Tests for visual inspection. (#103)

.github/workflows/pytest.yml

@@ -32,4 +32,4 @@ jobs:
       - name: Test with pytest
         run: |
           pip install pytest
-          pytest
+          pytest --ignore-glob=*_figures.py

.gitignore

@@ -9,3 +9,4 @@ build/
 checkpoints/
 lightning_logs/
 *.pt
+*.jpg

tests/test_modulation_figures.py

@@ -0,0 +1,119 @@
+from torchsig.datasets.synthetic import (
+    ConstellationDataset,
+    FSKDataset,
+    OFDMDataset,
+    default_const_map,
+    freq_map,
+)
+from matplotlib import pyplot as plt
+import numpy as np
+import pytest
+
+
+@pytest.mark.parametrize("modulation_name", default_const_map.keys())
+def test_can_generate_constellation_figures(modulation_name):
+    dataset = ConstellationDataset(
+        [modulation_name],
+        num_iq_samples=4096,
+        num_samples_per_class=1,
+        iq_samples_per_symbol=2,
+        pulse_shape_filter=None,
+        random_pulse_shaping=False,
+        random_data=False,
+        use_gpu=False,
+    )
+    item = dataset[0]
+    iq_data: np.ndarray = item[0]
+
+    # IQ Data
+    plt.figure(figsize=(9, 4))
+    plt.subplot(2, 2, 1)
+    plt.plot(iq_data.real)
+    plt.plot(iq_data.imag)
+    plt.legend(["real", "imaginary"])
+    plt.title("IQ Data")
+
+    plt.subplot(2, 2, 2)
+    _ = plt.scatter(iq_data.real, iq_data.imag)
+    plt.title("Constellation")
+
+    plt.subplot(2, 2, 3)
+    _ = plt.psd(iq_data)
+    plt.title("PSD")
+
+    plt.subplot(2, 2, 4)
+    _ = plt.specgram(iq_data)
+    plt.title("Spectrogram")
+    plt.savefig("tests/figures/synthetic_{}.jpg".format(modulation_name))
+
+
+@pytest.mark.parametrize("modulation_name", freq_map.keys())
+def test_can_generate_fsk_figures(modulation_name):
+    dataset = FSKDataset(
+        [modulation_name],
+        num_iq_samples=4096,
+        num_samples_per_class=1,
+        iq_samples_per_symbol=2,
+        random_pulse_shaping=False,
+        random_data=False,
+        use_gpu=False,
+    )
+    item = dataset[0]
+    iq_data: np.ndarray = item[0]
+
+    # IQ Data
+    plt.figure(figsize=(9, 4))
+    plt.subplot(2, 2, (1, 2))
+    plt.plot(iq_data.real[:256])
+    plt.plot(iq_data.imag[:256])
+    plt.legend(["real", "imaginary"])
+    plt.title("IQ Data")
+
+    plt.subplot(2, 2, 3)
+    _ = plt.psd(iq_data)
+    plt.title("PSD")
+
+    plt.subplot(2, 2, 4)
+    _ = plt.specgram(iq_data)
+    plt.title("Spectrogram")
+    plt.savefig("tests/figures/synthetic_{}.jpg".format(modulation_name))
+
+
+num_subcarriers = (64, 72, 128, 180, 256, 300, 512, 600, 900, 1024, 1200, 2048)
+
+
+@pytest.mark.parametrize("num_subcarriers", num_subcarriers)
+def test_can_generate_ofdm_figures(num_subcarriers):
+    constellations = ("bpsk", "qpsk", "16qam", "64qam", "256qam", "1024qam")
+    sidelobe_suppression_methods = ("lpf", "win_start")
+    dataset = OFDMDataset(
+        constellations,
+        num_subcarriers=(num_subcarriers,),
+        num_iq_samples=4096,
+        num_samples_per_class=1,
+        sidelobe_suppression_methods=sidelobe_suppression_methods,
+        use_gpu=False,
+    )
+    item = dataset[0]
+    iq_data: np.ndarray = item[0]
+
+    # IQ Data
+    plt.figure(figsize=(9, 4))
+    plt.subplot(2, 2, (1, 2))
+    plt.plot(iq_data.real[:256])
+    plt.plot(iq_data.imag[:256])
+    plt.legend(["real", "imaginary"])
+    plt.title("IQ Data")
+
+    plt.subplot(2, 2, 3)
+    _ = plt.psd(iq_data)
+    plt.title("PSD")
+
+    plt.subplot(2, 2, 4)
+    _ = plt.specgram(iq_data)
+    plt.title("Spectrogram")
+    plt.savefig("tests/figures/synthetic_ofdm_{}.jpg".format(num_subcarriers))
+
+
+if __name__ == "__main__":
+    pytest.main()

torchsig/datasets/synthetic.py

@@ -602,12 +602,11 @@ def _generate_samples(self, item: Tuple) -> np.ndarray:
                 if sym_mult < 1.0
                 else int(np.ceil(sym_mult))
             )
-            
+
         if self.num_iq_samples > 32768:
             # assume wideband task and reduce data for speed
             sym_mult = 0.3
 
-
         if mod_type == "random":
             # Randomized subcarrier modulations
             symbols = []