Python seaborn.pointplot() Examples

def violinplot(set_size, p_error, subplot, i):
    """Make learning cuves with violinplot.

    Parameters
    ----------
    set_size : list
       Size of sub-set of data/features which the model is based on.
    p_error : list
       The prediction error for plain vanilla ridge.
    subplot : int
        Which subplot being produced.
    i : int
       Which iteration in the featureselection.
    """
    plt.figure(1)
    plt.subplot(int("22" + str(subplot))).set_title('Feature size ' + str(i),
                                                    loc='left')
    plt.legend(loc='upper right')
    plt.ylabel('Prediction error')
    plt.xlabel('Data size')
    sns.violinplot(x=set_size, y=p_error, scale="count")
    sns.pointplot(x=set_size, y=p_error, ci=100, capsize=.2)
    if subplot == 4:
        plt.show() 

def featselect_featvar_plot(p_error_select, number_feat):
    """Create learning curve with data size and prediction error.

    Parameters
    ----------
    data_size : list
        Data_size for where the prediction were made.
    p_error : list
        Error for where the prediction were made.
    data_size_mean : list
        Mean of the data size in a sub-set.
    p_error_mean : list
        The mean error for the sub-set.
    corrected_std : array
        The standard deaviation for the sub-set of data.
    """
    fig = plt.figure()
    fig.add_subplot(111)
    sns.violinplot(x=number_feat, y=p_error_select, scale="count")
    sns.pointplot(x=number_feat, y=p_error_select)
    plt.legend(loc='upper right')
    plt.ylabel('Prediction error')
    plt.xlabel('Data size')
    plt.show() 

def main():
    args = get_args()
    region_df = read_mosdepth(args.mosdepth_combined, args.region)
    fig, ax = plt.subplots(figsize=(16, 8))
    ax = sns.pointplot(x="begin",
                       y="coverage",
                       hue="name",
                       data=region_df,
                       scale=0.1,
                       ax=ax)
    ax.set(xlabel="position",
           ylabel="normalizes coverage")
    plt.xticks([tick for i, tick in enumerate(list(plt.xticks()[0])) if not i % 5],
               rotation=30,
               ha='center')
    plt.savefig("Mosdepth_{}.png".format(args.region), dpi=500, bbox_inches='tight') 

def plotDailyStatsSleep(stats, columns=None):
    """
    Plot daily stats. Fill all data range, and put NaN for days without measures
    :param data: data to plot
    """
    MEASURE_NAME = 'date'
    if not columns:
        columns = ['sleep_inefficiency', 'sleep_hours']
    dataToPlot = _prepareDailyStats(stats, columns)

    f, axes = getAxes(2,1)
    xTicksDiv = min(10, len(dataToPlot))
    #xticks = [(x-pd.DateOffset(years=1, day=2)).date() for x in stats.date]
    xticks = [x.date() for x in dataToPlot.date]
    keptticks = xticks[::int(len(xticks)/xTicksDiv)]
    xticks = ['' for _ in xticks]
    xticks[::int(len(xticks)/xTicksDiv)] = keptticks
    for i, c in enumerate(columns):
        g =sns.pointplot(x=MEASURE_NAME, y=NAMES[c], data=dataToPlot, ax=axes[i])
        g.set_xticklabels([])
        g.set_xlabel('')
    g.set_xticklabels(xticks, rotation=45)
    sns.plt.show() 

def _plotWeekdayByMonthStats(stats, stat_name):
    dataToPlot = _prepareWeekdayByMonthStats(stats)

    # Plot
    g = sns.pointplot(data=dataToPlot, x="day", y=stat_name, hue="month", order=dayOfWeekOrder)
    g.set(xlabel='')
    g.set_ylabel(NAMES[stat_name])
    return g
    #sns.plt.show() 

def test_population_roi_over_time():
       # Style elements
       palette=["#56B4E9", "#E69F00"]

       data_dir = path.join(path.dirname(path.realpath(__file__)),"../../tests/data")
       data_path = path.join(data_dir,'drs_activity.csv')
       df = pd.read_csv(data_path)

       df = df.rename(columns={'t':'Mean t-Statistic'})
       df['Session']=df['Session'].map({
	       'ofM':'naïve',
	       'ofMaF':'acute',
	       'ofMcF1':'chronic/2w',
	       'ofMcF2':'chronic/4w',
	       'ofMpF':'post',
	       })


       # definitions for the axes
       left, width = 0.06, 0.9
       bottom, height = 0.06, 0.9

       session_coordinates = [left, bottom, width, height]
       roi_coordinates = [left+0.02, bottom+0.7, 0.3, 0.2]

       fig = plt.figure(1)

       ax1 = plt.axes(session_coordinates)
       sns.pointplot(
	      x='Session',
	      y='Mean t-Statistic',
	      units='subject',
	      data=df,
	      hue='treatment',
	      dodge=True,
	      palette=palette,
	      order=['naïve','acute','chronic/2w','chronic/4w','post'],
	      ax=ax1,
	      ci=95,
	      )

       ax2 = plt.axes(roi_coordinates)
       maps.atlas_label('/usr/share/mouse-brain-atlases/dsurqec_200micron_roi-dr.nii',
	       scale=0.3,
	       color="#E69F00",
	       ax=ax2,
	       annotate=False,
	       alpha=0.8,
	       )

       plt.savefig('_test_population_roi_over_time.png') 

def test_activity_timecourse_with_inlay():
	import pandas as pd
	import matplotlib.pyplot as plt
	import samri.plotting.maps as maps
	import seaborn as sns
	from os import path

	# Style elements
	palette=["#56B4E9", "#E69F00"]

	data_dir = path.join(path.dirname(path.realpath(__file__)),"../../tests/data")
	data_path = path.join(data_dir,'drs_activity.csv')
	df = pd.read_csv(data_path)

	df = df.rename(columns={'t':'Mean t-Statistic'})
	df['Session']=df['Session'].map({
		'ofM':'naïve',
		'ofMaF':'acute',
		'ofMcF1':'chronic/2w',
		'ofMcF2':'chronic/4w',
		'ofMpF':'post',
		})


	# definitions for the axes
	left, width = 0.06, 0.9
	bottom, height = 0.06, 0.9

	session_coordinates = [left, bottom, width, height]
	roi_coordinates = [left+0.02, bottom+0.7, 0.3, 0.2]

	fig = plt.figure(1)

	ax1 = plt.axes(session_coordinates)
	sns.pointplot(
	       x='Session',
	       y='Mean t-Statistic',
	       units='subject',
	       data=df,
	       hue='treatment',
	       dodge=True,
	       palette=palette,
	       order=['naïve','acute','chronic/2w','chronic/4w','post'],
	       ax=ax1,
	       ci=95,
	       )

	ax2 = plt.axes(roi_coordinates)
	maps.atlas_label('/usr/share/mouse-brain-atlases/dsurqec_200micron_roi-dr.nii',
		scale=0.3,
		color="#E69F00",
		ax=ax2,
		annotate=False,
		alpha=0.8,
		)

	plt.savefig('_activity_timecourse_with_inlay.png') 

def visualisationDF(df):    
    dataFrameInfoPrint(df)
    #graph-01
    # df['shapelyArea'].plot.hist(alpha=0.5)    
    #graph-02
    # df['shapelyArea'].plot.kde()    
    #graph-03
    # df[['shapelyLength','shapeIdx']].plot.scatter('shapelyLength','shapeIdx')    
    #normalize data in a range of columns
    cols_to_norm=['shapeIdx', 'FRAC']
    df[cols_to_norm]=df[cols_to_norm].apply(lambda x: (x - x.min()) / (x.max() - x.min()))
    
    a='shapeIdx'
    b='FRAC'
    c='park_class'
    
    #graph-04
    # sns.jointplot(a,b,df,kind='hex')
    
    #graph-05
    # sns.jointplot(a, b, df, kind='kde')
    
    #graph-06
    # sns.catplot(x='park_class',y=a,data=df)
    
    #graph-07
    '''
    # Initialize the figure
    f, ax = plt.subplots()
    sns.despine(bottom=True, left=True)
    # Show each observation with a scatterplot
    sns.stripplot(x=a, y=c, hue=c,data=df, dodge=True, alpha=.25, zorder=1)    
    # Show the conditional means
    sns.pointplot(x=a, y=c, hue=c,data=df, dodge=.532, join=False, palette="dark",markers="d", scale=.75, ci=None)
    # Improve the legend 
    handles, labels = ax.get_legend_handles_labels()
    ax.legend(handles[3:], labels[3:], title=b,handletextpad=0, columnspacing=1,loc="lower right", ncol=3, frameon=True)
    '''
    
    #graph-08
    # sns.catplot(x=c,y=a,data=df,kind='box')
    
    #graph-09
    # sns.catplot(x=c,y=a,data=df,kind='violin')
    
    #graph-10
    '''
    f, axs = plt.subplots(1, 2, figsize=(12, 6))
    # First axis    
    df[b].plot.hist(ax=axs[0])
    # Second axis
    df[b].plot.kde(ax=axs[1])
    # Title
    f.suptitle(b)
    # Display
    plt.show()
    '''        

#从新定义栅格投影，参考投影为vector .shp文件