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fixed pep8 styling problems

This commit is contained in:
Guillaume Raffy 2021-02-23 13:01:52 +00:00
parent 78d7e02285
commit 14e3e12571
1 changed files with 71 additions and 61 deletions
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74
cluster_stats.py
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@ -1,5 +1,5 @@
# encoding: utf-8 # encoding: utf-8
import sys # import sys
import os import os
import re import re
import datetime import datetime
@ -19,29 +19,33 @@ if r != 0:
matplotlib.use('Agg') # use Anti Grain Geometry backend for non-interactive rendering into pngs, svg, etc... 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.pyplot as plt # import matplotlib.pyplot as plt
import matplotlib.dates import matplotlib.dates
import abc import abc
from SimpaDbUtil import SqlDatabaseReader, SqlFile # from SimpaDbUtil import SqlDatabaseReader, SqlFile
from inventory import Inventory # from inventory import Inventory
def is_cluster_node_name(name): def is_cluster_node_name(name):
return re.match('^simpatix[0-9]+$', name) is not None or re.match('^physix[0-9]+$', name) is not None return re.match('^simpatix[0-9]+$', name) is not None or re.match('^physix[0-9]+$', name) is not None
def get_investment_over_time(time_value, price, purchase_time): def get_investment_over_time(time_value, price, purchase_time):
percent_decay_per_day = 0.0 # 1.0/(7.0*365.0) percent_decay_per_day = 0.0 # 1.0/(7.0*365.0)
f1 = (purchase_time-time_value)*percent_decay_per_day+1.0 f1 = (purchase_time - time_value) * percent_decay_per_day + 1.0
f2 = np.where( f1 < 0.0, 0.0, f1 ) f2 = np.where(f1 < 0.0, 0.0, f1)
f3 = np.where( time_value < purchase_time, 0.0, f2 ) f3 = np.where(time_value < purchase_time, 0.0, f2)
return f3 * price return f3 * price
def get_flops_over_time(inventory, time_value, computer_id, purchase_time): def get_flops_over_time(inventory, time_value, computer_id, purchase_time):
""" """
:param Inventory inventory: :param Inventory inventory:
""" """
return np.where( time_value < purchase_time, 0.0, inventory.get_computer_dflops(computer_id) ) return np.where(time_value < purchase_time, 0.0, inventory.get_computer_dflops(computer_id))
def get_flops_price_over_time(inventory, time_value): def get_flops_price_over_time(inventory, time_value):
""" """
@ -56,7 +60,7 @@ def get_flops_price_over_time(inventory, time_value):
flops_prices = [] flops_prices = []
for row in rows: 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 (name, serial_number, affectation, machine_spec_id, command_id, price_ex_vat, pos_x, pos_y, pos_z, inv_number) = row
is_cluster_node = is_cluster_node_name(name) is_cluster_node = is_cluster_node_name(name)
if is_cluster_node: if is_cluster_node:
purchase_date = inventory.get_machine_purchase_date(name) purchase_date = inventory.get_machine_purchase_date(name)
@ -64,20 +68,20 @@ def get_flops_price_over_time(inventory, time_value):
# print(name, price_ex_vat) # print(name, price_ex_vat)
purchase_time = matplotlib.dates.date2num(purchase_date.date()) purchase_time = matplotlib.dates.date2num(purchase_date.date())
computer_flops = inventory.get_computer_dflops(name) computer_flops = inventory.get_computer_dflops(name)
flops_price = ( price_ex_vat-inventory.get_computer_options_price(name) ) / computer_flops flops_price = (price_ex_vat - inventory.get_computer_options_price(name)) / computer_flops
# print ( purchase_date, name, price_ex_vat, computer_flops, flops_price ) # 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.append({'time': purchase_time, 'flops_price': flops_price, 'purchase_date': purchase_date})
flops_prices = sorted(flops_prices, key=get_key) flops_prices = sorted(flops_prices, key=get_key)
flops_price_over_time = np.where( True, 0.0, 0.0 ) flops_price_over_time = np.where(True, 0.0, 0.0)
for item in flops_prices: for item in flops_prices:
# print(item) # print(item)
flops_price_over_time = np.where( time_value < item['time'], flops_price_over_time, item['flops_price']) flops_price_over_time = np.where(time_value < item['time'], flops_price_over_time, item['flops_price'])
return flops_price_over_time return flops_price_over_time
def get_computer_value_over_time(inventory, computer_id, time_value, flops_price_over_time, purchase_time): def get_computer_value_over_time(inventory, computer_id, time_value, flops_price_over_time, purchase_time):
# print('flops_price_over_time = ', flops_price_over_time) # print('flops_price_over_time = ', flops_price_over_time)
computer_flops = inventory.get_computer_dflops(computer_id) computer_flops = inventory.get_computer_dflops(computer_id)
@ -110,6 +114,8 @@ def get_computer_value_over_time(inventory, computer_id, time_value, flops_price
# from_signal = y_stack[series_index-1,:] # 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]) # 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() # plt.legend()
def stackplot(ax, x_signal, y_signals): def stackplot(ax, x_signal, y_signals):
""" """
:param matplotlib.Axes ax: :param matplotlib.Axes ax:
@ -117,7 +123,7 @@ def stackplot(ax, x_signal, y_signals):
:param dict(str,numpy.array) y_signals: :param dict(str,numpy.array) y_signals:
""" """
if 'stackplot' in dir(ax): if 'stackplot' in dir(ax):
ax.stackplot(x_signal, list(y_signals.values()) ) ax.stackplot(x_signal, list(y_signals.values()))
plt.legend(list(y_signals.keys())) plt.legend(list(y_signals.keys()))
else: else:
# emulating missing Axes.stackplot method # emulating missing Axes.stackplot method
@ -130,12 +136,13 @@ def stackplot(ax, x_signal, y_signals):
if series_index == 0: if series_index == 0:
from_signal = 0 from_signal = 0
else: else:
from_signal = y_stack[series_index-1,:] 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) 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]) p = plt.Rectangle((0, 0), 0, 0, color=colors[series_index])
ax.add_patch(p) ax.add_patch(p)
plt.legend(list(y_signals.keys())) plt.legend(list(y_signals.keys()))
def draw_cluster_value_over_time_graph(inventory, from_date, to_date, graph_type): 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)) 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) flops_price_over_time = get_flops_price_over_time(inventory, time_value)
@ -144,7 +151,7 @@ def draw_cluster_value_over_time_graph(inventory, from_date, to_date, graph_type
rows = inventory.query("SELECT * FROM machines") rows = inventory.query("SELECT * FROM machines")
for row in rows: 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 (name, serial_number, affectation, machine_spec_id, command_id, price_ex_vat, pos_x, pos_y, pos_z, inv_number) = row
is_cluster_node = is_cluster_node_name(name) is_cluster_node = is_cluster_node_name(name)
if is_cluster_node: if is_cluster_node:
purchase_date = inventory.get_machine_purchase_date(name) purchase_date = inventory.get_machine_purchase_date(name)
@ -152,9 +159,9 @@ def draw_cluster_value_over_time_graph(inventory, from_date, to_date, graph_type
# print(name, price_ex_vat) # print(name, price_ex_vat)
purchase_time = matplotlib.dates.date2num(purchase_date.date()) purchase_time = matplotlib.dates.date2num(purchase_date.date())
item_value_over_time = { item_value_over_time = {
'cluster_cost_over_time':get_investment_over_time(time_value, price_ex_vat, purchase_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, name, time_value, flops_price_over_time, purchase_time), 'cluster_value_over_time': get_computer_value_over_time(inventory, name, time_value, flops_price_over_time, purchase_time),
'cluster_dp_gflops_over_time':get_flops_over_time(inventory, time_value, name, purchase_time)}[graph_type] 'cluster_dp_gflops_over_time': get_flops_over_time(inventory, time_value, name, purchase_time)}[graph_type]
for ownership in inventory.get_item_ownership(name): for ownership in inventory.get_item_ownership(name):
# print(ownership) # print(ownership)
# print(ownership['owner'], ownership['owner_ratio']) # print(ownership['owner'], ownership['owner_ratio'])
@ -175,16 +182,16 @@ def draw_cluster_value_over_time_graph(inventory, from_date, to_date, graph_type
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.set_title(graph_type) ax.set_title(graph_type)
#for dept, cluster_value_for_dept in cluster_value.iteritems(): # for dept, cluster_value_for_dept in cluster_value.iteritems():
# ax.plot(time_value, cluster_value_for_dept) # ax.plot(time_value, cluster_value_for_dept)
stackplot( ax, time_value, cluster_value) stackplot(ax, time_value, cluster_value)
plt.xlabel('time') plt.xlabel('time')
plt.ylabel( plt.ylabel({
{'cluster_cost_over_time':u'cluster investment (€)', 'cluster_cost_over_time': u'cluster investment (€)',
'cluster_value_over_time':u'cluster value (€)', 'cluster_value_over_time': u'cluster value (€)',
'cluster_dp_gflops_over_time':u'double prec gflops'}[graph_type]) 'cluster_dp_gflops_over_time': u'double prec gflops'}[graph_type])
years = matplotlib.dates.YearLocator() # every year years = matplotlib.dates.YearLocator() # every year
months = matplotlib.dates.MonthLocator() # every month months = matplotlib.dates.MonthLocator() # every month
@ -204,10 +211,11 @@ def draw_cluster_value_over_time_graph(inventory, from_date, to_date, graph_type
# fig.autofmt_xdate() # fig.autofmt_xdate()
ax.grid(True) ax.grid(True)
#plt.plot() # plt.plot()
# plt.plot(X,S) # plt.plot(X,S)
return fig return fig
def draw_dp_gflops_price_over_time_over_time_graph(inventory, from_date, to_date): def draw_dp_gflops_price_over_time_over_time_graph(inventory, from_date, to_date):
""" """
:param Inventory inventory: the inventory database :param Inventory inventory: the inventory database
@ -235,6 +243,7 @@ def draw_dp_gflops_price_over_time_over_time_graph(inventory, from_date, to_date
ax.grid(True) ax.grid(True)
return fig return fig
def draw_age_pyramid_graph(inventory): def draw_age_pyramid_graph(inventory):
""" """
:param Inventory inventory: the inventory database :param Inventory inventory: the inventory database
@ -246,12 +255,12 @@ def draw_age_pyramid_graph(inventory):
rows = inventory.query("SELECT * FROM machines") rows = inventory.query("SELECT * FROM machines")
for row in rows: 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 (name, serial_number, affectation, machine_spec_id, command_id, price_ex_vat, pos_x, pos_y, pos_z, inv_number) = row
is_cluster_node = is_cluster_node_name(name) is_cluster_node = is_cluster_node_name(name)
if is_cluster_node: if is_cluster_node:
purchase_date = inventory.get_machine_purchase_date(name) purchase_date = inventory.get_machine_purchase_date(name)
if purchase_date is not None: if purchase_date is not None:
purchase_time = matplotlib.dates.date2num(purchase_date.date()) purchase_time = matplotlib.dates.date2num(purchase_date.date()) # noqa: F841
age = datetime.datetime.now() - purchase_date age = datetime.datetime.now() - purchase_date
age_histogram[age.days / 365] += 1 age_histogram[age.days / 365] += 1
# print(name, age) # print(name, age)
@ -288,6 +297,7 @@ class IFigureHandler(object):
""" """
pass pass
class ScreenFigureHandler(IFigureHandler): class ScreenFigureHandler(IFigureHandler):
""" """
displays figures on screen displays figures on screen
@ -301,12 +311,11 @@ class ScreenFigureHandler(IFigureHandler):
def on_finalize(self): def on_finalize(self):
plt.show() plt.show()
class SvgFigureHandler(IFigureHandler): class SvgFigureHandler(IFigureHandler):
""" """
saves figures as svg files saves figures as svg files
""" """
def __init__(self):
pass
def __init__(self, out_svg_dir_path): def __init__(self, out_svg_dir_path):
""" """
@ -320,6 +329,7 @@ class SvgFigureHandler(IFigureHandler):
def on_finalize(self): def on_finalize(self):
pass pass
def draw_graphs(inventory, from_time, to_time, figure_handler): def draw_graphs(inventory, from_time, to_time, figure_handler):
""" """
:param Inventory inventory: the inventory database :param Inventory inventory: the inventory database