Stream_refactor

examples/plot_0_first_demo.py

@@ -91,7 +91,7 @@ def generate_random_walk(NUM_CHANNELS, TIME_DATA_SAMPLES):
 # DataFrame. There are some helper functions that let you create the
 # nm_channels without much effort:
 
-nm_channels = nm.utils.get_default_channels_from_data(data, car_rereferencing=True)
+nm_channels = nm.utils.create_default_channels_from_data(data, car_rereferencing=True)
 
 nm_channels
 
@@ -135,14 +135,15 @@ def generate_random_walk(NUM_CHANNELS, TIME_DATA_SAMPLES):
 # We are now ready to go to instantiate the *Stream* and call the *run* method for feature estimation:
 
 stream = nm.Stream(
+    data=data,
     settings=settings,
     channels=nm_channels,
     verbose=True,
     sfreq=sfreq,
     line_noise=50,
 )
 
-features = stream.run(data, save_csv=True)
+features = stream.run(save_csv=True)
 
 # %%
 # Feature Analysis

examples/plot_1_example_BIDS.py

@@ -43,7 +43,7 @@
     coord_names,
 ) = nm.io.read_BIDS_data(PATH_RUN=PATH_RUN)
 
-channels = nm.utils.set_channels(
+channels = nm.utils.create_channels(
     ch_names=raw.ch_names,
     ch_types=raw.get_channel_types(),
     reference="default",
@@ -94,6 +94,8 @@
 
 # %%
 stream = nm.Stream(
+    data=data,
+    experiment_name=RUN_NAME,
     sfreq=sfreq,
     channels=channels,
     settings=settings,
@@ -105,9 +107,7 @@
 
 # %%
 features = stream.run(
-    data=data,
     out_dir=PATH_OUT,
-    experiment_name=RUN_NAME,
     save_csv=True,
 )
 

examples/plot_3_example_sharpwave_analysis.py

@@ -56,6 +56,8 @@
     coord_names,
 ) = nm.io.read_BIDS_data(PATH_RUN=PATH_RUN)
 
+print(data.shape)
+
 # %%
 settings = NMSettings.get_fast_compute()
 
@@ -69,7 +71,7 @@
 for sw_feature in settings.sharpwave_analysis_settings.sharpwave_features.list_all():
     settings.sharpwave_analysis_settings.estimator["mean"].append(sw_feature)
 
-channels = nm.utils.set_channels(
+channels = nm.utils.create_channels(
     ch_names=raw.ch_names,
     ch_types=raw.get_channel_types(),
     reference="default",
@@ -79,7 +81,9 @@
     target_keywords=["MOV_RIGHT"],
 )
 
-stream = nm.Stream(
+data_plt = data[5, 1000:4000]
+
+data_processor = nm.DataProcessor(
     sfreq=sfreq,
     channels=channels,
     settings=settings,
@@ -88,14 +92,12 @@
     coord_names=coord_names,
     verbose=False,
 )
-sw_analyzer = cast(
-    SharpwaveAnalyzer, stream.data_processor.features.get_feature("sharpwave_analysis")
-)
+
+sw_analyzer = data_processor.features.get_feature("sharpwave_analysis")
+
 
 # %%
 # The plotted example time series, visualized on a short time scale, shows the relation of identified peaks, troughs, and estimated features:
-data_plt = data[5, 1000:4000]
-
 filtered_dat = fftconvolve(data_plt, sw_analyzer.list_filter[0][1], mode="same")
 
 troughs = signal.find_peaks(-filtered_dat, distance=10)[0]
@@ -297,6 +299,7 @@
 channels.loc[[3, 8], "used"] = 1
 
 stream = nm.Stream(
+    data=data[:, :30000],
     sfreq=sfreq,
     channels=channels,
     settings=settings,
@@ -306,7 +309,7 @@
     verbose=True,
 )
 
-df_features = stream.run(data=data[:, :30000], save_csv=True)
+df_features = stream.run(save_csv=True)
 
 # %%
 # We can then plot two exemplary features, prominence and interval, and see that the movement amplitude can be clustered with those two features alone:

examples/plot_4_example_gridPointProjection.py

@@ -54,7 +54,7 @@
 
 settings.postprocessing.project_cortex = True
 
-channels = nm.utils.set_channels(
+channels = nm.utils.create_channels(
     ch_names=raw.ch_names,
     ch_types=raw.get_channel_types(),
     reference="default",
@@ -65,6 +65,8 @@
 )
 
 stream = nm.Stream(
+    data=data[:, : int(sfreq * 5)],
+    experiment_name=RUN_NAME,
     sfreq=sfreq,
     channels=channels,
     settings=settings,
@@ -75,9 +77,7 @@
 )
 
 features = stream.run(
-    data=data[:, : int(sfreq * 5)],
     out_dir=PATH_OUT,
-    experiment_name=RUN_NAME,
     save_csv=True,
 )
 

examples/plot_6_real_time_demo.py

@@ -95,6 +95,7 @@ def get_fast_compute_settings():
 print("Computation time for single ECoG channel: ")
 data = np.random.random([1, 1000])
 stream = nm.Stream(sfreq=1000, data=data, sampling_rate_features_hz=10, verbose=False)
+
 print(
     f"{np.round(timeit.timeit(lambda: stream.data_processor.process(data), number=10)/10, 3)} s"
 )

examples/plot_7_lsl_example.py

@@ -11,6 +11,7 @@
 # %%
 from matplotlib import pyplot as plt
 import py_neuromodulation as nm
+import time
 
 # %%
 # Let’s get the example data from the provided BIDS dataset and create the channels DataFrame.
@@ -32,7 +33,7 @@
     coord_names,
 ) = nm.io.read_BIDS_data(PATH_RUN=PATH_RUN)
 
-channels = nm.utils.set_channels(
+channels = nm.utils.create_channels(
     ch_names=raw.ch_names,
     ch_types=raw.get_channel_types(),
     reference="default",
@@ -61,6 +62,9 @@
 player = nm.stream.LSLOfflinePlayer(raw=raw, stream_name="example_stream")
 
 player.start_player(chunk_size=30)
+
+time.sleep(2)  # Wait for stream to start
+
 # %%
 # Creating the LSLStream object
 # -----------------------------
@@ -78,6 +82,9 @@
 # %%
 stream = nm.Stream(
     sfreq=sfreq,
+    experiment_name=RUN_NAME,
+    is_stream_lsl=True,
+    stream_lsl_name="example_stream",
     channels=channels,
     settings=settings,
     coord_list=coord_list,
@@ -87,13 +94,7 @@
 # %%
 # We then simply have to set the `stream_lsl` parameter to be `True` and specify the `stream_lsl_name`.
 
-features = stream.run(
-    is_stream_lsl=True,
-    plot_lsl=False,
-    stream_lsl_name="example_stream",
-    out_dir=PATH_OUT,
-    experiment_name=RUN_NAME,
-)
+features = stream.run(out_dir=PATH_OUT)
 
 # %%
 # We can then look at the computed features and check if the streamed data was processed correctly.

py_neuromodulation/__init__.py

@@ -2,7 +2,6 @@
 import platform
 from pathlib import PurePath
 from importlib.metadata import version
-from py_neuromodulation.utils.logging import NMLogger
 
 #####################################
 # Globals and environment variables #
@@ -57,12 +56,6 @@
 user_features = {}
 
 
-######################################
-# Logger initialization and settings #
-######################################
-
-logger = NMLogger(__name__)  # logger initialization first to prevent circular import
-
 ####################################
 # API: Exposed classes and methods #
 ####################################

py_neuromodulation/analysis/decode.py

@@ -9,7 +9,7 @@
 from pathlib import PurePath
 import pickle
 
-from py_neuromodulation import logger
+from py_neuromodulation.utils import logger
 
 from typing import Callable
 

py_neuromodulation/analysis/plots.py

@@ -4,7 +4,7 @@
 from matplotlib import gridspec
 import seaborn as sb
 from pathlib import PurePath
-from py_neuromodulation import logger, PYNM_DIR
+from py_neuromodulation.utils import logger, PYNM_DIR
 from py_neuromodulation.utils.types import _PathLike
 
 

py_neuromodulation/features/__init__.py

@@ -28,4 +28,5 @@
     FeatureProcessors,
     add_custom_feature,
     remove_custom_feature,
+    USE_FREQ_RANGES,
 )

py_neuromodulation/features/coherence.py

@@ -11,7 +11,7 @@
     FrequencyRange,
     NMBaseModel,
 )
-from py_neuromodulation import logger
+from py_neuromodulation.utils import logger
 
 if TYPE_CHECKING:
     from py_neuromodulation import NMSettings

py_neuromodulation/features/feature_processor.py

@@ -6,6 +6,16 @@
     import numpy as np
     from py_neuromodulation import NMSettings
 
+USE_FREQ_RANGES: list[FeatureName] = [
+    "bandpass_filter",
+    "stft",
+    "fft",
+    "welch",
+    "bursts",
+    "coherence",
+    "nolds",
+    "bispectrum",
+]
 
 FEATURE_DICT: dict[FeatureName | str, str] = {
     "raw_hjorth": "Hjorth",

py_neuromodulation/filter/notch_filter.py

@@ -2,7 +2,7 @@
 from typing import cast
 
 from py_neuromodulation.utils.types import NMPreprocessor
-from py_neuromodulation import logger
+from py_neuromodulation.utils import logger
 
 
 class NotchFilter(NMPreprocessor):

py_neuromodulation/gui/backend/app_backend.py

@@ -12,7 +12,6 @@
     Query,
     WebSocket,
 )
-from fastapi.responses import FileResponse
 from fastapi.staticfiles import StaticFiles
 from fastapi.middleware.cors import CORSMiddleware
 
@@ -71,7 +70,9 @@ def __init__(
 
     def push_features_to_frontend(self, feature_queue: Queue) -> None:
         while True:
-            time.sleep(0.002) # NOTE: should be adapted depending on feature sampling rate
+            time.sleep(
+                0.002
+            )  # NOTE: should be adapted depending on feature sampling rate
             if feature_queue.empty() is False:
                 self.logger.info("data in feature queue")
                 features = feature_queue.get()
@@ -231,7 +232,6 @@ async def setup_offline_stream(data: dict):
         #######################
 
         @self.get("/api/app-info")
-        # TODO: fix this function
         async def get_app_info():
             metadata = importlib.metadata.metadata("py_neuromodulation")
             url_list = metadata.get_all("Project-URL")
@@ -353,25 +353,4 @@ def quick_access():
         ###########################
         @self.websocket("/ws")
         async def websocket_endpoint(websocket: WebSocket):
-            # if self.websocket_manager.is_connected:
-            #     self.logger.info(
-            #         "WebSocket connection attempted while already connected"
-            #     )
-            #     await websocket.close(
-            #         code=1008, reason="Another client is already connected"
-            #     )
-            #     return
-
-            await self.websocket_manager.connect(websocket)  
-        # # #######################
-        # # ### SPA ENTRY POINT ###
-        # # #######################
-        # if not self.dev:
-
-        #     @self.get("/app/{full_path:path}")
-        #     async def serve_spa(request, full_path: str):
-        #         # Serve the index.html for any path that doesn't match an API route
-        #         print(Path.cwd())
-        #         return FileResponse("frontend/index.html")
-
-
+            await self.websocket_manager.connect(websocket)