PESTPP-IES - Localization

In the previous tutorial (“ies_1_basics”) we introduced PESTPP-IES, demonstrated a rudimentary setup and explored some of the outcomes. In the current tutorial we are going to introduce “localization”.

As discussed in the previous tutorial, PESTPP-IES does not calculate derivatives using finite parameter differences. Instead, by running the model using each member of the parameter ensemble, it calculates approximate partial derivatives from cross-covariances between parameter values and model outputs that are calculated using members of the ensemble. This formulation of the inverse problem allows estimation of a virtually unlimited set of parameters, and drastically reduces the computational burden of estimating the relation between parameters and observations.

But it is not all sunshine and rainbows.

Calculating an empirical cross-covariance between large numbers of parameters and observations, with a limited number of realizations is likely to lead to spurious cross-correlations. Because (1) the relationship between observations and parameters is estimated empirically, and (2) in most cases, the number of realizations will be significantly smaller than the number of parameters the estimated cross-covariances may contain error.

This results in some parameters being adjusted, even if there was no information in the observation dataset to require their adjustment. In other words, artificial relations between parameters and observations can emerge. This can result in the cardinal sin of underestimating of forecast uncertainty - a phenomenon referred to as “ensemble collapse”.

Localization

To deal with these challenges we can employ “localization”. Localization refers to a strategy in which only “local” covariances are allowed to emerge. In essence, a modeller defines a “local” neighbourhood around each observation, specifying the parameters which are expected to influence it. Effectively, this creates a series of “local” history matching problems using subsets of parameters and observations. Conceptually, localization allows a form of expert knowledge to be expreseed in regard to how parametes and observations are or are not related. For example, an observation of groundwater level today cannot be correlated to the recharge which will occur tomorrow (e.g. information cannot flow backwards in time), or groundwater levels cannot inform porosity parameters, etc.

Localization Matrix

PESTPP-IES allows users to provide a localizing matrix to enforce physically plausible parameter-to-observation relations. This matrix must be preapred by the user. Matrix rows are observation names and/or observation group names, and columns are parameter names and/or parameter group names. Elements of the matrix should range between 0.0 and 1.0. A value of 0.0 removes any spurious sensitivities between the relevant observation and parameter. During this tutorial we will demosntrate how to construct such a amtrix using pyEMU using two localization strategies.

PESTPP-IES also has an option to automate this process by implementing a form of automatic adaptive localization. When employed, during each iteration, PESTPP-IES calculates the empirical correlation coefficient between each parameter and each observation. A “background” or “error” distribution for this correlation coeficeint is also calcualted. By comparing (in staitstical sense) these two, statstically significant corelations are identified and retained to construct a localization matrix. This matrix is then fed forward into the parameter adjustment process. Note taht automatic adaptive localization ncan be implemented in tandem with a user supplied localization matrix. In this manner, the automated process is only applied to non-zero elements in the user supplied matrix. We will implement this option during the current tutorial.

In practice, automatic localization doesn’t resolve the level of localization that can be achieved by a matrix explicitly constructed by the user. However, it is better than no localization at all. In general, implementing some form of localization is recommended.

The Current Tutorial

In the current notebook we are going to pick up after the “ies_1_basics” tutorial. We setup PEST++IES and ran it. We found that we can achieve great fits with historical data…but that (for some forecasts) the calculated posterior probabilities failed to cover the truth.

In this tutorial we are going to take a first stab at fixing that. We are going to implement localization to remove the potential for spurious correlations between observations and parameters incurred by using an “aproximate” partial deriviatives.

The next couple of cells load necessary dependencies and call a convenience function to prepare the PEST dataset folder for you. Simply press shift+enter to run the cells.

import os
import shutil
import warnings
warnings.filterwarnings("ignore")
warnings.filterwarnings("ignore", category=DeprecationWarning)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt;
import psutil

import sys
sys.path.insert(0,os.path.join("..", "..", "dependencies"))
import pyemu
import flopy
assert "dependencies" in flopy.__file__
assert "dependencies" in pyemu.__file__
sys.path.insert(0,"..")
import herebedragons as hbd



Prepare the template directory and copy across model files from the previous tutorial. Make sure you complete the previous tutorial first.

# specify the temporary working folder
t_d = os.path.join('freyberg6_template')

org_t_d = os.path.join("master_ies_1")
if not os.path.exists(org_t_d):
raise Exception("you need to run the '/freyberg_ies_1_basics.ipynb' notebook")

if os.path.exists(t_d):
shutil.rmtree(t_d)
shutil.copytree(org_t_d,t_d)


Load the PEST control file as a Pst object.

pst_path = os.path.join(t_d, 'freyberg_mf6.pst')
pst = pyemu.Pst(pst_path)
assert 'observed' in pst.observation_data.columns


Check that we are at the right stage to run ies:

assert os.path.exists(os.path.join(t_d, 'freyberg_mf6.3.pcs.csv')), "you need to run the '/freyberg_ies_1_basics.ipynb' notebook"


A quick reminder of the PEST++ optional control variables which have been specified:

pst.pestpp_options


PEST++IES with no localization

Just as a reminder, and so we can compare the results later on, let’s load in the results fmor theprevious tutorial and take a look at the (1) timeseries of measured and simulated heads, (2) the forecast probability distributions and (3) compare parameter prior and posterior distributions.

As in the previous tutorial, let’s write a couple of functions to helps os plot these up.

def plot_tseries_ensembles(pr_oe, pt_oe,noise, onames=["hds","sfr"]):
obs = pst.observation_data.copy()
obs = obs.loc[obs.oname.apply(lambda x: x in onames)]
obs = obs.loc[obs.obgnme.apply(lambda x: x in pst.nnz_obs_groups),:]
obs.obgnme.unique()

ogs = obs.obgnme.unique()
fig,axes = plt.subplots(len(ogs),1,figsize=(10,2*len(ogs)))
ogs.sort()
for ax,og in zip(axes,ogs):
oobs = obs.loc[obs.obgnme==og,:].copy()
oobs.loc[:,"time"] = oobs.time.astype(float)
oobs.sort_values(by="time",inplace=True)
tvals = oobs.time.values
onames = oobs.obsnme.values
[ax.plot(tvals,pr_oe.loc[i,onames].values,"0.5",lw=0.5,alpha=0.5) for i in pr_oe.index]
[ax.plot(tvals,pt_oe.loc[i,onames].values,"b",lw=0.5,alpha=1) for i in pt_oe.index]

oobs = oobs.loc[oobs.weight>0,:]
tvals = oobs.time.values
onames = oobs.obsnme.values
[ax.plot(tvals,noise.loc[i,onames].values,"r",lw=0.5,alpha=0.5) for i in noise.index]
ax.plot(oobs.time,oobs.obsval,"r-",lw=2)
ax.set_title(og,loc="left")
fig.tight_layout()
return fig

def plot_forecast_hist_compare(pt_oe,pr_oe, last_pt_oe=None,last_prior=None ):
num_plots = len(pst.forecast_names)
num_cols = 1
if last_pt_oe!=None:
num_cols=2
fig,axes = plt.subplots(num_plots, num_cols, figsize=(5*num_cols,num_plots * 2.5), sharex='row',sharey='row')
for axs,forecast in zip(axes, pst.forecast_names):
# plot first column with currrent outcomes
if num_cols==1:
axs=[axs]
ax = axs[0]
# just for aesthetics
bin_cols = [pt_oe.loc[:,forecast], pr_oe.loc[:,forecast],]
if num_cols>1:
bin_cols.extend([last_pt_oe.loc[:,forecast],last_prior.loc[:,forecast]])
bins=np.histogram(pd.concat(bin_cols),
bins=20)[1] #get the bin edges
pr_oe.loc[:,forecast].hist(facecolor="0.5",alpha=0.5, bins=bins, ax=ax)
pt_oe.loc[:,forecast].hist(facecolor="b",alpha=0.5, bins=bins, ax=ax)
ax.set_title(forecast)
fval = pst.observation_data.loc[forecast,"obsval"]
ax.plot([fval,fval],ax.get_ylim(),"r-")
# plot second column with other outcomes
if num_cols >1:
ax = axs[1]
last_prior.loc[:,forecast].hist(facecolor="0.5",alpha=0.5, bins=bins, ax=ax)
last_pt_oe.loc[:,forecast].hist(facecolor="b",alpha=0.5, bins=bins, ax=ax)
ax.set_title(forecast)
fval = pst.observation_data.loc[forecast,"obsval"]
ax.plot([fval,fval],ax.get_ylim(),"r-")
# set ax column titles
if num_cols >1:
axes.flatten()[0].text(0.5,1.2,"Current Attempt", transform=axes.flatten()[0].transAxes, weight='bold', fontsize=12, horizontalalignment='center')
axes.flatten()[1].text(0.5,1.2,"Previous Attempt", transform=axes.flatten()[1].transAxes, weight='bold', fontsize=12, horizontalalignment='center')
fig.tight_layout()
return fig



OK, now that that is out of the way, load the obsevration ensembles from the prior, the posterior and the measuered+noise:

pr_oe = pyemu.ObservationEnsemble.from_csv(pst=pst,filename=os.path.join(org_t_d,"freyberg_mf6.0.obs.csv"))
pt_oe = pyemu.ObservationEnsemble.from_csv(pst=pst,filename=os.path.join(org_t_d,"freyberg_mf6.{0}.obs.csv".format(pst.control_data.noptmax)))
noise = pyemu.ObservationEnsemble.from_csv(pst=pst,filename=os.path.join(org_t_d,"freyberg_mf6.obs+noise.csv"))


And finally plot them up. You should be famililar with thes plots fomr the previous tutorial.

fig = plot_tseries_ensembles(pr_oe, pt_oe,noise, onames=["hds","sfr"])


As you recall, the posterior fails to capture the truth for some forecass:

fig = plot_forecast_hist_compare(pt_oe=pt_oe, pr_oe=pr_oe)


Right then. Here is where things get interesting. Let’s take a look at the distribution of parameters, comparing their prior and posterior dsitributions. This will show us where the parameter adjustment process has ..well…adjusted parameters! (And by how much).

We will use pyemu.plot_utils.ensemble_helper() to display histograms of parameter groupings from the prior and posterior ensembles. We could display histograms of each parameter group by passing a dictionary of parameter group names (you would probably do so in real-world applications). This will result in a pretty large plot, because we have quite a few parameter groups.

For the purposes of this tutorial, let’s instead group parameters a bit more coarsely and just lump all parameters of the same hydraulic property together. The function in the next cell groups parmeters that share the first three letters in the parameter group name:

def group_pdict(pe_pr, pe_pt):
par = pst.parameter_data
pdict = {}
for string in pst.par_groups:
prefix= string[:3] #set first 3 letters as prefix
if prefix=='nel':
prefix='porosity'
group = pdict.setdefault(prefix, [])
parnames = par.loc[par.pargp==string, 'parnme'].tolist()
group.extend(parnames)
return pdict


Now, read in the parameter ensembles from the initial (prior) and last (posterior) PEST++IES iteration.

pe_pr = pd.read_csv(os.path.join(org_t_d,"freyberg_mf6.0.par.csv"),index_col=0)


Let’s create a dictionary of parameters “groupings” using the function we preapared above:

pdict = group_pdict(pe_pr, pe_pt)
# for example:
pdict['npf'][:5]


We can now plot histograms for each of our parameter groupings in the pdict dictionary. The grey and blue bars are the prior and posterior parameter distribution, respectively. Where the blue bars have shifted away fro the grey bars marks parameters which have been updated during history matching.

Now, this is a pretty coarse check. But it does allow us to pick up on parameters that are changing…but which shouldn’t. Take porosity parameters for example (the panel on the second row on the right: D)porosity). Our observation data set is only cmposed of groundwater levels and stream gage measurments. Neither of these types of masurements contain information which should inform porosity. In other words, porosity parameters should be insensitive to history matching. However, PEST++IES has adjsuted them from their prior values. A clear sign of spurrious correlation. And if it’s happening for porosity, who’s to say it isn’t happening for other prameters as well?

Right then, let’s fix this.

pyemu.plot_utils.ensemble_helper({"0.5":pe_pr,"b":pe_pt},plot_cols=pdict)


Simple Temporal Localization (and common sense)

As described a tthe beggining of the notebook, a user can provide PEST++IES with a localization matrix. This matrix explicilty enforces physically-plausible parameter-to-observation relations. Perhaps more importantly it enforces the non-existence of physically implausible relations. This matrix has rows that are observation names and/or observation group names, and columns that are parameter names and/or parameter group names. Elements of the matrix should range between 0.0 and 1.0.

Right then, let’s get started and add some localization. The obvious stuff is temporal localization - scenario parameters can’t influence historic observations (and the inverse is true) so let’s tell PEST++IES about this. Also, as discussed, should porosity be adjusted at all given the observations we have? (Not for history matching, but yes, it should be adjusted for forecast uncertainty analysis.)

This involves several steps:

• identify parameter names or parameter group names to specify in the matrix
• identify observation names to specify in the matrix
• construct a template matrix from the names
• assign values to elements of the matrix for each parameter/observation pair

In this next section, we are going to use good ole’ Python and functionality in pyEMU to construct such a matrix. As we constructed our PEST(++) interface using pyemu.PstFrom (see the “pstfrom pest setup” tutorial), we conveniently have lots of usefull metadata to draw on and help us processs parameters and observations.

For starters, let’s get the parameter_data and observation_data sections from the Pst control file. We are going to use them for some funcky slicing and dicing:

# get par data
par = pst.parameter_data
par.inst = par.inst.astype(int)
# get obs data
obs = pst.observation_data
obs.time = obs.time.astype(float)
# temporal units are different in obs and par:
par.inst.unique(), obs.time.unique()


Inconveniently, temporal parameters in par were recorded with the “stress period number” (or kper) instead of model time (see the par.inst column). But the observations in obs were recorded wiht the model time (see the obs.time column).

So we need to align these. We could go either way, but it is probalby more robust to align to model “time” instead of “stress period number”. The next cell updates the par parameter data to include a column with model time that corresponds to the time at the end of the stress period at which the parameter comes into existence.

rpar = par.loc[par.parnme.str.contains("recharge"),:]
rpar = rpar.loc[rpar.ptype=="cn",:]

par.loc[rpar.parnme,"inst"] = rpar.parnme.apply(lambda x: int(x.split("tcn")[0].split('_')[-1])-1)

# add a column for each stress period;
# we already have spd values assocaited to paranetemr names,
# so we will use this to associate parameters to observations in time
times = obs.time.unique()
times.sort()
for kper, time in enumerate(times):
par.loc[par.inst==int(kper), 'time'] = time

par.loc[rpar.parnme,["inst","time"]]

times


After tyding that up, let’s start preparing the parameter names (or parameter group names; PEST++IES accepts either) that we are gong to use as columns in the localization matrix.

Let’s start off with the easy ones: static parameters. These are parameters which do not vary in time. Let’s assume we cannot effectively rule out correlation between them and observations purely based on the time at which the observation occurs. (So things like hydraulic condutivty, storage, SFR conductance, etc.) Let’s make a list of parameter group names for these types of parameters:

# static parameters; these parameters will all be informed by historic obsevration data
prefixes = ['npf', 'sto', 'icstrt', 'ghb', 'sfrcondgr','sfrcondcn']
static_pargps = [i for i in pst.par_groups if any(i.startswith(s) for s in prefixes)]
# start making the list of localization matrix column names (i.e. parameter or parameter group names)
# as we dont need to do any further processing for the loc matrix, we can just use parameter group names
loc_matrix_cols = static_pargps.copy()


OK, so we keep going on about porosity not being informed by the data. So let’s give it special attention. Let’s make a list of parameter groups which should not be adjusted:

# should we really be adjusting porosity? lets just make a list fo use later on
prefixes = ['ne']
dont_pargps = [i for i in pst.par_groups if any(i.startswith(s) for s in prefixes)]
# keep building up our column name list
loc_matrix_cols.extend(dont_pargps)


Lastly, let us make a list of parameter names (not group names!) for parameters which vary over time. Why parameter names and not parameter group names? Because some of these (namely the wel and sfrgr groups) have parameters within a group which pertain to dfferent model times. So we need to drill down to explicitly asign values to specific parameters.

# temporal pars; parameters in the past cannot be informed by observations in the future
# so, an observation in stress period 2 cannot inform a rechange parameter in stress period 1, and so on...
prefixes = ['wel', 'rch', 'sfrgr']
temp_pargps = [i for i in pst.par_groups if any(i.startswith(s) for s in prefixes)]
temporal_pars = par.loc[par.apply(lambda x: "gr" not in x.parnme and "pp" not in x.parnme
and x.pargp in temp_pargps,axis=1),:].copy()
# for the localization matrix we are going to need to go parameter by parameter, so lets get the list of parameter _names_
#temporal_pars = par.loc[par.pargp.isin(temp_pargps)] = [t for t in temporal pars if gr]
# extend the column name list
loc_matrix_cols.extend(temporal_pars.parnme.tolist())


Right’o. So now we have a list of parameter group and paramer names which we are going to use to construct our localization matrix: loc_matrix_cols. We also have a couple of sub-lists to help us select specific columns form the matrix after we have constructed it (static_pargps, dont_pargps and temporal_pars).

Obviously, we also have the list of observations to use as rows in the matrix - they are simply all the non-zero observations in the pst control file: pst.nnz_obs_names.

Let’s get cooking! Generate a Matrix using the pyemu.Matrix class and then convert it to_dataframe() for easy manipulation:

# generate a Matrix object with the nz_obs names as rows and parameters/par groups as columns:
loc = pyemu.Matrix.from_names(pst.nnz_obs_names,loc_matrix_cols).to_dataframe()
# just to make sure, set evry cell/element to zero
loc.loc[:,:]= 0.0


OK, now we have the startings of a localization matrix. At this moment, every element is assigned a value of 0.0 (e.g. no parameter-to-observation correlation). We will now go through and assign a value of 1.0 to parameter-observation pairs for which a physically plausible relation might exist.

# we can now proceed to assign values to elements of the matrix
# assign a value of 1 to all rows for the static parameters, as they may be informed by all obsevrations
loc.loc[:,static_pargps] = 1.0
# see what that looks like


Here comes the tricky bit - assigning localization for time-dependent parameters. We are going to say that an observation can only inform parameters that are up to 180 days in the past (e.g. 180 days before the observation).

# here comes the tricky bit, assigning localization for time-dependent parameters
# we are going to say that an observation can _only_ inform parameters that are
# up to 180 days into the past; not in the future. Parameters that are beyond the historic period are not informed by observations
nz_obs = pst.observation_data.loc[pst.nnz_obs_names,:]
cutoff = 180
times = nz_obs.time.unique()
times.sort()
for time in times:
kper_obs_names = nz_obs.loc[nz_obs.time==time].obsnme.tolist()
# get pars from the same kper and up to -180 days backward in time
kper_par_names = temporal_pars.loc[temporal_pars.time.apply(lambda x: x>time-cutoff and x<=time)].parnme.tolist()
# update the loc matrix
loc.loc[kper_obs_names, kper_par_names] = 1.0
# see what that looks like:

fig,ax = plt.subplots(1,1,figsize=(20,20))
ax.imshow(loc.values)
ax.set_xticks(np.arange(loc.shape[1]))
ax.set_xticklabels(loc.columns.values,rotation=90)
ax.set_yticks(np.arange(loc.shape[0]))
_ = ax.set_yticklabels(loc.index.values)


OK! We should be good to go. Just a quick to check to see if we messed something up:

# make sure havent done somthing silly
assert loc.loc[:,dont_pargps].sum().sum()==0


All good? Excellent. Let’s rebuild the Matrix (don’t tell Neo) and then write it to an external file anmed loc.mat:

pyemu.Matrix.from_dataframe(loc).to_ascii(os.path.join(t_d,"loc.mat"))


Almost done! We need to tel PEST++IES what file to read. We do so by specifying the file name in the ies_localizer() PEST++ control variable:

pst.pestpp_options["ies_localizer"] = "loc.mat"


Don’t forget to re-write the control file!

pst.pestpp_options["ies_num_reals"] = 30 # in theory, with localization we can get by with less reals...lets see!

pst.write(os.path.join(t_d, 'freyberg_mf6.pst'))


OK, good to go. As usual, make sure to specify the number of workers that your machine can cope with.

num_workers = psutil.cpu_count(logical=False) #update this according to your resources
m_d = os.path.join('master_ies_2')

pyemu.os_utils.start_workers(t_d, # the folder which contains the "template" PEST dataset
'pestpp-ies', #the PEST software version we want to run
'freyberg_mf6.pst', # the control file to use with PEST
num_workers=num_workers, #how many agents to deploy
worker_root='.', #where to deploy the agent directories; relative to where python is running
master_dir=m_d, #the manager directory
)


Temporal Localization Outcomes

By now you should be familiar with the next few plots. Let’s blast through our plots of timeseries, forecast histograms and parameter distributions.

Start with the parameter changes. Hey whadya know! That looks a bit more reasonable, doesn’t it? Porosity parameters no longer change from the prior to the posterior. Variance for temporal parameters has also changed. Excelent. At least we’ve removed some potential for underestiating forecast uncertainty. Next check what this has done for history matching and, more importantly, the forecasts.

pe_pr_tloc = pd.read_csv(os.path.join(m_d,"freyberg_mf6.0.par.csv"),index_col=0)

pdict = group_pdict(pe_pr_tloc, pe_pt_tloc)
fig = pyemu.plot_utils.ensemble_helper({"0.5":pe_pr_tloc,"b":pe_pt_tloc},plot_cols=pdict)


Ok! Now we see that the porosity parameters are unchanged - just like we wanted! Do you think this will effect the travel time forecast???

Now read in the new posterior observation ensemble.

pt_oe_tloc = pyemu.ObservationEnsemble.from_csv(pst=pst,filename=os.path.join(m_d,"freyberg_mf6.{0}.obs.csv".format(pst.control_data.noptmax)))


Still getting relatively decent (although not as good as before) fits with historical data.

fig = plot_tseries_ensembles(pr_oe, pt_oe_tloc,noise, onames=["hds","sfr"])


What’s happend with our ever important forecasts?

fig = plot_forecast_hist_compare(pt_oe=pt_oe_tloc, pr_oe=pr_oe, last_pt_oe=pt_oe, last_prior=pr_oe)


Ok, much wider posterior distributions, but that could also be from a larger posterior objective function. In any event, much improvement…

Distance Based Localization

This is industrial strength localization that combines the temporal localization from before with a distance-based cutoff between each spatially-distributed parameter type and each spatially-discrete observation. In this way, we are defining a “window” around each observation and only parameters that are within this window are allowed to be conditioned from said observation. It’s painful to setup and subjective (since a circular windows around each obseravtion is a coarse approximation) but in practice, it seems to yield robust forecast estimates.

For the first time now, we will be using a fully-localized solve, meaning each parameter is upgraded independently. This means PEST++IES has to run through the upgrade calculations once for each parameter - this can be very slow. Currently, PESTPP-IES can multithread these calculations but the optimal number of threads is very problem specific. Through testing, 3 threads seems to be a good choice for this problem (the PEST++IES log file records the time it takes to solve groups of 1000 pars for each lambda so you can test for your problem too).

In the next few cells we are going to make use of flopy and some of the metadata that pyemu.PstFrom recorded when constructing our PEST(++) setup to calculate distances between parmaters and observations. We will do this only for groundwater level observations.

# load simulation
gwf = sim.get_model()


All of our head observation names conveniently contain the layer, row and column number. This allows us to use flopy to obtain their x and y coordinate:

# start by getting the locations of observation sites
# we will only do this for head obsevrations; other obs types in our PEST dataset arent applicable
hobs = nz_obs.loc[nz_obs.oname.isin(['hds','hdsvd','hdstd'])].copy()
hobs.loc[:,'i'] = hobs.obgnme.apply(lambda x: int(x.split('-')[-2]))
hobs.loc[:,'j'] = hobs.obgnme.apply(lambda x: int(x.split('-')[-1]))
# get x,y for cell of each obs
hobs.loc[:,'x'] = hobs.apply(lambda x: gwf.modelgrid.xcellcenters[x.i,x.j], axis=1)
hobs.loc[:,'y'] = hobs.apply(lambda x: gwf.modelgrid.ycellcenters[x.i,x.j], axis=1)
# group them for use later in identifying unique locations
hobs.loc[:,"xy"] = hobs.apply(lambda x: "{0}_{1}".format(x.x,x.y),axis=1)


We have the x and y coordinate of all grid and pilot point based parameters recorded in the Pst parameter_data section. Convenient.

par.x=par.x.astype(float)
par.y=par.y.astype(float)


First, create a Matrix for all adjsutable parameter names and non-zero observations:

spatial_loc = pyemu.Matrix.from_names(pst.nnz_obs_names,pst.adj_par_names).to_dataframe()
# set all elements to 1.0 to make sure all observation-parameter pairs are "active"
spatial_loc.values[:,:] = 1.0


Now the tricky bit. We are going to go through each obseravtion location and assign 0.0 to rows that correspond to spatially distributed parameters that are further away than a specified cutoff distance (loc_dist).

# the cutoff distance
loc_dist = 5000.0
# prepare a set of adjustable parameter names
#select only spatial params to avoid applying to layer-wide multiplier parameters
spatial_par = par.loc[par.x.notnull()].copy()
# group obs by location
xy_groups = hobs.groupby('xy').groups
print('Number of observation sites:',len(xy_groups))
# loop through each observation site and "blank out" correlation with parameters which are too far away
for xy,onames in xy_groups.items():
# get the obs site x and y coords
oname = onames[0]
xx,yy = hobs.loc[oname,['x','y']]
# calculate distance from obs to parameters
spatial_par.loc[:,'dist'] = spatial_par.apply(lambda x: (x.x - xx)**2 + (x.y - yy)**2,axis=1).apply(np.sqrt)
# select pars that are too far from obs
too_far = spatial_par.loc[spatial_par.dist > loc_dist,"parnme"]
too_far = too_far.loc[too_far.apply(lambda x: x in sadj)]
# assign zero to loc matrix for parameters that are too far from obs
spatial_loc.loc[onames, too_far] = 0.0



Now we need to update that with the temporal localization we prepared earlier:

# update the the loc matrix with temporal localization that we prepared previously
temporal_parnames = temporal_pars.parnme.tolist()
spatial_loc.loc[loc.index, temporal_parnames] = loc.loc[:, temporal_parnames]
# make sure the porosity pars are not; go down to parname level to make sure
dont_pars = set(par.loc[par.pargp.apply(lambda x: x in dont_pargps),"parnme"].tolist())
spatial_loc.loc[:,dont_pars] = 0.0


Always good to throw in some checks to make sure we aren’t missing something:

spatial_loc.loc[:, [i for i in spatial_loc.columns if ':ne' in i]].sum().sum()


Right then. Rebuild the Matrix from the dataframe, write it to an external file and update the relevant PEST++ option:

pyemu.Matrix.from_dataframe(spatial_loc).to_coo(os.path.join(t_d,"spatial_loc.jcb"))
pst.pestpp_options["ies_localizer"] = "spatial_loc.jcb"


A final consideration.

Through localization, a complex parameter estimation problem can be turned into a series of independent parameter estimation problems. If large numbers of parameters are being adjusted, the parameter upgrade calculation process for a given lambda will require as many truncated SVD solves as there are adjustable parameters. This can require considerable numerical effort. To overcome this problem, the localized upgrade solution process in PESTP++IES has been multithreaded; this is possible in circumstances such as these where each local solve is independent of every other local solve. The use of multiple threads is invoked through the ies_num_threads() control variable. It should be noted that the optimal number of threads to use is problem-specific. Furthermore, it should not exceed the number of physical cores of the host machine on which the PEST++IES master instance is running.

However, the fully localized solve is still sssslllloooooowwwwwww. So if you have heaps of parameters (>30,000 say) it may actually be faster to use more realizations rather than use localization in terms of wall time - more realizations will over come the issues related to spurious correlation simply by having more samples to calculate the empirical derivatives with…but this depends on the runtime of the forward model as well. As usual, the answer is: “It depends” - haha!

Just to make this notebook experience more enjoyable, lets limit the number of lambdas being tested (so that we only have to solve the fully localized solution once per iteration…):

pst.pestpp_options["ies_lambda_mults"] = 1.0


Rewrite the control file and deploy PEST++IES:

pst.write(os.path.join(t_d,"freyberg_mf6.pst"))
m_d = os.path.join('master_ies_3')

pyemu.os_utils.start_workers(t_d, # the folder which contains the "template" PEST dataset
'pestpp-ies', #the PEST software version we want to run
'freyberg_mf6.pst', # the control file to use with PEST
num_workers=num_workers, #how many agents to deploy
worker_root='.', #where to deploy the agent directories; relative to where python is running
master_dir=m_d, #the manager directory
)


Spatial Localization Outcomes

You know the drill by now. Let’s look at our parameter and observation distributions.

Starting with the parameters. Perhaps a bit less change. Hard to tell at this scale. What about the observations?

pe_pr_sloc = pd.read_csv(os.path.join(m_d,"freyberg_mf6.0.par.csv"),index_col=0)

pdict = group_pdict(pe_pr_sloc, pe_pt_sloc)
fig = pyemu.plot_utils.ensemble_helper({"0.5":pe_pr_sloc,"b":pe_pt_sloc},plot_cols=pdict)


Read in the new posterior ensemble:

pt_oe_sloc = pyemu.ObservationEnsemble.from_csv(pst=pst,filename=os.path.join(m_d,"freyberg_mf6.{0}.obs.csv".format(pst.control_data.noptmax)))


In this case, fits are similar to with the temporal localization:

fig = plot_tseries_ensembles(pr_oe, pt_oe_sloc,noise, onames=["hds","sfr"])


And the ever important foreacasts. Again, a bit more variance in the null-space dependent forecasts (i.e. particle travel time).

fig = plot_forecast_hist_compare(pt_oe=pt_oe_sloc, pr_oe=pr_oe, last_pt_oe=pt_oe_tloc, last_prior=pr_oe)


PEST++IES includes functionality for automatic localization. In practice, this form of localization doesnt resolve the level of localization that more rigorous explicit localization that you get through a localization matrix. However, its better than no localization at all.

A localization matrix supplied by the user can be used in combination with automatic adaptive localization (autoadaloc). When doing so, autoadaloc process is restricted to to the allowed parameter-to-observation relations in the user specified localization matrix. The automated process will only ever adjust values in the localization matrix downwards (i.e. decrease the correlation coefficients).

And, just like par-by-par distance based localization above, we need to solve the upgrade equations once for each parameter…this can take quite a bit of CPU time. Multithreading is a must.

How do we implement it? Easy peasy. Just activate the ies_autoadaloc() PEST++ option:

#pst.pestpp_options.pop("ies_localizer") #should you wish to try autoadaloc on its onw, simply drop the loc matrix

# these upgrade calcs take a while, so let's only do one lambda
pst.pestpp_options["ies_lambda_mults"] = 1.0


The one control value for autoadaloc is ies_autoadaloc_sigma_distance which is the statistical difference background or error correlation estimate and the current correlation coefficient. Any correlation coefficient that is less than the error mean plus/minus error standard deviation times ies_autoadaloc_sigma_distance is treated as a non-significant correlation and is localized out. So large ies_autoadaloc_sigma_distance values result in stronger localization. The default value is 1.0.

pst.pestpp_options["ies_autoadaloc_sigma_dist"] = 1


Cool beans. We are good to go. Just re-write the control file and let PEST++IES loose. Now, keep in mind that, even though we are using only a few hundred model runs, solving the parameter upgrade equations for a high-dimensional problem can be quite expensive. So this will take bit longer than previous runs.

pst.write(os.path.join(t_d,"freyberg_mf6.pst"))
m_d = os.path.join('master_ies_3')

pyemu.os_utils.start_workers(t_d, # the folder which contains the "template" PEST dataset
'pestpp-ies', #the PEST software version we want to run
'freyberg_mf6.pst', # the control file to use with PEST
num_workers=num_workers, #how many agents to deploy
worker_root='.', #where to deploy the agent directories; relative to where python is running
master_dir=m_d, #the manager directory
)


Right, let’s go straight for our forecasts!

pt_oe_autoloc = pyemu.ObservationEnsemble.from_csv(pst=pst,filename=os.path.join(m_d,"freyberg_mf6.{0}.obs.csv".format(pst.control_data.noptmax)))

fig = plot_forecast_hist_compare(pt_oe=pt_oe_autoloc,
pr_oe=pr_oe,
last_pt_oe=pt_oe_sloc,
last_prior=pr_oe)


Thus far we have implemented localization, a strategy to tackle spurious parameter-to-observation correlation. In doing so we reduce the potential for “ensemble colapse”, a fancy term that means an “underestimate of forecast uncertainty caused by artificial parameter-to-observation relations”. This solves history-matching induced through using ensemble based methods, but it does not solve a (the?) core issue - trying to “perfectly” fit data with an imperfect model will induce bias.

Now, as we have seen, for some forecasts this is not a huge problem (these are data-driven forecasts, which are well informed by available observation data). For others, it is (these are the forecasts which are influenced by parameter combinations in the null space, that are not informed by observation data). But when undertaking modelling in the real world, we will rarely know where our forecast lies on that spectrum (probably somewhere in the middle…). So, better safe than sorry.