df = df.ix[sp.x_min : sp.x_max]
except Exception:
lf.write("Parameters Error: unable to slice x_min and x_max range in directory %s\n\n" % folder)
transfer_attr(ts_full, df, speakup=False)
df.columns.name = timeunit # Useful for plotting 2d-3d for autodetection, but not necessary
###### Polygon Plot
spec_poly3d(df, title=options.rname + "3d Poly Spec")
plt_clrsave(outdir, options.rname + "polygon")
##### Basic spectral and absorbance plots
specplot(df, title=options.rname + "Full spectrum")
plt_clrsave(outdir, options.rname + "full_spectrum")
absplot(divby(df), title=options.rname + "Relative spectrum")
plt_clrsave(outdir, options.rname + "relative")
### Look for uv-vis ranges in data, if not found, default to equally slicing spectrum by 7
try:
uv_ranges = sp.uv_ranges
if isinstance(uv_ranges, float) or isinstance(uv_ranges, int):
uv_ranges = spec_slice(df.index, uv_ranges)
except AttributeError:
uv_ranges = spec_slice(df.index, 8)
### Time averaged plot, not scaled to 1 (relative intenisty dependson bin width and actual intensity)
dfsliced = wavelength_slices(df, ranges=uv_ranges, apply_fcn="mean")
range_timeplot(
dfsliced, ylabel="Average Intensity", xlabel="Time (" + timeunit + ")"
#uv_ranges=spec_slice(df.index, 8)
#### Time averaged plot, not scaled to 1 (relative intenisty dependson bin width and actual intensity)
#dfsliced=wavelength_slices(df, ranges=uv_ranges, apply_fcn='mean')
#range_timeplot(dfsliced, ylabel='Average Intensity', xlabel='Time ('+timeunit+')' ) #legstyle =1 for upper left
#plt_clrsave(outdir, options.rname+'raw_time')
#### Now scale curves to 1 for objective comparison
#dfsliced_norm=dfsliced.apply(lambda x: x/x[0], axis=1)
#range_timeplot(dfsliced_norm, title='Normalized Range Timeplot', ylabel='Scaled Average Intensity',\
#xlabel='Time ('+timeunit+')', legstyle=1 )
#plt_clrsave(outdir, options.rname+'norm_time')
#### Area plot using simpson method of integration
#dfarea=wavelength_slices(df, ranges=(min(df.index), max(df.index)), apply_fcn='simps')
#range_timeplot(dfarea, ylabel='Power', xlabel='Time ('+timeunit+')', legend=False,
#title='Spectral Power vs. Time (%i nm - %i nm)'% ## Eventually make nm a paremeter and class method
#(min(df.index), max(df.index)), color='r')
#plt_clrsave(outdir, options.rname+'full_area')
if __name__=='__main__':
print 'ya'
df=DataFrame([1,2,3,4])
s=specplot(df)
dfa=divby(df)
a=absplot(dfa)
plt.clf()
quadplot(df, s, a)
