How to calculate the statistics “t-test” with numpy
In a scipy.stats package there are few ttest_… functions. See example from here: >>> print ‘t-statistic = %6.3f pvalue = %6.4f’ % stats.ttest_1samp(x, m) t-statistic = 0.391 pvalue = 0.6955
scipy
Fit a gaussian function
Take a look at this answer for fitting arbitrary curves to data. Basically you can use scipy.optimize.curve_fit to fit any function you want to your data. The code below shows how you can fit a Gaussian to some random data (credit to this SciPy-User mailing list post). import numpy from scipy.optimize import curve_fit import matplotlib.pyplot … Read more
how to annotate heatmap with text in matplotlib
There is no automatic feature to do such a thing, but you could loop through each point and put text in the appropriate location: import matplotlib.pyplot as plt import numpy as np data = np.random.rand(5, 4) heatmap = plt.pcolor(data) for y in range(data.shape[0]): for x in range(data.shape[1]): plt.text(x + 0.5, y + 0.5, ‘%.4f’ % … Read more
Get coordinates of local maxima in 2D array above certain value
import numpy as np import scipy import scipy.ndimage as ndimage import scipy.ndimage.filters as filters import matplotlib.pyplot as plt fname=”/tmp/slice0000.png” neighborhood_size = 5 threshold = 1500 data = scipy.misc.imread(fname) data_max = filters.maximum_filter(data, neighborhood_size) maxima = (data == data_max) data_min = filters.minimum_filter(data, neighborhood_size) diff = ((data_max – data_min) > threshold) maxima[diff == 0] = 0 labeled, num_objects … Read more
scipy is not optimizing and returns “Desired error not necessarily achieved due to precision loss”
I copied your example and tried a little bit. Looks like if you stick with BFGS solver, after a few iteration the mu+ alpha * r will have some negative numbers, and that’s how you get the RuntimeWarning. The easiest fix I can think of is to switch to Nelder Mead solver. res = minimize(loglikelihood, … Read more
Linear regression of arrays containing NANs in Python/Numpy
You can remove NaNs using a mask: mask = ~np.isnan(varx) & ~np.isnan(vary) slope, intercept, r_value, p_value, std_err = stats.linregress(varx[mask], vary[mask])
scipy, lognormal distribution – parameters
The distributions in scipy are coded in a generic way wrt two parameter location and scale so that location is the parameter (loc) which shifts the distribution to the left or right, while scale is the parameter which compresses or stretches the distribution. For the two parameter lognormal distribution, the “mean” and “std dev” correspond … Read more
Evaluate sympy expression from an array of values
First of all, at the moment SymPy does not guarantee support for numpy arrays which is what you want in this case. Check this bug report http://code.google.com/p/sympy/issues/detail?id=537 Second, If you want to evaluate something numerically for many values SymPy is not the best choice (it is a symbolic library after all). Use numpy and scipy. … Read more
Concatenate sparse matrices in Python using SciPy/Numpy
You can use the scipy.sparse.hstack to concatenate sparse matrices with the same number of rows (horizontal concatenation): from scipy.sparse import hstack hstack((X, X2)) Similarly, you can use scipy.sparse.vstack to concatenate sparse matrices with the same number of columns (vertical concatenation). Using numpy.hstack or numpy.vstack will create an array with two sparse matrix objects.
How to get the indices list of all NaN value in numpy array?
np.isnan combined with np.argwhere x = np.array([[1,2,3,4], [2,3,np.nan,5], [np.nan,5,2,3]]) np.argwhere(np.isnan(x)) output: array([[1, 2], [2, 0]])