fix LSL stream timing

examples/plot_7_lsl_example.py

@@ -11,114 +11,119 @@
 # %%
 from matplotlib import pyplot as plt
 import py_neuromodulation as nm
+import asyncio
+from multiprocessing import freeze_support
 
 # %%
 # Let’s get the example data from the provided BIDS dataset and create the channels DataFrame.
 
-(
-    RUN_NAME,
-    PATH_RUN,
-    PATH_BIDS,
-    PATH_OUT,
-    datatype,
-) = nm.io.get_paths_example_data()
-
-(
-    raw,
-    data,
-    sfreq,
-    line_noise,
-    coord_list,
-    coord_names,
-) = nm.io.read_BIDS_data(PATH_RUN=PATH_RUN)
-
-channels = nm.utils.set_channels(
-    ch_names=raw.ch_names,
-    ch_types=raw.get_channel_types(),
-    reference="default",
-    bads=raw.info["bads"],
-    new_names="default",
-    used_types=("ecog", "dbs", "seeg"),
-    target_keywords=["MOV_RIGHT"],
-)
-
-# %%
-# Playing the Data
-# ----------------
-#
-# Now we need our data to be represeted in the LSL stream.
-# For this example an mne_lsl.Player is utilized, which is playing our earlier
-# recorded data. However, you could make use of any LSL source (live or
-# offline).
-# If you want to bind your own data source, make sure to specify the
-# necessary parameters (data type, type, name) accordingly.
-# If you are unsure about the parameters of your data source you can
-# always search for available lsl streams.
-#
-
-settings = nm.NMSettings.get_fast_compute()
-
-player = nm.stream.LSLOfflinePlayer(raw=raw, stream_name="example_stream")
-
-player.start_player(chunk_size=30)
-# %%
-# Creating the LSLStream object
-# -----------------------------
-#
-# Next let’s create a Stream analog to the First Demo’s example However as
-# we run the stream, we will set the *lsl-stream* value to True and pass
-# the stream name we earlier declared when initializing the player object.
-
-settings.features.welch = False
-settings.features.fft = True
-settings.features.bursts = False
-settings.features.sharpwave_analysis = False
-settings.features.coherence = False
-
-# %%
-stream = nm.Stream(
-    sfreq=sfreq,
-    channels=channels,
-    settings=settings,
-    coord_list=coord_list,
-    verbose=True,
-    line_noise=line_noise,
-)
-# %%
-# 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,
-)
-
-# %%
-# We can then look at the computed features and check if the streamed data was processed correctly.
-# This can be verified by the time label:
-
-plt.plot(features.time, features.MOV_RIGHT)
-
-
-######################################################################
-# Feature Analysis of Movement
-# ----------------------------
-# We can now check the movement averaged features of an ECoG channel.
-# Note that the path was here adapted to be documentation build compliant.
-
-
-feature_reader = nm.analysis.FeatureReader(feature_dir=PATH_OUT, feature_file=RUN_NAME)
-feature_reader.label_name = "MOV_RIGHT"
-feature_reader.label = feature_reader.feature_arr["MOV_RIGHT"]
-
-feature_reader.plot_target_averaged_channel(
-    ch="ECOG_RIGHT_0",
-    list_feature_keywords=None,
-    epoch_len=4,
-    threshold=0.5,
-    ytick_labelsize=7,
-    figsize_x=12,
-    figsize_y=12,
-)
+if __name__ == "__main__":
+    freeze_support()
+    (
+        RUN_NAME,
+        PATH_RUN,
+        PATH_BIDS,
+        PATH_OUT,
+        datatype,
+    ) = nm.io.get_paths_example_data()
+
+    (
+        raw,
+        data,
+        sfreq,
+        line_noise,
+        coord_list,
+        coord_names,
+    ) = nm.io.read_BIDS_data(PATH_RUN=PATH_RUN)
+
+    channels = nm.utils.set_channels(
+        ch_names=raw.ch_names,
+        ch_types=raw.get_channel_types(),
+        reference="default",
+        bads=raw.info["bads"],
+        new_names="default",
+        used_types=("ecog", "dbs", "seeg"),
+        target_keywords=["MOV_RIGHT"],
+    )
+
+    # %%
+    # Playing the Data
+    # ----------------
+    #
+    # Now we need our data to be represeted in the LSL stream.
+    # For this example an mne_lsl.Player is utilized, which is playing our earlier
+    # recorded data. However, you could make use of any LSL source (live or
+    # offline).
+    # If you want to bind your own data source, make sure to specify the
+    # necessary parameters (data type, type, name) accordingly.
+    # If you are unsure about the parameters of your data source you can
+    # always search for available lsl streams.
+    #
+
+    settings = nm.NMSettings.get_fast_compute()
+
+    player = nm.stream.LSLOfflinePlayer(raw=raw, stream_name="example_stream")
+
+    player.start_player(chunk_size=30, n_repeat=5999999)
+    import time
+    time.sleep(5)
+    # %%
+    # Creating the LSLStream object
+    # -----------------------------
+    #
+    # Next let’s create a Stream analog to the First Demo’s example However as
+    # we run the stream, we will set the *lsl-stream* value to True and pass
+    # the stream name we earlier declared when initializing the player object.
+
+    settings.features.welch = False
+    settings.features.fft = True
+    settings.features.bursts = False
+    settings.features.sharpwave_analysis = False
+    settings.features.coherence = False
+
+    # %%
+    stream = nm.Stream(
+        sfreq=sfreq,
+        channels=channels,
+        settings=settings,
+        coord_list=coord_list,
+        verbose=True,
+        line_noise=line_noise,
+    )
+    # %%
+    # We then simply have to set the `stream_lsl` parameter to be `True` and specify the `stream_lsl_name`.
+
+    features = asyncio.run(stream.run(
+        is_stream_lsl=True,
+        stream_lsl_name="example_stream",
+        out_dir=PATH_OUT,
+        experiment_name=RUN_NAME,
+    ))
+
+    # %%
+    # We can then look at the computed features and check if the streamed data was processed correctly.
+    # This can be verified by the time label:
+
+    plt.plot(features.time, features.MOV_RIGHT)
+
+
+    ######################################################################
+    # Feature Analysis of Movement
+    # ----------------------------
+    # We can now check the movement averaged features of an ECoG channel.
+    # Note that the path was here adapted to be documentation build compliant.
+
+
+    feature_reader = nm.analysis.FeatureReader(feature_dir=PATH_OUT, feature_file=RUN_NAME)
+    feature_reader.label_name = "MOV_RIGHT"
+    feature_reader.label = feature_reader.feature_arr["MOV_RIGHT"]
+
+    feature_reader.plot_target_averaged_channel(
+        ch="ECOG_RIGHT_0",
+        list_feature_keywords=None,
+        epoch_len=4,
+        threshold=0.5,
+        ytick_labelsize=7,
+        figsize_x=12,
+        figsize_y=12,
+    )

gui_dev/.gitignore

@@ -14,4 +14,8 @@ dist-ssr
 *.local
 
 # Dev-scripts
-gui_startup.py
+gui_startup.py
+
+# Remove once front was built
+py_neuromodulation/gui/frontend/*
+

gui_dev/package.json

@@ -35,7 +35,7 @@
     "babel-plugin-react-compiler": "latest",
     "eslint": "^9.14.0",
     "eslint-config-prettier": "^9.1.0",
-    "eslint-plugin-jsdoc": "^50.4.3",
+    "eslint-plugin-jsdoc": "^50.5.0",
     "eslint-plugin-react": "^7.37.2",
     "eslint-plugin-react-compiler": "latest",
     "eslint-plugin-react-hooks": "^4.6.2",

gui_dev/src/components/HeatmapGraph.jsx

@@ -111,6 +111,7 @@ export const HeatmapGraph = () => {
     if (!graphData || !selectedChannel || features.length === 0 || selectedFeatures.length === 0) return;
 
     // TODO: Always data in ms? (Time conversion here always necessary?)
+    // Timon: yes, let's define that the stream's time is always in ms
     let timestamp = graphData.time;
     if (timestamp === undefined) {
       timestamp = (Date.now() - hasInitialized.current) / 1000;
@@ -141,6 +142,7 @@ export const HeatmapGraph = () => {
 
     const currentTime = timestamp;
     const minTime = currentTime - maxTimeWindow; // TODO: What should be the visible window frame? adjustable? 10s?
+                                                 // Timon: Would be amazing if it's adjustable
 
     const validIndices = x.reduce((indices, time, index) => {
       if (time >= minTime) {
@@ -221,7 +223,7 @@ export const HeatmapGraph = () => {
                   <FormControlLabel
                     key={channel.id || index}
                     value={channel.name}
-                    control={<Radio />} // TODO: Should we make multiple selectable?
+                    control={<Radio />} // TODO: Should we make multiple selectable?  // Timon: No, let's keep with one
                     label={channel.name}
                   />
                 ))}

gui_dev/src/components/utils.jsx

@@ -1,5 +1,5 @@
 import React from "react";
-
+import { TextField } from "@mui/material";
 /**
  * @param {Object} props
  * @param {string} props.href - The URL to link to
@@ -41,3 +41,20 @@ export const getChannelAndFeature = (availableChannels, keystr) => {
   return { channelName, featureName};
 };
 
+export const MyTextField = ({ label, value, onChange }) => (
+  <TextField
+    label={label}
+    variant="outlined"
+    size="small"
+    fullWidth
+    sx={{
+      marginBottom: 2,
+      backgroundColor: "#616161",
+      color: "#f4f4f4",
+    }}
+    InputLabelProps={{ style: { color: "#cccccc" } }}
+    InputProps={{ style: { color: "#f4f4f4" } }}
+    value={value}
+    onChange={onChange}
+  />
+);

gui_dev/src/pages/SourceSelection/StreamParameters.jsx

@@ -1,24 +1,7 @@
 import { TextField } from "@mui/material";
 import { useSessionStore } from "@/stores";
 import { TitledBox } from "@/components";
-
-const MyTextField = ({ label, value, onChange }) => (
-  <TextField
-    label={label}
-    variant="outlined"
-    size="small"
-    fullWidth
-    sx={{
-      marginBottom: 2,
-      backgroundColor: "#616161",
-      color: "#f4f4f4",
-    }}
-    InputLabelProps={{ style: { color: "#cccccc" } }}
-    InputProps={{ style: { color: "#f4f4f4" } }}
-    value={value}
-    onChange={onChange}
-  />
-);
+import { MyTextField } from "@/components/utils";
 
 export const StreamParameters = () => {
   const streamParameters = useSessionStore((state) => state.streamParameters);
@@ -51,6 +34,16 @@ export const StreamParameters = () => {
         value={streamParameters.samplingRateFeatures}
         onChange={(event) => handleOnChange(event, "samplingRateFeatures")}
       />
+      <MyTextField
+        label="experiment name"
+        value={streamParameters.experimentName}
+        onChange={(event) => handleOnChange(event, "experimentName")}
+      />
+      <MyTextField
+        label="output directory"
+        value={streamParameters.outputDirectory}
+        onChange={(event) => handleOnChange(event, "outputDirectory")}
+      />
     </TitledBox>
   );
 };

gui_dev/src/stores/sessionStore.js

@@ -50,6 +50,8 @@ export const useSessionStore = createPersistStore("session", (set, get) => ({
     lineNoise: 50,
     samplingRateFeatures: 10,
     allValid: false,
+    experimentName: "sub",
+    outputDirectory: "default",
   },
 
   setSourceType: (type) => set({ sourceType: type }),
@@ -128,6 +130,8 @@ export const useSessionStore = createPersistStore("session", (set, get) => ({
           file_path: get().fileSource.path,
           sampling_rate_features: get().streamParameters.samplingRateFeatures,
           line_noise: get().streamParameters.lineNoise,
+          experimentName: get().streamParameters.experimentName,
+          outputDirectory: get().streamParameters.outputDirectory,
         }),
       });
 
@@ -168,6 +172,8 @@ export const useSessionStore = createPersistStore("session", (set, get) => ({
           stream_name: lslSource.availableStreams[0].name,
           sampling_rate_features: streamParameters.samplingRate,
           line_noise: streamParameters.lineNoise,
+          experimentName: streamParameters.experimentName,
+          outputDirectory: streamParameters.outputDirectory,
         }),
       });