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calculation_new.py
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Fri, Apr 26, 06:40

calculation_new.py
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#functions were modified by Jasmin, 07.11.2022
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
file_location = "C:\\Users\\leetseng\\TWtest"
# input_file_path = file_location+'\\input\\sludgeDatasetMergeCalculated.tsv'
input_file_path_1 = file_location+'\\input\\sludge_Original_raw.tsv' #'input/sludgeWithSmiles.tsv' "Your concatenate the different iterations, so your index is not continuous!!!!"
input_file_path_2 = file_location+'\\input\\sludge_Leo_raw.tsv'
input_file_path_3 = file_location+'\\input\\sludge_Rich_raw.tsv'
output_file_path_full = file_location+'\\output\\sludge_raw_bay3_check.tsv' #sludgeDatasetMergeCalculated.tsv
# output_file_path_full = file_location+'\\output\\sludgeDatasetMergeCalculated.tsv'
data1 = pd.read_csv(input_file_path_1, sep='\t')
data2 = pd.read_csv(input_file_path_2, sep='\t')
data3 = pd.read_csv(input_file_path_3, sep='\t')
# data1 = data1.reset_index(drop=True)
# data2 = data2.reset_index(drop=True)
# data3 = data3.reset_index(drop=True)
data_merge = pd.concat([data1, data2, data3])
data_merge.index = np.arange(1, len(data_merge) + 1)
data_merge = data_merge.drop('index', axis=1) #old column
data_merge.index.name = 'index'
# data_merge = data_merge.set_index('index')
# print(data_merge.head(2))
# print(data_merge.columns.tolist())
# plt.figure(figsize=(35, 5))
# sns.barplot(data=data0, x='compound_name', y=cpd, palette="Greens_d")
# plt.xticks(fontsize=8, rotation=70)
# plt.savefig(file_location+'\\output\\figures\\std_sludge_Merge_test.pdf')
# plt.close()
# rateconstant_list = []
# std_list = []
# k = row['combined']
# cpd = row['compound_name']
# for index, row in data_merge.iterrows():
# i = 0
# for i in cpd[i]: ###
# rateconstant_list.append(k[i])
# if cpd[i] == cpd[i+1]:
# rateconstant_list.append(k[i+1])
# n = len(rateconstant_list)
# std = np.std(rateconstant_list[i:n])
# std_list.append(std)
# print('checking the {}'.format(cpd[i]))
# print('std of {}:'.format(std_list[i]))
# elif cpd[i] != cpd[i+1]:
# i += 1
def main():
k_list = []
k_biomass_list = []
hl_list = []
hl_biomass_list = []
hl_biomass_list_2 = []
for index, row in data_merge.iterrows():
print(row['scenario_id']+'\n')
k = get_k(row)
k_biomass = get_k_biomass(row, k)
k_list.append(k)
k_biomass_list.append(k_biomass)
DT50 = get_DT50(row, k) #k_combined does not yet exist in table
DT50_biomass = get_DT50_biomass(row, DT50, k_biomass)
hl_list.append(DT50)
hl_biomass_list.append(DT50_biomass)
DT50_biomass_2 = get_DT50_biomass_double_check(row, DT50, k_biomass) ###############
hl_biomass_list_2.append(DT50_biomass_2) ##################
print(k, k_biomass, DT50, DT50_biomass, DT50_biomass_2) ##################
# data_merge['k_combined'] = k_list # k_combined = k given + k calculated from halflife
# data_merge['k_biomass_corrected'] = k_biomass_list
# data_merge['halflife'] = hl_list
# data_merge['hl_biomass_corrected'] = hl_biomass_list
# data_merge['log_k_combined'] = np.log10(data_merge['k_combined'])
# data_merge['log_k_biomass_corrected'] = np.log10(data_merge['k_biomass_corrected'])
# data_merge['halflife_log'] = np.log10(data_merge['halflife'])
# data_merge['log_hl_biomass_corrected'] = np.log10(data_merge['hl_biomass_corrected'])
# data_merge.to_csv(output_file_path_full, mode='w', sep="\t")
data_merge['k_combined'] = k_list # k_combined = k given + k calculated from halflife
data_merge['k_biomass_corrected'] = k_biomass_list
data_merge['halflife'] = hl_list
data_merge['hl_biomass_corrected'] = hl_biomass_list
data_merge['hl_biomass_corrected_2'] = hl_biomass_list_2 #####################
data_merge['log_k_combined'] = np.log10(data_merge['k_combined'])
data_merge['log_k_biomass_corrected'] = np.log10(data_merge['k_biomass_corrected'])
data_merge['halflife_log'] = np.log10(data_merge['halflife'])
data_merge['log_hl_biomass_corrected'] = np.log10(data_merge['hl_biomass_corrected'])
data_merge.to_csv(output_file_path_full, mode='w', sep="\t")
#check the size of the data before and after
#check you don't remove the line
#before the calculation of hl, check if you have the rateconstant
def get_k(row):
k_given = row['rateconstant']
k_unit = row['rateconstant_unit']
k_true = np.NaN
TSS = row['total_suspended_solids_concentration_start']
hl = row['halflife_raw']
order = row['halflife_model']
if not np.isnan(k_given):
if k_given != 0 and k_unit == '1 / day' and not np.isnan(TSS):
k_true = k_given
elif k_given != 0 and k_unit == 'L / (g TSS * day)' and not np.isnan(TSS):
k_true = k_given * TSS
elif k_given != 0 and k_unit == '㎍ / (g TSS * day)' and not np.isnan(TSS): ################
pass
else:
if np.isnan(TSS):
print('Problem: no TSS')
elif k_given == 0:
print('Problem: given rate constant is 0')
elif not np.isnan(hl): #elif not np.isnan(hl):
if order == 'Zero order':
k_true = TSS/(2 * hl)
elif order == 'First order':
k_true = np.log(2)/hl
elif order == 'Pseudo first order': # it's a biomass corrected hl
real_hl = hl / TSS
k_true = np.log(2)/real_hl
else: #By default, using the 1st order reaction formula
k_true = np.log(2)/hl
return k_true
def get_k_biomass(row, k):
k_given = row['rateconstant']
k_unit = row['rateconstant_unit']
TSS = row['total_suspended_solids_concentration_start']
hl = row['halflife_raw']
k_biomass = np.NaN
if not np.isnan(k_given) and k_given != 0:
if k_unit == '1 / day':
k_biomass = k / TSS ################# should be k_given / TSS
elif k_unit == 'L / (g TSS * day)':
k_biomass = k_given
else:
if k_given == 0:
print('Error: rate constant is 0')
elif np.isnan(k_given) and not np.isnan(hl): #add this conditional expressions for the Rich's dataset
k_biomass = k / TSS
return k_biomass
def get_DT50(row, k):
hl_given = row['halflife_raw']
hl = np.NaN
if np.isnan(hl_given):
if not np.isnan(k): # removed k_combined, does not yet exist at this point
hl = np.log(2)/k
else:
hl = np.NaN
elif hl_given != 0: #check yourself
hl = hl_given
else:
print('Error: half-life == 0')
return hl
def get_DT50_biomass(row, hl, k_biomass): #can generate in two ways 1. take hl list/TSS 2. ln2 / k_biomass need to be in consistent. just safety check.
TSS = row['total_suspended_solids_concentration_start']
hl_biomass = np.NaN
if not np.isnan(hl):
hl_biomass = hl/TSS
elif not np.isnan(k_biomass):
hl_biomass = np.log(2)/k_biomass
return hl_biomass
# We use an alternative way to see if the outcome of DT50 biomass is consistent in different ways.
def get_DT50_biomass_double_check(row, hl_list, k_biomass):
TSS = row['total_suspended_solids_concentration_start']
= row['halflife']
hl_biomass_2 = np.NaN
if not np.isnan(hl):
hl_biomass = hl_list / TSS
elif not np.isnan(k_biomass):
hl_biomass = np.log(2) / k_biomass
return hl_biomass_2
if __name__ == '__main__':
main()
#create the set of SMILES
# list_of_canonicalize_smiles = data_merge['canonicalize_smiles'].values.tolist()
# set_of_canonicalize_smiles = set(list_of_canonicalize_smiles)
# print(set_of_canonicalize_smiles)

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