Test parameters from gaussian fit

tests/end_to_end/test_generate_and_fit.py

@@ -1,5 +1,7 @@
 import numpy as np
+import pandas as pd
 
+import ramanchada2 as rc2
 from ramanchada2 import spectrum
 
 
@@ -20,3 +22,29 @@ def test_generate_and_fit():
     assert len(true_pos) == len(calc_pos), 'wrong number of peaks found'
     assert np.max(np.abs(true_pos - fit_pos - shift)) < 3, 'fit locations far from truth'
     assert np.max(np.abs(true_pos - calc_pos - shift)) < 5, 'find_peaks locations far from truth'
+
+
+def test_gaussian_fit_parameters():
+    x = np.linspace(-100, 3500, 2500)
+    params = [
+        dict(center=400, sigma=6, amplitude=500),
+        dict(center=430, sigma=3, amplitude=300),
+        dict(center=600, sigma=10, amplitude=500),
+        dict(center=625, sigma=5, amplitude=300),
+        dict(center=700, sigma=10, amplitude=500),
+        dict(center=780, sigma=5, amplitude=300),
+    ]
+    spe = rc2.spectrum.from_theoretical_lines(shapes=['gaussian']*len(params),
+                                              params=params, x=x)
+    spe = spe.add_gaussian_noise_drift(sigma=.5, coef=.2)
+    spe = spe.subtract_moving_minimum(100)
+    params_df = pd.DataFrame.from_records(params)
+
+    cand = spe.find_peak_multipeak(prominence=spe.y_noise_MAD()*10, width=3, wlen=100)
+    fitres = spe.fit_peak_multimodel(profile='Gaussian', candidates=cand, vary_baseline=True)
+    df = fitres.to_dataframe_peaks()
+
+    assert np.all(np.isclose(df['amplitude'], params_df['amplitude'], atol=df['amplitude_stderr']*5))
+    assert np.all(np.isclose(df['center'], params_df['center'], atol=df['center_stderr']*5))
+    assert np.all(np.isclose(df['sigma'], params_df['sigma'], atol=df['sigma_stderr']*5))
+    assert np.all(np.isclose(df['sigma']*np.sqrt(8*np.log(2)), df['fwhm']))