Fix tests (#31)

.codecov.yml

@@ -0,0 +1,10 @@
+comment: false
+
+coverage:
+  status:
+    project:
+      default:
+        target: 80%
+    patch:
+      default:
+        target: 50%

.github/workflows/ci.yml

@@ -29,7 +29,6 @@ jobs:
       - uses: actions/checkout@v4
         with:
           fetch-depth: 0
-          ref: ${{ github.event.pull_request.head.sha }}
       - name: create and activate env
         uses: conda-incubator/setup-miniconda@v3
         with:
@@ -64,7 +63,6 @@ jobs:
       - uses: actions/checkout@v4
         with:
           fetch-depth: 0
-          ref: ${{ github.event.pull_request.head.sha }}
       - name: create and activate env
         uses: conda-incubator/setup-miniconda@v3
         with:

mutis/lib/signal.py

@@ -152,8 +152,8 @@ def lc_gen_psd_nft(times, values):
     compute the power spectral density and to reconstruct the
     randomised signal."""
 
-    k = np.arange(-times.size // 2, times.size / 2)
-    n = k.size
+    k = np.arange(-times.size / 2, times.size / 2)
+    n = 2* (k.size//2)
 
     nft = nfft.nfft_adjoint(
         (times - (times.max() + times.min()) / 2) / np.ptp(times), values, n, use_fft=True

mutis/signal.py

@@ -51,15 +51,6 @@ class Signal:
 
     def __init__(self, times, values, dvalues=None, fgen=None):
 
-        if times.dtype is not np.float64:
-            log.warning('times.dtype not float64, which may cause erros when using numba, casting automatically.')
-
-        if values.dtype is not np.float64:
-            log.warning('values.dtype not float64, which It may cause erros when using numba, casting automatically.')
-
-        if dvalues.dtype is not np.float64:
-            log.warning('dvalues.dtype not float64, which may cause erros when using numba, casting automatically.')
-
         self.times = np.array(times, dtype=np.float64)
         self.values = np.array(values, dtype=np.float64)
         self.fgen = fgen

mutis/tests/test_bayblocks.py

@@ -4,13 +4,25 @@
 from mutis import Signal
 
 def test_bayblocks():
+
     # test the bayblock algorithm with a signal that corresponds to 3 gaussian pulses in a row
+
     t = np.linspace(0, 6, 50)
     y = np.exp(-(t-1)**2/0.1) + np.exp(-(t-3)**2/0.1) + np.exp(-(t-5)**2/0.1)
     dy = 0.1 * np.abs(y) 
+
     signal = Signal(t, y, dy)
     bayblocks = BayesianBlocks(signal, p=1e-1)
+
     bayblocks.get_flares()
+
+    # test that the signal and the bayblocks can be plotted
     bayblocks.signal.plot()
     bayblocks.plot()
-    assert len(bayblocks.get_flare_list()) == 3
+
+    assert len(bayblocks.get_flare_list()) == 3
+
+    ## test that the flares can be plotted too
+
+    for flare in bayblocks.get_flare_list():
+        flare.plot()