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110 lines
3.6 KiB
Python
110 lines
3.6 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Thu Mar 1 11:30:31 2018
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@author: z5030440
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Contains all the utilities, convenience functions and small functions that do simple things
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"""
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import matplotlib.pyplot as plt
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from datetime import datetime, timedelta
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import numpy as np
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import scipy.io as sio
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import pdb
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def ecdf(x):
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"""convenience function for computing the empirical CDF"""
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vals, counts = np.unique(x, return_counts=True)
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ecdf = np.cumsum(counts).astype(np.float64)
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ecdf /= ecdf[-1]
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return vals, ecdf
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def intensity_histogram(image):
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"""plots histogram and cumulative distribution of the pixel intensities in an image"""
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imSize = image.shape
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if len(imSize) == 2:
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im = image[:,:].reshape(imSize[0] * imSize[1])
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im = im[~np.isnan(im)]
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fig, (ax1, ax2) = plt.subplots(2,1, sharex=True, figsize = (8,6))
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ax1.hist(im, bins=300)
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ax1.set_title('Probability density function')
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ax2.hist(im, bins=300, cumulative=True, histtype='step')
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ax2.set_title('Cumulative distribution')
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plt.show()
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else:
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for i in range(imSize[2]):
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im = image[:,:,i].reshape(imSize[0] * imSize[1])
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im = im[~np.isnan(im)]
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fig, (ax1, ax2) = plt.subplots(2,1, sharex=True, figsize = (8,6))
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ax1.hist(im, bins=300)
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ax1.set_title('Probability density function')
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ax2.hist(im, bins=300, cumulative=True, histtype='step')
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ax2.set_title('Cumulative distribution')
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plt.show()
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def compare_images(im1, im2):
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"""plots 2 images next to each other, sharing the axis"""
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plt.figure()
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ax1 = plt.subplot(121)
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plt.imshow(im1, cmap='gray')
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ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
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plt.imshow(im2, cmap='gray')
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plt.show()
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def find_indices(lst, condition):
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"imitation of MATLAB find function"
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return [i for i, elem in enumerate(lst) if condition(elem)]
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def reject_outliers(data, m=2):
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"rejects outliers in a numpy array"
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return data[abs(data - np.mean(data)) < m * np.std(data)]
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def duplicates_dict(lst):
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"return duplicates and indices"
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# nested function
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def duplicates(lst, item):
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return [i for i, x in enumerate(lst) if x == item]
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return dict((x, duplicates(lst, x)) for x in set(lst) if lst.count(x) > 1)
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def datenum2datetime(datenum):
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"convert datenum to datetime"
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#takes in datenum and outputs python datetime
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time = [datetime.fromordinal(int(dn)) + timedelta(days=float(dn)%1) - timedelta(days = 366) for dn in datenum]
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return time
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def loadmat(filename):
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'''
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this function should be called instead of direct spio.loadmat
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as it cures the problem of not properly recovering python dictionaries
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from mat files. It calls the function check keys to cure all entries
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which are still mat-objects
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'''
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data = sio.loadmat(filename, struct_as_record=False, squeeze_me=True)
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return _check_keys(data)
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def _check_keys(dict):
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'''
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checks if entries in dictionary are mat-objects. If yes
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todict is called to change them to nested dictionaries
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'''
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for key in dict:
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if isinstance(dict[key], sio.matlab.mio5_params.mat_struct):
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dict[key] = _todict(dict[key])
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return dict
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def _todict(matobj):
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'''
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A recursive function which constructs from matobjects nested dictionaries
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'''
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dict = {}
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for strg in matobj._fieldnames:
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elem = matobj.__dict__[strg]
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if isinstance(elem, sio.matlab.mio5_params.mat_struct):
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dict[strg] = _todict(elem)
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else:
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dict[strg] = elem
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return dict |