def run_pipeline_single(initialize_Gillespie, runVector, path1):
# Prepare the reactions classes.
#[alpha, beta, yr, r0, c0, mu, cv, initialState, run_count, production, enzymatic_degradation, dilution ] = initialize_Gillespie
[alpha, beta, yr, r0, c0, mu, cv, initialState, run_count ] = initialize_Gillespie [0:9] #DMI
#[[alpha, beta, yr, R0, C0, mu, cv, initialState, run]]
initialState = np.array([initialState], dtype=int)
#params = [alpha, beta, yr, r0, c0, mu, cv, int(initialState), run_count] #DMI
[stopTime, runCount, burnInTime, sampleRate] = runVector
# Initialize the parameters and make a reaction list
dilution = Classy.Reaction(np.array([-1], dtype=int), 0, 0, [0, beta, 1, 0], 1, [0])
enzymatic_degradation = Classy.Reaction(np.array([-1], dtype=int), 0, 0, [0, yr, r0, 1], 1, [0])
production = Classy.Reaction(np.array([1], dtype=int), 0, 1, [alpha, c0, 2], 0, [mu, mu * cv])
reactionList = np.array([production, enzymatic_degradation, dilution])
# Name files and prepare them to receive new input
signalcsv = path1 + '{}signal.csv'.format( initialize_Gillespie )
peakscsv = path1 + "{}peaks.csv".format( initialize_Gillespie )
statscsv = path1 + "{}stats.csv".format( initialize_Gillespie )
tempOutFileSignal = open(signalcsv, 'a')
tempOutFilePeaks = open(peakscsv, 'a')
OutFileStats = open(statscsv, 'a')
# header = "alpha, beta, yr, r0, c0, mu, cv, initialState, peaks, run_count"
# np.savetxt("peaks.csv", header, delimiter=",")
tempSignal = Gill.gillespie(reactionList, stopTime, initialState)
#np.zeros(min(tempSignal.shape[1],9)) #DMI it worked with this but this does not do anything
np.savetxt(signalcsv, initialize_Gillespie, delimiter=",")
np.savetxt(signalcsv, tempSignal, delimiter=",")
print(tempSignal)
burned_in_signal = Post.burn_in_time_series(tempSignal, burnInTime) # this needs to be passed by reference.
uniform_sampling = Post.uniformly_sample(burned_in_signal, sampleRate) # this needs to be passed by reference.
peaks = Post.detect_peaks(uniform_sampling)
to_peak_file = initialize_Gillespie.extend(peaks) # DMI FIX!, issue: the peaks are in the form of time and height.
# thing = np.zeros(peaks.shape[1]))
# thing[:] = ' '.join(map(str, my_lst))
# to_peak_file[len(to_peak_file):] =
# need to add the parameters to each line
# recommended fix in format of making the peak data have a different set of deliminators than the other data
# other fix, just put the parameters near the peak amp and time, along with a slice number
#np.savetxt("peaks.csv", to_peak_file, delimiter=",") # might want to try json, might work
# know np.save(filename.npy,list) can save list to file. Cannot append multiple files
to_stat_file = initialize_Gillespie
to_stat_file[len(to_stat_file):] = Post.run_statistics(peaks)
to_stat_file = Post.run_statistics(peaks) # send the stats here
print(peaks)
np.savetxt(statscsv, to_stat_file, delimiter=",")
tempOutFileSignal.close()
tempOutFilePeaks.close()
OutFileStats.close()
# plotting
''' """
plt.plot(tempSignal[:, 0], tempSignal[:, 1])
plt.show()
'''
return
def run_pipeline(gillespie_parameters, processing_parameters, path):
[reaction_list, initial_state] = Initialize_Reactions(gillespie_parameters)
[stop_time, burn_in_time, sample_rate] = processing_parameters
signal = Gill.gillespie(reaction_list, stop_time, initial_state)
archive_signal(signal, path + '{}signal.npy'.format(gillespie_parameters),
"/home/spcampbe/servers/storage/data\ storage")
Post.post_process(signal,
path + "{}peaks.npy".format(gillespie_parameters),
burn_in_time, sample_rate, 7200)
def run_pipeline_single2(initialize_Gillespie, runVector, path1):
print(initialize_Gillespie)
reactionList = Initialize_Classes(initialize_Gillespie[0:7])
initialState = initialize_Gillespie[8] # remove runcount, remove indexing, get rid of extra shit in main.py
initialState = np.array([initialState],dtype=int) # Do we need to recreate initialState? Probably put this in main.py
[stopTime, runCount, burnInTime, sampleRate] = runVector # remove runcount
''' Rename temp_* to just * and make filenames "human readable" in lines 102,103
-SPC'''
signalcsv = path1 + '{}signal.csv'.format(initialize_Gillespie)
peakscsv = path1 + "{}peaks.csv".format(initialize_Gillespie)
''' move line 105 to main.py and put all stats in a single file. -SPC '''
statscsv = path1 + "{}stats.csv".format(initialize_Gillespie)
tempOutFileSignal = open(signalcsv, 'a') # refactor temp and open with 'w' instead of 'a'
tempOutFilePeaks = open(peakscsv, 'a') # refactor temp and open with 'w' instead of 'a'
OutFileStats = open(statscsv, 'a') # VERY IMPORTANT TO KEEP AS 'a'
print("Gillespie start")
tempSignal = Gill.gillespie(reactionList, stopTime, initialState) # refactor tempSignal to Signal
np.savetxt(signalcsv, initialize_Gillespie, delimiter=",") # maybe make this a header
np.savetxt(signalcsv, tempSignal, delimiter=",")
print(tempSignal)
burned_in_signal = Post.burn_in_time_series(tempSignal, burnInTime) # this needs to be passed by reference.
uniform_sampling = Post.uniformly_sample(burned_in_signal, sampleRate) # this needs to be passed by reference.
''' Break up time series here '''
#for i = 0; i < uniform_sampling.shape[0]/5000:
peaks = Post.detect_peaks(uniform_sampling)
to_peak_file = initialize_Gillespie.extend(peaks) # DMI FIX!, issue: the peaks are in the form of time and height.
# recommended fix in format of making the peak data have a different set of deliminators than the other data
# other fix, just put the parameters near the peak amp and time, along with a slice number
# np.savetxt("peaks.csv", to_peak_file, delimiter=",") # might want to try json, might work
# know np.save(filename.npy,list) can save list to file. Cannot append multiple files
to_stat_file = initialize_Gillespie
to_stat_file[len(to_stat_file):] = Post.run_statistics(peaks)
to_stat_file = Post.run_statistics(peaks) # send the stats here
print(peaks)
np.savetxt(statscsv, to_stat_file, delimiter=",")
tempOutFileSignal.close()
tempOutFilePeaks.close()
OutFileStats.close()
# plotting
''' """
plt.plot(tempSignal[:, 0], tempSignal[:, 1])
plt.show()
'''
return
def run_pipeline(gillespie_parameters, run_vector, path):
reaction_list = Initialize_Classes(gillespie_parameters[0:7])
initial_state = gillespie_parameters[7]
initial_state = np.array([initial_state], dtype=int)
[stop_time, burn_in_time, sample_rate] = run_vector # remove runcount
signal = Gill.gillespie(reaction_list, stop_time, initial_state)
signalcsv = path + '{}signal.csv'.format(gillespie_parameters)
np.savetxt(signalcsv, gillespie_parameters[:7], delimiter=",") # maybe make this a header
np.savetxt(signalcsv, signal, delimiter=",")
burned_in_signal = Post.burn_in_time_series(signal, burn_in_time) # this needs to be passed by reference.
uniform_sampling = Post.uniformly_sample(burned_in_signal, sample_rate) # this needs to be passed by reference.
peakscsv = path + "{}peaks.csv".format(gillespie_parameters)
peaks = Post.chop_peaks(uniform_sampling, peakscsv)
return peaks
def run_pipeline(gillespie_parameters, run_vector, path):
reaction_list = Initialize_Classes(gillespie_parameters[:-1])
initial_state = gillespie_parameters[-1]
[stop_time, burn_in_time, sample_rate] = run_vector
signal = Gill.gillespie(reaction_list, stop_time, initial_state)
signalcsv = path + '{}signal.csv'.format(gillespie_parameters)
np.savetxt(signalcsv, gillespie_parameters[:7],
delimiter=",") # maybe make this a header
np.savetxt(signalcsv, signal, delimiter=",")
burned_in_signal = Post.burn_in_time_series(
signal, burn_in_time) # this needs to be passed by reference.
uniform_sampling = Post.uniformly_sample(
burned_in_signal, sample_rate) # this needs to be passed by reference.
peakscsv = path + "{}peaks.csv".format(gillespie_parameters)
Post.chop_peaks(uniform_sampling[:, :2], peakscsv, 4000)
def run_pipeline(gillespie_parameters, run_vector, path):
reaction_list = Initialize_Classes(gillespie_parameters[:-1])
initial_state = gillespie_parameters[-1]
[stop_time, burn_in_time, sample_rate] = run_vector
signal = Gill.gillespie(reaction_list, stop_time, initial_state)
signalcsv = path + '{}signal.csv'.format(gillespie_parameters)
np.savetxt(signalcsv, gillespie_parameters[:7],
delimiter=",") # maybe make this a header
np.savetxt(signalcsv, signal, delimiter=",")
signalcsvbash = "'" + signalcsv
os.system(
"gzip {} ; mv {} /home/spcampbe/servers/storage/data\ storage".format(
signalcsvbash + "'", signalcsvbash + ".gz'"))
signal = Post.burn_in_time_series(
signal[:, :2], burn_in_time) # this needs to be passed by reference.
signal = Post.uniformly_sample(signal, sample_rate)
peakscsv = path + "{}peaks.csv".format(gillespie_parameters)
Post.chop_peaks(signal, peakscsv, 4000)
def run_pipeline(gillespie_parameters, run_vector, path):
try:
reaction_list = Initialize_Classes(gillespie_parameters[:-1])
initial_state = gillespie_parameters[-1]
[stop_time, burn_in_time, sample_rate] = run_vector
signal = Gill.gillespie(reaction_list, stop_time, initial_state)
signalcsv = path + '{}signal.csv'.format(gillespie_parameters)
np.savetxt(signalcsv, gillespie_parameters[:7],
delimiter=",") # maybe make this a header
np.savetxt(signalcsv, signal, delimiter=",")
signal = Post.burn_in_time_series(
signal[:, :2],
burn_in_time) # this needs to be passed by reference.
signal = Post.uniformly_sample(signal, sample_rate)
peakscsv = path + "{}peaks.csv".format(gillespie_parameters)
Post.chop_peaks(signal, peakscsv, 4000)
except:
print("Total, available, used, free virtual memory, active, inactive ",
file=path + 'error.log')
print("error" + str(gillespie_parameters) + " psutil memory " +
str(psutil.virtual_memory()),
file=path + 'error.log')
def run_pipeline_splits(initialize_Gillespie, runVector, path1):
# Prepare the reactions classes.
[alpha, beta, yr, r0, c0, mu, cv, initialState, run_count ] = initialize_Gillespie [0:9]
initialState = np.array([initialState], dtype=int)
[stopTime, runCount, burnInTime, sampleRate] = runVector
# print(run_count)
dilution = Classy.Reaction(np.array([-1], dtype=int), 0, 0, [0, beta, 1, 0], 1, [0])
enzymatic_degradation = Classy.Reaction(np.array([-1], dtype=int), 0, 0, [0, yr, r0, 1], 1, [0])
production = Classy.Reaction(np.array([1], dtype=int), 0, 1, [alpha, c0, 2], 0, [mu, mu * cv])
reactionList = np.array([production, enzymatic_degradation, dilution])
signalcsv = path1 + 'signal.csv'
peakscsv = path1 + "peaks.csv"
statscsv = path1 + "stats.csv"
signalcsv = path1 + '{}signal.csv'.format( initialize_Gillespie )
peakscsv = path1 + "{}peaks.csv".format( initialize_Gillespie )
statscsv = path1 + "stats.csv".format( initialize_Gillespie )
tempOutFileSignal = open(signalcsv, 'a')
tempOutFilePeaks = open(peakscsv, 'a')
OutFileStats = open(statscsv, 'a')
header = "alpha, beta, yr, r0, c0, mu, cv, initialState, peaks"
#np.savetxt("peaks.csv", header, delimiter=",")
tempSignal = Gill.gillespie(reactionList, stopTime, initialState)
np.savetxt("signal.csv", tempSignal, delimiter=",")
ichnks =0
chunks= int((stopTime - burnInTime)/run_count)
sliced_signal = dict.fromkeys(range(chunks))
sliced_signal[ichnks] = [Post.slice_up_time_series(tempSignal, (burnInTime+ ichnks* stopTime), (burnInTime+ (1+ichnks)* stopTime),run_count)]
print("slice")
print(sliced_signal)
for ichnks in range(chunks):
temp_slice = Post.slice_up_time_series(tempSignal, (burnInTime+ ichnks* stopTime), (burnInTime+ (1+ichnks)* stopTime),run_count)
print("pre uniform_sampling")
print(temp_slice)
print(temp_slice[0])
print(len(temp_slice))
uniform_sampling = Post.uniformly_sample(temp_slice[0], sampleRate) # this needs to be passed by reference.
print("uniform_sampling")
print(uniform_sampling)
# To Do: append uniform_sampling
peaks = Post.detect_peaks(uniform_sampling)
# To Do: apppend peaks to csv
sliced_signal[ichnks] = temp_slice
for xxn in sliced_signal:
uniform_sampling = Post.uniformly_sample(xxn, sampleRate) # this needs to be passed by reference.
peaks = Post.detect_peaks(uniform_sampling)
# Fix1, need to make it such that the things are appended to a something taht can be saved to file
# to_peak_file = initialize_Gillespie.extend(peaks) # DMI FIX!, issue: the peaks are in the form of time and height. Not good for 1 column format
# recomended fix in format of making the peak data have a different set of deliminators than the other data
# other fix, just put the parameters near the peak amp and time, along with a slice number
np.savetxt("peaks.csv", to_peak_file, delimiter=",")
to_stat_file = Post.run_statistics(peaks) # send the stats here
np.savetxt("stats.csv", to_stat_file, delimiter=",")
tempOutFileSignal.close()
tempOutFilePeaks.close()
OutFileStats.close()
plt.plot(tempSignal[:, 0], tempSignal[:, 1])
plt.show()
return