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<title>prms_python.optimizer — PRMS-Python v1.0.0</title>
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<h1>Source code for prms_python.optimizer</h1><div class="highlight"><pre>
<span></span><span class="sd">'''</span>
<span class="sd">optimizer.py -- holds ``Optimizer`` and ``OptimizationResult`` classes for </span>
<span class="sd">optimization routines and management conducted on PRMS parameters.</span>
<span class="sd">'''</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">print_function</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">os</span><span class="o">,</span> <span class="nn">sys</span><span class="o">,</span> <span class="nn">json</span><span class="o">,</span> <span class="nn">re</span><span class="o">,</span> <span class="nn">shutil</span>
<span class="kn">import</span> <span class="nn">multiprocessing</span> <span class="k">as</span> <span class="nn">mp</span>
<span class="kn">from</span> <span class="nn">copy</span> <span class="k">import</span> <span class="n">copy</span>
<span class="kn">from</span> <span class="nn">copy</span> <span class="k">import</span> <span class="n">deepcopy</span>
<span class="kn">from</span> <span class="nn">numpy</span> <span class="k">import</span> <span class="n">log10</span>
<span class="kn">from</span> <span class="nn">datetime</span> <span class="k">import</span> <span class="n">datetime</span>
<span class="kn">from</span> <span class="nn">.data</span> <span class="k">import</span> <span class="n">Data</span>
<span class="kn">from</span> <span class="nn">.parameters</span> <span class="k">import</span> <span class="n">Parameters</span>
<span class="kn">from</span> <span class="nn">.simulation</span> <span class="k">import</span> <span class="n">Simulation</span><span class="p">,</span> <span class="n">SimulationSeries</span>
<span class="kn">from</span> <span class="nn">.util</span> <span class="k">import</span> <span class="n">load_statvar</span><span class="p">,</span> <span class="n">nash_sutcliffe</span><span class="p">,</span> <span class="n">percent_bias</span><span class="p">,</span> <span class="n">rmse</span>
<span class="n">OPJ</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span>
<div class="viewcode-block" id="Optimizer"><a class="viewcode-back" href="../../api.html#prms_python.Optimizer">[docs]</a><span class="k">class</span> <span class="nc">Optimizer</span><span class="p">:</span>
<span class="sd">'''</span>
<span class="sd"> Container for PRMS parameter optimization routines that are </span>
<span class="sd"> defined as stages similar to what is described in Hay, et al, 2006</span>
<span class="sd"> (ftp://brrftp.cr.usgs.gov/pub/mows/software/luca_s/jawraHay.pdf).</span>
<span class="sd"> Currently the ``monte_carlo`` method provides random parameter </span>
<span class="sd"> resampling routines using uniform and normal random variables. </span>
<span class="sd"> Example:</span>
<span class="sd"> >>> from prms_python import Data, Optimizer, Parameters</span>
<span class="sd"> >>> params = Parameters('path/to/parameters')</span>
<span class="sd"> >>> data = Data('path/to/data')</span>
<span class="sd"> >>> control = 'path/to/control'</span>
<span class="sd"> >>> work_directory = 'path/to/create/simulations'</span>
<span class="sd"> >>> optr = Optimizer(</span>
<span class="sd"> params, </span>
<span class="sd"> data, </span>
<span class="sd"> control, </span>
<span class="sd"> work_directory, </span>
<span class="sd"> title='the title', </span>
<span class="sd"> description='desc')</span>
<span class="sd"> >>> measured = 'path/to/measured/csv' </span>
<span class="sd"> >>> statvar_name = 'basin_cfs' # or any other valid statvar </span>
<span class="sd"> >>> params_to_resample = ['dday_intcp', 'dday_slope'] # list of params</span>
<span class="sd"> >>> optr.monte_carlo(measured, params_to_resample, statvar_name)</span>
<span class="sd"> '''</span>
<span class="c1">#dic for min/max of parameter allowable ranges, add more when needed</span>
<span class="n">param_ranges</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'dday_intcp'</span><span class="p">:</span> <span class="p">(</span><span class="o">-</span><span class="mf">60.0</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">),</span>
<span class="s1">'dday_slope'</span><span class="p">:</span> <span class="p">(</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">),</span>
<span class="s1">'jh_coef'</span><span class="p">:</span> <span class="p">(</span><span class="mf">0.005</span><span class="p">,</span> <span class="mf">0.06</span><span class="p">),</span>
<span class="s1">'pt_alpha'</span><span class="p">:</span> <span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">),</span>
<span class="s1">'potet_coef_hru_mo'</span><span class="p">:</span> <span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">),</span>
<span class="s1">'tmax_index'</span><span class="p">:</span> <span class="p">(</span><span class="o">-</span><span class="mf">10.0</span><span class="p">,</span> <span class="mf">110.0</span><span class="p">),</span>
<span class="s1">'tmin_lapse'</span><span class="p">:</span> <span class="p">(</span><span class="o">-</span><span class="mf">10.0</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">),</span>
<span class="s1">'soil_moist_max'</span><span class="p">:</span> <span class="p">(</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">),</span>
<span class="s1">'rain_adj'</span><span class="p">:</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">),</span>
<span class="s1">'ppt_rad_adj'</span><span class="p">:</span> <span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">),</span>
<span class="s1">'radadj_intcp'</span><span class="p">:</span> <span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span>
<span class="s1">'radadj_slope'</span><span class="p">:</span> <span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span>
<span class="s1">'radj_sppt'</span><span class="p">:</span> <span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span>
<span class="s1">'radj_wppt'</span><span class="p">:</span> <span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span>
<span class="s1">'radmax'</span><span class="p">:</span> <span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
<span class="p">}</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">control_file</span><span class="p">,</span> <span class="n">working_dir</span><span class="p">,</span>
<span class="n">title</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span> <span class="n">Parameters</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">parameters</span> <span class="o">=</span> <span class="n">parameters</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'parameters must be instance of Parameters'</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">Data</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'data must be instance of Data'</span><span class="p">)</span>
<span class="n">input_dir</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">sep</span><span class="p">)</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">control_file</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">sep</span><span class="p">)[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">OPJ</span><span class="p">(</span><span class="n">input_dir</span><span class="p">,</span> <span class="s1">'statvar.dat'</span><span class="p">)):</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'You have no statvar.dat file in your model directory'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Running PRMS on original data in </span><span class="si">{}</span><span class="s1"> for later comparison'</span>\
<span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">input_dir</span><span class="p">))</span>
<span class="n">sim</span> <span class="o">=</span> <span class="n">Simulation</span><span class="p">(</span><span class="n">input_dir</span><span class="p">)</span>
<span class="n">sim</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">working_dir</span><span class="p">):</span>
<span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">working_dir</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">input_dir</span> <span class="o">=</span> <span class="n">input_dir</span>
<span class="bp">self</span><span class="o">.</span><span class="n">control_file</span> <span class="o">=</span> <span class="n">control_file</span>
<span class="bp">self</span><span class="o">.</span><span class="n">working_dir</span> <span class="o">=</span> <span class="n">working_dir</span>
<span class="bp">self</span><span class="o">.</span><span class="n">title</span> <span class="o">=</span> <span class="n">title</span>
<span class="bp">self</span><span class="o">.</span><span class="n">description</span> <span class="o">=</span> <span class="n">description</span>
<span class="bp">self</span><span class="o">.</span><span class="n">measured_arb</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># for arbitrary output methods</span>
<span class="bp">self</span><span class="o">.</span><span class="n">statvar_name</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">arb_outputs</span> <span class="o">=</span> <span class="p">[]</span>
<div class="viewcode-block" id="Optimizer.monte_carlo"><a class="viewcode-back" href="../../api.html#prms_python.Optimizer.monte_carlo">[docs]</a> <span class="k">def</span> <span class="nf">monte_carlo</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">reference_path</span><span class="p">,</span> <span class="n">param_names</span><span class="p">,</span> <span class="n">statvar_name</span><span class="p">,</span> \
<span class="n">stage</span><span class="p">,</span> <span class="n">n_sims</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'uniform'</span><span class="p">,</span> <span class="n">mu_factor</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>\
<span class="n">noise_factor</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">nproc</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">'''</span>
<span class="sd"> The ``monte_carlo`` method of ``Optimizer`` performs parameter</span>
<span class="sd"> random resampling techniques to a set of PRMS parameters and </span>
<span class="sd"> executes and manages the corresponding simulations. </span>
<span class="sd"> Args:</span>
<span class="sd"> reference_path (str): path to measured data for optimization</span>
<span class="sd"> param_names (list): list of parameter names to resample</span>
<span class="sd"> statvar_name (str): name of statisical variable output name for</span>
<span class="sd"> optimization </span>
<span class="sd"> stage (str): custom name of optimization stage e.g. 'ddsolrad' </span>
<span class="sd"> Kwargs:</span>
<span class="sd"> n_sims (int): number of simulations to conduct </span>
<span class="sd"> parameter optimization/uncertaitnty analysis.</span>
<span class="sd"> method (str): resampling method for parameters (normal or uniform)</span>
<span class="sd"> mu_factor (float): coefficient to scale mean of the parameter(s)</span>
<span class="sd"> to resample from when using the normal distribution to resample</span>
<span class="sd"> i.e. a value of 1.5 will sample from a normal rv with mean</span>
<span class="sd"> 50% higher than the original parameter mean</span>
<span class="sd"> noise_factor (float): scales the variance of noise to add to</span>
<span class="sd"> parameter values when using normal rv (method='normal')</span>
<span class="sd"> nproc (int): number of processors available to run PRMS simulations</span>
<span class="sd"> '''</span>
<span class="k">if</span> <span class="s1">'_'</span> <span class="ow">in</span> <span class="n">stage</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'stage name cannot contain an underscore'</span><span class="p">)</span>
<span class="c1"># assign the optimization object a copy of measured data for plots </span>
<span class="bp">self</span><span class="o">.</span><span class="n">measured_arb</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">from_csv</span><span class="p">(</span><span class="n">reference_path</span><span class="p">,</span>\
<span class="n">parse_dates</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># statistical variable output name </span>
<span class="bp">self</span><span class="o">.</span><span class="n">statvar_name</span> <span class="o">=</span> <span class="n">statvar_name</span>
<span class="n">start_time</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span><span class="o">.</span><span class="n">isoformat</span><span class="p">()</span>
<span class="c1"># resample params for all simulations- potential place to serialize</span>
<span class="n">params</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">param_names</span><span class="p">:</span> <span class="c1"># create list of lists of resampled params</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_sims</span><span class="p">):</span>
<span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">resample_param</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="n">method</span><span class="p">,</span>\
<span class="n">mu_factor</span><span class="o">=</span><span class="n">mu_factor</span><span class="p">,</span> <span class="n">noise_factor</span><span class="o">=</span><span class="n">noise_factor</span><span class="p">))</span>
<span class="n">params</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">tmp</span><span class="p">))</span>
<span class="c1"># SimulationSeries comprised of each resampled param set</span>
<span class="n">series</span> <span class="o">=</span> <span class="n">SimulationSeries</span><span class="p">(</span>
<span class="n">Simulation</span><span class="o">.</span><span class="n">from_data</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">_mod_params</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="p">,</span>\
<span class="p">[</span><span class="n">params</span><span class="p">[</span><span class="n">n</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">params</span><span class="p">))],</span>\
<span class="n">param_names</span><span class="p">),</span>
<span class="bp">self</span><span class="o">.</span><span class="n">control_file</span><span class="p">,</span>
<span class="n">OPJ</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">working_dir</span><span class="p">,</span> <span class="c1"># name of sim: first param and mean value</span>
<span class="s1">'</span><span class="si">{0}</span><span class="s1">_</span><span class="si">{1:.10f}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">param_names</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">]))</span>
<span class="p">)</span>
<span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_sims</span><span class="p">)</span>
<span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">nproc</span><span class="p">:</span>
<span class="n">nproc</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">()</span> <span class="o">//</span> <span class="mi">2</span>
<span class="c1"># run </span>
<span class="n">outputs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">series</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">nproc</span><span class="o">=</span><span class="n">nproc</span><span class="p">)</span><span class="o">.</span><span class="n">outputs_iter</span><span class="p">())</span>
<span class="bp">self</span><span class="o">.</span><span class="n">arb_outputs</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span> <span class="c1"># for current instance- add outputs </span>
<span class="n">end_time</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span><span class="o">.</span><span class="n">isoformat</span><span class="p">()</span>
<span class="c1"># json metadata for Monte Carlo run </span>
<span class="n">meta</span> <span class="o">=</span> <span class="p">{</span> <span class="s1">'params_adjusted'</span> <span class="p">:</span> <span class="n">param_names</span><span class="p">,</span>
<span class="s1">'statvar_name'</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">statvar_name</span><span class="p">,</span>
<span class="s1">'optimization_title'</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">title</span><span class="p">,</span>
<span class="s1">'optimization_description'</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">description</span><span class="p">,</span>
<span class="s1">'start_datetime'</span> <span class="p">:</span> <span class="n">start_time</span><span class="p">,</span>
<span class="s1">'end_datetime'</span> <span class="p">:</span> <span class="n">end_time</span><span class="p">,</span>
<span class="s1">'measured'</span> <span class="p">:</span> <span class="n">reference_path</span><span class="p">,</span>
<span class="s1">'method'</span> <span class="p">:</span> <span class="s1">'Monte Carlo'</span><span class="p">,</span>
<span class="s1">'mu_factor'</span> <span class="p">:</span> <span class="n">mu_factor</span><span class="p">,</span>
<span class="s1">'noise_factor'</span> <span class="p">:</span> <span class="n">noise_factor</span><span class="p">,</span>
<span class="s1">'resample'</span><span class="p">:</span> <span class="n">method</span><span class="p">,</span>
<span class="s1">'sim_dirs'</span> <span class="p">:</span> <span class="p">[],</span>
<span class="s1">'stage'</span><span class="p">:</span> <span class="n">stage</span><span class="p">,</span>
<span class="s1">'original_params'</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="o">.</span><span class="n">base_file</span><span class="p">,</span>
<span class="s1">'nproc'</span><span class="p">:</span> <span class="n">nproc</span><span class="p">,</span>
<span class="s1">'n_sims'</span> <span class="p">:</span> <span class="n">n_sims</span>
<span class="p">}</span>
<span class="k">for</span> <span class="n">output</span> <span class="ow">in</span> <span class="n">outputs</span><span class="p">:</span>
<span class="n">meta</span><span class="p">[</span><span class="s1">'sim_dirs'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">output</span><span class="p">[</span><span class="s1">'simulation_dir'</span><span class="p">])</span>
<span class="n">json_outfile</span> <span class="o">=</span> <span class="n">OPJ</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">working_dir</span><span class="p">,</span> <span class="n">_create_metafile_name</span><span class="p">(</span>\
<span class="bp">self</span><span class="o">.</span><span class="n">working_dir</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">title</span><span class="p">,</span> <span class="n">stage</span><span class="p">))</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">json_outfile</span><span class="p">,</span> <span class="s1">'w'</span><span class="p">)</span> <span class="k">as</span> <span class="n">outf</span><span class="p">:</span>
<span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">meta</span><span class="p">,</span> <span class="n">outf</span><span class="p">,</span> <span class="n">sort_keys</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>\
<span class="n">ensure_ascii</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="si">{0}</span><span class="se">\n</span><span class="s1">Output information sent to </span><span class="si">{1}</span><span class="se">\n</span><span class="s1">'</span><span class="o">.</span>\
<span class="nb">format</span><span class="p">(</span><span class="s1">'-'</span> <span class="o">*</span> <span class="mi">80</span><span class="p">,</span> <span class="n">json_outfile</span><span class="p">))</span></div>
<div class="viewcode-block" id="Optimizer.plot_optimization"><a class="viewcode-back" href="../../api.html#prms_python.Optimizer.plot_optimization">[docs]</a> <span class="k">def</span> <span class="nf">plot_optimization</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s1">'daily'</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'time_series'</span><span class="p">,</span>\
<span class="n">plot_vars</span><span class="o">=</span><span class="s1">'both'</span><span class="p">,</span> <span class="n">plot_1to1</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">return_fig</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>\
<span class="n">n_plots</span><span class="o">=</span><span class="mi">4</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Basic plotting of current optimization results with limited options. </span>
<span class="sd"> Plots measured, original simluated, and optimization simulated variabes</span>
<span class="sd"> Not recommended for plotting results when n_sims is very large, instead </span>
<span class="sd"> use options from an OptimizationResult object, or employ a user-defined </span>
<span class="sd"> method using the result data.</span>
<span class="sd"> Kwargs:</span>
<span class="sd"> freq (str): frequency of time series plots, value can be 'daily'</span>
<span class="sd"> or 'monthly' for solar radiation </span>
<span class="sd"> method (str): 'time_series' for time series sub plot of each</span>
<span class="sd"> simulation alongside measured radiation. Another choice is </span>
<span class="sd"> 'correlation' which plots each measured daily solar radiation</span>
<span class="sd"> value versus the corresponding simulated variable as subplots</span>
<span class="sd"> one for each simulation in the optimization. With coefficients</span>
<span class="sd"> of determiniationi i.e. square of pearson correlation coef. </span>
<span class="sd"> plot_vars (str): what to plot alongside simulated srad: </span>
<span class="sd"> 'meas': plot simulated along with measured swrad</span>
<span class="sd"> 'orig': plot simulated along with the original simulated swrad</span>
<span class="sd"> 'both': plot simulated, with original simulation and measured</span>
<span class="sd"> plot_1to1 (bool): if True plot one to one line on correlation </span>
<span class="sd"> scatter plot, otherwise exclude.</span>
<span class="sd"> return_fig (bool): flag whether to return matplotlib figure </span>
<span class="sd"> Returns: </span>
<span class="sd"> f (matplotlib.figure.Figure): If kwarg return_fig=True, then return</span>
<span class="sd"> copy of the figure that is generated to the user. </span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">arb_outputs</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'You have not run any optimizations'</span><span class="p">)</span>
<span class="n">var_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">statvar_name</span>
<span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">measured_arb</span>
<span class="n">idx</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">load_statvar</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">arb_outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>\
<span class="p">[</span><span class="s1">'statvar'</span><span class="p">])[</span><span class="s1">'</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">var_name</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
<span class="n">orig</span> <span class="o">=</span> <span class="n">load_statvar</span><span class="p">(</span><span class="n">OPJ</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_dir</span><span class="p">,</span> <span class="s1">'statvar.dat'</span><span class="p">))[</span><span class="s1">'</span><span class="si">{}</span><span class="s1">'</span>\
<span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">var_name</span><span class="p">)][</span><span class="n">idx</span><span class="p">]</span>
<span class="n">meas</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">measured_arb</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
<span class="n">sims</span> <span class="o">=</span> <span class="p">[</span><span class="n">load_statvar</span><span class="p">(</span><span class="n">out</span><span class="p">[</span><span class="s1">'statvar'</span><span class="p">])[</span><span class="s1">'</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">var_name</span><span class="p">)][</span><span class="n">idx</span><span class="p">]</span> <span class="k">for</span> \
<span class="n">out</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">arb_outputs</span><span class="p">]</span>
<span class="n">simdirs</span> <span class="o">=</span> <span class="p">[</span><span class="n">out</span><span class="p">[</span><span class="s1">'simulation_dir'</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">sep</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span>\
<span class="n">replace</span><span class="p">(</span><span class="s1">'_'</span><span class="p">,</span> <span class="s1">' '</span><span class="p">)</span> <span class="k">for</span> <span class="n">out</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">arb_outputs</span><span class="p">]</span>
<span class="n">var_name</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">statvar_name</span><span class="p">)</span>
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">arb_outputs</span><span class="p">)</span> <span class="c1"># number of simulations to plot</span>
<span class="c1"># user defined number of subplots from first n_plots results </span>
<span class="k">if</span> <span class="p">(</span><span class="n">n</span> <span class="o">></span> <span class="n">n_plots</span><span class="p">):</span> <span class="n">n</span> <span class="o">=</span> <span class="n">n_plots</span>
<span class="c1"># styles for each plot</span>
<span class="n">ms</span> <span class="o">=</span> <span class="mi">4</span> <span class="c1"># markersize for all points</span>
<span class="n">orig_sty</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">linestyle</span><span class="o">=</span><span class="s1">'none'</span><span class="p">,</span><span class="n">markersize</span><span class="o">=</span><span class="n">ms</span><span class="p">,</span>\
<span class="n">markerfacecolor</span><span class="o">=</span><span class="s1">'none'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'s'</span><span class="p">,</span>\
<span class="n">markeredgecolor</span><span class="o">=</span><span class="s1">'royalblue'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'royalblue'</span><span class="p">)</span>
<span class="n">meas_sty</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">linestyle</span><span class="o">=</span><span class="s1">'none'</span><span class="p">,</span><span class="n">markersize</span><span class="o">=</span><span class="n">ms</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span>\
<span class="n">markerfacecolor</span><span class="o">=</span><span class="s1">'none'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'1'</span><span class="p">,</span>\
<span class="n">markeredgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
<span class="n">sim_sty</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">linestyle</span><span class="o">=</span><span class="s1">'none'</span><span class="p">,</span><span class="n">markersize</span><span class="o">=</span><span class="n">ms</span><span class="p">,</span>\
<span class="n">markerfacecolor</span><span class="o">=</span><span class="s1">'none'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span>\
<span class="n">markeredgecolor</span><span class="o">=</span><span class="s1">'r'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'r'</span><span class="p">)</span>
<span class="c1">## number of subplots and rows (two plots per row) </span>
<span class="n">nrow</span> <span class="o">=</span> <span class="n">n</span><span class="o">//</span><span class="mi">2</span> <span class="c1"># round down if odd n</span>
<span class="n">ncol</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">odd_n</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">if</span> <span class="n">n</span><span class="o">/</span><span class="mf">2.</span> <span class="o">-</span> <span class="n">nrow</span> <span class="o">==</span> <span class="mf">0.5</span><span class="p">:</span>
<span class="n">nrow</span><span class="o">+=</span><span class="mi">1</span> <span class="c1"># odd number need extra row</span>
<span class="n">odd_n</span> <span class="o">=</span> <span class="kc">True</span>
<span class="c1">########</span>
<span class="c1">## Start plots depnding on key word arguments</span>
<span class="c1">########</span>
<span class="k">if</span> <span class="n">freq</span> <span class="o">==</span> <span class="s1">'daily'</span> <span class="ow">and</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">'time_series'</span><span class="p">:</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>\
<span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="n">n</span><span class="o">*</span><span class="mf">3.5</span><span class="p">))</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">sim</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sims</span><span class="p">[:</span><span class="n">n</span><span class="p">]):</span>
<span class="k">if</span> <span class="n">plot_vars</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'meas'</span><span class="p">,</span> <span class="s1">'both'</span><span class="p">):</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">meas</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Measured'</span><span class="p">,</span> <span class="o">**</span><span class="n">meas_sty</span><span class="p">)</span>
<span class="k">if</span> <span class="n">plot_vars</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'orig'</span><span class="p">,</span> <span class="s1">'both'</span><span class="p">):</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">orig</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Original sim.'</span><span class="p">,</span> <span class="o">**</span><span class="n">orig_sty</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">sim</span><span class="p">,</span> <span class="o">**</span><span class="n">sim_sty</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'sim: </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">simdirs</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">markerscale</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">'best'</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">autofmt_xdate</span><span class="p">()</span>
<span class="c1">#monthly means</span>
<span class="k">elif</span> <span class="n">freq</span> <span class="o">==</span> <span class="s1">'monthly'</span> <span class="ow">and</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">'time_series'</span><span class="p">:</span>
<span class="c1"># compute monthly means</span>
<span class="n">meas</span> <span class="o">=</span> <span class="n">meas</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">meas</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">month</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">orig</span> <span class="o">=</span> <span class="n">orig</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">orig</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">month</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="c1"># change line styles for monthly plots to lines not points</span>
<span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="p">(</span><span class="n">orig_sty</span><span class="p">,</span> <span class="n">meas_sty</span><span class="p">,</span> <span class="n">sim_sty</span><span class="p">):</span>
<span class="n">d</span><span class="p">[</span><span class="s1">'linestyle'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'-'</span>
<span class="n">d</span><span class="p">[</span><span class="s1">'marker'</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="n">nrow</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="n">ncol</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="n">n</span><span class="o">*</span><span class="mf">3.5</span><span class="p">))</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">sim</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sims</span><span class="p">[:</span><span class="n">n</span><span class="p">]):</span>
<span class="k">if</span> <span class="n">plot_vars</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'meas'</span><span class="p">,</span> <span class="s1">'both'</span><span class="p">):</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">meas</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Measured'</span><span class="p">,</span> <span class="o">**</span><span class="n">meas_sty</span><span class="p">)</span>
<span class="k">if</span> <span class="n">plot_vars</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'orig'</span><span class="p">,</span> <span class="s1">'both'</span><span class="p">):</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">orig</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Original sim.'</span><span class="p">,</span> <span class="o">**</span><span class="n">orig_sty</span><span class="p">)</span>
<span class="n">sim</span> <span class="o">=</span> <span class="n">sim</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">sim</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">month</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">sim</span><span class="p">,</span> <span class="o">**</span><span class="n">sim_sty</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'sim: </span><span class="si">{}</span><span class="se">\n</span><span class="s1">mean'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">simdirs</span><span class="p">[</span><span class="n">i</span><span class="p">]),</span>\
<span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">12.5</span><span class="p">)</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">markerscale</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">'best'</span><span class="p">)</span>
<span class="k">if</span> <span class="n">odd_n</span><span class="p">:</span> <span class="c1"># empty subplot if odd number of simulations </span>
<span class="n">fig</span><span class="o">.</span><span class="n">delaxes</span><span class="p">(</span><span class="n">axs</span><span class="p">[</span><span class="n">n</span><span class="p">])</span>
<span class="n">fig</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="s1">'month'</span><span class="p">)</span>
<span class="c1">#x-y scatter</span>
<span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">'correlation'</span><span class="p">:</span>
<span class="c1">## figure</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="n">nrow</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="n">ncol</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="n">n</span><span class="o">*</span><span class="mi">3</span><span class="p">))</span>
<span class="n">axs</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="c1">## subplot dimensions</span>
<span class="n">meas_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">meas_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">sim</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sims</span><span class="p">[:</span><span class="n">n</span><span class="p">]):</span>
<span class="n">Y</span> <span class="o">=</span> <span class="n">sim</span>
<span class="n">sim_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span>
<span class="n">sim_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span>
<span class="n">m</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">meas_max</span><span class="p">,</span><span class="n">sim_max</span><span class="p">)</span>
<span class="k">if</span> <span class="n">plot_1to1</span><span class="p">:</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="n">m</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">m</span><span class="p">],</span> <span class="s1">'k--'</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> <span class="c1">## one to one line</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">meas_min</span><span class="p">,</span><span class="n">meas_max</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">sim_min</span><span class="p">,</span> <span class="n">sim_max</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="o">**</span><span class="n">sim_sty</span><span class="p">)</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'sim: </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">simdirs</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Measured </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">var_name</span><span class="p">))</span>
<span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span><span class="sa">r</span><span class="s1">'$R^2 = </span><span class="si">{0:.2f}</span><span class="s1">$'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>\
<span class="n">X</span><span class="o">.</span><span class="n">corr</span><span class="p">(</span><span class="n">Y</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">),</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>\
<span class="n">ha</span><span class="o">=</span><span class="s1">'left'</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">transAxes</span><span class="p">)</span>
<span class="k">if</span> <span class="n">odd_n</span><span class="p">:</span> <span class="c1"># empty subplot if odd number of simulations </span>
<span class="n">fig</span><span class="o">.</span><span class="n">delaxes</span><span class="p">(</span><span class="n">axs</span><span class="p">[</span><span class="n">n</span><span class="p">])</span>
<span class="k">if</span> <span class="n">return_fig</span><span class="p">:</span>
<span class="k">return</span> <span class="n">fig</span></div></div>
<span class="k">def</span> <span class="nf">_create_metafile_name</span><span class="p">(</span><span class="n">out_dir</span><span class="p">,</span> <span class="n">opt_title</span><span class="p">,</span> <span class="n">stage</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Search through output directory where simulations are conducted</span>
<span class="sd"> look for all metadata simulation json files and find out if the</span>
<span class="sd"> current simulation is a replicate. Then use that information to </span>
<span class="sd"> build the correct file name for the output json file. The series</span>
<span class="sd"> are typically run in parallel that is why this step has to be </span>
<span class="sd"> done after running multiple simulations from an optimization stage.</span>
<span class="sd"> Args:</span>
<span class="sd"> out_dir (str): path to directory with model results, i.e. </span>
<span class="sd"> location where simulation series outputs and optimization</span>
<span class="sd"> json files are located, aka Optimizer.working_dir</span>
<span class="sd"> opt_title (str): optimization instance title for file search</span>
<span class="sd"> stage (str): stage of optimization, e.g. 'swrad', 'pet' </span>
<span class="sd"> Returns:</span>
<span class="sd"> name (str): file name for the current optimization simulation series</span>
<span class="sd"> metadata json file. E.g 'dry_creek_swrad_opt.json', or if</span>
<span class="sd"> this is the second time you have run an optimization titled</span>
<span class="sd"> 'dry_creek' the next json file will be returned as </span>
<span class="sd"> 'dry_creek_swrad_opt1.json' and so on with integer increments </span>
<span class="sd"> """</span>
<span class="n">meta_re</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="sa">r</span><span class="s1">'^</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_opt(\d*)\.json'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">opt_title</span><span class="p">,</span> <span class="n">stage</span><span class="p">))</span>
<span class="n">reps</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">out_dir</span><span class="p">):</span>
<span class="k">if</span> <span class="n">meta_re</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="n">f</span><span class="p">):</span>
<span class="n">nrep</span> <span class="o">=</span> <span class="n">meta_re</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="n">f</span><span class="p">)</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="n">nrep</span> <span class="o">==</span> <span class="s1">''</span><span class="p">:</span>
<span class="n">reps</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">reps</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nrep</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">reps</span><span class="p">:</span>
<span class="n">name</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_opt.json'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">opt_title</span><span class="p">,</span> <span class="n">stage</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># this is the nth optimization done under the same title</span>
<span class="n">n</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="n">reps</span><span class="p">))</span> <span class="o">+</span> <span class="mi">1</span>
<span class="n">name</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_opt</span><span class="si">{}</span><span class="s1">.json'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">opt_title</span><span class="p">,</span> <span class="n">stage</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
<span class="k">return</span> <span class="n">name</span>
<span class="k">def</span> <span class="nf">resample_param</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">param_name</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">'uniform'</span><span class="p">,</span> <span class="n">mu_factor</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>\
<span class="n">noise_factor</span><span class="o">=</span><span class="mf">0.1</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Resample PRMS parameter by shifting all values by a constant that is </span>
<span class="sd"> taken from a uniform distribution, where the range of the uniform </span>
<span class="sd"> values is equal to the difference between the min and max of the allowable </span>
<span class="sd"> range from PRMS. The parameter min and max are set in Optimizer.param_ranges </span>
<span class="sd"> If the resampling method (``how`` argument) is set to 'normal', randomly </span>
<span class="sd"> sample a normal distribution with mean = mean(parameter) X ``mu_factor`` and </span>
<span class="sd"> sigma = param allowable range multiplied by ``noise_factor``. If parameters have</span>
<span class="sd"> array length <= 366 then individual parameter values are resampled otherwise</span>
<span class="sd"> resample all param values at once, e.g. by taking a single random value </span>
<span class="sd"> from the uniform distribution. If they are taking all at once using the </span>
<span class="sd"> normal method then the original values are scaled by mu_factor and a normal </span>
<span class="sd"> random variable with mean=0 and std dev = parameter range X ``noise_factor``. </span>
<span class="sd"> Args:</span>
<span class="sd"> params (parameters.Parameters): ``Parameters`` object </span>
<span class="sd"> param_name (str): name of PRMS parameter to resample</span>
<span class="sd"> Kwargs: </span>
<span class="sd"> how (str): distribution to resample parameters from in the case </span>
<span class="sd"> that each parameter element can be resampled (len <=366)</span>
<span class="sd"> Currently works for uniform and normal distributions. </span>
<span class="sd"> noise_factor (float): factor to multiply parameter range by, </span>
<span class="sd"> use the result as the standard deviation for the normal rand.</span>
<span class="sd"> variable used to add element wise noise. i.e. higher </span>
<span class="sd"> noise_factor will result in higher variance. Must be > 0.</span>
<span class="sd"> Returns:</span>
<span class="sd"> ret (numpy.ndarry): ndarray of param after resampling </span>
<span class="sd"> """</span>
<span class="n">p_min</span><span class="p">,</span> <span class="n">p_max</span> <span class="o">=</span> <span class="n">Optimizer</span><span class="o">.</span><span class="n">param_ranges</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">param_name</span><span class="p">,(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span>
<span class="c1"># create dictionary of parameter basic info (not values)</span>
<span class="n">param_dic</span> <span class="o">=</span> <span class="p">{</span><span class="n">param</span><span class="p">[</span><span class="s1">'name'</span><span class="p">]:</span> <span class="n">param</span> <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">params</span><span class="o">.</span><span class="n">base_params</span><span class="p">}</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">param_dic</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">param_name</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="s1">'</span><span class="si">{}</span><span class="s1"> is not a valid parameter'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">param_name</span><span class="p">))</span>
<span class="k">if</span> <span class="n">p_min</span> <span class="o">==</span> <span class="n">p_max</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"""</span><span class="si">{}</span><span class="s2"> has not been added to the dictionary of</span>
<span class="s2"> parameters to resample, add it's allowable min and max value</span>
<span class="s2"> to the Optimizer.param_ranges attribute in</span>
<span class="s2"> Optimizer.py"""</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">param_name</span><span class="p">))</span>
<span class="n">dim_case</span> <span class="o">=</span> <span class="kc">None</span>
<span class="n">nhru</span> <span class="o">=</span> <span class="n">params</span><span class="o">.</span><span class="n">dimensions</span><span class="p">[</span><span class="s1">'nhru'</span><span class="p">]</span>
<span class="n">ndims</span> <span class="o">=</span> <span class="n">param_dic</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">param_name</span><span class="p">)[</span><span class="s1">'ndims'</span><span class="p">]</span>
<span class="n">dimnames</span> <span class="o">=</span> <span class="n">param_dic</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">param_name</span><span class="p">)[</span><span class="s1">'dimnames'</span><span class="p">]</span>
<span class="n">length</span> <span class="o">=</span> <span class="n">param_dic</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">param_name</span><span class="p">)[</span><span class="s1">'length'</span><span class="p">]</span>
<span class="n">param</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="n">param_name</span><span class="p">])</span>
<span class="c1"># could expand list and check parameter name also e.g. cascade_flg</span>
<span class="c1"># is a parameter that should not be changed </span>
<span class="n">dims_to_not_change</span> <span class="o">=</span> <span class="nb">set</span><span class="p">([</span><span class="s1">'ncascade'</span><span class="p">,</span><span class="s1">'ncascdgw'</span><span class="p">,</span><span class="s1">'nreach'</span><span class="p">,</span>\
<span class="s1">'nsegment'</span><span class="p">])</span>
<span class="k">if</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">dims_to_not_change</span><span class="p">,</span> <span class="nb">set</span><span class="p">(</span><span class="n">dimnames</span><span class="p">)))</span> <span class="o">></span> <span class="mi">0</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"""</span><span class="si">{}</span><span class="s2"> should not be resampled as</span>
<span class="s2"> it relates to the location of cascade flow</span>
<span class="s2"> parameters."""</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">param_name</span><span class="p">))</span>
<span class="c1"># use param info to get dimension info- e.g. if multidimensional</span>
<span class="k">if</span> <span class="p">(</span><span class="n">ndims</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">length</span> <span class="o"><=</span> <span class="mi">366</span><span class="p">):</span>
<span class="n">dim_case</span> <span class="o">=</span> <span class="s1">'resample_each_value'</span> <span class="c1"># for smaller param dimensions</span>
<span class="k">elif</span> <span class="p">(</span><span class="n">ndims</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">length</span> <span class="o">></span> <span class="mi">366</span><span class="p">):</span>
<span class="n">dim_case</span> <span class="o">=</span> <span class="s1">'resample_all_values_once'</span> <span class="c1"># covers nssr, ngw, etc. </span>
<span class="k">elif</span> <span class="p">(</span><span class="n">ndims</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">dimnames</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'nmonths'</span> <span class="ow">and</span> \
<span class="n">nhru</span> <span class="o">==</span> <span class="n">params</span><span class="o">.</span><span class="n">dimensions</span><span class="p">[</span><span class="n">dimnames</span><span class="p">[</span><span class="mi">0</span><span class="p">]]):</span>
<span class="n">dim_case</span> <span class="o">=</span> <span class="s1">'nhru_nmonths'</span>
<span class="k">elif</span> <span class="ow">not</span> <span class="n">dim_case</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'The </span><span class="si">{}</span><span class="s1"> parameter is not set for resampling'</span><span class="o">.</span>\
<span class="nb">format</span><span class="p">(</span><span class="n">param_name</span><span class="p">))</span>
<span class="c1"># #testing purposes </span>
<span class="c1"># print('name: ', param_name)</span>
<span class="c1"># print('max_val: ', p_max)</span>
<span class="c1"># print('min_val: ', p_min)</span>
<span class="c1"># print('ndims: ', ndims)</span>
<span class="c1"># print('dimnames: ', dimnames)</span>
<span class="c1"># print('length: ', length)</span>
<span class="c1"># print('resample_method: ', dim_case)</span>
<span class="n">s</span> <span class="o">=</span> <span class="p">(</span><span class="n">p_max</span> <span class="o">-</span> <span class="n">p_min</span><span class="p">)</span> <span class="o">*</span> <span class="n">noise_factor</span> <span class="c1"># std_dev (s) default: param_range/10 </span>
<span class="c1">#do resampling based on param dimensions and sampling distribution</span>
<span class="k">if</span> <span class="n">dim_case</span> <span class="o">==</span> <span class="s1">'resample_all_values_once'</span><span class="p">:</span>
<span class="k">if</span> <span class="n">how</span> <span class="o">==</span> <span class="s1">'uniform'</span><span class="p">:</span>
<span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="n">p_min</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="n">p_max</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">param</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">how</span> <span class="o">==</span> <span class="s1">'normal'</span><span class="p">:</span> <span class="c1"># scale parameter mean if mu_factor given </span>
<span class="n">param</span> <span class="o">*=</span> <span class="n">mu_factor</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">param</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">param</span>
<span class="k">elif</span> <span class="n">dim_case</span> <span class="o">==</span> <span class="s1">'resample_each_value'</span><span class="p">:</span>
<span class="n">ret</span> <span class="o">=</span> <span class="n">param</span>
<span class="k">if</span> <span class="n">how</span> <span class="o">==</span> <span class="s1">'uniform'</span><span class="p">:</span>
<span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="n">p_min</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="n">p_max</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">param</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">how</span> <span class="o">==</span> <span class="s1">'normal'</span><span class="p">:</span> <span class="c1"># the original value is considered the mean</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">ret</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">el</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">param</span><span class="p">):</span>
<span class="n">mu</span> <span class="o">=</span> <span class="n">el</span> <span class="o">*</span> <span class="n">mu_factor</span>
<span class="n">ret</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span> <span class="c1"># single value parameter</span>
<span class="n">mu</span> <span class="o">=</span> <span class="n">param</span> <span class="o">*</span> <span class="n">mu_factor</span>
<span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span>
<span class="c1"># nhru by nmonth dimensional params</span>
<span class="k">elif</span> <span class="n">dim_case</span> <span class="o">==</span> <span class="s1">'nhru_nmonths'</span><span class="p">:</span>
<span class="n">ret</span> <span class="o">=</span> <span class="n">param</span>
<span class="k">if</span> <span class="n">how</span> <span class="o">==</span> <span class="s1">'uniform'</span><span class="p">:</span>
<span class="k">for</span> <span class="n">month</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">):</span>
<span class="n">ret</span><span class="p">[</span><span class="n">month</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="n">p_min</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="n">p_max</span><span class="p">,</span>\
<span class="n">size</span><span class="o">=</span><span class="n">param</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">how</span> <span class="o">==</span> <span class="s1">'normal'</span><span class="p">:</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">el</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">param</span><span class="p">):</span>
<span class="n">el</span> <span class="o">*=</span> <span class="n">mu_factor</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">el</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">ret</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">el</span>
<span class="k">return</span> <span class="n">ret</span>
<span class="k">def</span> <span class="nf">_mod_params</span><span class="p">(</span><span class="n">parameters</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">param_names</span><span class="p">):</span>
<span class="c1"># loop through list of params and assign their values to Parameter instance</span>
<span class="n">ret</span> <span class="o">=</span> <span class="n">copy</span><span class="p">(</span><span class="n">parameters</span><span class="p">)</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">param</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">params</span><span class="p">):</span>
<span class="n">ret</span><span class="p">[</span><span class="n">param_names</span><span class="p">[</span><span class="n">idx</span><span class="p">]]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">param</span><span class="p">)</span>
<span class="k">return</span> <span class="n">ret</span>
<div class="viewcode-block" id="OptimizationResult"><a class="viewcode-back" href="../../api.html#prms_python.OptimizationResult">[docs]</a><span class="k">class</span> <span class="nc">OptimizationResult</span><span class="p">:</span>
<span class="sd">"""</span>
<span class="sd"> The ``OptimizationResult`` object serves to collect and manage output </span>
<span class="sd"> from an ``Optimizer`` method. Upon initialization and a given optimization</span>
<span class="sd"> stage that was used when running the Optimizer method, e.g. ``monte_carlo``,</span>
<span class="sd"> the class gathers all JSON metadata that was produced for the given stage. </span>
<span class="sd"> The ``OptimizationResult`` has three main user methods: first ``result_table`` </span>
<span class="sd"> which returns the top n simulations according to four model performance </span>
<span class="sd"> metrics (Nash-Sutcliffe efficiency (NSE), root-mean squared-error (RMSE), </span>
<span class="sd"> percent bias (PBIAS), and the coefficient of determination (COEF_DET) as</span>
<span class="sd"> calculated against measured data. For example the table may look like:</span>
<span class="sd"> </span>
<span class="sd"> >>> ddsolrad_res = OptimizationResult(work_directory, stage=stage)</span>
<span class="sd"> >>> top10 = ddsolrad_res.result_table(freq='monthly',top_n=10)</span>
<span class="sd"> >>> top10</span>
<span class="sd"> ======================== ======== ======= ========= ========</span>
<span class="sd"> ddsolrad parameters NSE RMSE PBIAS COEF_DET</span>
<span class="sd"> ======================== ======== ======= ========= ======== </span>
<span class="sd"> orig_params 0.956267 39.4725 -0.885715 0.963116</span>
<span class="sd"> tmax_index_54.2224631748 0.921626 47.6092 -0.849256 0.94402</span>
<span class="sd"> tmax_index_44.8823940703 0.879965 58.9194 5.79603 0.922021</span>
<span class="sd"> tmax_index_47.6835387480 0.764133 82.5918 -4.78896 0.837582</span>
<span class="sd"> ======================== ======== ======= ========= ========</span>
<span class="sd"> Second, the ``get_top_ranked_sims`` which returns a dictionary that map </span>
<span class="sd"> key information about the top n ranked simulations, an example returned </span>
<span class="sd"> dictionary may look like:</span>
<span class="sd"> >>> {</span>
<span class="sd"> 'dir_name' : ['pathToSim1', 'pathToSim2'],</span>
<span class="sd"> 'param_path' : ['pathToSim1/input/parameters', 'pathToSim2/input/parameters'],</span>
<span class="sd"> 'statvar_path' : ['pathToSim1/output/statvar.dat', 'pathToSim2/output/statvar.dat'],</span>
<span class="sd"> 'params_adjusted' : [[param_names_sim1], [param_names_sim2]]</span>
<span class="sd"> }</span>
<span class="sd"> The third method of ``OptimizationResult`` is ``archive`` which essentially </span>
<span class="sd"> opens all parameter and statvar files from each simulation of the given </span>
<span class="sd"> stage and archives the parameters that were modified and their modified values</span>
<span class="sd"> and the statistical variable (PRMS time series output) that is associated with </span>
<span class="sd"> the optimization stage. Other ``Optimizer`` simulation metadata is also gathered</span>
<span class="sd"> and new JSON metadata containing only this information is created and written</span>
<span class="sd"> within a newly created "archived" subdirectory within the same directory that </span>
<span class="sd"> the ``Optimizer`` routine managed simulations. The ``OptimizationResult.archive``</span>
<span class="sd"> method then recursively deletes the simulation data for each of the given stage. </span>
<span class="sd"> """</span>
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">working_dir</span><span class="p">,</span> <span class="n">stage</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Create an ``OptimizationResult`` instance to manage output and analyse parameter-</span>
<span class="sd"> output relationships as produced by the use of an ``Optimizer`` method of a user</span>
<span class="sd"> defined optimization stage. </span>
<span class="sd"> """</span>
<span class="bp">self</span><span class="o">.</span><span class="n">working_dir</span> <span class="o">=</span> <span class="n">working_dir</span>
<span class="bp">self</span><span class="o">.</span><span class="n">stage</span> <span class="o">=</span> <span class="n">stage</span>
<span class="bp">self</span><span class="o">.</span><span class="n">metadata_json_paths</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_optr_jsons</span><span class="p">(</span><span class="n">working_dir</span><span class="p">,</span> <span class="n">stage</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">total_sims</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_count_total_sims</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">statvar_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_statvar_name</span><span class="p">(</span><span class="n">stage</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">measured</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_measured</span><span class="p">(</span><span class="n">stage</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">input_dir</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_input_dir</span><span class="p">(</span><span class="n">stage</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">input_params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_input_params</span><span class="p">(</span><span class="n">stage</span><span class="p">)</span>
<span class="c1"># if there are more than one input param for given stage</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_params</span><span class="p">)</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span>
<span class="sd">"""Warning: there were more than one initial parameter sets used for the </span>
<span class="sd"> optimization for stage: {}. Make sure to compare the the correct input </span>
<span class="sd"> params with their corresponding output sims."""</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">stage</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">This optimization stage used the</span><span class="se">\</span>
<span class="s1"> following input parameter files:</span><span class="se">\n</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_params</span><span class="p">)))</span>
<span class="k">def</span> <span class="nf">_count_total_sims</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="c1"># total number of simulations of given stage in working directory</span>
<span class="n">tracked_dirs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metadata_json_paths</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">stage</span><span class="p">]:</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="k">as</span> <span class="n">fh</span><span class="p">:</span>
<span class="n">json_data</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fh</span><span class="p">)</span>
<span class="n">tracked_dirs</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">json_data</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'sim_dirs'</span><span class="p">))</span>
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">tracked_dirs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_get_optr_jsons</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">work_dir</span><span class="p">,</span> <span class="n">stage</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Retrieve locations of optimization output jsons which contain </span>
<span class="sd"> metadata needed to understand optimization results.</span>
<span class="sd"> Create dictionary of each optimization with stage as key and lists</span>
<span class="sd"> of corresponding json file paths as values. </span>
<span class="sd"> Args:</span>
<span class="sd"> work_dir (str): path to directory with model results, i.e. </span>
<span class="sd"> location where simulation series outputs and optimization</span>
<span class="sd"> json files are located, aka Optimizer.working_dir</span>
<span class="sd"> stage (str): the stage ('ddsolrad', 'jhpet', 'flow', etc.) of </span>
<span class="sd"> the optimization in which to gather the jsons</span>
<span class="sd"> Returns:</span>
<span class="sd"> ret (dict): dictionary of stage (keys) and lists of </span>
<span class="sd"> json file paths for that stage (values). </span>
<span class="sd"> """</span>
<span class="n">ret</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">optr_metafile_re</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="sa">r</span><span class="s1">'^.*_</span><span class="si">{}</span><span class="s1">_opt(\d*)\.json'</span><span class="o">.</span>\
<span class="nb">format</span><span class="p">(</span><span class="n">stage</span><span class="p">))</span>
<span class="n">ret</span><span class="p">[</span><span class="n">stage</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">OPJ</span><span class="p">(</span><span class="n">work_dir</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span>\
<span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">work_dir</span><span class="p">)</span> <span class="k">if</span>\
<span class="n">optr_metafile_re</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="n">f</span><span class="p">)]</span>
<span class="k">return</span> <span class="n">ret</span>
<span class="k">def</span> <span class="nf">_get_input_dir</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">stage</span><span class="p">):</span>
<span class="c1"># retrieves the input directory from the first json file of given stage</span>
<span class="n">json_file</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metadata_json_paths</span><span class="p">[</span><span class="n">stage</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">json_file</span><span class="p">)</span> <span class="k">as</span> <span class="n">jf</span><span class="p">:</span>
<span class="n">meta_dic</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">jf</span><span class="p">)</span>
<span class="k">return</span> <span class="s1">'</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">sep</span><span class="p">)</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">meta_dic</span><span class="p">[</span><span class="s1">'original_params'</span><span class="p">]</span><span class="o">.</span>\
<span class="n">split</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">sep</span><span class="p">)[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="k">def</span> <span class="nf">_get_input_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">stage</span><span class="p">):</span>
<span class="n">json_files</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metadata_json_paths</span><span class="p">[</span><span class="n">stage</span><span class="p">]</span>
<span class="n">param_paths</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">json_file</span> <span class="ow">in</span> <span class="n">json_files</span><span class="p">:</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">json_file</span><span class="p">)</span> <span class="k">as</span> <span class="n">jf</span><span class="p">:</span>
<span class="n">meta_dic</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">jf</span><span class="p">)</span>
<span class="n">param_paths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">meta_dic</span><span class="p">[</span><span class="s1">'original_params'</span><span class="p">])</span>
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">param_paths</span><span class="p">))</span>
<span class="k">def</span> <span class="nf">_get_sim_dirs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">stage</span><span class="p">):</span>
<span class="n">jsons</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metadata_json_paths</span><span class="p">[</span><span class="n">stage</span><span class="p">]</span>
<span class="n">json_files</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">sim_dirs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">inf</span> <span class="ow">in</span> <span class="n">jsons</span><span class="p">:</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">inf</span><span class="p">)</span> <span class="k">as</span> <span class="n">json_file</span><span class="p">:</span>
<span class="n">json_files</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">json_file</span><span class="p">))</span>
<span class="k">for</span> <span class="n">json_file</span> <span class="ow">in</span> <span class="n">json_files</span><span class="p">:</span>
<span class="n">sim_dirs</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">json_file</span><span class="p">[</span><span class="s1">'sim_dirs'</span><span class="p">])</span>
<span class="c1"># list of all simulation directory paths for stage </span>
<span class="k">return</span> <span class="n">sim_dirs</span>
<span class="k">def</span> <span class="nf">_get_measured</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">stage</span><span class="p">):</span>
<span class="c1"># only need to open one json file to get this information</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">metadata_json_paths</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">stage</span><span class="p">):</span>
<span class="k">return</span> <span class="c1"># no optimization json files exist for given stage</span>
<span class="n">first_json</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metadata_json_paths</span><span class="p">[</span><span class="n">stage</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">first_json</span><span class="p">)</span> <span class="k">as</span> <span class="n">json_file</span><span class="p">:</span>
<span class="n">json_data</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">json_file</span><span class="p">)</span>
<span class="n">measured_series</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="o">.</span><span class="n">from_csv</span><span class="p">(</span><span class="n">json_data</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'measured'</span><span class="p">),</span>\
<span class="n">parse_dates</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">return</span> <span class="n">measured_series</span>
<span class="k">def</span> <span class="nf">_get_statvar_name</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">stage</span><span class="p">):</span>
<span class="c1"># only need to open one json file to get this information</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">first_json</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metadata_json_paths</span><span class="p">[</span><span class="n">stage</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
<span class="k">except</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"""No optimization has been run for</span>
<span class="s2"> stage: </span><span class="si">{}</span><span class="s2">"""</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">stage</span><span class="p">))</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">first_json</span><span class="p">)</span> <span class="k">as</span> <span class="n">json_file</span><span class="p">:</span>
<span class="n">json_data</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">json_file</span><span class="p">)</span>
<span class="n">var_name</span> <span class="o">=</span> <span class="n">json_data</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'statvar_name'</span><span class="p">)</span>
<span class="k">return</span> <span class="n">var_name</span>
<span class="k">def</span> <span class="nf">result_table</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">freq</span><span class="o">=</span><span class="s1">'daily'</span><span class="p">,</span> <span class="n">top_n</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">latex</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="c1">##TODO: add stats for freq options annual (means or sum)</span>
<span class="n">sim_dirs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_sim_dirs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stage</span><span class="p">)</span>
<span class="k">if</span> <span class="n">top_n</span> <span class="o">>=</span> <span class="nb">len</span><span class="p">(</span><span class="n">sim_dirs</span><span class="p">):</span>
<span class="n">top_n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">sim_dirs</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span> <span class="c1"># for returning inclusive last sim</span>
<span class="n">sim_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">sep</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">sim_dirs</span><span class="p">]</span>
<span class="n">meas_var</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_measured</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stage</span><span class="p">)</span>
<span class="n">statvar_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_statvar_name</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stage</span><span class="p">)</span>
<span class="n">orig_statvar</span> <span class="o">=</span> <span class="n">load_statvar</span><span class="p">(</span><span class="n">OPJ</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_dir</span><span class="p">,</span><span class="s1">'statvar.dat'</span><span class="p">))</span>\
<span class="p">[</span><span class="n">statvar_name</span><span class="p">]</span>
<span class="n">result_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span>\
<span class="p">[</span><span class="s1">'NSE'</span><span class="p">,</span><span class="s1">'RMSE'</span><span class="p">,</span><span class="s1">'PBIAS'</span><span class="p">,</span><span class="s1">'COEF_DET'</span><span class="p">,</span><span class="s1">'ABS(PBIAS)'</span><span class="p">])</span>
<span class="n">orig_results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">'orig_params'</span><span class="p">],</span>\
<span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'NSE'</span><span class="p">,</span><span class="s1">'RMSE'</span><span class="p">,</span><span class="s1">'PBIAS'</span><span class="p">,</span><span class="s1">'COEF_DET'</span><span class="p">])</span>
<span class="c1"># get datetime indices that overlap from measured and simulated</span>
<span class="n">sim_out</span> <span class="o">=</span> <span class="n">load_statvar</span><span class="p">(</span><span class="n">OPJ</span><span class="p">(</span><span class="n">sim_dirs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">'outputs'</span><span class="p">,</span> <span class="s1">'statvar.dat'</span><span class="p">))</span>\
<span class="p">[</span><span class="n">statvar_name</span><span class="p">]</span>
<span class="n">idx</span> <span class="o">=</span> <span class="n">meas_var</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">sim_out</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
<span class="n">meas_var</span> <span class="o">=</span> <span class="n">copy</span><span class="p">(</span><span class="n">meas_var</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
<span class="c1">#sim_out = sim_out[idx] </span>
<span class="n">orig_statvar</span> <span class="o">=</span> <span class="n">orig_statvar</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
<span class="k">if</span> <span class="n">freq</span> <span class="o">==</span> <span class="s1">'monthly'</span><span class="p">:</span>
<span class="n">meas_mo</span> <span class="o">=</span> <span class="n">meas_var</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">meas_var</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">month</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">orig_mo</span> <span class="o">=</span> <span class="n">orig_statvar</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">orig_statvar</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">month</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">sim</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sim_dirs</span><span class="p">):</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">sim_out</span> <span class="o">=</span> <span class="n">load_statvar</span><span class="p">(</span><span class="n">OPJ</span><span class="p">(</span><span class="n">sim</span><span class="p">,</span> <span class="s1">'outputs'</span><span class="p">,</span> <span class="s1">'statvar.dat'</span><span class="p">))</span>\
<span class="p">[</span><span class="s1">'</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">statvar_name</span><span class="p">)]</span>
<span class="k">except</span><span class="p">:</span> <span class="c1"># simulation might have been removed or missing</span>
<span class="k">pass</span>
<span class="n">sim_out</span> <span class="o">=</span> <span class="n">sim_out</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
<span class="k">if</span> <span class="n">freq</span> <span class="o">==</span> <span class="s1">'daily'</span><span class="p">:</span>
<span class="n">result_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">sim_names</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[</span>\
<span class="n">nash_sutcliffe</span><span class="p">(</span><span class="n">meas_var</span><span class="p">,</span> <span class="n">sim_out</span><span class="p">),</span>\
<span class="n">rmse</span><span class="p">(</span><span class="n">meas_var</span><span class="p">,</span> <span class="n">sim_out</span><span class="p">),</span>\
<span class="n">percent_bias</span><span class="p">(</span><span class="n">meas_var</span><span class="p">,</span><span class="n">sim_out</span><span class="p">),</span>\
<span class="n">meas_var</span><span class="o">.</span><span class="n">corr</span><span class="p">(</span><span class="n">sim_out</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span>\
<span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">percent_bias</span><span class="p">(</span><span class="n">meas_var</span><span class="p">,</span> <span class="n">sim_out</span><span class="p">))</span> <span class="p">]</span>
<span class="n">orig_results</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'orig_params'</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>\
<span class="n">nash_sutcliffe</span><span class="p">(</span><span class="n">orig_statvar</span><span class="p">,</span><span class="n">meas_var</span><span class="p">),</span>\
<span class="n">rmse</span><span class="p">(</span><span class="n">orig_statvar</span><span class="p">,</span><span class="n">meas_var</span><span class="p">),</span>\
<span class="n">percent_bias</span><span class="p">(</span><span class="n">orig_statvar</span><span class="p">,</span><span class="n">meas_var</span><span class="p">),</span>\
<span class="n">orig_statvar</span><span class="o">.</span><span class="n">corr</span><span class="p">(</span><span class="n">meas_var</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">]</span>
<span class="k">elif</span> <span class="n">freq</span> <span class="o">==</span> <span class="s1">'monthly'</span><span class="p">:</span>
<span class="n">sim_out</span> <span class="o">=</span> <span class="n">sim_out</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">sim_out</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">month</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">result_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">sim_names</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[</span>\
<span class="n">nash_sutcliffe</span><span class="p">(</span><span class="n">meas_mo</span><span class="p">,</span> <span class="n">sim_out</span><span class="p">),</span>\
<span class="n">rmse</span><span class="p">(</span><span class="n">meas_mo</span><span class="p">,</span> <span class="n">sim_out</span><span class="p">),</span>\
<span class="n">percent_bias</span><span class="p">(</span><span class="n">meas_mo</span><span class="p">,</span> <span class="n">sim_out</span><span class="p">),</span>\
<span class="n">meas_mo</span><span class="o">.</span><span class="n">corr</span><span class="p">(</span><span class="n">sim_out</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span>\
<span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">percent_bias</span><span class="p">(</span><span class="n">meas_mo</span><span class="p">,</span> <span class="n">sim_out</span><span class="p">))</span> <span class="p">]</span>
<span class="n">orig_results</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">'orig_params'</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>\
<span class="n">nash_sutcliffe</span><span class="p">(</span><span class="n">orig_mo</span><span class="p">,</span><span class="n">meas_mo</span><span class="p">),</span>\
<span class="n">rmse</span><span class="p">(</span><span class="n">orig_mo</span><span class="p">,</span><span class="n">meas_mo</span><span class="p">),</span>\
<span class="n">percent_bias</span><span class="p">(</span><span class="n">orig_mo</span><span class="p">,</span><span class="n">meas_mo</span><span class="p">),</span>\
<span class="n">orig_mo</span><span class="o">.</span><span class="n">corr</span><span class="p">(</span><span class="n">meas_mo</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">]</span>
<span class="n">sorted_result</span> <span class="o">=</span> <span class="n">result_df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s1">'NSE'</span><span class="p">,</span><span class="s1">'RMSE'</span><span class="p">,</span><span class="s1">'ABS(PBIAS)'</span><span class="p">,</span>\
<span class="s1">'COEF_DET'</span><span class="p">],</span> <span class="n">ascending</span><span class="o">=</span><span class="p">[</span><span class="kc">False</span><span class="p">,</span><span class="kc">True</span><span class="p">,</span><span class="kc">True</span><span class="p">,</span><span class="kc">False</span><span class="p">])</span>
<span class="n">sorted_result</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">'</span><span class="si">{}</span><span class="s1"> parameters'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stage</span><span class="p">)</span>
<span class="n">sorted_result</span> <span class="o">=</span> <span class="n">sorted_result</span><span class="p">[[</span><span class="s1">'NSE'</span><span class="p">,</span><span class="s1">'RMSE'</span><span class="p">,</span><span class="s1">'PBIAS'</span><span class="p">,</span><span class="s1">'COEF_DET'</span><span class="p">]]</span>
<span class="n">sorted_result</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">orig_results</span><span class="p">,</span><span class="n">sorted_result</span><span class="p">])</span>
<span class="k">if</span> <span class="n">latex</span><span class="p">:</span> <span class="k">return</span> <span class="n">sorted_result</span><span class="p">[:</span><span class="n">top_n</span><span class="p">]</span><span class="o">.</span><span class="n">to_latex</span><span class="p">(</span><span class="n">escape</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span> <span class="k">return</span> <span class="n">sorted_result</span><span class="p">[:</span><span class="n">top_n</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">get_top_ranked_sims</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sorted_df</span><span class="p">):</span>
<span class="c1"># use result table to make dic with best param and statvar paths </span>
<span class="c1"># index of table are simulation directory names</span>
<span class="n">ret</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'dir_name'</span> <span class="p">:</span> <span class="p">[],</span>
<span class="s1">'param_path'</span> <span class="p">:</span> <span class="p">[],</span>
<span class="s1">'statvar_path'</span> <span class="p">:</span> <span class="p">[],</span>
<span class="s1">'params_adjusted'</span> <span class="p">:</span> <span class="p">[]</span>
<span class="p">}</span>
<span class="n">json_paths</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metadata_json_paths</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">stage</span><span class="p">]</span>
<span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="n">sorted_df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">'orig_params'</span><span class="p">)</span><span class="o">.</span><span class="n">index</span><span class="p">:</span>
<span class="n">ret</span><span class="p">[</span><span class="s1">'dir_name'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">el</span><span class="p">)</span>
<span class="n">ret</span><span class="p">[</span><span class="s1">'param_path'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">OPJ</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">working_dir</span><span class="p">,</span><span class="n">el</span><span class="p">,</span><span class="s1">'inputs'</span><span class="p">,</span>\
<span class="s1">'parameters'</span><span class="p">))</span>
<span class="n">ret</span><span class="p">[</span><span class="s1">'statvar_path'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">OPJ</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">working_dir</span><span class="p">,</span><span class="n">el</span><span class="p">,</span><span class="s1">'outputs'</span><span class="p">,</span>\
<span class="s1">'statvar.dat'</span><span class="p">))</span>
<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">json_paths</span><span class="p">:</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="k">as</span> <span class="n">fh</span><span class="p">:</span>
<span class="n">json_data</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fh</span><span class="p">)</span>
<span class="k">if</span> <span class="n">OPJ</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">working_dir</span><span class="p">,</span> <span class="n">el</span><span class="p">)</span> <span class="ow">in</span> <span class="n">json_data</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'sim_dirs'</span><span class="p">):</span>
<span class="n">ret</span><span class="p">[</span><span class="s1">'params_adjusted'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>\
<span class="n">json_data</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'params_adjusted'</span><span class="p">))</span>
<span class="k">return</span> <span class="n">ret</span>
<div class="viewcode-block" id="OptimizationResult.archive"><a class="viewcode-back" href="../../api.html#prms_python.OptimizationResult.archive">[docs]</a> <span class="k">def</span> <span class="nf">archive</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">remove_sims</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">remove_meta</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">metric_freq</span><span class="o">=</span><span class="s1">'daily'</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Create archive directory to hold json files that contain </span>
<span class="sd"> information of adjusted parameters, model output, and performance </span>
<span class="sd"> metrics for each Optimizer simulation of the </span>
<span class="sd"> OptimizationResult.stage in the OptimizationResult.working_dir. </span>
<span class="sd"> </span>
<span class="sd"> Kwargs: </span>
<span class="sd"> remove_sims (bool) : If True recursively delete all folders </span>
<span class="sd"> and files associated with original simulations of the </span>
<span class="sd"> OptimizationResult.stage in the </span>
<span class="sd"> OptimizationResult.working_dir, if False do not delete </span>
<span class="sd"> simulations.</span>
<span class="sd"> remove_meta (bool) : Whether to delete original Optimizer</span>
<span class="sd"> JSON metadata files, default is False.</span>
<span class="sd"> metric_freq (Str) : Frequency of output metric computation </span>
<span class="sd"> for recording of model performance. Can be 'daily' </span>
<span class="sd"> (default) or 'monthly'. Note, other results can be computed </span>
<span class="sd"> later with archived results. </span>
<span class="sd"> Returns: </span>
<span class="sd"> None </span>
<span class="sd"> """</span>
<span class="c1"># create output archive directory</span>
<span class="n">archive_dir</span> <span class="o">=</span> <span class="n">OPJ</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">working_dir</span><span class="p">,</span><span class="s2">"</span><span class="si">{}</span><span class="s2">_archived"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stage</span><span class="p">))</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">archive_dir</span><span class="p">):</span>
<span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">archive_dir</span><span class="p">)</span>
<span class="c1"># create table and use to make mapping dic </span>
<span class="n">table</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">result_table</span><span class="p">(</span><span class="n">freq</span><span class="o">=</span><span class="n">metric_freq</span><span class="p">,</span>\
<span class="n">top_n</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">total_sims</span><span class="p">,</span> <span class="n">latex</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">map_dic</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_top_ranked_sims</span><span class="p">(</span><span class="n">table</span><span class="p">)</span>
<span class="n">metadata_json_paths</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metadata_json_paths</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">stage</span><span class="p">]</span>
<span class="c1"># get measured optimization variable path</span>
<span class="n">first_json</span> <span class="o">=</span> <span class="n">metadata_json_paths</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">first_json</span><span class="p">)</span> <span class="k">as</span> <span class="n">json_file</span><span class="p">:</span>
<span class="n">json_data</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">json_file</span><span class="p">)</span>
<span class="n">measured_path</span> <span class="o">=</span> <span class="n">json_data</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'measured'</span><span class="p">)</span>
<span class="c1"># record info for each simulation and archive to JSONs</span>
<span class="c1"># pandas series and numpy arrays are converted to Python lists </span>
<span class="c1"># for JSON serialization</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">sim</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">map_dic</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'dir_name'</span><span class="p">)):</span>
<span class="n">json_path</span> <span class="o">=</span> <span class="n">OPJ</span><span class="p">(</span><span class="n">archive_dir</span><span class="p">,</span> <span class="s1">'</span><span class="si">{sim}</span><span class="s1">.json'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">sim</span><span class="o">=</span><span class="n">sim</span><span class="p">))</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">output_series</span> <span class="o">=</span> <span class="n">load_statvar</span><span class="p">(</span><span class="n">OPJ</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">working_dir</span><span class="p">,</span> <span class="n">sim</span><span class="p">,</span>\
<span class="s1">'outputs'</span><span class="p">,</span> <span class="s1">'statvar.dat'</span><span class="p">))</span>\
<span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">statvar_name</span><span class="p">]</span>
<span class="k">except</span><span class="p">:</span> <span class="c1"># simulation directory was already removed</span>
<span class="k">continue</span>
<span class="c1"># look for resampling method info for the particular simulation</span>
<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">metadata_json_paths</span><span class="p">:</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="k">as</span> <span class="n">tmp_file</span><span class="p">:</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">tmp_file</span><span class="p">)</span>
<span class="k">if</span> <span class="n">OPJ</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">working_dir</span><span class="p">,</span> <span class="n">sim</span><span class="p">)</span> <span class="ow">in</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'sim_dirs'</span><span class="p">):</span>
<span class="n">resample</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'resample'</span><span class="p">)</span>
<span class="n">noise_factor</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'noise_factor'</span><span class="p">)</span>
<span class="n">mu_factor</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'mu_factor'</span><span class="p">)</span>
<span class="n">json_data</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'param_names'</span> <span class="p">:</span> <span class="p">[],</span>
<span class="s1">'param_values'</span> <span class="p">:</span> <span class="p">[],</span>
<span class="s1">'original_param_path'</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_params</span><span class="p">,</span>
<span class="s1">'measured_path'</span> <span class="p">:</span> <span class="n">measured_path</span><span class="p">,</span>
<span class="s1">'output_name'</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">statvar_name</span><span class="p">,</span>
<span class="s1">'output_date_index'</span> <span class="p">:</span> <span class="n">output_series</span><span class="o">.</span>\
<span class="n">index</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
<span class="s1">'output_values'</span> <span class="p">:</span> <span class="n">output_series</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
<span class="s1">'metric_freq'</span> <span class="p">:</span> <span class="n">metric_freq</span><span class="p">,</span>
<span class="s1">'resample'</span> <span class="p">:</span> <span class="n">resample</span><span class="p">,</span>
<span class="s1">'stage'</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">stage</span><span class="p">,</span>
<span class="s1">'mu_factor'</span> <span class="p">:</span> <span class="n">mu_factor</span><span class="p">,</span>
<span class="s1">'noise_factor'</span> <span class="p">:</span> <span class="n">noise_factor</span><span class="p">,</span>
<span class="s1">'NSE'</span> <span class="p">:</span> <span class="n">table</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">sim</span><span class="p">,</span> <span class="s1">'NSE'</span><span class="p">],</span>
<span class="s1">'RMSE'</span> <span class="p">:</span> <span class="n">table</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">sim</span><span class="p">,</span> <span class="s1">'RMSE'</span><span class="p">],</span>
<span class="s1">'PBIAS'</span> <span class="p">:</span> <span class="n">table</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">sim</span><span class="p">,</span> <span class="s1">'PBIAS'</span><span class="p">],</span>
<span class="s1">'COEF_DET'</span> <span class="p">:</span> <span class="n">table</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">sim</span><span class="p">,</span> <span class="s1">'COEF_DET'</span><span class="p">]</span>
<span class="p">}</span>
<span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">map_dic</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'params_adjusted'</span><span class="p">)[</span><span class="n">i</span><span class="p">]:</span>
<span class="n">json_data</span><span class="p">[</span><span class="s1">'param_names'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">param</span><span class="p">)</span>
<span class="n">json_data</span><span class="p">[</span><span class="s1">'param_values'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Parameters</span><span class="p">(</span>\
<span class="n">map_dic</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'param_path'</span><span class="p">)[</span><span class="n">i</span><span class="p">])[</span><span class="n">param</span><span class="p">]</span>\
<span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
<span class="c1"># save JSON file into archive directory </span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">json_path</span><span class="p">,</span> <span class="s1">'w'</span><span class="p">)</span> <span class="k">as</span> <span class="n">outf</span><span class="p">:</span>
<span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">json_data</span><span class="p">,</span> <span class="n">outf</span><span class="p">,</span> <span class="n">sort_keys</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">indent</span> <span class="o">=</span> <span class="mi">4</span><span class="p">,</span>\
<span class="n">ensure_ascii</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span>
<span class="c1"># recursively delete all simulation directories after archiving</span>
<span class="k">if</span> <span class="n">remove_sims</span><span class="p">:</span>
<span class="n">path</span> <span class="o">=</span> <span class="n">OPJ</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">working_dir</span><span class="p">,</span> <span class="n">sim</span><span class="p">)</span>
<span class="k">for</span> <span class="n">dirpath</span><span class="p">,</span> <span class="n">dirnames</span><span class="p">,</span> <span class="n">filenames</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">walk</span><span class="p">(</span><span class="n">path</span><span class="p">,</span>\
<span class="n">topdown</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="n">dirpath</span><span class="p">,</span> <span class="n">ignore_errors</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">continue</span>
<span class="c1"># optional delete the original JSON metadata</span>
<span class="k">if</span> <span class="n">remove_meta</span><span class="p">:</span>
<span class="k">for</span> <span class="n">meta_file</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metadata_json_paths</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">stage</span><span class="p">]:</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">meta_file</span><span class="p">)</span>
<span class="k">except</span><span class="p">:</span>
<span class="k">continue</span></div></div>
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