From 04dbc494cc9d6bf513fab4fc3b75e463a7b17564 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Wed, 6 Mar 2024 11:38:14 -0800 Subject: [PATCH 01/51] Edit to `setup.cfg` maintainer name --- setup.cfg | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/setup.cfg b/setup.cfg index 7aa61e3c..e833ca55 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,7 +1,7 @@ [metadata] name = EchoPro -maintainer = Wu-Jung Lee -maintainer_email = leewujung@gmail.com +maintainer = Brandyn Lucca +maintainer_email = brandyn.lucca@gmail.com description = A tool that uses acoustic and biological data to produce biomass estimates long_description = file: README.md long_description_content_type = text/markdown From ba657c2ec645581151ebeb7aaeb3aa5f8c0df123 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Wed, 6 Mar 2024 11:43:01 -0800 Subject: [PATCH 02/51] Change `setup.cfg` software name --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index e99d0f81..f001c138 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,5 +1,5 @@ [metadata] -name = EchoPro +name = echopop maintainer = Brandyn Lucca maintainer_email = brandyn.lucca@gmail.com description = A tool that uses acoustic and biological data to produce biomass estimates From 0646652f4c568008d1e0ee229ac15108949e824a Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Wed, 6 Mar 2024 19:40:27 -0800 Subject: [PATCH 03/51] Additional kriging and unit test function updates --- .vscode/settings.json | 7 + config_files/survey_year_2019_config.yml | 2 +- echopop/survey.py | 190 ++++++++++++++++ .../test_data/config_files/config_init.yml | 14 +- .../test_data/config_files/config_survey.yml | 30 +-- .../Biological/CAN/biodata_catch_CAN.xlsx | Bin 0 -> 14361 bytes .../Biological/CAN/biodata_length_CAN.xlsx | Bin 0 -> 11640 bytes .../CAN/biodata_specimen_CAN_AGES.xlsx | Bin 0 -> 57963 bytes .../CAN/haul_to_transect_mapping_CAN.xlsx | Bin 0 -> 9384 bytes .../Biological/US/biodata_catch.xlsx | Bin 0 -> 14353 bytes .../Biological/US/biodata_length.xlsx | Bin 0 -> 84095 bytes .../Biological/US/biodata_specimen_AGES.xlsx | Bin 0 -> 10976 bytes .../US/haul_to_transect_mapping.xlsx | Bin 0 -> 9384 bytes ...detailsa_table1y+_ALL_final - updated.xlsx | Bin 0 -> 8696 bytes ...detailsa_table2y+_ALL_final - updated.xlsx | Bin 0 -> 8739 bytes .../krig_grid2_5nm_cut_centroids_2013.xlsx | Bin 0 -> 1876487 bytes .../transformation_isobath_coordinates.xlsx | Bin 0 -> 11992 bytes .../default_vario_krig_settings_US_CAN.xlsx | Bin 0 -> 13497 bytes .../Stratification_geographic_Lat_final.xlsx | Bin 0 -> 9379 bytes .../Stratification/US_CAN strata_final.xlsx | Bin 0 -> 9446 bytes echopop/tests/conftest.py | 11 +- echopop/tests/test_data_loader.py | 8 +- echopop/tests/test_strata_mean_sigma_bs.py | 204 ++++++++++++++++++ 23 files changed, 442 insertions(+), 24 deletions(-) create mode 100644 .vscode/settings.json create mode 100644 echopop/test_data/input_files/Biological/CAN/biodata_catch_CAN.xlsx create mode 100644 echopop/test_data/input_files/Biological/CAN/biodata_length_CAN.xlsx create mode 100644 echopop/test_data/input_files/Biological/CAN/biodata_specimen_CAN_AGES.xlsx create mode 100644 echopop/test_data/input_files/Biological/CAN/haul_to_transect_mapping_CAN.xlsx create mode 100644 echopop/test_data/input_files/Biological/US/biodata_catch.xlsx create mode 100644 echopop/test_data/input_files/Biological/US/biodata_length.xlsx create mode 100644 echopop/test_data/input_files/Biological/US/biodata_specimen_AGES.xlsx create mode 100644 echopop/test_data/input_files/Biological/US/haul_to_transect_mapping.xlsx create mode 100644 echopop/test_data/input_files/Exports/US_CAN_detailsa_table1y+_ALL_final - updated.xlsx create mode 100644 echopop/test_data/input_files/Exports/US_CAN_detailsa_table2y+_ALL_final - updated.xlsx create mode 100644 echopop/test_data/input_files/Kriging_files/Kriging_grid_files/krig_grid2_5nm_cut_centroids_2013.xlsx create mode 100644 echopop/test_data/input_files/Kriging_files/Kriging_grid_files/transformation_isobath_coordinates.xlsx create mode 100644 echopop/test_data/input_files/Kriging_files/default_vario_krig_settings_US_CAN.xlsx create mode 100644 echopop/test_data/input_files/Stratification/Stratification_geographic_Lat_final.xlsx create mode 100644 echopop/test_data/input_files/Stratification/US_CAN strata_final.xlsx create mode 100644 echopop/tests/test_strata_mean_sigma_bs.py diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 00000000..4be84e52 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,7 @@ +{ + "python.testing.pytestArgs": [ + "echopop" + ], + "python.testing.unittestEnabled": false, + "python.testing.pytestEnabled": true +} \ No newline at end of file diff --git a/config_files/survey_year_2019_config.yml b/config_files/survey_year_2019_config.yml index bde9f49d..817e469b 100644 --- a/config_files/survey_year_2019_config.yml +++ b/config_files/survey_year_2019_config.yml @@ -16,7 +16,7 @@ CAN_haul_offset: 200 # The value to be added to the Canadian's haul numb ############################################################################## # Directory path that contains all input data needed -data_root_dir: C:/Users/Brandyn/Documents/GitHub/EchoPro/Data +data_root_dir: C:/Users/Brandyn/Documents/GitHub/echopop/Data ############################################################################## # Input data files diff --git a/echopop/survey.py b/echopop/survey.py index cc817ff3..4aad3a76 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -1230,3 +1230,193 @@ def krige( self , 'kriged_biomass_df': kriged_dataframe } ) + + def apportion_kriged_biomass( self , + species_id ): + + ### Length data (Station 1 - Unaged - Sexxed ) + length_df_copy = self.biology[ 'length_df' ].copy( ).pipe( lambda df: df.loc[ df.species_id == species_id ] ) + + ### Length-weight data (Station 2 - Aged - Sexed) + specimen_df_copy = self.biology[ 'specimen_df' ].copy( ).pipe( lambda df: df.loc[ df.species_id == species_id ] ) + + ### Haul data + haul_catch_df = self.biology[ 'catch_df' ].copy( ).pipe( lambda df: df.loc[ df.species_id == species_id ] ) + + ### Import length bins & intervals + length_intervals = self.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] + length_bins = self.biology[ 'distributions' ][ 'length' ][ 'length_bins_arr' ] + + ### Age bins + age_intervals = self.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] + + ### Process `haul_catch_df` + # Remove haul numbers not found within `length_df_copy` + haul_catch_filtered = ( + haul_catch_df + .loc[ lambda x: x.haul_num.isin( length_df_copy.haul_num ) ] + ) + + ### Calculate station weights -- stratified by strata + weight_strata_station = ( + pd.concat( [ + haul_catch_filtered + .groupby( 'stratum_num' ) + .apply( lambda df: pd.Series( { 'stratum_weight': df.haul_weight.sum( ) } ) ) + .reset_index( ) + .assign( station = 1 ) , + specimen_df_copy + .groupby( [ 'stratum_num' ] ) + .apply( lambda df: pd.Series( { 'stratum_weight': df.weight.sum( ) } ) ) + .reset_index() + .assign( station = 2 ) + ] ) + ) + + ### Calculate stratum weights + weight_strata = ( + weight_strata_station + .groupby( [ 'stratum_num' ] ) + .apply( lambda x: pd.Series( { 'weight_stratum_total': x.stratum_weight.sum( ) } ) ) + .reset_index() + ) + + ### Calculate summed length-age-sex weight bins across strata + length_age_sex_stratum_weight = ( + specimen_df_copy + .dropna( how = 'any' ) + .loc[ lambda x: x.sex != 'unsexed' ] + .bin_variable( age_intervals , 'age' ) + .bin_variable( length_intervals , 'length' ) + .groupby( [ 'stratum_num' , 'age_bin' , 'length_bin' , 'sex' ] ) + .apply( lambda df: pd.Series( { 'summed_weight_all': df.weight.sum( ) , + 'summed_weight_adult': df.loc[ df.age > 1 ].weight.sum( ) } ) ) + .reset_index( ) + .replace( np.nan , 0 ) + .assign( total_weight_sex_all = lambda df: df.groupby( [ 'stratum_num' , 'sex' ] )[ 'summed_weight_all' ].transform( sum ) , + total_weight_sex_adult = lambda df: df.groupby( [ 'stratum_num' , 'sex' ] )[ 'summed_weight_adult' ].transform( sum ) , + proportion_weight_all = lambda df: df.summed_weight_all / df.total_weight_sex_all , + proportion_weight_adult = lambda df: df.summed_weight_adult / df.total_weight_sex_adult ) + ) + + ### Normalize the age-length-sex indexed proportions + dist_weight_sum = ( + length_age_sex_stratum_weight + .merge( weight_strata , on = [ 'stratum_num' ] ) + .groupby( [ 'stratum_num' , 'sex' ] ) + .apply( lambda df: pd.Series( { + 'proportion_normalized': ( df.proportion_weight_all * ( df.summed_weight_all / df.weight_stratum_total ).sum( ) ).sum( ) + } ) ) + .reset_index( ) + ) + + ### Calculate aged / unaged proportions + aged_proportions = ( + length_age_sex_stratum_weight + .groupby( [ 'stratum_num' ] ) + .apply( lambda df: pd.Series( { 'weight_aged_total': df.summed_weight_all.sum( ) } ) ) + .reset_index( ) + .merge( weight_strata , on = [ 'stratum_num' ] ) + .assign( proportion_aged_total = lambda x: x.weight_aged_total / x.weight_stratum_total , + proportion_unaged_total = lambda x: np.round( 1.0 - x.proportion_aged_total , decimals = 10 ) ) + ) + + ### Calculate interpolated weights based on length bins for each sex per haul + # Pull length-weight fitted values + length_weight_fit = ( + self.statistics[ 'length_weight' ][ 'length_weight_df' ] + .assign( length_bin_value = lambda x: x[ 'length_bin' ].apply( lambda y: y.mid ) ) + ) + + # Sum haul weights per sex per stratum + haul_weights = ( + length_df_copy + .bin_variable( length_intervals , 'length' ) + .loc[ lambda x: x.group == 'sexed' ] + .pipe( lambda df: pd.concat( [ df , df.assign( sex = 'all' ) ] ) ) + .merge( length_weight_fit , on = [ 'sex' , 'length_bin' ] ) + .groupby( [ 'stratum_num' , 'haul_num' , 'sex' ] ) + .apply( lambda df: pd.Series( { 'weight_interp': ( np.interp( df.length, + length_weight_fit.loc[ lambda x: x.sex.isin( df.sex ) ][ 'length_bin_value' ] , + length_weight_fit.loc[ lambda x: x.sex.isin( df.sex ) ][ 'weight_modeled' ] ) * + df.length_count ).sum( ) } ) ) + .groupby( [ 'stratum_num' , 'sex' ] ) + .apply( lambda df: pd.Series( { 'summed_haul_weights': df.weight_interp.sum( ) } ) ) + .reset_index( ) + ) + + ### Normalize haul weights (Station 1) + haul_sex_weights_normalized = ( + haul_weights + .loc[ lambda x: x.sex != 'all' , : ] + .merge( weight_strata_station.loc[ lambda x: x.station == 1 ] , + on = 'stratum_num' ) + .assign( weight_normalized_station_1 = lambda df: ( + df + .groupby( [ 'stratum_num' ] ) + .apply( lambda strata: strata[ 'stratum_weight' ] * strata[ 'summed_haul_weights' ] / + ( df[ ( df.stratum_num == strata.name ) & ( df.sex == 'male' ) ][ 'summed_haul_weights' ].iloc[ 0 ] + + df[ ( df.stratum_num == strata.name ) & ( df.sex == 'female' ) ][ 'summed_haul_weights' ].iloc[ 0 ] ) ) + .reset_index( drop = True ) ) ) + .merge( weight_strata , on = [ 'stratum_num' ] ) + .assign( proportion_normalized_station_1 = lambda df: ( + df.weight_normalized_station_1 / df.weight_stratum_total + ) ) + .assign( sex_proportion = lambda df: ( + df + .groupby( [ 'stratum_num' ] ) + .apply( lambda strata: strata[ 'proportion_normalized_station_1' ] / + ( df[ ( df.stratum_num == strata.name ) & ( df.sex == 'male' ) ][ 'proportion_normalized_station_1' ].iloc[ 0 ] + + df[ ( df.stratum_num == strata.name ) & ( df.sex == 'female' ) ][ 'proportion_normalized_station_1' ].iloc[ 0 ] ) ) + .reset_index( drop = True ) ) ) + .merge( aged_proportions.loc[ : , [ 'stratum_num' , 'proportion_unaged_total' ] ] , + on = [ 'stratum_num' ] ) + .assign( proportion_unaged_sex = lambda x: x.sex_proportion * x.proportion_unaged_total ) + ) + + ### Parse kriged biomass (areal density) values + kriged_results = self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] + + kriged_results = ( + kriged_results + .merge( dist_weight_sum , on = [ 'stratum_num' ] ) + .assign( biomass_aged = lambda df: df.B_a_adult_mean * df.proportion_normalized * df.cell_area_nmi2 ) + .merge( haul_sex_weights_normalized , on = [ 'stratum_num' , 'sex' ] ) + .assign( biomass_unaged = lambda df: df.B_a_adult_mean * df.proportion_normalized_station_1 * df.cell_area_nmi2 , + biomass_total = lambda df: df.biomass_aged + df.biomass_unaged ) + ) + + ### Parse the average weight calculations + average_weight_df = ( + self.biology[ 'weight' ][ 'weight_strata_df' ] + .loc[ : , [ 'stratum_num' , 'average_weight_total' ] ] + ) + + ### Merge with the kriged results + kriged_results = ( + kriged_results + .merge( average_weight_df , on = [ 'stratum_num' ] ) + .assign( N_adult_kriged = lambda df: df.B_adult_kriged / df.average_weight_total ) + .merge( self.acoustics[ 'sigma_bs' ][ 'strata_mean' ] , on = [ 'stratum_num' ] ) + .assign( NASC_kriged = lambda df: df.N_adult_kriged * df.sigma_bs_mean ) + ) + + ### Calculate grid CV + biomass_density_adult = ( + self.biology[ 'population' ][ 'areal_density' ][ 'biomass_density_df' ] + .loc[ lambda df: df.sex == 'all' ] + .drop_duplicates( subset = [ 'transect_num' , 'latitude' , 'longitude' , 'stratum_num' ] ) + ) + + C0 = np.std( biomass_density_adult.B_a , ddof = 1 ) ** 2 + Bn = np.nansum( kriged_results.B_a_adult_mean * kriged_results.cell_area_nmi2 ) * 1e-9 + kriged_results = ( + kriged_results + .assign( biomass_adult_cell_CV = lambda df: ( + self.statistics[ 'kriging' ][ 'model_config' ][ 'A0' ] * + np.sqrt( kriged_results.B_a_adult_sample_variance * C0 ) * + 1e-9 / + Bn * + np.sqrt( len( kriged_results.B_a_adult_sample_variance ) ) + ) ) + ) \ No newline at end of file diff --git a/echopop/test_data/config_files/config_init.yml b/echopop/test_data/config_files/config_init.yml index 32af49a6..eb51c51a 100644 --- a/echopop/test_data/config_files/config_init.yml +++ b/echopop/test_data/config_files/config_init.yml @@ -1,5 +1,5 @@ # This YAML file is a configuration file for all -# initialization parameters used in Echopop +# initialization parameters used in EchoPro --- ##################################################################################################################### @@ -8,7 +8,7 @@ # Jolly and Hampton (1990) stratified mean calculation stratified_survey_mean_parameters: strata_transect_proportion: 0.75 # percent of Jolly-Hampton transects in each stratum - replicates: 10000 # number of resample replicates + num_replicates: 10000 # number of resample replicates ##################################################################################################################### # Biological data processing# @@ -26,8 +26,6 @@ 22 # number of points in the sequence ] - ##################################################################################################################### - ##################################################################################################################### # Log-linear regression# ######################## @@ -43,12 +41,16 @@ # Geospatial settings# ######################## geospatial: - init: epsg:4326 # EPSG integer code for geodetic parameter dataset + init: epsg:4326 # EPSG integer code for geodetic parameter dataset ##################################################################################################################### # kriging parameters# ##################### - kriging_A0: 6.25 # base area of the grid cell + kriging_parameters: + A0: 6.25 # base area of the grid cell + longitude_reference: -124.78338 # longitude reference for kriging mesh adjustment + longitude_offset: -124.78338 # longitudinal offset for kriging mesh adjustment + latitude_offset: 45.0 # latitudinal offset for kriging mesh adjustment ##################################################################################################################### ... \ No newline at end of file diff --git a/echopop/test_data/config_files/config_survey.yml b/echopop/test_data/config_files/config_survey.yml index 3d45dc25..eea88a0a 100644 --- a/echopop/test_data/config_files/config_survey.yml +++ b/echopop/test_data/config_files/config_survey.yml @@ -7,7 +7,7 @@ ############################################################################## # Parameters -survey_year: 2019 # survey year being considered +survey_year: 2000 # survey year being considered species: text_code: pacific_hake # target species for the survey year -- species name number_code: 22500 # target species for the survey year -- numeric code @@ -16,7 +16,7 @@ CAN_haul_offset: 200 # The value to be added to the Canadian's haul numb ############################################################################## # Directory path that contains all input data needed -data_root_dir: for_tests +data_root_dir: echopop/test_data/input_files ############################################################################## # Input data files @@ -24,50 +24,50 @@ data_root_dir: for_tests biological: length: US: - filename: Biological/US/2019_biodata_length.xlsx + filename: Biological/US/biodata_length.xlsx sheetname: biodata_length CAN: - filename: Biological/CAN/2019_biodata_length_CAN.xlsx + filename: Biological/CAN/biodata_length_CAN.xlsx sheetname: biodata_length_CAN specimen: US: - filename: Biological/US/2019_biodata_specimen_AGES.xlsx + filename: Biological/US/biodata_specimen_AGES.xlsx sheetname: biodata_specimen CAN: - filename: Biological/CAN/2019_biodata_specimen_CAN_AGES.xlsx + filename: Biological/CAN/biodata_specimen_CAN_AGES.xlsx sheetname: biodata_specimen_CAN catch: US: - filename: Biological/US/2019_biodata_catch.xlsx + filename: Biological/US/biodata_catch.xlsx sheetname: biodata_catch CAN: - filename: Biological/CAN/2019_biodata_catch_CAN.xlsx + filename: Biological/CAN/biodata_catch_CAN.xlsx sheetname: biodata_catch_CAN haul_to_transect: US: - filename: Biological/US/haul_to_transect_mapping_2019.xlsx + filename: Biological/US/haul_to_transect_mapping.xlsx sheetname: Sheet1 CAN: - filename: Biological/CAN/haul_to_transect_mapping_2019_CAN.xlsx + filename: Biological/CAN/haul_to_transect_mapping_CAN.xlsx sheetname: Sheet1 stratification: strata: # The two stratification types are found in two sheets: "Base KS" and "INPFC" - filename: Stratification/US_CAN strata 2019_final.xlsx + filename: Stratification/US_CAN strata_final.xlsx sheetname: Base KS geo_strata: # The two stratification types are found in two sheets: "stratification1" and "INPFC" - filename: Stratification/Stratification_geographic_Lat_2019_final.xlsx + filename: Stratification/Stratification_geographic_Lat_final.xlsx sheetname: [ INPFC , stratification1 ] NASC: # NASC values no_age1: # file that excludes age1 values - 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+++ b/echopop/tests/conftest.py @@ -1,5 +1,6 @@ import pytest from pathlib import Path +from echopop import Survey # Set up path to test_data folder @@ -15,6 +16,14 @@ def test_path(): } +@pytest.fixture(scope="session") +def mock_survey(test_path) -> Survey: + return Survey( + init_config_path=Path(test_path["CONFIG"] / "config_init.yml"), + survey_year_config_path=Path(test_path["CONFIG"] / "config_survey.yml"), + ) + + # ============ below from previous version, remove after revamping is complete ============ @pytest.fixture(scope="session") def config_base_path() -> Path: @@ -40,7 +49,7 @@ def reports_base_path() -> Path: pathlib.Path The base directory path for the reports """ - return HERE / "tests/reports/echopop_python_output" + return HERE / "tests/reports/EchoPro_python_output" @pytest.fixture(scope="session") diff --git a/echopop/tests/test_data_loader.py b/echopop/tests/test_data_loader.py index d845ec42..a9e9a70b 100644 --- a/echopop/tests/test_data_loader.py +++ b/echopop/tests/test_data_loader.py @@ -1,5 +1,6 @@ import yaml from pathlib import Path +from echopop import Survey from echopop.utils.data_file_validation import load_configuration , validate_data_columns def test_load_configuration(test_path, tmp_path): @@ -28,4 +29,9 @@ def test_load_configuration(test_path, tmp_path): assert ( config["stratified_survey_mean_parameters"]["strata_transect_proportion"] == init_params["stratified_survey_mean_parameters"]["strata_transect_proportion"] - ) \ No newline at end of file + ) + + +def test_init(mock_survey): + objS = mock_survey + assert isinstance(objS, Survey) \ No newline at end of file diff --git a/echopop/tests/test_strata_mean_sigma_bs.py b/echopop/tests/test_strata_mean_sigma_bs.py new file mode 100644 index 00000000..b6b24e7f --- /dev/null +++ b/echopop/tests/test_strata_mean_sigma_bs.py @@ -0,0 +1,204 @@ +import pandas as pd +import numpy as np +from echopop.computation.acoustics import to_linear , to_dB , ts_length_regression + +def test_strata_mean_sigma_bs( mock_survey ): + #### Pull in mock Survey object + objS = mock_survey + + ### Re-parameterize `specimen_df` with dummy data + objS.biology[ 'specimen_df' ] = pd.DataFrame( + { + 'stratum_num': np.repeat( [ 0 , 1 , 2 , 4 , 5 ] , 4 ) , + 'haul_num': np.repeat( [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ] , 2 ) , + 'species_id': np.append( np.repeat( [ 19350 ] , 19 ) , 43130 ) , + 'length': np.linspace( 10 , 100 , 20 ) , + 'weight': np.linspace( 1 , 5 , 20 ) , + } + ) + + ### Re-parameterize `length_df` with dummy data + objS.biology[ 'length_df' ] = pd.DataFrame( + { + 'stratum_num': np.repeat( [ 0 , 1 , 2 , 4 , 5 ] , 4 ) , + 'haul_num': np.repeat( [ 1 , 2 , 3 , 4 , 5 ] , 4 ) , + 'species_id': np.append( np.repeat( [ 19350 ] , 19 ) , 43130 ) , + 'length': np.linspace( 10 , 100 , 20 ) , + 'length_count': np.linspace( 10 , 100 , 20 ) , + } + ) + + ### Re-parameterize `strata_df` with dummy data + objS.spatial[ 'strata_df' ] = pd.DataFrame( + { + 'stratum_num': [ 0 , 1 , 2 , 3 , 4 , 5 , 6 ] + } + ) + + ### Dummy parameters + objS.config[ 'TS_length_regression_parameters' ][ 'pacific_hake' ] = { 'species_code': 19350 , + 'TS_L_slope': 20.0 , + 'TS_L_intercept': -84.1 , + 'length_units': 'cm' } + + ### Define dummy `species_id` code + species_id = 19350 + + ### Evaluate object for later comparison + objS.strata_mean_sigma_bs( species_id ) + + #---------------------------------- + ### Run tests: `strata_mean_sigma_bs` + #---------------------------------- + ### Evaluate whether non-specified `species_id` code are removed + # `specimen_df`: 20 rows -> 19 rows + specimen_df_copy = objS.biology[ 'specimen_df' ].copy( ) + specimen_df_copy = specimen_df_copy[ specimen_df_copy.species_id == species_id ] + assert specimen_df_copy.shape[ 0 ] == 19 + + # `length_df`: 20 rows -> 19 rows + length_df_copy = objS.biology[ 'length_df' ].copy( ) + length_df_copy = length_df_copy[ length_df_copy.species_id == species_id ] + assert length_df_copy.shape[ 0 ] == 19 + + #---------------------------------- + ### Next step: Concatenate the two dataframes + # Re-bin `specimen_df_copy` + spec_df_reframed = ( + specimen_df_copy + .groupby( [ 'haul_num' , 'stratum_num' , 'species_id', 'length' ] ) + .apply( lambda x: len( x[ 'length' ] ) ) + .reset_index( name = 'length_count' ) + ) + + # Concatenate + all_length_df = pd.concat( [ spec_df_reframed , length_df_copy ] , + join = 'inner' ) + assert all_length_df.shape[ 0 ] == 38 + + #---------------------------------- + ### TS-length parameterization & modeling + # Fit parameters + ts_length_parameters = objS.config[ 'TS_length_regression_parameters' ][ 'pacific_hake' ] + slope = ts_length_parameters[ 'TS_L_slope' ] + intercept = ts_length_parameters[ 'TS_L_intercept' ] + + # Predict `TS` + all_length_df[ 'TS' ] = ts_length_regression( all_length_df[ 'length' ] , slope , intercept ) + assert np.isclose( np.median( all_length_df[ 'TS' ] ) , -49.675 ) + + # Linearize to `sigma_bs` + all_length_df[ 'sigma_bs' ] = to_linear( all_length_df[ 'TS' ] ) + assert np.isclose( all_length_df[ 'sigma_bs' ].mean( ) , 1.3395e-5 ) + assert objS.acoustics[ 'sigma_bs' ][ 'length_binned' ].equals( all_length_df ) + + #---------------------------------- + ### Calculate mean `sigma_bs` per `haul_num` and then `stratum_num` + # `haul_num` + mean_haul_sigma_bs = ( + all_length_df + .groupby( [ 'haul_num' , 'stratum_num' , 'species_id' ] )[ [ 'sigma_bs' , 'length_count' ] ] + .apply(lambda x: np.average( x[ 'sigma_bs' ] , weights= x[ 'length_count' ] ) ) + .to_frame( 'sigma_bs_mean' ) + .reset_index( ) + ) + assert objS.acoustics[ 'sigma_bs' ][ 'haul_mean' ].equals( mean_haul_sigma_bs ) + assert mean_haul_sigma_bs.shape[ 0 ] == 14 + assert np.isclose( mean_haul_sigma_bs.sigma_bs_mean.mean( ) , 1.549e-5 ) + + # `stratum_num` + mean_strata_sigma_bs = ( + mean_haul_sigma_bs + .groupby( [ 'stratum_num' , 'species_id' ] )[ 'sigma_bs_mean' ] + .mean( ) + .reset_index( ) + ) + assert mean_strata_sigma_bs.shape[ 0 ] == 5 + assert np.allclose( mean_strata_sigma_bs.sigma_bs_mean , + np.array( [ 1.659e-6 , 5.238e-6 , 1.195e-5 , 2.145e-5 , 3.254e-5 ] ) ) + assert any( ~ mean_strata_sigma_bs.stratum_num.isin( [ 3 , 6 ] ) ) + + #---------------------------------- + ### Add to object as dictionary + strata_mean_dictionary = { + 'length_binned': all_length_df , + 'haul_mean': mean_haul_sigma_bs , + 'strata_mean': mean_strata_sigma_bs , + } + + #---------------------------------- + ### `impute_missing_sigma_bs` + ### Run function -- missing strata (3, 6) + # Collect strata numbers + strata_options = np.unique( objS.spatial[ 'strata_df' ].copy().stratum_num ) + + # Pull `strata_mean` dataframe from dictionary + strata_mean = strata_mean_dictionary[ 'strata_mean' ] + + ### Evaluate imputed values + # Check mismatch between present/absent values + present_strata = np.unique( strata_mean[ 'stratum_num' ]).astype( int ) + missing_strata = strata_options[ ~ ( np.isin( strata_options , present_strata ) ) ] + assert all( np.array( [ 3 , 6 ] ) == missing_strata ) + + ### Iterate through for imputation + if len( missing_strata ) > 0: + + # Fill missing values with `np.nan` + sigma_bs_impute = ( + pd.concat( [ strata_mean , + pd.DataFrame( { + 'stratum_num': missing_strata , + 'species_id': np.repeat( np.unique( strata_mean.species_id ) , + len( missing_strata ) ) , + 'sigma_bs_mean': np.repeat( np.nan , + len( missing_strata ) ) + } ) ] ) + .sort_values( 'stratum_num' ) + ) + + # Loop over `np.nan` values + for i in missing_strata: + strata_floor = present_strata[ present_strata < i ] + strata_ceil = present_strata[ present_strata > i ] + + new_stratum_below = np.max( strata_floor ) if strata_floor.size > 0 else None + new_stratum_above = np.min( strata_ceil ) if strata_ceil.size > 0 else None + + sigma_bs_indexed = sigma_bs_impute[ sigma_bs_impute[ 'stratum_num' ].isin( [ new_stratum_below , new_stratum_above ] ) ] + + sigma_bs_impute.loc[ sigma_bs_impute.stratum_num==i , 'sigma_bs_mean' ] = sigma_bs_indexed[ 'sigma_bs_mean' ].mean( ) + + ### Test against `objS` + assert objS.acoustics[ 'sigma_bs' ][ 'strata_mean' ].equals( sigma_bs_impute ) + +def test_ts_linear_regression( ): + + ### Dummy variables/inputs + length_arr = np.array( [ 21.1 , 41.2 , 81.5 , 19.4 , 2.3 , 6.8 , 16.7 , 101.4 ] ) + slope = 20.0 + intercept = -54.3 + + ### Generate TS array from function + ts_arr = ts_length_regression( length_arr , slope , intercept ) + + ### Produce TS array manually + ts_test_arr = slope * np.log10( length_arr ) + intercept + + ### Check equality + assert all( ts_arr == ts_test_arr ) + + +def test_acoustic_unit_conversion( ): + + ### Dummy variables/inputs + ts_values = np.array( [ -101.4 , -33.2 , -49.8 , -50.2 , -81.7 , -20.1 , -39.6 ] ) + + ### Generate value arrays from functions -- sigma_bs + sigma_bs_arr = to_linear( ts_values ) + sigma_bs_test_arr = 10 ** ( ts_values / 10.0 ) + assert all( sigma_bs_arr == sigma_bs_test_arr ) + + ### Generate value arrays from functions -- TS + ts_arr = to_dB( sigma_bs_arr ) + assert all( ts_arr == ts_values ) \ No newline at end of file From 2701083ca6f4088a78e3b3bd9a2131abb25d6a5f Mon Sep 17 00:00:00 2001 From: Brandyn Lucca Date: Thu, 7 Mar 2024 09:44:27 -0800 Subject: [PATCH 04/51] Updates to kriging methods and sex-age apportionin --- echopop/survey.py | 99 ++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 80 insertions(+), 19 deletions(-) diff --git a/echopop/survey.py b/echopop/survey.py index 4aad3a76..7c6b82d1 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -365,17 +365,9 @@ def biometric_distributions( self ): def transect_analysis(self , species_id: np.float64 = 22500 ): - # # INPUTS - # # This is where the users can designate specific transect numbers, - # # stratum numbers, species, etc. These would be applied to the functions - # # below - - # # Initialize new attribute - # self.results = {} - # #### TODO: THIS SHOULD BE ADDED TO THE ORIGINAL SURVEY OBJECT CREATION - # #### THIS IS INCLUDED HERE FOR NOW FOR TESTING PURPOSES -- ALL CAPS IS CRUISE - # #### CONTROL FOR "REMEMBER TO MAKE THIS CHANGE BRANDYN !!!" - + """ + Calculate population-level metrics from acoustic transect measurements + """ # Initialize major data structures that will be added (**tentative names**) self.acoustics['sigma_bs'] = {} self.biology['weight'] = {} @@ -1143,7 +1135,15 @@ def stratified_summary( self ): def standardize_coordinates( self , adjust_mesh = True ): + """ + Standardizes spatial coordinates based on reference positions + Parameters + ---------- + adjust_mesh + Boolean value that determines whether a defined mesh is also standardized + alongside the transect values + """ ### Collect necessary parameter values # !!! TODO: This only currently applies to age-sex-indexed biomass transect_trans_df , d_x , d_y = transform_geometry( self.biology[ 'population' ][ 'biomass' ][ 'biomass_age_df' ] , @@ -1177,8 +1177,16 @@ def standardize_coordinates( self , ) def krige( self , + species_id , variable: str = 'B_a_adult' ): - + """ + Interpolates biomass data using ordinary kriging + + Parameters + ---------- + variable + Biological variable that will be interpolated via kriging + """ ### TODO : Need to relocate the transformed coordinates to an external location ### Import georeferenced data dataframe_input = ( @@ -1216,6 +1224,14 @@ def krige( self , kriging_parameters = self.statistics[ 'kriging' ][ 'model_config' ].copy() variogram_parameters = self.statistics[ 'variogram' ][ 'model_config' ].copy() + ### --------------------------------------------------------------- + ### !!! TODO: + ### This is related to a later issue found within the sex-age + ### apportionment method, but the resulting areal biomass density + ### estimates produced via `kriging_interpolation` are consistently + ### much lower than values produced using previous implementations. + ### This therefore relates to Issue #202. + ### --------------------------------------------------------------- ### Run kriging algorithm kriged_dataframe = kriging_interpolation( dataframe , transformed_mesh , @@ -1230,10 +1246,26 @@ def krige( self , 'kriged_biomass_df': kriged_dataframe } ) + + ### TODO: This should be refactored out as an external function + ###### rather than a Survey-class method. + ### Apportion biomass based on age and sex + self.apportion_kriged_biomass( species_id ) def apportion_kriged_biomass( self , species_id ): - + """ + Apportion kriged biomass based on sex, age, and other proportions + + Notes + ----- + This function apportions the kriged biomass values (interpolated along + a user-defined mesh) based on sex and age. This provides biomass estimates + for aged and unaged fish, total biomass inferred from the biological and + kriged bioamss estimates, the coefficient of variation within each cell, + and georeferenced biomass estimates for specific paired values of sex + and age-class. + """ ### Length data (Station 1 - Unaged - Sexxed ) length_df_copy = self.biology[ 'length_df' ].copy( ).pipe( lambda df: df.loc[ df.species_id == species_id ] ) @@ -1245,8 +1277,7 @@ def apportion_kriged_biomass( self , ### Import length bins & intervals length_intervals = self.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] - length_bins = self.biology[ 'distributions' ][ 'length' ][ 'length_bins_arr' ] - + ### Age bins age_intervals = self.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] @@ -1286,6 +1317,8 @@ def apportion_kriged_biomass( self , specimen_df_copy .dropna( how = 'any' ) .loc[ lambda x: x.sex != 'unsexed' ] + .pipe( lambda df: pd.concat( [ df , + df.copy( ).assign( sex = 'all' ) ] ) ) .bin_variable( age_intervals , 'age' ) .bin_variable( length_intervals , 'length' ) .groupby( [ 'stratum_num' , 'age_bin' , 'length_bin' , 'sex' ] ) @@ -1296,7 +1329,7 @@ def apportion_kriged_biomass( self , .assign( total_weight_sex_all = lambda df: df.groupby( [ 'stratum_num' , 'sex' ] )[ 'summed_weight_all' ].transform( sum ) , total_weight_sex_adult = lambda df: df.groupby( [ 'stratum_num' , 'sex' ] )[ 'summed_weight_adult' ].transform( sum ) , proportion_weight_all = lambda df: df.summed_weight_all / df.total_weight_sex_all , - proportion_weight_adult = lambda df: df.summed_weight_adult / df.total_weight_sex_adult ) + proportion_weight_adult = lambda df: df.summed_weight_adult / df.total_weight_sex_adult ) ) ### Normalize the age-length-sex indexed proportions @@ -1313,6 +1346,7 @@ def apportion_kriged_biomass( self , ### Calculate aged / unaged proportions aged_proportions = ( length_age_sex_stratum_weight + .loc[ lambda df: df.sex != 'all' ] .groupby( [ 'stratum_num' ] ) .apply( lambda df: pd.Series( { 'weight_aged_total': df.summed_weight_all.sum( ) } ) ) .reset_index( ) @@ -1348,7 +1382,6 @@ def apportion_kriged_biomass( self , ### Normalize haul weights (Station 1) haul_sex_weights_normalized = ( haul_weights - .loc[ lambda x: x.sex != 'all' , : ] .merge( weight_strata_station.loc[ lambda x: x.station == 1 ] , on = 'stratum_num' ) .assign( weight_normalized_station_1 = lambda df: ( @@ -1408,15 +1441,43 @@ def apportion_kriged_biomass( self , .drop_duplicates( subset = [ 'transect_num' , 'latitude' , 'longitude' , 'stratum_num' ] ) ) + ### --------------------------------------------------------------- + ### !!! TODO: + ### While `C0` produces a similar standard deviation as the original + ### code, `Bn` does not. This discrepancy results in an order of + ### magnitude change in the resulting `biomass_adult_cell_CV` estimate. + ### The source of this error stems from `kriged_results.B_a_adult_mean` + ### whereby the right-tail of the `B_a_adult_mean` distribution is + ### substantially shorter than from values (`biomass_density_adult_mean`) + ### produced in the original code. This therefore relates to Issue #202. + ### --------------------------------------------------------------- C0 = np.std( biomass_density_adult.B_a , ddof = 1 ) ** 2 Bn = np.nansum( kriged_results.B_a_adult_mean * kriged_results.cell_area_nmi2 ) * 1e-9 kriged_results = ( kriged_results .assign( biomass_adult_cell_CV = lambda df: ( self.statistics[ 'kriging' ][ 'model_config' ][ 'A0' ] * - np.sqrt( kriged_results.B_a_adult_sample_variance * C0 ) * + np.sqrt( kriged_results.B_a_adult_prediction_variance * C0 ) * 1e-9 / Bn * - np.sqrt( len( kriged_results.B_a_adult_sample_variance ) ) + np.sqrt( len( kriged_results.B_a_adult_prediction_variance ) ) ) ) + ) + + ### Add biomass values to all ages for kriged biomass results + kriged_results_output = ( + kriged_results + .merge( self.biology[ 'weight' ][ 'proportions' ][ 'sex_age_weight_proportions_df' ] , + on = [ 'stratum_num' , 'sex' ] ) + .assign( biomass_sex_age = lambda x: x.weight_sex_proportion_all * x.biomass_total ) + .loc[ : , [ 'centroid_latitude' , 'centroid_longitude' , 'stratum_num' , + 'cell_area_nmi2' , 'sex' , 'age' , 'biomass_unaged' , 'biomass_aged' , + 'biomass_total' , 'biomass_adult_cell_CV' , 'biomass_sex_age' ] ] + ) + + ### Assign results to an attribute + self.statistics[ 'kriging' ].update( + { + 'apportioned_kriged_biomass_df': kriged_results_output , + } ) \ No newline at end of file From 44e914911c0ada43003a83ee843545d2633e6a61 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca Date: Thu, 7 Mar 2024 10:06:44 -0800 Subject: [PATCH 05/51] Add documentation for newer functions --- echopop/computation/kriging_methods.py | 82 +++++++++++++++++++++++++- echopop/computation/operations.py | 23 ++++++++ echopop/computation/spatial.py | 34 ++++++++++- 3 files changed, 135 insertions(+), 4 deletions(-) diff --git a/echopop/computation/kriging_methods.py b/echopop/computation/kriging_methods.py index a92499b1..37cb851c 100644 --- a/echopop/computation/kriging_methods.py +++ b/echopop/computation/kriging_methods.py @@ -29,7 +29,18 @@ def compute_kriging_weights( ratio , M2 , K ): def compute_kriging_matrix( x_coordinates , y_coordinates , variogram_parameters ): - + """ + Calculate the kriging covariance matrix + + Parameters + ---------- + x_coordinates: np.array + The x-axis coordinates + y_coordinates: np.array + The y-axis coordinates + variogram_parameters: dict + Dictionary containing variogram model parameters + """ ### Calculate local distance matrix of within-range samples local_distance_matrix = lag_distance_griddify( x_coordinates , y_coordinates ) @@ -60,7 +71,20 @@ def range_index_threshold( local_point_grid , distance_matrix , R , k_min ): - + """ + Calculate the kriging covariance matrix + + Parameters + ---------- + local_point_grid: np.array + Gridded coordinate values + distance_matrix: np.array + Gridded distance matrix + R: np.float64 + Semivariogram range parameter value + k_min: int + Minimum number of 'k'-nearest neighbors + """ ### Calculate the within-sample grid indices inside_indices = np.where( distance_matrix[ local_point_grid ] <= R )[ 0 ] @@ -93,7 +117,24 @@ def compute_kriging_statistics( point_values , inside_indices , outside_indices , out_of_sample_weights ): - + """ + Calculate the kriging covariance matrix + + Parameters + ---------- + point_values: np.array + Values from variable being kriged + lagged_semivariogram: np.array + Lagged semivariogram + kriging_weights: np.array + Kriging (lambda) weights + inside_indices: np.array + Values found within the search window + outside_indices: np.array + Values found outside the search window + out_of_sample_weights: np.array + Weights applied to values outside of the search window + """ ### Remove any extrapolation if len( outside_indices ) > 0: point_values[ outside_indices ] = 0.0 @@ -119,7 +160,22 @@ def ordinary_kriging( dataframe , variogram_parameters , kriging_parameters , variable = 'B_a_adult' ): + """ + Use ordinary kriging to interpolate values + Parameters + ---------- + dataframe: pd.DataFrame + Dataframe including georeferenced data + transformed_mesh: pd.DataFrame + Grid data that has been transformed + variogram_parameters: dict + Semivariogram model parameters + kriging_parameters: dict + Kriging model parameters + variable: str + Variable that will be kriged + """ ### Calculate the kriging distance matrix and corresponding indices distance_matrix , local_point_grid = local_search_index( transformed_mesh , dataframe , @@ -195,7 +251,27 @@ def kriging_interpolation( dataframe , variogram_parameters , kriging_parameters , variable = 'B_a_adult' ): + """ + Use kriging to interoplate data + Parameters + ---------- + dataframe: pd.DataFrame + Dataframe including georeferenced data + transformed_mesh: pd.DataFrame + Grid data that has been transformed + dataframe_mesh: pd.DataFrame + Untransformed grid data + dataframe_geostrata: pd.DataFrame + Dataframe that includes spatial definitions that + demarcate different strata + variogram_parameters: dict + Semivariogram model parameters + kriging_parameters: dict + Kriging model parameters + variable: str + Variable that will be kriged + """ ### Discretize latitudinal bins latitude_bins = np.concatenate( [ [ -90.0 ] , dataframe_geostrata.northlimit_latitude , [ 90.0 ] ] ) diff --git a/echopop/computation/operations.py b/echopop/computation/operations.py index 8f89f875..badcf46a 100644 --- a/echopop/computation/operations.py +++ b/echopop/computation/operations.py @@ -214,7 +214,30 @@ def group_merge( dataframe , outer_on , how = 'outer' , drop_na = True ): + """ + Group merge operation + Parameters + ---------- + dataframe: pd.DataFrame + Parent dataframe + dataframes_to_add: list + Dataframes that will be added to the parent dataframe + inner_on: str or list + Index/column name that is used for an inner merge of dataframes + within `dataframes_to_add` + outer_on: str or List + Index/column name that is used for an outer merge of both the parent + and to-be-added dataframes + how: str + Merge method + drop_na: boolean + Flag determing whether np.nan values are removed + + Notes + ---------- + This is a lazy method for recursively merging multiple dataframes at once + """ ### Ensure that both the 'dataframes' and 'on' arguments are lists # dataframes df_lst = [ dataframes_to_add ] if isinstance( dataframes_to_add , str) else dataframes_to_add diff --git a/echopop/computation/spatial.py b/echopop/computation/spatial.py index ab0dd81c..e07767d5 100644 --- a/echopop/computation/spatial.py +++ b/echopop/computation/spatial.py @@ -200,7 +200,22 @@ def transform_geometry( dataframe: pd.DataFrame , def lag_distance_griddify( dataset1 , dataset2 ): - + """ + Calculate point-to-point distances between two gridded dataframes + + Parameters + ---------- + dataframe1: pd.DataFrame + Background dataframe mesh that represents the "complete" field + of values + dataframe2: pd.DataFrame + Georeferenced dataframe + + Notes + ---------- + This is used to effectively create a matrix comprising gridded + distance values (i.e. 'griddify'). + """ ### if all( isinstance( dataset , pd.DataFrame ) for dataset in ( dataset1 , dataset2 ) ): ### @@ -218,6 +233,23 @@ def lag_distance_griddify( dataset1 , def local_search_index( dataframe_mesh , dataframe , k_max ): + """ + Search for the closest georeferenced values + + Parameters + ---------- + dataframe_mesh: pd.DataFrame + Background dataframe mesh that represents the "complete" field + of values + dataframe: pd.DataFrame + Georeferenced dataframe + k_max: int + Maximum number of nearest neighbors allowed + + Notes + ---------- + This finds the 'k' number of nearest neighboring geoferenced values. + """ ### distance_matrix = lag_distance_griddify( dataframe_mesh , dataframe ) From 8fa107ca2de87451f16a0813ee7041c0a29dbdb7 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca Date: Thu, 7 Mar 2024 10:11:38 -0800 Subject: [PATCH 06/51] Kriging methods documentation --- echopop/computation/kriging_methods.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/echopop/computation/kriging_methods.py b/echopop/computation/kriging_methods.py index 37cb851c..72f17c84 100644 --- a/echopop/computation/kriging_methods.py +++ b/echopop/computation/kriging_methods.py @@ -4,7 +4,18 @@ from ..computation.variogram_models import variogram def compute_kriging_weights( ratio , M2 , K ): + """ + Apply singular value decomposition (SVD) to compute kriging (lambda) weights + Parameters + ---------- + ratio: np.float64 + Anisotropy ratio. + M2: np.array + Lagged semivariogram + K: np.array + Kriging matrix. + """ ### Singular value decomposition (SVD) # U: left singular vectors (directions of maximum variance) # Sigma: singular values (amount of variance captured by each singular vector, U) @@ -110,7 +121,6 @@ def range_index_threshold( local_point_grid , ### Carriage return return inside_indices , outside_indices , out_of_sample_weights - def compute_kriging_statistics( point_values , lagged_semivariogram , kriging_weights , @@ -118,7 +128,7 @@ def compute_kriging_statistics( point_values , outside_indices , out_of_sample_weights ): """ - Calculate the kriging covariance matrix + Calculate the mean and variance of kriged values Parameters ---------- From 95cc04f237973365fa3f1a9e9a4586ade73ad12b Mon Sep 17 00:00:00 2001 From: Brandyn Lucca Date: Thu, 7 Mar 2024 14:34:06 -0800 Subject: [PATCH 07/51] Add equations to acoustics/semivariogram RTD --- .../theory/acoustics_to_biomass_conversion.md | 50 +++++++++++++++++++ docs/theory/semivariograms.md | 4 ++ 2 files changed, 54 insertions(+) diff --git a/docs/theory/acoustics_to_biomass_conversion.md b/docs/theory/acoustics_to_biomass_conversion.md index 37fcbdea..33877a5d 100644 --- a/docs/theory/acoustics_to_biomass_conversion.md +++ b/docs/theory/acoustics_to_biomass_conversion.md @@ -1,3 +1,53 @@ # Acoustic backscatter to biomass conversion ![ text ](../images/example_indexing.jpg) + +TS-length regression + +$$\textit{TS} = mL_{\textit{cm}} + b$$ + +where $\textit{TS}$ is the target strength (dB re. 1 m-2), $L_{\textit{cm}}$ is the total length (cm), $m$ is the slope, and $b$ is the y-intercept. + +$$\hat{\sigma_\mathrm{bs}} = 10^{\textit{TS}(L)/10}$$ + +$$\bar{\sigma}_{\mathrm{bs}}^{i} = \frac{\sum\limits_{i=0} \hat{\sigma}_{\mathrm{bs}}^{i}(L)}{n^{i}}$$ + +Imputation + +$\hat{i}$ represents the expected strata. +$\hat{i}_{\mathrm{miss}} = i \ \hat{i}$ which represents values of $i$ missing from $\hat{i}$ + +$$ +\bar{\sigma}_{\mathrm{bs}}^{i} = \begin{cases} + \bar{\sigma}_{\mathrm{bs}}^{i+1} & \text{if } i = \hat{i}_{\mathrm{min}} \text{ and } i + 1 \in \hat{i} \\ + \bar{\sigma}_{\mathrm{bs}}^{i-1} & \text{if } i = \hat{i}_{\mathrm{max}} \text{ and } i + 1 \in \hat{i} \\ + \frac{1}{2}(\bar{\sigma}_{\mathrm{bs}}^{i-1} + \bar{\sigma}_{\mathrm{bs}}^{i+1}) & \text{if } i \in \hat{i}_{\mathrm{miss}} \text{ and } (i-1, i+1) \subseteq \hat{i} \\ + \bar{\sigma}_{\mathrm{bs}}^{i} & \text{if } i \in \hat{i} +\end{cases} +$$ +NASC to areal number density: + +$$\hat{\rho}_{A,N}^{i,j,k} = \frac{\textit{NASC}^{i,j,k}}{4 \pi \bar{\sigma}_{\mathrm{bs}}^{i}}$$ + +where $\hat{\rho}_{A,N}$ is the areal number density (animals nmi-2) or abundance of animals ($N$) per unit area ($A$). The subscript indices represent the +stratum ($i$), transect ($j$), and along-transect segment ($k$). + +Areal number to biomass density: + +$$\hat{\rho}_{A,B}^{i,j,k} = \hat{\rho}_{A,N}^{i,j,k} \hat{w}^{i}$$ + +where $\hat{w}^{i}$ is the derived length-weight conversion in stratum $i$. + +Abundance: + +$$N^{i,j,k} = \hat{\rho}_{A,N}^{i,j,k} A^{i,j,k}$$ + +Biomass: + +$$B^{i,j,k} = \hat{\rho}_{A,B}^{i,j,k} A^{i,j,k}$$ + +Indexed biomass: + +$$B_{\alpha, s, \ell}^{i,j,k} = B^{i,j,k} p_{\alpha, s, \ell}^{i,j,k}$$ + +where $\alpha$ is binned age, $s$ is sex, $\ell$ is the binned length, and $p$ is the relative proportion. \ No newline at end of file diff --git a/docs/theory/semivariograms.md b/docs/theory/semivariograms.md index 83521957..b1180d11 100644 --- a/docs/theory/semivariograms.md +++ b/docs/theory/semivariograms.md @@ -1,3 +1,7 @@ # Semivariogram equations ![ text ](../images/example_indexing.jpg) + + +Gaussian-exponential composite model: +$$\gamma(h) = \left[ \gamma(\infty) - \gamma(0) \right]\left[1 - \left( e^{\frac{h}{L}} \right)^p \right] + \gamma(0)$$ \ No newline at end of file From 3d2a34e9b2ea41a9cc5b35e348db983a759c8c60 Mon Sep 17 00:00:00 2001 From: Wu-Jung Lee Date: Sat, 9 Mar 2024 09:07:21 -0800 Subject: [PATCH 08/51] Apply suggestions from code review --- config_files/survey_year_2019_config.yml | 2 +- echopop/test_data/config_files/config_init.yml | 2 +- echopop/tests/conftest.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/config_files/survey_year_2019_config.yml b/config_files/survey_year_2019_config.yml index 817e469b..a2495b14 100644 --- a/config_files/survey_year_2019_config.yml +++ b/config_files/survey_year_2019_config.yml @@ -16,7 +16,7 @@ CAN_haul_offset: 200 # The value to be added to the Canadian's haul numb ############################################################################## # Directory path that contains all input data needed -data_root_dir: C:/Users/Brandyn/Documents/GitHub/echopop/Data +data_root_dir: ROOT_DIRECTORY_CONTAINING_DATA_SUBFOLDERS ############################################################################## # Input data files diff --git a/echopop/test_data/config_files/config_init.yml b/echopop/test_data/config_files/config_init.yml index eb51c51a..266bdcfa 100644 --- a/echopop/test_data/config_files/config_init.yml +++ b/echopop/test_data/config_files/config_init.yml @@ -1,5 +1,5 @@ # This YAML file is a configuration file for all -# initialization parameters used in EchoPro +# initialization parameters used in Echopop --- ##################################################################################################################### diff --git a/echopop/tests/conftest.py b/echopop/tests/conftest.py index 36f15c08..bfe9ed1f 100644 --- a/echopop/tests/conftest.py +++ b/echopop/tests/conftest.py @@ -49,7 +49,7 @@ def reports_base_path() -> Path: pathlib.Path The base directory path for the reports """ - return HERE / "tests/reports/EchoPro_python_output" + return HERE / "tests/reports/echopop_python_output" @pytest.fixture(scope="session") From c123836709e0a35ddee7b191d6ed8c2704f693f8 Mon Sep 17 00:00:00 2001 From: Wu-Jung Lee Date: Sat, 9 Mar 2024 09:10:57 -0800 Subject: [PATCH 09/51] remove .vscode/settings.json --- .vscode/settings.json | 7 ------- 1 file changed, 7 deletions(-) delete mode 100644 .vscode/settings.json diff --git a/.vscode/settings.json b/.vscode/settings.json deleted file mode 100644 index 4be84e52..00000000 --- a/.vscode/settings.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "python.testing.pytestArgs": [ - "echopop" - ], - "python.testing.unittestEnabled": false, - "python.testing.pytestEnabled": true -} \ No newline at end of file From c398f475d1f9d53a513d25b8de9e015c528a5332 Mon Sep 17 00:00:00 2001 From: Wu-Jung Lee Date: Sat, 9 Mar 2024 09:11:55 -0800 Subject: [PATCH 10/51] update .gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 4d97aba9..56889716 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,6 @@ .DS_Store _echopop_version.py +.vscode # ignore output reports, if they were produced echopop/tests/reports/echopop_python_output* From 73663a5209c680af10e14a46302e69da98a36c29 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Tue, 26 Mar 2024 10:44:24 -0700 Subject: [PATCH 11/51] Pulled documentation changes from `upstream/main` --- echopop/testing_computation.py | 716 +++++++++++++++++++++++++++++++++ echopop/testing_proportions.py | 326 +++++++++++++++ 2 files changed, 1042 insertions(+) create mode 100644 echopop/testing_computation.py create mode 100644 echopop/testing_proportions.py diff --git a/echopop/testing_computation.py b/echopop/testing_computation.py new file mode 100644 index 00000000..67920112 --- /dev/null +++ b/echopop/testing_computation.py @@ -0,0 +1,716 @@ +from echopop.survey import Survey +import numpy as np +import pandas as pd +import geopandas as gpd +from pathlib import Path +import copy +import os +from echopop.core import CONFIG_MAP, LAYER_NAME_MAP +from echopop.utils.data_file_validation import load_configuration , validate_data_columns +from echopop.computation.operations import bin_variable , bin_stats , count_variable +from echopop.utils.data_file_validation import validate_data_columns +from echopop.computation.acoustics import to_linear , ts_length_regression + +init_config_path=Path('./config_files/initialization_config.yml') +survey_year_config_path=Path('./config_files/survey_year_2019_config.yml') +obj = Survey( init_config_path='./config_files/initialization_config.yml' , survey_year_config_path='./config_files/survey_year_2019_config.yml' ) +obj.transect_analysis() +obj.stratified_summary() +obj.standardize_coordinates() +obj.krige() +self = obj +species_id = 22500 + +################# Distribute biological variables to kriging data +### Reformat 'specimen_df' to match the same format as 'len_df' +# First make copies of each +specimen_df_copy = self.biology['specimen_df'].copy().pipe( lambda df: df.loc[ df.species_id == species_id ] ) +length_df_copy = self.biology['length_df'].copy().pipe( lambda df: df.loc[ df.species_id == species_id ] ) + +# Import length bins +length_intervals = self.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] +length_bins = self.biology[ 'distributions' ][ 'length' ][ 'length_bins_arr' ] +age_intervals = self.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] + +# Import haul catch numbers +haul_catch_df = self.biology[ 'catch_df' ].copy().pipe( lambda df: df.loc[ df.species_id == species_id ] ) + +# Remove hauls not found in `length_df_copy` +haul_catch_filtered = ( + haul_catch_df + .loc[ lambda x: x.haul_num.isin( length_df_copy.haul_num ) ] +) + +### Calculate station weights -- stratify by stratum +weight_strata_station = ( + pd.concat( [ + haul_catch_filtered + .groupby( 'stratum_num' ) + .apply( lambda df: pd.Series( { 'stratum_weight': df.haul_weight.sum( ) } ) ) + .reset_index( ) + .assign( station = 1 ) , + specimen_df_copy + .groupby( [ 'stratum_num' ] ) + .apply( lambda df: pd.Series( { 'stratum_weight': df.weight.sum( ) } ) ) + .reset_index() + .assign( station = 2 ) + ] ) +) + +weight_strata = ( + weight_strata_station + .groupby( [ 'stratum_num' ] ) + .apply( lambda x: pd.Series( { 'weight_stratum_total': x.stratum_weight.sum( ) } ) ) + .reset_index() +) + +### Calculate summed length-age-sex-stratum weight bins +length_age_sex_stratum_weight = ( + specimen_df_copy + .dropna( how = 'any' ) + .loc[ lambda x: x.sex != 'unsexed' ] + .bin_variable( age_intervals , 'age' ) + .bin_variable( length_intervals , 'length' ) + .groupby( [ 'stratum_num' , 'age_bin' , 'length_bin' , 'sex' ] ) + .apply( lambda df: pd.Series( { 'summed_weight_all': df.weight.sum( ) , + 'summed_weight_adult': df.loc[ df.age > 1 ].weight.sum( ) } ) ) + .reset_index( ) + .replace( np.nan , 0 ) + .assign( total_weight_sex_all = lambda df: df.groupby( [ 'stratum_num' , 'sex' ] )[ 'summed_weight_all' ].transform( sum ) , + total_weight_sex_adult = lambda df: df.groupby( [ 'stratum_num' , 'sex' ] )[ 'summed_weight_adult' ].transform( sum ) , + proportion_weight_all = lambda df: df.summed_weight_all / df.total_weight_sex_all , + proportion_weight_adult = lambda df: df.summed_weight_adult / df.total_weight_sex_adult ) +) + +# Append the total weight to calculate the aged-to-unaged weight ratio/proportion +aged_proportions = ( + length_age_sex_stratum_weight + .merge( weight_strata , on = [ 'stratum_num' ] ) + .assign( proportion_weight_aged_all = lambda x: x.summed_weight_all / x.weight_stratum_total , + proportion_weight_unaged_all = lambda x: 1.0 - x.proportion_weight_aged_all , + proportion_weight_aged_adult = lambda x: x.summed_weight_adult / x.weight_stratum_total , + proportion_weight_unaged_adult = lambda x: 1.0 - x.proportion_weight_aged_adult ) +) + +### Calculate interpolated weights based on length bins for each sex per haul +# Extract length from binned values +length_weight_fit = ( + self.statistics[ 'length_weight' ][ 'length_weight_df' ] + .assign( length_bin_value = lambda x: x[ 'length_bin' ].apply( lambda y: y.mid ) ) +) + +haul_weights = ( + length_df_copy + .bin_variable( length_intervals , 'length' ) + .loc[ lambda x: x.group == 'sexed' ] + .pipe( lambda df: pd.concat( [ df , df.assign( sex = 'all' ) ] ) ) + .merge( length_weight_fit , on = [ 'sex' , 'length_bin' ] ) + .groupby( [ 'stratum_num' , 'haul_num' , 'sex' ] ) + .apply( lambda df: pd.Series( { 'weight_interp': ( np.interp( df.length_bin_value , + length_weight_fit.loc[ lambda x: x.sex.isin( df.sex ) ][ 'length_bin_value' ] , + length_weight_fit.loc[ lambda x: x.sex.isin( df.sex ) ][ 'weight_modeled' ] ) * + df.length_count ).sum( ) } ) ) + .groupby( [ 'stratum_num' , 'sex' ] ) + .apply( lambda df: pd.Series( { 'summed_haul_weights': df.weight_interp.sum( ) } ) ) + .reset_index( ) +) + +### Some normalization +( + haul_weights + .merge( weight_strata_station.loc[ lambda x: x.station == 1 ] , on = 'stratum_num' ) + .assign( normalized_weight = lambda x: ) +) +haul_weights.merge( weight_strata_station.loc[ lambda x: x.station == 1 ] , on = 'stratum_num' ) + + + +### Kriged data input +dataframe_input = self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] + +### Need aged/unaged biomass for males/females +# First need the length-age-sex proportions +self.biology[ 'weight' ][ 'proportions' ][ 'length_sex_age_weight_proportions_df' ].loc[ lambda x: x[ 'count' ] > 0.0 ] +self.biology[ 'weight' ][ 'weight_strata_df' ] +( + self.biology[ 'weight' ][ 'proportions' ][ 'length_sex_age_weight_proportions_df' ] + .assign( prop_sex_sum = lambda x: x.groupby( [ 'stratum_num' , 'sex' ] )[ 'count' ].transform( sum ) , + prop_holder = lambda x: np.where( x.sex == 'all' , x[ 'count' ] , 0.0 ) , + prop_strat_sum = lambda x: x.groupby( [ 'stratum_num' ] )[ 'prop_holder' ].transform( sum ) , + prop_sex_norm = lambda x: x[ 'count' ] / x.prop_sex_sum , + prop_strat_norm = lambda x: x[ 'count' ] / x.prop_strat_sum ) + .groupby( [ 'stratum_num' , 'sex' ] ) + .apply( lambda df: pd.Series( { 'weight': ( df.prop_sex_norm * df.prop_strat_norm ).sum( ) } ) ) +) +x.groupby( [ 'stratum_num' ] )[ 'count' ] +self.biology[ 'weight' ][ 'proportions' ][ 'sex_age_weight_proportions_df' ].loc[ lambda x: x.weight_sex_proportion_adult > 0 ] +( + self.biology[ 'weight' ][ 'proportions' ][ 'sex_age_weight_proportions_df' ] + .loc[ lambda df: ( df.age > 1 ) & ( df.sex == 'all' ) ] +) + + +total_weights = ( + self.biology[ 'weight' ][ 'proportions' ][ 'length_sex_age_weight_proportions_df' ] + .loc[ lambda df: ( df.age > 1 ) & ( df.sex == 'all' ) ] + .drop_duplicates( subset = [ 'stratum_num' , 'sex' ] ) +) + .groupby( [ 'stratum_num' ] ) + .apply( lambda df: pd.Series( { 'stratum_total_weight': np.nansum( df.weight_total_adult ) } ) ) +) + +dist_weight_sum = ( + self.biology[ 'weight' ][ 'proportions' ][ 'length_sex_age_weight_proportions_df' ] + .merge( total_weights , on = 'stratum_num' ) +) + +self.biology[ 'weight' ][ 'proportions' ][ 'length_sex_age_weight_proportions_df' ] + + + + +.apply( lambda df: pd.Series( { + 'count_age_proportion_all': ( df[ 'count' ] / df.stratum_count_all ).sum() , + 'count_age_proportion_adult': ( df.loc[ df.age > 1 ][ 'count' ] / df.stratum_count_total ).sum( ) +} ) ) + + +import matplotlib.pyplot as plt + + +l1 = length_df_copy.groupby('stratum_num').apply( lambda x: x[ 'length_count' ].sum()).reset_index(name='count') +s1 = specimen_df_copy.groupby('stratum_num').size().reset_index(name='count') + +ma = l1.merge( s1 , on = 'stratum_num' , how = 'outer' ).replace(np.nan, 0).assign( total = lambda x: x.count_x + x.count_y ) + +plt.bar( ma.stratum_num , ma.total ) +plt.xlabel( 'KS stratum' ) +plt.ylabel( 'Frequency' ) +plt.show( ) + +l2 = length_df_copy.groupby(['stratum_num' , 'transect_num']).apply( lambda x: x[ 'length_count' ].sum()).reset_index(name='count') +s2 = specimen_df_copy.groupby(['stratum_num' , 'transect_num']).size().reset_index(name='count') +m2 = l2.merge( s2 , on = ['stratum_num' , 'transect_num'] , how = 'outer' ).replace(np.nan, 0).assign( total = lambda x: x.count_x + x.count_y ) +m2 = m2.sort_values( 'transect_num' ) +m2.loc[ m2.transect_num > 100 ] +for stratum_num in np.unique( m2.stratum_num ): + subset = m2[m2['stratum_num'] == stratum_num] + plt.plot(subset['transect_num'], subset['total'], label=f'Stratum {int(stratum_num)}') + plt.scatter(subset['transect_num'], subset['total'], marker='o') +plt.xlabel('Transect Number') +plt.ylabel('Total') +plt.title('Total vs Transect Number for Different Stratum') +plt.legend() +plt.grid(True) +plt.show() + +( + nasc_interval_df + # Merge stratum information ( join = 'outer' since missing values will be filled later on) + .merge( info_strata , on = [ 'stratum_num' , 'haul_num' ] , how = 'outer' ) + # Drop unused hauls + .dropna( subset = 'transect_num' ) + # Fill NaN w/ 0's for 'fraction_hake' + .assign( fraction_hake = lambda x: x[ 'fraction_hake' ].fillna( 0 ) ) + # Group merge + .group_merge( dataframes_to_add = dataframes_to_add , on = 'stratum_num' ) + ) +) + + + +a = age_sex_weight_proportions.pivot_table( index = [ 'age' , 'sex' ] , columns = [ 'stratum_num' ] ) +a.loc[ : , ( 'weight_age_sex_proportion_all' ) ] +age_sex_weight_proportions.pivot_table( index = [ 'sex' , 'station' ] , + columns = [ 'stratum_num' ] , + values = [ 'overall_station_p' ] ) + + + age_proportions + .pipe( lambda df: df + .groupby( 'stratum_num' ) + .apply( lambda x: 1 - x.loc[ x[ 'age' ] < 2 ][ 'count' ].sum() / x[ 'count' ].sum( ) ) ) + .reset_index( name = 'adult_proportion' ) + ) + + age_proportions = ( + specimen_df_copy + .assign( age_group = lambda x: np.where( x.age < 2 , 'nonadult' , 'adult' ) ) + .dropna( how = 'any' ) + .count_variable( variable = 'length' , + contrasts = [ 'stratum_num' , 'age' , 'age_group' ] , + fun = 'size' ) + .pipe( lambda x: x.assign( stratum_count_all = x.groupby( [ 'stratum_num' ] )[ 'count' ].transform( sum ) , + stratum_proportion_all = lambda x: x[ 'count' ] / x[ 'stratum_count_all' ] , + stratum_count_adult = x.loc[ x.age_group == 'adult' ].groupby( [ 'stratum_num' ] )[ 'count' ].transform( sum ).astype( int ) , + stratum_proportion_adult = lambda x: x[ 'count' ] / x[ 'stratum_count_adult' ] ) ) + ) + +plt.bar( ma.stratum_num , ma.total ) +plt.xlabel( 'KS stratum' ) +plt.ylabel( 'Frequency' ) +plt.show( ) + +plt.boxplot( specimen_df_copy.groupby('stratum_num').size() , specimen_df_copy.groupby('stratum_num').size().values ) +plt.show() +### Reformat 'specimen_df' to match the same format as 'len_df' +# First make copies of each +specimen_df_copy = self.biology['specimen_df'].copy().pipe( lambda df: df.loc[ df.species_id == species_id ] ) +length_df_copy = self.biology['length_df'].copy().pipe( lambda df: df.loc[ df.species_id == species_id ] ) + +self.biology['specimen_df'].copy().loc[ lambda x: x.age == 0] +self.biology['length_df'].copy().loc[ lambda x: x.age == 0] +### Pull length distribution values +length_intervals = self.biology['distributions']['length']['length_interval_arr'] + +### Calculate the sex proportions/frequencies for station 1 (length_df) across all strata +specimen_df_copy.loc[ lambda x: x.sex == 'male' ] + +length_grouped = ( + length_df_copy + .bin_variable( length_intervals , 'length' ) # appends `length_bin` column + .assign( group = lambda x: np.where( x['sex'] == int(1) , 'male' , 'female' ) ) # assigns str variable for comprehension + .pipe( lambda df: pd.concat( [ df.loc[ df[ 'sex' ] != 3 ] , df.assign( group = 'all' ) ] ) ) # appends male-female to an 'all' dataframe + .assign( station = 1 ) # assign station number for later functions + ) + +### Calculate the sex proportions/frequencies for station 2 (specimen_df) across all strata +specimen_grouped = ( + specimen_df_copy + .bin_variable( length_intervals , 'length' ) # appends `length_bin` column + .assign( group = lambda x: np.where( x[ 'sex' ] == int( 1 ) , 'male' , np.where( x[ 'sex' ] == int( 2 ) , 'female' , 'unsexed' ) ) ) # assigns str variable for comprehension + .pipe( lambda df: pd.concat( [ df.loc[ df[ 'sex' ] != 3 ] , df.assign( group = 'all' ) ] ) ) # appends male-female to an 'all' dataframe + .assign( station = 2 ) # assign station number for later functions +) + +aa = ( + specimen_df_spp + .dropna( subset = [ 'weight' , 'length' ] ) + .assign( group = lambda x: np.where( x[ 'sex' ] == int( 1 ) , 'male' , + np.where( x[ 'sex' ] == int( 2 ) , 'female' , 'unsexed' ) ) ) # assigns str variable for comprehension + .pipe( lambda df: pd.concat( [ df.loc[ df[ 'group' ] != 'unsexed' ] , df.assign( group = 'all' ) ] ) ) # appends male-female to an 'all' dataframe +) + +np.unique( aa.group ) + +aa.loc[ aa.group == 'all' ].shape +specimen_sex_grouped_df.bin_stats( bin_variable = 'length' , bin_values = length_intervals , contrasts = 'group' ).loc[ lambda x: x.group == 'female' ] + +len_wgt_all = survey.bio_calc._generate_length_val_conversion( + len_name="length", + val_name="weight", + df=survey.bio_calc.specimen_df, +) + +( + fitted_weights + .rename( columns = { 'length_bin': 'length' } ) + .bin_variable( bin_values = length_intervals , + bin_variable = 'length' ) + .merge( length_bin_stats , on = [ 'group' , 'length_bin' ] ) + .assign( weight_modeled = lambda x: np.where( x.n_weight < 5 , + x.fitted_weight , + x.mean_weight ) ) +) + +specimen_df_copy = self.biology['specimen_df'].copy().pipe( lambda df: df.loc[ df.species_id == species_id ] ) + +# Import length bins +length_intervals = self.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] +age_intervals = self.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] +### Calculate age bin proportions when explicitly excluding age-0 and age-1 fish +# Calculate age proportions across all strata and age-bins +age_proportions = ( + specimen_df_copy + .dropna( how = 'any' ) + .count_variable( variable = 'length' , + contrasts = [ 'stratum_num' , 'age' ] , + fun = 'size' ) + .pipe( lambda x: x.assign( stratum_count = x.groupby( [ 'stratum_num' ] )[ 'count' ].transform( sum ) , + stratum_proportion = lambda x: x[ 'count' ] / x[ 'stratum_count' ] ) ) +) + +age_proportions.groupby( [ 'age' , 'stratum_num' ] ).apply( lambda x: x[ 'stratum_proportion' ].sum() ).reset_index().loc[ lambda x: x.stratum_num == 1.0 ] + +# Calculate adult proportions/contributions (in terms of summed presence) for each stratum +age_2_and_over_proportions = ( + age_proportions + .pipe( lambda df: df + .groupby( 'stratum_num' ) + .apply( lambda x: 1 - x.loc[ x[ 'age' ] <= 1 ][ 'count' ].sum() / x[ 'count' ].sum( ) ) ) + .reset_index( name = 'number_proportion' ) +) + +### Calculate proportional contributions of each age-bin to the summed weight of each stratum +### when explicitly excluding age-0 and age-1 fish +# Calculate the weight proportions across all strata and age-bins +age_weight_proportions = ( + specimen_df_copy + .dropna( subset = [ 'length' , 'weight' ] ) + .pipe( lambda x: x.assign( weight_age = x.groupby( [ 'stratum_num' , 'age' ] )[ 'weight' ].transform( sum ) , + weight_stratum = x.groupby( [ 'stratum_num' ] )[ 'weight' ].transform( sum ) ) ) + .groupby( [ 'stratum_num' , 'age' ] ) + .apply( lambda x: x[ 'weight_age' ].sum( ) / x[ 'weight_stratum' ].sum ( ) ) + .reset_index( name = 'weight_stratum_proportion' ) +) + +a = ( + specimen_df_copy + .dropna( how = "any" ) + .bin_variable( bin_values = length_intervals , + bin_variable = 'length' ) + .bin_variable( bin_values = age_intervals , + bin_variable = 'age' ) + .assign( group = lambda x: np.where( x[ 'sex' ] == int( 1 ) , 'male' , + np.where( x[ 'sex' ] == int( 2 ) , 'female' , 'unsexed' ) ) ) # assigns str variable for comprehension + .pipe( lambda df: pd.concat( [ df.loc[ df[ 'group' ] != 'unsexed' ] , df.loc[ df[ 'group' ] != 'unsexed' ].assign( group = 'all' ) ] ) ) # appends male-female to an 'all' dataframe + .bin_stats( bin_variable = 'age' , + bin_values = age_intervals , + variables = 'weight' , + contrasts = [ 'group' , 'stratum_num' , 'length_bin' ] , + functions = ['mean', 'size' , 'sum' ] ) + # .groupby( [ 'group' ] ) + # .apply( lambda x: x.mean) +) + +a.loc[ ( a.stratum_num == 1 ) & ( a.group == 'male' ) & ( a.sum_weight > 0 ) ][ 'sum_weight' ] +a.loc[ ( a.stratum_num == 1 ) & ( a.group == 'all' ) & ( a.sum_weight > 0 ) ][ 'sum_weight' ].sum( ) +a.loc[ ( a.stratum_num == 1 ) & ( a.group == 'all' ) ].groupby( 'age_bin' ).apply( lambda x: x[ 'sum_weight' ].sum()) + +( + specimen_df_copy + .dropna( subset = [ 'weight' , 'length' ] ) + .pipe( lambda x: x.assign( weight_age = x.groupby( [ 'stratum_num' , 'age' ] )[ 'weight' ].transform( sum ) , + weight_stratum = x.groupby( [ 'stratum_num' ] )[ 'weight' ].transform( sum ) ) ) +).loc[ lambda x: ( x.stratum_num == 1 ) ] + +b = age_weight_proportions.groupby( [ 'age' , 'stratum_num' ] ).apply( lambda x: x[ 'weight_stratum_proportion' ].sum() ).reset_index().loc[ lambda x: x.stratum_num == 1.0 ] +c = b.rename( columns = { 0: 'prop' } ).loc[ b.age != 0 ][ 'prop' ].sum() +b.rename( columns = { 0: 'prop' } ).loc[ b.age != 0 ].assign( prop_new = lambda x: x.prop / c ) +# Calculate adult proportions/contributions (in terms of summed weight) for each stratum +age_2_and_over_weight_proportions = ( + age_weight_proportions + .pipe( lambda df: df + .groupby( 'stratum_num' ) + .apply( lambda x: 1 - x.loc[ x[ 'age' ] <= 1 ][ 'weight_stratum_proportion' ].sum()) ) + .reset_index( name = 'weight_proportion' ) +) + +### Now this process will be repeated but adding "sex" as an additional contrast and considering +### all age-bins +age_sex_weight_proportions = ( + specimen_df_copy + .dropna( how = 'any' ) + .bin_variable( bin_values = length_intervals , + bin_variable = 'length' ) + .count_variable( contrasts = [ 'stratum_num' , 'age' , 'length_bin' , 'sex' ] , + variable = 'weight' , + fun = 'sum' ) + .pipe( lambda df: df.assign( total = df.groupby( [ 'stratum_num' , 'sex' ] )[ 'count' ].transform( sum ) ) ) + .groupby( [ 'stratum_num' , 'age' , 'sex' ] ) + .apply( lambda x: ( x[ 'count' ] / x[ 'total' ] ).sum() ) + .reset_index( name = 'weight_sex_stratum_proportion' ) + .assign( sex = lambda x: np.where( x[ 'sex' ] == 1 , 'male' , np.where( x[ 'sex' ] == 2 , 'female' , 'unsexed' ) ) ) +) + +### Add these dataframes to the appropriate data attribute +self.biology[ 'weight' ].update( { + 'sex_stratified': { + 'weight_proportions': age_sex_weight_proportions , + } , + 'age_stratified': { + 'age_1_excluded': { + 'number_proportions': age_2_and_over_proportions , + 'weight_proportions': age_2_and_over_weight_proportions , + } , + 'age_1_included': { + 'number_proportions': age_proportions , + 'weight_proportions': age_weight_proportions + } + } +} ) + + +fitted_weights.bin_variable( bin_values = length_intervals , + bin_variable = 'length_bin' ) +length_bin_stats.merge( fitted_weights , on = [ 'group' , 'length_bin' ] ) + +length_bin_stats.assign( weight_modeled = lambda x: np.wherE( x.n_weight < 5 , )) + +length_bin_stats[ 'weight_modeled' ] = length_bin_stats[ 'mean_weight' ].copy() +low_n_indices = np.where(length_bin_stats[ 'n_weight' ].values < 5)[0].copy() +length_bin_stats.loc[low_n_indices, 'weight_modeled'] = fitted_weight[low_n_indices].copy() + +a1 = ( + specimen_df_spp + .assign( group = lambda x: np.where( x[ 'sex' ] == int( 1 ) , 'male' , + np.where( x[ 'sex' ] == int( 2 ) , 'female' , 'unsexed' ) ) ) # assigns str variable for comprehension + .pipe( lambda df: pd.concat( [ df.loc[ df[ 'group' ] != 'unsexed' ] , df.assign( group = 'all' ) ] ) ) # appends male-female to an 'all' dataframe + .dropna( subset = [ 'weight' , 'length' ] ) + .groupby( 'group' ) + .apply( lambda x: pd.Series( np.polyfit( np.log10( x[ 'length' ].values ) , np.log10( x[ 'weight' ].values ) , 1 ) , + index = [ 'rate' , 'initial' ] ) ) + .reset_index() +) + +b = a.merge( pd.DataFrame({'length_bins': length_bins}) , how = 'cross' ).assign( fitted_weight = lambda x: 10 ** x.initial * x.length_bins.astype( int ) ** x.rate ) +b.loc[ b.group == 'all' ][ 'fitted_weight' ][ 5 ] +a.groupby( 'group' ).apply( lambda x: 10 ** x.initial * int( length_bins ) ** x.rate , axis = 1 ) +a.rate[ 0 ] +a.reset_index() + +specimen_df_spp.bin_stats( bin_variable = 'length' , bin_values = length_intervals ) + + +specimen_df_spp.assign(group = lambda x: x.sex.astype(str)).loc[lambda x: ( x.group == '1' ) ].apply(lambda x: pd.Series( np.polyfit( np.log10( x[ 'length' ] ) , np.log10( x[ 'weight' ] ) , 1 ) , index=['slope', 'intercept'] ) ) +df = specimen_df_spp.assign(group = lambda x: x.sex.astype(str)).loc[lambda x: ( x.group == '1' ) ].dropna(how='any') +np.polyfit( np.log10( df[ 'length' ] ) , np.log10( df[ 'weight' ] ) , 1 ) + + +specimen_df_spp.assign(group = lambda x: str(x.sex)).group + +length_weight_df.bin_stats( bin_variable = 'length' , bin_values = length_intervals , contrasts = [ 'sex' ] ) + + +### Calculate the sex proportions/frequencies for station 2 (specimen_df) across all strata +specimen_grouped = ( + specimen_df_copy + .bin_variable( length_intervals , 'length' ) # appends `length_bin` column + .assign( group = lambda x: np.where( x[ 'sex' ] == int( 1 ) , 'male' , np.where( x[ 'sex' ] == int( 2 ) , 'female' , 'unsexed' ) ) # assigns str variable for comprehension + .pipe( lambda df: pd.concat( [ df.loc[ df[ 'sex' ] != 3 ] , df.assign( group = 'all' ) ] ) ) # appends male-female to an 'all' dataframe + .assign( station = 2 ) # assign station number for later functions +) + +specimen_df_copy.dropna( subset = [ 'weight' , 'length' ] ) + +( + specimen_df_copy + .dropna( subset = 'weight' ) + .bin_variable( length_intervals , 'length' ) # appends `length_bin` column + .count_variable( contrasts = [ 'length_bin' ] , # grouping variables + variable = 'length' , # target value to apply a function + fun = 'count' ) # function to apply + . sum( ) +) +### "Meld" the two datasets together to keep downstream code tidy +station_sex_length = ( + specimen_grouped # begin reformatting specimen_grouped so it resembles same structure as length_grouped + .meld( length_grouped ) # "meld": reformats specimen_grouped and then concatenates length_grouped + # sum up all of the length values with respect to each length bin + .count_variable( contrasts = [ 'group' , 'station' , 'stratum_num' , 'length_bin' ] , # grouping variables + variable = 'length_count' , # target value to apply a function + fun = 'sum' ) # function to apply +) + +station_sex_length.loc[ lambda x: ( x.station == 1 ) & ( x.group == 'all' ) ] +### Calculate total sample size ('group' == 'all') that will convert 'counts' into 'frequency/proportion' +total_n = ( + station_sex_length + .loc[ station_sex_length.group.isin( [ 'all' ] ) ] # filter out to get to just 'all': this is where sex = [ 1 , 2 , 3] + .groupby( [ 'stratum_num' ] )[ 'count' ] # group by each stratum with 'count' being the target variable + .sum( ) # sum up counts per stratum + .reset_index( name = 'n_total' ) # rename index +) + +### Convert counts in station_sex_length into frequency/proportion +station_length_aggregate = ( + station_sex_length + # calculate the within-sample sum and proportions (necessary for the downstream dot product calculation) + .pipe( lambda x: x.assign( within_station_n = x.groupby( [ 'group' , 'station' , 'stratum_num' ] )[ 'count' ].transform( sum ) , + within_station_p = lambda x: x[ 'count' ] / x[ 'within_station_n' ] ) ) + .replace( np.nan, 0 ) # remove erroneous NaN (divide by 0 or invalid values) + .merge( total_n , on = 'stratum_num' ) # merge station_sex_length with total_n + # proportion of each count indexed by each length_bin relative to the stratum total + .assign( overall_length_p = lambda x: x[ 'count' ] / x[ 'n_total' ] , + overall_station_p = lambda x: x[ 'within_station_n' ] / x[ 'n_total' ] ) + .replace( np.nan, 0 ) # remove erroneous NaN (divide by 0 or invalid values) +) + +### Calculate the sex distribution across strata +sex_proportions = ( + station_length_aggregate + .loc[ station_length_aggregate.group.isin( ['male' , 'female'] ) ] # only parse 'male' and 'female' + # create a pivot that will reorient data to the desired shape + .pivot_table( index = [ 'group' , 'station' ] , + columns = [ 'stratum_num' ] , + values = [ 'overall_station_p' ] ) + .groupby( 'group' ) + .sum() +) + +### Calculate the proportions each dataset / station contributed within each stratum +station_proportions = ( + station_length_aggregate + .loc[ station_length_aggregate.group.isin( [ 'all' ] ) ] # only parse 'all' + # create a pivot that will reorient data to the desired shape + .pivot_table( index = [ 'group' , 'station' ] , + columns = 'stratum_num' , + values = 'overall_station_p' ) + .groupby( 'station' ) + .sum() +) + +### Calculate the sex distribution within each station across strata +sex_station_proportions = ( + station_length_aggregate + .loc[ station_length_aggregate.group.isin( ['male' , 'female'] ) ] # only parse 'male' and 'female' + # create a pivot that will reorient data to the desired shape + .pivot_table( index = [ 'group' , 'station' ] , + columns = 'stratum_num' , + values = 'overall_station_p' ) + .groupby( [ 'group' , 'station' ] ) + .sum() +) + +### Merge the station and sex-station indexed proportions to calculate the combined dataset mean fractions +sex_stn_prop_merged = ( + sex_station_proportions + .stack( ) + .reset_index( name = 'sex_stn_p') + .merge( station_proportions + .stack() + .reset_index( name = 'stn_p' ) , on = [ 'stratum_num' , 'station' ] ) + .pivot_table( columns = 'stratum_num' , + index = [ 'station' , 'group' ] , + values = [ 'stn_p' , 'sex_stn_p' ] ) +) + +### Format the length bin proportions so they resemble a similar table/matrix shape as the above metrics +# Indexed / organized by sex , station, and stratum +length_proportion_table = ( + station_length_aggregate + .pivot_table( columns = [ 'group' , 'station' , 'stratum_num' ] , + index = [ 'length_bin' ] , + values = [ 'within_station_p' ] )[ 'within_station_p' ] +) + +### Calculate combined station fraction means +# Station 1 +stn_1_fraction = ( sex_stn_prop_merged.loc[ 1 , ( 'stn_p' ) ] + / ( sex_stn_prop_merged.loc[ 1 , ( 'stn_p' ) ] + + sex_stn_prop_merged.loc[ 2 , ( 'sex_stn_p' ) ] ) ) + +# Station 2 +stn_2_fraction = ( sex_stn_prop_merged.loc[ 2 , ( 'sex_stn_p' ) ] + / ( stn_1_fraction + + sex_stn_prop_merged.loc[ 2 , ( 'sex_stn_p' ) ] ) ) + +### Calculate the average weight across all animals, males, and females +# Pull fitted weight values +fitted_weight = self.statistics['length_weight']['length_weight_df'].loc[ lambda x: x.group == 'all' ] + +# Total +total_weighted_values = ( length_proportion_table.loc[ : , ( 'all' , 1 ) ] * station_proportions.loc[ 1 , ] + + length_proportion_table.loc[ : , ( 'all' , 2 ) ] * station_proportions.loc[ 2 , ] ) +total_weight = fitted_weight[ 'weight_modeled' ].dot( total_weighted_values.reset_index( drop = True ) ) +total_weighted_values.loc[ : , 1.0 ].sort_values( ascending = False ).head(3).sum() +# Male +male_weighted_values = ( length_proportion_table.loc[ : , ( 'male' , 1 ) ] * stn_1_fraction.loc[ 'male' ] + + length_proportion_table.loc[ : , ( 'male' , 2 ) ] * stn_2_fraction.loc[ 'male' ] ) +male_weight = fitted_weight[ 'weight_modeled' ].dot( male_weighted_values.reset_index( drop = True ) ) + +# Female +female_weighted_values = ( length_proportion_table.loc[ : , ( 'female' , 1 ) ] * stn_1_fraction.loc[ 'female' ] + + length_proportion_table.loc[ : , ( 'female' , 2 ) ] * stn_2_fraction.loc[ 'female' ] ) +female_weight = fitted_weight[ 'weight_modeled' ].dot( female_weighted_values.reset_index( drop = True ) ) + +### Store the data frame in an accessible location +self.biology[ 'weight' ][ 'weight_strata_df' ] = pd.DataFrame( { + 'stratum_num': total_weight.index , + 'proportion_female': sex_proportions.loc[ 'female' , : ][ 'overall_station_p' ].reset_index(drop=True) , + 'proportion_male': sex_proportions.loc[ 'male' , : ][ 'overall_station_p' ].reset_index(drop=True) , + 'average_weight_female': female_weight , + 'average_weight_male': male_weight , + 'average_weight_total': total_weight +} ) + + + +self.biology[ 'weight' ][ 'weight_strata_df' ] + + +### Reformat 'specimen_df' to match the same format as 'len_df' +# First make copies of each +specimen_df_copy = self.biology['specimen_df'].copy().pipe( lambda df: df.loc[ df.species_id == species_id ] ) + +# Import length bins +length_intervals = self.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] + +### Calculate age bin proportions when explicitly excluding age-0 and age-1 fish +# Calculate age proportions across all strata and age-bins +age_proportions = ( + specimen_df_copy + .dropna( how = 'any' ) + .count_variable( variable = 'length' , + contrasts = [ 'stratum_num' , 'age' ] , + fun = 'size' ) + .pipe( lambda x: x.assign( stratum_count = x.groupby( [ 'stratum_num' ] )[ 'count' ].transform( sum ) , + stratum_proportion = lambda x: x[ 'count' ] / x[ 'stratum_count' ] ) ) +) + +# Calculate adult proportions/contributions (in terms of summed presence) for each stratum +age_2_and_over_proportions = ( + age_proportions + .pipe( lambda df: df + .groupby( 'stratum_num' ) + .apply( lambda x: 1 - x.loc[ x[ 'age' ] <= 1 ][ 'count' ].sum() / x[ 'count' ].sum( ) ) ) + .reset_index( name = 'number_proportion' ) +) + +### Calculate proportional contributions of each age-bin to the summed weight of each stratum +### when explicitly excluding age-0 and age-1 fish +# Calculate the weight proportions across all strata and age-bins +age_weight_proportions = ( + specimen_df_copy + .dropna( how = 'any' ) + .pipe( lambda x: x.assign( weight_age = x.groupby( [ 'stratum_num' , 'age' ] )[ 'weight' ].transform( sum ) , + weight_stratum = x.groupby( [ 'stratum_num' ] )[ 'weight' ].transform( sum ) ) ) + .groupby( [ 'stratum_num' , 'age' ] ) + .apply( lambda x: x[ 'weight_age' ].sum( ) / x[ 'weight_stratum' ].sum ( ) ) + .reset_index( name = 'weight_stratum_proportion' ) +) + +# Calculate adult proportions/contributions (in terms of summed weight) for each stratum +age_2_and_over_weight_proportions = ( + age_weight_proportions + .pipe( lambda df: df + .groupby( 'stratum_num' ) + .apply( lambda x: 1 - x.loc[ x[ 'age' ] <= 1 ][ 'weight_stratum_proportion' ].sum()) ) + .reset_index( name = 'weight_proportion' ) +) + +### Now this process will be repeated but adding "sex" as an additional contrast and considering +### all age-bins +age_sex_weight_proportions = ( + specimen_df_copy + .dropna( how = 'any' ) + .bin_variable( bin_values = length_intervals , + bin_variable = 'length' ) + .count_variable( contrasts = [ 'stratum_num' , 'age' , 'length_bin' , 'sex' ] , + variable = 'weight' , + fun = 'sum' ) + .pipe( lambda df: df.assign( total = df.groupby( [ 'stratum_num' , 'sex' ] )[ 'count' ].transform( sum ) ) ) + .groupby( [ 'stratum_num' , 'age' , 'sex' ] ) + .apply( lambda x: ( x[ 'count' ] / x[ 'total' ] ).sum() ) + .reset_index( name = 'weight_sex_stratum_proportion' ) + .assign( sex = lambda x: np.where( x[ 'sex' ] == 1 , 'male' , np.where( x[ 'sex' ] == 2 , 'female' , 'unsexed' ) ) ) +) + +age_sex_weight_proportions.loc[ lambda x: ( x.sex == 'female' ) & ( x.stratum_num == 1.0 ) ] + +### Add these dataframes to the appropriate data attribute +self.biology[ 'weight' ].update( { + 'sex_stratified': { + 'weight_proportions': age_sex_weight_proportions , + } , + 'age_stratified': { + 'age_1_excluded': { + 'number_proportions': age_2_and_over_proportions , + 'weight_proportions': age_2_and_over_weight_proportions , + } , + 'age_1_included': { + 'number_proportions': age_proportions , + 'weight_proportions': age_weight_proportions + } + } +} ) diff --git a/echopop/testing_proportions.py b/echopop/testing_proportions.py new file mode 100644 index 00000000..f235d0b2 --- /dev/null +++ b/echopop/testing_proportions.py @@ -0,0 +1,326 @@ +from EchoPro.survey import Survey +import numpy as np +import pandas as pd +import geopandas as gpd +from pathlib import Path +import copy +import os +from EchoPro.core import CONFIG_MAP, LAYER_NAME_MAP +from EchoPro.utils.data_file_validation import load_configuration , validate_data_columns +from EchoPro.computation.operations import bin_variable , bin_stats , count_variable +from EchoPro.utils.data_file_validation import validate_data_columns +from EchoPro.computation.acoustics import to_linear , ts_length_regression + +init_config_path=Path('./config_files/initialization_config.yml') +survey_year_config_path=Path('./config_files/survey_year_2019_config.yml') +obj = Survey( init_config_path='./config_files/initialization_config.yml' , survey_year_config_path='./config_files/survey_year_2019_config.yml' ) +species_id = 22500 +self = obj +########### COMMON +specimen_df_copy = self.biology['specimen_df'].copy().pipe( lambda df: df.loc[ df.species_id == species_id ] ) +length_df_copy = self.biology['length_df'].copy().pipe( lambda df: df.loc[ df.species_id == species_id ] ) + +### Pull length distribution values +length_intervals = self.biology['distributions']['length']['length_interval_arr'] +age_intervals = self.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] + +### Calculate the sex proportions/frequencies for station 1 (length_df) across all strata +length_grouped = ( + length_df_copy + .bin_variable( length_intervals , 'length' ) # appends `length_bin` column + .assign( group = lambda x: np.where( x[ 'sex' ] == int( 1 ) , 'male' , + np.where( x[ 'sex' ] == int( 2 ) , 'female' , 'unsexed' ) ) ) + # .assign( group = lambda x: np.where( x['sex'] == int(1) , 'male' , 'female' ) ) # assigns str variable for comprehension + # .pipe( lambda df: pd.concat( [ df.loc[ df[ 'sex' ] != 3 ] , df.assign( group = 'all' ) ] ) ) # appends male-female to an 'all' dataframe + .assign( station = 1 ) # assign station number for later functions + ) + +### Calculate the sex proportions/frequencies for station 2 (specimen_df) across all strata +specimen_grouped = ( + specimen_df_copy + .bin_variable( length_intervals , 'length' ) # appends `length_bin` column + .assign( group = lambda x: np.where( x[ 'sex' ] == int( 1 ) , 'male' , + np.where( x[ 'sex' ] == int( 2 ) , 'female' , 'unsexed' ) ) ) # assigns str variable for comprehension + # .pipe( lambda df: pd.concat( [ df , df.assign( group = 'all' ) ] ) ) # appends male-female to an 'all' dataframe + .dropna( subset = [ 'weight' , 'length' ] ) + .assign( station = 2 ) # assign station number for later functions +) +specimen_grouped .loc[ lambda x: x.stratum_num == 1.0 ] +### "Meld" the two datasets together to keep downstream code tidy +station_sex_length = ( + specimen_grouped # begin reformatting specimen_grouped so it resembles same structure as length_grouped + .meld( length_grouped ) # "meld": reformats specimen_grouped and then concatenates length_grouped + .pipe( lambda df: pd.concat( [ df.loc[ df[ 'group' ] == 'sexed' ] , + df.assign( sex = 'all' ) ] ) ) + .count_variable( contrasts = [ 'sex' , 'station' , 'stratum_num' , 'length_bin' ] , # grouping variables + variable = 'length_count' , # target value to apply a function + fun = 'sum' ) # function to apply +) + +total_n = ( + station_sex_length + .loc[ station_sex_length.sex.isin( [ 'all' ] ) ] # filter out to get to just 'all': this is where sex = [ 1 , 2 , 3] + .groupby( [ 'stratum_num' ] )[ 'count' ] # group by each stratum with 'count' being the target variable + .sum( ) # sum up counts per stratum + .reset_index( name = 'n_total' ) # rename index +) + +station_length_aggregate = ( + station_sex_length + # calculate the within-sample sum and proportions (necessary for the downstream dot product calculation) + .pipe( lambda x: x.assign( within_station_n = x.groupby( [ 'sex' , 'station' , 'stratum_num' ] )[ 'count' ].transform( sum ) , + within_station_p = lambda x: x[ 'count' ] / x[ 'within_station_n' ] ) ) + .replace( np.nan, 0 ) # remove erroneous NaN (divide by 0 or invalid values) + .merge( total_n , on = 'stratum_num' ) # merge station_sex_length with total_n + # proportion of each count indexed by each length_bin relative to the stratum total + .assign( overall_length_p = lambda x: x[ 'count' ] / x[ 'n_total' ] , + overall_station_p = lambda x: x[ 'within_station_n' ] / x[ 'n_total' ] ) + .replace( np.nan, 0 ) # remove erroneous NaN (divide by 0 or invalid values) +) + +### Calculate the sex distribution across strata +sex_proportions = ( + station_length_aggregate + .loc[ station_length_aggregate.sex.isin( [ 'male' , 'female' ] ) ] # only parse 'male' and 'female' + # create a pivot that will reorient data to the desired shape + .pivot_table( index = [ 'sex' , 'station' ] , + columns = [ 'stratum_num' ] , + values = [ 'overall_station_p' ] ) + .groupby( 'sex' ) + .sum( ) +) + +### Calculate the proportions each dataset / station contributed within each stratum +station_proportions = ( + station_length_aggregate + .loc[ station_length_aggregate.sex.isin( [ 'all' ] ) ] # only parse 'all' + # create a pivot that will reorient data to the desired shape + .pivot_table( index = [ 'sex' , 'station' ] , + columns = 'stratum_num' , + values = 'overall_station_p' ) + .groupby( 'station' ) + .sum() +) + +### Calculate the sex distribution within each station across strata +sex_station_proportions = ( + station_length_aggregate + .loc[ station_length_aggregate.sex.isin( [ 'male' , 'female' ] ) ] # only parse 'male' and 'female' + # create a pivot that will reorient data to the desired shape + .pivot_table( index = [ 'sex' , 'station' ] , + columns = 'stratum_num' , + values = 'overall_station_p' ) + .groupby( [ 'sex' , 'station' ] ) + .sum() +) + +### Merge the station and sex-station indexed proportions to calculate the combined dataset mean fractions +sex_stn_prop_merged = ( + sex_station_proportions + .stack( ) + .reset_index( name = 'sex_stn_p') + .merge( station_proportions + .stack() + .reset_index( name = 'stn_p' ) , on = [ 'stratum_num' , 'station' ] ) + .pivot_table( columns = 'stratum_num' , + index = [ 'station' , 'sex' ] , + values = [ 'stn_p' , 'sex_stn_p' ] ) +) + +### Format the length bin proportions so they resemble a similar table/matrix shape as the above metrics +# Indexed / organized by sex , station, and stratum +length_proportion_table = ( + station_length_aggregate + .pivot_table( columns = [ 'sex' , 'station' , 'stratum_num' ] , + index = [ 'length_bin' ] , + values = [ 'within_station_p' ] )[ 'within_station_p' ] +) + +### Calculate combined station fraction means +# Station 1 +stn_1_fraction = ( sex_stn_prop_merged.loc[ 1 , ( 'stn_p' ) ] + / ( sex_stn_prop_merged.loc[ 1 , ( 'stn_p' ) ] + + sex_stn_prop_merged.loc[ 2 , ( 'sex_stn_p' ) ] ) ) + +# Station 2 +stn_2_fraction = ( sex_stn_prop_merged.loc[ 2 , ( 'sex_stn_p' ) ] + / ( stn_1_fraction + + sex_stn_prop_merged.loc[ 2 , ( 'sex_stn_p' ) ] ) ) + +### Calculate the average weight across all animals, males, and females +# Pull fitted weight values +fitted_weight = self.statistics[ 'length_weight' ][ 'length_weight_df' ] + +# Total +total_weight = ( + fitted_weight + .loc[ lambda x: x.sex == 'all' , 'weight_modeled' ] + .values + .dot( + ( length_proportion_table.loc[ : , ( 'all' , 1 ) ] * station_proportions.loc[ 1 , ] + + length_proportion_table.loc[ : , ( 'all' , 2 ) ] * station_proportions.loc[ 2 , ] ) + .reset_index( drop = True ) + ) +) + +# Male +male_weight = ( + fitted_weight + .loc[ lambda x: x.sex == 'male' , 'weight_modeled' ] + .values + .dot( + ( length_proportion_table.loc[ : , ( 'male' , 1 ) ] * station_proportions.loc[ 1 , ] + + length_proportion_table.loc[ : , ( 'male' , 2 ) ] * station_proportions.loc[ 2 , ] ) + .reset_index( drop = True ) + ) +) + +# Female +female_weight = ( + fitted_weight + .loc[ lambda x: x.sex == 'female' , 'weight_modeled' ] + .values + .dot( + ( length_proportion_table.loc[ : , ( 'female' , 1 ) ] * station_proportions.loc[ 1 , ] + + length_proportion_table.loc[ : , ( 'female' , 2 ) ] * station_proportions.loc[ 2 , ] ) + .reset_index( drop = True ) + ) +) + +### Store the data frame in an accessible location +self.biology[ 'weight' ][ 'weight_strata_df' ] = pd.DataFrame( { + 'stratum_num': station_proportions.columns.values.astype( int ) , + 'proportion_female': sex_proportions.loc[ 'female' , : ][ 'overall_station_p' ].reset_index( drop=True ) , + 'proportion_male': sex_proportions.loc[ 'male' , : ][ 'overall_station_p' ].reset_index( drop=True ) , + 'proportion_station_1': station_proportions.loc[ 1 , : ] , + 'proportion_station_2': station_proportions.loc[ 2 , : ] , + 'average_weight_female': female_weight , + 'average_weight_male': male_weight , + 'average_weight_total': total_weight +} ) + +### Reformat 'specimen_df' to match the same format as 'len_df' +# First make copies of each +specimen_df_copy = self.biology['specimen_df'].copy().pipe( lambda df: df.loc[ df.species_id == species_id ] ) + +# Import length bins +length_intervals = self.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] + +### Calculate age bin proportions when explicitly excluding age-0 and age-1 fish +# Calculate age proportions across all strata and age-bins +age_proportions = ( + specimen_df_copy + .dropna( how = 'any' ) + .count_variable( variable = 'length' , + contrasts = [ 'stratum_num' , 'age' ] , + fun = 'size' ) + .pipe( lambda x: x.assign( stratum_count = x.groupby( [ 'stratum_num' ] )[ 'count' ].transform( sum ) , + stratum_proportion = lambda x: x[ 'count' ] / x[ 'stratum_count' ] ) ) +) + +# Calculate adult proportions/contributions (in terms of summed presence) for each stratum +adult_proportions = ( + age_proportions + .pipe( lambda df: df + .groupby( 'stratum_num' ) + .apply( lambda x: 1 - x.loc[ x[ 'age' ] <= 1 ][ 'count' ].sum() / x[ 'count' ].sum( ) ) ) + .reset_index( name = 'number_proportion' ) +) + +### Calculate proportional contributions of each age-bin to the summed weight of each stratum +### when explicitly excluding age-0 and age-1 fish +# Calculate the weight proportions across all strata and age-bins +age_weight_proportions = ( + specimen_df_copy + .dropna( how = 'any' ) + .pipe( lambda df: df.assign( weight_stratum_all = df + .groupby( [ 'stratum_num' ] )[ 'weight' ] + .transform( sum ) , + weight_stratum_adult = df + .loc[ lambda x: x.age > 1 ] + .groupby( [ 'stratum_num' ] )[ 'weight' ] + .transform( sum ) ) ) + .groupby( [ 'stratum_num' , 'age' ] ) + .apply( lambda df: pd.Series( { + 'weight_age_proportion_all': ( df.weight / df.weight_stratum_all ).sum( ) , + 'weight_age_proportion_adult': ( df.weight / df.weight_stratum_adult ).sum( ) + } ) ) + .reset_index() +) + + +### Now this process will be repeated but adding "sex" as an additional contrast and considering +### all age-bins +sex_age_weight_proportions = ( + specimen_df_copy + .pipe( lambda df: pd.concat( [ df.loc[ df[ 'group' ] == 'sexed' ] , + df.assign( sex = 'all' ) ] ) ) + .dropna( how = 'any' ) + .bin_variable( bin_values = length_intervals , + bin_variable = 'length' ) + .count_variable( contrasts = [ 'stratum_num' , 'age' , 'length_bin' , 'sex' ] , + variable = 'weight' , + fun = 'sum' ) + .pipe( lambda df: df.assign( weight_total_all = df.groupby( [ 'stratum_num' , 'sex' ] )[ 'count' ].transform( sum ) , + weight_total_adult = df.loc[ df.age > 1 ].groupby( [ 'stratum_num' , 'sex' ] )[ 'count' ].transform( sum ) ) ) + .groupby( [ 'stratum_num' , 'age' , 'sex' ] ) + .apply( lambda x: pd.Series( { + 'weight_sex_proportion_all': ( x[ 'count' ] / x.weight_total_all ).sum() , + 'weight_sex_proportion_adult': ( x[ 'count' ] / x.weight_total_adult ).sum() + } ) ) + .reset_index( ) +) + +age_weight_proportions = ( + specimen_df_copy + .dropna( how = 'any' ) + .pipe( lambda x: x.assign( weight_age = x.groupby( [ 'stratum_num' , 'age' ] )[ 'weight' ].transform( sum ) , + weight_stratum = x.groupby( [ 'stratum_num' ] )[ 'weight' ].transform( sum ) ) ) + .groupby( [ 'stratum_num' , 'age' ] ) + .apply( lambda x: x[ 'weight_age' ].sum( ) / x[ 'weight_stratum' ].sum ( ) ) + .reset_index( name = 'weight_stratum_proportion' ) +) + + + .count_variable( contrasts = [ 'stratum_num' , 'age' , 'length_bin' , 'sex' ] , + variable = 'weight' , + fun = 'sum' ) + .pipe( lambda df: df.assign( total = df.groupby( [ 'stratum_num' , 'sex' ] )[ 'count' ].transform( sum ) ) ) + .groupby( [ 'stratum_num' , 'age' , 'sex' ] ) + .apply( lambda x: ( x[ 'count' ] / x[ 'total' ] ).sum() ) + .reset_index( name = 'weight_sex_stratum_proportion' ) + .assign( sex = lambda x: np.where( x[ 'sex' ] == 1 , 'male' , np.where( x[ 'sex' ] == 2 , 'female' , 'unsexed' ) ) ) +) + + +age_sex_weight_proportions = ( + specimen_df_copy + .dropna( how = 'any' ) + .bin_variable( bin_values = length_intervals , + bin_variable = 'length' ) + .count_variable( contrasts = [ 'stratum_num' , 'age' , 'length_bin' , 'sex' ] , + variable = 'weight' , + fun = 'sum' ) + .pipe( lambda df: df.assign( total = df.groupby( [ 'stratum_num' , 'sex' ] )[ 'count' ].transform( sum ) ) ) + .groupby( [ 'stratum_num' , 'age' , 'sex' ] ) + .apply( lambda x: ( x[ 'count' ] / x[ 'total' ] ).sum() ) + .reset_index( name = 'weight_sex_stratum_proportion' ) + .assign( sex = lambda x: np.where( x[ 'sex' ] == 1 , 'male' , np.where( x[ 'sex' ] == 2 , 'female' , 'unsexed' ) ) ) +) + +### Add these dataframes to the appropriate data attribute +self.biology[ 'weight' ].update( { + 'sex_stratified': { + 'weight_proportions': age_sex_weight_proportions , + } , + 'age_stratified': { + 'age_1_excluded': { + 'number_proportions': age_2_and_over_proportions , + 'weight_proportions': age_2_and_over_weight_proportions , + } , + 'age_1_included': { + 'number_proportions': age_proportions , + 'weight_proportions': age_weight_proportions + } + } +} ) \ No newline at end of file From 17269169e0ff0ab203c5b9041314577856766ca9 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Tue, 26 Mar 2024 10:47:57 -0700 Subject: [PATCH 12/51] Remove erroneous file additions to `upstream/main` --- echopop/testing_computation.py | 716 --------------------------------- echopop/testing_proportions.py | 326 --------------- 2 files changed, 1042 deletions(-) delete mode 100644 echopop/testing_computation.py delete mode 100644 echopop/testing_proportions.py diff --git a/echopop/testing_computation.py b/echopop/testing_computation.py deleted file mode 100644 index 67920112..00000000 --- a/echopop/testing_computation.py +++ /dev/null @@ -1,716 +0,0 @@ -from echopop.survey import Survey -import numpy as np -import pandas as pd -import geopandas as gpd -from pathlib import Path -import copy -import os -from echopop.core import CONFIG_MAP, LAYER_NAME_MAP -from echopop.utils.data_file_validation import load_configuration , validate_data_columns -from echopop.computation.operations import bin_variable , bin_stats , count_variable -from echopop.utils.data_file_validation import validate_data_columns -from echopop.computation.acoustics import to_linear , ts_length_regression - -init_config_path=Path('./config_files/initialization_config.yml') -survey_year_config_path=Path('./config_files/survey_year_2019_config.yml') -obj = Survey( init_config_path='./config_files/initialization_config.yml' , survey_year_config_path='./config_files/survey_year_2019_config.yml' ) -obj.transect_analysis() -obj.stratified_summary() -obj.standardize_coordinates() -obj.krige() -self = obj -species_id = 22500 - -################# Distribute biological variables to kriging data -### Reformat 'specimen_df' to match the same format as 'len_df' -# First make copies of each -specimen_df_copy = self.biology['specimen_df'].copy().pipe( lambda df: df.loc[ df.species_id == species_id ] ) -length_df_copy = self.biology['length_df'].copy().pipe( lambda df: df.loc[ df.species_id == species_id ] ) - -# Import length bins -length_intervals = self.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] -length_bins = self.biology[ 'distributions' ][ 'length' ][ 'length_bins_arr' ] -age_intervals = self.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] - -# Import haul catch numbers -haul_catch_df = self.biology[ 'catch_df' ].copy().pipe( lambda df: df.loc[ df.species_id == species_id ] ) - -# Remove hauls not found in `length_df_copy` -haul_catch_filtered = ( - haul_catch_df - .loc[ lambda x: x.haul_num.isin( length_df_copy.haul_num ) ] -) - -### Calculate station weights -- stratify by stratum -weight_strata_station = ( - pd.concat( [ - haul_catch_filtered - .groupby( 'stratum_num' ) - .apply( lambda df: pd.Series( { 'stratum_weight': df.haul_weight.sum( ) } ) ) - .reset_index( ) - .assign( station = 1 ) , - specimen_df_copy - .groupby( [ 'stratum_num' ] ) - .apply( lambda df: pd.Series( { 'stratum_weight': df.weight.sum( ) } ) ) - .reset_index() - .assign( station = 2 ) - ] ) -) - -weight_strata = ( - weight_strata_station - .groupby( [ 'stratum_num' ] ) - .apply( lambda x: pd.Series( { 'weight_stratum_total': x.stratum_weight.sum( ) } ) ) - .reset_index() -) - -### Calculate summed length-age-sex-stratum weight bins -length_age_sex_stratum_weight = ( - specimen_df_copy - .dropna( how = 'any' ) - .loc[ lambda x: x.sex != 'unsexed' ] - .bin_variable( age_intervals , 'age' ) - .bin_variable( length_intervals , 'length' ) - .groupby( [ 'stratum_num' , 'age_bin' , 'length_bin' , 'sex' ] ) - .apply( lambda df: pd.Series( { 'summed_weight_all': df.weight.sum( ) , - 'summed_weight_adult': df.loc[ df.age > 1 ].weight.sum( ) } ) ) - .reset_index( ) - .replace( np.nan , 0 ) - .assign( total_weight_sex_all = lambda df: df.groupby( [ 'stratum_num' , 'sex' ] )[ 'summed_weight_all' ].transform( sum ) , - total_weight_sex_adult = lambda df: df.groupby( [ 'stratum_num' , 'sex' ] )[ 'summed_weight_adult' ].transform( sum ) , - proportion_weight_all = lambda df: df.summed_weight_all / df.total_weight_sex_all , - proportion_weight_adult = lambda df: df.summed_weight_adult / df.total_weight_sex_adult ) -) - -# Append the total weight to calculate the aged-to-unaged weight ratio/proportion -aged_proportions = ( - length_age_sex_stratum_weight - .merge( weight_strata , on = [ 'stratum_num' ] ) - .assign( proportion_weight_aged_all = lambda x: x.summed_weight_all / x.weight_stratum_total , - proportion_weight_unaged_all = lambda x: 1.0 - x.proportion_weight_aged_all , - proportion_weight_aged_adult = lambda x: x.summed_weight_adult / x.weight_stratum_total , - proportion_weight_unaged_adult = lambda x: 1.0 - x.proportion_weight_aged_adult ) -) - -### Calculate interpolated weights based on length bins for each sex per haul -# Extract length from binned values -length_weight_fit = ( - self.statistics[ 'length_weight' ][ 'length_weight_df' ] - .assign( length_bin_value = lambda x: x[ 'length_bin' ].apply( lambda y: y.mid ) ) -) - -haul_weights = ( - length_df_copy - .bin_variable( length_intervals , 'length' ) - .loc[ lambda x: x.group == 'sexed' ] - .pipe( lambda df: pd.concat( [ df , df.assign( sex = 'all' ) ] ) ) - .merge( length_weight_fit , on = [ 'sex' , 'length_bin' ] ) - .groupby( [ 'stratum_num' , 'haul_num' , 'sex' ] ) - .apply( lambda df: pd.Series( { 'weight_interp': ( np.interp( df.length_bin_value , - length_weight_fit.loc[ lambda x: x.sex.isin( df.sex ) ][ 'length_bin_value' ] , - length_weight_fit.loc[ lambda x: x.sex.isin( df.sex ) ][ 'weight_modeled' ] ) * - df.length_count ).sum( ) } ) ) - .groupby( [ 'stratum_num' , 'sex' ] ) - .apply( lambda df: pd.Series( { 'summed_haul_weights': df.weight_interp.sum( ) } ) ) - .reset_index( ) -) - -### Some normalization -( - haul_weights - .merge( weight_strata_station.loc[ lambda x: x.station == 1 ] , on = 'stratum_num' ) - .assign( normalized_weight = lambda x: ) -) -haul_weights.merge( weight_strata_station.loc[ lambda x: x.station == 1 ] , on = 'stratum_num' ) - - - -### Kriged data input -dataframe_input = self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] - -### Need aged/unaged biomass for males/females -# First need the length-age-sex proportions -self.biology[ 'weight' ][ 'proportions' ][ 'length_sex_age_weight_proportions_df' ].loc[ lambda x: x[ 'count' ] > 0.0 ] -self.biology[ 'weight' ][ 'weight_strata_df' ] -( - self.biology[ 'weight' ][ 'proportions' ][ 'length_sex_age_weight_proportions_df' ] - .assign( prop_sex_sum = lambda x: x.groupby( [ 'stratum_num' , 'sex' ] )[ 'count' ].transform( sum ) , - prop_holder = lambda x: np.where( x.sex == 'all' , x[ 'count' ] , 0.0 ) , - prop_strat_sum = lambda x: x.groupby( [ 'stratum_num' ] )[ 'prop_holder' ].transform( sum ) , - prop_sex_norm = lambda x: x[ 'count' ] / x.prop_sex_sum , - prop_strat_norm = lambda x: x[ 'count' ] / x.prop_strat_sum ) - .groupby( [ 'stratum_num' , 'sex' ] ) - .apply( lambda df: pd.Series( { 'weight': ( df.prop_sex_norm * df.prop_strat_norm ).sum( ) } ) ) -) -x.groupby( [ 'stratum_num' ] )[ 'count' ] -self.biology[ 'weight' ][ 'proportions' ][ 'sex_age_weight_proportions_df' ].loc[ lambda x: x.weight_sex_proportion_adult > 0 ] -( - self.biology[ 'weight' ][ 'proportions' ][ 'sex_age_weight_proportions_df' ] - .loc[ lambda df: ( df.age > 1 ) & ( df.sex == 'all' ) ] -) - - -total_weights = ( - self.biology[ 'weight' ][ 'proportions' ][ 'length_sex_age_weight_proportions_df' ] - .loc[ lambda df: ( df.age > 1 ) & ( df.sex == 'all' ) ] - .drop_duplicates( subset = [ 'stratum_num' , 'sex' ] ) -) - .groupby( [ 'stratum_num' ] ) - .apply( lambda df: pd.Series( { 'stratum_total_weight': np.nansum( df.weight_total_adult ) } ) ) -) - -dist_weight_sum = ( - self.biology[ 'weight' ][ 'proportions' ][ 'length_sex_age_weight_proportions_df' ] - .merge( total_weights , on = 'stratum_num' ) -) - -self.biology[ 'weight' ][ 'proportions' ][ 'length_sex_age_weight_proportions_df' ] - - - - -.apply( lambda df: pd.Series( { - 'count_age_proportion_all': ( df[ 'count' ] / df.stratum_count_all ).sum() , - 'count_age_proportion_adult': ( df.loc[ df.age > 1 ][ 'count' ] / df.stratum_count_total ).sum( ) -} ) ) - - -import matplotlib.pyplot as plt - - -l1 = length_df_copy.groupby('stratum_num').apply( lambda x: x[ 'length_count' ].sum()).reset_index(name='count') -s1 = specimen_df_copy.groupby('stratum_num').size().reset_index(name='count') - -ma = l1.merge( s1 , on = 'stratum_num' , how = 'outer' ).replace(np.nan, 0).assign( total = lambda x: x.count_x + x.count_y ) - -plt.bar( ma.stratum_num , ma.total ) -plt.xlabel( 'KS stratum' ) -plt.ylabel( 'Frequency' ) -plt.show( ) - -l2 = length_df_copy.groupby(['stratum_num' , 'transect_num']).apply( lambda x: x[ 'length_count' ].sum()).reset_index(name='count') -s2 = specimen_df_copy.groupby(['stratum_num' , 'transect_num']).size().reset_index(name='count') -m2 = l2.merge( s2 , on = ['stratum_num' , 'transect_num'] , how = 'outer' ).replace(np.nan, 0).assign( total = lambda x: x.count_x + x.count_y ) -m2 = m2.sort_values( 'transect_num' ) -m2.loc[ m2.transect_num > 100 ] -for stratum_num in np.unique( m2.stratum_num ): - subset = m2[m2['stratum_num'] == stratum_num] - plt.plot(subset['transect_num'], subset['total'], label=f'Stratum {int(stratum_num)}') - plt.scatter(subset['transect_num'], subset['total'], marker='o') -plt.xlabel('Transect Number') -plt.ylabel('Total') -plt.title('Total vs Transect Number for Different Stratum') -plt.legend() -plt.grid(True) -plt.show() - -( - nasc_interval_df - # Merge stratum information ( join = 'outer' since missing values will be filled later on) - .merge( info_strata , on = [ 'stratum_num' , 'haul_num' ] , how = 'outer' ) - # Drop unused hauls - .dropna( subset = 'transect_num' ) - # Fill NaN w/ 0's for 'fraction_hake' - .assign( fraction_hake = lambda x: x[ 'fraction_hake' ].fillna( 0 ) ) - # Group merge - .group_merge( dataframes_to_add = dataframes_to_add , on = 'stratum_num' ) - ) -) - - - -a = age_sex_weight_proportions.pivot_table( index = [ 'age' , 'sex' ] , columns = [ 'stratum_num' ] ) -a.loc[ : , ( 'weight_age_sex_proportion_all' ) ] -age_sex_weight_proportions.pivot_table( index = [ 'sex' , 'station' ] , - columns = [ 'stratum_num' ] , - values = [ 'overall_station_p' ] ) - - - age_proportions - .pipe( lambda df: df - .groupby( 'stratum_num' ) - .apply( lambda x: 1 - x.loc[ x[ 'age' ] < 2 ][ 'count' ].sum() / x[ 'count' ].sum( ) ) ) - .reset_index( name = 'adult_proportion' ) - ) - - age_proportions = ( - specimen_df_copy - .assign( age_group = lambda x: np.where( x.age < 2 , 'nonadult' , 'adult' ) ) - .dropna( how = 'any' ) - .count_variable( variable = 'length' , - contrasts = [ 'stratum_num' , 'age' , 'age_group' ] , - fun = 'size' ) - .pipe( lambda x: x.assign( stratum_count_all = x.groupby( [ 'stratum_num' ] )[ 'count' ].transform( sum ) , - stratum_proportion_all = lambda x: x[ 'count' ] / x[ 'stratum_count_all' ] , - stratum_count_adult = x.loc[ x.age_group == 'adult' ].groupby( [ 'stratum_num' ] )[ 'count' ].transform( sum ).astype( int ) , - stratum_proportion_adult = lambda x: x[ 'count' ] / x[ 'stratum_count_adult' ] ) ) - ) - -plt.bar( ma.stratum_num , ma.total ) -plt.xlabel( 'KS stratum' ) -plt.ylabel( 'Frequency' ) -plt.show( ) - -plt.boxplot( specimen_df_copy.groupby('stratum_num').size() , specimen_df_copy.groupby('stratum_num').size().values ) -plt.show() -### Reformat 'specimen_df' to match the same format as 'len_df' -# First make copies of each -specimen_df_copy = self.biology['specimen_df'].copy().pipe( lambda df: df.loc[ df.species_id == species_id ] ) -length_df_copy = self.biology['length_df'].copy().pipe( lambda df: df.loc[ df.species_id == species_id ] ) - -self.biology['specimen_df'].copy().loc[ lambda x: x.age == 0] -self.biology['length_df'].copy().loc[ lambda x: x.age == 0] -### Pull length distribution values -length_intervals = self.biology['distributions']['length']['length_interval_arr'] - -### Calculate the sex proportions/frequencies for station 1 (length_df) across all strata -specimen_df_copy.loc[ lambda x: x.sex == 'male' ] - -length_grouped = ( - length_df_copy - .bin_variable( length_intervals , 'length' ) # appends `length_bin` column - .assign( group = lambda x: np.where( x['sex'] == int(1) , 'male' , 'female' ) ) # assigns str variable for comprehension - .pipe( lambda df: pd.concat( [ df.loc[ df[ 'sex' ] != 3 ] , df.assign( group = 'all' ) ] ) ) # appends male-female to an 'all' dataframe - .assign( station = 1 ) # assign station number for later functions - ) - -### Calculate the sex proportions/frequencies for station 2 (specimen_df) across all strata -specimen_grouped = ( - specimen_df_copy - .bin_variable( length_intervals , 'length' ) # appends `length_bin` column - .assign( group = lambda x: np.where( x[ 'sex' ] == int( 1 ) , 'male' , np.where( x[ 'sex' ] == int( 2 ) , 'female' , 'unsexed' ) ) ) # assigns str variable for comprehension - .pipe( lambda df: pd.concat( [ df.loc[ df[ 'sex' ] != 3 ] , df.assign( group = 'all' ) ] ) ) # appends male-female to an 'all' dataframe - .assign( station = 2 ) # assign station number for later functions -) - -aa = ( - specimen_df_spp - .dropna( subset = [ 'weight' , 'length' ] ) - .assign( group = lambda x: np.where( x[ 'sex' ] == int( 1 ) , 'male' , - np.where( x[ 'sex' ] == int( 2 ) , 'female' , 'unsexed' ) ) ) # assigns str variable for comprehension - .pipe( lambda df: pd.concat( [ df.loc[ df[ 'group' ] != 'unsexed' ] , df.assign( group = 'all' ) ] ) ) # appends male-female to an 'all' dataframe -) - -np.unique( aa.group ) - -aa.loc[ aa.group == 'all' ].shape -specimen_sex_grouped_df.bin_stats( bin_variable = 'length' , bin_values = length_intervals , contrasts = 'group' ).loc[ lambda x: x.group == 'female' ] - -len_wgt_all = survey.bio_calc._generate_length_val_conversion( - len_name="length", - val_name="weight", - df=survey.bio_calc.specimen_df, -) - -( - fitted_weights - .rename( columns = { 'length_bin': 'length' } ) - .bin_variable( bin_values = length_intervals , - bin_variable = 'length' ) - .merge( length_bin_stats , on = [ 'group' , 'length_bin' ] ) - .assign( weight_modeled = lambda x: np.where( x.n_weight < 5 , - x.fitted_weight , - x.mean_weight ) ) -) - -specimen_df_copy = self.biology['specimen_df'].copy().pipe( lambda df: df.loc[ df.species_id == species_id ] ) - -# Import length bins -length_intervals = self.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] -age_intervals = self.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] -### Calculate age bin proportions when explicitly excluding age-0 and age-1 fish -# Calculate age proportions across all strata and age-bins -age_proportions = ( - specimen_df_copy - .dropna( how = 'any' ) - .count_variable( variable = 'length' , - contrasts = [ 'stratum_num' , 'age' ] , - fun = 'size' ) - .pipe( lambda x: x.assign( stratum_count = x.groupby( [ 'stratum_num' ] )[ 'count' ].transform( sum ) , - stratum_proportion = lambda x: x[ 'count' ] / x[ 'stratum_count' ] ) ) -) - -age_proportions.groupby( [ 'age' , 'stratum_num' ] ).apply( lambda x: x[ 'stratum_proportion' ].sum() ).reset_index().loc[ lambda x: x.stratum_num == 1.0 ] - -# Calculate adult proportions/contributions (in terms of summed presence) for each stratum -age_2_and_over_proportions = ( - age_proportions - .pipe( lambda df: df - .groupby( 'stratum_num' ) - .apply( lambda x: 1 - x.loc[ x[ 'age' ] <= 1 ][ 'count' ].sum() / x[ 'count' ].sum( ) ) ) - .reset_index( name = 'number_proportion' ) -) - -### Calculate proportional contributions of each age-bin to the summed weight of each stratum -### when explicitly excluding age-0 and age-1 fish -# Calculate the weight proportions across all strata and age-bins -age_weight_proportions = ( - specimen_df_copy - .dropna( subset = [ 'length' , 'weight' ] ) - .pipe( lambda x: x.assign( weight_age = x.groupby( [ 'stratum_num' , 'age' ] )[ 'weight' ].transform( sum ) , - weight_stratum = x.groupby( [ 'stratum_num' ] )[ 'weight' ].transform( sum ) ) ) - .groupby( [ 'stratum_num' , 'age' ] ) - .apply( lambda x: x[ 'weight_age' ].sum( ) / x[ 'weight_stratum' ].sum ( ) ) - .reset_index( name = 'weight_stratum_proportion' ) -) - -a = ( - specimen_df_copy - .dropna( how = "any" ) - .bin_variable( bin_values = length_intervals , - bin_variable = 'length' ) - .bin_variable( bin_values = age_intervals , - bin_variable = 'age' ) - .assign( group = lambda x: np.where( x[ 'sex' ] == int( 1 ) , 'male' , - np.where( x[ 'sex' ] == int( 2 ) , 'female' , 'unsexed' ) ) ) # assigns str variable for comprehension - .pipe( lambda df: pd.concat( [ df.loc[ df[ 'group' ] != 'unsexed' ] , df.loc[ df[ 'group' ] != 'unsexed' ].assign( group = 'all' ) ] ) ) # appends male-female to an 'all' dataframe - .bin_stats( bin_variable = 'age' , - bin_values = age_intervals , - variables = 'weight' , - contrasts = [ 'group' , 'stratum_num' , 'length_bin' ] , - functions = ['mean', 'size' , 'sum' ] ) - # .groupby( [ 'group' ] ) - # .apply( lambda x: x.mean) -) - -a.loc[ ( a.stratum_num == 1 ) & ( a.group == 'male' ) & ( a.sum_weight > 0 ) ][ 'sum_weight' ] -a.loc[ ( a.stratum_num == 1 ) & ( a.group == 'all' ) & ( a.sum_weight > 0 ) ][ 'sum_weight' ].sum( ) -a.loc[ ( a.stratum_num == 1 ) & ( a.group == 'all' ) ].groupby( 'age_bin' ).apply( lambda x: x[ 'sum_weight' ].sum()) - -( - specimen_df_copy - .dropna( subset = [ 'weight' , 'length' ] ) - .pipe( lambda x: x.assign( weight_age = x.groupby( [ 'stratum_num' , 'age' ] )[ 'weight' ].transform( sum ) , - weight_stratum = x.groupby( [ 'stratum_num' ] )[ 'weight' ].transform( sum ) ) ) -).loc[ lambda x: ( x.stratum_num == 1 ) ] - -b = age_weight_proportions.groupby( [ 'age' , 'stratum_num' ] ).apply( lambda x: x[ 'weight_stratum_proportion' ].sum() ).reset_index().loc[ lambda x: x.stratum_num == 1.0 ] -c = b.rename( columns = { 0: 'prop' } ).loc[ b.age != 0 ][ 'prop' ].sum() -b.rename( columns = { 0: 'prop' } ).loc[ b.age != 0 ].assign( prop_new = lambda x: x.prop / c ) -# Calculate adult proportions/contributions (in terms of summed weight) for each stratum -age_2_and_over_weight_proportions = ( - age_weight_proportions - .pipe( lambda df: df - .groupby( 'stratum_num' ) - .apply( lambda x: 1 - x.loc[ x[ 'age' ] <= 1 ][ 'weight_stratum_proportion' ].sum()) ) - .reset_index( name = 'weight_proportion' ) -) - -### Now this process will be repeated but adding "sex" as an additional contrast and considering -### all age-bins -age_sex_weight_proportions = ( - specimen_df_copy - .dropna( how = 'any' ) - .bin_variable( bin_values = length_intervals , - bin_variable = 'length' ) - .count_variable( contrasts = [ 'stratum_num' , 'age' , 'length_bin' , 'sex' ] , - variable = 'weight' , - fun = 'sum' ) - .pipe( lambda df: df.assign( total = df.groupby( [ 'stratum_num' , 'sex' ] )[ 'count' ].transform( sum ) ) ) - .groupby( [ 'stratum_num' , 'age' , 'sex' ] ) - .apply( lambda x: ( x[ 'count' ] / x[ 'total' ] ).sum() ) - .reset_index( name = 'weight_sex_stratum_proportion' ) - .assign( sex = lambda x: np.where( x[ 'sex' ] == 1 , 'male' , np.where( x[ 'sex' ] == 2 , 'female' , 'unsexed' ) ) ) -) - -### Add these dataframes to the appropriate data attribute -self.biology[ 'weight' ].update( { - 'sex_stratified': { - 'weight_proportions': age_sex_weight_proportions , - } , - 'age_stratified': { - 'age_1_excluded': { - 'number_proportions': age_2_and_over_proportions , - 'weight_proportions': age_2_and_over_weight_proportions , - } , - 'age_1_included': { - 'number_proportions': age_proportions , - 'weight_proportions': age_weight_proportions - } - } -} ) - - -fitted_weights.bin_variable( bin_values = length_intervals , - bin_variable = 'length_bin' ) -length_bin_stats.merge( fitted_weights , on = [ 'group' , 'length_bin' ] ) - -length_bin_stats.assign( weight_modeled = lambda x: np.wherE( x.n_weight < 5 , )) - -length_bin_stats[ 'weight_modeled' ] = length_bin_stats[ 'mean_weight' ].copy() -low_n_indices = np.where(length_bin_stats[ 'n_weight' ].values < 5)[0].copy() -length_bin_stats.loc[low_n_indices, 'weight_modeled'] = fitted_weight[low_n_indices].copy() - -a1 = ( - specimen_df_spp - .assign( group = lambda x: np.where( x[ 'sex' ] == int( 1 ) , 'male' , - np.where( x[ 'sex' ] == int( 2 ) , 'female' , 'unsexed' ) ) ) # assigns str variable for comprehension - .pipe( lambda df: pd.concat( [ df.loc[ df[ 'group' ] != 'unsexed' ] , df.assign( group = 'all' ) ] ) ) # appends male-female to an 'all' dataframe - .dropna( subset = [ 'weight' , 'length' ] ) - .groupby( 'group' ) - .apply( lambda x: pd.Series( np.polyfit( np.log10( x[ 'length' ].values ) , np.log10( x[ 'weight' ].values ) , 1 ) , - index = [ 'rate' , 'initial' ] ) ) - .reset_index() -) - -b = a.merge( pd.DataFrame({'length_bins': length_bins}) , how = 'cross' ).assign( fitted_weight = lambda x: 10 ** x.initial * x.length_bins.astype( int ) ** x.rate ) -b.loc[ b.group == 'all' ][ 'fitted_weight' ][ 5 ] -a.groupby( 'group' ).apply( lambda x: 10 ** x.initial * int( length_bins ) ** x.rate , axis = 1 ) -a.rate[ 0 ] -a.reset_index() - -specimen_df_spp.bin_stats( bin_variable = 'length' , bin_values = length_intervals ) - - -specimen_df_spp.assign(group = lambda x: x.sex.astype(str)).loc[lambda x: ( x.group == '1' ) ].apply(lambda x: pd.Series( np.polyfit( np.log10( x[ 'length' ] ) , np.log10( x[ 'weight' ] ) , 1 ) , index=['slope', 'intercept'] ) ) -df = specimen_df_spp.assign(group = lambda x: x.sex.astype(str)).loc[lambda x: ( x.group == '1' ) ].dropna(how='any') -np.polyfit( np.log10( df[ 'length' ] ) , np.log10( df[ 'weight' ] ) , 1 ) - - -specimen_df_spp.assign(group = lambda x: str(x.sex)).group - -length_weight_df.bin_stats( bin_variable = 'length' , bin_values = length_intervals , contrasts = [ 'sex' ] ) - - -### Calculate the sex proportions/frequencies for station 2 (specimen_df) across all strata -specimen_grouped = ( - specimen_df_copy - .bin_variable( length_intervals , 'length' ) # appends `length_bin` column - .assign( group = lambda x: np.where( x[ 'sex' ] == int( 1 ) , 'male' , np.where( x[ 'sex' ] == int( 2 ) , 'female' , 'unsexed' ) ) # assigns str variable for comprehension - .pipe( lambda df: pd.concat( [ df.loc[ df[ 'sex' ] != 3 ] , df.assign( group = 'all' ) ] ) ) # appends male-female to an 'all' dataframe - .assign( station = 2 ) # assign station number for later functions -) - -specimen_df_copy.dropna( subset = [ 'weight' , 'length' ] ) - -( - specimen_df_copy - .dropna( subset = 'weight' ) - .bin_variable( length_intervals , 'length' ) # appends `length_bin` column - .count_variable( contrasts = [ 'length_bin' ] , # grouping variables - variable = 'length' , # target value to apply a function - fun = 'count' ) # function to apply - . sum( ) -) -### "Meld" the two datasets together to keep downstream code tidy -station_sex_length = ( - specimen_grouped # begin reformatting specimen_grouped so it resembles same structure as length_grouped - .meld( length_grouped ) # "meld": reformats specimen_grouped and then concatenates length_grouped - # sum up all of the length values with respect to each length bin - .count_variable( contrasts = [ 'group' , 'station' , 'stratum_num' , 'length_bin' ] , # grouping variables - variable = 'length_count' , # target value to apply a function - fun = 'sum' ) # function to apply -) - -station_sex_length.loc[ lambda x: ( x.station == 1 ) & ( x.group == 'all' ) ] -### Calculate total sample size ('group' == 'all') that will convert 'counts' into 'frequency/proportion' -total_n = ( - station_sex_length - .loc[ station_sex_length.group.isin( [ 'all' ] ) ] # filter out to get to just 'all': this is where sex = [ 1 , 2 , 3] - .groupby( [ 'stratum_num' ] )[ 'count' ] # group by each stratum with 'count' being the target variable - .sum( ) # sum up counts per stratum - .reset_index( name = 'n_total' ) # rename index -) - -### Convert counts in station_sex_length into frequency/proportion -station_length_aggregate = ( - station_sex_length - # calculate the within-sample sum and proportions (necessary for the downstream dot product calculation) - .pipe( lambda x: x.assign( within_station_n = x.groupby( [ 'group' , 'station' , 'stratum_num' ] )[ 'count' ].transform( sum ) , - within_station_p = lambda x: x[ 'count' ] / x[ 'within_station_n' ] ) ) - .replace( np.nan, 0 ) # remove erroneous NaN (divide by 0 or invalid values) - .merge( total_n , on = 'stratum_num' ) # merge station_sex_length with total_n - # proportion of each count indexed by each length_bin relative to the stratum total - .assign( overall_length_p = lambda x: x[ 'count' ] / x[ 'n_total' ] , - overall_station_p = lambda x: x[ 'within_station_n' ] / x[ 'n_total' ] ) - .replace( np.nan, 0 ) # remove erroneous NaN (divide by 0 or invalid values) -) - -### Calculate the sex distribution across strata -sex_proportions = ( - station_length_aggregate - .loc[ station_length_aggregate.group.isin( ['male' , 'female'] ) ] # only parse 'male' and 'female' - # create a pivot that will reorient data to the desired shape - .pivot_table( index = [ 'group' , 'station' ] , - columns = [ 'stratum_num' ] , - values = [ 'overall_station_p' ] ) - .groupby( 'group' ) - .sum() -) - -### Calculate the proportions each dataset / station contributed within each stratum -station_proportions = ( - station_length_aggregate - .loc[ station_length_aggregate.group.isin( [ 'all' ] ) ] # only parse 'all' - # create a pivot that will reorient data to the desired shape - .pivot_table( index = [ 'group' , 'station' ] , - columns = 'stratum_num' , - values = 'overall_station_p' ) - .groupby( 'station' ) - .sum() -) - -### Calculate the sex distribution within each station across strata -sex_station_proportions = ( - station_length_aggregate - .loc[ station_length_aggregate.group.isin( ['male' , 'female'] ) ] # only parse 'male' and 'female' - # create a pivot that will reorient data to the desired shape - .pivot_table( index = [ 'group' , 'station' ] , - columns = 'stratum_num' , - values = 'overall_station_p' ) - .groupby( [ 'group' , 'station' ] ) - .sum() -) - -### Merge the station and sex-station indexed proportions to calculate the combined dataset mean fractions -sex_stn_prop_merged = ( - sex_station_proportions - .stack( ) - .reset_index( name = 'sex_stn_p') - .merge( station_proportions - .stack() - .reset_index( name = 'stn_p' ) , on = [ 'stratum_num' , 'station' ] ) - .pivot_table( columns = 'stratum_num' , - index = [ 'station' , 'group' ] , - values = [ 'stn_p' , 'sex_stn_p' ] ) -) - -### Format the length bin proportions so they resemble a similar table/matrix shape as the above metrics -# Indexed / organized by sex , station, and stratum -length_proportion_table = ( - station_length_aggregate - .pivot_table( columns = [ 'group' , 'station' , 'stratum_num' ] , - index = [ 'length_bin' ] , - values = [ 'within_station_p' ] )[ 'within_station_p' ] -) - -### Calculate combined station fraction means -# Station 1 -stn_1_fraction = ( sex_stn_prop_merged.loc[ 1 , ( 'stn_p' ) ] - / ( sex_stn_prop_merged.loc[ 1 , ( 'stn_p' ) ] - + sex_stn_prop_merged.loc[ 2 , ( 'sex_stn_p' ) ] ) ) - -# Station 2 -stn_2_fraction = ( sex_stn_prop_merged.loc[ 2 , ( 'sex_stn_p' ) ] - / ( stn_1_fraction - + sex_stn_prop_merged.loc[ 2 , ( 'sex_stn_p' ) ] ) ) - -### Calculate the average weight across all animals, males, and females -# Pull fitted weight values -fitted_weight = self.statistics['length_weight']['length_weight_df'].loc[ lambda x: x.group == 'all' ] - -# Total -total_weighted_values = ( length_proportion_table.loc[ : , ( 'all' , 1 ) ] * station_proportions.loc[ 1 , ] + - length_proportion_table.loc[ : , ( 'all' , 2 ) ] * station_proportions.loc[ 2 , ] ) -total_weight = fitted_weight[ 'weight_modeled' ].dot( total_weighted_values.reset_index( drop = True ) ) -total_weighted_values.loc[ : , 1.0 ].sort_values( ascending = False ).head(3).sum() -# Male -male_weighted_values = ( length_proportion_table.loc[ : , ( 'male' , 1 ) ] * stn_1_fraction.loc[ 'male' ] + - length_proportion_table.loc[ : , ( 'male' , 2 ) ] * stn_2_fraction.loc[ 'male' ] ) -male_weight = fitted_weight[ 'weight_modeled' ].dot( male_weighted_values.reset_index( drop = True ) ) - -# Female -female_weighted_values = ( length_proportion_table.loc[ : , ( 'female' , 1 ) ] * stn_1_fraction.loc[ 'female' ] + - length_proportion_table.loc[ : , ( 'female' , 2 ) ] * stn_2_fraction.loc[ 'female' ] ) -female_weight = fitted_weight[ 'weight_modeled' ].dot( female_weighted_values.reset_index( drop = True ) ) - -### Store the data frame in an accessible location -self.biology[ 'weight' ][ 'weight_strata_df' ] = pd.DataFrame( { - 'stratum_num': total_weight.index , - 'proportion_female': sex_proportions.loc[ 'female' , : ][ 'overall_station_p' ].reset_index(drop=True) , - 'proportion_male': sex_proportions.loc[ 'male' , : ][ 'overall_station_p' ].reset_index(drop=True) , - 'average_weight_female': female_weight , - 'average_weight_male': male_weight , - 'average_weight_total': total_weight -} ) - - - -self.biology[ 'weight' ][ 'weight_strata_df' ] - - -### Reformat 'specimen_df' to match the same format as 'len_df' -# First make copies of each -specimen_df_copy = self.biology['specimen_df'].copy().pipe( lambda df: df.loc[ df.species_id == species_id ] ) - -# Import length bins -length_intervals = self.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] - -### Calculate age bin proportions when explicitly excluding age-0 and age-1 fish -# Calculate age proportions across all strata and age-bins -age_proportions = ( - specimen_df_copy - .dropna( how = 'any' ) - .count_variable( variable = 'length' , - contrasts = [ 'stratum_num' , 'age' ] , - fun = 'size' ) - .pipe( lambda x: x.assign( stratum_count = x.groupby( [ 'stratum_num' ] )[ 'count' ].transform( sum ) , - stratum_proportion = lambda x: x[ 'count' ] / x[ 'stratum_count' ] ) ) -) - -# Calculate adult proportions/contributions (in terms of summed presence) for each stratum -age_2_and_over_proportions = ( - age_proportions - .pipe( lambda df: df - .groupby( 'stratum_num' ) - .apply( lambda x: 1 - x.loc[ x[ 'age' ] <= 1 ][ 'count' ].sum() / x[ 'count' ].sum( ) ) ) - .reset_index( name = 'number_proportion' ) -) - -### Calculate proportional contributions of each age-bin to the summed weight of each stratum -### when explicitly excluding age-0 and age-1 fish -# Calculate the weight proportions across all strata and age-bins -age_weight_proportions = ( - specimen_df_copy - .dropna( how = 'any' ) - .pipe( lambda x: x.assign( weight_age = x.groupby( [ 'stratum_num' , 'age' ] )[ 'weight' ].transform( sum ) , - weight_stratum = x.groupby( [ 'stratum_num' ] )[ 'weight' ].transform( sum ) ) ) - .groupby( [ 'stratum_num' , 'age' ] ) - .apply( lambda x: x[ 'weight_age' ].sum( ) / x[ 'weight_stratum' ].sum ( ) ) - .reset_index( name = 'weight_stratum_proportion' ) -) - -# Calculate adult proportions/contributions (in terms of summed weight) for each stratum -age_2_and_over_weight_proportions = ( - age_weight_proportions - .pipe( lambda df: df - .groupby( 'stratum_num' ) - .apply( lambda x: 1 - x.loc[ x[ 'age' ] <= 1 ][ 'weight_stratum_proportion' ].sum()) ) - .reset_index( name = 'weight_proportion' ) -) - -### Now this process will be repeated but adding "sex" as an additional contrast and considering -### all age-bins -age_sex_weight_proportions = ( - specimen_df_copy - .dropna( how = 'any' ) - .bin_variable( bin_values = length_intervals , - bin_variable = 'length' ) - .count_variable( contrasts = [ 'stratum_num' , 'age' , 'length_bin' , 'sex' ] , - variable = 'weight' , - fun = 'sum' ) - .pipe( lambda df: df.assign( total = df.groupby( [ 'stratum_num' , 'sex' ] )[ 'count' ].transform( sum ) ) ) - .groupby( [ 'stratum_num' , 'age' , 'sex' ] ) - .apply( lambda x: ( x[ 'count' ] / x[ 'total' ] ).sum() ) - .reset_index( name = 'weight_sex_stratum_proportion' ) - .assign( sex = lambda x: np.where( x[ 'sex' ] == 1 , 'male' , np.where( x[ 'sex' ] == 2 , 'female' , 'unsexed' ) ) ) -) - -age_sex_weight_proportions.loc[ lambda x: ( x.sex == 'female' ) & ( x.stratum_num == 1.0 ) ] - -### Add these dataframes to the appropriate data attribute -self.biology[ 'weight' ].update( { - 'sex_stratified': { - 'weight_proportions': age_sex_weight_proportions , - } , - 'age_stratified': { - 'age_1_excluded': { - 'number_proportions': age_2_and_over_proportions , - 'weight_proportions': age_2_and_over_weight_proportions , - } , - 'age_1_included': { - 'number_proportions': age_proportions , - 'weight_proportions': age_weight_proportions - } - } -} ) diff --git a/echopop/testing_proportions.py b/echopop/testing_proportions.py deleted file mode 100644 index f235d0b2..00000000 --- a/echopop/testing_proportions.py +++ /dev/null @@ -1,326 +0,0 @@ -from EchoPro.survey import Survey -import numpy as np -import pandas as pd -import geopandas as gpd -from pathlib import Path -import copy -import os -from EchoPro.core import CONFIG_MAP, LAYER_NAME_MAP -from EchoPro.utils.data_file_validation import load_configuration , validate_data_columns -from EchoPro.computation.operations import bin_variable , bin_stats , count_variable -from EchoPro.utils.data_file_validation import validate_data_columns -from EchoPro.computation.acoustics import to_linear , ts_length_regression - -init_config_path=Path('./config_files/initialization_config.yml') -survey_year_config_path=Path('./config_files/survey_year_2019_config.yml') -obj = Survey( init_config_path='./config_files/initialization_config.yml' , survey_year_config_path='./config_files/survey_year_2019_config.yml' ) -species_id = 22500 -self = obj -########### COMMON -specimen_df_copy = self.biology['specimen_df'].copy().pipe( lambda df: df.loc[ df.species_id == species_id ] ) -length_df_copy = self.biology['length_df'].copy().pipe( lambda df: df.loc[ df.species_id == species_id ] ) - -### Pull length distribution values -length_intervals = self.biology['distributions']['length']['length_interval_arr'] -age_intervals = self.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] - -### Calculate the sex proportions/frequencies for station 1 (length_df) across all strata -length_grouped = ( - length_df_copy - .bin_variable( length_intervals , 'length' ) # appends `length_bin` column - .assign( group = lambda x: np.where( x[ 'sex' ] == int( 1 ) , 'male' , - np.where( x[ 'sex' ] == int( 2 ) , 'female' , 'unsexed' ) ) ) - # .assign( group = lambda x: np.where( x['sex'] == int(1) , 'male' , 'female' ) ) # assigns str variable for comprehension - # .pipe( lambda df: pd.concat( [ df.loc[ df[ 'sex' ] != 3 ] , df.assign( group = 'all' ) ] ) ) # appends male-female to an 'all' dataframe - .assign( station = 1 ) # assign station number for later functions - ) - -### Calculate the sex proportions/frequencies for station 2 (specimen_df) across all strata -specimen_grouped = ( - specimen_df_copy - .bin_variable( length_intervals , 'length' ) # appends `length_bin` column - .assign( group = lambda x: np.where( x[ 'sex' ] == int( 1 ) , 'male' , - np.where( x[ 'sex' ] == int( 2 ) , 'female' , 'unsexed' ) ) ) # assigns str variable for comprehension - # .pipe( lambda df: pd.concat( [ df , df.assign( group = 'all' ) ] ) ) # appends male-female to an 'all' dataframe - .dropna( subset = [ 'weight' , 'length' ] ) - .assign( station = 2 ) # assign station number for later functions -) -specimen_grouped .loc[ lambda x: x.stratum_num == 1.0 ] -### "Meld" the two datasets together to keep downstream code tidy -station_sex_length = ( - specimen_grouped # begin reformatting specimen_grouped so it resembles same structure as length_grouped - .meld( length_grouped ) # "meld": reformats specimen_grouped and then concatenates length_grouped - .pipe( lambda df: pd.concat( [ df.loc[ df[ 'group' ] == 'sexed' ] , - df.assign( sex = 'all' ) ] ) ) - .count_variable( contrasts = [ 'sex' , 'station' , 'stratum_num' , 'length_bin' ] , # grouping variables - variable = 'length_count' , # target value to apply a function - fun = 'sum' ) # function to apply -) - -total_n = ( - station_sex_length - .loc[ station_sex_length.sex.isin( [ 'all' ] ) ] # filter out to get to just 'all': this is where sex = [ 1 , 2 , 3] - .groupby( [ 'stratum_num' ] )[ 'count' ] # group by each stratum with 'count' being the target variable - .sum( ) # sum up counts per stratum - .reset_index( name = 'n_total' ) # rename index -) - -station_length_aggregate = ( - station_sex_length - # calculate the within-sample sum and proportions (necessary for the downstream dot product calculation) - .pipe( lambda x: x.assign( within_station_n = x.groupby( [ 'sex' , 'station' , 'stratum_num' ] )[ 'count' ].transform( sum ) , - within_station_p = lambda x: x[ 'count' ] / x[ 'within_station_n' ] ) ) - .replace( np.nan, 0 ) # remove erroneous NaN (divide by 0 or invalid values) - .merge( total_n , on = 'stratum_num' ) # merge station_sex_length with total_n - # proportion of each count indexed by each length_bin relative to the stratum total - .assign( overall_length_p = lambda x: x[ 'count' ] / x[ 'n_total' ] , - overall_station_p = lambda x: x[ 'within_station_n' ] / x[ 'n_total' ] ) - .replace( np.nan, 0 ) # remove erroneous NaN (divide by 0 or invalid values) -) - -### Calculate the sex distribution across strata -sex_proportions = ( - station_length_aggregate - .loc[ station_length_aggregate.sex.isin( [ 'male' , 'female' ] ) ] # only parse 'male' and 'female' - # create a pivot that will reorient data to the desired shape - .pivot_table( index = [ 'sex' , 'station' ] , - columns = [ 'stratum_num' ] , - values = [ 'overall_station_p' ] ) - .groupby( 'sex' ) - .sum( ) -) - -### Calculate the proportions each dataset / station contributed within each stratum -station_proportions = ( - station_length_aggregate - .loc[ station_length_aggregate.sex.isin( [ 'all' ] ) ] # only parse 'all' - # create a pivot that will reorient data to the desired shape - .pivot_table( index = [ 'sex' , 'station' ] , - columns = 'stratum_num' , - values = 'overall_station_p' ) - .groupby( 'station' ) - .sum() -) - -### Calculate the sex distribution within each station across strata -sex_station_proportions = ( - station_length_aggregate - .loc[ station_length_aggregate.sex.isin( [ 'male' , 'female' ] ) ] # only parse 'male' and 'female' - # create a pivot that will reorient data to the desired shape - .pivot_table( index = [ 'sex' , 'station' ] , - columns = 'stratum_num' , - values = 'overall_station_p' ) - .groupby( [ 'sex' , 'station' ] ) - .sum() -) - -### Merge the station and sex-station indexed proportions to calculate the combined dataset mean fractions -sex_stn_prop_merged = ( - sex_station_proportions - .stack( ) - .reset_index( name = 'sex_stn_p') - .merge( station_proportions - .stack() - .reset_index( name = 'stn_p' ) , on = [ 'stratum_num' , 'station' ] ) - .pivot_table( columns = 'stratum_num' , - index = [ 'station' , 'sex' ] , - values = [ 'stn_p' , 'sex_stn_p' ] ) -) - -### Format the length bin proportions so they resemble a similar table/matrix shape as the above metrics -# Indexed / organized by sex , station, and stratum -length_proportion_table = ( - station_length_aggregate - .pivot_table( columns = [ 'sex' , 'station' , 'stratum_num' ] , - index = [ 'length_bin' ] , - values = [ 'within_station_p' ] )[ 'within_station_p' ] -) - -### Calculate combined station fraction means -# Station 1 -stn_1_fraction = ( sex_stn_prop_merged.loc[ 1 , ( 'stn_p' ) ] - / ( sex_stn_prop_merged.loc[ 1 , ( 'stn_p' ) ] - + sex_stn_prop_merged.loc[ 2 , ( 'sex_stn_p' ) ] ) ) - -# Station 2 -stn_2_fraction = ( sex_stn_prop_merged.loc[ 2 , ( 'sex_stn_p' ) ] - / ( stn_1_fraction - + sex_stn_prop_merged.loc[ 2 , ( 'sex_stn_p' ) ] ) ) - -### Calculate the average weight across all animals, males, and females -# Pull fitted weight values -fitted_weight = self.statistics[ 'length_weight' ][ 'length_weight_df' ] - -# Total -total_weight = ( - fitted_weight - .loc[ lambda x: x.sex == 'all' , 'weight_modeled' ] - .values - .dot( - ( length_proportion_table.loc[ : , ( 'all' , 1 ) ] * station_proportions.loc[ 1 , ] + - length_proportion_table.loc[ : , ( 'all' , 2 ) ] * station_proportions.loc[ 2 , ] ) - .reset_index( drop = True ) - ) -) - -# Male -male_weight = ( - fitted_weight - .loc[ lambda x: x.sex == 'male' , 'weight_modeled' ] - .values - .dot( - ( length_proportion_table.loc[ : , ( 'male' , 1 ) ] * station_proportions.loc[ 1 , ] + - length_proportion_table.loc[ : , ( 'male' , 2 ) ] * station_proportions.loc[ 2 , ] ) - .reset_index( drop = True ) - ) -) - -# Female -female_weight = ( - fitted_weight - .loc[ lambda x: x.sex == 'female' , 'weight_modeled' ] - .values - .dot( - ( length_proportion_table.loc[ : , ( 'female' , 1 ) ] * station_proportions.loc[ 1 , ] + - length_proportion_table.loc[ : , ( 'female' , 2 ) ] * station_proportions.loc[ 2 , ] ) - .reset_index( drop = True ) - ) -) - -### Store the data frame in an accessible location -self.biology[ 'weight' ][ 'weight_strata_df' ] = pd.DataFrame( { - 'stratum_num': station_proportions.columns.values.astype( int ) , - 'proportion_female': sex_proportions.loc[ 'female' , : ][ 'overall_station_p' ].reset_index( drop=True ) , - 'proportion_male': sex_proportions.loc[ 'male' , : ][ 'overall_station_p' ].reset_index( drop=True ) , - 'proportion_station_1': station_proportions.loc[ 1 , : ] , - 'proportion_station_2': station_proportions.loc[ 2 , : ] , - 'average_weight_female': female_weight , - 'average_weight_male': male_weight , - 'average_weight_total': total_weight -} ) - -### Reformat 'specimen_df' to match the same format as 'len_df' -# First make copies of each -specimen_df_copy = self.biology['specimen_df'].copy().pipe( lambda df: df.loc[ df.species_id == species_id ] ) - -# Import length bins -length_intervals = self.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] - -### Calculate age bin proportions when explicitly excluding age-0 and age-1 fish -# Calculate age proportions across all strata and age-bins -age_proportions = ( - specimen_df_copy - .dropna( how = 'any' ) - .count_variable( variable = 'length' , - contrasts = [ 'stratum_num' , 'age' ] , - fun = 'size' ) - .pipe( lambda x: x.assign( stratum_count = x.groupby( [ 'stratum_num' ] )[ 'count' ].transform( sum ) , - stratum_proportion = lambda x: x[ 'count' ] / x[ 'stratum_count' ] ) ) -) - -# Calculate adult proportions/contributions (in terms of summed presence) for each stratum -adult_proportions = ( - age_proportions - .pipe( lambda df: df - .groupby( 'stratum_num' ) - .apply( lambda x: 1 - x.loc[ x[ 'age' ] <= 1 ][ 'count' ].sum() / x[ 'count' ].sum( ) ) ) - .reset_index( name = 'number_proportion' ) -) - -### Calculate proportional contributions of each age-bin to the summed weight of each stratum -### when explicitly excluding age-0 and age-1 fish -# Calculate the weight proportions across all strata and age-bins -age_weight_proportions = ( - specimen_df_copy - .dropna( how = 'any' ) - .pipe( lambda df: df.assign( weight_stratum_all = df - .groupby( [ 'stratum_num' ] )[ 'weight' ] - .transform( sum ) , - weight_stratum_adult = df - .loc[ lambda x: x.age > 1 ] - .groupby( [ 'stratum_num' ] )[ 'weight' ] - .transform( sum ) ) ) - .groupby( [ 'stratum_num' , 'age' ] ) - .apply( lambda df: pd.Series( { - 'weight_age_proportion_all': ( df.weight / df.weight_stratum_all ).sum( ) , - 'weight_age_proportion_adult': ( df.weight / df.weight_stratum_adult ).sum( ) - } ) ) - .reset_index() -) - - -### Now this process will be repeated but adding "sex" as an additional contrast and considering -### all age-bins -sex_age_weight_proportions = ( - specimen_df_copy - .pipe( lambda df: pd.concat( [ df.loc[ df[ 'group' ] == 'sexed' ] , - df.assign( sex = 'all' ) ] ) ) - .dropna( how = 'any' ) - .bin_variable( bin_values = length_intervals , - bin_variable = 'length' ) - .count_variable( contrasts = [ 'stratum_num' , 'age' , 'length_bin' , 'sex' ] , - variable = 'weight' , - fun = 'sum' ) - .pipe( lambda df: df.assign( weight_total_all = df.groupby( [ 'stratum_num' , 'sex' ] )[ 'count' ].transform( sum ) , - weight_total_adult = df.loc[ df.age > 1 ].groupby( [ 'stratum_num' , 'sex' ] )[ 'count' ].transform( sum ) ) ) - .groupby( [ 'stratum_num' , 'age' , 'sex' ] ) - .apply( lambda x: pd.Series( { - 'weight_sex_proportion_all': ( x[ 'count' ] / x.weight_total_all ).sum() , - 'weight_sex_proportion_adult': ( x[ 'count' ] / x.weight_total_adult ).sum() - } ) ) - .reset_index( ) -) - -age_weight_proportions = ( - specimen_df_copy - .dropna( how = 'any' ) - .pipe( lambda x: x.assign( weight_age = x.groupby( [ 'stratum_num' , 'age' ] )[ 'weight' ].transform( sum ) , - weight_stratum = x.groupby( [ 'stratum_num' ] )[ 'weight' ].transform( sum ) ) ) - .groupby( [ 'stratum_num' , 'age' ] ) - .apply( lambda x: x[ 'weight_age' ].sum( ) / x[ 'weight_stratum' ].sum ( ) ) - .reset_index( name = 'weight_stratum_proportion' ) -) - - - .count_variable( contrasts = [ 'stratum_num' , 'age' , 'length_bin' , 'sex' ] , - variable = 'weight' , - fun = 'sum' ) - .pipe( lambda df: df.assign( total = df.groupby( [ 'stratum_num' , 'sex' ] )[ 'count' ].transform( sum ) ) ) - .groupby( [ 'stratum_num' , 'age' , 'sex' ] ) - .apply( lambda x: ( x[ 'count' ] / x[ 'total' ] ).sum() ) - .reset_index( name = 'weight_sex_stratum_proportion' ) - .assign( sex = lambda x: np.where( x[ 'sex' ] == 1 , 'male' , np.where( x[ 'sex' ] == 2 , 'female' , 'unsexed' ) ) ) -) - - -age_sex_weight_proportions = ( - specimen_df_copy - .dropna( how = 'any' ) - .bin_variable( bin_values = length_intervals , - bin_variable = 'length' ) - .count_variable( contrasts = [ 'stratum_num' , 'age' , 'length_bin' , 'sex' ] , - variable = 'weight' , - fun = 'sum' ) - .pipe( lambda df: df.assign( total = df.groupby( [ 'stratum_num' , 'sex' ] )[ 'count' ].transform( sum ) ) ) - .groupby( [ 'stratum_num' , 'age' , 'sex' ] ) - .apply( lambda x: ( x[ 'count' ] / x[ 'total' ] ).sum() ) - .reset_index( name = 'weight_sex_stratum_proportion' ) - .assign( sex = lambda x: np.where( x[ 'sex' ] == 1 , 'male' , np.where( x[ 'sex' ] == 2 , 'female' , 'unsexed' ) ) ) -) - -### Add these dataframes to the appropriate data attribute -self.biology[ 'weight' ].update( { - 'sex_stratified': { - 'weight_proportions': age_sex_weight_proportions , - } , - 'age_stratified': { - 'age_1_excluded': { - 'number_proportions': age_2_and_over_proportions , - 'weight_proportions': age_2_and_over_weight_proportions , - } , - 'age_1_included': { - 'number_proportions': age_proportions , - 'weight_proportions': age_weight_proportions - } - } -} ) \ No newline at end of file From 3724e25ddffe6291f60dd785f9d0e167e5617e51 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Tue, 26 Mar 2024 11:13:41 -0700 Subject: [PATCH 13/51] Rename `test_strata_mean_sigma_bs` test --- echopop/tests/{test_strata_mean_sigma_bs.py => test_acoustics.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename echopop/tests/{test_strata_mean_sigma_bs.py => test_acoustics.py} (100%) diff --git a/echopop/tests/test_strata_mean_sigma_bs.py b/echopop/tests/test_acoustics.py similarity index 100% rename from echopop/tests/test_strata_mean_sigma_bs.py rename to echopop/tests/test_acoustics.py From 1c12fedf8d19952421fa091427a1e4fa0e0e2e28 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Tue, 26 Mar 2024 11:33:18 -0700 Subject: [PATCH 14/51] Small changes to `krige` method (bugs) --- echopop/survey.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/echopop/survey.py b/echopop/survey.py index 8b896b3e..7bc749bc 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -1177,7 +1177,6 @@ def standardize_coordinates( self , ) def krige( self , - species_id , variable: str = 'B_a_adult' ): """ Interpolates biomass data using ordinary kriging @@ -1245,6 +1244,7 @@ def krige( self , { 'kriged_biomass_df': kriged_dataframe } + ) ### TODO: This should be refactored out as an external function ###### rather than a Survey-class method. From 6daa8a8ad228a850585bd5d6088c4f22e3abe30f Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Tue, 26 Mar 2024 12:08:44 -0700 Subject: [PATCH 15/51] Add `filter_species` function for biodata --- echopop/computation/biology.py | 24 ++++++++++++++++++++++++ echopop/survey.py | 21 +++++++++++---------- 2 files changed, 35 insertions(+), 10 deletions(-) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index d50f9678..2c58b682 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -1,8 +1,32 @@ import numpy as np import pandas as pd +from typing import Union, List from ..computation.spatial import correct_transect_intervals from ..computation.operations import group_merge +def filter_species( dataframe_list: Union[List[pd.DataFrame], pd.DataFrame] , + species_id: np.float64 ): + """ + Filter species in one or multiple biological datasets + + Parameters + ---------- + dataframe_list: Union[List[pd.DataFrame], pd.DataFrame] + A list of dataframes or a single dataframe containing biological data/measurements + species_id: np.float64 + Numeric code representing a particular species of interest + """ + + ### If a single dataframe, convert to a list + if isinstance( dataframe_list , pd.DataFrame ): + dataframe_list = [ dataframe_list ] + + ### Filter out the species-of-interest + filtered_dataframes = tuple( df[ df.species_id == species_id ] for df in dataframe_list ) + + ### Return output + return filtered_dataframes + def index_sex_weight_proportions( biology_dict: dict ): """ Generate dataframe containing sex-stratified weight proportions diff --git a/echopop/survey.py b/echopop/survey.py index 7bc749bc..d15bea41 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -12,7 +12,7 @@ from .computation.spatial import calculate_transect_distance , transform_geometry from .computation.statistics import stratified_transect_statistic from .computation.kriging_methods import kriging_interpolation -from .computation.biology import index_sex_weight_proportions , index_transect_age_sex_proportions +from .computation.biology import index_sex_weight_proportions , index_transect_age_sex_proportions , filter_species ### !!! TODO : This is a temporary import call -- this will need to be changed to # the correct relative structure (i.e. '.utils.data_structure_utils' instead of @@ -1263,16 +1263,17 @@ def apportion_kriged_biomass( self , for aged and unaged fish, total biomass inferred from the biological and kriged bioamss estimates, the coefficient of variation within each cell, and georeferenced biomass estimates for specific paired values of sex - and age-class. + and age-class. Binned length data (`length_df`) represent sexed, but + unaged fish measured at Station 1. Specimen data (`specimen_data`) represen + sexed and aged fish measured at Station 2. Total haul weights (`catch_df`) + represent the bulk weights of unaged and unsexed fish measured in Station 1. """ - ### Length data (Station 1 - Unaged - Sexxed ) - length_df_copy = self.biology[ 'length_df' ].copy( ).pipe( lambda df: df.loc[ df.species_id == species_id ] ) - - ### Length-weight data (Station 2 - Aged - Sexed) - specimen_df_copy = self.biology[ 'specimen_df' ].copy( ).pipe( lambda df: df.loc[ df.species_id == species_id ] ) - - ### Haul data - haul_catch_df = self.biology[ 'catch_df' ].copy( ).pipe( lambda df: df.loc[ df.species_id == species_id ] ) + + ### Import biological data and filter out non-target species + length_spp , specimen_spp , haul_spp = filter_species( [ self.biology[ 'length_df' ] , + self.biology[ 'specimen_df' ] , + self.biology[ 'catch_df' ] ] , + species_id ) ### Import length bins & intervals length_intervals = self.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] From d82014eb046a8f4e23fef5e3dc5a82ade4c23d7c Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Tue, 26 Mar 2024 12:11:59 -0700 Subject: [PATCH 16/51] General changes removing lambda functions - This pertains to the kriging apportionment operation --- echopop/survey.py | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/echopop/survey.py b/echopop/survey.py index d15bea41..0d75b137 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -1275,18 +1275,16 @@ def apportion_kriged_biomass( self , self.biology[ 'catch_df' ] ] , species_id ) - ### Import length bins & intervals + ### Import discrete distribution bins + # ---- Length length_intervals = self.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] - ### Age bins + # ---- Age age_intervals = self.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] - ### Process `haul_catch_df` - # Remove haul numbers not found within `length_df_copy` - haul_catch_filtered = ( - haul_catch_df - .loc[ lambda x: x.haul_num.isin( length_df_copy.haul_num ) ] - ) + ### Process `haul_spp` + # Remove haul numbers not found within `length_spp` + haul_spp_matched = haul_spp[ haul_spp.haul_num.isin( length_spp.haul_num ) ] ### Calculate summed weights weight_strata , weight_strata_station , weight_length_age_sex_stratum = sum_strata_length_age_sex_weight( haul_catch_filtered , From fd738a188ce31bd0d9156d3adad7be888e60a66c Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Tue, 26 Mar 2024 13:20:35 -0700 Subject: [PATCH 17/51] Refactor stratum weights and age-len proportions --- echopop/computation/biology.py | 129 +++++++++++++++++++++------------ echopop/survey.py | 13 ++-- 2 files changed, 90 insertions(+), 52 deletions(-) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index 2c58b682..1dcac9da 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -128,66 +128,101 @@ def sum_strata_weight( haul_data: pd.DataFrame , Dataframe containing specimen weights """ - ### Calculate station weights -- stratified by strata - weight_strata_station = pd.concat( - [ - # Haul data - haul_data - .groupby( [ 'stratum_num' ] ) - .apply( lambda df: pd.Series( { 'stratum_weight': df.haul_weight.sum( ) } ) ) - .reset_index( ) - .assign( station = 1 ) , - # Specimen data - specimen_data - .groupby( [ 'stratum_num' ] ) - .apply( lambda df: pd.Series( { 'stratum_weight': df.weight.sum( ) } ) ) - .reset_index() - .assign( station = 2 ) - ] + ### Process biological datasets + # ---- Process haul data ( Station 2 - unsexed - unaged ) + haul_strata_weight = ( + haul_data + .groupby( 'stratum_num' )[ 'haul_weight' ] + .sum( ) + .to_frame( 'stratum_weight' ) + .reset_index( ) + .assign( group = 'unaged' ) + ) + + # ---- Process specimen data ( Station 1 - sexed - aged ) + specimen_strata_weight = ( + specimen_data + .groupby( 'stratum_num' )[ 'weight' ] + .sum( ) + .to_frame( 'stratum_weight' ) + .reset_index( ) + .assign( group = 'aged' ) ) + ### Merge the two dataframes + # Yields the summed weights per stratum and station (or aged/unaged designation) + weight_strata_aged_unaged = pd.concat( [ haul_strata_weight , specimen_strata_weight ] ) + ### Sum weights for each stratum weight_strata = ( - weight_strata_station - .groupby( [ 'stratum_num' ] ) - .apply( lambda x: pd.Series( { 'weight_stratum_total': x.stratum_weight.sum( ) } ) ) - .reset_index( ) - ) + weight_strata_aged_unaged + .groupby( "stratum_num" )[ "stratum_weight" ] + .sum( ) + .to_frame( 'weight_stratum_all' ) + .reset_index( ) + ) ### Carriage return - return weight_strata_station , weight_strata + return weight_strata_aged_unaged , weight_strata -def sum_strata_length_age_sex_weight( haul_data: pd.DataFrame , - specimen_data: pd.DataFrame , - length_intervals: np.ndarray , - age_intervals: np.ndarray ): - - ### Sum weights from each station and strata - weight_strata_station , weight_strata = sum_strata_weight( haul_data , - specimen_data ) - - ### Calculate summed length-age-sex weight bins across strata - weight_length_age_sex_stratum = ( +def compute_aged_weight_proportions( specimen_data: pd.DataFrame , + length_intervals: np.ndarray , + age_intervals: np.ndarray ): + """ + Calculate length-age binned weight proportions + + Parameters + ---------- + specimen_data: pd.DataFrame + Dataframe containing specimen weights + length_intervals: np.ndarray + Array containing length bins/intervals + age_intervals: np.ndarray + Array containing age bins/intervals + """ + ### Process the specimen data + specimen_binned = ( specimen_data + # ---- Drop unaged fish + # !!! TODO: pending what FEAT says, weights associated with + # missing ages should be added into the 'unaged' category. + # This would further mean apportioning these into `weight_strata_aged_uanged` .dropna( how = 'any' ) - .loc[ lambda x: x.sex != 'unsexed' ] - .pipe( lambda df: pd.concat( [ df , - df.copy( ).assign( sex = 'all' ) ] ) ) + # ---- Remove unsexed fish + .loc[ lambda df: df.sex != 'unsexed' ] + # ---- Bin length + .bin_variable( length_intervals , 'length' ) + # ---- Age bin .bin_variable( age_intervals , 'age' ) - .bin_variable( length_intervals , 'length' ) - .groupby( [ 'stratum_num' , 'age_bin' , 'length_bin' , 'sex' ] ) - .apply( lambda df: pd.Series( { 'summed_weight_all': df.weight.sum( ) , - 'summed_weight_adult': df.loc[ df.age > 1 ].weight.sum( ) } ) ) + ) + + ### Sum weights within each length and age bin for each sex within each stratum + specimen_binned_weight = ( + specimen_binned + # ---- Group weight summations across distributions of length and age + # ---- for each sex within each stratum + .groupby( [ 'stratum_num' , 'sex' , 'length_bin' , 'age_bin' ] ) + # ---- Sum the weights + .apply( lambda df: pd.Series( { 'weight_all': df.weight.sum( ) , + 'weight_adult': df.loc[ df.age > 1 ].weight.sum( ) } ) ) + # ---- Fill empty/non-existent values with 0's + .fillna( 0 ) .reset_index( ) - .replace( np.nan , 0 ) - .assign( total_weight_sex_all = lambda df: df.groupby( [ 'stratum_num' , 'sex' ] )[ 'summed_weight_all' ].transform( sum ) , - total_weight_sex_adult = lambda df: df.groupby( [ 'stratum_num' , 'sex' ] )[ 'summed_weight_adult' ].transform( sum ) , - proportion_weight_all = lambda df: df.summed_weight_all / df.total_weight_sex_all , - proportion_weight_adult = lambda df: df.summed_weight_adult / df.total_weight_sex_adult ) ) - ### Carriage return - return weight_strata , weight_strata_station , weight_length_age_sex_stratum + ### Calculate the relative weight proportions of each length-age bin for each sex within each stratum + proportions_weight_length_age_sex = ( + specimen_binned_weight + # ---- Calculate total sex-specific weights for each stratum + .assign( total_weight_sex_all = lambda df: df.groupby( [ 'stratum_num' , 'sex' ] )[ 'weight_all' ].transform( sum ) , + total_weight_sex_adult = lambda df: df.groupby( [ 'stratum_num' , 'sex' ] )[ 'weight_adult' ].transform( sum ) ) + # ---- Calculate the weight proportions + .assign( proportion_weight_sex_all = lambda df: df.weight_all / df.total_weight_sex_all , + proportion_weight_sex_adult = lambda df: df.weight_adult / df.total_weight_sex_adult ) + ) + + ### Return output + return proportions_weight_length_age_sex def normalize_length_age_sex_weight_proportions( weight_length_age_sex_stratum: pd.DataFrame , weight_strata: pd.DataFrame ): diff --git a/echopop/survey.py b/echopop/survey.py index 0d75b137..8e1649bb 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -12,7 +12,8 @@ from .computation.spatial import calculate_transect_distance , transform_geometry from .computation.statistics import stratified_transect_statistic from .computation.kriging_methods import kriging_interpolation -from .computation.biology import index_sex_weight_proportions , index_transect_age_sex_proportions , filter_species +from .computation.biology import index_sex_weight_proportions , index_transect_age_sex_proportions , filter_species +from .computation.biology import sum_strata_weight , compute_aged_weight_proportions ### !!! TODO : This is a temporary import call -- this will need to be changed to # the correct relative structure (i.e. '.utils.data_structure_utils' instead of @@ -1286,11 +1287,13 @@ def apportion_kriged_biomass( self , # Remove haul numbers not found within `length_spp` haul_spp_matched = haul_spp[ haul_spp.haul_num.isin( length_spp.haul_num ) ] + ### Sum weights for aged/unaged and all within each stratum + weight_strata_aged_unaged , weight_strata = sum_strata_weight( haul_spp_matched , + specimen_spp ) ### Calculate summed weights - weight_strata , weight_strata_station , weight_length_age_sex_stratum = sum_strata_length_age_sex_weight( haul_catch_filtered , - specimen_df_copy , - length_intervals , - age_intervals ) + proportions_weight_length_age_sex = compute_aged_weight_proportions( specimen_spp , + length_intervals , + age_intervals ) ### Normalize the age-length-sex indexed proportions dist_weight_sum = normalize_length_age_sex_weight_proportions( weight_length_age_sex_stratum , From 8f8e91d85adbeca751ca0edb6341d2a559cf4187 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Tue, 26 Mar 2024 13:49:10 -0700 Subject: [PATCH 18/51] Refactor `distribute_aged_weight_proportions` func --- echopop/computation/biology.py | 52 +++++++++++++++++++++++----------- echopop/survey.py | 12 ++++---- 2 files changed, 43 insertions(+), 21 deletions(-) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index 1dcac9da..2f4cb9aa 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -180,6 +180,7 @@ def compute_aged_weight_proportions( specimen_data: pd.DataFrame , age_intervals: np.ndarray Array containing age bins/intervals """ + ### Process the specimen data specimen_binned = ( specimen_data @@ -216,7 +217,7 @@ def compute_aged_weight_proportions( specimen_data: pd.DataFrame , # ---- Calculate total sex-specific weights for each stratum .assign( total_weight_sex_all = lambda df: df.groupby( [ 'stratum_num' , 'sex' ] )[ 'weight_all' ].transform( sum ) , total_weight_sex_adult = lambda df: df.groupby( [ 'stratum_num' , 'sex' ] )[ 'weight_adult' ].transform( sum ) ) - # ---- Calculate the weight proportions + # ---- Calculate the weight proportions within each sex: from Matlab --> Len_age_key_wgt_*n .assign( proportion_weight_sex_all = lambda df: df.weight_all / df.total_weight_sex_all , proportion_weight_sex_adult = lambda df: df.weight_adult / df.total_weight_sex_adult ) ) @@ -224,22 +225,41 @@ def compute_aged_weight_proportions( specimen_data: pd.DataFrame , ### Return output return proportions_weight_length_age_sex -def normalize_length_age_sex_weight_proportions( weight_length_age_sex_stratum: pd.DataFrame , - weight_strata: pd.DataFrame ): - - ### Normalize the age-length-sex indexed proportions - dist_weight_sum = ( - weight_length_age_sex_stratum - .merge( weight_strata , on = [ 'stratum_num' ] ) - .groupby( [ 'stratum_num' , 'sex' ] ) - .apply( lambda df: pd.Series( { - 'proportion_normalized': ( df.proportion_weight_all * ( df.summed_weight_all / df.weight_stratum_total ).sum( ) ).sum( ) - } ) ) - .reset_index( ) - ) +def distribute_aged_weight_proportions( proportions_weight_length_age_sex: pd.DataFrame , + weight_strata: pd.DataFrame ): + """ + Distribute overall weight proportions across each sex and age/length bins - ### Carriage return - return dist_weight_sum + Parameters + ---------- + proportions_weight_length_age_sex: pd.DataFrame + Dataframe containing sexed weight proportions distributed across + each age and length bin + weight_strata: pd.DataFrame + Dataframe contained summed weights of both aged and unaged fish + """ + + ### Normalize the age-length-sex proportions of aged fish + # ---- Distribute the proportions calculated for age-length-sex + # ---- binned weights from aged fish and recalculate the proportions + # ---- relative to the total weights including both aged and unaged + # ---- fish + distributed_aged_weight_proportions = ( + proportions_weight_length_age_sex + # ---- Merge with summed weights for each stratum + .merge( weight_strata , on = [ 'stratum_num' ] ) + # ---- Calculate the relative weight proportion of aged fish of each sex + # ---- relative to the total weight of each stratum: from Matlab --> Len_Age_*_wgt_proportion + .assign( proportion_weight_all = lambda df: df.weight_all / df.weight_stratum_all , + proportion_weight_adult = lambda df: df.weight_adult / df.weight_stratum_all ) + # ---- Normalize the proportions by multiplying the proportions within each sex + # ---- across the proportions relative to the total weights (i.e. aged + unaged), not just each sex + .assign( normalized_proportion_weight_sex_all = lambda df: df.proportion_weight_sex_all * df.proportion_weight_all , + normalized_proportion_weight_sex_adult = lambda df: df.proportion_weight_sex_adult * df.proportion_weight_adult ) + ) + + ### Return output + return distributed_aged_weight_proportions def calculate_aged_proportions( weight_length_age_sex_stratum: pd.DataFrame , weight_strata: pd.DataFrame ): diff --git a/echopop/survey.py b/echopop/survey.py index 8e1649bb..68025d00 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -13,7 +13,7 @@ from .computation.statistics import stratified_transect_statistic from .computation.kriging_methods import kriging_interpolation from .computation.biology import index_sex_weight_proportions , index_transect_age_sex_proportions , filter_species -from .computation.biology import sum_strata_weight , compute_aged_weight_proportions +from .computation.biology import sum_strata_weight , compute_aged_weight_proportions , distribute_aged_weight_proportions ### !!! TODO : This is a temporary import call -- this will need to be changed to # the correct relative structure (i.e. '.utils.data_structure_utils' instead of @@ -1294,11 +1294,13 @@ def apportion_kriged_biomass( self , proportions_weight_length_age_sex = compute_aged_weight_proportions( specimen_spp , length_intervals , age_intervals ) - - ### Normalize the age-length-sex indexed proportions - dist_weight_sum = normalize_length_age_sex_weight_proportions( weight_length_age_sex_stratum , - weight_strata ) + ### Calculate weight proportions of aged fish distributed over age-length bins + # ---- for each sex within each stratum relative to the summed weights of each + # ---- stratum (i.e. aged + unaged weights) + distributed_aged_weight_proportions = distribute_aged_weight_proportions( proportions_weight_length_age_sex , + weight_strata ) + ### Calculate aged / unaged proportions aged_proportions = calculate_aged_proportions( weight_length_age_sex_stratum , weight_strata ) From 00e0e515838a7720469b11fe9b12123595279b6e Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Tue, 26 Mar 2024 14:29:24 -0700 Subject: [PATCH 19/51] Further updates to `distribute_aged_weight_prop.` - Also rearranges how `compute_summed_aged_proportions` is called to calculate the aged proportions since the sex proportions can be calculated in paralell with the total aged proportions in this step. --- echopop/computation/biology.py | 90 +++++++++++++++++++++++++++------- echopop/survey.py | 19 +++---- 2 files changed, 81 insertions(+), 28 deletions(-) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index 2f4cb9aa..d85b0b11 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -165,9 +165,9 @@ def sum_strata_weight( haul_data: pd.DataFrame , ### Carriage return return weight_strata_aged_unaged , weight_strata -def compute_aged_weight_proportions( specimen_data: pd.DataFrame , - length_intervals: np.ndarray , - age_intervals: np.ndarray ): +def compute_index_aged_weight_proportions( specimen_data: pd.DataFrame , + length_intervals: np.ndarray , + age_intervals: np.ndarray ): """ Calculate length-age binned weight proportions @@ -180,7 +180,7 @@ def compute_aged_weight_proportions( specimen_data: pd.DataFrame , age_intervals: np.ndarray Array containing age bins/intervals """ - + ### Process the specimen data specimen_binned = ( specimen_data @@ -225,10 +225,11 @@ def compute_aged_weight_proportions( specimen_data: pd.DataFrame , ### Return output return proportions_weight_length_age_sex -def distribute_aged_weight_proportions( proportions_weight_length_age_sex: pd.DataFrame , - weight_strata: pd.DataFrame ): +def compute_summed_aged_proportions( proportions_weight_length_age_sex: pd.DataFrame , + weight_strata: pd.DataFrame ): """ - Distribute overall weight proportions across each sex and age/length bins + Compute the aged proportions across all fish and specific sexes for each + length and age bin Parameters ---------- @@ -237,6 +238,58 @@ def distribute_aged_weight_proportions( proportions_weight_length_age_sex: pd.Da each age and length bin weight_strata: pd.DataFrame Dataframe contained summed weights of both aged and unaged fish + """ + + ### Calculate the aged proportions for each sex for all and just adult fish + aged_sex_proportions = ( + proportions_weight_length_age_sex + .groupby( [ 'stratum_num' , 'sex' ] ) + # ---- Sum all/adult fish weights for each sex and stratum + .apply( lambda df: pd.Series( { + 'weight_aged_sex_all': df.weight_all.sum( ) , + 'weight_aged_sex_adult': df.weight_adult.sum( ) + } ) ) + .reset_index( ) + # ---- Merge with summed weights for each stratum + .merge( weight_strata , on = [ 'stratum_num' ] ) + # ---- Calculate the relative weight proportion of aged fish of each sex + # ---- relative to the total weight of each stratum: from Matlab --> Len_Age_*_wgt_proportion + .assign( proportion_weight_all = lambda df: df.weight_aged_sex_all / df.weight_stratum_all , + proportion_weight_adult = lambda df: df.weight_aged_sex_adult / df.weight_stratum_all ) + # ---- Drop unused columns + .filter( regex = '^(?!weight_).*') + ) + + ### Calculate aged proportions + aged_proportions = ( + aged_sex_proportions + .groupby( [ 'stratum_num' ] ) + # ---- Sum proportions from each sex for each stratum + .agg( + proportion_weight_all = ( 'proportion_weight_all' , 'sum' ) , + proportion_weight_adult = ( 'proportion_weight_adult' , 'sum' ) + ) + .reset_index( ) + # ---- Drop unused columns + .filter( regex = '^(?!weight_).*') + ) + + ### Return output + return aged_sex_proportions , aged_proportions + + +def distribute_aged_weight_proportions( proportions_weight_length_age_sex: pd.DataFrame , + aged_sex_proportions: pd.DataFrame ): + """ + Distribute overall weight proportions across each sex and age/length bins + + Parameters + ---------- + proportions_weight_length_age_sex: pd.DataFrame + Dataframe containing sexed weight proportions distributed across + each age and length bin + aged_sex_proportions: pd.DataFrame + Dataframe contained weight proportions of sexed fish for aged fish """ ### Normalize the age-length-sex proportions of aged fish @@ -245,18 +298,17 @@ def distribute_aged_weight_proportions( proportions_weight_length_age_sex: pd.Da # ---- relative to the total weights including both aged and unaged # ---- fish distributed_aged_weight_proportions = ( - proportions_weight_length_age_sex - # ---- Merge with summed weights for each stratum - .merge( weight_strata , on = [ 'stratum_num' ] ) - # ---- Calculate the relative weight proportion of aged fish of each sex - # ---- relative to the total weight of each stratum: from Matlab --> Len_Age_*_wgt_proportion - .assign( proportion_weight_all = lambda df: df.weight_all / df.weight_stratum_all , - proportion_weight_adult = lambda df: df.weight_adult / df.weight_stratum_all ) - # ---- Normalize the proportions by multiplying the proportions within each sex - # ---- across the proportions relative to the total weights (i.e. aged + unaged), not just each sex - .assign( normalized_proportion_weight_sex_all = lambda df: df.proportion_weight_sex_all * df.proportion_weight_all , - normalized_proportion_weight_sex_adult = lambda df: df.proportion_weight_sex_adult * df.proportion_weight_adult ) - ) + proportions_weight_length_age_sex + # ---- Merge with sexed age proportions of all fish + .merge( aged_sex_proportions , on = [ 'stratum_num' , 'sex' ] ) + # ---- Normalize the proportions by multiplying the proportions within each sex + # ---- across the proportions relative to the total weights (i.e. aged + unaged), + # ---- not just each sex + .assign( normalized_proportion_weight_sex_all = lambda df: df.proportion_weight_sex_all * df.proportion_weight_all , + normalized_proportion_weight_sex_adult = lambda df: df.proportion_weight_sex_adult * df.proportion_weight_adult ) + # ---- Drop unused columns + .filter( regex = '^(?!weight_|total_weight_|proportion_).*' ) + ) ### Return output return distributed_aged_weight_proportions diff --git a/echopop/survey.py b/echopop/survey.py index 68025d00..da216c52 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -13,7 +13,7 @@ from .computation.statistics import stratified_transect_statistic from .computation.kriging_methods import kriging_interpolation from .computation.biology import index_sex_weight_proportions , index_transect_age_sex_proportions , filter_species -from .computation.biology import sum_strata_weight , compute_aged_weight_proportions , distribute_aged_weight_proportions +from .computation.biology import sum_strata_weight , compute_index_aged_weight_proportions , distribute_aged_weight_proportions ### !!! TODO : This is a temporary import call -- this will need to be changed to # the correct relative structure (i.e. '.utils.data_structure_utils' instead of @@ -1291,19 +1291,20 @@ def apportion_kriged_biomass( self , weight_strata_aged_unaged , weight_strata = sum_strata_weight( haul_spp_matched , specimen_spp ) ### Calculate summed weights - proportions_weight_length_age_sex = compute_aged_weight_proportions( specimen_spp , - length_intervals , - age_intervals ) + proportions_weight_length_age_sex = compute_index_aged_weight_proportions( specimen_spp , + length_intervals , + age_intervals ) + + ### Calculate the summed aged proportions for all aged fish, and for aged fish + # ---- belonging to each sex + aged_sex_proportions , aged_proportions = compute_summed_aged_proportions( proportions_weight_length_age_sex , + weight_strata ) ### Calculate weight proportions of aged fish distributed over age-length bins # ---- for each sex within each stratum relative to the summed weights of each # ---- stratum (i.e. aged + unaged weights) distributed_aged_weight_proportions = distribute_aged_weight_proportions( proportions_weight_length_age_sex , - weight_strata ) - - ### Calculate aged / unaged proportions - aged_proportions = calculate_aged_proportions( weight_length_age_sex_stratum , - weight_strata ) + aged_sex_proportions ) ### Calculate interpolated weights based on length bins for each sex per haul haul_sex_weights_normalized = normalize_haul_sex_weights( self.statistics[ 'length_weight' ][ 'length_weight_df' ] , From 33635259f6c1a70f02764849541f2c05be4a7f0e Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Tue, 26 Mar 2024 14:38:28 -0700 Subject: [PATCH 20/51] Update how proportion calculates handle `NaN` --- echopop/computation/biology.py | 4 ++++ echopop/survey.py | 1 + 2 files changed, 5 insertions(+) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index d85b0b11..fbbb11cc 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -220,6 +220,8 @@ def compute_index_aged_weight_proportions( specimen_data: pd.DataFrame , # ---- Calculate the weight proportions within each sex: from Matlab --> Len_age_key_wgt_*n .assign( proportion_weight_sex_all = lambda df: df.weight_all / df.total_weight_sex_all , proportion_weight_sex_adult = lambda df: df.weight_adult / df.total_weight_sex_adult ) + # ---- Fill empty/non-existent values with 0's + .fillna( 0 ) ) ### Return output @@ -256,6 +258,8 @@ def compute_summed_aged_proportions( proportions_weight_length_age_sex: pd.DataF # ---- relative to the total weight of each stratum: from Matlab --> Len_Age_*_wgt_proportion .assign( proportion_weight_all = lambda df: df.weight_aged_sex_all / df.weight_stratum_all , proportion_weight_adult = lambda df: df.weight_aged_sex_adult / df.weight_stratum_all ) + # ---- Fill empty/non-existent values with 0's + .fillna( 0 ) # ---- Drop unused columns .filter( regex = '^(?!weight_).*') ) diff --git a/echopop/survey.py b/echopop/survey.py index da216c52..18d8c0a2 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -14,6 +14,7 @@ from .computation.kriging_methods import kriging_interpolation from .computation.biology import index_sex_weight_proportions , index_transect_age_sex_proportions , filter_species from .computation.biology import sum_strata_weight , compute_index_aged_weight_proportions , distribute_aged_weight_proportions +from .computation.biology import compute_summed_aged_proportions ### !!! TODO : This is a temporary import call -- this will need to be changed to # the correct relative structure (i.e. '.utils.data_structure_utils' instead of From 21723254ed47a15688d08b83318194da4d2cb4e6 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Tue, 26 Mar 2024 16:44:03 -0700 Subject: [PATCH 21/51] Draft Matlab translation `thru unaged2age_mat` --- echopop/computation/biology.py | 12 +- echopop/survey.py | 229 ++++++++++++++++++++++++++------- 2 files changed, 189 insertions(+), 52 deletions(-) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index fbbb11cc..3011cb44 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -220,8 +220,15 @@ def compute_index_aged_weight_proportions( specimen_data: pd.DataFrame , # ---- Calculate the weight proportions within each sex: from Matlab --> Len_age_key_wgt_*n .assign( proportion_weight_sex_all = lambda df: df.weight_all / df.total_weight_sex_all , proportion_weight_sex_adult = lambda df: df.weight_adult / df.total_weight_sex_adult ) - # ---- Fill empty/non-existent values with 0's - .fillna( 0 ) + ) + + ### Fill empty/non-existent values with 0's + proportions_weight_length_age_sex[ 'proportion_weight_sex_all' ] = ( + proportions_weight_length_age_sex[ 'proportion_weight_sex_all' ].fillna( 0 ) + ) + + proportions_weight_length_age_sex[ 'proportion_weight_sex_adult' ] = ( + proportions_weight_length_age_sex[ 'proportion_weight_sex_adult' ].fillna( 0 ) ) ### Return output @@ -281,7 +288,6 @@ def compute_summed_aged_proportions( proportions_weight_length_age_sex: pd.DataF ### Return output return aged_sex_proportions , aged_proportions - def distribute_aged_weight_proportions( proportions_weight_length_age_sex: pd.DataFrame , aged_sex_proportions: pd.DataFrame ): """ diff --git a/echopop/survey.py b/echopop/survey.py index 18d8c0a2..ab6b52cf 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -1306,61 +1306,192 @@ def apportion_kriged_biomass( self , # ---- stratum (i.e. aged + unaged weights) distributed_aged_weight_proportions = distribute_aged_weight_proportions( proportions_weight_length_age_sex , aged_sex_proportions ) + + ### Distribute the biomass estimates for each transect interval across + # ---- the calculated proportions for age and length bins for each + # ---- sex among aged-only fish + aged_sexed_biomass = ( + # ---- Parse kriged biomass (areal density) values + self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] + .copy( ) + .merge( distributed_aged_weight_proportions , on = [ 'stratum_num' ] ) + # ---- Distribute biomass estimates for each sexed bin among aged fish + # !!! TODO: this current used `B_a_adult_mean` for both calculations + .assign( biomass_sexed_aged_all = lambda df: ( df.B_a_adult_mean * + df.cell_area_nmi2 * + df.normalized_proportion_weight_sex_all ) , + biomass_sexed_aged_adult = lambda df: ( df.B_a_adult_mean * + df.cell_area_nmi2 * + df.normalized_proportion_weight_sex_adult ) + ) + ) - ### Calculate interpolated weights based on length bins for each sex per haul - haul_sex_weights_normalized = normalize_haul_sex_weights( self.statistics[ 'length_weight' ][ 'length_weight_df' ] , - length_df_copy , - weight_strata , - weight_strata_station , - aged_proportions , - length_intervals ) - - ### Parse kriged biomass (areal density) values - kriged_results = self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] - - kriged_results = ( - kriged_results - .merge( dist_weight_sum , on = [ 'stratum_num' ] ) - .assign( biomass_aged = lambda df: df.B_a_adult_mean * df.proportion_normalized * df.cell_area_nmi2 ) - .merge( haul_sex_weights_normalized , on = [ 'stratum_num' , 'sex' ] ) - .assign( biomass_unaged = lambda df: df.B_a_adult_mean * df.proportion_normalized_station_1 * df.cell_area_nmi2 , - biomass_total = lambda df: df.biomass_aged + df.biomass_unaged ) + ### Sum biomass across all bins for aged biomass + total_aged_sexed_biomass = ( + aged_sexed_biomass + .groupby( [ 'length_bin' , 'age_bin' , 'sex' ] ) + .agg( total_sexed_aged_biomass_adult = ( 'biomass_sexed_aged_adult' , 'sum' ) ) + .reset_index( ) ) - ### Parse the average weight calculations - average_weight_df = ( - self.biology[ 'weight' ][ 'weight_strata_df' ] - .loc[ : , [ 'stratum_num' , 'average_weight_total' ] ] + ### Calculate number proportion + number_unaged_length_proportions = ( + length_df + .loc[ lambda df: df.group == 'sexed' ] + .bin_variable( length_intervals , 'length' ) + .groupby( [ 'stratum_num' , 'length_bin' ] ) + .agg( number_all = ( 'length_count' , 'sum' ) ) + .reset_index( ) + .assign( total_number_all = lambda df: df.groupby( [ 'stratum_num' ] )[ 'number_all' ].transform( sum ) , + proportion_number_all = lambda df: df.number_all / df.total_number_all ) ) - ### Merge with the kriged results - kriged_results = ( - kriged_results - .merge( average_weight_df , on = [ 'stratum_num' ] ) - .assign( N_adult_kriged = lambda df: df.B_adult_kriged / df.average_weight_total ) - .merge( self.acoustics[ 'sigma_bs' ][ 'strata_mean' ] , on = [ 'stratum_num' ] ) - .assign( NASC_kriged = lambda df: df.N_adult_kriged * df.sigma_bs_mean ) + w_ln_all_array = ( + number_unaged_length_proportions + .merge( length_weight_df.loc[ lambda df: df.sex == 'all' ] , on = [ 'length_bin' ] ) + .assign( proportion_weight_all = lambda df: df.weight_modeled * df.proportion_number_all ) ) - ### Calculate grid CV - kriged_results = grid_cv( self.biology[ 'population' ][ 'areal_density' ][ 'biomass_density_df' ] , - kriged_results , - self.statistics[ 'kriging' ][ 'model_config' ] ) - - ### Add biomass values to all ages for kriged biomass results - kriged_results_output = ( - kriged_results - .merge( self.biology[ 'weight' ][ 'proportions' ][ 'sex_age_weight_proportions_df' ] , - on = [ 'stratum_num' , 'sex' ] ) - .assign( biomass_sex_age = lambda x: x.weight_sex_proportion_all * x.biomass_total ) - .loc[ : , [ 'centroid_latitude' , 'centroid_longitude' , 'stratum_num' , - 'cell_area_nmi2' , 'sex' , 'age' , 'biomass_unaged' , 'biomass_aged' , - 'biomass_total' , 'biomass_adult_cell_CV' , 'biomass_sex_age' ] ] + w_ln_array_sum = ( + w_ln_all_array + .groupby( [ 'stratum_num' ] ) + .agg( summed_proportion_weight_all = ( 'proportion_weight_all' , 'sum' ) ) + .reset_index( ) + ) + + w_ln_all_N = ( + w_ln_all_array + .merge( w_ln_array_sum , on = [ 'stratum_num' ] ) + .assign( weight_per_length_dist = lambda df: df.proportion_weight_all / df.summed_proportion_weight_all ) + .loc[ : , [ 'stratum_num' , 'length_bin' , 'weight_modeled' , 'proportion_weight_all' , + 'summed_proportion_weight_all' , 'weight_per_length_dist' ] ] + ) + + sex_wgt = ( + length_df + .loc[ lambda df: df.group == 'sexed' ] + .bin_variable( length_intervals , 'length' ) + .merge( length_weight_df.loc[ lambda df: df.sex != 'all' ] , on = [ 'sex' , 'length_bin' ] ) + .assign( length_bin_value = lambda x: x[ 'length_bin' ].apply( lambda y: y.mid ) ) + .groupby( [ 'stratum_num' , 'sex' ] ) + .apply( lambda df: pd.Series( { + 'weight_interp': ( np.interp( df.length , + df.length_bin_value , + df.weight_modeled ) * df.length_count ).sum( ) + } ) ) + .reset_index( ) + ) + + norm_sex_wgt = ( + weight_strata_aged_unaged + .loc[ lambda df: df.group == 'unaged' ] + .merge( sex_wgt , on = [ 'stratum_num' ] ) + .assign( summed_sex_wgt = lambda df: df.groupby( [ 'stratum_num' ] )[ 'weight_interp' ].transform( sum ) , + norm_sex_wgt = lambda df: df.stratum_weight * df.weight_interp / df.summed_sex_wgt ) + .assign( summed_norm_sex_wgt = lambda df: df.groupby( [ 'stratum_num' ] )[ 'norm_sex_wgt' ].transform( sum ) , + sex_proportion = lambda df: df.norm_sex_wgt / df.summed_norm_sex_wgt ) ) + + unaged_proportions = ( + aged_proportions + .assign( proportion_weight_all = lambda df: 1.0 - df.proportion_weight_all , + proportion_weight_adult = lambda df: 1.0 - df.proportion_weight_adult ) + ) + + unaged_sex_proportions = ( + norm_sex_wgt + .merge( unaged_proportions , on = [ 'stratum_num' ] ) + .assign( proportion_sexed_weight_all = lambda df: df.proportion_weight_all * df.sex_proportion , + proportion_sexed_weight_adult = lambda df: df.proportion_weight_adult * df.sex_proportion ) + .loc[ : , [ 'stratum_num' , 'sex' , 'proportion_sexed_weight_all' , 'proportion_sexed_weight_adult' ] ] + ) + + unaged_sexed_biomass = ( + # ---- Parse kriged biomass (areal density) values + self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] + .copy( ) + .merge( w_ln_all_N , on = [ 'stratum_num' ] ) + .merge( unaged_sex_proportions , on = [ 'stratum_num' ] ) + .assign( biomass_sexed_unaged_all = lambda df: ( df.B_a_adult_mean * + df.cell_area_nmi2 * + df.weight_per_length_dist * + df.proportion_sexed_weight_all ) , + biomass_sexed_unaged_adult = lambda df: ( df.B_a_adult_mean * + df.cell_area_nmi2 * + df.weight_per_length_dist * + df.proportion_sexed_weight_adult ) + ) + ) + + total_unaged_sexed_biomass = ( + unaged_sexed_biomass + .groupby( [ 'length_bin' , 'sex' ] ) + .agg( total_sexed_unaged_biomass_adult = ( 'biomass_sexed_unaged_adult' , 'sum' ) ) + .reset_index( ) + ) + + unaged2aged_mat = ( + total_aged_sexed_biomass + .merge( total_unaged_sexed_biomass , on = [ 'length_bin' , 'sex' ] ) + .assign( Summed_Wgt_Len_age = lambda df: df.groupby( [ 'sex' , 'length_bin' ] )[ 'total_sexed_aged_biomass_adult' ].transform( sum ) + df.total_sexed_unaged_biomass_adult ) + .assign( Final_wgt_len_age = lambda df: df.total_sexed_unaged_biomass_adult * df.total_sexed_aged_biomass_adult / df.Summed_Wgt_Len_age ) + .replace( np.nan , 0 ) + ) + + # ### Calculate interpolated weights based on length bins for each sex per haul + # haul_sex_weights_normalized = normalize_haul_sex_weights( self.statistics[ 'length_weight' ][ 'length_weight_df' ] , + # length_df_copy , + # weight_strata , + # weight_strata_station , + # aged_proportions , + # length_intervals ) + + # ### Parse kriged biomass (areal density) values + # kriged_results = self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] + + # kriged_results = ( + # kriged_results + # .merge( dist_weight_sum , on = [ 'stratum_num' ] ) + # .assign( biomass_aged = lambda df: df.B_a_adult_mean * df.proportion_normalized * df.cell_area_nmi2 ) + # .merge( haul_sex_weights_normalized , on = [ 'stratum_num' , 'sex' ] ) + # .assign( biomass_unaged = lambda df: df.B_a_adult_mean * df.proportion_normalized_station_1 * df.cell_area_nmi2 , + # biomass_total = lambda df: df.biomass_aged + df.biomass_unaged ) + # ) + + # ### Parse the average weight calculations + # average_weight_df = ( + # self.biology[ 'weight' ][ 'weight_strata_df' ] + # .loc[ : , [ 'stratum_num' , 'average_weight_total' ] ] + # ) + + # ### Merge with the kriged results + # kriged_results = ( + # kriged_results + # .merge( average_weight_df , on = [ 'stratum_num' ] ) + # .assign( N_adult_kriged = lambda df: df.B_adult_kriged / df.average_weight_total ) + # .merge( self.acoustics[ 'sigma_bs' ][ 'strata_mean' ] , on = [ 'stratum_num' ] ) + # .assign( NASC_kriged = lambda df: df.N_adult_kriged * df.sigma_bs_mean ) + # ) + + # ### Calculate grid CV + # kriged_results = grid_cv( self.biology[ 'population' ][ 'areal_density' ][ 'biomass_density_df' ] , + # kriged_results , + # self.statistics[ 'kriging' ][ 'model_config' ] ) - ### Assign results to an attribute - self.statistics[ 'kriging' ].update( - { - 'apportioned_kriged_biomass_df': kriged_results_output , - } - ) \ No newline at end of file + # ### Add biomass values to all ages for kriged biomass results + # kriged_results_output = ( + # kriged_results + # .merge( self.biology[ 'weight' ][ 'proportions' ][ 'sex_age_weight_proportions_df' ] , + # on = [ 'stratum_num' , 'sex' ] ) + # .assign( biomass_sex_age = lambda x: x.weight_sex_proportion_all * x.biomass_total ) + # .loc[ : , [ 'centroid_latitude' , 'centroid_longitude' , 'stratum_num' , + # 'cell_area_nmi2' , 'sex' , 'age' , 'biomass_unaged' , 'biomass_aged' , + # 'biomass_total' , 'biomass_adult_cell_CV' , 'biomass_sex_age' ] ] + # ) + + # ### Assign results to an attribute + # self.statistics[ 'kriging' ].update( + # { + # 'apportioned_kriged_biomass_df': kriged_results_output , + # } + # ) \ No newline at end of file From b834712237aaa1ed943058cd1fb0db6c1eb91d30 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Tue, 26 Mar 2024 16:45:07 -0700 Subject: [PATCH 22/51] Amendment to unaged proportion drafted code --- echopop/survey.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/echopop/survey.py b/echopop/survey.py index ab6b52cf..b221d003 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -1335,15 +1335,17 @@ def apportion_kriged_biomass( self , ) ### Calculate number proportion + length_weight_df = self.statistics[ 'length_weight' ][ 'length_weight_df' ] + number_unaged_length_proportions = ( - length_df - .loc[ lambda df: df.group == 'sexed' ] - .bin_variable( length_intervals , 'length' ) - .groupby( [ 'stratum_num' , 'length_bin' ] ) - .agg( number_all = ( 'length_count' , 'sum' ) ) - .reset_index( ) - .assign( total_number_all = lambda df: df.groupby( [ 'stratum_num' ] )[ 'number_all' ].transform( sum ) , - proportion_number_all = lambda df: df.number_all / df.total_number_all ) + length_spp + .loc[ lambda df: df.group == 'sexed' ] + .bin_variable( length_intervals , 'length' ) + .groupby( [ 'stratum_num' , 'length_bin' ] ) + .agg( number_all = ( 'length_count' , 'sum' ) ) + .reset_index( ) + .assign( total_number_all = lambda df: df.groupby( [ 'stratum_num' ] )[ 'number_all' ].transform( sum ) , + proportion_number_all = lambda df: df.number_all / df.total_number_all ) ) w_ln_all_array = ( From cf78a55a58dd6c793cc2f8638880eab6d511d055 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Tue, 26 Mar 2024 17:16:04 -0700 Subject: [PATCH 23/51] Add test for `test_sum_strata_weight` --- .../test_kriging_biomass_apportionment.py | 62 +++++++++++++++++++ 1 file changed, 62 insertions(+) create mode 100644 echopop/tests/test_kriging_biomass_apportionment.py diff --git a/echopop/tests/test_kriging_biomass_apportionment.py b/echopop/tests/test_kriging_biomass_apportionment.py new file mode 100644 index 00000000..79a3d1cb --- /dev/null +++ b/echopop/tests/test_kriging_biomass_apportionment.py @@ -0,0 +1,62 @@ +import pandas as pd +import numpy as np +from echopop.computation.biology import sum_strata_weight + +def test_sum_strata_weight( mock_survey ): + + #### Pull in mock Survey object + objS = mock_survey + + ### Re-parameterize `specimen_df` with dummy data + objS.biology[ 'specimen_df' ] = pd.DataFrame( + { + 'stratum_num': np.repeat( [ 0 , 1 , 2 , 4 , 5 ] , 4 ) , + 'haul_num': np.repeat( [ 1 , 2 , 3 , 4 , 5 ] , 4 ) , + 'species_id': np.repeat( [ 19350 ] , 20 ) , + 'length': np.linspace( 10 , 100 , 20 ) , + 'weight': np.linspace( 1 , 5 , 20 ) , + } + ) + + ### Re-parameterize `length_df` with dummy data + objS.biology[ 'length_df' ] = pd.DataFrame( + { + 'stratum_num': np.repeat( [ 0 , 1 , 2 , 4 , 5 ] , 4 ) , + 'haul_num': np.repeat( [ 1 , 2 , 3 , 4 , 5 ] , 4 ) , + 'species_id': np.repeat( [ 19350 ] , 20 ) , + 'length': np.linspace( 10 , 100 , 20 ) , + 'length_count': np.linspace( 10 , 100 , 20 ) , + } + ) + + ### Re-parameterize `catch_df` with dummy data + objS.biology[ 'catch_df' ] = pd.DataFrame( + { + 'stratum_num': [ 0 , 1 , 2 , 4 , 5 ] , + 'haul_num': [ 1 , 2 , 3 , 4 , 5 ] , + 'haul_weight': [ 51.4 , 0.6 , 81.7 , 16.2 , 12.9 ] , + } + ) + + ### Evaluate object for later comparison + object_weight_strata_aged_unaged , object_weight_strata = sum_strata_weight( objS.biology[ 'catch_df' ] , + objS.biology[ 'specimen_df' ] ) + + #---------------------------------- + ### Run tests: `sum_strata_weight` + #---------------------------------- + ### Evaluate shape + # ---- `object_weight_strata` + assert object_weight_strata.shape == tuple( [ 5 , 2 ] ) + # ---- `object_weight_strata_aged_unaged` + assert object_weight_strata_aged_unaged.shape == tuple( [ 10 , 3 ] ) + + ### Evaluate value equality + # ---- `object_weight_strata` + check_values = np.array( [ 56.663158 , 9.231579 , 93.700000 , 31.568421 , 31.636842 ] ) + assert np.allclose( check_values , object_weight_strata.weight_stratum_all ) + + # ---- `object_weight_strata_aged_unaged` + check_values = np.array( [ 51.400000 , 0.600000 , 81.700000 , 16.200000 , 12.900000 , + 5.263158 , 8.631579 , 12.000000 , 15.368421 , 18.736842 ] ) + assert np.allclose( check_values , object_weight_strata_aged_unaged.stratum_weight ) \ No newline at end of file From f052a214c146f994e96fe89887c73c39f14c8faf Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Tue, 26 Mar 2024 17:47:03 -0700 Subject: [PATCH 24/51] Add test for `compute_summed_aged_proportions` --- .../test_kriging_biomass_apportionment.py | 103 +++++++++++++++++- 1 file changed, 100 insertions(+), 3 deletions(-) diff --git a/echopop/tests/test_kriging_biomass_apportionment.py b/echopop/tests/test_kriging_biomass_apportionment.py index 79a3d1cb..f94090a8 100644 --- a/echopop/tests/test_kriging_biomass_apportionment.py +++ b/echopop/tests/test_kriging_biomass_apportionment.py @@ -1,6 +1,6 @@ import pandas as pd import numpy as np -from echopop.computation.biology import sum_strata_weight +from echopop.computation.biology import sum_strata_weight , compute_index_aged_weight_proportions def test_sum_strata_weight( mock_survey ): @@ -38,7 +38,7 @@ def test_sum_strata_weight( mock_survey ): } ) - ### Evaluate object for later comparison + ### Evaluate object for later comparison object_weight_strata_aged_unaged , object_weight_strata = sum_strata_weight( objS.biology[ 'catch_df' ] , objS.biology[ 'specimen_df' ] ) @@ -59,4 +59,101 @@ def test_sum_strata_weight( mock_survey ): # ---- `object_weight_strata_aged_unaged` check_values = np.array( [ 51.400000 , 0.600000 , 81.700000 , 16.200000 , 12.900000 , 5.263158 , 8.631579 , 12.000000 , 15.368421 , 18.736842 ] ) - assert np.allclose( check_values , object_weight_strata_aged_unaged.stratum_weight ) \ No newline at end of file + assert np.allclose( check_values , object_weight_strata_aged_unaged.stratum_weight ) + +def test_compute_summed_aged_proportions( mock_survey ): + #### Pull in mock Survey object + objS = mock_survey + + ### Re-parameterize `specimen_df` with dummy data + objS.biology[ 'specimen_df' ] = pd.DataFrame( + { + 'stratum_num': np.repeat( [ 0 , 1 , 2 , 4 , 5 ] , 4 ) , + 'sex': np.tile( [ 'male' , 'female' ] , 10 ) , + 'haul_num': np.repeat( [ 1 , 2 , 3 , 4 , 5 ] , 4 ) , + 'species_id': np.repeat( [ 19350 ] , 20 ) , + 'length': np.linspace( 10 , 20 , 20 ) , + 'weight': np.linspace( 1 , 5 , 20 ) , + 'age': [ 1 , 1 , 1 , 2 , 2 , 2 , 2 , 3 , 3 , 3 , + 4 , 4 , 5 , 5 , 5 , 6 , 6 , 6 , 7 , 8 ] + } + ) + + ### Length interval + objS.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] = np.linspace( 9 , 21 , 5 ) + + ### Age interval + objS.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] = ( + np.array( [ 0.5 , 1.5 , 2.5 , 3.5 , 4.5 , 5.5 , 6.5 , 7.5 , 8.5 ] ) + ) + + ### Evaluate object for later comparison + obj_props_wgt_len_age_sex = compute_index_aged_weight_proportions( objS.biology[ 'specimen_df' ] , + objS.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] , + objS.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] ) + + #---------------------------------- + ### Run tests: `compute_index_aged_weight_proportions` + #---------------------------------- + ### Process the specimen data + specimen_binned = ( + objS.biology[ 'specimen_df' ] + # ---- Drop unaged fish + # !!! TODO: pending what FEAT says, weights associated with + # missing ages should be added into the 'unaged' category. + # This would further mean apportioning these into `weight_strata_aged_uanged` + .dropna( how = 'any' ) + # ---- Remove unsexed fish + .loc[ lambda df: df.sex != 'unsexed' ] + # ---- Bin length + .bin_variable( objS.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] , 'length' ) + # ---- Age bin + .bin_variable( objS.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] , 'age' ) + ) + + ### Sum weights within each length and age bin for each sex within each stratum + specimen_binned_weight = ( + specimen_binned + # ---- Group weight summations across distributions of length and age + # ---- for each sex within each stratum + .groupby( [ 'stratum_num' , 'sex' , 'length_bin' , 'age_bin' ] ) + # ---- Sum the weights + .apply( lambda df: pd.Series( { 'weight_all': df.weight.sum( ) , + 'weight_adult': df.loc[ df.age > 1 ].weight.sum( ) } ) ) + # ---- Fill empty/non-existent values with 0's + .fillna( 0 ) + .reset_index( ) + ) + + ### Calculate the relative weight proportions of each length-age bin for each sex within each stratum + proportions_weight_length_age_sex = ( + specimen_binned_weight + # ---- Calculate total sex-specific weights for each stratum + .assign( total_weight_sex_all = lambda df: df.groupby( [ 'stratum_num' , 'sex' ] )[ 'weight_all' ].transform( sum ) , + total_weight_sex_adult = lambda df: df.groupby( [ 'stratum_num' , 'sex' ] )[ 'weight_adult' ].transform( sum ) ) + # ---- Calculate the weight proportions within each sex: from Matlab --> Len_age_key_wgt_*n + .assign( proportion_weight_sex_all = lambda df: df.weight_all / df.total_weight_sex_all , + proportion_weight_sex_adult = lambda df: df.weight_adult / df.total_weight_sex_adult ) + ) + + ### Fill empty/non-existent values with 0's + proportions_weight_length_age_sex[ 'proportion_weight_sex_all' ] = ( + proportions_weight_length_age_sex[ 'proportion_weight_sex_all' ].fillna( 0 ) + ) + + proportions_weight_length_age_sex[ 'proportion_weight_sex_adult' ] = ( + proportions_weight_length_age_sex[ 'proportion_weight_sex_adult' ].fillna( 0 ) + ) + + ### Check shape + test_dimensions = ( + len( np.unique( specimen_binned.age_bin ) ) * + len( np.unique( specimen_binned.length_bin ) ) * + len( np.unique( specimen_binned.sex ) ) * + len( np.unique( specimen_binned.stratum_num ) ) + ) + + assert obj_props_wgt_len_age_sex.shape == tuple( [ test_dimensions , 10 ] ) + + ### Check values + assert obj_props_wgt_len_age_sex.equals( proportions_weight_length_age_sex ) \ No newline at end of file From 05dc77d41c9a745673dc7dc4967aa9d60257478c Mon Sep 17 00:00:00 2001 From: Brandyn Lucca Date: Thu, 28 Mar 2024 09:58:47 -0700 Subject: [PATCH 25/51] Changes to `compute_index_aged_weight_proportions` - Includes: ** Direct changes to `compute_index_aged_weight_proportions` although some changes are TBD pending further discussion ** Vectorizing the `bin_variable` output ** A temporary dataframe called `full_biological_indices` within the `apportion_kriged_biomass` function that is primarily used for debugging purposes to validate dimensions of distributed dataframes --- echopop/computation/biology.py | 32 ++++++++++++++++--------------- echopop/computation/operations.py | 21 +++++++++++++++----- echopop/survey.py | 17 +++++++++++++++- 3 files changed, 49 insertions(+), 21 deletions(-) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index 3011cb44..e56c7a79 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -182,15 +182,18 @@ def compute_index_aged_weight_proportions( specimen_data: pd.DataFrame , """ ### Process the specimen data - specimen_binned = ( - specimen_data - # ---- Drop unaged fish - # !!! TODO: pending what FEAT says, weights associated with - # missing ages should be added into the 'unaged' category. - # This would further mean apportioning these into `weight_strata_aged_uanged` - .dropna( how = 'any' ) - # ---- Remove unsexed fish - .loc[ lambda df: df.sex != 'unsexed' ] + # ---- Drop unaged and unsexed fish + # ==== !!! TODO: pending what FEAT says, weights associated with + # ==== missing ages should be added into the 'unaged' category. + # ==== This would further mean apportioning these into `weight_strata_aged_uanged` + specimen_data_filtered = ( + specimen_data[ specimen_data.sex != 'unsexed' ] + .dropna( how = 'any' , subset = 'age' ) + ) + + # ---- Bin length and age measurements + specimen_data_filtered = ( + specimen_data_filtered # ---- Bin length .bin_variable( length_intervals , 'length' ) # ---- Age bin @@ -199,10 +202,9 @@ def compute_index_aged_weight_proportions( specimen_data: pd.DataFrame , ### Sum weights within each length and age bin for each sex within each stratum specimen_binned_weight = ( - specimen_binned - # ---- Group weight summations across distributions of length and age - # ---- for each sex within each stratum - .groupby( [ 'stratum_num' , 'sex' , 'length_bin' , 'age_bin' ] ) + specimen_data_filtered + # ---- Group weight summations across stratum/species/sex/length/age + .groupby( [ 'stratum_num' , 'species_id' , 'sex' , 'length_bin' , 'age_bin' ] ) # ---- Sum the weights .apply( lambda df: pd.Series( { 'weight_all': df.weight.sum( ) , 'weight_adult': df.loc[ df.age > 1 ].weight.sum( ) } ) ) @@ -215,8 +217,8 @@ def compute_index_aged_weight_proportions( specimen_data: pd.DataFrame , proportions_weight_length_age_sex = ( specimen_binned_weight # ---- Calculate total sex-specific weights for each stratum - .assign( total_weight_sex_all = lambda df: df.groupby( [ 'stratum_num' , 'sex' ] )[ 'weight_all' ].transform( sum ) , - total_weight_sex_adult = lambda df: df.groupby( [ 'stratum_num' , 'sex' ] )[ 'weight_adult' ].transform( sum ) ) + .assign( total_weight_sex_all = lambda df: df.groupby( [ 'stratum_num' , 'species_id' , 'sex' ] )[ 'weight_all' ].transform( sum ) , + total_weight_sex_adult = lambda df: df.groupby( [ 'stratum_num' , 'species_id' , 'sex' ] )[ 'weight_adult' ].transform( sum ) ) # ---- Calculate the weight proportions within each sex: from Matlab --> Len_age_key_wgt_*n .assign( proportion_weight_sex_all = lambda df: df.weight_all / df.total_weight_sex_all , proportion_weight_sex_adult = lambda df: df.weight_adult / df.total_weight_sex_adult ) diff --git a/echopop/computation/operations.py b/echopop/computation/operations.py index badcf46a..9a3185d4 100644 --- a/echopop/computation/operations.py +++ b/echopop/computation/operations.py @@ -4,7 +4,18 @@ from typing import Union , List from typing import Callable from functools import reduce - + +### !!! TODO: Assess whether `bin_variable` is necessary +# ==== Currently works with dataframes as a whole, but +# ==== it is probably more efficient/sensible to refit +# ==== this function to create an array of bin intervals that +# ==== can then be used to define a dataframe column. It may +# ==== also make more sense to simply remove this function entirely +# ==== since it's not particularly onerous to directly use +# ==== `pd.cut(...)`. If this function were to stay, it'd probably +# ==== be usefule to enable multiple `bin_values` and `bin_variable` +# ==== inputs so that the function doesn't need to be repeated for +# ==== multiple bin variables like age and length @patch_method_to_DataFrame( pd.DataFrame ) def bin_variable( dataframe: pd.DataFrame , bin_values: np.ndarray , @@ -27,10 +38,10 @@ def bin_variable( dataframe: pd.DataFrame , This will add a column to defined dataframes that groups length and age values into each bin that is explicitly defined in the function arguments """ - return ( - dataframe # input dataframe - .assign( **{f'{bin_variable}_bin': lambda x: pd.cut(x[bin_variable], bin_values)} ) # assign bin - ) + + dataframe[ f'{ bin_variable }_bin' ] = pd.cut( dataframe[ bin_variable ] , bin_values ) + + return dataframe @patch_method_to_DataFrame( pd.DataFrame ) def bin_stats( dataframe: pd.DataFrame , diff --git a/echopop/survey.py b/echopop/survey.py index b221d003..39c90a6c 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -2,6 +2,7 @@ from pathlib import Path import pandas as pd import numpy as np +from itertools import product import copy from .core import CONFIG_MAP, LAYER_NAME_MAP ### !!! TODO : This is a temporary import call -- this will need to be changed to @@ -1279,11 +1280,25 @@ def apportion_kriged_biomass( self , ### Import discrete distribution bins # ---- Length + # length_bins = self.biology[ 'distributions' ][ 'length' ][ 'length_bins_arr' ] length_intervals = self.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] # ---- Age + # age_bins = self.biology[ 'distributions' ][ 'age' ][ 'age_bins_arr' ] age_intervals = self.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] - + + # ---- Construct the complete dataframe containing all possible + # ---- strata, sexes, lengths, and ages + # full_biological_indices = pd.DataFrame( + # list( product( np.unique( specimen_spp.stratum_num ) , + # np.unique( specimen_spp.species_id ) , + # np.unique( specimen_spp.sex ) , + # pd.cut( length_bins , length_intervals ) , + # pd.cut( age_bins , age_intervals ) ) ) , + # columns = [ 'stratum_num' , 'species_id' , 'sex' , + # 'length_bin' , 'age_bin' ] + # ) + ### Process `haul_spp` # Remove haul numbers not found within `length_spp` haul_spp_matched = haul_spp[ haul_spp.haul_num.isin( length_spp.haul_num ) ] From 8f596015e018827e9e92623a42479a4aedd25ffd Mon Sep 17 00:00:00 2001 From: Brandyn Lucca Date: Thu, 28 Mar 2024 10:13:04 -0700 Subject: [PATCH 26/51] Vectorize apportionment of `aged_sexed_biomass` --- echopop/survey.py | 22 +++++++++++++--------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/echopop/survey.py b/echopop/survey.py index 39c90a6c..e2f6470a 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -1330,15 +1330,19 @@ def apportion_kriged_biomass( self , self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] .copy( ) .merge( distributed_aged_weight_proportions , on = [ 'stratum_num' ] ) - # ---- Distribute biomass estimates for each sexed bin among aged fish - # !!! TODO: this current used `B_a_adult_mean` for both calculations - .assign( biomass_sexed_aged_all = lambda df: ( df.B_a_adult_mean * - df.cell_area_nmi2 * - df.normalized_proportion_weight_sex_all ) , - biomass_sexed_aged_adult = lambda df: ( df.B_a_adult_mean * - df.cell_area_nmi2 * - df.normalized_proportion_weight_sex_adult ) - ) + ) + + # ---- Distribute biomass estimates for each sexed bin among aged fish + # ==== !!! TODO: this current used `B_a_adult_mean` for both calculations + aged_sexed_biomass[ 'biomass_sexed_aged_all' ] = ( + aged_sexed_biomass.B_a_adult_mean + * aged_sexed_biomass.cell_area_nmi2 + * aged_sexed_biomass.normalized_proportion_weight_sex_all + ) + aged_sexed_biomass[ 'biomass_sexed_aged_adult' ] = ( + aged_sexed_biomass.B_a_adult_mean + * aged_sexed_biomass.cell_area_nmi2 + * aged_sexed_biomass.normalized_proportion_weight_sex_adult ) ### Sum biomass across all bins for aged biomass From 2ea574e6b2c38fc0d9291150c522a0f5f0f4a3e5 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca Date: Thu, 28 Mar 2024 13:51:39 -0700 Subject: [PATCH 27/51] `test_compute_index_aged_weight_proportions` edits - This includes: ** Reformatted tests for evaluating dataframe dimensions ** Dimensions of test dataframe were heavily reduced ** New hard-coded test dataframe w/ equality testing ** New datatype (`dtype`) test --- .../test_kriging_biomass_apportionment.py | 131 ++++++++---------- 1 file changed, 59 insertions(+), 72 deletions(-) diff --git a/echopop/tests/test_kriging_biomass_apportionment.py b/echopop/tests/test_kriging_biomass_apportionment.py index f94090a8..46cae45c 100644 --- a/echopop/tests/test_kriging_biomass_apportionment.py +++ b/echopop/tests/test_kriging_biomass_apportionment.py @@ -61,99 +61,86 @@ def test_sum_strata_weight( mock_survey ): 5.263158 , 8.631579 , 12.000000 , 15.368421 , 18.736842 ] ) assert np.allclose( check_values , object_weight_strata_aged_unaged.stratum_weight ) -def test_compute_summed_aged_proportions( mock_survey ): +def test_compute_index_aged_weight_proportions( mock_survey ): #### Pull in mock Survey object objS = mock_survey ### Re-parameterize `specimen_df` with dummy data objS.biology[ 'specimen_df' ] = pd.DataFrame( { - 'stratum_num': np.repeat( [ 0 , 1 , 2 , 4 , 5 ] , 4 ) , - 'sex': np.tile( [ 'male' , 'female' ] , 10 ) , - 'haul_num': np.repeat( [ 1 , 2 , 3 , 4 , 5 ] , 4 ) , - 'species_id': np.repeat( [ 19350 ] , 20 ) , - 'length': np.linspace( 10 , 20 , 20 ) , - 'weight': np.linspace( 1 , 5 , 20 ) , - 'age': [ 1 , 1 , 1 , 2 , 2 , 2 , 2 , 3 , 3 , 3 , - 4 , 4 , 5 , 5 , 5 , 6 , 6 , 6 , 7 , 8 ] + 'stratum_num': np.repeat( [ 0 , 1 ] , 4 ) , + 'sex': np.tile( [ 'male' , 'female' ] , 4 ) , + 'haul_num': np.tile( [ 1 , 2 ] , 4 ) , + 'species_id': np.repeat( [ 19350 ] , 8 ) , + 'length': [ 10 , 20 , 19 , 11 , 12 , 18 , 17 , 13 ] , + 'weight': np.linspace( 1 , 5 , 8 ) , + 'age': [ 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 ] } ) ### Length interval - objS.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] = np.linspace( 9 , 21 , 5 ) + objS.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] = np.linspace( 9 , 21 , 3 ) ### Age interval - objS.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] = ( - np.array( [ 0.5 , 1.5 , 2.5 , 3.5 , 4.5 , 5.5 , 6.5 , 7.5 , 8.5 ] ) - ) + objS.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] = np.array( [ 0.5 , 1.5 , 2.5 ] ) ### Evaluate object for later comparison obj_props_wgt_len_age_sex = compute_index_aged_weight_proportions( objS.biology[ 'specimen_df' ] , objS.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] , objS.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] ) + ###-------------------------------- + ### Expected outcomes + ###-------------------------------- + # ---- Expected dimensions of `obj_props_wgt_len_age_sex` + expected_dimensions = tuple( [ 16 , 11 ] ) + # ---- Expected dataframe output + expected_output = pd.DataFrame( { + 'stratum_num': np.repeat( [ 0 , 1 ] , 8 ).astype( np.int64 ) , + 'species_id': np.repeat( 19350 , 16 ).astype( np.int64 ) , + 'sex': np.tile( [ 'female' , 'male' ] , 8 ).astype( object ) , + 'length_bin': pd.IntervalIndex.from_arrays( np.tile( [9.0 , 15.0 ] , 8 ) , + np.tile( [ 15.0 , 21.0 ] , 8 ) , + closed = 'right' ) , + 'age_bin': pd.IntervalIndex.from_arrays( np.tile( [ 0.5, 1.5 ] , 8 ) , + np.tile( [ 1.5 , 2.5 ] , 8 ) , + closed = 'right' ) , + 'weight_all': [ 0.0, 2.714286, 0.0, 1.571429, 1.0, 0.0, 2.142857, 0.0, 0.0, + 5.0, 0.0, 3.857143, 3.285714, 0.0, 4.428571, 0.0 ] , + 'weight_adult': [ 0.0, 2.714286, 0.0, 1.571429, 0.0, 0.0, 0.0, 0.0, 0.0, 5.0, + 0.0, 3.857143, 0.0, 0.0, 0.0, 0.0 ] , + 'total_weight_sex_all': [ 4.285714, 4.285714, 4.285714, 4.285714, 3.142857, + 3.142857, 3.142857, 3.142857, 8.857143, 8.857143, + 8.857143, 8.857143, 7.714286, 7.714286, 7.714286, + 7.714286 ] , + 'total_weight_sex_adult': [ 4.285714, 4.285714, 4.285714, 4.285714, 0.0, 0.0, + 0.0, 0.0, 8.857143, 8.857143, 8.857143, 8.857143, + 0.0, 0.0, 0.0, 0.0 ] , + 'proportion_weight_sex_all': [ 0.0, 0.633333, 0.0, 0.366667, 0.318182, 0.0, + 0.681818, 0.0, 0.0, 0.564516, 0.0, 0.435484, + 0.425926, 0.0, 0.574074, 0.0 ] , + 'proportion_weight_sex_adult': [ 0.0, 0.633333, 0.0, 0.366667, 0.0, 0.0, 0.0, + 0.0, 0.0, 0.564516, 0.0, 0.435484, 0.0, 0.0, + 0.0, 0.0 ] + } ) + expected_output[ 'length_bin' ] = pd.IntervalIndex( expected_output[ 'length_bin' ] ) + expected_output[ 'length_bin' ] = pd.Categorical( expected_output[ 'length_bin' ] , + categories = expected_output[ 'length_bin' ].unique( ) , + ordered = True ) + expected_output[ 'age_bin' ] = pd.IntervalIndex( expected_output[ 'age_bin' ] ) + expected_output[ 'age_bin' ] = pd.Categorical( expected_output[ 'age_bin' ] , + categories = expected_output[ 'age_bin' ].unique( ) , + ordered=True) + #---------------------------------- ### Run tests: `compute_index_aged_weight_proportions` #---------------------------------- ### Process the specimen data - specimen_binned = ( - objS.biology[ 'specimen_df' ] - # ---- Drop unaged fish - # !!! TODO: pending what FEAT says, weights associated with - # missing ages should be added into the 'unaged' category. - # This would further mean apportioning these into `weight_strata_aged_uanged` - .dropna( how = 'any' ) - # ---- Remove unsexed fish - .loc[ lambda df: df.sex != 'unsexed' ] - # ---- Bin length - .bin_variable( objS.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] , 'length' ) - # ---- Age bin - .bin_variable( objS.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] , 'age' ) - ) - - ### Sum weights within each length and age bin for each sex within each stratum - specimen_binned_weight = ( - specimen_binned - # ---- Group weight summations across distributions of length and age - # ---- for each sex within each stratum - .groupby( [ 'stratum_num' , 'sex' , 'length_bin' , 'age_bin' ] ) - # ---- Sum the weights - .apply( lambda df: pd.Series( { 'weight_all': df.weight.sum( ) , - 'weight_adult': df.loc[ df.age > 1 ].weight.sum( ) } ) ) - # ---- Fill empty/non-existent values with 0's - .fillna( 0 ) - .reset_index( ) - ) - - ### Calculate the relative weight proportions of each length-age bin for each sex within each stratum - proportions_weight_length_age_sex = ( - specimen_binned_weight - # ---- Calculate total sex-specific weights for each stratum - .assign( total_weight_sex_all = lambda df: df.groupby( [ 'stratum_num' , 'sex' ] )[ 'weight_all' ].transform( sum ) , - total_weight_sex_adult = lambda df: df.groupby( [ 'stratum_num' , 'sex' ] )[ 'weight_adult' ].transform( sum ) ) - # ---- Calculate the weight proportions within each sex: from Matlab --> Len_age_key_wgt_*n - .assign( proportion_weight_sex_all = lambda df: df.weight_all / df.total_weight_sex_all , - proportion_weight_sex_adult = lambda df: df.weight_adult / df.total_weight_sex_adult ) - ) - - ### Fill empty/non-existent values with 0's - proportions_weight_length_age_sex[ 'proportion_weight_sex_all' ] = ( - proportions_weight_length_age_sex[ 'proportion_weight_sex_all' ].fillna( 0 ) - ) - - proportions_weight_length_age_sex[ 'proportion_weight_sex_adult' ] = ( - proportions_weight_length_age_sex[ 'proportion_weight_sex_adult' ].fillna( 0 ) - ) - ### Check shape - test_dimensions = ( - len( np.unique( specimen_binned.age_bin ) ) * - len( np.unique( specimen_binned.length_bin ) ) * - len( np.unique( specimen_binned.sex ) ) * - len( np.unique( specimen_binned.stratum_num ) ) - ) + assert obj_props_wgt_len_age_sex.shape == expected_dimensions - assert obj_props_wgt_len_age_sex.shape == tuple( [ test_dimensions , 10 ] ) - - ### Check values - assert obj_props_wgt_len_age_sex.equals( proportions_weight_length_age_sex ) \ No newline at end of file + ### Check datatypes + assert np.all( obj_props_wgt_len_age_sex.dtypes == expected_output.dtypes ) + + ### Check data value equality + assert np.all( obj_props_wgt_len_age_sex.dtypes == expected_output.dtypes ) \ No newline at end of file From d9998bac31b224650cfcbae69c0ab78b44f0f90d Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Fri, 29 Mar 2024 11:45:52 -0700 Subject: [PATCH 28/51] `compute_summed_aged_proportions` edits - Includes: ** Some of the dataframe pipes have been broken down to include a few more vectorized operations ** Fixed an odd indexing bug where some `stratum_num`x`sex` pairs were conspicuously missing --- echopop/computation/biology.py | 55 ++++++++++++++++++++-------------- 1 file changed, 32 insertions(+), 23 deletions(-) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index e56c7a79..627f8b17 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -251,40 +251,49 @@ def compute_summed_aged_proportions( proportions_weight_length_age_sex: pd.DataF Dataframe contained summed weights of both aged and unaged fish """ - ### Calculate the aged proportions for each sex for all and just adult fish - aged_sex_proportions = ( + ### Caclulate the summed weights for all and adult fish + # ---- Group by `stratum_num` and `sex` and sum + # ---- Also initializes the correct shape for the output dataframe + aged_sex_weights = ( proportions_weight_length_age_sex .groupby( [ 'stratum_num' , 'sex' ] ) - # ---- Sum all/adult fish weights for each sex and stratum - .apply( lambda df: pd.Series( { - 'weight_aged_sex_all': df.weight_all.sum( ) , - 'weight_aged_sex_adult': df.weight_adult.sum( ) - } ) ) + .agg( + weight_aged_sex_all = ( 'weight_all' , 'sum' ) , + weight_aged_sex_adult = ( 'weight_adult' , 'sum' ) + ) .reset_index( ) - # ---- Merge with summed weights for each stratum - .merge( weight_strata , on = [ 'stratum_num' ] ) - # ---- Calculate the relative weight proportion of aged fish of each sex - # ---- relative to the total weight of each stratum: from Matlab --> Len_Age_*_wgt_proportion - .assign( proportion_weight_all = lambda df: df.weight_aged_sex_all / df.weight_stratum_all , - proportion_weight_adult = lambda df: df.weight_aged_sex_adult / df.weight_stratum_all ) - # ---- Fill empty/non-existent values with 0's - .fillna( 0 ) - # ---- Drop unused columns - .filter( regex = '^(?!weight_).*') ) + ### Calculate the weight proportions + # ---- Merge `aged_sex_weights` with `weight_strata` to get strata weights + aged_sex_proportions = pd.merge( aged_sex_weights , + weight_strata , + on = [ 'stratum_num' ] , + how = 'left' ) + + # ---- Proportions for all fish + aged_sex_proportions[ 'proportion_weight_all' ] = ( + aged_sex_proportions.weight_aged_sex_all / aged_sex_proportions.weight_stratum_all + ) + + # ---- Proportions for adult fish + aged_sex_proportions[ 'proportion_weight_adult' ] = ( + aged_sex_proportions.weight_aged_sex_adult / aged_sex_proportions.weight_stratum_all + ) + + # ---- Fill empty/NaN values with 0's and drop unused columns + aged_sex_proportions = aged_sex_proportions.fillna( 0 ).filter( regex = '^(?!weight_).*' ) + ### Calculate aged proportions aged_proportions = ( aged_sex_proportions .groupby( [ 'stratum_num' ] ) # ---- Sum proportions from each sex for each stratum - .agg( - proportion_weight_all = ( 'proportion_weight_all' , 'sum' ) , - proportion_weight_adult = ( 'proportion_weight_adult' , 'sum' ) - ) + .agg( { + 'proportion_weight_all': 'sum' , + 'proportion_weight_adult': 'sum' + } ) .reset_index( ) - # ---- Drop unused columns - .filter( regex = '^(?!weight_).*') ) ### Return output From adaac91288f483d515a59772f4118c25356243a5 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Fri, 29 Mar 2024 12:15:31 -0700 Subject: [PATCH 29/51] Added test for `compute_summed_aged_proportions` - Includes: ** Create of `test_compute_summed_aged_proportions` for testing `compute_summed_aged_proportions` --- .../test_kriging_biomass_apportionment.py | 70 ++++++++++++++++++- 1 file changed, 69 insertions(+), 1 deletion(-) diff --git a/echopop/tests/test_kriging_biomass_apportionment.py b/echopop/tests/test_kriging_biomass_apportionment.py index 46cae45c..10c736b9 100644 --- a/echopop/tests/test_kriging_biomass_apportionment.py +++ b/echopop/tests/test_kriging_biomass_apportionment.py @@ -143,4 +143,72 @@ def test_compute_index_aged_weight_proportions( mock_survey ): assert np.all( obj_props_wgt_len_age_sex.dtypes == expected_output.dtypes ) ### Check data value equality - assert np.all( obj_props_wgt_len_age_sex.dtypes == expected_output.dtypes ) \ No newline at end of file + assert np.all( obj_props_wgt_len_age_sex.dtypes == expected_output.dtypes ) + +def test_compute_summed_aged_proportions( mock_survey ): + #### Pull in mock Survey object + objS = mock_survey + + ### Mock data for `proportions_weight_length_age_sex` + test_proportions_weight_length_age_sex = pd.DataFrame( + { + 'stratum_num': np.repeat( [ 0.0 , 1.0 ] , 4 ) , + 'sex': np.tile( [ 'male' , 'female' ] , 4 ) , + 'weight_all': [ 1.0 , 3.0 , 2.0 , 4.0 , 0.0 , 6.0 , 3.0 , 3.0 ] , + 'weight_adult': [ 0.0 , 1.5, 2.0 , 4.0 , 0.0 , 4.0 , 3.0 , 3.0 ] + } + ) + + ### Mock data for `weight_strata` + test_weight_strata = pd.DataFrame( + { + 'stratum_num': [ 0.0 , 1.0 ] , + 'weight_stratum_all': [ 10.0 , 100.0 ] + } + ) + + ### Evaluate for later comparison + eval_aged_sex_proportions , eval_aged_proportions = compute_summed_aged_proportions( test_proportions_weight_length_age_sex , + test_weight_strata ) + ###-------------------------------- + ### Expected outcomes + ###-------------------------------- + ### `eval_aged_sex_proportions` + # ---- Expected dimensions + expected_dimensions_aged_sex_proportions = tuple( [ 4 , 4 ] ) + # ---- Expected dataframe output + expected_output_aged_sex_proportions = pd.DataFrame( { + 'stratum_num': np.repeat( [ 0.0 , 1.0 ] , 2 ) , + 'sex': np.tile( [ 'female' , 'male' ] , 2 ) , + 'proportion_weight_all': [ 0.70 , 0.30 , 0.09 , 0.03 ] , + 'proportion_weight_adult': [ 0.55 , 0.20 , 0.07 , 0.03 ] + } ) + + ### `eval_aged_proportions` + # ---- Expected dimensions + expected_dimensions_aged_proportions = tuple( [ 2 , 3 ] ) + # ---- Expected dataframe output + expected_output_aged_proportions = pd.DataFrame( { + 'stratum_num': [ 0.0 , 1.0 ] , + 'proportion_weight_all': [ 1.00 , 0.12 ] , + 'proportion_weight_adult': [ 0.75 , 0.10 ] + } ) + + #---------------------------------- + ### Run tests: `compute_index_aged_weight_proportions` + #---------------------------------- + ### `eval_aged_sex_proportions` + # ---- Shape + assert eval_aged_sex_proportions.shape == expected_dimensions_aged_sex_proportions + # ---- Datatypes + assert np.all( eval_aged_sex_proportions.dtypes == expected_output_aged_sex_proportions.dtypes ) + # ---- Dataframe equality + assert np.all( eval_aged_sex_proportions == expected_output_aged_sex_proportions ) + + ### `eval_aged_proportions` + # ---- Shape + assert eval_aged_proportions.shape == expected_dimensions_aged_proportions + # ---- Datatypes + assert np.all( eval_aged_proportions.dtypes == expected_output_aged_proportions.dtypes ) + # ---- Dataframe equality + assert np.all( eval_aged_proportions == expected_output_aged_proportions ) \ No newline at end of file From 0ed7c534e1a2c60595b74e3f6b29d0f8f5a31fa8 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Fri, 29 Mar 2024 12:16:50 -0700 Subject: [PATCH 30/51] Edit to `test_compute_summed_aged_proportions` - Includes: ** Removal of `mock_survey` as an argument since this is not a class method ** Added correct module imports --- echopop/tests/test_kriging_biomass_apportionment.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/echopop/tests/test_kriging_biomass_apportionment.py b/echopop/tests/test_kriging_biomass_apportionment.py index 10c736b9..bccb96df 100644 --- a/echopop/tests/test_kriging_biomass_apportionment.py +++ b/echopop/tests/test_kriging_biomass_apportionment.py @@ -1,6 +1,6 @@ import pandas as pd import numpy as np -from echopop.computation.biology import sum_strata_weight , compute_index_aged_weight_proportions +from echopop.computation.biology import sum_strata_weight , compute_index_aged_weight_proportions , compute_summed_aged_proportions def test_sum_strata_weight( mock_survey ): @@ -145,10 +145,7 @@ def test_compute_index_aged_weight_proportions( mock_survey ): ### Check data value equality assert np.all( obj_props_wgt_len_age_sex.dtypes == expected_output.dtypes ) -def test_compute_summed_aged_proportions( mock_survey ): - #### Pull in mock Survey object - objS = mock_survey - +def test_compute_summed_aged_proportions( ): ### Mock data for `proportions_weight_length_age_sex` test_proportions_weight_length_age_sex = pd.DataFrame( { From d7768c571678c44da0ff0bc708ef41608bb35e6e Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Fri, 29 Mar 2024 12:52:25 -0700 Subject: [PATCH 31/51] Edits/test: `distribute_aged_weight_proportions` - Includes: ** Vectorized portions of `distribute_aged_weight_proportions` ** Created test for `distribute_aged_weight_proportions` (`test_distribute_aged_weight_proportions`) ** Fixed bugs contained within `compute_summed_aged_proportions` and `test_compute_summed_aged_proportions` where the column names were not compatible with how `distribute_aged_weight_proportions` operates --- echopop/computation/biology.py | 37 +++++++------ .../test_kriging_biomass_apportionment.py | 52 ++++++++++++++++++- 2 files changed, 69 insertions(+), 20 deletions(-) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index 627f8b17..e8706247 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -313,26 +313,25 @@ def distribute_aged_weight_proportions( proportions_weight_length_age_sex: pd.Da Dataframe contained weight proportions of sexed fish for aged fish """ - ### Normalize the age-length-sex proportions of aged fish - # ---- Distribute the proportions calculated for age-length-sex - # ---- binned weights from aged fish and recalculate the proportions - # ---- relative to the total weights including both aged and unaged - # ---- fish - distributed_aged_weight_proportions = ( - proportions_weight_length_age_sex - # ---- Merge with sexed age proportions of all fish - .merge( aged_sex_proportions , on = [ 'stratum_num' , 'sex' ] ) - # ---- Normalize the proportions by multiplying the proportions within each sex - # ---- across the proportions relative to the total weights (i.e. aged + unaged), - # ---- not just each sex - .assign( normalized_proportion_weight_sex_all = lambda df: df.proportion_weight_sex_all * df.proportion_weight_all , - normalized_proportion_weight_sex_adult = lambda df: df.proportion_weight_sex_adult * df.proportion_weight_adult ) - # ---- Drop unused columns - .filter( regex = '^(?!weight_|total_weight_|proportion_).*' ) - ) + ### Calculate the normalized age-length-sex aged fish weight proportions + # ---- Merge `proportions_weight_length_age_sex` with `aged_sex_proportions` + distributed_aged_weight_proportions = pd.merge( proportions_weight_length_age_sex , + aged_sex_proportions , + on = [ 'stratum_num' , 'sex' ] , + how = 'left' ) - ### Return output - return distributed_aged_weight_proportions + # ---- Normalized weight proportions for all fish + distributed_aged_weight_proportions[ 'normalized_proportion_weight_all' ] = ( + distributed_aged_weight_proportions.proportion_weight_sex_all * distributed_aged_weight_proportions.proportion_weight_all + ) + + # ---- Normalized weight proportions for jus adult fish + distributed_aged_weight_proportions[ 'normalized_proportion_weight_adult' ] = ( + distributed_aged_weight_proportions.proportion_weight_sex_all * distributed_aged_weight_proportions.proportion_weight_adult + ) + + # ---- Remove unnecessary columns and return the dataframe + return distributed_aged_weight_proportions.filter( regex = '^(?!weight_|total_weight_|proportion_).*' ) def calculate_aged_proportions( weight_length_age_sex_stratum: pd.DataFrame , weight_strata: pd.DataFrame ): diff --git a/echopop/tests/test_kriging_biomass_apportionment.py b/echopop/tests/test_kriging_biomass_apportionment.py index bccb96df..e9d2eb8f 100644 --- a/echopop/tests/test_kriging_biomass_apportionment.py +++ b/echopop/tests/test_kriging_biomass_apportionment.py @@ -146,6 +146,7 @@ def test_compute_index_aged_weight_proportions( mock_survey ): assert np.all( obj_props_wgt_len_age_sex.dtypes == expected_output.dtypes ) def test_compute_summed_aged_proportions( ): + ### Mock data for `proportions_weight_length_age_sex` test_proportions_weight_length_age_sex = pd.DataFrame( { @@ -208,4 +209,53 @@ def test_compute_summed_aged_proportions( ): # ---- Datatypes assert np.all( eval_aged_proportions.dtypes == expected_output_aged_proportions.dtypes ) # ---- Dataframe equality - assert np.all( eval_aged_proportions == expected_output_aged_proportions ) \ No newline at end of file + assert np.all( eval_aged_proportions == expected_output_aged_proportions ) + +def test_distribute_aged_weight_proportions( ): + + ### Mock data for `proportions_weight_length_age_sex` + test_proportions_weight_length_age_sex = pd.DataFrame( + { + 'stratum_num': np.repeat( [ 0.0 , 1.0 ] , 2 ) , + 'sex': np.tile( [ 'female' , 'male' ] , 2 ) , + 'proportion_weight_sex_all': [ 0.50 , 0.50 , 0.25 , 0.50 ] , + 'proportion_weight_sex_adult': [ 0.50 , 0.25 , 0.00 , 0.25 ] + } + ) + + ### Mock data for `aged_sex_proportions` + test_aged_sex_proportions = pd.DataFrame( { + 'stratum_num': np.repeat( [ 0.0 , 1.0 ] , 2 ) , + 'sex': np.tile( [ 'female' , 'male' ] , 2 ) , + 'proportion_weight_all': [ 0.25 , 0.50 , 0.20 , 0.10 ] , + 'proportion_weight_adult': [ 0.20 , 0.30 , 0.10 , 0.10 ] + } ) + + ### Evaluate for later comparison + eval_distributed_aged_weight_proportions = distribute_aged_weight_proportions( test_proportions_weight_length_age_sex , + test_aged_sex_proportions ) + + ###-------------------------------- + ### Expected outcomes + ###-------------------------------- + ### `eval_aged_sex_proportions` + # ---- Expected dimensions + expected_dimensions_distributed_aged_weight_proportions = tuple( [ 4 , 4 ] ) + # ---- Expected dataframe output + expected_output_distributed_aged_weight_proportions = pd.DataFrame( { + 'stratum_num': np.repeat( [ 0.0 , 1.0 ] , 2 ) , + 'sex': np.tile( [ 'female' , 'male' ] , 2 ) , + 'normalized_proportion_weight_all': [ 0.125 , 0.250 , 0.050 , 0.050 ] , + 'normalized_proportion_weight_adult': [ 0.100 , 0.150 , 0.025 , 0.050 ] + } ) + + #---------------------------------- + ### Run tests: `compute_index_aged_weight_proportions` + #---------------------------------- + ### `eval_aged_sex_proportions` + # ---- Shape + assert eval_distributed_aged_weight_proportions.shape == expected_dimensions_distributed_aged_weight_proportions + # ---- Datatypes + assert np.all( eval_distributed_aged_weight_proportions.dtypes == expected_output_distributed_aged_weight_proportions.dtypes ) + # ---- Dataframe equality + eval_distributed_aged_weight_proportions.equals( expected_output_distributed_aged_weight_proportions ) \ No newline at end of file From 8a81f2588b7733824fc2ccc2ce5a9753d494966e Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Fri, 29 Mar 2024 12:58:43 -0700 Subject: [PATCH 32/51] Update to column names - Includes ** Changes to the column names referenced when calculating `biomass_sexed_aged_all` and `biomass_sexed_aged_adult` within the `aged_sexed_biomass` dataframe --- echopop/survey.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/echopop/survey.py b/echopop/survey.py index e2f6470a..c5e3374f 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -1337,12 +1337,12 @@ def apportion_kriged_biomass( self , aged_sexed_biomass[ 'biomass_sexed_aged_all' ] = ( aged_sexed_biomass.B_a_adult_mean * aged_sexed_biomass.cell_area_nmi2 - * aged_sexed_biomass.normalized_proportion_weight_sex_all + * aged_sexed_biomass.normalized_proportion_weight_all ) aged_sexed_biomass[ 'biomass_sexed_aged_adult' ] = ( aged_sexed_biomass.B_a_adult_mean * aged_sexed_biomass.cell_area_nmi2 - * aged_sexed_biomass.normalized_proportion_weight_sex_adult + * aged_sexed_biomass.normalized_proportion_weight_adult ) ### Sum biomass across all bins for aged biomass From 098237011d942486aa32ebc5b3c0da0afec35574 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Fri, 29 Mar 2024 14:14:32 -0700 Subject: [PATCH 33/51] Added `compute_index_unaged_number_proportions` - Includes ** The `compute_index_unaged_number_proportions` function that calculates the number proportion for unaged fish ** The test (`test_compute_index_unaged_number_proportions`) for `compute_index_unaged_number_proportions` --- echopop/computation/biology.py | 52 +++++++++++++++- echopop/computation/operations.py | 2 +- echopop/survey.py | 17 ++---- .../test_kriging_biomass_apportionment.py | 60 ++++++++++++++++++- 4 files changed, 114 insertions(+), 17 deletions(-) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index e8706247..d9b9c4cc 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -411,4 +411,54 @@ def normalize_haul_sex_weights( length_weight_df: pd.DataFrame , ) ### Carriage return - return haul_sex_weights_normalized \ No newline at end of file + return haul_sex_weights_normalized + +def compute_index_unaged_number_proportions( length_data: pd.DataFrame , + length_intervals: np.ndarray ): + """ + Calculate length binned weight proportions among unaged fish + + Parameters + ---------- + length_data: pd.DataFrame + Dataframe containing length data measured from unaged fish + length_intervals: np.ndarray + Array containing length bins/intervals + """ + + ### Calculate number proportion + # ---- Remove unsexed + length_data_filtered = length_data[ length_data.sex != 'unsexed' ].copy( ) + + # ---- Bin length measurements + length_data_filtered = ( + length_data_filtered + # ---- Bin length + .bin_variable( length_intervals , 'length' ) + ) + + # ---- Sum the number of individuals within each bin + proportions_unaged_length_sex = ( + length_data_filtered + # ---- Group number summations across stratum/species/sex/length + .groupby( [ 'stratum_num' , 'species_id' , 'sex' , 'length_bin' ] ) + # ---- Sum count + .agg( number_all = ( 'length_count' , 'sum' ) ) + # ---- Fill empty/non-existent values with 0's + .fillna( 0 ) + .reset_index( ) + ) + + ### Calculate the number proportions + # --- Stratum total counts + proportions_unaged_length_sex[ 'stratum_number_all' ] = ( + proportions_unaged_length_sex.groupby( [ 'stratum_num' ] )[ 'number_all' ].transform( sum ) + ) + + # ---- Proportions of each sex-length bin pair relative to `stratum_number_all` + proportions_unaged_length_sex[ 'proportion_number_sex_all' ] = ( + proportions_unaged_length_sex.number_all / proportions_unaged_length_sex.stratum_number_all + ) + + ### Filter out unnecessary columns and return output + return proportions_unaged_length_sex.filter( regex = '^(?!number_|stratum_number_).*' ) diff --git a/echopop/computation/operations.py b/echopop/computation/operations.py index 9a3185d4..fed3693d 100644 --- a/echopop/computation/operations.py +++ b/echopop/computation/operations.py @@ -39,7 +39,7 @@ def bin_variable( dataframe: pd.DataFrame , into each bin that is explicitly defined in the function arguments """ - dataframe[ f'{ bin_variable }_bin' ] = pd.cut( dataframe[ bin_variable ] , bin_values ) + dataframe.loc[ f'{ bin_variable }_bin' ] = pd.cut( dataframe.loc[ : , bin_variable ] , bin_values ) return dataframe diff --git a/echopop/survey.py b/echopop/survey.py index c5e3374f..99ddcf13 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -15,7 +15,7 @@ from .computation.kriging_methods import kriging_interpolation from .computation.biology import index_sex_weight_proportions , index_transect_age_sex_proportions , filter_species from .computation.biology import sum_strata_weight , compute_index_aged_weight_proportions , distribute_aged_weight_proportions -from .computation.biology import compute_summed_aged_proportions +from .computation.biology import compute_summed_aged_proportions , compute_index_unaged_number_proportions ### !!! TODO : This is a temporary import call -- this will need to be changed to # the correct relative structure (i.e. '.utils.data_structure_utils' instead of @@ -1354,19 +1354,10 @@ def apportion_kriged_biomass( self , ) ### Calculate number proportion - length_weight_df = self.statistics[ 'length_weight' ][ 'length_weight_df' ] - - number_unaged_length_proportions = ( - length_spp - .loc[ lambda df: df.group == 'sexed' ] - .bin_variable( length_intervals , 'length' ) - .groupby( [ 'stratum_num' , 'length_bin' ] ) - .agg( number_all = ( 'length_count' , 'sum' ) ) - .reset_index( ) - .assign( total_number_all = lambda df: df.groupby( [ 'stratum_num' ] )[ 'number_all' ].transform( sum ) , - proportion_number_all = lambda df: df.number_all / df.total_number_all ) - ) + proportions_unaged_length_sex = compute_index_unaged_number_proportions( length_spp , + length_intervals ) + length_weight_df = self.statistics[ 'length_weight' ][ 'length_weight_df' ] w_ln_all_array = ( number_unaged_length_proportions .merge( length_weight_df.loc[ lambda df: df.sex == 'all' ] , on = [ 'length_bin' ] ) diff --git a/echopop/tests/test_kriging_biomass_apportionment.py b/echopop/tests/test_kriging_biomass_apportionment.py index e9d2eb8f..bc337d11 100644 --- a/echopop/tests/test_kriging_biomass_apportionment.py +++ b/echopop/tests/test_kriging_biomass_apportionment.py @@ -1,6 +1,7 @@ import pandas as pd import numpy as np from echopop.computation.biology import sum_strata_weight , compute_index_aged_weight_proportions , compute_summed_aged_proportions +from echopop.computation.biology import compute_index_unaged_number_proportions , distribute_aged_weight_proportions def test_sum_strata_weight( mock_survey ): @@ -62,6 +63,7 @@ def test_sum_strata_weight( mock_survey ): assert np.allclose( check_values , object_weight_strata_aged_unaged.stratum_weight ) def test_compute_index_aged_weight_proportions( mock_survey ): + #### Pull in mock Survey object objS = mock_survey @@ -250,7 +252,7 @@ def test_distribute_aged_weight_proportions( ): } ) #---------------------------------- - ### Run tests: `compute_index_aged_weight_proportions` + ### Run tests: `distribute_aged_weight_proportions` #---------------------------------- ### `eval_aged_sex_proportions` # ---- Shape @@ -258,4 +260,58 @@ def test_distribute_aged_weight_proportions( ): # ---- Datatypes assert np.all( eval_distributed_aged_weight_proportions.dtypes == expected_output_distributed_aged_weight_proportions.dtypes ) # ---- Dataframe equality - eval_distributed_aged_weight_proportions.equals( expected_output_distributed_aged_weight_proportions ) \ No newline at end of file + eval_distributed_aged_weight_proportions.equals( expected_output_distributed_aged_weight_proportions ) + +def test_compute_index_unaged_number_proportions( mock_survey ): + + #### Pull in mock Survey object + objS = mock_survey + + ### Re-parameterize `length_df` with dummy data + objS.biology[ 'length_df' ] = pd.DataFrame( + { + 'stratum_num': np.repeat( [ 0 , 1 ] , 4 ) , + 'sex': np.tile( [ 'male' , 'female' ] , 4 ) , + 'species_id': np.repeat( [ 19350 ] , 8 ) , + 'length': [ 10 , 12 , 20 , 18 , 12 , 16 , 18 , 14 ] , + 'length_count': [ 5 , 10 , 15 , 20 , 20 , 15 , 10 , 5 ] + } + ) + + ### Length interval + objS.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] = np.linspace( 9 , 21 , 3 ) + + ### Evaluate object for later comparison + obj_proportions_unaged_length_sex = compute_index_unaged_number_proportions( objS.biology[ 'length_df' ] , + objS.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] ) + + ###-------------------------------- + ### Expected outcomes + ###-------------------------------- + ### `eval_aged_sex_proportions` + # ---- Expected dimensions + expected_dimensions_proportions_unaged_length_sex = tuple( [ 8 , 5 ] ) + # ---- Expected dataframe output + expected_output = pd.DataFrame( { + 'stratum_num': np.repeat( [ 0 , 1 ] , 4 ).astype( np.int64 ) , + 'species_id': np.repeat( [ 19350 ] , 8 ).astype( np.int64 ) , + 'sex': [ 'female' , 'female' , 'male' , 'male' , 'female' , 'female' , 'male' , 'male' ] , + 'length_bin': pd.IntervalIndex.from_arrays( np.tile( [9.0 , 15.0 ] , 4 ) , + np.tile( [ 15.0 , 21.0 ] , 4 ) , + closed = 'right' ) , + 'proportion_number_sex_all': [ 0.2 , 0.4 , 0.1 , 0.3 , 0.1 , 0.3 , 0.4 , 0.2 ] + } ) + expected_output[ 'length_bin' ] = pd.IntervalIndex( expected_output[ 'length_bin' ] ) + expected_output[ 'length_bin' ] = pd.Categorical( expected_output[ 'length_bin' ] , + categories = expected_output[ 'length_bin' ].unique( ) , + ordered = True ) + #---------------------------------- + ### Run tests: `compute_index_unaged_number_proportions` + #---------------------------------- + ### `eval_aged_sex_proportions` + # ---- Shape + assert obj_proportions_unaged_length_sex.shape == expected_dimensions_proportions_unaged_length_sex + # ---- Datatypes + assert np.all( obj_proportions_unaged_length_sex.dtypes == expected_output.dtypes ) + # ---- Dataframe equality + obj_proportions_unaged_length_sex.equals( expected_output ) From f5520b68dd4b4396dbc337409dd492bf9c1a9061 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Fri, 29 Mar 2024 14:49:51 -0700 Subject: [PATCH 34/51] `test_compute_index_aged_weight_proportions` fixes - Includes ** Major simplification of the test dataset as well as the expected outcomes dataframe --- .../test_kriging_biomass_apportionment.py | 55 +++++++++---------- 1 file changed, 25 insertions(+), 30 deletions(-) diff --git a/echopop/tests/test_kriging_biomass_apportionment.py b/echopop/tests/test_kriging_biomass_apportionment.py index bc337d11..8acaeeae 100644 --- a/echopop/tests/test_kriging_biomass_apportionment.py +++ b/echopop/tests/test_kriging_biomass_apportionment.py @@ -41,7 +41,7 @@ def test_sum_strata_weight( mock_survey ): ### Evaluate object for later comparison object_weight_strata_aged_unaged , object_weight_strata = sum_strata_weight( objS.biology[ 'catch_df' ] , - objS.biology[ 'specimen_df' ] ) + objS.biology[ 'specimen_df' ] ) #---------------------------------- ### Run tests: `sum_strata_weight` @@ -74,9 +74,9 @@ def test_compute_index_aged_weight_proportions( mock_survey ): 'sex': np.tile( [ 'male' , 'female' ] , 4 ) , 'haul_num': np.tile( [ 1 , 2 ] , 4 ) , 'species_id': np.repeat( [ 19350 ] , 8 ) , - 'length': [ 10 , 20 , 19 , 11 , 12 , 18 , 17 , 13 ] , - 'weight': np.linspace( 1 , 5 , 8 ) , - 'age': [ 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 ] + 'length': [ 12 , 12 , 19 , 19 , 12 , 12 , 19 , 19 ] , + 'weight': [ 2 , 3 , 8 , 7 , 1 , 4 , 9 , 6 ] , + 'age': [ 1 , 1 , 2 , 2 , 1 , 1 , 2 , 2 ] } ) @@ -100,31 +100,25 @@ def test_compute_index_aged_weight_proportions( mock_survey ): expected_output = pd.DataFrame( { 'stratum_num': np.repeat( [ 0 , 1 ] , 8 ).astype( np.int64 ) , 'species_id': np.repeat( 19350 , 16 ).astype( np.int64 ) , - 'sex': np.tile( [ 'female' , 'male' ] , 8 ).astype( object ) , - 'length_bin': pd.IntervalIndex.from_arrays( np.tile( [9.0 , 15.0 ] , 8 ) , - np.tile( [ 15.0 , 21.0 ] , 8 ) , - closed = 'right' ) , - 'age_bin': pd.IntervalIndex.from_arrays( np.tile( [ 0.5, 1.5 ] , 8 ) , - np.tile( [ 1.5 , 2.5 ] , 8 ) , - closed = 'right' ) , - 'weight_all': [ 0.0, 2.714286, 0.0, 1.571429, 1.0, 0.0, 2.142857, 0.0, 0.0, - 5.0, 0.0, 3.857143, 3.285714, 0.0, 4.428571, 0.0 ] , - 'weight_adult': [ 0.0, 2.714286, 0.0, 1.571429, 0.0, 0.0, 0.0, 0.0, 0.0, 5.0, - 0.0, 3.857143, 0.0, 0.0, 0.0, 0.0 ] , - 'total_weight_sex_all': [ 4.285714, 4.285714, 4.285714, 4.285714, 3.142857, - 3.142857, 3.142857, 3.142857, 8.857143, 8.857143, - 8.857143, 8.857143, 7.714286, 7.714286, 7.714286, - 7.714286 ] , - 'total_weight_sex_adult': [ 4.285714, 4.285714, 4.285714, 4.285714, 0.0, 0.0, - 0.0, 0.0, 8.857143, 8.857143, 8.857143, 8.857143, - 0.0, 0.0, 0.0, 0.0 ] , - 'proportion_weight_sex_all': [ 0.0, 0.633333, 0.0, 0.366667, 0.318182, 0.0, - 0.681818, 0.0, 0.0, 0.564516, 0.0, 0.435484, - 0.425926, 0.0, 0.574074, 0.0 ] , - 'proportion_weight_sex_adult': [ 0.0, 0.633333, 0.0, 0.366667, 0.0, 0.0, 0.0, - 0.0, 0.0, 0.564516, 0.0, 0.435484, 0.0, 0.0, - 0.0, 0.0 ] - } ) + 'sex': np.tile( [ 'female' , 'female' , 'female' , 'female' , + 'male' , 'male' , 'male' , 'male' ] , 2 ).astype( object ) , + 'length_bin': pd.cut( [ 12 , 12 , 19 , 19 , 12 , 12 , 19 , 19 , + 12 , 12 , 19 , 19 , 12 , 12 , 19 , 19 ] , + np.linspace( 9 , 21 , 3 ) ) , + 'age_bin': pd.cut( [ 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , + 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 ] , + np.array( [ 0.5 , 1.5 , 2.5 ] ) ) , + 'weight_all': [ 3.0 , 0.0 , 0.0 , 7.0 , 2.0 , 0.0 , 0.0 , 8.0 , + 4.0 , 0.0 , 0.0 , 6.0 , 1.0 , 0.0 , 0.0 , 9.0 ] , + 'weight_adult': [ 0.0 , 0.0 , 0.0 , 7.0 , 0.0 , 0.0 , 0.0 , 8.0 , + 0.0 , 0.0 , 0.0 , 6.0 , 0.0 , 0.0 , 0.0 , 9.0 ] , + 'total_weight_sex_all': np.repeat( 10.0 , 16 ) , + 'total_weight_sex_adult': [ 7.0 , 7.0 , 7.0 , 7.0 , 8.0 , 8.0 , 8.0 , 8.0 , + 6.0 , 6.0 , 6.0 , 6.0 , 9.0 , 9.0 , 9.0 , 9.0 ] , + 'proportion_weight_sex_all': [ 0.3 , 0.0 , 0.0 , 0.7 , 0.2 , 0.0 , 0.0 , 0.8 , + 0.4 , 0.0 , 0.0 , 0.6 , 0.1 , 0.0 , 0.0 , 0.9 ] , + 'proportion_weight_sex_adult': [ 0.0 , 0.0 , 0.0 , 1.0 , 0.0 , 0.0 , 0.0 , 1.0 , + 0.0 , 0.0 , 0.0 , 1.0 , 0.0 , 0.0 , 0.0 , 1.0 ] } ) expected_output[ 'length_bin' ] = pd.IntervalIndex( expected_output[ 'length_bin' ] ) expected_output[ 'length_bin' ] = pd.Categorical( expected_output[ 'length_bin' ] , categories = expected_output[ 'length_bin' ].unique( ) , @@ -145,7 +139,8 @@ def test_compute_index_aged_weight_proportions( mock_survey ): assert np.all( obj_props_wgt_len_age_sex.dtypes == expected_output.dtypes ) ### Check data value equality - assert np.all( obj_props_wgt_len_age_sex.dtypes == expected_output.dtypes ) + assert expected_output.equals( obj_props_wgt_len_age_sex ) + def test_compute_summed_aged_proportions( ): From f1b0f20d1330a7850dd7af9f83097024161ffdc8 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Fri, 29 Mar 2024 14:55:56 -0700 Subject: [PATCH 35/51] Revert `bin_variable` vectorization - Includes: ** Reverted of `bin_variable` back to original `assign` format. For some reason, `test_compute_index_aged_weight_proportions` fails when trying to use the vectorized version or when `pandas.cut` is invoked directly. --- echopop/computation/operations.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/echopop/computation/operations.py b/echopop/computation/operations.py index fed3693d..0a62d51c 100644 --- a/echopop/computation/operations.py +++ b/echopop/computation/operations.py @@ -39,9 +39,10 @@ def bin_variable( dataframe: pd.DataFrame , into each bin that is explicitly defined in the function arguments """ - dataframe.loc[ f'{ bin_variable }_bin' ] = pd.cut( dataframe.loc[ : , bin_variable ] , bin_values ) - - return dataframe + return ( + dataframe # input dataframe + .assign( **{f'{bin_variable}_bin': lambda x: pd.cut(x[bin_variable], bin_values)} ) # assign bin + ) @patch_method_to_DataFrame( pd.DataFrame ) def bin_stats( dataframe: pd.DataFrame , From f98fbb2a091f165c6c6ecac59764dc85e0ca248d Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Fri, 29 Mar 2024 14:58:47 -0700 Subject: [PATCH 36/51] `test_compute_index_unaged_number_proportions` fix - Includes ** There was a missing `assert`, but that was not the source of the test failure ** Reverting the vectorized change for `bin_variable` resolves the test failure --- echopop/tests/test_kriging_biomass_apportionment.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/echopop/tests/test_kriging_biomass_apportionment.py b/echopop/tests/test_kriging_biomass_apportionment.py index 8acaeeae..eb5c96e9 100644 --- a/echopop/tests/test_kriging_biomass_apportionment.py +++ b/echopop/tests/test_kriging_biomass_apportionment.py @@ -309,4 +309,4 @@ def test_compute_index_unaged_number_proportions( mock_survey ): # ---- Datatypes assert np.all( obj_proportions_unaged_length_sex.dtypes == expected_output.dtypes ) # ---- Dataframe equality - obj_proportions_unaged_length_sex.equals( expected_output ) + assert obj_proportions_unaged_length_sex.equals( expected_output ) From ba85f1613f3722550385e117a6a68ef3066c64e3 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Fri, 29 Mar 2024 15:33:18 -0700 Subject: [PATCH 37/51] `compute_index_unaged_number_proportions` fixes - Includes: ** Removed `sex` as a contrast from `compute_index_unaged_number_proportions` since it is supposed to be calculated across all animals, not by sex ** Simplified `test_compute_index_unaged_number_proportions`mocked data and amended the expected outputs based on the above change --- echopop/computation/biology.py | 8 ++++---- .../tests/test_kriging_biomass_apportionment.py | 15 +++++++-------- 2 files changed, 11 insertions(+), 12 deletions(-) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index d9b9c4cc..bf369987 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -438,10 +438,10 @@ def compute_index_unaged_number_proportions( length_data: pd.DataFrame , ) # ---- Sum the number of individuals within each bin - proportions_unaged_length_sex = ( + proportions_unaged_length = ( length_data_filtered # ---- Group number summations across stratum/species/sex/length - .groupby( [ 'stratum_num' , 'species_id' , 'sex' , 'length_bin' ] ) + .groupby( [ 'stratum_num' , 'species_id' , 'length_bin' ] ) # ---- Sum count .agg( number_all = ( 'length_count' , 'sum' ) ) # ---- Fill empty/non-existent values with 0's @@ -451,8 +451,8 @@ def compute_index_unaged_number_proportions( length_data: pd.DataFrame , ### Calculate the number proportions # --- Stratum total counts - proportions_unaged_length_sex[ 'stratum_number_all' ] = ( - proportions_unaged_length_sex.groupby( [ 'stratum_num' ] )[ 'number_all' ].transform( sum ) + proportions_unaged_length[ 'stratum_number_all' ] = ( + proportions_unaged_length.groupby( [ 'stratum_num' ] )[ 'number_all' ].transform( sum ) ) # ---- Proportions of each sex-length bin pair relative to `stratum_number_all` diff --git a/echopop/tests/test_kriging_biomass_apportionment.py b/echopop/tests/test_kriging_biomass_apportionment.py index eb5c96e9..abb46923 100644 --- a/echopop/tests/test_kriging_biomass_apportionment.py +++ b/echopop/tests/test_kriging_biomass_apportionment.py @@ -268,7 +268,7 @@ def test_compute_index_unaged_number_proportions( mock_survey ): 'stratum_num': np.repeat( [ 0 , 1 ] , 4 ) , 'sex': np.tile( [ 'male' , 'female' ] , 4 ) , 'species_id': np.repeat( [ 19350 ] , 8 ) , - 'length': [ 10 , 12 , 20 , 18 , 12 , 16 , 18 , 14 ] , + 'length': [ 12 , 12 , 19 , 19 , 12 , 12 , 19 , 19 ] , 'length_count': [ 5 , 10 , 15 , 20 , 20 , 15 , 10 , 5 ] } ) @@ -285,16 +285,15 @@ def test_compute_index_unaged_number_proportions( mock_survey ): ###-------------------------------- ### `eval_aged_sex_proportions` # ---- Expected dimensions - expected_dimensions_proportions_unaged_length_sex = tuple( [ 8 , 5 ] ) + expected_dimensions_proportions_unaged_length_sex = tuple( [ 4 , 4 ] ) # ---- Expected dataframe output expected_output = pd.DataFrame( { - 'stratum_num': np.repeat( [ 0 , 1 ] , 4 ).astype( np.int64 ) , - 'species_id': np.repeat( [ 19350 ] , 8 ).astype( np.int64 ) , - 'sex': [ 'female' , 'female' , 'male' , 'male' , 'female' , 'female' , 'male' , 'male' ] , - 'length_bin': pd.IntervalIndex.from_arrays( np.tile( [9.0 , 15.0 ] , 4 ) , - np.tile( [ 15.0 , 21.0 ] , 4 ) , + 'stratum_num': np.repeat( [ 0 , 1 ] , 2 ).astype( np.int64 ) , + 'species_id': np.repeat( [ 19350 ] , 4 ).astype( np.int64 ) , + 'length_bin': pd.IntervalIndex.from_arrays( np.tile( [9.0 , 15.0 ] , 2 ) , + np.tile( [ 15.0 , 21.0 ] , 2 ) , closed = 'right' ) , - 'proportion_number_sex_all': [ 0.2 , 0.4 , 0.1 , 0.3 , 0.1 , 0.3 , 0.4 , 0.2 ] + 'proportion_number_sex_all': [ 0.3 , 0.7 , 0.7 , 0.3 ] } ) expected_output[ 'length_bin' ] = pd.IntervalIndex( expected_output[ 'length_bin' ] ) expected_output[ 'length_bin' ] = pd.Categorical( expected_output[ 'length_bin' ] , From 97fd66a9d7b19e19b683c729e0d862464653e56b Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Fri, 29 Mar 2024 15:35:54 -0700 Subject: [PATCH 38/51] Renamed vars w/in `compute_index_unaged_number...` - Includes: ** Changed the output variable to be `proportions_unaged_length` --- echopop/computation/biology.py | 44 ++++++++++++++++++++++++++++++++-- echopop/survey.py | 5 ++-- 2 files changed, 45 insertions(+), 4 deletions(-) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index bf369987..4332de1f 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -456,8 +456,48 @@ def compute_index_unaged_number_proportions( length_data: pd.DataFrame , ) # ---- Proportions of each sex-length bin pair relative to `stratum_number_all` - proportions_unaged_length_sex[ 'proportion_number_sex_all' ] = ( - proportions_unaged_length_sex.number_all / proportions_unaged_length_sex.stratum_number_all + proportions_unaged_length[ 'proportion_number_sex_all' ] = ( + proportions_unaged_length.number_all / proportions_unaged_length.stratum_number_all + ).fillna( 0 ) + + ### Filter out unnecessary columns and return output + return proportions_unaged_length.filter( regex = '^(?!number_|stratum_number_).*' ) + +def compute_summed_unaged_proportions( proportions_unaged_length_sex: pd.DataFrame , + length_weight_df: pd.DataFrame): + """ + Compute the unaged weight proportions across all fish and specific sexes for each + length bin + + Parameters + ---------- + proportions_unaged_length_sex: pd.DataFrame + Dataframe containing sexed weight proportions distributed across + each length bin + length_weight_df: pd.DataFrame + Dataframe containing the modeled weights for each sex and length bin + fit from the length-weight regression (fit for all animals) + """ + + ### Extract length-weight regression results calculated for all animals + length_weight_all = length_weight_df[ length_weight_df.sex == 'all' ][ [ 'length_bin' , 'weight_modeled' ] ] + + ### Calculate the weight proportion of each length bin that is 'weighted' by the number proportions + # ---- Merge `proportions_unaged_length_sex` and `length_weight_all` + proportions_unaged_weight_length_sex = pd.merge( proportions_unaged_length_sex , + length_weight_all , + on = [ 'length_bin' ] , + how = 'left' ) + + # ---- Calculate the weight proportion (`w_ln_all_array` in the original Matlab code) + proportions_unaged_weight_length_sex[ 'proportion_weight_all' ] = ( + proportions_unaged_weight_length_sex.weight_modeled * proportions_unaged_weight_length_sex.proportion_number_sex_all + ) + + ### Sum weight proportions for each strata to get the weight per length bin + # ---- Calculate the summed weights per stratum (`w_ln_array_sum` in the original Matlab code) + proportions_unaged_weight_length_sex[ 'proportion_weight_stratum' ] = ( + proportions_unaged_weight_length_sex.groupby( [ 'stratum_num' ] )[ 'proportion_weight_all' ].transform( sum ) ) ### Filter out unnecessary columns and return output diff --git a/echopop/survey.py b/echopop/survey.py index 99ddcf13..f789267d 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -1354,9 +1354,10 @@ def apportion_kriged_biomass( self , ) ### Calculate number proportion - proportions_unaged_length_sex = compute_index_unaged_number_proportions( length_spp , - length_intervals ) + proportions_unaged_length = compute_index_unaged_number_proportions( length_spp , + length_intervals ) + ### Import length-weight relationship calculated for all animals length_weight_df = self.statistics[ 'length_weight' ][ 'length_weight_df' ] w_ln_all_array = ( number_unaged_length_proportions From b180df65a5b6070ba9e24f281f056b4893d7c854 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Fri, 29 Mar 2024 16:07:21 -0700 Subject: [PATCH 39/51] Add `compute_summed_unaged_proportions` - Includes: ** Function for calculating the unaged weight proportions over each length bin across all animals (`compute_summed_unaged_weight_proportions`) ** Associated test function added (`test_compute_summed_unaged_weight_proportions`) --- echopop/computation/biology.py | 35 ++++++---- echopop/survey.py | 25 ++----- .../test_kriging_biomass_apportionment.py | 70 +++++++++++++++++-- 3 files changed, 88 insertions(+), 42 deletions(-) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index 4332de1f..98b7fd9d 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -456,23 +456,23 @@ def compute_index_unaged_number_proportions( length_data: pd.DataFrame , ) # ---- Proportions of each sex-length bin pair relative to `stratum_number_all` - proportions_unaged_length[ 'proportion_number_sex_all' ] = ( + proportions_unaged_length[ 'proportion_number_all' ] = ( proportions_unaged_length.number_all / proportions_unaged_length.stratum_number_all ).fillna( 0 ) ### Filter out unnecessary columns and return output return proportions_unaged_length.filter( regex = '^(?!number_|stratum_number_).*' ) -def compute_summed_unaged_proportions( proportions_unaged_length_sex: pd.DataFrame , - length_weight_df: pd.DataFrame): +def compute_summed_unaged_weight_proportions( proportions_unaged_length: pd.DataFrame , + length_weight_df: pd.DataFrame): """ Compute the unaged weight proportions across all fish and specific sexes for each length bin Parameters ---------- - proportions_unaged_length_sex: pd.DataFrame - Dataframe containing sexed weight proportions distributed across + proportions_unaged_length: pd.DataFrame + Dataframe containing the total number proportions distributed across each length bin length_weight_df: pd.DataFrame Dataframe containing the modeled weights for each sex and length bin @@ -484,21 +484,26 @@ def compute_summed_unaged_proportions( proportions_unaged_length_sex: pd.DataFra ### Calculate the weight proportion of each length bin that is 'weighted' by the number proportions # ---- Merge `proportions_unaged_length_sex` and `length_weight_all` - proportions_unaged_weight_length_sex = pd.merge( proportions_unaged_length_sex , - length_weight_all , - on = [ 'length_bin' ] , - how = 'left' ) + proportions_unaged_weight_length = pd.merge( proportions_unaged_length , + length_weight_all , + on = [ 'length_bin' ] , + how = 'left' ) # ---- Calculate the weight proportion (`w_ln_all_array` in the original Matlab code) - proportions_unaged_weight_length_sex[ 'proportion_weight_all' ] = ( - proportions_unaged_weight_length_sex.weight_modeled * proportions_unaged_weight_length_sex.proportion_number_sex_all + proportions_unaged_weight_length[ 'weight_proportion_all' ] = ( + proportions_unaged_weight_length.weight_modeled * proportions_unaged_weight_length.proportion_number_all ) ### Sum weight proportions for each strata to get the weight per length bin # ---- Calculate the summed weights per stratum (`w_ln_array_sum` in the original Matlab code) - proportions_unaged_weight_length_sex[ 'proportion_weight_stratum' ] = ( - proportions_unaged_weight_length_sex.groupby( [ 'stratum_num' ] )[ 'proportion_weight_all' ].transform( sum ) + proportions_unaged_weight_length[ 'weight_stratum_proportion' ] = ( + proportions_unaged_weight_length.groupby( [ 'stratum_num' ] )[ 'weight_proportion_all' ].transform( sum ) + ) + + # ---- Calculate the weight per length distribution bin (`w_ln_all_N` in the original Matlab code) + proportions_unaged_weight_length[ 'proportion_weight_length' ] = ( + proportions_unaged_weight_length.weight_proportion_all / proportions_unaged_weight_length.weight_stratum_proportion ) - ### Filter out unnecessary columns and return output - return proportions_unaged_length_sex.filter( regex = '^(?!number_|stratum_number_).*' ) + ### Drop unnecessary columns and return output + return proportions_unaged_weight_length.filter( regex = '^(?!weight_|proportion_number_).*' ) \ No newline at end of file diff --git a/echopop/survey.py b/echopop/survey.py index f789267d..86bbbd8c 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -15,7 +15,8 @@ from .computation.kriging_methods import kriging_interpolation from .computation.biology import index_sex_weight_proportions , index_transect_age_sex_proportions , filter_species from .computation.biology import sum_strata_weight , compute_index_aged_weight_proportions , distribute_aged_weight_proportions -from .computation.biology import compute_summed_aged_proportions , compute_index_unaged_number_proportions +from .computation.biology import compute_summed_aged_proportions , compute_index_unaged_number_proportions +from .computation.biology import compute_summed_unaged_proportions ### !!! TODO : This is a temporary import call -- this will need to be changed to # the correct relative structure (i.e. '.utils.data_structure_utils' instead of @@ -1359,26 +1360,10 @@ def apportion_kriged_biomass( self , ### Import length-weight relationship calculated for all animals length_weight_df = self.statistics[ 'length_weight' ][ 'length_weight_df' ] - w_ln_all_array = ( - number_unaged_length_proportions - .merge( length_weight_df.loc[ lambda df: df.sex == 'all' ] , on = [ 'length_bin' ] ) - .assign( proportion_weight_all = lambda df: df.weight_modeled * df.proportion_number_all ) - ) - w_ln_array_sum = ( - w_ln_all_array - .groupby( [ 'stratum_num' ] ) - .agg( summed_proportion_weight_all = ( 'proportion_weight_all' , 'sum' ) ) - .reset_index( ) - ) - - w_ln_all_N = ( - w_ln_all_array - .merge( w_ln_array_sum , on = [ 'stratum_num' ] ) - .assign( weight_per_length_dist = lambda df: df.proportion_weight_all / df.summed_proportion_weight_all ) - .loc[ : , [ 'stratum_num' , 'length_bin' , 'weight_modeled' , 'proportion_weight_all' , - 'summed_proportion_weight_all' , 'weight_per_length_dist' ] ] - ) + ### Calculate the normalized weight per unit length distribution (W_Ln_ALL in the original Matlab code) + proportions_unaged_weight_length = compute_summed_unaged_weight_proportions( proportions_unaged_length , + length_weight_df ) sex_wgt = ( length_df diff --git a/echopop/tests/test_kriging_biomass_apportionment.py b/echopop/tests/test_kriging_biomass_apportionment.py index abb46923..01d902fa 100644 --- a/echopop/tests/test_kriging_biomass_apportionment.py +++ b/echopop/tests/test_kriging_biomass_apportionment.py @@ -2,6 +2,7 @@ import numpy as np from echopop.computation.biology import sum_strata_weight , compute_index_aged_weight_proportions , compute_summed_aged_proportions from echopop.computation.biology import compute_index_unaged_number_proportions , distribute_aged_weight_proportions +from echopop.computation.biology import compute_summed_unaged_weight_proportions def test_sum_strata_weight( mock_survey ): @@ -277,15 +278,15 @@ def test_compute_index_unaged_number_proportions( mock_survey ): objS.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] = np.linspace( 9 , 21 , 3 ) ### Evaluate object for later comparison - obj_proportions_unaged_length_sex = compute_index_unaged_number_proportions( objS.biology[ 'length_df' ] , - objS.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] ) + obj_proportions_unaged_length = compute_index_unaged_number_proportions( objS.biology[ 'length_df' ] , + objS.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] ) ###-------------------------------- ### Expected outcomes ###-------------------------------- ### `eval_aged_sex_proportions` # ---- Expected dimensions - expected_dimensions_proportions_unaged_length_sex = tuple( [ 4 , 4 ] ) + expected_dimensions_proportions_unaged_length = tuple( [ 4 , 4 ] ) # ---- Expected dataframe output expected_output = pd.DataFrame( { 'stratum_num': np.repeat( [ 0 , 1 ] , 2 ).astype( np.int64 ) , @@ -293,7 +294,7 @@ def test_compute_index_unaged_number_proportions( mock_survey ): 'length_bin': pd.IntervalIndex.from_arrays( np.tile( [9.0 , 15.0 ] , 2 ) , np.tile( [ 15.0 , 21.0 ] , 2 ) , closed = 'right' ) , - 'proportion_number_sex_all': [ 0.3 , 0.7 , 0.7 , 0.3 ] + 'proportion_number_all': [ 0.3 , 0.7 , 0.7 , 0.3 ] } ) expected_output[ 'length_bin' ] = pd.IntervalIndex( expected_output[ 'length_bin' ] ) expected_output[ 'length_bin' ] = pd.Categorical( expected_output[ 'length_bin' ] , @@ -304,8 +305,63 @@ def test_compute_index_unaged_number_proportions( mock_survey ): #---------------------------------- ### `eval_aged_sex_proportions` # ---- Shape - assert obj_proportions_unaged_length_sex.shape == expected_dimensions_proportions_unaged_length_sex + assert obj_proportions_unaged_length.shape == expected_dimensions_proportions_unaged_length # ---- Datatypes - assert np.all( obj_proportions_unaged_length_sex.dtypes == expected_output.dtypes ) + assert np.all( obj_proportions_unaged_length.dtypes == expected_output.dtypes ) # ---- Dataframe equality - assert obj_proportions_unaged_length_sex.equals( expected_output ) + assert obj_proportions_unaged_length.equals( expected_output ) + +def test_compute_summed_unaged_weight_proportions( ): + + ### Create mock `proportions_unaged_length` + test_proportions_unaged_length = pd.DataFrame( { + 'stratum_num': np.repeat( [ 0 , 1 ] , 3 ) , + 'species_id': np.repeat( 19880 , 6 ) , + 'length_bin': [ 'small' , 'medium' , 'big' , + 'small' , 'medium' , 'big' ] , + 'proportion_number_all': [ 0.1 , 0.85 , 0.05 , + 0.0 , 0.75 , 0.25 ] + } ) + + ### Create mock `length_weight_df` + test_length_weight_df = pd.DataFrame( { + 'length_bin': [ 'small' , 'small' , 'small' , + 'medium' , 'medium' , 'medium' , + 'big' , 'big' , 'big' ] , + 'sex': [ 'all' , 'male' , 'female' , + 'all' , 'male' , 'female' , + 'all' , 'male' , 'female' ] , + 'weight_modeled': [ 0.5 , 0.242424 , 0.32562252 , + 1.0 , 1.141414 , 0.99999999 , + 2.0 , 1.942424 , 2.12313131 ] + } ) + + ### Evaluate for later comparison + eval_proportions_unaged_weight_length = compute_summed_unaged_weight_proportions( test_proportions_unaged_length , + test_length_weight_df ) + + ###-------------------------------- + ### Expected outcomes + ###-------------------------------- + ### `eval_proportions_unaged_weight_length` + # ---- Expected dimensions + expected_dimensions_proportions_unaged_length_sex = tuple( [ 6 , 4 ] ) + # ---- Expected dataframe output + expected_output = pd.DataFrame( { + 'stratum_num': np.repeat( [ 0 , 1 ] , 3 ) , + 'species_id': np.repeat( [ 19880 ] , 6 ) , + 'length_bin': [ 'small' , 'medium' , 'big' , + 'small' , 'medium' , 'big' ] , + 'proportion_weight_length': [ 0.05 , 0.85 , 0.10 , + 0.0 , 0.60 , 0.40 ] + } ) + #---------------------------------- + ### Run tests: `compute_index_unaged_number_proportions` + #---------------------------------- + ### `eval_aged_sex_proportions` + # ---- Shape + assert eval_proportions_unaged_weight_length.shape == expected_dimensions_proportions_unaged_length_sex + # ---- Datatypes + assert np.all( eval_proportions_unaged_weight_length.dtypes == expected_output.dtypes ) + # ---- Dataframe equality + assert eval_proportions_unaged_weight_length.equals( expected_output ) \ No newline at end of file From 15eb89ddaf74349a24ecef282c804511528e6c48 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Fri, 29 Mar 2024 17:46:10 -0700 Subject: [PATCH 40/51] Unaged sexed proportions and biomass calculation - Includes: ** Added a function for calculating the normalized sexed weight proportions (`compute_unaged_sex_proportions`) ** Reorganized the tail-end of the unaged biomass procedure (up to the redistribution of unaged biomass over age-classes in the next step of the procedure) --- echopop/computation/biology.py | 228 ++++++++++++++++++++++++--------- echopop/survey.py | 85 ++++++------ 2 files changed, 208 insertions(+), 105 deletions(-) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index 98b7fd9d..b7b35785 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -351,67 +351,67 @@ def calculate_aged_proportions( weight_length_age_sex_stratum: pd.DataFrame , ### Carriage return return aged_proportions -def normalize_haul_sex_weights( length_weight_df: pd.DataFrame , - length_df: pd.DataFrame , - weight_strata: pd.DataFrame , - weight_strata_station: pd.DataFrame , - aged_proportions: pd.DataFrame , - length_intervals: np.ndarray ): +# def normalize_haul_sex_weights( length_weight_df: pd.DataFrame , +# length_df: pd.DataFrame , +# weight_strata: pd.DataFrame , +# weight_strata_station: pd.DataFrame , +# aged_proportions: pd.DataFrame , +# length_intervals: np.ndarray ): - ### Calculate interpolated weights based on length bins for each sex per haul - length_weight_fit = ( - length_weight_df - .copy( ) - .assign( length_bin_value = lambda x: x[ 'length_bin' ].apply( lambda y: y.mid ) ) - ) - - # Sum haul weights per sex per stratum - haul_weights = ( - length_df - .bin_variable( length_intervals , 'length' ) - .loc[ lambda x: x.group == 'sexed' ] - .pipe( lambda df: pd.concat( [ df , df.assign( sex = 'all' ) ] ) ) - .merge( length_weight_fit , on = [ 'sex' , 'length_bin' ] ) - .groupby( [ 'stratum_num' , 'haul_num' , 'sex' ] ) - .apply( lambda df: pd.Series( { 'weight_interp': ( np.interp( df.length, - length_weight_fit.loc[ lambda x: x.sex.isin( df.sex ) ][ 'length_bin_value' ] , - length_weight_fit.loc[ lambda x: x.sex.isin( df.sex ) ][ 'weight_modeled' ] ) * - df.length_count ).sum( ) } ) ) - .groupby( [ 'stratum_num' , 'sex' ] ) - .apply( lambda df: pd.Series( { 'summed_haul_weights': df.weight_interp.sum( ) } ) ) - .reset_index( ) - ) +# ### Calculate interpolated weights based on length bins for each sex per haul +# length_weight_fit = ( +# length_weight_df +# .copy( ) +# .assign( length_bin_value = lambda x: x[ 'length_bin' ].apply( lambda y: y.mid ) ) +# ) + +# # Sum haul weights per sex per stratum +# haul_weights = ( +# length_df +# .bin_variable( length_intervals , 'length' ) +# .loc[ lambda x: x.group == 'sexed' ] +# .pipe( lambda df: pd.concat( [ df , df.assign( sex = 'all' ) ] ) ) +# .merge( length_weight_fit , on = [ 'sex' , 'length_bin' ] ) +# .groupby( [ 'stratum_num' , 'haul_num' , 'sex' ] ) +# .apply( lambda df: pd.Series( { 'weight_interp': ( np.interp( df.length, +# length_weight_fit.loc[ lambda x: x.sex.isin( df.sex ) ][ 'length_bin_value' ] , +# length_weight_fit.loc[ lambda x: x.sex.isin( df.sex ) ][ 'weight_modeled' ] ) * +# df.length_count ).sum( ) } ) ) +# .groupby( [ 'stratum_num' , 'sex' ] ) +# .apply( lambda df: pd.Series( { 'summed_haul_weights': df.weight_interp.sum( ) } ) ) +# .reset_index( ) +# ) - ### Normalize haul weights (Station 1) - haul_sex_weights_normalized = ( - haul_weights - .merge( weight_strata_station.loc[ lambda x: x.station == 1 ] , - on = 'stratum_num' ) - .assign( weight_normalized_station_1 = lambda df: ( - df - .groupby( [ 'stratum_num' ] ) - .apply( lambda strata: strata[ 'stratum_weight' ] * strata[ 'summed_haul_weights' ] / - ( df[ ( df.stratum_num == strata.name ) & ( df.sex == 'male' ) ][ 'summed_haul_weights' ].iloc[ 0 ] + - df[ ( df.stratum_num == strata.name ) & ( df.sex == 'female' ) ][ 'summed_haul_weights' ].iloc[ 0 ] ) ) - .reset_index( drop = True ) ) ) - .merge( weight_strata , on = [ 'stratum_num' ] ) - .assign( proportion_normalized_station_1 = lambda df: ( - df.weight_normalized_station_1 / df.weight_stratum_total - ) ) - .assign( sex_proportion = lambda df: ( - df - .groupby( [ 'stratum_num' ] ) - .apply( lambda strata: strata[ 'proportion_normalized_station_1' ] / - ( df[ ( df.stratum_num == strata.name ) & ( df.sex == 'male' ) ][ 'proportion_normalized_station_1' ].iloc[ 0 ] + - df[ ( df.stratum_num == strata.name ) & ( df.sex == 'female' ) ][ 'proportion_normalized_station_1' ].iloc[ 0 ] ) ) - .reset_index( drop = True ) ) ) - .merge( aged_proportions.loc[ : , [ 'stratum_num' , 'proportion_unaged_total' ] ] , - on = [ 'stratum_num' ] ) - .assign( proportion_unaged_sex = lambda x: x.sex_proportion * x.proportion_unaged_total ) - ) - - ### Carriage return - return haul_sex_weights_normalized +# ### Normalize haul weights (Station 1) +# haul_sex_weights_normalized = ( +# haul_weights +# .merge( weight_strata_station.loc[ lambda x: x.station == 1 ] , +# on = 'stratum_num' ) +# .assign( weight_normalized_station_1 = lambda df: ( +# df +# .groupby( [ 'stratum_num' ] ) +# .apply( lambda strata: strata[ 'stratum_weight' ] * strata[ 'summed_haul_weights' ] / +# ( df[ ( df.stratum_num == strata.name ) & ( df.sex == 'male' ) ][ 'summed_haul_weights' ].iloc[ 0 ] + +# df[ ( df.stratum_num == strata.name ) & ( df.sex == 'female' ) ][ 'summed_haul_weights' ].iloc[ 0 ] ) ) +# .reset_index( drop = True ) ) ) +# .merge( weight_strata , on = [ 'stratum_num' ] ) +# .assign( proportion_normalized_station_1 = lambda df: ( +# df.weight_normalized_station_1 / df.weight_stratum_total +# ) ) +# .assign( sex_proportion = lambda df: ( +# df +# .groupby( [ 'stratum_num' ] ) +# .apply( lambda strata: strata[ 'proportion_normalized_station_1' ] / +# ( df[ ( df.stratum_num == strata.name ) & ( df.sex == 'male' ) ][ 'proportion_normalized_station_1' ].iloc[ 0 ] + +# df[ ( df.stratum_num == strata.name ) & ( df.sex == 'female' ) ][ 'proportion_normalized_station_1' ].iloc[ 0 ] ) ) +# .reset_index( drop = True ) ) ) +# .merge( aged_proportions.loc[ : , [ 'stratum_num' , 'proportion_unaged_total' ] ] , +# on = [ 'stratum_num' ] ) +# .assign( proportion_unaged_sex = lambda x: x.sex_proportion * x.proportion_unaged_total ) +# ) + +# ### Carriage return +# return haul_sex_weights_normalize def compute_index_unaged_number_proportions( length_data: pd.DataFrame , length_intervals: np.ndarray ): @@ -464,7 +464,7 @@ def compute_index_unaged_number_proportions( length_data: pd.DataFrame , return proportions_unaged_length.filter( regex = '^(?!number_|stratum_number_).*' ) def compute_summed_unaged_weight_proportions( proportions_unaged_length: pd.DataFrame , - length_weight_df: pd.DataFrame): + length_weight_df: pd.DataFrame ): """ Compute the unaged weight proportions across all fish and specific sexes for each length bin @@ -506,4 +506,110 @@ def compute_summed_unaged_weight_proportions( proportions_unaged_length: pd.Data ) ### Drop unnecessary columns and return output - return proportions_unaged_weight_length.filter( regex = '^(?!weight_|proportion_number_).*' ) \ No newline at end of file + return proportions_unaged_weight_length.filter( regex = '^(?!weight_|proportion_number_).*' ) + +def compute_unaged_sex_proportions( length_data: pd.DataFrame , + length_intervals: pd.DataFrame , + length_weight_df: pd.DataFrame , + weight_strata: pd.DataFrame , + weight_strata_aged_unaged: pd.DataFrame ): + """ + Calculate the normalized weight proportions for unaged sexed fish + + Parameters + ---------- + length_data: pd.DataFrame + Dataframe containing length data measured from unaged fish + length_intervals: np.ndarray + Array containing length bins/intervals + length_weight_df: pd.DataFrame + Dataframe containing the modeled weights for each sex and length bin + fit from the length-weight regression (fit for all animals) + weight_strata: pd.DataFrame + Summed haul weights for each stratum + weight_strata_aged_uanged: pd.DataFrame + Summed haul weights for specifically aged and unaged fish across strata + """ + + ### Calculate sexed weights within each stratum + # ---- Remove unsexed + length_data_filtered = length_data[ length_data.sex != 'unsexed' ].copy( ) + + # ---- Extract sexed length-weight regression fits + length_weight_sex = length_weight_df[ length_weight_df.sex != 'all' ][ [ 'length_bin' , 'sex' , 'weight_modeled' ] ] + + # ---- Bin length measurements + length_data_filtered = ( + length_data_filtered + # ---- Bin length + .bin_variable( length_intervals , 'length' ) + ) + + # ---- Extract bin length values (i.e. mid) + length_data_filtered[ 'length_bin_value' ] = ( + length_data_filtered[ 'length_bin' ].apply( lambda x: x.mid ) + ) + + # ---- Merge `length_data_filtered ` and `length_weight_sex` + sexed_unaged_model_weights = pd.merge( length_data_filtered , + length_weight_sex , + on = [ 'length_bin' , 'sex' ] , + how = 'left' ) + + # ---- Interpolate weights over length values for each sex (weighted by `length_count`) + interpolated_weights = ( + sexed_unaged_model_weights + .groupby( [ 'stratum_num' , 'sex' ] ) + .apply( lambda df: pd.Series( { + 'weight_interp': ( np.interp( df.length , + df.length_bin_value , + df.weight_modeled ) * df.length_count ).sum( ) + } ) ) + .reset_index( ) + ) + + ### Calculate the unaged weight proportion relative to the haul catches + # ---- Parse out unaged haul totals + weight_strata_unaged = weight_strata_aged_unaged[ weight_strata_aged_unaged.group == 'unaged' ] + + # ---- Merge the unaged weight strata with the interpolated weights + proportions_unaged_weight_sex = pd.merge( interpolated_weights , + weight_strata_unaged , + on = [ 'stratum_num' ] , + how = 'left' ) + + # ---- Sum `weight_interp` over each stratum + proportions_unaged_weight_sex[ 'stratum_sex_weight_interp' ] = ( + proportions_unaged_weight_sex.groupby( ['stratum_num' ] )[ 'weight_interp' ].transform( sum ) + ) + + # ---- Calculate the normalized weights for each sex + proportions_unaged_weight_sex[ 'stratum_sex_weight_normalized' ] = ( + proportions_unaged_weight_sex.stratum_weight * + proportions_unaged_weight_sex.weight_interp / proportions_unaged_weight_sex.stratum_sex_weight_interp + ) + + # ---- Merge `proportions_unaged_weight_sex` with `weight strata` + proportions_unaged_weight_sex_normalized = pd.merge( proportions_unaged_weight_sex , + weight_strata , + on = [ 'stratum_num' ] , + how = 'left' ) + + # ---- Sum the normalized stratum sex weight across each stratum + proportions_unaged_weight_sex[ 'proportions_weight_sex' ] = ( + proportions_unaged_weight_sex_normalized.stratum_sex_weight_normalized / + proportions_unaged_weight_sex_normalized.weight_stratum_all + ) + + # ---- Sum 'proportions_weight_sex' + proportions_unaged_weight_sex[ 'proportions_weight_sex_total' ] = ( + proportions_unaged_weight_sex.groupby( [ 'stratum_num' ] )[ 'proportions_weight_sex' ].transform( sum ) + ) + + # ---- Calculate the final sex proportion + proportions_unaged_weight_sex[ 'proportion_weight_sex' ] = ( + proportions_unaged_weight_sex.proportions_weight_sex / proportions_unaged_weight_sex.proportions_weight_sex_total + ) + + ### Remove unnecessary columns and return output + return proportions_unaged_weight_sex[ [ 'stratum_num' , 'sex' , 'proportion_weight_sex' ] ] \ No newline at end of file diff --git a/echopop/survey.py b/echopop/survey.py index 86bbbd8c..9b50513a 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -16,7 +16,7 @@ from .computation.biology import index_sex_weight_proportions , index_transect_age_sex_proportions , filter_species from .computation.biology import sum_strata_weight , compute_index_aged_weight_proportions , distribute_aged_weight_proportions from .computation.biology import compute_summed_aged_proportions , compute_index_unaged_number_proportions -from .computation.biology import compute_summed_unaged_proportions +from .computation.biology import compute_summed_unaged_weight_proportions , compute_unaged_sex_proportions ### !!! TODO : This is a temporary import call -- this will need to be changed to # the correct relative structure (i.e. '.utils.data_structure_utils' instead of @@ -1365,66 +1365,63 @@ def apportion_kriged_biomass( self , proportions_unaged_weight_length = compute_summed_unaged_weight_proportions( proportions_unaged_length , length_weight_df ) - sex_wgt = ( - length_df - .loc[ lambda df: df.group == 'sexed' ] - .bin_variable( length_intervals , 'length' ) - .merge( length_weight_df.loc[ lambda df: df.sex != 'all' ] , on = [ 'sex' , 'length_bin' ] ) - .assign( length_bin_value = lambda x: x[ 'length_bin' ].apply( lambda y: y.mid ) ) - .groupby( [ 'stratum_num' , 'sex' ] ) - .apply( lambda df: pd.Series( { - 'weight_interp': ( np.interp( df.length , - df.length_bin_value , - df.weight_modeled ) * df.length_count ).sum( ) - } ) ) - .reset_index( ) - ) - - norm_sex_wgt = ( - weight_strata_aged_unaged - .loc[ lambda df: df.group == 'unaged' ] - .merge( sex_wgt , on = [ 'stratum_num' ] ) - .assign( summed_sex_wgt = lambda df: df.groupby( [ 'stratum_num' ] )[ 'weight_interp' ].transform( sum ) , - norm_sex_wgt = lambda df: df.stratum_weight * df.weight_interp / df.summed_sex_wgt ) - .assign( summed_norm_sex_wgt = lambda df: df.groupby( [ 'stratum_num' ] )[ 'norm_sex_wgt' ].transform( sum ) , - sex_proportion = lambda df: df.norm_sex_wgt / df.summed_norm_sex_wgt ) - ) - - unaged_proportions = ( - aged_proportions - .assign( proportion_weight_all = lambda df: 1.0 - df.proportion_weight_all , - proportion_weight_adult = lambda df: 1.0 - df.proportion_weight_adult ) + ### Calculate sex-specific weight proportions for unaged fish (within unaged fish) + proportions_unaged_weight_sex = compute_unaged_sex_proportions( length_spp , + length_intervals , + length_weight_df , + weight_strata , + weight_strata_aged_unaged ) + + ### Calculate the unaged proportions + unaged_proportions = pd.DataFrame( { + 'stratum_num': aged_proportions.stratum_num , + 'proportion_weight_all': 1.0 - aged_proportions.proportion_weight_all , + 'proportion_weight_adult': 1.0 - aged_proportions.proportion_weight_adult , + } ) + + ### Calculate sex-specific weight proportions for unaged fish (across all fish, aged and unaged) + # ---- Merge `proportions_unaged_weight_sex` and `unaged_proportions` + proportions_unaged_weight_sex_overall = pd.merge( proportions_unaged_weight_sex , + unaged_proportions , + on = [ 'stratum_num' ] , + how = 'left' ) + + # ---- Calculate the overall sexed proportions taking into account aged and unaged fish (all age class) + proportions_unaged_weight_sex_overall[ 'proportion_sexed_weight_all' ] = ( + proportions_unaged_weight_sex_overall.proportion_weight_sex * + proportions_unaged_weight_sex_overall.proportion_weight_all ) - unaged_sex_proportions = ( - norm_sex_wgt - .merge( unaged_proportions , on = [ 'stratum_num' ] ) - .assign( proportion_sexed_weight_all = lambda df: df.proportion_weight_all * df.sex_proportion , - proportion_sexed_weight_adult = lambda df: df.proportion_weight_adult * df.sex_proportion ) - .loc[ : , [ 'stratum_num' , 'sex' , 'proportion_sexed_weight_all' , 'proportion_sexed_weight_adult' ] ] + # ---- Calculate the overall sexed proportions taking into account aged and unaged fish (adults) + proportions_unaged_weight_sex_overall[ 'proportion_sexed_weight_adult' ] = ( + proportions_unaged_weight_sex_overall.proportion_weight_sex * + proportions_unaged_weight_sex_overall.proportion_weight_adult ) + ### Calculate unaged biomass for each sex unaged_sexed_biomass = ( # ---- Parse kriged biomass (areal density) values self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] .copy( ) - .merge( w_ln_all_N , on = [ 'stratum_num' ] ) - .merge( unaged_sex_proportions , on = [ 'stratum_num' ] ) + .merge( proportions_unaged_weight_length , on = [ 'stratum_num' ] ) + .merge( proportions_unaged_weight_sex_overall , on = [ 'stratum_num' ] ) .assign( biomass_sexed_unaged_all = lambda df: ( df.B_a_adult_mean * df.cell_area_nmi2 * - df.weight_per_length_dist * + df.proportion_weight_length * df.proportion_sexed_weight_all ) , - biomass_sexed_unaged_adult = lambda df: ( df.B_a_adult_mean * - df.cell_area_nmi2 * - df.weight_per_length_dist * - df.proportion_sexed_weight_adult ) + biomass_sexed_unaged_adult = lambda df: ( df.B_a_adult_mean * + df.cell_area_nmi2 * + df.proportion_weight_length * + df.proportion_sexed_weight_adult ) ) ) + ### Calculate total biomass total_unaged_sexed_biomass = ( unaged_sexed_biomass .groupby( [ 'length_bin' , 'sex' ] ) - .agg( total_sexed_unaged_biomass_adult = ( 'biomass_sexed_unaged_adult' , 'sum' ) ) + .agg( total_unaged_biomass_all = ( 'biomass_sexed_unaged_all' , 'sum' ) , + total_unaged_biomass_adult = ( 'biomass_sexed_unaged_adult' , 'sum' ) ) .reset_index( ) ) From 1efca374e913a51dc5d9d22c72dc8c1821ce0afd Mon Sep 17 00:00:00 2001 From: Brandyn Lucca <=> Date: Fri, 29 Mar 2024 18:44:56 -0700 Subject: [PATCH 41/51] `test_compute_unaged_sex_proportion` test - Includes: ** Test function for `compute_unaged_sex_proportion` --- .../test_kriging_biomass_apportionment.py | 89 ++++++++++++++++++- 1 file changed, 87 insertions(+), 2 deletions(-) diff --git a/echopop/tests/test_kriging_biomass_apportionment.py b/echopop/tests/test_kriging_biomass_apportionment.py index 01d902fa..01ca6fd3 100644 --- a/echopop/tests/test_kriging_biomass_apportionment.py +++ b/echopop/tests/test_kriging_biomass_apportionment.py @@ -2,7 +2,7 @@ import numpy as np from echopop.computation.biology import sum_strata_weight , compute_index_aged_weight_proportions , compute_summed_aged_proportions from echopop.computation.biology import compute_index_unaged_number_proportions , distribute_aged_weight_proportions -from echopop.computation.biology import compute_summed_unaged_weight_proportions +from echopop.computation.biology import compute_summed_unaged_weight_proportions , compute_unaged_sex_proportions def test_sum_strata_weight( mock_survey ): @@ -364,4 +364,89 @@ def test_compute_summed_unaged_weight_proportions( ): # ---- Datatypes assert np.all( eval_proportions_unaged_weight_length.dtypes == expected_output.dtypes ) # ---- Dataframe equality - assert eval_proportions_unaged_weight_length.equals( expected_output ) \ No newline at end of file + assert eval_proportions_unaged_weight_length.equals( expected_output ) + +def test_compute_unaged_sex_proportions( ): + + ### Mock data for `length_data` + test_length_data = pd.DataFrame( + { + 'stratum_num': np.repeat( [ 0 , 1 ] , 4 ) , + 'sex': np.tile( [ 'male' , 'female' ] , 4 ) , + 'species_id': np.repeat( [ 19350 ] , 8 ) , + 'length': [ 12 , 12 , 19 , 19 , 12 , 12 , 19 , 19 ] , + 'length_count': [ 5 , 10 , 15 , 20 , 20 , 15 , 10 , 5 ] + } + ) + + ### Mock data for `length_intervals` + test_length_intervals = np.linspace( 9 , 21 , 3 ) + + ### Mock data for `length_weight_df` + test_length_weight_df = pd.DataFrame( + { + 'length_bin': pd.cut( [ 12 , 12 , 12 , 19 , 19 , 19 , + 12 , 12 , 12 , 19 , 19 , 19 ] , + np.linspace( 9 , 21 , 3 ) ) , + 'sex': [ 'male' , 'female' , 'all' , 'male' , 'female' , 'all' , + 'male' , 'female' , 'all' , 'male' , 'female' , 'all' ] , + 'weight_modeled': [ 1 , 3 , 2 , 6 , 8 , 7 , + 4 , 2 , 3 , 9 , 11 , 10 ] + } + ) + test_length_weight_df[ 'length_bin' ] = pd.IntervalIndex( test_length_weight_df[ 'length_bin' ] ) + test_length_weight_df[ 'length_bin' ] = pd.Categorical( test_length_weight_df[ 'length_bin' ] , + categories = test_length_weight_df[ 'length_bin' ].unique( ) , + ordered = True ) + + ### Mock data for `weight_strata` + test_weight_strata = pd.DataFrame( + { + 'stratum_num': [ 0 , 1 ] , + 'weight_stratum_all': [ 50 , 100 ] , + } + ) + + ### Mock data for `weight_strata_aged_unaged` + test_weight_strata_aged_unaged = pd.DataFrame( + { + 'stratum_num': [ 0 , 1 , 0 , 1 ] , + 'stratum_weight': [ 500 , 400 , 50 , 100 ] , + 'group': [ 'unaged' , 'unaged' , 'aged' , 'aged' ] , + } + ) + + ### Evaluate for later comparison + eval_proportions_unaged_weight_sex = compute_unaged_sex_proportions( test_length_data , + test_length_intervals , + test_length_weight_df , + test_weight_strata , + test_weight_strata_aged_unaged ) + + ###-------------------------------- + ### Expected outcomes + ###-------------------------------- + # ---- Expected dimensions of `obj_props_wgt_len_age_sex` + expected_dimensions = tuple( [ 4 , 3 ] ) + + # ---- Expected dataframe output + expected_output = pd.DataFrame( { + 'stratum_num': [ 0 , 0 , 1 , 1 ] , + 'sex': [ 'female' , 'male' , 'female' , 'male' ] , + 'proportion_weight_sex': [ 0.607595 , 0.392405 , 0.333333 , 0.666667 ] + } ) + + #---------------------------------- + ### Run tests: `compute_index_aged_weight_proportions` + #---------------------------------- + ### Process the specimen data + ### Check shape + assert eval_proportions_unaged_weight_sex.shape == expected_dimensions + + ### Check datatypes + assert np.all( eval_proportions_unaged_weight_sex.dtypes == expected_output.dtypes ) + + ### Check data value equality + # ---- `stratum_num` + assert np.all( eval_proportions_unaged_weight_sex.stratum_num == expected_output.stratum_num ) + assert np.allclose( eval_proportions_unaged_weight_sex.proportion_weight_sex , expected_output.proportion_weight_sex ) \ No newline at end of file From 2f25f0832a685821bb646b75ae7b5699f05c1898 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca Date: Fri, 29 Mar 2024 19:00:52 -0700 Subject: [PATCH 42/51] Reorganization of the `unaged2agedmat` calculation - Includes ** Reorganization of the code so it can be a bit more accessible for downstream calculations (i.e. back-calculating abundance, NASC, etc) --- echopop/survey.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/echopop/survey.py b/echopop/survey.py index 9b50513a..8ee146ec 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -1417,7 +1417,7 @@ def apportion_kriged_biomass( self , ) ### Calculate total biomass - total_unaged_sexed_biomass = ( + total_unaged_biomass = ( unaged_sexed_biomass .groupby( [ 'length_bin' , 'sex' ] ) .agg( total_unaged_biomass_all = ( 'biomass_sexed_unaged_all' , 'sum' ) , @@ -1425,12 +1425,13 @@ def apportion_kriged_biomass( self , .reset_index( ) ) + ### Re-distribute unaged biomass so it is compatible with aged biomass to calculate the overall summed biomass unaged2aged_mat = ( total_aged_sexed_biomass - .merge( total_unaged_sexed_biomass , on = [ 'length_bin' , 'sex' ] ) - .assign( Summed_Wgt_Len_age = lambda df: df.groupby( [ 'sex' , 'length_bin' ] )[ 'total_sexed_aged_biomass_adult' ].transform( sum ) + df.total_sexed_unaged_biomass_adult ) - .assign( Final_wgt_len_age = lambda df: df.total_sexed_unaged_biomass_adult * df.total_sexed_aged_biomass_adult / df.Summed_Wgt_Len_age ) - .replace( np.nan , 0 ) + .merge( total_unaged_biomass , on = [ 'length_bin' , 'sex' ] ) + .assign( Summed_Wgt_Len_age = lambda df: df.groupby( [ 'sex' , 'length_bin' ] )[ 'total_sexed_aged_biomass_adult' ].transform( sum ) + df.total_unaged_biomass_adult ) + .assign( Final_wgt_len_age = lambda df: df.total_unaged_biomass_adult * df.total_sexed_aged_biomass_adult / df.Summed_Wgt_Len_age ) + .replace( np.nan , 0 ) ) # ### Calculate interpolated weights based on length bins for each sex per haul From 8b0c3f7ebfca2b3d39cb217751f6b9f049fd8951 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca Date: Mon, 1 Apr 2024 07:56:55 -0700 Subject: [PATCH 43/51] New function: `apply_age_bins` - This includes: ** The new function `apply_age_bins` that redistributes the bioamss of unaged fish over age bins based on the proportions calculated from the total aged biomass summed with the unaged biomass for each length bin ($\ell$) --- echopop/computation/biology.py | 48 +++++++++++++++++++++++++++++++++- echopop/survey.py | 13 +++------ 2 files changed, 51 insertions(+), 10 deletions(-) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index b7b35785..6424b516 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -612,4 +612,50 @@ def compute_unaged_sex_proportions( length_data: pd.DataFrame , ) ### Remove unnecessary columns and return output - return proportions_unaged_weight_sex[ [ 'stratum_num' , 'sex' , 'proportion_weight_sex' ] ] \ No newline at end of file + return proportions_unaged_weight_sex[ [ 'stratum_num' , 'sex' , 'proportion_weight_sex' ] ] + +def apply_age_bins( aged_biomass_df: pd.DataFrame , + unaged_biomass_df: pd.DataFrame ): + """ + Redistribute unaged biomass over the defined age distribution + + Parameters + ---------- + aged_biomass_df: pd.DataFrame + Dataframe containing biomass data of aged fish + unaged_biomass_df: pd.DataFrame + Dataframe containing biomass data of unaged fish + """ + + ### Merge unaged biomass length bins with aged bioamss length-age bins + aged_unaged_biomass = pd.merge( aged_biomass_df , + unaged_biomass_df , + on = [ 'length_bin' , 'sex' ] , + how = 'left' ) + + ### Calculate the total biomass for each sexed length bin (i.e. sum across age bins) + # ---- Adult fish + aged_unaged_biomass[ 'total_length_bin_biomass_adult' ] = ( + aged_unaged_biomass.groupby( [ 'sex' , 'length_bin' ] )[ 'total_sexed_aged_biomass_adult' ].transform( sum ) + + aged_unaged_biomass.total_unaged_biomass_adult + ) + + ### Redistribute unaged biomass over the length-age bins for each sex + # ---- Adult fish + aged_unaged_biomass[ 'total_unaged_biomass' ] = ( + aged_unaged_biomass.total_unaged_biomass_adult * + aged_unaged_biomass.total_sexed_aged_biomass_adult / + aged_unaged_biomass.total_length_bin_biomass_adult + ) + + ### Remove unnecessary columns + aged_unaged_biomass.drop( [ 'total_sexed_aged_biomass_adult' , + 'total_unaged_biomass_all' , + 'total_unaged_biomass_adult' , + 'total_length_bin_biomass_adult' , + 'total_length_bin_biomass_adult' ] , + axis = 1 , + inplace = True ) + + ### Replace NaN with 0's and return output + return aged_unaged_biomass.fillna( 0 ) \ No newline at end of file diff --git a/echopop/survey.py b/echopop/survey.py index 8ee146ec..d9ee0ba7 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -16,7 +16,7 @@ from .computation.biology import index_sex_weight_proportions , index_transect_age_sex_proportions , filter_species from .computation.biology import sum_strata_weight , compute_index_aged_weight_proportions , distribute_aged_weight_proportions from .computation.biology import compute_summed_aged_proportions , compute_index_unaged_number_proportions -from .computation.biology import compute_summed_unaged_weight_proportions , compute_unaged_sex_proportions +from .computation.biology import compute_summed_unaged_weight_proportions , compute_unaged_sex_proportions , apply_age_bins ### !!! TODO : This is a temporary import call -- this will need to be changed to # the correct relative structure (i.e. '.utils.data_structure_utils' instead of @@ -1253,7 +1253,7 @@ def krige( self , ### TODO: This should be refactored out as an external function ###### rather than a Survey-class method. ### Apportion biomass based on age and sex - self.apportion_kriged_biomass( species_id ) + # self.apportion_kriged_biomass( species_id ) def apportion_kriged_biomass( self , species_id ): @@ -1426,13 +1426,8 @@ def apportion_kriged_biomass( self , ) ### Re-distribute unaged biomass so it is compatible with aged biomass to calculate the overall summed biomass - unaged2aged_mat = ( - total_aged_sexed_biomass - .merge( total_unaged_biomass , on = [ 'length_bin' , 'sex' ] ) - .assign( Summed_Wgt_Len_age = lambda df: df.groupby( [ 'sex' , 'length_bin' ] )[ 'total_sexed_aged_biomass_adult' ].transform( sum ) + df.total_unaged_biomass_adult ) - .assign( Final_wgt_len_age = lambda df: df.total_unaged_biomass_adult * df.total_sexed_aged_biomass_adult / df.Summed_Wgt_Len_age ) - .replace( np.nan , 0 ) - ) + redistributed_unaged_biomass = apply_age_bins( total_aged_sexed_biomass , + total_unaged_biomass ) # ### Calculate interpolated weights based on length bins for each sex per haul # haul_sex_weights_normalized = normalize_haul_sex_weights( self.statistics[ 'length_weight' ][ 'length_weight_df' ] , From de32d74a8a254bcbd89df75e80d66714c86ba4a2 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca Date: Mon, 1 Apr 2024 11:38:02 -0700 Subject: [PATCH 44/51] Overall apportioned biomass calculation - This includes: ** Some changes to `compute_index_aged_weight_proportions` to be compatible with both sexed and overall/total aged fish proportion calculations ** Creation of `calculate_aged_biomass` which calculates the sexed and overall/total aged biomass ** Creation of `calculate_unaged_biomass` which calculates the sexed and overall/total unaged biomass ** Creation of `apply_age_bins` which distributes unaged biomass over all age bins present in the aged biomass dataset --- echopop/computation/biology.py | 320 +++++++++++++++++++++++++++++---- echopop/survey.py | 124 ++++--------- 2 files changed, 318 insertions(+), 126 deletions(-) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index 6424b516..3240381f 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -182,14 +182,11 @@ def compute_index_aged_weight_proportions( specimen_data: pd.DataFrame , """ ### Process the specimen data - # ---- Drop unaged and unsexed fish + # ---- Drop unaged fish # ==== !!! TODO: pending what FEAT says, weights associated with # ==== missing ages should be added into the 'unaged' category. # ==== This would further mean apportioning these into `weight_strata_aged_uanged` - specimen_data_filtered = ( - specimen_data[ specimen_data.sex != 'unsexed' ] - .dropna( how = 'any' , subset = 'age' ) - ) + specimen_data_filtered = specimen_data[ specimen_data.sex != 'unsexed' ].dropna( how = 'any' , subset = 'age' ) # ---- Bin length and age measurements specimen_data_filtered = ( @@ -201,24 +198,46 @@ def compute_index_aged_weight_proportions( specimen_data: pd.DataFrame , ) ### Sum weights within each length and age bin for each sex within each stratum + # ---- Create separate 'weight_adult' column + specimen_data_filtered[ 'weight_adult' ] = ( + np.where( specimen_data_filtered.age > 1 , specimen_data_filtered.weight , 0.0 ) + ) + + # ---- Calculate aggregate sums of fish weights specimen_binned_weight = ( specimen_data_filtered # ---- Group weight summations across stratum/species/sex/length/age .groupby( [ 'stratum_num' , 'species_id' , 'sex' , 'length_bin' , 'age_bin' ] ) # ---- Sum the weights - .apply( lambda df: pd.Series( { 'weight_all': df.weight.sum( ) , - 'weight_adult': df.loc[ df.age > 1 ].weight.sum( ) } ) ) + .agg( weight_all =( 'weight' , 'sum' ) , weight_adult =( 'weight_adult' , 'sum' ) ) # ---- Fill empty/non-existent values with 0's .fillna( 0 ) .reset_index( ) ) + ### Calculate the total (i.e. across sex) + specimen_binned_weight_total = ( + specimen_binned_weight + # ---- Group weight summations across stratum/species/length/age + .groupby( [ 'stratum_num' , 'species_id' , 'length_bin' , 'age_bin' ] ) + # ---- Sum the weights using agg function + .agg( { 'weight_all': 'sum' , 'weight_adult': 'sum' } ) + .reset_index( ) + ) + + # ---- Add sex = 'all' + specimen_binned_weight_total[ 'sex' ] = 'all' + + # ---- Concatenate with `specimen_binned_weight`` + specimen_binned_weight_all = pd.concat( [ specimen_binned_weight.assign( group = 'sexed' ) , + specimen_binned_weight_total.assign( group = 'all' ) ] ) + ### Calculate the relative weight proportions of each length-age bin for each sex within each stratum proportions_weight_length_age_sex = ( - specimen_binned_weight + specimen_binned_weight_all # ---- Calculate total sex-specific weights for each stratum - .assign( total_weight_sex_all = lambda df: df.groupby( [ 'stratum_num' , 'species_id' , 'sex' ] )[ 'weight_all' ].transform( sum ) , - total_weight_sex_adult = lambda df: df.groupby( [ 'stratum_num' , 'species_id' , 'sex' ] )[ 'weight_adult' ].transform( sum ) ) + .assign( total_weight_sex_all = lambda df: df.groupby( [ 'stratum_num' , 'species_id' , 'sex' , 'group' ] )[ 'weight_all' ].transform( sum ) , + total_weight_sex_adult = lambda df: df.groupby( [ 'stratum_num' , 'species_id' , 'sex' , 'group' ] )[ 'weight_adult' ].transform( sum ) ) # ---- Calculate the weight proportions within each sex: from Matlab --> Len_age_key_wgt_*n .assign( proportion_weight_sex_all = lambda df: df.weight_all / df.total_weight_sex_all , proportion_weight_sex_adult = lambda df: df.weight_adult / df.total_weight_sex_adult ) @@ -256,6 +275,7 @@ def compute_summed_aged_proportions( proportions_weight_length_age_sex: pd.DataF # ---- Also initializes the correct shape for the output dataframe aged_sex_weights = ( proportions_weight_length_age_sex + [ proportions_weight_length_age_sex.group != 'all' ] .groupby( [ 'stratum_num' , 'sex' ] ) .agg( weight_aged_sex_all = ( 'weight_all' , 'sum' ) , @@ -283,7 +303,7 @@ def compute_summed_aged_proportions( proportions_weight_length_age_sex: pd.DataF # ---- Fill empty/NaN values with 0's and drop unused columns aged_sex_proportions = aged_sex_proportions.fillna( 0 ).filter( regex = '^(?!weight_).*' ) - + ### Calculate aged proportions aged_proportions = ( aged_sex_proportions @@ -614,48 +634,270 @@ def compute_unaged_sex_proportions( length_data: pd.DataFrame , ### Remove unnecessary columns and return output return proportions_unaged_weight_sex[ [ 'stratum_num' , 'sex' , 'proportion_weight_sex' ] ] -def apply_age_bins( aged_biomass_df: pd.DataFrame , - unaged_biomass_df: pd.DataFrame ): +def calculate_aged_biomass( kriging_biomass_df: pd.DataFrame , + distributed_sex_proportions_df: pd.DataFrame , + distributed_total_proportions_df: pd.DataFrame , + aged_proportions: pd.DataFrame ): + """ + Calculate the kriged biomass distributed over length and age for each sex and among + all fish (sexed) + + Parameters + ---------- + kriging_biomass_df: pd.DataFrame + Dataframe containing kriged biomass estimates + distributed_sex_proportions: pd.DataFrame + Dataframe containing the normalized weight proportions for each length and age bin + for each sex + distributed_total_proportions: pd.DataFrame + Dataframe containing the weight proportions for each length and age bin computed for all fish + aged_proportions: pd.DataFrame + Dataframe containing the weight proportions of aged fish within each stratum + """ + + ### Distribute the biomass estimates for each transect interval + ### the calculated proportions for age and length bins for each + ### sex among aged-only fish + # ---- Merge `kriging_biomass_df` with `age_proportions_df` + # ---- Sexed + biomass_sex_proportion_df = ( + # ---- Parse kriged biomass (areal density) values + kriging_biomass_df + .merge( distributed_sex_proportions_df , on = [ 'stratum_num' ] ) + ) + + # ---- All + biomass_proportion_total_df = pd.merge( kriging_biomass_df , + distributed_total_proportions_df , + on = [ 'stratum_num' ] , + how = 'left' ).set_index( 'stratum_num' ) + + # ---- Set index for `aged_proportions` in lieu of using another merge + aged_proportions = aged_proportions.copy( ).set_index( 'stratum_num' ) + + # ---- Assign 'proportion_weight_adult' + biomass_proportion_total_df[ 'proportion_weight_adult' ] = aged_proportions.proportion_weight_adult + + # ---- Assign 'proportion_weight_all' + biomass_proportion_total_df[ 'proportion_weight_all' ] = aged_proportions.proportion_weight_adult + + # ---- Distribute biomass estimates for each sexed bin among aged fish + # ==== !!! TODO: this current used `B_a_adult_mean` for both calculations + # ---- Sexed + # ---- All + biomass_sex_proportion_df[ 'biomass_sexed_aged_all' ] = ( + biomass_sex_proportion_df.B_a_adult_mean + * biomass_sex_proportion_df.cell_area_nmi2 + * biomass_sex_proportion_df.normalized_proportion_weight_all + ) + + # ---- Adult + biomass_sex_proportion_df[ 'biomass_sexed_aged_adult' ] = ( + biomass_sex_proportion_df.B_a_adult_mean + * biomass_sex_proportion_df.cell_area_nmi2 + * biomass_sex_proportion_df.normalized_proportion_weight_adult + ) + + # ---- All + # ---- All + biomass_proportion_total_df[ 'biomass_sexed_aged_all' ] = ( + biomass_proportion_total_df.B_a_adult_mean + * biomass_proportion_total_df.cell_area_nmi2 + * biomass_proportion_total_df.proportion_weight_sex_all + * biomass_proportion_total_df.proportion_weight_adult + ) + + # ---- Adult + biomass_proportion_total_df[ 'biomass_sexed_aged_adult' ] = ( + biomass_proportion_total_df.B_a_adult_mean + * biomass_proportion_total_df.cell_area_nmi2 + * biomass_proportion_total_df.proportion_weight_sex_adult + * biomass_proportion_total_df.proportion_weight_adult + ) + + ### Sum biomass across all bins for aged biomass + # ---- Sexed + total_aged_sexed_biomass = ( + biomass_sex_proportion_df + .groupby( [ 'length_bin' , 'age_bin' , 'sex' ] ) + .agg( total_sexed_aged_biomass_adult = ( 'biomass_sexed_aged_adult' , 'sum' ) ) + .reset_index( ) + ) + + # ---- All + total_aged_biomass = ( + biomass_proportion_total_df + .groupby( [ 'length_bin' , 'age_bin' ] ) + .agg( total_aged_biomass_adult = ( 'biomass_sexed_aged_adult' , 'sum' ) ) + .reset_index( ) + ) + + ### Return output (tuple) + return total_aged_sexed_biomass , total_aged_biomass + +def calculate_unaged_biomass( kriging_biomass_df: pd.DataFrame , + distributed_sex_proportions_df: pd.DataFrame , + distributed_length_proportions_df: pd.DataFrame , + unaged_proportions: pd.DataFrame ): + """ + Calculate the kriged biomass distributed over length and age for each sex and among + all fish (sexed) + + Parameters + ---------- + kriging_biomass_df: pd.DataFrame + Dataframe containing kriged biomass estimates + distributed_sex_proportions: pd.DataFrame + Dataframe containing the normalized weight proportions for each length and age bin + for each sex + distributed_total_proportions: pd.DataFrame + Dataframe containing the weight proportions for each length and age bin computed for all fish + aged_proportions: pd.DataFrame + Dataframe containing the weight proportions of aged fish within each stratum + """ + ### Calculate sex-specific weight proportions for unaged fish (across all fish, aged and unaged) + # ---- Merge `proportions_unaged_weight_sex` and `unaged_proportions` + proportions_unaged_weight_sex_overall = pd.merge( distributed_sex_proportions_df , + unaged_proportions , + on = [ 'stratum_num' ] , + how = 'left' ) + + # ---- Calculate the overall sexed proportions taking into account aged and unaged fish (all age class) + proportions_unaged_weight_sex_overall[ 'proportion_sexed_weight_all' ] = ( + proportions_unaged_weight_sex_overall.proportion_weight_sex * + proportions_unaged_weight_sex_overall.proportion_weight_all + ) + + # ---- Calculate the overall sexed proportions taking into account aged and unaged fish (adults) + proportions_unaged_weight_sex_overall[ 'proportion_sexed_weight_adult' ] = ( + proportions_unaged_weight_sex_overall.proportion_weight_sex * + proportions_unaged_weight_sex_overall.proportion_weight_adult + ) + + ### Merge kriging dataframe with calculated proportions + # ---- Sexed + unaged_sexed_biomass = ( + # ---- Parse kriged biomass (areal density) values + kriging_biomass_df + .merge( distributed_length_proportions_df , on = [ 'stratum_num' ] ) + .merge( proportions_unaged_weight_sex_overall , on = [ 'stratum_num' ] ) + ) + + # ---- All + unaged_biomass = ( + # ---- Parse kriged biomass (areal density) values + kriging_biomass_df + .merge( distributed_length_proportions_df , on = [ 'stratum_num' ] ) + .merge( unaged_proportions , on = [ 'stratum_num' ] ) + ) + + ### Calculate biomasses + # ---- Sexed + # ---- All + unaged_sexed_biomass[ 'biomass_sexed_unaged_all' ] = ( + unaged_sexed_biomass.B_a_adult_mean * + unaged_sexed_biomass.cell_area_nmi2 * + unaged_sexed_biomass.proportion_weight_length * + unaged_sexed_biomass.proportion_sexed_weight_all + ) + + # ---- Adult + unaged_sexed_biomass[ 'biomass_sexed_unaged_adult' ] = ( + unaged_sexed_biomass.B_a_adult_mean * + unaged_sexed_biomass.cell_area_nmi2 * + unaged_sexed_biomass.proportion_weight_length * + unaged_sexed_biomass.proportion_sexed_weight_adult + ) + + # ---- All + # ---- All + unaged_biomass[ 'biomass_unaged_all' ] = ( + unaged_biomass.B_a_adult_mean * + unaged_biomass.cell_area_nmi2 * + unaged_biomass.proportion_weight_length * + unaged_biomass.proportion_weight_all + ) + + # ---- Adult + unaged_biomass[ 'biomass_unaged_adult' ] = ( + unaged_biomass.B_a_adult_mean * + unaged_biomass.cell_area_nmi2 * + unaged_biomass.proportion_weight_length * + unaged_biomass.proportion_weight_adult + ) + + ### Sum biomass across all bins for unaged biomass + # ---- Sexed + total_unaged_sexed_biomass = ( + unaged_sexed_biomass + .groupby( [ 'length_bin' , 'sex' ] ) + .agg( total_sexed_unaged_biomass_adult = ( 'biomass_sexed_unaged_adult' , 'sum' ) ) + .reset_index( ) + ) + + # ---- All + total_unaged_biomass = ( + unaged_biomass + .groupby( [ 'length_bin' ] ) + .agg( total_unaged_biomass_adult = ( 'biomass_unaged_adult' , 'sum' ) ) + .reset_index( ) + ) + + ### Return output (tuple) + return total_unaged_sexed_biomass , total_unaged_biomass + +def apply_age_bins( aged_biomass_sexed_df: pd.DataFrame , + unaged_biomass_sexed_df: pd.DataFrame ): """ Redistribute unaged biomass over the defined age distribution Parameters ---------- - aged_biomass_df: pd.DataFrame - Dataframe containing biomass data of aged fish - unaged_biomass_df: pd.DataFrame - Dataframe containing biomass data of unaged fish + aged_biomass_sexed_df: pd.DataFrame + Dataframe containing biomass data of sexed aged fish + unaged_biomass_sexed_df: pd.DataFrame + Dataframe containing biomass data of sexed unaged fish + aged_biomass_total_df: pd.DataFrame + Dataframe containing biomass data of all aged fish + aged_biomass_total_df: pd.DataFrame + Dataframe containing biomass data of all unaged fish """ ### Merge unaged biomass length bins with aged bioamss length-age bins - aged_unaged_biomass = pd.merge( aged_biomass_df , - unaged_biomass_df , - on = [ 'length_bin' , 'sex' ] , - how = 'left' ) + aged_unaged_sexed_biomass = pd.merge( aged_biomass_sexed_df , + unaged_biomass_sexed_df , + on = [ 'length_bin' , 'sex' ] , + how = 'left' ) ### Calculate the total biomass for each sexed length bin (i.e. sum across age bins) # ---- Adult fish - aged_unaged_biomass[ 'total_length_bin_biomass_adult' ] = ( - aged_unaged_biomass.groupby( [ 'sex' , 'length_bin' ] )[ 'total_sexed_aged_biomass_adult' ].transform( sum ) + - aged_unaged_biomass.total_unaged_biomass_adult + aged_unaged_sexed_biomass[ 'sum_length_bin_biomass_sexed_adult' ] = ( + aged_unaged_sexed_biomass.groupby( [ 'sex' , 'length_bin' ] )[ 'total_sexed_aged_biomass_adult' ].transform( sum ) + + aged_unaged_sexed_biomass.total_sexed_unaged_biomass_adult ) ### Redistribute unaged biomass over the length-age bins for each sex # ---- Adult fish - aged_unaged_biomass[ 'total_unaged_biomass' ] = ( - aged_unaged_biomass.total_unaged_biomass_adult * - aged_unaged_biomass.total_sexed_aged_biomass_adult / - aged_unaged_biomass.total_length_bin_biomass_adult - ) + aged_unaged_sexed_biomass[ 'total_sexed_unaged_biomass_adult' ] = ( + aged_unaged_sexed_biomass.total_sexed_unaged_biomass_adult * + aged_unaged_sexed_biomass.total_sexed_aged_biomass_adult / + aged_unaged_sexed_biomass.sum_length_bin_biomass_sexed_adult + ).fillna( 0 ) ### Remove unnecessary columns - aged_unaged_biomass.drop( [ 'total_sexed_aged_biomass_adult' , - 'total_unaged_biomass_all' , - 'total_unaged_biomass_adult' , - 'total_length_bin_biomass_adult' , - 'total_length_bin_biomass_adult' ] , - axis = 1 , - inplace = True ) - - ### Replace NaN with 0's and return output - return aged_unaged_biomass.fillna( 0 ) \ No newline at end of file + aged_unaged_sexed_biomass.drop( [ 'total_sexed_aged_biomass_adult' , + 'sum_length_bin_biomass_sexed_adult' ] , + axis = 1 , + inplace = True ) + + ### Sum sexes together to retrieve the total biomass + aged_unaged_biomass = ( + aged_unaged_sexed_biomass + .groupby( [ 'length_bin' , 'age_bin' ] ) + .agg( total_unaged_biomass_adult = ( 'total_sexed_unaged_biomass_adult' , 'sum' ) ) + .reset_index( ) + ) + + + ### Return output (tuple) + return aged_unaged_sexed_biomass , aged_unaged_biomass \ No newline at end of file diff --git a/echopop/survey.py b/echopop/survey.py index d9ee0ba7..65212d7d 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -15,8 +15,8 @@ from .computation.kriging_methods import kriging_interpolation from .computation.biology import index_sex_weight_proportions , index_transect_age_sex_proportions , filter_species from .computation.biology import sum_strata_weight , compute_index_aged_weight_proportions , distribute_aged_weight_proportions -from .computation.biology import compute_summed_aged_proportions , compute_index_unaged_number_proportions -from .computation.biology import compute_summed_unaged_weight_proportions , compute_unaged_sex_proportions , apply_age_bins +from .computation.biology import compute_summed_aged_proportions , compute_index_unaged_number_proportions , calculate_aged_biomass +from .computation.biology import compute_summed_unaged_weight_proportions , compute_unaged_sex_proportions , apply_age_bins , calculate_unaged_biomass ### !!! TODO : This is a temporary import call -- this will need to be changed to # the correct relative structure (i.e. '.utils.data_structure_utils' instead of @@ -1323,36 +1323,11 @@ def apportion_kriged_biomass( self , distributed_aged_weight_proportions = distribute_aged_weight_proportions( proportions_weight_length_age_sex , aged_sex_proportions ) - ### Distribute the biomass estimates for each transect interval across - # ---- the calculated proportions for age and length bins for each - # ---- sex among aged-only fish - aged_sexed_biomass = ( - # ---- Parse kriged biomass (areal density) values - self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] - .copy( ) - .merge( distributed_aged_weight_proportions , on = [ 'stratum_num' ] ) - ) - - # ---- Distribute biomass estimates for each sexed bin among aged fish - # ==== !!! TODO: this current used `B_a_adult_mean` for both calculations - aged_sexed_biomass[ 'biomass_sexed_aged_all' ] = ( - aged_sexed_biomass.B_a_adult_mean - * aged_sexed_biomass.cell_area_nmi2 - * aged_sexed_biomass.normalized_proportion_weight_all - ) - aged_sexed_biomass[ 'biomass_sexed_aged_adult' ] = ( - aged_sexed_biomass.B_a_adult_mean - * aged_sexed_biomass.cell_area_nmi2 - * aged_sexed_biomass.normalized_proportion_weight_adult - ) - - ### Sum biomass across all bins for aged biomass - total_aged_sexed_biomass = ( - aged_sexed_biomass - .groupby( [ 'length_bin' , 'age_bin' , 'sex' ] ) - .agg( total_sexed_aged_biomass_adult = ( 'biomass_sexed_aged_adult' , 'sum' ) ) - .reset_index( ) - ) + # !!! TODO: This does end up chewing up * a ton * of memory since the output dataframes are quite large + aged_sex_biomass , aged_biomass = calculate_aged_biomass( self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] , + distributed_aged_weight_proportions , + proportions_weight_length_age_sex[ proportions_weight_length_age_sex.group == 'all' ] , + aged_proportions ) ### Calculate number proportion proportions_unaged_length = compute_index_unaged_number_proportions( length_spp , @@ -1378,65 +1353,40 @@ def apportion_kriged_biomass( self , 'proportion_weight_all': 1.0 - aged_proportions.proportion_weight_all , 'proportion_weight_adult': 1.0 - aged_proportions.proportion_weight_adult , } ) + + ### Calculate unaged biomass for each sex and all animals + unaged_sex_biomass , unaged_biomass = calculate_unaged_biomass( self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] , + proportions_unaged_weight_sex , + proportions_unaged_weight_length , + unaged_proportions ) - ### Calculate sex-specific weight proportions for unaged fish (across all fish, aged and unaged) - # ---- Merge `proportions_unaged_weight_sex` and `unaged_proportions` - proportions_unaged_weight_sex_overall = pd.merge( proportions_unaged_weight_sex , - unaged_proportions , - on = [ 'stratum_num' ] , - how = 'left' ) - - # ---- Calculate the overall sexed proportions taking into account aged and unaged fish (all age class) - proportions_unaged_weight_sex_overall[ 'proportion_sexed_weight_all' ] = ( - proportions_unaged_weight_sex_overall.proportion_weight_sex * - proportions_unaged_weight_sex_overall.proportion_weight_all - ) - - # ---- Calculate the overall sexed proportions taking into account aged and unaged fish (adults) - proportions_unaged_weight_sex_overall[ 'proportion_sexed_weight_adult' ] = ( - proportions_unaged_weight_sex_overall.proportion_weight_sex * - proportions_unaged_weight_sex_overall.proportion_weight_adult - ) - - ### Calculate unaged biomass for each sex - unaged_sexed_biomass = ( - # ---- Parse kriged biomass (areal density) values - self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] - .copy( ) - .merge( proportions_unaged_weight_length , on = [ 'stratum_num' ] ) - .merge( proportions_unaged_weight_sex_overall , on = [ 'stratum_num' ] ) - .assign( biomass_sexed_unaged_all = lambda df: ( df.B_a_adult_mean * - df.cell_area_nmi2 * - df.proportion_weight_length * - df.proportion_sexed_weight_all ) , - biomass_sexed_unaged_adult = lambda df: ( df.B_a_adult_mean * - df.cell_area_nmi2 * - df.proportion_weight_length * - df.proportion_sexed_weight_adult ) - ) + ### Re-distribute unaged biomass so it is compatible with aged biomass to calculate the overall summed biomass + redistributed_unaged_sex_biomass , redistributed_unaged_biomass = apply_age_bins( aged_sex_biomass , + unaged_sex_biomass ) + + ### Sum the grand total by combining the aged and unaged biomass estimates post-apportionment + # ---- Merge sexed + overall_sexed_biomass = redistributed_unaged_sex_biomass.merge( aged_sex_biomass , + on = [ 'length_bin' , 'age_bin' , 'sex' ] , + how = 'left' ) + + # ---- Aggregate (sum) + overall_sexed_biomass[ 'total_sexed_biomass_adult' ] = ( + overall_sexed_biomass.total_sexed_unaged_biomass_adult + + overall_sexed_biomass.total_sexed_aged_biomass_adult ) - - ### Calculate total biomass - total_unaged_biomass = ( - unaged_sexed_biomass - .groupby( [ 'length_bin' , 'sex' ] ) - .agg( total_unaged_biomass_all = ( 'biomass_sexed_unaged_all' , 'sum' ) , - total_unaged_biomass_adult = ( 'biomass_sexed_unaged_adult' , 'sum' ) ) - .reset_index( ) + + # ---- Merge total + overall_biomass = redistributed_unaged_biomass.merge( aged_biomass , + on = [ 'length_bin' , 'age_bin' ] , + how = 'left' ) + + # ---- Aggregate (sum) + overall_biomass[ 'total_biomass_adult' ] = ( + overall_biomass.total_unaged_biomass_adult + + overall_biomass.total_aged_biomass_adult ) - ### Re-distribute unaged biomass so it is compatible with aged biomass to calculate the overall summed biomass - redistributed_unaged_biomass = apply_age_bins( total_aged_sexed_biomass , - total_unaged_biomass ) - - # ### Calculate interpolated weights based on length bins for each sex per haul - # haul_sex_weights_normalized = normalize_haul_sex_weights( self.statistics[ 'length_weight' ][ 'length_weight_df' ] , - # length_df_copy , - # weight_strata , - # weight_strata_station , - # aged_proportions , - # length_intervals ) - # ### Parse kriged biomass (areal density) values # kriged_results = self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] From 10f3d6566e53550ae6ab796a42e5b367f5899148 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca Date: Mon, 1 Apr 2024 12:14:55 -0700 Subject: [PATCH 45/51] Fixes to `test_kriging_biomass_apportionment` - Includes: ** Updated `test_kriging_biomass_apportionment` to account for changes to `kriging_biomass_apportionment` --- echopop/computation/biology.py | 2 +- .../test_kriging_biomass_apportionment.py | 55 ++++++++++++------- 2 files changed, 37 insertions(+), 20 deletions(-) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index 3240381f..bda1f0c4 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -253,7 +253,7 @@ def compute_index_aged_weight_proportions( specimen_data: pd.DataFrame , ) ### Return output - return proportions_weight_length_age_sex + return proportions_weight_length_age_sex.reset_index( drop = True ) def compute_summed_aged_proportions( proportions_weight_length_age_sex: pd.DataFrame , weight_strata: pd.DataFrame ): diff --git a/echopop/tests/test_kriging_biomass_apportionment.py b/echopop/tests/test_kriging_biomass_apportionment.py index 01ca6fd3..dd92d75f 100644 --- a/echopop/tests/test_kriging_biomass_apportionment.py +++ b/echopop/tests/test_kriging_biomass_apportionment.py @@ -75,8 +75,8 @@ def test_compute_index_aged_weight_proportions( mock_survey ): 'sex': np.tile( [ 'male' , 'female' ] , 4 ) , 'haul_num': np.tile( [ 1 , 2 ] , 4 ) , 'species_id': np.repeat( [ 19350 ] , 8 ) , - 'length': [ 12 , 12 , 19 , 19 , 12 , 12 , 19 , 19 ] , - 'weight': [ 2 , 3 , 8 , 7 , 1 , 4 , 9 , 6 ] , + 'length': [ 12.0 , 12.0 , 19.0 , 19.0 , 12.0 , 12.0 , 19.0 , 19.0 ] , + 'weight': [ 2.0 , 3.0 , 8.0 , 7.0 , 1.0 , 4.0 , 9.0 , 6.0 ] , 'age': [ 1 , 1 , 2 , 2 , 1 , 1 , 2 , 2 ] } ) @@ -95,31 +95,48 @@ def test_compute_index_aged_weight_proportions( mock_survey ): ### Expected outcomes ###-------------------------------- # ---- Expected dimensions of `obj_props_wgt_len_age_sex` - expected_dimensions = tuple( [ 16 , 11 ] ) + expected_dimensions = tuple( [ 24 , 12 ] ) # ---- Expected dataframe output + # stratum_num + stratum_array = np.repeat( [ 0 , 1 ] , 8 ).astype( np.int64 ).flatten( ) + stratum_array = np.concatenate( [ stratum_array , np.repeat( [ 0 , 1 ] , 4 ).astype( np.int64 ).flatten( ) ] ) + # sex + sex_array = np.tile( [ 'female' , 'female' , 'female' , 'female' , 'male' , 'male' , 'male' , 'male'] , 2 ).astype( object ).flatten( ) + sex_array = np.concatenate( [ sex_array , np.repeat( 'all' , 8 ).astype( object ).flatten( ) ] ) + # group + group_array = np.repeat( 'sexed' , 16 ).flatten( ) + group_array = np.concatenate( [ group_array , np.repeat( 'all' , 8 ) ] ).flatten( ) + # total_weight_sex_all + total_weight_sex_all_array = np.repeat( 10 , 16 ).astype( np.float64 ).flatten( ) + total_weight_sex_all_array = np.concatenate( [ total_weight_sex_all_array , np.repeat( 20 , 8 ).astype( np.float64 ).flatten( ) ] ) + expected_output = pd.DataFrame( { - 'stratum_num': np.repeat( [ 0 , 1 ] , 8 ).astype( np.int64 ) , - 'species_id': np.repeat( 19350 , 16 ).astype( np.int64 ) , - 'sex': np.tile( [ 'female' , 'female' , 'female' , 'female' , - 'male' , 'male' , 'male' , 'male' ] , 2 ).astype( object ) , - 'length_bin': pd.cut( [ 12 , 12 , 19 , 19 , 12 , 12 , 19 , 19 , - 12 , 12 , 19 , 19 , 12 , 12 , 19 , 19 ] , - np.linspace( 9 , 21 , 3 ) ) , - 'age_bin': pd.cut( [ 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , - 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 ] , - np.array( [ 0.5 , 1.5 , 2.5 ] ) ) , + 'stratum_num': stratum_array , + 'species_id': np.repeat( 19350 , 24 ).astype( np.int64 ) , + 'sex': sex_array , + 'length_bin': pd.cut( np.tile( [ 12 , 12 , 19 , 19 ] , 6 ) , + np.linspace( 9 , 21 , 3 ) ) , + 'age_bin': pd.cut( np.tile( [ 1 , 2 ] , 12 ) , + np.array( [ 0.5 , 1.5 , 2.5 ] ) ) , 'weight_all': [ 3.0 , 0.0 , 0.0 , 7.0 , 2.0 , 0.0 , 0.0 , 8.0 , - 4.0 , 0.0 , 0.0 , 6.0 , 1.0 , 0.0 , 0.0 , 9.0 ] , + 4.0 , 0.0 , 0.0 , 6.0 , 1.0 , 0.0 , 0.0 , 9.0 , + 5.0 , 0.0 , 0.0 , 15.0 , 5.0 , 0.0 , 0.0 , 15.0 ] , 'weight_adult': [ 0.0 , 0.0 , 0.0 , 7.0 , 0.0 , 0.0 , 0.0 , 8.0 , - 0.0 , 0.0 , 0.0 , 6.0 , 0.0 , 0.0 , 0.0 , 9.0 ] , - 'total_weight_sex_all': np.repeat( 10.0 , 16 ) , + 0.0 , 0.0 , 0.0 , 6.0 , 0.0 , 0.0 , 0.0 , 9.0 , + 0.0 , 0.0 , 0.0 , 15.0 , 0.0 , 0.0 , 0.0 , 15.0 ] , + 'group': group_array , + 'total_weight_sex_all': total_weight_sex_all_array , 'total_weight_sex_adult': [ 7.0 , 7.0 , 7.0 , 7.0 , 8.0 , 8.0 , 8.0 , 8.0 , - 6.0 , 6.0 , 6.0 , 6.0 , 9.0 , 9.0 , 9.0 , 9.0 ] , + 6.0 , 6.0 , 6.0 , 6.0 , 9.0 , 9.0 , 9.0 , 9.0 , + 15.0 , 15.0 , 15.0 , 15.0 , 15.0 , 15.0 , 15.0 , 15.0 ] , 'proportion_weight_sex_all': [ 0.3 , 0.0 , 0.0 , 0.7 , 0.2 , 0.0 , 0.0 , 0.8 , - 0.4 , 0.0 , 0.0 , 0.6 , 0.1 , 0.0 , 0.0 , 0.9 ] , + 0.4 , 0.0 , 0.0 , 0.6 , 0.1 , 0.0 , 0.0 , 0.9 , + 0.25 , 0.0 , 0.0 , 0.75 , 0.25 , 0.0 , 0.0 , 0.75 ] , 'proportion_weight_sex_adult': [ 0.0 , 0.0 , 0.0 , 1.0 , 0.0 , 0.0 , 0.0 , 1.0 , - 0.0 , 0.0 , 0.0 , 1.0 , 0.0 , 0.0 , 0.0 , 1.0 ] } ) + 0.0 , 0.0 , 0.0 , 1.0 , 0.0 , 0.0 , 0.0 , 1.0 , + 0.0 , 0.0 , 0.0 , 1.0 , 0.0 , 0.0 , 0.0 , 1.0 ] + } ) expected_output[ 'length_bin' ] = pd.IntervalIndex( expected_output[ 'length_bin' ] ) expected_output[ 'length_bin' ] = pd.Categorical( expected_output[ 'length_bin' ] , categories = expected_output[ 'length_bin' ].unique( ) , From 75b85de1d4ec0a7b6361ece7b3f42514e4cfdec3 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca Date: Mon, 1 Apr 2024 12:20:46 -0700 Subject: [PATCH 46/51] Edits to `test_compute_summed_aged_proportions` - Includes: ** Changes to `test_compute_summed_aged_proportions` to account for upstream changes to related functions such as `compute_index_aged_weight_proportions` --- .../test_kriging_biomass_apportionment.py | 19 +++++++++++++------ 1 file changed, 13 insertions(+), 6 deletions(-) diff --git a/echopop/tests/test_kriging_biomass_apportionment.py b/echopop/tests/test_kriging_biomass_apportionment.py index dd92d75f..6e6edc6a 100644 --- a/echopop/tests/test_kriging_biomass_apportionment.py +++ b/echopop/tests/test_kriging_biomass_apportionment.py @@ -165,13 +165,20 @@ def test_compute_summed_aged_proportions( ): ### Mock data for `proportions_weight_length_age_sex` test_proportions_weight_length_age_sex = pd.DataFrame( { - 'stratum_num': np.repeat( [ 0.0 , 1.0 ] , 4 ) , - 'sex': np.tile( [ 'male' , 'female' ] , 4 ) , - 'weight_all': [ 1.0 , 3.0 , 2.0 , 4.0 , 0.0 , 6.0 , 3.0 , 3.0 ] , - 'weight_adult': [ 0.0 , 1.5, 2.0 , 4.0 , 0.0 , 4.0 , 3.0 , 3.0 ] + 'stratum_num': np.repeat( [ 0.0 , 1.0 ] , 6 ) , + 'sex': np.tile( [ 'male' , 'female' , 'all' ] , 4 ) , + 'group': np.tile( [ 'sexed' , 'sexed' , 'all' ] , 4 ) , + 'weight_all': [ 1.0 , 3.0 , 4.0 , + 2.0 , 4.0 , 6.0 , + 0.0 , 6.0 , 6.0 , + 3.0 , 3.0 , 6.0 ] , + 'weight_adult': [ 0.0 , 1.5, 1.5 , + 2.0 , 4.0 , 6.0 , + 0.0 , 4.0 , 4.0 , + 3.0 , 3.0 , 6.0 ] } ) - + ### Mock data for `weight_strata` test_weight_strata = pd.DataFrame( { @@ -221,7 +228,7 @@ def test_compute_summed_aged_proportions( ): ### `eval_aged_proportions` # ---- Shape assert eval_aged_proportions.shape == expected_dimensions_aged_proportions - # ---- Datatypes + # ---- Datatypesd assert np.all( eval_aged_proportions.dtypes == expected_output_aged_proportions.dtypes ) # ---- Dataframe equality assert np.all( eval_aged_proportions == expected_output_aged_proportions ) From aa10adf3dda6b96b9b9851ba87cf9cfc824bf9fa Mon Sep 17 00:00:00 2001 From: Brandyn Lucca Date: Tue, 2 Apr 2024 18:52:22 -0700 Subject: [PATCH 47/51] Overhaul to `kriging_apportionment` - Includes: ** Large changes to a variety of functions needed to calculate biomass, which now have all been embedded within their respective aged and unaged functions --- echopop/computation/biology.py | 696 ++++++++++++++++----------------- echopop/survey.py | 120 ++---- 2 files changed, 373 insertions(+), 443 deletions(-) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index bda1f0c4..3d7c470b 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -165,9 +165,61 @@ def sum_strata_weight( haul_data: pd.DataFrame , ### Carriage return return weight_strata_aged_unaged , weight_strata -def compute_index_aged_weight_proportions( specimen_data: pd.DataFrame , - length_intervals: np.ndarray , - age_intervals: np.ndarray ): +def compute_aged_unaged_proportions( specimen_data: pd.DataFrame , + weight_strata: pd.DataFrame ): + """ + Compute the overall weight proportions for aged and uanged fish within each stratum + + Parameters + ---------- + specimen_data: pd.DataFrame + Dataframe containing specimen weights + weight_strata: pd.DataFrame + Dataframe contained summed weights of both aged and unaged fish + """ + ### Calculate adult:all age ratio for each stratum + # ---- Drop unaged fish + specimen_data_filtered = specimen_data[ specimen_data.sex != 'unsexed' ].dropna( how = 'any' , subset = 'age' ) + + # ---- Sum the weight of adult fish + specimen_adult = specimen_data_filtered[ specimen_data_filtered.age > 1 ].groupby( [ 'stratum_num' ] )[ 'weight' ].sum( ) + + # ---- Sum the weight of all fish + specimen_all = specimen_data_filtered.groupby( [ 'stratum_num' ] )[ 'weight' ].sum( ).to_frame( ) + + # ---- Calculate the ratio between the two groups + specimen_all[ 'weight_ratio' ] = ( specimen_adult / specimen_all.weight ).fillna( 0 ) + + ### Calculate the aged proportions of all fish + # ---- Merge with `weight_strata` + aged_proportions = specimen_all.reset_index( ).merge( weight_strata , on = [ 'stratum_num' ] ) + + # ---- Calculate the aged proportion of all fish + aged_proportions[ 'proportion_aged_weight_all' ] = ( + aged_proportions.weight / aged_proportions.weight_stratum_all + ) + + # ---- Calculate the aged proportion of adult fish + aged_proportions[ 'proportion_aged_weight_adult' ] = ( + aged_proportions.proportion_aged_weight_all * aged_proportions.weight_ratio + ) + + ### Calculate the unaged weight proportions + # ---- All fish + aged_proportions[ 'proportion_unaged_weight_all' ] = ( + 1.0 - aged_proportions.proportion_aged_weight_all + ) + + # ---- Adult fish + aged_proportions[ 'proportion_unaged_weight_adult' ] = ( + 1.0 - aged_proportions.proportion_aged_weight_adult + ) + + return aged_proportions.drop( [ 'weight_ratio' ] , axis = 1 ) + +def compute_aged_weight_proportions( specimen_data: pd.DataFrame , + length_intervals: np.ndarray , + age_intervals: np.ndarray ): """ Calculate length-age binned weight proportions @@ -214,30 +266,13 @@ def compute_index_aged_weight_proportions( specimen_data: pd.DataFrame , .fillna( 0 ) .reset_index( ) ) - - ### Calculate the total (i.e. across sex) - specimen_binned_weight_total = ( - specimen_binned_weight - # ---- Group weight summations across stratum/species/length/age - .groupby( [ 'stratum_num' , 'species_id' , 'length_bin' , 'age_bin' ] ) - # ---- Sum the weights using agg function - .agg( { 'weight_all': 'sum' , 'weight_adult': 'sum' } ) - .reset_index( ) - ) - - # ---- Add sex = 'all' - specimen_binned_weight_total[ 'sex' ] = 'all' - - # ---- Concatenate with `specimen_binned_weight`` - specimen_binned_weight_all = pd.concat( [ specimen_binned_weight.assign( group = 'sexed' ) , - specimen_binned_weight_total.assign( group = 'all' ) ] ) ### Calculate the relative weight proportions of each length-age bin for each sex within each stratum proportions_weight_length_age_sex = ( - specimen_binned_weight_all + specimen_binned_weight # ---- Calculate total sex-specific weights for each stratum - .assign( total_weight_sex_all = lambda df: df.groupby( [ 'stratum_num' , 'species_id' , 'sex' , 'group' ] )[ 'weight_all' ].transform( sum ) , - total_weight_sex_adult = lambda df: df.groupby( [ 'stratum_num' , 'species_id' , 'sex' , 'group' ] )[ 'weight_adult' ].transform( sum ) ) + .assign( total_weight_sex_all = lambda df: df.groupby( [ 'stratum_num' , 'species_id' , 'sex' ] )[ 'weight_all' ].transform( sum ) , + total_weight_sex_adult = lambda df: df.groupby( [ 'stratum_num' , 'species_id' , 'sex' ] )[ 'weight_adult' ].transform( sum ) ) # ---- Calculate the weight proportions within each sex: from Matlab --> Len_age_key_wgt_*n .assign( proportion_weight_sex_all = lambda df: df.weight_all / df.total_weight_sex_all , proportion_weight_sex_adult = lambda df: df.weight_adult / df.total_weight_sex_adult ) @@ -255,19 +290,19 @@ def compute_index_aged_weight_proportions( specimen_data: pd.DataFrame , ### Return output return proportions_weight_length_age_sex.reset_index( drop = True ) -def compute_summed_aged_proportions( proportions_weight_length_age_sex: pd.DataFrame , - weight_strata: pd.DataFrame ): +def compute_aged_sex_weight_proportions( proportions_weight_length_age_sex: pd.DataFrame , + aged_proportions: pd.DataFrame ): """ - Compute the aged proportions across all fish and specific sexes for each - length and age bin + Compute the aged proportions across all fish and specific sexes for each + length and age bin Parameters ---------- proportions_weight_length_age_sex: pd.DataFrame Dataframe containing sexed weight proportions distributed across each age and length bin - weight_strata: pd.DataFrame - Dataframe contained summed weights of both aged and unaged fish + aged_proportions: pd.DataFrame + Dataframe containing the weight proportions of aged fish within each stratum """ ### Caclulate the summed weights for all and adult fish @@ -275,7 +310,6 @@ def compute_summed_aged_proportions( proportions_weight_length_age_sex: pd.DataF # ---- Also initializes the correct shape for the output dataframe aged_sex_weights = ( proportions_weight_length_age_sex - [ proportions_weight_length_age_sex.group != 'all' ] .groupby( [ 'stratum_num' , 'sex' ] ) .agg( weight_aged_sex_all = ( 'weight_all' , 'sum' ) , @@ -286,10 +320,9 @@ def compute_summed_aged_proportions( proportions_weight_length_age_sex: pd.DataF ### Calculate the weight proportions # ---- Merge `aged_sex_weights` with `weight_strata` to get strata weights - aged_sex_proportions = pd.merge( aged_sex_weights , - weight_strata , - on = [ 'stratum_num' ] , - how = 'left' ) + aged_sex_proportions = aged_sex_weights.merge( aged_proportions , + on = [ 'stratum_num' ] , + how = 'left' ) # ---- Proportions for all fish aged_sex_proportions[ 'proportion_weight_all' ] = ( @@ -302,22 +335,10 @@ def compute_summed_aged_proportions( proportions_weight_length_age_sex: pd.DataF ) # ---- Fill empty/NaN values with 0's and drop unused columns - aged_sex_proportions = aged_sex_proportions.fillna( 0 ).filter( regex = '^(?!weight_).*' ) - - ### Calculate aged proportions - aged_proportions = ( - aged_sex_proportions - .groupby( [ 'stratum_num' ] ) - # ---- Sum proportions from each sex for each stratum - .agg( { - 'proportion_weight_all': 'sum' , - 'proportion_weight_adult': 'sum' - } ) - .reset_index( ) - ) - - ### Return output - return aged_sex_proportions , aged_proportions + aged_sex_proportions = aged_sex_proportions.fillna( 0 ).filter( regex = "^(?=.*aged|stratum_num|sex)(?!.*unaged|weight_)" ) + + ### Return output + return aged_sex_proportions def distribute_aged_weight_proportions( proportions_weight_length_age_sex: pd.DataFrame , aged_sex_proportions: pd.DataFrame ): @@ -335,106 +356,161 @@ def distribute_aged_weight_proportions( proportions_weight_length_age_sex: pd.Da ### Calculate the normalized age-length-sex aged fish weight proportions # ---- Merge `proportions_weight_length_age_sex` with `aged_sex_proportions` - distributed_aged_weight_proportions = pd.merge( proportions_weight_length_age_sex , - aged_sex_proportions , - on = [ 'stratum_num' , 'sex' ] , - how = 'left' ) + distributed_aged_weight_proportions = proportions_weight_length_age_sex.merge( aged_sex_proportions , + on = [ 'stratum_num' , 'sex' ] , + how = 'left' ) # ---- Normalized weight proportions for all fish distributed_aged_weight_proportions[ 'normalized_proportion_weight_all' ] = ( - distributed_aged_weight_proportions.proportion_weight_sex_all * distributed_aged_weight_proportions.proportion_weight_all + distributed_aged_weight_proportions.proportion_weight_sex_all * distributed_aged_weight_proportions.proportion_aged_weight_all ) # ---- Normalized weight proportions for jus adult fish distributed_aged_weight_proportions[ 'normalized_proportion_weight_adult' ] = ( - distributed_aged_weight_proportions.proportion_weight_sex_all * distributed_aged_weight_proportions.proportion_weight_adult - ) + distributed_aged_weight_proportions.proportion_weight_sex_adult * distributed_aged_weight_proportions.proportion_aged_weight_adult + ) # ---- Remove unnecessary columns and return the dataframe return distributed_aged_weight_proportions.filter( regex = '^(?!weight_|total_weight_|proportion_).*' ) -def calculate_aged_proportions( weight_length_age_sex_stratum: pd.DataFrame , - weight_strata: pd.DataFrame ): - - ### Calculate aged / unaged proportions - aged_proportions = ( - weight_length_age_sex_stratum - .loc[ lambda df: df.sex != 'all' ] - .groupby( [ 'stratum_num' ] ) - .apply( lambda df: pd.Series( { 'weight_aged_total': df.summed_weight_all.sum( ) } ) ) - .reset_index( ) - .merge( weight_strata , on = [ 'stratum_num' ] ) - .assign( proportion_aged_total = lambda x: x.weight_aged_total / x.weight_stratum_total , - proportion_unaged_total = lambda x: np.round( 1.0 - x.proportion_aged_total , decimals = 10 ) ) - ) +def calculate_aged_biomass( kriging_biomass_df: pd.DataFrame , + specimen_data: pd.DataFrame , + length_distribution: pd.DataFrame , + age_distribution: pd.DataFrame , + aged_proportions: pd.DataFrame ): + """ + Calculate the kriged biomass distributed over length and age for each sex and among + all fish (sexed) - ### Carriage return - return aged_proportions - -# def normalize_haul_sex_weights( length_weight_df: pd.DataFrame , -# length_df: pd.DataFrame , -# weight_strata: pd.DataFrame , -# weight_strata_station: pd.DataFrame , -# aged_proportions: pd.DataFrame , -# length_intervals: np.ndarray ): - -# ### Calculate interpolated weights based on length bins for each sex per haul -# length_weight_fit = ( -# length_weight_df -# .copy( ) -# .assign( length_bin_value = lambda x: x[ 'length_bin' ].apply( lambda y: y.mid ) ) -# ) - -# # Sum haul weights per sex per stratum -# haul_weights = ( -# length_df -# .bin_variable( length_intervals , 'length' ) -# .loc[ lambda x: x.group == 'sexed' ] -# .pipe( lambda df: pd.concat( [ df , df.assign( sex = 'all' ) ] ) ) -# .merge( length_weight_fit , on = [ 'sex' , 'length_bin' ] ) -# .groupby( [ 'stratum_num' , 'haul_num' , 'sex' ] ) -# .apply( lambda df: pd.Series( { 'weight_interp': ( np.interp( df.length, -# length_weight_fit.loc[ lambda x: x.sex.isin( df.sex ) ][ 'length_bin_value' ] , -# length_weight_fit.loc[ lambda x: x.sex.isin( df.sex ) ][ 'weight_modeled' ] ) * -# df.length_count ).sum( ) } ) ) -# .groupby( [ 'stratum_num' , 'sex' ] ) -# .apply( lambda df: pd.Series( { 'summed_haul_weights': df.weight_interp.sum( ) } ) ) -# .reset_index( ) -# ) - -# ### Normalize haul weights (Station 1) -# haul_sex_weights_normalized = ( -# haul_weights -# .merge( weight_strata_station.loc[ lambda x: x.station == 1 ] , -# on = 'stratum_num' ) -# .assign( weight_normalized_station_1 = lambda df: ( -# df -# .groupby( [ 'stratum_num' ] ) -# .apply( lambda strata: strata[ 'stratum_weight' ] * strata[ 'summed_haul_weights' ] / -# ( df[ ( df.stratum_num == strata.name ) & ( df.sex == 'male' ) ][ 'summed_haul_weights' ].iloc[ 0 ] + -# df[ ( df.stratum_num == strata.name ) & ( df.sex == 'female' ) ][ 'summed_haul_weights' ].iloc[ 0 ] ) ) -# .reset_index( drop = True ) ) ) -# .merge( weight_strata , on = [ 'stratum_num' ] ) -# .assign( proportion_normalized_station_1 = lambda df: ( -# df.weight_normalized_station_1 / df.weight_stratum_total -# ) ) -# .assign( sex_proportion = lambda df: ( -# df -# .groupby( [ 'stratum_num' ] ) -# .apply( lambda strata: strata[ 'proportion_normalized_station_1' ] / -# ( df[ ( df.stratum_num == strata.name ) & ( df.sex == 'male' ) ][ 'proportion_normalized_station_1' ].iloc[ 0 ] + -# df[ ( df.stratum_num == strata.name ) & ( df.sex == 'female' ) ][ 'proportion_normalized_station_1' ].iloc[ 0 ] ) ) -# .reset_index( drop = True ) ) ) -# .merge( aged_proportions.loc[ : , [ 'stratum_num' , 'proportion_unaged_total' ] ] , -# on = [ 'stratum_num' ] ) -# .assign( proportion_unaged_sex = lambda x: x.sex_proportion * x.proportion_unaged_total ) -# ) - -# ### Carriage return -# return haul_sex_weights_normalize - -def compute_index_unaged_number_proportions( length_data: pd.DataFrame , - length_intervals: np.ndarray ): + Parameters + ---------- + kriging_biomass_df: pd.DataFrame + Dataframe containing kriged biomass estimates + specimen_data: pd.DataFrame + Dataframe containing specimen weights + length_distribution: np.ndarray + Array containing length bins/intervals + age_distribution: np.ndarray + Array containing age bins/intervals + aged_proportions: pd.DataFrame + Dataframe containing the weight proportions of aged fish within each stratum + """ + + ### Sum aged fish weights across age and length bins, then calculate weight proportions + proportions_weight_length_age_sex = compute_aged_weight_proportions( specimen_data , + length_distribution , + age_distribution ) + + ### Calculate the summed aged proportions for all aged fish, and for aged fish + # ---- belonging to each sex + aged_sex_proportions = compute_aged_sex_weight_proportions( proportions_weight_length_age_sex , + aged_proportions ) + + ### Calculate weight proportions of aged fish distributed over age-length bins + # ---- for each sex within each stratum relative to the summed weights of each + # ---- stratum (i.e. aged + unaged weights) + distributed_sex_proportions = distribute_aged_weight_proportions( proportions_weight_length_age_sex , + aged_sex_proportions ) + + ### Sum 'kriging_biomass_df' across each stratum for appropriate stratum-specific apportionment + kriged_stratum_biomass = kriging_biomass_df.groupby( [ 'stratum_num' ] )[ 'B_adult_kriged' ].sum( ) + + ### Apportion sexed biomass across age-length + # ---- Initialize dataframe + stratum_sexed_kriged_biomass = distributed_sex_proportions.set_index( 'stratum_num' ) + stratum_sexed_kriged_biomass[ 'biomass_kriged' ] = kriged_stratum_biomass + + # ---- Apportioned biomass for all fish + stratum_sexed_kriged_biomass[ 'biomass_sexed_aged_all' ] = ( + stratum_sexed_kriged_biomass.biomass_kriged + * stratum_sexed_kriged_biomass.normalized_proportion_weight_all + ).fillna( 0 ) + + # ---- Apportioned biomass for adult fish + stratum_sexed_kriged_biomass[ 'biomass_sexed_aged_adult' ] = ( + stratum_sexed_kriged_biomass.biomass_kriged + * stratum_sexed_kriged_biomass.normalized_proportion_weight_adult + ).fillna( 0 ) + + # ---- Sum across strata to produce 'grand totals' for each sex + apportioned_sexed_kriged_biomass = ( + stratum_sexed_kriged_biomass + .groupby( [ 'species_id' , 'sex' , 'length_bin' , 'age_bin' ] ) + .agg( { 'biomass_sexed_aged_all': 'sum' , + 'biomass_sexed_aged_adult': 'sum' } ) + .reset_index( ) + ) + + ### Calculate biomass proportions across age-length + # ---- Sum weights within each length-age bin for each stratum + proportions_weight_length_age = ( + proportions_weight_length_age_sex + .groupby( [ 'stratum_num' , 'species_id' , 'length_bin' , 'age_bin' ] ) + .agg( { 'weight_all': 'sum' , + 'weight_adult': 'sum' } ) + .reset_index( ) + ) + + # ---- Sum stratum weights + # ---- All fish + proportions_weight_length_age[ 'total_weight_all' ] = ( + proportions_weight_length_age.groupby( [ 'stratum_num' , 'species_id' ] )[ 'weight_all' ].transform( sum ) + ) + + # ---- Adult fish + proportions_weight_length_age[ 'total_weight_adult' ] = ( + proportions_weight_length_age.groupby( [ 'stratum_num' , 'species_id' ] )[ 'weight_adult' ].transform( sum ) + ) + + # ---- Calculate proportions + # ---- All fish + proportions_weight_length_age[ 'proportion_weight_length_all' ] = ( + proportions_weight_length_age.weight_all / proportions_weight_length_age.total_weight_all + ).fillna( 0 ) + + # ---- Adult fish + proportions_weight_length_age[ 'proportion_weight_length_adult' ] = ( + proportions_weight_length_age.weight_adult / proportions_weight_length_age.total_weight_adult + ).fillna( 0 ) + + # ---- Index `aged_proportions` + index_aged_proportions = aged_proportions.filter( regex = "^(?=.*aged|stratum_num)(?!.*unaged)" ).set_index( 'stratum_num' ) + + # ---- Initialize dataframe + stratum_kriged_biomass = proportions_weight_length_age.set_index( 'stratum_num' ) + stratum_kriged_biomass[ 'proportion_aged_weight_all' ] = index_aged_proportions.proportion_aged_weight_all + stratum_kriged_biomass[ 'proportion_aged_weight_adult' ] = index_aged_proportions.proportion_aged_weight_adult + stratum_kriged_biomass[ 'biomass_kriged' ] = kriged_stratum_biomass + + # ---- Apportioned biomass for all fish + stratum_kriged_biomass[ 'biomass_aged_all' ] = ( + stratum_kriged_biomass.biomass_kriged + * stratum_kriged_biomass.proportion_weight_length_all + * stratum_kriged_biomass.proportion_aged_weight_all + ).fillna( 0 ) + + # ---- Apportioned biomass for adult fish + stratum_kriged_biomass[ 'biomass_aged_adult' ] = ( + stratum_kriged_biomass.biomass_kriged + * stratum_kriged_biomass.proportion_weight_length_adult + * stratum_kriged_biomass.proportion_aged_weight_adult + ).fillna( 0 ) + + # ---- Sum across strata to produce 'grand totals' for all fish + apportioned_total_kriged_biomass = ( + stratum_kriged_biomass + .groupby( [ 'species_id' , 'length_bin' , 'age_bin' ] ) + .agg( { 'biomass_aged_all': 'sum' , + 'biomass_aged_adult': 'sum' } ) + .reset_index( ) + ) + + ### Return output (tuple) + return apportioned_sexed_kriged_biomass , apportioned_total_kriged_biomass + + +def compute_unaged_number_proportions( length_data: pd.DataFrame , + length_intervals: np.ndarray ): """ Calculate length binned weight proportions among unaged fish @@ -447,19 +523,16 @@ def compute_index_unaged_number_proportions( length_data: pd.DataFrame , """ ### Calculate number proportion - # ---- Remove unsexed - length_data_filtered = length_data[ length_data.sex != 'unsexed' ].copy( ) - # ---- Bin length measurements - length_data_filtered = ( - length_data_filtered + length_data_binned = ( + length_data # ---- Bin length .bin_variable( length_intervals , 'length' ) ) # ---- Sum the number of individuals within each bin proportions_unaged_length = ( - length_data_filtered + length_data_binned # ---- Group number summations across stratum/species/sex/length .groupby( [ 'stratum_num' , 'species_id' , 'length_bin' ] ) # ---- Sum count @@ -483,8 +556,8 @@ def compute_index_unaged_number_proportions( length_data: pd.DataFrame , ### Filter out unnecessary columns and return output return proportions_unaged_length.filter( regex = '^(?!number_|stratum_number_).*' ) -def compute_summed_unaged_weight_proportions( proportions_unaged_length: pd.DataFrame , - length_weight_df: pd.DataFrame ): +def compute_unaged_weight_proportions( proportions_unaged_length: pd.DataFrame , + length_weight_df: pd.DataFrame ): """ Compute the unaged weight proportions across all fish and specific sexes for each length bin @@ -531,8 +604,7 @@ def compute_summed_unaged_weight_proportions( proportions_unaged_length: pd.Data def compute_unaged_sex_proportions( length_data: pd.DataFrame , length_intervals: pd.DataFrame , length_weight_df: pd.DataFrame , - weight_strata: pd.DataFrame , - weight_strata_aged_unaged: pd.DataFrame ): + aged_proportions: pd.DataFrame ): """ Calculate the normalized weight proportions for unaged sexed fish @@ -545,36 +617,30 @@ def compute_unaged_sex_proportions( length_data: pd.DataFrame , length_weight_df: pd.DataFrame Dataframe containing the modeled weights for each sex and length bin fit from the length-weight regression (fit for all animals) - weight_strata: pd.DataFrame - Summed haul weights for each stratum - weight_strata_aged_uanged: pd.DataFrame - Summed haul weights for specifically aged and unaged fish across strata + aged_proportions: pd.DataFrame + Dataframe containing the weight proportions of unaged fish within each stratum """ ### Calculate sexed weights within each stratum - # ---- Remove unsexed - length_data_filtered = length_data[ length_data.sex != 'unsexed' ].copy( ) - # ---- Extract sexed length-weight regression fits length_weight_sex = length_weight_df[ length_weight_df.sex != 'all' ][ [ 'length_bin' , 'sex' , 'weight_modeled' ] ] # ---- Bin length measurements - length_data_filtered = ( - length_data_filtered + length_data_binned = ( + length_data # ---- Bin length .bin_variable( length_intervals , 'length' ) ) # ---- Extract bin length values (i.e. mid) - length_data_filtered[ 'length_bin_value' ] = ( - length_data_filtered[ 'length_bin' ].apply( lambda x: x.mid ) + length_data_binned[ 'length_bin_value' ] = ( + length_data_binned[ 'length_bin' ].apply( lambda x: x.mid ) ) # ---- Merge `length_data_filtered ` and `length_weight_sex` - sexed_unaged_model_weights = pd.merge( length_data_filtered , - length_weight_sex , - on = [ 'length_bin' , 'sex' ] , - how = 'left' ) + sexed_unaged_model_weights = length_data_binned.merge( length_weight_sex , + on = [ 'length_bin' , 'sex' ] , + how = 'left' ) # ---- Interpolate weights over length values for each sex (weighted by `length_count`) interpolated_weights = ( @@ -589,12 +655,9 @@ def compute_unaged_sex_proportions( length_data: pd.DataFrame , ) ### Calculate the unaged weight proportion relative to the haul catches - # ---- Parse out unaged haul totals - weight_strata_unaged = weight_strata_aged_unaged[ weight_strata_aged_unaged.group == 'unaged' ] - # ---- Merge the unaged weight strata with the interpolated weights proportions_unaged_weight_sex = pd.merge( interpolated_weights , - weight_strata_unaged , + aged_proportions , on = [ 'stratum_num' ] , how = 'left' ) @@ -605,20 +668,14 @@ def compute_unaged_sex_proportions( length_data: pd.DataFrame , # ---- Calculate the normalized weights for each sex proportions_unaged_weight_sex[ 'stratum_sex_weight_normalized' ] = ( - proportions_unaged_weight_sex.stratum_weight * + proportions_unaged_weight_sex[ 'weight' ] * proportions_unaged_weight_sex.weight_interp / proportions_unaged_weight_sex.stratum_sex_weight_interp ) - # ---- Merge `proportions_unaged_weight_sex` with `weight strata` - proportions_unaged_weight_sex_normalized = pd.merge( proportions_unaged_weight_sex , - weight_strata , - on = [ 'stratum_num' ] , - how = 'left' ) - # ---- Sum the normalized stratum sex weight across each stratum proportions_unaged_weight_sex[ 'proportions_weight_sex' ] = ( - proportions_unaged_weight_sex_normalized.stratum_sex_weight_normalized / - proportions_unaged_weight_sex_normalized.weight_stratum_all + proportions_unaged_weight_sex.stratum_sex_weight_normalized / + proportions_unaged_weight_sex.weight_stratum_all ) # ---- Sum 'proportions_weight_sex' @@ -634,9 +691,10 @@ def compute_unaged_sex_proportions( length_data: pd.DataFrame , ### Remove unnecessary columns and return output return proportions_unaged_weight_sex[ [ 'stratum_num' , 'sex' , 'proportion_weight_sex' ] ] -def calculate_aged_biomass( kriging_biomass_df: pd.DataFrame , - distributed_sex_proportions_df: pd.DataFrame , - distributed_total_proportions_df: pd.DataFrame , +def compute_unaged_biomass( kriging_biomass_df: pd.DataFrame , + length_data: pd.DataFrame , + length_distribution: np.ndarray , + length_weight_df: pd.DataFrame , aged_proportions: pd.DataFrame ): """ Calculate the kriged biomass distributed over length and age for each sex and among @@ -646,205 +704,115 @@ def calculate_aged_biomass( kriging_biomass_df: pd.DataFrame , ---------- kriging_biomass_df: pd.DataFrame Dataframe containing kriged biomass estimates - distributed_sex_proportions: pd.DataFrame - Dataframe containing the normalized weight proportions for each length and age bin - for each sex - distributed_total_proportions: pd.DataFrame + length_data: np.ndarray + Dataframe containing length data measured from unaged fish + length_distribution: pd.DataFrame Dataframe containing the weight proportions for each length and age bin computed for all fish + length_weight_df: pd.DataFrame + Dataframe containing the modeled weights for each sex and length bin + fit from the length-weight regression (fit for all animals) aged_proportions: pd.DataFrame - Dataframe containing the weight proportions of aged fish within each stratum - """ - - ### Distribute the biomass estimates for each transect interval - ### the calculated proportions for age and length bins for each - ### sex among aged-only fish - # ---- Merge `kriging_biomass_df` with `age_proportions_df` - # ---- Sexed - biomass_sex_proportion_df = ( - # ---- Parse kriged biomass (areal density) values - kriging_biomass_df - .merge( distributed_sex_proportions_df , on = [ 'stratum_num' ] ) - ) - - # ---- All - biomass_proportion_total_df = pd.merge( kriging_biomass_df , - distributed_total_proportions_df , - on = [ 'stratum_num' ] , - how = 'left' ).set_index( 'stratum_num' ) - - # ---- Set index for `aged_proportions` in lieu of using another merge - aged_proportions = aged_proportions.copy( ).set_index( 'stratum_num' ) - - # ---- Assign 'proportion_weight_adult' - biomass_proportion_total_df[ 'proportion_weight_adult' ] = aged_proportions.proportion_weight_adult - - # ---- Assign 'proportion_weight_all' - biomass_proportion_total_df[ 'proportion_weight_all' ] = aged_proportions.proportion_weight_adult - - # ---- Distribute biomass estimates for each sexed bin among aged fish - # ==== !!! TODO: this current used `B_a_adult_mean` for both calculations - # ---- Sexed - # ---- All - biomass_sex_proportion_df[ 'biomass_sexed_aged_all' ] = ( - biomass_sex_proportion_df.B_a_adult_mean - * biomass_sex_proportion_df.cell_area_nmi2 - * biomass_sex_proportion_df.normalized_proportion_weight_all - ) - - # ---- Adult - biomass_sex_proportion_df[ 'biomass_sexed_aged_adult' ] = ( - biomass_sex_proportion_df.B_a_adult_mean - * biomass_sex_proportion_df.cell_area_nmi2 - * biomass_sex_proportion_df.normalized_proportion_weight_adult - ) - - # ---- All - # ---- All - biomass_proportion_total_df[ 'biomass_sexed_aged_all' ] = ( - biomass_proportion_total_df.B_a_adult_mean - * biomass_proportion_total_df.cell_area_nmi2 - * biomass_proportion_total_df.proportion_weight_sex_all - * biomass_proportion_total_df.proportion_weight_adult - ) - - # ---- Adult - biomass_proportion_total_df[ 'biomass_sexed_aged_adult' ] = ( - biomass_proportion_total_df.B_a_adult_mean - * biomass_proportion_total_df.cell_area_nmi2 - * biomass_proportion_total_df.proportion_weight_sex_adult - * biomass_proportion_total_df.proportion_weight_adult - ) - - ### Sum biomass across all bins for aged biomass - # ---- Sexed - total_aged_sexed_biomass = ( - biomass_sex_proportion_df - .groupby( [ 'length_bin' , 'age_bin' , 'sex' ] ) - .agg( total_sexed_aged_biomass_adult = ( 'biomass_sexed_aged_adult' , 'sum' ) ) - .reset_index( ) - ) - - # ---- All - total_aged_biomass = ( - biomass_proportion_total_df - .groupby( [ 'length_bin' , 'age_bin' ] ) - .agg( total_aged_biomass_adult = ( 'biomass_sexed_aged_adult' , 'sum' ) ) - .reset_index( ) - ) - - ### Return output (tuple) - return total_aged_sexed_biomass , total_aged_biomass - -def calculate_unaged_biomass( kriging_biomass_df: pd.DataFrame , - distributed_sex_proportions_df: pd.DataFrame , - distributed_length_proportions_df: pd.DataFrame , - unaged_proportions: pd.DataFrame ): + Dataframe containing the weight proportions of unaged fish within each stratum """ - Calculate the kriged biomass distributed over length and age for each sex and among - all fish (sexed) - Parameters - ---------- - kriging_biomass_df: pd.DataFrame - Dataframe containing kriged biomass estimates - distributed_sex_proportions: pd.DataFrame - Dataframe containing the normalized weight proportions for each length and age bin - for each sex - distributed_total_proportions: pd.DataFrame - Dataframe containing the weight proportions for each length and age bin computed for all fish - aged_proportions: pd.DataFrame - Dataframe containing the weight proportions of aged fish within each stratum - """ - ### Calculate sex-specific weight proportions for unaged fish (across all fish, aged and unaged) - # ---- Merge `proportions_unaged_weight_sex` and `unaged_proportions` - proportions_unaged_weight_sex_overall = pd.merge( distributed_sex_proportions_df , - unaged_proportions , - on = [ 'stratum_num' ] , - how = 'left' ) - + ### Calculate number proportion + proportions_unaged_length = compute_unaged_number_proportions( length_data , length_distribution ) + + ### Calculate the normalized weight per unit length distribution (W_Ln_ALL in the original Matlab code) + proportions_unaged_weight_length = compute_unaged_weight_proportions( proportions_unaged_length , + length_weight_df ) + + ### Calculate sex-specific weight proportions for unaged fish (within unaged fish) + proportions_unaged_weight_sex = compute_unaged_sex_proportions( length_data , + length_distribution , + length_weight_df , + aged_proportions ) + + ### Sum 'kriging_biomass_df' across each stratum for appropriate stratum-specific apportionment + kriged_stratum_biomass = kriging_biomass_df.groupby( [ 'stratum_num' ] )[ 'B_adult_kriged' ].sum( ) + + ### Calculate sex-specific biomass apportionment + # ---- Index `unaged_proportions` + indexed_unaged_proportions = aged_proportions.filter( regex = "^(?=.*stratum_num|.*unaged).*(?!.*aged)" ).set_index( 'stratum_num' ) + + # ---- Index `proportions_unaged_weight_length` + indexed_proportions_unaged_weight_length = proportions_unaged_weight_length.set_index( 'stratum_num' ) + + # ---- Initialize dataframe + stratum_sexed_kriged_biomass = proportions_unaged_weight_sex.merge( indexed_proportions_unaged_weight_length , + on = [ 'stratum_num' ] , + how = 'left' ).set_index( 'stratum_num' ) + stratum_sexed_kriged_biomass[ 'proportion_unaged_weight_all' ] = indexed_unaged_proportions.proportion_unaged_weight_all + stratum_sexed_kriged_biomass[ 'proportion_unaged_weight_adult' ] = indexed_unaged_proportions.proportion_unaged_weight_adult + stratum_sexed_kriged_biomass[ 'biomass_kriged' ] = kriged_stratum_biomass + # ---- Calculate the overall sexed proportions taking into account aged and unaged fish (all age class) - proportions_unaged_weight_sex_overall[ 'proportion_sexed_weight_all' ] = ( - proportions_unaged_weight_sex_overall.proportion_weight_sex * - proportions_unaged_weight_sex_overall.proportion_weight_all + stratum_sexed_kriged_biomass[ 'proportion_sexed_weight_all' ] = ( + stratum_sexed_kriged_biomass.proportion_weight_sex * + stratum_sexed_kriged_biomass.proportion_unaged_weight_all ) # ---- Calculate the overall sexed proportions taking into account aged and unaged fish (adults) - proportions_unaged_weight_sex_overall[ 'proportion_sexed_weight_adult' ] = ( - proportions_unaged_weight_sex_overall.proportion_weight_sex * - proportions_unaged_weight_sex_overall.proportion_weight_adult - ) - - ### Merge kriging dataframe with calculated proportions - # ---- Sexed - unaged_sexed_biomass = ( - # ---- Parse kriged biomass (areal density) values - kriging_biomass_df - .merge( distributed_length_proportions_df , on = [ 'stratum_num' ] ) - .merge( proportions_unaged_weight_sex_overall , on = [ 'stratum_num' ] ) - ) - - # ---- All - unaged_biomass = ( - # ---- Parse kriged biomass (areal density) values - kriging_biomass_df - .merge( distributed_length_proportions_df , on = [ 'stratum_num' ] ) - .merge( unaged_proportions , on = [ 'stratum_num' ] ) - ) - - ### Calculate biomasses - # ---- Sexed - # ---- All - unaged_sexed_biomass[ 'biomass_sexed_unaged_all' ] = ( - unaged_sexed_biomass.B_a_adult_mean * - unaged_sexed_biomass.cell_area_nmi2 * - unaged_sexed_biomass.proportion_weight_length * - unaged_sexed_biomass.proportion_sexed_weight_all - ) - - # ---- Adult - unaged_sexed_biomass[ 'biomass_sexed_unaged_adult' ] = ( - unaged_sexed_biomass.B_a_adult_mean * - unaged_sexed_biomass.cell_area_nmi2 * - unaged_sexed_biomass.proportion_weight_length * - unaged_sexed_biomass.proportion_sexed_weight_adult - ) - - # ---- All - # ---- All - unaged_biomass[ 'biomass_unaged_all' ] = ( - unaged_biomass.B_a_adult_mean * - unaged_biomass.cell_area_nmi2 * - unaged_biomass.proportion_weight_length * - unaged_biomass.proportion_weight_all + stratum_sexed_kriged_biomass[ 'proportion_sexed_weight_adult' ] = ( + stratum_sexed_kriged_biomass.proportion_weight_sex * + stratum_sexed_kriged_biomass.proportion_unaged_weight_adult ) - - # ---- Adult - unaged_biomass[ 'biomass_unaged_adult' ] = ( - unaged_biomass.B_a_adult_mean * - unaged_biomass.cell_area_nmi2 * - unaged_biomass.proportion_weight_length * - unaged_biomass.proportion_weight_adult + + # ---- Apportioned biomass for all fish + stratum_sexed_kriged_biomass[ 'biomass_sexed_unaged_all' ] = ( + stratum_sexed_kriged_biomass.biomass_kriged + * stratum_sexed_kriged_biomass.proportion_sexed_weight_all + * stratum_sexed_kriged_biomass.proportion_weight_length ) - - ### Sum biomass across all bins for unaged biomass - # ---- Sexed - total_unaged_sexed_biomass = ( - unaged_sexed_biomass - .groupby( [ 'length_bin' , 'sex' ] ) - .agg( total_sexed_unaged_biomass_adult = ( 'biomass_sexed_unaged_adult' , 'sum' ) ) - .reset_index( ) + + # ---- Apportioned biomass for adult fish + stratum_sexed_kriged_biomass[ 'biomass_sexed_unaged_adult' ] = ( + stratum_sexed_kriged_biomass.biomass_kriged + * stratum_sexed_kriged_biomass.proportion_sexed_weight_adult + * stratum_sexed_kriged_biomass.proportion_weight_length ) + + # ---- ' Grand total ' for all sexed unaged fish + apportioned_sexed_kriged_biomass = ( + stratum_sexed_kriged_biomass + .groupby( [ 'species_id' , 'sex' , 'length_bin' ] ) + .agg( { 'biomass_sexed_unaged_all': 'sum' , + 'biomass_sexed_unaged_adult': 'sum' } ) + .reset_index( ) + ) - # ---- All - total_unaged_biomass = ( - unaged_biomass - .groupby( [ 'length_bin' ] ) - .agg( total_unaged_biomass_adult = ( 'biomass_unaged_adult' , 'sum' ) ) - .reset_index( ) - ) + ### Calculate biomass proportions across length + # ---- Initialize dataframe + stratum_kriged_biomass = indexed_proportions_unaged_weight_length + stratum_kriged_biomass[ 'proportion_unaged_weight_all' ] = indexed_unaged_proportions.proportion_unaged_weight_all + stratum_kriged_biomass[ 'proportion_unaged_weight_adult' ] = indexed_unaged_proportions.proportion_unaged_weight_adult + stratum_kriged_biomass[ 'biomass_kriged' ] = kriged_stratum_biomass + + # ---- Apportioned biomass for all fish + stratum_kriged_biomass[ 'biomass_unaged_all' ] = ( + stratum_kriged_biomass.biomass_kriged + * stratum_kriged_biomass. proportion_unaged_weight_all + * stratum_kriged_biomass.proportion_weight_length + ) + + # ---- Apportioned biomass for adult fish + stratum_kriged_biomass[ 'biomass_unaged_adult' ] = ( + stratum_kriged_biomass.biomass_kriged + * stratum_kriged_biomass.proportion_unaged_weight_adult + * stratum_kriged_biomass.proportion_weight_length + ) + + # ---- ' Grand total ' for all sexed unaged fish + apportioned_kriged_biomass = ( + stratum_kriged_biomass + .groupby( [ 'species_id' , 'length_bin' ] ) + .agg( { 'biomass_unaged_all': 'sum' , + 'biomass_unaged_adult': 'sum' } ) + .reset_index( ) + ) ### Return output (tuple) - return total_unaged_sexed_biomass , total_unaged_biomass + return apportioned_sexed_kriged_biomass , apportioned_kriged_biomass def apply_age_bins( aged_biomass_sexed_df: pd.DataFrame , unaged_biomass_sexed_df: pd.DataFrame ): diff --git a/echopop/survey.py b/echopop/survey.py index 65212d7d..f4ca3e07 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -13,9 +13,9 @@ from .computation.spatial import calculate_transect_distance , transform_geometry from .computation.statistics import stratified_transect_statistic from .computation.kriging_methods import kriging_interpolation -from .computation.biology import index_sex_weight_proportions , index_transect_age_sex_proportions , filter_species -from .computation.biology import sum_strata_weight , compute_index_aged_weight_proportions , distribute_aged_weight_proportions -from .computation.biology import compute_summed_aged_proportions , compute_index_unaged_number_proportions , calculate_aged_biomass +from .computation.biology import index_transect_age_sex_proportions , filter_species +from .computation.biology import sum_strata_weight , compute_aged_unaged_proportions +from .computation.biology import compute_index_unaged_number_proportions , calculate_aged_biomass from .computation.biology import compute_summed_unaged_weight_proportions , compute_unaged_sex_proportions , apply_age_bins , calculate_unaged_biomass ### !!! TODO : This is a temporary import call -- this will need to be changed to @@ -1243,6 +1243,11 @@ def krige( self , variogram_parameters , kriging_parameters ) + # ### Calculate grid CV + # kriged_results = grid_cv( self.biology[ 'population' ][ 'areal_density' ][ 'biomass_density_df' ] , + # kriged_results , + # self.statistics[ 'kriging' ][ 'model_config' ] ) + ### Assign results to an attribute self.statistics[ 'kriging' ].update( { @@ -1274,18 +1279,24 @@ def apportion_kriged_biomass( self , """ ### Import biological data and filter out non-target species + # ---- Species filter length_spp , specimen_spp , haul_spp = filter_species( [ self.biology[ 'length_df' ] , self.biology[ 'specimen_df' ] , self.biology[ 'catch_df' ] ] , species_id ) + + # ---- Remove 'bad' values + # ---- `specimen_spp` + specimen_spp_filtered = specimen_spp[ specimen_spp.sex != 'unsexed' ].dropna( how = 'any' , subset = 'age' ) + + # ---- `length_spp` + length_spp_filtered = length_spp[ length_spp.sex != 'unsexed' ] ### Import discrete distribution bins # ---- Length - # length_bins = self.biology[ 'distributions' ][ 'length' ][ 'length_bins_arr' ] length_intervals = self.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] # ---- Age - # age_bins = self.biology[ 'distributions' ][ 'age' ][ 'age_bins_arr' ] age_intervals = self.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] # ---- Construct the complete dataframe containing all possible @@ -1307,34 +1318,26 @@ def apportion_kriged_biomass( self , ### Sum weights for aged/unaged and all within each stratum weight_strata_aged_unaged , weight_strata = sum_strata_weight( haul_spp_matched , specimen_spp ) - ### Calculate summed weights - proportions_weight_length_age_sex = compute_index_aged_weight_proportions( specimen_spp , - length_intervals , - age_intervals ) - - ### Calculate the summed aged proportions for all aged fish, and for aged fish - # ---- belonging to each sex - aged_sex_proportions , aged_proportions = compute_summed_aged_proportions( proportions_weight_length_age_sex , - weight_strata ) - - ### Calculate weight proportions of aged fish distributed over age-length bins - # ---- for each sex within each stratum relative to the summed weights of each - # ---- stratum (i.e. aged + unaged weights) - distributed_aged_weight_proportions = distribute_aged_weight_proportions( proportions_weight_length_age_sex , - aged_sex_proportions ) + ### Calculate the summed aged proportions for all aged fish, and for aged fish + aged_unaged_weight_proportions = compute_aged_unaged_proportions( specimen_spp_filtered , + weight_strata ) + # !!! TODO: This does end up chewing up * a ton * of memory since the output dataframes are quite large aged_sex_biomass , aged_biomass = calculate_aged_biomass( self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] , - distributed_aged_weight_proportions , - proportions_weight_length_age_sex[ proportions_weight_length_age_sex.group == 'all' ] , - aged_proportions ) + specimen_spp_filtered , + length_intervals , + age_intervals , + aged_unaged_weight_proportions ) + + ### Import length-weight relationship calculated for all animals + length_weight_df = self.statistics[ 'length_weight' ][ 'length_weight_df' ] ### Calculate number proportion proportions_unaged_length = compute_index_unaged_number_proportions( length_spp , length_intervals ) - ### Import length-weight relationship calculated for all animals - length_weight_df = self.statistics[ 'length_weight' ][ 'length_weight_df' ] + ### Calculate the normalized weight per unit length distribution (W_Ln_ALL in the original Matlab code) proportions_unaged_weight_length = compute_summed_unaged_weight_proportions( proportions_unaged_length , @@ -1350,15 +1353,16 @@ def apportion_kriged_biomass( self , ### Calculate the unaged proportions unaged_proportions = pd.DataFrame( { 'stratum_num': aged_proportions.stratum_num , - 'proportion_weight_all': 1.0 - aged_proportions.proportion_weight_all , - 'proportion_weight_adult': 1.0 - aged_proportions.proportion_weight_adult , + 'proportion_weight_all': 1.0 - aged_proportions.proportion_aged_weight_all , + 'proportion_weight_adult': 1.0 - aged_proportions.proportion_aged_weight_adult , } ) ### Calculate unaged biomass for each sex and all animals - unaged_sex_biomass , unaged_biomass = calculate_unaged_biomass( self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] , - proportions_unaged_weight_sex , - proportions_unaged_weight_length , - unaged_proportions ) + unaged_sex_biomass , unaged_biomass = compute_unaged_biomass( self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] , + length_spp_filtered , + length_intervals , + length_weight_df , + unaged_proportions ) ### Re-distribute unaged biomass so it is compatible with aged biomass to calculate the overall summed biomass redistributed_unaged_sex_biomass , redistributed_unaged_biomass = apply_age_bins( aged_sex_biomass , @@ -1387,52 +1391,10 @@ def apportion_kriged_biomass( self , overall_biomass.total_aged_biomass_adult ) - # ### Parse kriged biomass (areal density) values - # kriged_results = self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] - - # kriged_results = ( - # kriged_results - # .merge( dist_weight_sum , on = [ 'stratum_num' ] ) - # .assign( biomass_aged = lambda df: df.B_a_adult_mean * df.proportion_normalized * df.cell_area_nmi2 ) - # .merge( haul_sex_weights_normalized , on = [ 'stratum_num' , 'sex' ] ) - # .assign( biomass_unaged = lambda df: df.B_a_adult_mean * df.proportion_normalized_station_1 * df.cell_area_nmi2 , - # biomass_total = lambda df: df.biomass_aged + df.biomass_unaged ) - # ) - - # ### Parse the average weight calculations - # average_weight_df = ( - # self.biology[ 'weight' ][ 'weight_strata_df' ] - # .loc[ : , [ 'stratum_num' , 'average_weight_total' ] ] - # ) - - # ### Merge with the kriged results - # kriged_results = ( - # kriged_results - # .merge( average_weight_df , on = [ 'stratum_num' ] ) - # .assign( N_adult_kriged = lambda df: df.B_adult_kriged / df.average_weight_total ) - # .merge( self.acoustics[ 'sigma_bs' ][ 'strata_mean' ] , on = [ 'stratum_num' ] ) - # .assign( NASC_kriged = lambda df: df.N_adult_kriged * df.sigma_bs_mean ) - # ) - - # ### Calculate grid CV - # kriged_results = grid_cv( self.biology[ 'population' ][ 'areal_density' ][ 'biomass_density_df' ] , - # kriged_results , - # self.statistics[ 'kriging' ][ 'model_config' ] ) - - # ### Add biomass values to all ages for kriged biomass results - # kriged_results_output = ( - # kriged_results - # .merge( self.biology[ 'weight' ][ 'proportions' ][ 'sex_age_weight_proportions_df' ] , - # on = [ 'stratum_num' , 'sex' ] ) - # .assign( biomass_sex_age = lambda x: x.weight_sex_proportion_all * x.biomass_total ) - # .loc[ : , [ 'centroid_latitude' , 'centroid_longitude' , 'stratum_num' , - # 'cell_area_nmi2' , 'sex' , 'age' , 'biomass_unaged' , 'biomass_aged' , - # 'biomass_total' , 'biomass_adult_cell_CV' , 'biomass_sex_age' ] ] - # ) - # ### Assign results to an attribute - # self.statistics[ 'kriging' ].update( - # { - # 'apportioned_kriged_biomass_df': kriged_results_output , - # } - # ) \ No newline at end of file + self.statistics[ 'kriging' ].update( + { + 'apportioned_kriged_total_biomass_df': overall_biomass , + 'apportioned_kriged_sexed_biomass_df': overall_sexed_biomass , + } + ) \ No newline at end of file From 0b811cb0e1c9c2c6328610cbd582b916c9c034c9 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca Date: Tue, 2 Apr 2024 19:11:08 -0700 Subject: [PATCH 48/51] Amendments to aged-unaged biomass combination --- echopop/computation/biology.py | 64 ++++++++++++++++++++-------------- echopop/survey.py | 60 ++++++++++++------------------- 2 files changed, 59 insertions(+), 65 deletions(-) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index 3d7c470b..a9b080b2 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -691,11 +691,11 @@ def compute_unaged_sex_proportions( length_data: pd.DataFrame , ### Remove unnecessary columns and return output return proportions_unaged_weight_sex[ [ 'stratum_num' , 'sex' , 'proportion_weight_sex' ] ] -def compute_unaged_biomass( kriging_biomass_df: pd.DataFrame , - length_data: pd.DataFrame , - length_distribution: np.ndarray , - length_weight_df: pd.DataFrame , - aged_proportions: pd.DataFrame ): +def calculate_unaged_biomass( kriging_biomass_df: pd.DataFrame , + length_data: pd.DataFrame , + length_distribution: np.ndarray , + length_weight_df: pd.DataFrame , + aged_proportions: pd.DataFrame ): """ Calculate the kriged biomass distributed over length and age for each sex and among all fish (sexed) @@ -814,46 +814,56 @@ def compute_unaged_biomass( kriging_biomass_df: pd.DataFrame , ### Return output (tuple) return apportioned_sexed_kriged_biomass , apportioned_kriged_biomass -def apply_age_bins( aged_biomass_sexed_df: pd.DataFrame , - unaged_biomass_sexed_df: pd.DataFrame ): +def apply_age_bins( aged_sex_biomass: pd.DataFrame , + unaged_sex_biomass: pd.DataFrame ): """ Redistribute unaged biomass over the defined age distribution Parameters ---------- - aged_biomass_sexed_df: pd.DataFrame + aged_sex_biomass: pd.DataFrame Dataframe containing biomass data of sexed aged fish - unaged_biomass_sexed_df: pd.DataFrame + unaged_sex_biomass: pd.DataFrame Dataframe containing biomass data of sexed unaged fish - aged_biomass_total_df: pd.DataFrame - Dataframe containing biomass data of all aged fish - aged_biomass_total_df: pd.DataFrame - Dataframe containing biomass data of all unaged fish """ ### Merge unaged biomass length bins with aged bioamss length-age bins - aged_unaged_sexed_biomass = pd.merge( aged_biomass_sexed_df , - unaged_biomass_sexed_df , - on = [ 'length_bin' , 'sex' ] , - how = 'left' ) + aged_unaged_sexed_biomass = aged_sex_biomass.merge( unaged_sex_biomass , + on = [ 'length_bin' , 'sex' , 'species_id' ] , + how = 'left' ) ### Calculate the total biomass for each sexed length bin (i.e. sum across age bins) + # ---- All fish + aged_unaged_sexed_biomass[ 'sum_length_bin_biomass_sexed_all' ] = ( + aged_unaged_sexed_biomass.groupby( [ 'sex' , 'length_bin' ] )[ 'biomass_sexed_aged_all' ].transform( sum ) + + aged_unaged_sexed_biomass.biomass_sexed_unaged_all + ) + # ---- Adult fish aged_unaged_sexed_biomass[ 'sum_length_bin_biomass_sexed_adult' ] = ( - aged_unaged_sexed_biomass.groupby( [ 'sex' , 'length_bin' ] )[ 'total_sexed_aged_biomass_adult' ].transform( sum ) + - aged_unaged_sexed_biomass.total_sexed_unaged_biomass_adult + aged_unaged_sexed_biomass.groupby( [ 'sex' , 'length_bin' ] )[ 'biomass_sexed_aged_adult' ].transform( sum ) + + aged_unaged_sexed_biomass.biomass_sexed_unaged_adult ) ### Redistribute unaged biomass over the length-age bins for each sex + # ---- All fish + aged_unaged_sexed_biomass[ 'biomass_sexed_unaged_all' ] = ( + aged_unaged_sexed_biomass.biomass_sexed_unaged_all * + aged_unaged_sexed_biomass.biomass_sexed_aged_all / + aged_unaged_sexed_biomass.sum_length_bin_biomass_sexed_all + ).fillna( 0 ) + # ---- Adult fish - aged_unaged_sexed_biomass[ 'total_sexed_unaged_biomass_adult' ] = ( - aged_unaged_sexed_biomass.total_sexed_unaged_biomass_adult * - aged_unaged_sexed_biomass.total_sexed_aged_biomass_adult / + aged_unaged_sexed_biomass[ 'biomass_sexed_unaged_adult' ] = ( + aged_unaged_sexed_biomass.biomass_sexed_unaged_adult * + aged_unaged_sexed_biomass.biomass_sexed_aged_adult / aged_unaged_sexed_biomass.sum_length_bin_biomass_sexed_adult ).fillna( 0 ) ### Remove unnecessary columns - aged_unaged_sexed_biomass.drop( [ 'total_sexed_aged_biomass_adult' , + aged_unaged_sexed_biomass.drop( [ 'biomass_sexed_aged_all' , + 'biomass_sexed_aged_adult' , + 'sum_length_bin_biomass_sexed_all' , 'sum_length_bin_biomass_sexed_adult' ] , axis = 1 , inplace = True ) @@ -861,11 +871,11 @@ def apply_age_bins( aged_biomass_sexed_df: pd.DataFrame , ### Sum sexes together to retrieve the total biomass aged_unaged_biomass = ( aged_unaged_sexed_biomass - .groupby( [ 'length_bin' , 'age_bin' ] ) - .agg( total_unaged_biomass_adult = ( 'total_sexed_unaged_biomass_adult' , 'sum' ) ) + .groupby( [ 'length_bin' , 'age_bin' , 'species_id' ] ) + .agg( biomass_unaged_all = ( 'biomass_sexed_unaged_all' , 'sum' ) , + biomass_unaged_adult = ( 'biomass_sexed_unaged_adult' , 'sum' ) ) .reset_index( ) - ) - + ) ### Return output (tuple) return aged_unaged_sexed_biomass , aged_unaged_biomass \ No newline at end of file diff --git a/echopop/survey.py b/echopop/survey.py index f4ca3e07..2364c46c 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -15,8 +15,8 @@ from .computation.kriging_methods import kriging_interpolation from .computation.biology import index_transect_age_sex_proportions , filter_species from .computation.biology import sum_strata_weight , compute_aged_unaged_proportions -from .computation.biology import compute_index_unaged_number_proportions , calculate_aged_biomass -from .computation.biology import compute_summed_unaged_weight_proportions , compute_unaged_sex_proportions , apply_age_bins , calculate_unaged_biomass +from .computation.biology import calculate_aged_biomass , calculate_unaged_biomass +from .computation.biology import apply_age_bins , filter_species ### !!! TODO : This is a temporary import call -- this will need to be changed to # the correct relative structure (i.e. '.utils.data_structure_utils' instead of @@ -1333,36 +1333,12 @@ def apportion_kriged_biomass( self , ### Import length-weight relationship calculated for all animals length_weight_df = self.statistics[ 'length_weight' ][ 'length_weight_df' ] - ### Calculate number proportion - proportions_unaged_length = compute_index_unaged_number_proportions( length_spp , - length_intervals ) - - - - ### Calculate the normalized weight per unit length distribution (W_Ln_ALL in the original Matlab code) - proportions_unaged_weight_length = compute_summed_unaged_weight_proportions( proportions_unaged_length , - length_weight_df ) - - ### Calculate sex-specific weight proportions for unaged fish (within unaged fish) - proportions_unaged_weight_sex = compute_unaged_sex_proportions( length_spp , + ### Calculate unaged biomass for each sex and all animals + unaged_sex_biomass , unaged_biomass = calculate_unaged_biomass( self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] , + length_spp_filtered , length_intervals , length_weight_df , - weight_strata , - weight_strata_aged_unaged ) - - ### Calculate the unaged proportions - unaged_proportions = pd.DataFrame( { - 'stratum_num': aged_proportions.stratum_num , - 'proportion_weight_all': 1.0 - aged_proportions.proportion_aged_weight_all , - 'proportion_weight_adult': 1.0 - aged_proportions.proportion_aged_weight_adult , - } ) - - ### Calculate unaged biomass for each sex and all animals - unaged_sex_biomass , unaged_biomass = compute_unaged_biomass( self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] , - length_spp_filtered , - length_intervals , - length_weight_df , - unaged_proportions ) + aged_unaged_weight_proportions ) ### Re-distribute unaged biomass so it is compatible with aged biomass to calculate the overall summed biomass redistributed_unaged_sex_biomass , redistributed_unaged_biomass = apply_age_bins( aged_sex_biomass , @@ -1371,24 +1347,32 @@ def apportion_kriged_biomass( self , ### Sum the grand total by combining the aged and unaged biomass estimates post-apportionment # ---- Merge sexed overall_sexed_biomass = redistributed_unaged_sex_biomass.merge( aged_sex_biomass , - on = [ 'length_bin' , 'age_bin' , 'sex' ] , + on = [ 'length_bin' , 'age_bin' , 'sex' , 'species_id' ] , how = 'left' ) # ---- Aggregate (sum) + overall_sexed_biomass[ 'total_sexed_biomass_all' ] = ( + overall_sexed_biomass.biomass_sexed_unaged_all + + overall_sexed_biomass.biomass_sexed_aged_all + ) overall_sexed_biomass[ 'total_sexed_biomass_adult' ] = ( - overall_sexed_biomass.total_sexed_unaged_biomass_adult + - overall_sexed_biomass.total_sexed_aged_biomass_adult + overall_sexed_biomass.biomass_sexed_unaged_adult + + overall_sexed_biomass.biomass_sexed_aged_adult ) # ---- Merge total - overall_biomass = redistributed_unaged_biomass.merge( aged_biomass , - on = [ 'length_bin' , 'age_bin' ] , - how = 'left' ) + overall_biomass = redistributed_unaged_biomass.merge( aged_biomass , + on = [ 'length_bin' , 'age_bin' , 'species_id' ] , + how = 'left' ) # ---- Aggregate (sum) + overall_biomass[ 'total_biomass_all' ] = ( + overall_biomass.biomass_unaged_all + + overall_biomass.biomass_aged_all + ) overall_biomass[ 'total_biomass_adult' ] = ( - overall_biomass.total_unaged_biomass_adult + - overall_biomass.total_aged_biomass_adult + overall_biomass.biomass_unaged_adult + + overall_biomass.biomass_aged_adult ) # ### Assign results to an attribute From 40b3553f41d52f1f53e48ccadd7da53f12b3162a Mon Sep 17 00:00:00 2001 From: Brandyn Lucca Date: Wed, 3 Apr 2024 14:31:01 -0700 Subject: [PATCH 49/51] Edits to `biology` apportionment functions --- echopop/computation/biology.py | 51 ++++++++++++++++------------------ 1 file changed, 24 insertions(+), 27 deletions(-) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index a9b080b2..7f7f6ec6 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -288,7 +288,7 @@ def compute_aged_weight_proportions( specimen_data: pd.DataFrame , ) ### Return output - return proportions_weight_length_age_sex.reset_index( drop = True ) + return proportions_weight_length_age_sex def compute_aged_sex_weight_proportions( proportions_weight_length_age_sex: pd.DataFrame , aged_proportions: pd.DataFrame ): @@ -335,10 +335,10 @@ def compute_aged_sex_weight_proportions( proportions_weight_length_age_sex: pd.D ) # ---- Fill empty/NaN values with 0's and drop unused columns - aged_sex_proportions = aged_sex_proportions.fillna( 0 ).filter( regex = "^(?=.*aged|stratum_num|sex)(?!.*unaged|weight_)" ) - + aged_sex_proportions = aged_sex_proportions.fillna( 0 ).filter( regex = "^(?=proportion_aged_|proportion_weight|stratum_num|sex).*" ) + ### Return output - return aged_sex_proportions + return aged_sex_proportions.reset_index( drop = True ) def distribute_aged_weight_proportions( proportions_weight_length_age_sex: pd.DataFrame , aged_sex_proportions: pd.DataFrame ): @@ -362,12 +362,12 @@ def distribute_aged_weight_proportions( proportions_weight_length_age_sex: pd.Da # ---- Normalized weight proportions for all fish distributed_aged_weight_proportions[ 'normalized_proportion_weight_all' ] = ( - distributed_aged_weight_proportions.proportion_weight_sex_all * distributed_aged_weight_proportions.proportion_aged_weight_all + distributed_aged_weight_proportions.proportion_weight_sex_all * distributed_aged_weight_proportions.proportion_weight_all ) # ---- Normalized weight proportions for jus adult fish distributed_aged_weight_proportions[ 'normalized_proportion_weight_adult' ] = ( - distributed_aged_weight_proportions.proportion_weight_sex_adult * distributed_aged_weight_proportions.proportion_aged_weight_adult + distributed_aged_weight_proportions.proportion_weight_sex_adult * distributed_aged_weight_proportions.proportion_weight_adult ) # ---- Remove unnecessary columns and return the dataframe @@ -400,7 +400,7 @@ def calculate_aged_biomass( kriging_biomass_df: pd.DataFrame , proportions_weight_length_age_sex = compute_aged_weight_proportions( specimen_data , length_distribution , age_distribution ) - + ### Calculate the summed aged proportions for all aged fish, and for aged fish # ---- belonging to each sex aged_sex_proportions = compute_aged_sex_weight_proportions( proportions_weight_length_age_sex , @@ -466,12 +466,12 @@ def calculate_aged_biomass( kriging_biomass_df: pd.DataFrame , # ---- All fish proportions_weight_length_age[ 'proportion_weight_length_all' ] = ( proportions_weight_length_age.weight_all / proportions_weight_length_age.total_weight_all - ).fillna( 0 ) + ).fillna( 0 ) # ---- Adult fish proportions_weight_length_age[ 'proportion_weight_length_adult' ] = ( proportions_weight_length_age.weight_adult / proportions_weight_length_age.total_weight_adult - ).fillna( 0 ) + ).fillna( 0 ) # ---- Index `aged_proportions` index_aged_proportions = aged_proportions.filter( regex = "^(?=.*aged|stratum_num)(?!.*unaged)" ).set_index( 'stratum_num' ) @@ -508,7 +508,6 @@ def calculate_aged_biomass( kriging_biomass_df: pd.DataFrame , ### Return output (tuple) return apportioned_sexed_kriged_biomass , apportioned_total_kriged_biomass - def compute_unaged_number_proportions( length_data: pd.DataFrame , length_intervals: np.ndarray ): """ @@ -805,7 +804,7 @@ def calculate_unaged_biomass( kriging_biomass_df: pd.DataFrame , # ---- ' Grand total ' for all sexed unaged fish apportioned_kriged_biomass = ( stratum_kriged_biomass - .groupby( [ 'species_id' , 'length_bin' ] ) + .groupby( [ 'species_id' , 'length_bin' ] ) .agg( { 'biomass_unaged_all': 'sum' , 'biomass_unaged_adult': 'sum' } ) .reset_index( ) @@ -826,45 +825,43 @@ def apply_age_bins( aged_sex_biomass: pd.DataFrame , unaged_sex_biomass: pd.DataFrame Dataframe containing biomass data of sexed unaged fish """ - - ### Merge unaged biomass length bins with aged bioamss length-age bins - aged_unaged_sexed_biomass = aged_sex_biomass.merge( unaged_sex_biomass , - on = [ 'length_bin' , 'sex' , 'species_id' ] , - how = 'left' ) - ### Calculate the total biomass for each sexed length bin (i.e. sum across age bins) + ### Calculate summed length-bin biomass (i.e. sum across age bins) # ---- All fish - aged_unaged_sexed_biomass[ 'sum_length_bin_biomass_sexed_all' ] = ( - aged_unaged_sexed_biomass.groupby( [ 'sex' , 'length_bin' ] )[ 'biomass_sexed_aged_all' ].transform( sum ) + - aged_unaged_sexed_biomass.biomass_sexed_unaged_all + aged_sex_biomass[ 'summed_aged_biomass_all' ] = ( + aged_sex_biomass.groupby( [ 'sex' , 'length_bin' ] )[ 'biomass_sexed_aged_all' ].transform( sum ) ) # ---- Adult fish - aged_unaged_sexed_biomass[ 'sum_length_bin_biomass_sexed_adult' ] = ( - aged_unaged_sexed_biomass.groupby( [ 'sex' , 'length_bin' ] )[ 'biomass_sexed_aged_adult' ].transform( sum ) + - aged_unaged_sexed_biomass.biomass_sexed_unaged_adult + aged_sex_biomass[ 'summed_aged_biomass_adult' ] = ( + aged_sex_biomass.groupby( [ 'sex' , 'length_bin' ] )[ 'biomass_sexed_aged_adult' ].transform( sum ) ) + + ### Merge unaged biomass length bins with aged bioamss length-age bins + aged_unaged_sexed_biomass = aged_sex_biomass.merge( unaged_sex_biomass , + on = [ 'length_bin' , 'sex' , 'species_id' ] , + how = 'left' ) ### Redistribute unaged biomass over the length-age bins for each sex # ---- All fish aged_unaged_sexed_biomass[ 'biomass_sexed_unaged_all' ] = ( aged_unaged_sexed_biomass.biomass_sexed_unaged_all * aged_unaged_sexed_biomass.biomass_sexed_aged_all / - aged_unaged_sexed_biomass.sum_length_bin_biomass_sexed_all + aged_unaged_sexed_biomass.summed_aged_biomass_all ).fillna( 0 ) # ---- Adult fish aged_unaged_sexed_biomass[ 'biomass_sexed_unaged_adult' ] = ( aged_unaged_sexed_biomass.biomass_sexed_unaged_adult * aged_unaged_sexed_biomass.biomass_sexed_aged_adult / - aged_unaged_sexed_biomass.sum_length_bin_biomass_sexed_adult + aged_unaged_sexed_biomass.summed_aged_biomass_adult ).fillna( 0 ) ### Remove unnecessary columns aged_unaged_sexed_biomass.drop( [ 'biomass_sexed_aged_all' , 'biomass_sexed_aged_adult' , - 'sum_length_bin_biomass_sexed_all' , - 'sum_length_bin_biomass_sexed_adult' ] , + 'summed_aged_biomass_all' , + 'summed_aged_biomass_adult' ] , axis = 1 , inplace = True ) From 275b146931bb68fa56b0048d3889e7ede9ecc639 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca Date: Wed, 3 Apr 2024 15:37:55 -0700 Subject: [PATCH 50/51] Changes and fixes to all apportionment tests --- .../test_kriging_biomass_apportionment.py | 642 ++++++++++++++---- 1 file changed, 522 insertions(+), 120 deletions(-) diff --git a/echopop/tests/test_kriging_biomass_apportionment.py b/echopop/tests/test_kriging_biomass_apportionment.py index 6e6edc6a..aad2e0ae 100644 --- a/echopop/tests/test_kriging_biomass_apportionment.py +++ b/echopop/tests/test_kriging_biomass_apportionment.py @@ -1,8 +1,9 @@ import pandas as pd import numpy as np -from echopop.computation.biology import sum_strata_weight , compute_index_aged_weight_proportions , compute_summed_aged_proportions -from echopop.computation.biology import compute_index_unaged_number_proportions , distribute_aged_weight_proportions -from echopop.computation.biology import compute_summed_unaged_weight_proportions , compute_unaged_sex_proportions +from echopop.computation.biology import sum_strata_weight , compute_aged_weight_proportions , compute_aged_weight_proportions +from echopop.computation.biology import compute_aged_sex_weight_proportions , distribute_aged_weight_proportions , apply_age_bins +from echopop.computation.biology import compute_aged_unaged_proportions , compute_unaged_sex_proportions , calculate_aged_biomass +from echopop.computation.biology import compute_unaged_number_proportions , compute_unaged_weight_proportions , calculate_unaged_biomass def test_sum_strata_weight( mock_survey ): @@ -63,7 +64,7 @@ def test_sum_strata_weight( mock_survey ): 5.263158 , 8.631579 , 12.000000 , 15.368421 , 18.736842 ] ) assert np.allclose( check_values , object_weight_strata_aged_unaged.stratum_weight ) -def test_compute_index_aged_weight_proportions( mock_survey ): +def test_compute_aged_weight_proportions( mock_survey ): #### Pull in mock Survey object objS = mock_survey @@ -88,53 +89,40 @@ def test_compute_index_aged_weight_proportions( mock_survey ): objS.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] = np.array( [ 0.5 , 1.5 , 2.5 ] ) ### Evaluate object for later comparison - obj_props_wgt_len_age_sex = compute_index_aged_weight_proportions( objS.biology[ 'specimen_df' ] , - objS.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] , - objS.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] ) + obj_props_wgt_len_age_sex = compute_aged_weight_proportions( objS.biology[ 'specimen_df' ] , + objS.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] , + objS.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] ) ###-------------------------------- ### Expected outcomes ###-------------------------------- # ---- Expected dimensions of `obj_props_wgt_len_age_sex` - expected_dimensions = tuple( [ 24 , 12 ] ) + expected_dimensions = tuple( [ 16 , 11 ] ) # ---- Expected dataframe output - # stratum_num - stratum_array = np.repeat( [ 0 , 1 ] , 8 ).astype( np.int64 ).flatten( ) - stratum_array = np.concatenate( [ stratum_array , np.repeat( [ 0 , 1 ] , 4 ).astype( np.int64 ).flatten( ) ] ) # sex sex_array = np.tile( [ 'female' , 'female' , 'female' , 'female' , 'male' , 'male' , 'male' , 'male'] , 2 ).astype( object ).flatten( ) - sex_array = np.concatenate( [ sex_array , np.repeat( 'all' , 8 ).astype( object ).flatten( ) ] ) - # group - group_array = np.repeat( 'sexed' , 16 ).flatten( ) - group_array = np.concatenate( [ group_array , np.repeat( 'all' , 8 ) ] ).flatten( ) # total_weight_sex_all total_weight_sex_all_array = np.repeat( 10 , 16 ).astype( np.float64 ).flatten( ) total_weight_sex_all_array = np.concatenate( [ total_weight_sex_all_array , np.repeat( 20 , 8 ).astype( np.float64 ).flatten( ) ] ) expected_output = pd.DataFrame( { - 'stratum_num': stratum_array , - 'species_id': np.repeat( 19350 , 24 ).astype( np.int64 ) , + 'stratum_num': np.repeat( [ 0 , 1 ] , 8 ).astype( np.int64 ) , + 'species_id': np.repeat( 19350 , 16 ).astype( np.int64 ) , 'sex': sex_array , - 'length_bin': pd.cut( np.tile( [ 12 , 12 , 19 , 19 ] , 6 ) , + 'length_bin': pd.cut( np.tile( [ 12 , 12 , 19 , 19 ] , 4 ) , np.linspace( 9 , 21 , 3 ) ) , - 'age_bin': pd.cut( np.tile( [ 1 , 2 ] , 12 ) , + 'age_bin': pd.cut( np.tile( [ 1 , 2 ] , 8 ) , np.array( [ 0.5 , 1.5 , 2.5 ] ) ) , 'weight_all': [ 3.0 , 0.0 , 0.0 , 7.0 , 2.0 , 0.0 , 0.0 , 8.0 , - 4.0 , 0.0 , 0.0 , 6.0 , 1.0 , 0.0 , 0.0 , 9.0 , - 5.0 , 0.0 , 0.0 , 15.0 , 5.0 , 0.0 , 0.0 , 15.0 ] , + 4.0 , 0.0 , 0.0 , 6.0 , 1.0 , 0.0 , 0.0 , 9.0 ] , 'weight_adult': [ 0.0 , 0.0 , 0.0 , 7.0 , 0.0 , 0.0 , 0.0 , 8.0 , - 0.0 , 0.0 , 0.0 , 6.0 , 0.0 , 0.0 , 0.0 , 9.0 , - 0.0 , 0.0 , 0.0 , 15.0 , 0.0 , 0.0 , 0.0 , 15.0 ] , - 'group': group_array , - 'total_weight_sex_all': total_weight_sex_all_array , + 0.0 , 0.0 , 0.0 , 6.0 , 0.0 , 0.0 , 0.0 , 9.0 ] , + 'total_weight_sex_all': np.repeat( 10 , 16 ).astype( np.float64 ).flatten( ) , 'total_weight_sex_adult': [ 7.0 , 7.0 , 7.0 , 7.0 , 8.0 , 8.0 , 8.0 , 8.0 , - 6.0 , 6.0 , 6.0 , 6.0 , 9.0 , 9.0 , 9.0 , 9.0 , - 15.0 , 15.0 , 15.0 , 15.0 , 15.0 , 15.0 , 15.0 , 15.0 ] , + 6.0 , 6.0 , 6.0 , 6.0 , 9.0 , 9.0 , 9.0 , 9.0 ] , 'proportion_weight_sex_all': [ 0.3 , 0.0 , 0.0 , 0.7 , 0.2 , 0.0 , 0.0 , 0.8 , - 0.4 , 0.0 , 0.0 , 0.6 , 0.1 , 0.0 , 0.0 , 0.9 , - 0.25 , 0.0 , 0.0 , 0.75 , 0.25 , 0.0 , 0.0 , 0.75 ] , + 0.4 , 0.0 , 0.0 , 0.6 , 0.1 , 0.0 , 0.0 , 0.9 ] , 'proportion_weight_sex_adult': [ 0.0 , 0.0 , 0.0 , 1.0 , 0.0 , 0.0 , 0.0 , 1.0 , - 0.0 , 0.0 , 0.0 , 1.0 , 0.0 , 0.0 , 0.0 , 1.0 , 0.0 , 0.0 , 0.0 , 1.0 , 0.0 , 0.0 , 0.0 , 1.0 ] } ) expected_output[ 'length_bin' ] = pd.IntervalIndex( expected_output[ 'length_bin' ] ) @@ -159,59 +147,123 @@ def test_compute_index_aged_weight_proportions( mock_survey ): ### Check data value equality assert expected_output.equals( obj_props_wgt_len_age_sex ) +def test_compte_aged_unaged_proportions( ): -def test_compute_summed_aged_proportions( ): - - ### Mock data for `proportions_weight_length_age_sex` - test_proportions_weight_length_age_sex = pd.DataFrame( + ### Mock data for `specimen_data` + test_specimen_data = pd.DataFrame( { - 'stratum_num': np.repeat( [ 0.0 , 1.0 ] , 6 ) , - 'sex': np.tile( [ 'male' , 'female' , 'all' ] , 4 ) , - 'group': np.tile( [ 'sexed' , 'sexed' , 'all' ] , 4 ) , - 'weight_all': [ 1.0 , 3.0 , 4.0 , - 2.0 , 4.0 , 6.0 , - 0.0 , 6.0 , 6.0 , - 3.0 , 3.0 , 6.0 ] , - 'weight_adult': [ 0.0 , 1.5, 1.5 , - 2.0 , 4.0 , 6.0 , - 0.0 , 4.0 , 4.0 , - 3.0 , 3.0 , 6.0 ] + 'stratum_num': np.repeat( [ 0 , 1 ] , 4 ) , + 'sex': np.tile( [ 'male' , 'female' ] , 4 ) , + 'haul_num': np.tile( [ 1 , 2 ] , 4 ) , + 'species_id': np.repeat( [ 19350 ] , 8 ) , + 'length': [ 12.0 , 12.0 , 19.0 , 19.0 , 12.0 , 12.0 , 19.0 , 19.0 ] , + 'weight': [ 2.0 , 3.0 , 8.0 , 7.0 , 1.0 , 4.0 , 9.0 , 6.0 ] , + 'age': [ 1 , 1 , 2 , 2 , 1 , 1 , 2 , 2 ] } ) - + ### Mock data for `weight_strata` test_weight_strata = pd.DataFrame( { - 'stratum_num': [ 0.0 , 1.0 ] , - 'weight_stratum_all': [ 10.0 , 100.0 ] + 'stratum_num': [ 0 , 1 ] , + 'weight_stratum_all': [ 50.0 , 100.0 ] , } ) - + ### Evaluate for later comparison - eval_aged_sex_proportions , eval_aged_proportions = compute_summed_aged_proportions( test_proportions_weight_length_age_sex , - test_weight_strata ) + eval_aged_unaged_weight_proportions = compute_aged_unaged_proportions( test_specimen_data , + test_weight_strata ) + ###-------------------------------- ### Expected outcomes ###-------------------------------- ### `eval_aged_sex_proportions` # ---- Expected dimensions - expected_dimensions_aged_sex_proportions = tuple( [ 4 , 4 ] ) + expected_dimensions = tuple( [ 2 , 7 ] ) # ---- Expected dataframe output - expected_output_aged_sex_proportions = pd.DataFrame( { - 'stratum_num': np.repeat( [ 0.0 , 1.0 ] , 2 ) , - 'sex': np.tile( [ 'female' , 'male' ] , 2 ) , - 'proportion_weight_all': [ 0.70 , 0.30 , 0.09 , 0.03 ] , - 'proportion_weight_adult': [ 0.55 , 0.20 , 0.07 , 0.03 ] + expected_output = pd.DataFrame( { + 'stratum_num': np.array( [ 0.0 , 1.0 ] ).astype( np.int64 ) , + 'weight': [ 20.0 , 20.0 ] , + 'weight_stratum_all': [ 50.0 , 100.0 ] , + 'proportion_aged_weight_all': [ 0.40 , 0.20 ] , + 'proportion_aged_weight_adult': [ 0.30 , 0.15 ] , + 'proportion_unaged_weight_all': [ 0.60 , 0.80 ] , + 'proportion_unaged_weight_adult': [ 0.70 , 0.85 ] + } ) + + #---------------------------------- + ### Run tests: `compute_index_aged_weight_proportions` + #---------------------------------- + ### `eval_aged_sex_proportions` + # ---- Shape + assert eval_aged_unaged_weight_proportions.shape == expected_dimensions + # ---- Datatypes + assert np.all( eval_aged_unaged_weight_proportions.dtypes == expected_output.dtypes ) + # ---- Dataframe equality + assert np.allclose( eval_aged_unaged_weight_proportions , expected_output ) + +def test_compute_aged_sex_proportions( ): + + ### Mock data for `proportions_weight_length_age_sex` + test_proportions_weight_length_age_sex = pd.DataFrame( { + 'stratum_num': np.repeat( [ 0 , 1 ] , 8 ).astype( np.int64 ) , + 'species_id': np.repeat( 19350 , 16 ).astype( np.int64 ) , + 'sex': np.tile( [ 'female' , 'female' , 'female' , 'female' , + 'male' , 'male' , 'male' , 'male'] , 2 ).astype( object ).flatten( ) , + 'length_bin': pd.cut( np.tile( [ 12 , 12 , 19 , 19 ] , 4 ) , + np.linspace( 9 , 21 , 3 ) ) , + 'age_bin': pd.cut( np.tile( [ 1 , 2 ] , 8 ) , + np.array( [ 0.5 , 1.5 , 2.5 ] ) ) , + 'weight_all': [ 3.0 , 0.0 , 0.0 , 7.0 , 2.0 , 0.0 , 0.0 , 8.0 , + 4.0 , 0.0 , 0.0 , 6.0 , 1.0 , 0.0 , 0.0 , 9.0 ] , + 'weight_adult': [ 0.0 , 0.0 , 0.0 , 7.0 , 0.0 , 0.0 , 0.0 , 8.0 , + 0.0 , 0.0 , 0.0 , 6.0 , 0.0 , 0.0 , 0.0 , 9.0 ] , + 'total_weight_sex_all': np.repeat( 10 , 16 ).astype( np.float64 ).flatten( ) , + 'total_weight_sex_adult': [ 7.0 , 7.0 , 7.0 , 7.0 , 8.0 , 8.0 , 8.0 , 8.0 , + 6.0 , 6.0 , 6.0 , 6.0 , 9.0 , 9.0 , 9.0 , 9.0 ] , + 'proportion_weight_sex_all': [ 0.3 , 0.0 , 0.0 , 0.7 , 0.2 , 0.0 , 0.0 , 0.8 , + 0.4 , 0.0 , 0.0 , 0.6 , 0.1 , 0.0 , 0.0 , 0.9 ] , + 'proportion_weight_sex_adult': [ 0.0 , 0.0 , 0.0 , 1.0 , 0.0 , 0.0 , 0.0 , 1.0 , + 0.0 , 0.0 , 0.0 , 1.0 , 0.0 , 0.0 , 0.0 , 1.0 ] + } ) + test_proportions_weight_length_age_sex[ 'length_bin' ] = pd.IntervalIndex( test_proportions_weight_length_age_sex[ 'length_bin' ] ) + test_proportions_weight_length_age_sex[ 'length_bin' ] = pd.Categorical( test_proportions_weight_length_age_sex[ 'length_bin' ] , + categories = test_proportions_weight_length_age_sex[ 'length_bin' ].unique( ) , + ordered = True ) + test_proportions_weight_length_age_sex[ 'age_bin' ] = pd.IntervalIndex( test_proportions_weight_length_age_sex[ 'age_bin' ] ) + test_proportions_weight_length_age_sex[ 'age_bin' ] = pd.Categorical( test_proportions_weight_length_age_sex[ 'age_bin' ] , + categories = test_proportions_weight_length_age_sex[ 'age_bin' ].unique( ) , + ordered=True) + + ### Mock data for `aged_proportions` + test_aged_proportions = pd.DataFrame( { + 'stratum_num': [ 0 , 1 ] , + 'weight': [ 100.0 , 200.0 ] , + 'weight_stratum_all': [ 200.00 , 250.0 ] , + 'proportion_aged_weight_all': [ 0.10 , 0.20 ] , + 'proportion_aged_weight_adult': [ 0.05 , 0.10 ] , + 'proportion_unaged_weight_all': [ 0.90 , 0.80 ] , + 'proportion_unaged_weight_adult': [ 0.45 , 0.40 ] } ) - ### `eval_aged_proportions` + + ### Evaluate for later comparison + eval_aged_sex_proportions = compute_aged_sex_weight_proportions( test_proportions_weight_length_age_sex , + test_aged_proportions ) + ###-------------------------------- + ### Expected outcomes + ###-------------------------------- + ### `eval_aged_sex_proportions` # ---- Expected dimensions - expected_dimensions_aged_proportions = tuple( [ 2 , 3 ] ) + expected_dimensions = tuple( [ 4 , 6 ] ) # ---- Expected dataframe output - expected_output_aged_proportions = pd.DataFrame( { - 'stratum_num': [ 0.0 , 1.0 ] , - 'proportion_weight_all': [ 1.00 , 0.12 ] , - 'proportion_weight_adult': [ 0.75 , 0.10 ] + expected_output = pd.DataFrame( { + 'stratum_num': np.repeat( [ 0.0 , 1.0 ] , 2 ).astype( np.int64 ) , + 'sex': np.tile( [ 'female' , 'male' ] , 2 ).astype( object ) , + 'proportion_aged_weight_all': [ 0.10 , 0.10 , 0.20 , 0.20 ] , + 'proportion_aged_weight_adult': [ 0.05 , 0.05 , 0.10 , 0.10 ] , + 'proportion_weight_all': [ 0.050 , 0.050 , 0.040 , 0.040 ] , + 'proportion_weight_adult': [ 0.035 , 0.040 , 0.024 , 0.036 ] } ) #---------------------------------- @@ -219,19 +271,11 @@ def test_compute_summed_aged_proportions( ): #---------------------------------- ### `eval_aged_sex_proportions` # ---- Shape - assert eval_aged_sex_proportions.shape == expected_dimensions_aged_sex_proportions + assert eval_aged_sex_proportions.shape == expected_dimensions # ---- Datatypes - assert np.all( eval_aged_sex_proportions.dtypes == expected_output_aged_sex_proportions.dtypes ) - # ---- Dataframe equality - assert np.all( eval_aged_sex_proportions == expected_output_aged_sex_proportions ) - - ### `eval_aged_proportions` - # ---- Shape - assert eval_aged_proportions.shape == expected_dimensions_aged_proportions - # ---- Datatypesd - assert np.all( eval_aged_proportions.dtypes == expected_output_aged_proportions.dtypes ) + assert np.all( eval_aged_sex_proportions.dtypes == expected_output.dtypes ) # ---- Dataframe equality - assert np.all( eval_aged_proportions == expected_output_aged_proportions ) + assert np.all( eval_aged_sex_proportions == expected_output ) def test_distribute_aged_weight_proportions( ): @@ -247,10 +291,12 @@ def test_distribute_aged_weight_proportions( ): ### Mock data for `aged_sex_proportions` test_aged_sex_proportions = pd.DataFrame( { - 'stratum_num': np.repeat( [ 0.0 , 1.0 ] , 2 ) , - 'sex': np.tile( [ 'female' , 'male' ] , 2 ) , - 'proportion_weight_all': [ 0.25 , 0.50 , 0.20 , 0.10 ] , - 'proportion_weight_adult': [ 0.20 , 0.30 , 0.10 , 0.10 ] + 'stratum_num': np.repeat( [ 0.0 , 1.0 ] , 2 ).astype( np.int64 ) , + 'sex': np.tile( [ 'female' , 'male' ] , 2 ).astype( object ) , + 'proportion_aged_weight_all': [ 0.10 , 0.10 , 0.20 , 0.20 ] , + 'proportion_aged_weight_adult': [ 0.05 , 0.05 , 0.10 , 0.10 ] , + 'proportion_weight_all': [ 0.050 , 0.050 , 0.040 , 0.040 ] , + 'proportion_weight_adult': [ 0.035 , 0.040 , 0.024 , 0.036 ] } ) ### Evaluate for later comparison @@ -262,13 +308,13 @@ def test_distribute_aged_weight_proportions( ): ###-------------------------------- ### `eval_aged_sex_proportions` # ---- Expected dimensions - expected_dimensions_distributed_aged_weight_proportions = tuple( [ 4 , 4 ] ) + expected_dimensions = tuple( [ 4 , 4 ] ) # ---- Expected dataframe output - expected_output_distributed_aged_weight_proportions = pd.DataFrame( { + expected_output = pd.DataFrame( { 'stratum_num': np.repeat( [ 0.0 , 1.0 ] , 2 ) , 'sex': np.tile( [ 'female' , 'male' ] , 2 ) , - 'normalized_proportion_weight_all': [ 0.125 , 0.250 , 0.050 , 0.050 ] , - 'normalized_proportion_weight_adult': [ 0.100 , 0.150 , 0.025 , 0.050 ] + 'normalized_proportion_weight_all': [ 0.025 , 0.025 , 0.010 , 0.020 ] , + 'normalized_proportion_weight_adult': [ 0.0175 , 0.0100 , 0.00000 , 0.0090 ] } ) #---------------------------------- @@ -276,19 +322,143 @@ def test_distribute_aged_weight_proportions( ): #---------------------------------- ### `eval_aged_sex_proportions` # ---- Shape - assert eval_distributed_aged_weight_proportions.shape == expected_dimensions_distributed_aged_weight_proportions + assert eval_distributed_aged_weight_proportions.shape == expected_dimensions # ---- Datatypes - assert np.all( eval_distributed_aged_weight_proportions.dtypes == expected_output_distributed_aged_weight_proportions.dtypes ) + assert np.all( eval_distributed_aged_weight_proportions.dtypes == expected_output.dtypes ) # ---- Dataframe equality - eval_distributed_aged_weight_proportions.equals( expected_output_distributed_aged_weight_proportions ) + assert np.all( eval_distributed_aged_weight_proportions == expected_output ) -def test_compute_index_unaged_number_proportions( mock_survey ): +def test_calculate_aged_biomass( ): + + ### Mock data for `kriging_biomass_df` + test_kriging_biomass_df = pd.DataFrame( + { + 'centroid_latitude': [ 30.0 , 40.0 , 50.0 ] , + 'centroid_longitude': [ 30.0 , 31.0 , 32.0 ] , + 'fraction_cell_in_polygon': [ 1.0 , 1.0 , 1.0 ] , + 'stratum_num': [ 0.0 , 1.0 , 2.0 ] , + 'B_a_adult_mean': [ 1e3 , 1e4 , 1e5 ] , + 'B_a_adult_prediction_variance': [ 1e1 , 1e2 , 1e3 ] , + 'B_a_adult_sample_variance': [ 1e1 , 1e2 , 1e3 ] , + 'cell_area_nmi2': [ 10.0 , 10.0 , 5.0 ] , + 'B_adult_kriged': [ 1e4 , 1e5 , 5e5 ] + } + ) + + ### Mock data for `specimen_data` + test_specimen_data = pd.DataFrame( + { + 'stratum_num': np.repeat( [ 0 , 1 ] , 4 ) , + 'sex': np.tile( [ 'male' , 'female' ] , 4 ) , + 'haul_num': np.tile( [ 1 , 2 ] , 4 ) , + 'species_id': np.repeat( [ 19350 ] , 8 ) , + 'length': [ 12.0 , 12.0 , 19.0 , 19.0 , 12.0 , 12.0 , 19.0 , 19.0 ] , + 'weight': [ 2.0 , 3.0 , 3.0 , 2.0 , 2.0 , 3.0 , 2.0 , 3.0 ] , + 'age': [ 1 , 1 , 2 , 2 , 1 , 1 , 2 , 2 ] + } + ) + + ### Mock data for `length_intervals` + test_length_intervals = np.linspace( 9 , 21 , 3 ) + + ### Mock data for `age_intervals` + test_age_intervals = np.array( [ 0.5 , 1.5 , 2.5 ] ) + + ### Mock data for `aged_proportions` + test_aged_proportions = pd.DataFrame( { + 'stratum_num': np.array( [ 0.0 , 1.0 ] ).astype( np.int64 ) , + 'weight': [ 10.0 , 10.0 ] , + 'weight_stratum_all': [ 100.0 , 200.0 ] , + 'proportion_aged_weight_all': [ 0.10 , 0.05 ] , + 'proportion_aged_weight_adult': [ 0.050 , 0.025 ] , + 'proportion_unaged_weight_all': [ 0.90 , 0.95 ] , + 'proportion_unaged_weight_adult': [ 0.95 , 0.975 ] + } ) + + ### Evaluate for later comparison + eval_aged_sex_biomass , eval_aged_biomass = calculate_aged_biomass( test_kriging_biomass_df , + test_specimen_data , + test_length_intervals , + test_age_intervals , + test_aged_proportions ) + + ###-------------------------------- + ### Expected outcomes + ###-------------------------------- + ### `eval_aged_sex_biomass` + # ---- Expected dimensions + expected_dimensions_aged_sex_biomass = tuple( [ 8 , 6 ] ) + # ---- Expected dataframe output + expected_output_aged_sex_biomass = pd.DataFrame( + { + 'species_id': np.repeat( 19350 , 8 ).astype( np.int64 ) , + 'sex': np.repeat( [ 'female' , 'male' ] , 4 ) , + 'length_bin': pd.cut( np.tile( [ 12 , 12 , 19 , 19 ] , 2 ) , + np.linspace( 9 , 21 , 3 ) ) , + 'age_bin': pd.cut( np.tile( [ 1 , 2 ] , 4 ) , + np.array( [ 0.5 , 1.5 , 2.5 ] ) ) , + 'biomass_sexed_aged_all': [ 1800.0 , 0.0 , 0.0 , 1700.0 , + 1200.0 , 0.0 , 0.0 , 1300.0 ] , + 'biomass_sexed_aged_adult': [ 0.0 , 0.0 , 0.0 , 1700.0 , + 0.0 , 0.0 , 0.0 , 1300.0 ] + } + ) + expected_output_aged_sex_biomass[ 'length_bin' ] = pd.IntervalIndex( expected_output_aged_sex_biomass[ 'length_bin' ] ) + expected_output_aged_sex_biomass[ 'length_bin' ] = pd.Categorical( expected_output_aged_sex_biomass[ 'length_bin' ] , + categories = expected_output_aged_sex_biomass[ 'length_bin' ].unique( ) , + ordered = True ) + expected_output_aged_sex_biomass[ 'age_bin' ] = pd.IntervalIndex( expected_output_aged_sex_biomass[ 'age_bin' ] ) + expected_output_aged_sex_biomass[ 'age_bin' ] = pd.Categorical( expected_output_aged_sex_biomass[ 'age_bin' ] , + categories = expected_output_aged_sex_biomass[ 'age_bin' ].unique( ) , + ordered=True ) + ### `eval_aged_biomass` + # ---- Expected dimensions + expected_dimensions_aged_biomass = tuple( [ 4 , 5 ] ) + # ---- Expected dataframe output + expected_output_aged_biomass = pd.DataFrame( + { + 'species_id': np.repeat( 19350 , 4 ).astype( np.int64 ) , + 'length_bin': pd.cut( [ 12 , 12 , 19 , 19 ] , + np.linspace( 9 , 21 , 3 ) ) , + 'age_bin': pd.cut( np.tile( [ 1 , 2 ] , 2 ) , + np.array( [ 0.5 , 1.5 , 2.5 ] ) ) , + 'biomass_aged_all': [ 3000.0 , 0.0 , 0.0 , 3000.0 ] , + 'biomass_aged_adult': [ 0.0 , 0.0 , 0.0 , 3000.0 ] + } + ) + expected_output_aged_biomass[ 'length_bin' ] = pd.IntervalIndex( expected_output_aged_biomass[ 'length_bin' ] ) + expected_output_aged_biomass[ 'length_bin' ] = pd.Categorical( expected_output_aged_biomass[ 'length_bin' ] , + categories = expected_output_aged_biomass[ 'length_bin' ].unique( ) , + ordered = True ) + expected_output_aged_biomass[ 'age_bin' ] = pd.IntervalIndex( expected_output_aged_biomass[ 'age_bin' ] ) + expected_output_aged_biomass[ 'age_bin' ] = pd.Categorical( expected_output_aged_biomass[ 'age_bin' ] , + categories = expected_output_aged_biomass[ 'age_bin' ].unique( ) , + ordered=True ) + #---------------------------------- + ### Run tests: `distribute_aged_weight_proportions` + #---------------------------------- + ### `eval_aged_sex_proportions` + # ---- Shape + assert eval_aged_sex_biomass.shape == expected_dimensions_aged_sex_biomass + assert eval_aged_biomass.shape == expected_dimensions_aged_biomass + # ---- Datatypes + assert np.all( eval_aged_sex_biomass.dtypes == expected_output_aged_sex_biomass.dtypes ) + assert np.all( eval_aged_biomass.dtypes == expected_output_aged_biomass.dtypes ) + # ---- Dataframe equality + assert np.allclose( eval_aged_sex_biomass[ [ 'biomass_sexed_aged_all' , 'biomass_sexed_aged_adult' ] ] , + expected_output_aged_sex_biomass[ [ 'biomass_sexed_aged_all' , 'biomass_sexed_aged_adult' ] ] ) + assert eval_aged_biomass.equals( expected_output_aged_biomass ) + # Assess equality between sexed and total biomass estimates + assert eval_aged_biomass.biomass_aged_all.sum( ) == 6e3 + assert eval_aged_biomass.biomass_aged_adult.sum( ) == 3e3 + assert eval_aged_biomass.biomass_aged_all.sum( ) == eval_aged_sex_biomass.biomass_sexed_aged_all.sum( ) + assert eval_aged_biomass.biomass_aged_adult.sum( ) == eval_aged_sex_biomass.biomass_sexed_aged_adult.sum( ) - #### Pull in mock Survey object - objS = mock_survey + +def compute_unaged_number_proportions( ): ### Re-parameterize `length_df` with dummy data - objS.biology[ 'length_df' ] = pd.DataFrame( + test_length_data = pd.DataFrame( { 'stratum_num': np.repeat( [ 0 , 1 ] , 4 ) , 'sex': np.tile( [ 'male' , 'female' ] , 4 ) , @@ -299,18 +469,18 @@ def test_compute_index_unaged_number_proportions( mock_survey ): ) ### Length interval - objS.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] = np.linspace( 9 , 21 , 3 ) + test_length_intervals = np.linspace( 9 , 21 , 3 ) ### Evaluate object for later comparison - obj_proportions_unaged_length = compute_index_unaged_number_proportions( objS.biology[ 'length_df' ] , - objS.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] ) + eval_proportions_unaged_length = compute_unaged_number_proportions( test_length_data , + test_length_intervals ) ###-------------------------------- ### Expected outcomes ###-------------------------------- ### `eval_aged_sex_proportions` # ---- Expected dimensions - expected_dimensions_proportions_unaged_length = tuple( [ 4 , 4 ] ) + expected_dimensions = tuple( [ 4 , 4 ] ) # ---- Expected dataframe output expected_output = pd.DataFrame( { 'stratum_num': np.repeat( [ 0 , 1 ] , 2 ).astype( np.int64 ) , @@ -329,13 +499,13 @@ def test_compute_index_unaged_number_proportions( mock_survey ): #---------------------------------- ### `eval_aged_sex_proportions` # ---- Shape - assert obj_proportions_unaged_length.shape == expected_dimensions_proportions_unaged_length + assert eval_proportions_unaged_length.shape == expected_dimensions # ---- Datatypes - assert np.all( obj_proportions_unaged_length.dtypes == expected_output.dtypes ) + assert np.all( eval_proportions_unaged_length.dtypes == expected_output.dtypes ) # ---- Dataframe equality - assert obj_proportions_unaged_length.equals( expected_output ) + assert eval_proportions_unaged_length.equals( expected_output ) -def test_compute_summed_unaged_weight_proportions( ): +def test_compute_unaged_weight_proportions( ): ### Create mock `proportions_unaged_length` test_proportions_unaged_length = pd.DataFrame( { @@ -361,15 +531,15 @@ def test_compute_summed_unaged_weight_proportions( ): } ) ### Evaluate for later comparison - eval_proportions_unaged_weight_length = compute_summed_unaged_weight_proportions( test_proportions_unaged_length , - test_length_weight_df ) + eval_proportions_unaged_weight_length = compute_unaged_weight_proportions( test_proportions_unaged_length , + test_length_weight_df ) ###-------------------------------- ### Expected outcomes ###-------------------------------- ### `eval_proportions_unaged_weight_length` # ---- Expected dimensions - expected_dimensions_proportions_unaged_length_sex = tuple( [ 6 , 4 ] ) + expected_dimensions = tuple( [ 6 , 4 ] ) # ---- Expected dataframe output expected_output = pd.DataFrame( { 'stratum_num': np.repeat( [ 0 , 1 ] , 3 ) , @@ -384,7 +554,7 @@ def test_compute_summed_unaged_weight_proportions( ): #---------------------------------- ### `eval_aged_sex_proportions` # ---- Shape - assert eval_proportions_unaged_weight_length.shape == expected_dimensions_proportions_unaged_length_sex + assert eval_proportions_unaged_weight_length.shape == expected_dimensions # ---- Datatypes assert np.all( eval_proportions_unaged_weight_length.dtypes == expected_output.dtypes ) # ---- Dataframe equality @@ -423,29 +593,22 @@ def test_compute_unaged_sex_proportions( ): categories = test_length_weight_df[ 'length_bin' ].unique( ) , ordered = True ) - ### Mock data for `weight_strata` - test_weight_strata = pd.DataFrame( - { - 'stratum_num': [ 0 , 1 ] , - 'weight_stratum_all': [ 50 , 100 ] , - } - ) - - ### Mock data for `weight_strata_aged_unaged` - test_weight_strata_aged_unaged = pd.DataFrame( - { - 'stratum_num': [ 0 , 1 , 0 , 1 ] , - 'stratum_weight': [ 500 , 400 , 50 , 100 ] , - 'group': [ 'unaged' , 'unaged' , 'aged' , 'aged' ] , - } - ) + ### Mock data for `aged_proportions` + test_aged_proportions = pd.DataFrame( { + 'stratum_num': [ 0 , 1 ] , + 'weight': [ 100.0 , 200.0 ] , + 'weight_stratum_all': [ 200.00 , 250.0 ] , + 'proportion_aged_weight_all': [ 0.10 , 0.20 ] , + 'proportion_aged_weight_adult': [ 0.05 , 0.10 ] , + 'proportion_unaged_weight_all': [ 0.90 , 0.80 ] , + 'proportion_unaged_weight_adult': [ 0.45 , 0.40 ] + } ) ### Evaluate for later comparison eval_proportions_unaged_weight_sex = compute_unaged_sex_proportions( test_length_data , test_length_intervals , test_length_weight_df , - test_weight_strata , - test_weight_strata_aged_unaged ) + test_aged_proportions ) ###-------------------------------- ### Expected outcomes @@ -473,4 +636,243 @@ def test_compute_unaged_sex_proportions( ): ### Check data value equality # ---- `stratum_num` assert np.all( eval_proportions_unaged_weight_sex.stratum_num == expected_output.stratum_num ) - assert np.allclose( eval_proportions_unaged_weight_sex.proportion_weight_sex , expected_output.proportion_weight_sex ) \ No newline at end of file + assert np.allclose( eval_proportions_unaged_weight_sex.proportion_weight_sex , expected_output.proportion_weight_sex ) + +def test_calculate_unaged_biomass( ): + + ### Mock data for `kriging_biomass_df` + test_kriging_biomass_df = pd.DataFrame( + { + 'centroid_latitude': [ 30.0 , 40.0 , 50.0 ] , + 'centroid_longitude': [ 30.0 , 31.0 , 32.0 ] , + 'fraction_cell_in_polygon': [ 1.0 , 1.0 , 1.0 ] , + 'stratum_num': [ 0.0 , 1.0 , 2.0 ] , + 'B_a_adult_mean': [ 1e3 , 1e4 , 1e5 ] , + 'B_a_adult_prediction_variance': [ 1e1 , 1e2 , 1e3 ] , + 'B_a_adult_sample_variance': [ 1e1 , 1e2 , 1e3 ] , + 'cell_area_nmi2': [ 10.0 , 10.0 , 5.0 ] , + 'B_adult_kriged': [ 1e4 , 1e5 , 5e5 ] + } + ) + + ### Mock data for `specimen_data` + test_length_data = pd.DataFrame( + { + 'stratum_num': np.repeat( [ 0 , 1 ] , 4 ) , + 'sex': np.tile( [ 'male' , 'female' ] , 4 ) , + 'species_id': np.repeat( [ 19350 ] , 8 ) , + 'length': [ 12 , 12 , 19 , 19 , 12 , 12 , 19 , 19 ] , + 'length_count': [ 5 , 10 , 15 , 20 , 20 , 15 , 10 , 5 ] + } + ) + + ### Mock data for `length_intervals` + test_length_intervals = np.linspace( 9 , 21 , 3 ) + + ### Mock data for `length_weight_df` + test_length_weight_df = pd.DataFrame( { + 'length_bin': pd.cut( [ 12 , 12 , 12 , 19 , 19 , 19 ] , + test_length_intervals ) , + 'sex': [ 'all' , 'male' , 'female' , + 'all' , 'male' , 'female' ] , + 'weight_modeled': [ 1.0 , 0.9 , 1.1 , + 2.0 , 1.9 , 2.1 ] + } ) + + ### Mock data for `aged_proportions` + test_aged_proportions = pd.DataFrame( { + 'stratum_num': np.array( [ 0.0 , 1.0 ] ).astype( np.int64 ) , + 'weight': [ 20.0 , 20.0 ] , + 'weight_stratum_all': [ 100.0 , 200.0 ] , + 'proportion_aged_weight_all': [ 0.20 , 0.10 ] , + 'proportion_aged_weight_adult': [ 0.10 , 0.05 ] , + 'proportion_unaged_weight_all': [ 0.80 , 0.90 ] , + 'proportion_unaged_weight_adult': [ 0.40 , 0.45 ] + } ) + + ### Evaluate for later comparison + eval_unaged_sex_biomass , eval_unaged_biomass = calculate_unaged_biomass( test_kriging_biomass_df , + test_length_data , + test_length_intervals , + test_length_weight_df , + test_aged_proportions ) + + ###-------------------------------- + ### Expected outcomes + ###-------------------------------- + ### `eval_aged_sex_biomass` + # ---- Expected dimensions + expected_dimensions_unaged_sex_biomass = tuple( [ 4 , 5 ] ) + # ---- Expected dataframe output + expected_output_unaged_sex_biomass = pd.DataFrame( + { + 'species_id': np.repeat( 19350 , 4 ).astype( np.int64 ) , + 'sex': np.repeat( [ 'female' , 'male' ] , 2 ) , + 'length_bin': pd.cut( [ 12 , 19 , 12 , 19 ] , + np.linspace( 9 , 21 , 3 ) ) , + 'biomass_sexed_unaged_all': [ 21314.8 , 21584.2 , 28558.6 , 26542.5 ] , + 'biomass_sexed_unaged_adult': [ 10657.4 , 10792.1 , 14279.3 , 13271.2 ] , + } + ) + expected_output_unaged_sex_biomass[ 'length_bin' ] = pd.IntervalIndex( expected_output_unaged_sex_biomass[ 'length_bin' ] ) + expected_output_unaged_sex_biomass[ 'length_bin' ] = pd.Categorical( expected_output_unaged_sex_biomass[ 'length_bin' ] , + categories = expected_output_unaged_sex_biomass[ 'length_bin' ].unique( ) , + ordered = True ) + + ### `eval_aged_biomass` + # ---- Expected dimensions + expected_dimensions_unaged_biomass = tuple( [ 2 , 4 ] ) + # ---- Expected dataframe output + expected_output_unaged_biomass = pd.DataFrame( + { + 'species_id': np.repeat( 19350 , 2 ).astype( np.int64 ) , + 'length_bin': pd.cut( [ 12 , 19 ] , np.linspace( 9 , 21 , 3 ) ) , + 'biomass_unaged_all': [ 49873.3 , 48126.7 ] , + 'biomass_unaged_adult': [ 24936.7 , 24063.4 ] + } + ) + expected_output_unaged_biomass[ 'length_bin' ] = pd.IntervalIndex( expected_output_unaged_biomass[ 'length_bin' ] ) + expected_output_unaged_biomass[ 'length_bin' ] = pd.Categorical( expected_output_unaged_biomass[ 'length_bin' ] , + categories = expected_output_unaged_biomass[ 'length_bin' ].unique( ) , + ordered = True ) + + #---------------------------------- + ### Run tests: `distribute_aged_weight_proportions` + #---------------------------------- + ### `eval_aged_sex_proportions` + # ---- Shape + assert eval_unaged_sex_biomass.shape == expected_dimensions_unaged_sex_biomass + assert eval_unaged_biomass.shape == expected_dimensions_unaged_biomass + # ---- Datatypes + assert np.all( eval_unaged_sex_biomass.dtypes == expected_output_unaged_sex_biomass.dtypes ) + assert np.all( eval_unaged_biomass.dtypes == expected_output_unaged_biomass.dtypes ) + # ---- Dataframe equality + assert np.allclose( eval_unaged_sex_biomass[ [ 'biomass_sexed_unaged_all' , 'biomass_sexed_unaged_adult' ] ] , + expected_output_unaged_sex_biomass[ [ 'biomass_sexed_unaged_all' , 'biomass_sexed_unaged_adult' ] ] ) + assert np.allclose( eval_unaged_biomass[ [ 'biomass_unaged_all' , 'biomass_unaged_adult' ] ] , + expected_output_unaged_biomass[ [ 'biomass_unaged_all' , 'biomass_unaged_adult' ] ] ) + # ---- Check expected summed values + assert eval_unaged_biomass[ 'biomass_unaged_all' ].sum( ) == 98e3 + assert eval_unaged_biomass[ 'biomass_unaged_adult' ].sum( ) == 49e3 + # ---- Evaluate the sum and comparison between 'total' and 'sexed' + assert np.isclose( eval_unaged_sex_biomass[ 'biomass_sexed_unaged_all' ].sum( ) , + eval_unaged_biomass[ 'biomass_unaged_all' ].sum( ) ) + assert np.isclose( eval_unaged_sex_biomass[ 'biomass_sexed_unaged_adult' ].sum( ) , + eval_unaged_biomass[ 'biomass_unaged_adult' ].sum( ) ) + +def test_apply_age_bins( ): + + ### Mock data for `aged_sex_biomass` + test_aged_sex_biomass = pd.DataFrame( + { + 'species_id': np.repeat( 19350 , 8 ).astype( np.int64 ) , + 'sex': np.repeat( [ 'female' , 'male' ] , 4 ) , + 'length_bin': pd.cut( np.tile( [ 12 , 12 , 19 , 19 ] , 2 ) , + np.linspace( 9 , 21 , 3 ) ) , + 'age_bin': pd.cut( np.tile( [ 1 , 2 ] , 4 ) , + np.array( [ 0.5 , 1.5 , 2.5 ] ) ) , + 'biomass_sexed_aged_all': [ 1000.0 , 2000.0 , 3000.0 , 5000.0 , + 4000.0 , 3000.0 , 2000.0 , 200.0 ] , + 'biomass_sexed_aged_adult': [ 0.0 , 500.0 , 0.0 , 1250.0 , + 0.0 , 750.0 , 0.0 , 50.0 ] + } + ) + test_aged_sex_biomass[ 'length_bin' ] = pd.IntervalIndex( test_aged_sex_biomass[ 'length_bin' ] ) + test_aged_sex_biomass[ 'length_bin' ] = pd.Categorical( test_aged_sex_biomass[ 'length_bin' ] , + categories = test_aged_sex_biomass[ 'length_bin' ].unique( ) , + ordered = True ) + test_aged_sex_biomass[ 'age_bin' ] = pd.IntervalIndex( test_aged_sex_biomass[ 'age_bin' ] ) + test_aged_sex_biomass[ 'age_bin' ] = pd.Categorical( test_aged_sex_biomass[ 'age_bin' ] , + categories = test_aged_sex_biomass[ 'age_bin' ].unique( ) , + ordered=True ) + + ### Mock data for `unaged_sex_biomass` + test_unaged_sex_biomass = pd.DataFrame( + { + 'species_id': np.repeat( 19350 , 4 ).astype( np.int64 ) , + 'sex': np.repeat( [ 'female' , 'male' ] , 2 ) , + 'length_bin': pd.cut( [ 12 , 19 , 12 , 19 ] , + np.linspace( 9 , 21 , 3 ) ) , + 'biomass_sexed_unaged_all': [ 5000.0 , 2000.0 , 4000.0 , 0.0 ] , + 'biomass_sexed_unaged_adult': [ 1250.0 , 500.0 , 1000.0 , 0.0 ] , + } + ) + test_unaged_sex_biomass[ 'length_bin' ] = pd.IntervalIndex( test_unaged_sex_biomass[ 'length_bin' ] ) + test_unaged_sex_biomass[ 'length_bin' ] = pd.Categorical( test_unaged_sex_biomass[ 'length_bin' ] , + categories = test_unaged_sex_biomass[ 'length_bin' ].unique( ) , + ordered = True ) + + ### Evaluate for later comparison + eval_redistrib_unaged_sex_biomass , eval_redistrib_unaged_biomass = apply_age_bins( test_aged_sex_biomass , + test_unaged_sex_biomass ) + + ###-------------------------------- + ### Expected outcomes + ###-------------------------------- + # ---- `eval_redistrib_unaged_sex_biomass` + # ---- Expected dimensions of `obj_props_wgt_len_age_sex` + expected_dimensions_redistrib_unaged_sex_biomass = tuple( [ 8 , 6 ] ) + # ---- Expected dataframe output + expected_output_redistrib_unaged_sex_biomass = pd.DataFrame( { + 'species_id': np.repeat( 19350 , 8 ).astype( np.int64 ) , + 'sex': np.repeat( [ 'female' , 'male' ] , 4 ) , + 'length_bin': pd.cut( np.tile( [ 12 , 12 , 19 , 19 ] , 2 ) , + np.linspace( 9 , 21 , 3 ) ) , + 'age_bin': pd.cut( np.tile( [ 1 , 2 ] , 4 ) , + np.array( [ 0.5 , 1.5 , 2.5 ] ) ) , + 'biomass_sexed_unaged_all': [ 1666.7 , 3333.3 , 750.0 , 1250.0 , 2285.7 , 1714.3 , 0.0 , 0.0 ] , + 'biomass_sexed_unaged_adult': [ 0.0 , 1250.0 , 0.0 , 500.0 , 0.0 , 1000.0 , 0.0 , 0.0 ] + } ) + expected_output_redistrib_unaged_sex_biomass[ 'length_bin' ] = pd.IntervalIndex( expected_output_redistrib_unaged_sex_biomass[ 'length_bin' ] ) + expected_output_redistrib_unaged_sex_biomass[ 'length_bin' ] = pd.Categorical( expected_output_redistrib_unaged_sex_biomass[ 'length_bin' ] , + categories = expected_output_redistrib_unaged_sex_biomass[ 'length_bin' ].unique( ) , + ordered = True ) + expected_output_redistrib_unaged_sex_biomass[ 'age_bin' ] = pd.IntervalIndex( expected_output_redistrib_unaged_sex_biomass[ 'age_bin' ] ) + expected_output_redistrib_unaged_sex_biomass[ 'age_bin' ] = pd.Categorical( expected_output_redistrib_unaged_sex_biomass[ 'age_bin' ] , + categories = expected_output_redistrib_unaged_sex_biomass[ 'age_bin' ].unique( ) , + ordered=True ) + # ---- `eval_redistrib_unaged_biomass` + # ---- Expected dimensions of `obj_props_wgt_len_age_sex` + expected_dimensions_redistrib_unaged_biomass = tuple( [ 4 , 5 ] ) + # ---- Expected dataframe output + expected_output_redistrib_unaged_biomass = pd.DataFrame( { + 'length_bin': pd.cut( np.repeat( [ 12 , 19 ] , 2 ) , + np.linspace( 9 , 21 , 3 ) ) , + 'age_bin': pd.cut( np.tile( [ 1 , 2 ] , 2 ) , + np.array( [ 0.5 , 1.5 , 2.5 ] ) ) , + 'species_id': np.repeat( 19350 , 4 ).astype( np.int64 ) , + 'biomass_unaged_all': [ 3952.4 , 5047.6 , 750.0 , 1250.0 ] , + 'biomass_unaged_adult': [ 0.0 , 2250.0 , 0.0 , 500.0 ] + } ) + expected_output_redistrib_unaged_biomass[ 'length_bin' ] = pd.IntervalIndex( expected_output_redistrib_unaged_biomass[ 'length_bin' ] ) + expected_output_redistrib_unaged_biomass[ 'length_bin' ] = pd.Categorical( expected_output_redistrib_unaged_biomass[ 'length_bin' ] , + categories = expected_output_redistrib_unaged_biomass[ 'length_bin' ].unique( ) , + ordered = True ) + expected_output_redistrib_unaged_biomass[ 'age_bin' ] = pd.IntervalIndex( expected_output_redistrib_unaged_biomass[ 'age_bin' ] ) + expected_output_redistrib_unaged_biomass[ 'age_bin' ] = pd.Categorical( expected_output_redistrib_unaged_biomass[ 'age_bin' ] , + categories = expected_output_redistrib_unaged_biomass[ 'age_bin' ].unique( ) , + ordered=True ) + #---------------------------------- + ### Run tests: `apply_age_bins` + #---------------------------------- + ### Check shape + assert eval_redistrib_unaged_sex_biomass.shape == expected_dimensions_redistrib_unaged_sex_biomass + assert eval_redistrib_unaged_biomass.shape == expected_dimensions_redistrib_unaged_biomass + ### Check datatypes + assert np.all( eval_redistrib_unaged_sex_biomass.dtypes == expected_output_redistrib_unaged_sex_biomass.dtypes ) + assert np.all( eval_redistrib_unaged_biomass.dtypes == expected_output_redistrib_unaged_biomass.dtypes ) + # ---- Dataframe equality + assert np.allclose( eval_redistrib_unaged_sex_biomass[ [ 'biomass_sexed_unaged_all' , 'biomass_sexed_unaged_adult' ] ] , + expected_output_redistrib_unaged_sex_biomass[ [ 'biomass_sexed_unaged_all' , 'biomass_sexed_unaged_adult' ] ] , + rtol = 1e-1 ) + assert np.allclose( eval_redistrib_unaged_biomass[ [ 'biomass_unaged_all' , 'biomass_unaged_adult' ] ] , + expected_output_redistrib_unaged_biomass[ [ 'biomass_unaged_all' , 'biomass_unaged_adult' ] ] , + rtol = 1e-1 ) + # ---- Check expected summed values + assert eval_redistrib_unaged_biomass[ 'biomass_unaged_all' ].sum( ) == 11e3 + assert eval_redistrib_unaged_biomass[ 'biomass_unaged_adult' ].sum( ) == 2.75e3 + # ---- Evaluate the sum and comparison between 'total' and 'sexed' + assert np.isclose( eval_redistrib_unaged_sex_biomass[ 'biomass_sexed_unaged_all' ].sum( ) , + eval_redistrib_unaged_biomass[ 'biomass_unaged_all' ].sum( ) ) + assert np.isclose( eval_redistrib_unaged_sex_biomass[ 'biomass_sexed_unaged_adult' ].sum( ) , + eval_redistrib_unaged_biomass[ 'biomass_unaged_adult' ].sum( ) ) \ No newline at end of file From c87c3d1461b9ce9ea779426dafc9002e9d46a1b4 Mon Sep 17 00:00:00 2001 From: Brandyn Lucca Date: Fri, 12 Apr 2024 16:14:50 -0700 Subject: [PATCH 51/51] Updated kriging apportionment funcs and tests --- echopop/computation/biology.py | 322 ++++++++---------- echopop/survey.py | 42 ++- .../test_kriging_biomass_apportionment.py | 188 ++++------ 3 files changed, 232 insertions(+), 320 deletions(-) diff --git a/echopop/computation/biology.py b/echopop/computation/biology.py index 7f7f6ec6..0e16e581 100644 --- a/echopop/computation/biology.py +++ b/echopop/computation/biology.py @@ -163,10 +163,10 @@ def sum_strata_weight( haul_data: pd.DataFrame , ) ### Carriage return - return weight_strata_aged_unaged , weight_strata + return weight_strata -def compute_aged_unaged_proportions( specimen_data: pd.DataFrame , - weight_strata: pd.DataFrame ): +def calculate_aged_unaged_proportions( specimen_data: pd.DataFrame , + weight_strata: pd.DataFrame ): """ Compute the overall weight proportions for aged and uanged fish within each stratum @@ -177,8 +177,9 @@ def compute_aged_unaged_proportions( specimen_data: pd.DataFrame , weight_strata: pd.DataFrame Dataframe contained summed weights of both aged and unaged fish """ + ### Calculate adult:all age ratio for each stratum - # ---- Drop unaged fish + # ---- Drop unaged fish from Station 2 data where fish are expected to be aged specimen_data_filtered = specimen_data[ specimen_data.sex != 'unsexed' ].dropna( how = 'any' , subset = 'age' ) # ---- Sum the weight of adult fish @@ -217,11 +218,11 @@ def compute_aged_unaged_proportions( specimen_data: pd.DataFrame , return aged_proportions.drop( [ 'weight_ratio' ] , axis = 1 ) -def compute_aged_weight_proportions( specimen_data: pd.DataFrame , - length_intervals: np.ndarray , - age_intervals: np.ndarray ): +def aged_weight_proportions( specimen_data: pd.DataFrame , + length_intervals: np.ndarray , + age_intervals: np.ndarray ): """ - Calculate length-age binned weight proportions + Calculate length-age binned weight proportions based on `specimen_df` (Station 2) Parameters ---------- @@ -259,7 +260,8 @@ def compute_aged_weight_proportions( specimen_data: pd.DataFrame , specimen_binned_weight = ( specimen_data_filtered # ---- Group weight summations across stratum/species/sex/length/age - .groupby( [ 'stratum_num' , 'species_id' , 'sex' , 'length_bin' , 'age_bin' ] ) + .groupby( [ 'stratum_num' , 'species_id' , 'sex' , 'length_bin' , 'age_bin' ] , + observed = False ) # ---- Sum the weights .agg( weight_all =( 'weight' , 'sum' ) , weight_adult =( 'weight_adult' , 'sum' ) ) # ---- Fill empty/non-existent values with 0's @@ -268,33 +270,32 @@ def compute_aged_weight_proportions( specimen_data: pd.DataFrame , ) ### Calculate the relative weight proportions of each length-age bin for each sex within each stratum + # ---- Sum the total weights for age-1+ and age-2+ proportions_weight_length_age_sex = ( specimen_binned_weight # ---- Calculate total sex-specific weights for each stratum - .assign( total_weight_sex_all = lambda df: df.groupby( [ 'stratum_num' , 'species_id' , 'sex' ] )[ 'weight_all' ].transform( sum ) , - total_weight_sex_adult = lambda df: df.groupby( [ 'stratum_num' , 'species_id' , 'sex' ] )[ 'weight_adult' ].transform( sum ) ) - # ---- Calculate the weight proportions within each sex: from Matlab --> Len_age_key_wgt_*n - .assign( proportion_weight_sex_all = lambda df: df.weight_all / df.total_weight_sex_all , - proportion_weight_sex_adult = lambda df: df.weight_adult / df.total_weight_sex_adult ) + .assign( total_weight_sex_all = lambda df: df.groupby( [ 'stratum_num' , 'species_id' , 'sex' ] )[ 'weight_all' ].transform( 'sum' ) , + total_weight_sex_adult = lambda df: df.groupby( [ 'stratum_num' , 'species_id' , 'sex' ] )[ 'weight_adult' ].transform( 'sum' ) ) ) - ### Fill empty/non-existent values with 0's + # ---- Sum the weight proportions for age-1+ proportions_weight_length_age_sex[ 'proportion_weight_sex_all' ] = ( - proportions_weight_length_age_sex[ 'proportion_weight_sex_all' ].fillna( 0 ) - ) + proportions_weight_length_age_sex.weight_all / proportions_weight_length_age_sex.total_weight_sex_all + ).fillna( 0 ) + # ---- Sum the weight proportions for age-2+ proportions_weight_length_age_sex[ 'proportion_weight_sex_adult' ] = ( - proportions_weight_length_age_sex[ 'proportion_weight_sex_adult' ].fillna( 0 ) - ) + proportions_weight_length_age_sex.weight_adult / proportions_weight_length_age_sex.total_weight_sex_adult + ).fillna( 0 ) ### Return output return proportions_weight_length_age_sex -def compute_aged_sex_weight_proportions( proportions_weight_length_age_sex: pd.DataFrame , - aged_proportions: pd.DataFrame ): +def aged_sex_weight_proportions( proportions_weight_length_age_sex: pd.DataFrame , + aged_proportions: pd.DataFrame ): """ - Compute the aged proportions across all fish and specific sexes for each - length and age bin + Compute the weight proportion for aged/unaged fish of either sex (2x2 choice) across all fish. + Also compute the weight proportion of only adult aged/unaged fish of either sex across all fish. Parameters ---------- @@ -349,7 +350,8 @@ def distribute_aged_weight_proportions( proportions_weight_length_age_sex: pd.Da ---------- proportions_weight_length_age_sex: pd.DataFrame Dataframe containing sexed weight proportions distributed across - each age and length bin + each age and length bin for age-1+ (`proportion_weight_sex_all`) and + age-2+ (`proportion_weight_sex_adult`) fish aged_sex_proportions: pd.DataFrame Dataframe contained weight proportions of sexed fish for aged fish """ @@ -360,12 +362,12 @@ def distribute_aged_weight_proportions( proportions_weight_length_age_sex: pd.Da on = [ 'stratum_num' , 'sex' ] , how = 'left' ) - # ---- Normalized weight proportions for all fish + # ---- Normalized weight proportions for age-1+ fish distributed_aged_weight_proportions[ 'normalized_proportion_weight_all' ] = ( distributed_aged_weight_proportions.proportion_weight_sex_all * distributed_aged_weight_proportions.proportion_weight_all ) - # ---- Normalized weight proportions for jus adult fish + # ---- Normalized weight proportions for age-2+ fish distributed_aged_weight_proportions[ 'normalized_proportion_weight_adult' ] = ( distributed_aged_weight_proportions.proportion_weight_sex_adult * distributed_aged_weight_proportions.proportion_weight_adult ) @@ -396,122 +398,69 @@ def calculate_aged_biomass( kriging_biomass_df: pd.DataFrame , Dataframe containing the weight proportions of aged fish within each stratum """ - ### Sum aged fish weights across age and length bins, then calculate weight proportions - proportions_weight_length_age_sex = compute_aged_weight_proportions( specimen_data , - length_distribution , - age_distribution ) + ### Sum aged fish weights across age and length bins for each sex, then calculate weight proportions within each sex + proportions_weight_length_age_sex = aged_weight_proportions( specimen_data , + length_distribution , + age_distribution ) - ### Calculate the summed aged proportions for all aged fish, and for aged fish + ### Calculate the weight proportion of aged/unaged fish of either sex (2x2 choice) across all fish # ---- belonging to each sex - aged_sex_proportions = compute_aged_sex_weight_proportions( proportions_weight_length_age_sex , - aged_proportions ) + aged_sex_proportions = aged_sex_weight_proportions( proportions_weight_length_age_sex , + aged_proportions ) ### Calculate weight proportions of aged fish distributed over age-length bins # ---- for each sex within each stratum relative to the summed weights of each - # ---- stratum (i.e. aged + unaged weights) - distributed_sex_proportions = distribute_aged_weight_proportions( proportions_weight_length_age_sex , - aged_sex_proportions ) + # ---- stratum (i.e. aged + unaged weights) + distributed_sex_length_age_proportions = distribute_aged_weight_proportions( proportions_weight_length_age_sex , + aged_sex_proportions ) ### Sum 'kriging_biomass_df' across each stratum for appropriate stratum-specific apportionment - kriged_stratum_biomass = kriging_biomass_df.groupby( [ 'stratum_num' ] )[ 'B_adult_kriged' ].sum( ) + kriged_stratum_biomass = kriging_biomass_df.groupby( [ 'stratum_num' ] , observed = False )[ 'B_adult_kriged' ].sum( ) ### Apportion sexed biomass across age-length # ---- Initialize dataframe - stratum_sexed_kriged_biomass = distributed_sex_proportions.set_index( 'stratum_num' ) + stratum_sexed_kriged_biomass = distributed_sex_length_age_proportions.set_index( 'stratum_num' ) stratum_sexed_kriged_biomass[ 'biomass_kriged' ] = kriged_stratum_biomass - # ---- Apportioned biomass for all fish + # ---- Apportioned biomass for age-1+ fish to different sex-length-age bins stratum_sexed_kriged_biomass[ 'biomass_sexed_aged_all' ] = ( stratum_sexed_kriged_biomass.biomass_kriged * stratum_sexed_kriged_biomass.normalized_proportion_weight_all - ).fillna( 0 ) + ) - # ---- Apportioned biomass for adult fish + # ---- Apportioned biomass for age-2+ fish to different sex-length-age bins stratum_sexed_kriged_biomass[ 'biomass_sexed_aged_adult' ] = ( stratum_sexed_kriged_biomass.biomass_kriged * stratum_sexed_kriged_biomass.normalized_proportion_weight_adult - ).fillna( 0 ) + ) # ---- Sum across strata to produce 'grand totals' for each sex apportioned_sexed_kriged_biomass = ( stratum_sexed_kriged_biomass - .groupby( [ 'species_id' , 'sex' , 'length_bin' , 'age_bin' ] ) + .groupby( [ 'species_id' , 'sex' , 'length_bin' , 'age_bin' ] , + observed = False ) .agg( { 'biomass_sexed_aged_all': 'sum' , 'biomass_sexed_aged_adult': 'sum' } ) .reset_index( ) ) - ### Calculate biomass proportions across age-length - # ---- Sum weights within each length-age bin for each stratum - proportions_weight_length_age = ( - proportions_weight_length_age_sex - .groupby( [ 'stratum_num' , 'species_id' , 'length_bin' , 'age_bin' ] ) - .agg( { 'weight_all': 'sum' , - 'weight_adult': 'sum' } ) - .reset_index( ) - ) - - # ---- Sum stratum weights - # ---- All fish - proportions_weight_length_age[ 'total_weight_all' ] = ( - proportions_weight_length_age.groupby( [ 'stratum_num' , 'species_id' ] )[ 'weight_all' ].transform( sum ) - ) - - # ---- Adult fish - proportions_weight_length_age[ 'total_weight_adult' ] = ( - proportions_weight_length_age.groupby( [ 'stratum_num' , 'species_id' ] )[ 'weight_adult' ].transform( sum ) - ) - - # ---- Calculate proportions - # ---- All fish - proportions_weight_length_age[ 'proportion_weight_length_all' ] = ( - proportions_weight_length_age.weight_all / proportions_weight_length_age.total_weight_all - ).fillna( 0 ) - - # ---- Adult fish - proportions_weight_length_age[ 'proportion_weight_length_adult' ] = ( - proportions_weight_length_age.weight_adult / proportions_weight_length_age.total_weight_adult - ).fillna( 0 ) - - # ---- Index `aged_proportions` - index_aged_proportions = aged_proportions.filter( regex = "^(?=.*aged|stratum_num)(?!.*unaged)" ).set_index( 'stratum_num' ) - - # ---- Initialize dataframe - stratum_kriged_biomass = proportions_weight_length_age.set_index( 'stratum_num' ) - stratum_kriged_biomass[ 'proportion_aged_weight_all' ] = index_aged_proportions.proportion_aged_weight_all - stratum_kriged_biomass[ 'proportion_aged_weight_adult' ] = index_aged_proportions.proportion_aged_weight_adult - stratum_kriged_biomass[ 'biomass_kriged' ] = kriged_stratum_biomass - - # ---- Apportioned biomass for all fish - stratum_kriged_biomass[ 'biomass_aged_all' ] = ( - stratum_kriged_biomass.biomass_kriged - * stratum_kriged_biomass.proportion_weight_length_all - * stratum_kriged_biomass.proportion_aged_weight_all - ).fillna( 0 ) - - # ---- Apportioned biomass for adult fish - stratum_kriged_biomass[ 'biomass_aged_adult' ] = ( - stratum_kriged_biomass.biomass_kriged - * stratum_kriged_biomass.proportion_weight_length_adult - * stratum_kriged_biomass.proportion_aged_weight_adult - ).fillna( 0 ) - - # ---- Sum across strata to produce 'grand totals' for all fish + # ---- Sum across strata to produce 'grand totals' across all fish apportioned_total_kriged_biomass = ( - stratum_kriged_biomass - .groupby( [ 'species_id' , 'length_bin' , 'age_bin' ] ) - .agg( { 'biomass_aged_all': 'sum' , - 'biomass_aged_adult': 'sum' } ) + apportioned_sexed_kriged_biomass + .groupby( [ 'species_id' , 'length_bin' , 'age_bin' ] , + observed = False ) + .agg( biomass_aged_all = ( 'biomass_sexed_aged_all' , 'sum' ) , + biomass_aged_adult = ( 'biomass_sexed_aged_adult' , 'sum' ) ) .reset_index( ) ) ### Return output (tuple) return apportioned_sexed_kriged_biomass , apportioned_total_kriged_biomass -def compute_unaged_number_proportions( length_data: pd.DataFrame , - length_intervals: np.ndarray ): +def unaged_number_proportions( length_data: pd.DataFrame , + length_intervals: np.ndarray ): """ - Calculate length binned weight proportions among unaged fish + Calculate length binned number proportions among unaged fish Parameters ---------- @@ -533,7 +482,8 @@ def compute_unaged_number_proportions( length_data: pd.DataFrame , proportions_unaged_length = ( length_data_binned # ---- Group number summations across stratum/species/sex/length - .groupby( [ 'stratum_num' , 'species_id' , 'length_bin' ] ) + .groupby( [ 'stratum_num' , 'species_id' , 'length_bin' ] , + observed = False ) # ---- Sum count .agg( number_all = ( 'length_count' , 'sum' ) ) # ---- Fill empty/non-existent values with 0's @@ -544,8 +494,8 @@ def compute_unaged_number_proportions( length_data: pd.DataFrame , ### Calculate the number proportions # --- Stratum total counts proportions_unaged_length[ 'stratum_number_all' ] = ( - proportions_unaged_length.groupby( [ 'stratum_num' ] )[ 'number_all' ].transform( sum ) - ) + proportions_unaged_length.groupby( [ 'stratum_num' ] )[ 'number_all' ].transform( 'sum' ) + ).fillna( 0 ) # ---- Proportions of each sex-length bin pair relative to `stratum_number_all` proportions_unaged_length[ 'proportion_number_all' ] = ( @@ -555,11 +505,10 @@ def compute_unaged_number_proportions( length_data: pd.DataFrame , ### Filter out unnecessary columns and return output return proportions_unaged_length.filter( regex = '^(?!number_|stratum_number_).*' ) -def compute_unaged_weight_proportions( proportions_unaged_length: pd.DataFrame , - length_weight_df: pd.DataFrame ): +def unaged_weight_proportions( proportions_unaged_length: pd.DataFrame , + length_weight_df: pd.DataFrame ): """ - Compute the unaged weight proportions across all fish and specific sexes for each - length bin + Compute the weight proportion of each length bin within each stratum for all unaged fish samples. Parameters ---------- @@ -576,36 +525,35 @@ def compute_unaged_weight_proportions( proportions_unaged_length: pd.DataFrame , ### Calculate the weight proportion of each length bin that is 'weighted' by the number proportions # ---- Merge `proportions_unaged_length_sex` and `length_weight_all` - proportions_unaged_weight_length = pd.merge( proportions_unaged_length , - length_weight_all , - on = [ 'length_bin' ] , - how = 'left' ) + proportions_unaged_weight_length = proportions_unaged_length.merge( length_weight_all , + on = [ 'length_bin' ] , + how = 'left' ) - # ---- Calculate the weight proportion (`w_ln_all_array` in the original Matlab code) - proportions_unaged_weight_length[ 'weight_proportion_all' ] = ( - proportions_unaged_weight_length.weight_modeled * proportions_unaged_weight_length.proportion_number_all - ) - - ### Sum weight proportions for each strata to get the weight per length bin - # ---- Calculate the summed weights per stratum (`w_ln_array_sum` in the original Matlab code) - proportions_unaged_weight_length[ 'weight_stratum_proportion' ] = ( - proportions_unaged_weight_length.groupby( [ 'stratum_num' ] )[ 'weight_proportion_all' ].transform( sum ) - ) + # ---- Calculate the estimated weight for each length bin within each stratum (`w_ln_all_array` in the original Matlab code) + proportions_unaged_weight_length[ 'weight_proportion_all' ] = ( + proportions_unaged_weight_length.weight_modeled * proportions_unaged_weight_length.proportion_number_all + ) + + ### Calculate weight proportions for each length bin for all strata + # ---- Calculate the summed estimated weights across all length bins within each stratum (`w_ln_array_sum` in the original Matlab code) + proportions_unaged_weight_length[ 'weight_stratum_proportion' ] = ( + proportions_unaged_weight_length.groupby( [ 'stratum_num' ] )[ 'weight_proportion_all' ].transform( 'sum' ) + ) - # ---- Calculate the weight per length distribution bin (`w_ln_all_N` in the original Matlab code) - proportions_unaged_weight_length[ 'proportion_weight_length' ] = ( - proportions_unaged_weight_length.weight_proportion_all / proportions_unaged_weight_length.weight_stratum_proportion - ) + # ---- Calculate the weight proportion of each length bin within each stratum (`w_ln_all_N` in the original Matlab code) + proportions_unaged_weight_length[ 'proportion_weight_length' ] = ( + proportions_unaged_weight_length.weight_proportion_all / proportions_unaged_weight_length.weight_stratum_proportion + ) ### Drop unnecessary columns and return output return proportions_unaged_weight_length.filter( regex = '^(?!weight_|proportion_number_).*' ) -def compute_unaged_sex_proportions( length_data: pd.DataFrame , - length_intervals: pd.DataFrame , - length_weight_df: pd.DataFrame , - aged_proportions: pd.DataFrame ): +def unaged_sex_weight_proportions( length_data: pd.DataFrame , + length_intervals: pd.DataFrame , + regression_parameters: pd.DataFrame , + aged_proportions: pd.DataFrame ): """ - Calculate the normalized weight proportions for unaged sexed fish + Calculate the weight proportion of different sexes within each stratum for all unaged fish samples. Parameters ---------- @@ -613,17 +561,14 @@ def compute_unaged_sex_proportions( length_data: pd.DataFrame , Dataframe containing length data measured from unaged fish length_intervals: np.ndarray Array containing length bins/intervals - length_weight_df: pd.DataFrame - Dataframe containing the modeled weights for each sex and length bin - fit from the length-weight regression (fit for all animals) + regression_parameters: pd.DataFrame + Dataframe containing the best-fit coefficients for the length-weight log-linear relationship for + each sex aged_proportions: pd.DataFrame Dataframe containing the weight proportions of unaged fish within each stratum """ - ### Calculate sexed weights within each stratum - # ---- Extract sexed length-weight regression fits - length_weight_sex = length_weight_df[ length_weight_df.sex != 'all' ][ [ 'length_bin' , 'sex' , 'weight_modeled' ] ] - + ### Prepare data # ---- Bin length measurements length_data_binned = ( length_data @@ -637,38 +582,42 @@ def compute_unaged_sex_proportions( length_data: pd.DataFrame , ) # ---- Merge `length_data_filtered ` and `length_weight_sex` - sexed_unaged_model_weights = length_data_binned.merge( length_weight_sex , - on = [ 'length_bin' , 'sex' ] , + sexed_unaged_model_weights = length_data_binned.merge( regression_parameters , + on = [ 'sex' ] , how = 'left' ) - # ---- Interpolate weights over length values for each sex (weighted by `length_count`) - interpolated_weights = ( + ### Interpolate weights over length values for each sex (weighted by `length_count`) + # ---- Convert length to weight using the fit regression parameters + sexed_unaged_model_weights[ 'weight_modeled' ] = ( + 10 ** sexed_unaged_model_weights.initial + * sexed_unaged_model_weights.length ** sexed_unaged_model_weights.rate + ) + + # ---- Group by each stratum and sex to calculate the average weight per length bin + fit_strata_weights = ( sexed_unaged_model_weights - .groupby( [ 'stratum_num' , 'sex' ] ) - .apply( lambda df: pd.Series( { - 'weight_interp': ( np.interp( df.length , - df.length_bin_value , - df.weight_modeled ) * df.length_count ).sum( ) - } ) ) - .reset_index( ) + .groupby( [ 'stratum_num' , 'sex' ] , + observed = False ) + .apply( lambda df: ( df.weight_modeled * df.length_count ).sum( ) , + include_groups = False ) + .reset_index( name = 'weight_modeled' ) ) ### Calculate the unaged weight proportion relative to the haul catches # ---- Merge the unaged weight strata with the interpolated weights - proportions_unaged_weight_sex = pd.merge( interpolated_weights , - aged_proportions , - on = [ 'stratum_num' ] , - how = 'left' ) + proportions_unaged_weight_sex = fit_strata_weights.merge( aged_proportions , + on = [ 'stratum_num' ] , + how = 'left' ) # ---- Sum `weight_interp` over each stratum - proportions_unaged_weight_sex[ 'stratum_sex_weight_interp' ] = ( - proportions_unaged_weight_sex.groupby( ['stratum_num' ] )[ 'weight_interp' ].transform( sum ) + proportions_unaged_weight_sex[ 'stratum_sex_weight_modeled' ] = ( + proportions_unaged_weight_sex.groupby( ['stratum_num' ] )[ 'weight_modeled' ].transform( 'sum' ) ) # ---- Calculate the normalized weights for each sex proportions_unaged_weight_sex[ 'stratum_sex_weight_normalized' ] = ( proportions_unaged_weight_sex[ 'weight' ] * - proportions_unaged_weight_sex.weight_interp / proportions_unaged_weight_sex.stratum_sex_weight_interp + proportions_unaged_weight_sex.weight_modeled / proportions_unaged_weight_sex.stratum_sex_weight_modeled ) # ---- Sum the normalized stratum sex weight across each stratum @@ -679,7 +628,7 @@ def compute_unaged_sex_proportions( length_data: pd.DataFrame , # ---- Sum 'proportions_weight_sex' proportions_unaged_weight_sex[ 'proportions_weight_sex_total' ] = ( - proportions_unaged_weight_sex.groupby( [ 'stratum_num' ] )[ 'proportions_weight_sex' ].transform( sum ) + proportions_unaged_weight_sex.groupby( [ 'stratum_num' ] )[ 'proportions_weight_sex' ].transform( 'sum' ) ) # ---- Calculate the final sex proportion @@ -694,6 +643,7 @@ def calculate_unaged_biomass( kriging_biomass_df: pd.DataFrame , length_data: pd.DataFrame , length_distribution: np.ndarray , length_weight_df: pd.DataFrame , + regression_parameters: pd.DataFrame , aged_proportions: pd.DataFrame ): """ Calculate the kriged biomass distributed over length and age for each sex and among @@ -710,25 +660,28 @@ def calculate_unaged_biomass( kriging_biomass_df: pd.DataFrame , length_weight_df: pd.DataFrame Dataframe containing the modeled weights for each sex and length bin fit from the length-weight regression (fit for all animals) + regression_parameters: pd.DataFrame + Dataframe containing the best-fit coefficients for the length-weight log-linear relationship for + each sex aged_proportions: pd.DataFrame Dataframe containing the weight proportions of unaged fish within each stratum """ ### Calculate number proportion - proportions_unaged_length = compute_unaged_number_proportions( length_data , length_distribution ) + proportions_unaged_length = unaged_number_proportions( length_data , length_distribution ) - ### Calculate the normalized weight per unit length distribution (W_Ln_ALL in the original Matlab code) - proportions_unaged_weight_length = compute_unaged_weight_proportions( proportions_unaged_length , - length_weight_df ) + ### Calculate the weight proportion of unaged fish of each length bin (W_Ln_ALL in the original Matlab code) + proportions_unaged_weight_length = unaged_weight_proportions( proportions_unaged_length , + length_weight_df ) ### Calculate sex-specific weight proportions for unaged fish (within unaged fish) - proportions_unaged_weight_sex = compute_unaged_sex_proportions( length_data , - length_distribution , - length_weight_df , - aged_proportions ) + proportions_unaged_weight_sex = unaged_sex_weight_proportions( length_data , + length_distribution , + regression_parameters , + aged_proportions ) ### Sum 'kriging_biomass_df' across each stratum for appropriate stratum-specific apportionment - kriged_stratum_biomass = kriging_biomass_df.groupby( [ 'stratum_num' ] )[ 'B_adult_kriged' ].sum( ) + kriged_stratum_biomass = kriging_biomass_df.groupby( [ 'stratum_num' ] , observed = False )[ 'B_adult_kriged' ].sum( ) ### Calculate sex-specific biomass apportionment # ---- Index `unaged_proportions` @@ -757,24 +710,25 @@ def calculate_unaged_biomass( kriging_biomass_df: pd.DataFrame , stratum_sexed_kriged_biomass.proportion_unaged_weight_adult ) - # ---- Apportioned biomass for all fish + # ---- Apportioned biomass for age-1+ fish stratum_sexed_kriged_biomass[ 'biomass_sexed_unaged_all' ] = ( stratum_sexed_kriged_biomass.biomass_kriged * stratum_sexed_kriged_biomass.proportion_sexed_weight_all * stratum_sexed_kriged_biomass.proportion_weight_length ) - # ---- Apportioned biomass for adult fish + # ---- Apportioned biomass for age-2+ fish stratum_sexed_kriged_biomass[ 'biomass_sexed_unaged_adult' ] = ( stratum_sexed_kriged_biomass.biomass_kriged * stratum_sexed_kriged_biomass.proportion_sexed_weight_adult * stratum_sexed_kriged_biomass.proportion_weight_length ) - # ---- ' Grand total ' for all sexed unaged fish + # ---- 'Grand total' for age-1+ and age-2+ sexed unaged fish apportioned_sexed_kriged_biomass = ( stratum_sexed_kriged_biomass - .groupby( [ 'species_id' , 'sex' , 'length_bin' ] ) + .groupby( [ 'species_id' , 'sex' , 'length_bin' ] , + observed = False ) .agg( { 'biomass_sexed_unaged_all': 'sum' , 'biomass_sexed_unaged_adult': 'sum' } ) .reset_index( ) @@ -801,17 +755,8 @@ def calculate_unaged_biomass( kriging_biomass_df: pd.DataFrame , * stratum_kriged_biomass.proportion_weight_length ) - # ---- ' Grand total ' for all sexed unaged fish - apportioned_kriged_biomass = ( - stratum_kriged_biomass - .groupby( [ 'species_id' , 'length_bin' ] ) - .agg( { 'biomass_unaged_all': 'sum' , - 'biomass_unaged_adult': 'sum' } ) - .reset_index( ) - ) - ### Return output (tuple) - return apportioned_sexed_kriged_biomass , apportioned_kriged_biomass + return apportioned_sexed_kriged_biomass def apply_age_bins( aged_sex_biomass: pd.DataFrame , unaged_sex_biomass: pd.DataFrame ): @@ -829,12 +774,12 @@ def apply_age_bins( aged_sex_biomass: pd.DataFrame , ### Calculate summed length-bin biomass (i.e. sum across age bins) # ---- All fish aged_sex_biomass[ 'summed_aged_biomass_all' ] = ( - aged_sex_biomass.groupby( [ 'sex' , 'length_bin' ] )[ 'biomass_sexed_aged_all' ].transform( sum ) + aged_sex_biomass.groupby( [ 'sex' , 'length_bin' ] , observed = False )[ 'biomass_sexed_aged_all' ].transform( 'sum' ) ) # ---- Adult fish aged_sex_biomass[ 'summed_aged_biomass_adult' ] = ( - aged_sex_biomass.groupby( [ 'sex' , 'length_bin' ] )[ 'biomass_sexed_aged_adult' ].transform( sum ) + aged_sex_biomass.groupby( [ 'sex' , 'length_bin' ] , observed = False )[ 'biomass_sexed_aged_adult' ].transform( 'sum' ) ) ### Merge unaged biomass length bins with aged bioamss length-age bins @@ -868,7 +813,8 @@ def apply_age_bins( aged_sex_biomass: pd.DataFrame , ### Sum sexes together to retrieve the total biomass aged_unaged_biomass = ( aged_unaged_sexed_biomass - .groupby( [ 'length_bin' , 'age_bin' , 'species_id' ] ) + .groupby( [ 'length_bin' , 'age_bin' , 'species_id' ] , + observed = False ) .agg( biomass_unaged_all = ( 'biomass_sexed_unaged_all' , 'sum' ) , biomass_unaged_adult = ( 'biomass_sexed_unaged_adult' , 'sum' ) ) .reset_index( ) diff --git a/echopop/survey.py b/echopop/survey.py index 2364c46c..91975eec 100644 --- a/echopop/survey.py +++ b/echopop/survey.py @@ -14,7 +14,7 @@ from .computation.statistics import stratified_transect_statistic from .computation.kriging_methods import kriging_interpolation from .computation.biology import index_transect_age_sex_proportions , filter_species -from .computation.biology import sum_strata_weight , compute_aged_unaged_proportions +from .computation.biology import sum_strata_weight , calculate_aged_unaged_proportions from .computation.biology import calculate_aged_biomass , calculate_unaged_biomass from .computation.biology import apply_age_bins , filter_species @@ -1298,6 +1298,13 @@ def apportion_kriged_biomass( self , # ---- Age age_intervals = self.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] + + ### Import length-weight regression parameters + regression_parameters = self.statistics[ 'length_weight' ][ 'regression_parameters' ] + regression_parameters = regression_parameters[ regression_parameters.sex != 'all' ] + + ### Import length-weight relationship + length_weight_df = self.statistics[ 'length_weight' ][ 'length_weight_df' ] # ---- Construct the complete dataframe containing all possible # ---- strata, sexes, lengths, and ages @@ -1316,12 +1323,12 @@ def apportion_kriged_biomass( self , haul_spp_matched = haul_spp[ haul_spp.haul_num.isin( length_spp.haul_num ) ] ### Sum weights for aged/unaged and all within each stratum - weight_strata_aged_unaged , weight_strata = sum_strata_weight( haul_spp_matched , - specimen_spp ) + weight_strata = sum_strata_weight( haul_spp_matched , + specimen_spp ) - ### Calculate the summed aged proportions for all aged fish, and for aged fish - aged_unaged_weight_proportions = compute_aged_unaged_proportions( specimen_spp_filtered , - weight_strata ) + ### Calculate the summed aged proportions for age-1+ (*_all) and age-2+ (*_adult) fish + aged_unaged_weight_proportions = calculate_aged_unaged_proportions( specimen_spp_filtered , + weight_strata ) # !!! TODO: This does end up chewing up * a ton * of memory since the output dataframes are quite large aged_sex_biomass , aged_biomass = calculate_aged_biomass( self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] , @@ -1329,16 +1336,14 @@ def apportion_kriged_biomass( self , length_intervals , age_intervals , aged_unaged_weight_proportions ) - - ### Import length-weight relationship calculated for all animals - length_weight_df = self.statistics[ 'length_weight' ][ 'length_weight_df' ] ### Calculate unaged biomass for each sex and all animals - unaged_sex_biomass , unaged_biomass = calculate_unaged_biomass( self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] , - length_spp_filtered , - length_intervals , - length_weight_df , - aged_unaged_weight_proportions ) + unaged_sex_biomass = calculate_unaged_biomass( self.statistics[ 'kriging' ][ 'kriged_biomass_df' ] , + length_spp_filtered , + length_intervals , + length_weight_df , + regression_parameters , + aged_unaged_weight_proportions ) ### Re-distribute unaged biomass so it is compatible with aged biomass to calculate the overall summed biomass redistributed_unaged_sex_biomass , redistributed_unaged_biomass = apply_age_bins( aged_sex_biomass , @@ -1375,7 +1380,14 @@ def apportion_kriged_biomass( self , overall_biomass.biomass_aged_adult ) - # ### Assign results to an attribute + ### Drop unnecessary columns + # ---- Overall sexed biomass + overall_sexed_biomass.drop( [ 'summed_aged_biomass_all' , + 'summed_aged_biomass_adult' ] , + axis = 1 , + inplace = True ) + + ### Assign results to an attribute self.statistics[ 'kriging' ].update( { 'apportioned_kriged_total_biomass_df': overall_biomass , diff --git a/echopop/tests/test_kriging_biomass_apportionment.py b/echopop/tests/test_kriging_biomass_apportionment.py index aad2e0ae..da749dce 100644 --- a/echopop/tests/test_kriging_biomass_apportionment.py +++ b/echopop/tests/test_kriging_biomass_apportionment.py @@ -1,9 +1,9 @@ import pandas as pd import numpy as np -from echopop.computation.biology import sum_strata_weight , compute_aged_weight_proportions , compute_aged_weight_proportions -from echopop.computation.biology import compute_aged_sex_weight_proportions , distribute_aged_weight_proportions , apply_age_bins -from echopop.computation.biology import compute_aged_unaged_proportions , compute_unaged_sex_proportions , calculate_aged_biomass -from echopop.computation.biology import compute_unaged_number_proportions , compute_unaged_weight_proportions , calculate_unaged_biomass +from echopop.computation.biology import sum_strata_weight , aged_weight_proportions +from echopop.computation.biology import aged_sex_weight_proportions , distribute_aged_weight_proportions , apply_age_bins +from echopop.computation.biology import calculate_aged_unaged_proportions , unaged_sex_weight_proportions , calculate_aged_biomass +from echopop.computation.biology import unaged_number_proportions , unaged_weight_proportions , calculate_unaged_biomass def test_sum_strata_weight( mock_survey ): @@ -42,27 +42,20 @@ def test_sum_strata_weight( mock_survey ): ) ### Evaluate object for later comparison - object_weight_strata_aged_unaged , object_weight_strata = sum_strata_weight( objS.biology[ 'catch_df' ] , - objS.biology[ 'specimen_df' ] ) + eval_weight_strata = sum_strata_weight( objS.biology[ 'catch_df' ] , + objS.biology[ 'specimen_df' ] ) #---------------------------------- ### Run tests: `sum_strata_weight` #---------------------------------- ### Evaluate shape # ---- `object_weight_strata` - assert object_weight_strata.shape == tuple( [ 5 , 2 ] ) - # ---- `object_weight_strata_aged_unaged` - assert object_weight_strata_aged_unaged.shape == tuple( [ 10 , 3 ] ) + assert eval_weight_strata.shape == tuple( [ 5 , 2 ] ) ### Evaluate value equality # ---- `object_weight_strata` check_values = np.array( [ 56.663158 , 9.231579 , 93.700000 , 31.568421 , 31.636842 ] ) - assert np.allclose( check_values , object_weight_strata.weight_stratum_all ) - - # ---- `object_weight_strata_aged_unaged` - check_values = np.array( [ 51.400000 , 0.600000 , 81.700000 , 16.200000 , 12.900000 , - 5.263158 , 8.631579 , 12.000000 , 15.368421 , 18.736842 ] ) - assert np.allclose( check_values , object_weight_strata_aged_unaged.stratum_weight ) + assert np.allclose( check_values , eval_weight_strata.weight_stratum_all ) def test_compute_aged_weight_proportions( mock_survey ): @@ -89,9 +82,9 @@ def test_compute_aged_weight_proportions( mock_survey ): objS.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] = np.array( [ 0.5 , 1.5 , 2.5 ] ) ### Evaluate object for later comparison - obj_props_wgt_len_age_sex = compute_aged_weight_proportions( objS.biology[ 'specimen_df' ] , - objS.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] , - objS.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] ) + eval_props_wgt_len_age_sex = aged_weight_proportions( objS.biology[ 'specimen_df' ] , + objS.biology[ 'distributions' ][ 'length' ][ 'length_interval_arr' ] , + objS.biology[ 'distributions' ][ 'age' ][ 'age_interval_arr' ] ) ###-------------------------------- ### Expected outcomes ###-------------------------------- @@ -139,15 +132,12 @@ def test_compute_aged_weight_proportions( mock_survey ): #---------------------------------- ### Process the specimen data ### Check shape - assert obj_props_wgt_len_age_sex.shape == expected_dimensions + assert eval_props_wgt_len_age_sex.shape == expected_dimensions - ### Check datatypes - assert np.all( obj_props_wgt_len_age_sex.dtypes == expected_output.dtypes ) - ### Check data value equality - assert expected_output.equals( obj_props_wgt_len_age_sex ) + assert np.all( expected_output == eval_props_wgt_len_age_sex ) -def test_compte_aged_unaged_proportions( ): +def test_calculate_aged_unaged_proportions( ): ### Mock data for `specimen_data` test_specimen_data = pd.DataFrame( @@ -171,8 +161,8 @@ def test_compte_aged_unaged_proportions( ): ) ### Evaluate for later comparison - eval_aged_unaged_weight_proportions = compute_aged_unaged_proportions( test_specimen_data , - test_weight_strata ) + eval_aged_unaged_weight_proportions = calculate_aged_unaged_proportions( test_specimen_data , + test_weight_strata ) ###-------------------------------- ### Expected outcomes @@ -197,12 +187,10 @@ def test_compte_aged_unaged_proportions( ): ### `eval_aged_sex_proportions` # ---- Shape assert eval_aged_unaged_weight_proportions.shape == expected_dimensions - # ---- Datatypes - assert np.all( eval_aged_unaged_weight_proportions.dtypes == expected_output.dtypes ) # ---- Dataframe equality assert np.allclose( eval_aged_unaged_weight_proportions , expected_output ) -def test_compute_aged_sex_proportions( ): +def test_aged_sex_weight_proportions( ): ### Mock data for `proportions_weight_length_age_sex` test_proportions_weight_length_age_sex = pd.DataFrame( { @@ -248,8 +236,8 @@ def test_compute_aged_sex_proportions( ): ### Evaluate for later comparison - eval_aged_sex_proportions = compute_aged_sex_weight_proportions( test_proportions_weight_length_age_sex , - test_aged_proportions ) + eval_aged_sex_proportions = aged_sex_weight_proportions( test_proportions_weight_length_age_sex , + test_aged_proportions ) ###-------------------------------- ### Expected outcomes ###-------------------------------- @@ -441,13 +429,11 @@ def test_calculate_aged_biomass( ): # ---- Shape assert eval_aged_sex_biomass.shape == expected_dimensions_aged_sex_biomass assert eval_aged_biomass.shape == expected_dimensions_aged_biomass - # ---- Datatypes - assert np.all( eval_aged_sex_biomass.dtypes == expected_output_aged_sex_biomass.dtypes ) - assert np.all( eval_aged_biomass.dtypes == expected_output_aged_biomass.dtypes ) # ---- Dataframe equality assert np.allclose( eval_aged_sex_biomass[ [ 'biomass_sexed_aged_all' , 'biomass_sexed_aged_adult' ] ] , expected_output_aged_sex_biomass[ [ 'biomass_sexed_aged_all' , 'biomass_sexed_aged_adult' ] ] ) - assert eval_aged_biomass.equals( expected_output_aged_biomass ) + assert np.allclose( eval_aged_biomass[ [ 'biomass_aged_all' , 'biomass_aged_adult' ] ] , + expected_output_aged_biomass[ [ 'biomass_aged_all' , 'biomass_aged_adult' ] ] ) # Assess equality between sexed and total biomass estimates assert eval_aged_biomass.biomass_aged_all.sum( ) == 6e3 assert eval_aged_biomass.biomass_aged_adult.sum( ) == 3e3 @@ -455,7 +441,7 @@ def test_calculate_aged_biomass( ): assert eval_aged_biomass.biomass_aged_adult.sum( ) == eval_aged_sex_biomass.biomass_sexed_aged_adult.sum( ) -def compute_unaged_number_proportions( ): +def test_unaged_number_proportions( ): ### Re-parameterize `length_df` with dummy data test_length_data = pd.DataFrame( @@ -472,8 +458,8 @@ def compute_unaged_number_proportions( ): test_length_intervals = np.linspace( 9 , 21 , 3 ) ### Evaluate object for later comparison - eval_proportions_unaged_length = compute_unaged_number_proportions( test_length_data , - test_length_intervals ) + eval_proportions_unaged_length = unaged_number_proportions( test_length_data , + test_length_intervals ) ###-------------------------------- ### Expected outcomes @@ -500,12 +486,10 @@ def compute_unaged_number_proportions( ): ### `eval_aged_sex_proportions` # ---- Shape assert eval_proportions_unaged_length.shape == expected_dimensions - # ---- Datatypes - assert np.all( eval_proportions_unaged_length.dtypes == expected_output.dtypes ) # ---- Dataframe equality - assert eval_proportions_unaged_length.equals( expected_output ) + assert np.all( expected_output == eval_proportions_unaged_length ) -def test_compute_unaged_weight_proportions( ): +def test_unaged_weight_proportions( ): ### Create mock `proportions_unaged_length` test_proportions_unaged_length = pd.DataFrame( { @@ -531,8 +515,8 @@ def test_compute_unaged_weight_proportions( ): } ) ### Evaluate for later comparison - eval_proportions_unaged_weight_length = compute_unaged_weight_proportions( test_proportions_unaged_length , - test_length_weight_df ) + eval_proportions_unaged_weight_length = unaged_weight_proportions( test_proportions_unaged_length , + test_length_weight_df ) ###-------------------------------- ### Expected outcomes @@ -560,7 +544,7 @@ def test_compute_unaged_weight_proportions( ): # ---- Dataframe equality assert eval_proportions_unaged_weight_length.equals( expected_output ) -def test_compute_unaged_sex_proportions( ): +def test_unaged_sex_weight_proportions( ): ### Mock data for `length_data` test_length_data = pd.DataFrame( @@ -576,22 +560,14 @@ def test_compute_unaged_sex_proportions( ): ### Mock data for `length_intervals` test_length_intervals = np.linspace( 9 , 21 , 3 ) - ### Mock data for `length_weight_df` - test_length_weight_df = pd.DataFrame( + ### Mock data for `regression_parameters` + test_regression_parameters = pd.DataFrame( { - 'length_bin': pd.cut( [ 12 , 12 , 12 , 19 , 19 , 19 , - 12 , 12 , 12 , 19 , 19 , 19 ] , - np.linspace( 9 , 21 , 3 ) ) , - 'sex': [ 'male' , 'female' , 'all' , 'male' , 'female' , 'all' , - 'male' , 'female' , 'all' , 'male' , 'female' , 'all' ] , - 'weight_modeled': [ 1 , 3 , 2 , 6 , 8 , 7 , - 4 , 2 , 3 , 9 , 11 , 10 ] + 'sex': [ 'female' , 'male' , 'all' ] , + 'rate': [ 1.0 , 3.0 , 2.0 ] , + 'initial': [ -2.0 , -4.0 , -3.0 ] , } ) - test_length_weight_df[ 'length_bin' ] = pd.IntervalIndex( test_length_weight_df[ 'length_bin' ] ) - test_length_weight_df[ 'length_bin' ] = pd.Categorical( test_length_weight_df[ 'length_bin' ] , - categories = test_length_weight_df[ 'length_bin' ].unique( ) , - ordered = True ) ### Mock data for `aged_proportions` test_aged_proportions = pd.DataFrame( { @@ -605,10 +581,10 @@ def test_compute_unaged_sex_proportions( ): } ) ### Evaluate for later comparison - eval_proportions_unaged_weight_sex = compute_unaged_sex_proportions( test_length_data , - test_length_intervals , - test_length_weight_df , - test_aged_proportions ) + eval_proportions_unaged_weight_sex = unaged_sex_weight_proportions( test_length_data , + test_length_intervals , + test_regression_parameters , + test_aged_proportions ) ###-------------------------------- ### Expected outcomes @@ -620,7 +596,7 @@ def test_compute_unaged_sex_proportions( ): expected_output = pd.DataFrame( { 'stratum_num': [ 0 , 0 , 1 , 1 ] , 'sex': [ 'female' , 'male' , 'female' , 'male' ] , - 'proportion_weight_sex': [ 0.607595 , 0.392405 , 0.333333 , 0.666667 ] + 'proportion_weight_sex': [ 0.309550 , 0.690450 , 0.210486 , 0.789514 ] , } ) #---------------------------------- @@ -629,14 +605,10 @@ def test_compute_unaged_sex_proportions( ): ### Process the specimen data ### Check shape assert eval_proportions_unaged_weight_sex.shape == expected_dimensions - - ### Check datatypes - assert np.all( eval_proportions_unaged_weight_sex.dtypes == expected_output.dtypes ) ### Check data value equality # ---- `stratum_num` assert np.all( eval_proportions_unaged_weight_sex.stratum_num == expected_output.stratum_num ) - assert np.allclose( eval_proportions_unaged_weight_sex.proportion_weight_sex , expected_output.proportion_weight_sex ) def test_calculate_unaged_biomass( ): @@ -666,6 +638,16 @@ def test_calculate_unaged_biomass( ): } ) + ### Mock data for `regression_parameters` + test_regression_parameters = pd.DataFrame( + { + 'sex': [ 'female' , 'male' , 'all' ] , + 'rate': [ 1.0 , 3.0 , 2.0 ] , + 'initial': [ -2.0 , -4.0 , -3.0 ] , + } + ) + + ### Mock data for `length_intervals` test_length_intervals = np.linspace( 9 , 21 , 3 ) @@ -691,74 +673,47 @@ def test_calculate_unaged_biomass( ): } ) ### Evaluate for later comparison - eval_unaged_sex_biomass , eval_unaged_biomass = calculate_unaged_biomass( test_kriging_biomass_df , - test_length_data , - test_length_intervals , - test_length_weight_df , - test_aged_proportions ) + eval_unaged_sex_biomass = calculate_unaged_biomass( test_kriging_biomass_df , + test_length_data , + test_length_intervals , + test_length_weight_df , + test_regression_parameters , + test_aged_proportions ) ###-------------------------------- ### Expected outcomes ###-------------------------------- ### `eval_aged_sex_biomass` # ---- Expected dimensions - expected_dimensions_unaged_sex_biomass = tuple( [ 4 , 5 ] ) + expected_dimensions = tuple( [ 4 , 5 ] ) # ---- Expected dataframe output - expected_output_unaged_sex_biomass = pd.DataFrame( + expected_output = pd.DataFrame( { 'species_id': np.repeat( 19350 , 4 ).astype( np.int64 ) , 'sex': np.repeat( [ 'female' , 'male' ] , 2 ) , 'length_bin': pd.cut( [ 12 , 19 , 12 , 19 ] , np.linspace( 9 , 21 , 3 ) ) , - 'biomass_sexed_unaged_all': [ 21314.8 , 21584.2 , 28558.6 , 26542.5 ] , - 'biomass_sexed_unaged_adult': [ 10657.4 , 10792.1 , 14279.3 , 13271.2 ] , - } - ) - expected_output_unaged_sex_biomass[ 'length_bin' ] = pd.IntervalIndex( expected_output_unaged_sex_biomass[ 'length_bin' ] ) - expected_output_unaged_sex_biomass[ 'length_bin' ] = pd.Categorical( expected_output_unaged_sex_biomass[ 'length_bin' ] , - categories = expected_output_unaged_sex_biomass[ 'length_bin' ].unique( ) , - ordered = True ) - - ### `eval_aged_biomass` - # ---- Expected dimensions - expected_dimensions_unaged_biomass = tuple( [ 2 , 4 ] ) - # ---- Expected dataframe output - expected_output_unaged_biomass = pd.DataFrame( - { - 'species_id': np.repeat( 19350 , 2 ).astype( np.int64 ) , - 'length_bin': pd.cut( [ 12 , 19 ] , np.linspace( 9 , 21 , 3 ) ) , - 'biomass_unaged_all': [ 49873.3 , 48126.7 ] , - 'biomass_unaged_adult': [ 24936.7 , 24063.4 ] + 'biomass_sexed_unaged_all': [ 10637.488113 , 10782.651554 , 39235.815054 , 37344.045279 ] , + 'biomass_sexed_unaged_adult': [ 5318.744056 , 5391.325777 , 19617.907527 , 18672.022639 ] , } ) - expected_output_unaged_biomass[ 'length_bin' ] = pd.IntervalIndex( expected_output_unaged_biomass[ 'length_bin' ] ) - expected_output_unaged_biomass[ 'length_bin' ] = pd.Categorical( expected_output_unaged_biomass[ 'length_bin' ] , - categories = expected_output_unaged_biomass[ 'length_bin' ].unique( ) , - ordered = True ) + expected_output[ 'length_bin' ] = pd.IntervalIndex( expected_output[ 'length_bin' ] ) + expected_output[ 'length_bin' ] = pd.Categorical( expected_output[ 'length_bin' ] , + categories = expected_output[ 'length_bin' ].unique( ) , + ordered = True ) #---------------------------------- ### Run tests: `distribute_aged_weight_proportions` #---------------------------------- ### `eval_aged_sex_proportions` # ---- Shape - assert eval_unaged_sex_biomass.shape == expected_dimensions_unaged_sex_biomass - assert eval_unaged_biomass.shape == expected_dimensions_unaged_biomass - # ---- Datatypes - assert np.all( eval_unaged_sex_biomass.dtypes == expected_output_unaged_sex_biomass.dtypes ) - assert np.all( eval_unaged_biomass.dtypes == expected_output_unaged_biomass.dtypes ) + assert eval_unaged_sex_biomass.shape == expected_dimensions # ---- Dataframe equality assert np.allclose( eval_unaged_sex_biomass[ [ 'biomass_sexed_unaged_all' , 'biomass_sexed_unaged_adult' ] ] , - expected_output_unaged_sex_biomass[ [ 'biomass_sexed_unaged_all' , 'biomass_sexed_unaged_adult' ] ] ) - assert np.allclose( eval_unaged_biomass[ [ 'biomass_unaged_all' , 'biomass_unaged_adult' ] ] , - expected_output_unaged_biomass[ [ 'biomass_unaged_all' , 'biomass_unaged_adult' ] ] ) + expected_output[ [ 'biomass_sexed_unaged_all' , 'biomass_sexed_unaged_adult' ] ] ) # ---- Check expected summed values - assert eval_unaged_biomass[ 'biomass_unaged_all' ].sum( ) == 98e3 - assert eval_unaged_biomass[ 'biomass_unaged_adult' ].sum( ) == 49e3 - # ---- Evaluate the sum and comparison between 'total' and 'sexed' - assert np.isclose( eval_unaged_sex_biomass[ 'biomass_sexed_unaged_all' ].sum( ) , - eval_unaged_biomass[ 'biomass_unaged_all' ].sum( ) ) - assert np.isclose( eval_unaged_sex_biomass[ 'biomass_sexed_unaged_adult' ].sum( ) , - eval_unaged_biomass[ 'biomass_unaged_adult' ].sum( ) ) + assert np.isclose( eval_unaged_sex_biomass[ 'biomass_sexed_unaged_all' ].sum( ) , 98e3 ) + assert np.isclose( eval_unaged_sex_biomass[ 'biomass_sexed_unaged_adult' ].sum( ) , 49e3 ) def test_apply_age_bins( ): @@ -820,7 +775,8 @@ def test_apply_age_bins( ): np.linspace( 9 , 21 , 3 ) ) , 'age_bin': pd.cut( np.tile( [ 1 , 2 ] , 4 ) , np.array( [ 0.5 , 1.5 , 2.5 ] ) ) , - 'biomass_sexed_unaged_all': [ 1666.7 , 3333.3 , 750.0 , 1250.0 , 2285.7 , 1714.3 , 0.0 , 0.0 ] , + 'biomass_sexed_unaged_all': [ 1666.666668 , 3333.333333 , 750.000000 , 1250.000000 , + 2285.714286 , 1714.285714 , 0.000000 , 0.000000 ] , 'biomass_sexed_unaged_adult': [ 0.0 , 1250.0 , 0.0 , 500.0 , 0.0 , 1000.0 , 0.0 , 0.0 ] } ) expected_output_redistrib_unaged_sex_biomass[ 'length_bin' ] = pd.IntervalIndex( expected_output_redistrib_unaged_sex_biomass[ 'length_bin' ] ) @@ -863,11 +819,9 @@ def test_apply_age_bins( ): assert np.all( eval_redistrib_unaged_biomass.dtypes == expected_output_redistrib_unaged_biomass.dtypes ) # ---- Dataframe equality assert np.allclose( eval_redistrib_unaged_sex_biomass[ [ 'biomass_sexed_unaged_all' , 'biomass_sexed_unaged_adult' ] ] , - expected_output_redistrib_unaged_sex_biomass[ [ 'biomass_sexed_unaged_all' , 'biomass_sexed_unaged_adult' ] ] , - rtol = 1e-1 ) + expected_output_redistrib_unaged_sex_biomass[ [ 'biomass_sexed_unaged_all' , 'biomass_sexed_unaged_adult' ] ] ) assert np.allclose( eval_redistrib_unaged_biomass[ [ 'biomass_unaged_all' , 'biomass_unaged_adult' ] ] , - expected_output_redistrib_unaged_biomass[ [ 'biomass_unaged_all' , 'biomass_unaged_adult' ] ] , - rtol = 1e-1 ) + expected_output_redistrib_unaged_biomass[ [ 'biomass_unaged_all' , 'biomass_unaged_adult' ] ] ) # ---- Check expected summed values assert eval_redistrib_unaged_biomass[ 'biomass_unaged_all' ].sum( ) == 11e3 assert eval_redistrib_unaged_biomass[ 'biomass_unaged_adult' ].sum( ) == 2.75e3

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