From c8114b5ec43383c7e07b16f6867188f79d145628 Mon Sep 17 00:00:00 2001
From: "spaceBearAmadeus (Alex)" <alexanderpatrie@gmail.com>
Date: Wed, 11 Sep 2024 10:32:13 -0400
Subject: [PATCH] update version

---
 bio_compose/data_model.py             |    17 +-
 bio_compose/verifier.py               |    22 +-
 documentation/source/verification.rst |     8 +
 poetry.lock                           |    12 +-
 tests/test.ipynb                      | 66451 +++++++++++++++++++++++-
 5 files changed, 66483 insertions(+), 27 deletions(-)

diff --git a/bio_compose/data_model.py b/bio_compose/data_model.py
index c9e46d5..6a6b69b 100644
--- a/bio_compose/data_model.py
+++ b/bio_compose/data_model.py
@@ -1,6 +1,7 @@
 import asyncio
 import time 
 import os
+from warnings import warn 
 from dataclasses import asdict, dataclass
 from functools import wraps
 from typing import Dict, List, Union, Any, Callable
@@ -234,12 +235,16 @@ def save_plot(func):
     """
     @wraps(func)
     def wrapper(self, *args, **kwargs):
-        fig, data = func(self, *args, **kwargs)
-        save_dest = kwargs.get('save_dest', None)
-        if save_dest is not None:
-            dest = save_dest + '.pdf'
-            self.export_plot(fig=fig, save_dest=dest)
-        return data
+        try:
+            fig, data = func(self, *args, **kwargs)
+            save_dest = kwargs.get('save_dest', None)
+            if save_dest is not None:
+                dest = save_dest + '.pdf'
+                self.export_plot(fig=fig, save_dest=dest)
+            return data
+        except ValueError as e:   
+            warn(str(e))
+            return {}
 
     return wrapper
 
diff --git a/bio_compose/verifier.py b/bio_compose/verifier.py
index 1505df1..7230c7b 100644
--- a/bio_compose/verifier.py
+++ b/bio_compose/verifier.py
@@ -208,8 +208,8 @@ def get_rmse(self, job_id: str) -> dict:
         """
         try:
             output = self.get_output(job_id=job_id)
-            return output['content'].get('results').get('rmse') or {}
-        except Exception as e:
+            return output['content'].get('results').get('rmse', {}) 
+        except:
             import traceback
             tb_str = traceback.format_exc()
             error_message = (
@@ -406,19 +406,19 @@ def visualize_rmse(self, job_id: str, fig_dimensions: tuple[int, int] = None, co
         """
         Visualize the root-mean-squared error between simulator verification outputs as a heatmap.
 
-        Args:
-            - **job_id**: `str`: verification job id. This value can be easily derived from either of `Verifier`'s `.verify_...` methods.
-            - **fig_dimensions**: `Tuple[int, int], optional`: The value to use as the `figsize` parameter for a call to `matplotlib.pyplot.figure()`. If `None` is passed, default to (8, 6).
-            - **color_mapping**: `List[str], optional`: list of colors to use for each simulator in the grid. Defaults to None.
-            - **save_dest**: `str`: destination at which to save figure. Defaults to `None`.
+        :param job_id: (`str`) verification job id. This value can be easily derived from either of `Verifier`'s `.verify_...` methods.
+        :param fig_dimensions: (`Tuple[int, int], optional`) The value to use as the `figsize` parameter for a call to `matplotlib.pyplot.figure()`. If `None` is passed, default to (8, 6).
+        :param color_mapping: (`List[str], optional`) list of colors to use for each simulator in the grid. Defaults to None.
+        :param save_dest: `(str`) destination at which to save figure. Defaults to `None`.
 
-         Returns:
-            `Tuple[matplotlib.Figure, Dict]` of matplotlib Figure and simulator RMSE scores
+        :return: matplotlib Figure and simulator RMSE scores
+        :rtype: `Tuple[matplotlib.Figure, Dict]`
         """
         # extract data
         rmse_matrix = self.get_rmse(job_id)
-        if 'error' in rmse_matrix.keys():
-            raise IOError(f"The job for {job_id} is either not ready or has an error. Please check the output.")
+        if not rmse_matrix or 'error' in rmse_matrix.keys():
+            raise ValueError(f"The job for {job_id} is either not ready or has an error in rmse scoring. Please check the output.")
+
         simulators = list(rmse_matrix.keys())
         n_simulators = len(simulators)
 
diff --git a/documentation/source/verification.rst b/documentation/source/verification.rst
index 9e57b7c..a3bdd17 100644
--- a/documentation/source/verification.rst
+++ b/documentation/source/verification.rst
@@ -14,6 +14,14 @@ Running **OMEX verifications**:
 
     verification = verify(filepath, simulators)
 
+Visualizing verifications:
+
+.. code-block:: python
+    # visualize observables
+    verification.observables()
+
+    # visualize root-mean-square error scores for all simulators involved in the verification.
+    verification.rmse(save_dest='/my/save/path/for/observables')
 
 Running **SBML verifications**:
 
diff --git a/poetry.lock b/poetry.lock
index 040cf76..b81e236 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1236,13 +1236,13 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pytest"
-version = "8.3.2"
+version = "8.3.3"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pytest-8.3.2-py3-none-any.whl", hash = "sha256:4ba08f9ae7dcf84ded419494d229b48d0903ea6407b030eaec46df5e6a73bba5"},
-    {file = "pytest-8.3.2.tar.gz", hash = "sha256:c132345d12ce551242c87269de812483f5bcc87cdbb4722e48487ba194f9fdce"},
+    {file = "pytest-8.3.3-py3-none-any.whl", hash = "sha256:a6853c7375b2663155079443d2e45de913a911a11d669df02a50814944db57b2"},
+    {file = "pytest-8.3.3.tar.gz", hash = "sha256:70b98107bd648308a7952b06e6ca9a50bc660be218d53c257cc1fc94fda10181"},
 ]
 
 [package.dependencies]
@@ -1272,13 +1272,13 @@ six = ">=1.5"
 
 [[package]]
 name = "pytz"
-version = "2024.1"
+version = "2024.2"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
 files = [
-    {file = "pytz-2024.1-py2.py3-none-any.whl", hash = "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"},
-    {file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"},
+    {file = "pytz-2024.2-py2.py3-none-any.whl", hash = "sha256:31c7c1817eb7fae7ca4b8c7ee50c72f93aa2dd863de768e1ef4245d426aa0725"},
+    {file = "pytz-2024.2.tar.gz", hash = "sha256:2aa355083c50a0f93fa581709deac0c9ad65cca8a9e9beac660adcbd493c798a"},
 ]
 
 [[package]]
diff --git a/tests/test.ipynb b/tests/test.ipynb
index a29370a..5cbbd8f 100644
--- a/tests/test.ipynb
+++ b/tests/test.ipynb
@@ -21,13 +21,13 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "8680e1d5-120a-438b-be3c-7d1bd7b6917e",
+   "id": "d3364c99",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<bio_compose.verifier.Verifier at 0x1072bae30>"
+       "{}"
       ]
      },
      "execution_count": 2,
@@ -36,7 +36,61 @@
     }
    ],
    "source": [
-    "v"
+    "output = {'content': {'results': {'a': {}}}}\n",
+    "\n",
+    "output['content'].get('results').get('rmse', {}) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8680e1d5-120a-438b-be3c-7d1bd7b6917e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/alexanderpatrie/Desktop/repos/bio-compose/bio_compose/data_model.py:246: UserWarning: The job for verification-bio_check-request-0cdbb5cd-acc4-411e-86aa-36a842fe2a00-6903713f-429d-4418-a347-04bc36de31b8 is either not ready or has an error in rmse scoring. Please check the output.\n",
+      "  warn(str(e))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jobid = 'verification-bio_check-request-0cdbb5cd-acc4-411e-86aa-36a842fe2a00-6903713f-429d-4418-a347-04bc36de31b8'\n",
+    "data = v.visualize_rmse(job_id=jobid)\n",
+    "\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6e302f9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/alexanderpatrie/Desktop/repos/bio-compose/bio_compose/data_model.py:246: UserWarning: The job for simulation-execution-utc08d80451-07aa-4778-9bd3-bb887c6c815c is either not ready or has an error in rmse scoring. Please check the output.\n",
+      "  return warn(str(e))\n"
+     ]
+    }
+   ],
+   "source": [
+    "jobid2 = 'simulation-execution-utc08d80451-07aa-4778-9bd3-bb887c6c815c'\n",
+    "v.visualize_rmse(job_id=jobid2)"
    ]
   },
   {
@@ -1200,9 +1254,66398 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "4ab2393ae0dd7f6a",
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'content': {'job_id': 'verification-bio_check-request-a40e7cf2-c704-4cb6-89a3-c8b9ff034934-44c2b40c-d7b1-46ac-82e0-bb7cb7f523f8',\n",
+       "  'timestamp': '2024-09-06 04:21:27.765985',\n",
+       "  'status': 'COMPLETED',\n",
+       "  'results': {'Y': {'mse': {'amici': {'amici': 0.0,\n",
+       "      'copasi': 3.3792986348992197e-19,\n",
+       "      'tellurium': 1.1421292383284316e-18},\n",
+       "     'copasi': {'amici': 3.3792986348992197e-19,\n",
+       "      'copasi': 0.0,\n",
+       "      'tellurium': 2.5626951621647453e-19},\n",
+       "     'tellurium': {'amici': 1.1421292383284316e-18,\n",
+       "      'copasi': 2.5626951621647453e-19,\n",
+       "      'tellurium': 0.0}},\n",
+       "    'proximity': {'amici': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'copasi': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'tellurium': {'amici': True, 'copasi': True, 'tellurium': True}},\n",
+       "    'output_data': {'amici': [0.0,\n",
+       "      0.00010027114652816835,\n",
+       "      0.00010046432691371343,\n",
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+       "      ...]}},\n",
+       "   'Time': {'mse': {'amici': {'amici': 0.0,\n",
+       "      'copasi': 3.0694873472598097e-29,\n",
+       "      'tellurium': 0.0},\n",
+       "     'copasi': {'amici': 3.0694873472598097e-29,\n",
+       "      'copasi': 0.0,\n",
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+       "      'copasi': 3.0694873472598097e-29,\n",
+       "      'tellurium': 0.0}},\n",
+       "    'proximity': {'amici': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'copasi': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'tellurium': {'amici': True, 'copasi': True, 'tellurium': True}},\n",
+       "    'output_data': {'amici': [0.0,\n",
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+       "   'Reaction8': {'mse': {'amici': {'amici': 0.0,\n",
+       "      'copasi': 4.230986610154069e-12,\n",
+       "      'tellurium': 1.4287550941509143e-11},\n",
+       "     'copasi': {'amici': 4.230986610154069e-12,\n",
+       "      'copasi': 0.0,\n",
+       "      'tellurium': 3.1703373964686708e-12},\n",
+       "     'tellurium': {'amici': 1.4287550941509143e-11,\n",
+       "      'copasi': 3.1703373964686708e-12,\n",
+       "      'tellurium': 0.0}},\n",
+       "    'proximity': {'amici': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'copasi': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'tellurium': {'amici': True, 'copasi': True, 'tellurium': True}},\n",
+       "    'output_data': {'amici': [0.0,\n",
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+       "      ...]}},\n",
+       "   'time': {'mse': {'amici': {'amici': 0.0,\n",
+       "      'copasi': 3.0694873472598097e-29,\n",
+       "      'tellurium': 0.0},\n",
+       "     'copasi': {'amici': 3.0694873472598097e-29,\n",
+       "      'copasi': 0.0,\n",
+       "      'tellurium': 3.0694873472598097e-29},\n",
+       "     'tellurium': {'amici': 0.0,\n",
+       "      'copasi': 3.0694873472598097e-29,\n",
+       "      'tellurium': 0.0}},\n",
+       "    'proximity': {'amici': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'copasi': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'tellurium': {'amici': True, 'copasi': True, 'tellurium': True}},\n",
+       "    'output_data': {'amici': [0.0,\n",
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+       "   'Reaction7': {'mse': {'amici': {'amici': 0.0,\n",
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+       "    'proximity': {'amici': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'copasi': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'tellurium': {'amici': True, 'copasi': True, 'tellurium': True}},\n",
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+       "   'C2': {'mse': {'amici': {'amici': 0.0,\n",
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+       "      'tellurium': 0.0}},\n",
+       "    'proximity': {'amici': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'copasi': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'tellurium': {'amici': True, 'copasi': True, 'tellurium': True}},\n",
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+       "   'pM': {'mse': {'amici': {'amici': 0.0,\n",
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+       "      'tellurium': 2.0211844836657106e-10},\n",
+       "     'copasi': {'amici': 5.991334827129027e-11,\n",
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+       "      'copasi': 4.4935784670423836e-11,\n",
+       "      'tellurium': 0.0}},\n",
+       "    'proximity': {'amici': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'copasi': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'tellurium': {'amici': True, 'copasi': True, 'tellurium': True}},\n",
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+       "      ...]}},\n",
+       "   'YP': {'mse': {'amici': {'amici': 0.0,\n",
+       "      'copasi': 1.1752740583760312e-11,\n",
+       "      'tellurium': 3.968764150419283e-11},\n",
+       "     'copasi': {'amici': 1.1752740583760312e-11,\n",
+       "      'copasi': 0.0,\n",
+       "      'tellurium': 8.806492767970198e-12},\n",
+       "     'tellurium': {'amici': 3.968764150419283e-11,\n",
+       "      'copasi': 8.806492767970198e-12,\n",
+       "      'tellurium': 0.0}},\n",
+       "    'proximity': {'amici': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'copasi': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'tellurium': {'amici': True, 'copasi': True, 'tellurium': True}},\n",
+       "    'output_data': {'amici': [0.0,\n",
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+       "      0.004259480932507513,\n",
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+       "      0.004912858124080129,\n",
+       "      0.004952278325510343,\n",
+       "      ...]}},\n",
+       "   'CT': {'mse': {'amici': {'amici': 0.0,\n",
+       "      'copasi': 2.352151599638878e-20,\n",
+       "      'tellurium': 3.3639864887724236e-19},\n",
+       "     'copasi': {'amici': 2.352151599638878e-20,\n",
+       "      'copasi': 0.0,\n",
+       "      'tellurium': 1.9689831756634546e-19},\n",
+       "     'tellurium': {'amici': 3.3639864887724236e-19,\n",
+       "      'copasi': 1.9689831756634546e-19,\n",
+       "      'tellurium': 0.0}},\n",
+       "    'proximity': {'amici': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'copasi': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'tellurium': {'amici': True, 'copasi': True, 'tellurium': True}},\n",
+       "    'output_data': {'amici': [1.0,\n",
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+       "   'Reaction6': {'mse': {'amici': {'amici': 0.0,\n",
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+       "    'proximity': {'amici': {'amici': True, 'copasi': True, 'tellurium': True},\n",
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+       "     'tellurium': {'amici': True, 'copasi': True, 'tellurium': True}},\n",
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+       "   'Reaction2': {'mse': {'amici': {'amici': 0.0,\n",
+       "      'copasi': 2.4356233292206623e-05,\n",
+       "      'tellurium': 8.206903230592335e-05},\n",
+       "     'copasi': {'amici': 2.4356233292206623e-05,\n",
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+       "      'copasi': 1.819282249580589e-05,\n",
+       "      'tellurium': 0.0}},\n",
+       "    'proximity': {'amici': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'copasi': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'tellurium': {'amici': True, 'copasi': True, 'tellurium': True}},\n",
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+       "      ...]}},\n",
+       "   'pcyclin_cdc2': {'mse': {'amici': {'amici': 0.0,\n",
+       "      'copasi': 3.545674083502477e-11,\n",
+       "      'tellurium': 1.1969225278988108e-10},\n",
+       "     'copasi': {'amici': 3.545674083502477e-11,\n",
+       "      'copasi': 0.0,\n",
+       "      'tellurium': 2.6651537729549734e-11},\n",
+       "     'tellurium': {'amici': 1.1969225278988108e-10,\n",
+       "      'copasi': 2.6651537729549734e-11,\n",
+       "      'tellurium': 0.0}},\n",
+       "    'proximity': {'amici': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'copasi': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'tellurium': {'amici': True, 'copasi': True, 'tellurium': True}},\n",
+       "    'output_data': {'amici': [0.0,\n",
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+       "      ...]}},\n",
+       "   'Reaction4': {'mse': {'amici': {'amici': 0.0,\n",
+       "      'copasi': 2.434620837351125e-18,\n",
+       "      'tellurium': 8.657408788211303e-17},\n",
+       "     'copasi': {'amici': 2.434620837351125e-18,\n",
+       "      'copasi': 0.0,\n",
+       "      'tellurium': 8.561249395875602e-17},\n",
+       "     'tellurium': {'amici': 8.657408788211303e-17,\n",
+       "      'copasi': 8.561249395875602e-17,\n",
+       "      'tellurium': 0.0}},\n",
+       "    'proximity': {'amici': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'copasi': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'tellurium': {'amici': True, 'copasi': True, 'tellurium': True}},\n",
+       "    'output_data': {'amici': [0.0,\n",
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+       "      0.014998668375171989,\n",
+       "      ...]}},\n",
+       "   'Reaction9': {'mse': {'amici': {'amici': 0.0,\n",
+       "      'copasi': 5.241341148991054e-10,\n",
+       "      'tellurium': 1.7750841970498826e-09},\n",
+       "     'copasi': {'amici': 5.241341148991054e-10,\n",
+       "      'copasi': 0.0,\n",
+       "      'tellurium': 3.962043806701419e-10},\n",
+       "     'tellurium': {'amici': 1.7750841970498826e-09,\n",
+       "      'copasi': 3.962043806701419e-10,\n",
+       "      'tellurium': 0.0}},\n",
+       "    'proximity': {'amici': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'copasi': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'tellurium': {'amici': True, 'copasi': True, 'tellurium': True}},\n",
+       "    'output_data': {'amici': [0.0045,\n",
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+       "      0.002976878277523368,\n",
+       "      0.003004138643617492,\n",
+       "      0.003031458414219824,\n",
+       "      0.003058838652423124,\n",
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+       "      0.0036209427060789434,\n",
+       "      0.003649871315239308,\n",
+       "      ...]}},\n",
+       "   'total_cyclin': {'mse': {'amici': {'amici': 0.0,\n",
+       "      'copasi': 0.00845592871094892,\n",
+       "      'tellurium': 4.2443854491933725e-11},\n",
+       "     'copasi': {'amici': 0.00845592871094892,\n",
+       "      'copasi': 0.0,\n",
+       "      'tellurium': 0.008455888731499496},\n",
+       "     'tellurium': {'amici': 4.2443854491933725e-11,\n",
+       "      'copasi': 0.008455888731499496,\n",
+       "      'tellurium': 0.0}},\n",
+       "    'proximity': {'amici': {'amici': True, 'copasi': False, 'tellurium': True},\n",
+       "     'copasi': {'amici': False, 'copasi': True, 'tellurium': False},\n",
+       "     'tellurium': {'amici': True, 'copasi': False, 'tellurium': True}},\n",
+       "    'output_data': {'amici': [0.25,\n",
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+       "      0.18932309388474358,\n",
+       "      0.1905271420794214,\n",
+       "      ...]}},\n",
+       "   'YT': {'mse': {'amici': {'amici': 0.0,\n",
+       "      'copasi': 0.00845592871094892,\n",
+       "      'tellurium': 4.2443854491933725e-11},\n",
+       "     'copasi': {'amici': 0.00845592871094892,\n",
+       "      'copasi': 0.0,\n",
+       "      'tellurium': 0.008455888731499496},\n",
+       "     'tellurium': {'amici': 4.2443854491933725e-11,\n",
+       "      'copasi': 0.008455888731499496,\n",
+       "      'tellurium': 0.0}},\n",
+       "    'proximity': {'amici': {'amici': True, 'copasi': False, 'tellurium': True},\n",
+       "     'copasi': {'amici': False, 'copasi': True, 'tellurium': False},\n",
+       "     'tellurium': {'amici': True, 'copasi': False, 'tellurium': True}},\n",
+       "    'output_data': {'amici': [0.25,\n",
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+       "      ...]}},\n",
+       "   'Reaction1': {'mse': {'amici': {'amici': 0.0,\n",
+       "      'copasi': 3.545674083502477e-11,\n",
+       "      'tellurium': 1.1969225278988108e-10},\n",
+       "     'copasi': {'amici': 3.545674083502477e-11,\n",
+       "      'copasi': 0.0,\n",
+       "      'tellurium': 2.6651537729549734e-11},\n",
+       "     'tellurium': {'amici': 1.1969225278988108e-10,\n",
+       "      'copasi': 2.6651537729549734e-11,\n",
+       "      'tellurium': 0.0}},\n",
+       "    'proximity': {'amici': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'copasi': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'tellurium': {'amici': True, 'copasi': True, 'tellurium': True}},\n",
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+       "      ...]}},\n",
+       "   'Reaction3': {'mse': {'amici': {'amici': 0.0,\n",
+       "      'copasi': 2.435599277857626e-05,\n",
+       "      'tellurium': 8.206870070146097e-05},\n",
+       "     'copasi': {'amici': 2.435599277857626e-05,\n",
+       "      'copasi': 0.0,\n",
+       "      'tellurium': 1.819292590237251e-05},\n",
+       "     'tellurium': {'amici': 8.206870070146097e-05,\n",
+       "      'copasi': 1.819292590237251e-05,\n",
+       "      'tellurium': 0.0}},\n",
+       "    'proximity': {'amici': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'copasi': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'tellurium': {'amici': True, 'copasi': True, 'tellurium': True}},\n",
+       "    'output_data': {'amici': [750.0,\n",
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+       "      814.8669245530724,\n",
+       "      813.7035913407642,\n",
+       "      ...]}},\n",
+       "   'CP': {'mse': {'amici': {'amici': 0.0,\n",
+       "      'copasi': 2.4355992778578285e-11,\n",
+       "      'tellurium': 8.206870070143439e-11},\n",
+       "     'copasi': {'amici': 2.4355992778578285e-11,\n",
+       "      'copasi': 0.0,\n",
+       "      'tellurium': 1.8192925902365077e-11},\n",
+       "     'tellurium': {'amici': 8.206870070143439e-11,\n",
+       "      'copasi': 1.8192925902365077e-11,\n",
+       "      'tellurium': 0.0}},\n",
+       "    'proximity': {'amici': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'copasi': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'tellurium': {'amici': True, 'copasi': True, 'tellurium': True}},\n",
+       "    'output_data': {'amici': [0.75,\n",
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+       "      ...]}},\n",
+       "   'M': {'mse': {'amici': {'amici': 0.0,\n",
+       "      'copasi': 3.545674083502477e-11,\n",
+       "      'tellurium': 1.1969225278988108e-10},\n",
+       "     'copasi': {'amici': 3.545674083502477e-11,\n",
+       "      'copasi': 0.0,\n",
+       "      'tellurium': 2.6651537729549734e-11},\n",
+       "     'tellurium': {'amici': 1.1969225278988108e-10,\n",
+       "      'copasi': 2.6651537729549734e-11,\n",
+       "      'tellurium': 0.0}},\n",
+       "    'proximity': {'amici': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'copasi': {'amici': True, 'copasi': True, 'tellurium': True},\n",
+       "     'tellurium': {'amici': True, 'copasi': True, 'tellurium': True}},\n",
+       "    'output_data': {'amici': [0.0,\n",
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+       "      ...]}},\n",
+       "   'rmse': {'amici': {'amici': 0.0,\n",
+       "     'copasi': 0.027765716580250916,\n",
+       "     'tellurium': 0.0027314688873684495},\n",
+       "    'copasi': {'amici': 0.027765716580250916,\n",
+       "     'copasi': 0.0,\n",
+       "     'tellurium': 0.027755559445289364},\n",
+       "    'tellurium': {'amici': 0.0027314688873684495,\n",
+       "     'copasi': 0.027755559445289364,\n",
+       "     'tellurium': 0.0}}},\n",
+       "  'source': 'BIOMD0000000005.omex'}}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v.get_output('verification-bio_check-request-a40e7cf2-c704-4cb6-89a3-c8b9ff034934-44c2b40c-d7b1-46ac-82e0-bb7cb7f523f8')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1d200288",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = v.visualize_rmse('verification-bio_check-request-a40e7cf2-c704-4cb6-89a3-c8b9ff034934-44c2b40c-d7b1-46ac-82e0-bb7cb7f523f8')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ea82531b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'amici': [0.0, 0.027765716580250916, 0.0027314688873684495],\n",
+       " 'copasi': [0.027765716580250916, 0.0, 0.027755559445289364],\n",
+       " 'tellurium': [0.0027314688873684495, 0.027755559445289364, 0.0]}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee890ab2",
+   "metadata": {},
    "outputs": [],
    "source": []
   }