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generate_result.py
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generate_result.py
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import matplotlib
matplotlib.use('Agg')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import seaborn as sns
import os
from collections import OrderedDict
from openpyxl import load_workbook
#################################################################
#
# Date Edited: Jan 19, 2022
# Edited By: Kendra Garner, PhD
#
#################################################################
color_air_water = ['palegreen', 'lightgreen', 'forestgreen', 'limegreen', 'aquamarine']
color_air = ['lightblue', 'dodgerblue']
color_water = ['palegreen', 'lightgreen', 'forestgreen', 'limegreen',
'skyblue1', 'skyblue3', 'slateblue1', 'slateblue',
'aquamarine', 'turquoise', 'lightseagreen', 'teal']
color_soil = ['bisque', 'turquoise', 'tan', 'darkgoldenrod', 'bisque', 'turquoise', 'tan', 'darkgoldenrod',
'bisque', 'turquoise', 'tan', 'darkgoldenrod', 'bisque', 'turquoise', 'tan', 'darkgoldenrod']
class GenerateResult:
def __init__(self):
pass
def store_output(self, chem_type, chem_name, region_name, release_scenario, release,
date_array, process_array, V_bulk_list, funC_df_list, funM_df_list, output_file_path, file_name):
header = ['air', 'rw', 'rw_sed', 'fw', 'fw_sed', 'sw', 'sw_sed', 'undeveloped_soil', 'deep_undeveloped_soil',
'urban_soil', 'deep_urban_soil', 'agricultural_soil', 'deep_agricultural_soil', 'biosolids_soil',
'deep_biosolids_soil'] # 15
header_non_nano_sub = ['air', 'aerosol', 'rw', 'rw_sus_sed', 'rw_sed_water', 'rw_sed_solid',
'fw', 'fw_sus_sed', 'fw_sed_water', 'fw_sed_solid', 'sw', 'sw_sus_sed',
'sw_sed_water', 'sw_sed_solid', 'undeveloped_soil_air', 'undeveloped_soil_water',
'undeveloped_soil_solid', 'deep_undeveloped_soil', 'urban_soil_air', 'urban_soil_water',
'urban_soil_solid', 'deep_urban_soil', 'agricultural_soil_air',
'agricultural_soil_water',
'agricultural_soil_solid', 'deep_agricultural_soil', 'biosolids_soil_air',
'biosolids_soil_water', 'biosolids_soil_solid', 'deep_biosolids_soil'] # 29
# if funC_df_list only contains 2 items, it is NonionizableOrganic
writer_C = pd.ExcelWriter(os.path.join(output_file_path, 'chem_conc_' + file_name + '.xlsx'))
writer_M = pd.ExcelWriter(os.path.join(output_file_path, 'chem_mass_' + file_name + '.xlsx'))
df_1_C = pd.DataFrame(funC_df_list[0], columns=header, index=date_array)
df_1_M = pd.DataFrame(funM_df_list[0], columns=header, index=date_array)
if chem_type != 'Nanomaterial':
df_1_sub_C = pd.DataFrame(funC_df_list[1], columns=header_non_nano_sub, index=date_array)
df_1_M = pd.DataFrame(funM_df_list[0], columns=header, index=date_array)
df_1_sub_M = pd.DataFrame(funM_df_list[1], columns=header_non_nano_sub, index=date_array)
self.generate_release_bulk(release, chem_name, region_name, release_scenario,
os.path.join(output_file_path, 'release_bulk_' + file_name + '.png'))
if chem_type == 'NonionizableOrganic' or chem_type == 'IonizableOrganic':
txt = 'neutral'
df_1_C.to_excel(writer_C, txt)
df_1_sub_C.to_excel(writer_C, txt + '_sub')
df_1_M.to_excel(writer_M, txt)
df_1_sub_M.to_excel(writer_M, txt + '_sub')
self.generate_plot_bulk(df_1_C, txt, chem_name, region_name, release_scenario,
os.path.join(output_file_path, txt + '_bulk_' + file_name + '.png'))
self.generate_mass_bulk(df_1_C, txt, V_bulk_list, chem_name, region_name, release_scenario,
os.path.join(output_file_path, txt + '_bulk_mass_' + file_name + '.png'))
self.generate_plot_sub(df_1_sub_C, txt, chem_name, chem_type, region_name, release_scenario,
os.path.join(output_file_path, txt + '_sub_' + file_name + '.png'))
else:
# for metal and nanomaterial, no subcompartment
txt = 'particulate'
df_1_C.to_excel(writer_C, txt)
df_1_M.to_excel(writer_M, txt)
self.generate_plot_bulk(df_1_C, txt, chem_name, region_name, release_scenario,
os.path.join(output_file_path, txt + '_bulk_' + file_name + '.png'))
self.generate_mass_bulk(df_1_C, txt, V_bulk_list, chem_name, region_name, release_scenario,
os.path.join(output_file_path, txt + '_bulk_mass_' + file_name + '.png'))
if chem_type == 'IonizableOrganic':
df_2_C = pd.DataFrame(funC_df_list[2], columns=header, index=date_array)
df_2_sub_C = pd.DataFrame(funC_df_list[3], columns=header_non_nano_sub, index=date_array)
df_2_M = pd.DataFrame(funM_df_list[2], columns=header, index=date_array)
df_2_sub_M = pd.DataFrame(funM_df_list[3], columns=header_non_nano_sub, index=date_array)
df_2_C.to_excel(writer_C, 'ionic')
df_2_sub_C.to_excel(writer_C, 'ionic_sub')
df_2_M.to_excel(writer_M, 'ionic')
df_2_sub_M.to_excel(writer_M, 'ionic_sub')
df_sum_bulk = df_1_C.add(df_2_C)
df_sum_sub = df_1_sub_C.add(df_2_sub_C)
txt = 'ionic'
txt_sub = 'ionic_sub'
self.generate_plot_bulk(df_2_C, txt, chem_name, region_name, release_scenario,
os.path.join(output_file_path, txt + '_bulk_' + file_name + '.png'))
self.generate_mass_bulk(df_2_C, txt, V_bulk_list, chem_name, region_name, release_scenario,
os.path.join(output_file_path, txt + '_bulk_mass_' + file_name + '.png'))
self.generate_plot_sub(df_2_sub_C, txt, chem_name, chem_type, region_name, release_scenario,
os.path.join(output_file_path, txt_sub + '_' + file_name + '.png'))
self.generate_plot_bulk(df_sum_bulk, '', chem_name, region_name, release_scenario,
os.path.join(output_file_path, 'sum_bulk_' + file_name + '.png'))
self.generate_mass_bulk(df_sum_bulk, '', V_bulk_list, chem_name, region_name, release_scenario,
os.path.join(output_file_path, 'sum_bulk_mass_' + file_name + '.png'))
self.generate_plot_sub(df_sum_sub, '', chem_name, chem_type, region_name, release_scenario,
os.path.join(output_file_path, 'sum_sub_' + file_name + '.png'))
elif chem_type == 'Metal' or chem_type == 'Nanomaterial':
if chem_type == 'Metal':
index1, index2 = 2, 4
txt1, txt2 = 'colloidal', 'dissolved'
else:
index1, index2 = 1, 2
txt1, txt2 = 'free nano', 'dissolved'
df_2_C = pd.DataFrame(funC_df_list[index1], columns=header, index=date_array)
df_2_M = pd.DataFrame(funM_df_list[index1], columns=header, index=date_array)
df_3_C = pd.DataFrame(funC_df_list[index2], columns=header, index=date_array)
df_3_M = pd.DataFrame(funM_df_list[index2], columns=header, index=date_array)
df_2_C.to_excel(writer_C, txt1)
df_3_C.to_excel(writer_C, txt2)
df_2_M.to_excel(writer_M, txt1)
df_3_M.to_excel(writer_M, txt2)
df_sum1 = df_1_C.add(df_2_C)
df_sum = df_sum1.add(df_3_C)
self.generate_plot_bulk(df_2_C, txt1, chem_name, region_name, release_scenario,
os.path.join(output_file_path, txt1 + '_bulk_' + file_name + '.png'))
self.generate_plot_bulk(df_3_C, txt2, chem_name, region_name, release_scenario,
os.path.join(output_file_path, txt2 + '_bulk_' + file_name + '.png'))
self.generate_plot_bulk(df_sum, '', chem_name, region_name, release_scenario,
os.path.join(output_file_path, 'sum_bulk_' + file_name + '.png'))
self.generate_mass_bulk(df_2_C, txt1, V_bulk_list, chem_name, region_name, release_scenario,
os.path.join(output_file_path, txt1 + '_bulk_mass_' + file_name + '.png'))
self.generate_mass_bulk(df_3_C, txt2, V_bulk_list, chem_name, region_name, release_scenario,
os.path.join(output_file_path, txt2 + '_bulk_mass_' + file_name + '.png'))
self.generate_mass_bulk(df_sum, '', V_bulk_list, chem_name, region_name, release_scenario,
os.path.join(output_file_path, 'sum_bulk_mass_' + file_name + '.png'))
if chem_type == 'NonionizableOrganic':
txt = 'organoFate'
self.generate_heatmap_bulk(chem_type, [df_1_C], chem_name, region_name, release_scenario, V_bulk_list,
os.path.join(output_file_path, txt + '_heatmap_' + file_name + '.png'))
elif chem_type == 'IonizableOrganic':
self.generate_heatmap_bulk(chem_type, [df_1_C, df_2_C], chem_name, region_name, release_scenario,
V_bulk_list, os.path.join(output_file_path, 'ionOFate_heatmap_' + file_name + '.png'))
else:
if chem_type == 'Metal':
txt = 'metal'
else:
txt = 'nano'
self.generate_heatmap_bulk(chem_type, [df_1_C, df_2_C, df_3_C], chem_name, region_name, release_scenario,
V_bulk_list, os.path.join(output_file_path, txt + '_heatmap_' + file_name + '.png'))
writer_C.save()
writer_M.save()
print('Saved the results to excel file.')
# output process data
self.store_process_output(chem_type, date_array, process_array, output_file_path, file_name)
# insert unit into A1 cell in conc, mass and process file
conc_file = os.path.join(output_file_path, 'chem_conc_' + file_name + '.xlsx')
mass_file = os.path.join(output_file_path, 'chem_mass_' + file_name + '.xlsx')
process_file = os.path.join(output_file_path, 'process_' + file_name + '.xlsx')
conc_book = load_workbook(conc_file)
conc_sheetnames = conc_book.sheetnames
mass_book = load_workbook(mass_file)
for sheetname in conc_sheetnames:
conc_sheet = conc_book[sheetname]
mass_sheet = mass_book[sheetname]
conc_sheet['A1'] = 'g/L'
mass_sheet['A1'] = 'kg'
process_book = load_workbook(process_file)
process_sheet = process_book['process']
process_sheet['A1'] = 'kg/day'
conc_book.save(os.path.join(output_file_path, 'chem_conc_' + file_name + '.xlsx'))
mass_book.save(os.path.join(output_file_path, 'chem_mass_' + file_name + '.xlsx'))
process_book.save(os.path.join(output_file_path, 'process_' + file_name + '.xlsx'))
def store_process_output(self, chem_type, date_array, process_array, output_file_path, file_name):
if chem_type == 'IonizableOrganic':
header_list = ['adv_air_in', 'adv_air_out',
'adv_rw_in', 'adv_rw_out', 'adv_rwSed_in', 'adv_rwSed_out',
'adv_fw_in', 'adv_fw_out', 'adv_fwSed_in', 'adv_fwSed_out',
'adv_sw_in', 'adv_sw_out', 'adv_swSed_out',
'dep_dry_air', 'dep_dry_air_rw', 'dep_dry_air_fw', 'dep_dry_air_sw',
'dep_dry_air_soil1', 'dep_dry_air_soil2', 'dep_dry_air_soil3', 'dep_dry_air_soil4',
'dep_wet_air', 'dep_wet_air_rw', 'dep_wet_air_fw', 'dep_wet_air_sw',
'dep_wet_air_soil1', 'dep_wet_air_soil2', 'dep_wet_air_soil3', 'dep_wet_air_soil4',
'rain_dis_air', 'rain_dis_air_rw', 'rain_dis_air_fw', 'rain_dis_air_sw',
'rain_dis_air_soil1', 'rain_dis_air_soil2', 'rain_dis_air_soil3', 'rain_dis_air_soil4',
'dep_rSS', 'dep_fSS', 'dep_sSS',
'diff_air_rw', 'diff_air_fw', 'diff_air_sw', 'diff_rw_rSedW', 'diff_fw_fSedW',
'diff_sw_sSedW',
'diff_air_soil1', 'diff_air_soil2', 'diff_air_soil3', 'diff_air_soil4',
'diff_rw_air', 'diff_fw_air', 'diff_sw_air', 'diff_rSedW_rw', 'diff_fSedW_fw',
'diff_sSedW_sw',
'diff_soil1_air', 'diff_soil2_air', 'diff_soil3_air', 'diff_soil4_air',
'burial_rwSed', 'burial_fwSed', 'burial_swSed',
'resusp_rwSed', 'resusp_fwSed', 'resusp_swSed', 'aero_resusp_sSS',
'runoff_soil1_river', 'runoff_soil2_river', 'runoff_soil3_river', 'runoff_soil4_river',
'runoff_soil1_fresh', 'runoff_soil2_fresh', 'runoff_soil3_fresh', 'runoff_soil4_fresh',
'erosion_soil1_river', 'erosion_soil2_river', 'erosion_soil3_river', 'erosion_soil4_river',
'erosion_soil1_fresh', 'erosion_soil2_fresh', 'erosion_soil3_fresh', 'erosion_soil4_fresh',
'wind_erosion_soil1', 'wind_erosion_soil2', 'wind_erosion_soil3', 'wind_erosion_soil4',
'infiltra_soil1', 'infiltra_soil2', 'infiltra_soil3', 'infiltra_soil4',
'leach_soil1_river', 'leach_soil2_river', 'leach_soil3_river', 'leach_soil4_river',
'leach_soil1_fresh', 'leach_soil2_fresh', 'leach_soil3_fresh', 'leach_soil4_fresh',
'deg_air', 'deg_rw', 'deg_fw', 'deg_rwSed', 'deg_fwSed', 'deg_sw', 'deg_swSed',
'deg_soil1', 'deg_deepS1', 'deg_soil2', 'deg_deepS2', 'deg_soil3', 'deg_deepS3', 'deg_soil4',
'deg_deepS4']
elif chem_type == 'Metal':
header_list = ['adv_air_in', 'adv_air_out',
'adv_rw_in', 'adv_rw_out', 'adv_rwSed_in', 'adv_rwSed_out',
'adv_fw_in', 'adv_fw_out', 'adv_fwSed_in', 'adv_fwSed_out',
'adv_sw_in', 'adv_sw_out', 'adv_swSed_out',
'dep_dry_air', 'dep_dry_air_rw', 'dep_dry_air_fw', 'dep_dry_air_sw',
'dep_dry_air_soil1', 'dep_dry_air_soil2', 'dep_dry_air_soil3', 'dep_dry_air_soil4',
'dep_wet_air', 'dep_wet_air_rw', 'dep_wet_air_fw', 'dep_wet_air_sw',
'dep_wet_air_soil1', 'dep_wet_air_soil2', 'dep_wet_air_soil3', 'dep_wet_air_soil4',
'dep_rSS', 'dep_fSS', 'dep_sSS',
'diff_rw_rSedW', 'diff_fw_fSedW', 'diff_sw_sSedW', 'diff_rSedW_rw', 'diff_fSedW_fw',
'diff_sSedW_sw',
'burial_rwSed', 'burial_fwSed', 'burial_swSed',
'resusp_rwSed', 'resusp_fwSed', 'resusp_swSed', 'aero_resusp_sSS',
'runoff_soil1_river', 'runoff_soil2_river', 'runoff_soil3_river', 'runoff_soil4_river',
'runoff_soil1_fresh', 'runoff_soil2_fresh', 'runoff_soil3_fresh', 'runoff_soil4_fresh',
'erosion_soil1_river', 'erosion_soil2_river', 'erosion_soil3_river', 'erosion_soil4_river',
'erosion_soil1_fresh', 'erosion_soil2_fresh', 'erosion_soil3_fresh', 'erosion_soil4_fresh',
'wind_erosion_soil1', 'wind_erosion_soil2', 'wind_erosion_soil3', 'wind_erosion_soil4',
'infiltra_soil1', 'infiltra_soil2', 'infiltra_soil3', 'infiltra_soil4',
'leach_soil1_river', 'leach_soil2_river', 'leach_soil3_river', 'leach_soil4_river',
'leach_soil1_fresh', 'leach_soil2_fresh', 'leach_soil3_fresh', 'leach_soil4_fresh']
elif chem_type == 'NonionizableOrganic':
header_list = ['adv_air_in', 'adv_air_out',
'adv_rw_in', 'adv_rw_out', 'adv_rwSed_in', 'adv_rwSed_out',
'adv_fw_in', 'adv_fw_out', 'adv_fwSed_in', 'adv_fwSed_out',
'adv_sw_in', 'adv_sw_out', 'adv_swSed_out',
'dep_dry_air', 'dep_dry_air_rw', 'dep_dry_air_fw', 'dep_dry_air_sw',
'dep_dry_air_soil1', 'dep_dry_air_soil2', 'dep_dry_air_soil3', 'dep_dry_air_soil4',
'dep_wet_air', 'dep_wet_air_rw', 'dep_wet_air_fw', 'dep_wet_air_sw',
'dep_wet_air_soil1', 'dep_wet_air_soil2', 'dep_wet_air_soil3', 'dep_wet_air_soil4',
'rain_dis_air', 'rain_dis_air_rw', 'rain_dis_air_fw', 'rain_dis_air_sw',
'rain_dis_air_soil1', 'rain_dis_air_soil2', 'rain_dis_air_soil3', 'rain_dis_air_soil4',
'dep_rSS', 'dep_fSS', 'dep_sSS',
'diff_air_rw', 'diff_air_fw', 'diff_air_sw', 'diff_rw_rSedW', 'diff_fw_fSedW',
'diff_sw_sSedW',
'diff_air_soil1', 'diff_air_soil2', 'diff_air_soil3', 'diff_air_soil4',
'diff_rw_air', 'diff_fw_air', 'diff_sw_air', 'diff_rSedW_rw', 'diff_fSedW_fw',
'diff_sSedW_sw',
'diff_soil1_air', 'diff_soil2_air', 'diff_soil3_air', 'diff_soil4_air',
'burial_rwSed', 'burial_fwSed', 'burial_swSed',
'resusp_rwSed', 'resusp_fwSed', 'resusp_swSed',
'runoff_soil1', 'runoff_soil1_river', 'runoff_soil1_fresh',
'runoff_soil2', 'runoff_soil2_river', 'runoff_soil2_fresh',
'runoff_soil3', 'runoff_soil3_river', 'runoff_soil3_fresh',
'runoff_soil4', 'runoff_soil4_river', 'runoff_soil4_fresh',
'erosion_soil1', 'erosion_soil1_river', 'erosion_soil1_fresh',
'erosion_soil2', 'erosion_soil2_river', 'erosion_soil2_fresh',
'erosion_soil3', 'erosion_soil3_river', 'erosion_soil3_fresh',
'erosion_soil4', 'erosion_soil4_river', 'erosion_soil4_fresh',
'wind_erosion_soil1', 'wind_erosion_soil2', 'wind_erosion_soil3', 'wind_erosion_soil4',
'infiltra_soil1', 'infiltra_soil2', 'infiltra_soil3', 'infiltra_soil4',
'leach_soil1', 'leach_soil1_river', 'leach_soil1_fresh',
'leach_soil2', 'leach_soil2_river', 'leach_soil2_fresh',
'leach_soil3', 'leach_soil3_river', 'leach_soil3_fresh',
'leach_soil4', 'leach_soil4_river', 'leach_soil4_fresh',
'deg_air', 'deg_rw', 'deg_rwSed', 'deg_fw', 'deg_fwSed', 'deg_sw', 'deg_swSed',
'deg_soil1', 'deg_deepS1', 'deg_soil2', 'deg_deepS2', 'deg_soil3', 'deg_deepS3', 'deg_soil4',
'deg_deepS4']
else:
header_list = ['adv_air_in', 'adv_rw_out', 'adv_rwSed_out', 'adv_fw_out', 'adv_fwSed_out', 'adv_sw_out',
'adv_swSed_out',
'adv_rw_dissolved', 'adv_rwSed_dissolved',
'adv_fw_dissolved', 'adv_fwSed_dissolved',
'adv_sw_dissolved', 'adv_swSed_dissolved',
'dep_dry_air', 'dep_dry_air_rw', 'dep_dry_air_fw', 'dep_dry_air_sw',
'dep_dry_air_soil1', 'dep_dry_air_soil2', 'dep_dry_air_soil3', 'dep_dry_air_soil4',
'dep_wet_air', 'dep_wet_air_rw', 'dep_wet_air_fw', 'dep_wet_air_sw',
'dep_wet_air_soil1', 'dep_wet_air_soil2', 'dep_wet_air_soil3', 'dep_wet_air_soil4',
'dep_rSS', 'dep_fSS', 'dep_sSS',
'heteroagg_air', 'heteroagg_rw', 'heteroagg_fw', 'heteroagg_sw',
'dissolution_rw', 'dissolution_rwSed', 'dissolution_fw', 'dissolution_fwSed',
'dissolution_sw', 'dissolution_swSed',
'dissolution_soil1', 'dissolution_soil2', 'dissolution_soil3', 'dissolution_soil4',
'partition_soil2soilw1', 'partition_soil2soilw2', 'partition_soil2soilw3',
'partition_soil2soilw4',
'partition_soilw2soil1', 'partition_soilw2soil2', 'partition_soilw2soil3',
'partition_soilw2soil4',
'runoff_soil1_river', 'runoff_soil2_river', 'runoff_soil3_river', 'runoff_soil4_river',
'runoff_soil1_fresh', 'runoff_soil2_fresh', 'runoff_soil3_fresh', 'runoff_soil4_fresh',
'runoff_soil1_dissolved_river', 'runoff_soil2_dissolved_river',
'runoff_soil3_dissolved_river', 'runoff_soil4_dissolved_river',
'runoff_soil1_dissolved_fresh', 'runoff_soil2_dissolved_fresh',
'runoff_soil3_dissolved_fresh', 'runoff_soil4_dissolved_fresh',
'erosion_soil1_river', 'erosion_soil2_river', 'erosion_soil3_river', 'erosion_soil4_river',
'erosion_soil1_fresh', 'erosion_soil2_fresh', 'erosion_soil3_fresh', 'erosion_soil4_fresh',
'wind_erosion_soil1', 'wind_erosion_soil2', 'wind_erosion_soil3', 'wind_erosion_soil4',
'infiltra_soil1', 'infiltra_soil2', 'infiltra_soil3', 'infiltra_soil4',
'leach_soil1_river', 'leach_soil2_river', 'leach_soil3_river', 'leach_soil4_river',
'leach_soil1_fresh', 'leach_soil2_fresh', 'leach_soil3_fresh', 'leach_soil4_fresh',
'burial_rwSed', 'burial_fwSed', 'burial_swSed',
'resusp_rwSed', 'resusp_fwSed', 'resusp_swSed', 'aero_resusp_sSS']
writer = pd.ExcelWriter(os.path.join(output_file_path, 'process_' + file_name + '.xlsx'))
df = pd.DataFrame(process_array, columns=header_list, index=date_array)
df.to_excel(writer, 'process')
writer.save()
def generate_plot_bulk(self, df, txt, chem_name, region_name, release_scenario, output_figure_path):
df = df.reset_index(drop=True)
sns.set(style='white')
sim_days = df.shape[0]
X = np.linspace(0, sim_days, num=sim_days)
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 5), sharey=True)
line_labels = ['Air', 'RW', 'RW Sed', 'FW', 'FW Sed', 'SW', 'SW Sed', 'Natural Soil',
'Urban Soil', 'Ag. Soil', 'Bio. Soil']
sns.despine()
l1 = axes[0].plot(X, df['air'].tolist(), color='lightsteelblue')[0]
l2 = axes[0].plot(X, df['rw'].tolist(), color='slategrey')[0]
l3 = axes[0].plot(X, df['rw_sed'].tolist(), color='slategrey', linestyle=':')[0]
l4 = axes[0].plot(X, df['fw'].tolist(), color='deepskyblue')[0]
l5 = axes[0].plot(X, df['fw_sed'].tolist(), color='deepskyblue', linestyle=':')[0]
l6 = axes[0].plot(X, df['sw'].tolist(), color='royalblue')[0]
l7 = axes[0].plot(X, df['sw_sed'].tolist(), color='royalblue', linestyle=':')[0]
l8 = axes[1].plot(X, df['undeveloped_soil'].tolist(), color='goldenrod')[0]
l9 = axes[1].plot(X, df['urban_soil'].tolist(), color='darkgrey')[0]
l10 = axes[1].plot(X, df['agricultural_soil'].tolist(), color='limegreen')[0]
l11 = axes[1].plot(X, df['biosolids_soil'].tolist(), color='m')[0]
axes[0].set_yscale('log')
axes[1].set_yscale('log')
axes[0].set_xlim(xmin=0)
axes[1].set_xlim(xmin=0)
axes[0].set_ylabel('Concentration (g/L)', fontsize=14)
axes[0].set_title("Air and Water")
axes[1].set_title('Soil')
fig.suptitle(
txt.capitalize() + ' ' + chem_name + ' in ' + region_name + ' using ' + release_scenario + ' Release \n'
'Concentration in Major (Bulk) Compartments')
fig.text(0.5, 0.01, 'Simulation Time (day)', ha='center', fontsize=14)
fig.legend(handles=[l1, l2, l3, l4, l5, l6, l7, l8, l9, l10, l11],
labels=line_labels,
loc='center right', frameon=False)
plt.subplots_adjust(right=0.75, top=0.8)
plt.savefig(output_figure_path)
plt.close('all')
def generate_plot_sub(self, df, txt, chem_name, chem_type, region_name, release_scenario, output_figure_path):
df = df.reset_index(drop=True)
sns.set(style='white')
sim_days = df.shape[0]
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(10, 7), sharey=True)
sns.despine()
X = np.linspace(0, sim_days, num=sim_days)
if chem_type != 'Nanomaterial':
line_label_non_nano = ['Air', 'Aero', 'RW', 'RW Susp. Sed', 'RW Sed Water', 'RW Sed Solid',
'FW', 'FW Susp. Sed', 'FW Sed Water', 'FW Sed Solid',
'SW', 'SW Susp. Sed', 'SW Sed Water', 'SW Sed Solid',
'Natural Soil Air', 'Natural Soil Water', 'Natural Soil Solid',
'Natural Deep Soil', 'Urban Soil Air', 'Urban Soil Water', 'Urban Soil Solid',
'Urban Deep Soil',
'Ag. Soil Air', 'Ag. Soil Water', 'Ag. Soil Solid', 'Ag. Deep Soil',
'Ag. Deep Soil', 'Bio. Soil Air', 'Bio. Soil Water', 'Bio. Soil Solid',
'Bio. Deep Soil']
l1 = axes[0].plot(X, df['air'].tolist(), color='lightsteelblue')[0]
l2 = axes[0].plot(X, df['aerosol'].tolist(), color='steelblue')[0]
l3 = axes[1].plot(X, df['rw'].tolist(), color='#836fff')[0]
l4 = axes[1].plot(X, df['rw_sus_sed'].tolist(), color='#836fff', linestyle=':')[0]
l5 = axes[1].plot(X, df['rw_sed_water'].tolist(), color='#473c8b')[0]
l6 = axes[1].plot(X, df['rw_sed_solid'].tolist(), color='#473c8b', linestyle=':')[0]
l7 = axes[1].plot(X, df['fw'].tolist(), color='skyblue')[0]
l8 = axes[1].plot(X, df['fw_sus_sed'].tolist(), color='skyblue', linestyle=':')[0]
l9 = axes[1].plot(X, df['fw_sed_water'].tolist(), color='deepskyblue')[0]
l10 = axes[1].plot(X, df['fw_sed_solid'].tolist(), color='deepskyblue', linestyle=':')[0]
l11 = axes[1].plot(X, df['sw'].tolist(), color='blue')[0]
l12 = axes[1].plot(X, df['sw_sus_sed'].tolist(), color='blue', linestyle=':')[0]
l13 = axes[1].plot(X, df['sw_sed_water'].tolist(), color='slategray')[0]
l14 = axes[1].plot(X, df['sw_sed_solid'].tolist(), color='slategray', linestyle=':')[0]
l15 = axes[2].plot(X, df['undeveloped_soil_air'].tolist(), color='wheat')[0]
l16 = axes[2].plot(X, df['undeveloped_soil_water'].tolist(), color='gold')[0]
l17 = axes[2].plot(X, df['undeveloped_soil_solid'].tolist(), color='goldenrod')[0]
l18 = axes[2].plot(X, df['deep_agricultural_soil'].tolist(), color='goldenrod', linestyle=':')[0]
l19 = axes[2].plot(X, df['urban_soil_air'].tolist(), color='lightgray')[0]
l20 = axes[2].plot(X, df['urban_soil_water'].tolist(), color='darkgrey')[0]
l21 = axes[2].plot(X, df['urban_soil_solid'].tolist(), color='dimgray')[0]
l22 = axes[2].plot(X, df['deep_urban_soil'].tolist(), color='dimgray', linestyle=':')[0]
l23 = axes[2].plot(X, df['agricultural_soil_air'].tolist(), color='lightgreen')[0]
l24 = axes[2].plot(X, df['agricultural_soil_water'].tolist(), color='limegreen')[0]
l25 = axes[2].plot(X, df['agricultural_soil_solid'].tolist(), color='darkgreen')[0]
l26 = axes[2].plot(X, df['deep_agricultural_soil'].tolist(), color='darkgreen', linestyle=':')[0]
l27 = axes[2].plot(X, df['biosolids_soil_air'].tolist(), color='plum')[0]
l28 = axes[2].plot(X, df['biosolids_soil_water'].tolist(), color='m')[0]
l29 = axes[2].plot(X, df['biosolids_soil_solid'].tolist(), color='purple')[0]
l30 = axes[2].plot(X, df['deep_biosolids_soil'].tolist(), color='purple', linestyle=':')[0]
fig.legend(handles=[l1, l2, l3, l4, l5, l6, l7, l8, l9, l10, l11, l12, l13, l14, l15, l16, l17, l18,
l19, l20, l21, l22, l23, l24, l25, l26, l27, l28, l29, l30],
labels=line_label_non_nano,
loc='center right', frameon=False)
else:
line_label_nano = ['Air', 'Aero',
'RW', 'RW Susp. Sed', 'RW Dis.', 'RW Sed Solid', 'RW Sed Dis.',
'FW', 'FW Susp. Sed', 'FW Dis.', 'FW Sed Solid', 'FW Sed Dis.',
'SW', 'SW Susp. Sed', 'SW Dis.', 'SW Sed Solid', 'SW Sed Dis.',
'Natural Soil Water', 'Natural Soil Solid', 'Natural Deep Soil', 'Natural Soil Dis.',
'Urban Soil Water', 'Urban Soil Solid', 'Urban Deep Soil', 'Urban Soil Dis.',
'Ag. Soil Water', 'Ag. Soil Solid', 'Ag. Deep Soil', 'Ag. Soil Dis.',
'Bio. Soil Water', 'Bio. Soil Solid', 'Bio. Deep Soil', 'Bio. Soil Dis.']
l1 = axes[0].plot(X, df['air'].tolist(), color='lightsteelblue')[0]
l2 = axes[0].plot(X, df['aerosol'].tolist(), color='steelblue')[0]
l3 = axes[1].plot(X, df['rw'].tolist(), color='skyblue1')[0]
l4 = axes[1].plot(X, df['rw_sus_sed'].tolist(), color='deepskyblue1')[0]
l5 = axes[1].plot(X, df['rw_dis'].tolist(), color='skyblue1', linestyle=':')[0]
l6 = axes[1].plot(X, df['rw_sed'].tolist(), color='darkturquoise1')[0]
l7 = axes[1].plot(X, df['rwSed_dis'].tolist(), color='darkcyan1', linestyle=':')[0]
l8 = axes[1].plot(X, df['fw'].tolist(), color='skyblue')[0]
l9 = axes[1].plot(X, df['fw_sus_sed'].tolist(), color='deepskyblue')[0]
l10 = axes[1].plot(X, df['fw_dis'].tolist(), color='skyblue', linestyle=':')[0]
l11 = axes[1].plot(X, df['fw_sed'].tolist(), color='darkturquoise')[0]
l12 = axes[1].plot(X, df['fwSed_dis'].tolist(), color='darkcyan', linestyle=':')[0]
l13 = axes[1].plot(X, df['sw'].tolist(), color='cornflowerblue')[0]
l14 = axes[1].plot(X, df['sw_sus_sed'].tolist(), color='blue')[0]
l15 = axes[1].plot(X, df['sw_dis'].tolist(), color='royalblue', linestyle=':')[0]
l16 = axes[1].plot(X, df['sw_sed'].tolist(), color='slategray')[0]
l17 = axes[1].plot(X, df['swSed_dis'].tolist(), color='slategray', linestyle=':')[0]
l18 = axes[2].plot(X, df['undeveloped_soil_water'].tolist(), color='wheat')[0]
l19 = axes[2].plot(X, df['undeveloped_soil_solid'].tolist(), color='gold')[0]
l20 = axes[2].plot(X, df['deep_undeveloped_soil'].tolist(), color='goldenrod')[0]
l21 = axes[2].plot(X, df['undevSoilW_dis'].tolist(), color='goldenrod', linestyle=':')[0]
l22 = axes[2].plot(X, df['urban_soil_water'].tolist(), color='lightgray')[0]
l23 = axes[2].plot(X, df['urban_soil_solid'].tolist(), color='darkgray')[0]
l24 = axes[2].plot(X, df['deep_urban_soil'].tolist(), color='dimgray')[0]
l25 = axes[2].plot(X, df['urbanSoilW_dis'].tolist(), color='dimgray', linestyle=':')[0]
l26 = axes[2].plot(X, df['agricultural_soil_water'].tolist(), color='lightgreen')[0]
l27 = axes[2].plot(X, df['agricultural_soil_solid'].tolist(), color='limegreen')[0]
l28 = axes[2].plot(X, df['deep_agricultural_soil'].tolist(), color='darkgreen')[0]
l29 = axes[2].plot(X, df['agSoilW_dis'].tolist(), color='darkgreen', linestyle=':')[0]
l30 = axes[2].plot(X, df['biosolids_soil_water'].tolist(), color='plum')[0]
l31 = axes[2].plot(X, df['biosolids_soil_solid'].tolist(), color='m')[0]
l32 = axes[2].plot(X, df['deep_biosolids_soil'].tolist(), color='purple')[0]
l33 = axes[2].plot(X, df['bioSoilW_dis'].tolist(), color='purple', linestyle=':')[0]
fig.legend([l1, l2, l3, l4, l5, l6, l7, l8, l9, l10, l11, l12, l13, l14, l15, l16, l17, l18, l19, l20,
l21, l22, l23, l24, l25, l26, l27, l28, l29, l30, l31, l32, l33],
labels=line_label_nano,
loc='center right', frameon=False)
axes[0].set_yscale('log')
axes[1].set_yscale('log')
axes[2].set_yscale('log')
axes[0].set_ylabel('Concentration (g/L)', fontsize=14)
axes[0].set_xlim(xmin=0)
axes[1].set_xlim(xmin=0)
axes[2].set_xlim(xmin=0)
axes[0].set_title("Air")
axes[1].set_title('Water')
axes[2].set_title('Soil')
fig.suptitle(
txt.capitalize() + ' ' + chem_name + ' in ' + region_name + ' using ' + release_scenario + ' Release \n'
'Concentration in Subcompartments')
fig.text(0.5, 0.01, 'Simulation Time (day)', ha='center', fontsize=14)
plt.subplots_adjust(right=0.75, top=0.8)
# plt.tight_layout()
plt.savefig(output_figure_path)
plt.close('all')
def generate_heatmap_bulk(self, chem_type, df, chem_name, region_name, release_scenario,
V_bulk_list, output_figure_path):
data = OrderedDict()
data['conc'] = OrderedDict()
data['massFr'] = OrderedDict()
total_mass = 0
for i in range(0, len(df)):
if i == 0:
txt = 'first'
elif i == 1:
txt = 'second'
else:
txt = 'third'
data['conc'][txt] = OrderedDict()
data['conc'][txt]['Air'] = df[i]['air'].mean()
data['conc'][txt]['RW'] = df[i]['rw'].mean()
data['conc'][txt]['RW Sed'] = df[i]['rw_sed'].mean()
data['conc'][txt]['FW'] = df[i]['fw'].mean()
data['conc'][txt]['FW Sed'] = df[i]['fw_sed'].mean()
data['conc'][txt]['SW'] = df[i]['sw'].mean()
data['conc'][txt]['SW Sed'] = df[i]['sw_sed'].mean()
data['conc'][txt]['Natural Soil'] = df[i]['undeveloped_soil'].mean()
data['conc'][txt]['Urban Soil'] = df[i]['urban_soil'].mean()
data['conc'][txt]['Ag. Soil'] = df[i]['agricultural_soil'].mean()
data['conc'][txt]['Bio. Soil'] = df[i]['biosolids_soil'].mean()
total_mass = total_mass + df[i]['air'][-1] * V_bulk_list[0] + df[i]['rw'][-1] * V_bulk_list[1][-1] + \
df[i]['rw_sed'][-1] * V_bulk_list[2] \
+ df[i]['fw'][-1] * V_bulk_list[3] + df[i]['fw_sed'][-1] * V_bulk_list[4] \
+ df[i]['sw'][-1] * V_bulk_list[5] + df[i]['sw_sed'][-1] * V_bulk_list[6] + \
df[i]['undeveloped_soil'][-1] * V_bulk_list[7] + df[i]['urban_soil'][-1] * V_bulk_list[8] + \
df[i]['agricultural_soil'][-1] * V_bulk_list[9] + df[i]['biosolids_soil'][-1] * V_bulk_list[10]
for i in range(0, len(df)):
if i == 0:
txt = 'first'
elif i == 1:
txt = 'second'
else:
txt = 'third'
data['massFr'][txt] = OrderedDict()
data['massFr'][txt]['Air'] = (df[i]['air'][-1] * V_bulk_list[0] / total_mass) * 100
data['massFr'][txt]['RW'] = (df[i]['rw'][-1] * V_bulk_list[1][-1] / total_mass) * 100
data['massFr'][txt]['RW Sed'] = (df[i]['rw_sed'][-1] * V_bulk_list[2] / total_mass) * 100
data['massFr'][txt]['FW'] = (df[i]['fw'][-1] * V_bulk_list[3] / total_mass) * 100
data['massFr'][txt]['FW Sed'] = (df[i]['fw_sed'][-1] * V_bulk_list[4] / total_mass) * 100
data['massFr'][txt]['SW'] = (df[i]['sw'][-1] * V_bulk_list[5] / total_mass) * 100
data['massFr'][txt]['SW Sed'] = (df[i]['sw_sed'][-1] * V_bulk_list[6] / total_mass) * 100
data['massFr'][txt]['Natural Soil'] = (df[i]['undeveloped_soil'][-1] * V_bulk_list[7] / total_mass) * 100
data['massFr'][txt]['Urban Soil'] = (df[i]['urban_soil'][-1] * V_bulk_list[8] / total_mass) * 100
data['massFr'][txt]['Ag. Soil'] = (df[i]['agricultural_soil'][-1] * V_bulk_list[9] / total_mass) * 100
data['massFr'][txt]['Bio. Soil'] = (df[i]['biosolids_soil'][-1] * V_bulk_list[10] / total_mass) * 100
df_conc = pd.DataFrame.from_dict(data['conc'])
df_massFr = pd.DataFrame.from_dict(data['massFr'])
df_conc = df_conc.reindex(
['Air', 'RW', 'RW Sed', 'FW', 'FW Sed', 'SW', 'SW Sed', 'Natural Soil', 'Urban Soil', 'Ag. Soil',
'Bio. Soil'])
df_massFr = df_massFr.reindex(
['Air', 'RW', 'RW Sed', 'FW', 'FW Sed', 'SW', 'SW Sed', 'Natural Soil', 'Urban Soil', 'Ag. Soil',
'Bio. Soil'])
color_heatmap = ['#ffffcc', '#feb24c', '#fc4e2a', '#b10026']
cmap = mcolors.LinearSegmentedColormap.from_list("color", color_heatmap)
if len(df) == 1:
fig_width = 8
fig_height = 5
elif len(df) == 2:
fig_width = 8
fig_height = 6
else:
fig_width = 11
fig_height = 6
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(fig_width, fig_height))
if chem_type == 'NonionizableOrganic':
xlabels = ['Neutral']
elif chem_type == 'IonizableOrganic':
xlabels = ['Neutral', 'Ionic']
elif chem_type == 'Nanomaterial':
xlabels = ['Particulate', 'Free Nano', 'Dissolved']
else:
xlabels = ['Particulate', 'Colloidal', 'Dissolved']
sns.set(font_scale=1.1)
sns.heatmap(df_conc, ax=axes[0], cmap=cmap,
cbar_kws={'label': 'Concentration (g/L)', 'format': '%.0e'},
linecolor='white', linewidths=0.5, annot=True, fmt='.1e', annot_kws={"size": 11})
sns.heatmap(df_massFr, ax=axes[1], cmap=cmap,
cbar_kws={'label': 'Mass. Frac (%)'},
linecolor='white', linewidths=0.5, annot=True, fmt='.1e', annot_kws={"size": 11})
axes[0].set_title("Mean Conc.")
axes[1].set_title("Mass Frac.")
axes[0].set_xticklabels(xlabels, fontsize=12)
axes[1].set_xticklabels(xlabels, fontsize=12)
axes[0].set_ylim(len(df_conc), -0.2)
axes[1].set_ylim(len(df_conc), -0.2)
fig.suptitle(chem_name + ' in ' + region_name + ' using ' + release_scenario + ' Release \n'
'Overall mean concentration and last day mass fraction in major (bulk) compartments \n',
fontsize=13)
plt.subplots_adjust(wspace=0.8, top=0.8)
# plt.tight_layout()
plt.savefig(output_figure_path)
plt.close('all')
def generate_release_bulk(self, release, chem_name, region_name, release_scenario, output_figure_path):
air = release['air']
rw = release['rw']
rSS = release['rSS']
rwSed = release['rwSed']
fw = release['fw']
fSS = release['fSS']
fwSed = release['fwSed']
sw = release['sw']
sSS = release['sSS']
swSed = release['swSed']
soil1 = release['soil1']
dsoil1 = release['dsoil1']
soil2 = release['soil2']
dsoil2 = release['dsoil2']
soil3 = release['soil3']
dsoil3 = release['dsoil3']
soil4 = release['soil4']
dsoil4 = release['dsoil4']
compart_list = [air, rw, rSS, rwSed, fw, fSS, fwSed, sw, sSS, swSed, soil1, dsoil1, soil2, dsoil2, soil3,
dsoil3, soil4, dsoil4]
compart2_list = compart_list[:]
for i in range(0, len(compart_list)):
for j in range(0, len(compart_list[i])):
if j == 0:
compart2_list[i][j] = compart_list[i][j]
else:
compart2_list[i][j] = compart2_list[i][j - 1] + compart_list[i][j]
sns.set(style='white')
sim_days = len(air)
X = np.linspace(0, sim_days, num=sim_days)
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 5), sharey=True)
line_labels = ['Air', 'RW', 'RW Sus Sed', 'RW Sed', 'FW', 'FW Sus Sed', 'FW Sed', 'SW', 'SW Sus Sed', 'SW Sed',
'Natual Soil', 'Natural Deep Soil', 'Urban Soil', 'Urban Deep Soil',
'Ag. Soil', 'Ag. Deep Soil', 'Bio. Soil', 'Bio. Deep Soil']
sns.despine()
l1 = axes[0].plot(X, compart2_list[0], color='lightsteelblue')[0]
l2 = axes[0].plot(X, compart2_list[1], color='#836fff')[0] # slateblue1
l3 = axes[0].plot(X, compart2_list[2], color='#6959cd')[0] # slateblue3
l4 = axes[0].plot(X, compart2_list[3], color='#473c8b')[0] # slateblue4
l5 = axes[0].plot(X, compart2_list[4], color='skyblue')[0]
l6 = axes[0].plot(X, compart2_list[5], color='deepskyblue')[0]
l7 = axes[0].plot(X, compart2_list[6], color='deepskyblue', linestyle=':')[0]
l8 = axes[0].plot(X, compart2_list[7], color='royalblue')[0]
l9 = axes[0].plot(X, compart2_list[8], color='darkslateblue')[0]
l10 = axes[0].plot(X, compart2_list[9], color='darkslateblue', linestyle=':')[0]
l11 = axes[1].plot(X, compart2_list[10], color='goldenrod')[0]
l12 = axes[1].plot(X, compart2_list[11], color='goldenrod', linestyle=':')[0]
l13 = axes[1].plot(X, compart2_list[12], color='darkgray')[0]
l14 = axes[1].plot(X, compart2_list[13], color='darkgray', linestyle=':')[0]
l15 = axes[1].plot(X, compart2_list[14], color='limegreen')[0]
l16 = axes[1].plot(X, compart2_list[15], color='limegreen', linestyle=':')[0]
l17 = axes[1].plot(X, compart2_list[16], color='purple')[0]
l18 = axes[1].plot(X, compart2_list[17], color='purple', linestyle=':')[0]
# axes[0].set_yscale('log')
# axes[1].set_yscale('log')
plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
axes[0].set_xlim(xmin=0)
axes[1].set_xlim(xmin=0)
axes[0].set_ylim(ymin=0)
axes[0].set_ylim(ymin=0)
axes[0].set_ylabel('Accumulated Release Amount (kg)', fontsize=14)
axes[0].set_title("Air and Water")
axes[1].set_title('Soil')
fig.suptitle(chem_name + ' in ' + region_name + ' using ' + release_scenario + ' Release \n'
'Cumulative Mass Released to Major (Bulk) Compartments ')
fig.text(0.5, 0.01, 'Simulation Time (day)', ha='center', fontsize=14)
fig.legend(handles=[l1, l2, l3, l4, l5, l6, l7, l8, l9, l10, l11, l12, l13, l14, l15, l16, l17, l18],
labels=line_labels,
loc='center right', frameon=False)
plt.subplots_adjust(right=0.75, top=0.8)
plt.savefig(output_figure_path)
plt.close('all')
def generate_mass_bulk(self, df, txt, V_bulk_list, chem_name, region_name, release_scenario, output_figure_path):
df = df.reset_index(drop=True)
sns.set(style='white')
sim_days = df.shape[0]
X = np.linspace(0, sim_days, num=sim_days)
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 5), sharey=True)
line_labels = ['Air', 'RW', 'RW Sed', 'FW', 'FW Sed', 'SW', 'SW Sed', 'Natual Soil',
'Urban Soil', 'Ag. Soil', 'Bio. Soil']
sns.despine()
l1 = axes[0].plot(X, (df['air'] * V_bulk_list[0]).tolist(), color='lightsteelblue')[0]
l2 = axes[0].plot(X, (df['rw'] * V_bulk_list[1][-1]).tolist(), color='#6959cd')[0]
l3 = axes[0].plot(X, (df['rw_sed'] * V_bulk_list[2]).tolist(), color='#6959cd', linestyle=':')[0]
l4 = axes[0].plot(X, (df['fw'] * V_bulk_list[3]).tolist(), color='deepskyblue')[0]
l5 = axes[0].plot(X, (df['fw_sed'] * V_bulk_list[4]).tolist(), color='deepskyblue', linestyle=':')[0]
l6 = axes[0].plot(X, (df['sw'] * V_bulk_list[5]).tolist(), color='royalblue')[0]
l7 = axes[0].plot(X, (df['sw_sed'] * V_bulk_list[6]).tolist(), color='royalblue', linestyle=':')[0]
l8 = axes[1].plot(X, (df['undeveloped_soil'] * V_bulk_list[7]).tolist(), color='goldenrod')[0]
l9 = axes[1].plot(X, (df['urban_soil'] * V_bulk_list[8]).tolist(), color='darkgrey')[0]
l10 = axes[1].plot(X, (df['agricultural_soil'] * V_bulk_list[9]).tolist(), color='limegreen')[0]
l11 = axes[1].plot(X, (df['biosolids_soil'] * V_bulk_list[10]).tolist(), color='m')[0]
axes[0].set_yscale('log')
axes[1].set_yscale('log')
axes[0].set_xlim(xmin=0)
axes[1].set_xlim(xmin=0)
axes[0].set_ylabel('Mass (kg)', fontsize=14)
axes[0].set_title("Air and Water")
axes[1].set_title('Soil')
fig.suptitle(
txt.capitalize() + ' ' + chem_name + ' in ' + region_name + ' using ' + release_scenario + ' Release \n'
'Mass in Major (Bulk) Compartments')
fig.text(0.5, 0.01, 'Simulation Time (day)', ha='center', fontsize=14)
fig.legend(handles=[l1, l2, l3, l4, l5, l6, l7, l8, l9, l10, l11],
labels=line_labels,
loc='center right', frameon=False)
plt.subplots_adjust(right=0.75, top=0.8)
plt.savefig(output_figure_path)
plt.close('all')