222 lines
8.4 KiB
Python
222 lines
8.4 KiB
Python
# -*- coding: utf-8 -*-
|
|
import os
|
|
import datetime
|
|
import logging
|
|
|
|
import pandas as pd
|
|
from dateutil.relativedelta import relativedelta
|
|
|
|
L = logging.getLogger(__name__)
|
|
|
|
|
|
class AttributionReport(object):
|
|
def __init__(self, months=6, footer_length=None):
|
|
self.months = months
|
|
self.footer_length = footer_length
|
|
|
|
self.SF_DATE_COLUMN = "Date"
|
|
self.DP_DATE_COLUMN = "Date Received"
|
|
self.PI_COLUMN = "PI_Name"
|
|
self.ORG_COLUMN = "Org Name"
|
|
# Output the XLSX in this order
|
|
|
|
self.OUTPUT_COLUMN_ORDER = ["Addgene Assigned", "Plasmid ID", "Deposit ID", "Institute", "PI Name",
|
|
"Date Received", "Original Date", "Original ORG", "Original PI"]
|
|
|
|
self.ACCEPTABLE_EXTENSIONS = ["*.csv", "*.xls", "*.xlsx"]
|
|
|
|
# columns that need to be in the files
|
|
self.REQUIRED_SF_COLUMNS = ["First Name", "Last Name", "Account Name", "Date", "Assigned"]
|
|
self.REQUIRED_DP_COLUMNS = ["Org Name", "Deposit ID", "Plasmid ID", "PI_Name", "Date Received"]
|
|
|
|
# After load and merging, delete these columns
|
|
self.SF_TRIM_COLUMNS = ["Subject", "Created Date", "LIMS Organization ID",
|
|
"Account Description"]
|
|
self.DP_TRIM_COLUMNS = ["Org ID", "Deposit Status", "PI_ID", "Date Available", "# Orders",
|
|
"# Plasmids in the Deposit", "Addgene Contact", "Country"]
|
|
|
|
self.salesforce_df = None
|
|
self.deposit_df = None
|
|
self.output_dir = None
|
|
self.frames = None
|
|
|
|
def _get_dataframe_by_extension(self, path, date_cols):
|
|
"""
|
|
Gets a dataframe either by .csv, or .xls(x),
|
|
or erroring and exiting.
|
|
"""
|
|
_, ext = os.path.splitext(path)
|
|
|
|
if ext == ".csv":
|
|
df = pd.read_csv(path, parse_dates=date_cols, encoding='utf-8')
|
|
elif ext in [".xlsx", ".xls"]:
|
|
df = pd.read_excel(path, parse_dates=date_cols, encoding='utf-8')
|
|
else:
|
|
raise Exception("File was not of type {0}.\nQuitting".format(
|
|
" ".join(self.ACCEPTABLE_EXTENSIONS)))
|
|
return df
|
|
|
|
def set_dataframe_sf(self, fname):
|
|
self.salesforce_df = None
|
|
try:
|
|
salesforce_df = self._get_dataframe_by_extension(fname, date_cols=[self.SF_DATE_COLUMN, ])
|
|
except IndexError:
|
|
return False
|
|
except ValueError:
|
|
return False
|
|
except:
|
|
raise
|
|
|
|
if set(self.REQUIRED_SF_COLUMNS) < set(salesforce_df.columns):
|
|
self.salesforce_df = salesforce_df
|
|
return True
|
|
L.info("Wrong columns")
|
|
return False
|
|
|
|
def set_dataframe_deposit(self, fname):
|
|
self.deposit_df = None
|
|
try:
|
|
deposit_df = self._get_dataframe_by_extension(fname, date_cols=[self.DP_DATE_COLUMN, ])
|
|
except IndexError:
|
|
return False
|
|
except ValueError:
|
|
return False
|
|
except:
|
|
raise
|
|
if set(self.REQUIRED_DP_COLUMNS) < set(deposit_df.columns):
|
|
self.deposit_df = deposit_df
|
|
return True
|
|
L.info("Wrong columns")
|
|
return False
|
|
|
|
def set_output_dir(self, dir):
|
|
self.output_dir = dir
|
|
|
|
def get_dataframes(self):
|
|
salesforce_df, deposit_df = self.clean_dataframes()
|
|
return salesforce_df, deposit_df
|
|
|
|
def clean_dataframes(self):
|
|
# Get rid of the footer that Salesforce adds.
|
|
if self.footer_length:
|
|
length_with_footer = len(self.salesforce_df.index)
|
|
self.salesforce_df = self.salesforce_df.head(length_with_footer - self.footer_length)
|
|
|
|
# Clean up Salesforce
|
|
self.salesforce_df.sort(self.SF_DATE_COLUMN, ascending=1)
|
|
|
|
# Cleanup Deposit Data
|
|
self.deposit_df['Org Name'].fillna('', inplace=True)
|
|
self.deposit_df.sort(self.DP_DATE_COLUMN, ascending=1)
|
|
self.deposit_df['PI_Name'].astype(unicode)
|
|
|
|
# Cleanup not needed columns
|
|
for col in self.SF_TRIM_COLUMNS:
|
|
del self.salesforce_df[col]
|
|
for col in self.DP_TRIM_COLUMNS:
|
|
del self.deposit_df[col]
|
|
|
|
def get_filtered(self, filtered_df, sf_row, pi_name, pi_org, org=False):
|
|
"""
|
|
Assume kind is PI by default.
|
|
Filter where either the PI and PI match, or the Org and Org match
|
|
If both match, add it to the the double list
|
|
if only one matches, add it to the single list.
|
|
"""
|
|
filter_column = self.PI_COLUMN
|
|
filter_value = pi_name
|
|
single, double = [], []
|
|
|
|
if org:
|
|
filter_column = self.ORG_COLUMN
|
|
filter_value = pi_org
|
|
|
|
name_match = filtered_df[filtered_df[filter_column] == filter_value]
|
|
|
|
if not name_match.empty:
|
|
for _, row in name_match.iterrows():
|
|
data = {
|
|
"Addgene Assigned": sf_row['Assigned'],
|
|
"Plasmid ID": row['Plasmid ID'],
|
|
"Deposit ID": row['Deposit ID'],
|
|
"Institute": row['Org Name'],
|
|
"PI Name": row['PI_Name'],
|
|
"Date Received": row[self.DP_DATE_COLUMN],
|
|
"Original Date": sf_row[self.SF_DATE_COLUMN],
|
|
"Original ORG": pi_org,
|
|
"Original PI": pi_name,
|
|
}
|
|
if (data['Institute'] == data['Original ORG']) and \
|
|
(data['PI Name'] == data['Original PI']):
|
|
double.append(data)
|
|
else:
|
|
single.append(data)
|
|
return single, double
|
|
|
|
def get_attribution_dataframes(self):
|
|
self.clean_dataframes()
|
|
|
|
name_matches = []
|
|
org_matches = []
|
|
double_matches = []
|
|
mismatches = []
|
|
|
|
# Iterate through the Salesforce report as the master document
|
|
for index, sf_row in self.salesforce_df.iterrows():
|
|
# Get a start date and an end date for filtering.
|
|
start_date = sf_row[self.SF_DATE_COLUMN]
|
|
end_date = start_date + relativedelta(months=self.months)
|
|
|
|
start = self.deposit_df[self.DP_DATE_COLUMN].searchsorted(start_date)[0]
|
|
end = self.deposit_df[self.DP_DATE_COLUMN].searchsorted(end_date)[0]
|
|
|
|
# Filter the deposit data to grab only things within that timeframe.
|
|
filtered_df = self.deposit_df.ix[start:end]
|
|
|
|
# Variables for short names, and not having to type index a lot.
|
|
pi_name = unicode(sf_row['First Name']) + " " + unicode(sf_row['Last Name'])
|
|
pi_org = sf_row['Account Name']
|
|
|
|
# Get matches by the PI's name
|
|
by_name, pi_by_both = self.get_filtered(filtered_df, sf_row, pi_name, pi_org)
|
|
name_matches.extend(by_name)
|
|
mismatches.extend(by_name)
|
|
double_matches.extend(pi_by_both)
|
|
|
|
# Get matches by the organization name
|
|
by_org, org_by_both = self.get_filtered(filtered_df, sf_row, pi_name, pi_org, org=True)
|
|
org_matches.extend(by_org)
|
|
mismatches.extend(by_org)
|
|
double_matches.extend(org_by_both)
|
|
|
|
return (
|
|
("PI", pd.DataFrame(name_matches, columns=self.OUTPUT_COLUMN_ORDER)),
|
|
("Institute", pd.DataFrame(org_matches, columns=self.OUTPUT_COLUMN_ORDER)),
|
|
("Double", pd.DataFrame(double_matches, columns=self.OUTPUT_COLUMN_ORDER)),
|
|
("Single", pd.DataFrame(mismatches, columns=self.OUTPUT_COLUMN_ORDER))
|
|
)
|
|
|
|
def run(self):
|
|
self.frames = None
|
|
self.frames = self.get_attribution_dataframes()
|
|
|
|
def save(self):
|
|
for key, df in self.frames:
|
|
fname = '{0}_Attribution_Report_{1}_Match.xlsx'.format(datetime.date.today(), key)
|
|
|
|
output_path = os.path.join(self.output_dir, fname)
|
|
|
|
deduped_df = df.drop_duplicates()
|
|
|
|
with pd.ExcelWriter(output_path, engine='xlsxwriter') as writer:
|
|
deduped_df.to_excel(writer, sheet_name='Sheet1', index=False)
|
|
|
|
if __name__ == '__main__':
|
|
app = AttributionReport(months=6, footer_length=6)
|
|
app.set_dataframe_deposit("/Users/tyrelsouza/Dropbox (Addgene)/Addgene Shared/Dev/Attribution Report/deposit_data.csv")
|
|
app.set_dataframe_sf("/Users/tyrelsouza/Dropbox (Addgene)/Addgene Shared/Dev/Attribution Report/salesforce_report.xlsx")
|
|
app.set_output_dir("/Users/tyrelsouza/Dropbox (Addgene)/Addgene Shared/Dev/Attribution Report/Output/")
|
|
app.run()
|
|
app.save()
|
|
|