From 6e2a2799baef0a7242deee81c16352c38d4a3eb9 Mon Sep 17 00:00:00 2001 From: "roger@hammerdirt.ch" Date: Fri, 27 Sep 2024 10:00:51 +0200 Subject: [PATCH] added results from field excercise and historical values --- _build/.doctrees/environment.pickle | Bin 652325 -> 702331 bytes _build/.doctrees/excercise_2024.doctree | Bin 21006 -> 43519 bytes .../.doctrees/plastic_shotgun_wadding.doctree | Bin 110884 -> 125281 bytes .../plastic_shotgun_wadding.ipynb | 139 ++++++++++++++- data_processing.ipynb | 3 + docs/_sources/excercise_2024.md | 44 ++++- docs/_sources/plastic_shotgun_wadding.ipynb | 139 ++++++++++++++- docs/excercise_2024.html | 164 ++++++++++++++++-- docs/plastic_shotgun_wadding.html | 101 +++++++++++ docs/searchindex.js | 2 +- excercise_2024.md | 44 ++++- plastic_shotgun_wadding.ipynb | 139 ++++++++++++++- 12 files changed, 744 insertions(+), 31 deletions(-) diff --git a/_build/.doctrees/environment.pickle b/_build/.doctrees/environment.pickle index 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zKx2OsXD-{smCKAHmu=$4Wt%v0nQ`H=O&qvv6ZMyEqWm(W`ZA;VvQ5-pW|UsGiOQGy z=JljxBtYgOlOFSfTy)-uDGC>z*OUA((fJ(x#YE>%NRpW7+)9$fMCTcjA0|5AqQ98v zG$!yD6P>q{tT54`Q*gNGypR53qVsL~i-`^$YRt6RrUNLs=+M!3Opk3kAB!omO$P^Y z(V;|pn~4q`iNO@v{$2Wui4N`J%~aU-=r1NZv`HvaUYqvyW14Hz{$NaPZQ1~a>8wq= zA~1!usf3$pt4&3yOjT_vwPSi}Q#BD&Qk$v>nCRFv9qRiB)j^nK2vaYM>Si#kz{v4C zaQC_`4rJ^T?Al83^Zp5ydcW zVIb|-;2$w8k56AYgTBR_^v#V+pIyWk3DkXZ1Jsp=d(Y?>aONSLI;T}1-GgUyQh0i+ zDEx;Tpm1)O!pJ%2qe--LlGxlT66vV*Ef7%_qQM!lqp5pOPU`;FR#Erq4N!OQa0k*6 zpra}KJ2@%)>8+xSP9Yt)-;NAZhVu_c6ZV;$gneqO2%{qy$0h8|353xZaHC24T29jb z)mD*4hs%vi+Vn7K{0OYkwEb~T+Wx~<(MBhEjZ0g?P|jm0g^ZzY?=AU;@?Cgutf4$@ zsH3BW#-%P{sAJpR1;?_C?msOjg?DTfg>*8^xD=L#Wz(ZW7z&Ke(F7jNN#N2}5lAOU zj7wm`SRUE1el&>}a+3Jgts;^3%paG;1St(R7iW8gkEXDblfsKzMImihJ}!j`oyhet z*h_gdiI;Pd_~KTPNZUD&OJZ?Y5gCbW+tAVUeIzG+e|M|sqpe8Cr7uB9)jjY=Q~0Yn zDg4J>ihHqvxn!?G~ 1, 2, 3, and 4\n", + "conditions = t.assign(\n", + " greater_than_1=t['quantity'] > 1,\n", + " greater_than_2=t['quantity'] > 2,\n", + " greater_than_3=t['quantity'] > 3,\n", + " greater_than_4=t['quantity'] > 4\n", + ").groupby(['location', 'code']).agg(\n", + " greater_than_1=('greater_than_1', 'sum'),\n", + " greater_than_2=('greater_than_2', 'sum'),\n", + " greater_than_3=('greater_than_3', 'sum'),\n", + " greater_than_4=('greater_than_4', 'sum')\n", + ").reset_index()\n", + "\n", + "grouped = t.groupby(['object'])\n", + "# Step 2: Count samples per location and code\n", + "counts = grouped.size().reset_index(name='total_samples')\n", + "\n", + "# Step 3: Apply conditions and count how many times quantity > 1, 2, 3, and 4\n", + "conditions = t.assign(\n", + " greater_than_1=t['quantity'] > 1,\n", + " greater_than_2=t['quantity'] > 2,\n", + " greater_than_3=t['quantity'] > 3,\n", + " greater_than_4=t['quantity'] > 4,\n", + " greater_than_10=t['quantity'] > 10\n", + ").groupby(['object']).agg(\n", + " greater_than_1=('greater_than_1', 'sum'),\n", + " greater_than_2=('greater_than_2', 'sum'),\n", + " greater_than_3=('greater_than_3', 'sum'),\n", + " greater_than_4=('greater_than_4', 'sum'),\n", + " greater_than_10=('greater_than_10', 'sum')\n", + ").reset_index()\n", + "\n", + "\n", + "# Step 4: Merge the total sample count with the conditions\n", + "merged = pd.merge(counts, conditions, on=['object'])\n", + "# Step 5: Calculate the ratio of each condition to the total number of samples\n", + "merged['greater than 1'] = merged['greater_than_1'] / merged['total_samples']\n", + "merged['greater than 2'] = merged['greater_than_2'] / merged['total_samples']\n", + "merged['greater than 3'] = merged['greater_than_3'] / merged['total_samples']\n", + "merged['greater than 4'] = merged['greater_than_4'] / merged['total_samples']\n", + "merged['greater than 10'] = merged['greater_than_10'] / merged['total_samples']\n", + "\n", + "merged[['object', 'greater than 1', 'greater than 2', 'greater than 3', 'greater than 4', 'greater than 10']].round(2).to_markdown()" + ] + }, { "cell_type": "markdown", "id": "39f7bb8a-8472-46b6-a820-7986da6ae8d0", @@ -174,9 +308,6 @@ "id": "530dc284-919d-47b4-a2f1-f4fe75ab1f13", "metadata": { "editable": true, - "jupyter": { - "source_hidden": true - }, "slideshow": { "slide_type": "" }, diff --git a/data_processing.ipynb b/data_processing.ipynb index d4b4716..710d4b3 100644 --- a/data_processing.ipynb +++ b/data_processing.ipynb @@ -2044,6 +2044,9 @@ "execution_count": 23, "metadata": { "editable": true, + "jupyter": { + "source_hidden": true + }, "pycharm": { "name": "#%%\n" }, diff --git a/docs/_sources/excercise_2024.md b/docs/_sources/excercise_2024.md index a820783..0e60488 100644 --- a/docs/_sources/excercise_2024.md +++ b/docs/_sources/excercise_2024.md @@ -22,11 +22,17 @@ and snack wrappers (8% of the total). We find also objects that are not associat 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 mentioned are found in similar proportions in the marine environment in the 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), report included medical containers, straws and stirrers and toys as common objects on the beaches of lake Geneva. +The federal report of 2021, [IQAASL](https://hammerdirt-analyst.github.io/IQAASL-End-0f-Sampling-2021/), 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.__ +**Objective : in the field identify and count specific beach litter items.** + +*Survey locations:* + + + +### Items of interest 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 and counting 7 items: @@ -56,6 +62,40 @@ Record the number of each item found and the name of the beach where it was foun 4. What are the possible pathways for the object to reach the beach? 5. What are the possible prevention measures? +## Results + +_Legend :_ The cumulative results are the total number found on visits to the two beaches since 2016 up to the day before of the excercise. + + +| object | cumulative | found sep 26 | +|:----------------------------------------------------|-----------:|--------:| +| Biomass holder | 14 | 2 | +| Cotton bud/swab sticks | 368 | 8 | +| Industrial pellets (nurdles) | 22 | 9 | +| Medical; containers/tubes/ packaging | 133 | 7 | +| Plastic construction waste | 110 | 15| +| Sanitary pads /panty liners/tampons and applicators | 31 | 2 | +| Shotgun cartridges | 23 | 0 | +| Straws and stirrers | 29 | 2 | + + +### Probability of finding an amount + +_Legend :_ The historical probabilities (prior to field excercise) of finding a given quantity at the beaches surveyed (results combined): + + +| object | at least 1 | at least 2 | at least 3 | at least 4 | at least 10| +|:----------------------------------------------------|-----------------:|-----------------:|-----------------:|-----------------:|------------------:| +| Biomass holder | 0.5 | 0.38 | 0.38 | 0 | 0 | +| Cotton bud/swab sticks | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | +| Industrial pellets (nurdles) | 0.25 | 0.25 | 0.25 | 0.25 | 0.12 | +| Medical; containers/tubes/ packaging | 0.88 | 0.88 | 0.75 | 0.75 | 0.62 | +| Plastic construction waste | 0.86 | 0.86 | 0.86 | 0.71 | 0.43 | +| Sanitary pads /panty liners/tampons and applicators | 0.62 | 0.5 | 0.5 | 0.38 | 0 | +| Shotgun cartridges | 0.75 | 0.38 | 0.38 | 0.25 | 0 | +| Straws and stirrers | 0.62 | 0.5 | 0.5 | 0.38 | 0.12 | + + ## 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 diff --git a/docs/_sources/plastic_shotgun_wadding.ipynb b/docs/_sources/plastic_shotgun_wadding.ipynb index c4ba82d..bac1b7b 100644 --- a/docs/_sources/plastic_shotgun_wadding.ipynb +++ b/docs/_sources/plastic_shotgun_wadding.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 26, "id": "60d29688-34ea-4727-8c83-d1e52de7c808", "metadata": { "editable": true, @@ -65,6 +65,140 @@ "shotgun = work_data[work_data.code == 'G70'].copy()" ] }, + { + "cell_type": "code", + "execution_count": 27, + "id": "f2de3eb2-1cc2-43ba-a301-616aee1d9fed", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['parc-des-pierrettes', 'plage-de-dorigny', 'plage-de-st-sulpice',\n", + " 'saint-sulpice', 'tiger-duck-beach'], dtype=object)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "work_data[(work_data.city == 'Saint-Sulpice (VD)')].location.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "b420f011-d0d6-4b46-8b2a-6caeb8e75e19", + "metadata": {}, + "outputs": [], + "source": [ + "codesx = ['G70', 'G96', 'G112', 'G89', 'G95', 'G91', 'G35', 'G100']" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "aca6f768-f389-4371-ae59-68af51478a53", + "metadata": {}, + "outputs": [], + "source": [ + "t = work_data[(work_data.location.isin(['parc-des-pierrettes', 'plage-de-st-sulpice' ]))&(work_data.code.isin(codesx))].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "dca6f010-eee5-41a9-8393-1dd4af9fab3d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'| | object | quantity | found | sample_id |\\n|---:|:----------------------------------------------------|-----------:|--------:|------------:|\\n| 0 | Biomass holder | 14 | 0 | 8 |\\n| 1 | Cotton bud/swab sticks | 368 | 0 | 8 |\\n| 2 | Industrial pellets (nurdles) | 22 | 0 | 8 |\\n| 3 | Medical; containers/tubes/ packaging | 133 | 0 | 8 |\\n| 4 | Plastic construction waste | 110 | 0 | 7 |\\n| 5 | Sanitary pads /panty liners/tampons and applicators | 31 | 0 | 8 |\\n| 6 | Shotgun cartridges | 23 | 0 | 8 |\\n| 7 | Straws and stirrers | 29 | 0 | 8 |'" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "codes = pd.read_csv('resources/data/u_codes.csv')\n", + "codes.set_index('code', inplace=True)\n", + "t['object'] = t.code.map(lambda x: codes.loc[x, 'description'])\n", + "t['found'] = 0\n", + "t[['object', 'quantity', 'found', 'sample_id']].groupby('object', as_index=False).agg({'quantity':'sum', 'found':'sum', 'sample_id':'nunique'}).to_markdown()" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "043bdac8-cf93-4e8a-a76c-11ce617d887e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'| | object | greater than 1 | greater than 2 | greater than 3 | greater than 4 | greater than 10 |\\n|---:|:----------------------------------------------------|-----------------:|-----------------:|-----------------:|-----------------:|------------------:|\\n| 0 | Biomass holder | 0.5 | 0.38 | 0.38 | 0 | 0 |\\n| 1 | Cotton bud/swab sticks | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 |\\n| 2 | Industrial pellets (nurdles) | 0.25 | 0.25 | 0.25 | 0.25 | 0.12 |\\n| 3 | Medical; containers/tubes/ packaging | 0.88 | 0.88 | 0.75 | 0.75 | 0.62 |\\n| 4 | Plastic construction waste | 0.86 | 0.86 | 0.86 | 0.71 | 0.43 |\\n| 5 | Sanitary pads /panty liners/tampons and applicators | 0.62 | 0.5 | 0.5 | 0.38 | 0 |\\n| 6 | Shotgun cartridges | 0.75 | 0.38 | 0.38 | 0.25 | 0 |\\n| 7 | Straws and stirrers | 0.62 | 0.5 | 0.5 | 0.38 | 0.12 |'" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grouped = t.groupby(['object'])\n", + "# Step 2: Count samples per location and code\n", + "counts = grouped.size().reset_index(name='total_samples')\n", + "\n", + "# Step 3: Apply conditions and count how many times quantity > 1, 2, 3, and 4\n", + "conditions = t.assign(\n", + " greater_than_1=t['quantity'] > 1,\n", + " greater_than_2=t['quantity'] > 2,\n", + " greater_than_3=t['quantity'] > 3,\n", + " greater_than_4=t['quantity'] > 4\n", + ").groupby(['location', 'code']).agg(\n", + " greater_than_1=('greater_than_1', 'sum'),\n", + " greater_than_2=('greater_than_2', 'sum'),\n", + " greater_than_3=('greater_than_3', 'sum'),\n", + " greater_than_4=('greater_than_4', 'sum')\n", + ").reset_index()\n", + "\n", + "grouped = t.groupby(['object'])\n", + "# Step 2: Count samples per location and code\n", + "counts = grouped.size().reset_index(name='total_samples')\n", + "\n", + "# Step 3: Apply conditions and count how many times quantity > 1, 2, 3, and 4\n", + "conditions = t.assign(\n", + " greater_than_1=t['quantity'] > 1,\n", + " greater_than_2=t['quantity'] > 2,\n", + " greater_than_3=t['quantity'] > 3,\n", + " greater_than_4=t['quantity'] > 4,\n", + " greater_than_10=t['quantity'] > 10\n", + ").groupby(['object']).agg(\n", + " greater_than_1=('greater_than_1', 'sum'),\n", + " greater_than_2=('greater_than_2', 'sum'),\n", + " greater_than_3=('greater_than_3', 'sum'),\n", + " greater_than_4=('greater_than_4', 'sum'),\n", + " greater_than_10=('greater_than_10', 'sum')\n", + ").reset_index()\n", + "\n", + "\n", + "# Step 4: Merge the total sample count with the conditions\n", + "merged = pd.merge(counts, conditions, on=['object'])\n", + "# Step 5: Calculate the ratio of each condition to the total number of samples\n", + "merged['greater than 1'] = merged['greater_than_1'] / merged['total_samples']\n", + "merged['greater than 2'] = merged['greater_than_2'] / merged['total_samples']\n", + "merged['greater than 3'] = merged['greater_than_3'] / merged['total_samples']\n", + "merged['greater than 4'] = merged['greater_than_4'] / merged['total_samples']\n", + "merged['greater than 10'] = merged['greater_than_10'] / merged['total_samples']\n", + "\n", + "merged[['object', 'greater than 1', 'greater than 2', 'greater than 3', 'greater than 4', 'greater than 10']].round(2).to_markdown()" + ] + }, { "cell_type": "markdown", "id": "39f7bb8a-8472-46b6-a820-7986da6ae8d0", @@ -174,9 +308,6 @@ "id": "530dc284-919d-47b4-a2f1-f4fe75ab1f13", "metadata": { "editable": true, - "jupyter": { - "source_hidden": true - }, "slideshow": { "slide_type": "" }, diff --git a/docs/excercise_2024.html b/docs/excercise_2024.html index 39598bd..c82a87f 100644 --- a/docs/excercise_2024.html +++ b/docs/excercise_2024.html @@ -317,12 +317,17 @@

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