def gen_bars(data, headers, datasets, methods):
"""
Description:
Inputs:
Outputs:
"""
width = 0.5
x = np.arange(0, 16, 2)
labels_x = datasets.copy()
labels_x.insert(0,"")
fig, ax = plt.subplots()
bar1 = ax.bar(x - (0.3 + width/2), data[0,:], width, label = methods[0])
add_labels(ax, bar1)
if (len(methods) > 1):
bar2 = ax.bar(x , data[1,:], width, label = methods[1])
add_labels(ax, bar2)
if (len(methods) > 2):
bar3 = ax.bar(x + (0.3 + width/2), data[1,:], width, label = methods[2])
add_labels(ax, bar3)
ax.set_ylabel(metric)
ax.set_xlabel("Dataset")
ax.set_xticklabels(labels_x)
ax.set_title(metric + " Across Datasets")
ax.legend(bbox_to_anchor=(1, 1.15), fancybox=True, framealpha=0.5)
def allGraphs(self):
colors = ['r', 'g', 'b', 'c', 'm', 'y', 'c']
for f in range(len(self.features)):
ax = plt.subplot(111)
plt.xlabel('Standardized Feature Values')
plt.ylabel('Probability')
filename = self.resultsName + "_feature_" + str(
self.features[f]) + "_std.png"
title = "Histogram of feature: " + str(look.feat(self.features[f]))
plt.title(title)
plt.grid(True)
for j in range(len(self.H_)):
prob = self.H_[j][f][0]
bins = self.H_[j][f][1]
barBins = bins[0:-1]
bw = barBins[1] - barBins[0]
ax.bar(barBins,
prob,
width=bw,
facecolor=colors[j],
alpha=0.75,
label=look.classes(j))
ax.legend(loc=0)
plt.savefig(filename, bbox_inches='tight')
plt.close()
def plotBarMatplotlib(self, x_list, y_list, value, unit):
figureTitle = "{} for {} to {}".format(value, x_list[0], x_list[-1])
logging.debug("Plotting {}".format(figureTitle))
output_file = "{}Plot.png".format(value.title().replace(' ', ''))
df = pd.DataFrame({'date': x_list, value: y_list})
# plot
figure, axes = plt.subplots(figsize=(16, 6))
axes.bar('date', value, data=df, color='mediumvioletred')
plt.title(figureTitle)
plt.xlabel('Date')
plt.ylabel('{} ({})'.format(value, unit))
plt.legend()
plt.grid(axis='y')
plt.xticks(self.date)
logging.debug(plt.xticks())
if unit == "*C":
plt.ylim(0, 70)
else:
plt.ylim(0, 50)
plt.savefig(output_file)
plt.show()
def club_tags(unencoded_clubs_list):
"""
Creates a bar graph showing of the number of occurrances of each possible club tag.
By using a dictionary to count club tag occurrances, the function then creates a
matplotlib bar graph using numpy representations of the dictionary information.
The graph is formatted and pushed as a Response type for view on the server.
Returns:
--------
Response
The graph
"""
club_dict = {}
for clubs in unencoded_clubs_list:
for tag in clubs.get_category():
if tag in club_dict:
club_dict[tag] = club_dict[tag] + 1
else:
club_dict[tag] = 1
x = np.zeros(len(club_dict))
index_counter = 0
for tag in club_dict:
x[index_counter] = club_dict.get(tag)
index_counter = index_counter + 1
fig = plt.figure()
ax = fig.add_subplot()
bar = ax.bar(np.arange(len(club_dict)), x)
labels = club_dict.keys()
ax.set_xticks(np.arange(len(club_dict)))
ax.set_xticklabels(labels, rotation='45', ha='right')
ax.set_xlabel('Club Tags')
ax.set_ylabel('Number of Occurrances')
ax.set_title('Number of Club Tag Occurrances')
for rect in bar:
height = rect.get_height()
ax.annotate('{}'.format(height),
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3),
textcoords="offset points",
ha='center',
va='bottom')
plt.tight_layout()
output = io.BytesIO()
FigureCanvas(fig).print_png(output)
return Response(output.getvalue(), mimetype='image/png')
def printPost(self, prob):
colors = ['r', 'g', 'b', 'c', 'm', 'y', 'x']
ax = plt.subplot(111)
plt.xlabel("Class")
plt.ylabel("Relative Likelihood")
plt.title("Posterior Probability")
plt.tick_params(axis='x',
which='both',
bottom='off',
top='off',
labelbottom='off')
width = 5
for i, p in enumerate(prob):
ax.bar(i * width,
p * 100,
width=width,
facecolor=colors[i],
alpha=0.75,
label=look.classes(i))
ax.legend(loc=0)
plt.savefig(self.resultsName + "Img_" + str(self.currentImage) +
"_Prob.png",
bbox_inches='tight')
plt.close()
def clubs_per_user(user_list):
"""
Creates a bar graph of the number of clubs per user.
Using a dictionary to gather each user's name and the number of club each
user is in, numpy representations of the data is used to create a matplotlib
bar graph. The graph is then formatted and pushed for view on the server.
Returns:
--------
Response
The graph
"""
user_club_dict = {}
for user in user_list:
name = user.get_user_name()
user_club_dict[name] = len(user.get_user_clubs())
x = np.zeros(len(user_club_dict))
index_counter = 0
for user in user_club_dict:
x[index_counter] = user_club_dict.get(user)
index_counter = index_counter + 1
fig = plt.figure()
ax = fig.add_subplot()
bar = ax.bar(np.arange(len(user_club_dict)), x)
labels = user_club_dict.keys()
ax.set_xticks(np.arange(len(user_club_dict)))
ax.set_xticklabels(labels, rotation='45', ha='right')
ax.set_xlabel('User Name')
ax.set_ylabel('Number of Clubs')
ax.set_title('Number of Clubs per User')
for rect in bar:
height = rect.get_height()
ax.annotate('{}'.format(height),
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3),
textcoords="offset points",
ha='center',
va='bottom')
plt.tight_layout()
output = io.BytesIO()
FigureCanvas(fig).print_png(output)
return Response(output.getvalue(), mimetype='image/png')
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.axes as ax
import pylab
# change to proper directory
os.chdir('C:\Users\Matt\Desktop\Python Projects\Exploratory Data Analysis')
# load file, select proper date range, convert row to numeric dtypes
hpc = pd.read_csv('household_power_consumption.txt',
sep=';',
index_col=['Date'],
usecols=['Global_active_power', 'Date'])
hpc = hpc['1/2/2007':'2/2/2007'].convert_objects(convert_numeric=True)
# create plotting variables
y = pd.value_counts(hpc.Global_active_power,
bins=np.arange(0, 8, 0.5),
sort=False)
index = y.index
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.bar(index, y, width=0.5, color='r')
ax.set_xlabel('Global Active Power (kilowatts)')
ax.set_ylabel('Frequency')
ax.set_title('Global Active Power')
pylab.show()
# hpc[hpc['Global_active_power'] > 4]
# import needed libraries
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.axes as ax
import pylab
# change to proper directory
os.chdir('C:\Users\Matt\Desktop\Python Projects\Exploratory Data Analysis')
# load file, select proper date range, convert row to numeric dtypes
hpc = pd.read_csv('household_power_consumption.txt', sep=';', index_col=['Date'], usecols=['Global_active_power', 'Date'])
hpc = hpc['1/2/2007':'2/2/2007'].convert_objects(convert_numeric=True)
# create plotting variables
y = pd.value_counts(hpc.Global_active_power, bins=np.arange(0, 8, 0.5), sort=False)
index = y.index
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.bar(index, y, width=0.5, color='r')
ax.set_xlabel('Global Active Power (kilowatts)')
ax.set_ylabel('Frequency')
ax.set_title('Global Active Power')
pylab.show()
# hpc[hpc['Global_active_power'] > 4]
def create_bar_graph(data=[[[1, 2], [10, 20]]],
semilog=False,
add_bar_labels=True,
title='Insert Fancy Title',
add_legend=False,
bar_colors=[],
legend_labels=[]):
import matplotlib.patches as mpatches
from collections import deque
if not bar_colors:
bar_color_deque = deque([
'#1395ba', '#a2b86c', '#ebc844', '#f16c20', '#c02e1d', '#0d3c55',
'#ecaa38', '#117899', '#d94e1f', '#5ca793', '#ef8b2c', '#0f5b78'
])
else:
bar_color_deque = deque(bar_colors)
width = 0.33
xs = data[0][0]
legend_labels_queue = deque(legend_labels)
handles = []
if len(data) > 1:
width = 0.33 * 2 / len(data)
#plot comparison from multiple archives
all_unique_x = {}
for series in data:
for size in series[0]:
all_unique_x[size] = True
ind = np.arange(len(all_unique_x.keys()))
rect_refs = []
fig, ax = plt.subplots()
bar_shift = width / 2
#plot individual bars to allow for sparse data plots
for series in data:
if len(series) > 2:
label = series[2]
color = bar_color_deque.popleft()
if legend_labels:
legend_label = legend_labels_queue.popleft()
else:
legend_label = label
handles.append(mpatches.Patch(color=color, label=legend_label))
index = 0
labeled_yet = False
for ex in sorted(all_unique_x.keys()):
for i in range(0, len(series[0])):
if series[0][i] == ex:
if 'label' in locals() and not labeled_yet:
rects = ax.bar(index + bar_shift,
series[1][i],
width,
color=color,
label=label)
labeled_yet = True
else:
rects = ax.bar(index + bar_shift,
series[1][i],
width,
color=color)
rect_refs.append(rects[0])
if add_bar_labels:
bar_label(ax, rects, semilog)
index += 1
bar_shift += width
if semilog:
ax.set_yscale('log')
ax.set_xticks(ind + 0.59 - 0.045 * len(data))
if add_legend:
plt.legend(handles=handles, loc=2)
else:
color = bar_color_deque.popleft()
ys = data[0][1]
ind = np.arange(len(xs))
fig, ax = plt.subplots()
rects = ax.bar(ind + width, ys, color=color)
ax.set_xticks(ind + width * 2)
if semilog:
ax.set_yscale('log')
if add_bar_labels:
bar_label(ax, rects, semilog)
fig.set_size_inches(15, 8)
ax.set_xticklabels(xs, rotation=0)
ax.set_title(title)
ax.set_xlabel("Object Size (Bytes)")
ax.set_ylabel('MB/s')
plt.show()
plt.savefig('foo.png')
return
ax.ticklabel_format(style='plain')
ax.axis([0, 2000000, 0, 180000000])
plt.grid( linestyle='-', linewidth=1)
for tick in ax.get_xticklabels():
tick.set_rotation(55)
#plt.show()
pdf.savefig(bbox_inches='tight')
plt.close()
with PdfPages('ImagenFantasma_x_tamaño.pdf') as pdf:
#plt.figure(figsize=(7, 5))
labels = 'ASM', 'O3', 'O2', 'O0'
barValues = [resASM[7],resC3[7],resC2[7],resC0[7]]
x = [1,2,3,4]
fig, ax = plt.subplots()
rects1 = ax.bar(x, barValues,0.7, label='')
plt.ylabel('')
plt.xlabel("Implementaciones")
plt.ylabel("Ciclos de clock")
plt.title("Imagen Fantasma 1600x1200")
ax.ticklabel_format(style='plain')
ax.set_xticks(x)
ax.set_xticklabels(labels)
plt.grid(linestyle='-', linewidth=1, axis='y')
pdf.savefig(bbox_inches='tight')
plt.close()
def calcularPorcentaje(asm, c):
return int((asm*100)/c)
for i in range(0,8):