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processing.py
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processing.py
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import os
import numpy as np
from freyberg import pyemu
# from scipy import stats
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.colors as colors
from matplotlib import cm
import pandas as pd
from freyberg import flopy
from string import ascii_uppercase
def _get_annotation_colors(res, cmap=None, norm=None):
if cmap is None:
cmap = cm.RdBu_r
elif isinstance(cmap, str):
cmap = cm.get_cmap(cmap)
if norm is None:
norm = colors.CenteredNorm(0, 10)
tc = 'k'
fc = cmap(norm(res))
lu = (0.299 * fc[0] ** 2 + 0.587 * fc[1] ** 2 + 0.114 * fc[2] ** 2) ** 0.5
if lu < 0.4:
tc = 'w'
return fc, tc
def plot_results(m_d, ardim=None, pstf="test_run", log_oe=True,noptmax=None):
if ardim is None:
try:
sim = flopy.mf6.MFSimulation.load(sim_ws=m_d,
load_only=['DIS'])
m = sim.get_model("freyberg6")
nrow = m.modelgrid.nrow
ncol = m.modelgrid.ncol
ib = m.modelgrid.idomain
except:
nlay, nrow, ncol = (1, 50, 50)
ib = np.loadtxt(os.path.join("org_zone", "ib_resample.dat"), dtype=int)[np.newaxis]
else:
nrow, ncol = ardim
pst = pyemu.Pst(os.path.join(m_d, pstf+".pst"))
if noptmax is None:
noptmax = pst.control_data.noptmax
try:
pr_oe = pyemu.ObservationEnsemble.from_binary(
pst=pst, filename=os.path.join(m_d,"{0}.0.obs.jcb".format(pstf))
)
except:
pr_oe = pd.read_csv(os.path.join(m_d,"{0}.0.obs.csv".format(pstf)),
low_memory=False,
index_col=0)
try:
pt_oe = pyemu.ObservationEnsemble.from_binary(
pst=pst,
filename=os.path.join(m_d, "{0}.{1}.obs.jcb".format(
pstf, noptmax))
)
except:
pt_oe = pd.read_csv(
os.path.join(m_d, "{0}.{1}.obs.csv".format(
pstf, noptmax)),
low_memory=False,
index_col=0)
try:
noise = pyemu.ObservationEnsemble.from_binary(
pst=pst, filename=os.path.join(m_d, "{0}.obs+noise.jcb".format(pstf)))._df
except:
noise = pd.read_csv(
os.path.join(m_d, "{0}.obs+noise.csv".format(pstf)),
low_memory=False,
index_col=0)
if log_oe: # not already logged, transform now
pr_oe.loc[:, :] = np.log10(pr_oe.values)
pt_oe.loc[:, :] = np.log10(pt_oe.values)
noise = noise.copy()
noise.loc[:, :] = np.log10(noise.values)
pst.observation_data.loc[:, "obsval"] = np.log10(
pst.observation_data.obsval.values)
obs = pst.observation_data.copy()
obs = obs.loc[obs.otype=="arr"]
obs.loc[:, "i"] = obs.i.astype(int)
obs.loc[:, "j"] = obs.j.astype(int)
onames = obs.oname.unique()
onames = [o for o in onames if "dup" not in o]
onames.sort()
print(onames)
with PdfPages(os.path.join(m_d,"results.pdf")) as pdf:
for oname in onames:
# cheating to get layer from oname
k = int(oname[-1]) - 1
oobs = obs.loc[obs.oname==oname,:].copy()
nzobs = oobs.loc[oobs.weight > 0]
dups = obs.loc[obs.oname==oname+"dup",:]
dups = dups.loc[dups.weight > 0]
lsobs = nzobs.loc[nzobs.obgnme.str.contains("less")].index
grobs = nzobs.loc[nzobs.obgnme.str.contains("greater")].index
lsobs = lsobs.append(dups.loc[dups.obgnme.str.contains("less")].index)
grobs = grobs.append(dups.loc[dups.obgnme.str.contains("greater")].index)
nz_arr = np.zeros((nrow,ncol))
nz_arr[nzobs.i,nzobs.j] = nzobs.obsval
nz_arr[nz_arr==0] = np.NaN
# catch to not have too many scatters
if len(nzobs) < 100 and len(nzobs) > 0:
scatter = True
else:
scatter = False
# means
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
pr_arr = np.zeros((nrow,ncol))
pr_arr[oobs.i,oobs.j] = pr_oe.loc[:,oobs.obsnme].mean().values
pt_arr = np.zeros((nrow, ncol))
pt_arr[oobs.i, oobs.j] = pt_oe.loc[:, oobs.obsnme].mean().values
pr_arr[ib[k] <= 0] = np.NaN
pt_arr[ib[k] <= 0] = np.NaN
mn, mx = min(np.nanmin(pr_arr), np.nanmin(pt_arr)), max(np.nanmax(pr_arr), np.nanmax(pt_arr))
cb = axes[0].imshow(pr_arr,vmin=mn,vmax=mx)
plt.colorbar(cb,ax=axes[0],label="$log_{10}$")
cb = axes[1].imshow(pt_arr, vmin=mn, vmax=mx)
plt.colorbar(cb, ax=axes[1],label="$log_{10}$")
grp = oobs.obgnme.iloc[0]
axes[0].set_title("prior mean "+namer(grp),loc="left")
axes[1].set_title("posterior mean " + oname, loc="left")
if scatter:
ineqobs = nzobs.loc[nzobs.obgnme.str.contains("greater"),:]
#axes[0].scatter(nzobs.j,nzobs.i,marker=".",s=500,facecolor="none",edgecolor="r")
#axes[1].scatter(nzobs.j, nzobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
axes[0].scatter(ineqobs.j, ineqobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
axes[1].scatter(ineqobs.j, ineqobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
eqobs = nzobs.loc[nzobs.obgnme.apply(lambda x: "greater" not in x),:]
axes[0].scatter(eqobs.j, eqobs.i, marker="^", s=100, facecolor="none", edgecolor="r")
axes[1].scatter(eqobs.j, eqobs.i, marker="^", s=100, facecolor="none", edgecolor="r")
# ens mean max residual for group
tmp_df = pd.concat([nzobs, dups])
tmp_df['pr_res'] = pr_oe.loc[:, tmp_df.obsnme].mean() - tmp_df.obsval
tmp_df.loc[tmp_df.index.isin(lsobs) &
(tmp_df.pr_res < 0), "pr_res"] = 0
tmp_df.loc[tmp_df.index.isin(grobs) &
(tmp_df.pr_res > 0), 'pr_res'] = 0
tmp_df['pt_res'] = pt_oe.loc[:, tmp_df.obsnme].mean() - tmp_df.obsval
tmp_df.loc[tmp_df.index.isin(lsobs) &
(tmp_df.pt_res < 0), 'pt_res'] = 0
tmp_df.loc[tmp_df.index.isin(grobs) &
(tmp_df.pt_res > 0), 'pt_res'] = 0
# set to common obsname
tmp_df['obsnme'] = tmp_df.obsnme.str.replace(oname + "dup", oname)
tmp_df = tmp_df.reset_index(drop=True).groupby('obsnme').agg(
{'pr_res': lambda x: max(x, key=abs),
'pt_res': lambda x: max(x, key=abs)}
)
mx_res = tmp_df[['pr_res', "pt_res"]].abs().values.max()
norm = colors.CenteredNorm(0, mx_res)
for i,j,name in zip(nzobs.i,nzobs.j,nzobs.obsnme):
val = obs.loc[name, "obsval"]
prval = pr_arr[i, j]
ptval = pt_arr[i, j]
grp = obs.loc[name,"obgnme"]
dname = name.replace(oname,oname+"dup")
if dname in obs.index and obs.loc[dname,"weight"] > 0:
lb = "{0:2.2f}".format(obs.loc[dname,"obsval"])
ub = "{0:2.2f}".format(nzobs.loc[name, "obsval"])
valstr = "{0}<>{1}".format(lb, ub)
else:
valstr = "{0:2.2f}".format(val)
if "less" in grp:
valstr = "<" + valstr
elif "great" in grp:
valstr = ">" + valstr
fc, tc = _get_annotation_colors(tmp_df.pr_res[name], norm=norm)
axes[0].annotate(
"{0}\n{1:2.2f}".format(valstr, prval), (j, i),
xytext=(5, 5), textcoords="offset points",
va="bottom", ha="left", color=tc,
bbox=dict(boxstyle='round', facecolor=fc,
alpha=1.0)
)
fc, tc = _get_annotation_colors(tmp_df.pt_res[name], norm=norm)
axes[1].annotate(
"{0}\n{1:2.2f}".format(valstr, ptval), (j, i),
xytext=(5, 5), textcoords="offset points",
va="bottom", ha="left", color=tc,
bbox=dict(boxstyle='round',
facecolor=fc, alpha=1.0)
)
plt.tight_layout()
pdf.savefig()
plt.close(fig)
# stdev
nz_arr = np.zeros((nrow, ncol))
nz_arr[nzobs.i, nzobs.j] = 1. / nzobs.weight
nz_arr[nz_arr == 0] = np.NaN
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
pr_arr = np.zeros((nrow, ncol))
pr_arr[oobs.i, oobs.j] = pr_oe.loc[:, oobs.obsnme].std().values
pt_arr = np.zeros((nrow, ncol))
pt_arr[oobs.i, oobs.j] = pt_oe.loc[:, oobs.obsnme].std().values
pr_arr[ib[k] <= 0] = np.NaN
pt_arr[ib[k] <= 0] = np.NaN
mn, mx = min(np.nanmin(pr_arr), np.nanmin(pt_arr)), max(np.nanmax(pr_arr), np.nanmax(pt_arr))
cb = axes[0].imshow(pr_arr, vmin=mn, vmax=mx)
plt.colorbar(cb, ax=axes[0],label="$log_{10}$")
cb = axes[1].imshow(pt_arr, vmin=mn, vmax=mx)
plt.colorbar(cb, ax=axes[1],label="$log_{10}$")
axes[0].set_title("prior stdev " + oname, loc="left")
axes[1].set_title("posterior stdev " + oname, loc="left")
#for ax in axes.flatten():
#ax.imshow(nz_arr, vmin=mn, vmax=mx)
if scatter:
#axes[0].scatter(nzobs.j, nzobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
#axes[1].scatter(nzobs.j, nzobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
ineqobs = nzobs.loc[nzobs.obgnme.str.contains("greater"), :]
# axes[0].scatter(nzobs.j,nzobs.i,marker=".",s=500,facecolor="none",edgecolor="r")
# axes[1].scatter(nzobs.j, nzobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
axes[0].scatter(ineqobs.j, ineqobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
axes[1].scatter(ineqobs.j, ineqobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
eqobs = nzobs.loc[nzobs.obgnme.apply(lambda x: "greater" not in x), :]
axes[0].scatter(eqobs.j, eqobs.i, marker="^", s=100, facecolor="none", edgecolor="r")
axes[1].scatter(eqobs.j, eqobs.i, marker="^", s=100, facecolor="none", edgecolor="r")
# ens max std for group
mx_res = np.max([pr_oe.loc[:, nzobs.obsnme].std(),
pt_oe.loc[:, nzobs.obsnme].std()])
norm = colors.Normalize(0, mx_res)
for i,j,name in zip(nzobs.i,nzobs.j,nzobs.obsnme):
prval = pr_arr[i, j]
ptval = pt_arr[i, j]
val = noise.loc[:, name].std()
fc, tc = _get_annotation_colors(prval, cmap='BuGn', norm=norm)
axes[0].annotate("{0:2.2f}\n{1:2.2f}".format(val, prval), (j, i), xytext=(5, 5),
textcoords="offset points", va="bottom", ha="left", color=tc,
bbox=dict(boxstyle='round', facecolor=fc, alpha=1.0))
fc, tc = _get_annotation_colors(ptval, cmap='BuGn', norm=norm)
axes[1].annotate("{0:2.2f}\n{1:2.2f}".format(val, ptval), (j, i), xytext=(5, 5),
textcoords="offset points", va="bottom", ha="left", color=tc,
bbox=dict(boxstyle='round', facecolor=fc, alpha=1.0))
plt.tight_layout()
pdf.savefig()
plt.close(fig)
# min
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
pr_arr = np.zeros((nrow, ncol))
pr_arr[oobs.i, oobs.j] = pr_oe.loc[:, oobs.obsnme].min().values
pt_arr = np.zeros((nrow, ncol))
pt_arr[oobs.i, oobs.j] = pt_oe.loc[:, oobs.obsnme].min().values
pr_arr[ib[k] <= 0] = np.NaN
pt_arr[ib[k] <= 0] = np.NaN
mn, mx = min(np.nanmin(pr_arr), np.nanmin(pt_arr)), max(np.nanmax(pr_arr), np.nanmax(pt_arr))
cb = axes[0].imshow(pr_arr, vmin=mn, vmax=mx)
plt.colorbar(cb, ax=axes[0], label="$log_{10}$")
cb = axes[1].imshow(pt_arr, vmin=mn, vmax=mx)
plt.colorbar(cb, ax=axes[1], label="$log_{10}$")
axes[0].set_title("prior min " + oname, loc="left")
axes[1].set_title("posterior min " + oname, loc="left")
# for ax in axes.flatten():
# ax.imshow(nz_arr,vmin=mn,vmax=mx)
if scatter:
axes[0].scatter(nzobs.j, nzobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
axes[1].scatter(nzobs.j, nzobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
# ens max max residual for group
tmp_df = pd.concat([nzobs, dups])
tmp_df['obsval'] = noise.loc[:, tmp_df.obsnme].min()
tmp_df['pr_res'] = pr_oe.loc[:, tmp_df.obsnme].min() - tmp_df.obsval
tmp_df.loc[tmp_df.index.isin(lsobs) &
(tmp_df.pr_res < 0), "pr_res"] = 0
tmp_df.loc[tmp_df.index.isin(grobs) &
(tmp_df.pr_res > 0), 'pr_res'] = 0
tmp_df['pt_res'] = pt_oe.loc[:, tmp_df.obsnme].min() - tmp_df.obsval
tmp_df.loc[tmp_df.index.isin(lsobs) &
(tmp_df.pt_res < 0), 'pt_res'] = 0
tmp_df.loc[tmp_df.index.isin(grobs) &
(tmp_df.pt_res > 0), 'pt_res'] = 0
# set to common obsname
tmp_df['obsnme'] = tmp_df.obsnme.str.replace(oname + "dup", oname)
tmp_df = tmp_df.reset_index(drop=True).groupby('obsnme').agg(
{'pr_res': lambda x: max(x, key=abs),
'pt_res': lambda x: max(x, key=abs)}
)
mx_res = tmp_df[['pr_res', "pt_res"]].abs().values.max()
norm = colors.CenteredNorm(0, mx_res)
for i, j, name in zip(nzobs.i, nzobs.j, nzobs.obsnme):
val = noise.loc[:, name].min()
prval = pr_arr[i, j]
ptval = pt_arr[i, j]
grp = obs.loc[name, "obgnme"]
dname = name.replace(oname, oname + "dup")
if dname in obs.index and obs.loc[dname, "weight"] > 0:
lb = "{0:2.2f}".format(obs.loc[dname, "obsval"])
ub = "{0:2.2f}".format(nzobs.loc[name, "obsval"])
valstr = "{0}<>{1}".format(lb, ub)
else:
valstr = "{0:2.2f}".format(val)
if "less" in grp:
valstr = "<" + valstr
elif "great" in grp:
valstr = ">" + valstr
fc, tc = _get_annotation_colors(tmp_df.pr_res[name], norm=norm)
axes[0].annotate("{0}\n{1:2.2f}".format(valstr, prval), (j, i), xytext=(5, 5),
textcoords="offset points", va="bottom", ha="left", color=tc,
bbox=dict(boxstyle='round', facecolor=fc, alpha=1.0))
fc, tc = _get_annotation_colors(tmp_df.pt_res[name], norm=norm)
axes[1].annotate("{0}\n{1:2.2f}".format(valstr, ptval), (j, i), xytext=(5, 5),
textcoords="offset points", va="bottom", ha="left", color=tc,
bbox=dict(boxstyle='round', facecolor=fc, alpha=1.0))
plt.tight_layout()
pdf.savefig()
plt.close(fig)
# max
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
pr_arr = np.zeros((nrow, ncol))
pr_arr[oobs.i, oobs.j] = pr_oe.loc[:, oobs.obsnme].max().values
pt_arr = np.zeros((nrow, ncol))
pt_arr[oobs.i, oobs.j] = pt_oe.loc[:, oobs.obsnme].max().values
pr_arr[ib[k] <= 0] = np.NaN
pt_arr[ib[k] <= 0] = np.NaN
mn, mx = min(np.nanmin(pr_arr), np.nanmin(pt_arr)), max(np.nanmax(pr_arr), np.nanmax(pt_arr))
cb = axes[0].imshow(pr_arr, vmin=mn, vmax=mx)
plt.colorbar(cb, ax=axes[0], label="$log_{10}$")
cb = axes[1].imshow(pt_arr, vmin=mn, vmax=mx)
plt.colorbar(cb, ax=axes[1], label="$log_{10}$")
axes[0].set_title("prior max {0}".format(oname), loc="left")
axes[1].set_title("posterior max {0}".format(oname), loc="left")
# for ax in axes.flatten():
# ax.imshow(nz_arr,vmin=mn,vmax=mx)
if scatter:
axes[0].scatter(nzobs.j, nzobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
axes[1].scatter(nzobs.j, nzobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
# ens max max residual for group
tmp_df = pd.concat([nzobs, dups])
tmp_df['obsval'] = noise.loc[:, tmp_df.obsnme].max()
tmp_df['pr_res'] = pr_oe.loc[:, tmp_df.obsnme].max() - tmp_df.obsval
tmp_df.loc[tmp_df.index.isin(lsobs) &
(tmp_df.pr_res < 0), "pr_res"] = 0
tmp_df.loc[tmp_df.index.isin(grobs) &
(tmp_df.pr_res > 0), 'pr_res'] = 0
tmp_df['pt_res'] = pt_oe.loc[:, tmp_df.obsnme].max() - tmp_df.obsval
tmp_df.loc[tmp_df.index.isin(lsobs) &
(tmp_df.pt_res < 0), 'pt_res'] = 0
tmp_df.loc[tmp_df.index.isin(grobs) &
(tmp_df.pt_res > 0), 'pt_res'] = 0
# set to common obsname
tmp_df['obsnme'] = tmp_df.obsnme.str.replace(oname + "dup", oname)
tmp_df = tmp_df.reset_index(drop=True).groupby('obsnme').agg(
{'pr_res': lambda x: max(x, key=abs),
'pt_res': lambda x: max(x, key=abs)}
)
mx_res = tmp_df[['pr_res', "pt_res"]].abs().values.max()
norm = colors.CenteredNorm(0, mx_res)
for i, j, name in zip(nzobs.i, nzobs.j, nzobs.obsnme):
grp = obs.loc[name, "obgnme"]
dname = name.replace(oname, oname + "dup")
if dname in obs.index and obs.loc[dname, "weight"] > 0:
lb = "{0:2.2f}".format(obs.loc[dname, "obsval"])
ub = "{0:2.2f}".format(nzobs.loc[name, "obsval"])
valstr = "{0}<>{1}".format(lb, ub)
else:
valstr = "{0:2.2f}".format(noise.loc[:, name].max())
if "less" in grp:
valstr = "<" + valstr
elif "great" in grp:
valstr = ">" + valstr
fc, tc = _get_annotation_colors(tmp_df.pr_res[name], norm=norm)
axes[0].annotate("{0}\n{1:2.2f}".format(valstr, pr_arr[i, j]), (j, i), xytext=(5, 5),
textcoords="offset points", va="bottom", ha="left", color=tc,
bbox=dict(boxstyle='round', facecolor=fc, alpha=1.0))
fc, tc = _get_annotation_colors(tmp_df.pt_res[name], norm=norm)
axes[1].annotate("{0}\n{1:2.2f}".format(valstr, pt_arr[i, j]), (j, i), xytext=(5, 5),
textcoords="offset points", va="bottom", ha="left", color=tc,
bbox=dict(boxstyle='round', facecolor=fc, alpha=1.0))
plt.tight_layout()
pdf.savefig()
plt.close(fig)
# a few reals
for idx in pt_oe.index.values[:10]:
#nz_arr = np.zeros((nrow, ncol))
#nz_arr[nzobs.i, nzobs.j] = noise.loc[idx,nzobs.obsnme].values
#nz_arr[nz_arr == 0] = np.NaN
# means
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
pr_arr = np.zeros((nrow, ncol))
pr_arr[oobs.i, oobs.j] = pr_oe.loc[idx, oobs.obsnme].values
pt_arr = np.zeros((nrow, ncol))
pt_arr[oobs.i, oobs.j] = pt_oe.loc[idx, oobs.obsnme].values
pr_arr[ib[k] <= 0] = np.NaN
pt_arr[ib[k] <= 0] = np.NaN
mn, mx = min(np.nanmin(pr_arr), np.nanmin(pt_arr)), max(np.nanmax(pr_arr), np.nanmax(pt_arr))
cb = axes[0].imshow(pr_arr, vmin=mn, vmax=mx)
plt.colorbar(cb, ax=axes[0],label="$log_{10}$")
cb = axes[1].imshow(pt_arr, vmin=mn, vmax=mx)
plt.colorbar(cb, ax=axes[1],label="$log_{10}$")
axes[0].set_title("prior realization {0} {1} mn:{3:3.3f} std:{2:3.3f}".format(idx,oname,np.nanstd(pr_arr),np.nanmean(pr_arr)), loc="left")
axes[1].set_title("posterior realization {0} {1} mn:{3:3.3f} std:{2:3.3f} ".format(idx,oname,np.nanstd(pt_arr),np.nanmean(pt_arr)), loc="left")
#for ax in axes.flatten():
# ax.imshow(nz_arr,vmin=mn,vmax=mx)
if scatter:
# axes[0].scatter(nzobs.j, nzobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
# axes[1].scatter(nzobs.j, nzobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
ineqobs = nzobs.loc[nzobs.obgnme.str.contains("greater"), :]
# axes[0].scatter(nzobs.j,nzobs.i,marker=".",s=500,facecolor="none",edgecolor="r")
# axes[1].scatter(nzobs.j, nzobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
axes[0].scatter(ineqobs.j, ineqobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
axes[1].scatter(ineqobs.j, ineqobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
eqobs = nzobs.loc[nzobs.obgnme.apply(lambda x: "greater" not in x), :]
axes[0].scatter(eqobs.j, eqobs.i, marker="^", s=100, facecolor="none", edgecolor="r")
axes[1].scatter(eqobs.j, eqobs.i, marker="^", s=100, facecolor="none", edgecolor="r")
# ens max residual for group
tmp_df = pd.concat([nzobs, dups])
tmp_df['obsval'] = noise.loc[idx, tmp_df.obsnme]
tmp_df['pr_res'] = pr_oe.loc[idx, tmp_df.obsnme] - tmp_df.obsval
tmp_df.loc[tmp_df.index.isin(lsobs) &
(tmp_df.pr_res < 0), "pr_res"] = 0
tmp_df.loc[tmp_df.index.isin(grobs) &
(tmp_df.pr_res > 0), 'pr_res'] = 0
tmp_df['pt_res'] = pt_oe.loc[idx, tmp_df.obsnme] - tmp_df.obsval
tmp_df.loc[tmp_df.index.isin(lsobs) &
(tmp_df.pt_res < 0), 'pt_res'] = 0
tmp_df.loc[tmp_df.index.isin(grobs) &
(tmp_df.pt_res > 0), 'pt_res'] = 0
# set to common obsname
tmp_df['obsnme'] = tmp_df.obsnme.str.replace(oname + "dup", oname)
tmp_df = tmp_df.reset_index(drop=True).groupby('obsnme').agg(
{'pr_res': lambda x: max(x, key=abs),
'pt_res': lambda x: max(x, key=abs)}
)
mx_res = tmp_df[['pr_res', "pt_res"]].abs().values.max()
norm = colors.CenteredNorm(0, mx_res)
for i, j, name in zip(nzobs.i, nzobs.j, nzobs.obsnme):
grp = obs.loc[name, "obgnme"]
dname = name.replace(oname, oname + "dup")
if dname in obs.index and obs.loc[dname, "weight"] > 0:
lb = "{0:2.2f}".format(obs.loc[dname, "obsval"])
ub = "{0:2.2f}".format(nzobs.loc[name, "obsval"])
valstr = "{0}<>{1}".format(lb, ub)
else:
valstr = "{0:2.2f}".format(noise.loc[idx, name])
if "less" in grp:
valstr = "<{0:2.2f}".format(obs.loc[name, "obsval"])
elif "great" in grp:
valstr = ">{0:2.2f}".format(obs.loc[name, "obsval"])
fc, tc = _get_annotation_colors(tmp_df.pr_res[name], norm=norm)
axes[0].annotate("{0}\n{1:2.2f}".format(valstr, pr_arr[i,j]), (j, i), xytext=(5, 5),
textcoords="offset points", va="bottom", ha="left", color=tc,
bbox=dict(boxstyle='round', facecolor=fc, alpha=1.0))
fc, tc = _get_annotation_colors(tmp_df.pt_res[name], norm=norm)
axes[1].annotate("{0}\n{1:2.2f}".format(valstr, pt_arr[i, j]), (j, i), xytext=(5, 5),
textcoords="offset points", va="bottom", ha="left", color=tc,
bbox=dict(boxstyle='round', facecolor=fc, alpha=1.0))
plt.tight_layout()
pdf.savefig()
plt.close(fig)
def namer(name):
raw = name.split("_")[0]
print(raw)
if ":" in raw:
raw = raw.split(":")[1]
layer = int(raw[-1])
tag = raw.split("layer")[0]
full_name = None
if tag == "npfk":
full_name = "horizontal hydraulic conductivity"
elif tag == "stoss":
full_name = "specific storage"
elif tag == "npfk33":
full_name = "vertical hydraulic conductivity"
elif tag == "stosy":
full_name = "specific yield"
else:
raise Exception("{0}:{1}".format(tag,name))
full_name += ", layer:{0}".format(layer)
if "i:" in name:
row = int(name.split("i:")[1].split("_")[0]) + 1
col = int(name.split("j:")[1]) + 1
full_name += ", row:{0}, column:{1}".format(row,col)
return full_name
def plot_histo(m_d, pstf="test_run", log_oe=True,noptmax=None):
pst = pyemu.Pst(os.path.join(m_d, pstf+".pst"))
if noptmax is None:
noptmax = pst.control_data.noptmax
pr_oe = pyemu.ObservationEnsemble.from_binary(pst=pst, filename=os.path.join(m_d, "{0}.0.obs.jcb".format(pstf)))
pt_oe = pyemu.ObservationEnsemble.from_binary(pst=pst, filename=os.path.join(m_d, "{0}.{1}.obs.jcb". \
format(pstf,noptmax)))
noise = pyemu.ObservationEnsemble.from_binary(pst=pst, filename=os.path.join(m_d, "{0}.obs+noise.jcb".format(pstf)))
#pr_oe.loc[:, :] = np.log10(pr_oe.values)
#pt_oe.loc[:, :] = np.log10(pt_oe.values)
log_noise = noise.copy()
#log_noise.loc[:, :] = np.log10(log_noise.values)
#pst.observation_data.loc[:, "obsval"] = np.log10(pst.observation_data.obsval.values)
obs = pst.observation_data.copy()
obs = obs.loc[obs.otype == "arr"]
obs.loc[:, "i"] = obs.i.astype(int)
obs.loc[:, "j"] = obs.j.astype(int)
with PdfPages(os.path.join("histo_results_si.pdf")) as pdf:
for name in obs.loc[pst.nnz_obs_names].sort_values(['i', 'j']).index:
if "dup" in name:
continue
print(name)
grp = obs.loc[name, "obgnme"]
fig,ax = plt.subplots(1,1,figsize=(6,3))
ax.hist(pr_oe.loc[:,name],fc="0.5",ec="none",alpha=0.3,density=True)
ax.hist(pt_oe.loc[:, name], fc="b", ec="none", alpha=0.3,density=True)
if log_noise.loc[:,name].var() > 1e-12 and not "less" in grp and not "great" in grp:
ax.hist(log_noise.loc[:,name],fc="r",ec="none",alpha=0.3,density=True)
v = obs.loc[name,"obsval"]
ylim = ax.get_ylim()
ax.plot([v,v],ylim,"r-",lw=2)
dname = name.replace("oname:","oname:dup-")
tag = ""
if dname in pst.nnz_obs_names:
v = obs.loc[dname, "obsval"]
ylim = ax.get_ylim()
ax.plot([v, v], ylim, "r-", lw=2)
tag = ", double inequality"
ax.set_yticklabels([])
i,j = obs.loc[name,"i"],obs.loc[name,"j"]
if len(tag) == 0 and "less" in grp:
tag = ", less-than inequality"
elif len(tag) == 0 and "great" in grp:
tag = ", greater-than inequality"
ax.set_title("{0} {1}".format(namer(name)+" "+name,tag),loc="left")
plt.tight_layout()
pdf.savefig()
plt.close(fig)
def plot_histo_pub(m_d, pstf="test_run", log_oe=True, noptmax=None,
ineqfill=True):
# names = ["oname:stosslayer3_otype:arr_i:8_j:47","oname:npfklayer1_otype:arr_i:101_j:23","oname:npfklayer3_otype:arr_i:8_j:47","oname:npfklayer3_otype:arr_i:79_j:31"]
# units = ["$log_{10} \\frac{1}{m}$","$log_{10} \\frac{m}{d}$","$log_{10} \\frac{m}{d}$","$log_{10} \\frac{m}{d}$"]
names = ["oname:npfklayer3_otype:arr_i:8_j:47", "oname:npfklayer1_otype:arr_i:101_j:23",
"oname:npfklayer3_otype:arr_i:79_j:31", "oname:stosslayer1_otype:arr_i:29_j:5"]
units = ["$log_{10} \\frac{m}{d}$", "$log_{10} \\frac{m}{d}$", "$log_{10} \\frac{m}{d}$", "$log_{10} \\frac{1}{m}$"]
pst = pyemu.Pst(os.path.join(m_d, pstf+".pst"))
if noptmax is None:
noptmax = pst.control_data.noptmax
pr_oe = pyemu.ObservationEnsemble.from_binary(pst=pst, filename=os.path.join(m_d, "{0}.0.obs.jcb".format(pstf)))
pt_oe = pyemu.ObservationEnsemble.from_binary(pst=pst, filename=os.path.join(m_d, "{0}.{1}.obs.jcb". \
format(pstf,noptmax)))
noise = pyemu.ObservationEnsemble.from_binary(pst=pst, filename=os.path.join(m_d, "{0}.obs+noise.jcb".format(pstf)))
pr_oe = pr_oe.loc[pr_oe.index,:]
noise = noise.loc[pr_oe.index,:]
#pr_oe.loc[:, :] = np.log10(pr_oe.values)
#pt_oe.loc[:, :] = np.log10(pt_oe.values)
log_noise = noise.copy()
#log_noise.loc[:, :] = np.log10(log_noise.values)
#pst.observation_data.loc[:, "obsval"] = np.log10(pst.observation_data.obsval.values)
obs = pst.observation_data.copy()
obs = obs.loc[obs.otype == "arr"]
obs.loc[:, "i"] = obs.i.astype(int)
obs.loc[:, "j"] = obs.j.astype(int)
fig, axes = plt.subplots(len(names), 1, figsize=(6, 1.5*len(names)))
ax_count = 0
#for name in obs.loc[pst.nnz_obs_names].sort_values(['i', 'j']).index:
for name in names:
if name not in names:
continue
print(name)
ax = axes[ax_count]
grp = obs.loc[name, "obgnme"]
ax.hist(pr_oe.loc[:,name],fc="0.5",ec="none",alpha=0.3,label="prior ensemble",density=True)
ax.hist(pt_oe.loc[:, name], fc="b", ec="none", alpha=0.3,label="posterior ensemble",density=True)
if log_noise.loc[:,name].var() > 1e-12 and not "less" in grp and not "great" in grp:
ax.hist(log_noise.loc[:,name],fc="r",ec="none",alpha=0.3,label="noise ensemble",density=True)
v = obs.loc[name,"obsval"]
ylim = ax.get_ylim()
ax.plot([v,v],ylim,"r-",lw=2,label="datum", zorder=10)
dname = name.replace("oname:","oname:dup-")
tag = ""
if dname in pst.nnz_obs_names:
v0 = v
v = obs.loc[dname, "obsval"]
ax.plot([v, v], ylim, "r-", lw=2)
tag = ", double inequality"
if ineqfill:
xs = (v0, v)
ax.fill_between(xs, ylim[0], ylim[1], facecolor="r", alpha=0.3, zorder=9)
ax.set_yticklabels([])
xlim = ax.get_xlim()
if len(tag) == 0 and "less" in grp:
tag += ", less-than inequality"
if ineqfill:
xs = (xlim[0], v)
ax.fill_between(xs, ylim[0], ylim[1], facecolor="r", alpha=0.3, zorder=9)
elif len(tag) == 0 and "great" in grp:
tag += ", greater-than inequality"
if ineqfill:
xs = (v, xlim[1])
ax.fill_between(xs, ylim[0], ylim[1], facecolor="r", alpha=0.3, zorder=9)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
ax.set_xlabel(units[ax_count])
ax.set_yticks([])
ax.set_title("{2}) {0} {1}".format(namer(name),tag,ascii_uppercase[ax_count]),loc="left")
if ax_count == 0:
ax.legend()
ax_count += 1
plt.tight_layout()
plt.savefig("prop_histo_pub.pdf")
plt.close(fig)
def plot_results_pub(m_d, ardim=None, pstf="test_run", log_oe=True,noptmax=None):
if ardim is None:
try:
sim = flopy.mf6.MFSimulation.load(sim_ws=m_d,
load_only=['DIS'])
m = sim.get_model("freyberg6")
nrow = m.modelgrid.nrow
ncol = m.modelgrid.ncol
ib = m.modelgrid.idomain
except:
nlay, nrow, ncol = (1, 50, 50)
ib = np.loadtxt(os.path.join("org_zone", "ib_resample.dat"), dtype=int)[np.newaxis]
else:
nrow, ncol = ardim
pst = pyemu.Pst(os.path.join(m_d, pstf+".pst"))
if noptmax is None:
noptmax = pst.control_data.noptmax
try:
pr_oe = pyemu.ObservationEnsemble.from_binary(
pst=pst, filename=os.path.join(m_d,"{0}.0.obs.jcb".format(pstf))
)
except:
pr_oe = pd.read_csv(os.path.join(m_d,"{0}.0.obs.csv".format(pstf)),
low_memory=False,
index_col=0)
try:
pt_oe = pyemu.ObservationEnsemble.from_binary(
pst=pst,
filename=os.path.join(m_d, "{0}.{1}.obs.jcb".format(
pstf, noptmax))
)
except:
pt_oe = pd.read_csv(
os.path.join(m_d, "{0}.{1}.obs.csv".format(
pstf, noptmax)),
low_memory=False,
index_col=0)
try:
noise = pyemu.ObservationEnsemble.from_binary(
pst=pst, filename=os.path.join(m_d, "{0}.obs+noise.jcb".format(pstf)))._df
except:
noise = pd.read_csv(
os.path.join(m_d, "{0}.obs+noise.csv".format(pstf)),
low_memory=False,
index_col=0)
if log_oe: # not already logged, transform now
pr_oe.loc[:, :] = np.log10(pr_oe.values)
pt_oe.loc[:, :] = np.log10(pt_oe.values)
noise = noise.copy()
noise.loc[:, :] = np.log10(noise.values)
pst.observation_data.loc[:, "obsval"] = np.log10(
pst.observation_data.obsval.values)
pr_oe = pr_oe.loc[pt_oe.index,:]
obs = pst.observation_data.copy()
obs = obs.loc[obs.otype=="arr"]
obs.loc[:, "i"] = obs.i.astype(int)
obs.loc[:, "j"] = obs.j.astype(int)
onames = obs.oname.unique()
onames = [o for o in onames if "dup" not in o]
onames.sort()
print(onames)
ax_count = 0
with PdfPages("results_pub_si.pdf") as pdf:
for oname in onames:
fig, axes = plt.subplots(2, 3, figsize=(11,8.4))
# cheating to get layer from oname
#if "klayer3" not in oname and "klayer1" not in oname and "sslayer1" not in oname:
# continue
print(oname)
k = int(oname[-1]) - 1
oobs = obs.loc[obs.oname==oname,:].copy()
nzobs = oobs.loc[oobs.weight > 0]
dups = obs.loc[obs.oname==oname+"dup",:]
dups = dups.loc[dups.weight > 0]
lsobs = nzobs.loc[nzobs.obgnme.str.contains("less")].index
grobs = nzobs.loc[nzobs.obgnme.str.contains("greater")].index
lsobs = lsobs.append(dups.loc[dups.obgnme.str.contains("less")].index)
grobs = grobs.append(dups.loc[dups.obgnme.str.contains("greater")].index)
nz_arr = np.zeros((nrow,ncol))
nz_arr[nzobs.i.astype(int),nzobs.j.astype(int)] = nzobs.obsval
nz_arr[nz_arr==0] = np.NaN
# catch to not have too many scatters
if len(nzobs) < 100 and len(nzobs) > 0:
scatter = True
else:
scatter = False
# stdev
nz_arr = np.zeros((nrow, ncol))
nz_arr[nzobs.i.astype(int), nzobs.j.astype(int)] = 1. / nzobs.weight
nz_arr[nz_arr == 0] = np.NaN
pr_arr = np.zeros((nrow, ncol))
pr_arr[oobs.i.astype(int), oobs.j.astype(int)] = pr_oe.loc[:, oobs.obsnme].std().values
pt_arr = np.zeros((nrow, ncol))
pt_arr[oobs.i.astype(int), oobs.j.astype(int)] = pt_oe.loc[:, oobs.obsnme].std().values
pr_arr[ib[k] <= 0] = np.NaN
pt_arr[ib[k] <= 0] = np.NaN
mn, mx = min(np.nanmin(pr_arr), np.nanmin(pt_arr)), max(np.nanmax(pr_arr), np.nanmax(pt_arr))
irow = 0
cb = axes[0,irow].imshow(pr_arr, vmin=mn, vmax=mx)
plt.colorbar(cb, ax=axes[0,irow],label="$log_{10}$")
cb = axes[1,irow].imshow(pt_arr, vmin=mn, vmax=mx)
plt.colorbar(cb, ax=axes[1,irow],label="$log_{10}$")
grp = oobs.obgnme.iloc[0]
axes[0,irow].set_title("{0}) prior $\\sigma$ ".format(ax_count) + namer(grp), loc="left")
ax_count += 1
axes[1,irow].set_title("{0}) posterior $\\sigma$ ".format(ax_count) + namer(grp), loc="left")
ax_count += 1
#for ax in axes.flatten():
#ax.imshow(nz_arr, vmin=mn, vmax=mx)
if scatter:
#axes[0].scatter(nzobs.j, nzobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
#axes[1].scatter(nzobs.j, nzobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
ineqobs = nzobs.loc[nzobs.obgnme.str.contains("greater"), :]
# axes[0].scatter(nzobs.j,nzobs.i,marker=".",s=500,facecolor="none",edgecolor="r")
# axes[1].scatter(nzobs.j, nzobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
axes[0,irow].scatter(ineqobs.j, ineqobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
axes[1,irow].scatter(ineqobs.j, ineqobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
eqobs = nzobs.loc[nzobs.obgnme.apply(lambda x: "greater" not in x), :]
axes[0,irow].scatter(eqobs.j, eqobs.i, marker="^", s=100, facecolor="none", edgecolor="r")
axes[1,irow].scatter(eqobs.j, eqobs.i, marker="^", s=100, facecolor="none", edgecolor="r")
# ens max std for group
mx_res = np.max([pr_oe.loc[:, nzobs.obsnme].std(),
pt_oe.loc[:, nzobs.obsnme].std()])
norm = colors.Normalize(0, mx_res)
for i,j,name in zip(nzobs.i,nzobs.j,nzobs.obsnme):
prval = pr_arr[i, j]
ptval = pt_arr[i, j]
val = noise.loc[:, name].std()
fc, tc = _get_annotation_colors(prval, cmap='BuGn', norm=norm)
axes[0,irow].annotate("{0:2.2f}\n{1:2.2f}".format(val, prval), (j, i), xytext=(5, 5),
textcoords="offset points", va="bottom", ha="left", color=tc,
bbox=dict(boxstyle='round', facecolor=fc, alpha=1.0))
fc, tc = _get_annotation_colors(ptval, cmap='BuGn', norm=norm)
axes[1,irow].annotate("{0:2.2f}\n{1:2.2f}".format(val, ptval), (j, i), xytext=(5, 5),
textcoords="offset points", va="bottom", ha="left", color=tc,
bbox=dict(boxstyle='round', facecolor=fc, alpha=1.0))
# a few reals
irow = 1
for idx in [pt_oe.index.values[5],pt_oe.index.values[105]]:
print(idx,grp)
#nz_arr = np.zeros((nrow, ncol))
#nz_arr[nzobs.i, nzobs.j] = noise.loc[idx,nzobs.obsnme].values
#nz_arr[nz_arr == 0] = np.NaN
# means
pr_arr = np.zeros((nrow, ncol))
pr_arr[oobs.i.astype(int), oobs.j.astype(int)] = pr_oe.loc[idx, oobs.obsnme].values
pt_arr = np.zeros((nrow, ncol))
pt_arr[oobs.i.astype(int), oobs.j.astype(int)] = pt_oe.loc[idx, oobs.obsnme].values
pr_arr[ib[k] <= 0] = np.NaN
pt_arr[ib[k] <= 0] = np.NaN
mn, mx = min(np.nanmin(pr_arr), np.nanmin(pt_arr)), max(np.nanmax(pr_arr), np.nanmax(pt_arr))
cb = axes[0,irow].imshow(pr_arr, vmin=mn, vmax=mx)
cbar0 = plt.colorbar(cb, ax=axes[0,irow],label="$log_{10}$")
cb = axes[1,irow].imshow(pt_arr, vmin=mn, vmax=mx)
cbar1 = plt.colorbar(cb, ax=axes[1,irow],label="$log_{10}$")
axes[0,irow].set_title("{2}) prior realization {0}\n{1} ".format(idx,namer(grp),ax_count), loc="left")
ax_count += 1
axes[1,irow].set_title("{2}) posterior realization {0}\n{1}".format(idx,namer(grp),ax_count), loc="left")
ax_count += 1
#for ax in axes.flatten():
# ax.imshow(nz_arr,vmin=mn,vmax=mx)
if scatter:
cbar0.ax.zorder = -1
cbar1.ax.zorder = -1
# axes[0].scatter(nzobs.j, nzobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
# axes[1].scatter(nzobs.j, nzobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
ineqobs = nzobs.loc[nzobs.obgnme.apply(lambda x: "greater" in x or "less" in x), :]
# axes[0].scatter(nzobs.j,nzobs.i,marker=".",s=500,facecolor="none",edgecolor="r")
# axes[1].scatter(nzobs.j, nzobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
axes[0,irow].scatter(ineqobs.j, ineqobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
axes[1,irow].scatter(ineqobs.j, ineqobs.i, marker=".", s=500, facecolor="none", edgecolor="r")
eqobs = nzobs.loc[nzobs.obgnme.apply(lambda x: "greater" not in x and "less" not in x), :]
axes[0,irow].scatter(eqobs.j, eqobs.i, marker="^", s=100, facecolor="none", edgecolor="r")
axes[1,irow].scatter(eqobs.j, eqobs.i, marker="^", s=100, facecolor="none", edgecolor="r")
# ens max residual for group
tmp_df = pd.concat([nzobs, dups])
tmp_df['obsval'] = noise.loc[idx, tmp_df.obsnme]
tmp_df['pr_res'] = pr_oe.loc[idx, tmp_df.obsnme] - tmp_df.obsval
tmp_df.loc[tmp_df.index.isin(lsobs) &
(tmp_df.pr_res < 0), "pr_res"] = 0
tmp_df.loc[tmp_df.index.isin(grobs) &
(tmp_df.pr_res > 0), 'pr_res'] = 0
tmp_df['pt_res'] = pt_oe.loc[idx, tmp_df.obsnme] - tmp_df.obsval
tmp_df.loc[tmp_df.index.isin(lsobs) &
(tmp_df.pt_res < 0), 'pt_res'] = 0
tmp_df.loc[tmp_df.index.isin(grobs) &
(tmp_df.pt_res > 0), 'pt_res'] = 0
# set to common obsname
tmp_df['obsnme'] = tmp_df.obsnme.str.replace(oname + "dup", oname)
tmp_df = tmp_df.reset_index(drop=True).groupby('obsnme').agg(
{'pr_res': lambda x: max(x, key=abs),
'pt_res': lambda x: max(x, key=abs)}
)
mx_res = tmp_df[['pr_res', "pt_res"]].abs().values.max()
norm = colors.CenteredNorm(0, mx_res)
for i, j, name in zip(nzobs.i, nzobs.j, nzobs.obsnme):
ggrp = obs.loc[name, "obgnme"]
dname = name.replace("oname:", "oname:dup-")
#print(name,dname,dname in obs.index)
if dname in obs.index and obs.loc[dname, "weight"] > 0:
lb = "{0:2.2f}".format(obs.loc[dname, "obsval"])
ub = "{0:2.2f}".format(nzobs.loc[name, "obsval"])
valstr = "{0}<>{1}".format(lb, ub)
else:
valstr = "{0:2.2f}".format(noise.loc[idx, name])
if "less" in ggrp:
valstr = "<{0:2.2f}".format(obs.loc[name, "obsval"])
elif "great" in ggrp:
valstr = ">{0:2.2f}".format(obs.loc[name, "obsval"])
fc, tc = _get_annotation_colors(tmp_df.pr_res[name], norm=norm)
axes[0,irow].annotate("{0}\n{1:2.2f}".format(valstr, pr_arr[i,j]), (j, i), xytext=(5, 5),
textcoords="offset points", va="bottom", ha="left", color=tc,
bbox=dict(boxstyle='round', facecolor=fc, alpha=1.0))
fc, tc = _get_annotation_colors(tmp_df.pt_res[name], norm=norm)
axes[1,irow].annotate("{0}\n{1:2.2f}".format(valstr, pt_arr[i, j]), (j, i), xytext=(5, 5),
textcoords="offset points", va="bottom", ha="left", color=tc,
bbox=dict(boxstyle='round', facecolor=fc, alpha=1.0))
irow += 1
plt.tight_layout()
pdf.savefig()
plt.close(fig)
def plot_par_changes(m_d,noptmax=None):
pst = pyemu.Pst(os.path.join(m_d,"freyberg.pst"))
if noptmax is None:
noptmax = pst.control_data.noptmax
pr = pyemu.ParameterEnsemble.from_binary(pst=pst,filename=os.path.join(m_d,"freyberg.0.par.jcb"))
pt = pyemu.ParameterEnsemble.from_binary(pst=pst, filename=os.path.join(m_d, "freyberg.{0}.par.jcb".format(noptmax)))
commonidx = pr.index.intersection(pt.index)
pt = pt.loc[commonidx, :]
par = pst.parameter_data
pnames = par.pname.unique()
pnames.sort()
tdict = {"cn":"layer constant","pp":"pilot points","gr":"grid-scale"}
fig,axes = plt.subplots(len(pnames),3,figsize=(11.5,8))
for irow,pname in enumerate(pnames):
ppar = par.loc[par.pname==pname,:].copy()
grps = ppar.pargp.unique()
grps.sort()
for jcol,grp in enumerate(grps):
gpar = ppar.loc[ppar.pargp==grp,:]
ax = axes[irow,jcol]
ax.hist(pr.loc[:,gpar.parnme].apply(np.log10).values.flatten(),facecolor="0.5",edgecolor="none",alpha=0.5,density=True)
ax.hist(pt.loc[:, gpar.parnme].apply(np.log10).values.flatten(), facecolor="b", edgecolor="none", alpha=0.5,density=True)
ax.set_title("{0} {1}, {2} parameters".format(namer(pname),tdict[grp.split("_")[-1]],gpar.shape[0]),loc="left")
ax.set_yticks([])
#ax.set_xlabel()
plt.tight_layout()
plt.savefig("par_change.pdf")
plt.close(fig)