Sales average computation improvements

lacagette_addons/lacagette_products/models/products.py

 # -*- coding: utf-8 -*-
 
 from openerp import _, api, models, fields
+import datetime
+import numpy
 
 class LaCagetteProducts(models.Model):
     _name = "lacagette.products"
@@ -106,6 +108,80 @@ class LaCagetteProducts(models.Model):
 
         return res
 
+    def _compte_consecutive_non_sale_days(self, sale_days, days):
+        """
+        @sale_days : list of sale days
+        @days: list of full period days (except excluded one, such as sundays)
+        @return : integer days to remove from days number to compute average
+        """
+        minimum_significative_consecutive_days = 5  # arbitrary set (TODO ? : set it in odoo parameters)
+        consecutive_found = [] # each serie is added to compute total of them
+        missing_days = []
+        for d in days:
+            if not (d in sale_days):
+                missing_days.append(d)
+        current_consecutive_number = 1
+        for i in range(len(missing_days)):
+            if i > 0:
+                current_day =  datetime.datetime.strptime(missing_days[i], "%Y-%m-%d")
+                previous_day = datetime.datetime.strptime(missing_days[i-1], "%Y-%m-%d")
+                if (current_day - previous_day).days == 1:
+                    current_consecutive_number += 1
+                else:
+                    if current_consecutive_number >= minimum_significative_consecutive_days:
+                        consecutive_found.append(current_consecutive_number)
+                        current_consecutive_number = 1
+        return int(numpy.sum(consecutive_found))
+
+
+    def _compute_product_template_sales_average(self, ids, days, result):
+        res = []
+        products_qtys = {} # used to compute totals and sigma
+        products_discounts = {} # used to compute totals
+        products_sums = {} #  used to sum daily sales quantity 
+        found_ids = [] #  used to set not found product results to 0
+        products_sale_days = {} # used to compute consecutive non sale days (to fit average)
+        for p in result:
+            pid = p['tpl_id']
+            found_ids.append(pid)
+            if not (pid in products_sale_days):
+                products_sale_days[pid] = [p['day']]
+            else:
+                products_sale_days[pid].append(p['day'])
+            if not (pid in products_qtys):
+                products_qtys[pid] = [p['qtys']]
+            else:
+                products_qtys[pid].append(p['qtys'])
+            if not (pid in products_discounts):
+                products_discounts[pid] = [p['discounts']]
+            else:
+                products_discounts[pid].append(p['discounts'])
+
+            if not (pid in products_sums):
+                products_sums[pid] = {'total_qty': p['qtys'], 'total_discount': p['discounts']}
+            else:
+                products_sums[pid]['total_qty'] += p['qtys']
+                products_sums[pid]['total_discount'] += p['discounts']
+
+        for i in ids:
+            average_qty = average_discount = sigma = 0
+            vpc = 1 # Void PerCent (percant of non sales days)
+            if (i in found_ids):
+                days_nb_to_remove = self._compte_consecutive_non_sale_days(products_sale_days[i], days)
+                significative_days = len(days) - days_nb_to_remove
+                if significative_days > 0:
+                    average_qty = round(numpy.sum(products_qtys[i])/significative_days, 2)
+                    average_discount = round(numpy.sum(products_discounts[i])/significative_days, 2)
+                    # to compute sigma, add 0 for non_sales_days
+                    void = []
+                    for j in range(len(days) - len(products_sale_days[i])):
+                        void.append(0)
+                    sigma = round(numpy.std(products_qtys[i] + void), 2)
+                vpc = round((float(len(days)) - len(products_sale_days[i]))/len(days), 2)
+                
+            res.append({'id': i, 'average_qty': average_qty, 'average_discount': average_discount, 'sigma': sigma, 'vpc': vpc}) 
+        return res
+
     @api.model
     def get_template_products_sales_average(self, params={}):
         """Retrieve products sales average.
@@ -116,7 +192,6 @@ class LaCagetteProducts(models.Model):
         if 'ids' in params:
             ids = list(filter(lambda x: isinstance(x, int), params['ids']))
             if len(ids) > 0:
-                import datetime
                 today = datetime.date.today()
                 excluded_days = [0]
                 if 'excluded_days' in params:
@@ -161,21 +236,8 @@ class LaCagetteProducts(models.Model):
                     """
                     self.env.cr.execute(sql_dates)
                     days = list(filter(lambda x: not (x['dow'] in excluded_days), self.env.cr.dictfetchall()))
-                    
-                    res['list'] = []
-                    products_sums = {}
-                    for p in req_res:
-                        pid = p['tpl_id']
-                        if not (pid in products_sums):
-                            products_sums[pid] = {'total_qty': p['qtys'], 'total_discount': p['discounts']}
-                        else:
-                            products_sums[pid]['total_qty'] += p['qtys']
-                            products_sums[pid]['total_discount'] += p['discounts']
-                    for pid, val in products_sums.items():
-                        res['list'].append({'id': pid, 
-                                            'average_qty': round(val['total_qty']/len(days),2),
-                                            'average_discount': round(val['total_discount']/len(days),2)})
-
+                    days = map(lambda x: x['ddate'], days)
+                    res['list'] = self._compute_product_template_sales_average(ids, days, req_res)
                 except Exception as e:
                     res['error'] = str(e)
             else: