Autodetect sample rate

backend/classification/config/constants.py

@@ -36,8 +36,6 @@ def get_filename(self):
 FILE_MINIMUM_DURATION = EPOCH_DURATION
 
 DATASET_SAMPLE_RATE = 100
-OPENBCI_CYTON_SAMPLE_RATE = 250
-OPENBCI_GANGLION_SAMPLE_RATE = 200
 
 AGE_FEATURE_BINS = [
     [12, 49],

backend/classification/parser/__init__.py

@@ -12,11 +12,10 @@
 """
 from mne import create_info
 from mne.io import RawArray
-import numpy as np
 
-from classification.config.constants import OPENBCI_CYTON_SAMPLE_RATE, EEG_CHANNELS
-from classification.parser.constants import SCALE_V_PER_COUNT
-from classification.parser.file_type import FileType, detect_file_type
+from classification.config.constants import EEG_CHANNELS
+from classification.parser.file_type import detect_file_type
+from classification.parser.sample_rate import detect_sample_rate
 
 
 def get_raw_array(file):
@@ -32,14 +31,18 @@ def get_raw_array(file):
     Detected {filetype.name} format.
     """)
 
-    parse = filetype.parser
-    eeg_raw = parse(file)
+    sample_rate = detect_sample_rate(file, filetype)
+    print(f"""
+    Detected {sample_rate}Hz sample rate.
+    """)
+
+    eeg_raw = filetype.parser(file)
 
     raw_object = RawArray(
-        SCALE_V_PER_COUNT * np.transpose(eeg_raw),
+        eeg_raw,
         info=create_info(
             ch_names=EEG_CHANNELS,
-            sfreq=OPENBCI_CYTON_SAMPLE_RATE,
+            sfreq=sample_rate,
             ch_types='eeg'),
         verbose=False,
     )
@@ -48,7 +51,7 @@ def get_raw_array(file):
         First sample values: {raw_object[:, 0]}
         Second sample values: {raw_object[:, 1]}
         Number of samples: {raw_object.n_times}
-        Duration of signal (h): {raw_object.n_times / (3600 * OPENBCI_CYTON_SAMPLE_RATE)}
+        Duration of signal (h): {raw_object.n_times / (3600 * sample_rate)}
         Channel names: {raw_object.ch_names}
     """)
 

backend/classification/parser/constants.py

@@ -3,8 +3,11 @@
 ADS1299_Vref = 4.5
 ADS1299_gain = 24.
 SCALE_uV_PER_COUNT = ADS1299_Vref / ((2**23) - 1) / ADS1299_gain * 1000000
-SCALE_V_PER_COUNT = SCALE_uV_PER_COUNT / 1e6
+uV_TO_V = 1 / 1e6
+SCALE_V_PER_COUNT = SCALE_uV_PER_COUNT * uV_TO_V
 
 FILE_COLUMN_OFFSET = 1
 
+SESSION_FILE_HEADER_NB_LINES = 4
+
 RETAINED_COLUMNS = tuple(range(FILE_COLUMN_OFFSET, len(EEG_CHANNELS) + 1))

backend/classification/parser/csv.py

@@ -2,12 +2,13 @@
 
 from classification.exceptions import ClassificationError
 
+
 def read_csv(file, rows_to_skip=0, columns_to_read=None):
     try:
         raw_array = pd.read_csv(file,
-                              skiprows=rows_to_skip,
-                              usecols=columns_to_read
-                              ).to_numpy()
+                                skiprows=rows_to_skip,
+                                usecols=columns_to_read
+                                ).to_numpy()
     except Exception:
         raise ClassificationError()
 

backend/classification/parser/file_type.py

@@ -3,11 +3,13 @@
 from classification.parser.sd_file import parse_sd_file
 from classification.parser.session_file import parse_session_file
 
+
 class FileType(Enum):
-     SDFile = (parse_sd_file,)
-     SessionFile = (parse_session_file,)
-     def __init__(self, parser):
-             self.parser = parser
+    SDFile = (parse_sd_file,)
+    SessionFile = (parse_session_file,)
+
+    def __init__(self, parser):
+        self.parser = parser
 
 
 def detect_file_type(file) -> FileType:

backend/classification/parser/sample_rate.py

@@ -0,0 +1,28 @@
+import re
+
+from classification.parser.file_type import FileType
+from classification.parser.constants import SESSION_FILE_HEADER_NB_LINES
+from classification.exceptions import ClassificationError
+
+OPENBCI_CYTON_SD_DEFAULT_SAMPLE_RATE = 250
+
+SAMPLE_RATE_STRING = "Sample Rate"
+SAMPLE_RATE_REGEX = fr"^%{SAMPLE_RATE_STRING} = (\d+)"
+
+
+def detect_sample_rate(file, filetype):
+    if filetype == FileType.SDFile:
+        return OPENBCI_CYTON_SD_DEFAULT_SAMPLE_RATE
+
+    for _ in range(SESSION_FILE_HEADER_NB_LINES):
+        line = file.readline().decode("utf-8")
+        if SAMPLE_RATE_STRING not in line:
+            continue
+
+        try:
+            sample_rate_raw = re.search(SAMPLE_RATE_REGEX, line).group(1)
+            return int(sample_rate_raw)
+        except BaseException:
+            raise ClassificationError('Invalid sample rate')
+
+    raise ClassificationError("Couldn't find sample rate")

backend/classification/parser/sd_file.py

@@ -2,6 +2,7 @@
 
 from classification.parser.constants import RETAINED_COLUMNS
 from classification.parser.csv import read_csv
+from classification.parser.constants import SCALE_V_PER_COUNT
 
 ROWS_TO_SKIP = 2
 
@@ -26,5 +27,5 @@ def parse_sd_file(file):
     eeg_raw = read_csv(file, ROWS_TO_SKIP, RETAINED_COLUMNS)
     hexstr_to_int = np.vectorize(_hexstr_to_int)
     eeg_raw = hexstr_to_int(eeg_raw)
-
+    eeg_raw = np.transpose(eeg_raw) * SCALE_V_PER_COUNT
     return eeg_raw

backend/classification/parser/session_file.py

@@ -1,6 +1,6 @@
-import pandas as pd
+import numpy as np
 
-from classification.parser.constants import RETAINED_COLUMNS
+from classification.parser.constants import RETAINED_COLUMNS, uV_TO_V
 from classification.parser.csv import read_csv
 
 
@@ -14,4 +14,6 @@ def parse_session_file(file):
     Returns:
     - np.array of the two EEG channels of interest
     """
-    return read_csv(file, ROWS_TO_SKIP, RETAINED_COLUMNS)
+    eeg_raw = read_csv(file, ROWS_TO_SKIP, RETAINED_COLUMNS)
+    eeg_raw = np.transpose(eeg_raw) * uV_TO_V
+    return eeg_raw

web/src/views/analyze_sleep/upload_form/constants.js

@@ -1,9 +1,5 @@
 export const MAX_FILE_UPLOAD_SIZE = 1.6e9;
 export const ACCEPTED_FILE_TYPE = 'text/plain';
 export const ACCEPTED_FILE_EXTENSION = '.txt';
-export const DEVICES = {
-  CYTON: 'CYTON',
-  GANGLION: 'GANGLION',
-};
 export const MIN_AGE = 12;
 export const MAX_AGE = 125;

web/src/views/analyze_sleep/upload_form/index.js

@@ -3,14 +3,7 @@ import { useForm } from 'react-hook-form';
 import { Button, Container, CustomInput, Form, FormGroup, Label, Input, InputGroup, Col, Row, Alert } from 'reactstrap';
 import { DateTime } from 'luxon';
 
-import {
-  ACCEPTED_FILE_EXTENSION,
-  ACCEPTED_FILE_TYPE,
-  DEVICES,
-  MAX_AGE,
-  MAX_FILE_UPLOAD_SIZE,
-  MIN_AGE,
-} from './constants';
+import { ACCEPTED_FILE_EXTENSION, ACCEPTED_FILE_TYPE, MAX_AGE, MAX_FILE_UPLOAD_SIZE, MIN_AGE } from './constants';
 
 import './style.css';
 import useGlobalState from 'hooks/useGlobalState';
@@ -111,24 +104,6 @@ const UploadForm = () => {
               While recording your EEG data, you also gathered some informations about your device, yourself and your
               sleep:
             </p>
-            <FormGroup>
-              <Label for="openbci-device">OpenBCI device used</Label>
-              <Row>
-                <Col md={12}>
-                  <CustomInput
-                    innerRef={register({ required: 'A selection must be made.' })}
-                    type="select"
-                    id="openbci-device"
-                    name="device"
-                  >
-                    <option defaultValue></option>
-                    <option value={DEVICES.CYTON}>Cyton</option>
-                    <option value={DEVICES.GANGLION}>Ganglion</option>
-                  </CustomInput>
-                  <div className="upload-form__error-text">{errors.device?.message}</div>
-                </Col>
-              </Row>
-            </FormGroup>
 
             <Row form>
               <Col md={6}>