def setupplot(secondax=False, **kwargs):
ytickv = np.linspace(YR[0], YR[1], 6)
yticknames = map('{:0.0f}'.format, ytickv)
tmp = dict(
ylabel='Temperature [c]',
yr=minmax(ytickv),
ytickv=ytickv,
yticknames=yticknames,
)
tmp.update(kwargs)
ax = setup(**tmp)
if secondax:
subplt = kwargs.get('subplt', None)
f = lambda x: '{:0.0f}'.format(1.8 * x + 32.0)
yticknames = map(f, ytickv)
ax2 = ax.twinx()
ax2.set_ylabel(r"Temperature [F]")
ax2.set_ylim(minmax(ytickv))
ax2.yaxis.set_major_locator(matplotlib.ticker.FixedLocator(ytickv))
ax2.yaxis.set_major_formatter(
matplotlib.ticker.FixedFormatter(yticknames))
pylab.sca(ax)
# setup(ax=ax.twinx(),
# subplt=subplt,
# ylabel='Temperature [F]',
# yr=minmax(ytickv), ytickv=ytickv, yticknames=yticknames)
return ax
def setupplot(secondax=False, **kwargs):
ytickv = np.linspace(YR[0],YR[1],6)
yticknames = map('{:0.0f}'.format, ytickv)
tmp = dict(
ylabel='Temperature [c]',
yr=minmax(ytickv), ytickv=ytickv,
yticknames=yticknames,
)
tmp.update(kwargs)
ax = setup(**tmp)
if secondax:
subplt = kwargs.get('subplt',None)
f = lambda x: '{:0.0f}'.format(1.8*x + 32.0)
yticknames = map(f, ytickv)
ax2 = ax.twinx()
ax2.set_ylabel(r"Temperature [F]")
ax2.set_ylim(minmax(ytickv))
ax2.yaxis.set_major_locator(matplotlib.ticker.FixedLocator(ytickv))
ax2.yaxis.set_major_formatter(matplotlib.ticker.FixedFormatter(yticknames))
pylab.sca(ax)
# setup(ax=ax.twinx(),
# subplt=subplt,
# ylabel='Temperature [F]',
# yr=minmax(ytickv), ytickv=ytickv, yticknames=yticknames)
return ax
def getimrange(x, imrange):
xmin,xmax = minmax(x)
if imrange[0] > xmin:
imrange[0] = xmin
if imrange[1] < xmax:
imrange[1] = xmax
return imrange
def getimrange(x, imrange):
xmin, xmax = minmax(x)
if imrange[0] > xmin:
imrange[0] = xmin
if imrange[1] < xmax:
imrange[1] = xmax
return imrange
def fitgaussian(x, y, offset=0):
'''Fit a gaussian to the data points x,y.
offset == the assumed floor for the gaussian (subtracted from
the y array). Originally I fit for both the amplitude and offset
however this sometimes caused issues due to the degeneracy. '''
# set the parameters and some min values
p = Parameters()
# generally the background is 20-30, so require at least 10 above that
p.add('amplitude', value=np.max(y) - offset, min=10)
p.add('mean', value=np.mean(x), min=0)
p.add('sigma', value=np.std(x), min=0)
# minimise the fit.
out = minimize(gauss2, p, args=(x, y - offset))
# print the fit values and uncert. I may want to check the
# out.success value to ensure that everything worked.
# report_errors(p)
r = embiggen(minmax(x), 0.2)
xx = np.arange(r[0], r[1], 0.1)
return p, xx, gauss2(p, xx) + offset
def fitgaussian(x,y,offset=0):
'''Fit a gaussian to the data points x,y.
offset == the assumed floor for the gaussian (subtracted from
the y array). Originally I fit for both the amplitude and offset
however this sometimes caused issues due to the degeneracy. '''
# set the parameters and some min values
p = Parameters()
# generally the background is 20-30, so require at least 10 above that
p.add('amplitude', value=np.max(y)-offset, min=10)
p.add('mean', value=np.mean(x), min=0)
p.add('sigma', value=np.std(x), min=0)
# minimise the fit.
out = minimize(gauss2, p, args=(x, y-offset) )
# print the fit values and uncert. I may want to check the
# out.success value to ensure that everything worked.
# report_errors(p)
r = embiggen(minmax(x),0.2)
xx = np.arange(r[0], r[1], 0.1)
return p, xx, gauss2(p,xx)+offset
def make():
tag = 'Voltage'
tag = 'CurrentCapacity'
data = json.load(open(FILENAME,'r'))
date = date2num([datetime.fromtimestamp(x['date']) for x in data if len(x) > 1])
amp = [x[tag] for x in data if len(x) > 1]
damp = np.diff(amp)
ddate = date[:-1] + np.diff(date)/2.0
uii = np.where(damp > 2)
dii = np.where(damp < -2)
print len(data)
plot_params = dict(
marker = 's',
markersize=2,
alpha=0.2,
linestyle='_',
markeredgewidth=0,
markeredgecolor='none',
)
xr = embiggen(minmax(date), 0.02, 'both')
yr = [0,7000]
dyr = [-80,80]
if np.max(amp) > 7000:
yr = [0,10000]
dyr = [-100,100]
setup(figsize=(16,8))
setup(subplt=(2,3,1), autoticks=True,
title='Last Recorded: {}'.format(num2date(date[-1])),
xlabel='Date', xr=xr,
ylabel=tag, yr=yr)
pylab.plot(date, amp, **plot_params)
dateticks('%Y-%m-%d')
tmp = date % 7.0
setup(subplt=(2,3,2), autoticks=True,
title='Current Date: {}'.format(datetime.now()),
xlabel='Day of Week',
xtickv=np.arange(7), xr=[-0.2,6.2],
xticknames='sun mon tue wed thur fri sat'.split(),
ylabel=tag, yr=yr)
pylab.plot(tmp, amp, **plot_params)
tmp = (date % 1.0) * 24.0
setup(subplt=(2,3,3), autoticks=True,
xlabel='Hour of Day', xr=[-0.4,24.4],
xtickv=np.arange(0,25,4), xtickrotate=dict(rotation=90, ha='center'),
xticknames='mid 4am 8am noon 4pm 8pm mid'.split(),
ylabel=tag, yr=yr)
pylab.plot(tmp, amp, **plot_params)
# dateticks('%Y-%m-%d')
setup(subplt=(2,3,4), autoticks=True,
xlabel='Date', xr=xr,
ylabel='Delta', yr=dyr)
pylab.plot(ddate, damp, **plot_params)
dateticks('%Y-%m-%d', ha='center')
tmp = (date % 7.0)[:-1]
setup(subplt=(2,3,5), autoticks=True,
xlabel='Day of Week',
xtickv=np.arange(7), xr=[-0.2,6.2],
xticknames='sun mon tue wed thur fri sat'.split(),
ylabel=tag, yr=dyr)
pylab.plot(tmp, damp, **plot_params)
hist(tmp[uii], np.linspace(0,7,90), alpha=0.5, norm=dyr[1], filled=True)
hist(tmp[dii], np.linspace(0,7,90), alpha=0.5, norm=dyr[0], filled=True)
tmp = ((date % 1.0) * 24.0)[:-1]
setup(subplt=(2,3,6), autoticks=True,
xlabel='Hour of Day', xr=[-0.4,24.4],
xtickv=np.arange(0,25,4), xtickrotate=dict(rotation=90, ha='center'),
xticknames='mid 4am 8am noon 4pm 8pm mid'.split(),
ylabel=tag, yr=dyr)
pylab.plot(tmp, damp, **plot_params)
hist(tmp[uii], np.linspace(0,24,50), alpha=0.5, norm=dyr[1], filled=True)
hist(tmp[dii], np.linspace(0,24,50), alpha=0.5, norm=dyr[0], filled=True)
# dateticks('%Y-%m-%d')
# pylab.tight_layout()
setup(hspace=0, wspace=0)
pylab.show()
