d168fd4bfa
* fix: workspace draft issues * chore: changed the timezone key * chore: migration changes
270 lines
8.7 KiB
Python
270 lines
8.7 KiB
Python
# Python imports
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from datetime import timedelta
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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 (
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Case,
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CharField,
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Count,
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F,
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Sum,
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Value,
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When,
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FloatField,
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)
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from django.db.models.functions import (
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Coalesce,
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Concat,
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ExtractMonth,
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ExtractYear,
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TruncDate,
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Cast,
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)
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from django.utils import timezone
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# Module imports
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from plane.db.models import Issue, Project
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def annotate_with_monthly_dimension(queryset, field_name, attribute):
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# Get the year and the months
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year = ExtractYear(field_name)
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month = ExtractMonth(field_name)
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# Concat the year and month
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dimension = Concat(year, Value("-"), month, output_field=CharField())
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# Annotate the dimension
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return queryset.annotate(**{attribute: dimension})
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def extract_axis(queryset, x_axis):
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# Format the dimension when the axis is in date
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if x_axis in ["created_at", "start_date", "target_date", "completed_at"]:
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queryset = annotate_with_monthly_dimension(
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queryset, x_axis, "dimension"
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)
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return queryset, "dimension"
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else:
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return queryset.annotate(dimension=F(x_axis)), "dimension"
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def sort_data(data, temp_axis):
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# When the axis is in priority order by
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if temp_axis == "priority":
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order = ["low", "medium", "high", "urgent", "none"]
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return {key: data[key] for key in order if key in data}
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else:
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return dict(sorted(data.items(), key=lambda x: (x[0] == "none", x[0])))
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def build_graph_plot(queryset, x_axis, y_axis, segment=None):
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# temp x_axis
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temp_axis = x_axis
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# Extract the x_axis and queryset
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queryset, x_axis = extract_axis(queryset, x_axis)
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if x_axis == "dimension":
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queryset = queryset.exclude(dimension__isnull=True)
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#
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if segment in ["created_at", "start_date", "target_date", "completed_at"]:
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queryset = annotate_with_monthly_dimension(
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queryset, segment, "segmented"
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)
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segment = "segmented"
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queryset = queryset.values(x_axis)
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# Issue count
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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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queryset = (
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queryset.annotate(segment=F(segment)) if segment else queryset
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)
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queryset = (
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queryset.values("dimension", "segment")
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if segment
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else queryset.values("dimension")
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)
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queryset = queryset.annotate(count=Count("*")).order_by("dimension")
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# Estimate
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else:
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queryset = queryset.annotate(
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estimate=Sum(Cast("estimate_point__value", FloatField()))
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).order_by(x_axis)
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queryset = (
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queryset.annotate(segment=F(segment)) if segment else queryset
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)
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queryset = (
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queryset.values("dimension", "segment", "estimate")
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if segment
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else queryset.values("dimension", "estimate")
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)
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result_values = list(queryset)
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grouped_data = {
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str(key): list(items)
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for key, items in groupby(
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result_values, key=lambda x: x[str("dimension")]
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)
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}
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return sort_data(grouped_data, temp_axis)
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def burndown_plot(
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queryset,
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slug,
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project_id,
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plot_type,
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cycle_id=None,
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module_id=None,
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):
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# Total Issues in Cycle or Module
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total_issues = queryset.total_issues
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# check whether the estimate is a point or not
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estimate_type = Project.objects.filter(
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workspace__slug=slug,
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pk=project_id,
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estimate__isnull=False,
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estimate__type="points",
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).exists()
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if estimate_type and plot_type == "points" and cycle_id:
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issue_estimates = 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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estimate_point__isnull=False,
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).values_list("estimate_point__value", flat=True)
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issue_estimates = [float(value) for value in issue_estimates]
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total_estimate_points = sum(issue_estimates)
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if estimate_type and plot_type == "points" and module_id:
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issue_estimates = 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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estimate_point__isnull=False,
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).values_list("estimate_point__value", flat=True)
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issue_estimates = [float(value) for value in issue_estimates]
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total_estimate_points = sum(issue_estimates)
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if cycle_id:
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if queryset.end_date and queryset.start_date:
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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)).date()
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for x in range(
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(queryset.end_date - queryset.start_date).days + 1
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)
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]
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else:
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date_range = []
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chart_data = {str(date): 0 for date in date_range}
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if plot_type == "points":
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completed_issues_estimate_point_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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estimate_point__isnull=False,
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)
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.annotate(date=TruncDate("completed_at"))
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.values("date")
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.values("date", "estimate_point__value")
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.order_by("date")
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)
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else:
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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)).date()
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for x in range(
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(queryset.target_date - queryset.start_date).days + 1
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)
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]
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chart_data = {str(date): 0 for date in date_range}
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if plot_type == "points":
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completed_issues_estimate_point_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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estimate_point__isnull=False,
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)
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.annotate(date=TruncDate("completed_at"))
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.values("date")
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.values("date", "estimate_point__value")
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.order_by("date")
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)
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else:
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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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if plot_type == "points":
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for date in date_range:
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cumulative_pending_issues = total_estimate_points
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total_completed = 0
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total_completed = sum(
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float(item["estimate_point__value"])
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for item in completed_issues_estimate_point_distribution
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if item["date"] is not None and item["date"] <= date
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)
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cumulative_pending_issues -= total_completed
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if date > timezone.now().date():
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chart_data[str(date)] = None
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else:
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chart_data[str(date)] = cumulative_pending_issues
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else:
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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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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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cumulative_pending_issues -= total_completed
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if date > timezone.now().date():
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chart_data[str(date)] = None
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else:
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chart_data[str(date)] = cumulative_pending_issues
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return chart_data
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