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def gaussian(self, height, center_x, center_y, width_x, width_y, rotation): | |
"""Returns a gaussian function with the given parameters""" | |
width_x = float(width_x) | |
width_y = float(width_y) | |
rotation = np.deg2rad(rotation) | |
center_x = center_x * np.cos(rotation) - center_y * np.sin(rotation) | |
center_y = center_x * np.sin(rotation) + center_y * np.cos(rotation) | |
def rotgauss(x,y): |
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import cPickle as pickle | |
def save(obj, filename): | |
"""Simple wrapper to pickle an object on disk | |
:param: obj, any pickable object | |
:param: filename, string representation of the file to save to | |
""" | |
with open(filename, 'wb') as f: |
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import numpy as np | |
import matplotlib.pyplot as plt | |
def plot_array(npArray, axis=1, xlim=None, ylim=None): | |
plt.figure() | |
num_plots = npArray.shape[axis] | |
side = np.ceil(np.sqrt(num_plots)) | |
for current_plot in range(1, num_plots+1): |
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from scipy.optimize import curve_fit | |
def exponential(t, A, tau, C): | |
return A * np.exp(-tau * t) + C | |
start = None # redefine | |
ydata = data[start:] | |
xdata = np.arange(ydata.shape[0]) | |
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import numpy as np | |
def peakSort(values): | |
values = np.array(values) | |
low = np.floor(values.shape[0] / 2) | |
high = low+1 | |
new = np.zeros_like(values) | |
for i, item in enumerate(np.sort(values)[::-1]): | |
if i % 2: | |
new[high] = np.argwhere(values==item) | |
high += 1 |
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import numpy as np | |
def lifetimeSparseness(data): | |
N = data.shape[0] | |
top = (np.power(np.sum(data/N), 2)) | |
bottom = np.sum(np.power(data, 2) / N) | |
return (1 - top / bottom) |
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# good discussion here: http://stackoverflow.com/questions/4308168/sigmoidal-regression-with-scipy-numpy-python-etc | |
# curve_fit() example from here: http://permalink.gmane.org/gmane.comp.python.scientific.user/26238 | |
# other sigmoid functions here: http://en.wikipedia.org/wiki/Sigmoid_function | |
import numpy as np | |
import pylab | |
from scipy.optimize import curve_fit | |
def sigmoid(x, x0, k): | |
y = 1 / (1 + np.exp(-k*(x-x0))) |
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on open dropped_dir | |
tell application "Terminal" | |
set dirname to do shell script "perl -e \"print quotemeta ('" & POSIX path of item 1 of dropped_dir & "');\"" | |
do script "cd " & dirname & "; ipython notebook --pylab; exit" | |
end tell | |
end open |
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# found here: http://stackoverflow.com/questions/1589706/iterating-over-arbitrary-dimension-of-numpy-array | |
import numpy as np | |
def iterdim(a, axis=0) : | |
a = np.asarray(a); | |
leading_indices = (slice(None),)*axis | |
for i in xrange(a.shape[axis]) : | |
yield a[leading_indices+(i,)] | |