add first pytests

tests/io/minio_test.py

@@ -0,0 +1,49 @@
+import os
+import random
+import tempfile
+from string import ascii_letters
+
+from dotenv import dotenv_values
+
+from pytestpavement.helper.filehasher import calc_hash_of_file
+from pytestpavement.io.minio import MinioClient
+
+
+class TestMinio():
+
+    #read .env file
+    config = dotenv_values(".env")
+
+    bucket = 'pytest'
+
+    client = MinioClient(url=config['S3_HOST'],
+                         access_key=config['S3_ACCESS_KEY'],
+                         secret_key=config['S3_PASSWD'],
+                         secure=False,
+                         bucket=bucket)
+
+    def test_init_minio(self) -> None:
+        return
+
+    def test_upload_file(self) -> None:
+        """
+        generate dummy file and upload to s3 bucket
+        
+        """
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+
+            #generate testfile
+            n = 1024**2
+            n = 1000
+
+            file_orig = os.path.join(tmpdirname, 'textfile.txt')
+            with open(file_orig, 'w') as f:
+                f.write(''.join(
+                    [random.choice(ascii_letters) for l in range(n)]))
+
+            filehash_orig = calc_hash_of_file(file_orig)
+
+            self.client.compress_and_upload_file(file_orig,
+                                                 'test',
+                                                 metadata={'lab': '123'})

tests/labtests/sine_test.py

@@ -0,0 +1,117 @@
+from random import uniform
+
+import numpy as np
+
+from pytestpavement.analysis.regression import fit_cos, fit_cos_eval
+
+
+def fit(freq: float = 10,
+        ampl: float = 100.0,
+        offset: float = 20.0,
+        slope: float = 0.1,
+        phase: float = 0.05,
+        error: float = 0.001) -> None:
+
+    N: int = 5
+    num_samples_per_cycle: int = 50
+
+    t = np.linspace(0, N / freq, N * num_samples_per_cycle)
+    y = ampl * np.cos(2 * np.pi * freq * t + phase) + slope * t + offset
+
+    r = fit_cos(t, y)
+
+    error_min = (1 - error)
+    error_max = (1 + error)
+
+    # ampltude
+    rel_error = (r['amp'] / ampl)
+    assert error_min <= rel_error <= error_max
+
+    # offset
+    rel_error = (r['offset'] / offset)
+    assert error_min <= rel_error <= error_max
+
+    # slope
+    rel_error = (r['slope'] / slope)
+    assert error_min <= rel_error <= error_max
+
+    # phase
+    rel_error = (r['phase'] / phase)
+    assert error_min <= rel_error <= error_max
+
+    # freq
+    rel_error = (r['freq'] / freq)
+    assert error_min <= rel_error <= error_max
+
+
+def test_fit_simple_sine(ntest: int = 50) -> None:
+    """
+    fit a simple sine signal and evaluate amplitude
+    
+    error: percentage error of ampl, Error max 0.1 %
+    """
+
+    fit()
+
+    #run multiple tests with random parameters
+    for i in range(ntest):
+
+        fit(
+            ampl=uniform(1e-3, 1000),
+            offset=uniform(1e-3, 1),
+            slope=uniform(1e-5, 1),
+            phase=uniform(1e-5, 1),
+        )
+
+
+def fit_noise(freq: float = 10,
+              ampl: float = 100.0,
+              offset: float = 20.0,
+              slope: float = 0.1,
+              phase: float = 0.05,
+              noise_level: float = 0.01,
+              error: float = 0.01) -> None:
+
+    N: int = 5
+    num_samples_per_cycle: int = 50
+
+    t = np.linspace(0, N / freq, N * num_samples_per_cycle)
+    y = ampl * np.cos(2 * np.pi * freq * t + phase) + slope * t + offset
+    y_noise = np.random.normal(0, noise_level * ampl, len(t))
+
+    y = y + y_noise
+
+    r = fit_cos(t, y)
+
+    error_min = (1 - error)
+    error_max = (1 + error)
+
+    # ampltude
+    rel_error = (r['amp'] / ampl)
+    assert error_min <= rel_error <= error_max
+    
+    # freq
+    rel_error = (r['freq'] / freq)
+    assert error_min <= rel_error <= error_max
+
+
+def test_fit_simple_sine_with_noise(ntest: int = 50) -> None:
+    """
+    fit a simple sine signal and evaluate amplitude
+    
+    error: percentage error of ampl, Error max 0.1 %
+    """
+
+    fit_noise()
+
+    #run multiple tests with random parameters
+    for i in range(ntest):
+
+        fit_noise(
+            ampl=uniform(1e-3, 1000),
+            offset=uniform(1e-3, 1),
+            slope=uniform(1e-5, 1),
+            phase=uniform(1e-5, 1),
+            noise_level=uniform(0.01, 0.1),
+            error=0.02,
+        )