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spin_extractor.py
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spin_extractor.py
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import pandas, numpy, datetime
from matplotlib import pyplot as plt
from scipy.optimize import curve_fit as fit
from matplotlib import rc
#rc('text', usetex=True)
#font = {'size' : 30}
#rc('font', **font)
def spin_down(t, pmax, t0, tau, d):
# = args[0], args[1], args[2]
return pmax*(1-numpy.exp(-(t-t0)/tau))+d
def spin_up(t, p0, t0, tau, d):
# = args[0], args[1], args[2]
return p0*numpy.exp(-(t-t0)/tau)+d
def file_fetcher(filename, t):
with open(filename, 'r') as f:
df = pandas.read_csv(f)
df[t] = pandas.to_datetime(df[t], format="%Y-%m-%d %H:%M:%S")
df = df.sort_values(t)
return df
def df_trimmer(df, Sd, Sm, Sy, sh, sm, fh,fm, t, ss=0, fs=0, Fd=None,Fm=None,Fy=None):
if Fd is None:
Fd = Sd
if Fm is None:
Fm = Sm
if Fy is None:
Fy = Sy
startdate = datetime.datetime(year=Sy, month=Sm, day=Sd, hour=sh, minute=sm, second=ss)
enddate = datetime.datetime(year=Fy, month=Fm, day=Fd, hour=fh, minute=fm, second=fs)
df = df[(df[t]>startdate)&(df[t]<enddate)].sort_values(t)
return df
def get_x_for_fit(trimmed, Sd, Sm, Sy, t):
"""
Get the x data (of type datetime) into a format that scipy can interpret (seconds from the start of the day)
"""
end_time = (max(trimmed[t].tolist())-datetime.datetime(year=Sy, month=Sm, day=Sd)).total_seconds()
start_time = (min(trimmed[t].tolist())-datetime.datetime(year=Sy, month=Sm, day=Sd)).total_seconds()
timestamp_list = trimmed[t].tolist()
timesteps = []
for index, val in enumerate(timestamp_list):
if index == len(timestamp_list)-1:
break
timesteps.append((timestamp_list[index+1]-val).total_seconds())
avg_timestep = numpy.mean(timesteps)
xdata_for_fit = numpy.arange(start_time, end_time, avg_timestep).tolist()
xdata_for_fit.append(xdata_for_fit[-1]+avg_timestep)
return xdata_for_fit
def previewdata_gui(*args, **kwargs):
filename, title, Sd, Sm, Sy, sh, sm, fh,fm, p,t = args
ss=kwargs.pop('ss', 0)
fs=kwargs.pop('fs',0)
Fd=kwargs.pop('Fd',None)
Fm=kwargs.pop('Fm',None)
Fy=kwargs.pop('Fy',None)
preview=kwargs.pop('preview',True)
bounds=kwargs.pop('bounds',[1, 1, 1, 1])
up=kwargs.pop('up',True)
datetimeaxis = kwargs.pop('datetimeaxis', True)
df = file_fetcher(filename, t)
trimmed = df_trimmer(df, Sd, Sm, Sy, sh, sm, fh,fm, t, ss=ss, fs=fs, Fd=Fd,Fm=Fm,Fy=Fy)
fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(8.5, 11), constrained_layout=True)
ax[0].set_title(title)
ax[1].set_xlabel("Datetime")
ax[0].set_xlabel("Datetime")
ax[0].plot(df[t], df[p], c='b', label=title)
ax[0].plot(trimmed[t], trimmed[p], c='red')
ax[1].plot(trimmed[t], trimmed[p], c='red')
ax[0].grid(True)
ax[1].grid(True)
return fig
def getupdown(*args, **kwargs):
filename, title, Sd, Sm, Sy, sh, sm, fh,fm, p,t = args
ss=kwargs.pop('ss', 0)
fs=kwargs.pop('fs',0)
Fd=kwargs.pop('Fd',None)
Fm=kwargs.pop('Fm',None)
Fy=kwargs.pop('Fy',None)
preview=kwargs.pop('preview',False)
bounds=kwargs.pop('bounds',[1, 1, 1, 1])
up=kwargs.pop('up',True)
datetimeaxis = kwargs.pop('datetimeaxis', False)
df = file_fetcher(filename, t)
trimmed = df_trimmer(df, Sd, Sm, Sy, sh, sm, fh,fm, t, ss=ss, fs=fs, Fd=Fd,Fm=Fm,Fy=Fy)
xforfit = get_x_for_fit(trimmed, Sd, Sm, Sy, t)
yforfit = trimmed[p].to_list()
xforplot = trimmed[t]
fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(8.5, 11), constrained_layout=True)
ax[0].set_title(title)
ax[0].set_xlabel("Datetime")
ax[0].plot(df[t], df[p], c='b', label=title)
ax[0].plot(trimmed[t], trimmed[p], c='red')
ax[0].grid(True)
if up:
fitvars, _ = fit(spin_up, xforfit, yforfit)#, bounds=[(0, 83500, 0),(numpy.inf, 100000, numpy.inf)])
print("\n\nSpin up:", "pmax*(1-numpy.exp(-(t-t0)/tau))+shift\n")
print(title)
print("pmax", "t0", "tau", 'shift')
for i, val in enumerate(["pmax:", "t0:", "tau:", "shift:"]):
print(fitvars[i],end='\t')
print('')
fy = spin_up(xforfit, fitvars[0], fitvars[1], fitvars[2], fitvars[3])
if datetimeaxis:
ax[1].plot(xforplot, fy, c='green', label="Spin Up")
else:
ax[1].plot(xforfit, fy, c='green', label="Spin Up")
else:
fitvars, _ = fit(spin_down, xforfit, yforfit)#, bounds=[(0, 83500, 0),(numpy.inf, 100000, numpy.inf)])
print("\n\nSpin down:", "p0*numpy.exp(-(t-t0)/tau)+shift\n")
print(title)
print(["p0", "t0", "tau", "shift"])
for i, val in enumerate(["p0:", "t0:", "tau:","shift:"]):
print(fitvars[i],end='\t')
fy = spin_up(xforfit, fitvars[0], fitvars[1], fitvars[2], fitvars[3])
if datetimeaxis:
ax[1].plot(xforplot, fy, c='green', label="Spin Down")
else:
ax[1].plot(xforfit, fy, c='green', label="Spin Down")
if datetimeaxis:
ax[1].set_xlabel("Datetime")
ax[1].scatter(trimmed[t], trimmed[p], c='red')
else:
ax[1].set_xlabel("Seconds since Midnight")
ax[1].scatter(xforfit, trimmed[p], c='red')
ax[1].plot(xforfit, fy, c='green', label=("Spin "+("Up" if up else "Down")))
ax[1].grid(True)
ax[1].legend(loc='best')
ax[0].legend(loc='best')
return fig
if __name__ == "__main__":
#Needs some work
pass