Refactor footprint changes #167
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…into a single parameterized function extract_footprint(footprint_dict, footprint_key). Removed the print missing kwh/co2, footprint_dict since it was polluting the cell execution, and in all these cases footprint_dict was empty.
…he actual exception error reason, also while accounting the total number of trips where there were errors.
…red from prior labels
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Testing Scenario: Dataset used: Execution of notebooks- generic_metrics, mode_specific_metrics - to lookup for inferred label changes Detailed Execution of notebooks using generate_plots.py script
Results:
Note: Everything looks good |
…book_inferred_data(,..,add_footprint). Use base_mode AIR to filter out the trip without air mode, instead of internal label
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Fix for the following: Use base_mode to filter out the AIR related trips. Results:
Looks identical |
JGreenlee
reviewed
Oct 18, 2024
Comment on lines
+507
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| def extract_footprint(footprint_dict, footprint_key): | ||
| if footprint_key in footprint_dict.keys(): | ||
| return footprint_dict[footprint_key] |
| @@ -254,7 +257,7 @@ async def map_trip_data(expanded_trip_df, study_type, dynamic_labels): | |||
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| return expanded_trip_df | |||
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| async def load_viz_notebook_inferred_data(year, month, program, study_type, dynamic_labels, include_test_users=False): | |||
| async def load_viz_notebook_inferred_data(year, month, program, study_type, dynamic_labels, include_test_users=False, add_footprint=False): | |||
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For my understanding, is this cleanup from something that was meant to be added in a previous PR?
I saw that load_viz_notebook_data already had the add_footprint parameter, but load_viz_notebook_inferred_data was missing it
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No, this is not a cleanup which was meant to be added in the previous PR. We were using add_footprint parameter to compute footprint calculations in labeled trips in energy_calculations notebook. Earlier, we were not using add_footprint parameter for load_viz_notebook_inferred_data.
We need to pass add_footprint parameter as True to enable extraction of base_mode. Therefore, this change is required, such that we have base_mode as column added in the expanded_ct_inferred data frame. This enables us for filtering AIR as the base_mode, instead of using mode_confirm_w_other = 'air'.
| @@ -244,7 +246,7 @@ | |||
| " plot_and_text_stacked_bar_chart(expanded_ct, lambda df: (df.groupby(\"mode_confirm_w_other\").agg({distance_col: 'count'}).sort_values(by=distance_col, ascending=False)), \n", | |||
| " \"Labeled by user\\n\"+stacked_bar_quality_text_labeled, ax[0], text_results[0], colors_mode, debug_df, values_to_translations)\n", | |||
| " plot_and_text_stacked_bar_chart(expanded_ct_inferred, lambda df: (df.groupby(\"mode_confirm_w_other\").agg({distance_col: 'count'}).sort_values(by=distance_col, ascending=False)), \n", | |||
| " \"Labeled and Inferred by OpenPATH\\n\"+stacked_bar_quality_text_inferred, ax[1], text_results[1], colors_mode, debug_df_inferred, values_to_translations)\n", | |||
| " \"Inferred from prior labels\\n\"+stacked_bar_quality_text_inferred, ax[1], text_results[1], colors_mode, debug_df_inferred, values_to_translations)\n", | |||
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Good text change. More concise & clearer
Co-authored-by: Jack Greenlee <[email protected]>
Co-authored-by: Jack Greenlee <[email protected]>
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Handle Future cleanups mentioned in #152 Changes:
Type fixed from relecant to relevant.
Refactor extract_co2 and extract_kwh to a single consolidated parameterized function extract_footprint()