updating

.github/workflows/main.yml

@@ -5,7 +5,7 @@ on:
   push:
     branches: [ main ]
   pull_request:
-    branches: [ main ]
+    branches: [ main, dev ]
 
 jobs:
   lint:
@@ -40,9 +40,9 @@ jobs:
         #   - os: windows-latest
         #     python-version: 3.7
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
       - name: Set up Python
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
       - name: Install dependencies
@@ -52,14 +52,12 @@ jobs:
       - name: Test
         run: |
           coverage run --source=pynapple --branch -m pytest tests/
-          coverage report -m          
+          coverage report -m
 
-      - name: Coveralls
-        env:
-            GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        run: |
-            pip install coveralls
-            coveralls --service=github
+      - name: Upload coverage reports to Codecov
+        uses: codecov/[email protected]
+        with:
+            token: ${{ secrets.CODECOV_TOKEN }}
   check:
     if: always()
     needs:

pynapple/core/_core_functions.py

@@ -144,6 +144,6 @@ def _threshold(time_array, data_array, starts, ends, thr, method):
     if get_backend() == "jax":
         from pynajax.jax_core_threshold import threshold
 
-        return threshold(time_array, data_array, starts, ends, thr, method)
+        return threshold(time_array, data_array[:], starts, ends, thr, method)
     else:
-        return jitthreshold(time_array, data_array, starts, ends, thr, method)
+        return jitthreshold(time_array, data_array[:], starts, ends, thr, method)

pynapple/core/_jitted_functions.py

@@ -312,11 +312,16 @@ def jitthreshold(time_array, data_array, starts, ends, thr, method="above"):
     return (new_time_array, new_data_array, new_starts, new_ends)
 
 
-@jit(nopython=True)
 def jitbin_array(time_array, data_array, starts, ends, bin_size):
+    """Slice first for compatibility with lazy loading."""
     idx, countin = jitrestrict_with_count(time_array, starts, ends)
-    time_array = time_array[idx]
-    data_array = data_array[idx]
+    return _jitbin_array(
+        countin, time_array[idx], data_array[idx], starts, ends, bin_size
+    )
+
+
+@jit(nopython=True)
+def _jitbin_array(countin, time_array, data_array, starts, ends, bin_size):
 
     m = starts.shape[0]
     f = data_array.shape[1:]

pynapple/core/time_series.py

@@ -68,10 +68,18 @@ class BaseTsd(Base, NDArrayOperatorsMixin, abc.ABC):
     Implement most of the shared functions across concrete classes `Tsd`, `TsdFrame`, `TsdTensor`
     """
 
-    def __init__(self, t, d, time_units="s", time_support=None):
+    def __init__(self, t, d, time_units="s", time_support=None, load_array=True):
         super().__init__(t, time_units, time_support)
 
-        self.values = convert_to_array(d, "d")
+        if load_array:
+            self.values = convert_to_array(d, "d")
+        else:
+            if not is_array_like(d):
+                raise TypeError(
+                    "Data should be array-like, i.e. be indexable, iterable and, have attributes "
+                    "`shape`, `ndim` and, `dtype`)."
+                )
+            self.values = d
 
         assert len(self.index) == len(
             self.values
@@ -220,36 +228,38 @@ def __array_function__(self, func, types, args, kwargs):
 
     def as_array(self):
         """
-        Return the data as a numpy.ndarray
+        Return the data.
 
         Returns
         -------
-        out: numpy.ndarray
+        out: array-like
             _
         """
         return self.values
 
     def data(self):
         """
-        Return the data as a numpy.ndarray
+        Return the data.
 
         Returns
         -------
-        out: numpy.ndarray
+        out: array-like
             _
         """
         return self.values
 
     def to_numpy(self):
         """
-        Return the data as a numpy.ndarray. Mostly useful for matplotlib plotting when calling `plot(tsd)`
+        Return the data as a numpy.ndarray.
+
+        Mostly useful for matplotlib plotting when calling `plot(tsd)`.
         """
-        return self.values
+        return np.asarray(self.values)
 
     def copy(self):
         """Copy the data, index and time support"""
         return self.__class__(
-            t=self.index.copy(), d=self.values.copy(), time_support=self.time_support
+            t=self.index.copy(), d=self.values[:].copy(), time_support=self.time_support
         )
 
     def value_from(self, data, ep=None):
@@ -662,7 +672,9 @@ class TsdTensor(BaseTsd):
         The time support of the time series
     """
 
-    def __init__(self, t, d, time_units="s", time_support=None, **kwargs):
+    def __init__(
+        self, t, d, time_units="s", time_support=None, load_array=True, **kwargs
+    ):
         """
         TsdTensor initializer
 
@@ -677,7 +689,7 @@ def __init__(self, t, d, time_units="s", time_support=None, **kwargs):
         time_support : IntervalSet, optional
             The time support of the TsdFrame object
         """
-        super().__init__(t, d, time_units, time_support)
+        super().__init__(t, d, time_units, time_support, load_array)
 
         assert (
             self.values.ndim >= 3
@@ -831,7 +843,15 @@ class TsdFrame(BaseTsd):
         The time support of the time series
     """
 
-    def __init__(self, t, d=None, time_units="s", time_support=None, columns=None):
+    def __init__(
+        self,
+        t,
+        d=None,
+        time_units="s",
+        time_support=None,
+        columns=None,
+        load_array=True,
+    ):
         """
         TsdFrame initializer
         A pandas.DataFrame can be passed directly
@@ -859,7 +879,7 @@ def __init__(self, t, d=None, time_units="s", time_support=None, columns=None):
         else:
             assert d is not None, "Missing argument d when initializing TsdFrame"
 
-        super().__init__(t, d, time_units, time_support)
+        super().__init__(t, d, time_units, time_support, load_array)
 
         assert self.values.ndim <= 2, "Data should be 1 or 2 dimensional."
 
@@ -1084,7 +1104,7 @@ def save(self, filename):
         np.savez(
             filename,
             t=self.index.values,
-            d=self.values,
+            d=self.values[:],
             start=self.time_support.start,
             end=self.time_support.end,
             columns=cols_name,
@@ -1108,7 +1128,9 @@ class Tsd(BaseTsd):
         The time support of the time series
     """
 
-    def __init__(self, t, d=None, time_units="s", time_support=None, **kwargs):
+    def __init__(
+        self, t, d=None, time_units="s", time_support=None, load_array=True, **kwargs
+    ):
         """
         Tsd Initializer.
 
@@ -1129,7 +1151,7 @@ def __init__(self, t, d=None, time_units="s", time_support=None, **kwargs):
         else:
             assert d is not None, "Missing argument d when initializing Tsd"
 
-        super().__init__(t, d, time_units, time_support)
+        super().__init__(t, d, time_units, time_support, load_array)
 
         assert self.values.ndim == 1, "Data should be 1 dimensional"