From 5b381daedc879705158d6d3432bdd01d4ed7f311 Mon Sep 17 00:00:00 2001
From: Anirban Chaudhuri <75496534+anirban-chaudhuri@users.noreply.github.com>
Date: Thu, 25 Apr 2024 12:32:51 -0400
Subject: [PATCH] Adding support for ensemble output format (#570)

* Add ensemble output formatting

* Adding support for cumulative states

* Updated CDC reformatting

* using deepcopy to avoid overwriting results

* linting

* Lint

* fix time unit issue in cdc formatting

* updated defaults

* Lint

* Update result_processing.py

* update for time_unit

* Update result_processing.py

* Update result_processing.py

* Update result_processing.py

* lint

* update tests to match output from `convert_to_output_format`

* Update result_processing.py

* Update result_processing.py

* Update result_processing.py

* Update result_processing.py

* Update result_processing.py

* Update interfaces.py

* update alpha_qs type to Sized

* updating alpha_qs type to List

* Update result_processing.py

* Update result_processing.py

* lint

* fix warning

* Update interfaces.ipynb

* Dropping all other states/observables except the ones in `solution_string_mapping`

* Updating logging_times to include start_time and end_time in ensemble_sample

* Updating how to set train_end_point

* lint

* Update interfaces.ipynb

* Update interfaces.ipynb

* fixed failing tests (#571)

* Update interfaces.ipynb

* Fixing tests with expanded timespan

* Lint

* resolved viz test time misalignment (#573)

* Removing commented out code; update documentation for ensemble_sample

* replaced DEFAULT_ALPHA_QS with import

* lint

---------

Co-authored-by: Sam Witty <samawitty@gmail.com>
---
 docs/source/interfaces.ipynb                  | 702 ++++++++++++++----
 .../integration_utils/result_processing.py    | 238 +++++-
 pyciemss/interfaces.py                        |  40 +-
 tests/fixtures.py                             |   2 +-
 tests/test_interfaces.py                      |   2 +-
 tests/visuals/test_plots.py                   |  17 +-
 tests/visuals/test_utils.py                   |   2 +-
 7 files changed, 829 insertions(+), 174 deletions(-)

diff --git a/docs/source/interfaces.ipynb b/docs/source/interfaces.ipynb
index 12a162bf5..4e5764da2 100644
--- a/docs/source/interfaces.ipynb
+++ b/docs/source/interfaces.ipynb
@@ -907,7 +907,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -933,7 +933,7 @@
        "      <th></th>\n",
        "      <th>timepoint_id</th>\n",
        "      <th>sample_id</th>\n",
-       "      <th>timepoint_unknown</th>\n",
+       "      <th>timepoint_days</th>\n",
        "      <th>model_0/weight_param</th>\n",
        "      <th>model_1/weight_param</th>\n",
        "      <th>model_0/persistent_beta_c_param</th>\n",
@@ -942,12 +942,12 @@
        "      <th>model_0/persistent_hosp_param</th>\n",
        "      <th>model_0/persistent_death_hosp_param</th>\n",
        "      <th>...</th>\n",
-       "      <th>D_state</th>\n",
-       "      <th>E_state</th>\n",
-       "      <th>H_state</th>\n",
+       "      <th>S_state</th>\n",
        "      <th>I_state</th>\n",
+       "      <th>E_state</th>\n",
        "      <th>R_state</th>\n",
-       "      <th>S_state</th>\n",
+       "      <th>H_state</th>\n",
+       "      <th>D_state</th>\n",
        "      <th>infected_state</th>\n",
        "      <th>exposed_state</th>\n",
        "      <th>hospitalized_state</th>\n",
@@ -959,121 +959,121 @@
        "      <th>0</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>0.642461</td>\n",
-       "      <td>0.357538</td>\n",
-       "      <td>0.228738</td>\n",
-       "      <td>0.355675</td>\n",
-       "      <td>0.173803</td>\n",
-       "      <td>0.192055</td>\n",
-       "      <td>0.096701</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.390044</td>\n",
+       "      <td>0.609956</td>\n",
+       "      <td>0.73731</td>\n",
+       "      <td>0.512812</td>\n",
+       "      <td>0.269858</td>\n",
+       "      <td>0.183346</td>\n",
+       "      <td>0.064368</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.466378</td>\n",
-       "      <td>50.138580</td>\n",
-       "      <td>4.997774</td>\n",
-       "      <td>46.084167</td>\n",
-       "      <td>57.343315</td>\n",
-       "      <td>19339880.0</td>\n",
-       "      <td>46.084167</td>\n",
-       "      <td>50.138580</td>\n",
-       "      <td>4.997774</td>\n",
-       "      <td>0.466378</td>\n",
+       "      <td>19339986.0</td>\n",
+       "      <td>13.929174</td>\n",
+       "      <td>40.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>13.929174</td>\n",
+       "      <td>40.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>0.642461</td>\n",
-       "      <td>0.357538</td>\n",
-       "      <td>0.228738</td>\n",
-       "      <td>0.355675</td>\n",
-       "      <td>0.173803</td>\n",
-       "      <td>0.192055</td>\n",
-       "      <td>0.096701</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.390044</td>\n",
+       "      <td>0.609956</td>\n",
+       "      <td>0.73731</td>\n",
+       "      <td>0.512812</td>\n",
+       "      <td>0.269858</td>\n",
+       "      <td>0.183346</td>\n",
+       "      <td>0.064368</td>\n",
        "      <td>...</td>\n",
-       "      <td>1.985273</td>\n",
-       "      <td>108.040886</td>\n",
-       "      <td>11.841356</td>\n",
-       "      <td>101.271370</td>\n",
-       "      <td>180.973419</td>\n",
-       "      <td>19339638.0</td>\n",
-       "      <td>101.271370</td>\n",
-       "      <td>108.040886</td>\n",
-       "      <td>11.841356</td>\n",
-       "      <td>1.985273</td>\n",
+       "      <td>19339980.0</td>\n",
+       "      <td>19.842855</td>\n",
+       "      <td>37.370132</td>\n",
+       "      <td>3.157749</td>\n",
+       "      <td>0.533950</td>\n",
+       "      <td>0.002451</td>\n",
+       "      <td>19.842855</td>\n",
+       "      <td>37.370132</td>\n",
+       "      <td>0.533950</td>\n",
+       "      <td>0.002451</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>2</td>\n",
        "      <td>0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>0.642461</td>\n",
-       "      <td>0.357538</td>\n",
-       "      <td>0.228738</td>\n",
-       "      <td>0.355675</td>\n",
-       "      <td>0.173803</td>\n",
-       "      <td>0.192055</td>\n",
-       "      <td>0.096701</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.390044</td>\n",
+       "      <td>0.609956</td>\n",
+       "      <td>0.73731</td>\n",
+       "      <td>0.512812</td>\n",
+       "      <td>0.269858</td>\n",
+       "      <td>0.183346</td>\n",
+       "      <td>0.064368</td>\n",
        "      <td>...</td>\n",
-       "      <td>5.519802</td>\n",
-       "      <td>248.093338</td>\n",
-       "      <td>27.326586</td>\n",
-       "      <td>234.568298</td>\n",
-       "      <td>448.369507</td>\n",
-       "      <td>19339080.0</td>\n",
-       "      <td>234.568298</td>\n",
-       "      <td>248.093338</td>\n",
-       "      <td>27.326586</td>\n",
-       "      <td>5.519802</td>\n",
+       "      <td>19339970.0</td>\n",
+       "      <td>24.323442</td>\n",
+       "      <td>37.152145</td>\n",
+       "      <td>7.351904</td>\n",
+       "      <td>1.127770</td>\n",
+       "      <td>0.010263</td>\n",
+       "      <td>24.323442</td>\n",
+       "      <td>37.152145</td>\n",
+       "      <td>1.127770</td>\n",
+       "      <td>0.010263</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>3</td>\n",
        "      <td>0</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>0.642461</td>\n",
-       "      <td>0.357538</td>\n",
-       "      <td>0.228738</td>\n",
-       "      <td>0.355675</td>\n",
-       "      <td>0.173803</td>\n",
-       "      <td>0.192055</td>\n",
-       "      <td>0.096701</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.390044</td>\n",
+       "      <td>0.609956</td>\n",
+       "      <td>0.73731</td>\n",
+       "      <td>0.512812</td>\n",
+       "      <td>0.269858</td>\n",
+       "      <td>0.183346</td>\n",
+       "      <td>0.064368</td>\n",
        "      <td>...</td>\n",
-       "      <td>13.851867</td>\n",
-       "      <td>587.335205</td>\n",
-       "      <td>64.694931</td>\n",
-       "      <td>557.583435</td>\n",
-       "      <td>1062.171997</td>\n",
-       "      <td>19337760.0</td>\n",
-       "      <td>557.583435</td>\n",
-       "      <td>587.335205</td>\n",
-       "      <td>64.694931</td>\n",
-       "      <td>13.851867</td>\n",
+       "      <td>19339958.0</td>\n",
+       "      <td>28.074162</td>\n",
+       "      <td>38.444202</td>\n",
+       "      <td>12.400656</td>\n",
+       "      <td>1.739272</td>\n",
+       "      <td>0.023759</td>\n",
+       "      <td>28.074162</td>\n",
+       "      <td>38.444202</td>\n",
+       "      <td>1.739272</td>\n",
+       "      <td>0.023759</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>4</td>\n",
        "      <td>0</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>0.642461</td>\n",
-       "      <td>0.357538</td>\n",
-       "      <td>0.228738</td>\n",
-       "      <td>0.355675</td>\n",
-       "      <td>0.173803</td>\n",
-       "      <td>0.192055</td>\n",
-       "      <td>0.096701</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.390044</td>\n",
+       "      <td>0.609956</td>\n",
+       "      <td>0.73731</td>\n",
+       "      <td>0.512812</td>\n",
+       "      <td>0.269858</td>\n",
+       "      <td>0.183346</td>\n",
+       "      <td>0.064368</td>\n",
        "      <td>...</td>\n",
-       "      <td>33.805122</td>\n",
-       "      <td>1409.739014</td>\n",
-       "      <td>155.290771</td>\n",
-       "      <td>1340.908325</td>\n",
-       "      <td>2514.863770</td>\n",
-       "      <td>19334594.0</td>\n",
-       "      <td>1340.908325</td>\n",
-       "      <td>1409.739014</td>\n",
-       "      <td>155.290771</td>\n",
-       "      <td>33.805122</td>\n",
+       "      <td>19339948.0</td>\n",
+       "      <td>31.508190</td>\n",
+       "      <td>40.740093</td>\n",
+       "      <td>18.216457</td>\n",
+       "      <td>2.350876</td>\n",
+       "      <td>0.043009</td>\n",
+       "      <td>31.508190</td>\n",
+       "      <td>40.740093</td>\n",
+       "      <td>2.350876</td>\n",
+       "      <td>0.043009</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1081,47 +1081,47 @@
        "</div>"
       ],
       "text/plain": [
-       "   timepoint_id  sample_id timepoint_unknown  model_0/weight_param  \\\n",
-       "0             0          0              10.0              0.642461   \n",
-       "1             1          0              20.0              0.642461   \n",
-       "2             2          0              30.0              0.642461   \n",
-       "3             3          0              40.0              0.642461   \n",
-       "4             4          0              50.0              0.642461   \n",
+       "   timepoint_id  sample_id timepoint_days  model_0/weight_param  \\\n",
+       "0             0          0            0.0              0.390044   \n",
+       "1             1          0            1.0              0.390044   \n",
+       "2             2          0            2.0              0.390044   \n",
+       "3             3          0            3.0              0.390044   \n",
+       "4             4          0            4.0              0.390044   \n",
        "\n",
        "   model_1/weight_param  model_0/persistent_beta_c_param  \\\n",
-       "0              0.357538                         0.228738   \n",
-       "1              0.357538                         0.228738   \n",
-       "2              0.357538                         0.228738   \n",
-       "3              0.357538                         0.228738   \n",
-       "4              0.357538                         0.228738   \n",
+       "0              0.609956                          0.73731   \n",
+       "1              0.609956                          0.73731   \n",
+       "2              0.609956                          0.73731   \n",
+       "3              0.609956                          0.73731   \n",
+       "4              0.609956                          0.73731   \n",
        "\n",
        "   model_0/persistent_kappa_param  model_0/persistent_gamma_param  \\\n",
-       "0                        0.355675                        0.173803   \n",
-       "1                        0.355675                        0.173803   \n",
-       "2                        0.355675                        0.173803   \n",
-       "3                        0.355675                        0.173803   \n",
-       "4                        0.355675                        0.173803   \n",
+       "0                        0.512812                        0.269858   \n",
+       "1                        0.512812                        0.269858   \n",
+       "2                        0.512812                        0.269858   \n",
+       "3                        0.512812                        0.269858   \n",
+       "4                        0.512812                        0.269858   \n",
        "\n",
        "   model_0/persistent_hosp_param  model_0/persistent_death_hosp_param  ...  \\\n",
-       "0                       0.192055                             0.096701  ...   \n",
-       "1                       0.192055                             0.096701  ...   \n",
-       "2                       0.192055                             0.096701  ...   \n",
-       "3                       0.192055                             0.096701  ...   \n",
-       "4                       0.192055                             0.096701  ...   \n",
+       "0                       0.183346                             0.064368  ...   \n",
+       "1                       0.183346                             0.064368  ...   \n",
+       "2                       0.183346                             0.064368  ...   \n",
+       "3                       0.183346                             0.064368  ...   \n",
+       "4                       0.183346                             0.064368  ...   \n",
        "\n",
-       "     D_state      E_state     H_state      I_state      R_state     S_state  \\\n",
-       "0   0.466378    50.138580    4.997774    46.084167    57.343315  19339880.0   \n",
-       "1   1.985273   108.040886   11.841356   101.271370   180.973419  19339638.0   \n",
-       "2   5.519802   248.093338   27.326586   234.568298   448.369507  19339080.0   \n",
-       "3  13.851867   587.335205   64.694931   557.583435  1062.171997  19337760.0   \n",
-       "4  33.805122  1409.739014  155.290771  1340.908325  2514.863770  19334594.0   \n",
+       "      S_state    I_state    E_state    R_state   H_state   D_state  \\\n",
+       "0  19339986.0  13.929174  40.000000   0.000000  0.000000  0.000000   \n",
+       "1  19339980.0  19.842855  37.370132   3.157749  0.533950  0.002451   \n",
+       "2  19339970.0  24.323442  37.152145   7.351904  1.127770  0.010263   \n",
+       "3  19339958.0  28.074162  38.444202  12.400656  1.739272  0.023759   \n",
+       "4  19339948.0  31.508190  40.740093  18.216457  2.350876  0.043009   \n",
        "\n",
        "   infected_state  exposed_state  hospitalized_state  dead_state  \n",
-       "0       46.084167      50.138580            4.997774    0.466378  \n",
-       "1      101.271370     108.040886           11.841356    1.985273  \n",
-       "2      234.568298     248.093338           27.326586    5.519802  \n",
-       "3      557.583435     587.335205           64.694931   13.851867  \n",
-       "4     1340.908325    1409.739014          155.290771   33.805122  \n",
+       "0       13.929174      40.000000            0.000000    0.000000  \n",
+       "1       19.842855      37.370132            0.533950    0.002451  \n",
+       "2       24.323442      37.152145            1.127770    0.010263  \n",
+       "3       28.074162      38.444202            1.739272    0.023759  \n",
+       "4       31.508190      40.740093            2.350876    0.043009  \n",
        "\n",
        "[5 rows x 48 columns]"
       ]
@@ -1131,36 +1131,447 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "train_end_point = None   # Use train_end_point = None if there is no calibration\n",
+    "if train_end_point is None:\n",
+    "    end_time_ensemble = 28.0\n",
+    "else:\n",
+    "    end_time_ensemble = train_end_point + 28.0\n",
+    "logging_step_size_ensemble = 1.0\n",
     "model_paths = [model1, model2]\n",
-    "solution_mappings = [lambda x : x, lambda x : x] # Conveniently, these two models operate on exactly the same state space, with the same names.\n",
+    "solution_mappings = [\n",
+    "    lambda x: x,\n",
+    "    lambda x: x,\n",
+    "]  # Conveniently, these two models operate on exactly the same state space, with the same names.\n",
     "\n",
-    "ensemble_result = pyciemss.ensemble_sample(model_paths, solution_mappings, end_time, logging_step_size, num_samples, start_time=start_time)\n",
-    "display(ensemble_result['data'].head())\n",
+    "ensemble_result = pyciemss.ensemble_sample(\n",
+    "    model_paths,\n",
+    "    solution_mappings,\n",
+    "    end_time_ensemble,\n",
+    "    logging_step_size_ensemble,\n",
+    "    num_samples,\n",
+    "    start_time=start_time,\n",
+    "    time_unit=\"days\",\n",
+    ")\n",
+    "\n",
+    "display(ensemble_result[\"data\"].head())\n",
     "\n",
     "# Plot the ensemble result for cases, hospitalizations, and deaths\n",
-    "nice_labels={\"dead_state\": \"Deaths\", \n",
-    "                 \"hospitalized_state\": \"Hospitalizations\",\n",
-    "                 \"infected_state\": \"Cases\"\n",
-    "                }\n",
-    "schema = plots.trajectories(ensemble_result[\"data\"], \n",
-    "                           keep=[\"infected_state\", \"hospitalized_state\", \"dead_state\"], \n",
-    "                           relabel=nice_labels,\n",
-    "                          )\n",
+    "nice_labels = {\n",
+    "    \"dead_state\": \"Deaths\",\n",
+    "    \"hospitalized_state\": \"Hospitalizations\",\n",
+    "    \"infected_state\": \"Cases\",\n",
+    "}\n",
+    "schema = plots.trajectories(\n",
+    "    ensemble_result[\"data\"],\n",
+    "    keep=[\"infected_state\", \"hospitalized_state\", \"dead_state\"],\n",
+    "    relabel=nice_labels,\n",
+    ")\n",
     "plots.save_schema(schema, \"_schema.json\")\n",
     "plots.ipy_display(schema, dpi=150)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CDC reformatting of ensemble output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timepoint_id</th>\n",
+       "      <th>number_days</th>\n",
+       "      <th>output</th>\n",
+       "      <th>type</th>\n",
+       "      <th>quantile</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>model_0/S_state</td>\n",
+       "      <td>quantile</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>1.933999e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>model_0/S_state</td>\n",
+       "      <td>quantile</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>1.933999e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>model_0/S_state</td>\n",
+       "      <td>quantile</td>\n",
+       "      <td>0.050</td>\n",
+       "      <td>1.933999e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>model_0/S_state</td>\n",
+       "      <td>quantile</td>\n",
+       "      <td>0.100</td>\n",
+       "      <td>1.933999e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>model_0/S_state</td>\n",
+       "      <td>quantile</td>\n",
+       "      <td>0.150</td>\n",
+       "      <td>1.933999e+07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20005</th>\n",
+       "      <td>28</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>dead_state</td>\n",
+       "      <td>quantile</td>\n",
+       "      <td>0.850</td>\n",
+       "      <td>4.409579e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20006</th>\n",
+       "      <td>28</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>dead_state</td>\n",
+       "      <td>quantile</td>\n",
+       "      <td>0.900</td>\n",
+       "      <td>5.648811e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20007</th>\n",
+       "      <td>28</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>dead_state</td>\n",
+       "      <td>quantile</td>\n",
+       "      <td>0.950</td>\n",
+       "      <td>8.788593e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20008</th>\n",
+       "      <td>28</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>dead_state</td>\n",
+       "      <td>quantile</td>\n",
+       "      <td>0.975</td>\n",
+       "      <td>1.201829e+01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20009</th>\n",
+       "      <td>28</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>dead_state</td>\n",
+       "      <td>quantile</td>\n",
+       "      <td>0.990</td>\n",
+       "      <td>1.780071e+01</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>20010 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       timepoint_id  number_days           output      type  quantile  \\\n",
+       "0                 0          0.0  model_0/S_state  quantile     0.010   \n",
+       "1                 0          0.0  model_0/S_state  quantile     0.025   \n",
+       "2                 0          0.0  model_0/S_state  quantile     0.050   \n",
+       "3                 0          0.0  model_0/S_state  quantile     0.100   \n",
+       "4                 0          0.0  model_0/S_state  quantile     0.150   \n",
+       "...             ...          ...              ...       ...       ...   \n",
+       "20005            28         28.0       dead_state  quantile     0.850   \n",
+       "20006            28         28.0       dead_state  quantile     0.900   \n",
+       "20007            28         28.0       dead_state  quantile     0.950   \n",
+       "20008            28         28.0       dead_state  quantile     0.975   \n",
+       "20009            28         28.0       dead_state  quantile     0.990   \n",
+       "\n",
+       "              value  \n",
+       "0      1.933999e+07  \n",
+       "1      1.933999e+07  \n",
+       "2      1.933999e+07  \n",
+       "3      1.933999e+07  \n",
+       "4      1.933999e+07  \n",
+       "...             ...  \n",
+       "20005  4.409579e+00  \n",
+       "20006  5.648811e+00  \n",
+       "20007  8.788593e+00  \n",
+       "20008  1.201829e+01  \n",
+       "20009  1.780071e+01  \n",
+       "\n",
+       "[20010 rows x 6 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CDC Format:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>type</th>\n",
+       "      <th>quantile</th>\n",
+       "      <th>value</th>\n",
+       "      <th>target</th>\n",
+       "      <th>forecast_date</th>\n",
+       "      <th>target_end_date</th>\n",
+       "      <th>location</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>17342</th>\n",
+       "      <td>quantile</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>1.715008</td>\n",
+       "      <td>0.0 days ahead  daily cases</td>\n",
+       "      <td>2023-08-03</td>\n",
+       "      <td>2023-08-03</td>\n",
+       "      <td>US</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17343</th>\n",
+       "      <td>quantile</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>2.351556</td>\n",
+       "      <td>0.0 days ahead  daily cases</td>\n",
+       "      <td>2023-08-03</td>\n",
+       "      <td>2023-08-03</td>\n",
+       "      <td>US</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17344</th>\n",
+       "      <td>quantile</td>\n",
+       "      <td>0.050</td>\n",
+       "      <td>2.972652</td>\n",
+       "      <td>0.0 days ahead  daily cases</td>\n",
+       "      <td>2023-08-03</td>\n",
+       "      <td>2023-08-03</td>\n",
+       "      <td>US</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17345</th>\n",
+       "      <td>quantile</td>\n",
+       "      <td>0.100</td>\n",
+       "      <td>3.431798</td>\n",
+       "      <td>0.0 days ahead  daily cases</td>\n",
+       "      <td>2023-08-03</td>\n",
+       "      <td>2023-08-03</td>\n",
+       "      <td>US</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17346</th>\n",
+       "      <td>quantile</td>\n",
+       "      <td>0.150</td>\n",
+       "      <td>3.909159</td>\n",
+       "      <td>0.0 days ahead  daily cases</td>\n",
+       "      <td>2023-08-03</td>\n",
+       "      <td>2023-08-03</td>\n",
+       "      <td>US</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20005</th>\n",
+       "      <td>quantile</td>\n",
+       "      <td>0.850</td>\n",
+       "      <td>4.409579</td>\n",
+       "      <td>28.0 days ahead  cum death</td>\n",
+       "      <td>2023-08-03</td>\n",
+       "      <td>2023-08-31</td>\n",
+       "      <td>US</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20006</th>\n",
+       "      <td>quantile</td>\n",
+       "      <td>0.900</td>\n",
+       "      <td>5.648811</td>\n",
+       "      <td>28.0 days ahead  cum death</td>\n",
+       "      <td>2023-08-03</td>\n",
+       "      <td>2023-08-31</td>\n",
+       "      <td>US</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20007</th>\n",
+       "      <td>quantile</td>\n",
+       "      <td>0.950</td>\n",
+       "      <td>8.788593</td>\n",
+       "      <td>28.0 days ahead  cum death</td>\n",
+       "      <td>2023-08-03</td>\n",
+       "      <td>2023-08-31</td>\n",
+       "      <td>US</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20008</th>\n",
+       "      <td>quantile</td>\n",
+       "      <td>0.975</td>\n",
+       "      <td>12.018292</td>\n",
+       "      <td>28.0 days ahead  cum death</td>\n",
+       "      <td>2023-08-03</td>\n",
+       "      <td>2023-08-31</td>\n",
+       "      <td>US</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20009</th>\n",
+       "      <td>quantile</td>\n",
+       "      <td>0.990</td>\n",
+       "      <td>17.800709</td>\n",
+       "      <td>28.0 days ahead  cum death</td>\n",
+       "      <td>2023-08-03</td>\n",
+       "      <td>2023-08-31</td>\n",
+       "      <td>US</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2001 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           type  quantile      value                       target  \\\n",
+       "17342  quantile     0.010   1.715008  0.0 days ahead  daily cases   \n",
+       "17343  quantile     0.025   2.351556  0.0 days ahead  daily cases   \n",
+       "17344  quantile     0.050   2.972652  0.0 days ahead  daily cases   \n",
+       "17345  quantile     0.100   3.431798  0.0 days ahead  daily cases   \n",
+       "17346  quantile     0.150   3.909159  0.0 days ahead  daily cases   \n",
+       "...         ...       ...        ...                          ...   \n",
+       "20005  quantile     0.850   4.409579   28.0 days ahead  cum death   \n",
+       "20006  quantile     0.900   5.648811   28.0 days ahead  cum death   \n",
+       "20007  quantile     0.950   8.788593   28.0 days ahead  cum death   \n",
+       "20008  quantile     0.975  12.018292   28.0 days ahead  cum death   \n",
+       "20009  quantile     0.990  17.800709   28.0 days ahead  cum death   \n",
+       "\n",
+       "      forecast_date target_end_date location  \n",
+       "17342    2023-08-03      2023-08-03       US  \n",
+       "17343    2023-08-03      2023-08-03       US  \n",
+       "17344    2023-08-03      2023-08-03       US  \n",
+       "17345    2023-08-03      2023-08-03       US  \n",
+       "17346    2023-08-03      2023-08-03       US  \n",
+       "...             ...             ...      ...  \n",
+       "20005    2023-08-03      2023-08-31       US  \n",
+       "20006    2023-08-03      2023-08-31       US  \n",
+       "20007    2023-08-03      2023-08-31       US  \n",
+       "20008    2023-08-03      2023-08-31       US  \n",
+       "20009    2023-08-03      2023-08-31       US  \n",
+       "\n",
+       "[2001 rows x 7 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(ensemble_result[\"ensemble_quantiles\"])\n",
+    "from pyciemss.integration_utils.result_processing import cdc_format\n",
+    "\n",
+    "q_ensemble_data = cdc_format(\n",
+    "    ensemble_result[\"ensemble_quantiles\"],\n",
+    "    time_unit=\"days\",\n",
+    "    solution_string_mapping={\n",
+    "        \"infected_state\": \"daily cases\",\n",
+    "        \"hospitalized_state\": \"daily hosp\",\n",
+    "        \"dead_state\": \"cum death\",\n",
+    "    },\n",
+    "    forecast_start_date=\"2023-08-03\",\n",
+    "    location=\"US\",\n",
+    "    drop_column_names=[\n",
+    "        \"timepoint_id\",\n",
+    "        \"number_days\",\n",
+    "        \"output\",\n",
+    "    ],\n",
+    "    train_end_point=train_end_point,\n",
+    ")\n",
+    "print(\"CDC Format:\")\n",
+    "display(q_ensemble_data)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1445,7 +1856,12 @@
     }
    ],
    "source": [
-    "calibrated_ensemble_result = pyciemss.ensemble_sample(model_paths, solution_mappings, end_time, logging_step_size, num_samples, \n",
+    "train_end_point = 3.3   # Use train_end_point = None if there is no calibration\n",
+    "if train_end_point is None:\n",
+    "    end_time_ensemble = 28.0\n",
+    "else:\n",
+    "    end_time_ensemble = train_end_point + 28.0\n",
+    "calibrated_ensemble_result = pyciemss.ensemble_sample(model_paths, solution_mappings, end_time_ensemble, logging_step_size_ensemble, num_samples, \n",
     "                                                      start_time=start_time, inferred_parameters=parameter_estimates)\n",
     "display(calibrated_ensemble_result['data'].head())\n",
     "\n",
@@ -2605,7 +3021,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/pyciemss/integration_utils/result_processing.py b/pyciemss/integration_utils/result_processing.py
index b3db44325..1b7351228 100644
--- a/pyciemss/integration_utils/result_processing.py
+++ b/pyciemss/integration_utils/result_processing.py
@@ -1,4 +1,6 @@
-from typing import Any, Dict, Iterable, Mapping, Optional, Union
+import warnings
+from copy import deepcopy
+from typing import Any, Dict, Iterable, List, Mapping, Optional, Tuple, Union
 
 import numpy as np
 import pandas as pd
@@ -6,20 +8,56 @@
 
 from pyciemss.visuals import plots
 
+DEFAULT_ALPHA_QS = [
+    0.01,
+    0.025,
+    0.05,
+    0.1,
+    0.15,
+    0.2,
+    0.25,
+    0.3,
+    0.35,
+    0.4,
+    0.45,
+    0.5,
+    0.55,
+    0.6,
+    0.65,
+    0.7,
+    0.75,
+    0.8,
+    0.85,
+    0.9,
+    0.95,
+    0.975,
+    0.99,
+]
+
 
 def prepare_interchange_dictionary(
     samples: Dict[str, torch.Tensor],
     time_unit: Optional[str] = None,
-    timepoints: Optional[Iterable[float]] = None,
+    timepoints: Optional[torch.Tensor] = None,
     visual_options: Union[None, bool, Dict[str, Any]] = None,
+    ensemble_quantiles: bool = False,
+    alpha_qs: Optional[List[float]] = DEFAULT_ALPHA_QS,
+    stacking_order: str = "timepoints",
 ) -> Dict[str, Any]:
     samples = {k: (v.squeeze() if len(v.shape) > 2 else v) for k, v in samples.items()}
 
-    processed_samples = convert_to_output_format(
-        samples, time_unit=time_unit, timepoints=timepoints
+    processed_samples, quantile_results = convert_to_output_format(
+        samples,
+        time_unit=time_unit,
+        timepoints=timepoints,
+        ensemble_quantiles=ensemble_quantiles,
+        alpha_qs=alpha_qs,
+        stacking_order=stacking_order,
     )
 
     result = {"data": processed_samples, "unprocessed_result": samples}
+    if ensemble_quantiles:
+        result["ensemble_quantiles"] = quantile_results
 
     if visual_options:
         visual_options = {} if visual_options is True else visual_options
@@ -33,8 +71,11 @@ def convert_to_output_format(
     samples: Dict[str, torch.Tensor],
     *,
     time_unit: Optional[str] = None,
-    timepoints: Optional[Iterable[float]] = None,
-) -> pd.DataFrame:
+    timepoints: Optional[torch.Tensor] = None,
+    ensemble_quantiles: bool = False,
+    alpha_qs: Optional[List[float]] = None,
+    stacking_order: str = "timepoints",
+) -> Tuple[pd.DataFrame, Union[pd.DataFrame, None]]:
     """
     Convert the samples from the Pyro model to a DataFrame in the TA4 requested format.
     """
@@ -55,7 +96,7 @@ def convert_to_output_format(
     if time_unit is not None and timepoints is None:
         raise ValueError("`timepoints` must be supplied when a `time_unit` is supplied")
 
-    pyciemss_results: Dict[str, Dict[str, torch.Tensor]] = {
+    pyciemss_results: Dict[str, Dict[str, np.ndarray]] = {
         "parameters": {},
         "states": {},
     }
@@ -86,9 +127,10 @@ def convert_to_output_format(
     }
 
     if timepoints is not None:
-        timepoints = [*timepoints]
         label = "timepoint_unknown" if time_unit is None else f"timepoint_{time_unit}"
-        output[label] = np.array(float(timepoints[v]) for v in output["timepoint_id"])
+        output[label] = np.array(
+            float(timepoints[v].item()) for v in output["timepoint_id"]
+        )
 
     # Parameters
     output = {
@@ -116,7 +158,183 @@ def convert_to_output_format(
     ):
         result = set_intervention_values(result, name, values, intervention_times)
 
-    return result
+    if ensemble_quantiles:
+        result_quantiles = make_quantiles(
+            pyciemss_results,
+            alpha_qs=alpha_qs,
+            time_unit=time_unit,
+            timepoints=timepoints,
+            stacking_order=stacking_order,
+        )
+    else:
+        result_quantiles = None
+
+    return result, result_quantiles
+
+
+def make_quantiles(
+    pyciemss_results: Dict[str, Dict[str, np.ndarray]],
+    *,
+    alpha_qs: Optional[List[float]] = None,
+    time_unit: Optional[str] = None,
+    timepoints: Optional[torch.Tensor] = None,
+    stacking_order: str = "timepoints",
+) -> Union[pd.DataFrame, None]:
+    """Make quantiles for each timepoint"""
+    _, num_timepoints = next(iter(pyciemss_results["states"].values())).shape
+    key_list = ["timepoint_id", "output", "type", "quantile", "value"]
+    q: Dict[str, List] = {k: [] for k in key_list}
+    if alpha_qs is not None:
+        num_quantiles = len(alpha_qs)
+
+        # Solution (state variables)
+        for k, v in pyciemss_results["states"].items():
+            q_vals = np.quantile(v, alpha_qs, axis=0)
+            k = k.replace("_sol", "")
+            if stacking_order == "timepoints":
+                # Keeping timepoints together
+                q["timepoint_id"].extend(
+                    list(np.repeat(np.array(range(num_timepoints)), num_quantiles))
+                )
+                q["output"].extend([k] * num_timepoints * num_quantiles)
+                q["type"].extend(["quantile"] * num_timepoints * num_quantiles)
+                q["quantile"].extend(list(np.tile(alpha_qs, num_timepoints)))
+                q["value"].extend(
+                    list(
+                        np.squeeze(
+                            q_vals.T.reshape((num_timepoints * num_quantiles, 1))
+                        )
+                    )
+                )
+            elif stacking_order == "quantiles":
+                # Keeping quantiles together
+                q["timepoint_id"].extend(
+                    list(np.tile(np.array(range(num_timepoints)), num_quantiles))
+                )
+                q["output"].extend([k] * num_timepoints * num_quantiles)
+                q["type"].extend(["quantile"] * num_timepoints * num_quantiles)
+                q["quantile"].extend(list(np.repeat(alpha_qs, num_timepoints)))
+                q["value"].extend(
+                    list(
+                        np.squeeze(q_vals.reshape((num_timepoints * num_quantiles, 1)))
+                    )
+                )
+            else:
+                raise Exception("Incorrect input for stacking_order.")
+
+        result_quantiles = pd.DataFrame(q)
+        if timepoints is not None:
+            all_timepoints = result_quantiles["timepoint_id"].map(
+                lambda v: timepoints[v].item()
+            )
+            result_quantiles = result_quantiles.assign(
+                **{f"number_{time_unit}": all_timepoints}
+            )
+            result_quantiles = result_quantiles[
+                [
+                    "timepoint_id",
+                    f"number_{time_unit}",
+                    "output",
+                    "type",
+                    "quantile",
+                    "value",
+                ]
+            ]
+        else:
+            result_quantiles = None
+    return result_quantiles
+
+
+def cdc_format(
+    q_ensemble_input: pd.DataFrame,
+    solution_string_mapping: Dict[str, str],
+    *,
+    time_unit: Optional[str] = None,
+    forecast_start_date: Optional[str] = None,
+    location: Optional[str] = None,
+    drop_column_names: List[str] = [
+        "timepoint_id",
+        "output",
+    ],
+    train_end_point: Optional[float] = None,
+) -> pd.DataFrame:
+    """
+    Reformat the quantiles pandas dataframe file to CDC ensemble forecast format
+    Note that solution_string_mapping maps name of states/observables in the dictionary key to the dictionary value
+    and also drops any states/observables not available in the dictionary keys.
+    forecast_start_date is the date of last observed data.
+    """
+    q_ensemble_data = deepcopy(q_ensemble_input)
+    if time_unit != "days" or time_unit is None:
+        warnings.warn(
+            "cdc_format only works for time_unit=days"
+            "time_unit will default to days and overwrite previous time_unit."
+        )
+        q_ensemble_data.rename(columns={"number_None": "number_days"}, inplace=True)
+        if "number_days" not in q_ensemble_data:
+            raise ValueError("time_unit can only support days")
+        time_unit = "days"
+
+    if train_end_point is None:
+        q_ensemble_data["Forecast_Backcast"] = "Forecast"
+        number_data_days = 0.0
+    else:
+        q_ensemble_data["Forecast_Backcast"] = np.where(
+            q_ensemble_data[f"number_{time_unit}"] > train_end_point,
+            "Forecast",
+            "Backcast",
+        )
+        # Number of days for which data is available
+        number_data_days = max(
+            q_ensemble_data[
+                q_ensemble_data["Forecast_Backcast"].str.contains("Backcast")
+            ][f"number_{time_unit}"]
+        )
+    drop_column_names.extend(["Forecast_Backcast"])
+    # Subtracting number of backast days from number_days
+    q_ensemble_data[f"number_{time_unit}"] = (
+        q_ensemble_data[f"number_{time_unit}"] - number_data_days
+    )
+    # Drop rows that are backcasting
+    q_ensemble_data = q_ensemble_data[
+        ~q_ensemble_data["Forecast_Backcast"].str.contains("Backcast")
+    ]
+    # Changing name of state according to user provided strings
+    if solution_string_mapping:
+        # Drop rows that are not present in the solution_string_mapping keys
+        q_ensemble_data = q_ensemble_data[
+            q_ensemble_data["output"].str.contains(
+                "|".join(solution_string_mapping.keys())
+            )
+        ]
+        for k, v in solution_string_mapping.items():
+            q_ensemble_data["output"] = q_ensemble_data["output"].replace(k, v)
+
+    # Creating target column
+    q_ensemble_data["target"] = (
+        q_ensemble_data[f"number_{time_unit}"].astype("string")
+        + " days ahead "
+        # + q_ensemble_data["inc_cum"]
+        + " "
+        + q_ensemble_data["output"]
+    )
+
+    # Add dates
+    if forecast_start_date:
+        q_ensemble_data["forecast_date"] = pd.to_datetime(
+            forecast_start_date, format="%Y-%m-%d", errors="ignore"
+        )
+        q_ensemble_data["target_end_date"] = q_ensemble_data["forecast_date"].combine(
+            q_ensemble_data[f"number_{time_unit}"],
+            lambda x, y: x + pd.DateOffset(days=int(y)),
+        )
+    # Add location column
+    if location:
+        q_ensemble_data["location"] = location
+    # Dropping columns specified by user
+    if drop_column_names:
+        q_ensemble_data = q_ensemble_data.drop(columns=drop_column_names)
+    return q_ensemble_data
 
 
 # --- Intervention weaving utilities ----
diff --git a/pyciemss/interfaces.py b/pyciemss/interfaces.py
index 5df3ab18e..3152fdf1c 100644
--- a/pyciemss/interfaces.py
+++ b/pyciemss/interfaces.py
@@ -22,7 +22,10 @@
 from pyciemss.integration_utils.custom_decorators import pyciemss_logging_wrapper
 from pyciemss.integration_utils.interface_checks import check_solver
 from pyciemss.integration_utils.observation import compile_noise_model, load_data
-from pyciemss.integration_utils.result_processing import prepare_interchange_dictionary
+from pyciemss.integration_utils.result_processing import (
+    DEFAULT_ALPHA_QS,
+    prepare_interchange_dictionary,
+)
 from pyciemss.interruptions import (
     DynamicParameterIntervention,
     ParameterInterventionTracer,
@@ -52,7 +55,9 @@ def ensemble_sample(
     start_time: float = 0.0,
     inferred_parameters: Optional[pyro.nn.PyroModule] = None,
     time_unit: Optional[str] = None,
-):
+    alpha_qs: Optional[List[float]] = DEFAULT_ALPHA_QS,
+    stacking_order: str = "timepoints",
+) -> Dict[str, Any]:
     """
     Load a collection of models from files, compile them into an ensemble probabilistic program,
     and sample from the ensemble.
@@ -97,13 +102,22 @@ def ensemble_sample(
         - A Pyro module that contains the inferred parameters of the model.
           This is typically the result of `calibrate`.
         - If not provided, we will use the default values from the AMR model.
+    alpha_qs: Optional[List[float]]
+            - The quantiles required for estimating weighted interval score to test ensemble forecasting accuracy.
+    stacking_order: Optional[str]
+        - The stacking order requested for the ensemble quantiles to keep the selected quantity together for each state.
+        - Options: "timepoints" or "quantiles"
 
     Returns:
-        result: Dict[str, torch.Tensor]
-            - Dictionary of outputs from the model.
-                - Each key is the name of a parameter or state variable in the model.
-                - Each value is a tensor of shape (num_samples, num_timepoints) for state variables
+        result: Dict[str, Any]
+            - Dictionary of outputs with following attributes:
+                - data: The samples from the model as a pandas DataFrame.
+                - unprocessed_result: Dictionary of outputs from the model.
+                    - Each key is the name of a parameter or state variable in the model.
+                    - Each value is a tensor of shape (num_samples, num_timepoints) for state variables
                     and (num_samples,) for parameters.
+                - ensemble_quantiles: The quantiles for ensemble score calculation as a pandas DataFrames.
+                - schema: Visualization. (If visual_options is truthy)
     """
     check_solver(solver_method, solver_options)
 
@@ -116,7 +130,7 @@ def ensemble_sample(
         )
 
         logging_times = torch.arange(
-            start_time + logging_step_size, end_time, logging_step_size
+            start_time, end_time + logging_step_size, logging_step_size
         )
 
         # Check that num_samples is a positive integer
@@ -151,7 +165,12 @@ def wrapped_model():
         )()
 
         return prepare_interchange_dictionary(
-            samples, timepoints=logging_times, time_unit=time_unit
+            samples,
+            timepoints=logging_times,
+            time_unit=time_unit,
+            ensemble_quantiles=True,
+            alpha_qs=alpha_qs,
+            stacking_order=stacking_order,
         )
 
 
@@ -410,14 +429,13 @@ def sample(
             - Risk level for alpha-superquantile outputs in the results dictionary.
 
     Returns:
-        result: Dict[str, torch.Tensor]
+        result: Dict[str, Any]
             - Dictionary of outputs with following attributes:
                 - data: The samples from the model as a pandas DataFrame.
                 - unprocessed_result: Dictionary of outputs from the model.
                     - Each key is the name of a parameter or state variable in the model.
                     - Each value is a tensor of shape (num_samples, num_timepoints) for state variables
                     and (num_samples,) for parameters.
-                - quantiles: The quantiles for ensemble score calculation as a pandas DataFrames.
                 - risk: Dictionary with each key as the name of a state with
                 a dictionary of risk estimates for each state at the final timepoint.
                     - risk: alpha-superquantile risk estimate
@@ -435,7 +453,7 @@ def sample(
         model = CompiledDynamics.load(model_path_or_json)
 
         logging_times = torch.arange(
-            start_time + logging_step_size, end_time, logging_step_size
+            start_time, end_time + logging_step_size, logging_step_size
         )
 
         # Check that num_samples is a positive integer
diff --git a/tests/fixtures.py b/tests/fixtures.py
index fee615178..06a627228 100644
--- a/tests/fixtures.py
+++ b/tests/fixtures.py
@@ -244,7 +244,7 @@ def check_result_sizes(
         assert isinstance(v, torch.Tensor)
 
         num_timesteps = len(
-            torch.arange(start_time + logging_step_size, end_time, logging_step_size)
+            torch.arange(start_time, end_time + logging_step_size, logging_step_size)
         )
         if v.ndim == 2 and k == "model_weights":
             assert v.shape[0] == num_samples
diff --git a/tests/test_interfaces.py b/tests/test_interfaces.py
index 257c4fc35..4b58a5710 100644
--- a/tests/test_interfaces.py
+++ b/tests/test_interfaces.py
@@ -539,7 +539,7 @@ def test_output_format(
     )["data"]
     assert isinstance(processed_result, pd.DataFrame)
     assert processed_result.shape[0] == num_samples * len(
-        torch.arange(start_time + logging_step_size, end_time, logging_step_size)
+        torch.arange(start_time, end_time + logging_step_size, logging_step_size)
     )
     assert processed_result.shape[1] >= 2
     assert list(processed_result.columns)[:3] == [
diff --git a/tests/visuals/test_plots.py b/tests/visuals/test_plots.py
index 3dbe2566c..5cf037886 100644
--- a/tests/visuals/test_plots.py
+++ b/tests/visuals/test_plots.py
@@ -12,6 +12,8 @@
 from pyciemss.integration_utils.result_processing import convert_to_output_format
 from pyciemss.visuals import plots, vega
 
+START_TIME = 0.0
+
 
 def by_key_value(targets, key, value):
     for entry in targets:
@@ -33,7 +35,6 @@ def create_distributions(logging_step_size=20, time_unit="twenty"):
         "https://raw.githubusercontent.com/DARPA-ASKEM/simulation-integration"
         "/main/data/models/SEIRHD_NPI_Type1_petrinet.json"
     )
-    start_time = 0.0
     end_time = 100.0
     num_samples = 30
     sample = pyciemss.sample(
@@ -42,7 +43,7 @@ def create_distributions(logging_step_size=20, time_unit="twenty"):
         logging_step_size,
         num_samples,
         time_unit=time_unit,
-        start_time=start_time,
+        start_time=START_TIME,
         solver_method="euler",
         solver_options={"step_size": 1e-2},
     )["unprocessed_result"]
@@ -51,10 +52,10 @@ def create_distributions(logging_step_size=20, time_unit="twenty"):
         sample,
         # using same time point formula as in 'logging_times' from  pyciemms interfaces formula 'sample'
         timepoints=np.arange(
-            start_time + logging_step_size, end_time, logging_step_size
+            START_TIME, end_time + logging_step_size, logging_step_size
         ),
         time_unit=time_unit,
-    )
+    )[0]
 
 
 class TestTrajectory:
@@ -103,7 +104,10 @@ def test_timepoints(self, logging_step_size, time_unit, end_time):
         schema = plots.trajectories(new_distribution)
         df = pd.DataFrame(vega.find_named(schema["data"], "distributions")["values"])
         new_timepoints = [
-            float(x) for x in np.arange(logging_step_size, end_time, logging_step_size)
+            float(x)
+            for x in np.arange(
+                START_TIME, end_time + logging_step_size, logging_step_size
+            )
         ]
         # check timepoint created match the input logging_step_size and start and end time
         assert df.timepoint[: len(new_timepoints)].tolist() == new_timepoints
@@ -269,7 +273,6 @@ def simulation_result():
             "https://raw.githubusercontent.com/DARPA-ASKEM/simulation-integration/"
             "main/data/models/SEIRHD_NPI_Type1_petrinet.json"
         )
-        start_time = 0.0
         end_time = 100.0
         logging_step_size = 10.0
         num_samples = 3
@@ -279,7 +282,7 @@ def simulation_result():
             end_time,
             logging_step_size,
             num_samples,
-            start_time=start_time,
+            start_time=START_TIME,
             solver_method="euler",
             solver_options={"step_size": 0.1},
         )["unprocessed_result"]
diff --git a/tests/visuals/test_utils.py b/tests/visuals/test_utils.py
index 099f97a08..45e138f9d 100644
--- a/tests/visuals/test_utils.py
+++ b/tests/visuals/test_utils.py
@@ -36,7 +36,7 @@ def distributions():
         sample,
         timepoints=np.linspace(start_time, end_time, num_timepoints),
         time_unit="notional",
-    )
+    )[0]
 
 
 def test_resize(distributions):