def group(self, data, merger, drop_grouper=True):
"""Data should have same or higher frequency than merger."""
grouped = read.merge_series(data, self.instr['pluvio'].grouper())
result = pd.merge(grouped, pd.DataFrame(merger),
left_on='group', right_index=True)
if drop_grouper:
return result.drop('group', axis=1)
return result
hextent = np.array(bnds) + (-0.1, 0.1, -0.1, 0.1)
kws = {
'separate': True,
'source_style': 'hex',
'source_kws': {
'gridsize': 26,
'extent': hextent
},
'unfiltered': True
}
fitargs = {'force_flip': False, 'try_flip': False, 'fitclass': fit.PolFit}
limslist = sf.limitslist(rholimits)
data = param_table(e=e, debug=debug)
data.index = data.index.droplevel()
vdata = read.merge_series(e.pluv_grouper(), e.vel_data())
vtable = pd.merge(vdata,
pd.DataFrame(data.rhomin),
left_on='group',
right_index=True)
# TODO: check duplicates
vfits = dict()
for rhomin in rholimits[:-1]:
pipv = read.PipV(data=vtable.query('rhomin=={0}'.format(rhomin)))
vfits[rhomin], std, etc = pipv.pickler('v-d_rho_range', pipv.find_fit,
**fitargs)
fig, axarr = sf.plot_vfits_rho_intervals(dict2tuple(vfits), limslist, **kws)
axarr[0].axis(bnds)
for ax in axarr:
ax.tick_params(direction='out', top=False)
def scatterplot(x, y, c=None, kind='scatter', **kwargs):
"""scatter plot of two Series objects"""
plotdata = read.merge_series(x, y)
if c is not None:
kwargs['c'] = c
return plotdata.plot(kind=kind, x=x.name, y=y.name, **kwargs)
#plt.close('all')
plt.ioff()
hextent = np.array(bnds)+(-0.1,0.1,-0.1,0.1)
kws = {'separate': True,
'source_style': 'hex',
'source_kws': {'gridsize': 26, 'extent': hextent},
'unfiltered': True}
fitargs = {'force_flip': False,
'try_flip': False,
'fitclass': fit.PolFit}
limslist=sf.limitslist(rholimits)
data = param_table(e=e, debug=debug)
data.index = data.index.droplevel()
vdata = read.merge_series(e.pluv_grouper(), e.vel_data())
vtable = pd.merge(vdata, pd.DataFrame(data.rhomin), left_on='group', right_index=True)
# TODO: check duplicates
vfits = dict()
for rhomin in rholimits[:-1]:
pipv = read.PipV(data=vtable.query('rhomin=={0}'.format(rhomin)))
vfits[rhomin], std, etc = pipv.pickler('v-d_rho_range', pipv.find_fit, **fitargs)
fig, axarr = sf.plot_vfits_rho_intervals(dict2tuple(vfits), limslist, **kws)
axarr[0].axis(bnds)
for ax in axarr:
ax.tick_params(direction='out', top=False)
pc = pd.DataFrame(index=rholimits[:-1], columns=['count_all', 'count_hwfm', 'r_sq'])
for rhomin in vfits:
vfit = vfits[rhomin]
pc.loc[rhomin] = vfit.x_unfiltered.size, vfit.x.size, vfit.rsq(filtered=False)