainer_image,\n\u001b[1;32m 171\u001b[0m mode\u001b[38;5;241m=\u001b[39mmode,\n\u001b[1;32m 172\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcommon_kwargs,\n\u001b[1;32m 173\u001b[0m )\n\u001b[0;32m--> 175\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m 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\u001b[49m\u001b[43mraise_error\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m with_output:\n\u001b[1;32m 227\u001b[0m sorting \u001b[38;5;241m=\u001b[39m SorterClass\u001b[38;5;241m.\u001b[39mget_result_from_folder(output_folder, register_recording\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, sorting_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/spikeinterface/sorters/basesorter.py:258\u001b[0m, in \u001b[0;36mBaseSorter.run_from_folder\u001b[0;34m(cls, output_folder, raise_error, verbose)\u001b[0m\n\u001b[1;32m 255\u001b[0m t0 \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n\u001b[1;32m 257\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 258\u001b[0m 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ops[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfilename\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 237\u001b[0m n_chan_bin,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 252\u001b[0m file_object\u001b[38;5;241m=\u001b[39mfile_object,\n\u001b[1;32m 253\u001b[0m )\n\u001b[1;32m 255\u001b[0m \u001b[38;5;66;03m# TODO: don't think we need to do this actually\u001b[39;00m\n\u001b[1;32m 256\u001b[0m \u001b[38;5;66;03m# Save intermediate `ops` for use by GUI plots\u001b[39;00m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;66;03m# io.save_ops(ops, results_dir)\u001b[39;00m\n\u001b[1;32m 258\u001b[0m \n\u001b[1;32m 259\u001b[0m \u001b[38;5;66;03m# Sort spikes and save results\u001b[39;00m\n\u001b[0;32m--> 260\u001b[0m st, tF, _, _ \u001b[38;5;241m=\u001b[39m \u001b[43mdetect_spikes\u001b[49m\u001b[43m(\u001b[49m\u001b[43mops\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtic0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtic0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress_bar\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 261\u001b[0m clu, Wall \u001b[38;5;241m=\u001b[39m cluster_spikes(st, tF, ops, device, bfile, tic0\u001b[38;5;241m=\u001b[39mtic0, progress_bar\u001b[38;5;241m=\u001b[39mprogress_bar)\n\u001b[1;32m 262\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskip_kilosort_preprocessing\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/kilosort/run_kilosort.py:392\u001b[0m, in \u001b[0;36mdetect_spikes\u001b[0;34m(ops, device, bfile, tic0, progress_bar)\u001b[0m\n\u001b[1;32m 390\u001b[0m tic \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 391\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mExtracting spikes using templates\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 392\u001b[0m st0, tF, ops \u001b[38;5;241m=\u001b[39m \u001b[43mspikedetect\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mops\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress_bar\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 393\u001b[0m tF \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mfrom_numpy(tF)\n\u001b[1;32m 394\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(st0)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m spikes extracted in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtime\u001b[38;5;241m.\u001b[39mtime()\u001b[38;5;241m-\u001b[39mtic \u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m .2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124ms; \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \n\u001b[1;32m 395\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtotal \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtime\u001b[38;5;241m.\u001b[39mtime()\u001b[38;5;241m-\u001b[39mtic0 \u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m .2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124ms\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/kilosort/spikedetect.py:233\u001b[0m, in \u001b[0;36mrun\u001b[0;34m(ops, bfile, device, progress_bar)\u001b[0m\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ibatch \u001b[38;5;129;01min\u001b[39;00m tqdm(np\u001b[38;5;241m.\u001b[39marange(bfile\u001b[38;5;241m.\u001b[39mn_batches), miniters\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m200\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m progress_bar \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, \n\u001b[1;32m 230\u001b[0m mininterval\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m60\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m progress_bar \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 231\u001b[0m X \u001b[38;5;241m=\u001b[39m bfile\u001b[38;5;241m.\u001b[39mpadded_batch_to_torch(ibatch, ops)\n\u001b[0;32m--> 233\u001b[0m xy, imax, amp, adist \u001b[38;5;241m=\u001b[39m \u001b[43mtemplate_match\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mops\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miC\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miC2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweigh\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 234\u001b[0m yct \u001b[38;5;241m=\u001b[39m yweighted(yc, iC, adist, xy, device\u001b[38;5;241m=\u001b[39mdevice)\n\u001b[1;32m 235\u001b[0m nsp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(xy)\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/kilosort/spikedetect.py:156\u001b[0m, in \u001b[0;36mtemplate_match\u001b[0;34m(X, ops, iC, iC2, weigh, device)\u001b[0m\n\u001b[1;32m 154\u001b[0m Amaxs[:,\u001b[38;5;241m-\u001b[39mnt:] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 155\u001b[0m Amaxs \u001b[38;5;241m=\u001b[39m max_pool1d(Amaxs\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m), (\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39mnt0\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m), stride \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m, padding \u001b[38;5;241m=\u001b[39m nt0)\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m--> 156\u001b[0m xy \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlogical_and\u001b[49m\u001b[43m(\u001b[49m\u001b[43mAmaxs\u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43mAs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mAs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mops\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTh_universal\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnonzero\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 157\u001b[0m imax \u001b[38;5;241m=\u001b[39m imaxs[xy[:,\u001b[38;5;241m0\u001b[39m], xy[:,\u001b[38;5;241m1\u001b[39m]]\n\u001b[1;32m 158\u001b[0m amp \u001b[38;5;241m=\u001b[39m As[xy[:,\u001b[38;5;241m0\u001b[39m], xy[:,\u001b[38;5;241m1\u001b[39m]]\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "%%time\n", + "\n", + "# store sort summaries\n", + "sort_summary_all = []\n", + "\n", + "# loop through all birds / recordings\n", + "for this_bird in bird_rec_dict.keys():\n", + " # get session configurations\n", + " sess_all = bird_rec_dict[this_bird]\n", + " \n", + " # loop through session configurations\n", + " for this_sess_config in sess_all:\n", + " \n", + " # loop through sessions\n", + " for this_sess in this_sess_config['sess_par_list']:\n", + " log_dir = os.path.join('/mnt/cube/chronic_ephys/log', this_bird, this_sess)\n", + " \n", + " # build session parameter dictionary\n", + " sess_par = {'bird':this_bird,\n", + " 'sess':this_sess,\n", + " 'ephys_software':this_sess_config['ephys_software'],\n", + " 'sorter':this_sess_config['sorter'],\n", + " 'sort':this_sess_config['sort']}\n", + " # get epochs\n", + " sess_epochs = et.list_ephys_epochs(sess_par)\n", + " \n", + " for this_epoch in sess_epochs:\n", + " \n", + " # set output directory\n", + " epoch_struct = et.sgl_struct(sess_par,this_epoch,ephys_software=sess_par['ephys_software'])\n", + " sess_par['epoch'] = this_epoch\n", + " sort_folder = epoch_struct['folders']['derived'] + '/{}/{}/'.format(sess_par['sorter'],sess_par['sort'])\n", + " \n", + " # get spike sort log\n", + " try:\n", + " with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'r') as f:\n", + " log_message=f.readline() # read the first line of the log file\n", + " if log_message[:-1] == sess_par['bird']+' '+sess_par['sess']+' sort complete without error':\n", + " print(sess_par['bird'],sess_par['sess'],'already exists -- skipping sort')\n", + " run_proc = False\n", + " elif log_message[:-1] == sess_par['bird']+' '+sess_par['sess']+' sort failed':\n", + " if skip_failed:\n", + " print(sess_par['bird'],sess_par['sess'],'previously failed -- skipping sort')\n", + " run_proc = False\n", + " else:\n", + " run_proc = True\n", + " else: # uninterpretable log file\n", + " run_proc = True\n", + " except: # no existing log file\n", + " run_proc = True\n", + " \n", + " # run sort\n", + " if run_proc:\n", + " try:\n", + " print('___________',this_bird,this_sess,this_epoch,'___________')\n", + " # prepare recording for sorting\n", + " print('prep..')\n", + " if sess_par['ephys_software'] == 'sglx':\n", + " # load recording\n", + " rec_path = epoch_struct['folders']['sglx']\n", + " this_rec = si.read_spikeglx(folder_path=rec_path,stream_name='imec0.ap')\n", + " # save probe map prior to re-ordering for sorting\n", + " probe_df = this_rec.get_probe().to_dataframe()\n", + " probe_df.to_pickle(os.path.join(epoch_struct['folders']['derived'],'probe_map_df.pickle'))\n", + " # ibl destriping\n", + " this_rec = si.highpass_filter(recording=this_rec)\n", + " this_rec = si.phase_shift(recording=this_rec)\n", + " bad_good_channel_ids = si.detect_bad_channels(recording=this_rec)\n", + " if len(bad_good_channel_ids[0]) > 0:\n", + " this_rec = si.interpolate_bad_channels(recording=this_rec,bad_channel_ids=bad_good_channel_ids[0])\n", + " if this_sess_config['probe']['probe_type'] == 'neuropixels-2.0':\n", + " # highpass by shank\n", + " split_rec = this_rec.split_by(property='group',outputs='list') # split recording by shank\n", + " split_rec = [si.highpass_spatial_filter(recording=r,n_channel_pad=min(r.get_num_channels(),60)) for r in split_rec]\n", + " this_rec_p = si.aggregate_channels(split_rec) # recombine shanks\n", + " # stack shanks\n", + " p,_ = pm.stack_shanks(probe_df) # make new Probe object with shanks stacked\n", + " this_rec_p = this_rec.set_probe(p,group_mode='by_probe') # assign new Probe object to probe\n", + " else:\n", + " this_rec_p = si.highpass_spatial_filter(recording=this_rec)\n", + " elif sess_par['ephys_software'] =='oe':\n", + " # load recording\n", + " rec_path = [f.path for f in os.scandir(epoch_struct['folders']['oe']) if f.is_dir()][0]\n", + " this_rec = si.read_openephys(folder_path=rec_path)\n", + " # add probe\n", + " this_probe = pm.make_probes(this_sess_config['probe']['probe_type'],this_sess_config['probe']['probe_model']) # neuronexus, Buzsaki64\n", + " this_rec_p = this_rec.set_probe(this_probe,group_mode='by_shank')\n", + " # set sort params\n", + " this_rec_p = si.concatenate_recordings([this_rec_p])\n", + " sort_params = si.get_default_sorter_params(this_sess_config['sorter'])\n", + " for this_param in this_sess_config['sort_params'].keys():\n", + " sort_params[this_param] = this_sess_config['sort_params'][this_param]\n", + " # run sort\n", + " print('sort..')\n", + " torch.cuda.empty_cache()\n", + " this_sort = si.run_sorter(sorter_name=this_sess_config['sorter'],recording=this_rec_p,output_folder=sort_folder,\n", + " remove_existing_folder=True,delete_output_folder=False,delete_container_files=False,\n", + " verbose=verbose,raise_error=raise_error,**sort_params)\n", + " torch.cuda.empty_cache()\n", + " # bandpass recording before waveform extraction\n", + " print('bandpass..')\n", + " this_rec_pf = si.bandpass_filter(recording=this_rec_p)\n", + " # extract waveforms\n", + " print('waveform..')\n", + " wave_params = this_sess_config['wave_params']\n", + " wave = si.extract_waveforms(this_rec_pf,this_sort,folder=os.path.join(sort_folder,'waveforms'),\n", + " ms_before=wave_params['ms_before'],ms_after=wave_params['ms_after'],\n", + " max_spikes_per_unit=wave_params['max_spikes_per_unit'],\n", + " sparse=wave_params['sparse'],num_spikes_for_sparsity=wave_params['num_spikes_for_sparsity'],\n", + " method=wave_params['method'],radius_um=wave_params['radius_um'],overwrite=True,**job_kwargs)\n", + " # compute metrics\n", + " print('metrics..')\n", + " loc = si.compute_unit_locations(waveform_extractor=wave)\n", + " cor = si.compute_correlograms(waveform_or_sorting_extractor=wave)\n", + " sim = si.compute_template_similarity(waveform_extractor=wave)\n", + " amp = si.compute_spike_amplitudes(waveform_extractor=wave,**job_kwargs)\n", + " pca = si.compute_principal_components(waveform_extractor=wave,n_components=wave_params['n_components'],\n", + " mode=wave_params['mode'],**job_kwargs)\n", + " qms = si.get_quality_metric_list()\n", + " metric_names = []\n", + " bad_metrics = []\n", + " for qm in qms:\n", + " try:\n", + " si.compute_quality_metrics(waveform_extractor=wave,verbose=False,metric_names=[qm],**job_kwargs)\n", + " metric_names.append(qm)\n", + " except:\n", + " bad_metrics.append(qm)\n", + " met = si.compute_quality_metrics(waveform_extractor=wave,verbose=verbose,metric_names=metric_names,**job_kwargs)\n", + "\n", + " # mark complete\n", + " print('COMPLETE!!')\n", + "\n", + " # log complete sort\n", + " if not os.path.exists(log_dir): os.makedirs(log_dir)\n", + " with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'w') as f:\n", + " f.write(sess_par['bird']+' '+sess_par['sess']+' sort complete without error\\n\\n')\n", + " f.write('Sort method: '+this_sess_config['sorter']+'\\n\\n')\n", + " f.write('Sort params: '+str(sort_params)+'\\n\\n')\n", + " f.write('Computed quality metrics: '+str(metric_names)+'\\n\\n')\n", + " f.write('Failed quality metrics: '+str(bad_metrics)+'\\n')\n", + " sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'COMPLETE']\n", + " \n", + " except Exception as e:\n", + " # mark exception\n", + " print(\"An exception occurred:\", e)\n", + " \n", + " # log failed sort\n", + " if not os.path.exists(log_dir): os.makedirs(log_dir)\n", + " with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'w') as f:\n", + " f.write(sess_par['bird']+' '+sess_par['sess']+' sort failed\\n')\n", + " f.write(traceback.format_exc())\n", + " sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'FAIL']\n", + " else:\n", + " sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'EXISTS']\n", + " \n", + " # report and store sort summary\n", + " print(sort_summary)\n", + " sort_summary_all.append(sort_summary)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "spikeproc", + "language": "python", + "name": "spikeproc" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/3-sort_spikes_v0.100.8-checkpoint.py b/.ipynb_checkpoints/3-sort_spikes_v0.100.8-checkpoint.py new file mode 100644 index 0000000..2931dd7 --- /dev/null +++ b/.ipynb_checkpoints/3-sort_spikes_v0.100.8-checkpoint.py @@ -0,0 +1,236 @@ +### Spike sort +# +# Script within the chronic ephys processing pipeline +# - 1-preprocess_acoustics +# - 2-curate_acoustics +# - **3-sort_spikes** +# - 4-curate_spikes +# +# Use the environment **spikeproc** to run this notebook + + +## Import packages +import numpy as np +import os +import pickle +os.environ["NPY_MATLAB_PATH"] = '/mnt/cube/chronic_ephys/code/npy-matlab' +os.environ["KILOSORT2_PATH"] = '/mnt/cube/chronic_ephys/code/Kilosort2' +os.environ["KILOSORT3_PATH"] = '/mnt/cube/chronic_ephys/code/Kilosort' +import spikeinterface.full as si +import sys +import traceback +import torch +sys.path.append('/mnt/cube/lo/envs/ceciestunepipe/') +from ceciestunepipe.file import bcistructure as et +from ceciestunepipe.mods import probe_maps as pm + + +## Set parameters +si.get_default_sorter_params('kilosort4') + +# non default spike sorting parameters +sort_params_dict_ks3 = {'minFR':0.001, 'minfr_goodchannels':0.001} # kilosort 3 +sort_params_dict_ks4_npx = {'nblocks':5, 'Th_universal':8, 'Th_learned':7, 'dmin':15, 'dminx':32} # kilosort 4, neuropixels (set dmin and dminx to true pitch) +sort_params_dict_ks4_nnx64 = {'nblocks':0, 'nearest_templates':64, + 'Th_universal':8, 'Th_learned':7} # kilosort 4, neuronexus 64 chan + +# waveform extraction parameters +wave_params_dict = {'ms_before':1, 'ms_after':2, 'max_spikes_per_unit':500, + 'sparse':True, 'num_spikes_for_sparsity':100, 'method':'radius', + 'radius_um':40, 'n_components':5, 'mode':'by_channel_local'} + +# print stuff +verbose = True + +# errors break sorting +raise_error = False + +# restrict sorting to a specific GPU +restrict_to_gpu = 1 # 0 1 None + +# use specific GPU if specified +if restrict_to_gpu is not None: + os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" + os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(restrict_to_gpu) + +# parallel processing params +job_kwargs = dict(n_jobs=28,chunk_duration="1s",progress_bar=False) +si.set_global_job_kwargs(**job_kwargs) + +# force processing of previous failed sorts +skip_failed = False + +# session info +bird_rec_dict = { + 'z_r5r13_24':[ + {'sess_par_list':['2024-08-06'], # sessions (will process all epochs within) + 'probe':{'probe_type':'neuropixels-2.0'}, # probe specs + 'sort':'sort_1', # label for this sort instance + 'sorter':'kilosort4', # sort method + 'sort_params':sort_params_dict_ks4_npx, # non-default sort params + 'wave_params':wave_params_dict, # waveform extraction params + 'ephys_software':'sglx' # sglx or oe + }, + ], +} + + + +## Run sorts + +# store sort summaries +sort_summary_all = [] + +# loop through all birds / recordings +for this_bird in bird_rec_dict.keys(): + # get session configurations + sess_all = bird_rec_dict[this_bird] + + # loop through session configurations + for this_sess_config in sess_all: + + # loop through sessions + for this_sess in this_sess_config['sess_par_list']: + log_dir = os.path.join('/mnt/cube/chronic_ephys/log', this_bird, this_sess) + + # build session parameter dictionary + sess_par = {'bird':this_bird, + 'sess':this_sess, + 'ephys_software':this_sess_config['ephys_software'], + 'sorter':this_sess_config['sorter'], + 'sort':this_sess_config['sort']} + # get epochs + sess_epochs = et.list_ephys_epochs(sess_par) + + for this_epoch in sess_epochs: + + # set output directory + epoch_struct = et.sgl_struct(sess_par,this_epoch,ephys_software=sess_par['ephys_software']) + sess_par['epoch'] = this_epoch + sort_folder = epoch_struct['folders']['derived'] + '/{}/{}/'.format(sess_par['sorter'],sess_par['sort']) + + # get spike sort log + try: + with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'r') as f: + log_message=f.readline() # read the first line of the log file + if log_message[:-1] == sess_par['bird']+' '+sess_par['sess']+' sort complete without error': + print(sess_par['bird'],sess_par['sess'],'already exists -- skipping sort') + run_proc = False + elif log_message[:-1] == sess_par['bird']+' '+sess_par['sess']+' sort failed': + if skip_failed: + print(sess_par['bird'],sess_par['sess'],'previously failed -- skipping sort') + run_proc = False + else: + run_proc = True + else: # uninterpretable log file + run_proc = True + except: # no existing log file + run_proc = True + + # run sort + if run_proc: + try: + print('___________',this_bird,this_sess,this_epoch,'___________') + # prepare recording for sorting + print('prep..') + if sess_par['ephys_software'] == 'sglx': + # load recording + rec_path = epoch_struct['folders']['sglx'] + this_rec = si.read_spikeglx(folder_path=rec_path,stream_name='imec0.ap') + # save probe map prior to re-ordering for sorting + probe_df = this_rec.get_probe().to_dataframe() + probe_df.to_pickle(os.path.join(epoch_struct['folders']['derived'],'probe_map_df.pickle')) + # ibl destriping + this_rec = si.highpass_filter(recording=this_rec) + this_rec = si.phase_shift(recording=this_rec) + bad_good_channel_ids = si.detect_bad_channels(recording=this_rec) + if len(bad_good_channel_ids[0]) > 0: + this_rec = si.interpolate_bad_channels(recording=this_rec,bad_channel_ids=bad_good_channel_ids[0]) + if this_sess_config['probe']['probe_type'] == 'neuropixels-2.0': + # highpass by shank + split_rec = this_rec.split_by(property='group',outputs='list') # split recording by shank + split_rec = [si.highpass_spatial_filter(recording=r,n_channel_pad=min(r.get_num_channels(),60)) for r in split_rec] + this_rec_p = si.aggregate_channels(split_rec) # recombine shanks + # stack shanks + p,_ = pm.stack_shanks(probe_df) # make new Probe object with shanks stacked + this_rec_p = this_rec.set_probe(p,group_mode='by_probe') # assign new Probe object to probe + else: + this_rec_p = si.highpass_spatial_filter(recording=this_rec) + elif sess_par['ephys_software'] =='oe': + # load recording + rec_path = [f.path for f in os.scandir(epoch_struct['folders']['oe']) if f.is_dir()][0] + this_rec = si.read_openephys(folder_path=rec_path) + # add probe + this_probe = pm.make_probes(this_sess_config['probe']['probe_type'],this_sess_config['probe']['probe_model']) # neuronexus, Buzsaki64 + this_rec_p = this_rec.set_probe(this_probe,group_mode='by_shank') + # set sort params + this_rec_p = si.concatenate_recordings([this_rec_p]) + sort_params = si.get_default_sorter_params(this_sess_config['sorter']) + for this_param in this_sess_config['sort_params'].keys(): + sort_params[this_param] = this_sess_config['sort_params'][this_param] + # run sort + print('sort..') + torch.cuda.empty_cache() + this_sort = si.run_sorter(sorter_name=this_sess_config['sorter'],recording=this_rec_p,output_folder=sort_folder, + remove_existing_folder=True,delete_output_folder=False,delete_container_files=False, + verbose=verbose,raise_error=raise_error,**sort_params) + torch.cuda.empty_cache() + # bandpass recording before waveform extraction + print('bandpass..') + this_rec_pf = si.bandpass_filter(recording=this_rec_p) + # extract waveforms + print('waveform..') + wave_params = this_sess_config['wave_params'] + wave = si.extract_waveforms(this_rec_pf,this_sort,folder=os.path.join(sort_folder,'waveforms'), + ms_before=wave_params['ms_before'],ms_after=wave_params['ms_after'], + max_spikes_per_unit=wave_params['max_spikes_per_unit'], + sparse=wave_params['sparse'],num_spikes_for_sparsity=wave_params['num_spikes_for_sparsity'], + method=wave_params['method'],radius_um=wave_params['radius_um'],overwrite=True,**job_kwargs) + # compute metrics + print('metrics..') + loc = si.compute_unit_locations(waveform_extractor=wave) + cor = si.compute_correlograms(waveform_or_sorting_extractor=wave) + sim = si.compute_template_similarity(waveform_extractor=wave) + amp = si.compute_spike_amplitudes(waveform_extractor=wave,**job_kwargs) + pca = si.compute_principal_components(waveform_extractor=wave,n_components=wave_params['n_components'], + mode=wave_params['mode'],**job_kwargs) + qms = si.get_quality_metric_list() + metric_names = [] + bad_metrics = [] + for qm in qms: + try: + si.compute_quality_metrics(waveform_extractor=wave,verbose=False,metric_names=[qm],**job_kwargs) + metric_names.append(qm) + except: + bad_metrics.append(qm) + met = si.compute_quality_metrics(waveform_extractor=wave,verbose=verbose,metric_names=metric_names,**job_kwargs) + + # mark complete + print('COMPLETE!!') + + # log complete sort + if not os.path.exists(log_dir): os.makedirs(log_dir) + with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'w') as f: + f.write(sess_par['bird']+' '+sess_par['sess']+' sort complete without error\n\n') + f.write('Sort method: '+this_sess_config['sorter']+'\n\n') + f.write('Sort params: '+str(sort_params)+'\n\n') + f.write('Computed quality metrics: '+str(metric_names)+'\n\n') + f.write('Failed quality metrics: '+str(bad_metrics)+'\n') + sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'COMPLETE'] + + except Exception as e: + # mark exception + print("An exception occurred:", e) + + # log failed sort + if not os.path.exists(log_dir): os.makedirs(log_dir) + with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'w') as f: + f.write(sess_par['bird']+' '+sess_par['sess']+' sort failed\n') + f.write(traceback.format_exc()) + sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'FAIL'] + else: + sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'EXISTS'] + + # report and store sort summary + print(sort_summary) + sort_summary_all.append(sort_summary) diff --git a/.ipynb_checkpoints/3-sort_spikes_v0.101-checkpoint.ipynb b/.ipynb_checkpoints/3-sort_spikes_v0.101-checkpoint.ipynb new file mode 100644 index 0000000..da5cb5d --- /dev/null +++ b/.ipynb_checkpoints/3-sort_spikes_v0.101-checkpoint.ipynb @@ -0,0 +1,534 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Spike sort\n", + "\n", + "Notebook within the chronic ephys processing pipeline\n", + "- 1-preprocess_acoustics\n", + "- 2-curate_acoustics\n", + "- **3-sort_spikes**\n", + "- 4-curate_spikes\n", + "\n", + "Use the environment **spikeproc** to run this notebook" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import os\n", + "import pickle\n", + "os.environ[\"NPY_MATLAB_PATH\"] = '/mnt/cube/chronic_ephys/code/npy-matlab'\n", + "os.environ[\"KILOSORT2_PATH\"] = '/mnt/cube/chronic_ephys/code/Kilosort2'\n", + "os.environ[\"KILOSORT3_PATH\"] = '/mnt/cube/chronic_ephys/code/Kilosort'\n", + "import spikeinterface.full as si\n", + "import sys\n", + "import traceback\n", + "import torch\n", + "sys.path.append('/mnt/cube/lo/envs/ceciestunepipe/')\n", + "from ceciestunepipe.file import bcistructure as et\n", + "from ceciestunepipe.mods import probe_maps as pm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'batch_size': 60000,\n", + " 'nblocks': 1,\n", + " 'Th_universal': 9,\n", + " 'Th_learned': 8,\n", + " 'do_CAR': True,\n", + " 'invert_sign': False,\n", + " 'nt': 61,\n", + " 'artifact_threshold': None,\n", + " 'nskip': 25,\n", + " 'whitening_range': 32,\n", + " 'binning_depth': 5,\n", + " 'sig_interp': 20,\n", + " 'nt0min': None,\n", + " 'dmin': None,\n", + " 'dminx': None,\n", + " 'min_template_size': 10,\n", + " 'template_sizes': 5,\n", + " 'nearest_chans': 10,\n", + " 'nearest_templates': 100,\n", + " 'templates_from_data': True,\n", + " 'n_templates': 6,\n", + " 'n_pcs': 6,\n", + " 'Th_single_ch': 6,\n", + " 'acg_threshold': 0.2,\n", + " 'ccg_threshold': 0.25,\n", + " 'cluster_downsampling': 20,\n", + " 'cluster_pcs': 64,\n", + " 'duplicate_spike_bins': 15,\n", + " 'do_correction': True,\n", + " 'keep_good_only': False,\n", + " 'save_extra_kwargs': False,\n", + " 'skip_kilosort_preprocessing': False,\n", + " 'scaleproc': None}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "si.get_default_sorter_params('kilosort4')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Set `dmin` and `dminx`\n", + "**Setting these appropriately will greatly reduce sort time**\n", + "- The default value for dmin is the median distance between contacts -- if contacts are irregularly spaced, like in a modular Neuropixels 2.0 setup, will need to specify a value\n", + "- The default for dminx is 32um (designed for Neuropixels probes)\n", + "\n", + "Support documentation [here](https://kilosort.readthedocs.io/en/latest/parameters.html#dmin-and-dminx)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# non default spike sorting parameters\n", + "sort_params_dict_ks3 = {'minFR':0.001, 'minfr_goodchannels':0.001} # kilosort 3\n", + "sort_params_dict_ks4_npx = {'nblocks':5, 'Th_universal':8, 'Th_learned':7, 'dmin':15, 'dminx':32} # kilosort 4, neuropixels (set dmin and dminx to true pitch)\n", + "sort_params_dict_ks4_nnx64 = {'nblocks':0, 'nearest_templates':64,\n", + " 'Th_universal':8, 'Th_learned':7} # kilosort 4, neuronexus 64 chan\n", + "\n", + "# waveform extraction parameters\n", + "wave_params_dict = {'ms_before':1, 'ms_after':2, 'max_spikes_per_unit':500,\n", + " 'sparse':True, 'num_spikes_for_sparsity':100, 'method':'radius',\n", + " 'radius_um':40, 'n_components':5, 'mode':'by_channel_local'}\n", + "\n", + "# print stuff\n", + "verbose = True\n", + "\n", + "# errors break sorting\n", + "raise_error = False\n", + "\n", + "# restrict sorting to a specific GPU\n", + "restrict_to_gpu = 1 # 0 1 None\n", + "\n", + "# use specific GPU if specified\n", + "if restrict_to_gpu is not None:\n", + " os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n", + " os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"{}\".format(restrict_to_gpu)\n", + "\n", + "# parallel processing params\n", + "job_kwargs = dict(n_jobs=28,chunk_duration=\"1s\",progress_bar=False)\n", + "si.set_global_job_kwargs(**job_kwargs)\n", + "\n", + "# force processing of previous failed sorts\n", + "skip_failed = False" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "bird_rec_dict = {\n", + " 'z_p5y10_23':[\n", + " {'sess_par_list':['2024-05-16'], # sessions (will process all epochs within)\n", + " 'probe':{'probe_type':'neuropixels-2.0'}, # probe specs\n", + " 'sort':'sort_0', # label for this sort instance\n", + " 'sorter':'kilosort4', # sort method\n", + " 'sort_params':sort_params_dict_ks4_npx, # non-default sort params\n", + " 'wave_params':wave_params_dict, # waveform extraction params\n", + " 'ephys_software':'sglx' # sglx or oe\n", + " },\n", + " ],\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run sorts" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "___________ z_p5y10_23 2024-05-16 1246_g0 ___________\n", + "prep..\n", + "sort..\n", + "========================================\n", + "Loading recording with SpikeInterface...\n", + "number of samples: 368121306\n", + "number of channels: 384\n", + "number of segments: 1\n", + "sampling rate: 30000.0\n", + "dtype: int16\n", + "========================================\n", + "Preprocessing filters computed in 2077.34s; total 2077.34s\n", + "\n", + "computing drift\n", + "Re-computing universal templates from data.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6136/6136 [15:52:26<00:00, 9.31s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "drift computed in 59153.07s; total 61230.41s\n", + "\n", + "Extracting spikes using templates\n", + "Re-computing universal templates from data.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6136/6136 [16:23:41<00:00, 9.62s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "19999820 spikes extracted in 61300.19s; total 122530.61s\n", + "\n", + "First clustering\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 107/107 [19:49<00:00, 11.11s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1217 clusters found, in 1197.43s; total 123728.03s\n", + "\n", + "Extracting spikes using cluster waveforms\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6136/6136 [14:52:18<00:00, 8.73s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "37481401 spikes extracted in 53546.84s; total 177274.87s\n", + "\n", + "Final clustering\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 107/107 [26:00<00:00, 14.58s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "829 clusters found, in 1560.73s; total 178835.74s\n", + "\n", + "Merging clusters\n", + "742 units found, in 140.57s; total 178976.31s\n", + "\n", + "Saving to phy and computing refractory periods\n", + "338 units found with good refractory periods\n", + "\n", + "Total runtime: 179044.84s = 49:2984:4 h:m:s\n", + "kilosort4 run time 179047.45s\n", + "bandpass..\n", + "waveform..\n", + "metrics..\n", + "An exception occurred: [Errno 13] Permission denied: '/tmp/spikeinterface_cache/tmpnq7ml3iz'\n", + "['z_p5y10_23', '2024-05-16', 'sglx', '1246_g0', 'FAIL']\n", + "___________ z_p5y10_23 2024-05-16 1611_g0 ___________\n", + "prep..\n", + "sort..\n", + "========================================\n", + "Loading recording with SpikeInterface...\n", + "number of samples: 158273746\n", + "number of channels: 384\n", + "number of segments: 1\n", + "sampling rate: 30000.0\n", + "dtype: int16\n", + "========================================\n", + "Preprocessing filters computed in 878.17s; total 878.28s\n", + "\n", + "computing drift\n", + "Re-computing universal templates from data.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2638/2638 [7:23:47<00:00, 10.09s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "drift computed in 27475.47s; total 28353.88s\n", + "\n", + "Extracting spikes using templates\n", + "Re-computing universal templates from data.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 48%|████████████████████████████████████████████████████████████████████████████▉ | 1276/2638 [3:13:08<3:26:09, 9.08s/it]\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m:93\u001b[0m\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/spikeinterface/sorters/runsorter.py:175\u001b[0m, in \u001b[0;36mrun_sorter\u001b[0;34m(sorter_name, recording, output_folder, remove_existing_folder, delete_output_folder, verbose, raise_error, docker_image, singularity_image, delete_container_files, with_output, **sorter_params)\u001b[0m\n\u001b[1;32m 168\u001b[0m container_image \u001b[38;5;241m=\u001b[39m singularity_image\n\u001b[1;32m 169\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m run_sorter_container(\n\u001b[1;32m 170\u001b[0m container_image\u001b[38;5;241m=\u001b[39mcontainer_image,\n\u001b[1;32m 171\u001b[0m mode\u001b[38;5;241m=\u001b[39mmode,\n\u001b[1;32m 172\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcommon_kwargs,\n\u001b[1;32m 173\u001b[0m )\n\u001b[0;32m--> 175\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrun_sorter_local\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcommon_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/spikeinterface/sorters/runsorter.py:225\u001b[0m, in \u001b[0;36mrun_sorter_local\u001b[0;34m(sorter_name, recording, output_folder, remove_existing_folder, delete_output_folder, verbose, raise_error, with_output, **sorter_params)\u001b[0m\n\u001b[1;32m 223\u001b[0m SorterClass\u001b[38;5;241m.\u001b[39mset_params_to_folder(recording, output_folder, sorter_params, verbose)\n\u001b[1;32m 224\u001b[0m SorterClass\u001b[38;5;241m.\u001b[39msetup_recording(recording, output_folder, verbose\u001b[38;5;241m=\u001b[39mverbose)\n\u001b[0;32m--> 225\u001b[0m \u001b[43mSorterClass\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_from_folder\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_folder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mraise_error\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m with_output:\n\u001b[1;32m 227\u001b[0m sorting \u001b[38;5;241m=\u001b[39m SorterClass\u001b[38;5;241m.\u001b[39mget_result_from_folder(output_folder, register_recording\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, sorting_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/spikeinterface/sorters/basesorter.py:258\u001b[0m, in \u001b[0;36mBaseSorter.run_from_folder\u001b[0;34m(cls, output_folder, raise_error, verbose)\u001b[0m\n\u001b[1;32m 255\u001b[0m t0 \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n\u001b[1;32m 257\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 258\u001b[0m \u001b[43mSorterClass\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_from_folder\u001b[49m\u001b[43m(\u001b[49m\u001b[43msorter_output_folder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msorter_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 259\u001b[0m t1 \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n\u001b[1;32m 260\u001b[0m run_time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(t1 \u001b[38;5;241m-\u001b[39m t0)\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/spikeinterface/sorters/external/kilosort4.py:260\u001b[0m, in \u001b[0;36mKilosort4Sorter._run_from_folder\u001b[0;34m(cls, sorter_output_folder, params, verbose)\u001b[0m\n\u001b[1;32m 235\u001b[0m bfile \u001b[38;5;241m=\u001b[39m BinaryFiltered(\n\u001b[1;32m 236\u001b[0m ops[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfilename\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 237\u001b[0m n_chan_bin,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 252\u001b[0m file_object\u001b[38;5;241m=\u001b[39mfile_object,\n\u001b[1;32m 253\u001b[0m )\n\u001b[1;32m 255\u001b[0m \u001b[38;5;66;03m# TODO: don't think we need to do this actually\u001b[39;00m\n\u001b[1;32m 256\u001b[0m \u001b[38;5;66;03m# Save intermediate `ops` for use by GUI plots\u001b[39;00m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;66;03m# io.save_ops(ops, results_dir)\u001b[39;00m\n\u001b[1;32m 258\u001b[0m \n\u001b[1;32m 259\u001b[0m \u001b[38;5;66;03m# Sort spikes and save results\u001b[39;00m\n\u001b[0;32m--> 260\u001b[0m st, tF, _, _ \u001b[38;5;241m=\u001b[39m \u001b[43mdetect_spikes\u001b[49m\u001b[43m(\u001b[49m\u001b[43mops\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtic0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtic0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress_bar\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 261\u001b[0m clu, Wall \u001b[38;5;241m=\u001b[39m cluster_spikes(st, tF, ops, device, bfile, tic0\u001b[38;5;241m=\u001b[39mtic0, progress_bar\u001b[38;5;241m=\u001b[39mprogress_bar)\n\u001b[1;32m 262\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskip_kilosort_preprocessing\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/kilosort/run_kilosort.py:392\u001b[0m, in \u001b[0;36mdetect_spikes\u001b[0;34m(ops, device, bfile, tic0, progress_bar)\u001b[0m\n\u001b[1;32m 390\u001b[0m tic \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 391\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mExtracting spikes using templates\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 392\u001b[0m st0, tF, ops \u001b[38;5;241m=\u001b[39m \u001b[43mspikedetect\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mops\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress_bar\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 393\u001b[0m tF \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mfrom_numpy(tF)\n\u001b[1;32m 394\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(st0)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m spikes extracted in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtime\u001b[38;5;241m.\u001b[39mtime()\u001b[38;5;241m-\u001b[39mtic \u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m .2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124ms; \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \n\u001b[1;32m 395\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtotal \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtime\u001b[38;5;241m.\u001b[39mtime()\u001b[38;5;241m-\u001b[39mtic0 \u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m .2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124ms\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/kilosort/spikedetect.py:233\u001b[0m, in \u001b[0;36mrun\u001b[0;34m(ops, bfile, device, progress_bar)\u001b[0m\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ibatch \u001b[38;5;129;01min\u001b[39;00m tqdm(np\u001b[38;5;241m.\u001b[39marange(bfile\u001b[38;5;241m.\u001b[39mn_batches), miniters\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m200\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m progress_bar \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, \n\u001b[1;32m 230\u001b[0m mininterval\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m60\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m progress_bar \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 231\u001b[0m X \u001b[38;5;241m=\u001b[39m bfile\u001b[38;5;241m.\u001b[39mpadded_batch_to_torch(ibatch, ops)\n\u001b[0;32m--> 233\u001b[0m xy, imax, amp, adist \u001b[38;5;241m=\u001b[39m \u001b[43mtemplate_match\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mops\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miC\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miC2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweigh\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 234\u001b[0m yct \u001b[38;5;241m=\u001b[39m yweighted(yc, iC, adist, xy, device\u001b[38;5;241m=\u001b[39mdevice)\n\u001b[1;32m 235\u001b[0m nsp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(xy)\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/kilosort/spikedetect.py:156\u001b[0m, in \u001b[0;36mtemplate_match\u001b[0;34m(X, ops, iC, iC2, weigh, device)\u001b[0m\n\u001b[1;32m 154\u001b[0m Amaxs[:,\u001b[38;5;241m-\u001b[39mnt:] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 155\u001b[0m Amaxs \u001b[38;5;241m=\u001b[39m max_pool1d(Amaxs\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m), (\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39mnt0\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m), stride \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m, padding \u001b[38;5;241m=\u001b[39m nt0)\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m--> 156\u001b[0m xy \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlogical_and\u001b[49m\u001b[43m(\u001b[49m\u001b[43mAmaxs\u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43mAs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mAs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mops\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTh_universal\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnonzero\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 157\u001b[0m imax \u001b[38;5;241m=\u001b[39m imaxs[xy[:,\u001b[38;5;241m0\u001b[39m], xy[:,\u001b[38;5;241m1\u001b[39m]]\n\u001b[1;32m 158\u001b[0m amp \u001b[38;5;241m=\u001b[39m As[xy[:,\u001b[38;5;241m0\u001b[39m], xy[:,\u001b[38;5;241m1\u001b[39m]]\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "%%time\n", + "\n", + "# store sort summaries\n", + "sort_summary_all = []\n", + "\n", + "# loop through all birds / recordings\n", + "for this_bird in bird_rec_dict.keys():\n", + " # get session configurations\n", + " sess_all = bird_rec_dict[this_bird]\n", + " \n", + " # loop through session configurations\n", + " for this_sess_config in sess_all:\n", + " \n", + " # loop through sessions\n", + " for this_sess in this_sess_config['sess_par_list']:\n", + " log_dir = os.path.join('/mnt/cube/chronic_ephys/log', this_bird, this_sess)\n", + " \n", + " # build session parameter dictionary\n", + " sess_par = {'bird':this_bird,\n", + " 'sess':this_sess,\n", + " 'ephys_software':this_sess_config['ephys_software'],\n", + " 'sorter':this_sess_config['sorter'],\n", + " 'sort':this_sess_config['sort']}\n", + " # get epochs\n", + " sess_epochs = et.list_ephys_epochs(sess_par)\n", + " \n", + " for this_epoch in sess_epochs:\n", + " \n", + " # set output directory\n", + " epoch_struct = et.sgl_struct(sess_par,this_epoch,ephys_software=sess_par['ephys_software'])\n", + " sess_par['epoch'] = this_epoch\n", + " sort_path = epoch_struct['folders']['derived'] + '/{}/{}/'.format(sess_par['sorter'],sess_par['sort'])\n", + " sorting_analyzer_path = sort_path + 'sorting_analyzer/'\n", + " \n", + " # get spike sort log\n", + " try:\n", + " with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'r') as f:\n", + " log_message=f.readline() # read the first line of the log file\n", + " if log_message[:-1] == sess_par['bird']+' '+sess_par['sess']+' sort complete without error':\n", + " print(sess_par['bird'],sess_par['sess'],'already exists -- skipping sort')\n", + " run_proc = False\n", + " elif log_message[:-1] == sess_par['bird']+' '+sess_par['sess']+' sort failed':\n", + " if skip_failed:\n", + " print(sess_par['bird'],sess_par['sess'],'previously failed -- skipping sort')\n", + " run_proc = False\n", + " else:\n", + " run_proc = True\n", + " else: # uninterpretable log file\n", + " run_proc = True\n", + " except: # no existing log file\n", + " run_proc = True\n", + " \n", + " # run sort\n", + " if run_proc:\n", + " try:\n", + " print('___________',this_bird,this_sess,this_epoch,'___________')\n", + " # prepare recording for sorting\n", + " print('prep..')\n", + " if sess_par['ephys_software'] == 'sglx':\n", + " # load recording\n", + " rec_path = epoch_struct['folders']['sglx']\n", + " this_rec = si.read_spikeglx(folder_path=rec_path,stream_name='imec0.ap')\n", + " # save probe map prior to re-ordering for sorting\n", + " probe_df = this_rec.get_probe().to_dataframe()\n", + " probe_df.to_pickle(os.path.join(epoch_struct['folders']['derived'],'probe_map_df.pickle'))\n", + " # ibl destriping\n", + " this_rec = si.highpass_filter(recording=this_rec)\n", + " this_rec = si.phase_shift(recording=this_rec)\n", + " bad_good_channel_ids = si.detect_bad_channels(recording=this_rec)\n", + " if len(bad_good_channel_ids[0]) > 0:\n", + " this_rec = si.interpolate_bad_channels(recording=this_rec,bad_channel_ids=bad_good_channel_ids[0])\n", + " if this_sess_config['probe']['probe_type'] == 'neuropixels-2.0':\n", + " # highpass by shank\n", + " split_rec = this_rec.split_by(property='group',outputs='list') # split recording by shank\n", + " split_rec = [si.highpass_spatial_filter(recording=r,n_channel_pad=min(r.get_num_channels(),60)) for r in split_rec]\n", + " this_rec_p = si.aggregate_channels(split_rec) # recombine shanks\n", + " # stack shanks\n", + " p,_ = pm.stack_shanks(probe_df) # make new Probe object with shanks stacked\n", + " this_rec_p = this_rec.set_probe(p,group_mode='by_probe') # assign new Probe object to probe\n", + " else:\n", + " this_rec_p = si.highpass_spatial_filter(recording=this_rec)\n", + " elif sess_par['ephys_software'] =='oe':\n", + " # load recording\n", + " rec_path = [f.path for f in os.scandir(epoch_struct['folders']['oe']) if f.is_dir()][0]\n", + " this_rec = si.read_openephys(folder_path=rec_path)\n", + " # add probe\n", + " this_probe = pm.make_probes(this_sess_config['probe']['probe_type'],this_sess_config['probe']['probe_model']) # neuronexus, Buzsaki64\n", + " this_rec_p = this_rec.set_probe(this_probe,group_mode='by_shank')\n", + " # set sort params\n", + " this_rec_p = si.concatenate_recordings([this_rec_p])\n", + " sort_params = si.get_default_sorter_params(this_sess_config['sorter'])\n", + " for this_param in this_sess_config['sort_params'].keys():\n", + " sort_params[this_param] = this_sess_config['sort_params'][this_param]\n", + " # run sort\n", + " print('sort..')\n", + " torch.cuda.empty_cache()\n", + " this_sort = si.run_sorter(sorter_name=this_sess_config['sorter'],recording=this_rec_p,output_folder=sort_path,\n", + " remove_existing_folder=True,delete_output_folder=False,delete_container_files=False,\n", + " verbose=verbose,raise_error=raise_error,**sort_params)\n", + " torch.cuda.empty_cache()\n", + " # bandpass recording before running analyzer\n", + " this_rec_pf = si.bandpass_filter(recording=this_rec_p)\n", + " # run sorting analyzer\n", + " print('sorting analyzer..')\n", + " analyzer = si.create_sorting_analyzer(sorting=this_sort,recording=this_rec_pf,format=\"binary_folder\",\n", + " sparse=True,return_scaled=True,folder=sorting_analyzer_folder)\n", + " ext_compute_all = analyzer.get_computable_extensions()\n", + " for this_ext in ext_compute_all:\n", + " print(this_ext + '..')\n", + " analyzer.compute(this_ext)\n", + " \n", + " # mark complete\n", + " print('COMPLETE!!')\n", + "\n", + " # log complete sort\n", + " if not os.path.exists(log_dir): os.makedirs(log_dir)\n", + " with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'w') as f:\n", + " f.write(sess_par['bird']+' '+sess_par['sess']+' sort complete without error\\n\\n')\n", + " f.write('Sort method: '+this_sess_config['sorter']+'\\n\\n')\n", + " f.write('Sort params: '+str(sort_params)+'\\n\\n')\n", + " f.write('Computed quality metrics: '+str(metric_names)+'\\n\\n')\n", + " f.write('Failed quality metrics: '+str(bad_metrics)+'\\n')\n", + " sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'COMPLETE']\n", + " \n", + " except Exception as e:\n", + " # mark exception\n", + " print(\"An exception occurred:\", e)\n", + " \n", + " # log failed sort\n", + " if not os.path.exists(log_dir): os.makedirs(log_dir)\n", + " with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'w') as f:\n", + " f.write(sess_par['bird']+' '+sess_par['sess']+' sort failed\\n')\n", + " f.write(traceback.format_exc())\n", + " sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'FAIL']\n", + " else:\n", + " sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'EXISTS']\n", + " \n", + " # report and store sort summary\n", + " print(sort_summary)\n", + " sort_summary_all.append(sort_summary)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "spikeproc", + "language": "python", + "name": "spikeproc" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/3-sort_spikes_v0.101-checkpoint.py b/.ipynb_checkpoints/3-sort_spikes_v0.101-checkpoint.py new file mode 100644 index 0000000..214028d --- /dev/null +++ b/.ipynb_checkpoints/3-sort_spikes_v0.101-checkpoint.py @@ -0,0 +1,218 @@ +### Spike sort +# +# Script within the chronic ephys processing pipeline +# - 1-preprocess_acoustics +# - 2-curate_acoustics +# - **3-sort_spikes** +# - 4-curate_spikes +# +# Use the environment **spikeproc** to run this notebook + + +## Import packages +import numpy as np +import os +import pickle +os.environ["NPY_MATLAB_PATH"] = '/mnt/cube/chronic_ephys/code/npy-matlab' +os.environ["KILOSORT2_PATH"] = '/mnt/cube/chronic_ephys/code/Kilosort2' +os.environ["KILOSORT3_PATH"] = '/mnt/cube/chronic_ephys/code/Kilosort' +import spikeinterface.full as si +import sys +import traceback +import torch +sys.path.append('/mnt/cube/lo/envs/ceciestunepipe/') +from ceciestunepipe.file import bcistructure as et +from ceciestunepipe.mods import probe_maps as pm + + +## Set parameters +si.get_default_sorter_params('kilosort4') + +# non default spike sorting parameters +sort_params_dict_ks3 = {'minFR':0.001, 'minfr_goodchannels':0.001} # kilosort 3 +sort_params_dict_ks4_npx = {'batch_size':30000, 'nblocks':5, 'Th_universal':8, 'Th_learned':7, 'dmin':15, 'dminx':32} # kilosort 4, neuropixels (set dmin and dminx to true pitch) +sort_params_dict_ks4_nnx64 = {'nblocks':0, 'nearest_templates':64, + 'Th_universal':8, 'Th_learned':7} # kilosort 4, neuronexus 64 chan + +# waveform extraction parameters +wave_params_dict = {'ms_before':1, 'ms_after':2, 'max_spikes_per_unit':500, + 'sparse':True, 'num_spikes_for_sparsity':100, 'method':'radius', + 'radius_um':40, 'n_components':5, 'mode':'by_channel_local'} + +# print stuff +verbose = True + +# errors break sorting +raise_error = False + +# restrict sorting to a specific GPU +restrict_to_gpu = 1 # 0 1 None + +# use specific GPU if specified +if restrict_to_gpu is not None: + os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" + os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(restrict_to_gpu) + +# parallel processing params +job_kwargs = dict(n_jobs=28,chunk_duration="1s",progress_bar=False) +si.set_global_job_kwargs(**job_kwargs) + +# force processing of previous failed sorts +skip_failed = False + +# session info +bird_rec_dict = { + 'z_r5r13_24':[ + {'sess_par_list':['2024-08-06'], # sessions (will process all epochs within) + 'probe':{'probe_type':'neuropixels-2.0'}, # probe specs + 'sort':'sort_2', # label for this sort instance + 'sorter':'kilosort4', # sort method + 'sort_params':sort_params_dict_ks4_npx, # non-default sort params + 'wave_params':wave_params_dict, # waveform extraction params + 'ephys_software':'sglx' # sglx or oe + }, + ], +} + + + +## Run sorts + +# store sort summaries +sort_summary_all = [] + +# loop through all birds / recordings +for this_bird in bird_rec_dict.keys(): + # get session configurations + sess_all = bird_rec_dict[this_bird] + + # loop through session configurations + for this_sess_config in sess_all: + + # loop through sessions + for this_sess in this_sess_config['sess_par_list']: + log_dir = os.path.join('/mnt/cube/chronic_ephys/log', this_bird, this_sess) + + # build session parameter dictionary + sess_par = {'bird':this_bird, + 'sess':this_sess, + 'ephys_software':this_sess_config['ephys_software'], + 'sorter':this_sess_config['sorter'], + 'sort':this_sess_config['sort']} + # get epochs + sess_epochs = et.list_ephys_epochs(sess_par) + + for this_epoch in sess_epochs: + + # set output directory + epoch_struct = et.sgl_struct(sess_par,this_epoch,ephys_software=sess_par['ephys_software']) + sess_par['epoch'] = this_epoch + sort_path = epoch_struct['folders']['derived'] + '/{}/{}/'.format(sess_par['sorter'],sess_par['sort']) + sorting_analyzer_path = sort_path + 'sorting_analyzer/' + + # get spike sort log + try: + with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'r') as f: + log_message=f.readline() # read the first line of the log file + if log_message[:-1] == sess_par['bird']+' '+sess_par['sess']+' sort complete without error': + print(sess_par['bird'],sess_par['sess'],'already exists -- skipping sort') + run_proc = False + elif log_message[:-1] == sess_par['bird']+' '+sess_par['sess']+' sort failed': + if skip_failed: + print(sess_par['bird'],sess_par['sess'],'previously failed -- skipping sort') + run_proc = False + else: + run_proc = True + else: # uninterpretable log file + run_proc = True + except: # no existing log file + run_proc = True + + # run sort + if run_proc: + try: + print('___________',this_bird,this_sess,this_epoch,'___________') + # prepare recording for sorting + print('prep..') + if sess_par['ephys_software'] == 'sglx': + # load recording + rec_path = epoch_struct['folders']['sglx'] + this_rec = si.read_spikeglx(folder_path=rec_path,stream_name='imec0.ap') + # save probe map prior to re-ordering for sorting + probe_df = this_rec.get_probe().to_dataframe() + probe_df.to_pickle(os.path.join(epoch_struct['folders']['derived'],'probe_map_df.pickle')) + # ibl destriping + this_rec = si.highpass_filter(recording=this_rec) + this_rec = si.phase_shift(recording=this_rec) + bad_good_channel_ids = si.detect_bad_channels(recording=this_rec) + if len(bad_good_channel_ids[0]) > 0: + this_rec = si.interpolate_bad_channels(recording=this_rec,bad_channel_ids=bad_good_channel_ids[0]) + if this_sess_config['probe']['probe_type'] == 'neuropixels-2.0': + # highpass by shank + split_rec = this_rec.split_by(property='group',outputs='list') # split recording by shank + split_rec = [si.highpass_spatial_filter(recording=r,n_channel_pad=min(r.get_num_channels(),60)) for r in split_rec] + this_rec_p = si.aggregate_channels(split_rec) # recombine shanks + # stack shanks + p,_ = pm.stack_shanks(probe_df) # make new Probe object with shanks stacked + this_rec_p = this_rec.set_probe(p,group_mode='by_probe') # assign new Probe object to probe + else: + this_rec_p = si.highpass_spatial_filter(recording=this_rec) + elif sess_par['ephys_software'] =='oe': + # load recording + rec_path = [f.path for f in os.scandir(epoch_struct['folders']['oe']) if f.is_dir()][0] + this_rec = si.read_openephys(folder_path=rec_path) + # add probe + this_probe = pm.make_probes(this_sess_config['probe']['probe_type'],this_sess_config['probe']['probe_model']) # neuronexus, Buzsaki64 + this_rec_p = this_rec.set_probe(this_probe,group_mode='by_shank') + # set sort params + this_rec_p = si.concatenate_recordings([this_rec_p]) + sort_params = si.get_default_sorter_params(this_sess_config['sorter']) + for this_param in this_sess_config['sort_params'].keys(): + sort_params[this_param] = this_sess_config['sort_params'][this_param] + # run sort + print('sort..') + torch.cuda.empty_cache() + this_sort = si.run_sorter(sorter_name=this_sess_config['sorter'],recording=this_rec_p,output_folder=sort_path, + remove_existing_folder=True,delete_output_folder=False,delete_container_files=False, + verbose=verbose,raise_error=raise_error,**sort_params) + torch.cuda.empty_cache() + # bandpass recording before running analyzer + this_rec_pf = si.bandpass_filter(recording=this_rec_p) + # run sorting analyzer + print('sorting analyzer..') + analyzer = si.create_sorting_analyzer(sorting=this_sort,recording=this_rec_pf,format="binary_folder", + sparse=True,return_scaled=True,folder=sorting_analyzer_folder) + ext_compute_all = analyzer.get_computable_extensions() + for this_ext in ext_compute_all: + print(this_ext + '..') + analyzer.compute(this_ext) + + # mark complete + print('COMPLETE!!') + + # log complete sort + if not os.path.exists(log_dir): os.makedirs(log_dir) + with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'w') as f: + f.write(sess_par['bird']+' '+sess_par['sess']+' sort complete without error\n\n') + f.write('Sort method: '+this_sess_config['sorter']+'\n\n') + f.write('Sort params: '+str(sort_params)+'\n\n') + f.write('Computed quality metrics: '+str(metric_names)+'\n\n') + f.write('Failed quality metrics: '+str(bad_metrics)+'\n') + sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'COMPLETE'] + + except Exception as e: + # mark exception + print("An exception occurred:", e) + + # log failed sort + if not os.path.exists(log_dir): os.makedirs(log_dir) + with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'w') as f: + f.write(sess_par['bird']+' '+sess_par['sess']+' sort failed\n') + f.write(traceback.format_exc()) + sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'FAIL'] + else: + sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'EXISTS'] + + # report and store sort summary + print(sort_summary) + sort_summary_all.append(sort_summary) diff --git a/.ipynb_checkpoints/3-sort_spikes-checkpoint.py b/.ipynb_checkpoints/3.1-sort_spikes_concatenate-checkpoint.py similarity index 98% rename from .ipynb_checkpoints/3-sort_spikes-checkpoint.py rename to .ipynb_checkpoints/3.1-sort_spikes_concatenate-checkpoint.py index 4ba01e5..cb668ac 100644 --- a/.ipynb_checkpoints/3-sort_spikes-checkpoint.py +++ b/.ipynb_checkpoints/3.1-sort_spikes_concatenate-checkpoint.py @@ -19,6 +19,7 @@ import spikeinterface.full as si import sys import traceback +import torch sys.path.append('/mnt/cube/lo/envs/ceciestunepipe/') from ceciestunepipe.file import bcistructure as et from ceciestunepipe.mods import probe_maps as pm @@ -61,8 +62,8 @@ # session info bird_rec_dict = { - 'z_p5y10_23':[ - {'sess_par_list':['2024-05-16'], # sessions (will process all epochs within) + 'z_r5r13_24':[ + {'sess_par_list':['2024-08-06'], # sessions (will process all epochs within) 'probe':{'probe_type':'neuropixels-2.0'}, # probe specs 'sort':'sort_0', # label for this sort instance 'sorter':'kilosort4', # sort method @@ -169,9 +170,11 @@ sort_params[this_param] = this_sess_config['sort_params'][this_param] # run sort print('sort..') + torch.cuda.empty_cache() this_sort = si.run_sorter(sorter_name=this_sess_config['sorter'],recording=this_rec_p,output_folder=sort_folder, remove_existing_folder=True,delete_output_folder=False,delete_container_files=False, verbose=verbose,raise_error=raise_error,**sort_params) + torch.cuda.empty_cache() # bandpass recording before waveform extraction print('bandpass..') this_rec_pf = si.bandpass_filter(recording=this_rec_p) diff --git a/.ipynb_checkpoints/4-curate_spikes-checkpoint.ipynb b/.ipynb_checkpoints/4-curate_spikes-checkpoint.ipynb index 9adfaa4..5fc5258 100644 --- a/.ipynb_checkpoints/4-curate_spikes-checkpoint.ipynb +++ b/.ipynb_checkpoints/4-curate_spikes-checkpoint.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -40,13 +40,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sess_par = {\n", " 'bird':'z_y19o20_21', # bird ID\n", " 'sess':'2021-10-27', # session date\n", + " 'probe':{'probe_type':'neuropixels-1.0'}, # probe specs\n", " 'epoch':'1033_undirected_g0-1142_directed_g0', # epoch\n", " 'ephys_software':'sglx', # recording software, sglx or oe\n", " 'sorter':'kilosort3', # spike sorting algorithm\n", @@ -58,13 +59,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sess_par = {\n", " 'bird':'z_c5o30_23', # bird ID\n", " 'sess':'2023-06-15', # session date\n", + " 'probe':{'probe_type':'neuropixels-1.0'}, # probe specs\n", " 'epoch':'0913_g0', # epoch\n", " 'ephys_software':'sglx', # recording software, sglx or oe\n", " 'sorter':'kilosort3', # spike sorting algorithm\n", @@ -76,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -95,7 +97,26 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "sess_par = {\n", + " 'bird':'z_g9y18_23', # bird ID\n", + " 'sess':'2024-04-18', # session date\n", + " 'probe':{'probe_type':'neuropixels-1.0'}, # probe specs\n", + " 'epoch':'0959_g0', # epoch\n", + " 'ephys_software':'sglx', # recording software, sglx or oe\n", + " 'sorter':'kilosort4', # spike sorting algorithm\n", + " 'sort':'sort_0', # sort index\n", + "}\n", + "\n", + "labels = ['sua_1','sua_2','sua_3','mua','noise']" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -104,7 +125,6 @@ "isi_vr_thresh_high = 0.5\n", "snr_thresh_low = 1\n", "snr_thresh_high = 2\n", - "labels = []\n", "auto_merge_dict = {\n", " 'minimum_spikes':1000,\n", " 'maximum_distance_um':150,\n", @@ -132,19 +152,19 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/mnt/cube/chronic_ephys/der/z_p5y10_23/2024-05-16/sglx/1246_g0/kilosort4/sort_0/\n" + "/mnt/cube/chronic_ephys/der/z_g9y18_23/2024-04-18/sglx/0959_g0/kilosort4/sort_0/\n" ] } ], "source": [ - "sort_dir = '/net2/expData/speech_bci/derived_data/{}/{}/{}/{}/{}/{}/'.format(sess_par['bird'],sess_par['sess'],sess_par['ephys_software'],sess_par['epoch'],sess_par['sorter'],sess_par['sort'])\n", + "sort_dir = '/mnt/cube/chronic_ephys/der/{}/{}/{}/{}/{}/{}/'.format(sess_par['bird'],sess_par['sess'],sess_par['ephys_software'],sess_par['epoch'],sess_par['sorter'],sess_par['sort'])\n", "sort_path = sort_dir + 'sorter_output/'\n", "wave_path = sort_dir + 'waveforms/'\n", "metrics_path = wave_path + 'quality_metrics/metrics.csv'\n", @@ -153,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -170,15 +190,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "sua_1: 56\n", - "sua_2: 53\n", - "sua_3: 53\n", - "mua_4: 228\n", - "noise: 0\n", - "total: 337\n", - "KiloSortSortingExtractor: 337 units - 1 segments - 30.0kHz\n", - "WaveformExtractor: 384 channels - 337 units - 1 segments\n", - " before:30 after:60 n_per_units:500 - sparse\n" + "sua_1: 107\n", + "sua_2: 126\n", + "sua_3: 126\n", + "mua: 216\n", + "noise: 60\n", + "total: 526\n", + "KiloSortSortingExtractor: 526 units - 1 segments - 30.0kHz\n", + "WaveformExtractor: 384 channels - 526 units - 1 segments\n", + " before:29 after:59 n_per_units:500 - sparse\n" ] } ], @@ -200,7 +220,7 @@ "snr_label[np.where(sort.get_property('snr') > snr_thresh_high)[0]] = 'h' \n", "sort.set_property('snr_thresh',snr_label)\n", "quality_labels = np.full(sort.get_num_units(),'_____')\n", - "quality_labels[np.where(isi_vr_label == 'h')[0]] = 'mua_4'\n", + "quality_labels[np.where(isi_vr_label == 'h')[0]] = 'mua'\n", "quality_labels[np.where((isi_vr_label == 'l') & (snr_label == 'h'))[0]] = 'sua_1'\n", "quality_labels[np.where((isi_vr_label == 'l') & (snr_label == 'm'))[0]] = 'sua_2'\n", "quality_labels[np.where((isi_vr_label == 'm') & (snr_label == 'h'))[0]] = 'sua_2'\n", @@ -210,12 +230,12 @@ "print('sua_1:',len(np.where(quality_labels=='sua_1')[0]))\n", "print('sua_2:',len(np.where(quality_labels=='sua_2')[0]))\n", "print('sua_3:',len(np.where(quality_labels=='sua_2')[0]))\n", - "print('mua_4:',len(np.where(quality_labels=='mua_4')[0]))\n", + "print('mua:',len(np.where(quality_labels=='mua')[0]))\n", "print('noise:',len(np.where(quality_labels=='noise')[0]))\n", "print('total:',len(np.where(quality_labels=='sua_1')[0])+\n", " len(np.where(quality_labels=='sua_2')[0])+\n", " len(np.where(quality_labels=='sua_3')[0])+\n", - " len(np.where(quality_labels=='mua_4')[0])+\n", + " len(np.where(quality_labels=='mua')[0])+\n", " len(np.where(quality_labels=='noise')[0]))\n", "wave.sorting = sort\n", "unit_table_properties = ['quality_labels','KSLabel','isi_violations_ratio','snr','num_spikes']\n", @@ -225,14 +245,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[(63, 68)]\n" + "[(4, 9), (30, 32), (31, 32), (258, 259), (291, 301), (296, 298), (351, 355), (413, 451), (414, 425), (415, 425), (415, 429), (425, 431), (431, 437)]\n" ] } ], @@ -270,13 +290,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "IndexError", + "evalue": "boolean index did not match indexed array along dimension 0; dimension is 26133465 but corresponding boolean dimension is 26133466", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_sorting_summary\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwaveform_extractor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwave\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcuration\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbackend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msortingview\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munit_table_properties\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munit_table_properties\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel_choices\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/spikeinterface/widgets/sorting_summary.py:79\u001b[0m, in \u001b[0;36mSortingSummaryWidget.__init__\u001b[0;34m(self, waveform_extractor, unit_ids, sparsity, max_amplitudes_per_unit, min_similarity_for_correlograms, curation, unit_table_properties, label_choices, backend, **backend_kwargs)\u001b[0m\n\u001b[1;32m 66\u001b[0m unit_ids \u001b[38;5;241m=\u001b[39m sorting\u001b[38;5;241m.\u001b[39mget_unit_ids()\n\u001b[1;32m 68\u001b[0m plot_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m 69\u001b[0m waveform_extractor\u001b[38;5;241m=\u001b[39mwaveform_extractor,\n\u001b[1;32m 70\u001b[0m unit_ids\u001b[38;5;241m=\u001b[39munit_ids,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 76\u001b[0m max_amplitudes_per_unit\u001b[38;5;241m=\u001b[39mmax_amplitudes_per_unit,\n\u001b[1;32m 77\u001b[0m )\n\u001b[0;32m---> 79\u001b[0m \u001b[43mBaseWidget\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mplot_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbackend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbackend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mbackend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/spikeinterface/widgets/base.py:82\u001b[0m, in \u001b[0;36mBaseWidget.__init__\u001b[0;34m(self, data_plot, backend, immediate_plot, **backend_kwargs)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbackend_kwargs \u001b[38;5;241m=\u001b[39m backend_kwargs_\n\u001b[1;32m 81\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m immediate_plot:\n\u001b[0;32m---> 82\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_plot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/spikeinterface/widgets/base.py:103\u001b[0m, in \u001b[0;36mBaseWidget.do_plot\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdo_plot\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 102\u001b[0m func \u001b[38;5;241m=\u001b[39m 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min_similarity_for_correlograms \u001b[38;5;241m=\u001b[39m dp\u001b[38;5;241m.\u001b[39mmin_similarity_for_correlograms\n\u001b[1;32m 91\u001b[0m unit_ids \u001b[38;5;241m=\u001b[39m make_serializable(dp\u001b[38;5;241m.\u001b[39munit_ids)\n\u001b[0;32m---> 93\u001b[0m v_spike_amplitudes \u001b[38;5;241m=\u001b[39m \u001b[43mAmplitudesWidget\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 94\u001b[0m \u001b[43m \u001b[49m\u001b[43mwe\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 95\u001b[0m \u001b[43m \u001b[49m\u001b[43munit_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munit_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 96\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_spikes_per_unit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_amplitudes_per_unit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 97\u001b[0m \u001b[43m \u001b[49m\u001b[43mhide_unit_selector\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 98\u001b[0m \u001b[43m \u001b[49m\u001b[43mgenerate_url\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 99\u001b[0m \u001b[43m \u001b[49m\u001b[43mdisplay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[43m \u001b[49m\u001b[43mbackend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msortingview\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 101\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mview\n\u001b[1;32m 102\u001b[0m v_average_waveforms \u001b[38;5;241m=\u001b[39m UnitTemplatesWidget(\n\u001b[1;32m 103\u001b[0m we,\n\u001b[1;32m 104\u001b[0m unit_ids\u001b[38;5;241m=\u001b[39munit_ids,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 109\u001b[0m backend\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msortingview\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 110\u001b[0m )\u001b[38;5;241m.\u001b[39mview\n\u001b[1;32m 111\u001b[0m v_cross_correlograms \u001b[38;5;241m=\u001b[39m CrossCorrelogramsWidget(\n\u001b[1;32m 112\u001b[0m we,\n\u001b[1;32m 113\u001b[0m unit_ids\u001b[38;5;241m=\u001b[39munit_ids,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 118\u001b[0m backend\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msortingview\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 119\u001b[0m )\u001b[38;5;241m.\u001b[39mview\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/spikeinterface/widgets/amplitudes.py:56\u001b[0m, in \u001b[0;36mAmplitudesWidget.__init__\u001b[0;34m(self, waveform_extractor, unit_ids, unit_colors, segment_index, max_spikes_per_unit, hide_unit_selector, plot_histograms, bins, plot_legend, backend, **backend_kwargs)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcheck_extensions(waveform_extractor, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspike_amplitudes\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 55\u001b[0m sac \u001b[38;5;241m=\u001b[39m waveform_extractor\u001b[38;5;241m.\u001b[39mload_extension(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspike_amplitudes\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 56\u001b[0m amplitudes \u001b[38;5;241m=\u001b[39m \u001b[43msac\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mby_unit\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m unit_ids \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 59\u001b[0m unit_ids \u001b[38;5;241m=\u001b[39m sorting\u001b[38;5;241m.\u001b[39munit_ids\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/spikeinterface/postprocessing/spike_amplitudes.py:124\u001b[0m, in \u001b[0;36mSpikeAmplitudesCalculator.get_data\u001b[0;34m(self, outputs)\u001b[0m\n\u001b[1;32m 122\u001b[0m spike_labels \u001b[38;5;241m=\u001b[39m all_spikes[segment_index][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munit_index\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 123\u001b[0m mask \u001b[38;5;241m=\u001b[39m spike_labels \u001b[38;5;241m==\u001b[39m unit_index\n\u001b[0;32m--> 124\u001b[0m amps \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_extension_data\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mamplitude_segment_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43msegment_index\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmask\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 125\u001b[0m amplitudes_by_unit[segment_index][unit_id] \u001b[38;5;241m=\u001b[39m amps\n\u001b[1;32m 126\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m amplitudes_by_unit\n", + "\u001b[0;31mIndexError\u001b[0m: boolean index did not match indexed array along dimension 0; dimension is 26133465 but corresponding boolean dimension is 26133466" + ] + } + ], "source": [ - "# skip whatever metrics failed during sort\n", - "### remove this\n", - "si.plot_quality_metrics(wave, skip_metrics=['amplitude_cutoff'], backend=\"sortingview\");" + "si.plot_sorting_summary(waveform_extractor=wave, curation=True, backend='sortingview', unit_table_properties=unit_table_properties, label_choices=labels);" ] }, { @@ -285,7 +321,8 @@ "metadata": {}, "outputs": [], "source": [ - "si.plot_sorting_summary(waveform_extractor=wave, curation=True, backend='sortingview', label_choices=labels);" + "# # skip whatever metrics failed during sort\n", + "# si.plot_quality_metrics(wave, skip_metrics=['amplitude_cutoff', 'amplitude_median', 'amplitude_cv', 'sd_ratio'], backend=\"sortingview\");" ] }, { @@ -298,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -307,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -327,25 +364,26 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "435 units after curation:\n", - "- Units [68, 66] merged to 489\n", - "- Units [24, 31] merged to 490\n", - "- Units [25, 28, 32] merged to 491\n", - "- Units [39, 40] merged to 492\n", - "- Units [51, 53, 62] merged to 493\n", - "- Units [93, 95, 84] merged to 494\n", - "- Units [114, 112, 115, 116, 110] merged to 495\n", - "- Units [118, 108] merged to 496\n", - "- Units [111, 113] merged to 497\n", - "- Units [414, 409, 432, 426] merged to 498\n", - "- Units [164, 166, 165, 163] merged to 499\n" + "526 units after curation:\n", + "- Units [4, 9] merged to 526\n" + ] + }, + { + "ename": "IndexError", + "evalue": "index 0 is out of bounds for axis 0 with size 0", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[13], line 21\u001b[0m\n\u001b[1;32m 19\u001b[0m u_n_spks \u001b[38;5;241m=\u001b[39m sort\u001b[38;5;241m.\u001b[39mget_property(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnum_spikes\u001b[39m\u001b[38;5;124m'\u001b[39m)[idx]\n\u001b[1;32m 20\u001b[0m max_spikes_i \u001b[38;5;241m=\u001b[39m idx[np\u001b[38;5;241m.\u001b[39margmax(u_n_spks)]\n\u001b[0;32m---> 21\u001b[0m nui_i \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m(\u001b[49m\u001b[43munit_ids\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnui\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m this_metric \u001b[38;5;129;01min\u001b[39;00m metrics_list:\n\u001b[1;32m 23\u001b[0m sort_curated\u001b[38;5;241m.\u001b[39mget_property(this_metric)[nui_i] \u001b[38;5;241m=\u001b[39m sort\u001b[38;5;241m.\u001b[39mget_property(this_metric)[max_spikes_i]\n", + "\u001b[0;31mIndexError\u001b[0m: index 0 is out of bounds for axis 0 with size 0" ] } ], @@ -377,6 +415,73 @@ " orig_unit_ids[nui_i] = u" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use auto-curation & auto-merges\n", + "Created this in case spikeinterface curation module was throwing unsolvable errors" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "module 'spikeinterface.full' has no attribute 'create_sorting_analyzer'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_sorting_analyzer\u001b[49m()\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'spikeinterface.full' has no attribute 'create_sorting_analyzer'" + ] + } + ], + "source": [ + "si.create_sorting_analyzer()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sort_curated = sort\n", + "sort_curated = create_sorting_analyzer(sorting=sort, recording=recording)\n", + "sort_curated = sort_curated.merge_units(merge_unit_groups=merge_unit_groups)\n", + "\n", + "\n", + "\n", + "sort_curated = si.create_sorting_analyzer(sorting=sorting, recording=recording)\n", + "\n", + "sort_curated.labels = quality_labels\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -387,7 +492,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -401,7 +506,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 25, "metadata": { "scrolled": true }, @@ -434,107 +539,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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unitspike_trainContamPctAmplitudeKSLabelKSLabel_repeatUnnamed: 0num_spikesfiring_ratepresence_ratio...silhouettenn_hit_ratenn_miss_ratelabel_mualabel_1label_sualabel_2label_noiselabel_3orig_unit
434499[130, 15199, 15914, 21923, 23492, 24855, 36386...100.06.935584e-310muamua16353353.07.9209221.0...0.0918390.58550.035969TrueTrueTrueFalseTrueFalse[164, 166, 165, 163]
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" - ], - "text/plain": [ - " unit spike_train ContamPct \\\n", - "434 499 [130, 15199, 15914, 21923, 23492, 24855, 36386... 100.0 \n", - "\n", - " Amplitude KSLabel KSLabel_repeat Unnamed: 0 num_spikes \\\n", - "434 6.935584e-310 mua mua 163 53353.0 \n", - "\n", - " firing_rate presence_ratio ... silhouette nn_hit_rate nn_miss_rate \\\n", - "434 7.920922 1.0 ... 0.091839 0.5855 0.035969 \n", - "\n", - " label_mua label_1 label_sua label_2 label_noise label_3 \\\n", - "434 True True True False True False \n", - "\n", - " orig_unit \n", - "434 [164, 166, 165, 163] \n", - "\n", - "[1 rows x 31 columns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "spk_df.tail(1)" ] @@ -548,7 +555,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -579,7 +586,70 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# spk_df_save = spk_df" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['mua', 'sua_2', 'sua_1', 'noise', 'sua_3'], dtype=object)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spk_df.quality_labels.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "spk_df = spk_df[spk_df['quality_labels'] != 'noise']\n", + "# spk_df = spk_df[spk_df['quality_labels'] != 'mua']" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1907581/2241398020.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " spk_df['sort_key'] = spk_df['unit_locations'].apply(lambda x: x[1])\n" + ] + } + ], + "source": [ + "spk_df['sort_key'] = spk_df['unit_locations'].apply(lambda x: x[1])\n", + "spk_df = spk_df.sort_values(by='sort_key', ascending=False)\n", + "spk_df = spk_df.drop(columns='sort_key')" + ] + }, + { + "cell_type": "code", + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -592,7 +662,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 76, "metadata": {}, "outputs": [], "source": [ @@ -604,16 +674,16 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[8, 39, 52, 59, 64, 65, 69, 79, 127, 157, 177, 188]" + "[0, 1]" ] }, - "execution_count": 53, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -624,7 +694,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -637,7 +707,7 @@ ], "source": [ "# retrieve bout info\n", - "bout_idx = 52\n", + "bout_idx = 0\n", "if bout_df.loc[bout_idx, 'bout_check']:\n", " waveform = bout_df.loc[bout_idx, 'waveform']\n", " spectrogram = bout_df.loc[bout_idx, 'spectrogram']\n", @@ -651,7 +721,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -674,14 +744,14 @@ "spike_arr = make_raster(spk_df, spk_df.unit.to_list(), start_idx, end_idx)\n", "\n", "# reorder spike train by original unit\n", - "orig_unit = spk_df.orig_unit.to_list()\n", - "sorted_unit = np.argsort([np.mean(ou) for ou in orig_unit])\n", - "spike_arr = spike_arr[sorted_unit]" + "# orig_unit = spk_df.orig_unit.to_list()\n", + "# sorted_unit = np.argsort([np.mean(ou) for ou in orig_unit])\n", + "# spike_arr = spike_arr[sorted_unit]" ] }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 80, "metadata": { "tags": [] }, @@ -708,29 +778,14 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 81, "metadata": {}, "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4568797ea59a4ddd95c0ea2a5a423e15", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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", 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unitspike_trainunit_locationsabs_unit_locationsKSLabelKSLabel_repeatContamPctAmplitudeUnnamed: 0drift_ptp...rp_violationssliding_rp_violationsnrsync_spike_2sync_spike_4sync_spike_8isi_vr_threshsnr_threshquality_labelsorig_unit
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44[862897, 863614, 868115, 868783, 1060008, 1068...[36.141814351127465, 0.8324806436815474, 1.0][2036.1418143511276, 4300.832480643681, 301.0]goodgood0.039.74NaN...00.10018.6799930.1093290.0010300.000294lhsua_1[4]
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457457[10084244, 10704935, 27072676, 32277106, 36124...[1.8420477997573566, 3331.172939619838, 1.0][2001.8420477997574, 7631.172939619838, 301.0]muamua0.029.9457NaN...0NaN28.7723120.5964910.0263160.000000lhsua_1[457]
442442[7936614, 10084243, 10704934, 12391860, 129995...[1.6214948409849221, 3331.285092901966, 1.0][2001.621494840985, 7631.285092901966, 301.0]muamua0.023.9442NaN...0NaN27.6540800.6524820.0354610.000000lhsua_1[442]
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463463[7936608, 10084236, 10704928, 32277098, 424636...[2.2288280385007586, 3331.7660338278542, 1.0][2002.2288280385008, 7631.766033827855, 301.0]muamua0.035.4463NaN...0NaN31.7999441.0000000.4133330.013333lhsua_1[463]
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107 rows × 29 columns

\n", + "
" + ], + "text/plain": [ + " unit spike_train \\\n", + "7 7 [35264, 109017, 338275, 397747, 521440, 711554... \n", + "4 4 [862897, 863614, 868115, 868783, 1060008, 1068... \n", + "9 9 [1624, 5873, 11843, 13804, 27799, 35290, 39990... \n", + "2 2 [19768, 53114, 143131, 272889, 282368, 293545,... \n", + "8 8 [37733, 86645, 234208, 329221, 350448, 360985,... \n", + ".. ... ... \n", + "457 457 [10084244, 10704935, 27072676, 32277106, 36124... \n", + "442 442 [7936614, 10084243, 10704934, 12391860, 129995... \n", + "458 458 [7936607, 10084236, 10704927, 12391852, 129995... \n", + "463 463 [7936608, 10084236, 10704928, 32277098, 424636... \n", + "509 509 [83868, 271466, 290050, 470801, 541054, 546843... \n", + "\n", + " unit_locations \\\n", + "7 [31.173637502190775, -5.265224089156908, 1.0] \n", + "4 [36.141814351127465, 0.8324806436815474, 1.0] \n", + "9 [27.33218575392167, 3.9382619346881493, 5.6217... \n", + "2 [13.036294440970302, 10.323868440028003, 1.551... \n", + "8 [10.596645235287191, 14.858289054409507, 1.0] \n", + ".. ... \n", + "457 [1.8420477997573566, 3331.172939619838, 1.0] \n", + "442 [1.6214948409849221, 3331.285092901966, 1.0] \n", + "458 [2.0303124008646574, 3331.543695359722, 1.0] \n", + "463 [2.2288280385007586, 3331.7660338278542, 1.0] \n", + "509 [-0.015689899768380266, 3739.999820501117, 1.0] \n", + "\n", + " abs_unit_locations KSLabel KSLabel_repeat \\\n", + "7 [2031.1736375021908, 4294.734775910843, 301.0] good good \n", + "4 [2036.1418143511276, 4300.832480643681, 301.0] good good \n", + "9 [2027.3321857539217, 4303.938261934688, 305.62... good good \n", + "2 [2013.0362944409703, 4310.323868440028, 301.55... good good \n", + "8 [2010.5966452352873, 4314.858289054409, 301.0] good good \n", + ".. ... ... ... \n", + "457 [2001.8420477997574, 7631.172939619838, 301.0] mua mua \n", + "442 [2001.621494840985, 7631.285092901966, 301.0] mua mua \n", + "458 [2002.0303124008647, 7631.543695359722, 301.0] mua mua \n", + "463 [2002.2288280385008, 7631.766033827855, 301.0] mua mua \n", + "509 [1999.9843101002316, 8039.999820501117, 301.0] good good \n", + "\n", + " ContamPct Amplitude Unnamed: 0 drift_ptp ... rp_violations \\\n", + "7 0.0 21.6 7 NaN ... 0 \n", + "4 0.0 39.7 4 NaN ... 0 \n", + "9 5.6 23.2 9 NaN ... 6 \n", + "2 0.0 23.4 2 NaN ... 0 \n", + "8 2.8 28.6 8 NaN ... 6 \n", + ".. ... ... ... ... ... ... \n", + "457 0.0 29.9 457 NaN ... 0 \n", + "442 0.0 23.9 442 NaN ... 0 \n", + "458 0.0 46.4 458 NaN ... 0 \n", + "463 0.0 35.4 463 NaN ... 0 \n", + "509 0.0 16.8 509 NaN ... 0 \n", + "\n", + " sliding_rp_violation snr sync_spike_2 sync_spike_4 \\\n", + "7 0.245 17.554270 0.089304 0.001766 \n", + "4 0.100 18.679993 0.109329 0.001030 \n", + "9 0.050 17.821367 0.097092 0.000905 \n", + "2 0.075 20.067417 0.100830 0.001709 \n", + "8 0.035 22.820686 0.134239 0.001888 \n", + ".. ... ... ... ... \n", + "457 NaN 28.772312 0.596491 0.026316 \n", + "442 NaN 27.654080 0.652482 0.035461 \n", + "458 NaN 31.124668 0.851852 0.238095 \n", + "463 NaN 31.799944 1.000000 0.413333 \n", + "509 0.055 5.716975 0.094550 0.005691 \n", + "\n", + " sync_spike_8 isi_vr_thresh snr_thresh quality_labels orig_unit \n", + "7 0.000505 l h sua_1 [7] \n", + "4 0.000294 l h sua_1 [4] \n", + "9 0.000151 l h sua_1 [9] \n", + "2 0.000366 l h sua_1 [2] \n", + "8 0.000067 l h sua_1 [8] \n", + ".. ... ... ... ... ... \n", + "457 0.000000 l h sua_1 [457] \n", + "442 0.000000 l h sua_1 [442] \n", + "458 0.005291 l h sua_1 [458] \n", + "463 0.013333 l h sua_1 [463] \n", + "509 0.002845 l h sua_1 [509] \n", + "\n", + "[107 rows x 29 columns]" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spk_df[spk_df.quality_labels=='sua_1']" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/.ipynb_checkpoints/4-new_curate_spikes-checkpoint.ipynb b/.ipynb_checkpoints/4-new_curate_spikes-checkpoint.ipynb new file mode 100644 index 0000000..9487d23 --- /dev/null +++ b/.ipynb_checkpoints/4-new_curate_spikes-checkpoint.ipynb @@ -0,0 +1,707 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Set up" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "x=0" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pickle\n", + "import spikeinterface.full as si\n", + "import sys\n", + "sys.path.append('/mnt/cube/tsmcpher/code/')\n", + "from ephys_tsm import spike_util as su" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Prep data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Thresholds for quality metric curation\n", + "isi_vr_thresh = [0.1,0.5]\n", + "snr_thresh = [1,2]" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "# Probe absolute location (unit locations are relative)\n", + "# SI formatting: probe width (x), depth (y), othogonal (z)\n", + "# Assuming flat of probe extends M/L, foot is anterior, and vertical implant, use:\n", + "# Note at angle VENTRAL is how far probe is lowered into brain\n", + "# Angle is deviation from vertical\n", + "# M/L (x), D/V (y), A/P (z)\n", + "# s_b1484_24, HVC right: [3800,-200,800], angle: 0\n", + "# s_b1357_23, HVC left: [-3500,-800,1000], angle: 0\n", + "# s_b1253_21, RA right: [3380,-4500,630], angle: 0\n", + "# s_b1253_21, RA left: [-3380,-4500,630], angle: 52\n", + "probe_angle_deg = 38\n", + "probe_abs_loc = np.array([-3000,-4000,500])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['1108_g0', '0806_g0', '1410_g0']\n" + ] + } + ], + "source": [ + "bird_in = 's_b1360_24'\n", + "sess_in = '2024-07-30'\n", + "ephys_software_in = 'sglx'\n", + "path_in = '/mnt/cube/chronic_ephys/der/{}/{}/{}/'.format(bird_in,sess_in,ephys_software_in)\n", + "epochs = os.listdir(path_in)\n", + "print(epochs)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "({'bird': 's_b1360_24',\n", + " 'sess': '2024-07-30',\n", + " 'epoch': '0806_g0',\n", + " 'ephys_software': 'sglx',\n", + " 'sorter': 'kilosort4',\n", + " 'sort': 0},\n", + " '/mnt/cube/chronic_ephys/der/s_b1360_24/2024-07-30/sglx/0806_g0/kilosort4/0/')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "epoch_i = 1\n", + "epoch_in = epochs[epoch_i]\n", + "sess_par = {\n", + " 'bird':bird_in, # bird id\n", + " 'sess':sess_in, # session date\n", + " 'epoch':epoch_in, # epoch\n", + " 'ephys_software':ephys_software_in, # recording software, sglx or oe\n", + " 'sorter':'kilosort4', # spike sorting algorithm\n", + " 'sort':0} # sort index\n", + "sort_dir = '/mnt/cube/chronic_ephys/der/{}/{}/{}/{}/{}/{}/'.format(\n", + " sess_par['bird'],sess_par['sess'],sess_par['ephys_software'],\n", + " sess_par['epoch'],sess_par['sorter'],sess_par['sort'])\n", + "sess_par,sort_dir" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sorting analyzer..\n", + "sua_1: 49\n", + "sua_2: 65\n", + "sua_3: 65\n", + "mua_4: 194\n", + "noise: 9\n", + "total: 319\n", + "NumpySorting: 319 units - 1 segments - 30.0kHz\n", + "SortingAnalyzer: 384 channels - 319 units - 1 segments - binary_folder - sparse - has recording\n", + "Loaded 14 extensions: correlograms, template_similarity, principal_components, random_spikes, templates, unit_locations, waveforms, template_metrics, isi_histograms, amplitude_scalings, noise_levels, spike_locations, quality_metrics, spike_amplitudes\n" + ] + } + ], + "source": [ + "sort_path = sort_dir + 'sorter_output/'\n", + "analyzer_path = sort_dir + 'sorting_analyzer/'\n", + "waveforms_path = sort_dir + 'waveforms/'\n", + "if os.path.exists(analyzer_path):\n", + " print('sorting analyzer..')\n", + " use_analyzer_not_wave = True\n", + " metrics_path = analyzer_path + 'extensions/quality_metrics/metrics.csv'\n", + " analyzer = si.load_sorting_analyzer(analyzer_path)\n", + "else:\n", + " if os.path.exists(waveforms_path):\n", + " print('waveforms..')\n", + " use_analyzer_not_wave = False\n", + " metrics_path = waveforms_path + 'quality_metrics/metrics.csv'\n", + " analyzer = si.load_waveforms(waveforms_path)\n", + " else: print('no analyzer or waveforms..')\n", + "metrics_pd = pd.read_csv(metrics_path)\n", + "metrics_list = metrics_pd.keys().tolist()\n", + "for this_metric in metrics_list:\n", + " analyzer.sorting.set_property(this_metric,metrics_pd[this_metric].values)\n", + "isi_vr_label = np.full(analyzer.sorting.get_num_units(),'l')\n", + "isi_vr_label[np.where((analyzer.sorting.get_property('isi_violations_ratio') > isi_vr_thresh[0]) & \n", + " (analyzer.sorting.get_property('isi_violations_ratio') < isi_vr_thresh[1]))[0]] = 'm'\n", + "isi_vr_label[np.where(analyzer.sorting.get_property('isi_violations_ratio') > isi_vr_thresh[1])[0]] = 'h' \n", + "analyzer.sorting.set_property('isi_vr_thresh',isi_vr_label)\n", + "snr_label = np.full(analyzer.sorting.get_num_units(),'l')\n", + "snr_label[np.where((analyzer.sorting.get_property('snr') > snr_thresh[0]) & \n", + " (analyzer.sorting.get_property('snr') < snr_thresh[1]))[0]] = 'm'\n", + "snr_label[np.where(analyzer.sorting.get_property('snr') > snr_thresh[1])[0]] = 'h' \n", + "analyzer.sorting.set_property('snr_thresh',snr_label)\n", + "quality_labels = np.full(analyzer.sorting.get_num_units(),'_____')\n", + "quality_labels[np.where(isi_vr_label == 'h')[0]] = 'mua_4'\n", + "quality_labels[np.where((isi_vr_label == 'l') & (snr_label == 'h'))[0]] = 'sua_1'\n", + "quality_labels[np.where((isi_vr_label == 'l') & (snr_label == 'm'))[0]] = 'sua_2'\n", + "quality_labels[np.where((isi_vr_label == 'm') & (snr_label == 'h'))[0]] = 'sua_2'\n", + "quality_labels[np.where((isi_vr_label == 'm') & (snr_label == 'm'))[0]] = 'sua_3'\n", + "quality_labels[np.where(snr_label == 'l')[0]] = 'noise'\n", + "analyzer.sorting.set_property('quality_labels',quality_labels)\n", + "su.print_unit_counts(quality_labels)\n", + "print(analyzer.sorting); print(analyzer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Auto curation" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "similarity_correlograms..\n", + "[]\n", + "NumpySorting: 319 units - 1 segments - 30.0kHz\n", + "x_contaminations..\n", + "[[136, 143]]\n", + "MergeUnitsSorting: 318 units - 1 segments - 30.0kHz\n", + "temporal_splits..\n", + "[]\n", + "NumpySorting: 319 units - 1 segments - 30.0kHz\n", + "feature_neighbors..\n", + "[[1], [33, 2, 34, 5, 15, 16, 20, 21, 30, 31], [3], [4], [22], [26], [39], [40], [41], [44], [45], [48], [51, 54, 73, 59], [56], [61], [65], [66], [68], [69], [71], [72], [75], [76], [80], [82], [84], [88], [100], [101], [104], [109], [131, 136, 143], [137], [200], [202], [203], [208], [216], [217], [226], [232], [233], [256, 261], [293]]\n", + "MergeUnitsSorting: 304 units - 1 segments - 30.0kHz\n", + "CPU times: user 5min 7s, sys: 8.66 s, total: 5min 16s\n", + "Wall time: 4min 32s\n" + ] + } + ], + "source": [ + "%%time\n", + "merges_auto_init_all = []\n", + "merges_auto_all = []\n", + "sort_auto_all = []\n", + "\n", + "presets_all = ['similarity_correlograms','x_contaminations','temporal_splits','feature_neighbors']\n", + "for this_preset in presets_all:\n", + " print(this_preset + '..')\n", + " merges_auto_init = si.get_potential_auto_merge(analyzer,preset=this_preset)\n", + " merges_auto = su.merge_lists(merges_auto_init)\n", + " print(merges_auto)\n", + " if len(merges_auto) > 0: sort_auto = si.MergeUnitsSorting(analyzer.sorting,merges_auto)\n", + " else: sort_auto = analyzer.sorting\n", + " print(sort_auto)\n", + " \n", + " merges_auto_init_all.append(merges_auto_init)\n", + " merges_auto_all.append(merges_auto_all)\n", + " sort_auto_all.append(sort_auto)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Manual curation" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing sha1 of /home/AD/tsmcpher/.kachery-cloud/tmp_chVMxZKl/file.dat\n", + "https://figurl.org/f?v=npm://@fi-sci/figurl-sortingview@12/dist&d=sha1://8bd38be2b2a07d44872172e04718c9c740300c18\n" + ] + } + ], + "source": [ + "unit_table_properties = ['quality_labels','KSLabel','isi_violations_ratio','snr','num_spikes']\n", + "label_choices = ['sua_1','sua_2','sua_3','mua_4','noise']\n", + "pss = si.plot_sorting_summary(analyzer,curation=True,backend='sortingview',\n", + " unit_table_properties=unit_table_properties,label_choices=label_choices)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# sha_uri = 'sha1://eb06f8e626926b4dc905f7561617b2978530b2f7'" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# %%capture cap\n", + "# sort_curated = si.apply_sortingview_curation(sorting=analyzer.sorting,uri_or_json=sha_uri,verbose=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# merge_str_all = cap.stdout\n", + "# merge_starts = su.str_find('[',merge_str_all)\n", + "# merge_stops = su.str_find(']',merge_str_all)\n", + "# merges_curated = [merge_str_all[merge_starts[i]+1:merge_stops[i]].split(',') for i in range(len(merge_starts))]\n", + "# quality_labels = np.full(sort_curated.get_num_units(),'_____')\n", + "# for this_label in label_choices:\n", + "# quality_labels[np.where(sort_curated.get_property(this_label) == True)[0]] = this_label\n", + "# sort_curated.set_property('quality_labels',quality_labels)\n", + "# su.print_unit_counts(quality_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Depth labels" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hvc 44\n", + "ncm 32\n", + "bad 117\n", + "bad 126\n" + ] + }, + { + "data": { + "text/plain": [ + "{'hvc': [[1910, None]],\n", + " 'ncm': [[840, 1890]],\n", + " 'bad': [[None, 840], [1890, 1910]],\n", + " 'depth_labels': array(['bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'ncm', 'ncm', 'bad', 'ncm', 'bad', 'ncm', 'bad', 'ncm', 'bad',\n", + " 'ncm', 'bad', 'ncm', 'ncm', 'ncm', 'ncm', 'ncm', 'ncm', 'ncm',\n", + " 'ncm', 'ncm', 'ncm', 'ncm', 'ncm', 'ncm', 'ncm', 'ncm', 'ncm',\n", + " 'ncm', 'bad', 'ncm', 'bad', 'ncm', 'ncm', 'ncm', 'bad', 'ncm',\n", + " 'ncm', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'ncm', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'ncm', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'hvc', 'bad', 'hvc', 'bad',\n", + " 'bad', 'ncm', 'hvc', 'hvc', 'hvc', 'hvc', 'hvc', 'bad', 'bad',\n", + " 'hvc', 'hvc', 'hvc', 'bad', 'hvc', 'bad', 'hvc', 'bad', 'bad',\n", + " 'hvc', 'hvc', 'bad', 'hvc', 'hvc', 'hvc', 'bad', 'bad', 'hvc',\n", + " 'hvc', 'hvc', 'bad', 'bad', 'hvc', 'bad', 'bad', 'hvc', 'hvc',\n", + " 'bad', 'hvc', 'hvc', 'bad', 'bad', 'bad', 'hvc', 'bad', 'hvc',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'hvc', 'bad', 'bad', 'bad', 'hvc', 'hvc', 'bad', 'bad', 'bad',\n", + " 'hvc', 'bad', 'bad', 'bad', 'bad', 'hvc', 'bad', 'bad', 'bad',\n", + " 'bad', 'hvc', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'hvc', 'bad', 'hvc', 'hvc', 'hvc', 'bad', 'bad', 'hvc',\n", + " 'hvc', 'bad', 'hvc', 'hvc', 'bad', 'hvc', 'bad', 'hvc', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'hvc', 'bad'], dtype='= lower_bound) & (probe_depth <= upper_bound))[0])\n", + " labels_is_all.append(label_is)\n", + " depth_labels[label_is] = this_label\n", + " print(this_label,len(label_is))\n", + "assert len(sum(labels_is_all,[])) == len(depth_labels)\n", + "depth_dict['depth_labels'] = depth_labels\n", + "depth_dict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Save out" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "sort_in = sort_auto_all[0] # sort_curated sort_auto\n", + "merges_in = []#merges_auto_all[0] # merges_curated merges_auto" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "319 units after curation:\n" + ] + } + ], + "source": [ + "# get unit IDs\n", + "unit_ids = sort_in.get_unit_ids()\n", + "print(f\"{len(unit_ids)} units after curation:\")\n", + "iui = analyzer.sorting.get_unit_ids() # initial unit IDs\n", + "utm = [[int(x) for x in m] for m in merges_in] # units to merge\n", + "nui = np.arange(max(iui)+1, max(iui)+len(utm)+1) # new unit IDs\n", + "# set merged properties to unit with highest original spike rate\n", + "orig_unit_ids = [[x] for x in unit_ids]\n", + "not_max_spikes_is_all = []\n", + "for i, u in enumerate(utm):\n", + " print(f'- Units {u} merged to {nui[i]}')\n", + " idx = [np.where(iui == x)[0][0] for x in u]\n", + " u_n_spks = analyzer.sorting.get_property('num_spikes')[idx]\n", + " max_spikes_i = idx[np.argmax(u_n_spks)]\n", + " not_max_spikes_is = [idx[nmi] for nmi in list(np.where(idx != max_spikes_i)[0])]\n", + " nui_i = np.where(unit_ids == nui[i])[0][0]\n", + " for this_metric in analyzer.sorting.get_property_keys():\n", + " sort_in.get_property(this_metric)[nui_i] = analyzer.sorting.get_property(this_metric)[max_spikes_i]\n", + " sort_in.get_property('num_spikes')[nui_i] = np.sum(u_n_spks)\n", + " not_max_spikes_is_all.append(not_max_spikes_is)\n", + "if len(not_max_spikes_is_all) > 0:\n", + " merged_unit_locations = np.delete(np.array(unit_locations),np.concatenate(not_max_spikes_is_all),axis=0)\n", + "else:\n", + " merged_unit_locations = unit_locations\n", + "sort_final = sort_in" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'probe_abs_loc' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[66], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m spk_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munit_locations\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(merged_unit_locations)\n\u001b[1;32m 4\u001b[0m spk_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdepth_labels\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(depth_labels)\n\u001b[0;32m----> 5\u001b[0m spk_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprobe_location\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mspk_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43msu\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_probe_loc\u001b[49m\u001b[43m,\u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m spk_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprobe_angle\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m spk_df\u001b[38;5;241m.\u001b[39mapply(su\u001b[38;5;241m.\u001b[39madd_probe_angle,axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m prop \u001b[38;5;129;01min\u001b[39;00m sort_final\u001b[38;5;241m.\u001b[39mget_property_keys():\n", + "File \u001b[0;32m/mnt/cube/tsmcpher/envs/spike_prov_NEWNEW/lib/python3.9/site-packages/pandas/core/frame.py:10374\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[0;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 10360\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[1;32m 10362\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[1;32m 10363\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 10364\u001b[0m func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10372\u001b[0m kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[1;32m 10373\u001b[0m )\n\u001b[0;32m> 10374\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m/mnt/cube/tsmcpher/envs/spike_prov_NEWNEW/lib/python3.9/site-packages/pandas/core/apply.py:916\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[1;32m 914\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw(engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine, engine_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine_kwargs)\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/mnt/cube/tsmcpher/envs/spike_prov_NEWNEW/lib/python3.9/site-packages/pandas/core/apply.py:1063\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1061\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 1062\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 1063\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_series_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1064\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1065\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_numba()\n", + "File \u001b[0;32m/mnt/cube/tsmcpher/envs/spike_prov_NEWNEW/lib/python3.9/site-packages/pandas/core/apply.py:1081\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1078\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 1079\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[1;32m 1080\u001b[0m \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[0;32m-> 1081\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1082\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[1;32m 1083\u001b[0m \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[1;32m 1084\u001b[0m \u001b[38;5;66;03m# series_generator will swap out the underlying data\u001b[39;00m\n\u001b[1;32m 1085\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", + "File \u001b[0;32m/mnt/cube/tsmcpher/code/ephys_tsm/spike_util.py:492\u001b[0m, in \u001b[0;36madd_probe_loc\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m 491\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21madd_probe_loc\u001b[39m(row):\n\u001b[0;32m--> 492\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprobe_abs_loc\u001b[49m\n", + "\u001b[0;31mNameError\u001b[0m: name 'probe_abs_loc' is not defined" + ] + } + ], + "source": [ + "spk_df = pd.DataFrame({'unit': unit_ids})\n", + "spk_df['spike_train'] = spk_df['unit'].apply(lambda x: sort_final.get_unit_spike_train(unit_id=x, segment_index=0))\n", + "spk_df['unit_locations'] = list(merged_unit_locations)\n", + "spk_df['depth_labels'] = list(depth_labels)\n", + "spk_df['probe_location'] = spk_df.apply(su.add_probe_loc,axis=1)\n", + "spk_df['probe_angle'] = spk_df.apply(su.add_probe_angle,axis=1)\n", + "for prop in sort_final.get_property_keys():\n", + " spk_df[prop] = sort_final.get_property(prop)\n", + "spk_df = spk_df.drop(columns=['original_cluster_id'])\n", + "spk_df['orig_unit'] = orig_unit_ids\n", + "spk_df.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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unitspike_trainunit_locationsdepth_labels
00[123951, 147783, 152969, 153105, 191184, 23773...[-14.073065567470673, 17.670251844640863, 1.40...bad
11[73474, 96036, 138946, 160850, 173590, 266314,...[-6.960734860035025, 36.56431000266426, 9.0094...bad
22[363629, 1322175, 1322249, 2609953, 4171529, 4...[28.243145302930486, 16.397182659697762, 1.009...bad
33[501, 1010, 1484, 1906, 2419, 3113, 3427, 3741...[3.345220529178426, 32.80625933021123, 1.00000...bad
44[3434, 13800, 23463, 32599, 33698, 36838, 3748...[6.003732926989229, 59.70841970244248, 1.00000...bad
...............
314314[1321901, 1642489, 3872859, 4350387, 5900139, ...[32.29415469029789, 1903.1954691834296, 2.2189...bad
315315[2116, 2620, 6116, 10611, 11111, 11118, 11613,...[28.47801948110492, 1901.4852407894023, 1.0001...bad
316316[107, 605, 636, 1107, 1608, 3606, 3635, 4134, ...[28.080712798162818, 1901.98421932352, 1.00004...bad
317317[345041, 361299, 363166, 793182, 1313623, 1643...[21.119370966110907, 3828.3214553920816, 1.000...hvc
318318[793696, 793827, 794608, 1309637, 1310822, 131...[27.51656260594869, 1900.2085128547862, 3.0726...bad
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319 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " unit spike_train \\\n", + "0 0 [123951, 147783, 152969, 153105, 191184, 23773... \n", + "1 1 [73474, 96036, 138946, 160850, 173590, 266314,... \n", + "2 2 [363629, 1322175, 1322249, 2609953, 4171529, 4... \n", + "3 3 [501, 1010, 1484, 1906, 2419, 3113, 3427, 3741... \n", + "4 4 [3434, 13800, 23463, 32599, 33698, 36838, 3748... \n", + ".. ... ... \n", + "314 314 [1321901, 1642489, 3872859, 4350387, 5900139, ... \n", + "315 315 [2116, 2620, 6116, 10611, 11111, 11118, 11613,... \n", + "316 316 [107, 605, 636, 1107, 1608, 3606, 3635, 4134, ... \n", + "317 317 [345041, 361299, 363166, 793182, 1313623, 1643... \n", + "318 318 [793696, 793827, 794608, 1309637, 1310822, 131... \n", + "\n", + " unit_locations depth_labels \n", + "0 [-14.073065567470673, 17.670251844640863, 1.40... bad \n", + "1 [-6.960734860035025, 36.56431000266426, 9.0094... bad \n", + "2 [28.243145302930486, 16.397182659697762, 1.009... bad \n", + "3 [3.345220529178426, 32.80625933021123, 1.00000... bad \n", + "4 [6.003732926989229, 59.70841970244248, 1.00000... bad \n", + ".. ... ... \n", + "314 [32.29415469029789, 1903.1954691834296, 2.2189... bad \n", + "315 [28.47801948110492, 1901.4852407894023, 1.0001... bad \n", + "316 [28.080712798162818, 1901.98421932352, 1.00004... bad \n", + "317 [21.119370966110907, 3828.3214553920816, 1.000... hvc \n", + "318 [27.51656260594869, 1900.2085128547862, 3.0726... bad \n", + "\n", + "[319 rows x 4 columns]" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spk_df" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(sort_dir,'spk_df.pkl'), 'wb') as handle:\n", + " pickle.dump(spk_df,handle)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:tsmcpher-spike_prov_NEWNEW]", + "language": "python", + "name": "conda-env-tsmcpher-spike_prov_NEWNEW-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/0-reference_files.ipynb b/0-reference_files.ipynb index 69cd7ae..0e0437b 100755 --- a/0-reference_files.ipynb +++ b/0-reference_files.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "id": "ecd1bc70-88ed-447f-a19f-7f4f3d9530d6", "metadata": {}, "outputs": [], @@ -25,8 +25,8 @@ "from ceciestunepipe.file import bcistructure as et\n", "\n", "sess_par = {\n", - " 'bird': 'z_p5y10_23',\n", - " 'sess': '2024-05-17'\n", + " 'bird': 'z_r5r13_24',\n", + " 'sess': '2024-08-08'\n", "}\n", "\n", "rig_dict_path = et.get_exp_struct(sess_par['bird'],sess_par['sess'])['files']['rig']" @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "id": "c130bc8c-eb91-4e28-99b5-29ac6269e435", "metadata": {}, "outputs": [], @@ -50,7 +50,8 @@ " 'microphone_M': 'adc-00',\n", " 'microphone_F': 'adc-05',\n", " 'wav_stim': 'adc-01',\n", - " 'wav_syn': 'adc-02'},\n", + " 'wav_syn': 'adc-02'\n", + " },\n", " 'port': {\n", " 'probe_0': 'A-'\n", " }\n", @@ -58,7 +59,7 @@ " 'probe': {\n", " 'probe_0': { # will always be probe_0 unless multiple recordings from different probes on the same day\n", " 'model': 'NP2013',\n", - " 'serial': '22420012794', # update every time\n", + " 'serial': '22420013432', # update every time\n", " 'headstage': '23280347'\n", " }\n", " }\n", @@ -67,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "id": "8a27aec7-32d6-4523-912f-c0eaaebed071", "metadata": {}, "outputs": [], diff --git a/1-preprocess_acoustics-zebra_finch.ipynb b/1-preprocess_acoustics-zebra_finch.ipynb index 67683a2..f34b77e 100755 --- a/1-preprocess_acoustics-zebra_finch.ipynb +++ b/1-preprocess_acoustics-zebra_finch.ipynb @@ -26,7 +26,7 @@ "text": [ "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/spikeextractors/__init__.py:21: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n", " if StrictVersion(h5py.__version__) > '2.10.0':\n", - "2024-05-20 09:25:54,050 root INFO Running on pakhi.ucsd.edu\n" + "2024-08-14 19:59:35,803 root INFO Running on pakhi.ucsd.edu\n" ] }, { @@ -224,8 +224,21 @@ "# 'sort':'sort_0', # sort info\n", "# 'ephys_software':'sglx' # sglx or oe\n", "# }],\n", - " 'z_p5y10_23':[\n", - " {'sess_par_list':['2024-05-16','2024-05-17'], # sessions with this configuration\n", + " # 'z_p5y10_23':[\n", + " # {'sess_par_list':['2024-05-16','2024-05-17'], # sessions with this configuration\n", + " # 'stim_sess_list':[], # sessions where stimuli were presented\n", + " # 'mic_list':['microphone_M','microphone_F'], # list of mics of interest, by signal name in rig.json\n", + " # 'adc_list':[], # list of adc channels of interest\n", + " # 'stim_list':['wav_stim'], # list of adc chans with the stimulus\n", + " # 'nidq_ttl_list':[], # list of TTL signals form the nidq digital inputs to extract (besides the 'sync')\n", + " # 'ref_stream':'ap_0', # what to synchronize everything to (sglx only, oe already synced)\n", + " # 'trial_tag_chan':2, # sglx, what was the tag channel in the stimulus wave (this should come from meta et. al)\n", + " # 'on_signal':1, # sglx, whether signal on is hi or lo\n", + " # 'sort':'sort_0', # sort info\n", + " # 'ephys_software':'sglx' # sglx or oe\n", + " # }]\n", + " 'z_r5r13_24':[\n", + " {'sess_par_list':['2024-08-05','2024-08-05_reduced_chans','2024-08-06','2024-08-07','2024-08-08'], # sessions with this configuration\n", " 'stim_sess_list':[], # sessions where stimuli were presented\n", " 'mic_list':['microphone_M','microphone_F'], # list of mics of interest, by signal name in rig.json\n", " 'adc_list':[], # list of adc channels of interest\n", @@ -252,112 +265,24 @@ "execution_count": 4, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/mnt/cube/lo/envs/ceciestunepipe/ceciestunepipe/util/spikeextractors/extractors/spikeglxrecordingextractor/readSGLX.py:226: RuntimeWarning: divide by zero encountered in double_scalars\n", - " conv[i] = fI2V / APgain[k]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "z_p5y10_23 2024-05-16 no log file found -- running preprocessing\n", - "z_p5y10_23 2024-05-16 sglx preprocessing session..\n", - "derived data folder exists..\n", - "preprocessing..\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/mnt/cube/lo/envs/ceciestunepipe/ceciestunepipe/util/spikeextractors/extractors/spikeglxrecordingextractor/readSGLX.py:226: RuntimeWarning: divide by zero encountered in double_scalars\n", - " conv[i] = fI2V / APgain[k]\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "z_p5y10_23 2024-05-16 deriving bout information..\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "fa3c2c1a19424d18ae4509912379e640", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/10 [00:00 '2.10.0':\n", - "2024-04-29 09:39:13,742 root INFO Running on pakhi.ucsd.edu\n" + "2024-08-14 14:51:59,372 root INFO Running on pakhi.ucsd.edu\n" ] }, { @@ -144,8 +144,8 @@ "source": [ "# single session params\n", "sess_par = {\n", - " 'bird':'z_g9y18_23',\n", - " 'sess':'2024-04-19',\n", + " 'bird':'z_r5r13_24',\n", + " 'sess':'2024-08-07',\n", " 'stim_sess':[], # sessions where stimuli were presented\n", " 'mic_list':['microphone_M','microphone_F'], # list of mics of interest, by signal name in rig.json\n", " 'adc_list':[], # list of adc channels of interest\n", @@ -181,36 +181,16 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "derived data folder exists..\n", - "preprocessing..\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in double_scalars\n", - " ret = ret.dtype.type(ret / rcount)\n", - "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in double_scalars\n", - " ret = ret.dtype.type(ret / rcount)\n", - "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in double_scalars\n", - " ret = ret.dtype.type(ret / rcount)\n", - "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.\n", - " return _methods._mean(a, axis=axis, dtype=dtype,\n", - "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in double_scalars\n", - " ret = ret.dtype.type(ret / rcount)\n" + "preprocessing..\n", + "done.\n" ] } ], @@ -219,7 +199,8 @@ "if sess_par['ephys_software'] == 'sglx':\n", " preproc_sglx.preprocess_session(sess_par,force_redo=True)\n", "elif sess_par['ephys_software'] == 'oe':\n", - " preproc_oe.preprocess_session(sess_par,force_redo=True)" + " preproc_oe.preprocess_session(sess_par,force_redo=True)\n", + "print('done.')" ] }, { @@ -230,12 +211,82 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a9ab6f0e54654dce828b8f7d0d2f515a", + "model_id": "6bbefeb74d6a4f868ff3c340c824cc54", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/8 [00:00 27\u001b[0m \u001b[43msy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msync_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43mall_syn_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43msess_par\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mref_stream\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mforce\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;66;03m# load bouts\u001b[39;00m\n\u001b[1;32m 30\u001b[0m hparams, bout_pd \u001b[38;5;241m=\u001b[39m sb\u001b[38;5;241m.\u001b[39mload_bouts(sess_par[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbird\u001b[39m\u001b[38;5;124m'\u001b[39m],sess_par[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msess\u001b[39m\u001b[38;5;124m'\u001b[39m],\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m, derived_folder\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbouts_sglx\u001b[39m\u001b[38;5;124m'\u001b[39m,bout_file_key\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbout_auto_file\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m/mnt/cube/lo/envs/ceciestunepipe/ceciestunepipe/util/sglxsync.py:93\u001b[0m, in \u001b[0;36msync_all\u001b[0;34m(all_syn_dict, ref_stream, force)\u001b[0m\n\u001b[1;32m 86\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m t_prime file \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m not found or forced computation, getting the events\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(t_p_path))\n\u001b[1;32m 88\u001b[0m \u001b[38;5;66;03m# check if it had skipped beats\u001b[39;00m\n\u001b[1;32m 89\u001b[0m \u001b[38;5;66;03m# skipped_beat = check_skipped(one_syn_dict)\u001b[39;00m\n\u001b[1;32m 90\u001b[0m \u001b[38;5;66;03m# if skipped_beat:\u001b[39;00m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;66;03m# raise RuntimeError('Events array for {} had skipped heartbeats'.format(one_stream))\u001b[39;00m\n\u001b[0;32m---> 93\u001b[0m t_prime \u001b[38;5;241m=\u001b[39m \u001b[43msu\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msync_to_pattern\u001b[49m\u001b[43m(\u001b[49m\u001b[43mone_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mevt_arr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mone_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt_0\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 94\u001b[0m \u001b[43m \u001b[49m\u001b[43mref_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mevt_arr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mref_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt_0\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 95\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m saving t_prime array to \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m t_p_path) \n\u001b[1;32m 96\u001b[0m np\u001b[38;5;241m.\u001b[39msave(t_p_path, t_prime)\n", - "File \u001b[0;32m/mnt/cube/lo/envs/ceciestunepipe/ceciestunepipe/util/syncutil.py:51\u001b[0m, in \u001b[0;36msync_to_pattern\u001b[0;34m(x_ttl, t, x_0_ttl, t_0)\u001b[0m\n\u001b[1;32m 49\u001b[0m n_edges \u001b[38;5;241m=\u001b[39m x_ttl\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 50\u001b[0m n_edges_0 \u001b[38;5;241m=\u001b[39m x_0_ttl\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m---> 51\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mx_ttl\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m!=\u001b[39m x_0_ttl[\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m]:\n\u001b[1;32m 52\u001b[0m \u001b[38;5;66;03m# If the signals don't have the same number of edges there may be an error, better stop and debug\u001b[39;00m\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 54\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSign of first edge transition of pattern and target dont match\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_edges \u001b[38;5;241m!=\u001b[39m n_edges_0:\n\u001b[1;32m 56\u001b[0m \u001b[38;5;66;03m# If the signals don't have the same number of edges there may be an error, better stop and debug\u001b[39;00m\n", - "File \u001b[0;32m/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/numpy/core/memmap.py:334\u001b[0m, in \u001b[0;36mmemmap.__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m 333\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, index):\n\u001b[0;32m--> 334\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getitem__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 335\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(res) \u001b[38;5;129;01mis\u001b[39;00m memmap \u001b[38;5;129;01mand\u001b[39;00m res\u001b[38;5;241m.\u001b[39m_mmap \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 336\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39mndarray)\n", - "\u001b[0;31mIndexError\u001b[0m: index 0 is out of bounds for axis 1 with size 0" + "name": "stdout", + "output_type": "stream", + "text": [ + "Checking for skipped heartbeats in nidq stream:\n", + "Event array has 883 events\n", + "No skipped heartbeats\n", + "\n", + "Checking for skipped heartbeats in ap_0 stream:\n", + "Event array has 883 events\n", + "No skipped heartbeats\n", + "\n", + "Checking for skipped heartbeats in wav stream:\n", + "Event array has 883 events\n", + "No skipped heartbeats\n", + "\n" ] } ], "source": [ - "mismatched_streams = False\n", - "short_stream = 'nidq'\n", + "## debugging: check if streams have skipped heartbeats\n", + "def check_skipped(one_syn_dict: dict, round_ms=10):\n", + " no_skips = True\n", + " evt_arr = one_syn_dict['evt_arr']\n", + " evt_t = one_syn_dict['t_0'][evt_arr[0]]\n", + " print('Event array has {} events'.format(evt_arr.size//2))\n", + " \n", + " # get the unique periods, rounded at round_ms (default 50 ms)\n", + " evt_period_ms = np.unique(np.round((np.unique(np.diff(evt_t))*1000)/round_ms)*round_ms).astype(int)\n", + " if evt_period_ms.size > 1:\n", + " evt_diff = (np.round(np.diff(evt_t)*1000/round_ms)*round_ms).astype(int)\n", + " no_skips = False\n", + " bad_periods = np.where(evt_diff != np.argmax(np.bincount(evt_diff)))[0]\n", + " print('More than 1 different periods detected: {}'.format(evt_period_ms))\n", + " print('Most periods equal to {} -- bad periods:'.format(np.argmax(np.bincount(evt_diff))))\n", + " for bp in bad_periods:\n", + " print('evt_diff['+str(bp)+']='+str(evt_diff[bp]))\n", + " \n", + " # check that the diff between every other edge is zero\n", + " period_diff = np.hstack([np.diff(evt_arr[1][1:][::2]), np.diff(evt_arr[1][::2])])\n", + " if not (all(period_diff==0)):\n", + " no_skips = False\n", + " print('Difference between corresponding periodic edges is not zero: {}'.format(np.unique(period_diff)))\n", + " \n", + " if no_skips: print('No skipped heartbeats')\n", "\n", + "for stream in all_syn_dict.keys():\n", + " print('Checking for skipped heartbeats in',stream,'stream:')\n", + " check_skipped(all_syn_dict[stream]);print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Record epochs with mismatched streams and which stream is shortest" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "mismatched_streams = {\n", + " '0949_g0': (True, 'nidq'),\n", + " '1226_g0': (True, 'nidq'),\n", + " '1227_g0': (True, 'nidq'),\n", + " '1233_g0': (True, 'nidq'),\n", + " '1235_g0': (True, 'nidq'),\n", + " '1244_g0': (True, 'nidq'),\n", + " '1245_g0': (True, 'nidq'),\n", + " '2355_g0': (False,),\n", + " '2631_g0': (False,)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "z_r5r13_24 2024-08-07 0949_g0 syncing..\n", + "z_r5r13_24 2024-08-07 1226_g0 syncing..\n", + "z_r5r13_24 2024-08-07 1227_g0 syncing..\n", + "z_r5r13_24 2024-08-07 1233_g0 syncing..\n", + "z_r5r13_24 2024-08-07 1235_g0 syncing..\n", + "z_r5r13_24 2024-08-07 1245_g0 syncing..\n", + "z_r5r13_24 2024-08-07 2355_g0 syncing..\n", + "z_r5r13_24 2024-08-07 2631_g0 syncing..\n", + "done.\n" + ] + } + ], + "source": [ "# loop through epochs:\n", "epoch_list = sess_epochs # process all epochs\n", + "\n", "for this_epoch in epoch_list:\n", " \n", " sess_par['epoch'] = this_epoch\n", @@ -380,8 +552,8 @@ " # get sync pattern\n", " all_syn_dict = {k:sy.get_syn_pattern(run_recordings,epoch_struct,k,force=False) for k in all_streams}\n", " # run sync\n", - " if mismatched_streams:\n", - " syd.sync_all_mismatched_streams(all_syn_dict,sess_par['ref_stream'],short_stream,force=False)\n", + " if mismatched_streams[this_epoch][0]:\n", + " syd.sync_all_mismatched_streams(all_syn_dict,sess_par['ref_stream'],mismatched_streams[this_epoch][1],force=False)\n", " else:\n", " sy.sync_all(all_syn_dict,sess_par['ref_stream'],force=False)\n", "\n", @@ -393,7 +565,7 @@ " bout_pd.drop(bout_pd[drop_condition].index, inplace=True)\n", " bout_pd.reset_index(drop=True, inplace=True)\n", " # sync bouts to spike time base\n", - " if mismatched_streams:\n", + " if mismatched_streams[this_epoch][0]:\n", " bout_dict, bout_syn_pd = syd.bout_dict_from_pd_mismatched_streams(bout_pd,all_syn_dict,s_f_key='wav')\n", " else:\n", " bout_dict, bout_syn_pd = sy.bout_dict_from_pd(bout_pd,all_syn_dict,s_f_key='wav')\n", @@ -558,7 +730,9 @@ " with open(stim_dict_path,'wb') as handle:\n", " pickle.dump(trial_dict,handle)\n", " trial_syn_pd.to_pickle(stim_pd_path)\n", - " logger.info('saved syncronized stim dict and pandas dataframe to {}, {}'.format(stim_dict_path, stim_pd_path))" + " logger.info('saved syncronized stim dict and pandas dataframe to {}, {}'.format(stim_dict_path, stim_pd_path))\n", + "\n", + "print('done.')" ] }, { @@ -570,7 +744,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -603,16 +777,23 @@ "outputs": [], "source": [] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Debugging: check that all streams are the same length (address ttl events error)" + "#### To look up lengths of recordings to stitch them together in 2-curate_acoustics" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 40, "metadata": { "tags": [] }, @@ -621,90 +802,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "nidq recording ends at 45:04\n", - "lf_0 recording ends at 45:04\n", - "ap_0 recording ends at 45:04\n", - "wav recording ends at 45:04\n" + "n samples: 14681207\n" ] } ], "source": [ - "sess_par['epoch'] = '1340_g0' # problematic epoch\n", + "sess_par['epoch'] = '1235_g0'\n", "epoch_struct = et.sgl_struct(sess_par,sess_par['epoch'],ephys_software=sess_par['ephys_software'])\n", - "\n", - "# get epoch files\n", - "sgl_folders, sgl_files = sglu.sgl_file_struct(epoch_struct['folders']['sglx'])\n", - "run_meta_files = {k:v[0] for k,v in sgl_files.items()}\n", - "run_recordings = {k:sglex.SpikeGLXRecordingExtractor(sglu.get_data_meta_path(v)[0]) for k,v in run_meta_files.items()}\n", - "\n", - "# get streams, from raw recording extractors and preprocessed data\n", - "all_streams = list(run_recordings.keys()) + ['wav'] ### might want to just remove this\n", - "# get sync pattern\n", "all_syn_dict = {k:sy.get_syn_pattern(run_recordings,epoch_struct,k,force=False) for k in all_streams}\n", - "\n", - "for stream in all_syn_dict.keys():\n", - " time_end = np.shape(all_syn_dict[stream]['t_0'])[0]/all_syn_dict[stream]['s_f']/60\n", - " print(stream+' recording ends at '+str(int(np.floor(time_end)))+':'+f\"{round((time_end % 1)*60):02d}\")" + "print('n samples:',np.shape(all_syn_dict['ap_0']['t_0'])[0])" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Checking for skipped heartbeats in nidq stream:\n", - "Event array has 5407 events\n", - "No skipped heartbeats\n", - "\n", - "Checking for skipped heartbeats in lf_0 stream:\n", - "Event array has 5408 events\n", - "No skipped heartbeats\n", - "\n", - "Checking for skipped heartbeats in ap_0 stream:\n", - "Event array has 5408 events\n", - "No skipped heartbeats\n", - "\n", - "Checking for skipped heartbeats in wav stream:\n", - "Event array has 5407 events\n", - "No skipped heartbeats\n", - "\n" + "sampling rate: 29999.844262295082\n" ] } ], "source": [ - "## debugging: check if streams have skipped heartbeats\n", - "def check_skipped(one_syn_dict: dict, round_ms=10):\n", - " no_skips = True\n", - " evt_arr = one_syn_dict['evt_arr']\n", - " evt_t = one_syn_dict['t_0'][evt_arr[0]]\n", - " print('Event array has {} events'.format(evt_arr.size//2))\n", - " \n", - " # get the unique periods, rounded at round_ms (default 50 ms)\n", - " evt_period_ms = np.unique(np.round((np.unique(np.diff(evt_t))*1000)/round_ms)*round_ms).astype(int)\n", - " if evt_period_ms.size > 1:\n", - " evt_diff = (np.round(np.diff(evt_t)*1000/round_ms)*round_ms).astype(int)\n", - " no_skips = False\n", - " bad_periods = np.where(evt_diff != np.argmax(np.bincount(evt_diff)))[0]\n", - " print('More than 1 different periods detected: {}'.format(evt_period_ms))\n", - " print('Most periods equal to {} -- bad periods:'.format(np.argmax(np.bincount(evt_diff))))\n", - " for bp in bad_periods:\n", - " print('evt_diff['+str(bp)+']='+str(evt_diff[bp]))\n", - " \n", - " # check that the diff between every other edge is zero\n", - " period_diff = np.hstack([np.diff(evt_arr[1][1:][::2]), np.diff(evt_arr[1][::2])])\n", - " if not (all(period_diff==0)):\n", - " no_skips = False\n", - " print('Difference between corresponding periodic edges is not zero: {}'.format(np.unique(period_diff)))\n", - " \n", - " if no_skips: print('No skipped heartbeats')\n", - "\n", - "for stream in all_syn_dict.keys():\n", - " print('Checking for skipped heartbeats in',stream,'stream:')\n", - " check_skipped(all_syn_dict[stream]);print()" + "print('sampling rate:',all_syn_dict['ap_0']['s_f'])" ] }, { diff --git a/1.2-preprocess_acoustics-zebra_finch-one_session-off-and-on.ipynb b/1.2-preprocess_acoustics-zebra_finch-one_session-off-and-on.ipynb new file mode 100644 index 0000000..1919ad8 --- /dev/null +++ b/1.2-preprocess_acoustics-zebra_finch-one_session-off-and-on.ipynb @@ -0,0 +1,859 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Preprocess acoustic data and sync to neural data, one session at a time\n", + "\n", + "This notebook is a modified version of the *1-preprocess_acoustics* in the chronic ephys processing pipeline\n", + "\n", + "If *1-preprocess_acoustics* exits with errors, this notebook allows you to make manual adjustments\n", + "\n", + "Common errors include:\n", + "- data streams cannot be synched (ex. neural and audio data streams are of different lengths)\n", + "- TTL events were skipped (i.e., the machine clock malfunctioned or SpikeGLX crashed and the data streams terminated at different moments)\n", + "\n", + "Use the environment **songproc** to run this notebook\n", + "\n", + "(currently using environment spikesort)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/cube/lo/envs/songproc/lib/python3.8/site-packages/spikeextractors/__init__.py:21: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n", + " if StrictVersion(h5py.__version__) > '2.10.0':\n", + "2024-08-14 12:19:41,818 root INFO Running on pakhi.ucsd.edu\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "h5py version > 2.10.0. Some extractors might not work properly. It is recommended to downgrade to version 2.10.0: \n", + ">>> pip install h5py==2.10.0\n" + ] + } + ], + "source": [ + "%matplotlib widget\n", + "import numpy as np\n", + "import pandas as pd\n", + "import os\n", + "import pickle\n", + "from scipy.io import wavfile\n", + "import traceback\n", + "\n", + "import sys\n", + "sys.path.append('/mnt/cube/lo/envs/ceciestunepipe')\n", + "from ceciestunepipe.file import bcistructure as et\n", + "from ceciestunepipe.util.sound import boutsearch as bs\n", + "from ceciestunepipe.pipeline import searchbout as sb\n", + "from ceciestunepipe.util import stimutil as su\n", + "from ceciestunepipe.util import sglxutil as sglu\n", + "from ceciestunepipe.util import sglxsync as sy\n", + "from ceciestunepipe.mods import sglxsync_debug as syd\n", + "from ceciestunepipe.util.spikeextractors.extractors.spikeglxrecordingextractor import spikeglxrecordingextractor as sglex\n", + "from ceciestunepipe.util import oeutil as oeu\n", + "from ceciestunepipe.mods import preproc_sglx, preproc_oe\n", + "\n", + "import logging\n", + "logger = logging.getLogger()\n", + "handler = logging.StreamHandler()\n", + "formatter = logging.Formatter(\n", + " '%(asctime)s %(name)-12s %(levelname)-8s %(message)s')\n", + "handler.setFormatter(formatter)\n", + "logger.addHandler(handler)\n", + "logger.setLevel(logging.WARNING) # set to logging.INFO if you'd like to see the full readout" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "## default bout detection parameters that work well for zebra finches\n", + "hparams = {\n", + " # spectrogram\n", + " 'num_freq':1024, # how many channels to use in a spectrogram\n", + " 'preemphasis':0.97,\n", + " 'frame_shift_ms':5, # step size for fft\n", + " 'frame_length_ms':10, # frame length for fft FRAME SAMPLES < NUM_FREQ!!!\n", + " 'min_level_db':-55, # minimum threshold db for computing spectrogram\n", + " 'ref_level_db':110, # reference db for computing spectrogram\n", + " 'sample_rate':None, # sample rate of your data\n", + " \n", + " # mel filter\n", + " 'mel_filter':False, # should a mel filter be used?\n", + " 'num_mels':1024, # how many channels to use in the mel-spectrogram\n", + " 'fmin':300, # low frequency cutoff for mel filter\n", + " 'fmax':12000, # high frequency cutoff for mel filter\n", + " \n", + " # spectrogram inversion\n", + " 'max_iters':200,\n", + " 'griffin_lim_iters':20,\n", + " 'power':1.5,\n", + " \n", + " # bout searching\n", + " 'bout_auto_file':'bout_auto.pickle', # extension for saving the auto found files\n", + " 'bout_sync_file':'bout_sync.pickle', # extension for saving the synchronized auto bouts\n", + " 'stim_sync_file':'stim_sync.pickle', # extension for saving the synchronized stim if stim session\n", + " 'bout_curated_file':'bout_curated.pickle', # extension for manually curated files\n", + " \n", + " # if using deep_bout_search = False, the following parameters will apply for automatic bout detection:\n", + " 'read_wav_fun':bs.read_npy_chan, # function for loading the wav_like_stream (returns fs, ndarray)\n", + " 'file_order_fun':bs.sess_file_id, # function for extracting the file ID within the session\n", + " 'min_segment':20, # minimum length of supra_threshold to consider a 'syllable' (ms)\n", + " 'min_silence':3000, # minmum distance between groups of syllables to consider separate bouts (ms)\n", + " 'min_bout':500, # min bout duration (ms)\n", + " 'peak_thresh_rms':0.55, # threshold (rms) for peak acceptance,\n", + " 'thresh_rms':0.25, # threshold for detection of syllables\n", + " 'mean_syl_rms_thresh':0.3, # threshold for acceptance of mean rms across the syllable (relative to rms of the file)\n", + " 'max_bout':180000, # exclude bouts too long (ms)\n", + " 'l_p_r_thresh':100, # threshold for n of len_ms/peaks (typycally about 2-3 syllable spans)\n", + " 'waveform_edges':1000, # get number of ms before and after the edges of the bout for the waveform sample\n", + "}\n", + "\n", + "## other processing parameters\n", + "n_jobs = 1 # n_jobs for deriving bout info (errors when increased)\n", + "mic_file_ext = 'npy' # npy method more efficient than wav\n", + "force_preprocess = False # skip preprocessing for previously failed epochs\n", + "deep_bout_search = True # detect bouts using deep search -- see ceciestunepipe.mods.bout_detection_mf" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# single session params\n", + "sess_par = {\n", + " 'bird':'z_r5r13_24',\n", + " 'sess':'2024-08-08',\n", + " 'stim_sess':[], # sessions where stimuli were presented\n", + " 'mic_list':['microphone_M','microphone_F'], # list of mics of interest, by signal name in rig.json\n", + " 'adc_list':[], # list of adc channels of interest\n", + " 'stim_list':['wav_stim'], # list of adc chans with the stimulus\n", + " 'nidq_ttl_list':[], # list of TTL signals form the nidq digital inputs to extract (besides the 'sync')\n", + " 'ref_stream':'ap_0', # what to synchronize everything to (sglx only, oe already synced)\n", + " 'trial_tag_chan':2, # sglx, what was the tag channel in the stimulus wave (this should come from meta et. al)\n", + " 'on_signal':1, # sglx, whether signal on is hi or lo\n", + " 'sort':'sort_0', # sort index\n", + " 'ephys_software':'sglx'\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Preprocess and synchronize recordings" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# get experiment structure\n", + "exp_struct = et.get_exp_struct(sess_par['bird'],sess_par['sess'],sort=sess_par['sort'],ephys_software=sess_par['ephys_software'])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "preprocessing..\n", + "done.\n" + ] + } + ], + "source": [ + "##### preprocess acoustics #####\n", + "if sess_par['ephys_software'] == 'sglx':\n", + " preproc_sglx.preprocess_session(sess_par,force_redo=True)\n", + "elif sess_par['ephys_software'] == 'oe':\n", + " preproc_oe.preprocess_session(sess_par,force_redo=True)\n", + "print('done.')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "042c7905a373496688d27d7ffc3bdc9e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/9 [00:00 0:\n", + " trial_syn_pd_all = []" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['0700_g0', '1000_g0', '2355_g0', '2705_g0']\n" + ] + } + ], + "source": [ + "# get epochs\n", + "sess_epochs = et.list_ephys_epochs(sess_par)\n", + "print(sess_epochs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Debugging: check that all streams are the same length (address ttl events error)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nidq recording ends at 175:20\n", + "ap_0 recording ends at 175:17\n", + "wav recording ends at 175:19\n" + ] + } + ], + "source": [ + "sess_par['epoch'] = '1000_g0' # problematic epoch\n", + "epoch_struct = et.sgl_struct(sess_par,sess_par['epoch'],ephys_software=sess_par['ephys_software'])\n", + "\n", + "# get epoch files\n", + "sgl_folders, sgl_files = sglu.sgl_file_struct(epoch_struct['folders']['sglx'])\n", + "run_meta_files = {k:v[0] for k,v in sgl_files.items()}\n", + "run_recordings = {k:sglex.SpikeGLXRecordingExtractor(sglu.get_data_meta_path(v)[0]) for k,v in run_meta_files.items()}\n", + "\n", + "# get streams, from raw recording extractors and preprocessed data\n", + "all_streams = list(run_recordings.keys()) + ['wav'] ### might want to just remove this\n", + "# get sync pattern\n", + "all_syn_dict = {k:sy.get_syn_pattern(run_recordings,epoch_struct,k,force=False) for k in all_streams}\n", + "\n", + "for stream in all_syn_dict.keys():\n", + " time_end = np.shape(all_syn_dict[stream]['t_0'])[0]/all_syn_dict[stream]['s_f']/60\n", + " print(stream+' recording ends at '+str(int(np.floor(time_end)))+':'+f\"{round((time_end % 1)*60):02d}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Checking for skipped heartbeats in nidq stream:\n", + "Event array has 21040 events\n", + "More than 1 different periods detected: [130 440 500]\n", + "Most periods equal to 500 -- bad periods:\n", + "evt_diff[13797]=440\n", + "evt_diff[13798]=130\n", + "\n", + "Checking for skipped heartbeats in ap_0 stream:\n", + "Event array has 21034 events\n", + "More than 1 different periods detected: [140 500]\n", + "Most periods equal to 500 -- bad periods:\n", + "evt_diff[13792]=140\n", + "\n", + "Checking for skipped heartbeats in wav stream:\n", + "Event array has 21040 events\n", + "More than 1 different periods detected: [130 440 500]\n", + "Most periods equal to 500 -- bad periods:\n", + "evt_diff[13797]=440\n", + "evt_diff[13798]=130\n", + "\n" + ] + } + ], + "source": [ + "## debugging: check if streams have skipped heartbeats\n", + "def check_skipped(one_syn_dict: dict, round_ms=10):\n", + " no_skips = True\n", + " evt_arr = one_syn_dict['evt_arr']\n", + " evt_t = one_syn_dict['t_0'][evt_arr[0]]\n", + " print('Event array has {} events'.format(evt_arr.size//2))\n", + " \n", + " # get the unique periods, rounded at round_ms (default 50 ms)\n", + " evt_period_ms = np.unique(np.round((np.unique(np.diff(evt_t))*1000)/round_ms)*round_ms).astype(int)\n", + " if evt_period_ms.size > 1:\n", + " evt_diff = (np.round(np.diff(evt_t)*1000/round_ms)*round_ms).astype(int)\n", + " no_skips = False\n", + " bad_periods = np.where(evt_diff != np.argmax(np.bincount(evt_diff)))[0]\n", + " print('More than 1 different periods detected: {}'.format(evt_period_ms))\n", + " print('Most periods equal to {} -- bad periods:'.format(np.argmax(np.bincount(evt_diff))))\n", + " for bp in bad_periods:\n", + " print('evt_diff['+str(bp)+']='+str(evt_diff[bp]))\n", + " \n", + " # check that the diff between every other edge is zero\n", + " period_diff = np.hstack([np.diff(evt_arr[1][1:][::2]), np.diff(evt_arr[1][::2])])\n", + " if not (all(period_diff==0)):\n", + " no_skips = False\n", + " print('Difference between corresponding periodic edges is not zero: {}'.format(np.unique(period_diff)))\n", + " \n", + " if no_skips: print('No skipped heartbeats')\n", + "\n", + "for stream in all_syn_dict.keys():\n", + " print('Checking for skipped heartbeats in',stream,'stream:')\n", + " check_skipped(all_syn_dict[stream]);print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Need to\n", + "- cut all streams at evt_t = 13971\n", + "- resume streams at evt_t = [total length of stream] - 7240\n", + " - 13794 for ap_0\n", + " - 13800 for nidq\n", + " - 13800 for wav" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from ceciestunepipe.util import syncutil as scu\n", + "\n", + "def sync_all_mismatched_streams(all_syn_dict: dict, ref_stream: str, short_stream: str, force=False) -> dict:\n", + " ref_syn_dict = all_syn_dict[ref_stream]\n", + "\n", + " first_len_evt_arr = 13971 # number of events captured before streams were cut off\n", + " first_len = first_len_evt_arr/2 # time when streams got cut off (s)\n", + "\n", + " last_len_evt_arr = 7240 # number of events captured after streams resumed\n", + " last_len = last_len_evt_arr/2 # time after streams were resumed (s)\n", + " \n", + " # new_len = np.shape(all_syn_dict[short_stream]['t_0'])[0]/all_syn_dict[short_stream]['s_f'] # length of short stream (s)\n", + " ref_end = int(first_len * all_syn_dict[ref_stream]['s_f']) # where to truncate ref_stream (samples)\n", + " ref_restart = int((np.shape(all_syn_dict[ref_stream]['t_0'])[0] - last_len) * all_syn_dict[ref_stream]['s_f']) # where to restart ref_stream (samples)\n", + " ref_len_evt_arr = np.shape(all_syn_dict[short_stream]['evt_arr'])[1] # number of events captured in ref stream\n", + " \n", + " for one_stream, one_syn_dict in all_syn_dict.items():\n", + " if one_stream==ref_stream:\n", + " continue\n", + " \n", + " print(' sync {}...'.format(one_stream))\n", + " \n", + " t_0_folder = os.path.split(one_syn_dict['t_0_path'])[0]\n", + " t_p_path = os.path.join(t_0_folder, '{}-tp.npy'.format(one_stream))\n", + " \n", + " if not(os.path.exists(t_p_path) and (force is False)):\n", + " print(' t_prime file {} not found or forced computation, getting the events'.format(t_p_path))\n", + " \n", + " # Edit one_stream to length of short_stream:\n", + " one_end = int(first_len * all_syn_dict[one_stream]['s_f']) # where to truncate one_stream (samples)\n", + " one_restart = int((np.shape(all_syn_dict[one_stream]['t_0'])[0] - last_len) * all_syn_dict[one_stream]['s_f']) # where to resume one_stream (samples)\n", + " \n", + " # Sync to ref stream\n", + " one_len_evt_arr = np.shape(one_syn_dict['evt_arr'])[1]\n", + " t_prime = scu.sync_to_pattern(np.concatenate((one_syn_dict['evt_arr'][:,:first_len_evt_arr],\n", + " one_syn_dict['evt_arr'][:,one_len_evt_arr-last_len_evt_arr:]), axis=1), \n", + " np.concatenate((one_syn_dict['t_0'][:one_end],\n", + " one_syn_dict['t_0'][one_restart:])),\n", + " np.concatenate((ref_syn_dict['evt_arr'][:,:first_len_evt_arr],\n", + " ref_syn_dict['evt_arr'][:,ref_len_evt_arr-last_len_evt_arr:]), axis=1),\n", + " np.concatenate((ref_syn_dict['t_0'][:ref_end],\n", + " ref_syn_dict['t_0'][ref_restart:]))\n", + " )\n", + " print(' saving t_prime array to ' + t_p_path)\n", + " np.save(t_p_path, t_prime)\n", + " \n", + " # clear the memory, then load as memmap\n", + " del t_prime\n", + " \n", + " one_syn_dict['t_p_path'] = t_p_path\n", + " # save the dict with the path to the sync it\n", + " print(' saving synced dict to {}'.format(one_syn_dict['path']))\n", + " with open(one_syn_dict['path'], 'wb') as fp:\n", + " pickle.dump(one_syn_dict, fp)\n", + " \n", + " one_syn_dict['t_p'] = np.load(t_p_path, mmap_mode='r')\n", + " print('Done with sync_all')\n", + " return\n", + " \n", + "\n", + "def bout_dict_from_pd_mismatched_streams(bout_pd: pd.DataFrame, all_syn_dict: dict, s_f_key: str='wav') -> dict:\n", + " s_f = all_syn_dict[s_f_key]['s_f']\n", + "\n", + " start_ms = bout_pd['start_ms'].values\n", + " len_ms = bout_pd['len_ms'].values\n", + " \n", + " bout_dict = {\n", + " 's_f': s_f, # s_f used to get the spectrogram\n", + " 's_f_nidq': all_syn_dict['nidq']['s_f'],\n", + " 's_f_ap_0': all_syn_dict['ap_0']['s_f'],\n", + " 'start_ms': start_ms,\n", + " 'len_ms': len_ms,\n", + " 'start_sample_naive': ( start_ms * s_f * 0.001).astype(np.int64),\n", + " 'start_sample_nidq': np.array([np.where(all_syn_dict['nidq']['t_0'] > start)[0][0] for start in start_ms*0.001]),\n", + " 'start_sample_wav': np.array([np.where(all_syn_dict['wav']['t_0'] > start)[0][0] for start in start_ms*0.001])\n", + " }\n", + " \n", + " # Edit to remove bout starts > length of ap_0 recording\n", + " keep = bout_dict['start_sample_wav'] <= len(all_syn_dict['wav']['t_p'])\n", + " bout_dict['start_ms'] = bout_dict['start_ms'][keep]\n", + " bout_dict['len_ms'] = bout_dict['len_ms'][keep]\n", + " bout_dict['start_sample_naive'] = bout_dict['start_sample_naive'][keep]\n", + " bout_dict['start_sample_nidq'] = bout_dict['start_sample_nidq'][keep]\n", + " bout_dict['start_sample_wav'] = bout_dict['start_sample_wav'][keep]\n", + " \n", + " start_ms_ap_0 = all_syn_dict['wav']['t_p'][bout_dict['start_sample_wav']]*1000\n", + " \n", + " bout_dict['start_ms_ap_0'] = start_ms_ap_0\n", + " bout_dict['start_sample_ap_0'] = np.array([np.where(all_syn_dict['ap_0']['t_0'] > start)[0][0] for start in start_ms_ap_0*0.001])\n", + " bout_dict['start_sample_ap_0'] = (bout_dict['start_sample_ap_0']).astype(np.int64)\n", + " bout_dict['end_sample_ap_0'] = bout_dict['start_sample_ap_0'] + (bout_dict['len_ms'] * bout_dict['s_f_ap_0'] * 0.001).astype(np.int64)\n", + " \n", + " ## update the bout pandas dataframe with the synced columns\n", + " bout_pd = bout_pd.head(keep.sum()) # trim bout_pd to bouts within ap_0 recording\n", + " for k in ['start_ms_ap_0', 'start_sample_ap_0', 'len_ms', 'start_ms', 'start_sample_naive']:\n", + " warnings.simplefilter(action='ignore', category=SettingWithCopyWarning)\n", + " bout_pd[k] = bout_dict[k]\n", + " warnings.resetwarnings()\n", + "\n", + " return bout_dict, bout_pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Record epochs with mismatched streams and which stream is shortest" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "mismatched_streams = {\n", + " '0700_g0': (False,),\n", + " '1000_g0': (True, 'ap_0'),\n", + " '2355_g0': (False,),\n", + " '2705_g0': (False,)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "z_r5r13_24 2024-08-08 1000_g0 syncing..\n", + " sync nidq...\n", + " t_prime file /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-08/sglx/1000_g0/nidq-tp.npy not found or forced computation, getting the events\n", + "(2, 13971)\n", + "(2, 7240)\n" + ] + }, + { + "ename": "IndexError", + "evalue": "index 209570288 is out of bounds for axis 0 with size 209557048", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[23], line 24\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m# run sync\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mismatched_streams[this_epoch][\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m---> 24\u001b[0m \u001b[43msync_all_mismatched_streams\u001b[49m\u001b[43m(\u001b[49m\u001b[43mall_syn_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43msess_par\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mref_stream\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mmismatched_streams\u001b[49m\u001b[43m[\u001b[49m\u001b[43mthis_epoch\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mforce\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 26\u001b[0m sy\u001b[38;5;241m.\u001b[39msync_all(all_syn_dict,sess_par[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mref_stream\u001b[39m\u001b[38;5;124m'\u001b[39m],force\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", + "Cell \u001b[0;32mIn[22], line 38\u001b[0m, in \u001b[0;36msync_all_mismatched_streams\u001b[0;34m(all_syn_dict, ref_stream, short_stream, force)\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28mprint\u001b[39m(one_syn_dict[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mevt_arr\u001b[39m\u001b[38;5;124m'\u001b[39m][:,:first_len_evt_arr]\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28mprint\u001b[39m(one_syn_dict[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mevt_arr\u001b[39m\u001b[38;5;124m'\u001b[39m][:,one_len_evt_arr\u001b[38;5;241m-\u001b[39mlast_len_evt_arr:]\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m---> 38\u001b[0m t_prime \u001b[38;5;241m=\u001b[39m \u001b[43mscu\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msync_to_pattern\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mone_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mevt_arr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m:\u001b[49m\u001b[43mfirst_len_evt_arr\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 39\u001b[0m \u001b[43m \u001b[49m\u001b[43mone_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mevt_arr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43mone_len_evt_arr\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mlast_len_evt_arr\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 40\u001b[0m \u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mone_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt_0\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43mone_end\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 41\u001b[0m \u001b[43m \u001b[49m\u001b[43mone_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt_0\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mone_restart\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 42\u001b[0m \u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mref_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mevt_arr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m:\u001b[49m\u001b[43mfirst_len_evt_arr\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 43\u001b[0m \u001b[43m \u001b[49m\u001b[43mref_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mevt_arr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43mref_len_evt_arr\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mlast_len_evt_arr\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 44\u001b[0m \u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mref_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt_0\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43mref_end\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 45\u001b[0m \u001b[43m \u001b[49m\u001b[43mref_syn_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mt_0\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43mref_restart\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 46\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m saving t_prime array to \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m t_p_path)\n\u001b[1;32m 48\u001b[0m np\u001b[38;5;241m.\u001b[39msave(t_p_path, t_prime)\n", + "File \u001b[0;32m/mnt/cube/lo/envs/ceciestunepipe/ceciestunepipe/util/syncutil.py:61\u001b[0m, in \u001b[0;36msync_to_pattern\u001b[0;34m(x_ttl, t, x_0_ttl, t_0)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 58\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNumber of edges in the syn ttl events of pattern and target dont match\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 60\u001b[0m \u001b[38;5;66;03m# if all checks out, do the deed\u001b[39;00m\n\u001b[0;32m---> 61\u001b[0m t_0_edge \u001b[38;5;241m=\u001b[39m \u001b[43mt_0\u001b[49m\u001b[43m[\u001b[49m\u001b[43mx_0_ttl\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 62\u001b[0m sample_edge \u001b[38;5;241m=\u001b[39m x_ttl[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 63\u001b[0m \u001b[38;5;66;03m# the interpolation function. fill_value='extrapolate' allows extrapolation from zero and until the last time stamp\u001b[39;00m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;66;03m# careful, this could lead to negative time, but it is the correct way to do it.\u001b[39;00m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;66;03m# interpolation function interpolates time as a target, t_0=f(sample) with true values at the edges\u001b[39;00m\n", + "\u001b[0;31mIndexError\u001b[0m: index 209570288 is out of bounds for axis 0 with size 209557048" + ] + } + ], + "source": [ + "# loop through epochs:\n", + "# epoch_list = ['0700_g0', '1000_g0', '2355_g0','2705_g0']\n", + "epoch_list = ['1000_g0']\n", + "\n", + "for this_epoch in epoch_list:\n", + " \n", + " sess_par['epoch'] = this_epoch\n", + " epoch_struct = et.sgl_struct(sess_par,sess_par['epoch'],ephys_software=sess_par['ephys_software'])\n", + "\n", + " ##### synchronization - sglx #####\n", + " print(sess_par['bird'],sess_par['sess'],this_epoch,'syncing..')\n", + " if sess_par['ephys_software'] == 'sglx':\n", + " # get epoch files\n", + " sgl_folders, sgl_files = sglu.sgl_file_struct(epoch_struct['folders']['sglx'])\n", + " run_meta_files = {k:v[0] for k,v in sgl_files.items()}\n", + " run_recordings = {k:sglex.SpikeGLXRecordingExtractor(sglu.get_data_meta_path(v)[0]) for k,v in run_meta_files.items()}\n", + "\n", + " # get streams, from raw recording extractors and preprocessed data\n", + " all_streams = list(run_recordings.keys()) + ['wav'] ### might want to just remove this\n", + " # get sync pattern\n", + " all_syn_dict = {k:sy.get_syn_pattern(run_recordings,epoch_struct,k,force=False) for k in all_streams}\n", + " # run sync\n", + " if mismatched_streams[this_epoch][0]:\n", + " sync_all_mismatched_streams(all_syn_dict,sess_par['ref_stream'],mismatched_streams[this_epoch][1],force=False)\n", + " else:\n", + " sy.sync_all(all_syn_dict,sess_par['ref_stream'],force=False)\n", + "\n", + " # load bouts\n", + " hparams, bout_pd = sb.load_bouts(sess_par['bird'],sess_par['sess'],'', derived_folder='bouts_sglx',bout_file_key='bout_auto_file')\n", + " # keep only epoch bouts\n", + " logger.info('bouts from this epoch {}'.format(sess_par['epoch']))\n", + " drop_condition = ~bout_pd['file'].str.contains(sess_par['epoch'])\n", + " bout_pd.drop(bout_pd[drop_condition].index, inplace=True)\n", + " bout_pd.reset_index(drop=True, inplace=True)\n", + " # sync bouts to spike time base\n", + " bout_dict, bout_syn_pd = sy.bout_dict_from_pd(bout_pd,all_syn_dict,s_f_key='wav')\n", + " # if mismatched_streams[this_epoch][0]:\n", + " # bout_dict, bout_syn_pd = bout_dict_from_pd_mismatched_streams(bout_pd,all_syn_dict,s_f_key='wav')\n", + " # else:\n", + " # bout_dict, bout_syn_pd = sy.bout_dict_from_pd(bout_pd,all_syn_dict,s_f_key='wav')\n", + " # store epoch synced bout info\n", + " bout_syn_pd['bird'] = sess_par['bird']\n", + " bout_syn_pd['sess'] = sess_par['sess']\n", + " bout_syn_pd['epoch'] = sess_par['epoch']\n", + " bout_syn_pd_all.append(bout_syn_pd)\n", + " # save synced bouts\n", + " bout_dict_path = os.path.join(epoch_struct['folders']['derived'],'bout_dict_ap0.pkl')\n", + " with open(bout_dict_path, 'wb') as handle:\n", + " pickle.dump(bout_dict, handle)\n", + " bout_pd_path = os.path.join(epoch_struct['folders']['derived'],'bout_pd_ap0.pkl')\n", + " bout_pd.to_pickle(bout_pd_path)\n", + " logger.info('saved syncronized bout dict and pandas dataframe to {}, {}'.format(bout_dict_path, bout_pd_path))\n", + "\n", + " if len(sess_par['stim_sess']) > 0:\n", + " # syn_ttl comes from the digital pin, syn_sine_ttl from the sine\n", + " event_name = 'wav_stim'\n", + " ttl_ev_name = event_name + '_sync_sine_ttl' \n", + " # get the events npy file\n", + " npy_stim_path = os.path.join(epoch_struct['folders']['derived'],ttl_ev_name + '_evt.npy')\n", + " stream_stim_path = os.path.join(epoch_struct['folders']['derived'],event_name + '.npy')\n", + " trial_ttl = np.load(npy_stim_path)\n", + " # epoch may not have trials - if so ttl file will be empty\n", + " if len(trial_ttl) > 0:\n", + " trial_stream = np.load(stream_stim_path,mmap_mode='r')\n", + " # get sampling frequency\n", + " stim_s_f = int(all_syn_dict['nidq']['s_f'])\n", + " # load the stimulus name - frequency tag dictionary\n", + " stim_tags_dict = preproc_sglx.load_stim_tags_dict(sess_par['stim_sess'],sess_par['bird'])\n", + " # get trial tagged dataframe\n", + " trial_tagged_pd = su.get_trials_pd(trial_ttl, trial_stream, stim_s_f,on_signal=sess_par['on_signal'],\n", + " tag_chan=sess_par['trial_tag_chan'],stim_tags_dict=stim_tags_dict,\n", + " trial_is_onof=True)\n", + " # sync stim\n", + " trial_dict, trial_syn_pd = sy.trial_syn_from_pd(trial_tagged_pd,all_syn_dict,s_f_key='nidq')\n", + " # store epoch synced stim info\n", + " trial_syn_pd['bird'] = sess_par['bird']\n", + " trial_syn_pd['sess'] = sess_par['sess']\n", + " trial_syn_pd['epoch'] = this_epoch\n", + " trial_syn_pd_all.append(trial_syn_pd)\n", + " # save synced stim\n", + " stim_dict_path = os.path.join(epoch_struct['folders']['derived'],'stim_dict_ap0.pkl')\n", + " stim_pd_path = os.path.join(epoch_struct['folders']['derived'],'stim_pd_ap0.pkl')\n", + " with open(stim_dict_path,'wb') as handle:\n", + " pickle.dump(trial_dict,handle)\n", + " trial_syn_pd.to_pickle(stim_pd_path)\n", + " logger.info('saved syncronized stim dict and pandas dataframe to {}, {}'.format(stim_dict_path, stim_pd_path))\n", + "\n", + " ###### synchronization - oe #####\n", + " elif sess_par['ephys_software'] == 'oe':\n", + " # get epoch files\n", + " run_recordings = {'oeb':preproc_oe.get_oe_cont_recording(exp_struct,this_epoch)}\n", + "\n", + " # make an all_syn_dict\n", + " mic_file_name = os.path.join(exp_struct['folders']['derived'],this_epoch,'wav_mic-npy_meta.pickle')\n", + " with open(mic_file_name, 'rb') as handle:\n", + " wav_mic_meta = pickle.load(handle)\n", + " all_syn_dict = {'wav': {'s_f':wav_mic_meta['s_f']}, \n", + " 'ap_0': {'s_f':run_recordings['oeb'].get_sampling_frequency()},\n", + " 'nidq': {'s_f':run_recordings['oeb'].get_sampling_frequency()}}\n", + " # make bouts pandas file for this session - match sglx format, streams already synced\n", + " bout_oe_struct = et.get_exp_struct(sess_par['bird'],sess_par['sess'],sort=sess_par['sort'],ephys_software='bouts_oe')\n", + " bout_pd_path = os.path.join(bout_oe_struct['folders']['derived'], 'bout_auto.pickle')\n", + " bout_syn_pd = pd.read_pickle(bout_pd_path)\n", + " bout_dict = preproc_oe.bout_dict_from_pd(bout_syn_pd,all_syn_dict)\n", + " # store epoch synced bout info\n", + " bout_syn_pd['bird'] = sess_par['bird']\n", + " bout_syn_pd['sess'] = sess_par['sess']\n", + " bout_syn_pd['epoch'] = this_epoch\n", + " bout_syn_pd_all.append(bout_syn_pd)\n", + " # save synced bouts\n", + " bout_dict_path = os.path.join(epoch_struct['folders']['derived'],'bout_dict_oe.pkl')\n", + " bout_pd_path = os.path.join(epoch_struct['folders']['derived'],'bout_pd_oe.pkl')\n", + " with open(bout_dict_path,'wb') as handle:\n", + " pickle.dump(bout_dict,handle)\n", + " bout_syn_pd.to_pickle(bout_pd_path)\n", + "\n", + " if len(sess_par['stim_sess']) > 0:\n", + " # this epoch name - get recording events path\n", + " raw_folder = exp_struct['folders']['oe']\n", + " epoch_path = os.path.join(raw_folder,this_epoch)\n", + " node_path = preproc_oe.get_default_node(exp_struct,this_epoch)\n", + " rec_path = preproc_oe.get_default_recording(node_path)\n", + " events_path = os.path.join(rec_path,'events/Network_Events-102.0/TEXT_group_1/')\n", + " # load stim lables / onsets\n", + " stim_labels = np.load(os.path.join(events_path,'text.npy'))\n", + " stim_onsets = np.load(os.path.join(events_path,'timestamps.npy'))\n", + "\n", + " # get stim onsets and offsets\n", + " stim_on_all = []; stim_off_all = []; \n", + " stim_proc_path_all = []; stim_exp_path_all = [];\n", + " stim_map_dir_all = []; stim_id_all = [];\n", + " # loop through stim\n", + " for stim_i in range(len(stim_labels)):\n", + " this_stim_label = stim_labels[stim_i].astype('str')\n", + " this_stim_onset = stim_onsets[stim_i]\n", + " if this_stim_label[:4] == 'stim':\n", + " stim_exp_file = this_stim_label[5:]\n", + " # get stim preprocessing directory\n", + " stim_file_split = stim_exp_file.split('/')\n", + " stim_map_i = np.where([stim_file_split[i] in list(stim_map_dict.keys()) for i in range(len(stim_file_split))])[0][0]\n", + " stim_map_dir = stim_map_dict[stim_file_split[stim_map_i]]\n", + " # get remaining stim file path - identical for experiment and preprocessing\n", + " remaining_stim_file = '/'.join(stim_file_split[stim_map_i+1:])\n", + " # processing file location\n", + " stim_file = os.path.join(stim_map_dir,remaining_stim_file)\n", + " # load stim and get length\n", + " sf,this_wav = wavfile.read(stim_file,mmap=True)\n", + " stim_len = this_wav.shape[0]/sf\n", + " # get length of stim in samples - round up\n", + " stim_samp_len = int(np.ceil(stim_len * bout_dict['s_f']))\n", + " # get stim on / off\n", + " stim_on_all.append(this_stim_onset)\n", + " stim_off_all.append(this_stim_onset+stim_samp_len) \n", + " stim_proc_path_all.append(stim_file)\n", + " stim_exp_path_all.append(stim_exp_file)\n", + " stim_map_dir_all.append(stim_map_dir)\n", + " stim_id_all.append(remaining_stim_file)\n", + "\n", + " # make into a pd - oe already synced\n", + " stim_on_all_np = np.array(stim_on_all).astype('int')\n", + " stim_off_all_np = np.array(stim_off_all).astype('int')\n", + " stim_on_all_np_ms = 1000*(stim_on_all_np/bout_dict['s_f'])\n", + " stim_off_all_np_ms = 1000*(stim_off_all_np/bout_dict['s_f'])\n", + " trial_syn_pd = pd.DataFrame(np.vstack([stim_on_all_np,\n", + " stim_off_all_np,\n", + " stim_on_all_np_ms,\n", + " stim_off_all_np_ms,\n", + " stim_off_all_np_ms-stim_on_all_np_ms,\n", + " stim_proc_path_all,\n", + " stim_exp_path_all,\n", + " stim_map_dir_all,\n", + " stim_id_all]).T,\n", + " columns=['start_sample','end_sample','start_ms','end_ms','len_ms',\n", + " 'proc_file','exp_file','map_dir','stim_id'])\n", + " trial_syn_pd['start_sample'] = trial_syn_pd['start_sample'].astype('int')\n", + " trial_syn_pd['end_sample'] = trial_syn_pd['end_sample'].astype('int')\n", + " trial_syn_pd['start_ms'] = trial_syn_pd['start_ms'].astype('float')\n", + " trial_syn_pd['len_ms'] = trial_syn_pd['len_ms'].astype('float')\n", + " # store epoch synced stim info\n", + " trial_syn_pd['bird'] = sess_par['bird']\n", + " trial_syn_pd['sess'] = sess_par['sess']\n", + " trial_syn_pd['epoch'] = this_epoch\n", + " trial_syn_pd_all.append(trial_syn_pd)\n", + " trial_dict = {\n", + " 's_f': all_syn_dict['wav']['s_f'],\n", + " 'ap_0':all_syn_dict['ap_0']['s_f'],\n", + " 'nidq':all_syn_dict['nidq']['s_f'],\n", + " 'start_ms':trial_syn_pd['start_ms'],\n", + " 'len_ms':trial_syn_pd['len_ms'],\n", + " 'start_sample':trial_syn_pd['start_sample'],\n", + " 'end_sample':trial_syn_pd['end_sample'],\n", + " 'proc_file':trial_syn_pd['proc_file'],\n", + " 'exp_file':trial_syn_pd['exp_file'],\n", + " 'map_dir':trial_syn_pd['map_dir'],\n", + " 'stim_id':trial_syn_pd['stim_id']}\n", + " # save synced stim\n", + " stim_dict_path = os.path.join(epoch_struct['folders']['derived'],'stim_dict_ap0.pkl')\n", + " stim_pd_path = os.path.join(epoch_struct['folders']['derived'],'stim_pd_ap0.pkl')\n", + " with open(stim_dict_path,'wb') as handle:\n", + " pickle.dump(trial_dict,handle)\n", + " trial_syn_pd.to_pickle(stim_pd_path)\n", + " print('saved syncronized stim dict and pandas dataframe to {}, {}'.format(stim_dict_path, stim_pd_path))\n", + " \n", + "print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### After handling all errors, save outputs and log preprocessing complete" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# concatenate list of synced bout data frames from each epoch and save\n", + "bout_syn_pd_all_cat = pd.concat(bout_syn_pd_all)\n", + "sb.save_auto_bouts(bout_syn_pd_all_cat,sess_par,hparams,software=sess_par['ephys_software'],bout_file_key='bout_sync_file')\n", + "\n", + "# stim sess save the all sync epoch stim data frame as well\n", + "if len(sess_par['stim_sess']) > 0:\n", + " trial_syn_pd_all_cat = pd.concat(trial_syn_pd_all)\n", + " sb.save_auto_bouts(trial_syn_pd_all_cat,sess_par,hparams,software=sess_par['ephys_software'],bout_file_key='stim_sync_file')\n", + "\n", + "# # log preprocessing complete without error\n", + "# log_dir = os.path.join('/mnt/cube/chronic_ephys/log', sess_par['bird'], sess_par['sess'])\n", + "# with open(os.path.join(log_dir,'preprocessing.log'), 'w') as f:\n", + "# f.write(sess_par['bird']+' '+sess_par['sess']+' preprocessing complete without error\\nEpochs '+', '.join(sess_epochs)+' processed\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "songproc", + "language": "python", + "name": "songproc" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/2-curate_acoustics.ipynb b/2-curate_acoustics.ipynb index b57dcd6..abd18e1 100755 --- a/2-curate_acoustics.ipynb +++ b/2-curate_acoustics.ipynb @@ -65,16 +65,17 @@ "source": [ "# session parameters\n", "sess_par = {\n", - " 'bird':'z_p5y10_23', # bird ID\n", - " 'sess':'2024-05-16', # session date\n", + " 'bird':'z_r5r13_24', # bird ID\n", + " 'sess':'2024-08-08', # session date\n", " 'ephys_software':'sglx', # recording software, sglx or oe\n", - " 'stim_sess':False, # if song stimulus was played during the session, ignore detected bouts\n", + " 'stim_sess':True, # if song stimulus was played during the session\n", + " 'stim_epoch':['2355_g0'], # mark all detections as song for these overnight epochs\n", " 'trim_bouts':True, # manually trim bouts after curation\n", " 'sort':'sort_0', # sort index\n", "}\n", "\n", "# set type of ALSA bout dataframe to load, depending on how far it's been previously processed\n", - "bout_df_type = 'checked' # options are 'auto' (not checked), 'checked' (checked not trimmed), and 'curated' (checked and trimmed)" + "bout_df_type = 'auto' # options are 'auto' (not checked), 'checked' (checked not trimmed), and 'curated' (checked and trimmed)" ] }, { @@ -97,7 +98,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Bouts: 420\n" + "All bouts: 1273 | Post stim removal: 603\n", + "Bouts post stim removal begin at index 0 and end at index 602\n" ] } ], @@ -111,11 +113,18 @@ "fs, bout_dicts_all = cb.epoch_bout_dict_sample_rate_check(bout_df, sess_par)\n", "\n", "# if stim session, remove stim that overlap with bouts\n", + "bout_df_updated = bout_df.copy()\n", + "bout_df_updated = bout_df_updated.assign(bout_check=False, confusing=False, is_call=False)\n", + "# bout_df_updated = bout_df_updated.sort_values(by=['epoch', 'start_sample'])\n", "if sess_par['stim_sess']:\n", - " bout_df_updated = cb.remove_stim_bouts(bout_df, sess_par)\n", - " print('All bouts:',len(bout_df), ' | Post stim removal:',len(bout_df_updated)) \n", + " bout_df_updated.loc[bout_df_updated['epoch'].isin(sess_par['stim_epoch']), 'bout_check'] = True\n", + " len_bouts = len(bout_df_updated[bout_df_updated['bout_check']==False])\n", + " print('All bouts:',len(bout_df), '| Post stim removal:',len_bouts)\n", + " first_bout = bout_df_updated[~bout_df_updated['epoch'].isin(sess_par['stim_epoch'])].index[0]\n", + " last_bout = bout_df_updated[~bout_df_updated['epoch'].isin(sess_par['stim_epoch'])].index[-1]\n", + " print('Bouts post stim removal begin at index',first_bout,'and end at index',last_bout)\n", "else:\n", - " bout_df_updated = bout_df.copy()\n", + " len_bouts = len(bout_df)\n", " print('Bouts:',len(bout_df))" ] }, @@ -129,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "90c0ab08", "metadata": { "scrolled": true @@ -157,23 +166,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "34ba1212-7b8b-421b-a299-864a300dde3c", - "metadata": {}, - "outputs": [], - "source": [ - "bout_df_updated = bout_df_updated.assign(bout_check=True, confusing=False, is_call=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, "id": "8cc5bd8a-634b-4b5b-b2a5-d8a59d9e25f6", "metadata": {}, "outputs": [ { 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", + "image/png": 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"text/html": [ "\n", "
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\n", - " \n", + " \n", "
\n", " " ], @@ -256,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "95670f50-d805-4258-bfbc-0ac1766ff667", "metadata": { "tags": [] @@ -266,19 +265,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "Out of 420 potential bouts:\n", - "• 0 bouts\n", - "• 1 calls\n", - "• 419 noise\n" + "Out of 603 potential bouts:\n", + "• 45 songs\n", + "• 0 calls\n", + "• 558 noise\n" ] } ], "source": [ "# generate bout summaries\n", - "print(f\"Out of {len(bout_df_updated)} potential bouts:\")\n", - "print(f\"• {len(bout_df_updated[(bout_df_updated['bout_check'] == True) & (bout_df_updated['is_call'] == False)])} bouts\")\n", - "print(f\"• {bout_df_updated['is_call'].sum()} calls\")\n", - "print(f\"• {len(bout_df_updated) - bout_df_updated['bout_check'].sum()} noise\")" + "print(f\"Out of {len(bout_df_updated.head(len_bouts))} potential bouts:\")\n", + "print(f\"• {bout_df_updated['bout_check'].head(len_bouts).sum() - bout_df_updated['is_call'].head(len_bouts).sum()} songs\")\n", + "print(f\"• {bout_df_updated['is_call'].head(len_bouts).sum()} calls\")\n", + "print(f\"• {len(bout_df_updated.head(len_bouts)) - bout_df_updated['bout_check'].head(len_bouts).sum()} noise\")" ] }, { @@ -292,16 +291,48 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "id": "df27a2c0-b9e5-461e-a6e0-d1997bf066e0", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "No bouts to trim...\n" - ] + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7d7bf1940fde41a780dae9ce6c020654", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HBox(children=(Button(button_style='warning', description='Prev', icon='minus', style=ButtonSty…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cf03083b568146f0b32d84125eb661d4", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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8npb4hw0bpgBKSEiIUlpa2uHvnpNmkrhgDPi/4IIL8Pl8bR4bNmwIuI4hQ4YQHR3N5s2b+y3O888/H0VR8Pv9rf5VFIXvvvuuT481efJkNBoNGo2Gzz//nNNOO42EhISW43X0ANps++qrr1AUhbi4OCoqKto9XmhoaMvyQdHR0URGRrJnzx5GjhzJoEGDUKvVGI1GrFYrXq8Xn8/HN99806qOs846C4vFgkajQavVotVqOXjwIN9//z3p6ekkJSXR0NBAfHw8W7ZsIT4+HqPRiNls5qOPPmqpJzIykri4OGJjY/nqq6+A/87UfOmll1JfX09jYyM+nw+v14vX6+XIkSPU19djNptRFIXQ0NA253rRRRe1uce8Xm/LrJTXXHNNy3q1x76ex/7bV9s+++yzNtfd4/EEvK7zJZdcgl6vx+/3d/povl9VKlXLfdX82LBhA1lZWQwaNCigYzbr7u+Knvxu6e9jDKSYHA4Hr732Wpf7jRkzpmU9ZJVK1VJ/d37ubpmuZt7uzOWXX87HH3+My+XqcR2nuhtuuKFX17Sz55YsWXK8T0ecAC677DLee++9YIdxQrriiitaTeR37OeArrb1pMzxrjuQMiaTiYiIiG5NgNidv6/9+ff+RIpjx44dbN26tVsxHG+JiYlMnDix0/ZKZ///5JNPAj7Wsc1xaaAfZy+99BLXX399y/+/+uorJk6ciF6vb9lWWlpKZGQk4eHhwQhRCCGEEEIIIUQ/Gz58ODt27Gi17aRpoAP89NNPjBkzhldeeYUpU6YAsGXLFux2O2eccQYAdXV1REZGAuB2u9HpdC2N+02bNjFmzBgqKytJTk7m//7v/zj77LOJjo7mzjvv5NlnnwXgD3/4A+PHj8doNLZk89RqdcvPXq+XRx55hPfff58XXniBP/7xj8yZM4fbb7+dmJgYFEUhLS2Nv/71r+Tl5XV4PlarlT/+8Y/cf//9pKWlAfD555/z+eefY7FY8Pv9JCQktFmD1mAwkJaWxo4dO3jwwQdblptRqVSo1epWPzudTpYtW8brr7+OoihER0cD8Prrr3P11Ve3vKYAX3/9NZMnT+aVV15hxowZPPbYY+Tn51NUVMTVV1/dcn7NGd7y8nK2bNnCWWed1SojEujDbrfz/fffM3HiRB588EGKi4vR6XQkJiaiVqtJSUnhD3/4Azt27KCkpITzzz+fAwcO4PF4GD58OG63G6fTyZEjR4iKiiIqKgq3280DDzzAL3/5SxITE1EUhZycnJaMdnPsNpuN1157jcsvv5yUlBRsNhvV1dWYzWY0Gg3R0dE4nU62b9/OqlWr+Mtf/kJoaChGo5GamhqsVis6nY7s7GzWrVvHmWeeSWJiIlFRUS1Zeq1Wi8/nw+l0EhkZycaNG1GpVBiNRiZNmsSHH37Y8i3u0ZljrVaLRqOhpKSELVu2cNppp7VcV+g8o9hXz69cuZJPP/0UgJkzZ3LppZei1+v5/vvvO1yG5mhLly5FrVZ3+VAUBY1Gg6Io7fYi+Oabbxg3blyXx2vW3V93Pfn12N/HGGgx+f1+7r777i73e+KJJzAYDO1+y9zRz4Hu19HPP/74I2+++WbA59Js8uTJzJgxgwMHDjBv3rxulxdNHn/88Q57SB27rbv/X7x4cbvrPB8vc+bMYcGCBUE7/oksJCSExsZGNm/ezLJly/j73/8OwM0338zzzz/fozrDw8NJTU3l1ltvZc2aNQEtedaV4cOH9+v65gNJWFgYf/vb34DWCa9j//53ta0nZY533V09b7fbqaurI1Dd+XvZn3+LB8K+3dnf4XDw5z//uVt1d8fpp5/ODz/80Ok+X331FZMnT27VW2LPnj2sW7eORx55hEWLFrUsH9pRz67O/v/KK6/wj3/8o9Uxjxw5Qnx8PE899RS33XYba9eu5cYbb2wbnCKEEH3ouuuuazWu59lnn20ZfyNOTosXL253nJdGoxkQs6X+8Y9/DGg8ZkcTx8ybN6/bYzu3bdvWpp5f/vKXAZUdPHhwq8mjfvzxx24fPxgPv9+vVFRUKAcPHuy3a3m0f//7313GdNVVV7Vb9uhlr7rzOHLkSJu63njjjYDLHy0xMbFbx/7uu+/anZCxvx9vv/220tjY2Oa8x44d26169uzZE9B1tdvtAddZXV3dYT0XXXRRt8+1PXfddVfA938zv98fUJlf//rXit/vV0pLS1vKV1VVdXvViJ4+Opu5Xojj7bvvvlOuuuoqBVBefPHFLj8/HP138thlHxVFafX/uXPnKlVVVYrP51Psdnu7EzQOJNJAF0L0Kb/fr2zcuFFpaGhQampqgh2OOA7amwTzb3/7W7DDauH1erv9AftYffFBv6amJqCyGzZsaFUu0Maky+VSDh8+rHz99dfK6NGjlfT09OPyIR8G5jroGo2m07I2m61PrmtXcTQ/nnjiiVZlAllK7Mcff2z3ePv37z8ujfUHH3yww3Pet29fn7x27Xnvvff6pM6jl1bq6lFSUtJhPT2JpaslDk0mU6ex9/cyet1d+UiIga55ZYzmyax7+vtnIDjxIhZCCDGgHDhwoM2Hv+ZZVweK008/vdMPqyEhIZ2WD3SJNEB54IEHOqxn1KhRXZbv6YzZHTGbzf36QT+YH346iymQ7GBhYWHA55ifn99hPeeff36X5RsaGroVf2fHa3brrbf263WtrKzs8NhWq7Vf75FAssiBcDqdyiOPPKLcfffdyqWXXqoAyp///Odu1VVcXNztWJxOp/L6668r9fX1SmxsbJv9O1rW8WjtrZ9+or9nhTheTuT7/cSLWAghxIDTvBZ0II2KYDi2+9uxj0CySe19qO/JB9+eln/iiSd6VK67S7ydSB/2//a3v7Ubz/nnnx9wHYGe42effdZhHYF8gdPeFy8TJkxQEhISFKfTqTz55JOt9v/hhx+6jD3Q3iE9fXTWqyTQbtw9vUf8fr+i1Wo7rO/uu+/udp1H83g8yrJly5Rf//rXytatW7vcv6ioqMNYuip/bG+NHTt2BBTj0Uvc9uWjuLg4oOMLcSIbCH+jeuqkmiROCCFE8Njt9pYJVQaiBx54oN1JDM8+++yAJ5W6+uqru5x4rqs/q36/v2UJxvae62xVkn379pGSktJm+7///W/OOeecDsslJydTVlbWaVw9dfPNN/Pcc8/1S91d8Xq96HS6ljgWLVrEjh07OPfcc1u2d2Xr1q2MGjUKaFqm691338Xr9eJ0OgkNDUWlUnHhhRfywQcfdHjdoOvVZNq7L/x+Pz6fr1WszfXs27ePpKSkLuN/9913ueKKK7rcrye6upe7s4JOTz5uHn1923uus+vRH5SfJyl85513ePfdd9m9ezePPfYYEyZM6LKszWbj7bff5rLLLgt4lR5FUbq13NfRDh061OEypD6fr8f1CnGiOPr304nW3JUGuhBCiFPG+vXrmThxYqttO3fuZNiwYQHXoSgKKpWKsLAwGhoa2n0+UEc3rg4ePNjhB+qjzZ07l/nz53freF9//TVnn312wHF1R3l5OXFxcf1Sd6Car0lPeTwe6uvriYmJ6XEdu3btYvjw4e0+l5GRQVFRUUD1fPTRR9TW1rZakrUr/bHU7OLFi/nTn/7UJ8eNiIigtra2R3Ho9Xo8Hk/L/y+++GImTZp0yqywsHbtWqZOndqtMsXFxaSmpra5Pl9//TXjx4/HYDD0ZYhCDEiffvopubm5rFixgksuuSTY4XSLNNCFEEKcUvx+P4899hinn346F1xwQY8bNx6PB71e32Z7d/+sNmfNA43j6ON2lTk/Wn804qCp54TJZOqXuk80O3fu5F//+hfnnXceOTk5LdvvvPNOHnvssX47bn9c20DuY7PZjMPh6HK/HTt2dPjlRVdKSkpalpoNtFfByeaf//wnF198ccD7N1+7c845h6+++qrNdiHEwCYNdCGEEKKHjm0Y/elPf2Lx4sVBiqZz/dVAdzqdkpFrx913383//u//AmC1WgkNDe23Y/XltX3xxRf57W9/G9A1/fvf/87vf//7LvfrzT1is9kICQkBwO12Bzx04WTS2bCY9jR/tD+6i/wdd9zRsta5EGJgkwEoQgghRA/t3bu31f8ffvjhIEXStc2bN/dLvdI4b98jjzxCRkYGl156ab82zqEpQ99d7c3HAJCWlhbwNW1uOHemsrKyV/eIxWJh+/bt7Ny585RsnEPPv4BRqVS89dZbTJkyhQceeKCPoxJC9BfJoAshhBC9UFpayvjx41m1ahVXXXVVsMPpVLC6Qp+qujt8oad6MplYR+P2uzOBWEeTFja78soreeutt7oVl2iru9dX3pNCnNikgS6EEEKcInrSUHzggQdaTUp3LPkYMTB099p21EDv7vXs7LiHDh0iMTGxW/WJtqSBLsSpRbq4CyGEEKJDnc2WHRERcdziEJ07ejIwIYQQJy5poAshhBCniPLyct54441uleksQ3q816EWHTvrrLO6XaanM6sHyufz9Wv9p4r+HiIhhBhYpIEuhBBCnCLi4uI47bTTul3uggsuaHe73+/vbUgiiPq74Sf3hxBCdJ800IUQQgjRqY7WWo+JiTnOkYi+dGwDPZBZ2Y91//33d/icZNCPn1deeQWA6667LsiRCCF6SxroQgghxCmkJ1nT9rqyn3nmmbz55pt9EZIIkr///e+t/v/pp592u47Zs2d3+FxYWFi36xPt++yzzzp9fsqUKfh8Pl566aXjFJEQor9ogx2AEEIIIQa2YzOrGRkZfPPNN0GKRvSV8847D4fDgUajwW63Ex4e3u06EhMT+e6775g0aVKb56Kjo/siTAGcf/75Xe7T3aX2hBADk7yThRBCCNGpwsJCzj333GCHIfqB0WhEp9P1qHHe7IwzzsDn8zF37tyWbddee21fhCeEEKccaaALIYQQp5ChQ4cGvO+cOXMAMJvNfPHFF/0VkjgJqNVq5s2bx9dff83dd9/NmjVrgh3SSWfOnDkMHjw42GEIIfqZSlEUJdhBCCGEEOL4CXQc+muvvcY111zTplxGRgZFRUX9Epvoue7MLyAf/05MBw8eZMiQIa22TZgwge+//z5IEQkh+ppk0IUQQgjRwmQytfx8bCOuudvyX/7yl+Mak+hb119/fbBDEH1o0KBBwQ5BCNGHpIEuhBBCnGKuvvrqDp+zWq0tPx/bQF+3bh27du1i1qxZ/Rab6H+//OUvgx2C6KH2ej4YDIYgRCKE6C/SQBdCCCFOMa+88gp79+5tta2goID169ej1f53gZdjGwMajYasrKzjEqPoPr1eH9B+PVlqTwwMZrO5zbaEhIQgRCKE6C/SQBdCCCFOMRqNhrS0tFbbli5dyumnnw7Q0gj/xS9+cdxjEz338ccfk5iY2OV+gTbkxcATFRXFokWLWm2TL1yEOLlIA10IIYQ4xY0ZM6bV/7dt24bVaiUmJiZIEYmeOPfcczl06BC33357p/udeeaZxycg0S+OnQNixIgRQYpECNEftF3vIoQQQoiT2bFrYGu1WkJDQ4MUjeitjjKqX331FeXl5WRkZBzniER/OvYLNiHEiU0a6EIIIcQpTtZWPjWcddZZwQ5B9IMJEyYEOwQhRB+SLu5CCCGEECcRGZN8alGr5eO8ECcTeUcLIYQQp6gHHniAiIgIHnrooWCHIvqQNNCFEOLEJQ10IYQQ4hT14IMPUl1dTXp6erBDEf3siSeeCHYIQgghAiANdCGEEOIUJt1jTz7tZdD/+Mc/BiESIYQQ3aVSFEUJdhBCCCGEEKJvbN++nZEjR7baJh/3Ti5Hfwkj11aIk4s00IUQQgghTjJHjhwhMTERAKPRiMPhCHJEoi9JA12Ik5c00IUQQgghTkLNjTiTyYTdbg9yNKIvSQNdiJOXDDwTQgghhDiJGY3GYIcg+tgnn3wS7BCEEP1EGuhCCCGEECehN998k9TUVN5///1ghyL62C9/+Ut+/PFHDhw4EOxQhBB9TLq4CyGEEEIIIYQQA4Bk0IUQQgghhBBCiAFAGuhCCCGEEEIIIcQAIA10IYQQQgghhBBiAJAGuhBCCCGEEEIIMQBIA10IIYQQQgghhBgApIEuhBBCCCGEEEIMANJAF0IIIYQQQgghBgBpoAshhBBCCCGEEAOANNCFEEIIIYQQQogBQBvsAPqC3+/n0KFDhIaGolKpgh2OEEIIIYQQQgjRKUVRaGhoYNCgQajVTbnzk6KBfujQIYYOHRrsMIQQQgghhBBCiG7Zv38/Q4YMAU6SBnpoaOjPP6kByaALIUSw3D1kFlq1wiNlq4IdSpfmpUxnXunqPqvvniEzWXQg8PO+Z8hMsiOsWHQeUClU280U7Hyxw/3vT57BQ/ueabVtYVou9xav6WnIPJ5xM3fseb7D43l8KhYdWMVfk2a2XNMXRl1DlMmG16+hzBpBg1fXqxj6wsK0XAaZbTS49fxh99oe1XHP0Jks2j/w79uBZl7KdHIia1Gr/Bi0Hg43hjFr+8vdfj+I/nX0e1gIMZAogP+o9iyoFEVRghdQ37BarYSHhwMapIEuhBDB8XhGHiaNj8Jdz3S9swBgYWoBw8LrCTM4OdQYxi3bnjvuMdyfNJuHypZ0ud9zI28hKbyWBqeJKza8chwi656nsmaiBn6/Wxoh3TE/pZAHSpf2uPyitHzuKV7eJ3UJIcSpRwF81NfXExYWBsgkcUIIIYQQQgghxIAgGXQhhBCimx5OKSDR5OKI08CckmV9UufcpEIeLAs8+9jd/Y/1WHoeWrVCnVvLvH2B1bMqOxefoiJ/52oWphZwbx+du+hb9yfNJsHobulNcDyu1bzkwoDvo+5aMmwGWrWfvB1r+qX+nji654AQQvScZNCFEEIIIYQQQogBSTLoQgghTkmBjr3uzOMZeTR4NSf0uNsnM2dxW9FK4PhkWge6xzPySApppKQhBLvvxLy2c5MKSTC5GWJpBMDt13DNpvYnIGyeO8Lq0XJ3H2SEF6QW9FmvkoWpBQw2OzjiMOJTVPihz+oWQoiBQTLoQgghhBBCCCHEgCQNdCGEEKek3mbPAbRqP2E6bx9EE7h5yYV9Vtf9SbMZFlHDO+OuZW3OVExaX4f7PpxS0PLzmhHT+iyGvrAw9b+xrR4xjScyZ/W4rjv2rMDu1XJ+cgnZ4VaezprJ2+Ou5emsmX0R6nGhUYNXURFtaSA5pgKXT9PhvnfsWYFZ6yUpxMby4dNZPWIay4dPZ1FafreOuSC1gHnJhcwpWcby4dN5shfXoFmk3kNxo5m7i5dzb8kyPH7pJdkT9yfNDnYIQohukAa6EEIIIYQQQggxAEgDXQghxCllblJhq4xrb9y6exWhWi+L0/P6pL6uLBk2g5Cfs9wPpxSwoIfn8djP8T5UtoRISyPjR27jwgnrGRtb3mbfuUmFPJk5C4vWzxtjr+fLsy5Do/azNmdqz0+kj4XovKzNmcrjGXnUe3RE6DzMTep5T4O9DRY8Xi3j03fzq+HbCDc6qHTpAip7dE+DYFiQWkBqiI0RUVX4FRVFFYnsa7S0u2/za1Tj1mPz6MjfuZrp258lf+fqbs1Q/mhaPnNKlhFvdPPPib8lPaKGUJ2n1+dSuOuZVjPDz9u3tFe9I04mzddufkrX93lf9BYSQhw/0kAXQgghhBBCCCEGAGmgCyGEEEIIIYQQA4AssyaEEEIE6LH0PO7au6LVtrlJheREWrF5dORuf/a4xfJU1kzUQLVb1+2lwBamFpAR1kCspYG0ofsJS6jC69SzdctIPj84GK1K4b7SpuWsXh97A9HmRow6Nx6vlh3VcRy0G3iwbCnPjbyFW7Y91w9n1z1PZs4iOdSK26dlyuZ13Sq7ILWAFIsNvcbfquyK7Fx0KgWT1oPXr+amrS90WdeSYTPQqJSW1ycYVmTn4vZp+P3uVQHtvzZnKrFmGz5FzYHGUPJ2rOn2MZcPn45F6yHWbMPl1bG3IYzbf166TwghRGdkmTUhhBBCCCGEEGJAkgy6EEKIU9Li9DzuPCYb3pXH0vMwafxtspPzkgsxavzdmlhrVXYuPkWFSqV0O2v5ROYsrB5ttzPnx3p59I3EhzRw/jfv8PnkK/D51fzyu7dbnp+bVIgfFRcNLcPh0XPR92+yILWACJ2XCL0LvcZPhcMUcLa2P63KzkWn9nPQbuLekmUBl5uXXMggkwuPXz0gzuNEc3/SbEZGWIm3NLCzJobZu54JdkinjHnJha0m0WvP/JTCVr8nHk3L5+5u/J4SQvQ3yaALIYQQQgghhBADkmTQhRBCnLJ6kk16KmsmVo+WOd3I0rZnVXYu6ZHVaFR+Km2hXL3ppQ73nZdcSIrFQZ2naamvCJ2HBq+WW3uZ8X1u5C3oNT72WEMwafzoNf42dT6alk9qaCN6jZcrN7zCwtQC4owuZvZgrPKp4tG0fIwaP1aPpmUs/0D1cEoBRo2/zdwKgZqXXIhBrXBvyTKeyJwlY89PAIFk3oUQx4tk0IUQQgghhBBCiAFJMuhCCCFENy1Oz8PhU/dJdvTxjDyMGl+XY3efyJyFGritjzKUK7JzqXbpuad4OU9mzuq03kVp+aSE2KhwmNColAE1VvvobGBX59Hd+npqUVo+ETovGrW/R7Oi94VFafndmhNBCCFEMEgGXQghhBBCCCGEGJCkgS6EEEIIIYQQQgwAQW+gFxUVcf311zNkyBDMZjPDhw9n/vz52O32YIcmhBCim+anFAY7hOPizr0r0PTRiKo79qwIaGmq24tWclvRSh5Ny+/1MRen5xFlcDEp4TAfTbiaSpeu3f0eTilgQWoB9xQvp8JhItLgQqf28+qYG3sdQ1/RqhQeTikAwO1XsTZnKquyc3tcn1nj5/GMvF7FdE/xcgp3PRO07u2PZ+RJ93YhhDhBaYN58P379zNx4kTCw8P5wx/+QFRUFN9++y1z587lxx9/5O233+66EiGEEEIIIYQQ4iQQ1EniHnnkEebMmcPWrVsZOXJky/Zp06bx3HPPUVNTQ2RkZJf1yCRxQgghguHpn5dcu7eXS64FeqwEs50jdnOrSdp6OhnYwtQCxsdWEGays7Mygdztz7a737zkQswaP2NjKtFpfLi9Wr6tiGOo2Ynbrw4o+9/fFqXlE6r19dnkdauyc3H6NMdtMrz+WvZqQWpBr5cDFEII0Z8G2CRxVqsVgPj4+FbbExMTUavV6PX6YIQlhBBCCCGEEEIcd0FtoJ9//vkAzJw5k02bNrF//35efvlllixZwm233YbFYglmeEIIIUSnbF4Nw8LrWT58er8fy6eosHl0OHxNf7qXDJvBO+OuJd7oatlnblLgcwBYtD72N4Szuyoem7f9EW8LUwtQFPAoKg41hlHjsOBT1Jw/+CCRBidOn6Z3J9VH7ilejl7j4+msmX1S38wda9CoFF4dcyOrR0xjcXrvxqR3pa+z581zQQQre35/0uygHFcIIU4GQR2Dfskll/DQQw/xyCOP8I9//KNl+5w5c3j44Yc7LOdyuXC5/vuBpDkTL4QQQgghhBBCnKiCOgYd4IUXXuCFF17gmmuuITo6mvfee4/Vq1fz5JNP8oc//KHdMvPmzePBBx9s5xkZgy6EEOL4eiJzFrEGJ9UuI7cVrQyozP1Js3mobEmfHH9uUiEPlnUvA7swtQCjxs8de1YATdn4QMaSr/h5bPatx2ls9kDweEZey+skhBBC9K22Y9CDmkF/6aWXyM/PZ/fu3QwZMgSAq6++Gr/fz1/+8hduuOEGoqOj25S79957+dOf/tTyf6vVytChQ49b3EIIIYQQQgghRF8Lagb93HPPxefz8fXXX7fa/uabb3L11Vfz8ccfc+GFF3ZZj8ziLoQQQnTf/JRCHijt+9nDRXD012zwgXo0LZ+7Zf11IYTohgE2i3t5eTk+n6/Ndo/HA4DX6z3eIQkhhBBCCCGEEEER1AZ6VlYWGzduZPfu3a22v/jii6jVakaPHh2kyIQQQgghhBBCiOMrqA30P//5z/h8Ps455xweeugh/v73v3PppZfy1ltvMWPGDAYNGhTM8IQQQoiTmnRvb+3hlIJe17EoLZ8nMme1/H9Bau/rDNS8fUuZlxz4Unt9Tbq3CyFE7wV1krhzzz2Xb775hnnz5vH3v/+d6upqUlNTWbBgAXfffXcwQxNCCCGEEEIIIY6roGbQASZOnMj777/P4cOHcbvd7Nq1i7/+9a9otUH97kAIIYQQP5ufErys7PFk0fp7Xcc9xcuJ0Hl4fewNPDfyFizatnPt9KfmSeIWpeW32j43qe01XJye15Lhvz9pdq+uczAz90IIcTIJegNdCCGEEEIIIYQQQV5mra/IMmtCCCFE96zIzkWrUpi+/dlO91szYhoOnwa/osLhU+P0qYnUe/n97lXHKdLja+mwGRTueqbX9TyROQuj2t9lXU9kzsKg9lPn1nFvybJeH3duUiEPli1l6bAZVLl03FfaVOfi9Dzu3Lui1b4PpxQQZ3Tj8qu59SS9nkIIMbANsGXWhBBCCCGEEEII0UQGegshhBA9NC+5EJ+i4qGyJf1+rBXZuRg1Pg7bTX0yW3aF04Dbp2LpsBk4fBru2LOi3f0OO4yMjanC6jKwtS6cwSYXhbue4emsmQMqi97VeQTK7Vfz9rhr2VUX0avX+failQHt5/WriDZ4qHPrenysoz1YtpR3x09BURrRqc0t25uz5/cnzWao2YnVo8WvKOTvXM285EKeypqJTqVw0GE4LvezEEKI9kkGXQghhBBCCCGEGACkgS6EEEIIIYQQQgwA0kAXQggheihc5yM7vIHH0vP6/VjVLj1un6ZVt+unsma2LJMVqPkphSwZNoM5Jcvwo0Kj9mPUtF0KbOHP9SaYnCiKihizjWiDh8Jdz7AqO5da98AaJVe46xnsvt5/rLmtaCVXbniF1NBGnsqayfLh0/v1+t61dwVVTiMJJmev63osPY/nc25mU3UUG2uimLljTZt9Bptc5O9cTaO36V56Kmsm8/Yt5dbdqyjc9QxqTvi5g4UQ4oQmDXQhhBBCCCGEEGIAkGXWhBBCiF54PudmQnVuyhpDuS3AicF64umsmVS6dMzbt7TX9Th9au7cu4L5KYU8UBpYfWtGTCNU7+anmnAeLFvK4xl5vZ6QbSBbMmwGiWYbNo+Om7a+0G/HWZGdy2GHMeDrEIgnMmcFNEndvORC5u1rupYmjY/aXi719mhaPncXL+fJzFl4/Ko2y7oJIYQ4liyzJoQQQgghhBBCDEiSQRdCCCF6aXF6HnqNn1v7edmxJzNnoVf7UYBKl77bWddH0/KxedWE6nw0eDSoVGDW+ANaTuzxjDySQho5YjcTbXRy/U9re3gW/Wtxet4Jlbl9MnMWoToPhxxG5vQiex2ouUmFPFjWdN88nTWTBLMdl0/DjVte4OGUAjx+VcvzPfHy6Btp9OjbHf/eU/cnzcas8fcquy+EEAOTZNCFEEIIIYQQQogBSTLoQgghThlzkwqJMXrw+tUn/Pjph1MKuK/0+GUU56cUMtjkxKLzYHXryd+5+rgdW/SdozPozR7PyMPpU/cqQ/1k5ixijE5u3NJ/4/WFEOLkIxl0IYQQQgghhBBiQJIMuhBCCCFEH7o/aTYPlS0JdhhCCCEGPMmgCyGEEEIIIYQQA5I00IUQQgghhBBCiAFAGuhCCCHEKWh+SiHzkguDHcZJ6aGyJTyfczNLhs3ok/oWp+exOD0v4P2fzJwV0H7zkgtZkFrQ07BaPJ4ReGwng/kp8r4RQvQfaaALIYQQQgghhBADgEwSJ4QQok88mpaPH7ineHmwQzlujudSZ0uGzcCvqPj97lVAU1b1zr2tl4prbwmt9jyekUdSSCN6tY8rNrzS5vmlw2bgU1To1H6GhDRQ6zRy09YXWJszlZu2vtDusQeCR9PyubuX99/TWTPxKSpuK1rZ63gWpBYwp4uly9aNmopO7WdvQwiKourVUmdHe3f8FKxuQ5fLnr0x9npMOg8heieHGsK57qd13T7WwtQCVCoFn6Lq8nxPFo+l56FXK31ynwghTmUySZwQQgghhBBCCDEgSQNdCCFEn7i7ePkplT0HCNd7eXn0jdyfNLtfj3N/0mwGWRqJNLhatnWUwQ5kfKxW7eeaTS9S1hjK+6f/rtU45HnJhdR5tCSY7Vi0Hjx+NamR1Xx33sUccZh4Pudmogxu1uZM7f2J9TGnX81TWTN7VUe1W0eYzhPwOO7OBJJNNms9WHRuciJr+yx7/lTWTC7/8VV2W0PaPLdk2Awez/jvmParN73EocZQDjZEUO829Oh495Ys457i5adM9hzgrr0rsHo0QY3hVBv7L8SpQhroQgghhBBCCCHEACBj0IUQQpwy5qcUEmtwM3vXMywZNoNyp555+7oes92Z50begkHjZb/Nwl39OC77sfQ8tGqF2/thzOu85MIOX4d3x08h2tJAWV0Uhx1mEkwO9jaEDMhsaWfn0V0vjb6JSIODOpeRImvocZtroCP3J83mobIlXe63fPh08neubrVtblIhOrXCfaXLWmaWt3k1WLQ+ogxOLDoPtS4jN299nscz8rhjT+/u4/kphfgV+uxaPJE5C41K4daf51841S0dNoMIgwuHV8t+u4kHSvvmdRZCBIOMQRdCCCGEEEIIIQYkaaALIYQQQgghhBADgDTQhRBCnDKGhVnJjKjhowlXU+PW9UkX3Fu2PYdPURNjcPdBhJ27vWglj2fk8dzIW/qszodTCog3to59QWoBy4dPB2BnXQRn/vtD9jSEEqV3c9huZni4tWUytrlJXU9K15+OnhSur7pUA1z/01ouXv8G1/20Lujd24GAurcDbbq3AzxYtpQwnQ+A2bueYfauZ7hr7wpMGh8xZhs2jw6nT8O85ELu2LOi15MexhncfXYtFqXlY/NqCNF6WZSW3yd1nugUoMJhYvr2Z6V7uxAnIWmgCyGEEEIIIYQQA4A22AEIIYQ48S1KyydK76HOrePuAbrU2rzkQq77qX+yTTdueaFf6j3aILODLyZfjl8pZ2t1LACL0/OwejQ8WLaUJzNn4VNU2LxqVKq2S3wtSssnK8yK1W2g1q1Ho1LQqBQavAp1bl2rycHSQxuxeXQAhOm8fHnWZfiVg5z3zbuszZlKclQlSZEqXjbe2G+vaaBu3b2K53NuxuHVYvVoezRR3+MZeWjV/l5PQvbWaddhdRuodBo6XAbvaE9mziI51Eq9y8gt257rcL+HUwrQqOhwGbbmyfFWj5iGCsjd/myb5zUqT5tyVS49ud+9zfLh0znkMDBv31KWDJvB7F2BZeubPTfyFiqdBs5IPAjAd4f7bvmxo5dulAx6k9m7ngl2CEKIfiQZdCGEEEIIIYQQYgCQBroQQoheUxQV+TtXD9jsOfTt+OS+sGTYjG6N9fUpKswGFwatB6PGx6Np+dy5dwUPljWdl17tJ1LvJs7Y/lj4e4qXs68xlDizjRGR1ahUCj5FRbjOi92nbrW0VoXDRHZMOd+eewlDQ618sn8oYWY7ZdeNZVxyCdUNYeysTGBwWF2vXoO+olP7GRZVyfCIOp7MnMXDKQUBl10ybAZDLDYGm23dKtee3fXhxJjshOq8QFNm/o2x13eY+b2taCU6tZ9Ysw1oynS3577SZR1mzwFCtT7eHnctSWF1RBodvHXadbw7fgrrRk1l3aippIXYqXDq2pRr7m0QbXRy3qBDfHbmb7Bovd06Z4BBIVYuGbYTm8vIhvJB/bbc4D3Fy4M650FH1+dEIL0PhDhxSANdCCGEEEIIIYQYAFSKoijBDqK3rFYr4eHhgAZQBTscIYQ4JT2cUkBOZB11LmObMbCireXDpzMsqhKN2o/Hp+VAQzg3b32+3X2fz7mZSIMTn6Ki0a3noN3MYLOdOreB3/88bnpFdi5JIVYcXh1XbXy5ZVwywMLUAsJ0XiL0Lho8egAiDC521ocyKrKeGpcBh1fLbUUreTQtn6QQG5EGBx6/BpPOTVZqKSHRdTRWR1BeHktYSCM6nYd9hwfx2YHBLVn8YLk/aTZnxFZh8+i47qd1rB4xjendvAdXZOcSa3Sw3xbSq7HoT2bOYmR0JY6fX+cDjaHUunXtZsBfH3sDWfGHcLr1FFXF98tcButGTcWnqNColDb1f3LGVURYGqlsCOfX618HaHXfBOL1sTegV/vYURdBamgju+pD0aqVVmPHhRBCdEQBfNTX1xMWFgZIBl0IIYQQQgghhBgQZBZ3IYQQfSI5xM5+Wwi17rZjXU82T2XNJNrgosJp5PailT2qI3/nauanFBKt9xChd9Ho6fh1c3i1gLFNhv2JzFktP+ftWAM0jTV9eXTr2dXvLVnGmhHTUKnA7VejV/tJi6pkWNxhNh8aipqm8dALUgu4u7gp09sc2+93r2JH9Fn4fWoqq6LZVZGA87CWML2bYfGHOD2mBsp69BL0mYfKlkAZvDPuWj478zeU1ne/jrwda3g4pYDBZmevYrmtaCWrtLkMCbHS6DFQ2MmM23q1jyprOB6fBp+iYkV2LgftxnYz2E9nzcSo8XHEaWgzQ//C1AJMWl/LvfjqmBuZsnkd0HTvhOjcHLBZmJ9SSJjOy+1FK5mXXIhBu59Gp4kqh5nlw6dT7dIToW9/DoOO+BUVTp8Wn6JqOSY03T/9sUb3E5mzevye6ytzkwqJ0PtazdvQkccz8gAC2rc/9df1EEL0PcmgCyGEEEIIIYQQA4A00IUQQgghhBBCiAFAJokTQgjRa4vT87izn5ZW6ktPZs4iTOfhiNOAoqg6XbqqK6uyc4k2OihrDOW249Dldm3OVExaL1dveqnHdTyalo9PUZEa2kiY3oXbp0Gr9nP5j6+22m9eciFjo2rZWR/OPcXL+WDCNRTVRTI8shqD1kOFLYzDdjMjoqqwuoy9iqm3jp7UbM2IaTR4tTR6tD2+tk9lzWSopZGrNr7cp7G1Z0V2LmE6N2oVWHRufqiKadMNuXmCwEaPnut/WtumjvuTZvNQ2RLWjZqKQePD5tFh92pJC6/Fp6jZZw1HAWbveoa5SYU8WLaURWn53FO8nHfGXUu0pQG3T8t3RxIxa/z4oVtdyJvrfDJzVr+9D1Zk5xJlcHHQZjku77VAPJxSQJLFQYXT0Omycquyc7H7NL2aePBoT2TOwqj2U7jrGR7PyOuy6/yjafmYtL4+O74Qoq/JJHFCCCGEEEIIIcSAJBl0IYQQfeLhlALuK+15Rro/zU0qJMHkJtLgQq/24fGrue6ndV0X7MKK7FzSwmvZVh3br5m9xzPyGGS20ejRM3PHGlaPmEa1S99p5q49C1MLiNB7iDfZ2WMN4+7i5TyROYtGj6bNtXtp9E3Emhs50hhKtctInMlBlNFOQkQtPr+aPZUJTNm8jkfT8rk7iEtqLfh5CTmHV9NncTyWnsew8HoaPHoaPToqnPoe39udZdHnpxSSGmID4KDd1O7SZEuGzcDlV3ea1X7q5wnkbF4ttxetbMlqP5U1E0VRtbk3H04pINHkYlBIA5HmpmXWrtjwSo/Ob1FaPlqVwl17V3R7ibZALB8+nUSzDZPOTb3LxH6bJeiTxA0Ej6XnBfT+X5haQIjOKxl0IQYsyaALIYQQQgghhBADkmTQhRBC9Jn5KYWYNb5uZ3ZPZPNTCkkwuqhx69rNgPaFxzPyiDc5cHi1DAppoNZp4sYtL/S63hXZueTtWMOaEdPI3f5sm+eXDpvBYEsjZY2h/H73qpb9BnJvib7UnInuL4+l56FRgV+hz+ZwmJtUiB8VCUY3eo2vZfm99qweMY0jDiP3lizjicxZ2LyaNku4Bao5e96fr5ksFdba3KRCdGrllHgvCnHykgy6EEIIIYQQQggxIGmDHQDAhg0bmDdvHl999RVOp5O0tDTy8/O57bbbgh2aEKKb+mMMojhx6NV+tOoTvmNWt/Qko9c8bjvQ94vLr0JR4Kz03SiKitqyVD6ffAVGnZuD9VFcs+lFoGk8sE6lcOfeFSxMLWgzk/nanKk4fVqijQ5qXUbC9U4+nXQlRbWaNjPxvzPuWvw0EmluZHRGEVeePoqG+j0s908nM/IwL4XdhMOr7dFY+L723MhbSIuswuYyUtYY1mnWuDuaZzwP1fr4fRdjeOenFBKh82LU+MjfubrV9o7uERWQFlZPjdMENI0NjzZ4OOLQt2ShH04pIMbgoXDXMwCdztz9WHoeIyKriDQ3oigqKm2hQNPM3z6/qk2WfmhoPWMSD/Cb7HM4UFnD7vpIAJ7OmolZ62V6O70qOqJVKbw0+ibiLYf4xZDLqXOaOfBzz4u+8kBp/2boTxRPZc1Eq1Lw+L2dzn3xaFo+Fq0Ph08d9PeoECJwQW+g//Of/+SKK67gtNNO4/777yckJIS9e/dy4MCBYIcmhBBCCCGEEEIcN0FtoFutVm655RYuu+wyXnvtNdRq6XEvhBBCCCGEEOLUFNQW8bp16ygvL2fBggWo1WpsNht+vz+YIQkhekm6t5+a1oyYxpOZs7inePkJswTSY+l5zE8pDHj/p7JmsjZnKk9nzWzz3ILUAh7PyOPhlIKA6kowOVmRnUu4zgc0LYX0WHpeh/sPNduJMdnR691oND6MGi+KosLr0xBucLTsp1MpJJodfDThasZEV/PS6JtYkPrfmGLNNoaEWLlq48toVE1DEbw+DYW7niHO5OSpo87tiMNMetwRhiQeoaYmknd+nEB1fThjEw5i0Ho4LbmYnPhD+BUVi3+OfW5SIQtTA3sN+kLz9Wvw6PD51USHWlHTtERcoNaNmsoHE65hRXZuu8+7/WoavJou60kyO5g4aD8jYspbXsdV2blE6j3t3jMARo0fp1eLV1E1TchnduL0aVp14b6vdBkGjY+3TruO5cOn4/F3PBluZng9e+ojOPPfHzL5yw+4csMrLE7PI1znwe1XszC1gLlJTa/ZU1kziQppICK8ns37kwEYamlk+fDp1Hm0AXdvXzNiGt+ffxEXJpdyRuZOEqOrMBtc1LuMfdq9vdkQi7PVPX2qeTwjj1t3r8LlV3Nb0cpOf2/cXbycCL2LMJ0XaFqybsmwGccrVCFEDwW1gf7JJ58QFhbGwYMHGTZsGCEhIYSFhTF79mycTmcwQxNCCCGEEEIIIY6roDbQi4qK8Hq9XHnllVx88cW8/vrrzJgxg6VLlzJ9+vQOy7lcLqxWa6uHEGLgeDQtP9ghiOPo/qTZWD06UkOtLZnD7mSmj6cnM2cBTVmotNAG0kMaAy576+5V2Lw64kyONtlWRYFIvZuQnzPiXbll23Okhddy1pB9vDrmRqINbpJDbB3u7/Zr0Kj9/FSSzuZ9adS6jBxqCGNHdRwX/uctnsicxROZs7hz7wpu3PICG6piufSH17j+p7WYNf6Wc651mrB59Px00flo1X4cXi0Xr3+Dl0bfRJnNjFHz3/jjTHbK6yLx+9TYnEYuHvkTg+IqiI+rJDV1H+YQOyaDC4+iQv1zUvfBsqW4Osnw9rXmydd+v3sVW6viAZiYVIJeHdh1ANCp/ahRGJtwsN0sukal4A3gnA7YjXx7MAmPT0tqqLWl7jMG7cfWQQY+VOdBQUWUwUVaeC3Ttz/b7gRwLp+Gqza+TP7O1dzdzlJ+zRnlcKODSYPL+PKsy1g9Yhr/nPhbLFof07c/S6Tew70lyzBqmnpOKIoKrcaH0eJgfMpeDttCuWrjyxy0G7q11NqgECuJieVkjdzFwfJ4hr/7DVvLB3HLtucCrqM9zZn+Y1ndul7V21PLh0/nnXHX8tmZv+mwt0VHFqYWsCgtv1UPlZ5YkFrQ8h51+9Q8lTWTeo+W53NubrPvwykFPJk5C73Gj8vf9HE/f+dqZu96puX3oBBiYArqGPTGxkbsdjuFhYU8+eSTAFx99dW43W6WLVvG/PnzyczMbFNu4cKFPPjgg8c7XCGEEEIIIYQQot8ENYNuMjUtK3LDDTe02n7jjTcC8O2337Zb7t5776W+vr7lsX///v4NVAgRsPuTZreb4REnL4Paj17tJ8Tg5IxB+3lu5C08ULqUeckDJ4vePAY4NdTKZ2f+hjijg2qXkZu2vtCtevJ3rqbOrSfK4AJoGc+pVSvkbn+2W+PvDzaEU9UYRmbcYSYOLcXp63ic8/Ttz3Lhf95iW10EB2wWnD4NN219AbOmaWzp7UUrWx376OXVnD41z428hUFmG16/mqs3vYTLo+OcnC1cOGE9u644kxCdm9QQG3k71vBYeh5rRkzjyg2vsL0mBp3Bw+hJG7DbzVRUR+N269m3bygelw6Hy0BqiI16z39jP57zUCw+avytUeOjzhZCcWU8V296qdVznZmyeR0Xr3+DL8pSGBV3mHfGXduq7JySZdxX2nVG+b7SZdyxZwVbquIYElVFyTXjOWf4NmptIdh97X/ccvnVTBq2g0k5W4gKaeCLyZezbtRUnsycxarsXOYlF7I4PY9QnZt1o6Z2eE4ROi8fTLgGAL+iwuXVMSGphDCTncJdz7TMC/Bk5iws2qYMrEnrxedXc/DAICyhjSRaGvjkjKsYF1MT0OsGTctqZqcXo9b4+O7706m2h/DqmBupcekDrqMjRy8z98bY6/nnxN/y8ugbuXPvim5l+I8V6DwRx3L6NOyqD+frw4nkxB7h3fFTgKYl7O5Pmt1huRXZuUxOPMjZgw5gUDf1ZunJ78bnc25mcsJh4k12FqQWkBRio8qpI0rv5aDd1Gb/+0qXEWlwsa0ujFuPmQtAr/azesS0bscghDg+gppBHzRoENu2bSM+Pr7V9ri4OABqa2vbLWcwGDAYDP0enxBCCCGEEEIIcbwEtYE+fvx4Pv7445ZJ4podOnQIgNjY2GCFJoTooYfKlgQ7BHGcheh85MQeweYy4ldMZERVsnTYDAp3DZwZ/dPC69j0qwvw+mqot1vIii2nuDqW1SOmsd9uahnLHIi8HWtYM2IaP110Pj5/Ke+H/o69Vh+rsnOxenTtjiFuT/MY3edzbsai9XQ6bnphagFxRhcmrY19jRa0aj/vjp/C5T++wNqcqdi8OsL1Ltx+DTaPDptXg0Xro3DXMySanLj8ahIsDhIiatmRfBbrSxMZPOQQjXXhRMTUkOrTsqmsaSZvvVrB5tXy6aQrGRyzA1NYA/UH40lIPoDfq8UcV0NMg4XPvjqTaHMjtS5D0FZvGGyxs/PyySiKirqGI2w4Mpjf717FE5mz8HZzUZi79q5giXYGEwaXMTiympcsN3HEYeL2opWsys5lv90Y0HneVrSSt0OvJawxFKPOzb6G8A7Lef1qrPVhRMVUk5RaxsF9QzjcGI5F68XjVxNvdONXVC09PTqa3+P3u1fx0uibqLRbGBpWR+qgQ9hsZmrtITyfczNevxO7V4ufpt4Wa3OmkhpZTnxiOeFJR/DajeSodzP4ha28f/rveHf8FNZXRreaTb49ahU4bCbMITYGRVbz44FkZu5Y0ycz+T+cUkC0wcPY+H0kxFaiN7loqAvjq7BLKauPosxm5p4e9Na6r3QZC1MLcPlV3bpvb/u5h8qC1AJ2VsUzPKacH35xIdUNNVy8/o0Oy0UZXFidZg7aQjD8PH480OM+nFLQ0nvj5q3P88GEa0iKqkKv8VHrMnJadC0+RU2MuYEnNbNaYmxm1PgYG1UL+1rXe9ihx8/xmytCCNE9Qe3ifu211wKwalXrrjcrV65Eq9Vy/vnnByEqIYQQQgghhBDi+Osyg+7z+fjPf/7Dhg0bKC8vp7a2lsjISOLj4xk/fjwTJ05Eo+l6fdD2nHbaacyYMYNnnnkGr9fLeeedx+eff86rr77Kvffey6BBg3pUrxBCiONHp1Lw+LSY9S4qbaH88ru3gx1SG3vqI6hymNGp/fj8KsptoaRFVeJwG3o0XjZ3+7MsduVx1ejNJA45zJBD8RyoicFvC+l2XTdvfR7ofPWDEJ2XeLMNk87NmKH7iBt0BKfNzPqwX/HTES35O1fzaFo+6WENeP1qbN6mtc0Bqlx6IvUe9FovJVVxeP0aJqXvpr4mArXaj9boZlBGKTX1YTyR2ZSFWzpsBglRNYRF1lNzOA6TxYEhsgGN3kPFjjTUaj/R5kbO++bdNrEenfXrb5EGBxExtWj0HtSH/YTVxPLBhGtw+6xcueGVbtc3e9czvGO5lsSIGpIjqlERxTfn/Jo6RyM2b+CdDjdURXWZfW4+3sOuAuLK3Bg1PjKiKmnw6CizmdrtjdTZ/B7X/7SWRWn5DApR89ZPY0gKsVHhMBGhd1HpNBBndJH789rmN219gZdH30hIuYPS0iS0Gh8VDeGsvyARraaG6oYw9D/P9t6ZB0qXkh1+Iwdslp/nddCzZNgMZu9a9vPYeS/77cYe9ayKMXgYFlWFRu3nP0XDsbr1nJuxC4Abt3Rv7ohj3VuyrMdrqTePf189YhqRBieVzrbjv49W5zLg9Gla1oRfPnw6+TtXB3Sso99Ha3Om4vO7KKmOIzG0jsGR1SiKinJrBHVOMz6lbUZco/Jj1rl5afRNXP/T2pbtgdybQojg6fCvzVdffcXTTz/Ne++9h83236VfFEVBpfrvL4GQkBAuu+wyfv/733PWWWd1O4ClS5eSlJTE6tWrefPNN0lOTubxxx/n9ttv73ZdQgghhBBCCCHEiapNA/3LL7/kjjvuYOPGjSiKglqtZtSoUYwcOZLo6GjCwsKor6+nurqarVu3sn37dl566SVefvllxo0bx9/+9jfOOeecgAPQ6XTMnTuXuXPn9umJCSGEEEIIIYQQJxKVoigtfZiuv/56Xn31VbRaLZdeeim5ubn84he/IDQ0tMMKrFYrn376KWvWrOHDDz/E6/Vy7bXX8uKLLx6XE2iOITw8HNCATHohhBDH1arsXGrdOpJDbESZbHxfngC0XuprIFicnkedR0uC0d3S3RSalknqzvJoR/vq7EtJSdqPIcRBedkgcj78d7fruD9pdpddgD+YcA1JsRVsOZDEpGE7MIc14miw8NnW0S3dlruyZsQ0DjqMzClZxorsXKIMLkYO2UdYZD1et463NpyO06fm7uLlrBs1ldOSSrCE2LA1WggNtxKZfgB3fQgN5dEcOJTI5C8/YN2oqVS7DDR6tEG53mtzpmLReciIP4zJ5ORQRSw1Dgt6jY9fr3+9x/U+kTmLMTEVmPQuvjs0tMf3R1dWZOdS7jBwX+mylqEBa3Omdmv5v7lJhYTpfNy5t2lywsfS84gzurhl23MsHz4dp0+DSevF61cz++dhD4vS8vH6VYyLqabKYSZE58bt13D9T2t5MnMWbr+Ku/Z2Pdnh3KRCHixrWlJRq1K4r3RZq/fTgtSCXi2J1lw/NC2VqFEpRBhcrbpr90Zv4wvU/JTCbk1E2Z6XRt+Ex69Gr/Zx3U/r+OacX+PxabA6zWjUfi794bU2ZZ7MnIVKpbRZZk0IMZAogI/6+nrCwsKAYzLob775Jv/v//0/7rvvvjZLn3UkLCyM3/72t/z2t7+lvLyc+fPnt5n0TQghhBBCCCGEEJ1rlUEvLS0lJSWl15WWlJSQmpra63oCJRl0IYQInjUjppGTcJDYmGqcDiPfl2RQ7jAGlIE7Xh5Ny293gq3eZM+hKTM2IbaScKMdlUrhzH9/2On+TcvPNWUxuzOZ2utjb2DkkH2ER9WxfXcmO2tiOC+tCKdbz8bDQ8jbsabT8k9nzSTG6KC4IRSVSsHrV3Ff6TJ2Xj4Zg8FFY2MIh2ujuOj7N1vKfHX2pdTaQ9hRF0HBle+hMbr47l9n85+KeC5NL2JXRSKH7WaMGl9LZjYYHk3LJyu8nmhzIzUOCxFGO7UOC1dtfLnX9d5dvJxV2bnYfRocXk2nk7S1V7Yrz428hUEhVmwePU6vlut+Wsf9SbMZanYCUO3Sd9oz4ejeF6+PvYE4ixWtxofLq+Pzg4NRqyDB6CLG6MDp0+JTVFi0Hg7YQrB5NUyIK2doXDlut56K+gh+qorD41cFfJ4LUwta4ntn3LWUNIRh0XrRqBSSw2vZVRPTcr/3lXnJhUFb1q+35iYVEqH3YfOqezyR4qrsXOLNNtQqBadXy476cEZG1OH0advtWXAiv15CnBraZtBbLbPWF41z4Lg2zoUQQgghhBBCiJNBUNdBF0KcmOYmFfbqeXFyUQCj3s13O7PZV57AqMQDxJucnS4bdrzdXbycJzJnsXrEtJZta3OmUusKbOmsxel5rMrObXNvP1C6lCO2EPbWxlBrD2Fecuf3vkXnYf0Fv+K78y4mxuBp2b4oLZ/nc25mzVHxHS1U76SkPJE1X55DqNGBXuNDpVZQFBWJZhsLj1kyan5K6zg8ioo9DaFckFTKL5JKOW/wATZe+AsSM/cRm7UPi8XG3vpInht5C9DUs0Cj9hNqdDAutgK/T42zJoyc7F3M/vVHJGWWkDNoP6OiK9Gr/SwZNiOg17E/3F28nKs2vszumlhykksJt9i4auPLrM2Z2mXZpUfF/dLom1p+PjoD7variTU60Wv8ndb1fM7NfHvuJXxyxlXEGV0t258beQtPZc1st0xRgwWfX02jW891P63j3fFTOC2qFo9fTYjOg0nr6/SYD5Ut4bmRt/DPib9FhcLOmlhCzXaiQ61cP+onLhi8nwtHbCEnqZSUiGqGhNYTZbYxLKKG7Ig6UgcdIi75ELGJFZj1TTE3eANfOtei9fFk5izWjZrKYbuFSYPLuGLyN/z21/8kK72Y5LD6NvdioB5O+e89fX/S7JZ77ETOBj9YtpQ79qzoUfb8/qTZLErLZ+aONRRbw7n0h9fYUR9OmM7LVRtf5vqf1rb7WitK0725fPj0vjgFIcRx0GkDPS0tjaysLN57771OK/nzn/9Menp6nwYmhBBCCCGEEEKcSjptoJeWlrJnzx5++9vfsmxZx9/2VVVVUVpa2texCSEGKE0XfW+aZ90Vpwat2k9JRQLpMRUMjqlif3UsN299PuBxrMfDW6ddh06lMCbxAPuuPY1dV5xJiN5NgsndYdb6aLFGFyPjDnPjmM18edZlvD72hpZsul9R4fBq8SsqjF1kWaONDoqq4rG5jIxLPMDcpEIez8hjeHg9w2LKCdW72y1X6zTh9WmINrj57mASFq2XT4uGUdUYhtVtICOsodX+R88YPS+5EIvWy6/T9hAdWYdW4yMytIFGhwmfS4fHZsLaEEp2dCXJ4TW8O34Kkwbtx+owE2ayE2ay47GZMMXUEzr0CLoQB65GMwerYwgxOnD71TR4AuuJ0NceS89r+TnGZMfj0RIRUc+Oy84i3tLYZflQnZutl5zL3t9OZETCQaApU3n0vTt71zNUOEydzlUwL7mQIaH1xEXXkJFUhkXX1Dvi3fFTGBpWx2Czrd1yD5UtocJhIUzvZm3OVIxaD4lhdWhUClVOI7cXrWR+SmGnPTOijA4cXh1Xb3qJCUP28dbObH7Yn4LbrScq3EpNXQQ+nwaPT4PDo0eFgh8VZp0bm81MeelgKg/HUeewMMRiIyus69etWaVLR7zJzrmjNvO7X3zGyHPWY46rwV0fgrU2nH3W8B7NXn5/0uxWWeb0EDsev7rH2fi+sCC1gKXDZvB01kwW/dw7qDu9xZYMm8HqEdNa9eLpjgidl3uKlzM/pRCbV8M7467FqPajUymsyM7l7XHXMtjk5PGMvFblHixbitOnIVzv4v3Tf9fj4wshjp8uu7jn5ORgNBr5f//v/zFnzpzjEZMQQgghhBBCCHHK6bKBfvrpp/Pvf/+b+Ph4Fi1axM0334zX6z0esQkhhBBCCCGEEKeMgCaJGzt2LN9++y3Dhg1j3bp1XHLJJVit1v6OTQgxQHXWZXFuUiFrRkzrcrIscfLw+tVUO03sq4nl33szsXl1A25ColCDk0tGbiEyoo7a2ghMZgfDBh1gRHQFapXSZfn9dhOTvviIA5VxDI4vJ8bcgEoFL4++kWijg/ydq2n06BlsdnRaz76GMNw+DY1uA9GRdfzhkk+4+Zefctb4DcTFVlHpMLVb7rqf1rG1NrJp+apQK9FGOxekFZE1ZD8T0oto9Oha9j16ci0Ag1ohb8caDtVFoVH7sLsM6PVuUpIOsGvrcPweLR6vlkpbKHVOM2qVwoelqWQMOUBcfAXhoQ1oDW6s++Oxl0c31RliZ9K53xIZZmX2rmeCOpxh068uYNOvLiDCaGfXoSG8s/k0vinOpLg+ssuyDR49VdZwKqqj0Ovd7P3tRC4csh+AFdm5QOtu9B2JNngot4WS/ub37N6XTHbiAdbmTMWidxEZ0oBP6fjjVrTRQZjRzmGHiXCzjTP//SFJoVbCfu4m71ea6l9wzESAzfZYw7Ho3Pzwiwv5pDiDa0Zu5dcTv8dodFJWGcf7xRnUWsPwK2o2Vkfz7ZFBbK+J4ruKeD4vyeDj3dl8V5bK/oYw9BovPn/g8wc/VLaEGLMNvalpgrmNn51JyMIq/vndGWwsS+3xEmvNS8dB0/CUsYPL+GXmLoxqf6+6aC9Ky+/xhIbpoY2MjjvMOSl7uThtD2+ddh0jIqysys5tNcFge1Zl55IWVkesyc707c+ydNiMDicOPNajafmsys4lI7yeL8+6jNQQG7qfJ3A8I/EgZq2XvB1r2FEXgdOn4Y49bZe3zNuxBqtHz6U/vMYhh1EmchWnpGP/Ng5kAf8WTk5O5ptvvuHss8/mX//6F+eccw6HDh3qz9iEEEIIIYQQQohTRrdmdYmIiODjjz9m2rRpvPzyy0yaNKnLGd6FEKeW5gnins6ayfyUwh5NECROLBqVQmZ0BTqtF21dFFdueIXnc24OdlitfHk4EbvbgM2rQ6f2U9sYisenodJu4ZZtz3VZfk5J04RV26pjMWg9ZKaXMPr0zdQeiKe6tilTG2tuxOXVdVYNs3/OKK7KzmX3gaHoDyeSkngYjdbL4fI4Klz6Dsv6gUqXnhiTHaPOzSd7szhryD6qbaFM3/4sC1MLMGl93F7UdlLX18feQKihAbdbT9awPZhiazEk1BKv3Ur198OY8NnHvH/673B4dWypiSRE6+NQRSw6rZfS2hgmaH2ERddiiq7DURlJ1eE4IqJru3zd+ltGmJWoqKY49lfHcuWGV/hi8uW4vDrWV8Z1Wb45w7t8+HQsNTGMHryflCEH+fKsyzjS2DRhn0XrQwU8npHXbnYSmnqR1LkMABxsDMOsdzEpcze19WGM/fizlmx8e3x+NeEWG1eO+okfizP45pxf4/a5sFnDga6XFTt68rqtl5yLyewgYc0Onsic1fLcA6Xw2Zm/waeoGBNVg1qlsNcaTnZkNdGhVrw+DUfqI/EDYXpX+wfqQEriYWorojlSHY3To+fd8VOodGiYvv3ZbtVzrIdTCpgYV0GkpYZ91bEcsoXw68xdFFfG97jOe37u6bE2Zyp1bgO/370q4LJ1LgPXf/kBj2fkccmw7UwYuQ1rbTiNdjP2n699R2buWNPy8+oR0xhkqaPYGhHQcZt7+GyoimJCrMLkzF34FRVlRxL4oTyBu/au4LmRt3DLtvZ7sSwdNgOLzsPNW5tiiNJ7MGl8PJ01s1vnL8SJzuNXBTuEgHV72lW9Xs+LL77I0KFDeeyxxzj33HNJTk7uj9iEEEIIIYQQQohTRuADjY7x6KOP8tRTT9HQ0MCWLVv6MiYhxAlsblIhC1ML+P3uVZI9P0WoVApajQ9FURFraeDLsy7j5q3P80TmrGCH1iJC7+WKDa9w/U9rsXl0bK+OBSDK5Og0u3ms24pW8uXBoewtTqGhPJqEnD2kZxfx7vgpODx6LHpnQPXM3LGGLdXR/PK7t/lw2yhSXt3AlweSOn3PaFQK9xQvJzGsjlFjt/K7Cd/zfkkahxpDAbi3ZFm7S4FlhVsJNzSNjY9OqESt9VK3bxDemhCcpbF4PTreHT+FPfURXLPpRe4rXYbTp6bOYaHebiHGZONwZQy2+lAqdqVgqw9lf2Uchw8m4vVpeW7kLQG/fn3N7tWxtywJRVExbsR2Nv3qAlxeHSadu6XXQyDyd67mpq0vsOXQUEoODGFIwhHiLFZWj5hGcqiVOJMdp6/jj0x37FnRko0vbTRRUhdN+pvfs6MikcfS88g7KoN6rDq3gbEff0bmW/8hTO+iwWlChcLgAJaJa9a87NfeigRSXt3A+gt+xVlD9vHS6JtYlJbPt+dewp66KHQqhfWV0Vz6w2vc+nP2VK/3oFIppMSWs6Mugj3WMOYlF3J/0uyAjl1ZE4nNbsavqKl2mtlSE0WdW89bp13Hy6NvDPgcjuXyqznYGMaEzz7myg2v4FdUfF6SQZjByapuvGfbc9PWF3B3Y6w9/Le3RZ1bw8ayVPbvH4zJ7MCod1NcF91p2cVHzWOgV/sotkZg92oCOu5de1dQ6dKTHdG0lGJDowWX04CiqBgeUce746fg9GlYkZ3LU1kzeSJzVqvx7dFGZ0sM60ZNxebVkLv9WRyd3M9CnIxOpCWAO82gT5s2jbPPPrvD53//+98zZMgQ/vSnP6EoXU+yI4QQQgghhBBCiPaplJOgZW21WgkPDwc0wIkzvkAIIU4Gy4dPx+NXMybuMEmDD2FrtLDr0BCu2vhysENr1/yUQsZFVxNlbsTj03DeN+/2qJ4vJl9OZnoJkcP2oXg1NOxL4P3/TMLu1XZ7bOf8lEISTU4O2Iwdfsu/KjuXMYkHSEgox+PSExJppbYims370piyeV27ZZ7InMXp8YeIiahDpVJwu/VotV4abBb0Og+KosJocPHx7mxGRFWxuSqWO/euYEFqAWfElfOfinhyz/oag8VO+f7BVNWFY3WaCTE4CTE6MOg87DgyiOt+av/4x8MTmbOYmHCQxLgKNFofVVXRVDWE0eA2cs2mF7td1+1FK3l97A2MGFyGWu2nviGU7RWJ5HZjTPXi9Dzu3Nv+ePX2LEwtwKjxU+fW9DjLs3rENLyKiiqngXt/7j2wesQ0pm9/lvkphWSENrKnIYQHSpeyIjsXt0/D8KgqGlxGEsLq8Hi1aNR+dlXHUdpo6lYcC1MLWo65fPh08neu7tE5dKZ5TpMFqQWkWGzst5tbxpQHw6K0fBxeNRlhNm7e+ny/HuvprJmYtV6KG82tZrhflZ2LReeh0mls6RHRbEFqAUPNDvyKigavlii9iwaPvscz6wsh+osC+KivrycsLAzoRRd3IYQQQgghhBBC9B1poAsh+tSitPxWa03KeqsnP5VKISemHKPeTdLLm9h1aAjhxu6N7e5va0ZMY92oqXx51mUkGF0U1UcQE1mLRu3vcg3jjpRZI/l04zj2fTcan1OP3uIkJ/5QQOuqHyvW4OaAzYhJ62/3+VXZuYyKP4Re62XIui18uXMku3Zm4vHqsOjcHdbr9qmxOs14PDp0Og9DMksZMnIP2RM3c7guiti4SqLiqvjdpG8x611ckFLMo2n55ETW8l15PGclHuLggUFUH4onJKSRmIh6Thu+g5RBB/nuYBJH6iIxa73dPt++dHvRSn4sH8T3RcNoaAjFYrb3uC5FaeqFd82mF8l+72uGvfMt3xxMInf7sx2uQ94edTc7891bsox6T1P2/PGMpvHKC1ILmJ8S2O/PecmF6NU+rG4d95Ys493xU1iVncvgECtvnXYdQ80ODBpfyxwH9W4dfqDcFopfUbGvNhq/omJ/fSSHHEbCdL5uxe+HVu/3R9PyWfjz69VXaw8/ULqU+SmFzClZxk1bX+Ce4uXMSw7e35d7ipfzYNnSfs+eA/x+9ypcPg1pIXaWDJvBp5OuBMCk9RKicxOhd/PkMXN+zClZxi3bniN3+7PcunsVN219QbLn4pT1cErBCbUOeqsx6BpNYBNWtEelUuH1BvePtBBCCCGEEEIIcaJq1UA/CYajCyGEEEIIIYQQJ6RWDfTPPvus3Z0UReEXv/gFl1xyCX/5y1+OS2BCiBPTsZP2nEjLWoie8frVJERX43brWTpsBldtHHjdKMP0LqLMNpKHHGT0pA34HAZWfngRl2btRK/1Mi+5kHn7Or5X5yYVttzLzZNVNXp0zN71DM8ptzBBURERXUuoxYZX6bh/81NZMwnXedBrfJQ2Wkiy2LlxywtoVArjYmrwdbD008wda3hJ99+u+Ldsew5o6lYca3R0eLy7i5dDMbw7fgreCjWhhwYzZswWfG4doQYHdTWRDB1ZBH41g5wGamoi8SgqdGo/v8vZQoPNgsujw9oYQtGhIYwbsR2VSsHrUbh196o23WqD5dbdq3hj7PXU1IdxpDEcv0LAE9c9lp7HXUdN6HbsvRBtcPHRhKspa3R1Ws+C1ALmlCxjfkohEfqOr0lHBplcrMrOxeP3MTepELPGh8ffeV/553NuJsZkp8bRSIXTxK/Si9g14ky8viOYaqKaluIavJ/h737DF5MvZ2FqAQkmJ06fj0GWRkoawri9aCUrsnPZX55IjMFNdng90eZGPoq6mgONYczsZIk4aJqIzqxtoMpp4u1x13LYpmJ8XDlDYiu4POs8Ptnb/rCNQDRPPtd8jZLMTUNnQnUeNCqFIw5Pj+sOhqeyZqJVKczuRlfzuUmFhOl8xJoaUaHQaLcwOKaS/TeMYd/+GuxuA0aNr9NlAJsdPZmfEKcavfrESUS3aqCfd955ne6ckJDQ5T5CCCGEEEIIIYTovk7XQRdCCCG64ldU1DeEYjY6GR5d2ZKleTilgPtKB0a2Zl9jKEMjq2loCOXQD/GkpJaRe8HnlJUksbMyAUMX36znRFpZn/4rbC4jDc4aKAXXz9nuW7Y9xzdRv8bpMmC1m9ssd3S0EVFVDImtQK3xE18ez9bqWJ7OmkmcqRGr20CEvv0s7cMpBVjd7lbLVz2cUsABm4q8LjKcADvrIsgMr+eX373NrtgzqayN5Oyv3qcyPwO1zktVURJavZfIiDrmlCxjU8YFlNdEEWpy4PVr0Pj8JMdUsGXnMMx6Fw0uE7CB24pWdnns/rQ4PY+M8HoURYVG5edIYzhxFit1TnNA5Vdl52LSOvh88hXsrI7FrHXh8RtbLZOmUSlY3cYOezc0GxdTxbbsc3C7d1NnCwGaMtyZ0RU43Aa+PDyoZZK2Yz2ekUeIzkFqZBVGvZuPizOwaL1dLlfWPEHZBxOuYWJGEQaDC4PZgd0awoisahRFhdtpYMvF52E2VzDCaSba3Ei4xYbHq8Xr1/Du+Ckoip2RMQ00OE1cvP4NPphwDSadO6AJD6dvf5Z3xl3L8KgqahwWrj77K3QWBz6XDltteJe9ADpzb8kylg6bQUZEJUWjzmB7mYMrN7zCy6NvxObRdfpe6y8LUwvwKKqWa7koLb/L5d4eTikgyeJgcGgVDo+ep7NmUunSddprp9mDZUt5Kmsm12x6kS/PuoyRQ/dhd5gICWtg2IjdVB2MJ7QhFLVKYVV2LkecBuYckyVflJZPZlgDFSdYjwMh+opK9XOPshOEzOIuhBBCCCGEEEIMAJJBF0II0Ss2r4aJn/+TT864ijCTnZzIWh5V5XN38cDIngPcsWcFK3S5Ld9KOz16zHoXEz//J59PvoK9DV1nXE0GF0kpZTgaLbyrmsIhW9NSVAtTCzjSUE+Uz4bb2/mf1eL6SPZbIxg7uIwRw3eR5SmmuiIGlUohbtARivemcn/SbB4qW9KqXHaElSP2/8a4ZsQ0crc3vb5Lhs3ocEzrgtQCEowu/HhweHV8f/5F7D0cxpjhO9kRfhaOehv6UBtavRenw4hO52HrJecSElpPVFQte8uSONAQjqKoqHQamJhwCJPBhcOj59NJV7KlOpbbg5hFH2KxkZNcilrt43BFHJO//ICtl5yLQefhycxZ1Hm0HWatoWl5sEmZO4lIqGKc2YmtIorqihg27E9p2eeQ3cSIyFr0mo5XqpmbVEiooYzQ0AaMIXYSPFredU/Boq8nLaMEv09Do8sIpa3LPZaeR3KIjTB9NamJh4iMq8ZpMzOuIYwqh6XTc384pQCNqinL3Og20NhooaomEp3WS/qIIjwOAyqNn/LD8URG1vHmxvFcnvMTToeR4sp4QvQudtSFtfRyeWfctSSE17I2ZyrflYcEPH/I4xl5DInaS8rwvRjjamksi2f/znRqGkOpc5hxdtHzoDMvj76RzNh9qFQKxQcHMyqlmC+Ml7O50hxwZn5RWj6xBjcmrRdFgZu2vtDjeFZk55IUUsm+hvCWnkKpIY1dlruvdBlvjL2eotpobF4Nd+5dweL0vICPe+vuVTyVNROvvxKXy0B8YjkHDwzC5jISFdKA3W3gmk0vtlt2bc5URiYW4fboqHQMCfiYQpxMju1VMtBJBl0IIYQQQgghhBgAJIMuhBCiV9QqhbdOu46iOguz//NWsMPpUPNY7UVp+fhqYhibcJDdv5lEyWEdfjrPxk3ZvI7HbXmMroskK7WUM07bRGNtGFuSz6O4spbttZGM0vjQqX1dxrAwtYDo6liiY2qITDlI7Ok7Qa3gKY/AdGBQm+w5wDWbXmTdqKksTC1gfGwFceFl7M6YRE19OAfrO54xfE7JMh5Ny8ejqIgyuFCpFJJiKthRlMGkX3yFoqjY/NUE9FovsdHVVFTGYHcbcLr1HK6Lwu3XEKF3kRhRQ1REHRuLM8m0HGbcaZtxNlhweXVdv/D9qN5tYO/BwQxPLSEzu4jShHH8WDSYtJhyJg7az47KhC7K69h/JIGi/UkMia0kKWc3xogGvD4N821Ns/UPj6gj0tyIRt3xbOQpIQ58fjVfbBtFoqWBCZPXc+G17wCguPQ0lsUTG1rfppwKGBxWS3REHSHhDdRVRfHyprH8LmcLmuqYTmNvznyvys6l0aPg9WlISSnD49JjiKkjZHADKr0axaemtiKa34zajMHkIDTCitnsoLo2gvtKm8Z4J5htHLBZ2F0fjtuvJsni7OKVb3J/0mwi9Haio2tQ/Cpqi4bishsZ/c/PeWfctfgUNQnGzme/74zXr2ZHeSIH7WYsWh9un5ZR6XsYEl9ByaFBLFK6Hv999PMLUgt4//Tf4VdUWN0GbtzS/Wx6sTWCCzJ3oigqRkVNodhq5suzLuOcr9/rtNzVm17isfQ89GqFV8fcyE+13fsIHqL1UuswExnSwGcbT+P8sZtQq/3sLxtCUW30zysNhOHzqyk8qkdNjdtAg91MiMlBtDGw6yqECC7JoAshhBBCCCGEEANAq6/v0tLSOtxRpVLx2muv8fnnn3f4/N69e/s0OCGEEEIIIYQQ4lTRqoFeWlra6c6NjY00NrY/GYZK1fNlNIQQQpy4VIBB6yXeZA92KAG5p3g5b4y9Hr+iYmtZCiUNoei6WGYNmiaam+suRK1SSIiqJmZwOaZQOzanEY1KocZpIqyDZdKOdm/JMpbqZ1C9eSwRO4czKLyW2OhqXC4DGw8mtUw+dawbt7zAE5mzMOtdWCx2GhpCmPTFRzyVNbPT491dvJzHM/KodhrJ1HkwmZyMStyBSutDG+Jg1ITN1JQlkPr6j3x51mUoior3izMYE1WD169Co1Lh8Wqpqoli5JB9hEZYsVZGUVMXwY7ayC7Ptz81L0P2meY3JEZXode78fpV7CgfhEHjY/r2Zzstf9feFbC3adiD1W3kSE0UwzP2MijpILFlqcxLLsTtq8Og8/D9geQO6yl3Gpj+zbtAU12Dd6cQWRuG1uBi57bhHG6IaLecw6dm8pcftPz/owlXc2n6Hn4sTcPpC6wL9Mwda1g3aipflKVwucVGYuY+ljx/Lbfe8QyKDWrKY4iKr0KlUnDZjezYlYla5afCFsa3515CfMxP1NWHY9J6iAppwGxyUlKewIrsXGpd+k6XJnqobAmfnfkbyg4OYsumcUQZnOxpCOWTM4YSHVrFkfpI/ErPPh8+lp5HhbPpfdfsqayZhB4cjFbd1N29q+7tx5pTsgxK4JMzrsKkc/N8zs3cvPV5FqQWBDSJVPMwmS+iLycjrZQzkw4xtj6EopLUgI6faHKg1/gpa7SQGWrrVuxpEdWkDj1AQ0MoWrUfxa+iuiqGA3VRXD5+PS6HCWN5HNX2kFbl4owOdtfEMnPHGt4//XfdOqYQIjha/fZfvbrz9TaFEEIIIYQQQgjRP1SKonSdNhjgrFYr4eHhgAa6mOhHCCFE31qcnsede1fw3XkXY9B5WH8gGZVKack2DSSPZ+QRovOQt2MNn5xxFTqNF7vHwD5reKuJlbqyZsQ06jw6Jg8uIz6uEpfTQHl1NCV10ZTazJ0u7XW0h1MKuK90GU9mziJM5yG3k4zv3KRCxsXUEGZwUlQbjV7tZ0TcYaoaw7j0h9cCjjt3+7MsGTaDM5NK+LYslUtyNqM3ufhi81jSoipRFBVF1XHcsu05Hs/I4449K3hn3LVkDjqAyeygtjaC8rpILl7/RofZ/mBYnJ5HRng9QyOrKa2O5epNL3Wr/LzkQubtW8r7p/+OmBArqRkl+H1qnDYzu0pTuOj7NwOua23OVMpsFnRqP3ftXcGTmbNw+1VNGfujLEwtYIjFjlHjY8rmdTyWntdmn0C8O34KGYkHqayNJCrcytARRdQfjMdaH0bmRd+isvjxHAzlzdeuYEhYHSV10SSH11LjsDBicBlqtZ/D1TFU20MwaT24fRqu2vhyQMf+YMI1fFceS0qIg0iDk621ESSH2NGoFHyKCo1K6dFkbMdaOmwGVS4dKhWkhzZi1HjZWB0Z8HJwR3tp9E0MCaulwWViU1UM95Ysa7n+XZmbVMiDZUv59txLiI6ow9oYwuf7Uju8bvcnzcai9XFP8XKeyprJqJgKHB49RXWR3NaNJQo/OeMqNGo/0aFWAGobQxiRsxOvU0/ic9v58qzLiAq3MvL9L5mfUtjyO+iprJkMMttx+TTsaQjBrPH16B4TQvQXBfBRX19PWFgY0IeTxFVXV/dVVUIIIYQQQgghxCmn0wb6nXfeGVAl9fX1XHTRRX0SkBDi5DEvuTDYIYjjQKdW2HjhL0hO3s+goQe5cMQWsiKreDwjL9ihtWH3qTFrvHwx+XIO20JZX57I/oYwDjv03arngN3I7UUr2XB4CP/aOpqtZSnotF5UKiXg7Dn8d6ms24pWdpo9B3iwbClXbniFC779BwftBg47jEz8/J/8p6Lz5biOlrv9WVZl5xJnclBcGc+I6AqK9iexcUc245KLcXl0nPnvD7F5tSxKy28Z/3vQFsK3xZlsK05nb2UC22ujWZWdS6TeE/Cx+9ude1ewoSqKcZ9+2u3sOdCSPb30h9fYWZnA9u3DaKwLR6VSiLQ0djnWv9ncpEKqXUbuLVmGy69mblIhtxWtbDdr6fSpUKNw0G7mqayZaNUKj6blB3ScR9PyW73HjtREMzi+nNjEchzV4YRE15H1269RJ+hQhZtQ67xMSNuDQdd0zb4+nEicxYpfUXOgMg6XR8fg8BoSwmvRazpfLvBopdZwdGqF6dufxaeoua90GYftJlIiqhkeewS1qvcdNZ/MnEWFS48fFWclHmJwaB12r47UkI6XGOzM9T+t5YA1ksGR1fx25Ba+OvtSjJqOl9E7WnPGfnP5IP61awQl1XG4/B1/nH6obEnLWHmzxsdnBwajU/sYbAl8DPrrY28gzGTHoPXQ6DCRPGoXYyZsIjynhNDBFVTPTsWg81BTH8bb464lPaSR+SlNf39v3b2Kn2rCuXHLCzxQulSy50KcADptoD/++OMsWdJ2Pdaj2e12Lr30UjZt2tSXcQkhhBBCCCGEEKeUTqcIzcrK4rbbbiMpKYnLLruszfNut5srr7ySb7/9lquuuqq/YhRCnGCeyppJrVvXrUyiOHEZNT6iomo5eGAQbo+OyDAr533zLqtHTAt2aG0cPVPzE5mzepxNas58HztuPdDsZ281Z/EeTcvn7uLuvc8qnAZm7ljDk5mzqHUZGRpaj0btp6Q8EbtHz8ujb+S6n/57XovS8inctZwV2bl4/GrCDQ6GaT0Bj3s/XgIdQ9yVRWn57LeruGXbuy3zK7w7fgq1bl1A5fUaBdXPWeOj77fm+QaOdvT46cfS80gOsWHRuXlcnddq9vL2HD27+uU/vsqrY25EU+4nSeujwRrK8Ju+hcgofNHxqHwefJureWfbKO7cu4K1OVO5r3QZ70ZP4Zyv32P9Bb9iwmcf8+74KVz+46u8Mfb6gM4VIDnUyuxdr7F8+HS8fhcrsnPx+r1M/vIDvjzrMhJD6gOu61jzkgsZZHJxZnIRZrODsiMJ+PxqttXE8fvdq3pcL0BZowXfoSGE6d2EGJzdnhG++uf7oau5K+anFJIe0ojVoyctoorJadWUlCdyzaYXuzzGwykFJIfYuWbT82y79Bw0ah/RCZUUbRzJsImbcR+ORH9xGMZGK7oiLxM//yfP59yM16/GqP5vj4AHy5ayIjsXRVFh9Wgliy7EANdpBv39998nMjKS66+/no0bN7Z6zufzce211/Lpp5/yq1/9ipdfDmwyESGEEEIIIYQQQrTVaQM9LS2Nf/zjH/j9fi6//HL2798PgKIo3HzzzfzjH//g7LPP5q233kKnC+ybZSHEye/W3aske34K8fjVVFTGEBtTTWSYld2HB/P9+RcRaXC2jIMciG4vWsnDKQV9Wmdna0a3Z2Fq747fnePNTSrk3fFTCNN5gaZx7yE6NxMm/cCkM7/n9NM2c/ZpG4gPaeCdcdcCTWN/w3Ve5qcUkrdjDY1uPfUuU7fGvfe3xzPyeCJzFoNM/12Dvqf33ZOZs7ineHlL5vvOvStYOmwG9S4DsQZ3p2VXZOcCkBXWQKzR2bJ9UVo+z428pcNyb4y9npdH38hde1cQpneyuz6CO/as6Na98fLoG3H71KQml1FfFw6AkpiA44LbUE38E574NA5ty+DqsRv4dNKVxJptvD72Bi7/8dWm/8dUU/fHIUw6/UfKrhuLRdf5uR6twm7h3fFTiDfbGJNUSt6ONVx1xnfU3zGYuKgazAYXj6V3fz6K50bewmVpe7l03A/EJlYQFl3L8Mw9NLoNJIU08ETmrG7XebS7i5dz09YXqHMb2FwVF1CZJzJnsXrENB5Lz0OjUrpcO/3JzFlkhTVg1Pr49ehNDIqpJPu9r4kJsbJ02Iwuj5dkcXDz1udZmzMVt1uHXu9GrfMy5oZ/odZ50V01CH94DP6oWExGJ9+ddzGhOjeDLA34jlp/fnF6HkaND5tXi19RsbgH10MIcfx0OYv7pEmTeO655zhy5AiXXnop9fX15Ofn89JLLzFu3Djee+89TCbT8YhVCCGEEEIIIYQ4aXU6Br3ZNddcw//8z/9w9913M3z4cMrLy8nOzuajjz4iNDS0v2MUQgghhBBCCCFOegE10AHuuusuSkpKWLJkCWlpaXz66adER0f3Z2xCCCFOABqVwqf7UsmxRhATYuXKDa8EO6SAHTtpV1cWp+dh9WgI0/m4sw8mWrr35y6y7U0g1pcWphYQY3Ry+Y+v8mhaPkuGzaDOrSPMUIXfoyV0ZBmKW039tlSy0ot58ZvJADR4NcwpWQk0dSXXa2w0uPWtJjcLtgavhjFRNVQ5zCxKy+ee4uUBD7FZlJZPgtFFnNnGpT+8RpjOw/Lh09GoFGbuWMPC1AKSQqtx+zRUuowd1vNU1kxGxR3g0OkjqSo/RL3NAsDu30zC59tGo93MLZ993KbcY+l5XL1pBcuHT2fLxeeh1lSSGFnDRxFXs7m68+XJHk3LJyPMSojeRY1Thd2n5dChRDJG7EYf3ohqfy1k78CpKcFYuY+4VCvle4eiVfsw6dz4FDWfnfkbDFo3Gq2Pmv0J+PwadpYl46dpaa+d9WGdduN+LD2PkfGlxETVoCgq4oaV4jzHCN59uGrC8Pq02FxGorsYHtAei86DSqXwn+051LgMxBodjB++k0ljNlNXFQUHh3ZZx4LUAkZG1BGid+H2aTnQGEr+ztU8lp7HXXtXMDepkHKHjzijq8u61uZMxeP3YPXo8CsqDGqFFdm5JIfW4/LquKKd33u3Fa3k6ayZZEbUcKQilrjoavbfMIY9pUYKdz3D3KTCTt9Lt2x7jm/O+TVR4cWoVArx2SX4fWrcZeHofp2AOyED7aALcVV8yaDhn7Hl418QZ7Hi8rYednrn3hW8PvYG0sLq2dcQxm1FK7s8XyFE8LRqoM+Y0fl4GJ/Ph06nIyUlhb/+9a+tnlOpVKxa1bsZNYUQQgghhBBCiFOVSlGUlq9o1eouh6R3XJFKhc/n65OgustqtRIeHg5oAFVXuwshhOhD81MKGRNVQ1xIU+amwWmiwmFm5o41wQ6tS09nzcSs9XLIYexywidoWvYpM7SRcIOLy398tcfHXZyeR1Z4PalxR9BqvahUCtbGEDYeHkL+ztVdln959I1YdB58iirgHgvzUwrx+FWck3CE2LB6rHYzYWY7KcP3EjJiP2igcdtg6g7G8dKG0xlsdmDQeIkx27C7DURbGtBqfFQ0hKNR+6lxmNCq/Vz307oevw595fGMPDLD67DoXfgVFVV2S0BxPZqWz/lJpURG1BMS1kBDXRgVNVEU1cRS7jRg0foYamnE5dPg9mu4eevz7dbz6pgbGZ5wEKPJSfSgclRqhYN7kolPOoRlUCU1u5P4cWd2m2u1MLWA84aUEWJyUFIVR7E1nOSQBhJC66mxhwR0j225+Dz0ejdarQ9zaCM+r4bKiliyL/gPmkgH7sPh6BPrcZbG4mk0sXNLNruq48iJP4TNZSRl8EHMYY0YwmzYq8OpKo/loz1ZWLQ+ZnexhNjy4dP59WkbiB1dhCbOhXNXDPt+GkZdYwhDBx0mYnAF+7Zlcrg2iovXv9HluRztnXHXUvJztnd+SiGReg8pIU33oF7jJdTo4OsDyZ0uR7ciO5ec2CNEhDag1XnxeTVYG0Oos1v4riK+pafFU1kzafRoW3q0tOe78y5mUEI5BrODfSXJVNtCCTfaMerdeH0aJn7+z07PZ8mwGaSH11LjNBNucFLlMHd4PzV7MnMWZyeVAPCv0jR+N+5HYjLK2Lc5m4z7a/COm4nf24j6wJeoXvyeT/9xMREmGzUOC2WNoTi8GuKMLqyepox6pMFFoqWBHbXR3NrLZeqEEH1FAXzU19cTFhYGHJNBX7266w8F/W3BggXcd999jBw5kq1btwY7HCGEEEIIIYQQ4rho1UCfNm1asOIA4MCBAzzyyCNYLJagxiGEECJwD5QuhdJgRxGYp7NmkhFei9unZWNNFHUeBXUAyyU1m7evKeP20uib2PDLX3KoLopKhxmPX03+ztUsHTaDwi6yjtA0JvTtcddS1xhC8tCDGMwO9Ho3psqEDsssTC0gI6yBKJMNg7YOg85DrS0koLgXpeUTbXCSHFpPTKgVk9FJ4pDDOBrMeBwGKr4ZQWhCFVqjm9q6CO4tWcYXky8n3GLjQE0MGrWf8J+zs2qVQqXdQnbcYertA+PvdXMW9d3xU3D5tOy3WZiXXNhyvTri9Kv57mASt32+kk/OuIoxY7YSm3SYsN2pbD40FI1KIcbSQLjFxv7q2A7rOWw3s21nNkPNToZVRTN60gayLv8a/OAoabqmGlXbMeVOn4pGl5HSumhu3PICr465kXKHmXq3gcEh1oDOvbohjAhLI+V1kYzMLMIU3kjGkAr8Hi2e0lhMY+vBGIJRU4lncxIxkbX4/GomfPYxq0dMIzOthCP7B3G4NooJEzYQ6a3hro9WsCCAZd6ijU5CouvAr6Lis2z8Pg21DaHsrY3B69dQV5RFZuIhnL6ApzwCmrLNu+o13PXzPA8alYLbp+aKDa/w3MhbGD1oPxX1EZg0nffczNuxhgXOAuaULGNVdi4ZkVUkRFdjNjo5zatr+b2lUyntXp9mj6blU9VYS3lROBFGOyNydpLYYKGuJgKz2UHp4UTmpxS2O/fBorR8HF41s3ct5dG0fCxaH7UuA06fpsvX4bailcx1FTImqo7TYivwebXYjkRjMjrQVB5G9Z+/o9pRTOkHE4hLN5McU8Hof37OquzcVhnyJcNmEG+yo1Yp7LNGMGlwGW+HXHtCzRcixKmke78x+9ldd93FpEmT8Pl8VFVVBTscIYQQQgghhBDiuOn5oPM+9u9//5vXXnuNJ554ItihCCGEOAm9PPpGzhiyj+T4I4xMKeY3mbs4PaYSl7/7fwqv/2kte6viyU7axy9yfuL0wWW8ddp1OHwaHs/IC6iOKze8wtlfvc/X23KIW7WbjXszqXYZOtz/3pJlOLxafvnd25z91ftM+OxjLvr+TR5O6TrTeU/xcg47jGypjmFHeSIf7BzJP76bxM6yFCoOJuDzaHE3mKnbn0BVQxhPZ82kwWXC49USE2LF5tHjculRq/34FRV6jY/i6lhKrBGBvmT97v6k2bh8Wsw6Nx6/usvsOTT1/ritaCULUgu48D9v8eX3p1N1IIEhGaVMztzFmKH70Gp8jProC2wefYf11Li1DDK5mLljDWd/9T7b149h7weTOPjFWFa/+2t+3JlNUX1Em3IPli1lW00MoTo3i9PzmLJ5HbN3PYMKKK6PBJrmDujMlqo4qqzhZAw5gMHsxNVopujHHD5461JCHy3Hs0ePcqQRz+FQnA0W9h4eTFl9JB9MuIaRcYfZuSed0NAGDDoPzgYLepOLtTlTAbq8lysdJux1oRzZNIzDhxJY+fWZ7LdGEml0kJx4iNGpe7E7jBTVh3d+IY7h8qtbZhSan1JItN5DUogNgBHxhyiujOei79+k0dt1Frq5d8zMHWs475t3+WDnSOxOIxmJB3l1zI0sHz6dwl3PcHfx8g7r8CgqLv/xVa7c8Ao/VcWzcdMoVCo/cYOOUG8NZb81osOVA+4pXk6EvinTf3fxcqKNToZHVZFotgf03j09poakqCqGxFYQGlWH02amaH8SyoE6+KkER1EccakH8DSaiImvZEV2LvajsvPzkguxeTWkRFeSFFVFjMnO1vJBmLQeHk3L7/L4Qojjr9Wnkj/+8Y9UV1f3qsLKykpuu+22bpXx+XzceuutzJo1i1GjRvXq+EIIIYQQQgghxImoVQP96aefJjU1lXvvvZeioqJuVbRr1y7+/Oc/k56ezpIlS7pVdunSpezbt4+HHnoooP1dLhdWq7XVQwghhBBCCCGEOJG1GoO+fv16br31Vv7nf/6HRx99lDPPPJNf/vKXnHnmmWRnZxMdHU1ISAiNjY1UV1ezfft2vv32Wz7++GO+//57FEXhrLPO4qmnngo4gOrqah544AHuv/9+YmM7noDlaAsXLuTBBx/s3pkKIYTodx1NlDQQuHxaTv/XJyxILWB4uJVwg4NvK+JIsTi6Xdf8lEKmbF7KZ+bfMCS+nMTBhzGbHPgPJPFTbUTAdTxQupTrf1oLwPa6cO4rbX+yukfT8kkwOalwNnWBX/Rz19Rog5u8HYFNcDfU7GBiSjFqlZ9aaxgqlcKZ//6QV8fcyBnh29mzMxOzwUVceB0Orw63T0NZbQzXbHoRgDfGXk9CaD0unxaT1kNZY+iAWarpsfQ8vIqPSoeJOBQmxJWz3DA9oCXr4L/doK/e9BJrvVOp3DaK68/6mjB9DdFuHXujJlJyyN5heaPGj9Xz349Uk774qGVJvmiDix11ERg1/jblFqXlc0biftw+LUk+LU9lzeTW3auINjrwKSoWp+dx596O30/zUwoZH7+PMIuNiIRKHPUheFx6UobtJaHhCDuHTMbbuJeKremoNX60Og/nXfYJ1btS+GbbSIrqIrmtaCUvjb6JjJhy/D41jgYL6dGVhBtcVDrMnb5uNq+Gbbszuej7N/nkjKtIMLm5ccsLfDH5cv5TNJyLz/83sVn7GJp8gNTQG1rupa7cXrQSaOqePSaqhqzEgwB8arqSBoeCT1GzesQ0pm/veIm1zup+3nAzw1VHOHvMJnxeDVuSz+OTvVkdLtnm9v13Cd/me/5t77XUu4x4/OqW92VHTBofy4dPJzu6Au//Z+/Po+yoysX//11VZ55Pz/M8pTMnBMIMoqAgiiCgTJmT5udXv3hluXQ5EBEWfFi45ANfFiEhEMYrKiriVeEyykxC5qTT3el5ns48n1NVvz86adJ0dwheJdG7X2tlQXefvc8+VXW6u/p59vNoYaQjRelyzOlPXK8k6fT5swkNF1EbdFNSPIiGRPMfzsMfcRBNWVh++g48Z/UQP5CNqskYjil4t7F7E5vqV9M2mo/FkMGiZFh18HFgor2cIAinnik36IsXL+att97it7/9Lb/85S955513ePfdd487wdE26meddRbf/e53ueqqqz7VAn784x+TlZXFt7/97RMe88Mf/pD/+I//mPw4FApRWlr6qZ5XEARBEARBEARBEE4lM1bG+frXv87bb7/Nzp07+clPfsLZZ5+NzWZD1/XJfzabjXPOOYef/vSn7Ny5k7feeutT35y3tbWxefNmvvOd7zAwMEBXVxddXV0kEgnS6TRdXV34fL5p48xmMy6Xa8o/QRAE4eS6u2r9KRs9v7NyAzcdeIL7a9cCcNXu/8SfsHFazjj9ccunnu/o69w+XMBbrQ309xWh6xKRtAmn4fitnz4+x12VG7inav2s0XOYKC7VH7Nya/sWbitr4gcdm8loEilVYWP58YuIHVXmCtAzmofdEaW8vBd/zME7536JXHuYUNCF2ZjG4w3g8QQZidvwWmOY5InX8tLpX6MyZ4TmsXxMskq2LcLy4h7eOudS7jqBdlz/LE/Ou5Edn/s8i3NHMEo6FkXlyt2/YsdoHum/o/gfQGfETkqV+esHp3P/X7/AYF8hmYzCSGzmlnb3Vq/jBx2bubV9y5Rz4TCoxFQDNkMGm6KRUqevx25Q2TFUTJs/G4cpiceU4rUzvwJAscc3Y9QdJgri3V+7lp92bZqImBoyaGkDnvJBCk8/gGRQCfndxBMWYqMecuq7MNviuKv6URMmbJ4QBY4Qy4t7ePPsy2jIHyDLE6CvvwhZ0XBY4wxEHZOR1tl8r30LXSEPzy+5hmDSOvn5tGrgzIaD2KsGMbqjpOJmGor6PvHYH+vPp32db8zfyzlnbKdsyUEqz/uQhQv3EU2Zqc8f+MS1Hc+N+59kIOil5XA1yfjEugOp2QvOWQ3Tz8NA1EFCVQikjJjk6V//Zc06/rD4Wl46/Wu4TCmqPT4UWcNiTNEZyOLqPc+cUEvGnePZ9Ecd3HTgCQZCXvoHClh+5gfsGyihxZfLknkHcC3rRY9qGBxx5uUOkWNJcFtZ02SEvMrtp7Gwn/qiPjQkHjzy+VMlA0YQhKmO22Zt0aJFLFq0iI0bNwIQi8UIBoN4PB6sVuvxhp6Q/v5+NE3jO9/5zoyF5SorK/l//9//V1R2FwRBEARBEARBEP7tfao/L9tsNgoLC/8hN+cA8+bN4/e///20f3PnzqWsrIzf//73rFkj9scIgiD8K8g1p7ivdi0P1K3hzpMYVZ3J0T3GR///ofrVuM1xchwhFmePn1C7o2MdfX1pXaI7aqU/kMX7nTX0x6yEj9P66RfVH7Wtem7RN7mnaj1VzjDf79jMnZUbeKh+9axjf9j5ME/MvYmf9UxE3rPNab7VuhW3UeWuyg3cVtbEHRUbZmzLtblhFXZzgpriPjJpA+mkCYOsMhR2U1fdQXbeGNnZPjRNZnQsm/NrD9Hmz+bynb/mL8uuojx/CKMhg6pLNJR1U9/QRml5HxVlvVxU3smvFlz/qY7fP0qONYZBUWmsa+Omi15jaWkXAKfljpD6hAj6T8pu5rayiWP1i+p1k+e0yhnFomgoskbZkfoEfaN5KNLM0exb27ewdc5KYGK/79GMgjxLkmDKSH/UznfaHsEo69PGqrrEeNJAuTOIjM4ZVW0c8uVQW9SHL+LkW7NEOGudUapcQbbOWUk4aUXXJUYGCsjELJBRcDT04/IGyckdJ500kfK7sOX5QJMw2JLEgw5MhgwWU4r6hjYicSs9Q4WkMwbMtjjFNd1c2HCQ55dcc/wTAMRUha/u/DVzS7pxm1J8cMHFuG1R/H4PqWEPSlYCd9EoNuvse/hnYjMlsdri9LRVYv+5n7ZXz8BePMYFF7+G2xPk5TOuYMuR4/73aA54uOCdF9jRXsOhoeLJ99VMfjBDC7aMLrH+0GN8r33LjNeaqsNI3Mbe8Ry8ljiNdW2UlgxgMaWo8Y5NZvPcVtZ03HZnaU3CKGu8d/4lnLv0QxZe9C7+vnwuXv4+V3/+VZIxC763qvF/WMPI/mqqq7rItYfxmFSiGYVfLbgepyU+8R4vGKWmuI9sS4In5t70dxw1QRA+C8eNoP+z5eTkcMUVV0z7/NGI+UxfEwRBEARBEARBEIR/Ryf1Bl0QBEH417exvImN3Zvoj1tYnOWjL+qYErE+FTw97wZCaRMuYwKAm1seZfuFX8CgqJRkjTESsx93/JY5KzFKOjrQG7OSbUoBE9WdJQlCKTMNuUPkRZ1cuuO3s85zesEAzQ1nE4nZCMVj1KDTEXayqX41OmncR+adybMLrsOXNPCXZVcxGrMzFDfwzPwbiGfSfPfwNgDuq107WQH72HHVOb34Ik68rhCplAmAuvJubK4IJkeM4GAuRnMKb303eRkF/+FSLp6/h47G0+joV7FYE3jzxrmieAhNVTjUXMfLvaX8rGcT751fhEXJfNIp+Kf4cDSHH29/jh2f+zzlVd2UNrTT5VrCh23WWStyH1XliOEypXjAsoZvt27hxWVXssWyklBKJt8aw2bI0Bp044s4Kc4ZxeCbvbZAOGPgzbMvYzzmoCzrMBobKHYOkW9TCCQtvHveF2kbT04bV+/xU1UwgDdvfCLCDdw0v41wfx7Dviw21a+mqeXRyayIo3UL7MY0iqxR4x1DR6J/LJfBqJOyea3I7gSZMQc5S1sY31mHu6of2Zwm7XcSHfXirO7HUz6ImjGQiFswWpPMXbIPNWkkFbOSiptxVfWTZ07RM1Rw3GP4i+p1nFXczVv5l9Lwpz/TcWWKodFcFl30DoorRmbchWSRsTUOEhn1HneuYz0570aynT0c6qxkMOrk3up1BCID7Ht1OUVFgxzoqKY8b5h42nTcee6q3MAPj3wvurd6Hbe2f3RNHN3ff+3eZ3hu0TdPaF3bGlcQzRgIZxQyms691etIazLljui0x0aOZNIszRsmz+snHpmoiO/1BtjeV06+NTb5vRMmMjp+3vNRm+LbK5qodETxmMbJdwWorOvAP5RDPGyn5HO7kBSdVJ8XW8KEYkqjmNPYC8bR0gbqgb2j+fygY+L1vn7W5bz04Wksq+jAk+VHkXRGP6HyvCAIJ88peYP++uuvn+wlCIIgCIIgCIIgCMJn6u8rcSoIgiAIgiAIgiAIwj/UKRlBFwThX9s9Vev5/pGiOsemGAr/noyyzm8WXoemh7h8568BeGb+Deg69MZsMxZY+qxJElS5AhR6ffTMXUQ8ZiUc1WkdKaAt5KTccfwCVnXeMcoKB5EVjcHhPKLJidZsFkXHZcwwlrDgjbjItoe5raxp1oJTVnMSd1aA/PJ+etoqUTWZ0/KjpDIG6iu6+MuexTOO29a4gsG4kcW5Q2Q5w5y2eA+SpJGKWenrLYbmicfd0vbItOcv947jsMUoLBpiZCiPYMxOTUUXJmsSXZMA+LC1nrMX72J4Tx2yrOHMHyc65mFgtJB42oTJksTsitK6u5G2sXyKnQGcRpXHGlfQNv5R8bTjvfZ/hqOt6XYOlBJJWCnNHyYetzAS/+Ritg5jmvllnVxYMsSKrxQQHTmEvaOCc976Mw/UrcEsaziNGXJdQXRdwm2fnsZ81PKiXqrr23HV9qLFzaiaTCRhpbJwgKK5h4mNZNETyJo2TpE0Xjw0l4vUFtxxM0ND+VgtSQoreikvHqAv5AGY1r6wM+wg25zCarCgahLlHh/La1pRk0ZG35sDgKtkGE9dDzv+cj4LTt+NbMzgrBzAMN+GIRZH7tKIxmxoqoyWMIGsIysaRnOKaF8eyYgNq3H2LRcAp+UPYjKmmf/5d0ivkJHczZTZuiAjobbJxMfcyOY0kjk1ea19ktsrmihxDtA1mk972IVNUcm3JugPe9B0iWxvgEVzmnlzz0JU/fhxpnlePwfmnIvdHiUQOMzd+vrJ70eB1MSvwJvqVwNxNjesYv2hx2ac5+6q9dS6wpRn9WNQVAYDWXxp+3M8Oe9GSl0BXDMUwNvYvYlnF1zHhe/+kbsqN5BjSTI3ZxijIcPZVYfZ31eK40hLxolU94emjC+2JihyhHh7qBDZ5+USSSc3Z5x4zEpmzIFin9iuMz6Ui6op5Jf2Exn14qnsx+oNUeYIs7G8CbdRpTeU4Nz6ZjwFo6gpIzW5Q1y959UTOh+CIHz2RARdEARBEARBEARBEE4BUyLof/vb3ygoKKCuru5krUcQhH9hW+aspMAaw6CM87znGoyyxqU7RPT8350MZFmj2ExJnl1wHaou0x2xI0n6KRE9BxhJWGjIG8RoTKNpMjklQ4zvb2AkYeWcogHeGyo87vgWXy7jMQdL61qoqukkFnKw23kh4Vgf8bSJsbidjCaTSJuOG0EeD7s47cmXeefcLzFn8X7qskJIso6WNOLvLCKtS9xfu5bvfKzQ22DcwsLscfKzfDg9Ifo6yghEHUiSzt7RfB5rXMGqg48DTHv+0bCb9wZKafSOU1EwiEFRsecEkBUNTZWJjHpZPn8fyZgVd+Eo0TEPnQfqKKnoRdMlcp1BLK4omioz78L3qQ/Z8PcU4o/b8SctJFSFdc3bZnzuz8po0kROzE5u0ozdHmVh3iCbmCiwNpur9zzDi6YrsbsimFUZV+kwC/J8tOeezuDoINGkBYOi0jZSyIKKDrpGZi+YZlBUkhErwzvmkFPfxaJrXkGLK8huDS0ok+nL59q9z0wb9/ZQPguzAuzpLaf1wFzOLxrg4HARyySNdNpIQp34Ne3uqvWTRc1uaXuEW9u3cH/tWqo84+R4ghwaKGEs6mShksGV58NeMYiSk0YPS2Q0BV9PASZrElvChMMCUiqFJGkseeUVnhi6iYbcIU5//SX2XXI+FmuCd9vquenAEzy/ZO5xj3taNVB/7ocYGyTATmq/zFhzCXva6jj33HeQFY3kuAs1ZcQ/Pj2DYCY/7drENtsK7MY0AElNJseQxm2Jk1EVZFnFP55FJG1izZHrbjYjcRs5ETvxhJksT4Ablr/LN09bzGv7FxDOTMyfa42TVBUUaXobvKOKbXHmlXbh9IQwO2IUxIdgOxQ5QlSV9tLcUTXj+/boOf9h58P8ZuF1jEaduMwJhkMejLLGre1buL2iaVqGBEB/3MKa5ud58+zLCCettI7m44s4mVPVwQu/u4xiV4Bz3vozQyv9dHWWUWROYzMHJ45ZyE6R209XxEmlM4TdmCIet/Dkny/hKzVtGBSVB+rW8O1Z2vgJgnByTYmgX3DBBdx9992TH3/uc5/jnnvu+cwXJQiCIAiCIAiCIAj/20zbg67rH/0F8fXXX6eiouKzXI8gCP/CdF1C1SX8MTtz8iYiUI81rmA0YZ7cky78+7EbVPrDbhYV93BG/SFCQRfR3orJqOqpIM+SoN+fTUvQTak9Sq1vmMrKbgrzRtjfWf2Jkf71hx7j7qr1hPYvoNztp76mndrlu1EsKcLdhRzc34DXEWHQn8XvFn2DK3f/asZ59vuyeah+NTuGZNgFvpiDBTVteCv7MdsSRNKGGWs2WA0ql+74LW+efRnvdtSSVBXm5Q7hdYX5nCvIjp7KycfeXbUes6wTSCn8rGfTZF2AXy24nuGOWjymJOmMgbmX/Q3ZrWHqiBHsKSCrqo9wfx42T5i5S1pIjbkpjlnZ2VJP/mAOVmcMW8UQFleM4N4GFtW00dpdzsUf/P5/cGb+Zx5rXMFwwkyZPYos6Yz4vVijNmIpMyVH9uAebWM1k4Gok0ue2s+Ly65E1WUW1LVQtPgQhRmZzh3z0TWJBWUHOHSgnmRm9rI9mibzwcG52I0p8n1eSqq6cc3tRg9LBA9UEgk6eXreDVy//6nJMXdVbsBhTNMaclFqi7GxexNvlV7KGVVtdAwV0RLwEj/SqusHHZu5q3IDaf2jfdyFthjjMQdp1cC80i48OT5s+T6SfidkFBItboJ9eWQ7Q+w4XM+lX/kL5rkJGAPdlySVMNP+tdNp7w+TlztG+Pv5SMoBxttLybdFeGHJNbQE3cc9/mZDGtmaJLnHxuihSuzeEMmkmXxXgOGOUrLyx0hGbGiazHjYdaKnlZUHH+eeI1kDNe4AVQUDpNNGwjEbPYOFaLqM3Zj+xPO7pnnbZH2GxxpXYFZUipxBLj3zXZIxKwtyvkxfSOH6/U/x7ILrZp3Ha07QMVTEzv0LWOD1s3zJLhJ32pHt/0V63ElFzEp3ePqxOjZKbVEyeKxRFFmjIGscVVV4Yck1XL5z5vUfjaoPRdyUuH1Iks7iBfsB+PzZ75KM2BifX4nBFmBRfTex3jycC3tABpc5jWs0mwpHmCK3n0TKRPNgCecUDLHklVeOPMPrxz8JgiCcNFMi6E6nk8HBwZO1FkEQBEEQBEEQBEH4X2vKn4MXLFjAq6++yk9/+lNqamoAOHz4ME888cQJTXbTTTf941coCMK/jPWHHuMPi6+lIXeIaNLCcMSJpkuYFO2TBwv/sgJpAzmWBKomo2YMSJLOuuZtM+7JPFlUXWIkbuPW9i1sblhFvz+bnJxxgqGJqN6m+uPvVwaIZ2TGkyYIevHvm09k51IuO+9NXA3dnFE+SKQnn2TayPhY/ozjJ55jy+THd2Y28KPOh/nAeTEHOqrJdQZn7XhwS9sjPFi3huZxDbcpicuUwusKo8gqgaiTlQcf5ydlN/PznodIaTIGSZ3cD35P1XryLEnSmoYi6ai6xAd95ZQeKsfkjCIbM2RSRsL9eXjqetAzCsHmClx1PVjGYixfuIeQ38PYUC5Zp7WCBvVfeJd4Xy41qgwf/D1n5H/u7qr15FgCnFN3iHDYwf6hYrzWKGZjGpMhw5JXXuGeqvXHnWMobuGBujVcsn0iyvlk4kbOSZrIKhui+twPkZ1JtLCZmoSJrp1LZ5zjmfk30OnT0JHwR1yMJ2x0j+eS11yH/Ujmhi9uZfRI5f+jftj5MG+dcykmQ4aBoJcn592I2TjEqN/LWNxGhSPM4ZCL2yuacBkzpFR9yvXRGXaQ1iUuLAniC7rpG83D3JYmP2ectuY6lnzlVbzmFL96fzlfrG1Bzyi8+X+/wPwF+zHakkSjdszmJOd+5b8xlidB02n/7TKSSTPDMQdtYTvVn9DdYDzmIHCwAl2XCQTcdPaVcMiXgwSca2ujq70Chy1Gc38pJkU9gbM64faKJk4v6Kc4b4SswhEcVQPIrjTRg0W88eZZnLv8Awa7SkmplZ882RFHazTcXtGE2xalcm4ry+q7WOBz0Tt/IR80z7wHfXPDKhzmMVIZA4qkc/nOX/O65XJyBwoorOhFzRhoGyiZsQL8gpxhmmvPJhB2kszEKSkYwu4Jo6YNhPxupCP73m+vaMIka1MyeY5Wftf0FGMRF0saD+IoHsGQFWHHc19g7um7UeNmLIXjaDELmirT//Iiis7fg+KK0zOST3n2KAVFQ6gZBftwHgZFZVvjCvrjFiodUeIZwyfu4xcE4bM35Qb9+9//Pl//+te58847Jz/39ttv8/bbb5/QZOIGXRAEQRAEQRAEQRD+PlNu0C+//HI++OAD/vCHP9Dd3c22bduorq7m7LPPPlnrEz4jH68iejQSIwifxhNzb8JtCSDLGoG4jVjGiD9lJHJkH6Xw7+mnXZs4cOm5xBNmUikj8aSZ986/hG7/8aNvn5XbK5oYjqsY5Ilo1fpDj/HyGVdgdUYpzx/HMxikf++iT5xHkeF77Vv4Zc26yT2yH+xYQnF7OeXzW1CMKu/1l6HrM/d7Tmky2xpXsPJIJO9HnQ/zYN0a3uyVMSkaqiZzZ+VEVP3j/nza19H0EG1BD1ZDhsG4GWWwiBLvOIm06Ui2wkPcW72OW9un7mmNZmS6olZKbQlkSSfPFuGM7FGGuovJLxnE5Ihh84TobK0mnTRhzwqSddkQYMLjaqfnpdMoqOvClBtg8G8LMFmSOArGkQwqDm+IZxdcN2OF8n82o6RTmj2KJ8dHXmUfOTnjvLBrKZ+rPUQ6beT+2rWE0sfvvX00Ir2xvAmAQCrNjsP1FA7nMxxxs7S2hezKfmRFpTtim3GOUMqE3ZimJ2qjyJqgI2zHYbTgT1ipzxkmmTHgtSQY/Fhv9ofqVxNOBjGmVVqDbpblD+GPOrj4g99z4NJz2ddXBny0F/muyg1Txn+/YzP3Vq9jPOqkL+rg7PIOqpbuRzanqcgLkx5xolhS/D+rJ85NfDCHeXOb0VUFZA1vto900sTI3lrylRYkc5qCui5io16WyBpVEQdjUedxj18gaeHDffOZU96JImt0Bb0sLezD6wniyRtHkjQySTPWkQJ8J9Cb/qifdm1iV83n8OaPYrAlGNnZgN0bxHX2AJct+R3hDwux26OUu30nPOexc/+0C3Zon6e4ZABbrh+DMc14wjLj4xVJJxC3kVANkxHugbCb1/qKKeusxqSos77nY2kzJlOagtxRugeLiEbt7O2spip3mL+01U9mlx09x/dUrcdlzNDU8ujktbnzoovILxwGINRTyOA7hdTUtSMrGliTyO4MSm4MazKIe1EX6BBtK8KoqCTTRgb7C0mkTNTUtdPVXkFfzMKPux7m/tq1mGSR3SYIp6JpFU8WLlzIwoULAdi2bRvnnHMOjz56/LQ/QRAEQRAEQRAEQRD+Z2YvSQrcdtttLF68+LNaiyAIgiAIgiAIgiD8r/WJN+jC/w4eY4YH69bwrSPtQER6u3A8D9atIaXJfPfwlimfL3P7qSzpA8BkyADQM57LQNTxma9R+GylUkZKy3tJJ00EIw4yqkKp28/mhlUzFk/6LBVaE8zPG2T5Gy/yxNybSGoy20fMWA6lKCkcIpkwo5xAqudPuzZxT9V61CPtSL/fsRk6JlopLYraKcgdY453nM+//4cZx8dUGY2J1OvxpIGf9WziW61bJ4/RtsYVlNriM44di9uoyRqlFuiNODFKOh5rDJcjgt0apzDq5Ml5N6Lryckxm+pX4zKliGYSGCWdfHuEirwhIjEbRkOGdMaApGhYisbQ0wYW1PeiBuyko1Z6n66lZMMQUiqNp2CUp5+/jM/VH6Ti4u2gQWbMidGawuSOknMg+qnPyT/C99q38IL7Ghb992u8ftblLDxtN9d98SUio1kcPFzNd9oe4RfV605orqOtup5b9E1KPeMEYnZsxhTvtzRQPpJLtjfAHE9oxrGKrJFSFRrcIdpCTn7c9TCbG1ZNFCaMuFAknc6QG5M8tQjZzUeKEv5u0TfItSQJJS2cuXg340sr6W9XqPCOIZENTKQ+f79jIuX5jooN2A0a3z28hVvbt/Dm2ZdR7B1HknTC/Xlkn95CeshFsKsIkz2OvWQEZB1b+RBq1EKkNx+DJQXAWy2NfOnst+l6exFWWxx34SjhgIu84iFGDzaw3+857nErcQapLBpgT0cNu8a9zPOGCMTs5GT5cJQNoTgSDL4zn2x7mLaA94TOxU/Kbub0nHGslj4OHWgAYDDs4az5e5Hez8fkjpCOWhkaycVqSp3QnDN5v6+cQGc1NkVjUe7wrN+nZiqiFkyZJoswzuaOig10hdLEO41cuftX9F2XwpHrp3ROO3s/WDSt9eg9VetxHklvP5aqyfjHssgvm0jHt3pDKJYkmZgFoyuKFlZQsnUM2SEizcWko1aef+csTivuIRB1kJc9Tm5NL5oqE4rbOKdoALrgO22PsKl+9ac6ZoIgfDbkT36IIAiCIAiCIAiCIAj/bMeNoAv/e9gNGY6NH91RsQFJYsZiRYLgMqVIqdMLvymSRixqYzzopqRwiGjUTkpVqMsaOwmrFD5LO/rLqIk68DrCxFNmIkkLLUE3t7Zv+eTB/2T2I22sOq9aijv/b8jGDDGfm0TUSnNXJUZZRdWO//fq28omioh9v2MT99WunfK1b7duZZthBVnuEA5L/Ei0c/NM0+A0ZFjTvI0H69YA8OS8G7Eb4rx59mV4nZ2MhdzTxtxRsYHRhMZAfylzPQFqPT5KckfIr+hnqLOEsYAHVZe4cf+TAJPt7Y6NxN1fu5bl+YNkF4xSYEuAJpOOmyeinFkpJFMGzEakJVmYfGPYz2wls10j3F3MWH8+Swr7cHpCjH9Qj6t0mGBPAZ7KfhJjHmIp84mfjH+g2yuauHznJm6vaOK9IQ3H/jjFJQMYzSkWzDnEdscXeKnr+Of1zsoNWGSNLHOKtCZjM4YYDnkozR7ltc4aGr3jRBJWIoNWPJaZix6OJMzTflbmWWMEkhYqc0boGM0nxxLHbkxPecym+tUszB9gOKxT5gqQ7ZyI0BtsCfKKhgmMeekNZLOpfjWD8YnXsbG8iR93TY3cqrqMQVExm5P4xrLJMesYS0N0vnYmsZSZ8xf9EclpApMJJRzBnjbQ+eFcNE3mxv1Psj33C1TWtWPN85Pyu8gqHOHQgQaGo07OLBiCrtmPX1pT6BkqoL6oj2VzDzAylEdhySAGcxLZnCa4v5Lx8SySaSPhEywW+vOeh3i58Ao6hop4fySHJTk+sqxRip/az+4vXEhhyQDRoJMOXy7JGX4OnaijGYN3Vm5g9+jMrRFncnfVeuyGxOTHsxV2/HHXR597et4NdPX4qJR1Mikj4zEHt5U1TYnCz/Y9wx91MBp2k1EVjH2FlC9spmtXI5XL9tG3u4Gh8Wzq69pRTGniQQfhsJM8a4zFL7/K1jkrOe3c9zEXjRM9XIw/YcNhSvLisis56M+e0vZREIRTh4igC4IgCIIgCIIgCMIpQETQBWCijcix7TaO/cuvIHxcNG1EnaGtTE/Qi9GQobKih5Dfza/3z+XymsMMBU9s76HwryuaMbB7NB9pLI8aV5BAynxKRM8Brtv3FC8uuxKjOUUyYkPNKHjKB7EEHZQlrAz5sqbt+zzWxvImGtxh8u1h8ixrqHH7uK92LXZDhmjGwC1tj9AZsVIfsZOfM0ZdyDPjPClNxmtOsP+L55FKd/EH+7XMr2hhR3sNbnuU4YCXktwRHqhbQzSjTLZ0AiixR4lnjFy+89d0XrWUgoVtABQZM0ht5YSTH7WwCqUV7j8S5a90hvBYo2S0EdxZAaJBJx57HFNOAHUwG0NWDMluQo+mIJVETveix1TCe8qIBZy4y4ZIdJSxe6gYSdKZf/HbKO4E2dkhzP+RZnPD1/4BZ+jvY5R0NpZ/1CL0xewrKdIH0TIyiagNjztEsTUx6/gn5t6EQY5S5vYhSzop1UBGVfAnrBgUlYuq2hgNuQkmrCypbSWdNs44T0qVeOucSzEZMqjaRDTbaBjhg94Kiqu7ySsaxuyIsX/XvCnjBuMmzrIkWFwwTO9AIX2+HJyOKH99bzlZlolaBJXZI2hjeXjMSe43ryWQhmcXXIcsgdWQ5nDIjSINYjYn8eT6yDqvA4wmsFmYs/AgmZQRyW0Gh5307gQpfx5q3Ex23hh/2n46Lyy5hrGIinswj5LCcWRjhvCol/ln7KIhYqW3veK456A0e5TcwmGcpcN0fLCAORe+j1KQgPIiJJ8Bl9RNmaKyb+88FEk/7lxTj6mBtCaTY0ljUlS89gitX1mO2RJAknVcuT5q/cMse+2/T3hOYEqtnaM8xgwljvBx62X8smYd3z28hTsqNvCDjod5ZflXAfjVguvJtw/ygvcaPhzPmqxlcNRjjStwm5L4kwb2jBRiMaXI9vqpyBkh3xkkx7KGb39sPce6u2o9yUwAjzWKQVHJL+3HVOin/vw26A9RorVSELNgzgkQH8zBZE2S54ySTJomXps5ia+rCONgDo78cS772n+RGPHiH8zjku2/m9ZiVxCEU4OIoAuCIAiCIAiCIAjCKUBE0AUA4qpC7MhfXAXhk/THzTNW+u+M2Kn0jhPwedE1iSvqWln88qvcXbX+JKxS+KzcWbmBUnuI1qCLH3Y+zJ9P+zqlrgC/W/QNOsNOvncKRNLzPX4GBgrp8OVS4AjhGMqnsGiIWNxCT8jDQ/WrJ6tqf9zG7k28c+6XqKru4rSCd0kFHYy/dTbX7XuK3V+4kM3KKtYf2sTX55xPMmnGZpy5srRF1si1h9F1idy8MYrL+ohH7Fz6xVdQbAnq7Qn6353PeNI4JRL3466H+cuyq1hS1U7fovkEfHYGd9cxMp7N3uEiLqhrJpo28fS8G7h+/1P8uOth7qlaT4E1wVDcxlDcxrrmbfRWLKRgySHig9mkg46JSt5G0Avy2f3zOiqqu7AXjWIsCHH4YB0LLnkLNWJFkTW85iRWc5J4fy42wxAAryz/Kj0hjdHEydmD/sPOh3lhyTXco6xHkXQkaYz2jgo8jghGY5qOoSI6I9ZZx5uVDAvLuiie14a5yIdk1ckM20gMZXNg91zeGSjhvNJu6iq7SCeNxGMzz/Wznk18tfYiKurbsdf0I5k0El25fNBbgTXXj/s0Gb0vQFH/1H3OHpPKeNhFImXCYY0TStiQJI2vX/VHJINGbDCbfXvmce3eZybH/GXZVfRFnKRUhUpXirOKe/B6giiySipuJvxhIY6GfiRdw3Z9MUp/D8ldNsw1foyNdvTdcQ58sIiigmG+tPhDenpLiKXMpDNGFEccgyfKK6+dywXZAQD0GTKljqUoKs7SYYxnWalY5SXpWQ+yASk+hmG8A1PyHfb8dj6xlPlTRYQu3fFbHmtcga5LOM1xcnLGaesup7q0l30H5nDa2R9QXDLAG2d9mfPf+dOs8zwz/wbm5A9gt8UwmVNEwm285PkavoSN5oBzSicF7Tiv1SRr7P/iebi97/GtL5cTHu3mRfVKKgo6MJlS2D1hxt49c9o4hzFNTd4QsaSZ5W+8yF/cV9Hnz6a+qA9dl/h269Zpe9Hvq12LzEQmjK5DocdHcckA3vpuDJUqqDIt99dQfb6f+JgbNWUkMjrxM/d328/AasgwL3eIF5ddySXb/5MPPBdTUd1FOmpFMqjYqwZIx828c+6XaPefnA4MgiAcn4igC4IgCIIgCIIgCMIpQNygC4IgCIIgCIIgCMIpQKS4CwAkNRlFPvECLsL/bjOltwMsyRkn2xMgmTQzFPASzxh5rHEFBjn+Ga9Q+CzJgMOY5KLyTt4suoy3BrI5t7iXiuxRUtrf3wbpH+WBujXEEgPE0ya+sfdpfrPwOiR0zPYYjefsoHLES+vBemiZfY7msXw6/dmcVd9MwcI2rv5/nuEamxFt/E3ydw7zRtaXyagxogkLNlNyxjm+37GZFzzXoMgaybSRqobDeOZ2opQZwWRE61JJp43TCk3BRAtDszlJwOelZaiYeaVdLLjwPeanDUQGcqiODvNaV9Xk442yzk0HngBg1+c/x8Bpc3GX9TGwvZFIxEFhRR/um2RSuRdhbt7OnOW7sCyMoufkII0lWbLmNWJ7sgn0FnBwqJjGgn6MxjSRcQ+Oxl7UoIX2QBYDcTPmk/Sz4+l5N6DqaVac/waaqtDdXUokYcXpCqNmDLQGPUjHydAeS1jpGCrC6Qlhi1mwlY5gbJAw1o1xWuEb2F87gz5fDqMtbsyGiRZp99Wu5Za2R6bM88Tcm8jJ2YtszHDwz+dSUtWD98xOVn3+r2ByQ/8AGZ8No2nq1ofvHt7Cb+zXoekSqaiT8YSVeMJKMuCc2H4A2M0fFbl7ct6NqFqCLHMSRdKwGlOc/vpLvH7W5cybfwCjLcnBD+eztGYAcnORwwEwKPg6SsjzhsEfJ9RTw2lX/TfpUTexUS97Rgr50oLdpNNGoh1F2Gv6ufC8t/nw/aVE0ybiGeNk274Zj2HAQ21tlHTxPOREGMvbTzLwZAnRiJ3Cmm6UUona8m46e0v4fsevP9X5rc0apby0n6yaHkwVIYrsLWQ6JTRVBk3GXTFA1kjOcefwJ83sHyom3xbBY4uSlzvGouIhMikj9YP51Ltv4Lp9T7H+0GM8cKT14cfdUbEBuyGB0xXG4oyiphXylhyi8Py9qGETkqKRGMyma4btFIGUiY7RfDRd4idlN+M0dzOnYgizNUE44OLpeTcwnsxwV+UGfnikVdstbY/wi+p1FNsSlDqD9PmzGQ27yektxuWIUDSng+pzP6TrzSXIssb2jhrm5A8SS5qpcQcwyCqpjAFJ0rm9oglN7yQwmkXJkmaSYx6Sfiee+R3UW1Kk98z9VOdEEITPhoigC4IgCIIgCIIgCMIpQETQBQDiGYWkNhFquL2iCUXS+VGnaLUmfHpdQ4WkVAMea5S94zkszh1hLGY/2csS/okkSac77EaSdDy2KBZFQ9Vk/FHHyV4aANGMQn/Ii9cSA6AnYqfQGSDqd5MI21EMKiZj+rhzrGneBsBzhm8yN2kmK38M95wulAoLOQ1+crr/TKrPTcs7S+gNZM84xxNzb8JmCjIUduMwJRndvhTn3kYKc8cAUFWZ5r4yflE9b1phvWDSyqv7FlLh8aHpEiO+LAqCDkzZQazZQfr3LJwyJphW2NywigpXAEX2oxgyRAZycGYFsTpiOMsHkfs0zH296OEkqWAZ+m6J2LCN/MfivLL8q9RVdhEKuqjJGSYrZxzFoKLrEomebAy2JI05w6x/689/72n5HzMpGotq2nCWDqM449iyg2x/ZxmjY9kk08Zpke6P+1brVu6o2MClT+3njbO+TFZLGIs1QV51D7bqYeZd9TpzAhYSQ1lExz0MD+ZjmaFl5FDcQlt3Of37F1CTNUpbcy1FfhdWRxSDJUUmUcXfdi6l0BkAdk8Ze/WeZ3hy3o2UOIMU2sOk0wZa9jSS7Q0wOp6F1fxRNsZ40kxCVQiljCiyToFq4M2zL8NhjRLxeUgNmqhpOEznf5+O6Y0UJmsSWXbT21eE2RbHaE3iLB2i/82F7O2sxqJkyLfGSCVNOD0hJFkn3j1RyG446qAzYqfGFSGamT0LRtVlUu02wq+OYskKMdxVjsmapH8sF6stTnAwl+GRXKymmQsnzubOyg0kM0PYvUEM3gh6WEKPplBydEov2UmyK4uh/bVk1ONn6BzbUu3e6nUofeVIkk61M0RVwSDLqtt43Xk54YSVkbg64xw/7nqYXy24nt2Ha0lkDJgVlZK2cfLzR8mZ244WNRAb9bIwKwA9U8eua97GHRUb+HHXw7x0+tcIJWxoqowk61isCezGNNfvf2rKmNvKmvhe+yY21a+mM+Rhft4gQyEPwyEPRkOGyFA24UOV7OmpoNgVIJw2EU1YsFsSvD9YTKEtjq5Da8jBz3o2UWxdyUJFZWhvLc6sIOmkiQP/dR65eWNkOcPTitQJgnDyiQi6IAiCIAiCIAiCIJwCRARdAJjc+wQT7UQE4e+x15dFqS3GorJudE3iIms77/VWkPiEKIfwr82maJxW3EO/P5tQ3MZXF+ziUE8Fl+747cleGgA/6NjMbxZex+ff/wN7L76AUKwfg6LS01+EQVHp9OdgMxw/gn7UVbv/k2fUGzD3ldMwmE9Wzjj2PB+2+TIGd5RUxsDVe57hzsoN07KQEqrCvvFcbml7hPtq1+IxpmkLeMkOZJFjjZHWFK7Y9Sx3Vm6Y9ryDMdvkPuD7ateS1mT6X7kAqyFNVe4w7WEXv6heNxlF//g+9rfOuRSbOYkk6RgNGYYGChj8fRYea5ShsJsLz3ubt145j46gh5bLs9jdbcfUU4LZmOa9/jKWazKaLmExpTCaUxgsKWxHort3HVnvDz/jrKuWoJOcvhJauiqor+giu7KfufWtdHaW47LFeGLuTZP78Gfz466JNXcGsvHH7Vyx61leWHINnveiOKxx3K4QqZSJUGQiG6RphlZ83+/YDB0Te+Kbx/KxGjIc9uUSyxhoanmUrXNWUuXxcc4s2QY37n+Sl8+4gu6QB4Oi4rDE6egvpjx/iFDEMRnhnC0j4C/LrmL3QCnBtJHGoXGyHGF2tM7BbkyTUBVKHGGe+dt5ZDSJW9u38Mz8OVRljbJ7qJhFBf3U/fE9XlhyDbVFffztcD3LyzrJtUWnRXZnMhxx8tafP0euK4ivuYGCLB97O6s56PeSUg2UZI0RT5nwxz9dFlVak3hzsJCCLh9/efELNHgCeKxRugNZFLuCNNQdpm84j+bxvBOe89aPZaU8ULeGPEucZTWtxGI2mlsbZh0bTRtRJJ1r9z7D3VXruWLXs2wsb+KLAwUEY3bctihjiZnb8FU6orxx1pdp9btZXtZJS285HmsUf8yBxxqb9vij0eyj19pLzq9hM6bIcoTxegOEAy7ebGugPnuEaNKCqkvsHS1AlnQurOhgz2AJqw4+zn21awEYSpixjhbgDGSR7pApdAU4829/5c7KDdgUTUTPBeEUJCLogiAIgiAIgiAIgnAKEDfowjQ/6NjMDzo2n+xlCP+CftCxGYtBZcTvZTzk4vBoAQC+lEjW+XcmSToZVaGxvIvKogF2ddTSHnKf7GVNoUgarV9ZTtXS/Sy74mUWXfwWdXWHGY86uW7fU1yx69lZx24sb5ry8UjCQmfYQd9oLgdba/nvly+k+w9LCDZXkO0NcFflBrJM0yPyvtRH+6JlIK1LfKftEayGDH0RF1/+8DcAM9b/sBsyvHXOpbyy/KuU2qPIkk5jQT+NZd1EExbcxvS0fevHemughEMjhXjcQVJpIxlVoTR7FKMhw5ziXqJjHiryhjijpBuTKcVFZ75HeUk/mi5xbnkHOVk+6hcepHJeK/GwnRf/dg6yrPH8kmsotCY+8+g5TERZL3z3j3SEPGxvbeD1l88nHHBRmDeCJOnU5Qyf8FzhjIGhI7UyLt/5a3YMF7H45Vc52F1JJGYjlTHQG8jmmfk3zDrH9fufwm5ME0kbGUmYybPGuKNiA6G0kfQnZBGlNYUzyjuoqegiL3cMuzlBw5/e4fTXX/rECGdKVXCbkuRZEui6hMsR4QuN+9F1ieVlnVgMaYptMTRdYvuFX2BeYR/7RgrxmJOMht08Mfcmcp1Bekbyuey07eQWjBBOWXh+yTWfeNySqoFY2kTrSAHhpIWxgJuBqIPzS3oozxtmwUuvc3A8j1DK/IlzHavKEeOMvFHm/vlNTLLOWNxGTzCL9ogDizHFXz84nZGoC+PfkfF3W9nE+9kg6XSEnRzoqsIfdpJnSU5+7ePWNG/j6j3PcE/V+snfjzZ2b2I07KY5kMWuoWLWH3psxrGSBCZDhi/M20tB6QCnLdyL3ZIglDLz4XDBJ643kjJTnj9EeVU3akbBbEmyuKiXkYgLuzlBjdvPgtwh5mSP8n5fOasOPs4fFl/LLW2PsKl+NTKg6xPXdU/UwZl/+yvPL7mGH3U+zHcPz/49QxCEk0fcoAuCIAiCIAiCIAjCKUDcoAuC8A+zdc5K+qN2Lnz3j5z/zp+4es8z3Nzy6Kx904V/D9GMwus9FWxvr2VfVyVX7f5Pvn1M9eST7Zn5N1Di8WGxJnjnlXPp/tsSIj35jA7mczjk5rUzv8KT826cdfzR/dx3V60HJrpeSExEPQMJG3t9bkbGs4iH7Yz7PVxY1oV7hqrVmSOdMu6tXke+NYZBmugfLks6aW3ix/F9tWtn3IN+tIp8cc4oiyrb+dJFr1NU2UM47GAo5KE2a/S4x+AHHZtxGifW5HEH0XWJlw7XcWi0gD29FWTSRhRFpah4kFjcRuvBenr6iygv78VqSZBdPoiWNmB0R8hb0MbF57xNUVk/i2raWFTcw4Oz9JD+ZzoaWb655VGu3vMM4bQJTZMJBN0cHCymP5j1iXPcWbmBe6vXYVPUKfvLS+xRbitroi/ixGTIUFY8wPyyTvLt4WkZFQC/qF7HG2d9mRxrlN6YZXK/s8OoEs3IpNSZs4geqFvDc4u+idMcx5vjIxJyUvbsbsaiTrY1rph8zLGR3Y/36/7qzl9Tmz/I55Z+yPyGFhzuMJ6CUc6fu4/5L77BYMSFy5RgWf4QZRU9FJb185Xl73L23P2U54zwxeXv0bhsD2ec/QHeml5cpcMsqW7FKGuTa5hNIGUikLTQEnLhtUYxKip13nGyPQE8WX7Gb67k7PIOcqwx7q1e94nn46j+mJUvbX8OgO+0PUJalzArKpWOKFnuIBcv3UFjcS/KkffQ8dz1sfeTNPE2ZDhh4oedD2M2pMnxBFhU3EOBdfZq81vmrETVpcmPN5Y3EU6buKXtEQIpI3dUTH/f3l+7ljxbhGTGSCDg5vdvnUPLoVpSGQODMRsw0TnneGvvj9mJxa30dpZhMGbwFI9QXtfB0roWOvw5pNSJnuduW5TLl26n/Wunc8WuZ7mnaj1NLY9yWW0Lcwv76btuPt885006rjwN/5H98rNlDAiCcHKJG3RBEARBEARBEARBOAWIG3RBEARBEARBEARBOAWIG3QBmEixOl6alSCciNGkCYuiAvBQ/Wq2zFl5chckfCaMkk6JPcZowjJZbO3Bj6XlnkxmRSU3e5xgwI0/YaXuj++xfediSuo7uOFzr1JX3UFD7tCs45+cdyNb56zkBx2b+WXNRJpuoS1OUjWQVBV+1rOJDl8OmirTsOgAuVl+WkOOafP8uOth3jz7Mm5t34KOxHjSxG1lTVgNaZYU9nHoy2dxUVXbjEXi/nza1wFIJM14CkZB1kiE7XSN5xJNGycf9/S8G3jp9K/x+lmX86elV/NQ/erJr5mUDImEhWDIRW8gmy/WH+KixTu5+Jy36RssQNclkjELg+PZ5Hj9VFb0sOtgIyZTitBQDtZ8H6bqGBhUIuMenOVDFJ61j7LaThbnD/zd5+d/4ujWhHur1xHNGOgfy8VkSnNa9WEk9OP+XHuwbg0/6nyYWneQNc3beKBuDb84kobdF7WzLHecppZHUTUZNaNgMqe46L3np7Wwu7NyA0vzhzj/nT/R4s+mwh4HwG5MsTBnBJOsk1QNbDrmXByVbU5SX9BPrtfPjoONhCIOtl/4BZbUtXBmdSt/Wno1327disek8sz8G7i/di0OQ2bKloxn5t+Ayx1CVlR8o9nkLG3BWd+L0Zzivtq1pFSFPHeAsoIh/vOtcwmMZTE6mE8o6Joo8JgyYq0dwTZviN2vnEWguxB3/jjlecPUf0KhPUXSybbEubi8k/Pf+RNZ7iCKrOHJ8eGp7MdgS/BhXzktAe+0NmfH88POh3l2wXU8t+ibADiNaa7a/Z8cDtkZGMvF6o5QUN5Hzgxtyo66u2o9t5U14TBmuP9Iy7E7KjaQa07zUP1qCiwT6ey+hI14wgJMbJf4uK1zVvL6WZeTbU5wZuEAT867kftq11JqS+C1xNlY3kSlM0K+NTltbLkzREnuCC5rjN39ZSwp6gWgP5BFmSPC6QUD2I78zHxy3o08MfemybF3Vm7gh50PU+EIY7UkKCweRJJ1NFVGknTGx7M4vfIwNYX9OKxxcvNG2dtaTyJppvUry/nq3H10X7MYiyVBPGlGVjQMlhTu/HFcpom1ihZrgnBqEjfogiAIgiAIgiAIgnAKEL2PhEkyn1xsRRCO52j7mZdO/xouaz/BmJ1N9aunFF8S/v2E0gqnOQM05MV5LfsrBBNWrth16hSJaws5qQx4CMVtHAy4AHCa4yjWJNaSUWxhK6PDubOOj6aNXDx3L/6LyknH32CgpxhZ0okmLLzRV8ZD9auxGmLoukTc70JV5WlR1qOqKruJfs6LpPyJPa+eSfhIsaaS0n5kRUNTZbY1rmDlwcenjFMkDUXWMBozPPDnS8g2p7n6rHc4c8FedF3i7b0L2TJnJdfv3wbAY40rcJlSlDrC3FO1nu93bMZkyJBdMIok6STTRtJpA0GfB1nRyMvyYbYkGR3Lprqkl6yKAdS4mRxHCKsjhsUTxpAVwf9mJYmwnXTKhK+1jIH+QsoqenBY4/+AM/XpbG5YRSQt0fqV5YwHelFkjVTGgMsdIhJysD/godoRmXX8WWWd9CxeRDg8SM+1i4hFmznUX8qbZ1/G3wZkvvzhb3hi7k30+RIYFJXokSjrxy3JGePQeC5tV5xBTsnLqGmF+uxLsZmDFJf1Mae+DcWUZv++xinjNpY34bWMMu+vf2Nb4woshgyaLlE7p41MwsSOlgZ6ohOZGEfbYT1Uv5pYxsC3WrdyZ+UGftT5MLKkE4vaCIeclM1rJTnkRVY0ElErdkMGqyHNwaEiimNBvlh/EIOSIaraeKm1lqsW7sZb20uqz42kaDTMb6ZlfwPl5b0U13YiHy4/7jnIscQZT1hZtmAfPQWLsLpHKJnfirnGjxaUiQ1nM7+gnx39ZZ/m1E7MbYtSlD3G3rwLyC3Yhf+8ctoPHua0V18msKyEdMSG2zJ7BN2maFxSdRiLOYmmy+yuuBB/ZJBUxkB32E04PfEr8GDMxlxDBl/YOeM8dmOaQ+O5fK7+IKGIg0XOEPGkmXDCiqrJ3LR0B76Ah5GQZ9pYRdJ57sB85noCBNJGDIpKZUUPxXEzDm8I33AOt779Xzwz/wbK3OMossazC65DlmA8MdGqMZYx8FpbA/NyhpEknVpLAu/Sw8wpGiPWn4sk6Zizg8RHvGTZw/x3Wz2F1hjBlJlsS4KzFuwhv7YbAIMlRbCngLQm8+yC6xhLWPnWKVTQUxCECSKCLgiCIAiCIAiCIAinABFBFwD4aZfYhyT8z91e0cRcTxCrMcTe4SLWH3rsZC9J+Az8rGcTS3KuwWWLUVXaSzjk5Jn5N3DdvqdO9tIAyDanSKaNeOwRalxRAAJxOzveOJMcVxCARMo06/gqtx9vyTC2iiGUfIksqQd1QCPYXEFZWR89PSVkVIW9ndUUuAI4rHHurV43bc/t0/NuwOZ5E9mcRnEkWNb0OhgUMB3ZQ55RSXxoJq85Om0N3WE3UsTFQnmAL1cfZijoxeyKYHJHiI94KXb5SfmzgYnWScF0ZrJ1W7UrzHOLvonV5OPlD07nss+/xpKrX0ZPffQ3ekt3Ph++v5T5c5txVfUj25Okx53Issajb57LdUt2gCbjquvBBWSCdva+cQZt43mMht3kuQLcV7uWW9oe+XtP06eWY4mztK6FvHntVGRF0YIWBj6cg9GcIr+8n7VZAfp6i2cdb1BUrO4I+ac1I7vSYJCo9O8g3FaCxZSiwLKSmw5s48+nfZ35L77BPVXruevIvuBjDUUdfGnhLnIbOzCVR8BsZPlZPWhBHcmgI2WZ0UZSlI1Obfu2sXsT2+wr2HfJ+UhSJ/3jOQRjdkZ7CzEaMxS6Aly5+1cfe80JLMrEnurx5ETLr2/sfRr2TuyVdjgnMgZ0TSK3ppdLbQkOHa6mOGcUd1YA79wOtKiF5M5GohkZX8DNwT98kdLsUYrL+zDa4yiyhpoxkAzZsTuix30v241pltW04qntwVCSBKt54vn9Onpy4rrOzx+lPm77FGd2Qko1kFsyiLOmf2LOlIF5pcPEznNz6M16enw5ZNtmz5BQJB2XM0Lh/FYUVwxkHT1uQo1ZOCNiJTCQx/nln2coGKBjNB+ZiboEH48of2Pv0zy74DpyywapbOxFyjJDOoM6Cv69NeiaREN9F/G/nTFtDcGkmc+VdpNIm7DFbWiajMUZJauuh2h/DuMBDzDRarE7kMX1+5/ixWVXcsn23wET7+XuiMb87HE8jgjebB+yUaXjr2eQX9FPcDgbozmNpsp0tNSw/I0XubtqPfGMkUUF/aQyBgymNJbCcdAl2l45A3/YSYEjRJYzPJE10PqpT40gCP9kIoIuCIIgCIIgCIIgCKcAEUEXBOEfxiRr+JJmRuL5M1bDFf59fXXnr9nWuAJlqJhSV4DIMZXFT7bBuIWCiIv+YQdmReXNsy+jM2BF1yWM0QyBuJ0vf/ibWccHk1YO7JxP96vns6ism7ySQVy1vXhPO4yelvBW9eE7XIrUU0o6Y+DFtnoiGWXaPBaDynBXMYffPZ3K/CFy8kdw1/WiZIWQbAp6WCUVzGZshmhjUpM5raAfkzFNPGlmNG6jr6UKSdIxGDJ8OFyE05CZeB5F55a2R3hy3o3YDBnGExZGkyaGYja+0HCAVNjG2CtLAShobCfpdxL1u8lzBxjqLeLND5dy3unbMZjSWC1JLqlsp/K5D3njrC/z1n99kYsrOigoGMFqTnLTgSfYMmcll+74LRvLP9uq/f6khfHxLNTdChZ7HGfhGLl13RzeMZ9I3Epp0SCSNHttlR29FQQ7ajm/owNVk8nJ9pFd2Y+tcJx6Z4xyfzedjUvp6E/TcvmZ6Pp+XmmdM22e0YQZn88LB6sYeDUfVZNZfOG7mIqCSA4F3Zck2ZeLqk6PiYwlTXSP5WExpAmlLKQ1mV0H5+E2TuxHf2HJNVy+89eTjw+lTNjtKWrcAbSgZ/Lzd1ZuYCQB/7ljGZXOCKfXtODvLkRWVEpyRzCZUpjscfwHqjA7YoQiDpbnj7Lstf/mtTO/gt0eIzTuxRyz4HGF2NNaR2/ESZY5gXycYzgYszNP0Uj5naTDNrp2NdI7nsulO37L1jnXMDdvkOVvvMgbZ5We4Fn96PW4LT1E/W4OPd9Ay3geXnOCPEeI0qJBsrL8hONWdo8WzDpHSpPpHc6nZ6gAsyGNQVEpKhrEme/D6IqSn32YPE3Cvn0e4agdtzPMYFvDtHkerFtDsWsQZI1kbxZquxFzVgglK07O+a0gS6gjCm779MyX6/c/xe8WfYNCV4ByZ5APB0oJx60Y96u47FHOevMvABwOOSh3xHh63g28O+KYrC8wWWW9A94974vkmVOoKSOplIk/vHoBwwkzGU2i0JpkTs4wu79wIal0F5ou4Y86CKUsFPtdmIeykI0Z8ssGiLVWYbMkMJlSeHWJzQ2rRLabIJxiRARdEARBEARBEARBEE4B4gZdEARBEARBEARBEE4Bkq7r//K9tUKhEG63G1AA6WQvRxAE4X+dbY0rGE+aiKsydoOG3ZAhmjFMtoc62Z6edwP59ghvDhZwXtEgpXnDqKrCWMDDYX8OwbTxuAXONjesQpJ0Qikj2eYUc/MH6PLl0lDQj8MRYdzvpbhkgFjYwdutDdx04Ilpc9xe0cRPuzZxX+1a4hmFtC7x+dJuDIrKYMiDSVHxWKO82lvOjz5WiOzY4lV3VW4gqUlU2OP4UyasiorTmOL6/ccvynd/7VpK7BGCKTOrDj5O9zWLefdQIxYlQ4l3nF2DJaxr3sbzS64hkjKRa4sSzxj56s5f86elV/PlD3/DG2d9mfPf+RO7v3AhsqSz4KXXJ9NxT5aN5U0U2xI4jWm85okiZxZjinPe+jMP1K3h28dpI/VA3RpSqsz32rfwqwXXk28PcziQhVHSWXnwcZovO5uiui4yCRMDHWXMf/GNGee5v3Yt3zly/TzWuII5uUMk00aiKQtOc5zxmAODrE3bSnF/7VrmZo8ST5sIJs24TCnMhjQFHj8WS4JAyMXpr78043Me28Ly3up15FsTxDMGYqpCniXBSMKCqknMzx5D1WW+tP05Xj/rcpJH2n1d9N7z3F7RhEnWqHBEybVFqCnrQVY0/mvnUppaHuWBujVE0oZphfGOerBuDfNyhvHH7VTlDrPgpdf5Zc06NB2+176FfZecj80W52B3xZRU/RPxxNybCKaNJFWZtCZjkjWiqsJPuzbxl2VXoUgaF3/w++POcXtFEy5jhlvaHmFT/WryrDFSmsI39j7NK8u/yoJ5B3Ev6EALW+h5fz51f3xv1rn+tPRqHOYEkaSF6oIBdvVU4jEnWNx4EFlR2XOgccb1/LJmHd89vIUXl12J1x4hoypoukQibSKaNvHVTzgud1etx23MUGSPUJY1RkZVeK+/jAJrHLc5jlFRGY46cRhThFJmGgv6aRsppDXkxCxr2A0q83OH8LrCRGNWTMY0b3dX8a3WrWyqX43HnJwoNCgIwkmiAyrBYBCXa6IVrIigC4IgCIIgCIIgCMIp4KQWidu+fTuPP/44r732Gl1dXWRnZ7N8+XLuuOMO6urqTubSBEH4H5qpHZHw78ukqJxf3klGVejw5RJKmU6Z6DmASdHIdob4ojnBf/dUUB+zc9bcA6TTRla9/V/8ZdlVxx1/bBGlTfWr6Q9kcfWeZ7gjuAG7QWNu1hgOnweTKYWmz5zJ9dOuTWyds5JQWkI78vGS7KtRZA2A4agDf8I6YzTaKGs8t+ib7PO7WZg9hiTpVOQNYTYnCYZcjIdd3FO1nu93bAYmIsN5ljidEQf5liQmWWU8CRZDhiK3n7YrzqB/MAuLMlGMLJ0xsK55Gw/WrSGjxajKGuP9wRLyLAkerFvDWDwzuZbRtbVo6gCBsSyemX8Dqh77tKfjH2ai3Risa94GTEQ58z1+MqrC3VXrGUscPw5xbHS9M+wgrcmsa97GPVXrAWjuL6V9qIjqggFUbfa5TEfOIcCqg4/zwpJrCKVMjCYtgAdF0nEYMjOOy3MHcDgiJBIWVFVB1yWGA16UsEYoaZk25idlN+MxZmhq+ej9deuRDIBCt59IyswVu579KLOhfeIxb559GZquk+/x0zWWx0P1q7m5ZaII2bMLrqOquA+zLcH4SA7lriD31a7l263Hb5v3rdat/MZ6HaGUCVvAy9Y5KxlKyPyo82EOXHouNmuEwZG8Tx09BxhJmEmoMj/umng//LJmHT/t2sTT827Aafbhjzk+cY5jW8g2tTzKPVXrMco6v6xZx3A0znBfIWGfG2/RKIqiTXkPHesX1etIqhG+/M4LvLDkGg4PFqPrsGs8Gw420hdxYlHUaeOenHcjFe4BmuvPxpN9CIsnjJ5R6G8vx6HFscSt/GrB9bNGsDfVr8aiJPFa4iyqaUMxZBgbzWEkYSLXMlHAL5420VjQT+dYHvOLegnHbBwIuLApGre2b+GFJdfwRn8pNxR9gNsToKunlG+1buXOyg1YlASh47SYFATh5DipN+j/5//8H95++22uvvpqFixYwNDQEP/f//f/sWTJEt577z3mzZt3MpcnCIIgCIIgCIIgCJ+Zk3qD/h//8R8888wzmEwf/fXu2muvZf78+dx999089dTx99MJgnDqEtHz/12u2/cUT8+7gYUlPdRkj/B2X/nJXtKk+2vXkmsfZt9gCb6UaTKq1lx6NoVl/QzcOJf9Ldqs449GU7/fsZm7q9bT1LKZbY0rACaje0/MvYmClB8Ajzkx61wJVeG7h7dwe8VES7LBmJ2UquA0ptFh1miWUdaozRtkQUUHyYQZtzfI6EgOe3vLKXX7+fz7f2BzwypgYg+0zRDHl7Sg6hKrDj7O5oZVnF7Ui8cZxps7TiphwRdzEEqZ6Y9Z6Y442XfJ+QQiQ4xEXSQzRmpcQXIcIbQjkePNDasYiyXQdQlXdT+SrGPrK2MkbjtpGTPf+VjdgC9/+BueX3INKdXADzo2c2flhhOeS9UheqQ9oEWZuB76og4qnSH8IRfBxPT2d0dFMsqUY5BQDaQ0Bbshgwz0xizoM2RWRDIKvrCT5w7N4dzCQbIcYYIxOx1BL4W26Iz7k3/e89C0z01kVzj5at4gBYbMkT3aIzxgXEOd24+qyxz225lf0E/LcCHBlJm0JnNf7VryLAkiaQO6LmFxh3ElTeQnTXQc08bteNrDDn7QsZnHGlewpnkbv1l4HQCplBF/KJ+eYBZ3V63nBzNEpo/n1vapGTjHZuQYFBXHcd5nMBH1/t7H5ohm5I9alwH3JdayKHeYQNBNMm0kMUOWxJY5K6l0jfL59/8ATFwTOZYEQ/GJbJc79A38uOthHqhbM21stiWO3ZLguQPzmJ/lp9Q7TiJlIqUaSKaNbB/Nnfwecuy6bQYVg6yxrvlR7qrcwNLSLgaG8hkIeQmnTBRbk7hMSQajTmqyRukZz8VuTOELO/HH7XhMGaIZhV/WrGMskUIGNFUGDJPXoa7DyoOPH/cYCoJwcpzUG/Szzjpr2udqa2uZO3cuzc3NJ2FFgiAIgiAIgiAIgnBynHJF4nRdZ3h4mJycnJO9FEEQBOFTGIpbOTBQQipjoNYdONnLmWSUNZJpI7m26GSl9ofqVzPi99LWUkNrexVjcfus42VJx2NKA0xGAdMfi4ZqusR42EU6baTAFZhxnjsrN3A0Tn80ij8YtyBLOl1RG7GMAfMM+1hhYl/zeNhFS28Z/rCTSMhBXv4IVdmjfDhcxC9r1jGaNPGnpVeTa53YE35zy6NUOyNsnbOS9YceYzjsJpE009xSh6bKlGSNUeoKUGyLU+MKIisaTluMq3b/J6mMgSKPD68rRI7XT1pVGE2auGr3fzLcV0hixIujeISzluyi3Bk8qRkzmxtWsWXOSjbVrwYgyxqlOmeYHZ/7PJWO6AnP8+Ouh1F1iY3lTUQzCjARoW8NumkZzyOSMs861mXMkGdJTn4cSpnwJU30xyyMJk1s7N5E+Micx7q1fQvhpJWv1LSRURWCMTsua4wvzN1LfUkP7573xSmR2ftq13J7RRMbyycyMO6vXctzi75Jti3CN+bvxesNYDKlKMkaw5ew4U8ZUWSNcMqEIuls7y/DZshQ6x1nWWEf87LGUCQNVZfoHCgmHbWSVdNDbt4YC3KH+GXNuhM+fjZDmtsrmkhrMpvqV/N+bwUA+fYwbuP0/fcn6rayidd6NGslrU3UuUhlDDy36Juzjvt49Pz2iqYp0XOAW9oeYd9YHqMhNzB1z/pRg3EL+8ZzJz/O6BLxjGFy/qMR8OjHzu9D9asxG9KMhtwsyx2lL+JkKOid/HpPxEWZPT553R677hzLR9kBNa4w42EXiqxR6hlnQWEfpxX3kOsMUpM1itmYpsjjI8cVBMBqSGOUdFRdQpF0YhkDeZYksqLhyA5QV3eYl8+4YlrkXhCEU8cpd4P+9NNP09/fz7XXXjvrY5LJJKFQaMo/QRAEQRAEQRAEQfhXdkrdoB86dIhvfetbnHnmmaxYsWLWx91111243e7Jf6WlpZ/hKgVBEISZfK99C4fDTs5568/T+j2fTDe3PMol23+HP2GdjEbe3PIor/WXcNabf+H1/mKu2/cUPym7ecbxt7ZvmVLF/c7KDSTUiWjZ0TFpXWLPWB4vt9fR5c/hwRn2o/6o82G+3bp1cg1PzL0JTYekJuM1pRlJGFl18HHuqJi+b/qOig28PVhIKGWmJ+hlb285u1sacDkiXNJwgAU5IxRYknz5w9/w5Q9/w8HARC/VeMaAw5gC4Ks7f81/Ha5jNOoklTKRXzgMQLnbh8sS49BACYGog9fPupydY7m831fOyy2NDI7l0BnIptIR5YG6NYyF3Hzw7jJa311MYDQLpyXOy2dcwb3VJx5t/UcqtEU5q7Kdi+Yc4PWzLieeNnFguIhA1IHXEj+hOX5xZO1pXWJj9yaSmjR5br/XvoXRhJnOsGPGcwMwEDez5kgleQB/ysj32rewsXsT+Uci665Zosh7fFkseeUVUqqBXSMFvNtbQXtfKf2jeQyH3aTUj35Vu6XtEdLaxBpvr2jiO22PcNXu/2TvaD4Nf3qH3oFC0hkjb3dVU+Ed44sVHRz05dARdqLpEje3PEpL0M2bg0X0BLJIqQb6onbCaQNdQS+Hmuv45ZPXYHWFqa7qosj2yRkIdoPKg3VraAs58Rgz5NkiFNsjGGUNRdaQJZ2bj/Rr/zS2zlnJE3Nv4ryiQV5Z/lUa8wZpufxMzq1vpiprlMGok6t2/+cJzXVn5QbS2szdFb7dupX9vmzO/Ntfeehj0WyYiKp/9/CWyWyCYMpA+EitgmPZDVOzX0aTJi5673ku2f47do3nkNYlVE1mKOxmKOJiXu4QubboZC/7o+6pWs/Ve56Z7EzQGXbwt4Fidg8Vc/rrL5HKGOgczwPgzL/9lUTKxP6hYg4MFnP+O3/i3ZE8vtW6lbQmkdYkvtW6lTXN2xgdy+bBv1xCLOSgvHDwhI6bIJyK7qrcwG8WXsfvFn2DLXNWTmbZnKijNWBOZafMDfrQ0BCXXXYZbreb3/72tyjK9FSwo374wx8SDAYn//X29n6GKxUEQRAEQRAEQRCEf7yTWiTuqGAwyJe+9CUCgQBvvvkmRUVFx3282WzGbJ59L5ggCIIgCIIgCIIg/Ks56TfoiUSCyy+/nNbWVl5++WUaGxtP9pIEQRCEv9OPTuH2etGMgdMK+tlXeT6RuJWBUJB7lPXI0kRq6kztq47a1riCIkcIjy3KcMjPWMIKgNOg8pOym0mpqckCdMCMKXd3VGxgfpafXPsArQ3LCYSGuOnASzzWuIIa7xiKrHFZ1cW81DXzGn7c9TDPLfomsYyBa/c+w73V63BbYsTTJi7+4PdTHmtRdB6oW8Oqg1unfP5HnQ9zb/U66mNW+kfy2D5cwFca96MYVAI9NgyyiiJrFNviRNNGRhJGXu0r4Wc9EynVBZYkF777R+6tXscl23/H/bVr+U7bI9xbvY7IDEXQPgvRjJHtXVXMK+inpqKLkeE8YmnTZFus2WxrXEGJM4jFmEKW+vhL1lX0RSbS0N1GdUpbr4+35zrWxvImlmb7eLf8izhtMVJpIz2+ido4z8y/gflFXRyoOJeXWmdPWnxy3o3s9dv4UefDbKpfzeff/wN3VGygzB6fVuyswh7nibk3cdOBj9ZjN2R4dsF1mI1jGJQM117y34SHsxkazqPMEcZjjfF6fzEAZkUjx5zi2r3PcF/tWkyKxrdbt/LM/Bs49+3/4oMLLibi86AYVIpnKXh41Kb61ciSxvKyThSDiifLj7NgDINtoh1fbDCHv763/LhzzDZvXdYwDmuckaCHUMrC9pF8KhxRFpd34LTHsPrT3Fm54bjfcx6sW0Otx4fX3oGqyZTZV9IXtWA1aOi6NFnc8HvtW7ijYgM3t8w817bGFaT1NH9ZdhW94RR5RwoxbmtcgQ4UO0L4ElPbvmk6k+tzGlS+dWR7S73HR2nuCImkGWPSMmXMT8pu5vsdU78PWRSN73ds5qXTv8b+L55HfkkHC8veRY2bGKmqY98BEwuKemkZLuTOyg1UOiI8Oe9GIEJ72M5vFl5HJG3EFwnx/7vkJezFY0T7c3hu0TdPeIuAIJwMt5VNL+z4YN0aVF3l6j3PnPA8Pym7ecrP95mKQZ5qTuoNuqqqXHvttbz77rs8//zznHnmmSdzOYIgCIIgCIIgCIJw0ki6rusn68lvueUW/u///b9cfvnlXHPNNdO+fsMNN5zQPKFQCLfbDSjAzEVABEEQhH++zQ2r6I1Z+HnPQ9xRseGUaeXzwpJrqC3qw+EKY7ImMZhTjPUV0DVUOC36/HEP1q1hXs4wJkOGvGwfNmcEkz1OJmEinTQxOpLLaMhNWyCLb7VunXWeB+rWcFpBPwZFxWjIkFcwgtUbQk0akRWNTNJEy6Fazn37v2Ycf1/tWjKaxK1HIqq3lTVhN2icWdjP/rH8KYW4Hqxbg82QIZAyTYkE/2Hxtai6TH1BP9Xn7AQgE7ES7s+ju7uUaNKCImtc+O4fua2siQrHRJG1rogVj0ml1h3AbkryzlDhZOTy7qr1GCR9cl2fpaMRltvKmvCYVM4p7cJhi7Gzu5LWkANJgo3dM0dLtjWuwGVKUps/iMsdwmKPoZjShMe8pFImDvSW0x1x0ugdJ54xcsWuZ2ddx68WXE9jQT/ebD8WewyDLUE6asXiDaGmjAQHc/n9rqVTzsVRz8y/AaOsTUaEnl1wHY2F/ciKRihi5/3BkinZGUf9smbdlPl+t+gbzC3txmBQsTqi2PN8xEa9HGytxWZKkswYCSesDMVtrGveNpn9sLlhFUlNpsAa53DISY0rjMuU4JLtv/vEKOuT827k/Ln7cBeNYs7zgyaR8jvR0gai4x76+4p4f6CUbx/nfTGT5xZ9E4Osss/n5cddD0/5XvLamV/BaY3xTm8F35nhuBy1sbyJM/JGKcsdwWaNYbYlMFqT6KpMOmlicKCAXYMlrGvexjPzb+C6fU8ddz11+QPY7VFsrii6JhEJOQGwOaKEAy7+drh+SjHJe6vXoUjgMKaRYbKI4L3V6yixx7AoGa7Y9SwP1q2Z9n3jtrKmKdfuE3NvIssSp7pgAEnSsTuj2Dwhoj437zTPJZA0Y1ZUVh18nNvKmpjnDTEUt5JUZW5t38LG8iaKrEkumnMAV64PozVB18FaXuuswZcyHDd7SBBOpo3lTRgkfcrvEr+oXkdCk0/pjL1PRwdUgsEgLtdEgdeTGkHfvXs3AC+88AIvvPDCtK+f6A26IAiCIAiCIAiCIPyrO6kR9H8UEUEXBEE4NdxXuxZF0j91xOyf6b7atZNR0M6wi0hawapoXH/uWySiVnoHCmeNWn/cE3NvwqxkKHYFKC0axOYJY7LHScfM9LZXsPjlV9ncsGpKJO2oYyOeW+espD9uIduUZmlhH7Kk0zaWRyhtmrUl1UP1qzHJGmuat3Fn5QYWZvkoyx4lErfSFchmOG6dnP/uqvUA/KBj8+Q+2IfqV+Mypqj0jlOYN4LRnMLqjhAe8xIKuujz5TASszMYt2A3qHyn7RGemX8DdmOKr+78NR9ccDHJtJFdI4VYFJWRhOmUyZA46vkl15BrD9Puz+HG/U9OizLP5Oj5uqNiA/XuMKfVtCDJOvqRtlzjfi8vHK7BouiTe5Y/7t7qdZMZBI81riDfFqWqYID+sVzGYnY85gRjcRvX7/8oSntX5QZ+2DlRVyCtyYwmLHy7dSt/Pu3rjCeslDiDdAe9jCTMfL9j86zrPxoJb7n8TDKqgRcOzeG6ZR8gyTq9fUXEUuYp+/HvqVo/Zb6n591AIGVGlnQUSZ88r5+0v/voXF+oOky/P5s9vizOKezntf4SKuxxihyhT8xO+Xv8ZuF1lHrGGQ67+erOXx/3sbdXNGGSNX5w5PVublhFuTNISe4obk8ASdbp6SnhvYFSVJ1Zs0DurNyAy5ghkjagSDpLckcozhnFYMwQjdoIRB28PVg04/H6RfU6jLKO1ZAh25xA1WVcpgT+pJVv7H36E1/v0etk65yVVLj9mJQMkZQFmzGJpssMhF2kNAWTrHL9/qcmz9sDdWuwKSr5tuhk28vnl1yDDIwnrMzNH6A/kHXczBBBED4L0yPop0ybNUEQBEEQBEEQBEH430xE0AVBEIT/sdvKmpjjCRNNGyf3Wp4qHqhbM2NEv+XyM3F6QmRSRtq6y+kKek9o7XdXrecHHZt5oG4Nlc4Qo3Ebc3KHqKrpJBpw4g94eL6tdsaq33dVbiCtS1OqyD7WuIJVBx8/7j7Y18+6HIcljqbJnP76S7x25lfI8/o5NFCCzZii0ONj32AJN+5/Evhoz6zLHSKVMtE3nE9lSR9Z5QOYvGGSPhdtu+byTm8FC3KHGY44KXYFyMvykUqZ2NNXxvX7n2LLnJXMzRkGQJY1NE3mtb4ynAaVEnuEK3f/CpiIVH7WlXFvr2ii0hGdfM0wca7Pr2jH4w0QDjsZHM/+xGruH7d1zsop18E7534Jlz1KKGpn13DRjHUGts5ZCUzsM/5lzTosiorLmGI0acFpyExmPRwbYX2scQW1WaOMxxwMRh1cWNeMzRFjeDiXXYMlLMwfQNVk3hsoJZxR+GnXJrbOWUlak2lqeZRN9auJZBRK7VHKPD5KS/oxO+JE/S4OdlbhsUZx2mJkVAWXM4LBmCaZMNM/moeqyeR7/CTTRl7uqiKlyRRaE6z72PU/23vnqAfr1hBXZewGlUJbFEnS+erOX7OxvInF2T4ymvIPrxT+QN0a5mWPYjakOezL5aYDTxx3faNJ4+Re7qPX6dH32uaGVZMV6P0hJwfH8qdlv9xXu5Z6j5942sgun5ef9zzEvdXrsCracWtOHHu+/7T0amqL+gBoGyihJeChMcuHzZjkncHiycyMrXNWYlJUPKYkqi4RyxixG1NU5oxgt8foG87j1b4S7AaNlCYxxxOisbgHlzeIbzSb3vFczIY0w1EnZkWlzDtGRlWIpcx0HMnGOLqmE8kuEQThsyAi6IIgCIIgCIIgCIJwShI36IIgCIIgCIIgCIJwChA36IIg/MM9ULeGLUdSPoX/HX7Ws4nOsIORhBmYKIx0V+WGk7yqCeNJI3dUbODOj62n/oV3+fDAXP5r9xKGo05CaeO0sffVruX+2rWTHz9Yt2ay4NS3W7eyz5fFqoOPs3e4CN9wDjZXBJs1jvKxn653VGzgVwuup8YVnpIK/kDdGtKazLbGFbOmt29uWEUqY6B1tIBk2kj/DfOYN/8gLneIhqI+Fs89iMsZRpF0NpY3TbzmhGWieFxfMcGQk76wm96BQgK9BSR9Lsx5fhy2GOeWd6DrEmUeH6GEDV2X0DQZg6zxxNybKHGEMRnTBOJ2BoJeJEnnC+WdzM8Z4crdv+K+I8fms05vB1A1uHH/k9xTtZ67KjdwW1kTZlmjYzSfUNCFxZzEqGROeL7bKyaO3ZrmbVOu3cO+XEaDHlKqYcaU5nur1xHNGEioCgAxVUYCUprCLW2PTKbLOw3qlHGrDj5OMmMk3xnkjNIukkkz/QMFpDMGzqtpwWjIMBZx8d3DW/hp1ya2HEm9V2SNd879ErXecS4o76TM48NqTuIuG8JeMIbZmsBjnUg3bx0uYjTkxmyNY7IkSaVMDEZc+BM2Puyt4M2eShZk+TgtZ4wSR5iXTv8aW+asnLzmP6nY47dat+I1pbm55VEMsoaqydxdtR5dB6OsUZ41ymONK074HJyIaEbhwnf/yDsDJcd93G1lTXyrdeuMbfZaQw4A1h96jJbhIkYDHmIp84zFHUNpAy0BL1fu/hUyOpsbVmGSdVT9+FsqU6rEfbVrubtqPdG0ke7hAtoGSjDKKpXOMA5TAh0J9ZiNpmuat3Hj/idpDngwKSodYTvl2aP4Ik4SSTOFOWMsyx3HpGjkmNNI6Az5shnoL+TtrmoMskpvyENH2I6mS4xFXOwYKqY/7KYha5Qia2LyuUR6uyCcusQNuiAIgiAIgiAIgiCcAk5qH3RBEP793FbWREZTWde6lY3lTTNGL4R/T8e2oPreLO2KTgZZghxzmqZj2pdtql9NU8uj7Pd78ZjSs0avb2l7hLsqN0xGV7/VuomN5U3o+kTWwNHol6pLdAwV4Q25SGUM0yLKP+6aaHs0ljBOtrm6rawJi5LALGsEUiZ+Ub1uxuN2NKr39LwbkCSd7Qfm0hVxMi9rjMLscYb6C3ilo4ZFuSMMxqwA2A1pzvzbX3n9rMspLe9lzpm7MHgiSEYVPSOTGMymZaCEUu84VlOSRNpEWpM50FvOlbt/xS+q16HqErm2KPuHilF1iUDKyOGQi7OK+shxBXl+yTWMxDM8u+A6fEnLrO3h/lk0JP609GrKc1owm5P4gm56A9l0hl2oukwoZSKSOfFfc0yyxsbyJgosKZpaPrqWHcYUA2EX2ixFaG9t38JD9asZTpjYWN6E3ZDBZUoRzximFM/TPjZuW+MKLnrv8Smf+0nZzeRZUsxLm8hxBXFZYsBEJkd/zMBtZU3YlAgjERdVucOYTCmstjiJuIWEz43BlObpt8+m0hkho0kcDLi4oDjKyHAeoyE3l2z/HXdXrSejSZhkne93bJ78Pv3isivpCHppanl0StbIJwmkJjJPVF1CkTVSmozLqGI2pNk5UEpS+/tjQX9YfC1lWWOYjGnSGQOhmI3uYGJKW7vZfLxI491V61GP1EU+9udSKGVC1V1EZ8igAbDI2mQ0y2NSj/w3RXCWx8/2/Md6dsF19AS99MZsM7Yq/H7HZt4qupSr5/cjSTp5BSPY83wkA06iXVXYFJUcSxyjrDEWczASdTAne4SxmJOEqkyZ856q9ZOZENuOZDPcUbHhlGuRKAjCR0QEXRAEQRAEQRAEQRBOAaLNmiAIgvAPc0fFBqqcUYIp02ceUZ3NxvImqp1RSpxBsp0hZFkjEreSURUSaRM7R/P4fsfm47YK21S/mrGkEUWCbHOKtCZP2Y98tFXarxZcTyhlmnEvK0xEBPf6vfy0axO/rFlHoS2GxMSP4Wv3PvOJr+WBujWkVJnvtW/hlzXruLjuEBZzknjcwuHhQpzmBGZDmv6Qh86IgyU5o1iNKUoKhjDbElicUYyuKDveOJPayk4yaSPJhBmTKUU0aufAQAlGWSOhGjDKGlnWKLousWM0D7Osk9QkTssdYekZH2LN95EY8dK6p5EXOqppcIW5fv9T3FbWdNzo4T/KxvImzikYoix/CEnWsVgTGIxpQn43vSP5x22vdntFE2W2OCsPTkSw765aj92gIgOJI8f3zsoNFFiSrGnext1V6ym1xRiMW6dFbo9mRDw570Zu3P8kW+esJMcaI5Iy4UuZJ6OXmxtWTbsubq9ootQWJ6EqhNMGvn+kvsGBS8/FYk7iD7poGSnApExkWaxr3sbmhlXMzxvEaMiQk+0jq3wAoycCwNCuevqH89F1iSx3iO1dVdRlj6BqMpGUhS9tf44tc1YynjSxLG+YeNrEoYAHs6JR4w7QWNmBmlGIJ6yM+L1c+O4fP/E8PFi3BouiMidnGFWTCcTthNMm8mwRLMYUY1EnCdXAN/Y+faKnli1zVlLrHcdjjxCO2fDF7aiajMucoLxwkHDYQftYPgcCruNmaf2yZh2BlMLPejZxf+1aTLJGQpO5pe2Rycf8onodNoNKvjVGX9TBd4752sc91riCXGuMaNpIZ8QxWY8COG7G2LHviccaV3B6RQcmU4pgyMnOgVLWH3qMOyo2YDdo5Fvj2Axpdvu8fLGik7KyPqzeELIykYORjpnpaqvCbEwzEvQwEHGh6xIJVSHbkmA8YQEmsmhiqoF4xsB32h7hibk3EUwbJ6/Ho9+zBEE42USbNUEQBEEQBEEQBEE4JYkbdEEQBOEf4vaKJoptCeIZA9GMcrKXM2lj9yYq3D5qKzvJKxrGm+OjvLyXirJeNF1ifvY4T8678RMrkVc5o7iMGbItCYrtExHLo3vTpSP/3xOxzxo9f7BuDZoucXF550QUNHuUqqxRjLKG05TiL8uu4p1zv8RtZU1Txh1bff7brVsn96l/9/AWojErqiqTzhhwW+IMRFyEk1bKPD7meAJc/MHvCcTtVD73IW/vXUg6biYTsXHml1/BZE0yMJRPTskQWWVDvNxeR3PQxZW7f8V1+54imjbitUfwJ2y4jRkSqswPOjYzGHXi78sHg4qtepCcLB9fq20lmpnYkzvbXu1/tI3dm9g9lkvdH9/j93sX8qedS2nvqMBkSmExpoCJCuszcRkzKLLGk/Nu5NkF15FrTlFgjVNoi1JgjXN/7Vp+1Pkw0YyB+2vXUmaPYTVkMCsf30kO4YzCO+d+CRmdOys3oAFFbj+5tiiNWWNsnbOSB+rWUOEKTBv7065NaLqE25SajJ7fX7uWeMJMLGbFF3Fy/f6nGIlb8SdN3F21HrcpycHRAlpGCmjtLufR57/MgVeXM3agmrzGDuob2qirO4zJlKLUFaAnmMWBsXy+tP05/rD4WlKqwher28h2hrAaU2R0iW+3bmU46qCrrxhvyTDebB8dgSxeP+vyiWNd3jRt7TCxN77eO06F289IxEVx/jChlInxpJnKogEqynpJawpWQ/pTnduUqjAUcfGntjo+GCpi51gWV+7+Ff6Eje3ttZjNKUrcPjym2av0P7fomyQ1iZ/1bGJzwyqcxjSVrgBZphR3VGyYfF1pXcJhTCNLOhZFnTbP3VXreXbBdWydsxKrIYOqSxS7AtS5QgD8+bSvA7DAG5hxHXdXrafQmpr8eNXBx0mljMRjVoIxO6YjHRMcRhWDPHF9hVJm8i0plr/xIrub59DfWklfSxVjXcV0tlZTWDREQXk/CxfuQ0bnpgNPoEg6JlmlyB4h25JAQ0IGxpMTdRjsxjQXVrXxyvKvsv3CL+AwfrpzIginio//jPx3JG7QBUEQBEEQBEEQBOEUIKq4C4LwT/FZ7UMVTh0noxf2ibi3eh1O22FaOqrYO5aHIul8p+0R/rD4WuaWd5FKmZBHc2cd/8ZZX6a+7l3sBWMYHHG0uJnIQA5v2i4jnBzjAdMa7MY4xVaJCrcfOmaeJ61LnLlgL+6qfpZ4oqgRC8lRD/WyjpY2IJtThAdzqPHnTBn3oyPV8e+uWk+lI0Jf1D65RzqcGMKgqITiNj4cycdtSiOjE05YSRypYH75zl+zdc5Krt6zjT8ZrkaWdBY3HsRdMUC1JuGc20Oq30uuOcEtbU9NVsgeT5owGjKcXt/MwFA+b/SVARO9x3fkfR7L/mpkRSOrdAiA7PGJHssyn11pG4M88VxHo88P6GuIJi0UZ4/x4rIrGYvHZxynahJn1zWTVTqEyR2Z/Hx81EtoJIv2vtIj2RETmQOFjhAHx3OJpKf/2vTznof4as3nufSCN7FVDCKZVNSAncSIl0zKyPJcP5mEiaHWiinjjtY8WNO8jQfr1nBv9TrCaQWjnMLliKAoGq5EjD8svpYrdk3sG76naj3+pIWmlkd5oG4NobQJi6xhtSSJRWxEd82h+rL3kHLtZI23UdCey/tvnIkiTRyn0YSVGrcfsymFwZhhMJBFtnkiutsZsbLy4J8YqJ5L9sI2rp/XTrSrkBfTV3LJ9pnf243eceY3NuMsH8JYHEZymikztIBBgWCE5GEnNSO59PuzT/ykwpT6Dsf+LOkMO1B1iaKAB5c1Rp07MOscbnOcm87ey7euyUVW/oimymQiNhIhO7Gwg4vKvkg4MUx70IvTmOKKXc+yqX71tHl+0LGZDy64mOo5bZg9YQyeCFrcTHQgl53ZF+F09jK2tIqew6NTxv2k7GayzWkKrRFCaRMwkQ3zo86HGQl6yHUFUTUZqyFNW8g5rar6T8pu5p6q9fRHU2TZwwAU1XRRdukOKM5BSiTItGSoax3mNwuv4+o929gyZyVGSUeWdCo94wxF3PyseRsAiYxCftkAVafvJdybT48/h/tq12KU9CnHWxBOdT/r2cTtFU14jJlZa0b8pOxmft7z0AnNdyp2HBIRdEEQBEEQBEEQBEE4BYgbdEEQBEEQBEEQBEE4BYgUd0EQ/ilEertwKrijYgOl9jBtI4WkVJlwRsF2pBBUc8DDFbveB2DrnJWzzqHpMromkfQ7QZMxuiN45new/PQ2MMAXADKQGXXQ/cG8Wbd3+JMG0kkTscFsGMzGXjyGtXwY2aKCEbSAEX9PITfuf3La2Hur11HuiBBImUhpE39bPyNvmIIsH7G4haGIixJ7jM6wg3XNv5sy9hfV61jTvIXbypr48oebeKxxBamEGWNhiOwloI/qpMN28u0Rnph7E76Uzqb61SQ0nQFfNomUicO+XJblDfMLaR3fa9+CL+Lkr29W8+Ouh7m7aj3LCwYZiVsBsBumF1L7Z/hlzTpuaZva8uzbrVt5qH41dnMCty1Kd9g949jvtW/hc5WfQxlUcekSJncE2ahirxjEUd1PkbyXczUJZB09aSTen4vnUDVvdlfNOJ+mycjGDGrIhmTKoGRFcRRHkQwSekyDATexI8fnqGO3hDiNaWTJQKk9SpnHRyplYjzkoiOQzaqDj3N7RRMuY4YCa5T+qG3ytR61idU0tTxK9zWLCe0tJzSSRcHiFiRFRdMlImkjd1RsIJ5RGY45MIyr9IY8rDz4OM8uuI4XllxDV2SiaFgk6ETaW4PBMpH6rsizn09Vl7FmBzGVR8BqhmQa3ZdESyjoaQOBzmLSGQOdYdesc3ySY99LR7cyPGhcgyLpk++FmWi6RDppIhV0YLTHkY0ZzDkBrMWjZJnTlAB63MSZQQfxcTdv2i7jg6GZi1saFBVrrh9DThhkMOSlMdUl8V7QB8k06V47oX1zp4z5ec9D3F21nmv3PsN9tWsBOK+oj31152M0DtEyUMKOsewZ03C3zllJNJOeTN+9Pd7EPE+A6piFzLCN2E47liwVNWljx0ApC/MGASbb8NVnjaLqMoOxiWtloihelL6OMvLjZux5Pi448z0i4x4Ods58TQvCqeho6vonbak70fR24JRLbwcRQRcEQRAEQRAEQRCEU4KIoAuCIAj/tn7c9TBPzrsRXZcYiFum/NX9h50Ps7G8CYuisaZ586xz+OJ2Cp84yK8WXI9JVlFkjZG4DYuisrikm5L6DixFYyiuGDnFQ5w3lstdygZ+2PlR0adfVK8j1xKjp7+IWGclsqST3qkQSppJqgYG4hY+X9GBpsncX7t2WuGbW4+0Vts6ZyU/7HyY3y36BsGkxEhvOWZFxaJkGE1YSWpTW5w91riCVQe38MuadXz38MRrX3XwcbbnfwHTuw2MDuVht8dQVZnPv/8HNjes4pa2R7izcgNpTWL7aC7zVQMlziAXvvtH7jrS8m0o6sR0pECbRdG44J0XJqOER6Oc/2wxVWbLnJWsO1IECyYKrw3EJYZ6yjDK+rTCW8d6o7sK90Aa66GJVl0WJcNQzI7NkGF+YR/F5X0Y7XEMtgSSouL1BsgZTkybZ2N5E5reyc73lmI3JwgnrERSZsbiNtY0b+PlM67AYYkTSVinjT2qN2bFZcyg6jKhuI03+sootcVYdfBxNjesIpDSJiMqLtP09liBtIGN5U109A6wf/cSLq4/SPNrZ2A2p9g9louqS6Q1afK62lS/Gn/KyMbyJtpCOjnmNEW2GADtg8V86Y/v8cKSa9jj87IkZ3zWdX9p+3M0553NyDun4Y/bybGHycvyMTyezSt9pVw7bx9v9paT1v/+1nv3VK2fvKYeql/NzS2PnlBRM3/CSvmvd/H0vBsIp00U2yP0RyeKzAFcUNNKXskgZlcEgzlFljtE9scKNB6Vyhg49PZSPhwopdwZxJ+wouoy84t6cXuCSPL0woi3VzTxg45N3FO1nixTgrfOuRS3PYLRmOa9jtqJIowzjNtY3sSa5qkRvZ92beKh+tV0Hq7k7dcv4KsLdtHZUo0iazRkjfFybzl3V63HY8yw/tCj3F+7lpQmcWv7Fp6YexNDcR1fykz3eC7v9FTyrdatbCxv4vyiATLqqdMSUxA+yaeJjP8rExF0QRAEQRAEQRAEQTgFiAi6IAiC8G/txv1P8vS8GyYjZz8puxmbovHDzoeRpYk2SrO5t3odV+2eiF73ROxYFI20JvG9IxFt9sNLvq9hNU7s1y3KG+Wi955nY3nTlHmMsk6ONcb7g8V8r33LZFRP1SUCaQM/6nyYBwxrMB5pAXesY1vA9EQtbKpfjcvsI9sZot+fTTxj5Krdz3J7xfRWMasOPg7Adw9PrPe2siasBo3dQyk6xnO5du8zbKpfTSSj8NyiSq7a/diRqPvD3FGxgWJbgu6wi7yMkR2f+zztY2F+teB6cm1Bkkf2/6bUif/eMku7m38WlzFDuTPIjs99noyqEE1aCCSChFMmVh553cdzdL33/v/bu/P4KKt78eOf2Zckk52EJStLwo6URSw/QKVqVagFhMoatgBVvFq1tVctlsq19lrp9coiyKZoVXC7tlbr2ioohE1AgSCQQMi+TpbZ5/n9McmYmEkIhJAxft+v17xe5nme88yZOfMdOXPO+Z7eizFpfOusK5xaHj6xmb84F5FcFovdo8XjVWHSujlRFU68qfkIuloF7+akoCgQoffQK6SW83Vmat2+kckvS2OJMjj9bRHIQ2eeZVXKEsJ1Ls5YI7g6voDCGgsvDprNrKNbmly7qn4WQ2NalcKjuevZGjKPeJONf54YgNurJkTrppvRQcbX23i6fobD+rQF6NReepptJIZXMGH32+y7biIl1eG8NuwOHG7fe7GvNAq9RuHmfTtbrPcfUzN56xj0NNuYc/QFXhkyE7dXw5HSbjyau55HcwF2tdwILfhTaibdzTYSLJXERX7F1KGjcLm0aLRf8XHUZPKqwwPmamhsxuGXAJh1dDur+yzmq8oIAMwaL3effI4nPYvplZdImd1IN5MNewsjyU/2XszV/9rIhvT5ZB7fwsb+GVQ5db5ZLUd8s2NCtB4qnLom5Rpm66hVCnO/ep4N6fOJrrETZaplQLcCCqwRVDmMzZ6vpfWwXkXFp+eSiDU6eO+rISw7sZl1aQtQAR4vqNWw9MRmHk9ZQplDxdCoSgBMWjcmrYcal5Zal84/++DR3PWQ2+pbKIToJDKCLoQQQgghhBBCBAHpoAshhOjSNqTPJz60ml8MO0jxwn4s/8mHzBq1hw9G38bvctbzeMoSXh4yK2DZhrXf4FtbXevWUO1uOtJ2vsbCmcpoDhZ3Z883/dg6YF6zUbC7Tz7Hzft2oq8fqT1VE4rdq8ZaP3oOUOXUsvTE5mZ16GFy8OKg2QBEGtwsPbGZL0tjcbm1JMUU461fxtqQnb7x625YM/5I4jIA+lhq6R1W02TddqFdz/2nNuLyqtkyYB5lDn39tTXM/3obY3rlMm7oIXr2ymf88IP85Me7ibVUEWu0+d+XzuD0qAkz2oiKqKRXz3wG9T/ONUO+JC2miP/tt5AViUsvfBN8bVxo13Nn9ib/mvV7Tj5Hsc1Moc3ErKPbUVAxPKYk4Ch4pN5FlN6NRu2bqVBmN3LPyedw1ecD0KkVXC1kG39p8GxeGjybx5KX8NCZZ/EqKobG5RNqtBEfasWjNC/30Jmm6+r/lJpJUmgtABlfbyPcYGN59ibu/WYjmce3EG20sS5tgX9mRoFNj1Hjpn+3Avok51Dz2xiS+uQwNP04gxJyiLdUAr7s6U5P62vHHzy9gQdPb6DU7hsJzq8LodJmxu1V89bw6Xw8ZnKr5VsyPLaYq/tkk5pwDr3ehcejxmiyYzA4GJB+gvTYQl4cNJsN6fPbdD+XV+Wva6XLN3n0/lMbOVsTwp3Zm9Br3EQbbZyta54nwO5R88KgOWQe38LK5KVYG0bP6913aiM2j6ZJzgnwrUF/Z8Q0UsOqAcg8vgWvoiLMVIdKpfCzA6+2OGq/qX8GWwbMY0XiUh5LXsLbw6ejU3u579RGVCqFZfXfE8tObKZ/dDG/P7ueX5/ewJ9SM/EC43oUUGo3se+6iZTZjaRFlDM0uoxIo73Z7B4hRPCRDroQQgghhBBCCBEEpIMuhBBCCCGEEEIEAUkSJ4QQokszad0kxhdiDquhvCiGOpuJ0JBahg49SsXIJNz2jzif26t+K7Jvp66uTF5Kd5Mdq1PnTwpn96j4/dmm09fr3Fo8igqrS0O5M7TFJE8Ay+sTNLm8Kioc2ib3cnjVPJbsm5LeeHuwzONbOHD99RyMv44Txb5p5fed2siblhkcrYhgcr8THIq7lkPnnf4yf0rNJCGsjCFxNWwyZmDSVrM+bQED43JRqRQO/eRa9uf5prI31LeXpQK3VwOVUQCEG+wcnHgdqUOPYkoqRh2jA0soVFRhiKim8MPI+oRy21jdZzGO+mnEV4pe4+VIcXfG/Ptd/tJ3EfecfI5/jvo5A/udpEd8EUXFsfSPmMUvDr/YrOxjyUta3YINfEm53PXT1I9UhNM3rIYViUubtf/y7E1s7J9Bz5BaVvdZzNITvs+KUr/04O6TzzWbbv+n1EwsOjdDE06i07roWx3GwzlwqsaMXhODQeMhvUceCXFFvKa7g6mH/grAmn4L6RXqmzJdVBeCSqXg9rpxedVkT76aquowKmq/3Y5sdZ/FJMdm07dHHv+KvhWr3UyY8TyKoiI2pozIlHz0iRUYR7tBrSbqqzO4P/2Rv54mrZfVfRZT6dQ0e92N1bp94z33frORlwbP5t5vNvLJNZMoqwtt9T1uSZXDRG5Bdw6UdPMvodjUP4OFx7byj5FT+dGQI/ToWcD5vB5wvHn5R5OWkhRio8hu4MHTG5osw3A12oqw4fjBsijGdCsmMsAWdr3Mdoz1y0cab9PYWOPvDfAllgMPUSHVhBjt/HPUz7lh7xvUunQUVEZh0LrIuX04tbXfsFU9j7w6Y5PPY/eQGqqdenqHKYQb7JyqtjAoqpR/jvo5R8qM/s/vhvT5ONyVnJ0xDJdTT0l5LoqiIq8qimijnZS0UwyZ8jF4wVUUQfHx5FaT/gkhgoOMoAshhBBCCCGEEEFARtCFEEJ0aZVOPaXlkRzJSaHUbuJcnZHEEBspRZV0jy4jJKQWt0eDSqU0KadXe9GpFFLCqnkkcRl/OLsu4CiiTu3lzuO+rbD+Ur+VVSAb+2dQZDPwcM6z/OHsumbnzRovKpXSbBR6Tb+FREUdRK32ElkV4T/+jdXCDclnsIRbcdgNGLVu/zmjxkthbSg51nBG9DiHyeAgKrYMp92A2VKD26kjsiQO8G0TlRhayzflOpLCK0gIq2JVyhI83nJyy2KJOldB3rE+uFxaLGHV9BxzBE2InTCjjaLSWF4eMou82gu3w+XWMBsBvt0ybW9xN8KMNnrEFxHXrQSHSxew7HdHz7/bHisSl6LgpKo+odijuetZmdx89Bzg6b6L0KlclDsMVLk0rExeyu9y1mPSev3XhOmaJvAL0XpQAI3aQ2S3MnQ6V/12er56fD7uJrQaN+WV4eTXmZuUdXs1mHVOhsTlc74qisTIUjxeNZVWC927F6Ep9nK4LAbwjezu7v5TenYvpE9EDsWF3ThT2g2nV0PesYGMduhRDqvolpJHSGoB6jCF0NAa1qctYOmJzRjUin90vDWN30+nR8O6tAWcs7pIiSi7YNmWFNWGNhn5PlfnS0SXYw0n9HhfwkNqqbY3T+oGkGC2o1EpGNRKs3ON27ohrhu2HHs8wBZ2LW3Z19osDI0KLDoXoSYblnArKpXCyuSlZHy9nkcSlzE17ThejxqzqQ51/ffOmn4L/Qkoi+sc/CghB4PBgd1uJL3nORRFhU7npqQuhFlHtwO+2TUfjL6N8wXxWG1mDpbF8ODpDbw8ZBYp0cXYq0MwnI/E2K8CfUglUfZ8Xhw0m/N1ZlyKqlnCQSFEcJARdCGEEEIIIYQQIghIB10IIUSXtjx7E++fTWbKoZfJPL6FP5xdR4VDz8Q9bzLwnU9J3nGAT8+m+EdhG/i2ZNIx5dDLhGibjoA2rBV/uu8i9OpvR0q/qr2D1QAAHv5JREFUe4/GFEVFH0tNs+Mrk33rk3uabcQZHc3O35m9iWprGC6nvsm2W/ef2ojTrSX3fA/2nenN2ZoQ/7lat4ZzdSaMGg8er5qQkFrUGi+W2HIUrwqXQ4/VaWBT/wwGR5di92jI+HobJ8pjmLjnTVxeFaEGOz0jytm2dzR1Nt/opdOpx1lmQaXxkj7kGLcNOErPsErSIyqv6Ppz8K3j/q6Hc55l9/kE/v31IP55ZCjnrJEtlv9jo/KN14ivSFxKhN6DXu0lSv/trISW1h9rVArRRhuKoiLa4CLe6GB1n8Xo6kdGXxt2BwMiK5qUuTN7E8tObMZaE4qtOgSnU98sd0FuUTwjP34fRfl2zXS1W0N2lQWPV43Xq2Z472wSk8+S2ucMyb1zUOrXVyeE1PLioNm8MmQm+4t6cPibvryzfwRvnexX/3l2cao6hHePDaKwLJqP/j2W8kN98ZSHEJWcj1Hj4bVhd6BRKTyc82yr688fS17CC4PmsLrPYgAKbEYqnTpSIsqa1L2t/piaydRDf/WPEjdoeH+WndjM/9v1d4b88xP2FsUHvIfVpcPlVTdbGw6+eGuI37BGcb0yeWmzrdLA9zlb02+h/+8ViUt5PGUJerXCo0lLWd1nMW9eNaNJmXu/2cjCY1spqYrAbjPicmv9n58/nF2H26Mht6AHKa/tp8zhm1VzZ/YmHjy9AZtbjV7joazagkqt0LN3Lt1S8ujeNxe90UFObQgvD5nFJ9dM4p0R0yioDeNkWTe+qYr0x+AvDr/IieJ4em4/yrnDaZzceTW2Y7Fo9C6iTDZ+fXoDFp2bVSlLWp31I4ToHNJBF0IIIYQQQgghgoBKUZTmC3S+Z6xWK+Hh4YAGuPhfa4UQQnRdKxKXolLRanb11rw1fDoDEnOxRFaiNTrxOHW4nTo8bi0etwaXS0dJWRROj5YJu99u8T6b+mfw//oeJyK23H9M8apQ6zx4XRpqKsP55PgAFh7b2qzsK0Nm0j8+n6raEI6WxrHsxGZeGTKTSKMNs97BkZJ4VMDSE5sDPvfTfRdxd/3o/nsjp9ArtoTI6HLUGi/W8nD2nenDzCNNRyz/9qPbcXvV3HbwFV4ZMpMZh1/if/st5OqeZ4mKqMIcVoNW76KyNIrT53tyY9brF//mtsOfey9Grfo2g7ZvDXfb2nh92gKGd88jJqocY4jNn3/A5dDjcupxOnXY7EYcLh2KoqKwOpwph15u8X5vDZ9O/15nCQ2vRlFUeFxatHoXGq0bp91ISXEMn51N8bdBg5cGzya9WwFOt5aPzyZj1HiJ1DvpZq6lqC6EKpeuWbb/xh5PWcJvzzzL4ylL6GZ08KNeufRIPI9KpVBWGEtuUXyzdvnffguJ0DsJ0bo4URXuHzX+fNxNpPY+g8FSi9elpbo0knP53fmioCf3n2o+Eg3w+rBfMCgxh+ieRWjqM6BrzXY8Th3W/G58/U3vy/a5CJRBvzVbB8xjaI9zREZWojM48Xo0uJ1a1BovDruB2joztXYjlbYQSmxm5rew1hzg7eHTGZh8GoPZDvjiVqVW8Ho0OO0GSsojufpf7wWsw5Duedider4s7k643olZ60Kv8RBhquVcVSQzDr/U4vM2tG+Dk7eNJiYxHwBrYQwlpdEcLOjF4gDfGQ3r68HXtjFRFYRaqjFGVFNbGkFFWRRVtSGcLI9t9bULITqaAnioqqrCYrEAMoIuhBBCCCGEEEIEBcniLoQQokvzouIP9dmxG/bLhraNuK7usxiNqpLT53uiyu+BxVRHpMWKwehAZ3Ci0boxmOxYIiuxVkTw3sgpFNSGBcz8rFYplFeFcywvkbiwKsJDa9Dqvl3jXFYREXD0HOBYlYVuITXER5UxxlxHdtrV1NkKcLp07D6fiFnjIbM+k3wgd598jlUpS4jWu7gxazOfjb2ZsLBqPG4N5VXhzUbPAU5bw0ms33P760rfr/q1bg0jP36fNf0WkmKp4uZ9O3lp8GwK6gJn0+5IOrWCRqX4R/qtzjr+mJrpX/t9XwujvuCbafCCbg6ny2OJNdcQZrRh1DsxGHw5ADQaD2FhNUSoPTgcBhzuwNngATakzyfKVEx+aSz6ikjCzHXodC5UdQpqjZfKKguHC3s2Gz0HyK0JwenpRe/IUm7t59vQW1FUlFVbKLJHXXBdv6N+zbndo2Lhsa3sjvkpn+0fjsurJjGinLwaS7Myy7M3sbrPYs64Q5qsqz9RGseJ0jgsegdlDiN9I8sorLG0OHoOYNS68Xg0FOb28h+rsIah1XjQa93tGj1/YdAcdPX5HWJMtRi057g6bipVDmPAve2/SwG+KYnHWB6DVuMh3FiH2WjH61VjMtoJDa0lNLSWCHsNpspI/tx7ccDPzPq0BUSaCykqjUGj9mI22jGZ7KjVHrR6N3aHgcNFPQKO8Gd8vY1XtDNJCK9gQp8TqNVe6mwmbA4DH+clXjCL+m/PPNskW7ytzsS/Pr0GrdpLt7AqiqvDA46eA/68GY+nLOF0eS1nK6OIMddS5zSQEF0CgN2ll9FzIYKQjKALIYQQQgghhBBBQDroQgghhBBCCCFEEJAkcUIIIbq8t4dPJ85SSVhILSazLymYtcpCQUUUFXZTq4mawLf1U8N047/0XUSY1o0XKLUb/EmcXhg0B41KCThdHGDH0JmcrzNzz8nn+GNqJga1QndzHdnWsBa38Grs0aSljO5WQoSpFq3Gg9lkp6QygoPF8dz7zUY29c+g0qlrdWp3Q+KoTf0zOG8z8ruc9U0SyDXWWlKuhi3KrvTWao09P3AuqZGlRIdXode7UKt9U3prakLJLe3Grft3sCplyQWnEYMv4Zxe45tOXevWNHtdLw+ZRYXDwLIASfgaPht/TM2s32rNSbXLt4KwtbZo8GjSUqINvgRrvS1VGLQuqh1GDldE8ruc9axPW4Ddq251C78GWwbMY/7X29jYP4NSuwEg4NZhLdnUP4NCu4GHzjzb4ueiJU/3XYRJ6/ZPuV7TbyF3Zm9qc/nGnh84l34xRURarL7lAvXLFjRaD16vmprqUAoropi4582Lum/j6eKNl7i8MGgOlU49ywPUd3WfxU22a1uftoBKl5YwrYc7szc1+W4I5JHEZUTo3Ji1HpLCrESaayirDePW/TtYn7YAo8ZDsd3Ar9sQS88PnMvcr55nfdoClp7Y3CSOW7MhfT6Zx7c0SzonhAgGkiROCCGEEEIIIYQISjKCLoQQostb028h0UY7Jq0Lq9NApMFOfHgFADV2EzmVUQETu7WmraOzgfwpNRONSkGBVpNwXeh516UtoNypu6R6NB5NvJjnDFaPJS8hzuSgX2Qpeq2bSlsI31RFBBwVbUlrW/K15f1albIEi859Uc95OTVOgngp/tx7MXavus1t/WTvxXgVVcDR3wuNLLdmU/8MPIoqYOLD9WkLSI8uwebSc67a0mpyxLZamby0TbNYGjSeXXIx2/u113dnNfwpNZNfn95wwS3oLnY2hBDiSpIRdCGEEEIIIYQQIijJNmtCCCG6vEtdC9uSFYlLeejMpY+a2b1qvErgkdoLaTy6GWhNdFupLmLCWbCPngNtng3QmtZGIdty/4fOPMuTvRe3ux6XSlHaN4tQrbq4tm5t9kd7RoBa2m4QfFvkXW4XM3rum0nx7fVXavQcoM7T9F3VqX2TYFv73AL+NfxCiO+HLtFB/3aWvnwBCSGE6HgOr5P2/D/H7nXiVWjXPdrL3s7XIALrzPe1vc99OeveVT9fwdS+ba2LzdM120KIrsEXm41XnXeJNeh5eXkkJCR0djWEEEIIIYQQQoiLcu7cOXr16gV0kQ661+slPz+fsLAwVBczZ08EDavVSkJCAufOnfMnSBDfP9KOXYO0Y9chbdk1SDt2DdKOXYO0Y9cRDG2pKArV1dX06NEDtdq3jKVLTHFXq9X+XxzE95vFYpEvuy5A2rFrkHbsOqQtuwZpx65B2rFrkHbsOjq7LX27kX1LsrgLIYQQQgghhBBBQDroQgghhBBCCCFEEJAOuggKBoOBFStWYDAYOrsqoh2kHbsGaceuQ9qya5B27BqkHbsGaceuI1jbskskiRNCCCGEEEIIIb7vZARdCCGEEEIIIYQIAtJBF0IIIYQQQgghgoB00IUQQgghhBBCiCAgHXTRoRwOB7/5zW/o0aMHJpOJ0aNH8/7771+w3KOPPopKpWr2MBqNV6DW4rtqampYsWIFN910E1FRUahUKrZu3drm8pWVlWRmZhIbG0tISAjXXnstBw4c6LgKi4Da045bt24NGJMqlYrCwsKOrbhoIisri7vuuouBAwcSEhJCYmIi06dPJzs7u03lJR6DQ3vaUeIxeHz11VfcfvvtpKamYjabiYmJYdy4cbz99tttKi/xGBza044Sj8Ft1apVqFQqBg0a1Kbrz58/z/Tp04mIiMBisfCzn/2M06dPd3Atm9Je0WcTPzgZGRns3LmTe+65h759+7J161ZuvvlmPv74Y8aOHXvB8uvWrSM0NNT/t0aj6cjqihaUlpaycuVKEhMTGTp0KJ988kmby3q9Xm655Ra+/PJLHnjgAWJiYli7di0TJkxg//799O3bt+MqLppoTzs2WLlyJSkpKU2ORUREXJ4KijZ54okn2LVrF7fffjtDhgyhsLCQZ555huHDh/PFF1+0+o8Qicfg0Z52bCDx2Plyc3Oprq5m3rx59OjRg7q6Ol577TUmT57Ms88+S2ZmZotlJR6DR3vasYHEY/DJy8vjv/7rvwgJCWnT9TU1NVx77bVUVVXxn//5n+h0OlavXs348eM5dOgQ0dHRHVzjeooQHWTPnj0KoPz3f/+3/5jNZlN69+6tjBkzptWyK1asUAClpKSko6sp2sButysFBQWKoihKVlaWAihbtmxpU9lXXnlFAZQdO3b4jxUXFysRERHKHXfc0RHVFS1oTztu2bJFAZSsrKwOrKFoi127dikOh6PJsezsbMVgMCizZs1qtazEY/BoTztKPAY3t9utDB06VElLS2v1OonH4NbWdpR4DF4zZsxQrrvuOmX8+PHKwIEDL3j9E088oQDK3r17/ceOHTumaDQa5be//W1HVrUJmeIuOszOnTvRaDRNfnU0Go0sXLiQzz//nHPnzl3wHoqiYLVaUWQ3wE5lMBiIj4+/pLI7d+4kLi6OKVOm+I/FxsYyffp03nrrLRwOx+WqpriA9rRjY9XV1Xg8nstQI3EprrnmGvR6fZNjffv2ZeDAgRw7dqzVshKPwaM97diYxGPw0Wg0JCQkUFlZ2ep1Eo/Bra3t2JjEY/D497//zc6dO/nLX/7S5jI7d+5k5MiRjBw50n8sPT2d66+/nldffbUDahmYdNBFhzl48CD9+vXDYrE0OT5q1CgADh06dMF7pKamEh4eTlhYGLNnz6aoqKgjqio60MGDBxk+fDhqddOvm1GjRlFXV9fmdbMiOFx77bVYLBbMZjOTJ0/m5MmTnV0lge/HzKKiImJiYlq9TuIxuLW1HRtIPAaP2tpaSktLOXXqFKtXr+Yf//gH119/fatlJB6Dz6W0YwOJx+Dh8XhYvnw5ixYtYvDgwW0q4/V6OXz4MCNGjGh2btSoUZw6dYrq6urLXdWAZA266DAFBQV079692fGGY/n5+S2WjYyM5K677mLMmDEYDAY+/fRT1qxZw969e9m3b1+zTr8IXgUFBYwbN67Z8cafg7Z+eYrOYzabycjI8P8DZP/+/Tz11FNcc801HDhwgISEhM6u4g/aiy++yPnz51m5cmWr10k8Bre2tqPEY/C57777ePbZZwFQq9VMmTKFZ555ptUyEo/B51LaUeIx+Kxfv57c3Fw++OCDNpcpLy/H4XBcsO+SlpZ22erZEumgiw5js9kwGAzNjjdkYrfZbC2W/Y//+I8mf0+dOpVRo0Yxa9Ys1q5dy4MPPnh5Kys6THs+ByJ4TJ8+nenTp/v/vu2227jxxhsZN24cq1atYv369Z1Yux+248ePc+eddzJmzBjmzZvX6rUSj8HrYtpR4jH43HPPPUybNo38/HxeffVVPB4PTqez1TISj8HnUtpR4jG4lJWV8bvf/Y5HHnmE2NjYNpdriLdgiEmZ4i46jMlkCrh+ym63+89fjJkzZxIfH39Rv4aJzne5PwcieIwdO5bRo0dLTHaiwsJCbrnlFsLDw/15P1oj8RicLrYdA5F47Fzp6elMnDiRuXPn8re//Y2amhomTZrUag4dicfgcyntGIjEY+d5+OGHiYqKYvny5RdVriHegiEmpYMuOkz37t0pKChodrzhWI8ePS76ngkJCZSXl7e7buLK6YjPgQgeEpOdp6qqip/+9KdUVlby7rvvtimWJB6Dz6W0Y0skHoPHtGnTyMrKanUducRj8GtLO7ZE4vHKO3nyJBs2bODuu+8mPz+fnJwccnJysNvtuFwucnJyWmyTqKgoDAZDUMSkdNBFhxk2bBjZ2dlYrdYmx/fs2eM/fzEURSEnJ+eipquIzjds2DAOHDiA1+ttcnzPnj2YzWb69evXSTUTl8Pp06clJjuB3W5n0qRJZGdn87e//Y0BAwa0qZzEY3C51HZsicRj8GiYCltVVdXiNRKPwa8t7dgSiccr7/z583i9Xu6++25SUlL8jz179pCdnU1KSkqLOT7UajWDBw9m3759zc7t2bOH1NRUwsLCOvol+OpyRZ5F/CBNmzYNj8fDhg0b/MccDgdbtmxh9OjR/qQZZ8+e5fjx403KlpSUNLvfunXrKCkp4aabburYiotLVlBQwPHjx3G5XP5j06ZNo6ioiNdff91/rLS0lB07djBp0qSAa31E5wrUjoFi8p133mH//v0Sk1eYx+NhxowZfP755+zYsYMxY8YEvE7iMbi1px0lHoNHcXFxs2Mul4vnn38ek8nk/9FF4jG4tacdJR6Dx6BBg3jjjTeaPQYOHEhiYiJvvPEGCxcuBAL3PxpmTDTupJ84cYKPPvqI22+//Yq9DpUiG0yLDjR9+nTeeOMN7r33Xvr06cO2bdvYu3cvH374oT9z6YQJE/jXv/7VZH2P2WxmxowZDB48GKPRyGeffcbLL7/M0KFD2bVrF2azubNe0g/WM888Q2VlJfn5+axbt44pU6Zw1VVXAbB8+XLCw8PJyMhg27ZtnDlzhuTkZMD3j9CxY8dy9OhRHnjgAWJiYli7di1nz54lKyvrimTDFN+61Hbs27cvV111FSNGjCA8PJwDBw6wefNmunfvTlZWFnFxcZ34qn5Y7rnnHv7nf/6HSZMmNUlM1GD27NkAEo9Brj3tKPEYPH7+859jtVoZN24cPXv2pLCwkBdffJHjx4/z5z//mV/96leAxGOwa087SjwGvwkTJlBaWsrRo0ebHPtu/6O6upqrrrqK6upq7r//fnQ6HU899RQej4dDhw5duRkRihAdyGazKffff78SHx+vGAwGZeTIkcq7777b5Jrx48cr3/0oLlq0SBkwYIASFham6HQ6pU+fPspvfvMbxWq1Xsnqi0aSkpIUIODjzJkziqIoyrx585r83aC8vFxZuHChEh0drZjNZmX8+PFKVlbWlX8R4pLb8aGHHlKGDRumhIeHKzqdTklMTFSWLVumFBYWds4L+QFr+M5s6dFA4jG4tacdJR6Dx1//+ldl4sSJSlxcnKLVapXIyEhl4sSJyltvvdXkOonH4NaedpR4DH7jx49XBg4c2OxYoK7wuXPnlGnTpikWi0UJDQ1Vbr31VuXkyZNXqqqKoiiKjKALIYQQQgghhBBBQNagCyGEEEIIIYQQQUA66EIIIYQQQgghRBCQDroQQgghhBBCCBEEpIMuhBBCCCGEEEIEAemgCyGEEEIIIYQQQUA66EIIIYQQQgghRBCQDroQQgghhBBCCBEEpIMuhBBCCCGEEEIEAemgCyGEEEIIIYQQQUA66EIIIUQQy8nJQaVSkZyc3NlVaTev18uIESOIj4+ntrb2ku+zfft2VCoVa9euvYy1E0IIITqfdNCFEEKITpScnIxKpSInJ6ezq9LhNm3axP79+3nkkUcICQm55PvMnDmTwYMH88gjj1BeXn4ZayiEEEJ0LumgCyGEEEGsZ8+eHDt2jA8//LCzq9IuNpuNhx56iB49epCZmdmue6nValasWEF5eTmPPfbYZaqhEEII0fmkgy6EEEIEMZ1OR3p6Or179+7sqrTL9u3bKSkpYe7cueh0unbfb/LkycTGxrJp0yZqamouQw2FEEKIzicddCGEEKITbN26FZVKRW5uLgApKSmoVCr/45NPPgFaX4PecC34OsCjRo0iNDSU2NhY7rjjDs6ePQuAoig888wzDBs2jJCQEGJiYsjIyKC4uLjF+mVnZ7NkyRJ69+6N0WgkPDyccePGsX379kt6vc888wwAGRkZAc+fPHmSBQsWkJKSgsFgIDQ0lKSkJG655Ra2bNnS7HqdTsfMmTOxWq288MILl1QnIYQQItioFEVROrsSQgghxA/NZ599xnPPPcfOnTupra1l6tSphIaG+s8/+OCDpKenk5OTQ0pKCklJSc3WqTd0zh988EGefPJJxo0bR1RUFHv37uXs2bMkJCTw5ZdfsnTpUv7v//6PCRMmYDKZ2LVrF8XFxQwZMoSsrCz0en2T++7YsYO5c+dit9tJT0+nf//+VFVVsWfPHmpra5k/fz6bN29u82s9c+YMqamp9OrVi3PnzjU7f/ToUX784x9jtVpJS0tj4MCBaDQa8vLyOHLkCL179+bQoUPNyv3973/n1ltv5YYbbuC9995rc32EEEKIYKXt7AoIIYQQP0Rjx45l7NixfPLJJ9TW1vLkk09ecqb2jRs3sm/fPoYOHQr41nvfcMMNfPbZZ4wfP566ujqOHz9OUlISAKWlpYwZM4bDhw+zY8cOZs2a5b/XkSNHmDNnDiqVitdee40pU6b4z+Xm5jJp0iS2bNnChAkTmDt3bpvq99FHHwEwZsyYgOefeuoprFYrjz32GA899FCTczabjaysrIDlxowZg0ql4rPPPsPpdDb7oUEIIYT4vpEp7kIIIcT33MqVK/2dcwCTycSvfvUrwNfhfvrpp/2dc4CYmBiWLVsG0Cz53KpVq3A4HDz22GNNOucASUlJbNq0CYCnn366zfU7ePAgAP379w94vqioCICbb7652TmTycS4ceMClouKiiI+Pt7/A4QQQgjxfScddCGEEOJ7LlDHtm/fvgBotVpuuOGGFs/n5+f7j3m9Xv7xj38AMGPGjIDPNWLECEJDQzl48CB2u71N9WvogEdHRwc8P2rUKACWLVvGe++91+b7Nr5nw3MIIYQQ32fSQRdCCCG+5xITE5sda1jP3r17d7Ta5ivawsLCAJp0hsvKyrBarQAkJCQ0SVrX8FCr1dTU1OD1eikrK2tT/aqqqgCwWCwBzz/wwANMnDiRPXv2cNNNN2GxWBg5ciT33Xdfi9PbGzTcs6Kiok11EUIIIYKZrEEXQgghvufU6pZ/b2/t3Hd5vV7/f8+bN++C1xsMhjbdNyIiAsDf+f8us9nM+++/T1ZWFu+++y67d+9m9+7d7Nu3j6eeeopf/vKXrFmzJmDZhs5/ZGRkm+oihBBCBDPpoAshhBAC8K1NN5lM2Gw2nnzySWJiYi7Lfbt16wZwwRH3kSNHMnLkSADcbjdvvvkmc+fOZe3atUybNo1rr722WZmGe8bFxV2WugohhBCdSaa4CyGEEJ2oIfO42+3u5JqARqPhJz/5CQCvvvrqZbvv8OHDAfj666/bXEar1TJt2jRuvPFGgIDbrJWVlVFYWIjZbG4xAZ0QQgjxfSIddCGEEKIT9erVC4Cvvvqqk2vis2LFCvR6PQ888ADbtm1rMu29wdGjR3n99dfbfM+Gke/PP/884Pm1a9dy4sSJZscLCwvZt28fQJMs9A12794N+Las0+l0ba6PEEIIEaykgy6EEEJ0oqlTpwIwe/Zspk6dyqJFi1i0aFHADuuVMHz4cLZv3w5ARkYGSUlJ3HjjjcyePZubb76ZhIQEBg8efFEj7CkpKQwZMoTz589z7NixZuc3bNhAeno6qampTJ48mdmzZ3PjjTeSmppKXl4e1113HZMnT25W7oMPPgDgtttuu7QXK4QQQgQZWYMuhBBCdKJly5ZRXV3N9u3beeedd/xZ1WfPnk1aWlqn1On2229n5MiRPP3007z//vvs2rULj8dDXFwcffr04a677mLatGkXdc+77rqLzMxMtm7dyhNPPNHk3KpVq/j73//OF198wRdffEFVVRXdunVj9OjRzJ8/nzvuuKNZJnqXy8VLL72ExWJhzpw57X7NQgghRDBQKYqidHYlhBBCCNG11dXVkZycjFarJScnx7/2/lK99tprTJs2jXvvvZennnrqMtVSCCGE6FwyxV0IIYQQHc5sNrNq1SoKCgrYsGFDu+7l9Xr5/e9/T1RUFA8//PBlqqEQQgjR+WQEXQghhBBXhNfrZdSoUeTl5XHq1ClCQkIu6T7bt29nzpw5rFmzhl/+8peXuZZCCCFE55EOuhBCCCGEEEIIEQRkirsQQgghhBBCCBEEpIMuhBBCCCGEEEIEAemgCyGEEEIIIYQQQUA66EIIIYQQQgghRBD4/zgcqwSiOvb1AAAAAElFTkSuQmCC", + "text/html": [ + "\n", + "
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8:37:41 (2.6s long)\n", + "index 413: 8:53:05 - 8:53:12 (6.9s long)\n", + "index 415: 8:53:21 - 8:53:24 (3.4s long)\n", + "index 416: 8:53:31 - 8:53:32 (1.5s long)\n", + "index 450: 9:03:23 - 9:03:32 (9.3s long)\n", + "index 453: 9:04:31 - 9:04:37 (6.2s long)\n", + "index 457: 9:10:35 - 9:10:42 (7.0s long)\n", + "index 470: 9:14:35 - 9:14:40 (5.5s long)\n", + "index 475: 9:16:27 - 9:16:34 (7.0s long)\n", + "index 494: 9:23:01 - 9:23:05 (4.2s long)\n", + "index 508: 9:28:25 - 9:28:30 (4.6s long)\n", + "index 509: 9:28:33 - 9:28:36 (3.4s long)\n", + "index 510: 9:28:55 - 9:28:59 (4.0s long)\n", + "index 511: 9:29:38 - 9:29:41 (3.6s long)\n", + "index 517: 9:32:08 - 9:32:14 (6.5s long)\n", + "index 518: 9:32:20 - 9:32:26 (5.7s long)\n", + "index 519: 9:32:35 - 9:32:38 (3.2s long)\n", + "index 521: 9:32:49 - 9:32:52 (3.1s long)\n", + "index 522: 9:33:09 - 9:33:17 (7.9s long)\n", + "index 523: 9:33:27 - 9:33:31 (3.9s long)\n", + "index 524: 9:34:03 - 9:34:07 (3.7s long)\n", + "index 525: 9:34:11 - 9:34:16 (5.5s long)\n", + "index 537: 9:37:34 - 9:37:38 (4.6s long)\n", + "index 539: 9:38:18 - 9:38:23 (4.8s long)\n", + "index 541: 9:38:51 - 9:38:55 (4.5s long)\n", + "index 543: 9:40:36 - 9:40:40 (3.9s long)\n", + "index 544: 9:41:03 - 9:41:08 (5.4s long)\n", + "index 545: 9:41:29 - 9:41:32 (2.5s long)\n", + "index 547: 9:42:37 - 9:42:40 (3.4s long)\n", + "index 549: 9:43:08 - 9:43:12 (4.2s long)\n", + "index 552: 9:43:55 - 9:43:58 (2.4s long)\n", + "index 556: 9:44:31 - 9:44:34 (2.4s long)\n", + "index 558: 9:44:51 - 9:44:54 (3.3s long)\n", + "index 560: 9:45:29 - 9:45:34 (5.4s long)\n", + "index 581: 9:49:27 - 9:49:31 (3.7s long)\n", + "index 588: 9:50:58 - 9:51:03 (5.1s long)\n", + "index 590: 9:51:21 - 9:51:26 (4.3s long)\n", + "index 591: 9:51:27 - 9:51:31 (3.9s long)\n", + "index 594: 9:55:25 - 9:55:29 (4.0s long)\n", + "index 595: 9:55:49 - 9:55:55 (6.0s long)\n", + "index 597: 9:57:28 - 9:57:29 (1.4s long)\n", + "index 598: 9:57:34 - 9:57:37 (2.6s long)\n" + ] + } + ], "source": [ "for i, r in bout_df_final.iterrows():\n", - " \n", - " if i > 345: # overnight stim\n", - " break\n", + " if i > last_bout: break\n", " \n", " hr_start = int(r['file'][-19:-17])\n", " hr_end = hr_start\n", @@ -517,44 +598,7 @@ }, { "cell_type": "code", - "execution_count": 36, - "id": "85d75a43-4b9c-4cae-adbb-06ce66cb5f76", - "metadata": {}, - "outputs": [], - "source": [ - "with open('/mnt/cube/chronic_ephys/der/z_y19o20_21/2021-10-27/bouts_sglx/bout_curated.pickle', 'rb') as f:\n", - " bout_df_final = pickle.load(f)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "2a776ca6-912a-4aeb-8892-1d380aa5bd42", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['start_ms', 'end_ms', 'start_sample', 'end_sample', 'p_step', 'rms_p',\n", - " 'peak_p', 'bout_check', 'file', 'len_ms', 'syl_in', 'n_syl', 'peaks_p',\n", - " 'n_peaks', 'l_p_ratio', 'waveform', 'confusing', 'valid_waveform',\n", - " 'valid', 'spectrogram', 'start_ms_ap_0', 'start_sample_ap_0',\n", - " 'start_sample_naive', 'bird', 'sess', 'epoch', 'is_call'],\n", - " dtype='object')" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bout_df_final.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, + "execution_count": 67, "id": "40708862-b1b1-4058-b875-9249df6dbf28", "metadata": {}, "outputs": [ @@ -579,437 +623,623 @@ " \n", " \n", " \n", - " start_ms\n", - " end_ms\n", + " file\n", " start_sample\n", " end_sample\n", - " p_step\n", - " rms_p\n", - " peak_p\n", - " bout_check\n", - " file\n", + " start_ms\n", + " end_ms\n", " len_ms\n", + " waveform\n", + " fem_waveform\n", + " spectrogram\n", + " sample_rate\n", " ...\n", - " valid_waveform\n", " valid\n", - " spectrogram\n", " start_ms_ap_0\n", " start_sample_ap_0\n", " start_sample_naive\n", " bird\n", " sess\n", " epoch\n", + " bout_check\n", + " confusing\n", " is_call\n", " \n", " \n", " \n", " \n", - " 8\n", - " 187128\n", - " 189488\n", - " 7485120\n", - " 7579520\n", - " [12.668582973409952, 41.513719119620895, 59.29...\n", - " 6.615156\n", - " 134.106723\n", - " True\n", - " /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1...\n", - " 2360\n", + " 45\n", + " /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08...\n", + " 88356760\n", + " 88673640\n", + " 2208919\n", + " 2216841\n", + " 7922\n", + " [471, 486, 469, 502, 489, 472, 453, 528, 544, ...\n", + " [-255, -238, -251, -283, -266, -251, -247, -24...\n", + " [[0.1069199546870063, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", + " 40000\n", " ...\n", " True\n", + " 2208960\n", + " 66268458\n", + " 88293400\n", + " z_r5r13_24\n", + " 2024-08-07\n", + " 0949_g0\n", " True\n", - " [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", - " 187131\n", - " 5643920\n", - " 7389000\n", - " z_y19o20_21\n", - " 2021-10-27\n", - " 1033_undirected_g0\n", + " False\n", " False\n", " \n", " \n", - " 39\n", - " 1071263\n", - " 1074653\n", - " 42850520\n", - " 42986120\n", - " [5.338206375397029, 3.0978681633708787, 3.9398...\n", - " 6.804745\n", - " 121.659218\n", - " True\n", - " /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1...\n", - " 3390\n", + " 142\n", + " /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08...\n", + " 306126440\n", + " 306319280\n", + " 7653161\n", + " 7657982\n", + " 4821\n", + " [690, 720, 722, 670, 711, 687, 702, 641, 663, ...\n", + " [-202, -205, -234, -205, -210, -204, -202, -22...\n", + " [[0.04617063032697592, 0.0, 0.0, 0.0, 0.0, 0.0...\n", + " 40000\n", " ...\n", " True\n", + " 7653305\n", + " 229597967\n", + " 306070440\n", + " z_r5r13_24\n", + " 2024-08-07\n", + " 0949_g0\n", " True\n", - " [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", - " 1071280\n", - " 32168339\n", - " 42187320\n", - " z_y19o20_21\n", - " 2021-10-27\n", - " 1033_undirected_g0\n", + " False\n", " False\n", " \n", " \n", - " 52\n", - " 1506162\n", - " 1509293\n", - " 60246480\n", - " 60371720\n", - " [11.552444728799006, 40.35527971348254, 57.706...\n", - " 6.804745\n", - " 153.937348\n", - " True\n", - " /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1...\n", - " 3131\n", + " 153\n", + " /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08...\n", + " 319266360\n", + " 319406360\n", + " 7981659\n", + " 7985159\n", + " 3500\n", + " [619, 598, 635, 569, 599, 605, 559, 618, 579, ...\n", + " [-326, -328, -328, -329, -331, -327, -330, -32...\n", + " [[0.021999940761009317, 0.0, 0.0, 0.0, 0.0, 0....\n", + " 40000\n", " ...\n", " True\n", + " 7981810\n", + " 239453041\n", + " 319266360\n", + " z_r5r13_24\n", + " 2024-08-07\n", + " 0949_g0\n", " True\n", - 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" 1782892\n", - " 53516648\n", - " 71354520\n", - " z_y19o20_21\n", - " 2021-10-27\n", - " 1033_undirected_g0\n", + " False\n", " False\n", " \n", " \n", - " 64\n", - " 2002208\n", - " 2007923\n", - " 80088320\n", - " 80316920\n", - " [1.9347568605351688, 7.7485843417036575, 4.873...\n", - " 6.804745\n", - " 141.213069\n", - " True\n", - " /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1...\n", - " 5715\n", + " 156\n", + " /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08...\n", + " 324018040\n", + " 324186040\n", + " 8100451\n", + " 8104651\n", + " 4200\n", + " [367, 315, 366, 313, 327, 350, 351, 298, 368, ...\n", + " [-272, -295, -278, -276, -274, -271, -278, -27...\n", + " [[0.04861262692254681, 0.0, 0.0, 0.0, 0.0, 0.0...\n", + " 40000\n", " ...\n", " True\n", + " 8100604\n", + " 243016851\n", + " 323998040\n", + " z_r5r13_24\n", + " 2024-08-07\n", + " 0949_g0\n", " True\n", - " [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", - " 2002240\n", - " 60097092\n", - " 80128320\n", - " z_y19o20_21\n", - 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12 rows × 27 columns

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18 rows × 21 columns

\n", "" ], "text/plain": [ - " start_ms end_ms start_sample end_sample \\\n", - "8 187128 189488 7485120 7579520 \n", - "39 1071263 1074653 42850520 42986120 \n", - "52 1506162 1509293 60246480 60371720 \n", - "59 1782863 1787293 71314520 71491720 \n", - "64 2002208 2007923 80088320 80316920 \n", - "65 2010033 2014278 80401320 80571120 \n", - "69 2319316 2322816 92772640 92912640 \n", - "79 2622221 2625901 104888840 105036040 \n", - "127 25835 33745 1033400 1349800 \n", - "157 818867 822846 32754680 32913840 \n", - "177 1509479 1513209 60379160 60528360 \n", - "188 2086656 2089610 83466240 83584400 \n", + " file start_sample \\\n", + "45 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 88356760 \n", + "142 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 306126440 \n", + "153 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 319266360 \n", + "155 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 323347800 \n", + "156 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 324018040 \n", + "157 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 324624120 \n", + "158 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 325398080 \n", + "159 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 331901560 \n", + "160 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 332316080 \n", + "161 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 333613360 \n", + "162 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 334314600 \n", + "163 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 335430280 \n", + "164 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 336185240 \n", + "165 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 359295480 \n", + "166 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 360171640 \n", + "193 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 2924520 \n", + "194 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 14719440 \n", + "196 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 12219400 \n", "\n", - " p_step rms_p peak_p \\\n", - "8 [12.668582973409952, 41.513719119620895, 59.29... 6.615156 134.106723 \n", - "39 [5.338206375397029, 3.0978681633708787, 3.9398... 6.804745 121.659218 \n", - "52 [11.552444728799006, 40.35527971348254, 57.706... 6.804745 153.937348 \n", - "59 [19.734600855974794, 55.380104136620304, 88.21... 6.804745 124.455970 \n", - "64 [1.9347568605351688, 7.7485843417036575, 4.873... 6.804745 141.213069 \n", - "65 [4.194721552563428, 34.957344918072536, 81.087... 6.804745 139.213337 \n", - "69 [3.2451614405857594, 3.9892908524229553, 4.173... 4.810822 149.524894 \n", - "79 [2.362219435555598, 3.7773330065235813, 2.1025... 4.810822 120.014404 \n", - "127 [12.678209512221926, 45.38018960916082, 19.775... 6.670950 221.077734 \n", - "157 [1.8657256449298205, 4.105385240576828, 3.9668... 6.670950 150.351373 \n", - "177 [1.8458635010324818, 1.8480902459641393, 2.793... 4.953947 167.735870 \n", - "188 [1.405343200739505, 2.480036543067559, 2.35920... 4.293242 130.600130 \n", + " end_sample start_ms end_ms len_ms \\\n", + "45 88673640 2208919 2216841 7922 \n", + "142 306319280 7653161 7657982 4821 \n", + "153 319406360 7981659 7985159 3500 \n", + "155 323568320 8083695 8089208 5513 \n", + "156 324186040 8100451 8104651 4200 \n", + "157 324808040 8115603 8120201 4598 \n", + "158 325617240 8134952 8140431 5479 \n", + "159 332095280 8297539 8302382 4843 \n", + "160 332481600 8307902 8312040 4138 \n", + "161 333827600 8340334 8345690 5356 \n", + "162 334561880 8357865 8364047 6182 \n", + "163 335612000 8385757 8390300 4543 \n", + "164 336384920 8404631 8409623 4992 \n", + "165 359460160 8982387 8986504 4117 \n", + "166 360456560 9004291 9011414 7123 \n", + "193 3117120 73113 77928 4815 \n", + "194 14939240 367986 373481 5495 \n", + "196 12347520 305485 308688 3203 \n", "\n", - " bout_check file len_ms \\\n", - "8 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 2360 \n", - "39 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3390 \n", - "52 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3131 \n", - "59 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 4430 \n", - "64 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 5715 \n", - "65 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 4245 \n", - "69 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3500 \n", - "79 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3680 \n", - "127 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 7910 \n", - "157 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3979 \n", - "177 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3730 \n", - "188 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 2954 \n", + " waveform \\\n", + "45 [471, 486, 469, 502, 489, 472, 453, 528, 544, ... \n", + "142 [690, 720, 722, 670, 711, 687, 702, 641, 663, ... \n", + "153 [619, 598, 635, 569, 599, 605, 559, 618, 579, ... \n", + "155 [424, 430, 441, 415, 436, 395, 462, 408, 470, ... \n", + "156 [367, 315, 366, 313, 327, 350, 351, 298, 368, ... \n", + "157 [730, 701, 703, 694, 681, 706, 734, 694, 691, ... \n", + "158 [606, 575, 590, 593, 554, 601, 563, 605, 568, ... \n", + "159 [746, 732, 730, 750, 728, 778, 685, 742, 734, ... \n", + "160 [547, 567, 574, 512, 527, 506, 507, 524, 513, ... \n", + "161 [928, 981, 879, 913, 945, 921, 937, 964, 924, ... \n", + "162 [443, 389, 413, 397, 386, 365, 364, 375, 399, ... \n", + "163 [309, 326, 327, 298, 291, 292, 289, 292, 264, ... \n", + "164 [765, 722, 734, 756, 821, 771, 787, 790, 741, ... \n", + "165 [540, 541, 519, 547, 537, 498, 577, 542, 531, ... \n", + "166 [251, 261, 250, 248, 302, 285, 277, 265, 234, ... \n", + "193 [162, 142, 159, 175, 141, 143, 149, 126, 177, ... \n", + "194 [461, 436, 453, 399, 438, 454, 410, 398, 442, ... \n", + "196 [510, 526, 497, 519, 524, 532, 503, 514, 528, ... \n", "\n", - " ... valid_waveform valid \\\n", - "8 ... True True \n", - "39 ... True True \n", - "52 ... True True \n", - "59 ... True True \n", - "64 ... True True \n", - "65 ... True True \n", - "69 ... True True \n", - "79 ... True True \n", - "127 ... True True \n", - "157 ... True True \n", - "177 ... True True \n", - "188 ... True True \n", + " fem_waveform \\\n", + "45 [-255, -238, -251, -283, -266, -251, -247, -24... \n", + "142 [-202, -205, -234, -205, -210, -204, -202, -22... \n", + "153 [-326, -328, -328, -329, -331, -327, -330, -32... \n", + "155 [-212, -208, -209, -216, -216, -183, -213, -20... \n", + "156 [-272, -295, -278, -276, -274, -271, -278, -27... \n", + "157 [-194, -191, -185, -190, -181, -189, -177, -18... \n", + "158 [-271, -288, -273, -275, -283, -281, -267, -28... \n", + "159 [-290, -257, -271, -257, -266, -270, -253, -25... \n", + "160 [-242, -234, -237, -236, -238, -238, -233, -22... \n", + "161 [-194, -196, -190, -194, -204, -183, -201, -21... \n", + "162 [-268, -273, -259, -268, -267, -258, -277, -27... \n", + "163 [-242, -250, -243, -240, -251, -245, -245, -24... \n", + "164 [-239, -234, -249, -245, -224, -253, -272, -22... \n", + "165 [-268, -270, -265, -262, -264, -261, -241, -25... \n", + "166 [-265, -276, -278, -277, -278, -280, -279, -27... \n", + "193 [-229, -224, -215, -221, -223, -217, -220, -23... \n", + "194 [-166, -173, -171, -175, -174, -205, -174, -20... \n", + "196 [-212, -190, -214, -210, -219, -220, -202, -22... \n", "\n", - " spectrogram start_ms_ap_0 \\\n", - "8 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 187131 \n", - "39 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1071280 \n", - "52 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1506186 \n", - "59 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1782892 \n", - "64 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2002240 \n", - "65 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2010065 \n", - "69 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2319353 \n", - "79 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2622263 \n", - "127 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 25835 \n", - "157 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 818880 \n", - "177 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1509503 \n", - "188 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2086690 \n", + " spectrogram sample_rate ... \\\n", + "45 [[0.1069199546870063, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "142 [[0.04617063032697592, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "153 [[0.021999940761009317, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "155 [[0.034754266281730006, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "156 [[0.04861262692254681, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "157 [[0.020798472369377728, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "158 [[0.053783507904516886, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "159 [[0.10646443110827125, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "160 [[0.0417131455845805, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "161 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "162 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "163 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "164 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "165 [[0.07653305031436872, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "166 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "193 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "194 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "196 [[0.06698295276872104, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", "\n", - " start_sample_ap_0 start_sample_naive bird sess \\\n", - "8 5643920 7389000 z_y19o20_21 2021-10-27 \n", - "39 32168339 42187320 z_y19o20_21 2021-10-27 \n", - "52 45215500 60211720 z_y19o20_21 2021-10-27 \n", - "59 53516648 71354520 z_y19o20_21 2021-10-27 \n", - "64 60097092 80128320 z_y19o20_21 2021-10-27 \n", - "65 60331845 80441320 z_y19o20_21 2021-10-27 \n", - "69 69610458 91998240 z_y19o20_21 2021-10-27 \n", - "79 78697746 104812240 z_y19o20_21 2021-10-27 \n", - "127 805057 498600 z_y19o20_21 2021-10-27 \n", - "157 24596361 32651000 z_y19o20_21 2021-10-27 \n", - "177 45315010 59732760 z_y19o20_21 2021-10-27 \n", - "188 62630574 83468760 z_y19o20_21 2021-10-27 \n", + " valid start_ms_ap_0 start_sample_ap_0 start_sample_naive bird \\\n", + "45 True 2208960 66268458 88293400 z_r5r13_24 \n", + "142 True 7653305 229597967 306070440 z_r5r13_24 \n", + "153 True 7981810 239453041 319266360 z_r5r13_24 \n", + "155 True 8083847 242514164 323328320 z_r5r13_24 \n", + "156 True 8100604 243016851 323998040 z_r5r13_24 \n", + "157 True 8115756 243471416 324576680 z_r5r13_24 \n", + "158 True 8135105 244051894 325377240 z_r5r13_24 \n", + "159 True 8297695 248929571 331855280 z_r5r13_24 \n", + "160 True 8308059 249240465 332281600 z_r5r13_24 \n", + "161 True 8340491 250213439 333587600 z_r5r13_24 \n", + "162 True 8358023 250739375 334314600 z_r5r13_24 \n", + "163 True 8385915 251576146 335430280 z_r5r13_24 \n", + "164 True 8404789 252142374 336164920 z_r5r13_24 \n", + "165 True 8982556 269475292 359273120 z_r5r13_24 \n", + "166 True 9004461 270132420 360171640 z_r5r13_24 \n", + "193 True 73114 2193422 2924520 z_r5r13_24 \n", + "194 True 367992 11039732 14719440 z_r5r13_24 \n", + "196 True 305490 9164677 12084080 z_r5r13_24 \n", "\n", - " epoch is_call \n", - "8 1033_undirected_g0 False \n", - "39 1033_undirected_g0 False \n", - "52 1033_undirected_g0 False \n", - "59 1033_undirected_g0 False \n", - "64 1033_undirected_g0 False \n", - "65 1033_undirected_g0 False \n", - "69 1033_undirected_g0 False \n", - "79 1033_undirected_g0 False \n", - "127 1142_directed_g0 False \n", - "157 1142_directed_g0 False \n", - "177 1142_directed_g0 False \n", - "188 1142_directed_g0 False \n", + " sess epoch bout_check confusing is_call \n", + "45 2024-08-07 0949_g0 True False False \n", + "142 2024-08-07 0949_g0 True False False \n", + "153 2024-08-07 0949_g0 True False False \n", + "155 2024-08-07 0949_g0 True False False \n", + "156 2024-08-07 0949_g0 True False False \n", + "157 2024-08-07 0949_g0 True False False \n", + "158 2024-08-07 0949_g0 True False False \n", + "159 2024-08-07 0949_g0 True False False \n", + "160 2024-08-07 0949_g0 True False False \n", + "161 2024-08-07 0949_g0 True False False \n", + "162 2024-08-07 0949_g0 True False False \n", + "163 2024-08-07 0949_g0 True False False \n", + "164 2024-08-07 0949_g0 True False False \n", + "165 2024-08-07 0949_g0 True False False \n", + "166 2024-08-07 0949_g0 True False False \n", + "193 2024-08-07 1233_g0 True False False \n", + "194 2024-08-07 1235_g0 True False False \n", + "196 2024-08-07 1245_g0 True False False \n", "\n", - "[12 rows x 27 columns]" + "[18 rows x 21 columns]" ] }, - "execution_count": 37, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "bout_df_final" + "bout_df_final.head(18)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 34, "id": "4533d2b8-9887-4d49-bae4-bddf0db77ca9", "metadata": {}, "outputs": [], @@ -1021,18 +1251,24 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 35, "id": "f24c3baf-4366-4a9e-8f8c-a950433e55dd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['/mnt/cube/chronic_ephys/der/z_y19o20_21/2021-10-27/sglx/1033_undirected_g0/wav_mic.npy',\n", - " '/mnt/cube/chronic_ephys/der/z_y19o20_21/2021-10-27/sglx/1142_directed_g0/wav_mic.npy']" + "['/mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-07/sglx/0949_g0/wav_mic.npy',\n", + " '/mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-07/sglx/1226_g0/wav_mic.npy',\n", + " '/mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-07/sglx/1227_g0/wav_mic.npy',\n", + " '/mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-07/sglx/1233_g0/wav_mic.npy',\n", + " '/mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-07/sglx/1235_g0/wav_mic.npy',\n", + " '/mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-07/sglx/1245_g0/wav_mic.npy',\n", + " '/mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-07/sglx/2355_g0/wav_mic.npy',\n", + " '/mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-07/sglx/2631_g0/wav_mic.npy']" ] }, - "execution_count": 15, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1043,29 +1279,29 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 59, "id": "e9fb4106-165a-4cc2-9cc2-9038a63f556b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'/mnt/cube/chronic_ephys/der/z_y19o20_21/2021-10-27/sglx/1033_undirected_g0/wav_mic.npy'" + "'/mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-07/sglx/1235_g0/wav_mic.npy'" ] }, - "execution_count": 16, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "file_path = wav_path_list[0]\n", + "file_path = wav_path_list[4]\n", "file_path" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 60, "id": "f55d7abe-8b4f-4dfe-8f92-95146e418a15", "metadata": {}, "outputs": [], @@ -1080,17 +1316,17 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 38, "id": "4fe618b5-debb-47ce-a887-0cea78bc96c5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "166720513" + "376411488" ] }, - "execution_count": 45, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -1102,17 +1338,17 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 39, "id": "4b983bc3-1af1-4d7f-8594-dc49e0c46473", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "4168013" + "9410287" ] }, - "execution_count": 46, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1124,7 +1360,30 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 61, + "id": "3e70150e-7a03-4198-8cb7-ede2513d9540", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "410945921" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## just kept looping through this when I had to stitch 6 recordings together\n", + "# sample_offset = sample_offset + len(x)\n", + "# ms_offset = ms_offset + round(len(x)/s_f*1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, "id": "cab76a1f-1621-49d1-bd1a-2d54a19e9e76", "metadata": {}, "outputs": [], @@ -1134,21 +1393,43 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 63, + "id": "ee5bdf65-c144-4048-8179-222d0a7be0d6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[196]" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "offset_idx = bout_df_new[bout_df_new['epoch']=='1245_g0'].index.tolist()\n", + "offset_idx" + ] + }, + { + "cell_type": "code", + "execution_count": 64, "id": "ef9c47c6-713b-43d3-9bdb-aeae6e5092ec", "metadata": {}, "outputs": [], "source": [ - "for i in [127, 157, 177, 188]:\n", - " bout_df_new.at[i, 'start_sample'] = bout_df_final.at[i, 'start_sample'] + 166720513\n", - " bout_df_new.at[i, 'end_sample'] = bout_df_final.at[i, 'end_sample'] + 166720513\n", - " bout_df_new.at[i, 'start_ms'] = bout_df_final.at[i, 'start_ms'] + 4168013\n", - " bout_df_new.at[i, 'end_ms'] = bout_df_final.at[i, 'end_ms'] + 4168013" + "for i in offset_idx:\n", + " bout_df_new.at[i, 'start_sample'] = bout_df_final.at[i, 'start_sample'] + sample_offset\n", + " bout_df_new.at[i, 'end_sample'] = bout_df_final.at[i, 'end_sample'] + sample_offset\n", + " bout_df_new.at[i, 'start_ms'] = bout_df_final.at[i, 'start_ms'] + ms_offset\n", + " bout_df_new.at[i, 'end_ms'] = bout_df_final.at[i, 'end_ms'] + ms_offset" ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 66, "id": "375ae28c-78bc-4a9d-9ee6-7f52c71fce09", "metadata": {}, "outputs": [ @@ -1173,426 +1454,612 @@ " \n", " \n", " \n", - " start_ms\n", - " end_ms\n", + " file\n", " start_sample\n", " end_sample\n", - " p_step\n", - " rms_p\n", - " peak_p\n", - " bout_check\n", - " file\n", + " start_ms\n", + " end_ms\n", " len_ms\n", + " waveform\n", + " fem_waveform\n", + " spectrogram\n", + " sample_rate\n", " ...\n", - " valid_waveform\n", " valid\n", - " spectrogram\n", " start_ms_ap_0\n", " start_sample_ap_0\n", " start_sample_naive\n", " bird\n", " sess\n", " epoch\n", + " bout_check\n", + " confusing\n", " is_call\n", " \n", " \n", " \n", " \n", - " 8\n", - " 187128\n", - " 189488\n", - " 7485120\n", - " 7579520\n", - " [12.668582973409952, 41.513719119620895, 59.29...\n", - " 6.615156\n", - " 134.106723\n", - " True\n", - " /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1...\n", - " 2360\n", + " 45\n", + " /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08...\n", + " 88356760\n", + " 88673640\n", + " 2208919\n", + " 2216841\n", + " 7922\n", + " [471, 486, 469, 502, 489, 472, 453, 528, 544, ...\n", + " [-255, -238, -251, -283, -266, -251, -247, -24...\n", + " [[0.1069199546870063, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", + " 40000\n", " ...\n", " True\n", + " 2208960\n", + " 66268458\n", + " 88293400\n", + " z_r5r13_24\n", + " 2024-08-07\n", + " 0949_g0\n", " True\n", - " [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", - " 187131\n", - " 5643920\n", - " 7389000\n", - " z_y19o20_21\n", - " 2021-10-27\n", - " 1033_undirected_g0\n", + " False\n", " False\n", " \n", " \n", - " 39\n", - " 1071263\n", - " 1074653\n", - " 42850520\n", - " 42986120\n", - " [5.338206375397029, 3.0978681633708787, 3.9398...\n", - " 6.804745\n", - " 121.659218\n", - " True\n", - " /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1...\n", - " 3390\n", + " 142\n", + " /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08...\n", + " 306126440\n", + " 306319280\n", + " 7653161\n", + " 7657982\n", + " 4821\n", + " [690, 720, 722, 670, 711, 687, 702, 641, 663, ...\n", + " [-202, -205, -234, -205, -210, -204, -202, -22...\n", + " [[0.04617063032697592, 0.0, 0.0, 0.0, 0.0, 0.0...\n", + " 40000\n", " ...\n", " True\n", + " 7653305\n", + " 229597967\n", + " 306070440\n", + " z_r5r13_24\n", + " 2024-08-07\n", + " 0949_g0\n", " True\n", - " [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", - 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18 rows × 21 columns

\n", "" ], "text/plain": [ - " start_ms end_ms start_sample end_sample \\\n", - "8 187128 189488 7485120 7579520 \n", - "39 1071263 1074653 42850520 42986120 \n", - "52 1506162 1509293 60246480 60371720 \n", - "59 1782863 1787293 71314520 71491720 \n", - "64 2002208 2007923 80088320 80316920 \n", - "65 2010033 2014278 80401320 80571120 \n", - "69 2319316 2322816 92772640 92912640 \n", - "79 2622221 2625901 104888840 105036040 \n", - "127 4193848 4201758 167753913 168070313 \n", - "157 4986880 4990859 199475193 199634353 \n", - "177 5677492 5681222 227099673 227248873 \n", - "188 6254669 6257623 250186753 250304913 \n", + " file start_sample \\\n", + "45 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 88356760 \n", + "142 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 306126440 \n", + "153 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 319266360 \n", + "155 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 323347800 \n", + "156 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 324018040 \n", + "157 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 324624120 \n", + "158 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 325398080 \n", + "159 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 331901560 \n", + "160 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 332316080 \n", + "161 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 333613360 \n", + "162 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 334314600 \n", + "163 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 335430280 \n", + "164 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 336185240 \n", + "165 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 359295480 \n", + "166 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 360171640 \n", + "193 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 388693608 \n", + "194 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 406133713 \n", + "196 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 423165321 \n", "\n", - " p_step rms_p peak_p \\\n", - "8 [12.668582973409952, 41.513719119620895, 59.29... 6.615156 134.106723 \n", - "39 [5.338206375397029, 3.0978681633708787, 3.9398... 6.804745 121.659218 \n", - "52 [11.552444728799006, 40.35527971348254, 57.706... 6.804745 153.937348 \n", - "59 [19.734600855974794, 55.380104136620304, 88.21... 6.804745 124.455970 \n", - "64 [1.9347568605351688, 7.7485843417036575, 4.873... 6.804745 141.213069 \n", - "65 [4.194721552563428, 34.957344918072536, 81.087... 6.804745 139.213337 \n", - "69 [3.2451614405857594, 3.9892908524229553, 4.173... 4.810822 149.524894 \n", - "79 [2.362219435555598, 3.7773330065235813, 2.1025... 4.810822 120.014404 \n", - "127 [12.678209512221926, 45.38018960916082, 19.775... 6.670950 221.077734 \n", - "157 [1.8657256449298205, 4.105385240576828, 3.9668... 6.670950 150.351373 \n", - "177 [1.8458635010324818, 1.8480902459641393, 2.793... 4.953947 167.735870 \n", - "188 [1.405343200739505, 2.480036543067559, 2.35920... 4.293242 130.600130 \n", + " end_sample start_ms end_ms len_ms \\\n", + "45 88673640 2208919 2216841 7922 \n", + "142 306319280 7653161 7657982 4821 \n", + "153 319406360 7981659 7985159 3500 \n", + "155 323568320 8083695 8089208 5513 \n", + "156 324186040 8100451 8104651 4200 \n", + "157 324808040 8115603 8120201 4598 \n", + "158 325617240 8134952 8140431 5479 \n", + "159 332095280 8297539 8302382 4843 \n", + "160 332481600 8307902 8312040 4138 \n", + "161 333827600 8340334 8345690 5356 \n", + "162 334561880 8357865 8364047 6182 \n", + "163 335612000 8385757 8390300 4543 \n", + "164 336384920 8404631 8409623 4992 \n", + "165 359460160 8982387 8986504 4117 \n", + "166 360456560 9004291 9011414 7123 \n", + "193 388886208 9717340 9722155 4815 \n", + "194 406353513 10153343 10158838 5495 \n", + "196 423293441 10579133 10582336 3203 \n", "\n", - " bout_check file len_ms \\\n", - "8 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 2360 \n", - "39 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3390 \n", - "52 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3131 \n", - "59 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 4430 \n", - "64 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 5715 \n", - "65 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 4245 \n", - "69 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3500 \n", - "79 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3680 \n", - "127 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 7910 \n", - "157 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3979 \n", - "177 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3730 \n", - "188 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 2954 \n", + " waveform \\\n", + "45 [471, 486, 469, 502, 489, 472, 453, 528, 544, ... \n", + "142 [690, 720, 722, 670, 711, 687, 702, 641, 663, ... \n", + "153 [619, 598, 635, 569, 599, 605, 559, 618, 579, ... \n", + "155 [424, 430, 441, 415, 436, 395, 462, 408, 470, ... \n", + "156 [367, 315, 366, 313, 327, 350, 351, 298, 368, ... \n", + "157 [730, 701, 703, 694, 681, 706, 734, 694, 691, ... \n", + "158 [606, 575, 590, 593, 554, 601, 563, 605, 568, ... \n", + "159 [746, 732, 730, 750, 728, 778, 685, 742, 734, ... \n", + "160 [547, 567, 574, 512, 527, 506, 507, 524, 513, ... \n", + "161 [928, 981, 879, 913, 945, 921, 937, 964, 924, ... \n", + "162 [443, 389, 413, 397, 386, 365, 364, 375, 399, ... \n", + "163 [309, 326, 327, 298, 291, 292, 289, 292, 264, ... \n", + "164 [765, 722, 734, 756, 821, 771, 787, 790, 741, ... \n", + "165 [540, 541, 519, 547, 537, 498, 577, 542, 531, ... \n", + "166 [251, 261, 250, 248, 302, 285, 277, 265, 234, ... \n", + "193 [162, 142, 159, 175, 141, 143, 149, 126, 177, ... \n", + "194 [461, 436, 453, 399, 438, 454, 410, 398, 442, ... \n", + "196 [510, 526, 497, 519, 524, 532, 503, 514, 528, ... \n", "\n", - " ... valid_waveform valid \\\n", - "8 ... True True \n", - "39 ... True True \n", - "52 ... True True \n", - "59 ... True True \n", - "64 ... True True \n", - "65 ... True True \n", - "69 ... True True \n", - "79 ... True True \n", - "127 ... True True \n", - "157 ... True True \n", - "177 ... True True \n", - "188 ... True True \n", + " fem_waveform \\\n", + "45 [-255, -238, -251, -283, -266, -251, -247, -24... \n", + "142 [-202, -205, -234, -205, -210, -204, -202, -22... \n", + "153 [-326, -328, -328, -329, -331, -327, -330, -32... \n", + "155 [-212, -208, -209, -216, -216, -183, -213, -20... \n", + "156 [-272, -295, -278, -276, -274, -271, -278, -27... \n", + "157 [-194, -191, -185, -190, -181, -189, -177, -18... \n", + "158 [-271, -288, -273, -275, -283, -281, -267, -28... \n", + "159 [-290, -257, -271, -257, -266, -270, -253, -25... \n", + "160 [-242, -234, -237, -236, -238, -238, -233, -22... \n", + "161 [-194, -196, -190, -194, -204, -183, -201, -21... \n", + "162 [-268, -273, -259, -268, -267, -258, -277, -27... \n", + "163 [-242, -250, -243, -240, -251, -245, -245, -24... \n", + "164 [-239, -234, -249, -245, -224, -253, -272, -22... \n", + "165 [-268, -270, -265, -262, -264, -261, -241, -25... \n", + "166 [-265, -276, -278, -277, -278, -280, -279, -27... \n", + "193 [-229, -224, -215, -221, -223, -217, -220, -23... \n", + "194 [-166, -173, -171, -175, -174, -205, -174, -20... \n", + "196 [-212, -190, -214, -210, -219, -220, -202, -22... \n", "\n", - " spectrogram start_ms_ap_0 \\\n", - "8 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 187131 \n", - "39 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1071280 \n", - "52 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1506186 \n", - "59 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1782892 \n", - "64 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2002240 \n", - "65 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2010065 \n", - "69 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2319353 \n", - "79 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2622263 \n", - "127 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 25835 \n", - "157 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 818880 \n", - "177 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1509503 \n", - "188 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2086690 \n", + " spectrogram sample_rate ... \\\n", + "45 [[0.1069199546870063, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "142 [[0.04617063032697592, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "153 [[0.021999940761009317, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "155 [[0.034754266281730006, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "156 [[0.04861262692254681, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "157 [[0.020798472369377728, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "158 [[0.053783507904516886, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "159 [[0.10646443110827125, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "160 [[0.0417131455845805, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "161 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "162 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "163 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "164 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "165 [[0.07653305031436872, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "166 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "193 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "194 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "196 [[0.06698295276872104, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", "\n", - " start_sample_ap_0 start_sample_naive bird sess \\\n", - "8 5643920 7389000 z_y19o20_21 2021-10-27 \n", - "39 32168339 42187320 z_y19o20_21 2021-10-27 \n", - "52 45215500 60211720 z_y19o20_21 2021-10-27 \n", - "59 53516648 71354520 z_y19o20_21 2021-10-27 \n", - "64 60097092 80128320 z_y19o20_21 2021-10-27 \n", - "65 60331845 80441320 z_y19o20_21 2021-10-27 \n", - "69 69610458 91998240 z_y19o20_21 2021-10-27 \n", - "79 78697746 104812240 z_y19o20_21 2021-10-27 \n", - "127 805057 498600 z_y19o20_21 2021-10-27 \n", - "157 24596361 32651000 z_y19o20_21 2021-10-27 \n", - "177 45315010 59732760 z_y19o20_21 2021-10-27 \n", - "188 62630574 83468760 z_y19o20_21 2021-10-27 \n", + " valid start_ms_ap_0 start_sample_ap_0 start_sample_naive bird \\\n", + "45 True 2208960 66268458 88293400 z_r5r13_24 \n", + "142 True 7653305 229597967 306070440 z_r5r13_24 \n", + "153 True 7981810 239453041 319266360 z_r5r13_24 \n", + "155 True 8083847 242514164 323328320 z_r5r13_24 \n", + "156 True 8100604 243016851 323998040 z_r5r13_24 \n", + "157 True 8115756 243471416 324576680 z_r5r13_24 \n", + "158 True 8135105 244051894 325377240 z_r5r13_24 \n", + "159 True 8297695 248929571 331855280 z_r5r13_24 \n", + "160 True 8308059 249240465 332281600 z_r5r13_24 \n", + "161 True 8340491 250213439 333587600 z_r5r13_24 \n", + "162 True 8358023 250739375 334314600 z_r5r13_24 \n", + "163 True 8385915 251576146 335430280 z_r5r13_24 \n", + "164 True 8404789 252142374 336164920 z_r5r13_24 \n", + "165 True 8982556 269475292 359273120 z_r5r13_24 \n", + "166 True 9004461 270132420 360171640 z_r5r13_24 \n", + "193 True 73114 2193422 2924520 z_r5r13_24 \n", + "194 True 367992 11039732 14719440 z_r5r13_24 \n", + "196 True 305490 9164677 12084080 z_r5r13_24 \n", "\n", - " epoch is_call \n", - "8 1033_undirected_g0 False \n", - "39 1033_undirected_g0 False \n", - "52 1033_undirected_g0 False \n", - "59 1033_undirected_g0 False \n", - "64 1033_undirected_g0 False \n", - "65 1033_undirected_g0 False \n", - "69 1033_undirected_g0 False \n", - "79 1033_undirected_g0 False \n", - "127 1142_directed_g0 False \n", - "157 1142_directed_g0 False \n", - "177 1142_directed_g0 False \n", - "188 1142_directed_g0 False \n", + " sess epoch bout_check confusing is_call \n", + "45 2024-08-07 0949_g0 True False False \n", + "142 2024-08-07 0949_g0 True False False \n", + "153 2024-08-07 0949_g0 True False False \n", + "155 2024-08-07 0949_g0 True False False \n", + "156 2024-08-07 0949_g0 True False False \n", + "157 2024-08-07 0949_g0 True False False \n", + "158 2024-08-07 0949_g0 True False False \n", + "159 2024-08-07 0949_g0 True False False \n", + "160 2024-08-07 0949_g0 True False False \n", + "161 2024-08-07 0949_g0 True False False \n", + "162 2024-08-07 0949_g0 True False False \n", + "163 2024-08-07 0949_g0 True False False \n", + "164 2024-08-07 0949_g0 True False False \n", + "165 2024-08-07 0949_g0 True False False \n", + "166 2024-08-07 0949_g0 True False False \n", + "193 2024-08-07 1233_g0 True False False \n", + "194 2024-08-07 1235_g0 True False False \n", + "196 2024-08-07 1245_g0 True False False \n", "\n", - "[12 rows x 27 columns]" + "[18 rows x 21 columns]" ] }, - "execution_count": 62, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -1603,37 +2070,37 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 98, "id": "8714a5fd-6d7c-460c-a3fa-057d95fc9e19", "metadata": {}, "outputs": [], "source": [ - "ap_offset = 125042153" + "ap_offset = 282322836" ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 99, "id": "2613932d-2157-4a80-b73d-19a3be70ec90", "metadata": {}, "outputs": [], "source": [ - "ap_sr = 29999.933405327574" + "ap_sr = 29999.842180774747" ] }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 100, "id": "dc223e68-601e-4cc9-8abd-e6dbddaefc58", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "4168081.019066337" + "9410810.70689516" ] }, - "execution_count": 71, + "execution_count": 100, "metadata": {}, "output_type": "execute_result" } @@ -1644,17 +2111,17 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 101, "id": "17a21ad5-f8f1-46c6-8275-7ef392e45220", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "4168081" + "9410811" ] }, - "execution_count": 72, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" } @@ -1666,7 +2133,19 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 115, + "id": "415ff3c7-2188-4b93-ba6a-a79b31ba0213", + "metadata": {}, + "outputs": [], + "source": [ + "## just kept looping through this when I had to stitch 6 recordings together\n", + "# ap_offset = ap_offset + 14681207\n", + "# ap_ms_offset = ap_ms_offset + round(14681207/29999.844262295082*1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 102, "id": "41251ffb-f1bf-469c-ac89-49502bcc0d16", "metadata": {}, "outputs": [], @@ -1676,19 +2155,41 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 117, + "id": "ff7eab70-559c-4a4a-9611-3260f2846db7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[196]" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# offset_idx = bout_df_new[bout_df_new['epoch']=='1245_g0'].index.tolist()\n", + "# offset_idx" + ] + }, + { + "cell_type": "code", + "execution_count": 118, "id": "8d3d2cfc-182e-472b-aedd-56fbe299089c", "metadata": {}, "outputs": [], "source": [ - "for i in [127, 157, 177, 188]:\n", + "for i in offset_idx:\n", " bout_df_concat.at[i, 'start_sample_ap_0'] = bout_df_new.at[i, 'start_sample_ap_0'] + ap_offset\n", " bout_df_concat.at[i, 'start_ms_ap_0'] = bout_df_new.at[i, 'start_ms_ap_0'] + ap_ms_offset" ] }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 120, "id": "5bf75bb0-d890-4dea-86b8-f736920b45ce", "metadata": {}, "outputs": [ @@ -1713,426 +2214,612 @@ " \n", " \n", " \n", - " start_ms\n", - " end_ms\n", + " file\n", " start_sample\n", " end_sample\n", - " p_step\n", - " rms_p\n", - " peak_p\n", - " bout_check\n", - " file\n", + " start_ms\n", + " end_ms\n", " len_ms\n", + " waveform\n", + " fem_waveform\n", + " spectrogram\n", + " sample_rate\n", " ...\n", - " valid_waveform\n", " valid\n", - " spectrogram\n", " start_ms_ap_0\n", " start_sample_ap_0\n", " start_sample_naive\n", " bird\n", " sess\n", " epoch\n", + " bout_check\n", + " confusing\n", " is_call\n", " \n", " \n", " \n", " \n", - " 8\n", - " 187128\n", - " 189488\n", - " 7485120\n", - " 7579520\n", - " [12.668582973409952, 41.513719119620895, 59.29...\n", - " 6.615156\n", - " 134.106723\n", - " True\n", - " /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1...\n", - " 2360\n", + " 45\n", + " /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08...\n", + " 88356760\n", + " 88673640\n", + " 2208919\n", + " 2216841\n", + " 7922\n", + " [471, 486, 469, 502, 489, 472, 453, 528, 544, ...\n", + " [-255, -238, -251, -283, -266, -251, -247, -24...\n", + " [[0.1069199546870063, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", + " 40000\n", " ...\n", " True\n", + " 2208960\n", + " 66268458\n", + " 88293400\n", + " z_r5r13_24\n", + " 2024-08-07\n", + " 0949_g0\n", " True\n", - " [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", - " 187131\n", - " 5643920\n", - " 7389000\n", - " z_y19o20_21\n", - " 2021-10-27\n", - " 1033_undirected_g0\n", + " False\n", " False\n", " \n", " \n", - " 39\n", - " 1071263\n", - " 1074653\n", - " 42850520\n", - " 42986120\n", - " [5.338206375397029, 3.0978681633708787, 3.9398...\n", - " 6.804745\n", - " 121.659218\n", - " True\n", - " /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1...\n", - " 3390\n", + " 142\n", + " /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08...\n", + " 306126440\n", + " 306319280\n", + " 7653161\n", + " 7657982\n", + " 4821\n", + " [690, 720, 722, 670, 711, 687, 702, 641, 663, ...\n", + " [-202, -205, -234, -205, -210, -204, -202, -22...\n", + " [[0.04617063032697592, 0.0, 0.0, 0.0, 0.0, 0.0...\n", + " 40000\n", " ...\n", " True\n", + " 7653305\n", + " 229597967\n", + " 306070440\n", + " z_r5r13_24\n", + " 2024-08-07\n", + " 0949_g0\n", " True\n", - " [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", - " 1071280\n", - " 32168339\n", - " 42187320\n", - " z_y19o20_21\n", - " 2021-10-27\n", - " 1033_undirected_g0\n", + " False\n", " False\n", " \n", " \n", - " 52\n", - " 1506162\n", - " 1509293\n", - " 60246480\n", - " 60371720\n", - " [11.552444728799006, 40.35527971348254, 57.706...\n", - " 6.804745\n", - " 153.937348\n", - " True\n", - " /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1...\n", - " 3131\n", + " 153\n", + " /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08...\n", + " 319266360\n", + " 319406360\n", + " 7981659\n", + " 7985159\n", + " 3500\n", + " [619, 598, 635, 569, 599, 605, 559, 618, 579, ...\n", + " [-326, -328, -328, -329, -331, -327, -330, -32...\n", + " [[0.021999940761009317, 0.0, 0.0, 0.0, 0.0, 0....\n", + " 40000\n", " ...\n", " True\n", + " 7981810\n", + " 239453041\n", + " 319266360\n", + " z_r5r13_24\n", + " 2024-08-07\n", + " 0949_g0\n", " True\n", - " [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...\n", - " 1506186\n", - " 45215500\n", - " 60211720\n", - " z_y19o20_21\n", - " 2021-10-27\n", - " 1033_undirected_g0\n", + " False\n", " False\n", " \n", " \n", - " 59\n", - " 1782863\n", - " 1787293\n", - " 71314520\n", - " 71491720\n", - " [19.734600855974794, 55.380104136620304, 88.21...\n", - " 6.804745\n", - " 124.455970\n", - " True\n", - " /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1...\n", - " 4430\n", + " 155\n", + " /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08...\n", + " 323347800\n", + " 323568320\n", + " 8083695\n", + " 8089208\n", + " 5513\n", + " [424, 430, 441, 415, 436, 395, 462, 408, 470, ...\n", + " [-212, -208, -209, -216, -216, -183, -213, -20...\n", + " [[0.034754266281730006, 0.0, 0.0, 0.0, 0.0, 0....\n", + " 40000\n", " ...\n", " True\n", + " 8083847\n", + " 242514164\n", + " 323328320\n", + " z_r5r13_24\n", + " 2024-08-07\n", + " 0949_g0\n", " True\n", - 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12 rows × 27 columns

\n", + "

18 rows × 21 columns

\n", "" ], "text/plain": [ - " start_ms end_ms start_sample end_sample \\\n", - "8 187128 189488 7485120 7579520 \n", - "39 1071263 1074653 42850520 42986120 \n", - "52 1506162 1509293 60246480 60371720 \n", - "59 1782863 1787293 71314520 71491720 \n", - "64 2002208 2007923 80088320 80316920 \n", - "65 2010033 2014278 80401320 80571120 \n", - "69 2319316 2322816 92772640 92912640 \n", - "79 2622221 2625901 104888840 105036040 \n", - "127 4193848 4201758 167753913 168070313 \n", - "157 4986880 4990859 199475193 199634353 \n", - "177 5677492 5681222 227099673 227248873 \n", - "188 6254669 6257623 250186753 250304913 \n", + " file start_sample \\\n", + "45 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 88356760 \n", + "142 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 306126440 \n", + "153 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 319266360 \n", + "155 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 323347800 \n", + "156 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 324018040 \n", + "157 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 324624120 \n", + "158 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 325398080 \n", + "159 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 331901560 \n", + "160 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 332316080 \n", + "161 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 333613360 \n", + "162 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 334314600 \n", + "163 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 335430280 \n", + "164 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 336185240 \n", + "165 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 359295480 \n", + "166 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 360171640 \n", + "193 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 388693608 \n", + "194 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 406133713 \n", + "196 /mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08... 423165321 \n", "\n", - " p_step rms_p peak_p \\\n", - "8 [12.668582973409952, 41.513719119620895, 59.29... 6.615156 134.106723 \n", - "39 [5.338206375397029, 3.0978681633708787, 3.9398... 6.804745 121.659218 \n", - "52 [11.552444728799006, 40.35527971348254, 57.706... 6.804745 153.937348 \n", - "59 [19.734600855974794, 55.380104136620304, 88.21... 6.804745 124.455970 \n", - "64 [1.9347568605351688, 7.7485843417036575, 4.873... 6.804745 141.213069 \n", - "65 [4.194721552563428, 34.957344918072536, 81.087... 6.804745 139.213337 \n", - "69 [3.2451614405857594, 3.9892908524229553, 4.173... 4.810822 149.524894 \n", - "79 [2.362219435555598, 3.7773330065235813, 2.1025... 4.810822 120.014404 \n", - "127 [12.678209512221926, 45.38018960916082, 19.775... 6.670950 221.077734 \n", - "157 [1.8657256449298205, 4.105385240576828, 3.9668... 6.670950 150.351373 \n", - "177 [1.8458635010324818, 1.8480902459641393, 2.793... 4.953947 167.735870 \n", - "188 [1.405343200739505, 2.480036543067559, 2.35920... 4.293242 130.600130 \n", + " end_sample start_ms end_ms len_ms \\\n", + "45 88673640 2208919 2216841 7922 \n", + "142 306319280 7653161 7657982 4821 \n", + "153 319406360 7981659 7985159 3500 \n", + "155 323568320 8083695 8089208 5513 \n", + "156 324186040 8100451 8104651 4200 \n", + "157 324808040 8115603 8120201 4598 \n", + "158 325617240 8134952 8140431 5479 \n", + "159 332095280 8297539 8302382 4843 \n", + "160 332481600 8307902 8312040 4138 \n", + "161 333827600 8340334 8345690 5356 \n", + "162 334561880 8357865 8364047 6182 \n", + "163 335612000 8385757 8390300 4543 \n", + "164 336384920 8404631 8409623 4992 \n", + "165 359460160 8982387 8986504 4117 \n", + "166 360456560 9004291 9011414 7123 \n", + "193 388886208 9717340 9722155 4815 \n", + "194 406353513 10153343 10158838 5495 \n", + "196 423293441 10579133 10582336 3203 \n", "\n", - " bout_check file len_ms \\\n", - "8 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 2360 \n", - "39 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3390 \n", - "52 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3131 \n", - "59 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 4430 \n", - "64 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 5715 \n", - "65 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 4245 \n", - "69 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3500 \n", - "79 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3680 \n", - "127 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 7910 \n", - "157 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3979 \n", - "177 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 3730 \n", - "188 True /mnt/cube/chronic_ephys/der/z_y19o20_21/2021-1... 2954 \n", + " waveform \\\n", + "45 [471, 486, 469, 502, 489, 472, 453, 528, 544, ... \n", + "142 [690, 720, 722, 670, 711, 687, 702, 641, 663, ... \n", + "153 [619, 598, 635, 569, 599, 605, 559, 618, 579, ... \n", + "155 [424, 430, 441, 415, 436, 395, 462, 408, 470, ... \n", + "156 [367, 315, 366, 313, 327, 350, 351, 298, 368, ... \n", + "157 [730, 701, 703, 694, 681, 706, 734, 694, 691, ... \n", + "158 [606, 575, 590, 593, 554, 601, 563, 605, 568, ... \n", + "159 [746, 732, 730, 750, 728, 778, 685, 742, 734, ... \n", + "160 [547, 567, 574, 512, 527, 506, 507, 524, 513, ... \n", + "161 [928, 981, 879, 913, 945, 921, 937, 964, 924, ... \n", + "162 [443, 389, 413, 397, 386, 365, 364, 375, 399, ... \n", + "163 [309, 326, 327, 298, 291, 292, 289, 292, 264, ... \n", + "164 [765, 722, 734, 756, 821, 771, 787, 790, 741, ... \n", + "165 [540, 541, 519, 547, 537, 498, 577, 542, 531, ... \n", + "166 [251, 261, 250, 248, 302, 285, 277, 265, 234, ... \n", + "193 [162, 142, 159, 175, 141, 143, 149, 126, 177, ... \n", + "194 [461, 436, 453, 399, 438, 454, 410, 398, 442, ... \n", + "196 [510, 526, 497, 519, 524, 532, 503, 514, 528, ... \n", "\n", - " ... valid_waveform valid \\\n", - "8 ... True True \n", - "39 ... True True \n", - "52 ... True True \n", - "59 ... True True \n", - "64 ... True True \n", - "65 ... True True \n", - "69 ... True True \n", - "79 ... True True \n", - "127 ... True True \n", - "157 ... True True \n", - "177 ... True True \n", - "188 ... True True \n", + " fem_waveform \\\n", + "45 [-255, -238, -251, -283, -266, -251, -247, -24... \n", + "142 [-202, -205, -234, -205, -210, -204, -202, -22... \n", + "153 [-326, -328, -328, -329, -331, -327, -330, -32... \n", + "155 [-212, -208, -209, -216, -216, -183, -213, -20... \n", + "156 [-272, -295, -278, -276, -274, -271, -278, -27... \n", + "157 [-194, -191, -185, -190, -181, -189, -177, -18... \n", + "158 [-271, -288, -273, -275, -283, -281, -267, -28... \n", + "159 [-290, -257, -271, -257, -266, -270, -253, -25... \n", + "160 [-242, -234, -237, -236, -238, -238, -233, -22... \n", + "161 [-194, -196, -190, -194, -204, -183, -201, -21... \n", + "162 [-268, -273, -259, -268, -267, -258, -277, -27... \n", + "163 [-242, -250, -243, -240, -251, -245, -245, -24... \n", + "164 [-239, -234, -249, -245, -224, -253, -272, -22... \n", + "165 [-268, -270, -265, -262, -264, -261, -241, -25... \n", + "166 [-265, -276, -278, -277, -278, -280, -279, -27... \n", + "193 [-229, -224, -215, -221, -223, -217, -220, -23... \n", + "194 [-166, -173, -171, -175, -174, -205, -174, -20... \n", + "196 [-212, -190, -214, -210, -219, -220, -202, -22... \n", "\n", - " spectrogram start_ms_ap_0 \\\n", - "8 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 187131 \n", - "39 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1071280 \n", - "52 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1506186 \n", - "59 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 1782892 \n", - "64 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2002240 \n", - "65 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2010065 \n", - "69 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2319353 \n", - "79 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 2622263 \n", - "127 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 4193916 \n", - "157 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 4986961 \n", - "177 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 5677584 \n", - "188 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 6254771 \n", + " spectrogram sample_rate ... \\\n", + "45 [[0.1069199546870063, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "142 [[0.04617063032697592, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "153 [[0.021999940761009317, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "155 [[0.034754266281730006, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "156 [[0.04861262692254681, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "157 [[0.020798472369377728, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "158 [[0.053783507904516886, 0.0, 0.0, 0.0, 0.0, 0.... 40000 ... \n", + "159 [[0.10646443110827125, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "160 [[0.0417131455845805, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "161 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "162 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "163 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "164 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "165 [[0.07653305031436872, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", + "166 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "193 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "194 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,... 40000 ... \n", + "196 [[0.06698295276872104, 0.0, 0.0, 0.0, 0.0, 0.0... 40000 ... \n", "\n", - " start_sample_ap_0 start_sample_naive bird sess \\\n", - "8 5643920 7389000 z_y19o20_21 2021-10-27 \n", - "39 32168339 42187320 z_y19o20_21 2021-10-27 \n", - "52 45215500 60211720 z_y19o20_21 2021-10-27 \n", - "59 53516648 71354520 z_y19o20_21 2021-10-27 \n", - "64 60097092 80128320 z_y19o20_21 2021-10-27 \n", - "65 60331845 80441320 z_y19o20_21 2021-10-27 \n", - "69 69610458 91998240 z_y19o20_21 2021-10-27 \n", - "79 78697746 104812240 z_y19o20_21 2021-10-27 \n", - "127 125847210 498600 z_y19o20_21 2021-10-27 \n", - "157 149638514 32651000 z_y19o20_21 2021-10-27 \n", - "177 170357163 59732760 z_y19o20_21 2021-10-27 \n", - "188 187672727 83468760 z_y19o20_21 2021-10-27 \n", + " valid start_ms_ap_0 start_sample_ap_0 start_sample_naive bird \\\n", + "45 True 2208960 66268458 88293400 z_r5r13_24 \n", + "142 True 7653305 229597967 306070440 z_r5r13_24 \n", + "153 True 7981810 239453041 319266360 z_r5r13_24 \n", + "155 True 8083847 242514164 323328320 z_r5r13_24 \n", + "156 True 8100604 243016851 323998040 z_r5r13_24 \n", + "157 True 8115756 243471416 324576680 z_r5r13_24 \n", + "158 True 8135105 244051894 325377240 z_r5r13_24 \n", + "159 True 8297695 248929571 331855280 z_r5r13_24 \n", + "160 True 8308059 249240465 332281600 z_r5r13_24 \n", + "161 True 8340491 250213439 333587600 z_r5r13_24 \n", + "162 True 8358023 250739375 334314600 z_r5r13_24 \n", + "163 True 8385915 251576146 335430280 z_r5r13_24 \n", + "164 True 8404789 252142374 336164920 z_r5r13_24 \n", + "165 True 8982556 269475292 359273120 z_r5r13_24 \n", + "166 True 9004461 270132420 360171640 z_r5r13_24 \n", + "193 True 9718933 291566464 2924520 z_r5r13_24 \n", + "194 True 10156036 304679494 14719440 z_r5r13_24 \n", + "196 True 10582910 317485646 12084080 z_r5r13_24 \n", "\n", - " epoch is_call \n", - "8 1033_undirected_g0 False \n", - "39 1033_undirected_g0 False \n", - "52 1033_undirected_g0 False \n", - "59 1033_undirected_g0 False \n", - "64 1033_undirected_g0 False \n", - "65 1033_undirected_g0 False \n", - "69 1033_undirected_g0 False \n", - "79 1033_undirected_g0 False \n", - "127 1142_directed_g0 False \n", - "157 1142_directed_g0 False \n", - "177 1142_directed_g0 False \n", - "188 1142_directed_g0 False \n", + " sess epoch bout_check confusing is_call \n", + "45 2024-08-07 0949_g0 True False False \n", + "142 2024-08-07 0949_g0 True False False \n", + "153 2024-08-07 0949_g0 True False False \n", + "155 2024-08-07 0949_g0 True False False \n", + "156 2024-08-07 0949_g0 True False False \n", + "157 2024-08-07 0949_g0 True False False \n", + "158 2024-08-07 0949_g0 True False False \n", + "159 2024-08-07 0949_g0 True False False \n", + "160 2024-08-07 0949_g0 True False False \n", + "161 2024-08-07 0949_g0 True False False \n", + "162 2024-08-07 0949_g0 True False False \n", + "163 2024-08-07 0949_g0 True False False \n", + "164 2024-08-07 0949_g0 True False False \n", + "165 2024-08-07 0949_g0 True False False \n", + "166 2024-08-07 0949_g0 True False False \n", + "193 2024-08-07 1233_g0 True False False \n", + "194 2024-08-07 1235_g0 True False False \n", + "196 2024-08-07 1245_g0 True False False \n", "\n", - "[12 rows x 27 columns]" + "[18 rows x 21 columns]" ] }, - "execution_count": 78, + "execution_count": 120, "metadata": {}, "output_type": "execute_result" } @@ -2151,12 +2838,12 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 123, "id": "ab63a138-b2a9-42e6-aa43-9c8ab1c0e274", "metadata": {}, "outputs": [], "source": [ - "bout_df_concat.to_pickle('/mnt/cube/chronic_ephys/der/z_y19o20_21/2021-10-27/sglx/1033_undirected_g0-1142_directed_g0/bout_pd_ap0_curated.pkl')" + "bout_df_concat.to_pickle('/mnt/cube/chronic_ephys/der/z_r5r13_24/2024-08-07/sglx/0949_g0-1226_g0-1227_g0-1233_g0-1235_g0-1245_g0/bout_pd_ap0_curated.pkl')" ] }, { diff --git a/2.log b/2.log new file mode 100644 index 0000000..d79578f --- /dev/null +++ b/2.log @@ -0,0 +1,20 @@ +INFO:kilosort.io:======================================== +INFO:kilosort.io:Loading recording with SpikeInterface... +INFO:kilosort.io:number of samples: 325893811 +INFO:kilosort.io:number of channels: 384 +INFO:kilosort.io:numbef of segments: 1 +INFO:kilosort.io:sampling rate: 30000.0 +INFO:kilosort.io:dtype: int16 +INFO:kilosort.io:======================================== +INFO:kilosort.run_kilosort: +INFO:kilosort.run_kilosort:Computing preprocessing variables. +INFO:kilosort.run_kilosort:---------------------------------------- +INFO:kilosort.run_kilosort:Preprocessing filters computed in 556.56s; total 556.56s +INFO:kilosort.run_kilosort: +INFO:kilosort.run_kilosort:Computing drift correction. +INFO:kilosort.run_kilosort:---------------------------------------- +INFO:kilosort.spikedetect:Re-computing universal templates from data. +___________ z_r5r13_24 2024-08-06 0659_g0 ___________ +prep.. +sort.. + 0%| | 0/10864 [00:00 0: + this_rec = si.interpolate_bad_channels(recording=this_rec,bad_channel_ids=bad_good_channel_ids[0]) + if this_sess_config['probe']['probe_type'] == 'neuropixels-2.0': + # highpass by shank + split_rec = this_rec.split_by(property='group',outputs='list') # split recording by shank + split_rec = [si.highpass_spatial_filter(recording=r,n_channel_pad=min(r.get_num_channels(),60)) for r in split_rec] + this_rec_p = si.aggregate_channels(split_rec) # recombine shanks + # stack shanks + p,_ = pm.stack_shanks(probe_df) # make new Probe object with shanks stacked + this_rec_p = this_rec.set_probe(p,group_mode='by_probe') # assign new Probe object to probe + else: + this_rec_p = si.highpass_spatial_filter(recording=this_rec) + elif sess_par['ephys_software'] =='oe': + # load recording + rec_path = [f.path for f in os.scandir(epoch_struct['folders']['oe']) if f.is_dir()][0] + this_rec = si.read_openephys(folder_path=rec_path) + # add probe + this_probe = pm.make_probes(this_sess_config['probe']['probe_type'],this_sess_config['probe']['probe_model']) # neuronexus, Buzsaki64 + this_rec_p = this_rec.set_probe(this_probe,group_mode='by_shank') + # set sort params + this_rec_p = si.concatenate_recordings([this_rec_p]) + sort_params = si.get_default_sorter_params(this_sess_config['sorter']) + for this_param in this_sess_config['sort_params'].keys(): + sort_params[this_param] = this_sess_config['sort_params'][this_param] + # run sort + print('sort..') + torch.cuda.empty_cache() + this_sort = si.run_sorter(sorter_name=this_sess_config['sorter'],recording=this_rec_p,output_folder=sort_folder, + remove_existing_folder=True,delete_output_folder=False,delete_container_files=False, + verbose=verbose,raise_error=raise_error,**sort_params) + torch.cuda.empty_cache() + # bandpass recording before waveform extraction + print('bandpass..') + this_rec_pf = si.bandpass_filter(recording=this_rec_p) + # extract waveforms + print('waveform..') + wave_params = this_sess_config['wave_params'] + wave = si.extract_waveforms(this_rec_pf,this_sort,folder=os.path.join(sort_folder,'waveforms'), + ms_before=wave_params['ms_before'],ms_after=wave_params['ms_after'], + max_spikes_per_unit=wave_params['max_spikes_per_unit'], + sparse=wave_params['sparse'],num_spikes_for_sparsity=wave_params['num_spikes_for_sparsity'], + method=wave_params['method'],radius_um=wave_params['radius_um'],overwrite=True,**job_kwargs) + # compute metrics + print('metrics..') + loc = si.compute_unit_locations(waveform_extractor=wave) + cor = si.compute_correlograms(waveform_or_sorting_extractor=wave) + sim = si.compute_template_similarity(waveform_extractor=wave) + amp = si.compute_spike_amplitudes(waveform_extractor=wave,**job_kwargs) + pca = si.compute_principal_components(waveform_extractor=wave,n_components=wave_params['n_components'], + mode=wave_params['mode'],**job_kwargs) + qms = si.get_quality_metric_list() + metric_names = [] + bad_metrics = [] + for qm in qms: + try: + si.compute_quality_metrics(waveform_extractor=wave,verbose=False,metric_names=[qm],**job_kwargs) + metric_names.append(qm) + except: + bad_metrics.append(qm) + met = si.compute_quality_metrics(waveform_extractor=wave,verbose=verbose,metric_names=metric_names,**job_kwargs) + + # mark complete + print('COMPLETE!!') + + # log complete sort + if not os.path.exists(log_dir): os.makedirs(log_dir) + with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'w') as f: + f.write(sess_par['bird']+' '+sess_par['sess']+' sort complete without error\n\n') + f.write('Sort method: '+this_sess_config['sorter']+'\n\n') + f.write('Sort params: '+str(sort_params)+'\n\n') + f.write('Computed quality metrics: '+str(metric_names)+'\n\n') + f.write('Failed quality metrics: '+str(bad_metrics)+'\n') + sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'COMPLETE'] + + except Exception as e: + # mark exception + print("An exception occurred:", e) + + # log failed sort + if not os.path.exists(log_dir): os.makedirs(log_dir) + with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'w') as f: + f.write(sess_par['bird']+' '+sess_par['sess']+' sort failed\n') + f.write(traceback.format_exc()) + sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'FAIL'] + else: + sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'EXISTS'] + + # report and store sort summary + print(sort_summary) + sort_summary_all.append(sort_summary) diff --git a/3-sort_spikes_v0.101.ipynb b/3-sort_spikes_v0.101.ipynb new file mode 100644 index 0000000..da5cb5d --- /dev/null +++ b/3-sort_spikes_v0.101.ipynb @@ -0,0 +1,534 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Spike sort\n", + "\n", + "Notebook within the chronic ephys processing pipeline\n", + "- 1-preprocess_acoustics\n", + "- 2-curate_acoustics\n", + "- **3-sort_spikes**\n", + "- 4-curate_spikes\n", + "\n", + "Use the environment **spikeproc** to run this notebook" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import os\n", + "import pickle\n", + "os.environ[\"NPY_MATLAB_PATH\"] = '/mnt/cube/chronic_ephys/code/npy-matlab'\n", + "os.environ[\"KILOSORT2_PATH\"] = '/mnt/cube/chronic_ephys/code/Kilosort2'\n", + "os.environ[\"KILOSORT3_PATH\"] = '/mnt/cube/chronic_ephys/code/Kilosort'\n", + "import spikeinterface.full as si\n", + "import sys\n", + "import traceback\n", + "import torch\n", + "sys.path.append('/mnt/cube/lo/envs/ceciestunepipe/')\n", + "from ceciestunepipe.file import bcistructure as et\n", + "from ceciestunepipe.mods import probe_maps as pm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'batch_size': 60000,\n", + " 'nblocks': 1,\n", + " 'Th_universal': 9,\n", + " 'Th_learned': 8,\n", + " 'do_CAR': True,\n", + " 'invert_sign': False,\n", + " 'nt': 61,\n", + " 'artifact_threshold': None,\n", + " 'nskip': 25,\n", + " 'whitening_range': 32,\n", + " 'binning_depth': 5,\n", + " 'sig_interp': 20,\n", + " 'nt0min': None,\n", + " 'dmin': None,\n", + " 'dminx': None,\n", + " 'min_template_size': 10,\n", + " 'template_sizes': 5,\n", + " 'nearest_chans': 10,\n", + " 'nearest_templates': 100,\n", + " 'templates_from_data': True,\n", + " 'n_templates': 6,\n", + " 'n_pcs': 6,\n", + " 'Th_single_ch': 6,\n", + " 'acg_threshold': 0.2,\n", + " 'ccg_threshold': 0.25,\n", + " 'cluster_downsampling': 20,\n", + " 'cluster_pcs': 64,\n", + " 'duplicate_spike_bins': 15,\n", + " 'do_correction': True,\n", + " 'keep_good_only': False,\n", + " 'save_extra_kwargs': False,\n", + " 'skip_kilosort_preprocessing': False,\n", + " 'scaleproc': None}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "si.get_default_sorter_params('kilosort4')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Set `dmin` and `dminx`\n", + "**Setting these appropriately will greatly reduce sort time**\n", + "- The default value for dmin is the median distance between contacts -- if contacts are irregularly spaced, like in a modular Neuropixels 2.0 setup, will need to specify a value\n", + "- The default for dminx is 32um (designed for Neuropixels probes)\n", + "\n", + "Support documentation [here](https://kilosort.readthedocs.io/en/latest/parameters.html#dmin-and-dminx)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# non default spike sorting parameters\n", + "sort_params_dict_ks3 = {'minFR':0.001, 'minfr_goodchannels':0.001} # kilosort 3\n", + "sort_params_dict_ks4_npx = {'nblocks':5, 'Th_universal':8, 'Th_learned':7, 'dmin':15, 'dminx':32} # kilosort 4, neuropixels (set dmin and dminx to true pitch)\n", + "sort_params_dict_ks4_nnx64 = {'nblocks':0, 'nearest_templates':64,\n", + " 'Th_universal':8, 'Th_learned':7} # kilosort 4, neuronexus 64 chan\n", + "\n", + "# waveform extraction parameters\n", + "wave_params_dict = {'ms_before':1, 'ms_after':2, 'max_spikes_per_unit':500,\n", + " 'sparse':True, 'num_spikes_for_sparsity':100, 'method':'radius',\n", + " 'radius_um':40, 'n_components':5, 'mode':'by_channel_local'}\n", + "\n", + "# print stuff\n", + "verbose = True\n", + "\n", + "# errors break sorting\n", + "raise_error = False\n", + "\n", + "# restrict sorting to a specific GPU\n", + "restrict_to_gpu = 1 # 0 1 None\n", + "\n", + "# use specific GPU if specified\n", + "if restrict_to_gpu is not None:\n", + " os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n", + " os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"{}\".format(restrict_to_gpu)\n", + "\n", + "# parallel processing params\n", + "job_kwargs = dict(n_jobs=28,chunk_duration=\"1s\",progress_bar=False)\n", + "si.set_global_job_kwargs(**job_kwargs)\n", + "\n", + "# force processing of previous failed sorts\n", + "skip_failed = False" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "bird_rec_dict = {\n", + " 'z_p5y10_23':[\n", + " {'sess_par_list':['2024-05-16'], # sessions (will process all epochs within)\n", + " 'probe':{'probe_type':'neuropixels-2.0'}, # probe specs\n", + " 'sort':'sort_0', # label for this sort instance\n", + " 'sorter':'kilosort4', # sort method\n", + " 'sort_params':sort_params_dict_ks4_npx, # non-default sort params\n", + " 'wave_params':wave_params_dict, # waveform extraction params\n", + " 'ephys_software':'sglx' # sglx or oe\n", + " },\n", + " ],\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run sorts" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "___________ z_p5y10_23 2024-05-16 1246_g0 ___________\n", + "prep..\n", + "sort..\n", + "========================================\n", + "Loading recording with SpikeInterface...\n", + "number of samples: 368121306\n", + "number of channels: 384\n", + "number of segments: 1\n", + "sampling rate: 30000.0\n", + "dtype: int16\n", + "========================================\n", + "Preprocessing filters computed in 2077.34s; total 2077.34s\n", + "\n", + "computing drift\n", + "Re-computing universal templates from data.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6136/6136 [15:52:26<00:00, 9.31s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "drift computed in 59153.07s; total 61230.41s\n", + "\n", + "Extracting spikes using templates\n", + "Re-computing universal templates from data.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6136/6136 [16:23:41<00:00, 9.62s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "19999820 spikes extracted in 61300.19s; total 122530.61s\n", + "\n", + "First clustering\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 107/107 [19:49<00:00, 11.11s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1217 clusters found, in 1197.43s; total 123728.03s\n", + "\n", + "Extracting spikes using cluster waveforms\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6136/6136 [14:52:18<00:00, 8.73s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "37481401 spikes extracted in 53546.84s; total 177274.87s\n", + "\n", + "Final clustering\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 107/107 [26:00<00:00, 14.58s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "829 clusters found, in 1560.73s; total 178835.74s\n", + "\n", + "Merging clusters\n", + "742 units found, in 140.57s; total 178976.31s\n", + "\n", + "Saving to phy and computing refractory periods\n", + "338 units found with good refractory periods\n", + "\n", + "Total runtime: 179044.84s = 49:2984:4 h:m:s\n", + "kilosort4 run time 179047.45s\n", + "bandpass..\n", + "waveform..\n", + "metrics..\n", + "An exception occurred: [Errno 13] Permission denied: '/tmp/spikeinterface_cache/tmpnq7ml3iz'\n", + "['z_p5y10_23', '2024-05-16', 'sglx', '1246_g0', 'FAIL']\n", + "___________ z_p5y10_23 2024-05-16 1611_g0 ___________\n", + "prep..\n", + "sort..\n", + "========================================\n", + "Loading recording with SpikeInterface...\n", + "number of samples: 158273746\n", + "number of channels: 384\n", + "number of segments: 1\n", + "sampling rate: 30000.0\n", + "dtype: int16\n", + "========================================\n", + "Preprocessing filters computed in 878.17s; total 878.28s\n", + "\n", + "computing drift\n", + "Re-computing universal templates from data.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2638/2638 [7:23:47<00:00, 10.09s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "drift computed in 27475.47s; total 28353.88s\n", + "\n", + "Extracting spikes using templates\n", + "Re-computing universal templates from data.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 48%|████████████████████████████████████████████████████████████████████████████▉ | 1276/2638 [3:13:08<3:26:09, 9.08s/it]\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m:93\u001b[0m\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/spikeinterface/sorters/runsorter.py:175\u001b[0m, in \u001b[0;36mrun_sorter\u001b[0;34m(sorter_name, recording, output_folder, remove_existing_folder, delete_output_folder, verbose, raise_error, docker_image, singularity_image, delete_container_files, with_output, **sorter_params)\u001b[0m\n\u001b[1;32m 168\u001b[0m container_image \u001b[38;5;241m=\u001b[39m singularity_image\n\u001b[1;32m 169\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m run_sorter_container(\n\u001b[1;32m 170\u001b[0m container_image\u001b[38;5;241m=\u001b[39mcontainer_image,\n\u001b[1;32m 171\u001b[0m mode\u001b[38;5;241m=\u001b[39mmode,\n\u001b[1;32m 172\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcommon_kwargs,\n\u001b[1;32m 173\u001b[0m )\n\u001b[0;32m--> 175\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrun_sorter_local\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcommon_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/spikeinterface/sorters/runsorter.py:225\u001b[0m, in \u001b[0;36mrun_sorter_local\u001b[0;34m(sorter_name, recording, output_folder, remove_existing_folder, delete_output_folder, verbose, raise_error, with_output, **sorter_params)\u001b[0m\n\u001b[1;32m 223\u001b[0m SorterClass\u001b[38;5;241m.\u001b[39mset_params_to_folder(recording, output_folder, sorter_params, verbose)\n\u001b[1;32m 224\u001b[0m SorterClass\u001b[38;5;241m.\u001b[39msetup_recording(recording, output_folder, verbose\u001b[38;5;241m=\u001b[39mverbose)\n\u001b[0;32m--> 225\u001b[0m \u001b[43mSorterClass\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_from_folder\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput_folder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mraise_error\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m with_output:\n\u001b[1;32m 227\u001b[0m sorting \u001b[38;5;241m=\u001b[39m SorterClass\u001b[38;5;241m.\u001b[39mget_result_from_folder(output_folder, register_recording\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, sorting_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/spikeinterface/sorters/basesorter.py:258\u001b[0m, in \u001b[0;36mBaseSorter.run_from_folder\u001b[0;34m(cls, output_folder, raise_error, verbose)\u001b[0m\n\u001b[1;32m 255\u001b[0m t0 \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n\u001b[1;32m 257\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 258\u001b[0m \u001b[43mSorterClass\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_from_folder\u001b[49m\u001b[43m(\u001b[49m\u001b[43msorter_output_folder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msorter_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 259\u001b[0m t1 \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n\u001b[1;32m 260\u001b[0m run_time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mfloat\u001b[39m(t1 \u001b[38;5;241m-\u001b[39m t0)\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/spikeinterface/sorters/external/kilosort4.py:260\u001b[0m, in \u001b[0;36mKilosort4Sorter._run_from_folder\u001b[0;34m(cls, sorter_output_folder, params, verbose)\u001b[0m\n\u001b[1;32m 235\u001b[0m bfile \u001b[38;5;241m=\u001b[39m BinaryFiltered(\n\u001b[1;32m 236\u001b[0m ops[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfilename\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 237\u001b[0m n_chan_bin,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 252\u001b[0m file_object\u001b[38;5;241m=\u001b[39mfile_object,\n\u001b[1;32m 253\u001b[0m )\n\u001b[1;32m 255\u001b[0m \u001b[38;5;66;03m# TODO: don't think we need to do this actually\u001b[39;00m\n\u001b[1;32m 256\u001b[0m \u001b[38;5;66;03m# Save intermediate `ops` for use by GUI plots\u001b[39;00m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;66;03m# io.save_ops(ops, results_dir)\u001b[39;00m\n\u001b[1;32m 258\u001b[0m \n\u001b[1;32m 259\u001b[0m \u001b[38;5;66;03m# Sort spikes and save results\u001b[39;00m\n\u001b[0;32m--> 260\u001b[0m st, tF, _, _ \u001b[38;5;241m=\u001b[39m \u001b[43mdetect_spikes\u001b[49m\u001b[43m(\u001b[49m\u001b[43mops\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtic0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtic0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress_bar\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 261\u001b[0m clu, Wall \u001b[38;5;241m=\u001b[39m cluster_spikes(st, tF, ops, device, bfile, tic0\u001b[38;5;241m=\u001b[39mtic0, progress_bar\u001b[38;5;241m=\u001b[39mprogress_bar)\n\u001b[1;32m 262\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskip_kilosort_preprocessing\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/kilosort/run_kilosort.py:392\u001b[0m, in \u001b[0;36mdetect_spikes\u001b[0;34m(ops, device, bfile, tic0, progress_bar)\u001b[0m\n\u001b[1;32m 390\u001b[0m tic \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 391\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mExtracting spikes using templates\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 392\u001b[0m st0, tF, ops \u001b[38;5;241m=\u001b[39m \u001b[43mspikedetect\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mops\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprogress_bar\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 393\u001b[0m tF \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mfrom_numpy(tF)\n\u001b[1;32m 394\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(st0)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m spikes extracted in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtime\u001b[38;5;241m.\u001b[39mtime()\u001b[38;5;241m-\u001b[39mtic \u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m .2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124ms; \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \n\u001b[1;32m 395\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtotal \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtime\u001b[38;5;241m.\u001b[39mtime()\u001b[38;5;241m-\u001b[39mtic0 \u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m .2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124ms\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/kilosort/spikedetect.py:233\u001b[0m, in \u001b[0;36mrun\u001b[0;34m(ops, bfile, device, progress_bar)\u001b[0m\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ibatch \u001b[38;5;129;01min\u001b[39;00m tqdm(np\u001b[38;5;241m.\u001b[39marange(bfile\u001b[38;5;241m.\u001b[39mn_batches), miniters\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m200\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m progress_bar \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, \n\u001b[1;32m 230\u001b[0m mininterval\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m60\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m progress_bar \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 231\u001b[0m X \u001b[38;5;241m=\u001b[39m bfile\u001b[38;5;241m.\u001b[39mpadded_batch_to_torch(ibatch, ops)\n\u001b[0;32m--> 233\u001b[0m xy, imax, amp, adist \u001b[38;5;241m=\u001b[39m \u001b[43mtemplate_match\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mops\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miC\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miC2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweigh\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 234\u001b[0m yct \u001b[38;5;241m=\u001b[39m yweighted(yc, iC, adist, xy, device\u001b[38;5;241m=\u001b[39mdevice)\n\u001b[1;32m 235\u001b[0m nsp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(xy)\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc/lib/python3.8/site-packages/kilosort/spikedetect.py:156\u001b[0m, in \u001b[0;36mtemplate_match\u001b[0;34m(X, ops, iC, iC2, weigh, device)\u001b[0m\n\u001b[1;32m 154\u001b[0m Amaxs[:,\u001b[38;5;241m-\u001b[39mnt:] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 155\u001b[0m Amaxs \u001b[38;5;241m=\u001b[39m max_pool1d(Amaxs\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m), (\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39mnt0\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m), stride \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m, padding \u001b[38;5;241m=\u001b[39m nt0)\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m--> 156\u001b[0m xy \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlogical_and\u001b[49m\u001b[43m(\u001b[49m\u001b[43mAmaxs\u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43mAs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mAs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mops\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTh_universal\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnonzero\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 157\u001b[0m imax \u001b[38;5;241m=\u001b[39m imaxs[xy[:,\u001b[38;5;241m0\u001b[39m], xy[:,\u001b[38;5;241m1\u001b[39m]]\n\u001b[1;32m 158\u001b[0m amp \u001b[38;5;241m=\u001b[39m As[xy[:,\u001b[38;5;241m0\u001b[39m], xy[:,\u001b[38;5;241m1\u001b[39m]]\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "%%time\n", + "\n", + "# store sort summaries\n", + "sort_summary_all = []\n", + "\n", + "# loop through all birds / recordings\n", + "for this_bird in bird_rec_dict.keys():\n", + " # get session configurations\n", + " sess_all = bird_rec_dict[this_bird]\n", + " \n", + " # loop through session configurations\n", + " for this_sess_config in sess_all:\n", + " \n", + " # loop through sessions\n", + " for this_sess in this_sess_config['sess_par_list']:\n", + " log_dir = os.path.join('/mnt/cube/chronic_ephys/log', this_bird, this_sess)\n", + " \n", + " # build session parameter dictionary\n", + " sess_par = {'bird':this_bird,\n", + " 'sess':this_sess,\n", + " 'ephys_software':this_sess_config['ephys_software'],\n", + " 'sorter':this_sess_config['sorter'],\n", + " 'sort':this_sess_config['sort']}\n", + " # get epochs\n", + " sess_epochs = et.list_ephys_epochs(sess_par)\n", + " \n", + " for this_epoch in sess_epochs:\n", + " \n", + " # set output directory\n", + " epoch_struct = et.sgl_struct(sess_par,this_epoch,ephys_software=sess_par['ephys_software'])\n", + " sess_par['epoch'] = this_epoch\n", + " sort_path = epoch_struct['folders']['derived'] + '/{}/{}/'.format(sess_par['sorter'],sess_par['sort'])\n", + " sorting_analyzer_path = sort_path + 'sorting_analyzer/'\n", + " \n", + " # get spike sort log\n", + " try:\n", + " with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'r') as f:\n", + " log_message=f.readline() # read the first line of the log file\n", + " if log_message[:-1] == sess_par['bird']+' '+sess_par['sess']+' sort complete without error':\n", + " print(sess_par['bird'],sess_par['sess'],'already exists -- skipping sort')\n", + " run_proc = False\n", + " elif log_message[:-1] == sess_par['bird']+' '+sess_par['sess']+' sort failed':\n", + " if skip_failed:\n", + " print(sess_par['bird'],sess_par['sess'],'previously failed -- skipping sort')\n", + " run_proc = False\n", + " else:\n", + " run_proc = True\n", + " else: # uninterpretable log file\n", + " run_proc = True\n", + " except: # no existing log file\n", + " run_proc = True\n", + " \n", + " # run sort\n", + " if run_proc:\n", + " try:\n", + " print('___________',this_bird,this_sess,this_epoch,'___________')\n", + " # prepare recording for sorting\n", + " print('prep..')\n", + " if sess_par['ephys_software'] == 'sglx':\n", + " # load recording\n", + " rec_path = epoch_struct['folders']['sglx']\n", + " this_rec = si.read_spikeglx(folder_path=rec_path,stream_name='imec0.ap')\n", + " # save probe map prior to re-ordering for sorting\n", + " probe_df = this_rec.get_probe().to_dataframe()\n", + " probe_df.to_pickle(os.path.join(epoch_struct['folders']['derived'],'probe_map_df.pickle'))\n", + " # ibl destriping\n", + " this_rec = si.highpass_filter(recording=this_rec)\n", + " this_rec = si.phase_shift(recording=this_rec)\n", + " bad_good_channel_ids = si.detect_bad_channels(recording=this_rec)\n", + " if len(bad_good_channel_ids[0]) > 0:\n", + " this_rec = si.interpolate_bad_channels(recording=this_rec,bad_channel_ids=bad_good_channel_ids[0])\n", + " if this_sess_config['probe']['probe_type'] == 'neuropixels-2.0':\n", + " # highpass by shank\n", + " split_rec = this_rec.split_by(property='group',outputs='list') # split recording by shank\n", + " split_rec = [si.highpass_spatial_filter(recording=r,n_channel_pad=min(r.get_num_channels(),60)) for r in split_rec]\n", + " this_rec_p = si.aggregate_channels(split_rec) # recombine shanks\n", + " # stack shanks\n", + " p,_ = pm.stack_shanks(probe_df) # make new Probe object with shanks stacked\n", + " this_rec_p = this_rec.set_probe(p,group_mode='by_probe') # assign new Probe object to probe\n", + " else:\n", + " this_rec_p = si.highpass_spatial_filter(recording=this_rec)\n", + " elif sess_par['ephys_software'] =='oe':\n", + " # load recording\n", + " rec_path = [f.path for f in os.scandir(epoch_struct['folders']['oe']) if f.is_dir()][0]\n", + " this_rec = si.read_openephys(folder_path=rec_path)\n", + " # add probe\n", + " this_probe = pm.make_probes(this_sess_config['probe']['probe_type'],this_sess_config['probe']['probe_model']) # neuronexus, Buzsaki64\n", + " this_rec_p = this_rec.set_probe(this_probe,group_mode='by_shank')\n", + " # set sort params\n", + " this_rec_p = si.concatenate_recordings([this_rec_p])\n", + " sort_params = si.get_default_sorter_params(this_sess_config['sorter'])\n", + " for this_param in this_sess_config['sort_params'].keys():\n", + " sort_params[this_param] = this_sess_config['sort_params'][this_param]\n", + " # run sort\n", + " print('sort..')\n", + " torch.cuda.empty_cache()\n", + " this_sort = si.run_sorter(sorter_name=this_sess_config['sorter'],recording=this_rec_p,output_folder=sort_path,\n", + " remove_existing_folder=True,delete_output_folder=False,delete_container_files=False,\n", + " verbose=verbose,raise_error=raise_error,**sort_params)\n", + " torch.cuda.empty_cache()\n", + " # bandpass recording before running analyzer\n", + " this_rec_pf = si.bandpass_filter(recording=this_rec_p)\n", + " # run sorting analyzer\n", + " print('sorting analyzer..')\n", + " analyzer = si.create_sorting_analyzer(sorting=this_sort,recording=this_rec_pf,format=\"binary_folder\",\n", + " sparse=True,return_scaled=True,folder=sorting_analyzer_folder)\n", + " ext_compute_all = analyzer.get_computable_extensions()\n", + " for this_ext in ext_compute_all:\n", + " print(this_ext + '..')\n", + " analyzer.compute(this_ext)\n", + " \n", + " # mark complete\n", + " print('COMPLETE!!')\n", + "\n", + " # log complete sort\n", + " if not os.path.exists(log_dir): os.makedirs(log_dir)\n", + " with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'w') as f:\n", + " f.write(sess_par['bird']+' '+sess_par['sess']+' sort complete without error\\n\\n')\n", + " f.write('Sort method: '+this_sess_config['sorter']+'\\n\\n')\n", + " f.write('Sort params: '+str(sort_params)+'\\n\\n')\n", + " f.write('Computed quality metrics: '+str(metric_names)+'\\n\\n')\n", + " f.write('Failed quality metrics: '+str(bad_metrics)+'\\n')\n", + " sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'COMPLETE']\n", + " \n", + " except Exception as e:\n", + " # mark exception\n", + " print(\"An exception occurred:\", e)\n", + " \n", + " # log failed sort\n", + " if not os.path.exists(log_dir): os.makedirs(log_dir)\n", + " with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'w') as f:\n", + " f.write(sess_par['bird']+' '+sess_par['sess']+' sort failed\\n')\n", + " f.write(traceback.format_exc())\n", + " sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'FAIL']\n", + " else:\n", + " sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'EXISTS']\n", + " \n", + " # report and store sort summary\n", + " print(sort_summary)\n", + " sort_summary_all.append(sort_summary)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "spikeproc", + "language": "python", + "name": "spikeproc" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/3-sort_spikes_v0.101.py b/3-sort_spikes_v0.101.py new file mode 100644 index 0000000..214028d --- /dev/null +++ b/3-sort_spikes_v0.101.py @@ -0,0 +1,218 @@ +### Spike sort +# +# Script within the chronic ephys processing pipeline +# - 1-preprocess_acoustics +# - 2-curate_acoustics +# - **3-sort_spikes** +# - 4-curate_spikes +# +# Use the environment **spikeproc** to run this notebook + + +## Import packages +import numpy as np +import os +import pickle +os.environ["NPY_MATLAB_PATH"] = '/mnt/cube/chronic_ephys/code/npy-matlab' +os.environ["KILOSORT2_PATH"] = '/mnt/cube/chronic_ephys/code/Kilosort2' +os.environ["KILOSORT3_PATH"] = '/mnt/cube/chronic_ephys/code/Kilosort' +import spikeinterface.full as si +import sys +import traceback +import torch +sys.path.append('/mnt/cube/lo/envs/ceciestunepipe/') +from ceciestunepipe.file import bcistructure as et +from ceciestunepipe.mods import probe_maps as pm + + +## Set parameters +si.get_default_sorter_params('kilosort4') + +# non default spike sorting parameters +sort_params_dict_ks3 = {'minFR':0.001, 'minfr_goodchannels':0.001} # kilosort 3 +sort_params_dict_ks4_npx = {'batch_size':30000, 'nblocks':5, 'Th_universal':8, 'Th_learned':7, 'dmin':15, 'dminx':32} # kilosort 4, neuropixels (set dmin and dminx to true pitch) +sort_params_dict_ks4_nnx64 = {'nblocks':0, 'nearest_templates':64, + 'Th_universal':8, 'Th_learned':7} # kilosort 4, neuronexus 64 chan + +# waveform extraction parameters +wave_params_dict = {'ms_before':1, 'ms_after':2, 'max_spikes_per_unit':500, + 'sparse':True, 'num_spikes_for_sparsity':100, 'method':'radius', + 'radius_um':40, 'n_components':5, 'mode':'by_channel_local'} + +# print stuff +verbose = True + +# errors break sorting +raise_error = False + +# restrict sorting to a specific GPU +restrict_to_gpu = 1 # 0 1 None + +# use specific GPU if specified +if restrict_to_gpu is not None: + os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" + os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(restrict_to_gpu) + +# parallel processing params +job_kwargs = dict(n_jobs=28,chunk_duration="1s",progress_bar=False) +si.set_global_job_kwargs(**job_kwargs) + +# force processing of previous failed sorts +skip_failed = False + +# session info +bird_rec_dict = { + 'z_r5r13_24':[ + {'sess_par_list':['2024-08-06'], # sessions (will process all epochs within) + 'probe':{'probe_type':'neuropixels-2.0'}, # probe specs + 'sort':'sort_2', # label for this sort instance + 'sorter':'kilosort4', # sort method + 'sort_params':sort_params_dict_ks4_npx, # non-default sort params + 'wave_params':wave_params_dict, # waveform extraction params + 'ephys_software':'sglx' # sglx or oe + }, + ], +} + + + +## Run sorts + +# store sort summaries +sort_summary_all = [] + +# loop through all birds / recordings +for this_bird in bird_rec_dict.keys(): + # get session configurations + sess_all = bird_rec_dict[this_bird] + + # loop through session configurations + for this_sess_config in sess_all: + + # loop through sessions + for this_sess in this_sess_config['sess_par_list']: + log_dir = os.path.join('/mnt/cube/chronic_ephys/log', this_bird, this_sess) + + # build session parameter dictionary + sess_par = {'bird':this_bird, + 'sess':this_sess, + 'ephys_software':this_sess_config['ephys_software'], + 'sorter':this_sess_config['sorter'], + 'sort':this_sess_config['sort']} + # get epochs + sess_epochs = et.list_ephys_epochs(sess_par) + + for this_epoch in sess_epochs: + + # set output directory + epoch_struct = et.sgl_struct(sess_par,this_epoch,ephys_software=sess_par['ephys_software']) + sess_par['epoch'] = this_epoch + sort_path = epoch_struct['folders']['derived'] + '/{}/{}/'.format(sess_par['sorter'],sess_par['sort']) + sorting_analyzer_path = sort_path + 'sorting_analyzer/' + + # get spike sort log + try: + with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'r') as f: + log_message=f.readline() # read the first line of the log file + if log_message[:-1] == sess_par['bird']+' '+sess_par['sess']+' sort complete without error': + print(sess_par['bird'],sess_par['sess'],'already exists -- skipping sort') + run_proc = False + elif log_message[:-1] == sess_par['bird']+' '+sess_par['sess']+' sort failed': + if skip_failed: + print(sess_par['bird'],sess_par['sess'],'previously failed -- skipping sort') + run_proc = False + else: + run_proc = True + else: # uninterpretable log file + run_proc = True + except: # no existing log file + run_proc = True + + # run sort + if run_proc: + try: + print('___________',this_bird,this_sess,this_epoch,'___________') + # prepare recording for sorting + print('prep..') + if sess_par['ephys_software'] == 'sglx': + # load recording + rec_path = epoch_struct['folders']['sglx'] + this_rec = si.read_spikeglx(folder_path=rec_path,stream_name='imec0.ap') + # save probe map prior to re-ordering for sorting + probe_df = this_rec.get_probe().to_dataframe() + probe_df.to_pickle(os.path.join(epoch_struct['folders']['derived'],'probe_map_df.pickle')) + # ibl destriping + this_rec = si.highpass_filter(recording=this_rec) + this_rec = si.phase_shift(recording=this_rec) + bad_good_channel_ids = si.detect_bad_channels(recording=this_rec) + if len(bad_good_channel_ids[0]) > 0: + this_rec = si.interpolate_bad_channels(recording=this_rec,bad_channel_ids=bad_good_channel_ids[0]) + if this_sess_config['probe']['probe_type'] == 'neuropixels-2.0': + # highpass by shank + split_rec = this_rec.split_by(property='group',outputs='list') # split recording by shank + split_rec = [si.highpass_spatial_filter(recording=r,n_channel_pad=min(r.get_num_channels(),60)) for r in split_rec] + this_rec_p = si.aggregate_channels(split_rec) # recombine shanks + # stack shanks + p,_ = pm.stack_shanks(probe_df) # make new Probe object with shanks stacked + this_rec_p = this_rec.set_probe(p,group_mode='by_probe') # assign new Probe object to probe + else: + this_rec_p = si.highpass_spatial_filter(recording=this_rec) + elif sess_par['ephys_software'] =='oe': + # load recording + rec_path = [f.path for f in os.scandir(epoch_struct['folders']['oe']) if f.is_dir()][0] + this_rec = si.read_openephys(folder_path=rec_path) + # add probe + this_probe = pm.make_probes(this_sess_config['probe']['probe_type'],this_sess_config['probe']['probe_model']) # neuronexus, Buzsaki64 + this_rec_p = this_rec.set_probe(this_probe,group_mode='by_shank') + # set sort params + this_rec_p = si.concatenate_recordings([this_rec_p]) + sort_params = si.get_default_sorter_params(this_sess_config['sorter']) + for this_param in this_sess_config['sort_params'].keys(): + sort_params[this_param] = this_sess_config['sort_params'][this_param] + # run sort + print('sort..') + torch.cuda.empty_cache() + this_sort = si.run_sorter(sorter_name=this_sess_config['sorter'],recording=this_rec_p,output_folder=sort_path, + remove_existing_folder=True,delete_output_folder=False,delete_container_files=False, + verbose=verbose,raise_error=raise_error,**sort_params) + torch.cuda.empty_cache() + # bandpass recording before running analyzer + this_rec_pf = si.bandpass_filter(recording=this_rec_p) + # run sorting analyzer + print('sorting analyzer..') + analyzer = si.create_sorting_analyzer(sorting=this_sort,recording=this_rec_pf,format="binary_folder", + sparse=True,return_scaled=True,folder=sorting_analyzer_folder) + ext_compute_all = analyzer.get_computable_extensions() + for this_ext in ext_compute_all: + print(this_ext + '..') + analyzer.compute(this_ext) + + # mark complete + print('COMPLETE!!') + + # log complete sort + if not os.path.exists(log_dir): os.makedirs(log_dir) + with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'w') as f: + f.write(sess_par['bird']+' '+sess_par['sess']+' sort complete without error\n\n') + f.write('Sort method: '+this_sess_config['sorter']+'\n\n') + f.write('Sort params: '+str(sort_params)+'\n\n') + f.write('Computed quality metrics: '+str(metric_names)+'\n\n') + f.write('Failed quality metrics: '+str(bad_metrics)+'\n') + sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'COMPLETE'] + + except Exception as e: + # mark exception + print("An exception occurred:", e) + + # log failed sort + if not os.path.exists(log_dir): os.makedirs(log_dir) + with open(os.path.join(log_dir, this_epoch+'_spikesort_'+this_sess_config['sort']+'.log'), 'w') as f: + f.write(sess_par['bird']+' '+sess_par['sess']+' sort failed\n') + f.write(traceback.format_exc()) + sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'FAIL'] + else: + sort_summary = [this_bird,this_sess,sess_par['ephys_software'],this_epoch,'EXISTS'] + + # report and store sort summary + print(sort_summary) + sort_summary_all.append(sort_summary) diff --git a/3.1-sort_spikes_concatenate.ipynb b/3.1-sort_spikes_concatenate.ipynb index c82c44e..c89f43b 100644 --- a/3.1-sort_spikes_concatenate.ipynb +++ b/3.1-sort_spikes_concatenate.ipynb @@ -28,6 +28,7 @@ "import spikeinterface.full as si\n", "import sys\n", "import traceback\n", + "import torch\n", "sys.path.append('/mnt/cube/lo/envs/ceciestunepipe/')\n", "from ceciestunepipe.file import bcistructure as et\n", "from ceciestunepipe.mods import probe_maps as pm" @@ -116,7 +117,7 @@ "raise_error = False\n", "\n", "# restrict sorting to a specific GPU\n", - "restrict_to_gpu = 0 # 0 1 None\n", + "restrict_to_gpu = 1 # 0 1 2 None\n", "\n", "# use specific GPU if specified\n", "if restrict_to_gpu is not None:\n", diff --git a/3-sort_spikes.py b/3.1-sort_spikes_concatenate.py similarity index 98% rename from 3-sort_spikes.py rename to 3.1-sort_spikes_concatenate.py index 4ba01e5..cb668ac 100644 --- a/3-sort_spikes.py +++ b/3.1-sort_spikes_concatenate.py @@ -19,6 +19,7 @@ import spikeinterface.full as si import sys import traceback +import torch sys.path.append('/mnt/cube/lo/envs/ceciestunepipe/') from ceciestunepipe.file import bcistructure as et from ceciestunepipe.mods import probe_maps as pm @@ -61,8 +62,8 @@ # session info bird_rec_dict = { - 'z_p5y10_23':[ - {'sess_par_list':['2024-05-16'], # sessions (will process all epochs within) + 'z_r5r13_24':[ + {'sess_par_list':['2024-08-06'], # sessions (will process all epochs within) 'probe':{'probe_type':'neuropixels-2.0'}, # probe specs 'sort':'sort_0', # label for this sort instance 'sorter':'kilosort4', # sort method @@ -169,9 +170,11 @@ sort_params[this_param] = this_sess_config['sort_params'][this_param] # run sort print('sort..') + torch.cuda.empty_cache() this_sort = si.run_sorter(sorter_name=this_sess_config['sorter'],recording=this_rec_p,output_folder=sort_folder, remove_existing_folder=True,delete_output_folder=False,delete_container_files=False, verbose=verbose,raise_error=raise_error,**sort_params) + torch.cuda.empty_cache() # bandpass recording before waveform extraction print('bandpass..') this_rec_pf = si.bandpass_filter(recording=this_rec_p) diff --git a/4-curate_spikes.ipynb b/4-curate_spikes.ipynb index 9adfaa4..aea5bf1 100644 --- a/4-curate_spikes.ipynb +++ b/4-curate_spikes.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -40,13 +40,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sess_par = {\n", " 'bird':'z_y19o20_21', # bird ID\n", " 'sess':'2021-10-27', # session date\n", + " 'probe':{'probe_type':'neuropixels-1.0'}, # probe specs\n", " 'epoch':'1033_undirected_g0-1142_directed_g0', # epoch\n", " 'ephys_software':'sglx', # recording software, sglx or oe\n", " 'sorter':'kilosort3', # spike sorting algorithm\n", @@ -65,18 +66,19 @@ "sess_par = {\n", " 'bird':'z_c5o30_23', # bird ID\n", " 'sess':'2023-06-15', # session date\n", + " 'probe':{'probe_type':'neuropixels-1.0'}, # probe specs\n", " 'epoch':'0913_g0', # epoch\n", " 'ephys_software':'sglx', # recording software, sglx or oe\n", " 'sorter':'kilosort3', # spike sorting algorithm\n", " 'sort':'sort_0', # sort index\n", "}\n", "\n", - "labels = ['sua','mua','noise','1','2','3']" + "labels = ['sua_1','sua_2','sua_3','mua','noise']" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -90,61 +92,43 @@ " 'sort':'sort_0', # sort index\n", "}\n", "\n", - "labels = ['sua','mua','noise','1','2','3']" + "labels = ['sua_1','sua_2','sua_3','mua','noise']" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "# Trevor's thresholds for automatic unit labeling (don't seem to transfer well to z_c5o30_23)\n", - "isi_vr_thresh_low = 0.1\n", - "isi_vr_thresh_high = 0.5\n", - "snr_thresh_low = 1\n", - "snr_thresh_high = 2\n", - "labels = []\n", - "auto_merge_dict = {\n", - " 'minimum_spikes':1000,\n", - " 'maximum_distance_um':150,\n", - " 'peak_sign':'neg',\n", - " 'bin_ms':0.25,\n", - " 'window_ms':100,\n", - " 'corr_diff_thresh':0.16,\n", - " 'template_diff_thresh':0.5, #0.25\n", - " 'censored_period_ms':0.3,\n", - " 'refractory_period_ms':1,\n", - " 'sigma_smooth_ms':0.6,\n", - " 'contamination_threshold':0.2,\n", - " 'adaptative_window_threshold':0.5,\n", - " 'censor_correlograms_ms':0.15,\n", - " 'num_channels':5,\n", - " 'num_shift':5,\n", - " 'firing_contamination_balance':1.5,\n", - " 'extra_outputs':False,\n", - " 'steps':['min_spikes','remove_contaminated','unit_positions',\n", - " 'template_similarity','check_increase_score']\n", - "} \n", - "# ['min_spikes','remove_contaminated','unit_positions',\n", - "# 'correlogram','template_similarity','check_increase_score']" - ] - }, - { - "cell_type": "code", - "execution_count": 17, + "sess_par = {\n", + " 'bird':'z_g9y18_23', # bird ID\n", + " 'sess':'2024-04-18', # session date\n", + " 'probe':{'probe_type':'neuropixels-1.0'}, # probe specs\n", + " 'epoch':'0959_g0', # epoch\n", + " 'ephys_software':'sglx', # recording software, sglx or oe\n", + " 'sorter':'kilosort4', # spike sorting algorithm\n", + " 'sort':'sort_0', # sort index\n", + "}\n", + "\n", + "labels = ['sua_1','sua_2','sua_3','mua','noise']" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/mnt/cube/chronic_ephys/der/z_p5y10_23/2024-05-16/sglx/1246_g0/kilosort4/sort_0/\n" + "/mnt/cube/chronic_ephys/der/z_c5o30_23/2023-06-15/sglx/0913_g0/kilosort3/sort_0/\n" ] } ], "source": [ - "sort_dir = '/net2/expData/speech_bci/derived_data/{}/{}/{}/{}/{}/{}/'.format(sess_par['bird'],sess_par['sess'],sess_par['ephys_software'],sess_par['epoch'],sess_par['sorter'],sess_par['sort'])\n", + "sort_dir = '/mnt/cube/chronic_ephys/der/{}/{}/{}/{}/{}/{}/'.format(sess_par['bird'],sess_par['sess'],sess_par['ephys_software'],sess_par['epoch'],sess_par['sorter'],sess_par['sort'])\n", "sort_path = sort_dir + 'sorter_output/'\n", "wave_path = sort_dir + 'waveforms/'\n", "metrics_path = wave_path + 'quality_metrics/metrics.csv'\n", @@ -153,9 +137,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/cube/lo/envs/spikeproc100.8/lib/python3.8/site-packages/spikeinterface/core/base.py:1079: UserWarning: Versions are not the same. This might lead to compatibility errors. Using spikeinterface==0.98.2 is recommended\n", + " warnings.warn(\n" + ] + } + ], "source": [ "sort = si.read_kilosort(sort_path)\n", "wave = si.load_waveforms(wave_path)" @@ -173,7 +166,7 @@ "sua_1: 56\n", "sua_2: 53\n", "sua_3: 53\n", - "mua_4: 228\n", + "mua: 228\n", "noise: 0\n", "total: 337\n", "KiloSortSortingExtractor: 337 units - 1 segments - 30.0kHz\n", @@ -183,6 +176,11 @@ } ], "source": [ + "isi_vr_thresh_low=0.1\n", + "isi_vr_thresh_high=0.5\n", + "snr_thresh_low=1\n", + "snr_thresh_high=2\n", + "\n", "metrics_pd = pd.read_csv(metrics_path)\n", "metrics_list = metrics_pd.keys().tolist()\n", "for this_metric in metrics_list:\n", @@ -200,7 +198,7 @@ "snr_label[np.where(sort.get_property('snr') > snr_thresh_high)[0]] = 'h' \n", "sort.set_property('snr_thresh',snr_label)\n", "quality_labels = np.full(sort.get_num_units(),'_____')\n", - "quality_labels[np.where(isi_vr_label == 'h')[0]] = 'mua_4'\n", + "quality_labels[np.where(isi_vr_label == 'h')[0]] = 'mua'\n", "quality_labels[np.where((isi_vr_label == 'l') & (snr_label == 'h'))[0]] = 'sua_1'\n", "quality_labels[np.where((isi_vr_label == 'l') & (snr_label == 'm'))[0]] = 'sua_2'\n", "quality_labels[np.where((isi_vr_label == 'm') & (snr_label == 'h'))[0]] = 'sua_2'\n", @@ -210,12 +208,12 @@ "print('sua_1:',len(np.where(quality_labels=='sua_1')[0]))\n", "print('sua_2:',len(np.where(quality_labels=='sua_2')[0]))\n", "print('sua_3:',len(np.where(quality_labels=='sua_2')[0]))\n", - "print('mua_4:',len(np.where(quality_labels=='mua_4')[0]))\n", + "print('mua:',len(np.where(quality_labels=='mua')[0]))\n", "print('noise:',len(np.where(quality_labels=='noise')[0]))\n", "print('total:',len(np.where(quality_labels=='sua_1')[0])+\n", " len(np.where(quality_labels=='sua_2')[0])+\n", " len(np.where(quality_labels=='sua_3')[0])+\n", - " len(np.where(quality_labels=='mua_4')[0])+\n", + " len(np.where(quality_labels=='mua')[0])+\n", " len(np.where(quality_labels=='noise')[0]))\n", "wave.sorting = sort\n", "unit_table_properties = ['quality_labels','KSLabel','isi_violations_ratio','snr','num_spikes']\n", @@ -225,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -274,9 +272,7 @@ "metadata": {}, "outputs": [], "source": [ - "# skip whatever metrics failed during sort\n", - "### remove this\n", - "si.plot_quality_metrics(wave, skip_metrics=['amplitude_cutoff'], backend=\"sortingview\");" + "si.plot_sorting_summary(waveform_extractor=wave, curation=True, backend='sortingview', unit_table_properties=unit_table_properties, label_choices=labels);" ] }, { @@ -285,7 +281,8 @@ "metadata": {}, "outputs": [], "source": [ - "si.plot_sorting_summary(waveform_extractor=wave, curation=True, backend='sortingview', label_choices=labels);" + "# skip whatever metrics failed during sort\n", + "si.plot_quality_metrics(wave, skip_metrics=['amplitude_cutoff', 'amplitude_median', 'amplitude_cv', 'sd_ratio'], backend=\"sortingview\");" ] }, { @@ -298,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -307,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -317,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -327,25 +324,18 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "435 units after curation:\n", - "- Units [68, 66] merged to 489\n", - "- Units [24, 31] merged to 490\n", - "- Units [25, 28, 32] merged to 491\n", - "- Units [39, 40] merged to 492\n", - "- Units [51, 53, 62] merged to 493\n", - "- Units [93, 95, 84] merged to 494\n", - "- Units [114, 112, 115, 116, 110] merged to 495\n", - "- Units [118, 108] merged to 496\n", - "- Units [111, 113] merged to 497\n", - "- Units [414, 409, 432, 426] merged to 498\n", - "- Units [164, 166, 165, 163] merged to 499\n" + "ename": "NameError", + "evalue": "name 'sort_curated' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[10], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# get unit IDs\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m unit_ids \u001b[38;5;241m=\u001b[39m \u001b[43msort_curated\u001b[49m\u001b[38;5;241m.\u001b[39mget_unit_ids()\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(unit_ids)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m units after curation:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# get merged units\u001b[39;00m\n", + "\u001b[0;31mNameError\u001b[0m: name 'sort_curated' is not defined" ] } ], @@ -377,6 +367,74 @@ " orig_unit_ids[nui_i] = u" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use auto-curation & auto-merges\n", + "Created this in case spikeinterface curation module was throwing unsolvable errors" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'merge_lists' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# spikeinterface 0.100.8 workaround\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m merges_auto \u001b[38;5;241m=\u001b[39m \u001b[43mmerge_lists\u001b[49m(potential_merges)\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(merges_auto)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(merges_auto) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m: sort_curated \u001b[38;5;241m=\u001b[39m si\u001b[38;5;241m.\u001b[39mMergeUnitsSorting(sort, merges_auto)\n", + "\u001b[0;31mNameError\u001b[0m: name 'merge_lists' is not defined" + ] + } + ], + "source": [ + "# spikeinterface 0.100.8 workaround\n", + "merges_auto = merge_lists(potential_merges)\n", + "print(merges_auto)\n", + "if len(merges_auto) > 0: sort_curated = si.MergeUnitsSorting(sort, merges_auto)\n", + "else: sort_curated = sort\n", + "sort_curated.labels = quality_labels" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# might work in spikeinterface 0.101.0\n", + "recording = ...\n", + "sort_curated = si.create_sorting_analyzer(sorting=sort, recording=recording)\n", + "sort_curated = sort_curated.merge_units(merge_unit_groups=merge_unit_groups)\n", + "sort_curated.labels = quality_labels" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -387,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -401,7 +459,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 25, "metadata": { "scrolled": true }, @@ -434,107 +492,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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unitspike_trainContamPctAmplitudeKSLabelKSLabel_repeatUnnamed: 0num_spikesfiring_ratepresence_ratio...silhouettenn_hit_ratenn_miss_ratelabel_mualabel_1label_sualabel_2label_noiselabel_3orig_unit
434499[130, 15199, 15914, 21923, 23492, 24855, 36386...100.06.935584e-310muamua16353353.07.9209221.0...0.0918390.58550.035969TrueTrueTrueFalseTrueFalse[164, 166, 165, 163]
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1 rows × 31 columns

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" - ], - "text/plain": [ - " unit spike_train ContamPct \\\n", - "434 499 [130, 15199, 15914, 21923, 23492, 24855, 36386... 100.0 \n", - "\n", - " Amplitude KSLabel KSLabel_repeat Unnamed: 0 num_spikes \\\n", - "434 6.935584e-310 mua mua 163 53353.0 \n", - "\n", - " firing_rate presence_ratio ... silhouette nn_hit_rate nn_miss_rate \\\n", - "434 7.920922 1.0 ... 0.091839 0.5855 0.035969 \n", - "\n", - " label_mua label_1 label_sua label_2 label_noise label_3 \\\n", - "434 True True True False True False \n", - "\n", - " orig_unit \n", - "434 [164, 166, 165, 163] \n", - "\n", - "[1 rows x 31 columns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "spk_df.tail(1)" ] @@ -548,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -579,7 +539,111 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "with open('/mnt/cube/chronic_ephys/der/z_y19o20_21/2021-10-27/sglx/1033_undirected_g0-1142_directed_g0/kilosort3/sort_0/spk_df.pickle', 'rb') as f:\n", + " spk_df = pickle.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# spk_df_save = spk_df" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, False])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spk_df.label_mua.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "spk_df = spk_df[(spk_df['label_noise'] == False) & (spk_df['label_mua'] == False)]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'DataFrame' object has no attribute 'quality_labels'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mspk_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquality_labels\u001b[49m\u001b[38;5;241m.\u001b[39munique()\n", + "File \u001b[0;32m/mnt/cube/lo/envs/spikeproc100.8/lib/python3.8/site-packages/pandas/core/generic.py:5902\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 5895\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 5896\u001b[0m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_internal_names_set\n\u001b[1;32m 5897\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metadata\n\u001b[1;32m 5898\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessors\n\u001b[1;32m 5899\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info_axis\u001b[38;5;241m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[1;32m 5900\u001b[0m ):\n\u001b[1;32m 5901\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[name]\n\u001b[0;32m-> 5902\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getattribute__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'quality_labels'" + ] + } + ], + "source": [ + "spk_df.quality_labels.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "spk_df = spk_df[spk_df['quality_labels'] != 'noise']\n", + "# spk_df = spk_df[spk_df['quality_labels'] != 'mua']" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1907581/2241398020.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " spk_df['sort_key'] = spk_df['unit_locations'].apply(lambda x: x[1])\n" + ] + } + ], + "source": [ + "spk_df['sort_key'] = spk_df['unit_locations'].apply(lambda x: x[1])\n", + "spk_df = spk_df.sort_values(by='sort_key', ascending=False)\n", + "spk_df = spk_df.drop(columns='sort_key')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -592,7 +656,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -604,7 +668,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -613,7 +677,7 @@ "[8, 39, 52, 59, 64, 65, 69, 79, 127, 157, 177, 188]" ] }, - "execution_count": 53, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -624,7 +688,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -637,7 +701,7 @@ ], "source": [ "# retrieve bout info\n", - "bout_idx = 52\n", + "bout_idx = 0\n", "if bout_df.loc[bout_idx, 'bout_check']:\n", " waveform = bout_df.loc[bout_idx, 'waveform']\n", " spectrogram = bout_df.loc[bout_idx, 'spectrogram']\n", @@ -651,7 +715,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -674,14 +738,14 @@ "spike_arr = make_raster(spk_df, spk_df.unit.to_list(), start_idx, end_idx)\n", "\n", "# reorder spike train by original unit\n", - "orig_unit = spk_df.orig_unit.to_list()\n", - "sorted_unit = np.argsort([np.mean(ou) for ou in orig_unit])\n", - "spike_arr = spike_arr[sorted_unit]" + "# orig_unit = spk_df.orig_unit.to_list()\n", + "# sorted_unit = np.argsort([np.mean(ou) for ou in orig_unit])\n", + "# spike_arr = spike_arr[sorted_unit]" ] }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 80, "metadata": { "tags": [] }, @@ -708,29 +772,14 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 81, "metadata": {}, "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4568797ea59a4ddd95c0ea2a5a423e15", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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", 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unitspike_trainunit_locationsabs_unit_locationsKSLabelKSLabel_repeatContamPctAmplitudeUnnamed: 0drift_ptp...rp_violationssliding_rp_violationsnrsync_spike_2sync_spike_4sync_spike_8isi_vr_threshsnr_threshquality_labelsorig_unit
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44[862897, 863614, 868115, 868783, 1060008, 1068...[36.141814351127465, 0.8324806436815474, 1.0][2036.1418143511276, 4300.832480643681, 301.0]goodgood0.039.74NaN...00.10018.6799930.1093290.0010300.000294lhsua_1[4]
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457457[10084244, 10704935, 27072676, 32277106, 36124...[1.8420477997573566, 3331.172939619838, 1.0][2001.8420477997574, 7631.172939619838, 301.0]muamua0.029.9457NaN...0NaN28.7723120.5964910.0263160.000000lhsua_1[457]
442442[7936614, 10084243, 10704934, 12391860, 129995...[1.6214948409849221, 3331.285092901966, 1.0][2001.621494840985, 7631.285092901966, 301.0]muamua0.023.9442NaN...0NaN27.6540800.6524820.0354610.000000lhsua_1[442]
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463463[7936608, 10084236, 10704928, 32277098, 424636...[2.2288280385007586, 3331.7660338278542, 1.0][2002.2288280385008, 7631.766033827855, 301.0]muamua0.035.4463NaN...0NaN31.7999441.0000000.4133330.013333lhsua_1[463]
509509[83868, 271466, 290050, 470801, 541054, 546843...[-0.015689899768380266, 3739.999820501117, 1.0][1999.9843101002316, 8039.999820501117, 301.0]goodgood0.016.8509NaN...00.0555.7169750.0945500.0056910.002845lhsua_1[509]
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107 rows × 29 columns

\n", + "
" + ], + "text/plain": [ + " unit spike_train \\\n", + "7 7 [35264, 109017, 338275, 397747, 521440, 711554... \n", + "4 4 [862897, 863614, 868115, 868783, 1060008, 1068... \n", + "9 9 [1624, 5873, 11843, 13804, 27799, 35290, 39990... \n", + "2 2 [19768, 53114, 143131, 272889, 282368, 293545,... \n", + "8 8 [37733, 86645, 234208, 329221, 350448, 360985,... \n", + ".. ... ... \n", + "457 457 [10084244, 10704935, 27072676, 32277106, 36124... \n", + "442 442 [7936614, 10084243, 10704934, 12391860, 129995... \n", + "458 458 [7936607, 10084236, 10704927, 12391852, 129995... \n", + "463 463 [7936608, 10084236, 10704928, 32277098, 424636... \n", + "509 509 [83868, 271466, 290050, 470801, 541054, 546843... \n", + "\n", + " unit_locations \\\n", + "7 [31.173637502190775, -5.265224089156908, 1.0] \n", + "4 [36.141814351127465, 0.8324806436815474, 1.0] \n", + "9 [27.33218575392167, 3.9382619346881493, 5.6217... \n", + "2 [13.036294440970302, 10.323868440028003, 1.551... \n", + "8 [10.596645235287191, 14.858289054409507, 1.0] \n", + ".. ... \n", + "457 [1.8420477997573566, 3331.172939619838, 1.0] \n", + "442 [1.6214948409849221, 3331.285092901966, 1.0] \n", + "458 [2.0303124008646574, 3331.543695359722, 1.0] \n", + "463 [2.2288280385007586, 3331.7660338278542, 1.0] \n", + "509 [-0.015689899768380266, 3739.999820501117, 1.0] \n", + "\n", + " abs_unit_locations KSLabel KSLabel_repeat \\\n", + "7 [2031.1736375021908, 4294.734775910843, 301.0] good good \n", + "4 [2036.1418143511276, 4300.832480643681, 301.0] good good \n", + "9 [2027.3321857539217, 4303.938261934688, 305.62... good good \n", + "2 [2013.0362944409703, 4310.323868440028, 301.55... good good \n", + "8 [2010.5966452352873, 4314.858289054409, 301.0] good good \n", + ".. ... ... ... \n", + "457 [2001.8420477997574, 7631.172939619838, 301.0] mua mua \n", + "442 [2001.621494840985, 7631.285092901966, 301.0] mua mua \n", + "458 [2002.0303124008647, 7631.543695359722, 301.0] mua mua \n", + "463 [2002.2288280385008, 7631.766033827855, 301.0] mua mua \n", + "509 [1999.9843101002316, 8039.999820501117, 301.0] good good \n", + "\n", + " ContamPct Amplitude Unnamed: 0 drift_ptp ... rp_violations \\\n", + "7 0.0 21.6 7 NaN ... 0 \n", + "4 0.0 39.7 4 NaN ... 0 \n", + "9 5.6 23.2 9 NaN ... 6 \n", + "2 0.0 23.4 2 NaN ... 0 \n", + "8 2.8 28.6 8 NaN ... 6 \n", + ".. ... ... ... ... ... ... \n", + "457 0.0 29.9 457 NaN ... 0 \n", + "442 0.0 23.9 442 NaN ... 0 \n", + "458 0.0 46.4 458 NaN ... 0 \n", + "463 0.0 35.4 463 NaN ... 0 \n", + "509 0.0 16.8 509 NaN ... 0 \n", + "\n", + " sliding_rp_violation snr sync_spike_2 sync_spike_4 \\\n", + "7 0.245 17.554270 0.089304 0.001766 \n", + "4 0.100 18.679993 0.109329 0.001030 \n", + "9 0.050 17.821367 0.097092 0.000905 \n", + "2 0.075 20.067417 0.100830 0.001709 \n", + "8 0.035 22.820686 0.134239 0.001888 \n", + ".. ... ... ... ... \n", + "457 NaN 28.772312 0.596491 0.026316 \n", + "442 NaN 27.654080 0.652482 0.035461 \n", + "458 NaN 31.124668 0.851852 0.238095 \n", + "463 NaN 31.799944 1.000000 0.413333 \n", + "509 0.055 5.716975 0.094550 0.005691 \n", + "\n", + " sync_spike_8 isi_vr_thresh snr_thresh quality_labels orig_unit \n", + "7 0.000505 l h sua_1 [7] \n", + "4 0.000294 l h sua_1 [4] \n", + "9 0.000151 l h sua_1 [9] \n", + "2 0.000366 l h sua_1 [2] \n", + "8 0.000067 l h sua_1 [8] \n", + ".. ... ... ... ... ... \n", + "457 0.000000 l h sua_1 [457] \n", + "442 0.000000 l h sua_1 [442] \n", + "458 0.005291 l h sua_1 [458] \n", + "463 0.013333 l h sua_1 [463] \n", + "509 0.002845 l h sua_1 [509] \n", + "\n", + "[107 rows x 29 columns]" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spk_df[spk_df.quality_labels=='sua_1']" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/4-new_curate_spikes.ipynb b/4-new_curate_spikes.ipynb new file mode 100644 index 0000000..047020e --- /dev/null +++ b/4-new_curate_spikes.ipynb @@ -0,0 +1,707 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Set up" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "x=0" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pickle\n", + "import spikeinterface.full as si\n", + "import sys\n", + "sys.path.append('/mnt/cube/tsmcpher/code/')\n", + "from ephys_tsm import spike_util as su" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Prep data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Thresholds for quality metric curation\n", + "isi_vr_thresh = [0.1,0.5]\n", + "snr_thresh = [1,2]" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "# Probe absolute location (unit locations are relative)\n", + "# SI formatting: probe width (x), depth (y), othogonal (z)\n", + "# Assuming flat of probe extends M/L, foot is anterior, and vertical implant, use:\n", + "# Note at angle VENTRAL is how far probe is lowered into brain\n", + "# Angle is deviation from vertical\n", + "# M/L (x), D/V (y), A/P (z)\n", + "# s_b1484_24, HVC right: [3800,-200,800], angle: 0\n", + "# s_b1357_23, HVC left: [-3500,-800,1000], angle: 0\n", + "# s_b1253_21, RA right: [3380,-4500,630], angle: 0\n", + "# s_b1253_21, RA left: [-3380,-4500,630], angle: 52\n", + "probe_angle_deg = 38\n", + "probe_abs_loc = np.array([-3000,-4000,500])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['1108_g0', '0806_g0', '1410_g0']\n" + ] + } + ], + "source": [ + "bird_in = 's_b1360_24'\n", + "sess_in = '2024-07-30'\n", + "ephys_software_in = 'sglx'\n", + "path_in = '/mnt/cube/chronic_ephys/der/{}/{}/{}/'.format(bird_in,sess_in,ephys_software_in)\n", + "epochs = os.listdir(path_in)\n", + "print(epochs)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "({'bird': 's_b1360_24',\n", + " 'sess': '2024-07-30',\n", + " 'epoch': '0806_g0',\n", + " 'ephys_software': 'sglx',\n", + " 'sorter': 'kilosort4',\n", + " 'sort': 0},\n", + " '/mnt/cube/chronic_ephys/der/s_b1360_24/2024-07-30/sglx/0806_g0/kilosort4/0/')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "epoch_i = 1\n", + "epoch_in = epochs[epoch_i]\n", + "sess_par = {\n", + " 'bird':bird_in, # bird id\n", + " 'sess':sess_in, # session date\n", + " 'epoch':epoch_in, # epoch\n", + " 'ephys_software':ephys_software_in, # recording software, sglx or oe\n", + " 'sorter':'kilosort4', # spike sorting algorithm\n", + " 'sort':0} # sort index\n", + "sort_dir = '/mnt/cube/chronic_ephys/der/{}/{}/{}/{}/{}/{}/'.format(\n", + " sess_par['bird'],sess_par['sess'],sess_par['ephys_software'],\n", + " sess_par['epoch'],sess_par['sorter'],sess_par['sort'])\n", + "sess_par,sort_dir" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sorting analyzer..\n", + "sua_1: 49\n", + "sua_2: 65\n", + "sua_3: 65\n", + "mua_4: 194\n", + "noise: 9\n", + "total: 319\n", + "NumpySorting: 319 units - 1 segments - 30.0kHz\n", + "SortingAnalyzer: 384 channels - 319 units - 1 segments - binary_folder - sparse - has recording\n", + "Loaded 14 extensions: correlograms, template_similarity, principal_components, random_spikes, templates, unit_locations, waveforms, template_metrics, isi_histograms, amplitude_scalings, noise_levels, spike_locations, quality_metrics, spike_amplitudes\n" + ] + } + ], + "source": [ + "sort_path = sort_dir + 'sorter_output/'\n", + "analyzer_path = sort_dir + 'sorting_analyzer/'\n", + "waveforms_path = sort_dir + 'waveforms/'\n", + "if os.path.exists(analyzer_path):\n", + " print('sorting analyzer..')\n", + " use_analyzer_not_wave = True\n", + " metrics_path = analyzer_path + 'extensions/quality_metrics/metrics.csv'\n", + " analyzer = si.load_sorting_analyzer(analyzer_path)\n", + "else:\n", + " if os.path.exists(waveforms_path):\n", + " print('waveforms..')\n", + " use_analyzer_not_wave = False\n", + " metrics_path = waveforms_path + 'quality_metrics/metrics.csv'\n", + " analyzer = si.load_waveforms(waveforms_path)\n", + " else: print('no analyzer or waveforms..')\n", + "metrics_pd = pd.read_csv(metrics_path)\n", + "metrics_list = metrics_pd.keys().tolist()\n", + "for this_metric in metrics_list:\n", + " analyzer.sorting.set_property(this_metric,metrics_pd[this_metric].values)\n", + "isi_vr_label = np.full(analyzer.sorting.get_num_units(),'l')\n", + "isi_vr_label[np.where((analyzer.sorting.get_property('isi_violations_ratio') > isi_vr_thresh[0]) & \n", + " (analyzer.sorting.get_property('isi_violations_ratio') < isi_vr_thresh[1]))[0]] = 'm'\n", + "isi_vr_label[np.where(analyzer.sorting.get_property('isi_violations_ratio') > isi_vr_thresh[1])[0]] = 'h' \n", + "analyzer.sorting.set_property('isi_vr_thresh',isi_vr_label)\n", + "snr_label = np.full(analyzer.sorting.get_num_units(),'l')\n", + "snr_label[np.where((analyzer.sorting.get_property('snr') > snr_thresh[0]) & \n", + " (analyzer.sorting.get_property('snr') < snr_thresh[1]))[0]] = 'm'\n", + "snr_label[np.where(analyzer.sorting.get_property('snr') > snr_thresh[1])[0]] = 'h' \n", + "analyzer.sorting.set_property('snr_thresh',snr_label)\n", + "quality_labels = np.full(analyzer.sorting.get_num_units(),'_____')\n", + "quality_labels[np.where(isi_vr_label == 'h')[0]] = 'mua_4'\n", + "quality_labels[np.where((isi_vr_label == 'l') & (snr_label == 'h'))[0]] = 'sua_1'\n", + "quality_labels[np.where((isi_vr_label == 'l') & (snr_label == 'm'))[0]] = 'sua_2'\n", + "quality_labels[np.where((isi_vr_label == 'm') & (snr_label == 'h'))[0]] = 'sua_2'\n", + "quality_labels[np.where((isi_vr_label == 'm') & (snr_label == 'm'))[0]] = 'sua_3'\n", + "quality_labels[np.where(snr_label == 'l')[0]] = 'noise'\n", + "analyzer.sorting.set_property('quality_labels',quality_labels)\n", + "su.print_unit_counts(quality_labels)\n", + "print(analyzer.sorting); print(analyzer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Auto curation" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "similarity_correlograms..\n", + "[]\n", + "NumpySorting: 319 units - 1 segments - 30.0kHz\n", + "x_contaminations..\n", + "[[136, 143]]\n", + "MergeUnitsSorting: 318 units - 1 segments - 30.0kHz\n", + "temporal_splits..\n", + "[]\n", + "NumpySorting: 319 units - 1 segments - 30.0kHz\n", + "feature_neighbors..\n", + "[[1], [33, 2, 34, 5, 15, 16, 20, 21, 30, 31], [3], [4], [22], [26], [39], [40], [41], [44], [45], [48], [51, 54, 73, 59], [56], [61], [65], [66], [68], [69], [71], [72], [75], [76], [80], [82], [84], [88], [100], [101], [104], [109], [131, 136, 143], [137], [200], [202], [203], [208], [216], [217], [226], [232], [233], [256, 261], [293]]\n", + "MergeUnitsSorting: 304 units - 1 segments - 30.0kHz\n", + "CPU times: user 5min 7s, sys: 8.66 s, total: 5min 16s\n", + "Wall time: 4min 32s\n" + ] + } + ], + "source": [ + "%%time\n", + "merges_auto_init_all = []\n", + "merges_auto_all = []\n", + "sort_auto_all = []\n", + "\n", + "presets_all = ['similarity_correlograms','x_contaminations','temporal_splits','feature_neighbors']\n", + "for this_preset in presets_all:\n", + " print(this_preset + '..')\n", + " merges_auto_init = si.get_potential_auto_merge(analyzer,preset=this_preset)\n", + " merges_auto = su.merge_lists(merges_auto_init)\n", + " print(merges_auto)\n", + " if len(merges_auto) > 0: sort_auto = si.MergeUnitsSorting(analyzer.sorting,merges_auto)\n", + " else: sort_auto = analyzer.sorting\n", + " print(sort_auto)\n", + " \n", + " merges_auto_init_all.append(merges_auto_init)\n", + " merges_auto_all.append(merges_auto_all)\n", + " sort_auto_all.append(sort_auto)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Manual curation" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computing sha1 of /home/AD/tsmcpher/.kachery-cloud/tmp_chVMxZKl/file.dat\n", + "https://figurl.org/f?v=npm://@fi-sci/figurl-sortingview@12/dist&d=sha1://8bd38be2b2a07d44872172e04718c9c740300c18\n" + ] + } + ], + "source": [ + "unit_table_properties = ['quality_labels','KSLabel','isi_violations_ratio','snr','num_spikes']\n", + "label_choices = ['sua_1','sua_2','sua_3','mua_4','noise']\n", + "pss = si.plot_sorting_summary(analyzer,curation=True,backend='sortingview',\n", + " unit_table_properties=unit_table_properties,label_choices=label_choices)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# sha_uri = 'sha1://eb06f8e626926b4dc905f7561617b2978530b2f7'" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# %%capture cap\n", + "# sort_curated = si.apply_sortingview_curation(sorting=analyzer.sorting,uri_or_json=sha_uri,verbose=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# merge_str_all = cap.stdout\n", + "# merge_starts = su.str_find('[',merge_str_all)\n", + "# merge_stops = su.str_find(']',merge_str_all)\n", + "# merges_curated = [merge_str_all[merge_starts[i]+1:merge_stops[i]].split(',') for i in range(len(merge_starts))]\n", + "# quality_labels = np.full(sort_curated.get_num_units(),'_____')\n", + "# for this_label in label_choices:\n", + "# quality_labels[np.where(sort_curated.get_property(this_label) == True)[0]] = this_label\n", + "# sort_curated.set_property('quality_labels',quality_labels)\n", + "# su.print_unit_counts(quality_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Depth labels" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hvc 44\n", + "ncm 32\n", + "bad 117\n", + "bad 126\n" + ] + }, + { + "data": { + "text/plain": [ + "{'hvc': [[1910, None]],\n", + " 'ncm': [[840, 1890]],\n", + " 'bad': [[None, 840], [1890, 1910]],\n", + " 'depth_labels': array(['bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'ncm', 'ncm', 'bad', 'ncm', 'bad', 'ncm', 'bad', 'ncm', 'bad',\n", + " 'ncm', 'bad', 'ncm', 'ncm', 'ncm', 'ncm', 'ncm', 'ncm', 'ncm',\n", + " 'ncm', 'ncm', 'ncm', 'ncm', 'ncm', 'ncm', 'ncm', 'ncm', 'ncm',\n", + " 'ncm', 'bad', 'ncm', 'bad', 'ncm', 'ncm', 'ncm', 'bad', 'ncm',\n", + " 'ncm', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'ncm', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'ncm', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'hvc', 'bad', 'hvc', 'bad',\n", + " 'bad', 'ncm', 'hvc', 'hvc', 'hvc', 'hvc', 'hvc', 'bad', 'bad',\n", + " 'hvc', 'hvc', 'hvc', 'bad', 'hvc', 'bad', 'hvc', 'bad', 'bad',\n", + " 'hvc', 'hvc', 'bad', 'hvc', 'hvc', 'hvc', 'bad', 'bad', 'hvc',\n", + " 'hvc', 'hvc', 'bad', 'bad', 'hvc', 'bad', 'bad', 'hvc', 'hvc',\n", + " 'bad', 'hvc', 'hvc', 'bad', 'bad', 'bad', 'hvc', 'bad', 'hvc',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'hvc', 'bad', 'bad', 'bad', 'hvc', 'hvc', 'bad', 'bad', 'bad',\n", + " 'hvc', 'bad', 'bad', 'bad', 'bad', 'hvc', 'bad', 'bad', 'bad',\n", + " 'bad', 'hvc', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'hvc', 'bad', 'hvc', 'hvc', 'hvc', 'bad', 'bad', 'hvc',\n", + " 'hvc', 'bad', 'hvc', 'hvc', 'bad', 'hvc', 'bad', 'hvc', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad', 'bad',\n", + " 'bad', 'bad', 'hvc', 'bad'], dtype='= lower_bound) & (probe_depth <= upper_bound))[0])\n", + " labels_is_all.append(label_is)\n", + " depth_labels[label_is] = this_label\n", + " print(this_label,len(label_is))\n", + "assert len(sum(labels_is_all,[])) == len(depth_labels)\n", + "depth_dict['depth_labels'] = depth_labels\n", + "depth_dict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Save out" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "sort_in = sort_auto_all[0] # sort_curated sort_auto\n", + "merges_in = []#merges_auto_all[0] # merges_curated merges_auto" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "319 units after curation:\n" + ] + } + ], + "source": [ + "# get unit IDs\n", + "unit_ids = sort_in.get_unit_ids()\n", + "print(f\"{len(unit_ids)} units after curation:\")\n", + "iui = analyzer.sorting.get_unit_ids() # initial unit IDs\n", + "utm = [[int(x) for x in m] for m in merges_in] # units to merge\n", + "nui = np.arange(max(iui)+1, max(iui)+len(utm)+1) # new unit IDs\n", + "# set merged properties to unit with highest original spike rate\n", + "orig_unit_ids = [[x] for x in unit_ids]\n", + "not_max_spikes_is_all = []\n", + "for i, u in enumerate(utm):\n", + " print(f'- Units {u} merged to {nui[i]}')\n", + " idx = [np.where(iui == x)[0][0] for x in u]\n", + " u_n_spks = analyzer.sorting.get_property('num_spikes')[idx]\n", + " max_spikes_i = idx[np.argmax(u_n_spks)]\n", + " not_max_spikes_is = [idx[nmi] for nmi in list(np.where(idx != max_spikes_i)[0])]\n", + " nui_i = np.where(unit_ids == nui[i])[0][0]\n", + " for this_metric in analyzer.sorting.get_property_keys():\n", + " sort_in.get_property(this_metric)[nui_i] = analyzer.sorting.get_property(this_metric)[max_spikes_i]\n", + " sort_in.get_property('num_spikes')[nui_i] = np.sum(u_n_spks)\n", + " not_max_spikes_is_all.append(not_max_spikes_is)\n", + "if len(not_max_spikes_is_all) > 0:\n", + " merged_unit_locations = np.delete(np.array(unit_locations),np.concatenate(not_max_spikes_is_all),axis=0)\n", + "else:\n", + " merged_unit_locations = unit_locations\n", + "sort_final = sort_in" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'probe_abs_loc' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[66], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m spk_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munit_locations\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(merged_unit_locations)\n\u001b[1;32m 4\u001b[0m spk_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdepth_labels\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(depth_labels)\n\u001b[0;32m----> 5\u001b[0m spk_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprobe_location\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mspk_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43msu\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_probe_loc\u001b[49m\u001b[43m,\u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m spk_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mprobe_angle\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m spk_df\u001b[38;5;241m.\u001b[39mapply(su\u001b[38;5;241m.\u001b[39madd_probe_angle,axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m prop \u001b[38;5;129;01min\u001b[39;00m sort_final\u001b[38;5;241m.\u001b[39mget_property_keys():\n", + "File \u001b[0;32m/mnt/cube/tsmcpher/envs/spike_prov_NEWNEW/lib/python3.9/site-packages/pandas/core/frame.py:10374\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[0;34m(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 10360\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[1;32m 10362\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[1;32m 10363\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 10364\u001b[0m func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10372\u001b[0m kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[1;32m 10373\u001b[0m )\n\u001b[0;32m> 10374\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m/mnt/cube/tsmcpher/envs/spike_prov_NEWNEW/lib/python3.9/site-packages/pandas/core/apply.py:916\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[1;32m 914\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw(engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine, engine_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine_kwargs)\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/mnt/cube/tsmcpher/envs/spike_prov_NEWNEW/lib/python3.9/site-packages/pandas/core/apply.py:1063\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1061\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 1062\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 1063\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_series_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1064\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1065\u001b[0m results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_series_numba()\n", + "File \u001b[0;32m/mnt/cube/tsmcpher/envs/spike_prov_NEWNEW/lib/python3.9/site-packages/pandas/core/apply.py:1081\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1078\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 1079\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[1;32m 1080\u001b[0m \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[0;32m-> 1081\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1082\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[1;32m 1083\u001b[0m \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[1;32m 1084\u001b[0m \u001b[38;5;66;03m# series_generator will swap out the underlying data\u001b[39;00m\n\u001b[1;32m 1085\u001b[0m results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", + "File \u001b[0;32m/mnt/cube/tsmcpher/code/ephys_tsm/spike_util.py:492\u001b[0m, in \u001b[0;36madd_probe_loc\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m 491\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21madd_probe_loc\u001b[39m(row):\n\u001b[0;32m--> 492\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprobe_abs_loc\u001b[49m\n", + "\u001b[0;31mNameError\u001b[0m: name 'probe_abs_loc' is not defined" + ] + } + ], + "source": [ + "spk_df = pd.DataFrame({'unit': unit_ids})\n", + "spk_df['spike_train'] = spk_df['unit'].apply(lambda x: sort_final.get_unit_spike_train(unit_id=x, segment_index=0))\n", + "spk_df['unit_locations'] = list(merged_unit_locations)\n", + "spk_df['depth_labels'] = list(depth_labels)\n", + "spk_df['probe_location'] = spk_df.apply(su.add_probe_loc,axis=1)\n", + "spk_df['probe_angle'] = spk_df.apply(su.add_probe_angle,axis=1)\n", + "for prop in sort_final.get_property_keys():\n", + " spk_df[prop] = sort_final.get_property(prop)\n", + "spk_df = spk_df.drop(columns=['original_cluster_id'])\n", + "spk_df['orig_unit'] = orig_unit_ids\n", + "spk_df.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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unitspike_trainunit_locationsdepth_labels
00[123951, 147783, 152969, 153105, 191184, 23773...[-14.073065567470673, 17.670251844640863, 1.40...bad
11[73474, 96036, 138946, 160850, 173590, 266314,...[-6.960734860035025, 36.56431000266426, 9.0094...bad
22[363629, 1322175, 1322249, 2609953, 4171529, 4...[28.243145302930486, 16.397182659697762, 1.009...bad
33[501, 1010, 1484, 1906, 2419, 3113, 3427, 3741...[3.345220529178426, 32.80625933021123, 1.00000...bad
44[3434, 13800, 23463, 32599, 33698, 36838, 3748...[6.003732926989229, 59.70841970244248, 1.00000...bad
...............
314314[1321901, 1642489, 3872859, 4350387, 5900139, ...[32.29415469029789, 1903.1954691834296, 2.2189...bad
315315[2116, 2620, 6116, 10611, 11111, 11118, 11613,...[28.47801948110492, 1901.4852407894023, 1.0001...bad
316316[107, 605, 636, 1107, 1608, 3606, 3635, 4134, ...[28.080712798162818, 1901.98421932352, 1.00004...bad
317317[345041, 361299, 363166, 793182, 1313623, 1643...[21.119370966110907, 3828.3214553920816, 1.000...hvc
318318[793696, 793827, 794608, 1309637, 1310822, 131...[27.51656260594869, 1900.2085128547862, 3.0726...bad
\n", + "

319 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " unit spike_train \\\n", + "0 0 [123951, 147783, 152969, 153105, 191184, 23773... \n", + "1 1 [73474, 96036, 138946, 160850, 173590, 266314,... \n", + "2 2 [363629, 1322175, 1322249, 2609953, 4171529, 4... \n", + "3 3 [501, 1010, 1484, 1906, 2419, 3113, 3427, 3741... \n", + "4 4 [3434, 13800, 23463, 32599, 33698, 36838, 3748... \n", + ".. ... ... \n", + "314 314 [1321901, 1642489, 3872859, 4350387, 5900139, ... \n", + "315 315 [2116, 2620, 6116, 10611, 11111, 11118, 11613,... \n", + "316 316 [107, 605, 636, 1107, 1608, 3606, 3635, 4134, ... \n", + "317 317 [345041, 361299, 363166, 793182, 1313623, 1643... \n", + "318 318 [793696, 793827, 794608, 1309637, 1310822, 131... \n", + "\n", + " unit_locations depth_labels \n", + "0 [-14.073065567470673, 17.670251844640863, 1.40... bad \n", + "1 [-6.960734860035025, 36.56431000266426, 9.0094... bad \n", + "2 [28.243145302930486, 16.397182659697762, 1.009... bad \n", + "3 [3.345220529178426, 32.80625933021123, 1.00000... bad \n", + "4 [6.003732926989229, 59.70841970244248, 1.00000... bad \n", + ".. ... ... \n", + "314 [32.29415469029789, 1903.1954691834296, 2.2189... bad \n", + "315 [28.47801948110492, 1901.4852407894023, 1.0001... bad \n", + "316 [28.080712798162818, 1901.98421932352, 1.00004... bad \n", + "317 [21.119370966110907, 3828.3214553920816, 1.000... hvc \n", + "318 [27.51656260594869, 1900.2085128547862, 3.0726... bad \n", + "\n", + "[319 rows x 4 columns]" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spk_df" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(sort_dir,'spk_df.pkl'), 'wb') as handle:\n", + " pickle.dump(spk_df,handle)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "spikeproc", + "language": "python", + "name": "spikeproc" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/archive/4-curate_spikes.ipynb b/archive/4-curate_spikes.ipynb index 76cc5d3..084744e 100644 --- a/archive/4-curate_spikes.ipynb +++ b/archive/4-curate_spikes.ipynb @@ -135,6 +135,17 @@ "si.plot_quality_metrics(wave, skip_metrics=['snr','amplitude_cutoff'], backend=\"sortingview\");" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "label_choices = ['sua_1','sua_2','sua_3','mua_4','noise']\n", + "pss = si.plot_sorting_summary(waveform_extractor=wave,curation=True,backend='sortingview',\n", + " unit_table_properties=unit_table_properties,label_choices=label_choices)" + ] + }, { "cell_type": "code", "execution_count": 7, diff --git a/spec-file.txt b/spec-file.txt new file mode 100644 index 0000000..905cdd6 --- /dev/null +++ b/spec-file.txt @@ -0,0 +1,4 @@ +# This file may be used to create an environment using: +# $ conda create --name --file +# platform: linux-64 +@EXPLICIT