英文:
How to create a 2D density plot color coded with the fraction of points that are in two dataframes
问题
我有两个2D数据框，定义在(x, y)平面上，df1和df2，并且它们位于x，y平面的相似区域。我想创建一个以df2中的点数除以df1中的点数着色的图。我尝试了一些方法，但看起来有点像素化，你有什么建议吗？
英文:
I have two 2D dataframes, defined in the (x,y) plane, df1 and df2 and they are located in a similar region of the x,y plane.
I would like to create a plot color coded with number of points in df2 divided by the number of points in df1.
I try something, but it looks pixelated, do you have any recommendation?
df1 = pd.DataFrame(np.random.random((10000,2)), columns=list('AB'))df2 = pd.DataFrame(np.random.random((10000,2)), columns=list('AB'))df1upper_lim_A = 0.8lower_lim_A = 0.3upper_lim_B = 0.7lower_lim_B = 0.2fontsize = 8N_bins = 10hist_threshold = 1 #Number of points in a bin to be considered populated%matplotlib inline#Creating the histogramshist_all, *edges_all = np.histogram2d(df1['B'], df1['A'], bins=N_bins, range = ((lower_lim_B, upper_lim_B), (lower_lim_A,upper_lim_A)))hist_vars, *edges_vars = np.histogram2d(df2['B'], df2['A'], bins=N_bins, range = ((lower_lim_B, upper_lim_B), (lower_lim_A,upper_lim_A)))fig, ax1 = plt.subplots( figsize=(10.5,2.5*3))ax1.tick_params(direction='out', length=6, width=2, colors='k', grid_alpha=1, labelsize=size_la)#Finding the ratio df2/df1ratio = hist_vars/hist_all#Replace Nan with 0ratio[np.isnan(ratio)] = 0#Deep copy the ratio arrayratio_copy = np.copy(ratio)#Set the ratio to nan if hist_all < hist_thresholdratio[hist_all < hist_threshold*0.9] = np.nancolor_style = 'inferno'vmax = 1.1im = ax1.imshow(ratio, alpha=1,origin='lower',cmap=color_style,vmin=0, aspect='auto', extent=[lower_lim_A,upper_lim_A,lower_lim_B, upper_lim_B])from mpl_toolkits.axes_grid1 import make_axes_locatableax0 = fig.add_subplot(111)ax0.set_frame_on(False)ax0.xaxis.set_ticks([])ax0.yaxis.set_ticks([])# create a divider for the colorbardivider = make_axes_locatable(ax0)cax = divider.append_axes("top", size="5%", pad=0.1)# add the colorbar to the new axiscbar = fig.colorbar(im, cax=cax, orientation='horizontal')cbar.ax.tick_params(labelsize=size_la)cbar.set_label(r'$\mathrm{N}_{\mathrm{df2}}/\mathrm{N}_{\mathrm{df1}}$', fontsize=25)cax.xaxis.set_ticks_position('top')cax.xaxis.set_label_position('top')ax1.invert_yaxis()
答案1
得分: 1
I removed much code that was only related for showing the image. There is already a parameter for the number of bins, that you can use to make the result smoother.
Also, please note that I changed the way the ratio is calculated. I guess first / (first + second) is a little more meaningful?
import pandas as pdimport numpy as npimport matplotlib.pyplot as pltdf1 = pd.DataFrame(np.random.random((10000, 2)), columns=list('AB'))df2 = pd.DataFrame(np.random.random((10000, 2)), columns=list('AB'))upper_lim_A = 0.8lower_lim_A = 0.3upper_lim_B = 0.7lower_lim_B = 0.2# increase this for more binsN_bins = 40hist1, *edges_all = np.histogram2d(df1['B'], df1['A'],bins=N_bins,range=((lower_lim_B, upper_lim_B), (lower_lim_A, upper_lim_A)))hist2, *edges_vars = np.histogram2d(df2['B'], df2['A'],bins=N_bins,range=((lower_lim_B, upper_lim_B), (lower_lim_A, upper_lim_A)))# ratio is now share of total (not share of hist 1) + small epsilon to avoid div by 0ratio = hist2/(hist1 + hist2 + 10**-6)plt.imshow(ratio)plt.colorbar()
This is an alternative to a larger bin number. This uses open cv, since it is very convenient, but you can use any image processing library that allows you to blur images (or even scipy).
import cv2blurred_ratio = cv2.blur(ratio, ksize=(5,5)) # choose any kernel size for Gaussian blurplt.imshow(blurred_ratio)
英文:
I removed much code that was only related for showing the image. There is already a parameter for the number of bins, that you can use to make the result smoother.
Also, please note that I changed the way the ratio is calculated. I guess first / (first + second) is a little more meaningful?
import pandas as pdimport numpy as npimport matplotlib.pyplot as pltdf1 = pd.DataFrame(np.random.random((10000, 2)), columns=list('AB'))df2 = pd.DataFrame(np.random.random((10000, 2)), columns=list('AB'))upper_lim_A = 0.8lower_lim_A = 0.3upper_lim_B = 0.7lower_lim_B = 0.2# increase this for more binsN_bins = 40hist1, *edges_all = np.histogram2d(df1['B'], df1['A'],bins=N_bins,range=((lower_lim_B, upper_lim_B), (lower_lim_A,upper_lim_A)))hist2, *edges_vars = np.histogram2d(df2['B'], df2['A'],bins=N_bins,range=((lower_lim_B, upper_lim_B), (lower_lim_A,upper_lim_A)))# ratio is now share of total (not share of hist 1) + small epsilon to avoid div by 0ratio = hist2/(hist1 + hist2 + 10**-6)plt.imshow(ratio)plt.colorbar()
This is an alternative to lager bin number. This uses open cv, since it is very convenient, but you can use any image processing library that allows you to blur images (or even scipy).
import cv2blurred_ratio = cv2.blur(ratio, ksize=(5,5)) # choose any kernel size for gaussian blurplt.imshow(blurred_ratio)
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