added 2024project

hammerdirt-analyst · Sep 26, 2024 · 39ce9b2 · 39ce9b2
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diff --git a/_build/.doctrees/environment.pickle b/_build/.doctrees/environment.pickle
diff --git a/_build/.doctrees/excercise_2024.doctree b/_build/.doctrees/excercise_2024.doctree
diff --git a/_build/.doctrees/grid_approximation.doctree b/_build/.doctrees/grid_approximation.doctree
diff --git a/_build/.doctrees/grids_2023.doctree b/_build/.doctrees/grids_2023.doctree
diff --git a/_build/.doctrees/plastic_shotgun_wadding.doctree b/_build/.doctrees/plastic_shotgun_wadding.doctree
diff --git a/_build/.doctrees/titlepage.doctree b/_build/.doctrees/titlepage.doctree
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diff --git a/_build/jupyter_execute/grid_approximation.glue.json b/_build/jupyter_execute/grid_approximation.glue.json
diff --git a/_build/jupyter_execute/grid_approximation.ipynb b/_build/jupyter_execute/grid_approximation.ipynb
@@ -954,80 +954,6 @@
     "cbdu.rename(columns={\"pcs/m\":\"pcs_m\"}, inplace=True)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "editable": true,
-    "pycharm": {
-     "name": "#%%\n"
-    },
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": [
-     "remove-input"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "# !!Bug these locations not found in the \n",
-    "# land use index \"['cully-p', 'preverenges-p', 'tolochenaz-p', 'vidy-p']\n",
-    "# raises exception with: \n",
-    "# trc = lu_scaled.loc[tr_locs][attribute_columns]\n",
-    "\n",
-    "# tst_locs = cbdu[(cbdu.Project == 'Testing')].slug.unique()\n",
-    "# tr_locs = cbdu[(cbdu.Project == 'Training')].slug.unique()\n",
-    "\n",
-    "\n",
-    "# english_column_names = {\n",
-    "#     \"Obstanlage\":\"Orchards\",\n",
-    "#     \"Reben\": \"Vineyards\",\n",
-    "#     \"Siedl\": \"Buildings\",\n",
-    "#     \"Strasse\": \"Streets\",\n",
-    "#     \"Wald\": \"Woods\",\n",
-    "#     \"infrastructure\":\"Infrastructure\",\n",
-    "#     \"recreation\":\"Recreation\",\n",
-    "#     \"undefined\":\"Undefined\"\n",
-    "# }\n",
-    "\n",
-    "# trc = lu_scaled.loc[tr_locs][attribute_columns]\n",
-    "# tst = lu_scaled.loc[tst_locs][attribute_columns]\n",
-    "\n",
-    "# trc.rename(columns=english_column_names, inplace=True)\n",
-    "# tst.rename(columns=english_column_names, inplace=True)\n",
-    "\n",
-    "# corr_tst = tst.corr()\n",
-    "# corr_trc = trc.corr()\n",
-    "\n",
-    "# mask_tr = np.triu(np.ones_like(corr_trc, dtype=bool))\n",
-    "# mask_ts = np.triu(np.ones_like(corr_tst, dtype=bool))\n",
-    "# fig, ax = plt.subplots()\n",
-    "\n",
-    "# sns.heatmap(corr_trc, mask=mask_tr, cmap=\"YlOrBr\", ax=ax)\n",
-    "\n",
-    "\n",
-    "# ax.set_ylabel(\"\")\n",
-    "# ax.set_xlabel(\"\")\n",
-    "# ax.set_title(\"Training data\", loc=\"left\")\n",
-    "# plt.tight_layout()\n",
-    "\n",
-    "# glue(\"corr_training\", fig, display=False)\n",
-    "# plt.close()\n",
-    "\n",
-    "# fig, ax = plt.subplots()\n",
-    "\n",
-    "# sns.heatmap(corr_tst, mask=mask_ts, cmap=\"YlOrBr\", ax=ax)\n",
-    "\n",
-    "# ax.set_ylabel(\"\")\n",
-    "# ax.set_xlabel(\"\")\n",
-    "# ax.set_title(\"Testing data\", loc=\"left\")\n",
-    "# plt.tight_layout()\n",
-    "\n",
-    "# glue(\"corr_testing\", fig, display=False)\n",
-    "# plt.close()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -2279,7 +2205,7 @@
       "text/markdown": [
        "\n",
        "\n",
-       "This script updated 22/08/2024 in Biel, CH\n",
+       "This script updated 26/09/2024 in Biel, CH\n",
        "\n",
        "❤️ __what you do everyday:__ *analyst at hammerdirt*\n"
       ],
@@ -2330,10 +2256,10 @@
       "\n",
       "Git branch: main\n",
       "\n",
-      "seaborn   : 0.13.2\n",
-      "matplotlib: 3.8.4\n",
-      "pandas    : 2.2.2\n",
       "numpy     : 1.26.4\n",
+      "pandas    : 2.2.2\n",
+      "matplotlib: 3.8.4\n",
+      "seaborn   : 0.13.2\n",
       "\n"
      ]
     }

diff --git a/_build/jupyter_execute/grids_2023.glue.json b/_build/jupyter_execute/grids_2023.glue.json
diff --git a/_build/jupyter_execute/grids_2023.ipynb b/_build/jupyter_execute/grids_2023.ipynb
diff --git a/_build/jupyter_execute/plastic_shotgun_wadding.glue.json b/_build/jupyter_execute/plastic_shotgun_wadding.glue.json
diff --git a/_build/jupyter_execute/plastic_shotgun_wadding.ipynb b/_build/jupyter_execute/plastic_shotgun_wadding.ipynb
@@ -6,9 +6,6 @@
    "id": "60d29688-34ea-4727-8c83-d1e52de7c808",
    "metadata": {
     "editable": true,
-    "jupyter": {
-     "source_hidden": true
-    },
     "pycharm": {
      "name": "#%%\n"
     },
@@ -1404,7 +1401,7 @@
       "text/markdown": [
        "\n",
        "\n",
-       "This script updated 22/08/2024 in Biel, CH\n",
+       "This script updated 26/09/2024 in Biel, CH\n",
        "\n",
        "❤️ __what you do everyday:__ *analyst at hammerdirt*\n"
       ],

diff --git a/_toc.yml b/_toc.yml
@@ -6,8 +6,9 @@ root: titlepage
 options:
     numbered: True
 chapters:
-- file: plastic_shotgun_wadding
+- file: excercise_2024
 - file: grids_2023
+- file: plastic_shotgun_wadding
 - file: grid_approximation
 
 

diff --git a/data_processing.ipynb b/data_processing.ipynb
@@ -2140,7 +2140,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,

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diff --git a/docs/_sources/excercise_2024.md b/docs/_sources/excercise_2024.md
@@ -0,0 +1,88 @@
+# How did it get there?
+
+Today, we’re looking at how plastics, an essential part of modern life, ends up on the beach of Lake Geneva. 
+
+In december 2022 the Association for the Safeguard of Lake Geneva (ASL) completed a year-long project of identifying and
+quantifying trash on the beaches of Lake Geneva [Pla'stock](https://associationsauvegardeleman.github.io/plastock/index.html). 
+This project was the latest in a series of initiatives that have been ongoing since 2015. These reports provide information 
+about the quantity and type of objects found. However, they do not provide information about how the objects got to the beach.
+
+__Todays schedule:__
+
+1. Introduction
+2. Discussion of what we expect to find
+3. Field experience: identify and count 
+4. Return to lab - tabulate results
+5. Discussion of results
+
+## Most common items
+
+Among the most common items found on the lakes beaches in 2022 are the usual suspects: cigarette butts (11% of the total), plastic bottle caps, (4% of the total) 
+and snack wrappers (8% of the total). We find also objects that are not associated with activities at the beach. For example, fragmented plastics (41% of the total)
+industrial plastic pellets (6% of the total), cotton swabs (4% of the total) plastic construction materials (3% of the total) and shotgun cartridges (1% of the total).
+
+All the previous items have are found in similar proportions in the marine environment in european space, [EEA data viewer](https://www.eea.europa.eu/en/analysis/maps-and-charts/marine-litterwatch-data-viewer-marine-litterwatch-data-viewer).
+The federal report of 2021, [IQAASL](https://www.bafu.admin.ch/bafu/en/home/topics/waste/state/data-and-indicators/indicators/indicators-on-waste-and-resources.html), reported included 
+medical containers, straws and stirrers and toys as common objects on the beaches of lake Geneva.
+
+## First encounters
+
+__Objective: In the field identify and count specific beach litter items.__
+
+The guide for monitoring beach litter, [MLW Guide](https://publications.jrc.ec.europa.eu/repository/handle/JRC83985), lists 200 items that can be found on the beach. The OSPAR 
+commission has a similar list of 150 items [OSPAR](https://www.ospar.org/documents?v=44122). Today we are going to focus on identifying the counting 7 items:
+
+1. Plastic medical containers
+2. Cotton swabs
+3. Shotgun cartridges / wadding
+4. Construction plastics
+5. Industrial pellets
+6. Media filters / biomass holders
+7. Straws and stirrers
+
+All other objects will be collected and discarded. Each item on the list has a distinct use or origin. 
+
+### Tabulate results
+
+Record the number of each item found and the name of the beach where it was found. Reflections:
+
+1. Did you find more or less than you expected?
+2. How do the results compare to the federal report of 2021?
+3. Were there items that were found on one beach and not another?
+
+### Discussion 
+
+1. Did you notice any immediate sources of the items found? If so were their any visible signs of prevention measures?
+2. If the source is not apparent, what are the possible sources?
+4. What are the possible pathways for the object to reach the beach?
+5. What are the possible prevention measures?
+
+## Semester project: pathways
+
+You can choose to make your video project on the potential pathways to the beach of a specific item. Your video project should also include
+a video short (think tiktok or google shorts) that can be shared on social media and give a brief overview of the problem and potential solutions.
+
+### Resources for the video project
+
+Interested in the topic? I am at your disposal for any questions on the topic: Here are some resources to get you started:
+
+__General information:__
+
+1. [The ocean cleanup](https://theoceancleanup.com/updates/)
+2. [EEA data viewer](https://www.eea.europa.eu/en/analysis/maps-and-charts/marine-litterwatch-data-viewer-marine-litterwatch-data-viewer)
+3. [MLW Guide](https://publications.jrc.ec.europa.eu/repository/handle/JRC83985)
+4. [OSPAR](https://www.ospar.org/documents?v=44122)
+5. [Pla'stock](https://associationsauvegardeleman.github.io/plastock/index.html)
+6. [Identifying sources of marine litter](https://publications.jrc.ec.europa.eu/repository/handle/JRC104038)
+7. contact [email protected] for specific resources on a topic
+
+
+
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+