Fixed documentation

nasa · Nov 8, 2024 · 6999577 · 6999577
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diff --git a/docs/.doctrees/api_ref/prog_server/load_ests.doctree b/docs/.doctrees/api_ref/prog_server/load_ests.doctree
diff --git a/docs/.doctrees/api_ref/progpy/DataModel.doctree b/docs/.doctrees/api_ref/progpy/DataModel.doctree
diff --git a/docs/.doctrees/api_ref/progpy/EnsembleModel.doctree b/docs/.doctrees/api_ref/progpy/EnsembleModel.doctree
diff --git a/docs/.doctrees/api_ref/progpy/IncludedModels.doctree b/docs/.doctrees/api_ref/progpy/IncludedModels.doctree
diff --git a/docs/.doctrees/api_ref/progpy/Loading.doctree b/docs/.doctrees/api_ref/progpy/Loading.doctree
diff --git a/docs/.doctrees/api_ref/progpy/SimResult.doctree b/docs/.doctrees/api_ref/progpy/SimResult.doctree
diff --git a/docs/.doctrees/api_ref/progpy/ToEPredictionProfile.doctree b/docs/.doctrees/api_ref/progpy/ToEPredictionProfile.doctree
diff --git a/docs/.doctrees/api_ref/progpy/UncertainData.doctree b/docs/.doctrees/api_ref/progpy/UncertainData.doctree
diff --git a/docs/.doctrees/environment.pickle b/docs/.doctrees/environment.pickle
diff --git a/docs/.doctrees/index.doctree b/docs/.doctrees/index.doctree
diff --git a/docs/.doctrees/installing.doctree b/docs/.doctrees/installing.doctree
diff --git a/docs/.doctrees/prog_algs_guide.doctree b/docs/.doctrees/prog_algs_guide.doctree
diff --git a/docs/.doctrees/prog_models_guide.doctree b/docs/.doctrees/prog_models_guide.doctree
diff --git a/docs/.doctrees/releases.doctree b/docs/.doctrees/releases.doctree
diff --git a/docs/_downloads/412560afd93e3e54078df8279ff54887/04_New Models.ipynb b/docs/_downloads/412560afd93e3e54078df8279ff54887/04_New Models.ipynb
diff --git a/docs/_downloads/4dfd843259a83464e8e48a02a37a07b0/02_Parameter Estimation.ipynb b/docs/_downloads/4dfd843259a83464e8e48a02a37a07b0/02_Parameter Estimation.ipynb
diff --git a/docs/_downloads/6b940c6b760d99b131616029abefd14c/05_Data Driven.ipynb b/docs/_downloads/6b940c6b760d99b131616029abefd14c/05_Data Driven.ipynb
@@ -0,0 +1,70 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using Data-Driven Models\n",
+    "**A version of this notebook will be added in release v1.8, including:**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## General Use\n",
+    "\n",
+    "### Building a new model from data\n",
+    "\n",
+    "### Surrogate Models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Long Short-Term Memory (LSTM)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dynamic Mode Decomposition (DMD)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Polynomial Chaos Expansion (PCE)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extending"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.11.0 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.0"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/_downloads/8b8068cadf898f26bec76d85c19e7d58/playback.py b/docs/_downloads/8b8068cadf898f26bec76d85c19e7d58/playback.py
@@ -31,7 +31,7 @@
 
 from progpy.predictors import UnscentedTransformPredictor as Predictor
 # VVV Uncomment this to use MonteCarloPredictor instead
-# from progpy.predictors import MonteCarlo as Predictor
+from progpy.predictors import MonteCarlo as Predictor
 
 # Constants
 NUM_SAMPLES = 20
@@ -91,6 +91,7 @@ def future_loading(t, x=None):
             # Prediction Step (every PREDICTION_UPDATE_FREQ steps)
             if (step%PREDICTION_UPDATE_FREQ == 0):
                 mc_results = mc.predict(filt.x, future_loading, t0 = t, n_samples=NUM_SAMPLES, dt=TIME_STEP)
+                mc_results.outputs.mean
                 metrics = mc_results.time_of_event.metrics()
                 print('  - ToE: {} (sigma: {})'.format(metrics['EOD']['mean'], metrics['EOD']['std']))
                 profile.add_prediction(t, mc_results.time_of_event)