308 lines
11 KiB
Python
308 lines
11 KiB
Python
from flask import Blueprint, render_template, request, send_file, flash
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from app.models.subcontractor_model import Subcontractor
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from app.models.manhole_excavation_model import ManholeExcavation
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from app.models.trench_excavation_model import TrenchExcavation
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from app.models.manhole_domestic_chamber_model import ManholeDomesticChamber
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from app.models.mh_ex_client_model import ManholeExcavationClient
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from app.models.tr_ex_client_model import TrenchExcavationClient
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from app.models.mh_dc_client_model import ManholeDomesticChamberClient
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from app import db
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import pandas as pd
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import io
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from enum import Enum
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file_report_bp = Blueprint("file_report", __name__, url_prefix="/file")
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class BillType(Enum):
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Client = 1
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Subcontractor = 2
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class SubcontractorBill:
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df_tr = []
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df_mh = []
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df_dc =[]
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def Fetch(self, RA_Bill_No):
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# CLIENT DATA (RA BILL WISE)
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trench = TrenchExcavation.query.filter_by(RA_Bill_No=RA_Bill_No).all()
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mh = ManholeExcavation.query.filter_by(RA_Bill_No=RA_Bill_No).all()
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dc = ManholeDomesticChamber.query.filter_by(RA_Bill_No=RA_Bill_No).all()
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self.df_tr = pd.DataFrame([c.__dict__ for c in trench])
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self.df_mh = pd.DataFrame([c.__dict__ for c in mh])
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self.df_dc = pd.DataFrame([c.__dict__ for c in dc])
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# CLEAN COLUMNS
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drop_cols = ["id","created_at","Remarks","_sa_instance_state"]
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for df in [self.df_tr, self.df_mh, self.df_dc]:
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if not df.empty:
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df.drop(columns=drop_cols, errors="ignore", inplace=True)
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mh_exc_columns = [
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"Location", "MH_NO", "Upto_IL_Depth", "Cutting_Depth", "ID_of_MH_m",
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"Ex_Dia_of_Manhole", "Area_of_Manhole",
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"Soft_Murum_0_to_1_5", "Soft_Murum_1_5_to_3_0", "Soft_Murum_3_0_to_4_5",
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"Hard_Murum_0_to_1_5", "Hard_Murum_1_5_to_3_0",
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"Soft_Rock_0_to_1_5", "Soft_Rock_1_5_to_3_0",
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"Hard_Rock_0_to_1_5", "Hard_Rock_1_5_to_3_0",
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"Hard_Rock_3_0_to_4_5", "Hard_Rock_4_5_to_6_0", "Hard_Rock_6_0_to_7_5",
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"Soft_Murum_0_to_1_5_total", "Soft_Murum_1_5_to_3_0_total",
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"Soft_Murum_3_0_to_4_5_total",
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"Hard_Murum_0_to_1_5_total", "Hard_Murum_1_5_and_above_total",
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"Soft_Rock_0_to_1_5_total", "Soft_Rock_1_5_and_above_total",
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"Hard_Rock_0_to_1_5_total", "Hard_Rock_1_5_and_above_total",
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"Hard_Rock_3_0_to_4_5_total", "Hard_Rock_4_5_to_6_0_total",
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"Hard_Rock_6_0_to_7_5_total",
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"Remarks", "Total"
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]
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trench_columns = [
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"Location", "MH_NO", "CC_length", "Invert_Level", "MH_Top_Level",
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"Ground_Level", "ID_of_MH_m", "Actual_Trench_Length", "Pipe_Dia_mm",
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"Width_0_to_2_5", "Width_2_5_to_3_0", "Width_3_0_to_4_5", "Width_4_5_to_6_0",
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"Upto_IL_Depth", "Cutting_Depth", "Avg_Depth",
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"Soft_Murum_0_to_1_5", "Soft_Murum_1_5_to_3_0", "Soft_Murum_3_0_to_4_5",
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"Hard_Murum_0_to_1_5", "Hard_Murum_1_5_to_3_0",
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"Soft_Rock_0_to_1_5", "Soft_Rock_1_5_to_3_0",
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"Hard_Rock_0_to_1_5", "Hard_Rock_1_5_to_3_0",
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"Hard_Rock_3_0_to_4_5", "Hard_Rock_4_5_to_6_0", "Hard_Rock_6_0_to_7_5",
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"Soft_Murum_0_to_1_5_total", "Soft_Murum_1_5_to_3_0_total",
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"Soft_Murum_3_0_to_4_5_total",
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"Hard_Murum_0_to_1_5_total", "Hard_Murum_1_5_and_above_total",
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"Soft_Rock_0_to_1_5_total", "Soft_Rock_1_5_and_above_total",
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"Hard_Rock_0_to_1_5_total", "Hard_Rock_1_5_and_above_total",
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"Hard_Rock_3_0_to_4_5_total", "Hard_Rock_4_5_to_6_0_total",
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"Hard_Rock_6_0_to_7_5_total",
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"Remarks", "Total"
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]
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domestic_columns = [
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"Location", "Node_No", "Depth_of_MH",
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"d_0_to_0_75", "d_1_06_to_1_65", "d_1_66_to_2_15",
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"d_2_16_to_2_65", "d_2_66_to_3_15", "d_3_16_to_3_65",
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"d_3_66_to_4_15", "d_4_16_to_4_65", "d_4_66_to_5_15",
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"d_5_16_to_5_65", "d_5_66_to_6_15", "d_6_16_to_6_65",
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"d_6_66_to_7_15", "d_7_16_to_7_65", "d_7_66_to_8_15",
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"d_8_16_to_8_65", "d_8_66_to_9_15", "d_9_16_to_9_65",
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"Domestic_Chambers"
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]
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# Reorder columns serial wise
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df_mh_exc = self.df_mh.reindex(columns=mh_exc_columns, fill_value="")
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df_trench = self.df_tr.reindex(columns=trench_columns, fill_value="")
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df_domestic = self.df_dc.reindex(columns=domestic_columns, fill_value="")
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# get report by contractor id and dowanload
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@file_report_bp.route("/report", methods=["GET", "POST"])
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def report_file():
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subcontractors = Subcontractor.query.all()
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if request.method == "POST":
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subcontractor_id = request.form.get("subcontractor_id")
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if not subcontractor_id:
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flash("Please select a subcontractor.", "danger")
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return render_template("report.html", subcontractors=subcontractors)
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# Fetch subcontractor for file name
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subcontractor = Subcontractor.query.get(subcontractor_id)
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subcontractorBill = SubcontractorBill()
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# WRITE EXCEL FILE
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output = io.BytesIO()
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file_name = f"{subcontractor.subcontractor_name}_Report.xlsx"
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with pd.ExcelWriter(output, engine="xlsxwriter") as writer:
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subcontractorBill.df_tr.to_excel(writer, index=False, sheet_name="Tr.Ex.")
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subcontractorBill.df_mh.to_excel(writer, index=False, sheet_name="MH.Ex.")
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subcontractorBill.df_dc.to_excel(writer, index=False, sheet_name="MH & DC")
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output.seek(0)
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return send_file(
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output,
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download_name=file_name,
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as_attachment=True,
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mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
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)
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return render_template("report.html", subcontractors=subcontractors)
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class ClientBill:
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df_tr = []
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df_mh = []
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df_dc =[]
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def Fetch(self, RA_Bill_No):
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# CLIENT DATA (RA BILL WISE)
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trench = TrenchExcavationClient.query.filter_by(RA_Bill_No=RA_Bill_No).all()
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mh = ManholeExcavationClient.query.filter_by(RA_Bill_No=RA_Bill_No).all()
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dc = ManholeDomesticChamberClient.query.filter_by(RA_Bill_No=RA_Bill_No).all()
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self.df_tr = pd.DataFrame([c.__dict__ for c in trench])
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self.df_mh = pd.DataFrame([c.__dict__ for c in mh])
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self.df_dc = pd.DataFrame([c.__dict__ for c in dc])
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# CLEAN COLUMNS
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drop_cols = ["id","created_at","Remarks","_sa_instance_state"]
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for df in [self.df_tr, self.df_mh, self.df_dc]:
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if not df.empty:
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df.drop(columns=drop_cols, errors="ignore", inplace=True)
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@file_report_bp.route("/client_vs_subcont", methods=["GET", "POST"])
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def client_vs_all_subcontractor():
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if request.method == "POST":
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RA_Bill_No = request.form.get("RA_Bill_No")
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if not RA_Bill_No:
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flash("Please select RA Bill No.", "danger")
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return render_template("generate_comparison_client_vs_subcont.html")
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clientBill = ClientBill()
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clientBill.Fetch(RA_Bill_No=RA_Bill_No)
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contractorBill = SubcontractorBill()
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contractorBill.Fetch(RA_Bill_No=RA_Bill_No)
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# GROUP SUBCONTRACTOR DATA
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qty_cols = [
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"Soft_Murum_0_to_1_5_total",
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"Soft_Murum_1_5_to_3_0_total",
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"Soft_Murum_3_0_to_4_5_total",
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"Hard_Murum_0_to_1_5_total",
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"Hard_Murum_1_5_and_above_total",
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"Soft_Rock_0_to_1_5_total",
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"Soft_Rock_1_5_and_above_total",
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"Hard_Rock_0_to_1_5_total",
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"Hard_Rock_1_5_to_3_0_total",
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"Hard_Rock_3_0_to_4_5_total",
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"Hard_Rock_4_5_to_6_0_total",
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"Hard_Rock_6_0_to_7_5_total"
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]
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mh_dc_qty_cols = [
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"d_0_to_0_75",
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"d_0_76_to_1_05",
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"d_1_06_to_1_65",
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"d_1_66_to_2_15",
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"d_2_16_to_2_65",
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"d_2_66_to_3_15",
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"d_3_16_to_3_65",
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"d_3_66_to_4_15",
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"d_4_16_to_4_65",
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"d_4_66_to_5_15",
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"d_5_16_to_5_65",
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"d_5_66_to_6_15",
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"d_6_16_to_6_65",
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"d_6_66_to_7_15",
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"d_7_16_to_7_65",
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"d_7_66_to_8_15",
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"d_8_16_to_8_65",
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"d_8_66_to_9_15",
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"d_9_16_to_9_65",
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"Domestic_Chambers"
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]
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df_sub_tr_grp = (contractorBill.df_tr.groupby(["Location", "MH_NO"], as_index=False)[qty_cols].sum())
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df_sub_mh_grp = (contractorBill.df_mh.groupby(["Location", "MH_NO"], as_index=False)[qty_cols].sum())
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df_sub_dc_grp = (contractorBill.df_dc.groupby(["Location", "Node_No"], as_index=False)[mh_dc_qty_cols].sum())
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# MERGE CLIENT VS SUBCONTRACTOR
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df_tr_cmp = clientBill.df_tr.merge(
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df_sub_tr_grp,
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on=["Location", "MH_NO"],
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how="left",
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suffixes=("_Client", "_Sub")
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)
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df_mh_cmp = clientBill.df_mh.merge(
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df_sub_mh_grp,
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on=["Location", "MH_NO"],
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how="left",
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suffixes=("_Client", "_Sub")
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)
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if "MH_NO" in clientBill.df_dc.columns:
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clientBill.df_dc.rename(columns={"MH_NO": "Node_No"}, inplace=True)
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df_dc_cmp = clientBill.df_dc.merge(
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df_sub_dc_grp,
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on=["Location", "Node_No"],
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how="left",
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suffixes=("_Client", "_Sub")
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)
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# CALCULATE DIFFERENCE
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for col in qty_cols:
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if f"{col}_Client" in df_tr_cmp.columns:
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df_tr_cmp[f"{col}_Diff"] = (
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df_tr_cmp[f"{col}_Client"].fillna(0)
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- df_tr_cmp[f"{col}_Sub"].fillna(0)
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)
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print("Sum of df_tr_cmp::",df_tr_cmp)
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df_mh_cmp[f"{col}_Diff"] = (
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df_mh_cmp[f"{col}_Client"].fillna(0)
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- df_mh_cmp[f"{col}_Sub"].fillna(0)
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)
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print("Sum of df_mh_cmp::",df_mh_cmp)
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df_dc_cmp["Domestic_Chambers_Diff"] = (
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df_dc_cmp["Domestic_Chambers_Client"].fillna(0)
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- df_dc_cmp["Domestic_Chambers_Sub"].fillna(0)
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)
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# EXPORT EXCEL
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output = io.BytesIO()
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file_name = f"Client_vs_All_Subcontractor_RA_{RA_Bill_No}.xlsx"
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with pd.ExcelWriter(output, engine="xlsxwriter") as writer:
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df_tr_cmp.to_excel(writer, sheet_name="Tr.Ex", index=False)
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df_mh_cmp.to_excel(writer, sheet_name="Mh.Ex", index=False)
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df_dc_cmp.to_excel(writer, sheet_name="MH & DC", index=False)
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output.seek(0)
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return send_file(
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output,
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download_name=file_name,
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as_attachment=True,
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mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
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)
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return render_template("generate_comparison_client_vs_subcont.html")
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