Bug 3098 - mettre à jour les graphiques montrant l'évolution du cluster

- improved the handling of colors. As a result, the number of owners is no longer restricted, as the color palette is now no longer restricted in size
- improved readability by putting the legend on the upper left part of the graphs, where it no longer hides the upper right part of the graph
- fixed clutter of years on the x axis which made it difficult to read the years. Now, there is a limit in the number of years displayed on the x axis
- improved the legend showng the owners, which now displays the lab in addition to the department

cluster_stats.py

@@ -4,6 +4,7 @@ import os
 import re
 import datetime
 import numpy as np
+import colorsys
 # fix to prevent the following error when run from www-data
 # Failed to create /var/www/.matplotlib; consider setting MPLCONFIGDIR to a writable directory for matplotlib configuration data
 # root@intranet:~# echo ~www-data
@@ -116,18 +117,26 @@ def get_computer_value_over_time(inventory, computer_id, time_value, flops_price
 #         plt.legend()
 
 
-def stackplot(ax, x_signal, y_signals):
+def get_rgb_palette(num_colors, saturation=0.5, value=0.5):
+    hsv_tuples = [(x * 1.0 / num_colors, saturation, value) for x in range(num_colors)]
+    rgb_tuples = map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples)
+    return rgb_tuples
+
+
+def stackplot(ax, x_signal, y_signals, legend_location='best'):
     """
     :param matplotlib.Axes ax:
     :param numpy.array x_signal:
     :param dict(str,numpy.array) y_signals:
+    :param str legend_location: one of the values allowed in loc argument of matplotlib's plt.legend() function, or:
+        'outside right': outside the graph, at its right
     """
     if 'stackplot' in dir(ax):
         ax.stackplot(x_signal, list(y_signals.values()))
         plt.legend(list(y_signals.keys()))
     else:
         # emulating missing Axes.stackplot method
-        colors = ['blue', 'orange', 'green', 'purple', 'yellow']
+        colors = get_rgb_palette(num_colors=len(y_signals), saturation=1.0, value=0.8)  # ['blue', 'orange', 'green', 'purple', 'yellow', 'cyan']
         y = np.row_stack(list(y_signals.itervalues()))
         # this call to 'cumsum' (cumulative sum), passing in your y data,
         # is necessary to avoid having to manually order the datasets
@@ -140,7 +149,11 @@ def stackplot(ax, x_signal, y_signals):
             ax.fill_between(x_signal, from_signal, y_stack[series_index, :], color=colors[series_index], lw=0.0)
             p = plt.Rectangle((0, 0), 0, 0, color=colors[series_index])
             ax.add_patch(p)
-        plt.legend(list(y_signals.keys()))
+
+        if legend_location == 'outside right':
+            plt.legend(list(y_signals.keys()), bbox_to_anchor=(1.10, 1.00), loc='upper left')  # force the legend into the bounding box
+        else:
+            plt.legend(list(y_signals.keys()), loc=legend_location)
 
 
 def draw_cluster_value_over_time_graph(inventory, from_date, to_date, graph_type):
@@ -166,7 +179,7 @@ def draw_cluster_value_over_time_graph(inventory, from_date, to_date, graph_type
                     # print(ownership)
                     # print(ownership['owner'], ownership['owner_ratio'])
                     owner = ownership['owner']
-                    owner_dept = owner.split('.')[1]
+                    owner_dept = '.'.join(owner.split('.')[0:2])
                     # if owner_dept == 'matnano':
                     # print(name, owner, purchase_date, price_ex_vat)
                     if owner_dept in cluster_value.keys():
@@ -185,7 +198,7 @@ def draw_cluster_value_over_time_graph(inventory, from_date, to_date, graph_type
     # for dept, cluster_value_for_dept in cluster_value.iteritems():
     #    ax.plot(time_value, cluster_value_for_dept)
 
-    stackplot(ax, time_value, cluster_value)
+    stackplot(ax, time_value, cluster_value, legend_location='upper left')
 
     plt.xlabel('time')
     plt.ylabel({
@@ -193,7 +206,15 @@ def draw_cluster_value_over_time_graph(inventory, from_date, to_date, graph_type
         'cluster_value_over_time': u'cluster value (€)',
         'cluster_dp_gflops_over_time': u'double prec gflops'}[graph_type])
 
-    years = matplotlib.dates.YearLocator()   # every year
+    datemin = datetime.date(from_date.year, 1, 1)
+    datemax = datetime.date(to_date.year + 1, 1, 1)
+    ax.set_xlim(datemin, datemax)
+
+    max_num_years = 8
+    num_years = (datemax - datemin).days // 365
+    year_step = num_years / max_num_years
+
+    years = matplotlib.dates.YearLocator(year_step)   # every year_step year
     months = matplotlib.dates.MonthLocator()  # every month
     yearsFmt = matplotlib.dates.DateFormatter('%Y')
 
@@ -202,9 +223,6 @@ def draw_cluster_value_over_time_graph(inventory, from_date, to_date, graph_type
     ax.xaxis.set_major_formatter(yearsFmt)
     ax.xaxis.set_minor_locator(months)
 
-    datemin = datetime.date(from_date.year, 1, 1)
-    datemax = datetime.date(to_date.year + 1, 1, 1)
-    ax.set_xlim(datemin, datemax)
 
     # rotates and right aligns the x labels, and moves the bottom of the
     # axes up to make room for them