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yield_report_handler
Commit 4a33d5db authored by dasharatha.vamshi's avatar dasharatha.vamshi
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init

parent 56d09898
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output.csv 0 → 100644
View file @ 4a33d5db
,Date,Shift,"A1A023, A1A024",7303011675,"E5E014,E5E003, E5E016",IV component,7302011030,Recovered 7302011030,Reaction Product ,Package Scrubber Over Flow ,Package Scrubber Make Up ,MEE To RPT,Main 7302011030 Totaliser,Flash Column Feed,Flash Column Bottom,R#4 7302011030 Recovery Feed(FT-801),Flash Column Ejector UIST01,T-101 level (%),E5E014 E5E003 E5E016 Conc Shift Average,Surge Tank Pyr,Surge Tank Beta,Make Up To P/S (Pyr) MEE Tank,Make Up To P/S (Beta) MEE Tank,Package O/F Pyr,Package O/F Beta,Alpha in Rxn (PPM),Gamma to Beta Ratio,7303011675.1,Reactor coke,Regenerator coke,Fresh 7302011030,7302011030 totaliser,Calculated 7303011675,Calculated IV component,"Calculated E5E014,E5E003, E5E016",TOTAL 7302011030,Fresh 7302011030 / Total 7302011030,"Calculated A1A023, A1A024","(A1A023, A1A024)+7303011675+IV component",PRODN. RXN PDT,Calculated Scrubber,Calculated MEE To RPT,Surge Tank (PB) Total,Make Up To P/S (PB) MEE Tank,Norm (Surge Tank Stage),Norm (RPT Stage),Yield,"A1A023, A1A024 to E5E014 E5E003 E5E016 Molar ratio","A1A023, A1A024 to Amm Molar ratio","E5E014 E5E003 E5E016 to A1A023, A1A024 ratio @ 37%"
0,16-Jun-20,A,84010,90872,20947614,0,4170949,13912668,38505,3127685,25884,7220725,19282442,60026708,146480896,60026708,12497817,49,46.7,13.56,13.06,,,,,608,0.43,,,,,,,,,,,,,,,,26.62,,,,,,,
1,16-Jun-20,B,8445,96441,20990822,0,4178243,13935717,18630,3215626,93779,7246067,19290064,60097936,146553200,51907,12506987,66,46.62,14.12,13.26,3.86,1.33,3.72,1.38,504,0.34,5569.0,1.27,1.03,7294.0,7622.0,5569.0,0.0,43208.0,23049.0,31.65%,24435.0,30004.0,80125.0,20046.0,25342.0,27.38,5.19,1.368,1.29,88.35%,1.2090813210558624,2.02,2.228036832412523
2,16-Jun-20,C,32944,2025,21034178,0,4185799,13957892,98988,3304077,61739,7263277,19296874,60181620,146637552,67193,12514134,57,46.62,13.03,16.51,3.92,1.27,3.61,1.21,510,0.18,5584.0,,,7556.0,6810.0,5584.0,0.0,43356.0,22175.0,34.07%,24499.0,30083.0,80358.0,1120491.0,17210.0,29.54,5.19,1.267,1.221,89.26%,1.2100534127923588,1.96,2.2298281562512754
3,17-Jun-20,A,57633,7648,21077852,0,4193419,13980316,80229,3393286,30556,7277307,19302896,60251764,146709136,82163,12522326,52,46.67,13.42,13.17,2.98,1.03,3.9,1.15,469,0.15,5623.0,,,7620.0,6022.0,5623.0,0.0,43674.0,22424.0,33.98%,24689.0,30312.0,81241.0,1120392.0,14030.0,26.59,4.01,1.403,1.368,89.49%,1.2108453902547693,1.96,2.2312875741795506
4,17-Jun-20,B,81596,13112,21120998,0,4200325,14002027,58385,3479779,97142,7298859,19306896,60327168,146785408,96818,12530364,51,46.84,12.59,12.66,3.16,1.12,4.13,1.21,455,0.29,5464.0,2.21,1.26,6906.0,4000.0,5464.0,0.0,43146.0,21711.0,31.81%,23963.0,29427.0,78156.0,19907.0,21552.0,25.25,4.28,1.491,1.425,87.99%,1.2369372248883697,1.95,2.2793683505313935
5,17-Jun-20,C,7008,18903,21165954,0,4207826,14025105,42550,3571557,67942,7324324,19314654,60411128,146870896,12538,12538937,45,46.6,14.41,16.01,3.01,1.1,3.51,1.1,611,0.44,5791.0,,,7501.0,7758.0,5791.0,0.0,44956.0,23078.0,32.5%,25412.0,31203.0,84165.0,1120978.0,25465.0,30.42,4.11,1.219,1.171,90.07%,1.2091109502072512,1.95,2.228091431585844
6,18-Jun-20,A,31019,24377,21208458,0,4215741,14046827,21637,3655150,31688,7339559,19322662,60482024,146944528,25020,12546907,64,46.75,12.34,12.86,2.6,0.93,2.91,1.03,389,0.26,5474.0,,,7915.0,8008.0,5474.0,0.0,42504.0,21722.0,36.44%,24011.0,29485.0,79087.0,1119847.0,15235.0,25.2,3.53,1.479,1.441,89.63%,1.2137593602931989,1.97,2.2366572978375903
7,18-Jun-20,B,54349,29851,21250762,108,4223191,14068487,250,3740988,98107,7362143,19326866,60552000,147015200,39376,12554967,57,46.79,12.74,12.93,2.14,0.76,3.12,0.76,426,0.3,5474.0,1.49,0.99,7450.0,4204.0,5474.0,108.0,42304.0,21660.0,34.4%,23330.0,28912.0,78613.0,19419.0,22584.0,25.67,2.9,1.433,1.388,90.06%,1.2443746683811971,2.01,2.2930737132331647
8,18-Jun-20,C,78320,35325,21294384,487,4231048,14090848,81320,3830045,66638,7380538,19334790,60632876,147098880,53807,12563201,49,46.79,14.63,16.01,3.01,0.82,3.01,1.42,436,0.25,5474.0,1.56,1.21,7857.0,7924.0,5474.0,379.0,43622.0,22361.0,35.14%,23971.0,29824.0,81070.0,1120526.0,18395.0,30.64,3.83,1.201,1.168,90.12%,1.2488316259925187,2.02,2.301286780084494
test.csv 0 → 100644
View file @ 4a33d5db
Date,Shift,"A1A023, A1A024",7303011675,"E5E014,E5E003, E5E016",IV component,7302011030,Recovered 7302011030,Reaction Product ,Package Scrubber Over Flow ,Package Scrubber Make Up ,MEE To RPT,Main 7302011030 Totaliser,Flash Column Feed,Flash Column Bottom,R#4 7302011030 Recovery Feed(FT-801),Flash Column Ejector UIST01,T-101 level (%),E5E014 E5E003 E5E016 Conc Shift Average,Surge Tank Pyr,Surge Tank Beta,Make Up To P/S (Pyr) MEE Tank,Make Up To P/S (Beta) MEE Tank,Package O/F Pyr,Package O/F Beta,Alpha in Rxn (PPM),Gamma to Beta Ratio,7303011675,Reactor coke,Regenerator coke
16-Jun-20,A,84010,90872,20947614,0,4170949,13912668,38505,3127685,25884,7220725,19282442,60026708,146480896,60026708,12497817,49,46.7,13.56,13.06,,,,,608,0.43,,,
16-Jun-20,B,8445,96441,20990822,0,4178243,13935717,18630,3215626,93779,7246067,19290064,60097936,146553200,51907,12506987,66,46.62,14.12,13.26,3.86,1.33,3.72,1.38,504,0.34,5569,1.27,1.03
16-Jun-20,C,32944,2025,21034178,0,4185799,13957892,98988,3304077,61739,7263277,19296874,60181620,146637552,67193,12514134,57,46.62,13.03,16.51,3.92,1.27,3.61,1.21,510,0.18,5584,,
17-Jun-20,A,57633,7648,21077852,0,4193419,13980316,80229,3393286,30556,7277307,19302896,60251764,146709136,82163,12522326,52,46.67,13.42,13.17,2.98,1.03,3.9,1.15,469,0.15,5623,,
17-Jun-20,B,81596,13112,21120998,0,4200325,14002027,58385,3479779,97142,7298859,19306896,60327168,146785408,96818,12530364,51,46.84,12.59,12.66,3.16,1.12,4.13,1.21,455,0.29,5464,2.21,1.26
17-Jun-20,C,7008,18903,21165954,0,4207826,14025105,42550,3571557,67942,7324324,19314654,60411128,146870896,12538,12538937,45,46.6,14.41,16.01,3.01,1.1,3.51,1.1,611,0.44,5791,,
18-Jun-20,A,31019,24377,21208458,0,4215741,14046827,21637,3655150,31688,7339559,19322662,60482024,146944528,25020,12546907,64,46.75,12.34,12.86,2.6,0.93,2.91,1.03,389,0.26,5474,,
18-Jun-20,B,54349,29851,21250762,108,4223191,14068487,250,3740988,98107,7362143,19326866,60552000,147015200,39376,12554967,57,46.79,12.74,12.93,2.14,0.76,3.12,0.76,426,0.3,5474,1.49,0.99
18-Jun-20,C,78320,35325,21294384,487,4231048,14090848,81320,3830045,66638,7380538,19334790,60632876,147098880,53807,12563201,49,46.79,14.63,16.01,3.01,0.82,3.01,1.42,436,0.25,5474,1.56,1.21
\ No newline at end of file
yield_calculated_columns.py 0 → 100644
View file @ 4a33d5db
import os
import sys
import traceback
import pandas as pd
import logging
from logging import StreamHandler
logger = logging.getLogger("YieldReport")
logger.setLevel(os.environ.get("logger", "INFO"))
__formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(module)s - %(lineno)d - %(message)s')
__console_handler = StreamHandler(sys.stdout)
__console_handler.setFormatter(__formatter)
logger.addHandler(__console_handler)
enable_traceback = True
class AppConstants(object):
"""
In this Constants class we will be having the new column names and the dependant column names
"""
fresh_7302011030 = {
"new_column": "Fresh 7302011030",
"dependant_column": "7302011030"
}
totaliser_7302011030 = {
"new_column": "7302011030 totaliser",
"dependant_column": "Main 7302011030 Totaliser"
}
calculated_7303011675 = {
"new_column": "Calculated 7303011675",
"dependant_column": "7303011675",
"condition": [0, 100000]
}
calculated_IV_component = {
"new_column": "Calculated IV component",
"dependant_column": "IV component"
}
calculated_E5E014_E5E003_E5E016 = {
"new_column": "Calculated E5E014,E5E003, E5E016",
"dependant_column": "E5E014,E5E003, E5E016"
}
total_7302011030 = {
"new_column": "TOTAL 7302011030",
"dependant_column": "Recovered 7302011030"
}
fresh_div_total_7302011030 = {
"new_column": "Fresh 7302011030 / Total 7302011030",
"numerator": "Fresh 7302011030",
"denominator": "TOTAL 7302011030"
}
A1A023_A1A024 = {
"new_column": "Calculated A1A023, A1A024",
"dependant_column": "A1A023, A1A024",
"condition": [0, 100000]
}
A1A023_A1A024_plus_7303011675_plus_IV_component = {
"new_column": "(A1A023, A1A024)+7303011675+IV component",
"dependant_column": ["Calculated A1A023, A1A024", "Calculated 7303011675", "Calculated IV component"]
}
PRODN_RXNPDT = {
"new_column": "PRODN. RXN PDT",
"dependant_column": "Reaction Product ",
"condition": [0, 100000]
}
Calculated_Scrubber = {
"new_column": "Calculated Scrubber",
"dependant_column_1": "Package Scrubber Over Flow ",
"condition_1": [0, 1000000],
"dependant_column_2": "Package Scrubber Make Up ",
"condition_2": [0, 1000000]
}
Calculated_MEE_to_RPT = {
"new_column": "Calculated MEE To RPT",
"dependant_column": "MEE To RPT",
"condition": [0, 1000000]
}
surge_tank_total = {
"new_column": "Surge Tank (PB) Total",
"dependant_column": ["Surge Tank Pyr", "Surge Tank Beta"]
}
make_up_to_mee_tank = {
"new_column": "Make Up To P/S (PB) MEE Tank",
"dependant_column": ["Make Up To P/S (Pyr) MEE Tank", "Make Up To P/S (Beta) MEE Tank"]
}
norm_surge_tank_stage = {
"new_column": "Norm (Surge Tank Stage)",
"dependant_column": ["(A1A023, A1A024)+7303011675+IV component", "PRODN. RXN PDT", "Surge Tank (PB) Total"]
}
norm_rpt_stage = {
"new_column": "Norm (RPT Stage)",
"dependant_column": ["(A1A023, A1A024)+7303011675+IV component", "PRODN. RXN PDT", "Surge Tank (PB) Total",
"Calculated MEE To RPT", "Make Up To P/S (PB) MEE Tank"]
}
calculate_yield = {
"new_column": "Yield",
"dependant_column": ["PRODN. RXN PDT", "Calculated A1A023, A1A024", "Calculated E5E014,E5E003, E5E016",
"TOTAL 7302011030"]
}
A1A023_A1A024_to_E5E014_E5E003_E5E016_Molar_ratio = {
"new_column": "A1A023, A1A024 to E5E014 E5E003 E5E016 Molar ratio",
"dependant_column": ["Calculated E5E014,E5E003, E5E016","E5E014 E5E003 E5E016 Conc Shift Average","Calculated A1A023, A1A024"],
"constants": [44,30]
}
A1A023_A1A024_to_Amm_Molar_ratio = {
"new_column": "A1A023, A1A024 to Amm Molar ratio",
"dependant_column": ["TOTAL 7302011030","Fresh 7302011030","Calculated A1A023, A1A024"],
"constants": [0.75,44,17]
}
E5E014_E5E003_E5E016_to_A1A023_A1A024_ratio_37 = {
"new_column": "E5E014 E5E003 E5E016 to A1A023, A1A024 ratio @ 37%",
"dependant_column": ["Calculated E5E014,E5E003, E5E016","Calculated A1A023, A1A024","E5E014 E5E003 E5E016 Conc Shift Average"],
"constants": [37]
}
class CalculatedColumns:
def fresh_7302011030(self, df):
my_dict = AppConstants.fresh_7302011030
try:
logger.info(f"Calculating the column {my_dict['new_column']}")
df[my_dict['new_column']] = df[my_dict['dependant_column']].diff()
return df
except Exception as e:
logger.error(f"Failed to calculate {my_dict['new_column']} because of error {e}", exc_info=enable_traceback)
def totaliser_7302011030(self, df):
my_dict = AppConstants.totaliser_7302011030
try:
logger.info(f"Calculating the column {my_dict['new_column']}")
df[my_dict['new_column']] = df[my_dict['dependant_column']].diff()
return df
except Exception as e:
logger.error(f"Failed to calculate {my_dict['new_column']} because of error {e}", exc_info=enable_traceback)
def calculated_7303011675(self, df):
# check negative
my_dict = AppConstants.calculated_7303011675
try:
logger.info(f"Calculating the column {my_dict['new_column']}")
df[my_dict['new_column']] = df[my_dict['dependant_column']].diff()
my_list = list(df[my_dict['new_column']])
new_list = []
for i in my_list:
if i > 0:
new_list.append(i + my_dict['condition'][0])
elif i < 0:
new_list.append(i + my_dict['condition'][1])
else:
new_list.append(i)
df[my_dict['new_column']] = pd.Series(new_list)
return df
except Exception as e:
logger.error(f"Failed to calculate {my_dict['new_column']} because of error {e}", exc_info=enable_traceback)
def calculated_IV_component(self, df):
my_dict = AppConstants.calculated_IV_component
try:
logger.info(f"Calculating the column {my_dict['new_column']}")
df[my_dict['new_column']] = df[my_dict['dependant_column']].diff()
return df
except Exception as e:
logger.error(f"Failed to calculate {my_dict['new_column']} because of error {e}", exc_info=enable_traceback)
def calculated_E5E014_E5E003_E5E016(self, df):
my_dict = AppConstants.calculated_E5E014_E5E003_E5E016
try:
logger.info(f"Calculating the column {my_dict['new_column']}")
df[my_dict['new_column']] = df[my_dict['dependant_column']].diff()
return df
except Exception as e:
logger.error(f"Failed to calculate {my_dict['new_column']} because of error {e}", exc_info=enable_traceback)
def total_7302011030(self, df):
my_dict = AppConstants.total_7302011030
try:
logger.info(f"Calculating the column {my_dict['new_column']}")
df[my_dict['new_column']] = df[my_dict['dependant_column']].diff()
return df
except Exception as e:
logger.error(f"Failed to calculate {my_dict['new_column']} because of error {e}", exc_info=enable_traceback)
def fresh_div_total_7302011030(self, df):
my_dict = AppConstants.fresh_div_total_7302011030
try:
logger.info(f"Calculating the column {my_dict['new_column']}")
df[my_dict['new_column']] = (((df[my_dict['numerator']] / df[my_dict['denominator']]) * 100))
my_list = list(df[my_dict['new_column']])
new_list = []
for i in my_list:
if i > 0:
new_list.append(str(round(i, 2)) + "%")
else:
new_list.append(i)
df[my_dict['new_column']] = pd.Series(new_list)
return df
except Exception as e:
logger.error(f"Failed to calculate {my_dict['new_column']} because of error {e}", exc_info=enable_traceback)
def A1A023_A1A024(self, df):
my_dict = AppConstants.A1A023_A1A024
try:
logger.info(f"Calculating the column {my_dict['new_column']}")
df[my_dict['new_column']] = df[my_dict['dependant_column']].diff()
my_list = list(df[my_dict['new_column']])
new_list = []
for i in my_list:
if i > 0:
new_list.append(i + my_dict['condition'][0])
elif i < 0:
new_list.append(i + my_dict['condition'][1])
else:
new_list.append(i)
df[my_dict['new_column']] = pd.Series(new_list)
return df
except Exception as e:
logger.error(f"Failed to calculate {my_dict['new_column']} because of error {e}", exc_info=enable_traceback)
def A1A023_A1A024_plus_7303011675_plus_IV_component(self, df):
my_dict = AppConstants.A1A023_A1A024_plus_7303011675_plus_IV_component
try:
logger.info(f"Calculating the column {my_dict['new_column']}")
df[my_dict['new_column']] = df[my_dict['dependant_column'][0]] + df[my_dict['dependant_column'][1]] + df[
my_dict['dependant_column'][2]]
return df
except Exception as e:
logger.error(f"Failed to calculate {my_dict['new_column']} because of error {e}", exc_info=enable_traceback)
def PRODN_RXNPDT(self, df):
my_dict = AppConstants.PRODN_RXNPDT
try:
logger.info(f"Calculating the column {my_dict['new_column']}")
df[my_dict['new_column']] = df[my_dict['dependant_column']].diff()
my_list = list(df[my_dict['new_column']])
new_list = []
for i in my_list:
if i > 0:
new_list.append(i + my_dict['condition'][0])
elif i < 0:
new_list.append(i + my_dict['condition'][1])
else:
new_list.append(i)
df[my_dict['new_column']] = pd.Series(new_list)
return df
except Exception as e:
logger.error(f"Failed to calculate {my_dict['new_column']} because of error {e}", exc_info=enable_traceback)
def Calculated_Scrubber(self, df):
my_dict = AppConstants.Calculated_Scrubber
try:
logger.info(f"Calculating the column {my_dict['new_column']}")
df["temp1"] = df[my_dict['dependant_column_1']].diff()
df["temp2"] = df[my_dict['dependant_column_2']].diff()
df[my_dict['new_column']] = df["temp1"] - df["temp2"]
final_list = list(df[my_dict['new_column']])
my_list = list(df["temp1"])
print(my_list)
new_list = []
for i in range(0, len(my_list)):
if my_list[i] > 0:
new_list.append(final_list[i] + my_dict['condition_1'][0])
elif my_list[i] < 0:
new_list.append(final_list[i] + my_dict['condition_1'][1])
else:
new_list.append(final_list[i])
df["temp1"] = pd.Series(new_list)
print(new_list)
my_list_1 = list(df["temp2"])
new_list_1 = []
print(my_list_1)
for i in range(0, len(my_list_1)):
if my_list_1[i] > 0:
new_list_1.append(new_list[i] + my_dict['condition_2'][0])
elif my_list_1[i] < 0:
new_list_1.append(new_list[i] + my_dict['condition_2'][1])
else:
new_list_1.append(new_list[i])
df["temp2"] = pd.Series(new_list_1)
df[my_dict['new_column']] = pd.Series(new_list_1)
df = df.drop(["temp1", "temp2"], axis=1)
return df
except Exception as e:
logger.error(f"Failed to calculate {my_dict['new_column']} because of error {e}", exc_info=enable_traceback)
def Calculated_MEE_to_RPT(self, df):
my_dict = AppConstants.Calculated_MEE_to_RPT
try:
logger.info(f"Calculating the column {my_dict['new_column']}")
df[my_dict['new_column']] = df[my_dict['dependant_column']].diff()
my_list = list(df[my_dict['new_column']])
new_list = []
for i in my_list:
if i > 0:
new_list.append(i + my_dict['condition'][0])
elif i < 0:
new_list.append(i + my_dict['condition'][1])
else:
new_list.append(i)
df[my_dict['new_column']] = pd.Series(new_list)
return df
except Exception as e:
logger.error(f"Failed to calculate {my_dict['new_column']} because of error {e}", exc_info=enable_traceback)
def surge_tank_total(self, df):
my_dict = AppConstants.surge_tank_total
try:
logger.info(f"Calculating the column {my_dict['new_column']}")
df[my_dict['new_column']] = df[my_dict['dependant_column'][0]] + df[my_dict['dependant_column'][1]]
return df
except Exception as e:
logger.error(f"Failed to calculate {my_dict['new_column']} because of error {e}", exc_info=enable_traceback)
def make_up_to_mee_tank(self, df):
my_dict = AppConstants.make_up_to_mee_tank
try:
logger.info(f"Calculating the column {my_dict['new_column']}")
df[my_dict['new_column']] = round(df[my_dict['dependant_column'][0]] + df[my_dict['dependant_column'][1]],
2)
return df
except Exception as e:
logger.error(f"Failed to calculate {my_dict['new_column']} because of error {e}", exc_info=enable_traceback)
def norm_surge_tank_stage(self, df):
my_dict = AppConstants.norm_surge_tank_stage
try:
logger.info(f"Calculating the column {my_dict['new_column']}")
df[my_dict['new_column']] = round((df[my_dict['dependant_column'][0]]) / (
(df[my_dict['dependant_column'][1]] * df[my_dict['dependant_column'][2]]) / 100), 3)
return df
except Exception as e:
logger.error(f"Failed to calculate {my_dict['new_column']} because of error {e}", exc_info=enable_traceback)
def norm_rpt_stage(self, df):
my_dict = AppConstants.norm_rpt_stage
try:
logger.info(f"Calculating the column {my_dict['new_column']}")
df[my_dict['new_column']] = round((df[my_dict['dependant_column'][0]]) /
((df[my_dict['dependant_column'][1]] * df[
my_dict['dependant_column'][2]]) / 100 + (
df[my_dict['dependant_column'][3]] * df[
my_dict['dependant_column'][4]]) / 100), 3)
return df
except Exception as e:
logger.error(f"Failed to calculate {my_dict['new_column']} because of error {e}", exc_info=enable_traceback)
def calculate_yield(self, df):
my_dict = AppConstants.calculate_yield
try:
logger.info(f"Calculating the column {my_dict['new_column']}")
df[my_dict['new_column']] = round((df[my_dict['dependant_column'][0]]/(df[my_dict['dependant_column'][1]] + df[my_dict['dependant_column'][2]] + df[my_dict['dependant_column'][3]]))*100,2)
my_list = list(df[my_dict['new_column']])
new_list = []
for i in my_list:
if i > 0:
new_list.append(str(i) + "%")
else:
new_list.append(i)
df[my_dict['new_column']] = pd.Series(new_list)
return df
except Exception as e:
logger.error(f"Failed to calculate {my_dict['new_column']} because of error {e}", exc_info=enable_traceback)
def A1A023_A1A024_to_E5E014_E5E003_E5E016_Molar_ratio(self, df):
my_dict = AppConstants.A1A023_A1A024_to_E5E014_E5E003_E5E016_Molar_ratio
try:
logger.info(f"Calculating the column {my_dict['new_column']}")
df[my_dict['new_column']] = (df[my_dict['dependant_column'][0]] * df[my_dict['dependant_column'][1]] * my_dict['constants'][0]) / (df[my_dict['dependant_column'][2]] * my_dict['constants'][1])/100
return df
except Exception as e:
logger.error(f"Failed to calculate {my_dict['new_column']} because of error {e}", exc_info=enable_traceback)
def A1A023_A1A024_to_Amm_Molar_ratio(self, df):
my_dict = AppConstants.A1A023_A1A024_to_Amm_Molar_ratio
try:
logger.info(f"Calculating the column {my_dict['new_column']}")
nr = ((df[my_dict['dependant_column'][0]] - df[my_dict['dependant_column'][1]]) * my_dict['constants'][0]) + df[my_dict['dependant_column'][1]]
df[my_dict['new_column']] = round((nr * my_dict['constants'][1]) / (df[my_dict['dependant_column'][2]] * my_dict['constants'][2]),2)
return df
except Exception as e:
logger.error(f"Failed to calculate {my_dict['new_column']} because of error {e}", exc_info=enable_traceback)
def E5E014_E5E003_E5E016_to_A1A023_A1A024_ratio_37(self, df):
my_dict = AppConstants.E5E014_E5E003_E5E016_to_A1A023_A1A024_ratio_37
try:
logger.info(f"Calculating the column {my_dict['new_column']}")
nr = ((df[my_dict['dependant_column'][0]])/df[my_dict['dependant_column'][1]]) * df[my_dict['dependant_column'][2]]
df[my_dict['new_column']] = nr / my_dict['constants'][0]
return df
except Exception as e:
logger.error(f"Failed to calculate {my_dict['new_column']} because of error {e}", exc_info=enable_traceback)
\ No newline at end of file
yield_report.py 0 → 100644
View file @ 4a33d5db
import os
import dask
import dask.dataframe as dd
import os
import sys
import traceback
import pandas as pd
import logging
from logging import StreamHandler
from yield_calculated_columns import CalculatedColumns
logger = logging.getLogger("YieldReport")
logger.setLevel(os.environ.get("logger", "INFO"))
__formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(module)s - %(lineno)d - %(message)s')
__console_handler = StreamHandler(sys.stdout)
__console_handler.setFormatter(__formatter)
logger.addHandler(__console_handler)
enable_traceback = False
class yield_report:
def __init__(self, path):
self.df = self.read_csv(path)
def read_csv(self, path):
"""
This function is used to read dataframe using dask
:param path: Path of CSV file
:return: Data Frame
"""
df = pd.read_csv(path)
return df
def add_calculate_columns(self):
cal_obj = CalculatedColumns()
try:
self.df = cal_obj.fresh_7302011030(self.df)
self.df = cal_obj.totaliser_7302011030(self.df)
self.df = cal_obj.calculated_7303011675(self.df)
self.df = cal_obj.calculated_IV_component(self.df)
self.df = cal_obj.calculated_E5E014_E5E003_E5E016(self.df)
self.df = cal_obj.total_7302011030(self.df)
self.df = cal_obj.fresh_div_total_7302011030(self.df)
self.df = cal_obj.A1A023_A1A024(self.df)
self.df = cal_obj.A1A023_A1A024_plus_7303011675_plus_IV_component(self.df)
self.df = cal_obj.PRODN_RXNPDT(self.df)
self.df = cal_obj.Calculated_Scrubber(self.df)
self.df = cal_obj.Calculated_MEE_to_RPT(self.df)
self.df = cal_obj.surge_tank_total(self.df)
self.df = cal_obj.make_up_to_mee_tank(self.df)
self.df = cal_obj.norm_surge_tank_stage(self.df)
self.df = cal_obj.norm_rpt_stage(self.df)
self.df = cal_obj.calculate_yield(self.df)
self.df = cal_obj.A1A023_A1A024_to_E5E014_E5E003_E5E016_Molar_ratio(self.df)
self.df = cal_obj.A1A023_A1A024_to_Amm_Molar_ratio(self.df)
self.df = cal_obj.E5E014_E5E003_E5E016_to_A1A023_A1A024_ratio_37(self.df)
return self.df
except Exception as e:
logger.error(f"failed to calculate because of error {e}", exc_info=enable_traceback)
if __name__ == '__main__':
my_obj = yield_report("test.csv")
df = my_obj.add_calculate_columns()
# df = df.compute()
df.to_csv("output.csv")
\ No newline at end of file
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