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All model filter are work and show bar chat and data table.

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Pankaj J Patil
2026-07-31 16:43:38 +05:30
parent 604e948986
commit 2d7d146ec3
3 changed files with 546 additions and 212 deletions
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app/services/comparison_service.py
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@@ -1,204 +1,204 @@
from collections import defaultdict
import pandas as pd
from app.utils.regex_utils import RegularExpression
# from collections import defaultdict
# import pandas as pd
# from app.utils.regex_utils import RegularExpression
class ComparisonService:
# class ComparisonService:
TRENCH_MAPPING = [
{
"label": "Marshi 0 to 1.5",
"client": "Client_Marshi_Muddy_Slushy_0_to_1_5_total",
"sub": None
},
{
"label": "Marshi 1.5 to 3.0",
"client": "Client_Marshi_Muddy_Slushy_1_5_to_3_0_total",
"sub": None
},
{
"label": "Marshi 3.0 to 4.5",
"client": "Client_Marshi_Muddy_Slushy_3_0_to_4_5_total",
"sub": None
},
{
"label": "Soft Murum 0 to 1.5",
"client": "Client_Soft_Murum_0_to_1_5_total",
"sub": "Sub_Soft_Murum_0_to_1_5_total"
},
{
"label": "Soft Murum 1.5 to 3.0",
"client": "Client_Soft_Murum_1_5_to_3_0_total",
"sub": "Sub_Soft_Murum_1_5_to_3_0_total"
},
{
"label": "Soft Murum 3.0 to 4.5",
"client": "Client_Soft_Murum_3_0_to_4_5_total",
"sub": "Sub_Soft_Murum_3_0_to_4_5_total"
},
{
"label": "Hard Murum 0 to 1.5",
"client": "Client_Hard_Murum_0_to_1_5_total",
"sub": "Sub_Hard_Murum_0_to_1_5_total"
},
{
"label": "Hard Murum 1.5+",
"client": "Client_Hard_Murum_1_5_to_3_0_total",
"sub": "Sub_Hard_Murum_1_5_and_above_total"
},
{
"label": "Soft Rock 0 to 1.5",
"client": "Client_Soft_Rock_0_to_1_5_total",
"sub": "Sub_Soft_Rock_0_to_1_5_total"
},
{
"label": "Soft Rock 1.5+",
"client": "Client_Soft_Rock_1_5_to_3_0_total",
"sub": "Sub_Soft_Rock_1_5_and_above_total"
},
{
"label": "Hard Rock 0 to 1.5",
"client": "Client_Hard_Rock_0_to_1_5_total",
"sub": "Sub_Hard_Rock_0_to_1_5_total"
},
{
"label": "Hard Rock 1.5 to 3.0",
"client": "Client_Hard_Rock_1_5_to_3_0_total",
"sub": "Sub_Hard_Rock_1_5_to_3_0_total"
},
{
"label": "Hard Rock 3.0 to 4.5",
"client": "Client_Hard_Rock_3_0_to_4_5_total",
"sub": "Sub_Hard_Rock_3_0_to_4_5_total"
},
{
"label": "Hard Rock 4.5 to 6.0",
"client": "Client_Hard_Rock_4_5_to_6_0_total",
"sub": "Sub_Hard_Rock_4_5_to_6_0_total"
},
{
"label": "Hard Rock 6.0 to 7.5",
"client": "Client_Hard_Rock_6_0_to_7_5_total",
"sub": "Sub_Hard_Rock_6_0_to_7_5_total"
}
]
# TRENCH_MAPPING = [
# {
# "label": "Marshi 0 to 1.5",
# "client": "Client_Marshi_Muddy_Slushy_0_to_1_5_total",
# "sub": None
# },
# {
# "label": "Marshi 1.5 to 3.0",
# "client": "Client_Marshi_Muddy_Slushy_1_5_to_3_0_total",
# "sub": None
# },
# {
# "label": "Marshi 3.0 to 4.5",
# "client": "Client_Marshi_Muddy_Slushy_3_0_to_4_5_total",
# "sub": None
# },
# {
# "label": "Soft Murum 0 to 1.5",
# "client": "Client_Soft_Murum_0_to_1_5_total",
# "sub": "Sub_Soft_Murum_0_to_1_5_total"
# },
# {
# "label": "Soft Murum 1.5 to 3.0",
# "client": "Client_Soft_Murum_1_5_to_3_0_total",
# "sub": "Sub_Soft_Murum_1_5_to_3_0_total"
# },
# {
# "label": "Soft Murum 3.0 to 4.5",
# "client": "Client_Soft_Murum_3_0_to_4_5_total",
# "sub": "Sub_Soft_Murum_3_0_to_4_5_total"
# },
# {
# "label": "Hard Murum 0 to 1.5",
# "client": "Client_Hard_Murum_0_to_1_5_total",
# "sub": "Sub_Hard_Murum_0_to_1_5_total"
# },
# {
# "label": "Hard Murum 1.5+",
# "client": "Client_Hard_Murum_1_5_to_3_0_total",
# "sub": "Sub_Hard_Murum_1_5_and_above_total"
# },
# {
# "label": "Soft Rock 0 to 1.5",
# "client": "Client_Soft_Rock_0_to_1_5_total",
# "sub": "Sub_Soft_Rock_0_to_1_5_total"
# },
# {
# "label": "Soft Rock 1.5+",
# "client": "Client_Soft_Rock_1_5_to_3_0_total",
# "sub": "Sub_Soft_Rock_1_5_and_above_total"
# },
# {
# "label": "Hard Rock 0 to 1.5",
# "client": "Client_Hard_Rock_0_to_1_5_total",
# "sub": "Sub_Hard_Rock_0_to_1_5_total"
# },
# {
# "label": "Hard Rock 1.5 to 3.0",
# "client": "Client_Hard_Rock_1_5_to_3_0_total",
# "sub": "Sub_Hard_Rock_1_5_to_3_0_total"
# },
# {
# "label": "Hard Rock 3.0 to 4.5",
# "client": "Client_Hard_Rock_3_0_to_4_5_total",
# "sub": "Sub_Hard_Rock_3_0_to_4_5_total"
# },
# {
# "label": "Hard Rock 4.5 to 6.0",
# "client": "Client_Hard_Rock_4_5_to_6_0_total",
# "sub": "Sub_Hard_Rock_4_5_to_6_0_total"
# },
# {
# "label": "Hard Rock 6.0 to 7.5",
# "client": "Client_Hard_Rock_6_0_to_7_5_total",
# "sub": "Sub_Hard_Rock_6_0_to_7_5_total"
# }
# ]
@staticmethod
def normalize_key(value):
if value is None:
return ""
return str(value).strip().upper()
# @staticmethod
# def normalize_key(value):
# if value is None:
# return ""
# return str(value).strip().upper()
@classmethod
def make_lookup(cls, rows, key_field):
"""
Create lookup dictionary using:
(Location, MH_NO)
"""
# @classmethod
# def make_lookup(cls, rows, key_field):
# """
# Create lookup dictionary using:
# (Location, MH_NO)
# """
lookup = defaultdict(list)
# lookup = defaultdict(list)
for row in rows:
# for row in rows:
location = cls.normalize_key(row.get("Location"))
key = cls.normalize_key(row.get(key_field))
# location = cls.normalize_key(row.get("Location"))
# key = cls.normalize_key(row.get(key_field))
if location and key:
lookup[(location, key)].append(row)
# if location and key:
# lookup[(location, key)].append(row)
return lookup
# return lookup
@classmethod
def build_comparison(cls, client_rows, subcontractor_rows, key_field="MH_NO"):
# @classmethod
# def build_comparison(cls, client_rows, subcontractor_rows, key_field="MH_NO"):
subcontractor_lookup = cls.make_lookup(
subcontractor_rows,
key_field
)
# subcontractor_lookup = cls.make_lookup(
# subcontractor_rows,
# key_field
# )
used = defaultdict(int)
# used = defaultdict(int)
output = []
# output = []
for client in client_rows:
# for client in client_rows:
location = cls.normalize_key(client.get("Location"))
key = cls.normalize_key(client.get(key_field))
# location = cls.normalize_key(client.get("Location"))
# key = cls.normalize_key(client.get(key_field))
if not location or not key:
continue
# if not location or not key:
# continue
rows = subcontractor_lookup.get((location, key))
# rows = subcontractor_lookup.get((location, key))
if not rows:
continue
# if not rows:
# continue
index = used[(location, key)]
# index = used[(location, key)]
if index >= len(rows):
continue
# if index >= len(rows):
# continue
subcontractor = rows[index]
# subcontractor = rows[index]
used[(location, key)] += 1
# used[(location, key)] += 1
client_total = sum(
float(v or 0)
for k, v in client.items()
if k.endswith("_total")
or RegularExpression.D_RANGE_PATTERN.match(k)
or RegularExpression.PIPE_MM_PATTERN.match(k)
)
# client_total = sum(
# float(v or 0)
# for k, v in client.items()
# if k.endswith("_total")
# or RegularExpression.D_RANGE_PATTERN.match(k)
# or RegularExpression.PIPE_MM_PATTERN.match(k)
# )
subcontractor_total = sum(
float(v or 0)
for k, v in subcontractor.items()
if k.endswith("_total")
or RegularExpression.D_RANGE_PATTERN.match(k)
or RegularExpression.PIPE_MM_PATTERN.match(k)
)
# subcontractor_total = sum(
# float(v or 0)
# for k, v in subcontractor.items()
# if k.endswith("_total")
# or RegularExpression.D_RANGE_PATTERN.match(k)
# or RegularExpression.PIPE_MM_PATTERN.match(k)
# )
row = {
# row = {
"Location": location,
# "Location": location,
key_field: key,
# key_field: key,
"Client_Total": round(client_total, 2),
# "Client_Total": round(client_total, 2),
"Subcontractor_Total": round(subcontractor_total, 2),
# "Subcontractor_Total": round(subcontractor_total, 2),
"Difference": round(
client_total - subcontractor_total,
2
)
}
# "Difference": round(
# client_total - subcontractor_total,
# 2
# )
# }
# Client Columns
for column, value in client.items():
# # Client Columns
# for column, value in client.items():
if column in [
"id",
"created_at"
]:
continue
# if column in [
# "id",
# "created_at"
# ]:
# continue
row[f"Client_{column}"] = value
# row[f"Client_{column}"] = value
# Subcontractor Columns
for column, value in subcontractor.items():
# # Subcontractor Columns
# for column, value in subcontractor.items():
if column in [
"id",
"created_at",
"subcontractor_id"
]:
continue
# if column in [
# "id",
# "created_at",
# "subcontractor_id"
# ]:
# continue
row[f"Sub_{column}"] = value
# row[f"Sub_{column}"] = value
output.append(row)
# output.append(row)
return pd.DataFrame(output)
# return pd.DataFrame(output)