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View matplotlib_layout_model.py
import matplotlib.pyplot as plt
def main():
resized = (8, 3)
basic_example()
basic_example(resized)
fixed_aspect()
fixed_aspect(resized)
View pandas_merge_example.py
import numpy as np
import pandas as pd
#-- Generate some data similar to yours
idx = np.arange(20)
np.random.shuffle(idx)
idx1 = idx[:15]
np.random.shuffle(idx)
idx2 = idx[:10]
View bachelors_mpl_pr.py
import matplotlib.pyplot as plt
from matplotlib.mlab import csv2rec
from matplotlib.cbook import get_sample_data
#fname = get_sample_data('percent_bachelors_degrees_women_usa.csv')
fname = 'percent_bachelors_degrees_women_usa.csv'
gender_degree_data = csv2rec(fname)
# These are the colors that will be used in the plot
View datacursor_example_lookup.py
import random
import matplotlib.pyplot as plt
from mpldatacursor import datacursor
def main():
accounts = generate_accounts()
lookup = plot(accounts)
datacursor(formatter=Formatter(lookup), bbox=dict(alpha=1))
plt.show()
View datacursor_example_formatter_label.py
import random
import matplotlib.pyplot as plt
from mpldatacursor import datacursor
def main():
accounts = generate_accounts()
plot(accounts)
datacursor(formatter='Account #\n{label}'.format, bbox=dict(alpha=1))
plt.show()
@joferkington
joferkington / masked_hist.py
Created Jun 10, 2015
Quick example of masking 0-count portions of a 2D histogram
View masked_hist.py
import numpy as np
import matplotlib.pyplot as plt
x, y = np.random.normal(0, 1, (2, 1000))
counts, ybins, xbins = np.histogram2d(y, x, bins=30)
counts = np.ma.masked_equal(counts, 0)
fig, ax = plt.subplots()
ax.pcolormesh(xbins, ybins, counts)
@joferkington
joferkington / point_drag_add_delete.py
Created Mar 26, 2015
General example of the type of framework you need to efficiently implement drawable/draggable/deleteable artists in matplotlib.
View point_drag_add_delete.py
import numpy as np
import matplotlib.pyplot as plt
class DrawDragPoints(object):
"""
Demonstrates a basic example of the "scaffolding" you need to efficiently
blit drawable/draggable/deleteable artists on top of a background.
"""
def __init__(self):
self.fig, self.ax = self.setup_axes()
View gist:6789f086769527cc3157
import matplotlib.pyplot as plt
from matplotlib.transforms import Affine2D
import mpl_toolkits.axisartist.floating_axes as floating_axes
fig = plt.figure()
plot_extents = 0, 10, 0, 10
transform = Affine2D().rotate_deg(45)
helper = floating_axes.GridHelperCurveLinear(transform, plot_extents)
ax = floating_axes.FloatingSubplot(fig, 111, grid_helper=helper)
View gist:bbadb22da6949a285f95
import matplotlib.pyplot as plt
import cPickle as pickle
def main():
fig, ax = plt.subplots()
ax.plot(range(10))
ax.bar(range(10), range(10))
fig2 = copy_figure(fig)
fig2.axes[0].plot(range(10)[::-1], color='red')
View dashed_contours.py
import numpy as np
import matplotlib.pyplot as plt
x, y = np.mgrid[:10, :10]
z = np.hypot(x - 4.5, y - 4.5)
#-- Create two masked arrays, one with the upper region and one with the lower.
z1 = np.ma.masked_where(y > 5, z)
# If we just invert the previous masked region, we'll have a gap. There are
# better ways to do this, but for simple cases, we can just ensure a one-pixel
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