76 lines
2.8 KiB
Python
76 lines
2.8 KiB
Python
# Python imports
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from itertools import groupby
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# Django import
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from django.db import models
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from django.db.models import Count, F, Sum, Value, Case, When, CharField
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from django.db.models.functions import Coalesce, ExtractMonth, ExtractYear, Concat
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def build_graph_plot(queryset, x_axis, y_axis, segment=None):
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temp_axis = x_axis
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if x_axis in ["created_at", "start_date", "target_date", "completed_at"]:
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year = ExtractYear(x_axis)
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month = ExtractMonth(x_axis)
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dimension = Concat(year, Value("-"), month, output_field=CharField())
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queryset = queryset.annotate(dimension=dimension)
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x_axis = "dimension"
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else:
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queryset = queryset.annotate(dimension=F(x_axis))
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x_axis = "dimension"
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if x_axis in ["created_at", "start_date", "target_date", "completed_at"]:
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queryset = queryset.exclude(x_axis__is_null=True)
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if segment in ["created_at", "start_date", "target_date", "completed_at"]:
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year = ExtractYear(segment)
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month = ExtractMonth(segment)
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dimension = Concat(year, Value("-"), month, output_field=CharField())
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queryset = queryset.annotate(segmented=dimension)
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segment = "segmented"
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queryset = queryset.values(x_axis)
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# Group queryset by x_axis field
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if y_axis == "issue_count":
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queryset = queryset.annotate(
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is_null=Case(
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When(dimension__isnull=True, then=Value("None")),
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default=Value("not_null"),
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output_field=models.CharField(max_length=8),
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),
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dimension_ex=Coalesce("dimension", Value("null")),
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).values("dimension")
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if segment:
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queryset = queryset.annotate(segment=F(segment)).values(
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"dimension", "segment"
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)
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else:
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queryset = queryset.values("dimension")
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queryset = queryset.annotate(count=Count("*")).order_by("dimension")
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if y_axis == "estimate":
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queryset = queryset.annotate(estimate=Sum("estimate_point")).order_by(x_axis)
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if segment:
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queryset = queryset.annotate(segment=F(segment)).values(
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"dimension", "segment", "estimate"
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)
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else:
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queryset = queryset.values("dimension", "estimate")
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result_values = list(queryset)
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grouped_data = {}
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for key, items in groupby(result_values, key=lambda x: x[str("dimension")]):
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grouped_data[str(key)] = list(items)
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sorted_data = grouped_data
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if temp_axis == "priority":
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order = ["low", "medium", "high", "urgent", "None"]
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sorted_data = {key: grouped_data[key] for key in order if key in grouped_data}
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else:
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sorted_data = dict(sorted(grouped_data.items(), key=lambda x: (x[0] == "None", x[0])))
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return sorted_data
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