72 lines
3.2 KiB
Python
72 lines
3.2 KiB
Python
import os #compability with windows
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from IPython import embed
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def parse_dataset(dataset_name):
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assert(os.path.exists(dataset_name)) #see if data exists
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f = open(dataset_name, 'r') #open data we gave in
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lines = f.readlines() #read data
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f.close() #?
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# metadata lists for every loop
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eodfs = []
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deltafs = []
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stimulusfs = []
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# data itself
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times = []
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frequencies = []
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amplitudes = []
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# temporary lists with data we put in the lists above
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time = []
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ampl = []
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freq = []
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for i in range(len(lines)):
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l = lines[i].strip() #all lines of textdata, exclude all empty lines (empty () default for spacebar)
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if "#" in l and "EODf" in l: #if line starts with # EODf:
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eodfs.append(float(l.split(':')[-1].strip()[:-2])) #append: line splitted by ':' the 2nd part ([-1],
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if "#" in l and "Delta f" in l: #which got striped so we sure there is no space at the end,
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deltafs.append(float(l.split(':')[-1].strip()[:-2])) #from that all expect the last two signs (Hz unit)
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if "#" in l and "StimulusFrequency" in l: #this for different metadata in different lists
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stimulusfs.append(float(l.split(':')[-1].strip()[:-2]))
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if '#Key' in l:
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#print('KEY')
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if len(time) != 0: #therefore empty in the first round
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times.append(time) #2nd loop means time != 0, so we put the times/amplitudes/frequencies to
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amplitudes.append(ampl) #the data of the first loop
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frequencies.append(freq)
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time = [] #temporary lists to overwrite the lists with the same name we made before
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ampl = [] #so they are empty again
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freq = []
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print(len(times))
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if len(l) > 0 and l[0] is not '#': #line not empty and doesnt start with #
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temporary = list(map(float, l.split())) #temporary list where we got 3 index splitted by spacebar, map to find them
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time.append(temporary[0]) #temporary lists with the data at that place, respectively
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freq.append(temporary[1])
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ampl.append(temporary[2])
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times.append(time) #append data from one list to another
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amplitudes.append(ampl) #these append the data from the first loop to the final lists, because we overwrite them (?)
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frequencies.append(freq)
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return times, frequencies, amplitudes, eodfs, deltafs, stimulusfs #output of the function
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def noise_reduce(dataset_name):
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assert (os.path.exists(dataset_name)) # see if data exists
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f = open(dataset_name, 'r') # open data we gave in
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lines = f.readlines() # read data
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f.close()
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n = 10
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cutf = []
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for i in np.arange(0, len(dataset_name), n): #dataset_name sollte Frequenzen sein?
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mean = np.mean(dataset_name[i:i+n]) #sollte nach i+n weitergehen?
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cutf.append(mean)
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return cutf |