Python pyplot.margins方法代码示例

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import margins [as 别名]
def distplot_messages_per_hour(msgs, path_to_save):
    sns.set(style="whitegrid")

    ax = sns.distplot([msg.date.hour for msg in msgs], bins=range(25), color="m", kde=False)
    ax.set_xticklabels(stools.get_hours())
    ax.set(xlabel="hour", ylabel="messages")
    ax.margins(x=0)

    plt.xticks(range(24), rotation=65)
    plt.tight_layout()
    fig = plt.gcf()
    fig.set_size_inches(11, 8)

    fig.savefig(os.path.join(path_to_save, distplot_messages_per_hour.__name__ + ".png"), dpi=500)
    # plt.show()
    log_line(f"{distplot_messages_per_hour.__name__} was created.")
    plt.close("all") 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import margins [as 别名]
def distplot_messages_per_day(msgs, path_to_save):
    sns.set(style="whitegrid")

    data = stools.get_messages_per_day(msgs)

    max_day_len = len(max(data.values(), key=len))
    ax = sns.distplot([len(day) for day in data.values()], bins=list(range(0, max_day_len, 50)) + [max_day_len],
                      color="m", kde=False)
    ax.set(xlabel="messages", ylabel="days")
    ax.margins(x=0)

    fig = plt.gcf()
    fig.set_size_inches(11, 8)

    fig.savefig(os.path.join(path_to_save, distplot_messages_per_day.__name__ + ".png"), dpi=500)
    # plt.show()
    log_line(f"{distplot_messages_per_day.__name__} was created.")
    plt.close("all") 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import margins [as 别名]
def distplot_messages_per_month(msgs, path_to_save):
    sns.set(style="whitegrid")

    start_date = msgs[0].date.date()
    (xticks, xticks_labels, xlabel) = _get_xticks(msgs)

    ax = sns.distplot([(msg.date.date() - start_date).days for msg in msgs],
                      bins=xticks + [(msgs[-1].date.date() - start_date).days], color="m", kde=False)
    ax.set_xticklabels(xticks_labels)
    ax.set(xlabel=xlabel, ylabel="messages")
    ax.margins(x=0)

    plt.xticks(xticks, rotation=65)
    plt.tight_layout()
    fig = plt.gcf()
    fig.set_size_inches(11, 8)

    fig.savefig(os.path.join(path_to_save, distplot_messages_per_month.__name__ + ".png"), dpi=500)
    # plt.show()
    log_line(f"{distplot_messages_per_month.__name__} was created.")
    plt.close("all") 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import margins [as 别名]
def _show_plot(x_values, y_values, x_labels=None, y_labels=None):
    try:
        import matplotlib.pyplot as plt
    except ImportError:
        raise ImportError('The plot function requires matplotlib to be installed.'
                         'See http://matplotlib.org/')

    plt.locator_params(axis='y', nbins=3)
    axes = plt.axes()
    axes.yaxis.grid()
    plt.plot(x_values, y_values, 'ro', color='red')
    plt.ylim(ymin=-1.2, ymax=1.2)
    plt.tight_layout(pad=5)
    if x_labels:
        plt.xticks(x_values, x_labels, rotation='vertical')
    if y_labels:
        plt.yticks([-1, 0, 1], y_labels, rotation='horizontal')
    # Pad margins so that markers are not clipped by the axes
    plt.margins(0.2)
    plt.show()

#////////////////////////////////////////////////////////////
#{ Parsing and conversion functions
#//////////////////////////////////////////////////////////// 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import margins [as 别名]
def generate_plot(x, y, title, save_path):
    """
    generates the plot given the indices and fid values
    :param x: the indices (epochs)
    :param y: fid values
    :param title: title of the generated plot
    :param save_path: path to save the file
    :return: None (saves file)
    """
    font = {'family': 'normal', 'size': 20}
    matplotlib.rc('font', **font)
    plt.figure(figsize=(10, 6))
    annot_min(x, y)
    plt.margins(.05, .05)
    plt.title(title)
    plt.xlabel("Epochs")
    plt.ylabel("FID scores")
    plt.plot(x, y, linewidth=4)
    plt.tight_layout()
    plt.savefig(save_path, bbox_inches='tight') 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import margins [as 别名]
def generate_plot(x, y, title, save_path):
    """
    generates the plot given the indices and is values
    :param x: the indices (epochs)
    :param y: IS values
    :param title: title of the generated plot
    :param save_path: path to save the file
    :return: None (saves file)
    """
    font = {'family': 'normal', 'size': 20}
    matplotlib.rc('font', **font)
    plt.figure(figsize=(10, 6))
    annot_max(x, y)
    plt.margins(.05, .05)
    plt.title(title)
    plt.xlabel("Epochs")
    plt.ylabel("Inception scores")
    plt.ylim(0, max(y) + 2)
    plt.plot(x, y, linewidth=4)
    plt.tight_layout()
    plt.savefig(save_path, bbox_inches='tight') 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import margins [as 别名]
def save_data_and_plot(self, data, filename, xlabel, ylabel):
        """
        Produce a plot of performance of the agent over the session and save the relative data to txt
        """
        min_val = min(data)
        max_val = max(data)

        plt.rcParams.update({'font.size': 24})  # set bigger font size

        plt.plot(data)
        plt.ylabel(ylabel)
        plt.xlabel(xlabel)
        plt.margins(0)
        plt.ylim(min_val - 0.05 * abs(min_val), max_val + 0.05 * abs(max_val))
        fig = plt.gcf()
        fig.set_size_inches(20, 11.25)
        fig.savefig(os.path.join(self._path, 'plot_'+filename+'.png'), dpi=self._dpi)
        plt.close("all")

        with open(os.path.join(self._path, 'plot_'+filename + '_data.txt'), "w") as file:
            for value in data:
                    file.write("%s\n" % value) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import margins [as 别名]
def plotAdriansFile():
    abf = pyabf.ABF(PATH_DATA+"/190619B_0003.abf")

    print(abf)
    # OUTPUT:
    #   ABF (version 1.8.3.0) with 2 channels (mV, pA),
    #   sampled at 20.0 kHz, containing 10 sweeps,
    #   having no tags, with a total length of 0.28 minutes,
    #   recorded with protocol "IV_FI_IN0_saray".

    plt.figure(figsize=(10, 4))
    plt.grid(alpha=.2, ls='--')
    for sweepNumber in abf.sweepList:
        abf.setSweep(sweepNumber)
        plt.plot(abf.sweepX, abf.sweepY, label=f"sweep {sweepNumber+1}")
    plt.margins(0, .1)
    plt.legend(fontsize=8)
    plt.title(abf.abfID+".abf")
    plt.ylabel(abf.sweepLabelY)
    plt.xlabel(abf.sweepLabelX)
    plt.tight_layout()
    plt.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import margins [as 别名]
def makeFigure1(colormap):
    abf=pyabf.ABF("../../data/17o05028_ic_steps.abf")
    plt.figure(figsize=(6,3))
    for sweep in abf.sweepList[::3]:
        color = plt.cm.get_cmap(colormap)(sweep/abf.sweepCount)
        abf.setSweep(sweep)
        plt.plot(abf.dataX,abf.dataY,color=color)
    plt.margins(0,.1)
    plt.axis([0,1,None,None])
    plt.gca().axis('off') # remove square around edges
    plt.xticks([]) # remove x labels
    plt.yticks([]) # remove y labels
    plt.tight_layout()
    plt.savefig("_output/1_%s.png"%colormap,dpi=150)
    plt.show()
    plt.close()
    return 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import margins [as 别名]
def makeFigure2(colormap):
    abf=pyabf.ABF("../../data/17o05026_vc_stim.abf")
    plt.figure(figsize=(6,3))
    for sweep in abf.sweepList[::-1]:
        color = plt.cm.get_cmap(colormap)(sweep/abf.sweepCount)
        abf.setSweep(sweep)
        abf.dataY[:-int(abf.pointsPerSec*1)]=np.nan
        abf.dataY+=4*sweep
        plt.plot(abf.dataX+.05*sweep,abf.dataY,color=color,alpha=.7)
    plt.margins(0,0)
    plt.gca().axis('off') # remove square around edges
    plt.xticks([]) # remove x labels
    plt.yticks([]) # remove y labels
    plt.tight_layout()
    plt.savefig("_output/2_%s.png"%colormap,dpi=150)
    plt.show()
    plt.close()
    return 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import margins [as 别名]
def demo_02a_plot_matplotlib_sweep(self):
        """
        ## Plot a Sweep with Matplotlib

        Matplotlib is a fantastic plotting library for Python. This example
        shows how to plot an ABF sweep using matplotlib.
        ABF `setSweep()` is used to tell the ABF class what sweep to load
        into memory. After that you can just plot `sweepX` and `sweepY`.
        """

        import pyabf
        abf = pyabf.ABF("data/abfs/17o05028_ic_steps.abf")
        abf.setSweep(14)
        plt.figure(figsize=self.figsize)
        plt.plot(abf.sweepX, abf.sweepY)
        plt.grid(alpha=.2)  # ignore
        plt.margins(0, .1)  # ignore
        plt.tight_layout()  # ignore
        self.saveAndClose() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import margins [as 别名]
def demo_03a_decorate_matplotlib_plot(self):
        """
        ## Decorate Plots with ABF Information

        The ABF class provides easy access to lots of information about the ABF.
        This example shows how to use these class methods to create a prettier
        plot of several sweeps from the same file.
        """

        import pyabf
        abf = pyabf.ABF("data/abfs/17o05028_ic_steps.abf")
        plt.figure(figsize=self.figsize)
        plt.title("pyABF and Matplotlib are a great pair!")
        plt.ylabel(abf.sweepLabelY)
        plt.xlabel(abf.sweepLabelX)
        for i in [0, 5, 10, 15]:
            abf.setSweep(i)
            plt.plot(abf.sweepX, abf.sweepY, alpha=.5, label="sweep %d" % (i))
        plt.margins(0, .1)  # ignore
        plt.tight_layout()  # ignore
        plt.grid(alpha=.2)  # ignore
        plt.legend()
        self.saveAndClose() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import margins [as 别名]
def demo_08a_xy_offset(self):
        """
        ## Plot Sweeps in 3D

        The previous example how to plot stacked sweeps by adding a Y offset
        to each sweep. If you add an X and Y offset to each sweep, you can
        create a 3D effect.
        """

        import pyabf
        abf = pyabf.ABF("data/abfs/171116sh_0018.abf")

        plt.figure(figsize=self.figsize)
        for sweepNumber in abf.sweepList:
            abf.setSweep(sweepNumber)
            i1, i2 = 0, int(abf.dataRate * 1)  # plot part of the sweep
            dataX = abf.sweepX[i1:i2] + .025 * sweepNumber
            dataY = abf.sweepY[i1:i2] + 15 * sweepNumber
            plt.plot(dataX, dataY, color='C0', alpha=.5)

        plt.gca().axis('off')  # hide axes to enhance floating effect
        plt.margins(.02, .02)  # ignore
        plt.tight_layout()  # ignore
        self.saveAndClose() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import margins [as 别名]
def demo_11a_gap_free(self):
        """
        ## Plotting Gap-Free ABFs

        The pyABF treats every ABF like it's episodic (with sweeps). As such,
        gap free ABF files are loaded as if they were episodic files with
        a single sweep. When an ABF is loaded, `setSweep(0)` is called
        automatically, so the entire gap-free set of data is already available
        by plotting `sweepX` and `sweepY`.
        """

        import pyabf
        abf = pyabf.ABF("data/abfs/abf1_with_tags.abf")
        plt.figure(figsize=self.figsize)
        plt.plot(abf.sweepX, abf.sweepY, lw=.5)
        plt.axis([725, 825, -150, -15])
        plt.ylabel(abf.sweepLabelY)
        plt.xlabel(abf.sweepLabelX)
        plt.title("Example Gap Free File")
        plt.margins(0, .1)  # ignore
        plt.grid(alpha=.2)  # ignore
        self.saveAndClose() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import margins [as 别名]
def plot_scatter(pt, data_name, plt_path):
    fig = plt.figure()
    fig.set_size_inches(20.0 / 3, 20.0 / 3)
    ax = fig.gca(projection='3d')
    ax.set_aspect('equal')
    ax.grid(color='r', linestyle='-',)
    ax.set_yticklabels([])
    ax.set_xticklabels([])
    ax.set_zticklabels([])
    X = pt[:, 0]
    Y = pt[:, 1]
    Z = pt[:, 2]

    scat = ax.scatter(X, Y, Z, depthshade=True, marker='.')

    max_range = np.array([X.max() - X.min(), Y.max() - Y.min(), Z.max() - Z.min()]).max() / 2.0

    mid_x = (X.max() + X.min()) * 0.5
    mid_y = (Y.max() + Y.min()) * 0.5
    mid_z = (Z.max() + Z.min()) * 0.5
    ax.set_xlim(mid_x - max_range, mid_x + max_range)
    ax.set_ylim(mid_y - max_range, mid_y + max_range)
    ax.set_zlim(mid_z - max_range, mid_z + max_range)

    plt.margins(0, 0)
    fig.savefig(os.path.join(plt_path, data_name.replace('.dat', '.png')), format='png', transparent=True, dpi=300, pad_inches=0, bbox_inches='tight')