# encoding: utf-8 import sys import os import re import datetime import numpy as np # 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 # /var/www os.environ['MPLCONFIGDIR'] = "/tmp/cluster_stats" import matplotlib # fix to TclError at /cluster/ClusterEvolution/ # no display name and no $DISPLAY environment variable # https://matplotlib.org/tutorials/introductory/usage.html#what-is-a-backend r = os.system('python -c "import matplotlib.pyplot as plt;plt.figure()"') if r != 0: matplotlib.use('Agg') # use Anti Grain Geometry backend for non-interactive rendering into pngs, svg, etc... import matplotlib.pyplot as plt #import matplotlib.pyplot as plt import matplotlib.dates import abc from SimpaDbUtil import SqlDatabaseReader, SqlFile from inventory import Inventory def get_investment_over_time(time_value, price, purchase_time): percent_decay_per_day = 0.0 # 1.0/(7.0*365.0) f1 = (purchase_time-time_value)*percent_decay_per_day+1.0 f2 = np.where( f1 < 0.0, 0.0, f1 ) f3 = np.where( time_value < purchase_time, 0.0, f2 ) return f3 * price def get_flops_over_time(inventory, time_value, computer_serial_number, purchase_time): """ :param Inventory inventory: """ return np.where( time_value < purchase_time, 0.0, inventory.get_computer_dflops(computer_serial_number) ) def get_flops_price_over_time(inventory, time_value): """ :param Inventory inventory: the inventory database """ rows = inventory.query("SELECT * FROM machines") def get_key(item): return item['time'] flops_prices = [] for row in rows: (name, serial_number, affectation, machine_spec_id, command_id, price_ex_vat, pos_x, pos_y, pos_z, inv_number)=row is_cluster_node = re.match('^simpatix[0-9]+$', name) if is_cluster_node: purchase_date = inventory.get_machine_purchase_date(serial_number) if purchase_date is not None: # print(name, price_ex_vat) purchase_time = matplotlib.dates.date2num(purchase_date.date()) computer_flops = inventory.get_computer_dflops(serial_number ) flops_price = ( price_ex_vat-inventory.get_computer_options_price(name) ) / computer_flops # print ( purchase_date, name, price_ex_vat, computer_flops, flops_price ) flops_prices.append({'time':purchase_time, 'flops_price':flops_price, 'purchase_date':purchase_date}) flops_prices = sorted(flops_prices, key=get_key) flops_price_over_time = np.where( True, 0.0, 0.0 ) for item in flops_prices: # print(item) flops_price_over_time = np.where( time_value < item['time'], flops_price_over_time, item['flops_price']) return flops_price_over_time def get_computer_value_over_time(inventory, computer_serial_number, time_value, flops_price_over_time, purchase_time): # print('flops_price_over_time = ', flops_price_over_time) computer_flops = inventory.get_computer_dflops(computer_serial_number) computer_flops_over_time = np.where(time_value < purchase_time, 0.0, computer_flops) computer_value_over_time = computer_flops_over_time * flops_price_over_time return computer_value_over_time # def stackplot(ax, x_signal, y_signals): # """ # :param matplotlib.Axes ax: # :param numpy.array x_signal: # :param dict(str,numpy.array) y_signals: # """ # # matplot 1.1.1 doesn't have the stackplot method in Axes # if 'toto_stackplot' in dir(ax): # ax.stackplot(x_signal, list(y_signals.itervalues()) ) # plt.legend(list(y_signals.keys())) # else: # colors = ['blue', 'orange', 'green', 'purple', 'yellow'] # # emulating missing Axes.stackplot method # 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 # y_stack = np.cumsum(y, axis=0) # a 3x10 array # for series_index in range(len(y_signals)): # if series_index == 0: # from_signal = 0 # else: # from_signal = y_stack[series_index-1,:] # ax.fill_between(x_signal, from_signal, y_stack[series_index,:], color=colors[series_index], lw=0.0, label=y_signals.keys()[series_index]) # plt.legend() def stackplot(ax, x_signal, y_signals): """ :param matplotlib.Axes ax: :param numpy.array x_signal: :param dict(str,numpy.array) y_signals: """ if 'stackplot' in dir(ax): ax.stackplot(x_signal, list(y_signals.itervalues()) ) plt.legend(list(y_signals.keys())) else: # emulating missing Axes.stackplot method colors = ['blue', 'orange', 'green', 'purple', 'yellow'] 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 y_stack = np.cumsum(y, axis=0) # a 3x10 array for series_index in range(len(y_signals)): if series_index == 0: from_signal = 0 else: from_signal = y_stack[series_index-1,:] 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())) def draw_cluster_value_over_time_graph(inventory, from_date, to_date, graph_type): time_value = matplotlib.dates.drange(dstart=from_date, dend=to_date, delta=datetime.timedelta(days=1)) flops_price_over_time = get_flops_price_over_time(inventory, time_value) cluster_value = {} rows = inventory.query("SELECT * FROM machines") for row in rows: (name, serial_number, affectation, machine_spec_id, command_id, price_ex_vat, pos_x, pos_y, pos_z, inv_number)=row is_cluster_node = re.match('^simpatix[0-9]+$', name) if is_cluster_node: purchase_date = inventory.get_machine_purchase_date(serial_number) if purchase_date is not None: # print(name, price_ex_vat) purchase_time = matplotlib.dates.date2num(purchase_date.date()) item_value_over_time = { 'cluster_cost_over_time':get_investment_over_time(time_value, price_ex_vat, purchase_time), 'cluster_value_over_time':get_computer_value_over_time(inventory, serial_number, time_value, flops_price_over_time, purchase_time), 'cluster_dp_gflops_over_time':get_flops_over_time(inventory, time_value, serial_number, purchase_time)}[graph_type] for ownership in inventory.get_item_ownership(serial_number): # print(ownership) # print(ownership['owner'], ownership['owner_ratio']) owner = ownership['owner'] owner_dept = owner.split('.')[1] # if owner_dept == 'matnano': # print(name, owner, purchase_date, price_ex_vat) if owner_dept in cluster_value.keys(): cluster_value[owner_dept] += item_value_over_time else: cluster_value[owner_dept] = np.zeros_like(time_value) # print(purchase_date) # print(type(from_date)) # print(type(to_date)) # X = np.linspace(-np.pi, np.pi, 256, endpoint=True) # C,S = np.cos(X), np.sin(X) fig, ax = plt.subplots() ax.set_title(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) plt.xlabel('time') plt.ylabel( {'cluster_cost_over_time':u'cluster investment (€)', 'cluster_value_over_time':u'cluster value (€)', 'cluster_dp_gflops_over_time':u'double prec gflops'}[graph_type]) years = matplotlib.dates.YearLocator() # every year months = matplotlib.dates.MonthLocator() # every month yearsFmt = matplotlib.dates.DateFormatter('%Y') # format the ticks ax.xaxis.set_major_locator(years) 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 # fig.autofmt_xdate() ax.grid(True) #plt.plot() # plt.plot(X,S) return fig def draw_dp_gflops_price_over_time_over_time_graph(inventory, from_date, to_date): """ :param Inventory inventory: the inventory database :param datetime from_time: :param datetime to_time: """ time_value = matplotlib.dates.drange(dstart=from_date, dend=to_date, delta=datetime.timedelta(days=1)) flops_price_over_time = get_flops_price_over_time(inventory, time_value) fig, ax = plt.subplots() ax.set_yscale('log') ax.plot(time_value, flops_price_over_time) ax.set_xlabel('time') ax.set_ylabel(u'double precision flops price (€/gflops)') ax.set_title('gflops_price_over_time') years = matplotlib.dates.YearLocator() # every year months = matplotlib.dates.MonthLocator() # every month yearsFmt = matplotlib.dates.DateFormatter('%Y') # format the ticks ax.xaxis.set_major_locator(years) ax.xaxis.set_major_formatter(yearsFmt) ax.xaxis.set_minor_locator(months) ax.grid(True) return fig class IFigureHandler(object): """ specifies what to do with generated figures """ @abc.abstractmethod def on_figure_ended(self, fig): """ :param matplotlib.Figure fig: """ pass @abc.abstractmethod def on_finalize(self): """ called after all figures have been created """ pass class ScreenFigureHandler(IFigureHandler): """ displays figures on screen """ def __init__(self): pass def on_figure_ended(self, fig): pass def on_finalize(self): plt.show() class SvgFigureHandler(IFigureHandler): """ saves figures as svg files """ def __init__(self): pass def __init__(self, out_svg_dir_path): """ :param str out_svg_dir_path: where to save the svg files """ self._out_svg_dir_path = out_svg_dir_path def on_figure_ended(self, fig): fig.savefig(self._out_svg_dir_path + '/' + fig.axes[0].get_title() + '.svg') def on_finalize(self): pass def draw_graphs(inventory, from_time, to_time, figure_handler): """ :param Inventory inventory: the inventory database :param datetime from_time: :param datetime to_time: :param IFigureHandler figure_handler: """ fig = draw_cluster_value_over_time_graph(inventory, from_time.date(), to_time.date(), 'cluster_value_over_time') figure_handler.on_figure_ended(fig) fig = draw_cluster_value_over_time_graph(inventory, from_time.date(), to_time.date(), 'cluster_dp_gflops_over_time') figure_handler.on_figure_ended(fig) fig = draw_cluster_value_over_time_graph(inventory, from_time.date(), to_time.date(), 'cluster_cost_over_time') figure_handler.on_figure_ended(fig) fig = draw_dp_gflops_price_over_time_over_time_graph(inventory, from_time.date(), to_time.date()) figure_handler.on_figure_ended(fig) figure_handler.on_finalize()