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fidle/img/00-Fidle-logo-00.svg

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fidle/img/00-Fidle-logo-01.svg

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fidle/img/00-Fidle-pdf.svg

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fidle/img/00-Notebooks.svg

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fidle/img/00-Videos.svg

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\ No newline at end of file

fidle/mplstyles/custom.mplstyle

-
-# See : https://matplotlib.org/users/customizing.html
-
-axes.titlesize : 24
-axes.labelsize : 20
-axes.edgecolor      : dimgrey
-axes.labelcolor     : dimgrey
-axes.linewidth      : 2
-axes.grid           : False
-
-axes.prop_cycle    : cycler('color', ['steelblue', 'tomato', '2ca02c', 'd62728', '9467bd', '8c564b', 'e377c2', '7f7f7f', 'bcbd22', '17becf'])
-
-lines.linewidth     : 3
-lines.markersize    : 10
-
-xtick.color         : black
-xtick.labelsize     : 18
-ytick.color         : black
-ytick.labelsize     : 18
-
-axes.spines.left   : True
-axes.spines.bottom : True
-axes.spines.top    : False
-axes.spines.right  : False
-
-savefig.dpi         : 300      # figure dots per inch or 'figure'
-savefig.facecolor   : white    # figure facecolor when saving
-savefig.edgecolor   : white    # figure edgecolor when saving
-savefig.format      : svg
-savefig.bbox        : tight
-savefig.pad_inches  : 0.1
-savefig.transparent : True

fidle/pwk.py

-
-# ==================================================================
-#  ____                 _   _           _  __        __         _
-# |  _ \ _ __ __ _  ___| |_(_) ___ __ _| | \ \      / /__  _ __| | __
-# | |_) | '__/ _` |/ __| __| |/ __/ _` | |  \ \ /\ / / _ \| '__| |/ /
-# |  __/| | | (_| | (__| |_| | (_| (_| | |   \ V  V / (_) | |  |   <
-# |_|   |_|  \__,_|\___|\__|_|\___\__,_|_|    \_/\_/ \___/|_|  |_|\_\
-#                                                        module pwk                                   
-# ==================================================================
-# A simple module to host some common functions for practical work
-# Jean-Luc Parouty 2020
-
-import os
-import glob
-import shutil
-from datetime import datetime
-import itertools
-import datetime, time
-
-import math
-import numpy as np
-from collections.abc import Iterable
-
-import tensorflow as tf
-from tensorflow import keras
-from sklearn.metrics import confusion_matrix
-
-import pandas as pd
-import matplotlib
-import matplotlib.pyplot as plt
-import seaborn as sn     #IDRIS : module en cours d'installation
-
-from IPython.display import display,Image,Markdown,HTML
-
-import fidle.config as config
-
-_save_figs = False
-_figs_dir  = './figs'
-_figs_name = 'fig_'
-_figs_id   = 0
-
-# -------------------------------------------------------------
-# init_all
-# -------------------------------------------------------------
-#
-def init( mplstyle='../fidle/mplstyles/custom.mplstyle', 
-          cssfile='../fidle/css/custom.css',
-          places={ 'SOMEWHERE' : '/path/to/datasets'}):
-    
-    update_keras_cache=False
- 
-    # ---- Predifined places
-    #
-    predefined_places = config.locations
-    
-    # ---- Load matplotlib style and css
-    #
-    matplotlib.style.use(mplstyle)
-    load_cssfile(cssfile)
-    
-    # ---- Create subdirs
-    #
-    mkdir('./run')
-    
-    # ---- Try to find where we are
-    #
-    place_name, dataset_dir = where_we_are({**places, **predefined_places})
-    
-    
-    # ---- If we are at IDRIS, we need to copy datasets/keras_cache to keras cache...
-    #
-    if place_name=='Fidle at IDRIS':
-        from_dir = f'{dataset_dir}/keras_cache/*.*'
-        to_dir   = os.path.expanduser('~/.keras/datasets')
-        mkdir(to_dir)
-        for pathname in glob.glob(from_dir):
-            filename=os.path.basename(pathname)
-            destname=f'{to_dir}/{filename}'
-            if not os.path.isfile(destname):
-                shutil.copy(pathname, destname)
-        update_keras_cache=True
-    
-    # ---- Hello world
-    print('\nFIDLE 2020 - Practical Work Module')
-    print('Version              :', config.VERSION)
-    print('Run time             : {}'.format(time.strftime("%A %-d %B %Y, %H:%M:%S")))
-    print('TensorFlow version   :',tf.__version__)
-    print('Keras version        :',tf.keras.__version__)
-    print('Current place        :',place_name )
-    print('Dataset dir          :',dataset_dir)
-    if update_keras_cache:
-        print('Update keras cache   : Done')
-    
-    return place_name, dataset_dir
-
-# -------------------------------------------------------------
-# Folder cooking
-# -------------------------------------------------------------
-#
-def tag_now():
-    return datetime.datetime.now().strftime("%Y-%m-%d_%Hh%Mm%Ss")
-
-def mkdir(path):
-    os.makedirs(path, mode=0o750, exist_ok=True)
-      
-def get_directory_size(path):
-    """
-    Return the directory size, but only 1 level
-    args:
-        path : directory path
-    return:
-        size in Mo
-    """
-    size=0
-    for f in os.listdir(path):
-        if os.path.isfile(path+'/'+f):
-            size+=os.path.getsize(path+'/'+f)
-    return size/(1024*1024)
-
-# ------------------------------------------------------------------
-# Where we are ?
-# ------------------------------------------------------------------
-#
-def where_we_are(places):
-        
-    for place_name, place_dir in places.items():
-        if os.path.isdir(place_dir):
-            return place_name,place_dir
-
-    print('** ERROR ** : Le dossier datasets est introuvable\n')
-    print('              Vous devez :\n')
-    print('                 1/ Récupérer le dossier datasets')
-    print('                    Une archive (datasets.tar) est disponible via le repo Fidle.\n')
-    print("                 2/ Préciser la localisation de ce dossier datasets")
-    print("                    Soit dans le fichier fidle/config.py (préférable)")
-    print("                    Soit via un paramètre à la fonction ooo.init()\n")
-    print('   Par exemple :')
-    print("         ooo.init( places={ 'Chez-moi':'/tmp/datasets',  'Sur-mon-cluster':'/tests/datasets'}')\n")
-    print('   Note : Vous pouvez également déposer le dossier datasets directement dans votre home : ~/datasets\n\n')
-    assert False, 'datasets folder not found : Abort all.'
-
-
-# -------------------------------------------------------------
-# shuffle_dataset
-# -------------------------------------------------------------
-#
-def shuffle_np_dataset(x, y):
-    """
-    Shuffle a dataset (x,y)
-    args:
-        x,y : dataset
-    return:
-        x,y mixed
-    """
-    assert (len(x) == len(y)), "x and y must have same size"
-    p = np.random.permutation(len(x))
-    return x[p], y[p]
-
-
-def update_progress(what,i,imax, redraw=False):
-    """
-    Display a text progress bar, as :
-    My progress bar : ############# 34%
-    args:
-        what  : Progress bas name
-        i     : Current progress
-        imax  : Max value for i
-    return:
-        nothing
-    """
-    bar_length = min(40,imax)
-    if (i%int(imax/bar_length))!=0 and i<imax and not redraw:
-        return
-    progress  = float(i/imax)
-    block     = int(round(bar_length * progress))
-    endofline = '\r' if progress<1 else '\n'
-    text = "{:16s} [{}] {:>5.1f}% of {}".format( what, "#"*block+"-"*(bar_length-block), progress*100, imax)
-    print(text, end=endofline)
-
-    
-def rmax(l):
-    """
-    Recursive max() for a given iterable of iterables
-    Should be np.array of np.array or list of list, etc.
-    args:
-        l : Iterable of iterables
-    return: 
-        max value
-    """
-    maxi = float('-inf')
-    for item in l:
-        if isinstance(item, Iterable):
-            t = rmax(item)
-        else:
-            t = item
-        if t > maxi:
-            maxi = t
-    return maxi
-
-def rmin(l):
-    """
-    Recursive min() for a given iterable of iterables
-    Should be np.array of np.array or list of list, etc.
-    args:
-        l : Iterable of iterables
-    return: 
-        min value
-    """
-    mini = float('inf')
-    for item in l:
-        if isinstance(item, Iterable):
-            t = rmin(item)
-        else:
-            t = item
-        if t < mini:
-            mini = t
-    return mini
-
-# -------------------------------------------------------------
-# show_images
-# -------------------------------------------------------------
-#
-def plot_images(x,y=None, indices='all', columns=12, x_size=1, y_size=1,
-                colorbar=False, y_pred=None, cm='binary',y_padding=0.35, spines_alpha=1,
-                fontsize=20, save_as='auto'):
-    """
-    Show some images in a grid, with legends
-    args:
-        x             : images - Shapes must be (-1,lx,ly) (-1,lx,ly,1) or (-1,lx,ly,3)
-        y             : real classes or labels or None (None)
-        indices       : indices of images to show or None for all (None)
-        columns       : number of columns (12)
-        x_size,y_size : figure size (1), (1)
-        colorbar      : show colorbar (False)
-        y_pred        : predicted classes (None)
-        cm            : Matplotlib color map (binary)
-        y_padding     : Padding / rows (0.35)
-        spines_alpha  : Spines alpha (1.)
-        font_size     : Font size in px (20)
-        save_as       : Filename to use if save figs is enable ('auto')
-    returns: 
-        nothing
-    """
-    if indices=='all': indices=range(len(x))
-    draw_labels = (y is not None)
-    draw_pred   = (y_pred is not None)
-    rows        = math.ceil(len(indices)/columns)
-    fig=plt.figure(figsize=(columns*x_size, rows*(y_size+y_padding)))
-    n=1
-    for i in indices:
-        axs=fig.add_subplot(rows, columns, n)
-        n+=1
-        # ---- Shape is (lx,ly)
-        if len(x[i].shape)==2:
-            xx=x[i]
-        # ---- Shape is (lx,ly,n)
-        if len(x[i].shape)==3:
-            (lx,ly,lz)=x[i].shape
-            if lz==1: 
-                xx=x[i].reshape(lx,ly)
-            else:
-                xx=x[i]
-        img=axs.imshow(xx,   cmap = cm, interpolation='lanczos')
-        axs.spines['right'].set_visible(True)
-        axs.spines['left'].set_visible(True)
-        axs.spines['top'].set_visible(True)
-        axs.spines['bottom'].set_visible(True)
-        axs.spines['right'].set_alpha(spines_alpha)
-        axs.spines['left'].set_alpha(spines_alpha)
-        axs.spines['top'].set_alpha(spines_alpha)
-        axs.spines['bottom'].set_alpha(spines_alpha)
-        axs.set_yticks([])
-        axs.set_xticks([])
-        if draw_labels and not draw_pred:
-            axs.set_xlabel(y[i],fontsize=fontsize)
-        if draw_labels and draw_pred:
-            if y[i]!=y_pred[i]:
-                axs.set_xlabel(f'{y_pred[i]} ({y[i]})',fontsize=fontsize)
-                axs.xaxis.label.set_color('red')
-            else:
-                axs.set_xlabel(y[i],fontsize=fontsize)
-        if colorbar:
-            fig.colorbar(img,orientation="vertical", shrink=0.65)
-    save_fig(save_as)
-    plt.show()
-
-    
-def plot_image(x,cm='binary', figsize=(4,4),save_as='auto'):
-    """
-    Draw a single image.
-    Image shape can be (lx,ly), (lx,ly,1) or (lx,ly,n)
-    args:
-        x       : image as np array
-        cm      : color map ('binary')
-        figsize : fig size (4,4)
-    """
-    # ---- Shape is (lx,ly)
-    if len(x.shape)==2:
-        xx=x
-    # ---- Shape is (lx,ly,n)
-    if len(x.shape)==3:
-        (lx,ly,lz)=x.shape
-        if lz==1: 
-            xx=x.reshape(lx,ly)
-        else:
-            xx=x
-    # ---- Draw it
-    plt.figure(figsize=figsize)
-    plt.imshow(xx,   cmap = cm, interpolation='lanczos')
-    save_fig(save_as)
-    plt.show()
-
-
-# -------------------------------------------------------------
-# show_history
-# -------------------------------------------------------------
-#
-def plot_history(history, figsize=(8,6), 
-                 plot={"Accuracy":['accuracy','val_accuracy'], 'Loss':['loss', 'val_loss']},
-                 save_as='auto'):
-    """
-    Show history
-    args:
-        history: history
-        figsize: fig size
-        plot: list of data to plot : {<title>:[<metrics>,...], ...}
-    """
-    fig_id=0
-    for title,curves in plot.items():
-        plt.figure(figsize=figsize)
-        plt.title(title)
-        plt.ylabel(title)
-        plt.xlabel('Epoch')
-        for c in curves:
-            plt.plot(history.history[c])
-        plt.legend(curves, loc='upper left')
-        if save_as=='auto':
-            figname='auto'
-        else:
-            figname=f'{save_as}_{fig_id}'
-            fig_id+=1
-        save_fig(figname)
-        plt.show()
-
-    
-    
-# -------------------------------------------------------------
-# plot_confusion_matrix
-# -------------------------------------------------------------
-# Bug in Matplotlib 3.1.1
-#
-def plot_confusion_matrix(cm,
-                          title='Confusion matrix',
-                          figsize=(12,8),
-                          cmap="gist_heat_r",
-                          vmin=0,
-                          vmax=1,
-                          xticks=5,yticks=5,
-                          annot=True,
-                          save_as='auto'):
-    """
-    given a sklearn confusion matrix (cm), make a nice plot
-    Note:bug in matplotlib 3.1.1
-
-    Args:
-        cm:           confusion matrix from sklearn.metrics.confusion_matrix
-        title:        the text to display at the top of the matrix
-        figsize:      Figure size (12,8)
-        cmap:         color map (gist_heat_r)
-        vmi,vmax:     Min/max 0 and 1
-        annot:        Annotation or just colors (True)
-        
-    """
- 
-    accuracy = np.trace(cm) / float(np.sum(cm))
-    misclass = 1 - accuracy
-
-    plt.figure(figsize=figsize)
-    sn.heatmap(cm, linewidths=1, linecolor="#ffffff",square=True, 
-               cmap=cmap, xticklabels=xticks, yticklabels=yticks,
-               vmin=vmin,vmax=vmax,annot=annot)
-    plt.ylabel('True label')
-    plt.xlabel('Predicted label\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))
-    save_fig(save_as)
-    plt.show()
-
-
-    
-def display_confusion_matrix(y_true,y_pred,labels=None,color='green',
-                             font_size='12pt', title="#### Confusion matrix is :"):
-    """
-    Show a confusion matrix for a predictions.
-    see : sklearn.metrics.confusion_matrix
-
-    Args:
-        y_true        Real classes
-        y_pred        Predicted classes
-        labels        List of classes to show in the cm
-        color:        Color for the palette (green)
-        font_size:    Values font size 
-        title:        the text to display at the top of the matrix        
-    """
-    assert (labels!=None),"Label must be set"
-    
-    if title != None :  display(Markdown(title)) 
-    
-    cm = confusion_matrix( y_true,y_pred, normalize="true", labels=labels)
-    df=pd.DataFrame(cm)
-
-    cmap = sn.light_palette(color, as_cmap=True)
-    df.style.set_properties(**{'font-size': '20pt'})
-    display(df.style.format('{:.2f}') \
-            .background_gradient(cmap=cmap)
-            .set_properties(**{'font-size': font_size}))
-    
-    
-def plot_donut(values, labels, colors=["lightsteelblue","coral"], figsize=(6,6), title=None, save_as='auto'):
-    """
-    Draw a donut
-    args:
-        values   : list of values
-        labels   : list of labels
-        colors   : list of color (["lightsteelblue","coral"])
-        figsize  : size of figure ( (6,6) )
-    return:
-        nothing
-    """
-    # ---- Title or not
-    if title != None :  display(Markdown(title))
-    # ---- Donut
-    plt.figure(figsize=figsize)
-    # ---- Draw a pie  chart..
-    plt.pie(values, labels=labels, 
-            colors = colors, autopct='%1.1f%%', startangle=70, pctdistance=0.85,
-            textprops={'fontsize': 18},
-            wedgeprops={"edgecolor":"w",'linewidth': 5, 'linestyle': 'solid', 'antialiased': True})
-    # ---- ..with a white circle
-    circle = plt.Circle((0,0),0.70,fc='white')
-    ax = plt.gca()
-    ax.add_artist(circle)
-    # Equal aspect ratio ensures that pie is drawn as a circle
-    plt.axis('equal')  
-    plt.tight_layout()
-    save_fig(save_as)
-    plt.show()
-    
-
-    
-def plot_multivariate_serie(sequence, labels=None, predictions=None, only_features=None,
-                            columns=3, width=5,height=4,wspace=0.3,hspace=0.2,
-                            save_as='auto', time_dt=1):
-    
-    sequence_len = len(sequence)
-    features_len = sequence.shape[1]
-    if only_features is None : only_features=range(features_len)
-    if labels is None        : labels=range(features_len)
-    
-    t  = np.arange(sequence_len)    
-    if predictions is None:
-        dt = 0
-    else:
-        dt = len(predictions)
-        sequence_with_pred = sequence.copy()
-        sequence_with_pred[-dt:]=predictions
-
-    rows = math.ceil(features_len/columns)
-    fig  = plt.figure(figsize=(columns*width, rows*height))
-    fig.subplots_adjust(wspace=0.3,hspace=0.2)
-    n=1
-    for i in only_features:
-        ax=fig.add_subplot(rows, columns, n)
-        ax.plot(t[:-dt],       sequence[:-dt,i],    '-',  linewidth=1,  color='steelblue', label=labels[i])
-        ax.plot(t[:-dt],       sequence[:-dt,i],    'o',  markersize=4, color='steelblue')
-        ax.plot(t[-dt-1:], sequence[-dt-1:,i],'--o', linewidth=1, fillstyle='none',  markersize=6, color='steelblue')
-        if predictions is not None:
-            ax.plot(t[-dt-1:],     sequence_with_pred[-dt-1:,i],     '--',  linewidth=1, fillstyle='full',  markersize=6, color='red')
-            ax.plot(t[-dt:],       predictions[:,i],     'o',  linewidth=1, fillstyle='full',  markersize=6, color='red')
-
-        ax.legend(loc="upper left")
-        n+=1
-    save_fig(save_as)
-    plt.show()
-
- 
-    
-    
-    
-def set_save_fig(save=True, figs_dir='./figs', figs_name='fig_', figs_id=0):
-    """
-    Set save_fig parameters
-    Default figs name is <figs_name><figs_id>.{png|svg}
-    args:
-        save      : Boolean, True to save figs (True)
-        figs_dir  : Path to save figs (./figs)
-        figs_name : Default basename for figs (figs_)
-        figs_id   : Start id for figs name (0)
-    """
-    global _save_figs, _figs_dir, _figs_name, _figs_id
-    _save_figs = save
-    _figs_dir  = figs_dir
-    _figs_name = figs_name
-    _figs_id   = figs_id
-    print(f'Save figs            : {_save_figs}')
-    print(f'Path figs            : {_figs_dir}')
-    
-    
-def save_fig(filename='auto', png=True, svg=False):
-    """
-    Save current figure
-    args:
-        filename : Image filename ('auto')
-        png      : Boolean. Save as png if True (True)
-        svg      : Boolean. Save as svg if True (False)
-    """
-    global _save_figs, _figs_dir, _figs_name, _figs_id
-    if not _save_figs : return
-    mkdir(_figs_dir)
-    if filename=='auto': 
-        path=f'{_figs_dir}/{_figs_name}{_figs_id:02d}'
-    else:
-        path=f'{_figs_dir}/{filename}'
-    if png : plt.savefig( f'{path}.png')
-    if svg : plt.savefig( f'{path}.png')
-    if filename=='auto': _figs_id+=1
-    
-
-def subtitle(t):
-    display(Markdown(f'<br>**{t}**'))
-    
-def display_md(md_text):
-    display(Markdown(md_text))
-    
-def display_img(img):
-    display(Image(img))
-    
-def hdelay(sec):
-    return str(datetime.timedelta(seconds=int(sec)))
-
-def hsize(num, suffix='o'):
-    for unit in ['','K','M','G','T','P','E','Z']:
-        if abs(num) < 1024.0:
-            return f'{num:3.1f} {unit}{suffix}'
-        num /= 1024.0
-    return f'{num:.1f} Y{suffix}'
-
-def load_cssfile(cssfile):
-    if cssfile is None: return
-    styles = open(cssfile, "r").read()
-    display(HTML(styles))
-    
-     
-        
-def np_print(*args, format={'float': '{:6.3f}'.format}):
-    with np.printoptions(formatter=format):
-        for a in args:
-            print(a)
-     
-     
-     
-     
-     
-