python类jointplot()的实例源码

plots.py 文件源码 项目:guacml 作者: guacml 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def predictions_vs_actual_regression(model_results, model_name, size=6, bins=None,
                                     gridsize=30, outlier_ratio=None, **kwargs):
    holdout = model_results.holdout_data
    target = model_results.target

    if outlier_ratio is not None:
        holdout = utils.remove_outlier_rows(holdout, 'prediction', outlier_ratio)
        holdout = utils.remove_outlier_rows(holdout, target, outlier_ratio)

    sns.set(style="white", color_codes=True)

    marginal_kws = dict(hist_kws=dict(edgecolor='black'))
    plt.suptitle('{0}: Predictions vs Actual'.format(model_name), fontsize=14)
    grid = sns.jointplot('prediction', target, holdout, 'hexbin', gridsize=gridsize,
                         size=size, bins=bins, space=0, marginal_kws=marginal_kws, **kwargs)
    plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1)  # shrink fig so cbar is visible
    cax = grid.fig.add_axes([.95, .18, .04, .5])  # x, y, width, height
    color_bar = sns.plt.colorbar(cax=cax)

    if bins is None:
        color_bar.set_label('count')
    elif bins == 'log':
        color_bar.set_label('log_10(count)')
    return grid
tools.py 文件源码 项目:Harmonbot 作者: Harmon758 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def graph_alternative(self, ctx, *, data : str):
        '''WIP'''
        filename = "data/temp/graph_alternative.png"
        seaborn.jointplot(**eval(data)).savefig(name)
        await self.bot.send_file(destination = ctx.message.channel, fp = filename, content = ctx.message.author.display_name + ':')
report.py 文件源码 项目:temci 作者: parttimenerd 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def _jointplot(self, first: RunData, second: RunData, property: str, size: int, filename: str = None,
                   show_ticks: bool = True):
        import matplotlib.pyplot as plt
        import seaborn as sns
        import numpy
        filename = filename or self._get_new_figure_filename()
        length = min(len(first[property]), len(second[property]))
        first_prop = first[property][0:length]
        second_prop = second[property][0:length]
        lim = (0, max(max(first_prop), max(second_prop)))
        self._set_fig_size(size)
        x1 = pd.Series(first_prop, name="{descr}: {prop}".format(descr=first.description(), prop=property))
        x2 = pd.Series(second_prop, name="{descr}: {prop}".format(descr=second.description(), prop=property))
        plt.xlim(lim)
        g = None
        try:
            g = sns.jointplot(x1, x2, kind=self.misc["pair_kind"], size=size, space=0,
                              stat_func=self.stats_helper.tester.test, xlim=lim, ylim=lim)
            if not show_ticks:
                g.ax_joint.set_xticklabels([])
                g.ax_joint.set_yticklabels([])
            g.savefig(filename)
            plt.close()
        except BaseException as ex:
            logging.warning(ex)
        return filename
51cto??_??????.py 文件源码 项目:Data_Analysis 作者: crown-prince 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def stock():
     #?????????????????, ?????????
    stock_list = {"zsyh":"600036","jsyh":"601939","szzs":"000001","pfyh":"600000","msyh":"600061"}
    for stock, code in stock_list.items():
        globals()[stock] = tsh.get_hist_data(code,start="2015-01-01",end="2016-04-16")
    stock_list2 = stock_list.keys()
    #print(stock_list2)
    sl = [globals()[st]["close"] for st in stock_list2]
    df_close = pd.concat(sl,axis=1,join='inner')
    df_close.columns = stock_list2
    #print(df_close)
    df_close.sort_index(ascending=True,inplace=True) #ascending ??????????????????
    pc_ret = df_close.pct_change() #????????????????
    print(pc_ret)
    make_end_line()
    print(pc_ret.mean())
    make_end_line()
    #????????????
    plt.show(sns.jointplot("zsyh","jsyh",pc_ret,kind="hex")) #?? ????????1?????????? 0????? -1????????
    plt.show(sns.jointplot("zsyh","jsyh",pc_ret,kind="scatter"))
    plt.show(sns.jointplot("zsyh","szzs",pc_ret,kind="scatter"))
    plt.show(sns.pairplot(pc_ret[["jsyh","zsyh","pfyh","msyh"]].dropna())) #??????????
    print(pc_ret.std()) #????????????????????????????
    make_end_line()
    rets = pc_ret.dropna()
    print(rets.mean())
    make_end_line()
    area = np.pi *20 #????
    plt.scatter(rets.mean(),rets.std())    #???rets?????????xy? 
    plt.xlabel("Expected Return")#????xy????
    plt.ylabel("Risk")
    for label,x,y in zip(rets.columns,rets.mean(),rets.std()):
        plt.annotate(
            label,
            xy = (x,y),xytext = (50,50),
            textcoords = "offset points",ha = "right",va = "bottom",
            arrowprops = dict(arrowstyle = "-",connectionstyle = "arc3,rad=-0.3"))
    plt.show()
cohort.py 文件源码 项目:cohorts 作者: hammerlab 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def plot_correlation(self, on, x_col=None, plot_type="jointplot", stat_func=pearsonr, show_stat_func=True, plot_kwargs={}, **kwargs):
        """Plot the correlation between two variables.

        Parameters
        ----------
        on : list or dict of functions or strings
            See `cohort.load.as_dataframe`
        x_col : str, optional
            If `on` is a dict, this guarantees we have the expected ordering.
        plot_type : str, optional
            Specify "jointplot", "regplot", "boxplot", or "barplot".
        stat_func : function, optional.
            Specify which function to use for the statistical test.
        show_stat_func : bool, optional
            Whether or not to show the stat_func result in the plot itself.
        plot_kwargs : dict, optional
            kwargs to pass through to plotting functions.
        """
        if plot_type not in ["boxplot", "barplot", "jointplot", "regplot"]:
            raise ValueError("Invalid plot_type %s" % plot_type)
        plot_cols, df = self.as_dataframe(on, return_cols=True, **kwargs)
        if len(plot_cols) != 2:
            raise ValueError("Must be comparing two columns, but there are %d columns" % len(plot_cols))
        for plot_col in plot_cols:
            df = filter_not_null(df, plot_col)
        if x_col is None:
            x_col = plot_cols[0]
            y_col = plot_cols[1]
        else:
            if x_col == plot_cols[0]:
                y_col = plot_cols[1]
            else:
                y_col = plot_cols[0]
        series_x = df[x_col]
        series_y = df[y_col]
        coeff, p_value = stat_func(series_x, series_y)
        if plot_type == "jointplot":
            plot = sb.jointplot(data=df, x=x_col, y=y_col,
                                stat_func=stat_func if show_stat_func else None,
                                **plot_kwargs)
        elif plot_type == "regplot":
            plot = sb.regplot(data=df, x=x_col, y=y_col,
                              **plot_kwargs)
        elif plot_type == "boxplot":
            plot = stripboxplot(data=df, x=x_col, y=y_col, **plot_kwargs)
        else:
            plot = sb.barplot(data=df, x=x_col, y=y_col, **plot_kwargs)
        return CorrelationResults(coeff=coeff, p_value=p_value, stat_func=stat_func,
                                  series_x=series_x, series_y=series_y, plot=plot)
analysis.py 文件源码 项目:learning-to-prune 作者: timvieira 项目源码 文件源码 阅读 29 收藏 0 点赞 0 评论 0
def main():
    from argparse import ArgumentParser
    p = ArgumentParser()
    p.add_argument('--minlength', type=int, default=5)
    p.add_argument('--maxlength', type=int, default=30)
    p.add_argument('--examples', type=int, required=True)
    p.add_argument('--seed', type=int, default=None)
    p.add_argument('--profile', action='store_true')
    p.add_argument('--grammar', choices=('medium','big'), default='medium')
    p.add_argument('--aggressive', type=float, default=0,
                   help='Pruning rate (zero=no pruning, one=lots of pruning).')

    args = p.parse_args()

    if args.profile:
        profile_run(examples = args.examples,
                    grammar = args.grammar,
                    maxlength = args.maxlength,
                    minlength = args.minlength,
                    aggressive = args.aggressive,
                    seed = args.seed)

    else:
        d = run(examples = args.examples,
                grammar = args.grammar,
                maxlength = args.maxlength,
                minlength = args.minlength,
                aggressive = args.aggressive,
                seed = args.seed)

        filename_base = 'tmp/cp-analysis-' + '-'.join('%s_%s' % (k,v) for k,v in sorted(args.__dict__.items()))
        d.to_csv('%s.csv' % filename_base)
        p = sns.jointplot('ratio', 'speedup', d, kind='reg')
        p.savefig('%s.png' % filename_base)
        print '[info] wrote %s.csv' % filename_base

    print '== DONE =='

    pl.ioff()
    pl.show()
pycore.py 文件源码 项目:sci-pype 作者: jay-johnson 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def sb_jointplot(self, req, debug=False):

        image_list      = []

        image_filename  = req["ImgFile"]

        import seaborn as sns 
        import numpy as np
        from matplotlib import pyplot
        import matplotlib.pyplot as plt
        import matplotlib.dates as mdates
        from matplotlib.patches import Rectangle
        from matplotlib.finance import volume_overlay
        import pandas as pd
        from pandas.tseries.offsets import BDay

        source_df       = req["SourceDF"]
        ds_name         = req["DSName"]

        sns.set_style("whitegrid", {'axes.grid' : True})
        sns.color_palette("Set1", n_colors=8, desat=.5)

        cur_xlabel      = "measurement"
        cur_ylabel      = "value"
        cur_kind        = "reg"
        cur_width       = 15.0
        cur_height      = 15.0
        if "X" in req:
            cur_xlabel  = str(req["X"])
        if "Y" in req:
            cur_ylabel  = str(req["Y"])
        if "Width" in req:
            cur_width   = float(req["Width"])
        if "Height" in req:
            cur_height  = float(req["Height"])
        if "Kind" in req:
            cur_kind    = str(req["Kind"])
        # end of parsing inputs

        # Add custom plots here

        grid            = sns.jointplot(cur_xlabel, cur_ylabel, kind=cur_kind, data=source_df, annot_kws=dict(stat="r"))

        if debug:
            self.lg("Saving File(" + str(image_filename) + ")", 6)

        grid.fig.set_figwidth(cur_width)
        grid.fig.set_figheight(cur_height)

        ax              = grid.ax_joint
        self.pd_add_footnote(ax.figure)
        ax.figure.savefig(image_filename)
        image_list.append(image_filename)

        if req["ShowPlot"] == True:
            plt.show()

        return image_list
    # end of sb_jointplot
seaborn_usage.py 文件源码 项目:base_function 作者: Rockyzsu 项目源码 文件源码 阅读 20 收藏 0 点赞 0 评论 0
def testcase1():
    tips=sns.load_dataset('tips')
    sns.jointplot('total_bill','tips',tips,kind='reg')
pycore.py 文件源码 项目:datanode 作者: jay-johnson 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def sb_jointplot(self, req, debug=False):

        image_list      = []

        image_filename  = req["ImgFile"]

        import seaborn as sns 
        import numpy as np
        from matplotlib import pyplot
        import matplotlib.pyplot as plt
        import matplotlib.dates as mdates
        from matplotlib.patches import Rectangle
        from matplotlib.finance import volume_overlay
        import pandas as pd
        from pandas.tseries.offsets import BDay

        source_df       = req["SourceDF"]
        ds_name         = req["DSName"]

        sns.set_style("whitegrid", {'axes.grid' : True})
        sns.color_palette("Set1", n_colors=8, desat=.5)

        cur_xlabel      = "measurement"
        cur_ylabel      = "value"
        cur_kind        = "reg"
        cur_width       = 15.0
        cur_height      = 15.0
        if "X" in req:
            cur_xlabel  = str(req["X"])
        if "Y" in req:
            cur_ylabel  = str(req["Y"])
        if "Width" in req:
            cur_width   = float(req["Width"])
        if "Height" in req:
            cur_height  = float(req["Height"])
        if "Kind" in req:
            cur_kind    = str(req["Kind"])
        # end of parsing inputs

        # Add custom plots here

        grid            = sns.jointplot(cur_xlabel, cur_ylabel, kind=cur_kind, data=source_df, annot_kws=dict(stat="r"))

        if debug:
            self.lg("Saving File(" + str(image_filename) + ")", 6)

        grid.fig.set_figwidth(cur_width)
        grid.fig.set_figheight(cur_height)

        ax              = grid.ax_joint
        self.pd_add_footnote(ax.figure)
        ax.figure.savefig(image_filename)
        image_list.append(image_filename)

        if req["ShowPlot"] == True:
            plt.show()

        return image_list
    # end of sb_jointplot
genomic_distribution_cor.py 文件源码 项目:TEIBA 作者: brguez 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def scatterCorr(arrayA, arrayB, threshold, outPath):
    """

        Interpretation of strength of correlation

        very weak: < 0,15 
        weak: 0,15-0,25  
        moderate: 0,25-0,40 
        strong: 0,40-0,75
        very strong: >0,75

    """
    corr = stats.spearmanr(arrayA, arrayB)
    coefficient = float(format(corr[0], '.3f'))
    pvalue = float(corr[1])
    print "pvalue: ", pvalue

    ## Make scatterplot if rho >= threshold or <= -theshold
    if (coefficient >= threshold) or (coefficient <= -threshold):

        # Make scatterplot
        fig = plt.figure(figsize=(6,6))
        ax1 = fig.add_subplot(1, 1, 1)
        #plot = sns.jointplot(x=arrayA, y=arrayB, kind="hex", xlim=(0,40), gridsize=50, dropna=True, cmap="Blues", stat_func=spearmanr)
        plot = sns.jointplot(x=arrayA, y=arrayB, kind="kde", space=0, xlim=(0,30), gridsize=50, dropna=True, cmap="Blues", stat_func=spearmanr)
        plt.xlabel('# L1', fontsize=12)
        plt.ylabel('Replication time', fontsize=12)

#        sns.plt.subplots_adjust(left=0.2, right=0.8, top=0.8, bottom=0.2)  # shrink fig so cbar is visible
#        cax = plot.fig.add_axes([.85, .25, .05, .4])  # x, y, width, height
#        sns.plt.colorbar(cax=cax)

        #sns.jointplot(x=arrayA, y=arrayB, kind="kde", space=0, color="b", xlim=(0,30))



        ## Save figure
        fileName = outPath + '_' + str(coefficient) + '_correlation.pdf' 

        plt.savefig(fileName)

    return coefficient, pvalue


#### MAIN ####

## Import modules ##


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