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View gist:2465280
import numpy as np
def process_file(filename, num_cols, delimiter='\t'):
def items(infile):
for line in infile:
for item in line.rstrip().split(delimiter):
yield float(item)
with open(filename, 'r') as infile:
data = np.fromiter(items(infile))
View zeroing.py
# Make up some data in nested lists
strain_data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
# To make things a bit more readable, let's define a function that operates
# on a single list...
def zero(data):
"""Returns the difference between the items in "data" and its first item."""
# This is a "list comprehension". It's basically a 1-line for loop
return [item - data[0] for item in data]
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)
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')
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