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@joferkington
joferkington / silly.py
Created Aug 28, 2015
Lovely dynamic typing
View silly.py
import random
class BadIdea(object):
def __getattr__(self, key):
return random.randint(0, 1000)
def __setattr__(self, key, value):
pass
x = BadIdea()
@joferkington
joferkington / yo_dawg.py
Created Feb 21, 2014
I'm having a bit too much fun with this...
View yo_dawg.py
import matplotlib.pyplot as plt
import numpy as np
def main():
t = np.linspace(0, 4*np.pi, 1000)
fig, ax = plt.subplots()
ax.plot(t, np.cos(t))
ax.plot(t, np.sin(t))
inception(inception(inception(fig)))
View outline.py
import scipy.ndimage as ndimage
import numpy as np
import matplotlib.pyplot as plt
data = np.zeros((40,40), dtype=bool)
data[5:20, 5:20] = True
data[15:35, 15:35] = True
footprint = np.ones((3,3))
outside = ndimage.binary_dilation(data, structure=footprint) - data
View BenchMapCoordinates.py
# -*- coding: utf-8 -*-
"""
Benchmark for spline interpolation of a 3D wind field, using the function map_coordinates.
The spline interpolation is about 46000 times slower than a linear interpolation.
It is also about 10000 times slower than an equivalent program, written in the programming
language Julia.
"""
import time
import numpy as np
from scipy import ndimage
View gist:0b858cecc1ee659dd0d8
pipeline = sklearn.pipeline.Pipeline([
('Replace nans',
preprocessing.Imputer(strategy='mean')),
('Scale data',
preprocessing.StandardScaler()),
('Feature Selection',
SelectPercentile(f_regression, percentile=60)),
('Regression',
ensemble.ExtraTreesRegressor(
n_estimators=550,
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
@joferkington
joferkington / projection.py
Created Dec 27, 2014
Spießbürger's stereonet: Fixed coordinate conversions. See http://stackoverflow.com/questions/27622007
View projection.py
import matplotlib
from matplotlib.axes import Axes
from matplotlib.patches import Circle
from matplotlib.path import Path
from matplotlib.ticker import NullLocator, Formatter, FixedLocator
from matplotlib.transforms import Affine2D, BboxTransformTo, Transform
from matplotlib.projections import register_projection
import matplotlib.spines as mspines
import matplotlib.axis as maxis
import matplotlib.pyplot as plt
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 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)
@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()
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