diff --git "a/example.html" "b/example.html" new file mode 100644--- /dev/null +++ "b/example.html" @@ -0,0 +1,15383 @@ + + +
+ + +There are three ways to use this dataset:
+First, Unzip the "aware_raw.zip" to the same folder. After then:
+ +import os
+from scipy import io
+import numpy as np
+import matplotlib.pyplot as plt
+
+plt.figure(figsize=(8,6))
+path = './aware_raw/1/' # Take subject #1 as an example
+for idx, filename in enumerate(os.listdir(path)):
+ fs, data = io.wavfile.read(path+filename)
+ data = data/(2**15) # Normalize from 16-bit PCM to [-1,1]
+ plt.subplot(3,3,idx+1)
+ plt.plot(np.array(range(len(data)))/fs, data)
+ plt.title(filename)
+ plt.xlabel('Time (sec)')
+ plt.ylabel('Amplitude')
+plt.tight_layout()
+plt.show()
+
import pandas as pd
+import matplotlib.pyplot as plt
+
+df = pd.read_pickle('aware_segmented.pkl')
+display(df.loc[[100]]) # Take record #0 as an example
+
+data = df['Inhale_1'][100] # Let's try to see how record #100's signals in phase "Inhale_1" looks like
+data = data/(2**15) # Normalize from 16-bit PCM to [-1,1]
+plt.figure(figsize=(12,9))
+for idx in range(data.shape[0]):
+ plt.subplot(5, 5, idx+1)
+ plt.plot(data[idx,:])
+plt.show()
+
+ | AWARE STUDY ID: | +Calculated age (years): | +Sex: | +Race/ethnicity: (choice=White) | +Race/ethnicity: (choice=Black / African American) | +Race/ethnicity: (choice=Hispanic / Latino) | +Race/ethnicity: (choice=Asian) | +Race/ethnicity: (choice=Other) | +Height (cm): | +Weight (kg): | +... | +Calibration_1 | +Calibration_2 | +Calibration_3 | +Nasal | +Inhale_1 | +Inhale_2 | +Inhale_3 | +Exhale_1 | +Exhale_2 | +Exhale_3 | +
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
100 | +28 | +38.7 | +Male | +Checked | +Unchecked | +Unchecked | +Unchecked | +Unchecked | +177.8 | +87.5 | +... | +[[-2, -4, -4, -3, -1, 0, -2, -1, -3, -1, -1, -... | +[[-21, -18, -15, -16, -21, -22, -21, -18, -15,... | +[[-7, -5, -6, -5, -5, -5, -3, -3, -2, -1, -2, ... | +[[5, 7, 3, 3, 2, 1, 0, 0, -3, -6, -7, -9, -8, ... | +[[21, 23, 28, 31, 31, 30, 31, 32, 36, 39, 39, ... | +[[-13, -12, -11, -10, -10, -11, -12, -13, -11,... | +[[-39, -46, -45, -40, -32, -31, -37, -38, -39,... | +[[-7, -7, -6, -5, -6, -6, -4, -3, -2, -5, -6, ... | +[[198, 185, 171, 154, 136, 122, 110, 102, 86, ... | +[[-54, -48, -43, -38, -31, -26, -22, -20, -16,... | +
1 rows × 56 columns
+NOTE: Reading whole file is time consuming
+ +import pandas as pd
+import matplotlib.pyplot as plt
+
+df = pd.read_csv('aware_segmented.csv', header=None, skiprows=100, nrows=1) # Read the 101-th line (record #100) for example
+display(df)
+
+data = df.iloc[0, 292846:412846].to_numpy() # 292846:412846 correspond to phase "Inhale_1", see README.md
+data = data.reshape(25,-1)
+data = data/(2**15) # Normalize from 16-bit PCM to [-1,1]
+plt.figure(figsize=(12,9))
+for idx in range(data.shape[0]):
+ plt.subplot(5, 5, idx+1)
+ plt.plot(data[idx,:])
+plt.show()
+
+ | 0 | +1 | +2 | +3 | +4 | +5 | +6 | +7 | +8 | +9 | +... | +1012836 | +1012837 | +1012838 | +1012839 | +1012840 | +1012841 | +1012842 | +1012843 | +1012844 | +1012845 | +
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | +28 | +38.7 | +Male | +Checked | +Unchecked | +Unchecked | +Unchecked | +Unchecked | +177.8 | +87.5 | +... | +-60 | +-59 | +-55 | +-50 | +-52 | +-58 | +-61 | +-62 | +-56 | +-53 | +
1 rows × 1012846 columns
+import pandas as pd
+import matplotlib.pyplot as plt
+
+df = pd.read_csv('aware_csa.csv')
+display(df)
+
+plt.plot(df.iloc[0:4, 46:].T.to_numpy()) # Let's take a look at subject #70's airway CSA curve (record #0 to #3)
+plt.grid('on')
+plt.show()
+
+ | AWARE STUDY ID: | +Calculated age (years): | +Sex: | +Race/ethnicity: (choice=White) | +Race/ethnicity: (choice=Black / African American) | +Race/ethnicity: (choice=Hispanic / Latino) | +Race/ethnicity: (choice=Asian) | +Race/ethnicity: (choice=Other) | +Height (cm): | +Weight (kg): | +... | +CSA_75 | +CSA_76 | +CSA_77 | +CSA_78 | +CSA_79 | +CSA_80 | +CSA_81 | +CSA_82 | +CSA_83 | +CSA_84 | +
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | +70 | +30.7 | +Female | +Checked | +Unchecked | +Unchecked | +Unchecked | +Unchecked | +160.02 | +80.0 | +... | +3.810551 | +3.913885 | +3.957559 | +3.970113 | +4.004002 | +4.103503 | +4.280572 | +4.504991 | +4.712451 | +4.833306 | +
1 | +70 | +30.7 | +Female | +Checked | +Unchecked | +Unchecked | +Unchecked | +Unchecked | +160.02 | +80.0 | +... | +4.490665 | +4.274807 | +4.022432 | +3.807408 | +3.686022 | +3.682560 | +3.784819 | +3.943260 | +4.080129 | +4.119503 | +
2 | +70 | +30.7 | +Female | +Checked | +Unchecked | +Unchecked | +Unchecked | +Unchecked | +160.02 | +80.0 | +... | +3.219514 | +3.399397 | +3.595761 | +3.768916 | +3.900902 | +3.999388 | +4.086103 | +4.178455 | +4.277437 | +4.368749 | +
3 | +70 | +30.7 | +Female | +Checked | +Unchecked | +Unchecked | +Unchecked | +Unchecked | +160.02 | +80.0 | +... | +4.317590 | +4.736081 | +5.098569 | +5.315797 | +5.369527 | +5.325483 | +5.294827 | +5.382279 | +5.654416 | +6.126306 | +
4 | +72 | +19.6 | +Female | +Checked | +Checked | +Unchecked | +Unchecked | +Unchecked | +152.40 | +50.6 | +... | +4.063526 | +3.973345 | +3.926251 | +3.907793 | +3.893328 | +3.864602 | +3.824250 | +3.801414 | +3.845476 | +4.012830 | +
... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +
1053 | +66 | +18.0 | +Male | +Unchecked | +Checked | +Unchecked | +Unchecked | +Unchecked | +177.20 | +100.9 | +... | +4.317066 | +4.419419 | +4.453888 | +4.432593 | +4.382772 | +4.338273 | +4.329225 | +4.376190 | +4.486610 | +4.652911 | +
1054 | +66 | +18.0 | +Male | +Unchecked | +Checked | +Unchecked | +Unchecked | +Unchecked | +177.20 | +100.9 | +... | +5.058151 | +5.262232 | +5.407480 | +5.526130 | +5.672834 | +5.898386 | +6.228152 | +6.649212 | +7.106533 | +7.515035 | +
1055 | +66 | +18.0 | +Male | +Unchecked | +Checked | +Unchecked | +Unchecked | +Unchecked | +177.20 | +100.9 | +... | +5.700953 | +5.885066 | +5.989681 | +6.028290 | +6.022268 | +6.000034 | +5.994446 | +6.038448 | +6.157008 | +6.358157 | +
1056 | +68 | +19.9 | +Male | +Unchecked | +Checked | +Unchecked | +Unchecked | +Unchecked | +182.30 | +85.0 | +... | +5.396123 | +5.364110 | +5.456679 | +5.680443 | +6.016404 | +6.422066 | +6.833527 | +7.169713 | +7.347199 | +7.307952 | +
1057 | +68 | +19.9 | +Male | +Unchecked | +Checked | +Unchecked | +Unchecked | +Unchecked | +182.30 | +85.0 | +... | +6.678286 | +6.753770 | +6.664450 | +6.450338 | +6.209805 | +6.062980 | +6.115728 | +6.443510 | +7.083921 | +8.019280 | +
1058 rows × 130 columns
+
+