Supprimer 'functions/utils.py'

functions/utils.py

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