[ADD] base modules
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Muhammad
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web_cohort/models/__init__.py
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6
web_cohort/models/__init__.py
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# -*- coding: utf-8 -*-
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# Part of Odoo. See LICENSE file for full copyright and licensing details.
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from . import ir_action_act_window
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from . import ir_ui_view
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from . import models
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web_cohort/models/ir_action_act_window.py
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web_cohort/models/ir_action_act_window.py
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# -*- coding: utf-8 -*-
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from odoo import fields, models
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class ActWindowView(models.Model):
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_inherit = 'ir.actions.act_window.view'
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view_mode = fields.Selection(selection_add=[
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('cohort', 'Cohort')
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], ondelete={'cohort': 'cascade'})
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8
web_cohort/models/ir_ui_view.py
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web_cohort/models/ir_ui_view.py
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# -*- coding: utf-8 -*-
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from odoo import fields, models
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class View(models.Model):
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_inherit = 'ir.ui.view'
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type = fields.Selection(selection_add=[('cohort', 'Cohort')])
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181
web_cohort/models/models.py
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181
web_cohort/models/models.py
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# -*- coding: utf-8 -*-
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# Part of Odoo. See LICENSE file for full copyright and licensing details.
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from collections import defaultdict
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from datetime import datetime
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from dateutil.relativedelta import relativedelta
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from odoo import api, fields, models
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from odoo.tools import DEFAULT_SERVER_DATE_FORMAT
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from odoo.osv import expression
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DISPLAY_FORMATS = {
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'day': '%d %b %Y',
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'week': 'W%W %Y',
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'month': '%B %Y',
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'year': '%Y',
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}
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class Base(models.AbstractModel):
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_inherit = 'base'
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@api.model
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def get_cohort_data(self, date_start, date_stop, measure, interval, domain, mode, timeline):
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"""
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Get all the data needed to display a cohort view
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:param date_start: the starting date to use in the group_by clause
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:param date_stop: the date field which mark the change of state
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:param measure: the field to aggregate
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:param interval: the interval of time between two cells ('day', 'week', 'month', 'year')
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:param domain: a domain to limit the read_group
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:param mode: the mode of aggregation ('retention', 'churn') [default='retention']
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:param timeline: the direction to display data ('forward', 'backward') [default='forward']
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:return: dictionary containing a total amount of records considered and a
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list of rows each of which contains 16 cells.
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"""
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rows = []
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columns_avg = defaultdict(lambda: dict(percentage=0, count=0))
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total_value = 0
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initial_churn_value = 0
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measure_is_many2one = self._fields.get(measure) and self._fields.get(measure).type == 'many2one'
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field_measure = (
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[measure + ':count_distinct']
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if measure_is_many2one
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else ([measure] if self._fields.get(measure) else [])
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)
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row_groups = self._read_group_raw(
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domain=domain,
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fields=[date_start] + field_measure,
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groupby=date_start + ':' + interval
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)
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for group in row_groups:
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dates = group['%s:%s' % (date_start, interval)]
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if not dates:
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continue
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# Split with space for smoothly format datetime field
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clean_start_date = dates[0].split('/')[0].split(' ')[0]
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cohort_start_date = fields.Datetime.from_string(clean_start_date)
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if measure == '__count__':
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value = float(group[date_start + '_count'])
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else:
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value = float(group[measure] or 0.0)
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total_value += value
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sub_group = self._read_group_raw(
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domain=group['__domain'],
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fields=[date_stop] + field_measure,
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groupby=date_stop + ':' + interval
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)
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sub_group_per_period = {}
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for g in sub_group:
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d_stop = g["%s:%s" % (date_stop, interval)]
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if d_stop:
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date_group = fields.Datetime.from_string(d_stop[0].split('/')[0])
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group_interval = date_group.strftime(DISPLAY_FORMATS[interval])
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sub_group_per_period[group_interval] = g
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columns = []
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initial_value = value
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col_range = range(-15, 1) if timeline == 'backward' else range(0, 16)
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for col_index, col in enumerate(col_range):
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col_start_date = cohort_start_date
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if interval == 'day':
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col_start_date += relativedelta(days=col)
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col_end_date = col_start_date + relativedelta(days=1)
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elif interval == 'week':
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col_start_date += relativedelta(days=7 * col)
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col_end_date = col_start_date + relativedelta(days=7)
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elif interval == 'month':
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col_start_date += relativedelta(months=col)
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col_end_date = col_start_date + relativedelta(months=1)
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else:
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col_start_date += relativedelta(years=col)
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col_end_date = col_start_date + relativedelta(years=1)
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if col_start_date > datetime.today():
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columns_avg[col_index]
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columns.append({
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'value': '-',
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'churn_value': '-',
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'percentage': '',
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})
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continue
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significative_period = col_start_date.strftime(DISPLAY_FORMATS[interval])
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col_group = sub_group_per_period.get(significative_period, {})
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if not col_group:
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col_value = 0.0
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elif measure == '__count__':
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col_value = col_group[date_stop + '_count']
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else:
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col_value = col_group[measure] or 0.0
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# In backward timeline, if columns are out of given range, we need
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# to set initial value for calculating correct percentage
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if timeline == 'backward' and col_index == 0:
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outside_timeline_domain = expression.AND(
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[
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group['__domain'],
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['|',
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(date_stop, '=', False),
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(date_stop, '>=', fields.Datetime.to_string(col_start_date)),
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]
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]
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)
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col_group = self._read_group_raw(
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domain=outside_timeline_domain,
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fields=field_measure,
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groupby=[]
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)
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if measure == '__count__':
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initial_value = float(col_group[0]['__count'])
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else:
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initial_value = float(col_group[0][measure] or 0.0)
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initial_churn_value = value - initial_value
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previous_col_remaining_value = initial_value if col_index == 0 else columns[-1]['value']
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col_remaining_value = previous_col_remaining_value - col_value
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percentage = value and (col_remaining_value) / value or 0
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if mode == 'churn':
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percentage = 1 - percentage
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percentage = round(100 * percentage, 1)
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columns_avg[col_index]['percentage'] += percentage
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columns_avg[col_index]['count'] += 1
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# For 'week' interval, we display a better tooltip (range like : '02 Jul - 08 Jul')
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if interval == 'week':
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period = "%s - %s" % (col_start_date.strftime('%d %b'), (col_end_date - relativedelta(days=1)).strftime('%d %b'))
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else:
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period = col_start_date.strftime(DISPLAY_FORMATS[interval])
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if mode == 'churn':
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domain = [
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(date_stop, '<', col_end_date.strftime(DEFAULT_SERVER_DATE_FORMAT)),
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]
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else:
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domain = ['|',
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(date_stop, '>=', col_end_date.strftime(DEFAULT_SERVER_DATE_FORMAT)),
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(date_stop, '=', False),
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]
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columns.append({
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'value': col_remaining_value,
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'churn_value': col_value + (columns[-1]['churn_value'] if col_index > 0 else initial_churn_value),
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'percentage': percentage,
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'domain': domain,
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'period': period,
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})
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rows.append({
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'date': dates[1],
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'value': value,
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'domain': group['__domain'],
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'columns': columns,
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})
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return {
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'rows': rows,
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'avg': {'avg_value': total_value / len(rows) if rows else 0, 'columns_avg': columns_avg},
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}
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