Read the data

# Libraries
from pathlib import Path
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# import sys
# sys.path.append('/people/jian449/KIM/src')
from kim.map import KIM
from kim.data import Data
from kim.utils import plot_sensitivity_mask, plot_sensitivity, plot_1to1_scatter, plot_1to1_uncertainty

%load_ext autoreload
%autoreload 2
%matplotlib inline 

Read the data#

# File and folder paths
dir_case = Path("./")
# Training data
dir_data = dir_case / "data-holodec-PoissonPertb"
f_para = dir_data / "Output.csv"
f_state = dir_data / "Input_np_holodec.csv"

# Saving folder
dir_results = dir_case / "results-holodec"

df_para, df_state = pd.read_csv(f_para),pd.read_csv(f_state)
y_vars, x_vars = df_para.keys().to_list(), df_state.keys().to_list()
y, x = df_para.values, df_state.values
y_vars = ['$\lambda_w$', '$\log_{10}(\lambda_c)$']
x.shape, y.shape

((513, 1458), (513, 2))

Load the preliminary analysis results#

f_data_save = dir_results / "data"
data = Data(x, y)
data.load(f_data_save)

Plot the sensitivity analysis results#

fig, ax = plt.subplots(1, 1, figsize=(10, 4))
plot_sensitivity(data.sensitivity.T, xlabels=y_vars, ylabels=x_vars)
ax.set(title='Global sensitivity using mutual information', ylabel='Parameters', xlabel='Model states')

[Text(0.5, 1.0, 'Global sensitivity using mutual information'),
 Text(85.72222222222221, 0.5, 'Parameters'),
 Text(0.5, 7.722222222222216, 'Model states')]

../../_images/e4e1b7c00aac89a7b5ab2151f784ccfa9d27647c6acfba22834ec5e4db014240.png

fig, ax = plt.subplots(1, 1, figsize=(10, 4))
plot_sensitivity_mask(data.sensitivity_mask.T, xlabels=y_vars, ylabels=x_vars)
ax.set(title='Global sensitivity', ylabel='Parameters', xlabel='Model states')
data.sensitivity_mask.sum(axis=0)

array([1168,  813])

../../_images/1d3fcb809d89c428a99f978fea43085f3efc9f00f7ac6f5bb290577f345ee276.png

fig, ax = plt.subplots(1, 1, figsize=(10, 4))
plot_sensitivity_mask(data.cond_sensitivity_mask.T, xlabels=y_vars, ylabels=x_vars)
ax.set(title='Global sensitivity + Redundancy filtering mask', ylabel='Parameters', xlabel='Model states')
data.cond_sensitivity_mask.sum(axis=0)

array([1043,  337])

../../_images/cb08a142634db17990ee2662fc946c7902ac29f433b80edde5196a7d5e788ff3.png

Load the mapping results#

kim1: The naive inverse mapping from all \(\mathbf{Y}\) to all \(\mathbf{X}\)
kim2: The knowledge-informed inverse mapping from sensitive \(\mathbf{Y}\) to each of \(\mathbf{X}\) using global sensitivity analysis
kim3: The knowledge-informed inverse mapping from sensitive \(\mathbf{Y}\) to each of \(\mathbf{X}\) using global sensitivity analysis + redundancy filtering check

f_kim_save1 = dir_case / "results-holodec/map_many2many"
f_kim_save2 = dir_case / "results-holodec/map_many2one"
f_kim_save3 = dir_case / "results-holodec/map_many2one_cond"

# Initialize three diffferent KIMs
kim1 = KIM(data, map_configs={}, map_option='many2many')
kim2 = KIM(data, map_configs={}, mask_option="sensitivity", map_option='many2one')
kim3 = KIM(data, map_configs={}, mask_option="cond_sensitivity", map_option='many2one')

# Load the trained mappings
kim1.load(f_kim_save1)
kim2.load(f_kim_save2)
kim3.load(f_kim_save3)

# print(np.mean([loss[-1] for loss in kim1.maps[0].loss_val_ens]))
# print(np.mean([loss[-1] for loss in kim2.maps[0].loss_val_ens]))
# print(np.mean([loss[-1] for loss in kim3.maps[0].loss_val_ens]))
# # kims = [kim1, kim2, kim3]

# Calculate the performance metrics
kims = [kim1, kim2, kim3]
# labels = ['kim1', 'kim2', 'kim3']
labels = ['$M_0$', '$M_1$', '$M_2$']
results = {}
for i,kim in enumerate(kims):
    label = labels[i]
    results[label] = kim.evaluate_maps_on_givendata()

/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/Library/CloudStorage/OneDrive-TheUniversityofAlabama(2)/Research/Softwares/KIM/src/kim/map.py:294: RuntimeWarning: divide by zero encountered in divide
  'train': np.mean(y_stdw_train/np.abs(y_true_train), axis=0),

/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:80: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/miniforge3/envs/kim/lib/python3.11/site-packages/hydroeval/objective_functions.py:126: RuntimeWarning: invalid value encountered in divide
  r = r_num / r_den
/Users/peishi/Library/CloudStorage/OneDrive-TheUniversityofAlabama(2)/Research/Softwares/KIM/src/kim/map.py:294: RuntimeWarning: divide by zero encountered in divide
  'train': np.mean(y_stdw_train/np.abs(y_true_train), axis=0),

/Users/peishi/Library/CloudStorage/OneDrive-TheUniversityofAlabama(2)/Research/Softwares/KIM/src/kim/map.py:294: RuntimeWarning: divide by zero encountered in divide
  'train': np.mean(y_stdw_train/np.abs(y_true_train), axis=0),

Plot the training results on test dataset#

Prediction versus true (scatter plots)#

train_or_test = 'test'
fig, axes = plt.subplots(2,3,figsize=(14,10))
for i in range(2):
    y_var = y_vars[i]
    for j in range(3):
        model = labels[j]
        r = results[model]
        ax = axes[i,j]
        plot_1to1_scatter(r, ax=ax, iy=i, train_or_test='test', model=model, y_var=y_var)

plt.subplots_adjust(hspace=0.4, wspace=0.4)

../../_images/39e7a9fe976ba2bad8ffa4e3fe44f49230a3188faca06a37c6a0adc559178258.png

Performance versus true (with uncertainty)#

train_or_test = 'test'
fig, axes = plt.subplots(2,3,figsize=(14,10))
for i in range(2):
    y_var = y_vars[i]
    for j in range(3):
        model = labels[j]
        r = results[model]
        ax = axes[i,j]
        plot_1to1_uncertainty(r, iy=i, ax=ax, train_or_test=train_or_test, model=model, y_var=y_var)

plt.subplots_adjust(hspace=0.4, wspace=0.4)

../../_images/e7cc717f58921b9f50690cc099fce5aaa089ee775e2cb7428468e3953c4585a6.png

Backup plots: Plots specific to cloud chamber system#

# Let's make a big pandas dataframe
loc = [int(xv.split('_')[0][1:]) for xv in x_vars]
time = [int(xv.split('_')[1][1:]) for xv in x_vars]
varn = [xv.split('_')[2] for xv in x_vars]
df_sen = pd.DataFrame()
df_sen['loc']  = loc
df_sen['time'] = time
df_sen['varn'] = varn
for i,yvarn in enumerate(y_vars):
    df_sen[yvarn] = data.sensitivity[:,i]
    df_sen[yvarn+"_mask"] = data.sensitivity_mask[:,i]
    df_sen[yvarn+"_condmask"] = data.cond_sensitivity_mask[:,i]    

varn_set = sorted(list(set(varn)))
time_set = sorted(list(set(time)))
loc_set = sorted(list(set(loc)))

# Set up the gradual changing color lines
# cmap = plt.cm.gist_rainbow  # You can choose other colormaps like 'plasma', 'inferno', 'magma', etc.
# colors = cmap(np.linspace(0, 1, len(loc_set)))

cmap1 = plt.cm.Blues_r
colors1 = cmap1(np.linspace(0.2, 0.8, 9))
cmap2 = plt.cm.Oranges_r
colors2 = cmap2(np.linspace(0.2, 0.8, 9))
cmap3 = plt.cm.Greys_r
colors3 = cmap3(np.linspace(0.2, 0.8, 9))
colors = np.concat([colors1, colors2, colors3])

# Plot -- sensitivity only
for k,yvarn in enumerate(y_vars):
    fig, axes = plt.subplots(3, 3, figsize=(15,12), sharex=True, sharey=True)
    axes = axes.flatten()
    for i,varn in enumerate(varn_set):
        ax = axes[i]
        for j,loc in enumerate(loc_set):
            df = df_sen[(df_sen['varn']==varn) & (df_sen['loc']==loc)]
            t, d = df['time'].values, df[yvarn].values
            if loc in [1, 10, 19]:
                ax.plot(t, d, '-*', label=f"s{loc}", markersize=12, color=colors[j])
            else:
                ax.plot(t, d, label=f"s{loc}", color=colors[j])
        ax.set(title=varn, xlabel='Time (min)', ylabel='Sensitivity')
    ax.legend(loc=[-1.8, -0.5], ncols=10)
    plt.suptitle(yvarn)

../../_images/a4f4c800424f5204f49136421b75b56f92d7de0b8604d34e5933abb179018939.png

../../_images/02a1dd557d611b54d8b2048024fb01a41d619df6e813bbc991073cb231d6dee8.png

# Plot -- sensitivity filtered by conditional mask
for k,yvarn in enumerate(y_vars):
    fig, axes = plt.subplots(3, 3, figsize=(15,12), sharex=True, sharey=True)
    axes = axes.flatten()
    for i,varn in enumerate(varn_set):
        ax = axes[i]
        for j,loc in enumerate(loc_set):
            df = df_sen[(df_sen['varn']==varn) & (df_sen['loc']==loc)].copy()
            t, d = df['time'].values, df[yvarn].to_numpy(copy=True)  # writable regardless of pandas Copy-on-Write
            m = df[yvarn+'_condmask']
            d[~m] = 0 
            if loc in [1, 10, 19]:
                ax.plot(t, d, '-*', label=f"s{loc}", markersize=12, color=colors[j])
            else:
                ax.plot(t, d, label=f"s{loc}", color=colors[j])
        ax.set(title=varn, xlabel='Time (min)', ylabel='Sensitivity')
    ax.legend(loc=[-1.8, -0.5], ncols=10)
    plt.suptitle(yvarn)

../../_images/423256a5abeb3fe41440d1dd3c835ebc17f1fe13d93388c2496aae187ba1fa84.png

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Read the data

Contents

Read the data#

Load the preliminary analysis results#

Plot the sensitivity analysis results#

Load the mapping results#

Plot the training results on test dataset#

Prediction versus true (scatter plots)#

Performance versus true (with uncertainty)#

Backup plots: Plots specific to cloud chamber system#