def retrieve_footer(file, comment_marker='#'):
"""Retrieves the footer of a .txt file saved with np.savetxt.
Parameters
----------
file : str
File's root (must include directory and termination).
comment_marker='#' : str, optional
Sign that indicates a line is a comment on np.savetxt.
Returns
-------
last_line : str, dict
File's footer
Raises
------
ValueError : "Footer not found. Sorry!"
When the last line doesn't begin with 'comment_marker'.
See Also
--------
fwp_save.savetxt
"""
with open(file, 'r') as f:
for line in f:
last_line = line
if last_line[0] == comment_marker:
try:
last_line = last_line.split(comment_marker + ' ')[-1]
last_line = last_line.split('\n')[0]
footer = eval('dict({})'.format(last_line))
for key, value in footer.items():
try:
number = fst.find_numbers(value)
if len(number) == 1:
number = number[0]
if len(value.split(' ')) == 2:
footer[key] = (
number,
value.split(' ')[-1]
)
else:
footer[key] = number
except TypeError:
value = value
except:
footer = last_line
return footer
else:
raise ValueError("No footer found. Sorry!")
else:
if len(temp) != 0:
rawfiles_by_sr[current_sr] = temp
current_sr = this_sr
temp = [f]
# sorted list of sampling rates
samplingrates = sorted(list(rawfiles_by_sr.keys()))
cual = 10
archivos = rawfiles_by_sr[samplingrates[cual]]
signal_freqs = {}
#time, data = np.loadtxt(f, unpack=True)
for f in archivos:
freq = fstr.find_numbers(f)[1]
signal_freqs[freq] = Struct()
signal_freqs[freq].file = f
time, data = np.loadtxt(f, unpack=True)
signal_freqs[freq].duration = time[-1]
signal_freqs[freq].nperiods = len(find_peaks(data)[0])
signal_freqs[freq].maybe_freq = len(find_peaks(data)[0]) / time[-1]
#to be able to plot with lines, keys must be sorted
actual_freq = sorted(list(signal_freqs.keys()))
plt.plot(actual_freq, [signal_freqs[f].maybe_freq for f in actual_freq], '-o')
#%% interbuffer time
def calc_vel(time, singal, **kwargs):
ds = np.diff(signal)
picos = find_peaks(ds, **kwargs)[0]
vel = np.diff(picos).astype(float)
t = time[picos[:-1]]
return t, 1 / vel
#def fix_vel(vel, threshold):
#%%
#Select file and extract stuff
gen_freq = 10e3 #in Hz
file = contenidos_completos[1]
salto = fst.find_numbers(file)[0:1]
samplerate = fst.find_1st_number(retrieve_footer(file)) #in Hz
points_per_gen_period = samplerate / gen_freq
#load data
time, signal, gen = np.loadtxt(file, unpack=True)
signal = np.abs(signal)
gen /= np.mean(gen[gen > 4]) # gen now goes between 0 and 1, approx
cada = 200
#integral = np.array([np.trapz(gen[i:i+cada]) for i in range(len(gen)-cada)])
#duty_cycle = np.array([np.mean(gen[i:i+cada]) for i in range(len(gen)-cada)])
ds = normalize(np.diff(signal), 3)
signal = normalize(signal, 4)
picos = find_peaks(ds, height=.6, prominence=.4)[0]