- add option in nm_stream_offline to pass data

 - add option in nm_stream to add feature_sampling_frequency_hz
 - fix path in examples to data is loaded by absolute path
 - add test for running all examples

examples/plot_example_BIDS.py

@@ -42,7 +42,7 @@
 
 RUN_NAME = "sub-000_ses-right_task-force_run-3_ieeg"
 
-PATH_BIDS = Path.cwd() / "data"
+PATH_BIDS = Path(__file__).absolute().parent / "data"
 
 PATH_RUN = PATH_BIDS / "sub-000" / "sess-right" / "ieeg" / (RUN_NAME + ".vhdr")
 

examples/plot_example_gridPointProjection.py

@@ -40,7 +40,7 @@
 # %%
 RUN_NAME = "sub-000_ses-right_task-force_run-3_ieeg"
 
-PATH_BIDS = Path.cwd() / "data"
+PATH_BIDS = Path(__file__).absolute().parent / "data"
 
 PATH_RUN = PATH_BIDS / "sub-000" / "sess-right" / "ieeg" / (RUN_NAME + ".vhdr")
 

examples/plot_example_sharpwave_analysis.py

@@ -43,7 +43,7 @@
 
 RUN_NAME = "sub-000_ses-right_task-force_run-3_ieeg"
 
-PATH_BIDS = Path.cwd() / "data"
+PATH_BIDS = Path(__file__).absolute().parent / "data"
 
 PATH_RUN = PATH_BIDS / "sub-000" / "sess-right" / "ieeg" / (RUN_NAME + ".vhdr")
 

examples/plot_first_demo.py

@@ -45,7 +45,6 @@ def generate_random_walk(NUM_CHANNELS, TIME_DATA_SAMPLES):
 plt.xlabel("Time [s]")
 plt.ylabel("Amplitude")
 plt.title("Example random walk data")
-plt.show()
 
 # %%
 # Now let’s define the necessary setup files will be use for data

py_neuromodulation/nm_stream_abc.py

@@ -26,6 +26,7 @@ class PNStream(ABC):
     features: nm_features.Features
     coords: dict
     sfreq: int | float
+    sfreq_feature: int | float = None
     path_grids: _PathLike | None
     model: base.BaseEstimator | None
     sess_right: bool | None
@@ -39,22 +40,45 @@ def __init__(
         nm_channels: pd.DataFrame | _PathLike,
         settings: dict | _PathLike | None = None,
         line_noise: int | float | None = 50,
+        sampling_rate_features_hz: int | float | None = None,
         path_grids: _PathLike | None = None,
-        coords: dict | None = None,
         coord_names: list | None = None,
         coord_list: list | None = None,
         verbose: bool = True,
     ) -> None:
+        """Stream initialization
+
+        Parameters
+        ----------
+        sfreq : int | float
+            sampling frequency of data in Hertz
+        nm_channels : pd.DataFrame | _PathLike
+            parametrization of channels (see nm_define_channels.py for initialization)
+        settings : dict | _PathLike | None, optional
+            features settings can be a dictionary or path to the nm_settings.json, by default the py_neuromodulation/nm_settings.json are read
+        line_noise : int | float | None, optional
+            line noise, by default 50
+        sampling_rate_features_hz : int | float | None, optional
+            feature sampling rate, by default None
+        path_grids : _PathLike | None, optional
+            path to grid_cortex.tsv and/or gird_subcortex.tsv, by default Non
+        coord_names : list | None, optional
+            coordinate name in the form [coord_1_name, coord_2_name, etc], by default None
+        coord_list : list | None, optional
+            coordinates in the form [[coord_1_x, coord_1_y, coord_1_z], [coord_2_x, coord_2_y, coord_2_z],], by default None
+        verbose : bool, optional
+            print out stream computation time information, by default True
+        """
         self.settings = self._load_settings(settings)
+
+        if sampling_rate_features_hz is not None:
+            self.settings["sampling_rate_features_hz"] = sampling_rate_features_hz
+
         self.nm_channels = self._load_nm_channels(nm_channels)
         if path_grids is None:
             path_grids = pathlib.Path(__file__).parent.resolve()
         self.path_grids = path_grids
         self.verbose = verbose
-        if coords is None:
-            self.coords = {}
-        else:
-            self.coords = coords
         self.sfreq = sfreq
         self.sess_right = None
         self.projection = None
@@ -119,27 +143,6 @@ def load_model(self, model_name: _PathLike) -> None:
         with open(model_name, "rb") as fid:
             self.model = cPickle.load(fid)
 
-    def plot_cortical_projection(self) -> None:
-        """plot projection of cortical grid electrodes on cortex"""
-        ecog_strip = None
-        if self.projection is not None:
-            ecog_strip = self.projection.ecog_strip
-
-        grid_cortex = None
-        if self.projection is not None:
-            grid_cortex = self.projection.grid_cortex
-
-        sess_right = None
-        if self.projection is not None:
-            sess_right = self.projection.sess_right
-
-        nmplotter = nm_plots.NM_Plot(
-            ecog_strip=ecog_strip,
-            grid_cortex=grid_cortex,
-            sess_right=sess_right,
-        )
-        nmplotter.plot_cortex(set_clim=False)
-
     def save_after_stream(
         self,
         out_path_root: _PathLike | None = None,

py_neuromodulation/nm_stream_offline.py

@@ -5,12 +5,20 @@
 import numpy as np
 import pandas as pd
 
-from py_neuromodulation import nm_generator, nm_IO, nm_stream_abc
+from py_neuromodulation import nm_generator, nm_IO, nm_stream_abc, nm_define_nmchannels
 
 _PathLike = str | os.PathLike
 
 
 class _OfflineStream(nm_stream_abc.PNStream):
+    """Offline stream base class. 
+    This class can be inhereted for different types of offline streams, e.g. epoch-based or continuous.
+
+    Parameters
+    ----------
+    nm_stream_abc : nm_stream_abc.PNStream
+    """
+
     def _add_labels(
         self, features: pd.DataFrame, data: np.ndarray
     ) -> pd.DataFrame:
@@ -105,13 +113,74 @@ def _run_offline(
 
 
 class Stream(_OfflineStream):
+    def __init__(
+        self,
+        sfreq: int | float,
+        data: np.ndarray | pd.DataFrame = None,
+        nm_channels: pd.DataFrame | _PathLike = None,
+        settings: dict | _PathLike | None = None,
+        sampling_rate_features_hz: float = None,
+        line_noise: int | float | None = 50,
+        path_grids: _PathLike | None = None,
+        coord_names: list | None = None,
+        coord_list: list | None = None,
+        verbose: bool = True,     
+    ) -> None:
+        """Stream initialization
+
+        Parameters
+        ----------
+        sfreq : int | float
+            sampling frequency of data in Hertz
+        data : np.ndarray | pd.DataFrame | None, optional
+            data to be streamed with shape (n_channels, n_time), by default None
+        nm_channels : pd.DataFrame | _PathLike
+            parametrization of channels (see nm_define_channels.py for initialization)
+        settings : dict | _PathLike | None, optional
+            features settings can be a dictionary or path to the nm_settings.json, by default the py_neuromodulation/nm_settings.json are read
+        line_noise : int | float | None, optional
+            line noise, by default 50
+        sampling_rate_features_hz : int | float | None, optional
+            feature sampling rate, by default None
+        path_grids : _PathLike | None, optional
+            path to grid_cortex.tsv and/or gird_subcortex.tsv, by default Non
+        coord_names : list | None, optional
+            coordinate name in the form [coord_1_name, coord_2_name, etc], by default None
+        coord_list : list | None, optional
+            coordinates in the form [[coord_1_x, coord_1_y, coord_1_z], [coord_2_x, coord_2_y, coord_2_z],], by default None
+        verbose : bool, optional
+            print out stream computation time information, by default True
+        """
+
+        if nm_channels is None and data is not None:
+            nm_channels = nm_define_nmchannels.get_default_channels_from_data(data)
+
+        if nm_channels is None and data is None:
+            raise ValueError(
+                "Either `nm_channels` or `data` must be passed to `Stream`."
+            )
+
+        super().__init__(
+            sfreq,
+            nm_channels,
+            settings,
+            line_noise,
+            sampling_rate_features_hz,
+            path_grids,
+            coord_names,
+            coord_list,
+            verbose,
+        )
+
+        self.data = data
+
     def run(
         self,
-        data: np.ndarray | pd.DataFrame,
+        data: np.ndarray | pd.DataFrame = None,
         out_path_root: _PathLike | None = None,
         folder_name: str = "sub",
     ) -> pd.DataFrame:
-        """Call run function for offline stream. 
+        """Call run function for offline stream.
 
         Parameters
         ----------
@@ -129,6 +198,13 @@ def run(
             feature DataFrame
         """
 
-        data = self._handle_data(data)
+        if self.data is not None:
+            data = self._handle_data(self.data)
+        elif data is not None:
+            data = self._handle_data(data)
+        elif self.data is None and data is None:
+            raise ValueError(
+                "No data passed to run function."
+            )
 
         return self._run_offline(data, out_path_root, folder_name)

tests/test_all_examples.py

@@ -0,0 +1,8 @@
+from pathlib import Path
+import pytest
+import subprocess
+
+@pytest.mark.parametrize("example_filename", Path('examples').glob('*.py'))
+def test_run_through_all_test(example_filename):    
+    print(f'Running {example_filename}')
+    subprocess.run(['python', example_filename], check=True)

tests/test_initalization_offline_stream.py

@@ -0,0 +1,14 @@
+import numpy as np
+
+import py_neuromodulation as nm
+
+
+def test_stream_init():
+    """Test if stream initialization with passed data will setup nm_channels correctly
+    """
+    data = np.random.random((10, 1000))
+    sfreq = 100
+    stream = nm.Stream(sfreq=sfreq, data=data, sampling_rate_features_hz=11)
+
+    assert stream.nm_channels.shape[0] == 10
+    assert stream.settings["sampling_rate_features_hz"] == 11