def visualize_array(image_data, label='data_figure'):
"""
Produce a visual representation of the image_data matrix.
Parameters
----------
image_data : 2D array of floats
The pixel values to make into an image.
label : str
The string label to affix to the image. It is used both
to generate a figure number and as the title.
"""
# Treat nan values like zeros for display purposes
image_data = np.nan_to_num(np.copy(image_data))
fig = plt.figure(str_to_int(label))
# Diane made the brilliant suggestion to leave this plot in color.
# It looks much prettier.
plt.summer()
im = plt.imshow(image_data)
im.set_interpolation('nearest')
plt.title(label)
plt.xlabel('Max = {0:.3}, Min = {1:.3}'.format(np.max(image_data),
np.min(image_data)))
fig.show()
fig.canvas.draw()
def visualize_array(image_data, label="data_figure"):
"""
Produce a visual representation of the image_data matrix.
Parameters
----------
image_data : 2D array of floats
The pixel values to make into an image.
label : str
The string label to affix to the image. It is used both
to generate a figure number and as the title.
"""
# Treat nan values like zeros for display purposes
image_data = np.nan_to_num(np.copy(image_data))
fig = plt.figure(str_to_int(label))
# Diane made the brilliant suggestion to leave this plot in color.
# It looks much prettier.
plt.summer()
im = plt.imshow(image_data)
im.set_interpolation("nearest")
plt.title(label)
plt.xlabel("Max = {0:.3}, Min = {1:.3}".format(np.max(image_data), np.min(image_data)))
fig.show()
fig.canvas.draw()
def visualize_array(image_data, shape=None, save_eps=False,
label='data_figure', epsfilename=None, show=True):
"""
Produce a visual representation of the image_data matrix
"""
if shape is None:
shape = image_data.shape
if epsfilename is None:
epsfilename = 'log/' + label + '.eps'
fig = plt.figure(str_to_int(label))
# Treat nan values like zeros for display purposes
image_data = np.nan_to_num(np.copy(image_data))
# Diane made the brilliant suggestion to leave this plot in color.
# It looks much prettier.
plt.summer()
im = plt.imshow(image_data[0:shape[0], 0:shape[1]])
im.set_interpolation('nearest')
plt.title(label)
if show:
fig.show()
fig.canvas.draw()
if save_eps:
fig.savefig(epsfilename, format='eps')
return
def stream_plot():
"""範囲指定"""
plt.xlim(-2.0, 2.0)
plt.ylim(-2.0, 2.0)
"""流線を描く"""
plt.contour(xi[:, :],
eta[:, :],
g[:, :],
locator=plt.MultipleLocator(0.05))
plt.summer()
"""翼を描く(翼はξ、ηの配列のr=0の部分)"""
plt.plot(xi[:, 0], eta[:, 0])
plt.title('Sream Line')
plt.xlabel("ξ")
plt.ylabel("η")
cbar = plt.colorbar()
cbar.set_label("ψ")
plt.show()
def visualize_array(image_data,
shape=None,
save_eps=False,
label='data_figure',
epsfilename=None):
""" Produce a visual representation of the image_data matrix """
if shape is None:
shape = image_data.shape
if epsfilename is None:
epsfilename = 'log/' + label + '.eps'
fig = plt.figure(str_to_int(label))
# Diane made the brilliant suggestion to leave this plot in color.
# It looks much prettier.
plt.summer()
im = plt.imshow(image_data[0:shape[0], 0:shape[1]])
im.set_interpolation('nearest')
plt.title(label)
fig.show()
fig.canvas.draw()
if save_eps:
fig.savefig(epsfilename, format='eps')
return
intensity = make_ward_dictionary(df, "Primary Type", "NARCOTICS",
"Case Number")
row = 10
column = 5
count = 1
w_array = [[0] * column for _ in range(row)]
for i in range(row):
for j in range(column):
w_array[i][j] = intensity[count]
count += 1
intens_list1 = list(intensity.values())
max_intens1 = max(intens_list1)
plt.pcolor(w_array)
plt.colorbar()
plt.summer()
plt.title("Chicago Narcotics by Ward")
plt.xlabel("Ward Number Mod 5 on right")
plt.show()
with open("311_Service_Requests_-_Vacant_and_Abandoned_Buildings_Reported.csv"
) as vacant:
'''Second Heatmap for Vacant and Abandoned Buildings in Chicago By Ward'''
data = pd.read_csv(vacant, low_memory=False)
intensity = make_ward_dictionary(data, "SERVICE REQUEST TYPE",
"Vacant/Abandoned Building",
"SERVICE REQUEST NUMBER")
row = 10
column = 5
count = 1
bad
temp_new_M = np.delete(M, bad, axis =0)
new_M = np.delete(temp_new_M, bad, axis =1)
num_cohorts
midpoint_list = []
i = 0
for pop in pop_list:
midpoint_list.append(num_cohorts * (i + .5) )
i += 1
import matplotlib.pyplot as plt
plt.matshow(new_M)
plt.summer()
#x = midpoint_dict.values()
#names = midpoint_dict.keys()
plt.xticks(midpoint_list, pop_list)
plt.yticks(midpoint_list, pop_list)
title = ' L2 distance between deciles of length dist, '+str(2*num_samples)+' haps = '+str(num_samples)+' ind'
plt.title(title)
#plt.subplot(121)
plt.show()
