Test norm.pdf and update travis for newer python

.travis.yml

@@ -5,6 +5,9 @@ dist: xenial
 python:
     - "3.6"
     - "3.7"
+    - "3.8"
+    - "3.9"
+    - "3.10"
 
 services:
   - xvfb
@@ -24,6 +27,7 @@ install:
     - conda install --yes phconvert
     - pip install pybroom
     - python setup.py build
+    - pip install pybroom
     - pip install .
     - rm -rf build/
 

fretbursts/burstlib.py

@@ -22,7 +22,7 @@
 import numpy as np
 import copy
 from numpy import zeros, size, r_
-import scipy.stats as SS
+from scipy.stats import norm
 
 from .utils.misc import pprint, clk_to_s, deprecate
 from .poisson_threshold import find_optimal_T_bga
@@ -2996,7 +2996,7 @@ def fit_E_m(self, E1=-1, E2=2, weights='size', gamma=1.):
                 np.dot(w, (E[mask] - fit_res[ich, 0])**2)/w.sum())
             fit_model_F[ich] = mask.sum()/mask.size
 
-        fit_model = lambda x, p: SS.norm.pdf(x, p[0], p[1])
+        fit_model = lambda x, p: norm.pdf(x, p[0], p[1])
         self.add(fit_E_res=fit_res, fit_E_name='Moments',
                  E_fit=fit_res[:, 0], fit_E_curve=True, fit_E_E1=E1,
                  fit_E_E2=E2, fit_E_model=fit_model,
@@ -3091,7 +3091,7 @@ def fit_E_generic(self, E1=-1, E2=2, fit_fun=two_gaussian_fit_hist,
             For EM fitting use :meth:`fit_E_two_gauss_EM()`.
         """
         if fit_fun.__name__.startswith("gaussian_fit"):
-            fit_model = lambda x, p: SS.norm.pdf(x, p[0], p[1])
+            fit_model = lambda x, p: norm.pdf(x, p[0], p[1])
             if 'mu0' not in fit_kwargs: fit_kwargs.update(mu0=0.5)
             if 'sigma0' not in fit_kwargs: fit_kwargs.update(sigma0=0.3)
             iE, nparam = 0, 2

fretbursts/tests/test_burstlib.py

@@ -15,6 +15,7 @@
 from collections import namedtuple
 import pytest
 import numpy as np
+from scipy.stats import norm
 
 try:
     import matplotlib
@@ -78,6 +79,9 @@ def load_fake_pax():
     d.burst_search(L=10, m=10, F=6, pax=True)
     return d
 
+def normpdf(x, c=0, mu=0.5):
+    return np.exp(-(x-c)**2/((mu**2)*2))/(mu*np.sqrt(2*np.pi))
+
 
 @pytest.fixture(scope="module", params=[
                                     load_dataset_1ch,
@@ -1163,5 +1167,11 @@ def test_collapse(data_8ch):
         else:
             assert np.allclose(dc1[name][0], dc2[name][0])
 
+def test_norm_pdf():
+    x = np.arange(0, 1, 0.01)
+    for c in np.arange(0.1, 1.0, 0.1):
+        for mu in np.logspace(np.log(5e-2)/np.log(10), np.log(0.5)/np.log(10), 10):
+            assert np.allclose(normpdf(x, c, mu), norm.pdf(x, loc=c, scale=mu))
+
 if __name__ == '__main__':
     pytest.main("-x -v fretbursts/tests/test_burstlib.py")