def readlabels(pathname):
dt_i8 = np.dtype("<u1")
with open(pathname, "rb") as bytestream:
#Check magic is 2049
magic = _read32(bytestream)
num_inputs = _read32(bytestream)
buf = bytestream.read(num_inputs*dt_i8.itemsize)
labels = np.frombuffer(buf, dtype=dt_i8)
labels.shape = (num_inputs)
return(labels)
def readlabels(pathname):
dt_i8 = np.dtype("<u1")
with open(pathname, "rb") as bytestream:
#Check magic is 2049
magic = _read32(bytestream)
num_inputs = _read32(bytestream)
buf = bytestream.read(num_inputs * dt_i8.itemsize)
labels = np.frombuffer(buf, dtype=dt_i8)
labels.shape = (num_inputs)
return (labels)
def readdata(pathname):
dt_f32 = np.dtype("<f4")
with open(pathname, "rb") as bytestream:
#Check magic is 2049
magic = _read32(bytestream)
samples = _read32(bytestream)
rows = _read32(bytestream)
cols = _read32(bytestream)
buf = bytestream.read(rows*cols*samples*dt_f32.itemsize)
dataset = np.frombuffer(buf, dtype=dt_f32)
dataset.shape = (samples, rows, cols)
return(dataset)
def readdata(pathname):
dt_f32 = np.dtype("<f4")
with open(pathname, "rb") as bytestream:
#Check magic is 2049
magic = _read32(bytestream)
samples = _read32(bytestream)
rows = _read32(bytestream)
cols = _read32(bytestream)
buf = bytestream.read(rows * cols * samples * dt_f32.itemsize)
dataset = np.frombuffer(buf, dtype=dt_f32)
dataset.shape = (samples, rows, cols)
return (dataset)
def readlabels2(pathname):
dt_i8 = np.dtype("<u1")
labels = []
with open(pathname, "rb") as bytestream:
#Check magic is 2049
magic = _read32(bytestream)
num_tests = _read32(bytestream)
for i in range(0, num_tests):
num_inputs = _read32(bytestream)
buf = bytestream.read(num_inputs*dt_i8.itemsize)
labels_temp = np.frombuffer(buf, dtype=dt_i8)
labels_temp.shape = (num_inputs)
labels.append(labels_temp)
return(labels)
def readlabels2(pathname):
dt_i8 = np.dtype("<u1")
labels = []
with open(pathname, "rb") as bytestream:
#Check magic is 2049
magic = _read32(bytestream)
num_tests = _read32(bytestream)
for i in range(0, num_tests):
num_inputs = _read32(bytestream)
buf = bytestream.read(num_inputs * dt_i8.itemsize)
labels_temp = np.frombuffer(buf, dtype=dt_i8)
labels_temp.shape = (num_inputs)
labels.append(labels_temp)
return (labels)
def readdata2(pathname):
dt_f32 = np.dtype("<f4")
dataset = []
with open(pathname, "rb") as bytestream:
#Check magic is 2049
magic = _read32(bytestream)
num_tests = _read32(bytestream)
rows = _read32(bytestream)
cols = _read32(bytestream)
for i in range(0, num_tests):
samples = _read32(bytestream)
buf = bytestream.read(rows*cols*samples*dt_f32.itemsize)
dataset_temp = np.frombuffer(buf, dtype=dt_f32)
dataset_temp.shape = (samples, rows, cols)
dataset.append(dataset_temp)
return(dataset)
def readdata2(pathname):
dt_f32 = np.dtype("<f4")
dataset = []
with open(pathname, "rb") as bytestream:
#Check magic is 2049
magic = _read32(bytestream)
num_tests = _read32(bytestream)
rows = _read32(bytestream)
cols = _read32(bytestream)
for i in range(0, num_tests):
samples = _read32(bytestream)
buf = bytestream.read(rows * cols * samples * dt_f32.itemsize)
dataset_temp = np.frombuffer(buf, dtype=dt_f32)
dataset_temp.shape = (samples, rows, cols)
dataset.append(dataset_temp)
return (dataset)
path = samplespath+"/"+samplefolder
# Get Inputs
with open(path+"/input", "rb") as bytestream:
num_inputs = _read8(bytestream)
buf = bytestream.read(num_inputs * dt_i8.itemsize)
labels = np.frombuffer(buf, dtype=dt_i8)
# Get Samples and Presamples
dataset = []
predataset = []
allsamples = sorted(os.listdir(path+"/sample"))
for sample in allsamples:
with open(path+"/sample/"+sample, "rb") as bytestream:
#Check magic is 2049
magic = _read32(bytestream)
rows = _read32(bytestream)
cols = _read32(bytestream)
buf = bytestream.read(max_samples * cols * dt_f32.itemsize)
dataset.append(np.frombuffer(buf, dtype=dt_f32))
with open(path+"/presample/"+sample, "rb") as bytestream:
num_samples += 1
#Check magic is 2049
magic = _read32(bytestream)
rows = _read32(bytestream)
cols = _read32(bytestream)
buf = bytestream.read(max_presamples * cols * dt_f32.itemsize)
predataset.append(np.frombuffer(buf, dtype=dt_f32))
# Reshape
dataset = np.array(dataset)
path = samplespath + "/" + samplefolder
# Get Inputs
with open(path + "/input", "rb") as bytestream:
num_inputs = _read8(bytestream)
buf = bytestream.read(num_inputs * dt_i8.itemsize)
labels = np.frombuffer(buf, dtype=dt_i8)
# Get Samples and Presamples
dataset = []
predataset = []
allsamples = sorted(os.listdir(path + "/sample"))
for sample in allsamples:
with open(path + "/sample/" + sample, "rb") as bytestream:
#Check magic is 2049
magic = _read32(bytestream)
rows = _read32(bytestream)
cols = _read32(bytestream)
buf = bytestream.read(max_samples * cols * dt_f32.itemsize)
dataset.append(np.frombuffer(buf, dtype=dt_f32))
with open(path + "/presample/" + sample, "rb") as bytestream:
num_samples += 1
#Check magic is 2049
magic = _read32(bytestream)
rows = _read32(bytestream)
cols = _read32(bytestream)
buf = bytestream.read(max_presamples * cols * dt_f32.itemsize)
predataset.append(np.frombuffer(buf, dtype=dt_f32))
# Reshape
dataset = np.array(dataset)
dt_f32 = np.dtype("<f4")
electrodes = [False, False, False, False, False, False, False, False]
masterfolder = "testing"
samplefolder = "sample"
#Read raw data
path = os.path.abspath(os.path.join(__file__,"../"))
testingpath = path + "/"+ masterfolder + "/" + samplefolder
filename = os.listdir(testingpath)[0]
max_rows = 260
with open(testingpath+"/"+filename, "rb") as readstream:
magic = _read32(readstream)
rows = _read32(readstream)
cols = _read32(readstream)
buf = readstream.read(max_rows * cols * dt_f32.itemsize)
data = np.frombuffer(buf, dtype=dt_f32)
data.shape = (max_rows, cols)
min_thresh = -60000
max_thresh = -10000
#min_thresh = -38000
#max_thresh = -12000
for i in range(0,data.shape[1]):
if max(data[:,i]) < max_thresh and min(data[:,i]) > min_thresh:
electrodes[i] = True
#DC Filter
import numpy as np
from readbytes import _read8, _read32
from datafilters import apply_dc_filter
from sklearn.ensemble import RandomForestClassifier
num_channels = 8
max_rows = 360
masterfolder = "predicting"
dt_f32 = np.dtype("<f4")
#Read data from Hardware
path = os.path.abspath(os.path.join(__file__, "../"))
testingpath = path + "/predicting/sample"
with open(testingpath + "/" + sys.argv[1], "rb") as readstream:
magic = _read32(readstream)
rows = _read32(readstream)
cols = _read32(readstream)
buf = readstream.read(max_rows * cols * dt_f32.itemsize)
data = np.frombuffer(buf, dtype=dt_f32)
data.shape = (max_rows, cols)
#DC Filter
fs = 250
enable_dc = True
dc_lowcut = 1.0 #Only get alpha and beta, most related to movement
dc_highcut = 13.0
dc_order = 2
dc_type = "bandpass"
dc_func_type = "butter"
