python类cubehelix_palette()的实例源码

def __init__(self, parent):
        fig = Figure(figsize=(4, 4), dpi=100, tight_layout=True)
        super(DefaultGraph, self).__init__(fig)
        self.setParent(parent)
        sns.set(style="dark")

        for index, s in zip(range(9), np.linspace(0, 3, 10)):
            axes = fig.add_subplot(3, 3, index + 1)
            x, y = np.random.randn(2, 50)
            cmap = sns.cubehelix_palette(start=s, light=1, as_cmap=True)
            sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes)
            axes.set_xlim(-3, 3)
            axes.set_ylim(-3, 3)
            axes.set_xticks([])
            axes.set_yticks([])

        fig.suptitle("Activity Browser", y=0.5, fontsize=30, backgroundcolor=(1, 1, 1, 0.5))

        self.setSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)
        self.updateGeometry()

def __init__(self, parent, mlca, width=6, height=6, dpi=100):
        figure = Figure(figsize=(width, height), dpi=dpi, tight_layout=True)
        axes = figure.add_subplot(111)

        super(LCAResultsPlot, self).__init__(figure)
        self.setParent(parent)
        activity_names = [format_activity_label(next(iter(f.keys()))) for f in mlca.func_units]
        # From https://stanford.edu/~mwaskom/software/seaborn/tutorial/color_palettes.html
        cmap = sns.cubehelix_palette(8, start=.5, rot=-.75, as_cmap=True)
        hm = sns.heatmap(
            # mlca.results / np.average(mlca.results, axis=0), # Normalize to get relative results
            mlca.results,
            annot=True,
            linewidths=.05,
            cmap=cmap,
            xticklabels=["\n".join(x) for x in mlca.methods],
            yticklabels=activity_names,
            ax=axes,
            square=False,
        )
        hm.tick_params(labelsize=8)

        self.setMinimumSize(self.size())
        # sns.set_context("notebook")

def plot_heatmaps(data, mis, column_label, cont, topk=30, prefix=''):
    cmap = sns.cubehelix_palette(as_cmap=True, light=.9)
    m, nv = mis.shape
    for j in range(m):
        inds = np.argsort(- mis[j, :])[:topk]
        if len(inds) >= 2:
            plt.clf()
            order = np.argsort(cont[:,j])
            subdata = data[:, inds][order].T
            subdata -= np.nanmean(subdata, axis=1, keepdims=True)
            subdata /= np.nanstd(subdata, axis=1, keepdims=True)
            columns = [column_label[i] for i in inds]
            sns.heatmap(subdata, vmin=-3, vmax=3, cmap=cmap, yticklabels=columns, xticklabels=False, mask=np.isnan(subdata))
            filename = '{}/heatmaps/group_num={}.png'.format(prefix, j)
            if not os.path.exists(os.path.dirname(filename)):
                os.makedirs(os.path.dirname(filename))
            plt.title("Latent factor {}".format(j))
            plt.yticks(rotation=0)
            plt.savefig(filename, bbox_inches='tight')
            plt.close('all')
            #plot_rels(data[:, inds], map(lambda q: column_label[q], inds), colors=cont[:, j],
            #          outfile=prefix + '/relationships/group_num=' + str(j), latent=labels[:, j], alpha=0.1)

def plot_crossval_auc(roc_curves):
    cmap = sns.cubehelix_palette(11)
    aucs = []
    ax = plt.axes()
    for fold in roc_curves.keys():
        (f, p) = roc_curves[fold]
        aucs.append(area_under_curve(f, p))
        label_str = "fold {}, roc auc: {:.2f}".format(fold, aucs[-1])
        ax.plot(f, p, label=label_str, color=cmap[fold])
    ax.plot([0, 1], [0, 1], label="random, roc auc: 0.5", color="black")
    ax.legend(loc="lower right")
    plt.xlabel("False positive rate")
    plt.ylabel("True positive rate")
    plt.title(
        "ROC curves across 10 different validation folds(tiny convnet "
        "trained on small datasets)")
    plt.show()

def print_heatmap( points,label,id_map ):
    '''
    points: N_samples * N_features
    label: (int) N_samples
    id_map: map label id to its name
    '''
    # = sns.color_palette("RdBu_r", max(label)+1)
    #cNorm = colors.Normalize(vmin=0,vmax=max(label)) #normalise the colormap
    #scalarMap = cm.ScalarMappable(norm=cNorm,cmap='Paired') #map numbers to colors

    index = [id_map[i] for i in label]
    df = DataFrame( 
            points,
            columns = list(range(points.shape[1])),
            index = index
            )
    row_color = [current_palette[i] for i in label]

    cmap = sns.cubehelix_palette(as_cmap=True, rot=-.3, light=1)
    g = sns.clustermap( df,cmap=cmap,row_colors=row_color,col_cluster=False,xticklabels=False,yticklabels=False) #,standard_scale=1 )

    return g.fig

def dendrogram(df, number_of_clusters=int(df.shape[1] / 1.2)):
        # Create Dendrogram
        agglomerated_features = FeatureAgglomeration(n_clusters=number_of_clusters)
        used_networks = np.arange(0, number_of_clusters, dtype=int)

        # Create a custom palette to identify the networks
        network_pal = sns.cubehelix_palette(len(used_networks),
                                            light=.9, dark=.1, reverse=True,
                                            start=1, rot=-2)
        network_lut = dict(zip(map(str, df.columns), network_pal))

        # Convert the palette to vectors that will be drawn on the side of the matrix
        networks = df.columns.get_level_values(None)
        network_colors = pd.Series(networks, index=df.columns).map(network_lut)
        sns.set(font="monospace")
        # Create custom colormap
        cmap = sns.diverging_palette(h_neg=210, h_pos=350, s=90, l=30, as_cmap=True)
        cg = sns.clustermap(df.astype(float).corr(), cmap=cmap, linewidths=.5, row_colors=network_colors,
                            col_colors=network_colors)
        plt.setp(cg.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
        plt.setp(cg.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)
        plt.show()

def plot_heatmaps(data, labels, alpha, mis, column_label, cont, topk=20, prefix='', focus=''):
    cmap = sns.cubehelix_palette(as_cmap=True, light=.9)
    m, nv = mis.shape
    for j in range(m):
        inds = np.where(np.logical_and(alpha[j] > 0, mis[j] > 0.))[0]
        inds = inds[np.argsort(- alpha[j, inds] * mis[j, inds])][:topk]
        if focus in column_label:
            ifocus = column_label.index(focus)
            if not ifocus in inds:
                inds = np.insert(inds, 0, ifocus)
        if len(inds) >= 2:
            plt.clf()
            order = np.argsort(cont[:,j])
            subdata = data[:, inds][order].T
            subdata -= np.nanmean(subdata, axis=1, keepdims=True)
            subdata /= np.nanstd(subdata, axis=1, keepdims=True)
            columns = [column_label[i] for i in inds]
            sns.heatmap(subdata, vmin=-3, vmax=3, cmap=cmap, yticklabels=columns, xticklabels=False, mask=np.isnan(subdata))
            filename = '{}/heatmaps/group_num={}.png'.format(prefix, j)
            if not os.path.exists(os.path.dirname(filename)):
                os.makedirs(os.path.dirname(filename))
            plt.title("Latent factor {}".format(j))
            plt.savefig(filename, bbox_inches='tight')
            plt.close('all')
            #plot_rels(data[:, inds], list(map(lambda q: column_label[q], inds)), colors=cont[:, j],
            #          outfile=prefix + '/relationships/group_num=' + str(j), latent=labels[:, j], alpha=0.1)

def plot_pairplots(data, labels, alpha, mis, column_label, topk=5, prefix='', focus=''):
    cmap = sns.cubehelix_palette(as_cmap=True, light=.9)
    plt.rcParams.update({'font.size': 32})
    m, nv = mis.shape
    for j in range(m):
        inds = np.where(np.logical_and(alpha[j] > 0, mis[j] > 0.))[0]
        inds = inds[np.argsort(- alpha[j, inds] * mis[j, inds])][:topk]
        if focus in column_label:
            ifocus = column_label.index(focus)
            if not ifocus in inds:
                inds = np.insert(inds, 0, ifocus)
        if len(inds) >= 2:
            plt.clf()
            subdata = data[:, inds]
            columns = [column_label[i] for i in inds]
            subdata = pd.DataFrame(data=subdata, columns=columns)

            try:
                sns.pairplot(subdata, kind="reg", diag_kind="kde", size=5, dropna=True)
                filename = '{}/pairplots_regress/group_num={}.pdf'.format(prefix, j)
                if not os.path.exists(os.path.dirname(filename)):
                    os.makedirs(os.path.dirname(filename))
                plt.suptitle("Latent factor {}".format(j), y=1.01)
                plt.savefig(filename, bbox_inches='tight')
                plt.clf()
            except:
                pass

            subdata['Latent factor'] = labels[:,j]
            try:
                sns.pairplot(subdata, kind="scatter", dropna=True, vars=subdata.columns.drop('Latent factor'), hue="Latent factor", diag_kind="kde", size=5)
                filename = '{}/pairplots/group_num={}.pdf'.format(prefix, j)
                if not os.path.exists(os.path.dirname(filename)):
                    os.makedirs(os.path.dirname(filename))
                plt.suptitle("Latent factor {}".format(j), y=1.01)
                plt.savefig(filename, bbox_inches='tight')
                plt.close('all')
            except:
                pass

def dendrogram(df, number_of_clusters, agglomerated_feature_labels):
        import seaborn as sns
        # Todo: Create Dendrogram
        # used networks are the labels occuring in agglomerated_features.labels_
        # which corresponds to np.arange(0, number_of_clusters)
        # number_of_clusters = int(df.shape[1] / 1.2)
        # used_networks = np.arange(0, number_of_clusters, dtype=int)
        used_networks = np.unique(agglomerated_feature_labels)
        # used_networks = [1, 5, 6, 7, 8, 11, 12, 13, 16, 17]

        # In our case all columns are clustered, which means used_columns is true in every element
        # used_columns = (df.columns.get_level_values(None)
                        # .astype(int)
                        # .isin(used_networks))
        # used_columns = (agglomerated_feature_labels.astype(int).isin(used_networks))
        # df = df.loc[:, used_columns]

        # Create a custom palette to identify the networks
        network_pal = sns.cubehelix_palette(len(used_networks),
                                            light=.9, dark=.1, reverse=True,
                                            start=1, rot=-2)
        network_lut = dict(zip(map(str, df.columns), network_pal))

        # Convert the palette to vectors that will be drawn on the side of the matrix
        networks = df.columns.get_level_values(None)
        # networks = agglomerated_feature_labels
        network_colors = pd.Series(networks, index=df.columns).map(network_lut)
        # plt.figure()
        # cg = sns.clustermap(df, metric="correlation")
        # plt.setp(cg.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
        sns.set(font="monospace")
        # Create custom colormap
        cmap = sns.diverging_palette(h_neg=210, h_pos=350, s=90, l=30, as_cmap=True)
        cg = sns.clustermap(df.astype(float).corr(), cmap=cmap, linewidths=.5, row_colors=network_colors,
                            col_colors=network_colors)
        plt.setp(cg.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
        plt.setp(cg.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)
        # plt.xticks(rotation=90)
        plt.show()