This it's related with #14

.gitignore

@@ -1,3 +1,4 @@
+.tests/*.png
 .vscode
 # Byte-compiled / optimized / DLL files
 __pycache__/

requirements_cpu.txt

@@ -1,3 +1,3 @@
-numpy==1.19.0
-scipy==1.5.0
+numpy
+scipy
 tensorflow==1.15.3

requirements_gpu.txt

@@ -1,3 +1,3 @@
-numpy==1.19.0
-scipy==1.5.0
-tensorflow-gpu==1.15.2
+numpy
+scipy
+tensorflow-gpu==1.15.3

requirements_test.txt

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+numpy
+scipy
+tensorflow-gpu==1.15.3
+networkx
+sklearn
+matplotlib

tests/context.py

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+import os
+import sys
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+import emate

tests/test_kpm.py

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+from sklearn.neighbors import KernelDensity
+from scipy import integrate
+import networkx as nx
+import numpy as np
+import matplotlib.pyplot as plt
+
+from .context import emate
+
+try:
+    import cupy as cp
+    textCupyImplementation = True
+except:
+    textCupyImplementation = False
+
+
+def calc_cupykpm(W, vals, kde):
+    num_moments = 140
+    num_vecs = 140
+    extra_points = 25
+    ek, rho = emate.hermitian.cupykpm(
+        W.tocsr().astype("complex64"), num_moments, num_vecs, extra_points)
+
+    print("Saving the cupyKPM plot..")
+    plt.hist(vals, density=True, bins=100, alpha=.9, color="steelblue")
+    plt.scatter(ek.get(), rho.get(), c="tomato", zorder=999, alpha=0.9, marker="d")
+    plt.savefig("tests/test_kpm_cupy.png", filetype="png")
+
+    log_dens = kde.score_samples(ek.get()[:, np.newaxis])
+
+    return integrate.simps(ek.get(), np.abs(rho.get()-np.exp(log_dens))) < 0.01
+
+def calc_tfkpm(W, vals, kde):
+    num_moments = 40
+    num_vecs = 40
+    extra_points = 25
+    ek, rho = emate.hermitian.cupykpm(
+        W.tocsr().astype("complex64"), num_moments, num_vecs, extra_points, device="cpu:0")
+
+    print("Saving the tfKPM plot..")
+    plt.hist(vals, density=True, bins=100, alpha=.9, color="steelblue")
+    plt.scatter(ek, rho, c="tomato", zorder=999, alpha=0.9, marker="d")
+    plt.savefig("tests/test_kpm_tf.png", filetype="png")
+
+    log_dens = kde.score_samples(ek[:, np.newaxis])
+
+    return integrate.simps(ek, np.abs(rho-np.exp(log_dens))) < 0.01
+
+def test_kpm():
+    n = 1000
+    g = nx.erdos_renyi_graph(n , 3/n)
+    W = nx.adjacency_matrix(g)
+
+    vals  = np.linalg.eigvals(W.todense()).real
+
+    kde = KernelDensity(kernel='gaussian', bandwidth=0.1).fit(vals[:, np.newaxis])
+
+    assert calc_tfkpm(W, vals, kde)
+
+    if textCupyImplementation:
+        assert calc_cupykpm(W, vals, kde)
+