deploy: ef05e11

R/README.html

@@ -208,6 +208,12 @@
   </li>
       </ul>
   </li>
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../docs/docker-configurations.qmd" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Docker</span></a>
+  </div>
+</li>
     </ul>
     </div>
 </nav>

R/comparing-r-and-python/HADs-reprojection/WIP-Comparing-HADs-grids.html

@@ -242,6 +242,12 @@
   </li>
       </ul>
   </li>
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../../../docs/docker-configurations.qmd" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Docker</span></a>
+  </div>
+</li>
     </ul>
     </div>
 </nav>

R/misc/Identifying_Runs.html

@@ -242,6 +242,12 @@
   </li>
       </ul>
   </li>
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../../docs/docker-configurations.qmd" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Docker</span></a>
+  </div>
+</li>
     </ul>
     </div>
 </nav>

README.html

@@ -242,6 +242,12 @@
   </li>
       </ul>
   </li>
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./docs/docker-configurations.qmd" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Docker</span></a>
+  </div>
+</li>
     </ul>
     </div>
 </nav>

docs/contributing.html

@@ -242,6 +242,12 @@
   </li>
       </ul>
   </li>
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../docs/docker-configurations.qmd" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Docker</span></a>
+  </div>
+</li>
     </ul>
     </div>
 </nav>

docs/pipeline.html

@@ -211,6 +211,12 @@
   </li>
       </ul>
   </li>
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../docs/docker-configurations.qmd" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Docker</span></a>
+  </div>
+</li>
     </ul>
     </div>
 </nav>
@@ -251,7 +257,7 @@ <h1 class="title"><strong>Workflow</strong></h1>
 ````
 --&gt;
 
-```fxvtwhbay
+```qwvxmcvob
 graph TB
 
 subgraph Legend

docs/pipeline_guidance.html

@@ -242,6 +242,12 @@
   </li>
       </ul>
   </li>
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../docs/docker-configurations.qmd" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Docker</span></a>
+  </div>
+</li>
     </ul>
     </div>
 </nav>

python/README.html

@@ -242,6 +242,12 @@
   </li>
       </ul>
   </li>
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../docs/docker-configurations.qmd" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Docker</span></a>
+  </div>
+</li>
     </ul>
     </div>
 </nav>

search.json

@@ -293,7 +293,7 @@
     "href": "docs/pipeline.html",
     "title": "Workflow",
     "section": "",
-    "text": "Workflow diagram\n<!– Note a syntax error is raised if referencing the mermaid file in docs/assets/. Ideally this gets resolved to simply import that file here like so:\n\n\n\n\n{% include assets/pipeline.mermaid %}\n````\n-->\n\n```fxvtwhbay\ngraph TB\n\nsubgraph Legend\n    direction RL\n    data_external[(external data)]\n    data_fileshare[path to fileshare]\n    script_r([R script])\n    script_py([Python script])\n    script_bash([Bash script])\n    var[parameter]:::var\nend\n\n%%% INPUT DATA\nsubgraph CEDA\n    data_hads[(HADS)]\n    data_cpm[(UKCP2.2)]\n    data_hads --> script_load\n    data_cpm --> script_load\n    data_hads --> script_load\nend\n\nsubgraph Core pipeline\n    subgraph Data Ingress\n        %%% Loading data to disk\n        script_load([ceda_ftp_download.py])\n        data_hads_raw[RAW/HadsUKgrid/../*.nc]\n        data_cpm_raw[RAW/UKCP2.2/../*.nc]\n        script_load --> data_hads_raw\n        script_load --> data_cpm_raw\n    end\n    subgraph Preprocessing\n        %% resampling & reprojecting\n        script_resampling([resampling_hads.py])\n        script_reproject([reproject_all.sh])\n\n        data_hads_res[Processed/HadsUKgrid/../*.nc]\n        data_cpm_rep[Reprojected/UKCP2.2/../*.tiff]\n\n        script_resampling --> data_hads_res\n        script_reproject --> data_cpm_rep\n\n        %% cropping\n        script_crop_city([Cropping_Rasters_to_three_cities.R])\n\n        data_cropped_cpm[Cropped/cpm/..]\n        data_cropped_hads[Cropped/hads/..]\n        script_crop_city --> data_cropped_cpm\n        script_crop_city --> data_cropped_hads\n\n\n    end\n\n    subgraph Data Splitting\n        data_outdir[Cropped/preprocessed/..]\n\n        script_preproc([preprocess_data.py])\n\n        data_out_train[../simh..]\n        data_out_calibrate[../simp..]\n        data_out_groundtruth_h[../obsh..]\n        data_out_groundtruth_p[../obsp..]\n\n        script_preproc --> data_outdir\n\n        data_outdir --> data_out_train\n        data_outdir --> data_out_calibrate\n        data_outdir --> data_out_groundtruth_h\n        data_outdir --> data_out_groundtruth_p\n    end\n\n    subgraph bc[Bias Correction]\n        script_bc_py([run_cmethods.py])\n        script_bc_r([run_cmethods.R])\n        function_bc_r[[fitQmapQUANT.R]]\n\n\n        data_out_py[Debiased/...]\n        data_out_r[Debiased/R/QuantileMapping/resultsL*]\n\n        data_out_train --> script_bc_py\n        data_out_calibrate --> script_bc_py\n        data_out_groundtruth_h --> script_bc_py\n        data_out_train --> script_bc_r\n        data_out_calibrate --> script_bc_r\n        data_out_groundtruth_h --> script_bc_r\n        script_bc_r --> function_bc_r\n\n        script_bc_py-->data_out_py\n        function_bc_r-->data_out_r\n    end\n\n    subgraph Assessment\n        script_asses[tbc]\n        data_out_groundtruth_p --> script_asses\n    end\n    data_out_py --> script_asses\n    data_out_r --> script_asses\nend\n\n\nsubgraph nner_py[Execute Python pipeline for MO dataset]\n    data_shape_uk[(shape London)]\n    data_shape_gl[(shape Glasgow)]\n    data_shape_ma[(shape Manchester)]\n\n\n    script_BC_wrapper[three_cities_debiasing.sh]\n    param1[\"metric (eg tasmax)\"]:::var\n    param2[\"runs (eg 05)\"]:::var\n    param3[\"BC method (eg quantile_mapping)\"]:::var\n    param4[city]:::var\n\n    script_BC_wrapper --> param1\n    param1 --> param2\n    param2 --> param3\n    param3 --> param4\n    param4 -- for loop --> script_preproc\n\n    %% Looping connections\n    param4 -.-> param3\n    param3 -.-> param2\n    param2 -.-> param1\nend\n\nsubgraph nner_jupyter[Jupyter Notebook for Guidance]\n    direction BT\n    data_shape_gl2[(shape Glasgow)]\n    data_cpm2[(UKCP2.2_Monthly)]\n\n    param5[\"tasmax\"]:::var\n    param6[\"quantile_mapping\"]:::var\n    param7[Glasgow]:::var\n\n    script_BC_wrapper --> param1\n    param5 --> script_preproc\n    param6 --> script_preproc\n    param7 --> script_preproc\n\n    data_cpm2 --> script_load\n    data_shape_gl2 --> script_crop_city\nend\n\n%% between block connections\n%% input preproc 1\ndata_hads_raw --> script_resampling\ndata_cpm_raw --> script_reproject\n%% input cropping\ndata_cpm_rep --> script_crop_city\n\ndata_hads_res --> script_crop_city\ndata_shape_uk --> script_crop_city\ndata_shape_ma --> script_crop_city\ndata_shape_gl --> script_crop_city\n\n%% input preproc2\ndata_cropped_cpm --> script_preproc\ndata_cropped_hads --> script_preproc\n\nparam4 -- for loop --> script_bc_py\n\n\n%% class styles\nclassDef python fill:#4CAF50;\nclassDef r fill:#FF5722;\nclassDef bash fill:#f9f\nclassDef var fill:none,stroke:#0f0;\nclassDef dashed stroke-dasharray: 5 5;\n\nclass script_crop_city,script_crop_uk,function_bc_r,script_r,script_df_uk,function_bc,function_crop_bc,fn_crop_cpm,fn_crop_hads,fn_bc,script_bc_r r;\nclass script_load,script_resampling,script_preproc,script_bc_py,script_py python;\nclass script_reproject,script_BC_wrapper,script_bash bash;\nclass inner_py dashed;\nclass inner_r dashed;\n\n\n\n\n\n\n\n\n Back to top",
+    "text": "Workflow diagram\n<!– Note a syntax error is raised if referencing the mermaid file in docs/assets/. Ideally this gets resolved to simply import that file here like so:\n\n\n\n\n{% include assets/pipeline.mermaid %}\n````\n-->\n\n```qwvxmcvob\ngraph TB\n\nsubgraph Legend\n    direction RL\n    data_external[(external data)]\n    data_fileshare[path to fileshare]\n    script_r([R script])\n    script_py([Python script])\n    script_bash([Bash script])\n    var[parameter]:::var\nend\n\n%%% INPUT DATA\nsubgraph CEDA\n    data_hads[(HADS)]\n    data_cpm[(UKCP2.2)]\n    data_hads --> script_load\n    data_cpm --> script_load\n    data_hads --> script_load\nend\n\nsubgraph Core pipeline\n    subgraph Data Ingress\n        %%% Loading data to disk\n        script_load([ceda_ftp_download.py])\n        data_hads_raw[RAW/HadsUKgrid/../*.nc]\n        data_cpm_raw[RAW/UKCP2.2/../*.nc]\n        script_load --> data_hads_raw\n        script_load --> data_cpm_raw\n    end\n    subgraph Preprocessing\n        %% resampling & reprojecting\n        script_resampling([resampling_hads.py])\n        script_reproject([reproject_all.sh])\n\n        data_hads_res[Processed/HadsUKgrid/../*.nc]\n        data_cpm_rep[Reprojected/UKCP2.2/../*.tiff]\n\n        script_resampling --> data_hads_res\n        script_reproject --> data_cpm_rep\n\n        %% cropping\n        script_crop_city([Cropping_Rasters_to_three_cities.R])\n\n        data_cropped_cpm[Cropped/cpm/..]\n        data_cropped_hads[Cropped/hads/..]\n        script_crop_city --> data_cropped_cpm\n        script_crop_city --> data_cropped_hads\n\n\n    end\n\n    subgraph Data Splitting\n        data_outdir[Cropped/preprocessed/..]\n\n        script_preproc([preprocess_data.py])\n\n        data_out_train[../simh..]\n        data_out_calibrate[../simp..]\n        data_out_groundtruth_h[../obsh..]\n        data_out_groundtruth_p[../obsp..]\n\n        script_preproc --> data_outdir\n\n        data_outdir --> data_out_train\n        data_outdir --> data_out_calibrate\n        data_outdir --> data_out_groundtruth_h\n        data_outdir --> data_out_groundtruth_p\n    end\n\n    subgraph bc[Bias Correction]\n        script_bc_py([run_cmethods.py])\n        script_bc_r([run_cmethods.R])\n        function_bc_r[[fitQmapQUANT.R]]\n\n\n        data_out_py[Debiased/...]\n        data_out_r[Debiased/R/QuantileMapping/resultsL*]\n\n        data_out_train --> script_bc_py\n        data_out_calibrate --> script_bc_py\n        data_out_groundtruth_h --> script_bc_py\n        data_out_train --> script_bc_r\n        data_out_calibrate --> script_bc_r\n        data_out_groundtruth_h --> script_bc_r\n        script_bc_r --> function_bc_r\n\n        script_bc_py-->data_out_py\n        function_bc_r-->data_out_r\n    end\n\n    subgraph Assessment\n        script_asses[tbc]\n        data_out_groundtruth_p --> script_asses\n    end\n    data_out_py --> script_asses\n    data_out_r --> script_asses\nend\n\n\nsubgraph nner_py[Execute Python pipeline for MO dataset]\n    data_shape_uk[(shape London)]\n    data_shape_gl[(shape Glasgow)]\n    data_shape_ma[(shape Manchester)]\n\n\n    script_BC_wrapper[three_cities_debiasing.sh]\n    param1[\"metric (eg tasmax)\"]:::var\n    param2[\"runs (eg 05)\"]:::var\n    param3[\"BC method (eg quantile_mapping)\"]:::var\n    param4[city]:::var\n\n    script_BC_wrapper --> param1\n    param1 --> param2\n    param2 --> param3\n    param3 --> param4\n    param4 -- for loop --> script_preproc\n\n    %% Looping connections\n    param4 -.-> param3\n    param3 -.-> param2\n    param2 -.-> param1\nend\n\nsubgraph nner_jupyter[Jupyter Notebook for Guidance]\n    direction BT\n    data_shape_gl2[(shape Glasgow)]\n    data_cpm2[(UKCP2.2_Monthly)]\n\n    param5[\"tasmax\"]:::var\n    param6[\"quantile_mapping\"]:::var\n    param7[Glasgow]:::var\n\n    script_BC_wrapper --> param1\n    param5 --> script_preproc\n    param6 --> script_preproc\n    param7 --> script_preproc\n\n    data_cpm2 --> script_load\n    data_shape_gl2 --> script_crop_city\nend\n\n%% between block connections\n%% input preproc 1\ndata_hads_raw --> script_resampling\ndata_cpm_raw --> script_reproject\n%% input cropping\ndata_cpm_rep --> script_crop_city\n\ndata_hads_res --> script_crop_city\ndata_shape_uk --> script_crop_city\ndata_shape_ma --> script_crop_city\ndata_shape_gl --> script_crop_city\n\n%% input preproc2\ndata_cropped_cpm --> script_preproc\ndata_cropped_hads --> script_preproc\n\nparam4 -- for loop --> script_bc_py\n\n\n%% class styles\nclassDef python fill:#4CAF50;\nclassDef r fill:#FF5722;\nclassDef bash fill:#f9f\nclassDef var fill:none,stroke:#0f0;\nclassDef dashed stroke-dasharray: 5 5;\n\nclass script_crop_city,script_crop_uk,function_bc_r,script_r,script_df_uk,function_bc,function_crop_bc,fn_crop_cpm,fn_crop_hads,fn_bc,script_bc_r r;\nclass script_load,script_resampling,script_preproc,script_bc_py,script_py python;\nclass script_reproject,script_BC_wrapper,script_bash bash;\nclass inner_py dashed;\nclass inner_r dashed;\n\n\n\n\n\n\n\n\n Back to top",
     "crumbs": [
       "Pipeline Diagram"
     ]