Python matplotlib.pyplot 模块,tick_params() 实例源码

我们从Python开源项目中,提取了以下50个代码示例,用于说明如何使用matplotlib.pyplot.tick_params()

项目:code-uai16    作者:thanhan    | 项目源码 | 文件源码
def plot_gold(g1, g2, lc, p = 0):
    """
    plot sen/spe of g1 against g2
    only consider workers in lc
    """

    mv = crowd_model.mv_model(lc)
    s1 = []; s2 = []

    for w in g1.keys():
        if w in g2 and g1[w][p] != None and g2[w][p] != None and w in mv.dic_ss:
            s1.append(g1[w][p])
            s2.append(g2[w][p])

    plt.xticks((0, 0.5, 1), ("0", "0.5", "1"))
    plt.tick_params(labelsize = 25)
    plt.yticks((0, 0.5, 1), ("0", "0.5", "1"))

    plt.xlim(0,1)
    plt.ylim(0,1)
    plt.scatter(s1, s2, marker = '.', s=50, c = 'black')

    plt.xlabel('task 1 sen.', fontsize = 25)
    plt.ylabel('task 2 sen.', fontsize = 25)
项目:MicroGrids    作者:squoilin    | 项目源码 | 文件源码
def Energy_Flow(Time_Series):


    Energy_Flow = {'Energy_Demand':0, 'Lost Load':0, 'Energy PV':0,'Curtailment':0, 'Energy Diesel':0, 'Discharge energy from the Battery':0, 'Charge energy to the Battery':0}

    for v in Energy_Flow.keys():
        if v == 'Energy PV':
            Energy_Flow[v] = round((Time_Series[v].sum() - Time_Series['Curtailment'].sum()- Time_Series['Charge energy to the Battery'].sum())/1000000, 2)
        else:
            Energy_Flow[v] = round((Time_Series[v].sum())/1000000, 2)


    c = ['From Generator', 'To Battery', 'Demand', 'From PV', 'From Battery', 'Curtailment', 'Lost Load']       
    plt.figure()    
    plt.bar((1,2,3,4,5,6,7), Energy_Flow.values(), color= 'b', alpha=0.3, align='center')

    plt.xticks((1.2,2.2,3.2,4.2,5.2,6.2,7.2), c)
    plt.xlabel('Technology')
    plt.ylabel('Energy Flow (MWh)')
    plt.tick_params(axis='x', which='both', bottom='off', top='off', labelbottom='on')
    plt.xticks(rotation=-30)
    plt.savefig('Results/Energy_Flow.png', bbox_inches='tight')
    plt.show()    

    return Energy_Flow
项目:autonomio    作者:autonomio    | 项目源码 | 文件源码
def paramagg(data):

    '''
    USE: paramagg(df)

    Provides an overview in one plot for a parameter scan. Useful
    to understand rough distribution of accuracacy and loss for both
    test and train.

    data = a pandas dataframe from hyperscan()
    '''

    plt.figure(num=None, figsize=(8, 8), dpi=80, facecolor='w', edgecolor='k')

    plt.scatter(data.train_loss, data.train_acc, label='train')
    plt.scatter(data.test_loss, data.test_acc, label='test')

    plt.legend(loc='upper right')
    plt.tick_params(axis='both', which='major', pad=15)

    plt.xlabel('loss', fontsize=18, labelpad=15, color="gray")
    plt.ylabel('accuracy', fontsize=18, labelpad=15, color="gray")

    plt.show()
项目:actions-for-actions    作者:gsig    | 项目源码 | 文件源码
def finalize_plot(allticks,handles):
    plt.locator_params(axis='x', nticks=Noracles,nbins=Noracles)
    plt.yticks([x[0] for x in allticks], [x[1] for x in allticks])
    plt.tick_params(
        axis='y',          # changes apply to the x-axis
        which='both',      # both major and minor ticks are affected
        left='off',      # ticks along the bottom edge are off
        right='off'         # ticks along the top edge are off
    )
    if LEGEND:
        plt.legend([h[0] for h in handles],seriesnames,
                   loc='upper right',borderaxespad=0.,
                   ncol=1,fontsize=10,numpoints=1)
    plt.gcf().tight_layout()


######################################################
# Data processing
项目:twitter_LDA_topic_modeling    作者:kenneth-orton    | 项目源码 | 文件源码
def draw_dual_line_graph(title, x_label, y_label, y_axis_1, y_axis_2, line_1_label, line_2_label, output_path):
    x_axis = np.arange(0, len(y_axis_1))
    fig = plt.figure()
    fig.suptitle(title, fontsize=14, fontweight='bold')
    ax = fig.add_subplot(111)
    ax.set_xlabel(x_label)
    ax.set_ylabel(y_label)
    ax.plot(x_axis, y_axis_1, 'b')
    ax.plot(x_axis, y_axis_2, 'g', alpha=0.7)
    ax.legend([line_1_label, line_2_label], loc='center', bbox_to_anchor=(0.5, -0.18), ncol=2)
    ax.axis([0, np.amax(x_axis), 0, np.log(2) + .001])
    plt.margins(0.2)
    plt.tick_params(labelsize=10)
    fig.subplots_adjust(bottom=0.2)
    plt.savefig(output_path + '.eps', format='eps')
    plt.savefig(output_path)
    plt.close(fig)
项目:movement-quadrants    作者:sealneaward    | 项目源码 | 文件源码
def plot_shot(data):
    plt.figure(figsize=(12,11))
    plt.scatter(data.LOC_X, data.LOC_Y, c=data.shot_zone_range_area, s=30)
    draw_court()
    # Adjust plot limits to just fit in half court
    plt.xlim(-250,250)
    # Descending values along th y axis from bottom to top
    # in order to place the hoop by the top of plot
    plt.ylim(422.5, -47.5)
    # get rid of axis tick labels
    # plt.tick_params(labelbottom=False, labelleft=False)
    plt.savefig('./data/img/half/fully_converted_with_range_areas.jpg')
    plt.close()

###########################################################################
# Visualization of court: http://savvastjortjoglou.com/nba-shot-sharts.html
###########################################################################
项目:movement-quadrants    作者:sealneaward    | 项目源码 | 文件源码
def plot_half_court_movement(data):
    plt.figure(figsize=(12,11))
    plt.scatter(data.x_loc, data.y_loc, c=data.game_clock,
                cmap=plt.cm.Blues, s=250, zorder=1)

    draw_half_court()
    # Adjust plot limits to just fit in half court
    plt.xlim(-250,250)
    # Descending values along th y axis from bottom to top
    # in order to place the hoop by the top of plot
    plt.ylim(422.5, -47.5)
    # get rid of axis tick labels
    # plt.tick_params(labelbottom=False, labelleft=False)
    plt.savefig('./data/img/half/event_movement_convert_half.jpg')
    plt.close()


########################################################################
# Convert all full court coordinates in data to half court coordinates
########################################################################
项目:recognizeFitExercise    作者:tyiannak    | 项目源码 | 文件源码
def visualizeFeatures(Features, Files, Names):    
    y_eig, coeff = pcaDimRed(Features, 2)    
    plt.close("all")
    print y_eig
    plt.subplot(2,1,1);
    ax = plt.gca()
    for i in range(len(Files)):
        im = cv2.imread(Files[i], cv2.CV_LOAD_IMAGE_COLOR)    
        Width = 0.2;  Height = 0.2; startX = y_eig[i][0]; startY = y_eig[i][1];
        print startX, startY
        myaximage = ax.imshow(cv2.cvtColor(im, cv2.cv.CV_RGB2BGR), extent=(startX-Width/2.0, startX+Width/2.0, startY-Height/2.0, startY+Height/2.0), alpha=1.0, zorder=-1)
        plt.axis((-3,3,-3,3))
    # Plot feaures
    plt.subplot(2,1,2)    
    ax = plt.gca()
    for i in range(len(Files)):            
        plt.plot(numpy.array(Features[i,:].T));
    plt.xticks(range(len(Names)))
    plt.legend(Files)
    ax.set_xticklabels(Names)
    plt.setp(plt.xticks()[1], rotation=90)
    plt.tick_params(axis='both', which='major', labelsize=8)
    plt.tick_params(axis='both', which='minor', labelsize=8)

    plt.show()
项目:ddnn    作者:kunglab    | 项目源码 | 文件源码
def plot_positions(df, img_path, frame, cam):
    color_dict = {'car': '#fc8d59', 'bus': '#ffffbf', 'person': '#91cf60'}
    frame_pos = df[(df['frame'] == frame) & (df['cam'] == cam)]
    fig = plt.figure()
    ax = fig.add_subplot(111, aspect='equal')
    im = plt.imread(img_path)
    ax.imshow(im)
    for i, f in frame_pos.iterrows():
        add_rect(ax, f['x'], f['y'], f['w'], f['h'], color=color_dict[f['class_name']], name=f['id'])


    legend_handles = []
    for k, v in color_dict.iteritems():
        handle = patches.Patch(color=v, label=k)
        legend_handles.append(handle)

    plt.legend(loc=0, handles=legend_handles)
    plt.xlim((0, 360))
    plt.ylim((0, 288))
    plt.ylim(plt.ylim()[::-1])
    plt.tight_layout()
    plt.tick_params(axis='both', left='off', top='off', right='off',
                    bottom='off', labelleft='off', labeltop='off',
                    labelright='off', labelbottom='off')
    plt.show()
项目:bates_galaxies_lab    作者:aleksds    | 项目源码 | 文件源码
def plot_image():
    std = np.std(stamp[stamp==stamp])
    plt.imshow(stamp, interpolation='nearest', origin = 'lower', vmin = -1.*std, vmax = 3.*std, cmap='bone')
    plt.tick_params(axis='both', which='major', labelsize=8)
    circle0 = plt.Circle((dx,dy),0.1)
    x1, y1 = centroid_com(stamp)
    circle1 = plt.Circle((x1,y1),0.1,color='r')
    ax.add_artist(circle1)
    x2, y2 = centroid_1dg(stamp)
    circle2 = plt.Circle((x2,y2),0.1,color='b')
    ax.add_artist(circle2)
    x3, y3 = centroid_2dg(stamp)
    circle3 = plt.Circle((x3,y3),0.1,color='g')
    ax.add_artist(circle3)
    print(x1, x2, x3)
    print(y1, y2, y3)

# define the directory that contains the images
项目:bates_galaxies_lab    作者:aleksds    | 项目源码 | 文件源码
def plot_image():
    std = np.std(stamp[stamp==stamp])
    plt.imshow(stamp, interpolation='nearest', origin = 'lower', vmin = -1.*std, vmax = 3.*std, cmap='bone')
    plt.tick_params(axis='both', which='major', labelsize=8)
    circle0 = plt.Circle((dx,dy),0.1)
    x1, y1 = centroid_com(stamp)
    circle1 = plt.Circle((x1,y1),0.1,color='r')
    ax.add_artist(circle1)
    x2, y2 = centroid_1dg(stamp)
    circle2 = plt.Circle((x2,y2),0.1,color='b')
    ax.add_artist(circle2)
    x3, y3 = centroid_2dg(stamp)
    circle3 = plt.Circle((x3,y3),0.1,color='g')
    ax.add_artist(circle3)
    print(x1, x2, x3)
    print(y1, y2, y3)

# define the directory that contains the images
项目:bates_galaxies_lab    作者:aleksds    | 项目源码 | 文件源码
def plot_image():
    std = np.std(stamp[stamp==stamp])
    plt.imshow(stamp, interpolation='nearest', origin = 'lower', vmin = -1.*std, vmax = 3.*std, cmap='bone')
    plt.tick_params(axis='both', which='major', labelsize=8)
    circle0 = plt.Circle((dx,dy),0.1)
    x1, y1 = centroid_com(stamp)
    circle1 = plt.Circle((x1,y1),0.1,color='r')
    ax.add_artist(circle1)
    x2, y2 = centroid_1dg(stamp)
    circle2 = plt.Circle((x2,y2),0.1,color='b')
    ax.add_artist(circle2)
    x3, y3 = centroid_2dg(stamp)
    circle3 = plt.Circle((x3,y3),0.1,color='g')
    ax.add_artist(circle3)
    return ((x1, y1),(x2, y2),(x3, y3))
    #print(x1, x2, x3)
    #print('now some y values')
    #print(y1, y2, y3)

# define the directory that contains the images
项目:astetik    作者:mikkokotila    | 项目源码 | 文件源码
def swarm(data,x,y,xscale='linear',yscale='linear'):

    # set default pretty settings from Seaborn

    sns.set(style="white", palette="muted")
    sns.set_context("notebook", font_scale=1, rc={"lines.linewidth": 0.2}) 

    # createthe plot

    g = sns.swarmplot(x=x, y=y, data=data, palette='RdYlGn')

    plt.tick_params(axis='both', which='major', pad=10)

    g.set(xscale=xscale)
    g.set(yscale=yscale)

    # Setting plot limits

    start = data[y].min().min()
    plt.ylim(start,);

    sns.despine()
项目:astetik    作者:mikkokotila    | 项目源码 | 文件源码
def correlation(data,title=''):

    corr = data.corr(method='spearman')
    mask = np.zeros_like(corr)
    mask[np.triu_indices_from(mask)] = True

    sns.set(style="white")
    sns.set_context("notebook", font_scale=2, rc={"lines.linewidth": 0.3})

    rcParams['figure.figsize'] = 25, 12
    rcParams['font.family'] = 'Verdana'
    rcParams['figure.dpi'] = 300

    g = sns.heatmap(corr, mask=mask, linewidths=1, cmap="RdYlGn", annot=False)
    g.set_xticklabels(data,rotation=25,ha="right");
    plt.tick_params(axis='both', which='major', pad=15);
项目:DeepScript    作者:mikekestemont    | 项目源码 | 文件源码
def plot_confusion_matrix(cm, target_names,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.tick_params(labelsize=6)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(target_names))
    plt.xticks(tick_marks, target_names, rotation=90)
    plt.yticks(tick_marks, target_names)
    thresh = cm.max() / 2.
    for i, j in product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, round(cm[i, j], 2),
                 horizontalalignment='center',
                 color='white' if cm[i, j] > thresh else 'black',
                 fontsize=5)
    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
项目:pyImageClassification    作者:tyiannak    | 项目源码 | 文件源码
def visualizeFeatures(Features, Files, Names):    
    y_eig, coeff = pcaDimRed(Features, 2)    
    plt.close("all")
    print y_eig
    plt.subplot(2,1,1);
    ax = plt.gca()
    for i in range(len(Files)):
        im = cv2.imread(Files[i], cv2.CV_LOAD_IMAGE_COLOR)    
        Width = 0.2;  Height = 0.2; startX = y_eig[i][0]; startY = y_eig[i][1];
        print startX, startY
        myaximage = ax.imshow(cv2.cvtColor(im, cv2.cv.CV_RGB2BGR), extent=(startX-Width/2.0, startX+Width/2.0, startY-Height/2.0, startY+Height/2.0), alpha=1.0, zorder=-1)
        plt.axis((-3,3,-3,3))
    # Plot feaures
    plt.subplot(2,1,2)    
    ax = plt.gca()
    for i in range(len(Files)):            
        plt.plot(numpy.array(Features[i,:].T));
    plt.xticks(range(len(Names)))
    plt.legend(Files)
    ax.set_xticklabels(Names)
    plt.setp(plt.xticks()[1], rotation=90)
    plt.tick_params(axis='both', which='major', labelsize=8)
    plt.tick_params(axis='both', which='minor', labelsize=8)

    plt.show()
项目:orthopy    作者:nschloe    | 项目源码 | 文件源码
def plot(L):
    xlim = [-2.0, +2.0]
    x = numpy.linspace(xlim[0], xlim[1], 500)
    vals = tree(L, x)

    for val in vals:
        plt.plot(x, val)

    plt.xlim(*xlim)
    # plt.ylim(-2, +2)
    plt.tick_params(
        axis='both',
        which='both',
        bottom='off',
        top='off',
        left='off',
        right='off',
        labelbottom='off',
        labelleft='off'
        )
    plt.grid()
    return
项目:taylor-swift-lyrics    作者:irenetrampoline    | 项目源码 | 文件源码
def plot_bar_chart(values, labels):
    fig, ax = plt.subplots(figsize=(6,4.5))
    N = len(values)
    ind = np.arange(N)
    width = 0.75

    ax.bar(ind, values, width, color='#FFB7AA', edgecolor='none')
    ax.set_xticks(ind + width)
    ax.set_xticklabels(labels)

    # make plot prettier
    ax.spines["top"].set_visible(False)
    ax.spines["bottom"].set_visible(False)
    ax.spines["right"].set_visible(False)
    ax.spines["left"].set_visible(False)
    plt.tick_params(axis="both", which="both", bottom="off", top="off",
                labelbottom="on", left="off", right="off", labelleft="on")

    plt.xticks(rotation=35, ha='right')
    plt.title('The 20 Most Common Taylor Swift Words')
    plt.xlabel('Word (excl stop words)')
    plt.ylabel('Uses per song')
    plt.savefig('top_words.png', bbox_inches='tight')
项目:devito    作者:opesci    | 项目源码 | 文件源码
def show(self, idx=0, time=None, wavelet=None):
        """
        Plot the wavelet of the specified source.

        :param idx: Index of the source point for which to plot wavelet
        :param wavelet: Prescribed wavelet instead of one from this symbol
        :param time: Prescribed time instead of time from this symbol
        """
        wavelet = wavelet or self.data[:, idx]
        time = time or self.time
        plt.figure()
        plt.plot(time, wavelet)
        plt.xlabel('Time (ms)')
        plt.ylabel('Amplitude')
        plt.tick_params()
        plt.show()
项目:HRG    作者:nddsg    | 项目源码 | 文件源码
def draw_diam_plot(orig_g, mG):
    df = pd.DataFrame(mG)
    gD = bfs_eff_diam(orig_g, 20, .9)
    ori_degree_seq = []
    for i in range(0, len(max(mG))):
        ori_degree_seq.append(gD)

    plt.fill_between(df.columns, df.mean() - df.sem(), df.mean() + df.sem(), color='blue', alpha=0.2, label="se")
    h, = plt.plot(df.mean(), color='blue', aa=True, linewidth=4, ls='--', label="H*")
    orig, = plt.plot(ori_degree_seq, color='black', linewidth=2, ls='-', label="H")

    plt.title('Diameter Plot')
    plt.ylabel('Diameter')
    plt.xlabel('Growth')

    plt.tick_params(
        axis='x',  # changes apply to the x-axis
        which='both',  # both major and minor ticks are affected
        bottom='off',  # ticks along the bottom edge are off
        top='off',  # ticks along the top edge are off
        labelbottom='off')  # labels along the bottom edge are off
    plt.legend([orig, h], ['$H$', 'HRG $H^*$'], loc=4)
    # fig = plt.gcf()
    # fig.set_size_inches(5, 4, forward=True)
    plt.show()
项目:code-uai16    作者:thanhan    | 项目源码 | 文件源码
def plot_sen_spe(dic_sen, dic_spe, vals = None):
    """
    """
    label  = {'single': 'Single', 'accum': 'Accum', 'multi': 'Multi'}
    marker = {'single': '.', 'accum': 'x', 'multi': 's'}
    algo = ['single', 'accum', 'multi']

    if vals == None:
        vals = [64, 323, 1295, 6476]

    plt.xlim(0,3)
    plt.ylim(0, 0.3)

    for a in algo:
        y = []
        for v in vals:
            x = np.mean(dic_sen[(v, a)])
            y.append(x)
        print a, y
        plt.plot([0, 1, 2, 3], y, label = label[a], marker = marker[a], markersize = 15, linewidth = 5)


    plt.xlabel('Percentage of target task labels', fontsize = 25)
    plt.ylabel('RMSE', fontsize = 30)
    plt.legend(loc = 'upper right', fontsize = 30)

    plt.tick_params(labelsize = 25)
    plt.xticks([0,1,2,3], [1, 5, 20, 100])
    plt.yticks((0, 0.15, 0.3), ("0", "0.15", "0.3"))
    #plt.set_xticklabels(['1','','5','','20','','100'])
项目:MicroGrids    作者:squoilin    | 项目源码 | 文件源码
def Percentage_Of_Use(Time_Series):
    '''
    This model creates a plot with the percentage of the time that each technologies is activate during the analized 
    time.
    :param Time_series: The results of the optimization model that depend of the periods.
    '''    

    # Creation of the technolgy dictonary    
    PercentageOfUse= {'Lost Load':0, 'Energy PV':0,'Curtailment':0, 'Energy Diesel':0, 'Discharge energy from the Battery':0, 'Charge energy to the Battery':0}

    # Count the quantity of times each technology has energy production
    for v in PercentageOfUse.keys():
        foo = 0
        for i in range(len(Time_Series)):
            if Time_Series[v][i]>0: 
                foo = foo + 1      
            PercentageOfUse[v] = (round((foo/float(len(Time_Series))), 3))*100 

    # Create the names in the plot
    c = ['From Generator', 'Curtailment', 'To Battery', 'From PV', 'From Battery', 'Lost Load']       

#     Create the bar plot  
    plt.figure()
    plt.bar((1,2,3,4,5,6), PercentageOfUse.values(), color= 'b', alpha=0.5, align='center')

    plt.xticks((1.2,2.2,3.2,4.2,5.2,6.2), c) # Put the names and position for the ticks in the x axis 
    plt.xticks(rotation=-30) # Rotate the ticks
    plt.xlabel('Technology') # Create a label for the x axis
    plt.tick_params(axis='x', which='both', bottom='off', top='off', labelbottom='on')
    plt.ylabel('Percentage of use (%)') # Create a label for the y axis
    plt.savefig('Results/Percentge_of_Use.png', bbox_inches='tight') # Save the plot 
    plt.show() 

    return PercentageOfUse
项目:fabric8-analytics-common    作者:fabric8-analytics    | 项目源码 | 文件源码
def create_summary_graph(title, y_axis_label, labels, values):
    """Create summary (column) graph for any measurement."""
    N = len(values)
    indexes = np.arange(N)

    fig = plt.figure()
    plt.xlabel("call #")
    plt.ylabel(y_axis_label)
    plt.grid(True)
    plt.xticks(indexes, labels)
    locs, plt_labels = plt.xticks()
    plt.setp(plt_labels, rotation=90)
    plt.bar(indexes, values, 0.80, color='yellow',
            edgecolor='black', label=title)

    # plt.legend(loc='lower right')

    for tick in plt_labels:
        tick.set_horizontalalignment("left")
        tick.set_verticalalignment("top")
        tick.set_visible(False)

    for tick in plt_labels[::5]:
        tick.set_visible(True)

    plt.tick_params(axis='x', which='major', labelsize=10)

    fig.subplots_adjust(bottom=0.4)
    fig.suptitle(title)
    return fig
项目:fabric8-analytics-common    作者:fabric8-analytics    | 项目源码 | 文件源码
def create_statistic_graph(title, y_axis_label, labels, min_values, max_values, avg_values,
                           x_axis_label="pause time (seconds)", width=DEFAULT_WIDTH,
                           height=DEFAULT_HEIGHT, dpi=DPI):
    """Create summary (column) graph with min, average, and max values."""
    N = len(labels)
    indexes = np.arange(N)

    fig = plt.figure(figsize=(1.0 * width / dpi, 1.0 * height / dpi), dpi=dpi)
    plt.xlabel(x_axis_label)
    plt.ylabel(y_axis_label)
    plt.grid(True)
    plt.xticks(indexes, labels)
    locs, plt_labels = plt.xticks()
    plt.setp(plt_labels, rotation=90)

    plt.bar(indexes - 0.27, min_values, 0.25, color='red',
            edgecolor='black', label='min values')
    plt.bar(indexes, avg_values, 0.25, color='yellow',
            edgecolor='black', label='avg values')
    plt.bar(indexes + 0.27, max_values, 0.25, color='green',
            edgecolor='black', label='max values')

    plt.legend(loc='upper left')
    for tick in plt_labels:
        tick.set_horizontalalignment("left")
        tick.set_verticalalignment("top")
    plt.tick_params(axis='x', which='major', labelsize=10)
    # fig.subplots_adjust(bottom=0.4)
    fig.suptitle(title)
    return fig
项目:autonomio    作者:autonomio    | 项目源码 | 文件源码
def lstm_plot(predicted_data, true_data, prediction_len=None):

    fig = plt.figure(facecolor='white', figsize=(16, 4))
    ax = fig.add_subplot(111)
    ax.plot(true_data, label='True Data')
    plt.tick_params(axis='both', which='major', pad=15)

    plt.plot(predicted_data, label='Prediction')
    plt.legend()
    plt.show()
项目:autonomio    作者:autonomio    | 项目源码 | 文件源码
def histplot(x, y, bins=50):

    plt.figure(num=None, figsize=(16, 4), dpi=80, facecolor='w', edgecolor='k')
    plt.hist(y, bins=bins, label='actual')
    plt.hist(x, bins=bins, label='prediction', alpha=.8)
    plt.grid(b=False)
    plt.tick_params(axis='both', which='major', pad=15)
    plt.legend()
    plt.show()
项目:autonomio    作者:autonomio    | 项目源码 | 文件源码
def prediction_distribution(x, bins):

    plt.figure(num=None, figsize=(16, 4), dpi=80, facecolor='w', edgecolor='k')
    plt.hist(x, bins=bins, label='prediction')
    plt.grid(b=False)
    plt.tick_params(axis='both', which='major', pad=15)
    plt.legend()
    plt.show()
项目:ReGraph    作者:eugeniashurko    | 项目源码 | 文件源码
def _ticks_off():
    plt.tick_params(
        axis='x',           # changes apply to the x-axis
        which='both',       # both major and minor ticks are affected
        bottom='off',       # ticks along the bottom edge are off
        top='off',          # ticks along the top edge are off
        labelbottom='off')  # labels along the bottom edge are off
    plt.tick_params(
        axis='y',           # changes apply to the x-axis
        which='both',       # both major and minor ticks are affected
        left='off',         # ticks along the bottom edge are off
        right='off',        # ticks along the top edge are off
        labelleft='off')    # labels along the bottom edge are off
项目:CustomerSim    作者:sisl    | 项目源码 | 文件源码
def plot_validate(data_true, data_predicted, xlab, ylab, name, n_bins, x_range, y_range, font = 15, legend = False, bar_width = 0.4):

    hist_true, bin_edges = np.histogram(data_true, bins=n_bins, range=x_range)
    hist_predicted, bin_edges = np.histogram(data_predicted, bins=n_bins, range=x_range)
    hist_true = hist_true / float(sum(hist_true))
    hist_predicted = hist_predicted / float(sum(hist_predicted))

    plt.figure(num=None, figsize=(8, 6), dpi=150, facecolor='w', edgecolor='w')
    plt.bar(bin_edges[:-1],hist_true, bar_width,color="#60BD68",label='Actual Data')
    plt.bar(bin_edges[:-1]+bar_width,hist_predicted,bar_width,color="#FAA43A",alpha=1,label='Simulated Data') 
    plt.xlabel(xlab, fontsize=font, labelpad=15)
    if ylab:
        plt.ylabel(ylab, fontsize=font, labelpad=15)
    plt.xlim(x_range[0], x_range[1])
    plt.ylim(y_range[0], y_range[1])

    xt_val = list(set([int(e) for e in bin_edges[:-1]]))
    xt_pos = [float(e) + bar_width for e in xt_val]

    plt.tick_params(axis='both', which='major', labelsize=15)
    plt.tick_params(axis='both', which='minor', labelsize=15)

    plt.xticks(xt_pos, xt_val)
    if legend:
        plt.legend(fontsize=font)
    plt.savefig(name, bbox_inches='tight')
    plt.close()

# PERCENTILE KL DIVERGENCE BOOTSTRAP TEST
项目:CustomerSim    作者:sisl    | 项目源码 | 文件源码
def roc(y_label,y_score,name):

    fpr = dict()
    tpr = dict()
    thresholds = dict()
    roc_auc = dict()
    for i in range(2):
        fpr[i], tpr[i], thresholds[i] = roc_curve(y_label[:, i], y_score[:, i])
        roc_auc[i] = auc(fpr[i], tpr[i])

    ind_max = np.argmax(1 - fpr[1] + tpr[1])
    # Compute micro-average ROC curve and ROC area
    fpr["micro"], tpr["micro"], _ = roc_curve(y_label.ravel(), y_score.ravel())
    roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])

    # Plot of a ROC curve for a specific class
    plt.figure(num=None, figsize=(8, 6), dpi=150, facecolor='w', edgecolor='w')
    plt.plot(fpr[1], tpr[1], label='ROC Curve (Area = %0.2f)' % roc_auc[1],color="g")

    plt.plot([fpr[1][ind_max], fpr[1][ind_max]], [fpr[1][ind_max], tpr[1][ind_max]], 'k:')
    plt.annotate(r'$\bf J$', xy=(fpr[1][ind_max]-0.04, (fpr[1][ind_max] + tpr[1][ind_max])/2), color='black', 
             fontsize=20)

    plt.plot(fpr[1][ind_max], tpr[1][ind_max], marker ='v', markersize=10, linestyle='None', color='brown', 
         label="Decision Threshold (DT),\nMax. Youden's J Statistic")
    plt.annotate('DT: %0.2f\nTPR: %0.2f\nFPR: %0.2f' % (thresholds[1][ind_max], tpr[1][ind_max], fpr[1][ind_max]), 
             xy=(fpr[1][ind_max]+0.015, tpr[1][ind_max]-0.175), color='black', fontsize=20)

    plt.plot([0, 1], [0, 1], 'k--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.tick_params(axis='both', which='major', labelsize=15)
    plt.tick_params(axis='both', which='minor', labelsize=15)
    plt.xlabel('False Positive Rate (1 - Specificity)',fontsize=20, labelpad=15)
    plt.ylabel('True Positive Rate (Sensitivity)',fontsize=20, labelpad=15)
    plt.legend(loc="lower right",fontsize=20,numpoints=1)
    plt.savefig(name, bbox_inches='tight')
    plt.close()
项目:GASP-python    作者:henniggroup    | 项目源码 | 文件源码
def get_system_size_plot(self):
        """
        Returns a plot of the system size versus the number of energy
        calculations, as a matplotlib plot object.
        """

        # set the font to Times, rendered with Latex
        plt.rc('font', **{'family': 'serif', 'serif': ['Times']})
        plt.rc('text', usetex=True)

        # parse the compositions and numbers of energy calculations
        compositions = []
        num_calcs = []
        for i in range(4, len(self.lines)):
            line = self.lines[i].split()
            compositions.append(line[1])
            num_calcs.append(int(line[4]))

        # get the numbers of atoms from the compositions
        nums_atoms = []
        for composition in compositions:
            comp = Composition(composition)
            nums_atoms.append(comp.num_atoms)

        # make the plot
        plt.plot(num_calcs, nums_atoms, 'D', markersize=5,
                 markeredgecolor='blue', markerfacecolor='blue')
        plt.xlabel(r'Number of energy calculations', fontsize=22)
        plt.ylabel(r'Number of atoms in the cell', fontsize=22)
        plt.tick_params(which='both', width=1, labelsize=18)
        plt.tick_params(which='major', length=8)
        plt.tick_params(which='minor', length=4)
        plt.xlim(xmin=0)
        plt.ylim(ymin=0)
        plt.tight_layout()
        return plt
项目:twitter_LDA_topic_modeling    作者:kenneth-orton    | 项目源码 | 文件源码
def draw_scatter_graph(title, x_label, y_label, x_axis, y_axis, min_x, max_x, min_y, max_y, output_path):
    fig = plt.figure()
    fig.suptitle(title, fontsize=14, fontweight='bold')
    ax = fig.add_subplot(111)
    ax.set_xlabel(x_label)
    ax.set_ylabel(y_label)
    ax.plot(x_axis, y_axis, 'o')
    ax.axis([min_x, max_x, min_y, max_y])
    plt.margins(0.2)
    plt.tick_params(labelsize=10)
    fig.subplots_adjust(bottom=0.2)
    plt.savefig(output_path)
    plt.close(fig)
项目:PyBGMM    作者:junlulocky    | 项目源码 | 文件源码
def plot_variance_cuve():
        ################# plot for variance ##########################
        K = 5
        alpha = a = 0.3
        all_num = 1000
        b = np.linspace(0, 20, num=all_num)

        # print upper_incomplete_gamma_function(0.1, 1)
        # print uppergamma(0.1, 1)
        # a = uppergamma(0.1, 1)
        # print float(a)
        # print compute_gdir_variance(K, a, 1)

        symmetric_dir_var = [compute_symmetric_dir_variance(K, alpha)] * all_num
        gdir_var = [compute_gdir_variance(K, a, local_b) for local_b in b]
        # print gdir_var

        save_path = os.path.dirname(__file__) + '/res_gdir/res_variance'


        plt.figure(1)

        plt.rc('xtick', labelsize=20)
        plt.rc('ytick', labelsize=20)
        plt.tick_params(axis='both', which='major', labelsize=20)
        plt.plot(b, gdir_var)
        plt.plot(b, symmetric_dir_var)
        plt.plot((a, a), (0, 1./K), 'k-')

        plt.savefig(save_path + '/gdir_K{}_a{}.png'.format(K, a))
        plt.savefig(save_path + '/gdir_K{}_a{}.pdf'.format(K, a))



        plt.show()
项目:Twitter_Geolocation    作者:shawn-terryah    | 项目源码 | 文件源码
def plot_error_histogram(df):
    '''
    Input: DataFrame that contains a 'error_in_miles' column
    Output: Histogram of errors
    '''

    plt.figure(figsize=(11,7))
    plt.hist(df['error_in_miles'], bins=20, normed=1)
    plt.xlabel('Error in miles', fontsize=20)
    plt.ylabel('Percentage', fontsize=22)
    plt.tick_params(labelsize=14)
    plt.show()
项目:hylaa    作者:stanleybak    | 项目源码 | 文件源码
def create_plot(self):
        'create the plot'

        if self.settings.plot_mode != PlotSettings.PLOT_NONE and self.settings.plot_mode != PlotSettings.PLOT_MATLAB:
            self.fig, self.axes = plt.subplots(nrows=1, figsize=self.settings.plot_size)
            ha = self.engine.hybrid_automaton

            title = self.settings.label.title
            title = title if title is not None else ha.name

            x_label = self.settings.label.x_label
            x_label = x_label if x_label is not None else ha.variables[self.settings.xdim].capitalize()
            y_label = self.settings.label.y_label
            y_label = y_label if y_label is not None else ha.variables[self.settings.ydim].capitalize()

            self.axes.set_xlabel(x_label, fontsize=self.settings.label.label_size)
            self.axes.set_ylabel(y_label, fontsize=self.settings.label.label_size)
            self.axes.set_title(title, fontsize=self.settings.label.title_size)

            if self.settings.label.axes_limits is not None:
                # hardcoded axes limits
                xmin, xmax, ymin, ymax = self.settings.label.axes_limits

                self.axes.set_xlim(xmin, xmax)
                self.axes.set_ylim(ymin, ymax)

            if self.settings.grid:
                self.axes.grid(True)

            # make the x and y axis animated in case of rescaling
            self.axes.xaxis.set_animated(True)
            self.axes.yaxis.set_animated(True)

            plt.tick_params(axis='both', which='major', labelsize=self.settings.label.tick_label_size)
            plt.tight_layout()

            self.shapes = DrawnShapes(self)
项目:LowPoly    作者:Shlw    | 项目源码 | 文件源码
def TriAndPaint(img, points, outputIMG):
    tri = Delaunay(points)
    triList = points[tri.simplices]
    cMap = ListedColormap(
        np.array([ChooseColor(img, tr) for tr in triList]))
    # use core rgb
    # center = np.sum(points[tri.simplices], axis=1) / 3
    # print(center)
    # cMap = ListedColormap(
    #     np.array([img.getpixel((x, y)) for x, y in center]) / 256)
    color = np.array(range(len(triList)))
    # print(color)

    width, height = img.size
    plt.figure(figsize=(width, height), dpi=1)
    plt.tripcolor(points[:, 0], points[:, 1],
                  tri.simplices.copy(), facecolors=color, cmap=cMap)
    # plt.tick_params(labelbottom='off', labelleft='off',
    #                 left='off', right='off', bottom='off', top='off')
    # plt.tight_layout(pad=0)
    plt.axis('off')
    plt.subplots_adjust(left=0, right=1, top=1, bottom=0)
    plt.xlim(0, width)
    plt.ylim(0, height)
    plt.gca().invert_yaxis()
    plt.savefig(outputIMG)
    # uncomment show() if you want to view when it's done
    # plt.show()
项目:bates_galaxies_lab    作者:aleksds    | 项目源码 | 文件源码
def scat_plot():
    f = plt.figure()
    # filename = 'MLP5_dap_multi_' + str(plate) + '_quicklook.pdf'
    mpl5_dir = os.environ['MANGADIR_MPL5']
    drp = fits.open(mpl5_dir + 'drpall-v2_0_1.fits')
    drpdata = drp[1].data
    absmag = drpdata.field('nsa_elpetro_absmag')


    plt.xlim(-16,-24)
    plt.ylim(1,7)
    plt.scatter(absmag[:,5], absmag[:,1]-absmag[:,5], marker='.',color=['blue'], s=0.5)
    plt.xlabel('i-band absolute magnitude', fontsize=16)
    plt.ylabel('NUV - i', fontsize=16)
    plt.tick_params(axis='both', labelsize=14)

    ifu_list = drpdata.field('plateifu')
    for i in good_galaxies:
        ithname = str(i[0]) + str(i[1])
        for e in range(0, len(ifu_list)):
            ethname = ifu_list[e]
            ethname = ethname.replace("-","")
            if ithname == ethname:
                plt.scatter(absmag[e, 5], absmag[e, 1] - absmag[e, 5], marker='*',color=['red'])
    f.savefig("scatter.pdf", bbox_inches='tight')
    # pp = PdfPages('scatter.pdf')
    # pp.savefig(plot_1)
    plt.close()
    os.system("open %s &" % 'scatter.pdf')
项目:bates_galaxies_lab    作者:aleksds    | 项目源码 | 文件源码
def plot_image():
    std = np.std(stamp[stamp==stamp])
    plt.imshow(stamp, interpolation='nearest', origin = 'lower', vmin = -1.*std, vmax = 3.*std, cmap='bone')
    plt.tick_params(axis='both', which='major', labelsize=8)

# define the directory that contains the images
项目:bates_galaxies_lab    作者:aleksds    | 项目源码 | 文件源码
def plot_image(stamp):
    std = np.std(stamp[stamp==stamp])
    plt.imshow(stamp, interpolation='nearest', origin = 'lower', vmin = -1.*std, vmax = 3.*std, cmap='bone')
    plt.tick_params(axis='both', which='major', labelsize=8)

# define the directory that contains the images
项目:bates_galaxies_lab    作者:aleksds    | 项目源码 | 文件源码
def plot_image(stamp):
    std = np.std(stamp[stamp==stamp])
    plt.imshow(stamp, interpolation='nearest', origin = 'lower', vmin = -1.*std, vmax = 3.*std, cmap='bone')
    plt.tick_params(axis='both', which='major', labelsize=8)

# define the directory that contains the images
项目:bates_galaxies_lab    作者:aleksds    | 项目源码 | 文件源码
def plot_image():
    std = np.std(stamp[stamp==stamp])
    plt.imshow(stamp, interpolation='nearest', origin = 'lower', vmin = -1.*std, vmax = 3.*std, cmap='bone')
    plt.tick_params(axis='both', which='major', labelsize=8)

# define a function for the gaussian model
项目:bates_galaxies_lab    作者:aleksds    | 项目源码 | 文件源码
def plot_image(stamp):
    std = np.std(stamp[stamp==stamp])
    plt.imshow(stamp, interpolation='nearest', origin = 'lower', vmin = -1.*std, vmax = 3.*std, cmap='bone')
    plt.tick_params(axis='both', which='major', labelsize=8)

# define the directory that contains the images
项目:bates_galaxies_lab    作者:aleksds    | 项目源码 | 文件源码
def plot_image():
    std = np.std(stamp[stamp==stamp])
    plt.imshow(stamp, interpolation='nearest', origin = 'lower', vmin = -1.*std, vmax = 3.*std, cmap='bone')
    plt.tick_params(axis='both', which='major', labelsize=8)

# define the directory that contains the images
项目:bates_galaxies_lab    作者:aleksds    | 项目源码 | 文件源码
def plot_image():
    std = np.std(stamp[stamp==stamp])
    plt.imshow(stamp, interpolation='nearest', origin = 'lower', vmin = -1.*std, vmax = 3.*std, cmap='bone')
    plt.tick_params(axis='both', which='major', labelsize=8)

# define the directory that contains the images
项目:bates_galaxies_lab    作者:aleksds    | 项目源码 | 文件源码
def plot_image():
    std = np.std(stamp[stamp==stamp])
    plt.imshow(stamp, interpolation='nearest', origin = 'lower', vmin = -1.*std, vmax = 3.*std, cmap='Greys')
    plt.tick_params(axis='both', which='major', labelsize=8)
项目:bates_galaxies_lab    作者:aleksds    | 项目源码 | 文件源码
def plot_image(stamp):
    std = np.std(stamp[stamp==stamp])
    plt.imshow(stamp, interpolation='nearest', origin = 'lower', vmin = -1.*std, vmax = 3.*std, cmap='bone')
    plt.tick_params(axis='both', which='major', labelsize=8)

# define the directory that contains the images
项目:bates_galaxies_lab    作者:aleksds    | 项目源码 | 文件源码
def plot_image(stamp):
    std = np.std(stamp[stamp==stamp])
    plt.imshow(stamp, interpolation='nearest', origin = 'lower', vmin = -1.*std, vmax = 3.*std, cmap='bone')
    plt.tick_params(axis='both', which='major', labelsize=8)

# define the directory that contains the images
项目:bates_galaxies_lab    作者:aleksds    | 项目源码 | 文件源码
def plot_image(stamp):
    std = np.std(stamp[stamp==stamp])
    plt.imshow(stamp, interpolation='nearest', origin = 'lower', vmin = -1.*std, vmax = 3.*std, cmap='bone')
    plt.tick_params(axis='both', which='major', labelsize=8)

# define the directory that contains the images
项目:soinn    作者:fukatani    | 项目源码 | 文件源码
def draw_digit(data, n, row, col, title):
    import matplotlib.pyplot as plt
    size = 28
    plt.subplot(row, col, n)
    Z = data.reshape(size,size)   # convert from vector to 28x28 matrix
    Z = Z[::-1,:]                 # flip vertical
    plt.xlim(0,28)
    plt.ylim(0,28)
    plt.pcolor(Z)
    plt.title("title=%s"%(title), size=8)
    plt.gray()
    plt.tick_params(labelbottom="off")
    plt.tick_params(labelleft="off")
项目:astetik    作者:mikkokotila    | 项目源码 | 文件源码
def kde(x,y,title='',color='YlGnBu',xscale='linear',yscale='linear'):

    sns.set_style('white')
    sns.set_context('notebook', font_scale=1, rc={"lines.linewidth": 0.5})
    g = sns.kdeplot(x,y,shade=True, cut=2, cmap=color, shade_lowest=False, legend=True, set_title="test")
    plt.tick_params(axis='both', which='major', pad=10)
    sns.plt.title(title)

    g.set(xscale=xscale)
    g.set(yscale=yscale)

    sns.despine()