mirror of
https://github.com/makeplane/plane
synced 2024-06-14 14:31:34 +00:00
147 lines
4.9 KiB
Python
147 lines
4.9 KiB
Python
# Python imports
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from itertools import groupby
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from datetime import timedelta
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# Django import
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from django.db import models
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from django.db.models.functions import TruncDate
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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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# Module imports
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from plane.db.models import Issue
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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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def burndown_plot(queryset, slug, project_id, cycle_id=None, module_id=None):
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# Total Issues in Cycle or Module
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total_issues = queryset.total_issues
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if cycle_id:
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# Get all dates between the two dates
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date_range = [
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queryset.start_date + timedelta(days=x)
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for x in range((queryset.end_date - queryset.start_date).days + 1)
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]
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chart_data = {str(date): 0 for date in date_range}
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completed_issues_distribution = (
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Issue.issue_objects.filter(
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workspace__slug=slug,
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project_id=project_id,
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issue_cycle__cycle_id=cycle_id,
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)
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.annotate(date=TruncDate("completed_at"))
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.values("date")
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.annotate(total_completed=Count("id"))
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.values("date", "total_completed")
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.order_by("date")
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)
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if module_id:
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# Get all dates between the two dates
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date_range = [
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queryset.start_date + timedelta(days=x)
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for x in range((queryset.target_date - queryset.start_date).days + 1)
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]
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chart_data = {str(date): 0 for date in date_range}
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completed_issues_distribution = (
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Issue.issue_objects.filter(
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workspace__slug=slug,
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project_id=project_id,
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issue_module__module_id=module_id,
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)
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.annotate(date=TruncDate("completed_at"))
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.values("date")
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.annotate(total_completed=Count("id"))
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.values("date", "total_completed")
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.order_by("date")
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)
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for date in date_range:
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cumulative_pending_issues = total_issues
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total_completed = 0
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total_completed = sum(
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[
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item["total_completed"]
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for item in completed_issues_distribution
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if item["date"] is not None and item["date"] <= date
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]
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)
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cumulative_pending_issues -= total_completed
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chart_data[str(date)] = cumulative_pending_issues
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return chart_data |