Who looks at examples, eh?

Makefile

@@ -1,3 +1,4 @@
+LINT_TARGET_DIRS := PyEMD doc example
 
 test:
 	python -m PyEMD.tests.test_all
@@ -10,8 +11,11 @@ doc:
 	cd doc && make html
 
 format:
-	python -m black PyEMD doc
+	python -m black $(LINT_TARGET_DIRS)
 
 lint-check:
 	python -m isort --check PyEMD
-	python -m black --check PyEMD doc
+	python -m black --check $(LINT_TARGET_DIRS)
+
+perf/build:
+	docker build -t pyemd-perf -f perf_test/Dockerfile .

example/eemd_example.py

@@ -12,7 +12,7 @@
 S += 5 * sin(11, 2.7)
 S += 3 * sin(14, 1.6)
 S += 1 * np.sin(4 * 2 * np.pi * (t - 0.8) ** 2)
-S += t ** 2.1 - t
+S += t**2.1 - t
 
 # Assign EEMD to `eemd` variable
 eemd = EEMD()

example/emd_comparison.py

@@ -6,6 +6,7 @@
 a lot of the same computation within a single execution. For a single script
 with a single EMD execution, it's still much more performant to use normal EMD.
 """
+
 import time
 import numpy as np
 
@@ -17,11 +18,13 @@
 t = get_timeline(len(s), s.dtype)
 n_repeat = 20
 
-print(f"""Comparing EEMD execution on a larger signal with classic and JIT EMDs.
+print(
+    f"""Comparing EEMD execution on a larger signal with classic and JIT EMDs.
 Signal is random (uniform) noise of length: {len(s)}. The test is done by executing
 EEMD with either classic or JIT EMD {n_repeat} times and taking the average. Such
 setup favouries JitEMD which is compiled once and then reused {n_repeat-1} times.
-Compiltion is quite costly.""")
+Compiltion is quite costly."""
+)
 
 time_0 = time.time()
 emd = EMD()

example/example_spline_capabilities.py

@@ -10,7 +10,7 @@
 from PyEMD.utils import get_timeline
 
 
-def test_spline(X,T,s_kind):
+def test_spline(X, T, s_kind):
     """
     Test the fitting with the given spline.
 
@@ -38,11 +38,11 @@ def test_spline(X,T,s_kind):
 
     emd = EMD()
     emd.spline_kind = s_kind
-    max_env, min_env, eMax, eMin = emd.extract_max_min_spline(T,X)
+    max_env, min_env, eMax, eMin = emd.extract_max_min_spline(T, X)
     return max_env, min_env, eMax, eMin
 
 
-def test_akima(X,T,ax):
+def test_akima(X, T, ax):
     """
     test the fitting with akima spline.
 
@@ -65,15 +65,15 @@ def test_akima(X,T,ax):
 
     max_env, min_env, eMax, eMin = test_spline(X, T, "akima")
 
-    ax.plot(max_env,label='max akima')
-    ax.plot(min_env,label='min akima')
+    ax.plot(max_env, label="max akima")
+    ax.plot(min_env, label="min akima")
     return eMax, eMin
 
 
-def test_cubic(X,T,ax):
+def test_cubic(X, T, ax):
     """
     test the fitting with cubic spline
-    
+
     Parameters
     ----------
     see 'test_akima'
@@ -85,14 +85,14 @@ def test_cubic(X,T,ax):
 
     max_env, min_env, eMax, eMin = test_spline(X, T, "cubic")
 
-    ax.plot(max_env,label='max cubic')
-    ax.plot(min_env,label='min cubic')
+    ax.plot(max_env, label="max cubic")
+    ax.plot(min_env, label="min cubic")
     return eMax, eMin
 
 
-def test_pchip(X,T,ax):
+def test_pchip(X, T, ax):
     """
-    test the fitting with pchip spline 
+    test the fitting with pchip spline
     'Piecewise Cubic Hermite Interpolating Polynomial'
 
     Parameters
@@ -106,14 +106,14 @@ def test_pchip(X,T,ax):
 
     max_env, min_env, eMax, eMin = test_spline(X, T, "pchip")
 
-    ax.plot(max_env,label='max pchip')
-    ax.plot(min_env,label='min pchip')
+    ax.plot(max_env, label="max pchip")
+    ax.plot(min_env, label="min pchip")
     return eMax, eMin
 
 
-def test_cubic_hermite(X,T,ax):
+def test_cubic_hermite(X, T, ax):
     """
-    test the fitting with cubic_hermite spline 
+    test the fitting with cubic_hermite spline
 
     Parameters
     ----------
@@ -126,24 +126,23 @@ def test_cubic_hermite(X,T,ax):
 
     max_env, min_env, eMax, eMin = test_spline(X, T, "cubic_hermite")
 
-    ax.plot(max_env,label='max cubic_hermite')
-    ax.plot(min_env,label='min cubic_hermite')
+    ax.plot(max_env, label="max cubic_hermite")
+    ax.plot(min_env, label="min cubic_hermite")
     return eMax, eMin
 
 
-
 if __name__ == "__main__":
 
     X = np.random.normal(size=200)
-    T = get_timeline(len(X),X.dtype)
+    T = get_timeline(len(X), X.dtype)
     T = EMD._normalize_time(T)
 
     fig, ax = plt.subplots()
-    ax.plot(X,'--',lw=2,c='k')
+    ax.plot(X, "--", lw=2, c="k")
     emax_akima, emin_akima = test_akima(X, T, ax)
     emax_cubic, emin_cubic = test_cubic(X, T, ax)
     emax_pchip, emin_pchip = test_pchip(X, T, ax)
     emax_chermite, emin_chermite = test_cubic_hermite(X, T, ax)
-    ax.plot(emax_akima[0],emax_akima[1],'--')
+    ax.plot(emax_akima[0], emax_akima[1], "--")
     ax.legend()
-    plt.show()
+    plt.show()

example/hht_example.py

@@ -21,7 +21,7 @@ def instant_phase(imfs):
 S += 5 * sin(11, 2.7)
 S += 3 * sin(14, 1.6)
 S += 1 * np.sin(4 * 2 * np.pi * (t - 0.8) ** 2)
-S += t ** 2.1 - t
+S += t**2.1 - t
 
 # Compute IMFs with EMD
 emd = EMD()