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62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
from __future__ import print_function
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import sys
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import os
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sys.path.append(os.getcwd())
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#[ getting_started_listing_03
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import histogram as hg
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import numpy as np
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# create 2d-histogram with two axes with 10 equidistant bins from -3 to 3
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h = hg.histogram(hg.axis.regular(10, -3, 3, "x"),
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hg.axis.regular(10, -3, 3, "y"))
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# generate some numpy arrays with data to fill into histogram,
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# in this case normal distributed random numbers in x and y
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x = np.random.randn(1000)
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y = 0.5 * np.random.randn(1000)
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# fill histogram with numpy arrays, this is very fast
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h(x, y)
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# get representations of the bin edges as Numpy arrays, this representation
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# differs from `list(h.axis(0))`, because it is optimised for compatibility
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# with existing Numpy code, i.e. to replace numpy.histogram
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x = np.array(h.axis(0))
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y = np.array(h.axis(1))
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# creates a view of the counts (no copy involved)
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count_matrix = np.asarray(h)
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# cut off the under- and overflow bins to not confuse matplotib (no copy)
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reduced_count_matrix = count_matrix[:-2,:-2]
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try:
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# draw the count matrix
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import matplotlib.pyplot as plt
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plt.pcolor(x, y, reduced_count_matrix.T)
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plt.xlabel(h.axis(0).label)
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plt.ylabel(h.axis(1).label)
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plt.savefig("example_2d_python.png")
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except ImportError:
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# ok, no matplotlib, then just print the full count matrix
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print(count_matrix)
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# output of the print looks something like this, the two right-most rows
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# and two down-most columns represent under-/overflow bins
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# [[ 0 0 0 1 5 0 0 1 0 0 0 0]
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# [ 0 0 0 1 17 11 6 0 0 0 0 0]
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# [ 0 0 0 5 31 26 4 1 0 0 0 0]
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# [ 0 0 3 20 59 62 26 4 0 0 0 0]
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# [ 0 0 1 26 96 89 16 1 0 0 0 0]
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# [ 0 0 4 21 86 84 20 1 0 0 0 0]
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# [ 0 0 1 24 71 50 15 2 0 0 0 0]
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# [ 0 0 0 6 26 37 7 0 0 0 0 0]
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# [ 0 0 0 0 11 10 2 0 0 0 0 0]
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# [ 0 0 0 1 2 3 1 0 0 0 0 0]
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# [ 0 0 0 0 0 2 0 0 0 0 0 0]
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# [ 0 0 0 0 0 1 0 0 0 0 0 0]]
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#]
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