Updated links and checked X-ray examples

ptycho · Jun 10, 2024 · 44270f1 · 44270f1
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diff --git a/docs/intro.md b/docs/intro.md
@@ -17,24 +17,24 @@ The table below gives an overview of all examples with associated data and a lin
 |  [](notebooks/basic_examples/06_stochastic_engines)         |   No Data Required  | - | - |
 |  [](notebooks/basic_examples/07_gradient_based_engines)     |   No Data Required  | - | - |
 | **Experimental X-ray Data** |
-|  [](notebooks/experimental_xray_data/00_data_loading)                 |  [small_data.zip](https://zenodo.org/records/11501765/files/small_data.zip?download=1)     |  126 MB  |  [doi](https://zenodo.org/doi/10.5281/zenodo.11501764) |
-|  [](notebooks/experimental_xray_data/01_working_with_large_data)      |  [dls_i08_nanogold_spiral.zip](https://zenodo.org/records/11501765/files/dls_i08_nanogold_spiral.zip?download=1) |  1.11 GB |  [doi](https://zenodo.org/doi/10.5281/zenodo.11501764) |
-|  [](notebooks/experimental_xray_data/02_fixing_data_issues)           |  [small_data.zip](https://zenodo.org/records/11501765/files/small_data.zip?download=1)     |  126 MB  |  [doi](https://zenodo.org/doi/10.5281/zenodo.11501764) |
-|  [](notebooks/experimental_xray_data/03_partial_coherence)            |  [dls_i08_nanogold_spiral.zip](https://zenodo.org/records/11501765/files/dls_i08_nanogold_spiral.zip?download=1) |  1.11 GB |  [doi](https://zenodo.org/doi/10.5281/zenodo.11501764) |
-|  [](notebooks/experimental_xray_data/04_position_refinement)          |  [dls_i14_test_structure.zip](https://zenodo.org/records/11501765/files/dls_i14_test_structure.zip?download=1)  |  3.68 GB |  [doi](https://zenodo.org/doi/10.5281/zenodo.11501764) |
-|  [](notebooks/experimental_xray_data/05_missing_detector_frames)      |  [dls_i08_nanogold_raster.zip](https://zenodo.org/records/11501765/files/dls_i08_nanogold_raster.zip?download=1) |  993 MB  |  [doi](https://zenodo.org/doi/10.5281/zenodo.11501764) |
-|  [](notebooks/experimental_xray_data/06_testing_different_algorithms) |  [dls_i13_butterfly.zip](https://zenodo.org/records/11501765/files/dls_i13_butterfly.zip?download=1)       |  286 MB  |  [doi](https://zenodo.org/doi/10.5281/zenodo.11501764) |
-|  [](notebooks/experimental_xray_data/07_multi_gpu)                    |  [dls_i08_nanogold_spiral.zip](https://zenodo.org/records/11501765/files/dls_i08_nanogold_spiral.zip?download=1) |  1.11 GB |  [doi](https://zenodo.org/doi/10.5281/zenodo.11501764) |
+|  [](notebooks/experimental_xray_data/00_data_loading)                 |  [small_data.zip](https://zenodo.org/records/11501765/files/small_data.zip?download=1)     |  126 MB  |  [doi](https://doi.org/10.5281/zenodo.11501765) |
+|  [](notebooks/experimental_xray_data/01_working_with_large_data)      |  [dls_i08_nanogold_spiral.zip](https://zenodo.org/records/11501765/files/dls_i08_nanogold_spiral.zip?download=1) |  1.11 GB |  [doi](https://doi.org/10.5281/zenodo.11501765) |
+|  [](notebooks/experimental_xray_data/02_fixing_data_issues)           |  [small_data.zip](https://zenodo.org/records/11501765/files/small_data.zip?download=1)     |  126 MB  |  [doi](https://doi.org/10.5281/zenodo.11501765) |
+|  [](notebooks/experimental_xray_data/03_partial_coherence)            |  [dls_i08_nanogold_spiral.zip](https://zenodo.org/records/11501765/files/dls_i08_nanogold_spiral.zip?download=1) |  1.11 GB |  [doi](https://doi.org/10.5281/zenodo.11501765) |
+|  [](notebooks/experimental_xray_data/04_position_refinement)          |  [dls_i14_test_structure.zip](https://zenodo.org/records/11501765/files/dls_i14_test_structure.zip?download=1)  |  3.68 GB |  [doi](https://doi.org/10.5281/zenodo.11501765) |
+|  [](notebooks/experimental_xray_data/05_missing_detector_frames)      |  [dls_i08_nanogold_raster.zip](https://zenodo.org/records/11501765/files/dls_i08_nanogold_raster.zip?download=1) |  993 MB  |  [doi](https://doi.org/10.5281/zenodo.11501765) |
+|  [](notebooks/experimental_xray_data/06_testing_different_algorithms) |  [dls_i13_butterfly.zip](https://zenodo.org/records/11501765/files/dls_i13_butterfly.zip?download=1)       |  286 MB  |  [doi](https://doi.org/10.5281/zenodo.11501765) |
+|  [](notebooks/experimental_xray_data/07_multi_gpu)                    |  [dls_i08_nanogold_spiral.zip](https://zenodo.org/records/11501765/files/dls_i08_nanogold_spiral.zip?download=1) |  1.11 GB |  [doi](https://doi.org/10.5281/zenodo.11501765) |
 | **Electron Ptychography Data** |
-|  [](notebooks/ptychography_with_electrons/00_electron_data)                       | [dls_epsic_80kV_graphene.zip](https://zenodo.org/records/11501765/files/dls_epsic_80kV_graphene.zip?download=1)  |  270 MB  |  [doi](https://zenodo.org/doi/10.5281/zenodo.11501764) |
-|  [](notebooks/ptychography_with_electrons/01_chaining_multiple_engines)           | [dls_epsic_80kV_graphene.zip](https://zenodo.org/records/11501765/files/dls_epsic_80kV_graphene.zip?download=1)  |  270 MB  |  [doi](https://zenodo.org/doi/10.5281/zenodo.11501764) |
-|  [](notebooks/ptychography_with_electrons/02_start_from_previous_reconstruction)  | [dls_epsic_80kV_graphene.zip](https://zenodo.org/records/11501765/files/dls_epsic_80kV_graphene.zip?download=1)  |  270 MB  |  [doi](https://zenodo.org/doi/10.5281/zenodo.11501764) |
+|  [](notebooks/ptychography_with_electrons/00_electron_data)                       | [dls_epsic_80kV_graphene.zip](https://zenodo.org/records/11501765/files/dls_epsic_80kV_graphene.zip?download=1)  |  270 MB  |  [doi](https://doi.org/10.5281/zenodo.11501765) |
+|  [](notebooks/ptychography_with_electrons/01_chaining_multiple_engines)           | [dls_epsic_80kV_graphene.zip](https://zenodo.org/records/11501765/files/dls_epsic_80kV_graphene.zip?download=1)  |  270 MB  |  [doi](https://doi.org/10.5281/zenodo.11501765) |
+|  [](notebooks/ptychography_with_electrons/02_start_from_previous_reconstruction)  | [dls_epsic_80kV_graphene.zip](https://zenodo.org/records/11501765/files/dls_epsic_80kV_graphene.zip?download=1)  |  270 MB  |  [doi](https://doi.org/10.5281/zenodo.11501765) |
 | **Supplementary Examples** |
 |  [](notebooks/supplementary_examples/00_nearfield_ptychography)  |  esrf_id16_AlNi_nearfield.zip        	|  532 MB  |        TBA       	|
 |  [](notebooks/supplementary_examples/01_object_regularisation)   |  esrf_id16_AlNi_nearfield.zip        	|  532 MB  |        TBA       	|
-|  [](notebooks/supplementary_examples/02_live_visualisation)      |  [dls_i08_nanogold_spiral.zip](https://zenodo.org/records/11501765/files/dls_i08_nanogold_spiral.zip?download=1)           |  1.11 GB |  [doi](https://zenodo.org/doi/10.5281/zenodo.11501764) |
+|  [](notebooks/supplementary_examples/02_live_visualisation)      |  [dls_i08_nanogold_spiral.zip](https://zenodo.org/records/11501765/files/dls_i08_nanogold_spiral.zip?download=1)           |  1.11 GB |  [doi](https://doi.org/10.5281/zenodo.11501765) |
 |  [](notebooks/supplementary_examples/03_simulating_data)         |  No Data Required                      |  -       | -                                       |
-|  [](notebooks/supplementary_examples/04_modified_initial_probe)  |  [small_data.zip](https://zenodo.org/records/11501765/files/small_data.zip?download=1)        |  126 MB  |  [doi](https://zenodo.org/doi/10.5281/zenodo.11501764) |
+|  [](notebooks/supplementary_examples/04_modified_initial_probe)  |  [small_data.zip](https://zenodo.org/records/11501765/files/small_data.zip?download=1)        |  126 MB  |  [doi](https://doi.org/10.5281/zenodo.11501765) |
 
 
 ## Jupyter Lab/Notebooks

diff --git a/notebooks/experimental_xray_data/00_data_loading.ipynb b/notebooks/experimental_xray_data/00_data_loading.ipynb
@@ -20,7 +20,7 @@
     "\n",
     "| **Data file** |  **Type** | **Download** | **Courtesy of** | **Reference** |\n",
     "| :- | :- | :- | :-: | :-: |\n",
-    "| dls_i08_nanogold_small.h5 | Raw | small_data.zip | Majid Kazemian | [doi](https://10.5281/zenodo.11501765) |\n",
+    "| dls_i08_nanogold_small.h5 | Raw | [small_data.zip](https://zenodo.org/records/11501765/files/small_data.zip?download=1) | Majid Kazemian | [DOI](https://doi.org/10.5281/zenodo.11501765) |\n",
     "\n",
     "In this example, we use an experimental data set collected at the I08-1 instrument at the Diamond Light Source on a test sample (gold nanostructures deposited on a carbon film). A subset of the experimental data including some meta information has been saved in an HDF5 file named \"dls_i08_nanogold_small.h5\" that was specifically created for the purpose of this tutorial. We can use the following code\n",
     "```python\n",
@@ -57,7 +57,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "e8d3874c",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import h5py, os\n",
@@ -188,7 +190,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "b0743148",
    "metadata": {},
@@ -210,7 +211,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "8faefcc7-8b44-426f-8246-c3a93e471942",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import ptypy, os\n",
@@ -318,9 +321,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel) [User Conda - cuda12.2]",
+   "display_name": "PtyPy (cupy)",
    "language": "python",
-   "name": "conda-env-User_Conda_-_cuda12.2-python3"
+   "name": "ptypy_cupy"
   },
   "language_info": {
    "codemirror_mode": {
@@ -332,7 +335,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,

diff --git a/notebooks/experimental_xray_data/01_working_with_large_data.ipynb b/notebooks/experimental_xray_data/01_working_with_large_data.ipynb
@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "413a3048-7624-4fa6-a0e5-a2d92e55f9d0",
    "metadata": {},
@@ -21,7 +20,7 @@
     "\n",
     "| **Data file** | **Type** | **Download** | **Courtesy of** | **Reference** |\n",
     "| :- | :- | :- | :-: | :-: | \n",
-    "| i08-1-5776_cropped.h5 | Raw | dls_i08_nanogold_spiral.zip | Majid Kazemian | [doi](https://10.5281/zenodo.11501765) |\n",
+    "| i08-1-5776_cropped.h5 | Raw | [dls_i08_nanogold_spiral.zip](https://zenodo.org/records/11501765/files/dls_i08_nanogold_spiral.zip?download=1) | Majid Kazemian | [DOI](https://doi.org/10.5281/zenodo.11501765) |\n",
     "\n",
     "At the I08-1 instrument at the Diamond Light Source, raw data is written into HDF5/Nexus files. As an example, we can look at the original raw data from the nanogold test sample which is stored in ```\"dls_i08_nanogold_spiral/i08-1-5776_cropped.h5\"``` and inspect the relevant entries\n",
     "```python\n",
@@ -60,7 +59,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "f12c532f-6e99-4855-b11b-507ca27e2264",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import h5py, os\n",
@@ -201,7 +202,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "64f75030-3d41-4665-bb27-b66305fd2cf2",
    "metadata": {},
@@ -223,7 +223,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "ee3cbe0e-7393-4e07-8e6c-a0063eb55dbe",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import ptypy, os\n",
@@ -233,7 +235,7 @@
     "ptypy.load_ptyscan_module(\"hdf5_loader\")\n",
     "\n",
     "# This will import the GPU engines\n",
-    "ptypy.load_gpu_engines(\"cuda\")  \n",
+    "ptypy.load_gpu_engines(\"cupy\")  \n",
     "\n",
     "# Root directory of tutorial data\n",
     "tutorial_data_home = \"../../data/\"\n",
@@ -353,21 +355,13 @@
     "# Run reconstruction\n",
     "P = ptypy.core.Ptycho(p,level=5)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f40ea151-03f7-43fe-9651-80def27f3c89",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel) [User Conda - cuda12.2]",
+   "display_name": "PtyPy (cupy)",
    "language": "python",
-   "name": "conda-env-User_Conda_-_cuda12.2-python3"
+   "name": "ptypy_cupy"
   },
   "language_info": {
    "codemirror_mode": {
@@ -379,7 +373,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,

diff --git a/notebooks/experimental_xray_data/02_fixing_data_issues.ipynb b/notebooks/experimental_xray_data/02_fixing_data_issues.ipynb
@@ -22,7 +22,7 @@
    "metadata": {},
    "source": [
     "<div class=\"alert alert-info\" markdown=\"1\">\n",
-    "<strong>Note</strong><br>Since PtyPy 0.8.1 the megapixel limit has been increase to 100M therefore you might run out of memory before seeing this RuntimeError. \n",
+    "<strong>Note</strong><br>Since PtyPy 0.8.1 the megapixel limit has been increased to 100M therefore you might run out of memory before seeing this RuntimeError. \n",
     "</div>"
    ]
   },
@@ -35,7 +35,7 @@
     "\n",
     "| **Data file** | **Type** | **Download** | **Courtesy of** | **Reference** |\n",
     "| :- | :- | :- | :-: | :-: |\n",
-    "| dls_i08_nanogold_issue.h5 | Raw data | small_data.zip | Majid Kazemian | [doi](https://10.5281/zenodo.11501765) |\n",
+    "| dls_i08_nanogold_issue.h5 | Raw data | [small_data.zip](https://zenodo.org/records/11501765/files/small_data.zip?download=1) | Majid Kazemian | [DOI](https://doi.org/10.5281/zenodo.11501765) |\n",
     "\n",
     "Based on the small I08-1 data set that was used in the tutorial on [Loading HDF5 Data](./00_data_loading.ipynb) we have a created a new file named ```\"dls_i08_nanogold_issue.h5\"``` for which the coordinate system has been modified sligthly. The task for this tutorial is to figure out the correct ```orientation``` based on the data that has been provided. As a starting point, we set it to zero.\n",
     "\n",
@@ -59,7 +59,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "f5dd1b69-425a-41bf-b9a3-1ba2a46579a1",
    "metadata": {},
@@ -81,7 +80,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "8faefcc7-8b44-426f-8246-c3a93e471942",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import ptypy, os\n",
@@ -177,9 +178,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel) [User Conda - cuda12.2]",
+   "display_name": "PtyPy (cupy)",
    "language": "python",
-   "name": "conda-env-User_Conda_-_cuda12.2-python3"
+   "name": "ptypy_cupy"
   },
   "language_info": {
    "codemirror_mode": {
@@ -191,7 +192,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,

diff --git a/notebooks/experimental_xray_data/03_partial_coherence.ipynb b/notebooks/experimental_xray_data/03_partial_coherence.ipynb
diff --git a/notebooks/experimental_xray_data/04_position_refinement.ipynb b/notebooks/experimental_xray_data/04_position_refinement.ipynb
@@ -20,8 +20,8 @@
     "\n",
     "| **Data file** | **Type** | **Download** | **Courtesy of** | **Reference** |\n",
     "| :- | :- | :- | :-: | :-: |\n",
-    "| i14-logo-128.h5 | Raw | dls_i14_test_structure.zip | Miguel Gomez Gonzalez | [doi](https://10.5281/zenodo.11501765) |\n",
-    "| scan_103937.ptyr | Reconstruction | dls_i14_test_structure.zip | Miguel Gomez Gonzalez | [doi](https://10.5281/zenodo.11501765) |\n",
+    "| i14-logo-128.h5 | Raw | [dls_i14_test_structure.zip](https://zenodo.org/records/11501765/files/dls_i14_test_structure.zip?download=1) | Miguel Gomez Gonzalez | [DOI](https://doi.org/10.5281/zenodo.11501765) |\n",
+    "| scan_103937.ptyr | Reconstruction | [dls_i14_test_structure.zip](https://zenodo.org/records/11501765/files/dls_i14_test_structure.zip?download=1) | Miguel Gomez Gonzalez | [DOI](https://doi.org/10.5281/zenodo.11501765) |\n",
     "\n",
     "\n",
     "In this example, we are using a test dataset that has been collected at the I14 instrument at the Diamond Light Source. The raw data has been collected with a MediPix3 detector and was stored in an HDF5/Nexus file with the following relevant entries for the intensities and scan positions\n",
@@ -192,7 +192,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "28be44d4-4715-44bb-8d5e-1caae987f2d0",
    "metadata": {},
@@ -214,7 +213,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "ee3cbe0e-7393-4e07-8e6c-a0063eb55dbe",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import ptypy, os\n",
@@ -365,9 +366,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel) [User Conda - cuda12.2]",
+   "display_name": "PtyPy (cupy)",
    "language": "python",
-   "name": "conda-env-User_Conda_-_cuda12.2-python3"
+   "name": "ptypy_cupy"
   },
   "language_info": {
    "codemirror_mode": {
@@ -379,7 +380,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,

diff --git a/notebooks/experimental_xray_data/05_missing_detector_frames.ipynb b/notebooks/experimental_xray_data/05_missing_detector_frames.ipynb
@@ -20,7 +20,7 @@
     "\n",
     "| **Data file** | **Type** | **Download** | **Courtesy of** | **Refernce** |\n",
     "| :- | :- | :- | :-: | :-: | \n",
-    "| i08-1-5778_cropped.h5 | Raw data | dls_i08_nanogold_raster.zip | Majid Kazemian | [doi](https://10.5281/zenodo.11501765) |\n",
+    "| i08-1-5778_cropped.h5 | Raw data | [dls_i08_nanogold_raster.zip](https://zenodo.org/records/11501765/files/dls_i08_nanogold_raster.zip?download=1) | Majid Kazemian | [DOI](https://doi.org/10.5281/zenodo.11501765) |\n",
     "\n",
     "We are again revisting nanogold data from I08-1, similar to the data used in [Working With Large Data](./01_working_with_large_data.ipynb) but this time collected with a raster grid scan (instead of a spiral scan). The raw data is located in the HDF5/Nexus file ```\"dls_i08_nanogold_raster/i08-1-5778_cropped.h5\"``` with the following relevant entries\n",
     "\n",
@@ -143,6 +143,16 @@
     "![](./_assets/i08_nanogold_framefilter.png)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4956626c-a6f7-4c0a-9cf5-cac4598e850c",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-warning\" markdown=\"1\">\n",
+    "    <strong>Challenge</strong><br>Compare reconstructions without and with using the framefilter to exclude the missing detector frame.\n",
+    "</div>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "bc8f7e85-7830-406d-813f-89aae9f47459",
@@ -155,7 +165,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "afaaf9cc-6d5e-487c-8980-aa9a20fa19d0",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import ptypy, os\n",
@@ -261,9 +273,9 @@
     "p.scans.scan_00.data.darkfield.file = path_to_data\n",
     "p.scans.scan_00.data.darkfield.key = \"dark\"\n",
     "\n",
-    "p.scans.scan_00.data.framefilter = u.Param()\n",
-    "p.scans.scan_00.data.framefilter.file = path_to_data\n",
-    "p.scans.scan_00.data.framefilter.key = \"datasum\"\n",
+    "# p.scans.scan_00.data.framefilter = u.Param()\n",
+    "# p.scans.scan_00.data.framefilter.file = path_to_data\n",
+    "# p.scans.scan_00.data.framefilter.key = \"datasum\"\n",
     "\n",
     "p.scans.scan_00.data.energy = 0.710\n",
     "p.scans.scan_00.data.distance = 0.072\n",
@@ -298,9 +310,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel) [User Conda - cuda12.2]",
+   "display_name": "PtyPy (cupy)",
    "language": "python",
-   "name": "conda-env-User_Conda_-_cuda12.2-python3"
+   "name": "ptypy_cupy"
   },
   "language_info": {
    "codemirror_mode": {
@@ -312,7 +324,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,