Python pyplot.GridSpec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 GridSpec [as 別名]
def __init__(self, n: int, max_cols=3, scale=3):
        """
        :param n: number of axes to generate
        :param max_cols: maximum number of axes in a given row
        """

        self.n = n
        self.nrows = int(np.ceil(n / max_cols))
        self.ncols = int(min((max_cols, n)))
        figsize = self.ncols * scale, self.nrows * scale

        # create figure
        self.gs = plt.GridSpec(nrows=self.nrows, ncols=self.ncols)
        self.figure = plt.figure(figsize=figsize)

        # create axes
        self.axes = {}
        for i in range(n):
            row = int(i // self.ncols)
            col = int(i % self.ncols)
            self.axes[i] = plt.subplot(self.gs[row, col]) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import GridSpec [as 別名]
def _create_likelihood_axis(self, figure=None, subplot_spec=None, **kwargs):
        # Making the axes:
        if figure is None:
            figsize = kwargs.get('figsize', None)
            fig, _ = plt.subplots(0, 0, figsize=figsize, constrained_layout=False)
        else:
            fig = figure

        if subplot_spec is None:
            grid = plt.GridSpec(1, 1, hspace=0.1, wspace=0.1, figure=fig)
        else:
            grid = gridspect.GridSpecFromSubplotSpec(1, 1, subplot_spec=subplot_spec)

        ax_like = fig.add_subplot(grid[0, 0])
        ax_like.spines['bottom'].set_position(('data', 0.0))
        ax_like.yaxis.tick_right()

        ax_like.spines['right'].set_position(('axes', 1.03))
        ax_like.spines['top'].set_color('none')
        ax_like.spines['left'].set_color('none')
        ax_like.set_xlabel('Thickness Obs.')
        ax_like.set_title('Likelihood')
        return ax_like 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import GridSpec [as 別名]
def plot_fp_analysis(fp_error_analysis, save_filename, 
                     colors=['#33a02c','#b2df8a','#1f78b4','#fb9a99','#e31a1c','#a6cee3'],
                     error_names=['True Postive', 'Double Detection Err','Wrong Lable Err', 'Localization Err', 'Confusion Err', 'Background Err'],
                     figsize=(10,5), fontsize=24):

    values,labels = [],[]
    _, _, fp_error_types_precentage_df = split_predictions_by_score_ranges(fp_error_analysis,fp_error_analysis.limit_factor)
    order = np.array([4,2,5,3,1,0])
    for this_limit_factor, this_fp_error_types_precentage_df  in fp_error_types_precentage_df.iteritems():
        values+=[this_fp_error_types_precentage_df['avg'].values[order]]
        labels+=['$%dG$' % (this_limit_factor+1)]

    fig = plt.figure(figsize=figsize)
    grid = plt.GridSpec(1, 5)

    lgd = subplot_fp_profile(fig=fig, ax=fig.add_subplot(grid[:-2]),
                             values=values, labels=labels, colors=colors,
                             xticks=error_names,
                             xlabel='Top Predicitons', ylabel='Error Breakdown ($\%$)',
                             title='False Postive Profile', fontsize=fontsize, 
                             ncol=3, legend_loc=(-0.15,1.15))

    order = np.array([4,0,1,3,2])
    subplot_error_type_impact(fig=fig, ax=fig.add_subplot(grid[-2:]),
                              values=np.array([fp_error_analysis.average_mAP_gain.values()]).T[order,:], 
                              labels=np.array(fp_error_analysis.average_mAP_gain.keys())[order], 
                              colors=colors[::-1],
                              xlabel='Error Type', ylabel='Average-mAP$_N$\nImprovment $(\%)$',
                              title='Removing Error Impact', fontsize=fontsize,
                              top=np.ceil(np.max(fp_error_analysis.average_mAP_gain.values())*100*1.1))
    
    plt.tight_layout()
    fig.savefig(save_filename,box_extra_artists=(lgd,), bbox_inches='tight')
    print('[Done] Output analysis is saved in %s' % save_filename) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import GridSpec [as 別名]
def DEBUG_print_ksn_filters(self):
        """
            This function is used to print one ksn profile per river to check the effect of the different filters on the dataset
            BG - 12/01/2018
        """
        plt.clf()
        print("I will now print ksn(chi) with the different filter")
        svdir = self.fpath+'river_plots/'
        if not os.path.isdir(svdir):
            os.makedirs(svdir)

        for SK in self.df_river["source_key"].unique():
            print("printing river: " +str(SK))

            # Selecting the river
            df = self.df_river[self.df_river["source_key"] == SK]

            fig = plt.figure(1, facecolor='white',figsize=(9,5))

            gs = plt.GridSpec(100,100,bottom=0.10,left=0.10,right=0.95,top=0.95)
            ax1 = fig.add_subplot(gs[0:100,0:100])

            ax1.scatter(df["chi"], df["m_chi"], c = "r", s = 1, marker = "o", label = "ksn")
            ax1.scatter(df["chi"], df["lumped_ksn"], c = "g", s = 1, marker = "s", label = "lumped ksn")
            ax1.scatter(df["chi"], df["TVD_ksn"], c = "k", s = 1, marker = "+", label = "TVD ksn")

            ax1.legend()

            ax1.set_xlabel(r'$ \chi$')
            ax1.set_ylabel(r'$ k_{sn}$')

            plt.savefig(svdir + self.fprefix + "_ksn_SK_" +str(SK)+".png", dpi = 300)
            plt.clf() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import GridSpec [as 別名]
def DEBUG_print_ksn_outliers(self):
        """
            This function is used to print one ksn profile per river to check the effect of the different filters on the dataset
            BG - 12/01/2018
        """
        plt.clf()
        print("I will now print ksn(chi) with outliers")
        svdir = self.fpath+'river_plots/'
        if not os.path.isdir(svdir):
            os.makedirs(svdir)

        for SK in self.df_river["source_key"].unique():
            print("printing river: " +str(SK))

            # Selecting the river
            df = self.df_river[self.df_river["source_key"] == SK]
            dfo = self.df_kp[self.df_kp["source_key"] == SK]

            fig = plt.figure(1, facecolor='white',figsize=(9,5))

            gs = plt.GridSpec(100,100,bottom=0.10,left=0.10,right=0.95,top=0.95)
            ax1 = fig.add_subplot(gs[0:100,0:100])

            ax1.scatter(df["chi"], df["m_chi"], c = "r", s = 1, marker = "o", label = "ksn", alpha = 0.15)
            # ax1.scatter(df["chi"], df["lumped_ksn"], c = "g", s = 1, marker = "s", label = "lumped ksn")
            # ax1.scatter(df["chi"], df["TVD_ksn_NC"], c = "purple", s = 2, marker = "x", label = "TVD ksn non corrected")
            ax1.scatter(df["chi"], df["TVD_ksn"], c = "k", s = 1, marker = "+", label = "TVD ksn")

            ax1.scatter(dfo["chi"][dfo["out"]==1], dfo["delta_ksn"][dfo["out"]==1], c = "purple" , marker = "s", s = 4)
            lim = ax1.get_ylim()
            ax1.vlines(dfo["chi"][dfo["out"]==1],-1000,1000, lw = 0.5, alpha = 0.5)
            ax1.set_ylim(lim)


            ax1.legend()

            ax1.set_xlabel(r'$ \chi$')
            ax1.set_ylabel(r'$ k_{sn}$')

            plt.savefig(svdir + self.fprefix + "_outksn_SK_" +str(SK)+".png", dpi = 300)
            plt.clf() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import GridSpec [as 別名]
def DEBUG_print_KDE(self):
        """
            This function is used to print one ksn profile per river to check the effect of the different filters on the dataset
            BG - 12/01/2018
        """
        plt.clf()
        print("I will now print ksn(chi) with the different KDE")
        svdir = self.fpath+'river_plots/'
        if not os.path.isdir(svdir):
            os.makedirs(svdir)

        for SK in self.df_kp_raw["source_key"].unique():
            print("printing river: " +str(SK))

            # Selecting the river
            df = self.df_kp_raw[self.df_kp_raw["source_key"] == SK]

            fig = plt.figure(1, facecolor='white',figsize=(9,5))

            gs = plt.GridSpec(100,100,bottom=0.10,left=0.10,right=0.95,top=0.95)
            ax1 = fig.add_subplot(gs[0:100,0:100])

            ax1.scatter(df["dksn/dchi"], df["KDE"], c = "k", s = 1, marker = "+", label = "ksn")

            ax1.set_xlabel(r'$ \frac{dk_{sn}}{\chi}$')
            ax1.set_ylabel(r'$ KDE_pdf $')

            plt.savefig(svdir + self.fprefix + "_KDE_SK_" +str(SK)+".png", dpi = 300)
            plt.clf() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import GridSpec [as 別名]
def plot_base_to_lip_from_knickpoint_profile(self, df = 0, comparison_point = [], size_format='ESURF', FigFormat='png'):

        if(isinstance(df,int)):
            df = self.knickpoint_raw
        raster_directory = self.fpath+'raster_plots/'
        if not os.path.isdir(raster_directory):
            os.makedirs(raster_directory)

        self.get_base_to_lip_from_knickpoint(df)

        for source in df["source_key"].unique():
            print(source)
            # make a figure with required dimensions
            if size_format == "geomorphology":
                fig = plt.figure(1, facecolor='white',figsize=(6.25,3.5))            
            elif size_format == "big":
                fig = plt.figure(1, facecolor='white',figsize=(16,9))            
            else:
                fig = plt.figure(1, facecolor='white',figsize=(4.92126,3.5))

            # create the axis and its position
            ## axis 1: The Chi profile and the knickpoints
            gs = plt.GridSpec(100,100,bottom=0.15,left=0.15,right=0.95,top=0.95)
            ax = fig.add_subplot(gs[0:100,0:100])
            CN = self.chanNet[self.chanNet["source_key"] == source]
            ax.plot(CN["chi"], CN["elevation"],zorder = 100)
            for i in self.base_to_lip_from_knickpoint:
                if i.iloc[0]["source_key"] == source:
                    ax.scatter(i.iloc[0]["chi"],i.iloc[0]["segmented_elevation"], s = 10, c = "r", zorder = 150)
                    ax.scatter(i.iloc[-1]["chi"],i.iloc[-1]["segmented_elevation"], s = 10, c = "b", zorder = 150)

            if(len(comparison_point) == 2):
                print("I am adding the comparison_point to the profiles")
                ax.scatter(comparison_point[0]["chi"][comparison_point[0]["source_key"] == source], comparison_point[0]["elevation"][comparison_point[0]["source_key"] == source], marker = "x", c = "r", s = 20,lw = 0.8, zorder = 500)
                ax.scatter(comparison_point[1]["chi"][comparison_point[1]["source_key"] == source], comparison_point[1]["elevation"][comparison_point[1]["source_key"] == source], marker = "x", c = "y", s = 20,lw = 0.8, zorder = 500)


            plt.savefig(raster_directory +"profile_base_to_lip"+str(source)+".png",dpi = 300)
            plt.clf() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import GridSpec [as 別名]
def __enter__(self):
        plt.ion()
        plt.figure(figsize=self.figsize)
        self.gs = plt.GridSpec(*self.grid)
        return self 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import GridSpec [as 別名]
def correlation_plots(plotted_methods, file_name):
        """Shortcut to create correlation plots."""
        n_methods = len(plotted_methods)

        n_cols = 5
        n_rows = int(np.floor(n_methods/(n_cols-1)))
        plot_size = 7.25 / n_cols
        fig = plt.figure(figsize=(n_cols*plot_size, n_rows*plot_size))
        grid = plt.GridSpec(nrows=n_rows, ncols=n_cols*2)
        # All rows have 4 plots except for last one which has 5.
        axes = []
        for row_idx in range(n_rows-1):
            axes.extend([fig.add_subplot(grid[row_idx, c:c+2]) for c in range(1,9,2)])
        axes.extend([fig.add_subplot(grid[-1, c:c+2]) for c in range(0,10,2)])

        # Associate a color to each host.
        for method, ax in zip(plotted_methods, axes):
            # Isolate statistics of the method.
            data = collection_all.data[collection_all.data.method == method]
            # Build palette.
            palette = [HOST_PALETTE[host_name] for host_name in sorted(data.host_name.unique())]
            # Add color for regression line over all data points.
            palette += [HOST_PALETTE['other1']]
            # Plot correlations.
            plot_correlation(x='$\Delta$G (expt) [kcal/mol]', y='$\Delta$G (calc) [kcal/mol]',
                             data=data, title=method, hue='host_name', color=palette,
                             shaded_area_color=HOST_PALETTE['other2'], ax=ax)
            # Remove legend and axes labels.
            ax.legend_.remove()
            ax.set_xlabel('')
            ax.set_ylabel('')
            # Make title and axes labels closer to axes.
            ax.set_title(ax.get_title(), pad=1.5)
            ax.tick_params(pad=3.0)

        # Use a single label for the figure.
        fig.text(0.015, 0.5, '$\Delta$G (calc) [kcal/mol]', va='center', rotation='vertical', size='large')
        fig.text(0.5, 0.015, '$\Delta$G (exp) [kcal/mol]', ha='center', size='large')

        plt.tight_layout(pad=0.9, rect=[0.0, 0.025, 1.0, 1.0])
        plt.savefig('../Accuracy/PaperImages/{}.pdf'.format(file_name)) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import GridSpec [as 別名]
def plot_image_components(x, coefficients=None, mean=0, components=None,
                          imshape=(8, 8), n_components=6, fontsize=12):
    if coefficients is None:
        coefficients = x
        
    if components is None:
        components = np.eye(len(coefficients), len(x))
        
    mean = np.zeros_like(x) + mean
        

    fig = plt.figure(figsize=(1.2 * (5 + n_components), 1.2 * 2))
    g = plt.GridSpec(2, 5 + n_components, hspace=0.3)

    def show(i, j, x, title=None):
        ax = fig.add_subplot(g[i, j], xticks=[], yticks=[])
        ax.imshow(x.reshape(imshape), interpolation='nearest')
        if title:
            ax.set_title(title, fontsize=fontsize)

    show(slice(2), slice(2), x, "True")

    approx = mean.copy()
    show(0, 2, np.zeros_like(x) + mean, r'$\mu$')
    show(1, 2, approx, r'$1 \cdot \mu$')

    for i in range(0, n_components):
        approx = approx + coefficients[i] * components[i]
        show(0, i + 3, components[i], r'$c_{0}$'.format(i + 1))
        show(1, i + 3, approx,
             r"${0:.2f} \cdot c_{1}$".format(coefficients[i], i + 1))
        plt.gca().text(0, 1.05, '$+$', ha='right', va='bottom',
                       transform=plt.gca().transAxes, fontsize=fontsize)

    show(slice(2), slice(-2, None), approx, "Approx") 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import GridSpec [as 別名]
def gridspec(ncols=4, nrows=1, figsize=None, dpi=None):
    figsize, dpi = get_figure_params(figsize, dpi, ncols)
    gs = pl.GridSpec(
        nrows, ncols, pl.figure(None, (figsize[0] * ncols, figsize[1] * nrows), dpi=dpi)
    )
    return gs 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import GridSpec [as 別名]
def __init__(self, ncols=4, nrows=1, figsize=None, dpi=None, **scatter_kwargs):
        """Specifies the geometry of the grid that a subplots can be placed in

        Example

        .. code:: python

            with scv.GridSpec() as pl:
                pl.scatter(adata, basis='pca')
                pl.scatter(adata, basis='umap')
                pl.hist(adata.obs.initial_size)

        Parameters
        ----------
        ncols: `int` (default: 4)
            Number of panels per row.
        nrows: `int` (default: 1)
            Number of panels per column.
        figsize: tuple (default: `None`)
            Figure size.
        dpi: `int` (default: `None`)
            Figure dpi.
        scatter_kwargs:
            Arguments to be passed to all scatter plots, e.g. `frameon=False`.
        """
        self.ncols, self.nrows, self.figsize, self.dpi = ncols, nrows, figsize, dpi
        self.scatter_kwargs = scatter_kwargs
        self.scatter_kwargs.update({"show": False})
        self.get_new_grid()
        self.new_row = None 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import GridSpec [as 別名]
def plot_phase_portraits(self, genes: List[str]) -> None:
        """Plot spliced-unspliced scatterplots resembling phase portraits

        Arguments
        ---------
        genes: List[str]
            A list of gene symbols.
        """
        n = len(genes)
        sqrtn = int(np.ceil(np.sqrt(n)))
        gs = plt.GridSpec(sqrtn, int(np.ceil(n / sqrtn)))
        for i, gn in enumerate(genes):
            self._plot_phase_portrait(gn, gs[i]) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import GridSpec [as 別名]
def plot_fit(F, n_hat, C_hat, theta_hat, dt):

    sigma, alpha, beta, lamb, gamma = theta_hat

    fig = plt.figure()
    gs = plt.GridSpec(3, 1)
    ax1 = fig.add_subplot(gs[0:2])
    ax2 = fig.add_subplot(gs[2:], sharex=ax1)
    axes = np.array([ax1, ax2])

    t = np.arange(F.shape[1]) * dt
    F_hat = alpha[:, None] * C_hat[None, :] + beta[:, None]

    axes[0].hold(True)
    axes[0].plot(t, F.sum(0), '-b', label=r'$F$')
    axes[0].plot(t, F_hat.sum(0), '-r', lw=1,
                 label=r'$\hat{\alpha}\hat{C}+\hat{\beta}$')
    axes[0].legend(loc=1, fancybox=True, fontsize='large')
    axes[0].tick_params(labelbottom=False)

    axes[1].plot(t, n_hat, '-k')
    axes[1].set_xlabel('Time (s)')
    axes[1].set_ylabel(r'$\hat{n}$', fontsize='large')
    axes[1].set_xlim(0, t[-1])

    fig.tight_layout()

    return fig, axes 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import GridSpec [as 別名]
def create_figure(self, marginal=True, likelihood=True, joyplot=True,
                      figsize=None, textsize=None,
                      n_samples=11):

        figsize, self.ax_labelsize, _, self.xt_labelsize, self.linewidth, _ = _scale_fig_size(figsize, textsize)
        self.fig, axes = plt.subplots(0, 0, figsize=figsize, constrained_layout=False)
        gs_0 = gridspect.GridSpec(3, 6, figure=self.fig, hspace=.1)

        if marginal is True:
            # Testing
            if likelihood is False:
                self.marginal_axes = self._create_joint_axis(figure=self.fig, subplot_spec=gs_0[0:2, 0:4])
            elif likelihood is False and joyplot is False:
                self.marginal_axes = self._create_joint_axis(figure=self.fig, subplot_spec=gs_0[:, :])
    
            else:
                self.marginal_axes = self._create_joint_axis(figure=self.fig, subplot_spec=gs_0[0:2, 0:3])

        if likelihood is True:
            if marginal is False:
                self.likelihood_axes = self._create_likelihood_axis(figure=self.fig, subplot_spec=gs_0[0:2, 0:4])
            elif joyplot is False:
                self.likelihood_axes = self._create_likelihood_axis(figure=self.fig, subplot_spec=gs_0[0:2, 4:])
            else:
                self.likelihood_axes = self._create_likelihood_axis(figure=self.fig, subplot_spec=gs_0[0:1, 4:])

        if joyplot is True:
            self.n_samples = n_samples
            if marginal is False and likelihood is False:
                self.joy = self._create_joy_axis(self.fig, gs_0[:, :])
            else:
                self.joy = self._create_joy_axis(self.fig, gs_0[1:2, 4:])