Skip to content

Don't use deprecated np.random.random_integers. #13088

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Jan 3, 2019
Merged

Don't use deprecated np.random.random_integers. #13088

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
71 changes: 34 additions & 37 deletions examples/statistics/boxplot_demo.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -82,9 +82,8 @@
# properties of the original sample, and a boxplot is one visual tool
# to make this assessment

numDists = 5
randomDists = ['Normal(1,1)', ' Lognormal(1,1)', 'Exp(1)', 'Gumbel(6,4)',
'Triangular(2,9,11)']
random_dists = ['Normal(1,1)', ' Lognormal(1,1)', 'Exp(1)', 'Gumbel(6,4)',
'Triangular(2,9,11)']
N = 500

norm = np.random.normal(1, 1, N)
Expand All @@ -95,15 +94,14 @@

# Generate some random indices that we'll use to resample the original data
# arrays. For code brevity, just use the same random indices for each array
bootstrapIndices = np.random.random_integers(0, N - 1, N)
Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

the deprecated call

normBoot = norm[bootstrapIndices]
expoBoot = expo[bootstrapIndices]
gumbBoot = gumb[bootstrapIndices]
lognBoot = logn[bootstrapIndices]
triaBoot = tria[bootstrapIndices]

data = [norm, normBoot, logn, lognBoot, expo, expoBoot, gumb, gumbBoot,
tria, triaBoot]
bootstrap_indices = np.random.randint(0, N, N)
data = [
norm, norm[bootstrap_indices],
logn, logn[bootstrap_indices],
expo, expo[bootstrap_indices],
gumb, gumb[bootstrap_indices],
tria, tria[bootstrap_indices],
]

fig, ax1 = plt.subplots(figsize=(10, 6))
fig.canvas.set_window_title('A Boxplot Example')
Expand All @@ -126,21 +124,19 @@
ax1.set_ylabel('Value')

# Now fill the boxes with desired colors
boxColors = ['darkkhaki', 'royalblue']
numBoxes = numDists*2
medians = list(range(numBoxes))
Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

medians gets fully overwritted in the loop below.

for i in range(numBoxes):
box_colors = ['darkkhaki', 'royalblue']
num_boxes = len(data)
medians = np.empty(num_boxes)
for i in range(num_boxes):
box = bp['boxes'][i]
boxX = []
boxY = []
for j in range(5):
boxX.append(box.get_xdata()[j])
boxY.append(box.get_ydata()[j])
boxCoords = np.column_stack([boxX, boxY])
box_coords = np.column_stack([boxX, boxY])
# Alternate between Dark Khaki and Royal Blue
k = i % 2
boxPolygon = Polygon(boxCoords, facecolor=boxColors[k])
ax1.add_patch(boxPolygon)
ax1.add_patch(Polygon(box_coords, facecolor=box_colors[i % 2]))
# Now draw the median lines back over what we just filled in
med = bp['medians'][i]
medianX = []
Expand All @@ -149,39 +145,40 @@
medianX.append(med.get_xdata()[j])
medianY.append(med.get_ydata()[j])
ax1.plot(medianX, medianY, 'k')
medians[i] = medianY[0]
Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

this line can be lifted out of the internal loop (and overwrites medians, as promised above).

medians[i] = medianY[0]
# Finally, overplot the sample averages, with horizontal alignment
# in the center of each box
ax1.plot([np.average(med.get_xdata())], [np.average(data[i])],
ax1.plot(np.average(med.get_xdata()), np.average(data[i]),
color='w', marker='*', markeredgecolor='k')

# Set the axes ranges and axes labels
ax1.set_xlim(0.5, numBoxes + 0.5)
ax1.set_xlim(0.5, num_boxes + 0.5)
top = 40
bottom = -5
ax1.set_ylim(bottom, top)
ax1.set_xticklabels(np.repeat(randomDists, 2),
ax1.set_xticklabels(np.repeat(random_dists, 2),
rotation=45, fontsize=8)

# Due to the Y-axis scale being different across samples, it can be
# hard to compare differences in medians across the samples. Add upper
# X-axis tick labels with the sample medians to aid in comparison
# (just use two decimal places of precision)
pos = np.arange(numBoxes) + 1
upperLabels = [str(np.round(s, 2)) for s in medians]
pos = np.arange(num_boxes) + 1
upper_labels = [str(np.round(s, 2)) for s in medians]
weights = ['bold', 'semibold']
for tick, label in zip(range(numBoxes), ax1.get_xticklabels()):
for tick, label in zip(range(num_boxes), ax1.get_xticklabels()):
k = tick % 2
ax1.text(pos[tick], top - (top*0.05), upperLabels[tick],
horizontalalignment='center', size='x-small', weight=weights[k],
color=boxColors[k])
ax1.text(pos[tick], .95, upper_labels[tick],
transform=ax1.get_xaxis_transform(),
horizontalalignment='center', size='x-small',
weight=weights[k], color=box_colors[k])

# Finally, add a basic legend
fig.text(0.80, 0.08, str(N) + ' Random Numbers',
backgroundcolor=boxColors[0], color='black', weight='roman',
fig.text(0.80, 0.08, f'{N} Random Numbers',
backgroundcolor=box_colors[0], color='black', weight='roman',
size='x-small')
fig.text(0.80, 0.045, 'IID Bootstrap Resample',
backgroundcolor=boxColors[1],
backgroundcolor=box_colors[1],
color='white', weight='roman', size='x-small')
fig.text(0.80, 0.015, '*', color='white', backgroundcolor='silver',
weight='roman', size='medium')
Expand Down Expand Up @@ -213,10 +210,10 @@ def fakeBootStrapper(n):
return med, CI

inc = 0.1
e1 = np.random.normal(0, 1, size=(500,))
e2 = np.random.normal(0, 1, size=(500,))
e3 = np.random.normal(0, 1 + inc, size=(500,))
e4 = np.random.normal(0, 1 + 2*inc, size=(500,))
e1 = np.random.normal(0, 1, size=500)
e2 = np.random.normal(0, 1, size=500)
e3 = np.random.normal(0, 1 + inc, size=500)
e4 = np.random.normal(0, 1 + 2*inc, size=500)

treatments = [e1, e2, e3, e4]
med1, CI1 = fakeBootStrapper(1)
Expand Down