Plotting 2D efficiency with matplotlib

Question

I am trying to plot a 2D (in)efficiency in matplotlib, basically, I need the ration of two histograms. I am using panda DF to fill them:

X_arr = np.array(df['X'])
Y_arr = np.array(df['Y'])

Xeff_arr = np.array(df['X'][(df['condition'] == 1)]
Yeff_arr = np.array(df['Y'][(df['condition'] == 1)]

plt.figure()

denom_histo, xedges, yedges = np.histogram2d(Y_arr, X_arr, bins=(100, 100))
eff_histo, xedges, yedges = np.histogram2d(Yeff_arr, Xeff_arr, bins=(100, 100), weights=-1)
ones, xedges, yedges = np.histogram2d(np.array(100*[1]), np.array(100*[1]), bins=(100, 100))

ineff_histo = ones - eff_histo
ineff_histo = ineff_histo / denom_histo

plt.show()

I need inefficiency, so I calculate it by "1 - (data_passing_condition)/(all_data)" Also doing this prevents division by zero.

But I get errors, like "ValueError: object of too small depth for desired array"

Could you let me know what's the best way to deal with 2D plots in matplot?

Cheers

Which line are you getting the error in? Can you provide us some sample data so we can reproduce your error? Also, it doesn't seem to me that you ever actually tell your code to plot the ratio. You could do something like this example to plot it. — m13op22
– m13op22, Commented Feb 28, 2019 at 14:53

Gedas Sarpis · Accepted Answer · 2019-03-13 14:32:52Z

0

after some tinkering I found this solution to work:

def plot_eff2D(df):

        dfe = df[(df['Condition']==1)]

        x = np.array(df['X'])
        y = np.array(df['Y'])

        xe = np.array(dfe['X'])
        ye = np.array(dfe['Y'])

        fig = plt.figure()
        plt.title('Inefficiency map', fontsize=16)

        den, yedges, xedges = np.histogram2d(x, y, bins=100)
        num, _, _ = np.histogram2d(xe, ye, bins=(yedges, xedges))

        H = (den - num)/den  # 1-eff = inefficiency

        img = plt.imshow(H, interpolation='nearest', origin='low', vmin=0, vmax=0.2, extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])
        fig.colorbar(img, fraction=0.025, pad=0.04) # above plot H

        plt.tight_layout()

answered Mar 13, 2019 at 14:32

Gedas Sarpis

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