Python：如何在一个直方图中 plot 多列

Question

我正在学习 python 并且我坚持的事情是根据 FLT 列绘制直方图以指示每个过滤器。 我有这个数据框：

 VARLIST: MJD FLT FIELD FLUXCAL FLUXCALERR MAG MAGERR \ 0 OBS: 55161.6 g NaN -62.016 23.428 NaN -0.410 1 OBS: 55176.6 g NaN -8.183 21.252 NaN -2.820 2 OBS: 55179.6 g NaN 0.451 19.109 -4.134 46.053 3 OBS: 55188.6 g NaN 511.964 21.218 -11.773 0.045 4 OBS: 55206.6 g NaN 682.704 22.329 -12.086 0.036.. ... ... .. ... ... ... ... ... 259 OBS: 56659.6 z NaN 193.577 44.434 -10.717 0.249 260 OBS: 56662.6 z NaN 2.728 30.422 -6.089 12.109 261 OBS: 56667.7 z NaN 51.009 30.915 -9.269 0.658 262 OBS: 56681.5 z NaN 8.945 30.450 -7.379 3.696 263 OBS: 56754.3 z NaN 12.488 60.586 -7.741 5.268 peakMJD mag_zpt27.5 snr_zpt27.5 magerr_zpt27.5 0 55206.6 23.018741 -2.647089 -0.410262 1 55206.6 25.217719 -0.385046 -2.820441 2 55206.6 28.364559 0.023601 46.014133 3 55206.6 20.726901 24.128759 0.045009 4 55206.6 20.414419 30.574768 0.035519.. ... ... ... ... 259 55206.6 21.782866 4.356506 0.249282 260 55206.6 26.410389 0.089672 12.110811 261 55206.6 23.230883 1.649976 0.658191 262 55206.6 25.121049 0.293760 3.696892 263 55206.6 24.758768 0.206120 5.268770

我的目标是 plot FLT 列，但要指示不同的过滤器：g、i、r 和 z。 通过搜索如何制作直方图，我可以 plot 是基于 MAG（幅度）的基本图。 理想情况下，我想要的是 4 个不同的 colors 来指示不同的过滤器。 我知道该图会与过滤器重叠 Mag，但我可以接受它，因为我需要视觉效果，如果需要，我可以放大。

我下面的代码就是我现在所拥有的。 它现在很重要，因为我一直在查看文档并且无处可去。 需要注意的是，中间有一堆关于我的尝试的评论。 一种是遍历每一行并查看过滤器然后绘制图表，但我无法弄清楚。 另一个想法是创建 4 个新列，每个过滤器一个，然后绘制每一列。 我被卡住了，无法弄清楚。

 #HISTOGRAM def plot_histogram(source, data_file): if source == 'villar': filename, ext = os.path.splitext(data_file) SnName_villar = filename[34:-6] # read in for clarifying info ps1_phot_info = pd.read_csv(data_file) ra_deg = ps1_phot_info.loc[2][0][11:19] #deg dec_deg = ps1_phot_info.loc[3][0][12:19] #deg final_z_villar = ps1_phot_info.loc[5][0][17:23] # read in for data ps1_phot = pd.read_csv(data_file, skiprows=15, delim_whitespace=True) ps1_phot.drop(ps1_phot.tail(1).index,inplace=True) ps1_phot['peakMJD'] = ps1_phot.iloc[ps1_phot['FLUXCAL'].idxmax()]["MJD"] # Calculate mag. Look at zeropoints (A. Villar = 32.5, YSE = 27.5) ps1_phot['mag_zpt27.5'] = np.array(-2.5*np.log10(np.abs(ps1_phot['FLUXCAL'])))+27.5 ps1_phot['snr_zpt27.5'] = (ps1_phot['FLUXCAL'] / np.abs(ps1_phot['FLUXCALERR'])) ps1_phot['magerr_zpt27.5'] = np.array(1.086 / ps1_phot['snr_zpt27.5']) mask = (ps1_phot['FLUXCAL'].notna()) #& (ps1_phot['FLUXCALERR'] <= 50) ps1_masked = ps1_phot.loc[mask] #has mag obs and reasonable error print(ps1_masked) ps1_phot['peakMJD'] = ps1_phot.iloc[ps1_phot['FLUXCAL'].idxmax()]["MJD"] print('here') print(ps1_phot) #Calculate mag. Look at zeropoints (A. Villar = 32.5, YSE = 27.5) ps1_phot['mag_zpt27.5'] = np.array(-2.5*np.log10(np.abs(ps1_phot['FLUXCAL'])))+27.5 ps1_phot['snr_zpt27.5'] = (ps1_phot['FLUXCAL'] / np.abs(ps1_phot['FLUXCALERR'])) ps1_phot['magerr_zpt27.5'] = np.array(1.086 / ps1_phot['snr_zpt27.5']) mask = (ps1_phot['FLUXCAL'].notna()) #& (ps1_phot['FLUXCALERR'] <= 50) ps1_masked = ps1_phot.loc[mask] #has mag obs and reasonable error #print(ps1_masked) #ps1_phot['g_band'] = ps1_phot.iloc[ps1_phot['FLT']=='g'] #print("THIS IS THE G BAND") #print(ps1_masked) #ps1_phot['r_band'] = ps1_phot.iloc[ps1_phot['FLT']=='r'] #print("THIS IS THE R BAND") #print(ps1_masked) #ps1_phot['i_band'] = ps1_phot.iloc[ps1_phot['FLT']=='i'] #print("THIS IS THE I BAND") #print(ps1_masked) #ps1_phot['z_band'] = ps1_phot.iloc[ps1_phot['FLT']=='z'] #print("THIS IS THE Z BAND") #print(ps1_masked) #for pb in passbands: # plt.hist(x=ps1_masked['']) #numpy.histogram(a, bins=10, range=None, normed=None, weights=None, density=None)[source] #passbands = ('g', 'r', 'i', 'z') #for pb in passbands: # #x = passbands[pd] # #blah blah code --> Plot histogram # #errorbar only for scatter plot # ax1.errorbar(x=ps1_masked[passbands[pd]]['MJD'] - ps1_masked[passbands[pd]]['peakMJD'], y=ps1_masked[passbands[pd]][f'{yaxis_is}'], yerr=ps1_masked[passbands[pd]][f'{yaxiserr_is}'], # fmt='o', alpha=0.5, color='g', label=f'{x}-PS1') #ax1.ticklabel_format(useOffset=False, style='plain') yaxis_is = 'mag_zpt27.5' # or FLUXCAL yaxiserr_is = 'magerr_zpt27.5' # or FLUXCALERR fig = plt.figure(figsize=(12, 4)) ax1 = fig.add_subplot(121) ax1.set_ylabel('Observations', fontsize=16) ax1.set_xlabel('Mag', fontsize=16) ax1.tick_params(labelsize=12) ax1.set_title(f'{SnName_yse}, z_yse={final_z_yse}; {SnName_villar}, z_villar={final_z_villar}', fontsize=16) #Plot ps1_phot['mag_zpt27.5'].hist() plt.savefig(f"path/to/file/my_plot_{pb}.png") plt.tight_layout() plt.show() fig.savefig(f'./Villar_Data_Graphs/{SnName_villar}_{SnName_yse}_{yaxis_is}_Histogram.png', format='png', bbox_inches='tight', dpi=300) plt.close(fig) #Read all the files "fileV = sorted(glob.glob('./Villar/ps1_sne_zenodo/*.dat'))" and then supernova type (has the same) # check the redshift to see if the are comparable (range +/- 0.1)

这就是我在上面调用 function 的地方

 x = './Villar/ps1_sne_zenodo/PS1_PS1MD_PSc000186.snana.dat' SnName_villar, final_z_villar, ps1_masked_g, ps1_masked_r, ps1_masked_i, ps1_masked_z = get_dataframes(source='villar', data_file=x, plot_color='c', sntype_label='VIa') y = './Photpipe/yselc_v3_photoz/GPC1v3_2020add.snana.dat' SnName_yse, final_z_yse, pp_phot_g, pp_phot_r, pp_phot_i, pp_phot_z = get_dataframes(source='yse', data_file=y, plot_color='b', sntype_label='VIa') plot_villar_and_yse(SnName_villar, final_z_villar, ps1_masked_g, ps1_masked_r, ps1_masked_i, ps1_masked_z, SnName_yse, final_z_yse, pp_phot_g, pp_phot_r, pp_phot_i, pp_phot_z) plot_histogram(source='villar', data_file=x) #Call and plot histogram function

请让我知道您的想法和建议。

Answer 1

这可以使用Seaborn 的 histplot function相对简单地完成。

import pandas as pd
import numpy as np
import seaborn as sns

# construct some sample data
ps1_phot = pd.concat([pd.DataFrame({'FLT': flt, 'MAG': np.random.randn(1000) + i}) for i, flt in enumerate('girz')])

# plot overlapping histograms
sns.histplot(ps1_phot.dropna(subset=['MAG']), x='MAG', hue='FLT')

结果是

Answer 2

seaborn 是 go 的方式。 对于 plot 分布，您在 seaborn 库中有几个选项：sns.distplot()、sns.kdeplot()、sns.boxplot()、sns.histplot()。 我个人喜欢使用 sns.distplot()，因为它为您提供了显示直方图和曲线的选项。