From 414c72998b74a732ebcde09ce44c1e848041352a Mon Sep 17 00:00:00 2001
From: KyleKlenk <kyle.c.klenk@gmail.com>
Date: Thu, 11 Aug 2022 17:13:03 -0600
Subject: [PATCH] added the laugh tests to the dir

---
 build/makefile                                |  16 +-
 .../laugh_tests/celia1990/output/runinfo.txt  |   2 +-
 ... summa-actors_celia1990GRU1-1_timestep.nc} | Bin
 .../celia1990/plot_lt_celia1990.ipynb         | 155 ++++++++++++++++++
 utils/laugh_tests/celia1990/run_test_summa.sh |   2 +-
 .../settings/summa_fileManager_celia1990.txt  |   2 +-
 .../summa_fileManager_verify_celia1990.txt    |  20 +++
 .../celia1990/verification_data/runinfo.txt   |   1 +
 .../summa_celia1990_G1-1_timestep.nc}         | Bin
 utils/laugh_tests/celia1990/verify_celia.py   | 114 +++++++++++++
 utils/laugh_tests/dir_setup.sh                |  11 +-
 11 files changed, 307 insertions(+), 16 deletions(-)
 rename utils/laugh_tests/celia1990/output/{celia1990GRU1-1_timestep.nc => summa-actors_celia1990GRU1-1_timestep.nc} (100%)
 create mode 100644 utils/laugh_tests/celia1990/plot_lt_celia1990.ipynb
 create mode 100644 utils/laugh_tests/celia1990/settings/summa_fileManager_verify_celia1990.txt
 create mode 100644 utils/laugh_tests/celia1990/verification_data/runinfo.txt
 rename utils/laugh_tests/celia1990/{output/celia1990_G1-1_timestep.nc => verification_data/summa_celia1990_G1-1_timestep.nc} (100%)
 create mode 100644 utils/laugh_tests/celia1990/verify_celia.py

diff --git a/build/makefile b/build/makefile
index 13369cb..7fa0ce3 100644
--- a/build/makefile
+++ b/build/makefile
@@ -14,16 +14,16 @@ ACTORS_LIBRARIES = -L/usr/lib -L/usr/local/lib -L/Summa-Actors/bin -lcaf_core -l
 
 
 # Production runs
-# FLAGS_NOAH = -O3 -ffree-form -ffree-line-length-none -fmax-errors=0 -fPIC -Wfatal-errors
-# FLAGS_COMM = -O3 -ffree-line-length-none -fmax-errors=0 -fPIC -Wfatal-errors
-# FLAGS_SUMMA = -O3 -ffree-line-length-none -fmax-errors=0 -fPIC -Wfatal-errors
-# FLAGS_ACTORS = -O3 -Wfatal-errors -std=c++17
+FLAGS_NOAH = -O3 -ffree-form -ffree-line-length-none -fmax-errors=0 -fPIC -Wfatal-errors
+FLAGS_COMM = -O3 -ffree-line-length-none -fmax-errors=0 -fPIC -Wfatal-errors
+FLAGS_SUMMA = -O3 -ffree-line-length-none -fmax-errors=0 -fPIC -Wfatal-errors
+FLAGS_ACTORS = -O3 -Wfatal-errors -std=c++17
 
 # # Debug runs
-FLAGS_NOAH = -g -O0 -ffree-form -ffree-line-length-none -fmax-errors=0 -fbacktrace -Wno-unused -Wno-unused-dummy-argument -fPIC
-FLAGS_COMM = -g -O0 -Wall -ffree-line-length-none -fmax-errors=0 -fbacktrace -fcheck=bounds -fPIC
-FLAGS_SUMMA = -g -O0 -Wall -ffree-line-length-none -fmax-errors=0 -fbacktrace -fcheck=bounds -fPIC
-FLAGS_ACTORS = -g -O0 -Wall -std=c++17
+# FLAGS_NOAH = -g -O0 -ffree-form -ffree-line-length-none -fmax-errors=0 -fbacktrace -Wno-unused -Wno-unused-dummy-argument -fPIC
+# FLAGS_COMM = -g -O0 -Wall -ffree-line-length-none -fmax-errors=0 -fbacktrace -fcheck=bounds -fPIC
+# FLAGS_SUMMA = -g -O0 -Wall -ffree-line-length-none -fmax-errors=0 -fbacktrace -fcheck=bounds -fPIC
+# FLAGS_ACTORS = -g -O0 -Wall -std=c++17
 
 
 
diff --git a/utils/laugh_tests/celia1990/output/runinfo.txt b/utils/laugh_tests/celia1990/output/runinfo.txt
index d1d8b8e..0ae02e8 100644
--- a/utils/laugh_tests/celia1990/output/runinfo.txt
+++ b/utils/laugh_tests/celia1990/output/runinfo.txt
@@ -1 +1 @@
- Run start time on system:  ccyy=2022 - mm=08 - dd=11 - hh=18 - mi=58 - ss=23.254
+ Run start time on system:  ccyy=2022 - mm=08 - dd=11 - hh=19 - mi=12 - ss=22.249
diff --git a/utils/laugh_tests/celia1990/output/celia1990GRU1-1_timestep.nc b/utils/laugh_tests/celia1990/output/summa-actors_celia1990GRU1-1_timestep.nc
similarity index 100%
rename from utils/laugh_tests/celia1990/output/celia1990GRU1-1_timestep.nc
rename to utils/laugh_tests/celia1990/output/summa-actors_celia1990GRU1-1_timestep.nc
diff --git a/utils/laugh_tests/celia1990/plot_lt_celia1990.ipynb b/utils/laugh_tests/celia1990/plot_lt_celia1990.ipynb
new file mode 100644
index 0000000..fa005fa
--- /dev/null
+++ b/utils/laugh_tests/celia1990/plot_lt_celia1990.ipynb
@@ -0,0 +1,155 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# modules\n",
+    "from pathlib import Path\n",
+    "from datetime import datetime\n",
+    "import xarray as xr # note, also needs netcdf4 library installed\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Specify the data locations relative to the notebook\n",
+    "sim_path = Path(\"/home/local/kck540/SUMMA-Projects/Summa-Actors/utils/laugh_tests/celia1990/output\")\n",
+    "sim_name = \"summa-actors_celia1990GRU1-1_timestep.nc\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Specify plotting dimensions\n",
+    "timesteps = [10,32,49]\n",
+    "midToto = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Specify the base time\n",
+    "time_ref = datetime.strptime('2000-01-01 0:00:00', '%Y-%m-%d %H:%M:%S')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Load the data\n",
+    "ds = xr.open_dataset( sim_path / sim_name ).isel(hru=0, gru=0).load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove the mising data layers\n",
+    "ds = ds.where(ds['mLayerDepth'] != -9999, drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ensure that we can actually read the figure labels\n",
+    "font = {'weight' : 'normal',\n",
+    "        'size'   : 18}\n",
+    "\n",
+    "plt.rc('font', **font)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1440x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Open a figure\n",
+    "fig = plt.figure(figsize=(18, 10), dpi= 80, facecolor='w', edgecolor='k');\n",
+    "\n",
+    "# Plot the data\n",
+    "for time in timesteps:\n",
+    "    \n",
+    "    # legend label\n",
+    "    time_cur = ds['time'][time].dt.round('s') # Extract time @ timestep, rounded to the nearest second\n",
+    "    time_dif = pd.to_datetime(time_cur.data) - time_ref # time_cur is a datetime64 object, needs to be datetime\n",
+    "    lbl = str(round(time_dif.total_seconds())) + 's' # round() gets rid of the decimal 0 that's added by default\n",
+    "    \n",
+    "    # data\n",
+    "    plt.plot(ds['mLayerMatricHead'].isel(time=time,midToto=midToto), ds['mLayerHeight'].isel(time=time), \\\n",
+    "             marker='.', label=lbl);\n",
+    "\n",
+    "# Make sure that increasing depth points downward\n",
+    "plt.gca().invert_yaxis()\n",
+    "\n",
+    "# Labels\n",
+    "plt.xlabel('Pressure head [m]'); # note, ';' supresses output from the Text object that is created for the labels\n",
+    "plt.ylabel('Depth [m]');\n",
+    "plt.legend();\n",
+    "\n",
+    "# Save the figure\n",
+    "plt.savefig('summa-actors-lt1_celia1990.png');"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.10 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/utils/laugh_tests/celia1990/run_test_summa.sh b/utils/laugh_tests/celia1990/run_test_summa.sh
index 056571b..640d339 100755
--- a/utils/laugh_tests/celia1990/run_test_summa.sh
+++ b/utils/laugh_tests/celia1990/run_test_summa.sh
@@ -1,3 +1,3 @@
 #! /bin/bash
 
-/SUMMA/bin/summa.exe -g 1 1 -m /Summa-Actors/utils/laugh_tests/celia1990/settings/summa_fileManager_celia1990.txt
\ No newline at end of file
+/SUMMA/bin/summa.exe -g 1 1 -m /Summa-Actors/utils/laugh_tests/celia1990/settings/summa_fileManager_verify_celia1990.txt
\ No newline at end of file
diff --git a/utils/laugh_tests/celia1990/settings/summa_fileManager_celia1990.txt b/utils/laugh_tests/celia1990/settings/summa_fileManager_celia1990.txt
index 2432269..d2d4772 100644
--- a/utils/laugh_tests/celia1990/settings/summa_fileManager_celia1990.txt
+++ b/utils/laugh_tests/celia1990/settings/summa_fileManager_celia1990.txt
@@ -15,7 +15,7 @@ attributeFile        'summa_zLocalAttributes.nc' !  local_attr
 trialParamFile       'summa_zParamTrial_celia1990.nc' !  para_trial
 forcingListFile      'summa_zForcingFileList.txt' !  forcing_list
 initConditionFile    'summa_zInitialCond_celia1990.nc' !  initial_cond
-outFilePrefix        'celia1990' !  output_prefix
+outFilePrefix        'summa-actors_celia1990' !  output_prefix
 vegTableFile         'VEGPARM.TBL' ! 
 soilTableFile        'SOILPARM.TBL' ! 
 generalTableFile     'GENPARM.TBL' ! 
diff --git a/utils/laugh_tests/celia1990/settings/summa_fileManager_verify_celia1990.txt b/utils/laugh_tests/celia1990/settings/summa_fileManager_verify_celia1990.txt
new file mode 100644
index 0000000..a874ea8
--- /dev/null
+++ b/utils/laugh_tests/celia1990/settings/summa_fileManager_verify_celia1990.txt
@@ -0,0 +1,20 @@
+controlVersion       'SUMMA_FILE_MANAGER_V3.0.0' !  fman_ver
+simStartTime         '2000-01-01 00:30' ! 
+simEndTime           '2000-01-03 12:00' ! 
+tmZoneInfo           'localTime' ! 
+settingsPath         '/Summa-Actors/utils/laugh_tests/celia1990/settings/' !  setting_path
+forcingPath          '/Summa-Actors/utils/laugh_tests/celia1990/forcing_data/' !  input_path
+outputPath           '/Summa-Actors/utils/laugh_tests/celia1990/verification_data/' ! 
+decisionsFile        'summa_zDecisions_celia1990.txt' !  decision
+outputControlFile    'Model_Output.txt' !  OUTPUT_CONTROL
+globalHruParamFile   'summa_zLocalParamInfo.txt' !  local_par
+globalGruParamFile   'summa_zBasinParamInfo.txt' !  basin_par
+attributeFile        'summa_zLocalAttributes.nc' !  local_attr
+trialParamFile       'summa_zParamTrial_celia1990.nc' !  para_trial
+forcingListFile      'summa_zForcingFileList.txt' !  forcing_list
+initConditionFile    'summa_zInitialCond_celia1990.nc' !  initial_cond
+outFilePrefix        'summa_celia1990' !  output_prefix
+vegTableFile         'VEGPARM.TBL' ! 
+soilTableFile        'SOILPARM.TBL' ! 
+generalTableFile     'GENPARM.TBL' ! 
+noahmpTableFile      'MPTABLE.TBL' ! 
\ No newline at end of file
diff --git a/utils/laugh_tests/celia1990/verification_data/runinfo.txt b/utils/laugh_tests/celia1990/verification_data/runinfo.txt
new file mode 100644
index 0000000..50770b1
--- /dev/null
+++ b/utils/laugh_tests/celia1990/verification_data/runinfo.txt
@@ -0,0 +1 @@
+ Run start time on system:  ccyy=2022 - mm=08 - dd=11 - hh=19 - mi=08 - ss=25.918
diff --git a/utils/laugh_tests/celia1990/output/celia1990_G1-1_timestep.nc b/utils/laugh_tests/celia1990/verification_data/summa_celia1990_G1-1_timestep.nc
similarity index 100%
rename from utils/laugh_tests/celia1990/output/celia1990_G1-1_timestep.nc
rename to utils/laugh_tests/celia1990/verification_data/summa_celia1990_G1-1_timestep.nc
diff --git a/utils/laugh_tests/celia1990/verify_celia.py b/utils/laugh_tests/celia1990/verify_celia.py
new file mode 100644
index 0000000..1d7a336
--- /dev/null
+++ b/utils/laugh_tests/celia1990/verify_celia.py
@@ -0,0 +1,114 @@
+from os import listdir
+from os.path import isfile, join
+from pathlib import Path
+import xarray as xr
+import numpy as np
+
+numHRU = 1
+
+time = "time" 
+nSnow = "nSnow" 
+nSoil = "nSoil" 
+nLayers = "nLayers" 
+mLayerHeight = "mLayerHeight" 
+iLayerLiqFluxSoil = "iLayerLiqFluxSoil" 
+mLayerDepth = "mLayerDepth" 
+mLayerVolFracIce = "mLayerVolFracIce" 
+mLayerVolFracLiq = "mLayerVolFracLiq" 
+mLayerMatricHead = "mLayerMatricHead" 
+mLayerTranspire = "mLayerTranspire" 
+mLayerBaseflow = "mLayerBaseflow" 
+mLayerCompress = "mLayerCompress" 
+iLayerNrgFlux = "iLayerNrgFlux" 
+basin__TotalArea = "basin__TotalArea" 
+scalarGroundEvaporation = "scalarGroundEvaporation" 
+scalarSoilBaseflow = "scalarSoilBaseflow" 
+scalarSoilDrainage = "scalarSoilDrainage" 
+scalarInfiltration = "scalarInfiltration" 
+scalarSnowDrainage = "scalarSnowDrainage" 
+scalarSnowSublimation = "scalarSnowSublimation" 
+scalarThroughfallRain = "scalarThroughfallRain" 
+scalarThroughfallSnow = "scalarThroughfallSnow" 
+scalarRainfall = "scalarRainfall" 
+scalarSnowfall = "scalarSnowfall" 
+scalarRainPlusMelt = "scalarRainPlusMelt" 
+pptrate = "pptrate" 
+averageRoutedRunoff = "averageRoutedRunoff" 
+scalarSWE = "scalarSWE"
+fieldCapacity = "fieldCapacity"
+
+output_variables = [time, nSnow, nSoil, nLayers, mLayerHeight, iLayerLiqFluxSoil, \
+    mLayerDepth, mLayerVolFracIce, mLayerVolFracLiq, mLayerMatricHead, mLayerTranspire, \
+    mLayerBaseflow, mLayerCompress, iLayerNrgFlux, basin__TotalArea, scalarGroundEvaporation, \
+    scalarSoilBaseflow, scalarSoilDrainage, scalarInfiltration, scalarSnowDrainage, \
+    scalarSnowSublimation, scalarThroughfallRain, scalarThroughfallSnow, scalarRainfall, \
+    scalarSnowfall, scalarRainPlusMelt, pptrate, averageRoutedRunoff, \
+    scalarSWE, fieldCapacity]
+
+# find the output files
+verified_data_path = Path("/home/local/kck540/SUMMA-Projects/Summa-Actors/utils/laugh_tests/celia1990/verification_data/summa_celia1990_G1-1_timestep.nc")
+data_to_compare_path = Path("/home/local/kck540/SUMMA-Projects/Summa-Actors/utils/laugh_tests/celia1990/output/summa-actors_celia1990GRU1-1_timestep.nc")
+    
+try:
+    verified_dataset = xr.open_dataset(verified_data_path)
+    to_compare_dataset = xr.open_dataset(data_to_compare_path)
+except FileNotFoundError:
+    print("Check the variables \'verified_data_path\' and \'data_to_compare_path\'. They may not point to the correct output files or the output filenames may have changed.")
+    exit()
+
+# Get the HRUs from the dataset into a list
+for iHRU in range(0, numHRU):
+    verified_hru = verified_dataset.isel(hru=iHRU).copy()
+    hru_to_compare = to_compare_dataset.isel(hru=iHRU).copy()
+
+    for var in output_variables:
+        try:
+            if len(verified_hru[var].values) != len(hru_to_compare[var].values):
+                print("ERROR: output variable", var, "does not contain the same amount of data")
+                print("     verified_hru = ", len(verified_hru[var].values))
+                print("     hru_to_compare = ", len(hru_to_compare[var].values))
+            
+            verified_data = []
+            to_verify_data = []
+            if (verified_hru[var].values.ndim > 1):
+                # 2D output case
+                for list in verified_hru[var].values:
+                    for data in list:
+                        verified_data.append(data)
+                
+                for list in hru_to_compare[var].values:
+                    for data in list:
+                        to_verify_data.append(data)
+
+            else:
+                # 1D output case
+                for data in verified_hru[var].values:
+                    verified_data.append(data)
+                
+                for data in hru_to_compare[var].values:
+                    to_verify_data.append(data)
+
+                                
+            # check length
+            if len(verified_data) != len(to_verify_data):
+                print("ERROR: output variable", var, "does not contain the same amount of data")
+                print("     verified_hru = ", len(verified_data))
+                print("     hru_to_compare = ", len(to_verify_data))
+
+            # check values
+            for elem in range(0, len(verified_data)):
+                if verified_data[elem] != to_verify_data[elem]:
+                    print("variable -",var, "has different values at", elem)
+                    print("     verified_hru = ", verified_data[elem])
+                    print("     hru_to_compare = ", to_verify_data[elem])
+
+            # if (verified_hru[var].values != hru_to_compare[var].values).all():
+            #     print("ERROR: Output data is not the same in",var)
+            #     print("     verified_hru = ", verified_hru[var].values)
+            #     print("     hru_to_compare = ", hru_to_compare[var].values)
+        except TypeError:
+            print("variable - ", var, "Cannot be compared with len")
+            print("     verified_hru = ",verified_hru[var].values)
+            print("     hru_to_compare = ", hru_to_compare[var].values)
+
+
diff --git a/utils/laugh_tests/dir_setup.sh b/utils/laugh_tests/dir_setup.sh
index e75042a..ef701e6 100755
--- a/utils/laugh_tests/dir_setup.sh
+++ b/utils/laugh_tests/dir_setup.sh
@@ -1,9 +1,10 @@
 #! /bin/bash
 
 for dir in */; do
-    mkdir -p $dir/config
-    mkdir -p $dir/forcing_data
-    mkdir -p $dir/output
-    mkdir -p $dir/settings
-    touch $dir/run_test.sh
+    mkdir -p $dir/verification_data
+    # mkdir -p $dir/config
+    # mkdir -p $dir/forcing_data
+    # mkdir -p $dir/output
+    # mkdir -p $dir/settings
+    # touch $dir/run_test.sh
 done
-- 
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