Python pyplot.tick_params方法代碼示例

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tick_params [as 別名]
def chisq_dist():
    fig = plt.figure(figsize=(6,4))
    ivar = np.load("%s/val_ivar_norm.npz" %DATA_DIR)['arr_0']
    npix = np.sum(ivar>0, axis=1)
    chisq = np.load("%s/val_chisq.npz" %DATA_DIR)['arr_0']
    redchisq = chisq/npix
    nbins = 25
    plt.hist(redchisq, bins=nbins, color='k', histtype="step",
            lw=2, normed=False, alpha=0.3, range=(0,3))
    plt.legend()
    plt.xlabel("Reduced $\chi^2$", fontsize=16)
    plt.tick_params(axis='both', labelsize=16)
    plt.ylabel("Count", fontsize=16)
    plt.axvline(x=1.0, linestyle='--', c='k')
    fig.tight_layout()
    #plt.show()
    plt.savefig("chisq_dist.png") 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tick_params [as 別名]
def make_plot(files, labels):
	plt.figure()
	for file_idx in range(len(files)):
		rot_err, trans_err = read_csv(files[file_idx])
		success_dict = count_success(trans_err)

		x_range = success_dict.keys()
		x_range.sort()
		success = []
		for i in x_range:
			success.append(success_dict[i])
		success = np.array(success)/total_cases

		plt.plot(x_range, success, linewidth=3, label=labels[file_idx])
		# plt.scatter(x_range, success, s=50)
	plt.ylabel('Success Ratio', fontsize=40)
	plt.xlabel('Threshold for Translation Error', fontsize=40)
	plt.tick_params(labelsize=40, width=3, length=10)
	plt.grid(True)
	plt.ylim(0,1.005)
	plt.yticks(np.arange(0,1.2,0.2))
	plt.xticks(np.arange(0,2.1,0.2))
	plt.xlim(0,2)
	plt.legend(fontsize=30, loc=4) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tick_params [as 別名]
def plot_DOY(dates, y, mpl_cmap):
    """ Create a DOY plot

    Args:
        dates (iterable): sequence of datetime
        y (np.ndarray): variable to plot
        mpl_cmap (colormap): matplotlib colormap
    """
    doy = np.array([d.timetuple().tm_yday for d in dates])
    year = np.array([d.year for d in dates])

    sp = plt.scatter(doy, y, c=year, cmap=mpl_cmap,
                     marker='o', edgecolors='none', s=35)
    plt.colorbar(sp)

    months = mpl.dates.MonthLocator()  # every month
    months_fmrt = mpl.dates.DateFormatter('%b')

    plt.tick_params(axis='x', which='minor', direction='in', pad=-10)
    plt.axes().xaxis.set_minor_locator(months)
    plt.axes().xaxis.set_minor_formatter(months_fmrt)

    plt.xlim(1, 366)
    plt.xlabel('Day of Year') 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tick_params [as 別名]
def snr_dist():
    fig = plt.figure(figsize=(6,4))
    tr_snr = np.load("../tr_SNR.npz")['arr_0']
    snr = np.load("../val_SNR.npz")['arr_0']
    nbins = 25
    plt.hist(tr_snr, bins=nbins, color='k', histtype="step",
            lw=2, normed=True, alpha=0.3, label="Training Set")
    plt.hist(snr, bins=nbins, color='r', histtype="step",
            lw=2, normed=True, alpha=0.3, label="Validation Set")
    plt.legend()
    plt.xlabel("S/N", fontsize=16)
    plt.tick_params(axis='both', labelsize=16)
    plt.ylabel("Normalized Count", fontsize=16)
    fig.tight_layout()
    plt.show()
    #plt.savefig("snr_dist.png") 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tick_params [as 別名]
def render(self, filename):
        """
        Renders the attention graph over timesteps.

        Args:
          filename (string): filename to save the figure to.
        """
        figure, axes = plt.subplots()
        graph = np.stack(self.attentions)

        axes.imshow(graph, cmap=plt.cm.Blues, interpolation="nearest")
        axes.xaxis.tick_top()
        axes.set_xticks(range(len(self.keys)))
        axes.set_xticklabels(self.keys)
        plt.setp(axes.get_xticklabels(), rotation=90)
        axes.set_yticks(range(len(self.generated_values)))
        axes.set_yticklabels(self.generated_values)
        axes.set_aspect(1, adjustable='box')
        plt.tick_params(axis='x', which='both', bottom='off', top='off')
        plt.tick_params(axis='y', which='both', left='off', right='off')

        figure.savefig(filename) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tick_params [as 別名]
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() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tick_params [as 別名]
def plot_bar_chart(self, data):
        x = []
        y = []
        for item in data:
            y.append(item['count'])
            x.append(item['Implemented_by_partial_function'])
        plt.barh(x, y)
        plt.title("Top apis", fontsize=10)
        plt.xlabel("Number of API Calls", fontsize=8)
        plt.xticks([])
        plt.ylabel("Partial function", fontsize=8)
        plt.tick_params(axis='y', labelsize=8)
        for i, j in zip(y, x):
            plt.text(i, j, str(i), clip_on=True, ha='center',va='center', fontsize=8)
        plt.tight_layout()
        buf = BytesIO()
        plt.savefig(buf, format='png')
        image_base64 = base64.b64encode(buf.getvalue()).decode('utf-8').replace('\n', '')
        buf.close()
        # Clear the previous plot.
        plt.gcf().clear()
        return image_base64 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tick_params [as 別名]
def plot_topconsumer_bar_chart(self, data):
        x = []
        y = []
        for item in data:
            y.append(item['count'])
            x.append(item['app_name'])
        plt.barh(x, y)
        plt.title("Top consumers", fontsize=10)
        plt.xlabel("Number of API Calls", fontsize=8)
        plt.xticks([])
        plt.ylabel("Consumers", fontsize=8)
        plt.tick_params(axis='y', labelsize=8)
        for i, j in zip(y, x):
            plt.text(i, j, str(i), clip_on=True, ha='center',va='center', fontsize=8)
        plt.tight_layout()
        buf = BytesIO()
        plt.savefig(buf, format='png')
        image_base64 = base64.b64encode(buf.getvalue()).decode('utf-8').replace('\n', '')
        buf.close()
        # Clear the previous plot.
        plt.gcf().clear()
        return image_base64 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tick_params [as 別名]
def plot_dendrogram(self, **kwargs):
        # Distances between each pair of children
        distance = np.arange(self.children.shape[0])
        position = np.arange(self.children.shape[0])

        # Create linkage matrix and then plot the dendrogram
        linkage_matrix = np.column_stack([
            self.children, distance, position]
        ).astype(float)

        # Plot the corresponding dendrogram
        fig, ax = plt.subplots(figsize=(15, 7))  # set size
        ax = dendrogram(linkage_matrix, **kwargs)
        plt.tick_params(axis='x', bottom='off', top='off', labelbottom='off')
        plt.tight_layout()
        plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tick_params [as 別名]
def plot_setup(title="", ylabel="edits"):
    fig = plt.figure(figsize=(12, 9))
    ax = fig.add_subplot(111)
    plt.title(title)
    plt.xlabel("date")
    plt.ylabel(ylabel)

    # x-ticks formatting
    plt.gca().xaxis.set_major_formatter(mpl.dates.DateFormatter('%Y-%m-%d'))
    plt.gca().xaxis.set_major_locator(mpl.dates.MonthLocator(interval=3))
    plt.tick_params(axis="x", which="both", direction="out")

    # y-ticks
    plt.gca().yaxis.set_major_locator(mpl.ticker.MaxNLocator(nbins=10))

    # show grid
    plt.grid(True, which="both")

    return ax 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tick_params [as 別名]
def plot_trajectory(proj_file, dir_file, show=False):
    """ Plot optimization trajectory on the plane spanned by given directions."""

    assert exists(proj_file), 'Projection file does not exist.'
    f = h5py.File(proj_file, 'r')
    fig = plt.figure()
    plt.plot(f['proj_xcoord'], f['proj_ycoord'], marker='.')
    plt.tick_params('y', labelsize='x-large')
    plt.tick_params('x', labelsize='x-large')
    f.close()

    if exists(dir_file):
        f2 = h5py.File(dir_file,'r')
        if 'explained_variance_ratio_' in f2.keys():
            ratio_x = f2['explained_variance_ratio_'][0]
            ratio_y = f2['explained_variance_ratio_'][1]
            plt.xlabel('1st PC: %.2f %%' % (ratio_x*100), fontsize='xx-large')
            plt.ylabel('2nd PC: %.2f %%' % (ratio_y*100), fontsize='xx-large')
        f2.close()

    fig.savefig(proj_file + '.pdf', dpi=300, bbox_inches='tight', format='pdf')
    if show: plt.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tick_params [as 別名]
def show(self, idx=0, wavelet=None):
        """
        Plot the wavelet of the specified source.

        Parameters
        ----------
        idx : int
            Index of the source point for which to plot wavelet.
        wavelet : ndarray or callable
            Prescribed wavelet instead of one from this symbol.
        """
        wavelet = wavelet or self.data[:, idx]
        plt.figure()
        plt.plot(self.time_values, wavelet)
        plt.xlabel('Time (ms)')
        plt.ylabel('Amplitude')
        plt.tick_params()
        plt.show()

    # Pickling support 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tick_params [as 別名]
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 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tick_params [as 別名]
def plot(t, plots, shot_ind):
    n = len(plots)

    for i in range(0,n):
        label, data = plots[i]

        plt = py.subplot(n, 1, i+1)
        plt.tick_params(labelsize=8)
        py.grid()
        py.xlim([t[0], t[-1]])
        py.ylabel(label)

        py.plot(t, data, 'k-')
        py.scatter(t[shot_ind], data[shot_ind], marker='*', c='g')

    py.xlabel("Time")
    py.show()
    py.close() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tick_params [as 別名]
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