Deployed d4c077d with MkDocs version: 1.6.1

Changelog/index.html

@@ -3371,7 +3371,7 @@ <h3 id="changed_14">Changed</h3>
 </ul>
 <h3 id="fixed_18">Fixed</h3>
 <ul>
-<li>Object link recomputation logic: the launch_attributes_computation_chain function in the <a href="https://github.com/publicissapient-france/e-footprint/tree/main/efootprint/abstract_modeling_classes/modeling_object.py">ModelingObject class</a> now allows for a breadth first exploration of the object link graph to recompute object attributes in the right order. </li>
+<li>Object link recomputation logic: the launch_attributes_computation_chain function in the <a href="https://github.com/Boavizta/e-footprint/tree/main/efootprint/abstract_modeling_classes/modeling_object.py">ModelingObject class</a> now allows for a breadth first exploration of the object link graph to recompute object attributes in the right order. </li>
 </ul>
 <h2 id="111-2023-11-03">[1.1.1] - 2023-11-03</h2>
 <h3 id="added_16">Added</h3>
@@ -3380,8 +3380,8 @@ <h3 id="fixed_19">Fixed</h3>
 <ul>
 <li>Possibility to have a null service as input for user journey steps (in cases when the user simply uses the device without any service call).</li>
 <li>UserJourney’s add_step method didn’t trigger setattr because of the use of the self.uj_steps.append(new_step) syntax, and hence didn’t trigger the appropriate recomputation logic. Fixed by replacing it with the self.uj_steps = self.uj_steps + [new_step] syntax.</li>
-<li><a href="https://github.com/publicissapient-france/e-footprint/tree/main/efootprint/utils/graph_tools.py">graph_tools</a> module doesn’t depend any more on special selenium screenshot functions that are only used during development. Such functions have been moved to the <a href="https://github.com/publicissapient-france/e-footprint/tree/main/efootprint/utils/dev_utils">dev_utils</a> package that only contains modules not used in the project because they are work in progress or dev helper functions.</li>
-<li>Fixed the <a href="https://github.com/publicissapient-france/e-footprint/tree/main/tests/abstract_modeling_classes/test_explainable_objects.py">convert_to_utc_test</a> that had broken because of time change  </li>
+<li><a href="https://github.com/Boavizta/e-footprint/tree/main/efootprint/utils/graph_tools.py">graph_tools</a> module doesn’t depend any more on special selenium screenshot functions that are only used during development. Such functions have been moved to the <a href="https://github.com/Boavizta/e-footprint/tree/main/efootprint/utils/dev_utils">dev_utils</a> package that only contains modules not used in the project because they are work in progress or dev helper functions.</li>
+<li>Fixed the <a href="https://github.com/Boavizta/e-footprint/tree/main/tests/abstract_modeling_classes/test_explainable_objects.py">convert_to_utc_test</a> that had broken because of time change  </li>
 </ul>
 <h2 id="110-2023-10-26">[1.1.0] - 2023-10-26</h2>
 <p>State of project at time of open sourcing.</p>

Network/index.html

@@ -1400,8 +1400,8 @@ <h2 id="calculated-attributes">Calculated attributes</h2>
 <h3 id="energy_footprint">energy_footprint</h3>
 <p>hourly network energy footprint in kilogram.  </p>
 <p>Example value: 26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in kg:<br />
-    first 10 vals [0.02, 0.02, 0.02, 0.03, 0.03, 0.01, 0.0, 0.0, 0.03, 0.01],<br />
-    last 10 vals [0.02, 0.01, 0.03, 0.03, 0.01, 0.02, 0.0, 0.03, 0.01, 0.02]  </p>
+    first 10 vals [0.01, 0.02, 0.03, 0.0, 0.01, 0.03, 0.02, 0.01, 0.01, 0.01],<br />
+    last 10 vals [0.01, 0.02, 0.02, 0.02, 0.0, 0.01, 0.02, 0.01, 0.01, 0.0]  </p>
 <p>Depends directly on:  </p>
 <ul>
 <li><a href="../Job/#hourly_data_upload_per_usage_pattern">Hourly data upload for streaming in usage pattern</a></li>
@@ -1443,7 +1443,7 @@ <h1></h1>
         </center>
         <style type="text/css">
 
-             #mynetwork_00bc23 {
+             #mynetwork_4ae4c9 {
                  width: 760px;
                  height: 300px;
                  background-color: #ffffff;
@@ -1465,7 +1465,7 @@ <h1></h1>
         <div class="card" style="width: 100%">
 
 
-            <div id="mynetwork_00bc23" class="card-body"></div>
+            <div id="mynetwork_4ae4c9" class="card-body"></div>
         </div>
 
 
@@ -1495,12 +1495,12 @@ <h1></h1>
 
               // This method is responsible for drawing the graph, returns the drawn network
               function drawGraph() {
-                  var container = document.getElementById('mynetwork_00bc23');
+                  var container = document.getElementById('mynetwork_4ae4c9');
 
 
 
                   // parsing and collecting nodes and edges from the python
-                  nodes = new vis.DataSet([{"color": null, "id": "Hourly network energy footprint", "label": "Hourly network\nenergy footprint", "shape": "dot", "size": 15, "title": "Hourly network energy footprint\n=\nusage pattern network energy consumption * Average carbon intensity of devices\ncountry\n=\n26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in kWh:\nfirst 10 vals [0.2, 0.28, 0.2, 0.32, 0.36, 0.16, 0.04, 0.04, 0.32, 0.12],\nlast 10 vals [0.24, 0.08, 0.32, 0.32, 0.08, 0.24, 0.04, 0.36, 0.16, 0.24] * 85.0\ngram / kilowatt_hour\n=\n26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in kg:\nfirst 10 vals [0.02, 0.02, 0.02, 0.03, 0.03, 0.01, 0.0, 0.0, 0.03, 0.01],\nlast 10 vals [0.02, 0.01, 0.03, 0.03, 0.01, 0.02, 0.0, 0.03, 0.01, 0.02]", "x": 0.0, "y": 900}, {"color": null, "id": "usage pattern network energy consumption", "label": "usage pattern\nnetwork energy\nconsumption", "shape": "dot", "size": 15, "title": "usage pattern network energy consumption\n=\n(Hourly data upload for streaming in usage pattern + Hourly data download for\nstreaming in usage pattern) * bandwith energy intensity of network\n=\n(26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in TB:\nfirst 10 vals [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\nlast 10 vals [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + 26281 values\nfrom 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in MB:\nfirst 10 vals [4000.0, 5600.0, 4000.0, 6400.0, 7200.0, 3200.0, 800.0, 800.0,\n6400.0, 2400.0],\nlast 10 vals [4800.0, 1600.0, 6400.0, 6400.0, 1600.0, 4800.0, 800.0, 7200.0,\n3200.0, 4800.0]) * 0.05 kilowatt_hour / gigabyte\n=\n26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in kWh:\nfirst 10 vals [0.2, 0.28, 0.2, 0.32, 0.36, 0.16, 0.04, 0.04, 0.32, 0.12],\nlast 10 vals [0.24, 0.08, 0.32, 0.32, 0.08, 0.24, 0.04, 0.36, 0.16, 0.24]", "x": -150.0, "y": 750}, {"color": null, "id": "Hourly data upload for streaming in usage pattern", "label": "Hourly data upload\nfor streaming in\nusage pattern", "shape": "dot", "size": 15, "title": "Hourly data upload for streaming in usage pattern\n=\n(shift by 0 of (Hourly streaming occurrences in usage pattern)) * data upload\nper hour for job streaming in usage pattern\n=\n(shift by 0 of (26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in\ndimensionless:\nfirst 10 vals [5, 7, 5, 8, 9, 4, 1, 1, 8, 3],\nlast 10 vals [6, 2, 8, 8, 2, 6, 1, 9, 4, 6])) * 0.05 megabyte\n=\n26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in TB:\nfirst 10 vals [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\nlast 10 vals [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]", "x": -200.0, "y": 600}, {"color": null, "id": "Hourly data download for streaming in usage pattern", "label": "Hourly data download\nfor streaming in\nusage pattern", "shape": "dot", "size": 15, "title": "Hourly data download for streaming in usage pattern\n=\n(shift by 0 of (Hourly streaming occurrences in usage pattern)) * data download\nper hour for job streaming in usage pattern\n=\n(shift by 0 of (26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in\ndimensionless:\nfirst 10 vals [5, 7, 5, 8, 9, 4, 1, 1, 8, 3],\nlast 10 vals [6, 2, 8, 8, 2, 6, 1, 9, 4, 6])) * 800.0 megabyte\n=\n26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in MB:\nfirst 10 vals [4000.0, 5600.0, 4000.0, 6400.0, 7200.0, 3200.0, 800.0, 800.0,\n6400.0, 2400.0],\nlast 10 vals [4800.0, 1600.0, 6400.0, 6400.0, 1600.0, 4800.0, 800.0, 7200.0,\n3200.0, 4800.0]", "x": 0.0, "y": 600}, {"color": null, "id": "bandwith energy intensity of network", "label": "bandwith energy\nintensity of network", "shape": "dot", "size": 15, "title": "bandwith energy intensity of network = 0.05 kilowatt_hour / gigabyte", "x": 200.0, "y": 600}, {"color": null, "id": "Average carbon intensity of devices country", "label": "Average carbon\nintensity of devices\ncountry", "shape": "dot", "size": 15, "title": "Average carbon intensity of devices country = 85.0 gram / kilowatt_hour", "x": 150.0, "y": 750}]);
+                  nodes = new vis.DataSet([{"color": null, "id": "Hourly network energy footprint", "label": "Hourly network\nenergy footprint", "shape": "dot", "size": 15, "title": "Hourly network energy footprint\n=\nusage pattern network energy consumption * Average carbon intensity of devices\ncountry\n=\n26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in kWh:\nfirst 10 vals [0.08, 0.2, 0.36, 0.04, 0.12, 0.32, 0.24, 0.16, 0.08, 0.08],\nlast 10 vals [0.16, 0.28, 0.2, 0.28, 0.04, 0.12, 0.28, 0.16, 0.12, 0.04] * 85.0\ngram / kilowatt_hour\n=\n26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in kg:\nfirst 10 vals [0.01, 0.02, 0.03, 0.0, 0.01, 0.03, 0.02, 0.01, 0.01, 0.01],\nlast 10 vals [0.01, 0.02, 0.02, 0.02, 0.0, 0.01, 0.02, 0.01, 0.01, 0.0]", "x": 0.0, "y": 900}, {"color": null, "id": "usage pattern network energy consumption", "label": "usage pattern\nnetwork energy\nconsumption", "shape": "dot", "size": 15, "title": "usage pattern network energy consumption\n=\n(Hourly data upload for streaming in usage pattern + Hourly data download for\nstreaming in usage pattern) * bandwith energy intensity of network\n=\n(26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in TB:\nfirst 10 vals [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\nlast 10 vals [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] + 26281 values\nfrom 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in MB:\nfirst 10 vals [1600.0, 4000.0, 7200.0, 800.0, 2400.0, 6400.0, 4800.0, 3200.0,\n1600.0, 1600.0],\nlast 10 vals [3200.0, 5600.0, 4000.0, 5600.0, 800.0, 2400.0, 5600.0, 3200.0,\n2400.0, 800.0]) * 0.05 kilowatt_hour / gigabyte\n=\n26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in kWh:\nfirst 10 vals [0.08, 0.2, 0.36, 0.04, 0.12, 0.32, 0.24, 0.16, 0.08, 0.08],\nlast 10 vals [0.16, 0.28, 0.2, 0.28, 0.04, 0.12, 0.28, 0.16, 0.12, 0.04]", "x": -150.0, "y": 750}, {"color": null, "id": "Hourly data upload for streaming in usage pattern", "label": "Hourly data upload\nfor streaming in\nusage pattern", "shape": "dot", "size": 15, "title": "Hourly data upload for streaming in usage pattern\n=\n(shift by 0 of (Hourly streaming occurrences in usage pattern)) * data upload\nper hour for job streaming in usage pattern\n=\n(shift by 0 of (26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in\ndimensionless:\nfirst 10 vals [2, 5, 9, 1, 3, 8, 6, 4, 2, 2],\nlast 10 vals [4, 7, 5, 7, 1, 3, 7, 4, 3, 1])) * 0.05 megabyte\n=\n26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in TB:\nfirst 10 vals [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\nlast 10 vals [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]", "x": -200.0, "y": 600}, {"color": null, "id": "Hourly data download for streaming in usage pattern", "label": "Hourly data download\nfor streaming in\nusage pattern", "shape": "dot", "size": 15, "title": "Hourly data download for streaming in usage pattern\n=\n(shift by 0 of (Hourly streaming occurrences in usage pattern)) * data download\nper hour for job streaming in usage pattern\n=\n(shift by 0 of (26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in\ndimensionless:\nfirst 10 vals [2, 5, 9, 1, 3, 8, 6, 4, 2, 2],\nlast 10 vals [4, 7, 5, 7, 1, 3, 7, 4, 3, 1])) * 800.0 megabyte\n=\n26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in MB:\nfirst 10 vals [1600.0, 4000.0, 7200.0, 800.0, 2400.0, 6400.0, 4800.0, 3200.0,\n1600.0, 1600.0],\nlast 10 vals [3200.0, 5600.0, 4000.0, 5600.0, 800.0, 2400.0, 5600.0, 3200.0,\n2400.0, 800.0]", "x": 0.0, "y": 600}, {"color": null, "id": "bandwith energy intensity of network", "label": "bandwith energy\nintensity of network", "shape": "dot", "size": 15, "title": "bandwith energy intensity of network = 0.05 kilowatt_hour / gigabyte", "x": 200.0, "y": 600}, {"color": null, "id": "Average carbon intensity of devices country", "label": "Average carbon\nintensity of devices\ncountry", "shape": "dot", "size": 15, "title": "Average carbon intensity of devices country = 85.0 gram / kilowatt_hour", "x": 150.0, "y": 750}]);
                   edges = new vis.DataSet([{"arrows": "to", "from": "usage pattern network energy consumption", "to": "Hourly network energy footprint"}, {"arrows": "to", "from": "Hourly data upload for streaming in usage pattern", "to": "usage pattern network energy consumption"}, {"arrows": "to", "from": "Hourly data download for streaming in usage pattern", "to": "usage pattern network energy consumption"}, {"arrows": "to", "from": "bandwith energy intensity of network", "to": "usage pattern network energy consumption"}, {"arrows": "to", "from": "Average carbon intensity of devices country", "to": "Hourly network energy footprint"}]);
 
                   nodeColors = {};