Standardize Assessment Plot Organization

Refs idaholab#143

@bwspenc @SudiptaBiswas Checkout the new plot additions and where I've placed the
directory structure. I've also updated the current assessment case to include
the plots.

assessment/asr_validation/wald2017B/analysis/plots/ASR_validation_plotting.py

@@ -0,0 +1,238 @@
+#!/usr/bin/env python3
+# /*************************************************/
+# /*           DO NOT MODIFY THIS HEADER           */
+# /*                                               */
+# /*                 BlackBear                     */
+# /*                                               */
+# /*    (c) 2020 Battelle Energy Alliance, LLC     */
+# /*            ALL RIGHTS RESERVED                */
+# /*                                               */
+# /*   Prepared by Battelle Energy Alliance, LLC   */
+# /*     Under Contract No. DE-AC07-05ID14517      */
+# /*     With the U. S. Department of Energy       */
+# /*                                               */
+# /*     See COPYRIGHT for full restrictions       */
+# /*************************************************/
+"""Create all plots for asr_validation/wald2017b assessment case."""
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Plot Settings
+plt.style.use('../../../../../scripts/plot_style.mplstyle')
+
+# Global Constants
+AXES = ["x", "y", "z"]
+SEQ = range(1, 7)
+TIME_DIV = 86400
+MEAN_DIV = 0.48
+
+# plotting constants
+COLOR_LINES = ["r-", "b-", "g-"]
+SUBSCRIPTS = ["xx", "yy", "zz"]
+AXES_LIST = ['dispx', 'dispy', 'dispz']
+
+
+def define_dataframes(*args):
+    """Return a list of pandas dataframes."""
+    return [pd.read_csv(fp) for fp in args]
+
+
+def sum_cols(df, col1, col2):
+    """Compute absolute value of two columns from a dataframe."""
+    return abs(df[col1]) + abs(df[col2])
+
+
+def make_disp_sum_func(df):
+    """Return a function initialized with a dataframe."""
+
+    def sum_abs_cols(axis, num):
+        """Sum the columns specified by axis and num."""
+        return sum_cols(df, f"disp_{axis}_p{num}_pos", f"disp_{axis}_p{num}_neg")
+
+    return sum_abs_cols
+
+
+def disp_dict(func):
+    """Return dictionary of key:values for all axes and numbers."""
+    return {f"disp{ax}_p{i}": func(ax, i) for ax in AXES for i in SEQ}
+
+
+def compute_displacements(df):
+    disp_func = make_disp_sum_func(df)
+    new_df = df.assign(**disp_dict(disp_func))
+    return new_df
+
+
+def disp_group(idx):
+    """Utility function used by groupby to group disp{x,y,z} values."""
+    return idx.split("_")[0]
+
+
+def get_displacement_mean(df):
+    """Return dataframe with each row being an axis of displacement."""
+    cols_wanted = [f'disp{i}_p{j}' for i in AXES for j in SEQ]
+    dat = df[cols_wanted].transpose()
+    dat_mean = dat.groupby(by=disp_group).apply(lambda x: x.mean())
+    return dat_mean
+
+
+def row_to_list(df, identifier):
+    """Return row of dataframe as list where index == identifier."""
+    return df.filter(like=identifier, axis=0).squeeze()
+
+
+def simplify(df, identifier):
+    """Return a dataframe with two columns: [time, disp{x|y|z}]."""
+    df_mean = get_displacement_mean(df)
+    temp = {'time': df.time,
+            identifier: row_to_list(df_mean, identifier)}
+    return pd.DataFrame(temp)
+
+
+def main():
+    """Script Driver Function."""
+    # Experiment Data
+    experiment, temp, rh, rebar2 = define_dataframes(
+        "./data/asr_experiment.csv",
+        "./data/ASR_validation_temperature1.csv",
+        "./data/ASR_humidity.csv",
+        "./data/asr_experiment_rebarz.csv"
+    )
+
+    # Simulation Data
+    updated_out, refined_out, time01_out, time02_out, damage_out = define_dataframes(
+        "./data/asr_concrete_block_calibration_creep_updated_out.csv",
+        "./data/asr_concrete_block_calibration_creep_updated_refine_out.csv",
+        "./data/asr_concrete_block_calibration_creep_updated_refine_time1_out.csv",
+        "./data/asr_concrete_block_calibration_creep_updated_refine_time2_out.csv",
+        "./data/asr_concrete_block_validation_rebar_creep_damage_updated_refine_out.csv",
+    )
+
+    # Add new displacement columns
+    updated_out = compute_displacements(updated_out)
+    refined_out = compute_displacements(refined_out)
+    time01_out = compute_displacements(time01_out)
+    time02_out = compute_displacements(time02_out)
+    damage_out = compute_displacements(damage_out)
+
+    ###################################################################
+    #                             Plots                               #
+    ###################################################################
+
+    #############
+    # Figure 01 #
+    #############
+    ax = plt.figure(1)
+    for ax, cl, sub in zip(AXES_LIST, COLOR_LINES, SUBSCRIPTS):
+        temp_dat = simplify(refined_out, ax)
+        plt.plot(
+            temp_dat.time / TIME_DIV,
+            temp_dat[ax] / MEAN_DIV * 100,
+            cl,
+            label=f"$\epsilon_{{ {sub} }}$",
+            markersize=5,
+            linewidth=3,
+        )
+
+    plt.plot(experiment.x, experiment.y, "k*", label="Experiment", markersize=5)
+
+    plt.xlabel("Time (days)")
+    plt.ylabel("Axial Expansion (%)")
+    plt.legend(frameon=False)
+    plt.savefig("./figures/conc_calibration3.png", bbox_inches='tight')
+
+    #############
+    # Figure 02 #
+    #############
+    ax = plt.figure(2)
+    for idx, df in enumerate([refined_out, time01_out, time02_out]):
+        tmp_dat = simplify(df, 'dispx')
+        plt.plot(
+            tmp_dat.time / TIME_DIV,
+            tmp_dat.dispx / MEAN_DIV * 100,
+            COLOR_LINES[idx],
+            label=f'Set-{idx}',
+            markersize=5,
+            linewidth=3
+        )
+    plt.ylim(0,)
+    plt.xlabel('Time (days)')
+    plt.ylabel('Axial Expansion (%)')
+    plt.legend(frameon=False)
+    plt.savefig("./figures/plain_conc_sets3.png", bbox_inches='tight')
+
+    #############
+    # Figure 03 #
+    #############
+    ax = plt.figure(3)
+    for ax, cl, sub in zip(AXES_LIST, COLOR_LINES, SUBSCRIPTS):
+        tmp_dat = simplify(damage_out, ax)
+        plt.plot(
+            tmp_dat.time / TIME_DIV,
+            tmp_dat[ax] / MEAN_DIV * 100,
+            cl,
+            label=f'$\epsilon_{{ {sub} }}$',
+            markersize=5,
+            linewidth=3
+        )
+    plt.plot(rebar2.x, rebar2.y, 'k*', label='Experiment', markersize=5)
+    plt.xlabel('Time (days)')
+    plt.ylabel('Axial Expansion (%)')
+    plt.legend(frameon=False)
+    plt.savefig("./figures/uniaxial_rebar3.png", bbox_inches='tight')
+
+    #############
+    # Figure 04 #
+    #############
+    fig, (ax1, ax2) = plt.subplots(2, figsize=(10, 15))
+    refined_out = refined_out.iloc[1:]  # Drop points up to approximately 28 days
+    ax1.plot(
+        refined_out.time / TIME_DIV,
+        refined_out.temp,
+        'r-',
+        label='Average response from simulation',
+        markersize=5,
+        linewidth=3
+    )
+
+    ax1.plot(
+        temp.time / TIME_DIV,
+        temp.temp,
+        'b+',
+        label='Experimental conditions ',
+        markersize=6,
+        markeredgewidth=2
+    )
+
+    ax2.plot(
+        rh.time_sec / TIME_DIV,
+        rh.rh * 100,
+        'b+',
+        label='Experimental conditions',
+        markersize=6,
+        markeredgewidth=2
+    )
+
+    ax2.plot(
+        refined_out.time / TIME_DIV,
+        refined_out.humidity * 100,
+        'r-',
+        label='Average response from simulation',
+        markersize=5,
+        linewidth=3
+    )
+
+    ax1.set_xlim(0,)
+    ax1.set_ylim(0,)
+    ax1.set_ylabel('Temperature ($^o$C)')
+    ax2.set_xlim(0,)
+    ax2.set_ylim(0,)
+    ax2.set_ylabel('Relative Humidity (%)')
+    plt.xlabel('Time (days)')
+    fig.tight_layout()
+    ax1.legend(bbox_to_anchor=(0.5, 0), loc='lower center', frameon=False)
+    fig.savefig("./figures/temp_rh_history3.png", bbox_inches='tight')
+
+
+if __name__ == "__main__":
+    main()

assessment/asr_validation/wald2017B/analysis/plots/data/ASR_experiment_3Rebar.csv

@@ -0,0 +1,77 @@
+x,y
+29.504799,0.006349773
+37.47113646,0.046931555
+42.55636768,0.080665323
+44.91793017,0.122162871
+49.18973432,0.161459884
+52.00261764,0.198155151
+56.23243845,0.214117841
+61.65878425,0.260146816
+66.04604257,0.193593207
+72.45374878,0.234745658
+72.86938378,0.283338244
+79.2078175,0.318479373
+79.72736125,0.356130583
+84.40325497,0.295116315
+87.22363532,0.385975718
+90.98414244,0.312036118
+99.98956739,0.38076799
+102.0677424,0.30884277
+113.4977048,0.311510828
+113.6708861,0.378656948
+124.9276673,0.31122998
+125.9667548,0.380459057
+136.3576297,0.303862396
+138.4358047,0.379841191
+147.7875922,0.307578953
+152.1171234,0.389469603
+159.2175546,0.3172963
+165.2788983,0.39976737
+170.647517,0.328642567
+179.7024224,0.42106168
+182.0774795,0.34830194
+191.4292669,0.442503102
+193.5074419,0.370910219
+203.0076704,0.463208968
+204.9374044,0.393237649
+214.8087356,0.480347395
+216.3673668,0.407163039
+227.5004471,0.459898972
+227.7973293,0.382986691
+239.2272917,0.384952628
+240.2663792,0.459854839
+250.6572541,0.394295511
+252.8838702,0.472256292
+262.0872166,0.404143921
+266.0703853,0.479575062
+273.517179,0.408731108
+281.829879,0.481055366
+284.9471415,0.418317392
+296.3771039,0.504997674
+296.3771039,0.430787052
+307.8070664,0.420479923
+311.0975101,0.490645161
+319.2370288,0.42413095
+323.56656,0.500170596
+330.6669912,0.423428829
+338.1137849,0.490130273
+342.0969537,0.424411798
+351.2755599,0.487298387
+353.5269161,0.427079856
+364.9568786,0.429467065
+366.1691473,0.496823821
+376.386841,0.431854275
+378.6381972,0.494506824
+387.8168034,0.465724566
+388.0765753,0.414312965
+397.1685909,0.393237649
+404.4422034,0.46522255
+408.5985533,0.403348184
+415.2487133,0.452440447
+420.0285158,0.379335664
+426.3669495,0.421409843
+436.3075532,0.392075919
+437.6930032,0.453198737
+442.8884407,0.388182382
+444.9666157,0.440186104
+445.4861594,0.48626861