project_scope.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:#008891\">Description of survey results: temporal, geopgraphic and administrative scope</span>\n",
    "\n",
    "#### <span style=\"color:#008891\">Identification, quantification and analysis of observable anthropogenic debris along swiss river and lakes</span>\n",
    "\n",
    "The summary of the survey results and survey characteristics is provided. The results are presented nationally and by river bassin for all surveys since April 1, 2020.  For now this function only works on data later than January 1st 2020. We are in the process of integrating the data from prior projects.\n",
    "\n",
    "#### <span style=\"color:#008891\">Contents</span>\n",
    "\n",
    "1. [A summary](#scope) of the number of locations and the effected population as well as an exhaustive list of lakes, rivers and municipalites where surveys were conducted.\n",
    "\n",
    "2. [The dimensional records](#validdata) are matched to the survey records, missing records are identified and records that do not have a match in both data sets are excluded. The list of excluded surveys and the percentage of all the surveys that are being used to calculate the summary is provided.\n",
    "\n",
    "3. [The dimensional summary](#datasummary) records are summarized to the river bassin.\n",
    "\n",
    "4. [Figure 1](#figureone): relationship of \"number of items found per survey\" to the measured variables, grouped by river bassin\n",
    "\n",
    "\n",
    "#### <span style=\"color:#008891\">More information </span>\n",
    "\n",
    "For information on a specific catchment area or a water feature see the notebook for that catchment area (or make one and send a pull request). For more information on the project visit https://www.plagespropres.ch/ ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sys, file and nav packages:\n",
    "import os\n",
    "import datetime as dt\n",
    "import csv\n",
    "import json\n",
    "\n",
    "# math packages:\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "# charting:\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# images and display\n",
    "import base64, io, IPython\n",
    "from PIL import Image as PILImage\n",
    "from IPython.display import Markdown as md\n",
    "\n",
    "# home brew utitilties\n",
    "import utilities.utility_functions as ut\n",
    "import utilities.abundance_classes as ac\n",
    "\n",
    "\n",
    "# easy variables to the directory tree\n",
    "most_recent, survey_data, location_data, code_defs, stat_ent, geo_data, output = ut.make_local_paths()\n",
    "\n",
    "today = dt.datetime.now().date().strftime(\"%Y-%m-%d\")\n",
    "\n",
    "# set some parameters:\n",
    "start_date = '2020-01-01'\n",
    "end_date =today\n",
    "\n",
    "# name the folder:\n",
    "name_of_project = 'project_scope'\n",
    "\n",
    "# use this to store things:\n",
    "project_directory = ut.make_project_folder(output, name_of_project)\n",
    "\n",
    "# keep track of output\n",
    "files_generated = []\n",
    "figure_num = 0\n",
    "data_num = 0\n",
    "\n",
    "# kwargs for the abundance class\n",
    "group_names_locations = ut.json_file_get(F\"{output}/code_groups/code_group2.json\")\n",
    "\n",
    "# convenience method to organize output\n",
    "def add_output(**kwargs):\n",
    "    files_generated.append({'tag':kwargs['tag'], 'number':kwargs['figure_num'], 'file':kwargs['file'],'type':kwargs['a_type']})\n",
    "    if kwargs['a_type'] == 'data':\n",
    "        kwargs['data'].to_csv(F\"{kwargs['file']}.csv\", index=False)\n",
    "    else:\n",
    "        plt.savefig(F\"{kwargs['file']}.jpeg\", dpi=300)\n",
    "\n",
    "\n",
    "data_sources = {    \n",
    "    \"survey_data\":\"results_with_zeroes.csv\",\n",
    "    \"river_bassins\":\"river_basins.json\",\n",
    "    \"beaches\":\"beaches_with_gis.csv\",\n",
    "    \"codes\":\"mlw_codes.csv\",\n",
    "    \"code_groups\":\"code_group2.json\",\n",
    "    \"dims_data\":\"dims_data.csv\"\n",
    "}\n",
    "\n",
    "# define the methods to use for the .JSON and .csv files:\n",
    "my_data_methods = {'json':ut.json_file_get, 'csv':pd.read_csv}\n",
    "\n",
    "# get your data:\n",
    "survey_data, river_bassins, dfBeaches, dfCodes, code_groups, dfDims = ac.get_data_from_most_recent(data_sources, data_methods=my_data_methods)\n",
    "\n",
    "# format the date to timestamp and slice the data by start/end date\n",
    "dfSurveys = ac.fo_rmat_and_slice_date(survey_data.copy(), a_format=\"%Y-%m-%d\", start_date=start_date, end_date=end_date)\n",
    "\n",
    "# add the grouping column defined by river_bassins\n",
    "data = ac.add_a_grouping_column(dfSurveys, river_bassins, column_to_match=\"water_name_slug\")\n",
    "\n",
    "# set the index of the beach data to location slug\n",
    "dfBeaches.set_index('slug', inplace=True)\n",
    "\n",
    "# set the index of dfCodes to code:\n",
    "dfCodes.set_index('code', inplace=True)\n",
    "\n",
    "# assign river bassin labels to locations:\n",
    "for k,v in river_bassins.items():\n",
    "    dfBeaches.loc[dfBeaches.water_name_slug.isin(v), 'river_bassin'] = k\n",
    "\n",
    "# dimensional data\n",
    "# the loc date key needs updating:\n",
    "dfDims['loc_date'] = list(zip(dfDims.location, dfDims.date))\n",
    "\n",
    "# make a copy of the survey data and tag the records as either lake or river:\n",
    "data = dfSurveys.copy()\n",
    "\n",
    "# columns can be added to the data before prcessing it\n",
    "\n",
    "# map lake or river from dfBeaches\n",
    "lakes = dfBeaches[dfBeaches.water == 'l'].water_name_slug.unique()\n",
    "rivers = dfBeaches[dfBeaches.water == 'r'].water_name_slug.unique()\n",
    "\n",
    "# tagging each record as either lake or river:\n",
    "# map values to new column t:\n",
    "data['type'] = 't'\n",
    "for a_place in data.water_name_slug.unique():\n",
    "    data.loc[data.water_name_slug.isin(lakes), 'type'] = 'l'\n",
    "    data.loc[data.water_name_slug.isin(rivers), 'type'] = 'r'\n",
    "\n",
    "# check if there are any un categorized records:\n",
    "if len(data[data['type']=='t']) > 0:\n",
    "    print(F\"\\nThere are {len(data[data['type']=='t'])} records that were not classified as either lake or river:\\n\\n{data[data['type']=='t']}\\n\")\n",
    "else:\n",
    "    pass\n",
    "\n",
    "# put the data into a class\n",
    "# kwargs for the data class\n",
    "a_class_kwargs = dict(\n",
    "    code_group_data=group_names_locations,\n",
    "    levels=['river_bassin', 'water_name_slug', 'city'],\n",
    "    river_bassins=river_bassins,\n",
    "    exp_variables=['population','buildings', 'streets', 'intersects'],\n",
    "    code_group_loc=output,\n",
    ")\n",
    "\n",
    "# this puts the data into an abundance class\n",
    "a = ac.PreprocessData(data, dfBeaches,**a_class_kwargs)\n",
    "\n",
    "# define the final data set here:\n",
    "a_data = a.survey_data.copy()\n",
    "\n",
    "n_samps_rivers_lakes = a_data[['loc_date', 'type']].groupby('type').nunique()\n",
    "\n",
    "# locations included in this report, export to GIS for mapping\n",
    "geo_df = dfBeaches.loc[a_data.location.unique()]\n",
    "filename = F\"{project_directory}/report_locations.csv\"\n",
    "geo_df.to_csv(filename, index=False)\n",
    "\n",
    "# describe the data set:\n",
    "num_obs = len(a_data)\n",
    "num_samps = len(a_data.loc_date.unique())\n",
    "num_obj = a_data.quantity.sum()\n",
    "num_locs = len(a_data.location.unique())\n",
    "num_rivs = a_data[a_data['type']=='r'].water_name_slug.nunique()\n",
    "num_lakes = a_data[a_data['type'] == 'l'].water_name_slug.nunique()\n",
    "\n",
    "# number of municipalities\n",
    "a_map = dfBeaches['city']\n",
    "munis = [a_map[x] for x in a_data.location.unique()]\n",
    "munis = sorted(list(set(munis)))\n",
    "num_munis = len(munis)\n",
    "\n",
    "# population\n",
    "total_pop_d = dfBeaches.loc[a_data.location.unique()][['city', 'population']]\n",
    "total_pop_c = total_pop_d.drop_duplicates('city')\n",
    "total_pop = total_pop_c.population.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['emme', 'neuenburgersee', 'thunersee', 'aare', 'bielersee',\n",
       "       'schuss', 'brienzersee', 'la-thiele', 'aarenidau-buren-kanal'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_data[a_data.river_bassin == 'aare']['water_name_slug'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='scope'></a>\n",
    "#### <span style=\"color:#008891\"> Effective dates, river bassins, number of obervations, number of surveys and number of objects</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "This summary is valid for all surveys between 2020-01-01 and 2021-05-19 from the following catchment areas:\n",
       "1. linth\n",
       "2. aare\n",
       "3. reuss\n",
       "4. rhone\n",
       "5. ticino\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_list = [F\"{i+1}. {x}\\n\" for i,x in enumerate(a_data.river_bassin.unique())]\n",
    "a_list=''.join(a_list)\n",
    "\n",
    "description = F\"\"\"This summary is valid for all surveys between {start_date} and {end_date} from the following catchment areas:\\n{a_list}\n",
    "\"\"\"\n",
    "md(description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "There were 51,987 objects collected in the course of 368 surveys. This includes 131 different survey locations from 74 municipalities with a total population of ~1,669,684. There were 319 samples from 10 different lakes and 49 samples from 16 different rivers."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surveys_objects = F\"There were {'{:,}'.format(num_obj)} objects collected in the course of {num_samps} surveys.\"\n",
    "pop_and_munis = F\"This includes {num_locs} different survey locations from {num_munis} municipalities with a total population of ~{'{:,}.'.format(total_pop)}\"\n",
    "num_samps_rl = F\"There were {n_samps_rivers_lakes.loc['l'][0]} samples from {num_lakes} different lakes and {n_samps_rivers_lakes.loc['r'][0]} samples from {num_rivs} different rivers.\"\n",
    "\n",
    "the_lakes = [F\"{x},\" for x in sorted(a_data[a_data['type'] == 'l'].water_name_slug.unique())[:-1]]\n",
    "the_lakes.append(F\"and {sorted(a_data[a_data['type'] == 'l'].water_name_slug.unique())[-1]}\")\n",
    "the_lakes=' '.join(the_lakes)\n",
    "t_l = F\"\\n**The lakes where the surveys were conducted**:\\n\\n{the_lakes}\\n\"\n",
    "\n",
    "the_rivs = [F\"{x},\" for x in sorted(a_data[a_data['type'] == 'r'].water_name_slug.unique())[:-1]]\n",
    "the_rivs.append(F\"and {sorted(a_data[a_data['type'] == 'r'].water_name_slug.unique())[-1]}\")\n",
    "the_rivs =' '.join(the_rivs)\n",
    "\n",
    "t_r = F\"**The rivers where the surveys were conducted**:\\n\\n{the_rivs}\"\n",
    "\n",
    "the_munis = [F\"{x},\" for x in sorted(munis)[:-1]]\n",
    "the_munis.append(F\"and {sorted(munis)[-1]}.\")\n",
    "the_munis = ' '.join(the_munis)\n",
    "\n",
    "t_m = F\"**The municpalities where the surveys were conducted**:\\n\\n{the_munis}\"\n",
    "\n",
    "a_para = F\"{surveys_objects} {pop_and_munis} {num_samps_rl}\"\n",
    "md(a_para)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "**The lakes where the surveys were conducted**:\n",
       "\n",
       "bielersee, brienzersee, lac-leman, lago-maggiore, neuenburgersee, quatre-cantons, thunersee, walensee, zugersee, and zurichsee\n",
       "\n",
       "**The rivers where the surveys were conducted**:\n",
       "\n",
       "aare, aarenidau-buren-kanal, dorfbach, emme, escherkanal, jona, la-thiele, limmat, linthkanal, maggia, reuss, rhone, schuss, seez, sihl, and ticino\n",
       "\n",
       "**The municpalities where the surveys were conducted**:\n",
       "\n",
       "Aarau, Allaman, Ascona, Beatenberg, Bellinzona, Bern, Biel/Bienne, Boudry, Bourg-en-Lavaux, Brienz (BE), Brugg, Brügg, Burgdorf, Bönigen, Cheyres-Châbles, Cudrefin, Dietikon, Estavayer, Freienbach, Gals, Gambarogno, Gebenstorf, Genève, Gland, Glarus Nord, Grandson, Hauterive (NE), Hünenberg, Kallnach, Köniz, Küsnacht (ZH), La Tour-de-Peilz, Lausanne, Lavey-Morcles, Le Landeron, Leuk, Ligerz, Locarno, Luterbach, Lüscherz, Merenschwand, Minusio, Montreux, Neuchâtel, Nidau, Port, Préverenges, Quarten, Rapperswil-Jona, Richterswil, Riddes, Rubigen, Saint-Gingolph, Saint-Sulpice (VD), Salgesch, Schmerikon, Sion, Solothurn, Spiez, Stäfa, Thun, Tolochenaz, Unterengstringen, Unterseen, Versoix, Vevey, Vinelz, Walenstadt, Walperswil, Weesen, Weggis, Yverdon-les-Bains, Zug, and Zürich."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md(F\"{t_l}\\n{t_r}\\n\\n{t_m}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='validdata'></a>\n",
    "## <span style=\"color:#008891\">The dimensional data summary</span>\n",
    "\n",
    "### <span style=\"color:#008891\"> Match the dimensional records to the survey records</span>\n",
    "\n",
    "#### There are two components to a survey:\n",
    "\n",
    "1. The objects that are found\n",
    "2. The context in which they were found -- dimensional data\n",
    "\n",
    "These two elements are transmitted seperately when the surveyor submits the data. The context for each survey is unique and each survey can generate up to 220 observations with the same context, sending the dimensional data only once is more efficient and faster.\n",
    "\n",
    "Before analysis the dimensional data records need to be matched up to the survey data. The *location_date* key is the unique indentifier for each survey. Here we match up the location_date keys from both data sets.\n",
    "\n",
    "Dimensional data with no matching survey is dropped. An inquiry is made to the surveyor to identify the nature of the discrepancy. There are paper records generated at each survey. Records are dropped from the server or ammended once the surveyor has replied to the inquiry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "<span style='color:#008891'>These are the surveys that do not have matching dimensional data</span>:\n",
       "\n",
       "('baby-plage-geneva', '2021-04-14'), ('quai-maria-belgia', '2021-02-28'), ('quai-maria-belgia', '2021-01-31')\n",
       "\n",
       "! THE DIMENSIONAL DATA SUMMARY IS CALCULATED USING 99.2% of the survey data !\n",
       "Requests have been made to surveyors to submit the missing records.\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# gather the dimensional data for the time frame\n",
    "som_dims = dfDims[(dfDims.date >= start_date)&(dfDims.date <= end_date)].copy()\n",
    "\n",
    "# map survey total to loc date from the survey data\n",
    "some_q = a.daily_totals_all[['loc_date', 'quantity']].set_index('loc_date')\n",
    "\n",
    "# use only data from the surveys in a_data:\n",
    "som_dims = som_dims[som_dims.loc_date.isin(a_data.loc_date.unique())].copy()\n",
    "\n",
    "# keep the dims that don't match\n",
    "no_matching_locdate = som_dims[~som_dims.loc_date.isin(a_data.loc_date.unique())]\n",
    "\n",
    "# get a list of unique loc_date values from each set\n",
    "ldu = a_data.loc_date.unique()\n",
    "ddu = som_dims.loc_date.unique()\n",
    "\n",
    "# identify the surveys with no matching dimensional data\n",
    "no_dim_locdate = [x for x in list(ldu) if x not in list(ddu)]\n",
    "\n",
    "# map quantity per survey to the dims data\n",
    "som_dims['quantity'] = som_dims.loc_date.map(lambda x: ut.use_this_key(x,some_q, column='quantity'))\n",
    "\n",
    "# keep the dims that did not get tagged with a qty\n",
    "no_qty = som_dims[som_dims.quantity == 'no data'][['location', 'date']].copy()\n",
    "\n",
    "# drop the dims that did not match\n",
    "som_dims = som_dims[som_dims.quantity != 'no data'].copy()\n",
    "\n",
    "# map locations to feature names\n",
    "location_wname_key = dfBeaches.water_name_slug\n",
    "\n",
    "# assign a water feature name to each record\n",
    "som_dims['water_name_slug'] = som_dims.location.map(lambda x: location_wname_key.loc[x])\n",
    "\n",
    "# assign a river bassin name to each record\n",
    "for k,v in river_bassins.items():\n",
    "    som_dims.loc[som_dims.water_name_slug.isin(v), 'river_bassin'] = k\n",
    "\n",
    "# map the number of samples per water feature or bassin:\n",
    "samples_feature = a_data.groupby('water_name_slug').loc_date.nunique()\n",
    "samples_bassin = a_data.groupby('river_bassin').loc_date.nunique()\n",
    "\n",
    "# map total quantity per river bassin:\n",
    "qty_bassin = a_data.groupby('river_bassin').quantity.sum()\n",
    "qty_feature = a_data.groupby('water_name_slug').quantity.sum()\n",
    "\n",
    "# 120 minutes covers the travel, equipment preparation and maintenance and incidental time on location:\n",
    "som_dims['total_time'] = som_dims.time_minutes+120\n",
    "som_dims['total_time'] = som_dims.total_time/60\n",
    "som_dims['total_time'] = som_dims.total_time.round(2)\n",
    "\n",
    "dim_recs_no_survey = len(no_qty) > 0\n",
    "if dim_recs_no_survey:\n",
    "    no_surveys = \"\\nThe dims records with no matching survey records have been dropped.\"\n",
    "else:\n",
    "    no_surveys = \"All dimensional records found a home in the survey data!\"\n",
    "\n",
    "no_dimensional_data = len(no_dim_locdate) > 0\n",
    "\n",
    "if no_dimensional_data:\n",
    "    no_dims = ', '.join([str(x) for x in no_dim_locdate])\n",
    "    no_dimensions = F\"\\n<span style='color:#008891'>These are the surveys that do not have matching dimensional data</span>:\\n\\n{no_dims}\\n\"\n",
    "    per_cent_surveys = F\"\\n! THE DIMENSIONAL DATA SUMMARY IS CALCULATED USING {np.round(((1-len(no_dim_locdate)/len(ldu))*100),1)}% of the survey data !\"\n",
    "    requests = \"\\nRequests have been made to surveyors to submit the missing records.\\n\"\n",
    "    dimensional_data = F\"{no_dimensions}{per_cent_surveys}{requests}\"\n",
    "else:\n",
    "    dimensional_data = \"\\nAll the surveys found a home in the dimensional data\"\n",
    "\n",
    "md(dimensional_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'resources/maps/national.png'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-9481b5918ef1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPILImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"resources/maps/national.png\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'PNG'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mencoded_string\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbase64\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mb64encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/refactor_process/lib/python3.8/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode, formats)\u001b[0m\n\u001b[1;32m   2889\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2890\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2891\u001b[0;31m         \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2892\u001b[0m         \u001b[0mexclusive_fp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2893\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'resources/maps/national.png'"
     ]
    }
   ],
   "source": [
    "image = PILImage.open(\"resources/maps/national.png\")\n",
    "\n",
    "output = io.BytesIO()\n",
    "image.save(output, format='PNG')\n",
    "encoded_string = base64.b64encode(output.getvalue()).decode()\n",
    "\n",
    "html = '<img src=\"data:image/png;base64,{}\"/>'.format(encoded_string)\n",
    "IPython.display.HTML(html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='datasummary'></a>\n",
    "### <span style=\"color:#008891\">The dimensional records</span>\n",
    "\n",
    "The following information is recorded at each survey:\n",
    "\n",
    "1. Length of survey area\n",
    "2. Surface area\n",
    "3. Weight of all objects\n",
    "4. Weight of all plastics\n",
    "5. Weight of all plastics < 5mm\n",
    "6. Time to complete a survey\n",
    "7. Number of participants\n",
    "8. Participating groups\n",
    "\n",
    "For objects that are greater than 5mm the weights are recorded on location. The smaller objects are weighed on a more appropriate scale. The length and the surface area are recorded at each survey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "new_cols = {\n",
    "    'total_time':'time to survey',\n",
    "    'length':'meters surveyed',\n",
    "    'area':'m² surveyed',\n",
    "    'total_w':'total weight',\n",
    "    'mac_plast_w':'plastic > 5mm weight',\n",
    "    'mic_plas_w':'plastic < 5mm weight',\n",
    "    'num_parts_staff':'staff',\n",
    "    'num_parts_other':'help',\n",
    "    'participants':'groups'\n",
    "}\n",
    "\n",
    "som_dims.rename(columns=new_cols, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "#### <span style='color:#008891'>Cumulative totals all data</span>\n",
       "The cumulative results from 365 samples, weights are in kilograms, time is in hours"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by river bassin\n",
    "agg_this = {\n",
    "    'time to survey':'sum',\n",
    "    'meters surveyed':'sum',\n",
    "    'm² surveyed':'sum',\n",
    "    'total weight':'sum',\n",
    "    'plastic > 5mm weight':'sum',\n",
    "    'plastic < 5mm weight':'sum',\n",
    "    'staff':'sum',\n",
    "    'help':'sum',\n",
    "    'loc_date':'nunique'}\n",
    "dims_summary_stats = som_dims.groupby('river_bassin').agg(agg_this)\n",
    "dims_summary_stats.rename(columns={'loc_date':'# samples'}, inplace=True)\n",
    "\n",
    "# convert the plastic weights to kilos\n",
    "dims_summary_stats['plastic > 5mm weight'] = dims_summary_stats['plastic > 5mm weight']/1000\n",
    "dims_summary_stats['plastic < 5mm weight'] = dims_summary_stats['plastic < 5mm weight']/1000\n",
    "\n",
    "# get the quantity found per river bassin\n",
    "dims_summary_stats['quantity'] = dims_summary_stats.index.map(lambda x: ut.use_this_key(x, qty_bassin))\n",
    "\n",
    "som_cols = [\n",
    "    'time to survey',\n",
    "    'meters surveyed',\n",
    "    'm² surveyed',\n",
    "    'total weight',\n",
    "    'plastic > 5mm weight',\n",
    "    'staff', 'help',\n",
    "    '# samples',\n",
    "    'quantity'\n",
    "]\n",
    "ints = som_cols = [\n",
    "    'time to survey',\n",
    "    'meters surveyed',\n",
    "    'm² surveyed',\n",
    "    'total weight',\n",
    "    'staff', 'help',\n",
    "    '# samples',\n",
    "    'quantity'\n",
    "]\n",
    "def make_series_an_int(x):\n",
    "    return x.astype('int')\n",
    "col_order = ['# samples',  'meters surveyed', 'm² surveyed', 'total weight', 'plastic > 5mm weight','plastic < 5mm weight', 'quantity', 'time to survey', 'help', 'staff']\n",
    "\n",
    "# reorder the columns\n",
    "dims_summary_stats[som_cols] = dims_summary_stats[som_cols].apply(lambda x: x.astype('int'))\n",
    "dims_summary_stats = dims_summary_stats[col_order]\n",
    "\n",
    "# make a summary of all data\n",
    "asum = pd.DataFrame(dims_summary_stats.sum()).round(2)\n",
    "asum.reset_index(inplace=True)\n",
    "\n",
    "# pandas gymnastics os that inst are displayed as ints:\n",
    "asum['summary total'] = 'summary total'\n",
    "asum['# samples'] = len(som_dims)\n",
    "asum = asum.pivot(columns='index', values=0, index='summary total')\n",
    "asum[ints] = asum[ints].apply(lambda x: x.astype('int'))\n",
    "asum = asum[col_order]\n",
    "\n",
    "cum_results = F\"#### <span style='color:#008891'>Cumulative totals all data</span>\\nThe cumulative results from {len(som_dims.loc_date.unique())} samples, weights are in kilograms, time is in hours\"\n",
    "md(cum_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>index</th>\n",
       "      <th># samples</th>\n",
       "      <th>meters surveyed</th>\n",
       "      <th>m² surveyed</th>\n",
       "      <th>total weight</th>\n",
       "      <th>plastic &gt; 5mm weight</th>\n",
       "      <th>plastic &lt; 5mm weight</th>\n",
       "      <th>quantity</th>\n",
       "      <th>time to survey</th>\n",
       "      <th>help</th>\n",
       "      <th>staff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>summary total</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>summary total</th>\n",
       "      <td>365</td>\n",
       "      <td>18961</td>\n",
       "      <td>91841</td>\n",
       "      <td>284</td>\n",
       "      <td>87.74</td>\n",
       "      <td>0.22</td>\n",
       "      <td>51987</td>\n",
       "      <td>1438</td>\n",
       "      <td>130</td>\n",
       "      <td>378</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "index          # samples  meters surveyed  m² surveyed  total weight  \\\n",
       "summary total                                                          \n",
       "summary total        365            18961        91841           284   \n",
       "\n",
       "index          plastic > 5mm weight  plastic < 5mm weight  quantity  \\\n",
       "summary total                                                         \n",
       "summary total                 87.74                  0.22     51987   \n",
       "\n",
       "index          time to survey  help  staff  \n",
       "summary total                               \n",
       "summary total            1438   130    378  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<span style='color:#008891'>Cumulative totals by river basin</span>\n",
       "\n",
       "The cumulative results for each river basin, weights are in kilograms, time is in hours:\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rb_cum_results = \"<span style='color:#008891'>Cumulative totals by river basin</span>\\n\\nThe cumulative results for each river basin, weights are in kilograms, time is in hours:\\n\"\n",
    "md(rb_cum_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># samples</th>\n",
       "      <th>meters surveyed</th>\n",
       "      <th>m² surveyed</th>\n",
       "      <th>total weight</th>\n",
       "      <th>plastic &gt; 5mm weight</th>\n",
       "      <th>plastic &lt; 5mm weight</th>\n",
       "      <th>quantity</th>\n",
       "      <th>time to survey</th>\n",
       "      <th>help</th>\n",
       "      <th>staff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>river_bassin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aare</th>\n",
       "      <td>138</td>\n",
       "      <td>7834</td>\n",
       "      <td>36229</td>\n",
       "      <td>72</td>\n",
       "      <td>31.361170</td>\n",
       "      <td>0.167588</td>\n",
       "      <td>13858</td>\n",
       "      <td>555</td>\n",
       "      <td>6</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>linth</th>\n",
       "      <td>104</td>\n",
       "      <td>5137</td>\n",
       "      <td>24862</td>\n",
       "      <td>34</td>\n",
       "      <td>12.496621</td>\n",
       "      <td>0.004475</td>\n",
       "      <td>9289</td>\n",
       "      <td>391</td>\n",
       "      <td>7</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reuss</th>\n",
       "      <td>8</td>\n",
       "      <td>225</td>\n",
       "      <td>832</td>\n",
       "      <td>1</td>\n",
       "      <td>0.301000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>123</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rhone</th>\n",
       "      <td>98</td>\n",
       "      <td>4710</td>\n",
       "      <td>24930</td>\n",
       "      <td>144</td>\n",
       "      <td>41.757375</td>\n",
       "      <td>0.049155</td>\n",
       "      <td>27287</td>\n",
       "      <td>407</td>\n",
       "      <td>115</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ticino</th>\n",
       "      <td>17</td>\n",
       "      <td>1055</td>\n",
       "      <td>4988</td>\n",
       "      <td>33</td>\n",
       "      <td>1.825180</td>\n",
       "      <td>0.000249</td>\n",
       "      <td>1430</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              # samples  meters surveyed  m² surveyed  total weight  \\\n",
       "river_bassin                                                          \n",
       "aare                138             7834        36229            72   \n",
       "linth               104             5137        24862            34   \n",
       "reuss                 8              225          832             1   \n",
       "rhone                98             4710        24930           144   \n",
       "ticino               17             1055         4988            33   \n",
       "\n",
       "              plastic > 5mm weight  plastic < 5mm weight  quantity  \\\n",
       "river_bassin                                                         \n",
       "aare                     31.361170              0.167588     13858   \n",
       "linth                    12.496621              0.004475      9289   \n",
       "reuss                     0.301000              0.000000       123   \n",
       "rhone                    41.757375              0.049155     27287   \n",
       "ticino                    1.825180              0.000249      1430   \n",
       "\n",
       "              time to survey  help  staff  \n",
       "river_bassin                               \n",
       "aare                     555     6    140  \n",
       "linth                    391     7    109  \n",
       "reuss                     21     2      8  \n",
       "rhone                    407   115    104  \n",
       "ticino                    64     0     17  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dims_summary_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='figureone'></a>\n",
    "<br></br>\n",
    "#### <span style=\"color:#008891\">The number of objects indentified versus time to survey, length, weights, surface area</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "greater_than_50 = som_dims[som_dims['total weight'] >= 50]['loc_date'].unique()\n",
    "greater_than_ten = som_dims[som_dims['plastic < 5mm weight'] >= 10]['loc_date'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "The following Surveys *are not included* in the charts below:\n",
       "\n",
       "Surveys with a total weight greater than 50kg:\n",
       "\n",
       "  ('lavey-les-bains-2', '2021-02-24')\n",
       "\n",
       "Surveys with a micro plastic weight greater than 10g:\n",
       "\n",
       "  ('baby-plage-geneva', '2020-07-07'), ('wycheley', '2020-11-07')\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print(F\"\\nThe following Surveys are not included in the charts below:\\n\")\n",
    "g_50 = ', '.join([str(x) for x in greater_than_50])\n",
    "t_wght = F\"Surveys with a total weight greater than 50kg:\\n\\n  {g_50}\\n\"\n",
    "g_10 = ', '.join([str(x) for x in greater_than_ten])\n",
    "micro_wght = F\"Surveys with a micro plastic weight greater than 10g:\\n\\n  {g_10}\\n\"\n",
    "\n",
    "n_ot_included = F\"\"\"\\nThe following Surveys *are not included* in the charts below:\\n\\n{t_wght}\\n{micro_wght}\"\"\"\n",
    "md(n_ot_included)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"\\n\")\n",
    "\n",
    "# chart kwargs\n",
    "title_k14 = {'loc':'left', 'pad':16, 'linespacing':1.5, 'fontsize':14}\n",
    "xlab_k14 = {'labelpad':14, 'fontsize':14}\n",
    "\n",
    "# chart variables against number of objects found\n",
    "fig, axs = plt.subplots(3,2,figsize=(12,12), sharex=False, sharey=True)\n",
    "sns.scatterplot(data=som_dims, x='time to survey', y='quantity', hue='river_bassin', palette='husl', ax=axs[0,0])\n",
    "axs[0,0].get_legend().remove()\n",
    "axs[0,0].set_title(\"Time in hours to complete the survey\", **title_k14 )\n",
    "axs[0,0].set_xlabel(\"\")\n",
    "axs[0,0].set_ylabel(\"Number of objects identified\", **xlab_k14)\n",
    "\n",
    "\n",
    "sns.scatterplot(data=som_dims, x='meters surveyed', y='quantity', hue='river_bassin', palette='husl', ax=axs[0,1])\n",
    "axs[0,1].set_title(\"Length of shoreline\", **title_k14 )\n",
    "axs[0,1].set_xlabel(\"\")\n",
    "axs[0,1].set_ylabel(\"Number of objects identified\", **xlab_k14)\n",
    "\n",
    "\n",
    "sns.scatterplot(data=som_dims, x='m² surveyed', y='quantity', hue='river_bassin', palette='husl', ax=axs[1,0])\n",
    "axs[1,0].get_legend().remove()\n",
    "axs[1,0].set_title(\"Surface area\", **title_k14 )\n",
    "axs[1,0].set_xlabel(\"\")\n",
    "axs[1,0].set_ylabel(\"Number of objects identified\", **xlab_k14)\n",
    "\n",
    "sns.scatterplot(data=som_dims[som_dims['total weight'] < 50], x='total weight', y='quantity', hue='river_bassin', palette='husl', ax=axs[1,1])\n",
    "axs[1,1].set_title(\"Total weight in kilograms\", **title_k14 )\n",
    "axs[1,1].set_xlabel(\"\" )\n",
    "axs[1,1].set_ylabel(\"Number of objects identified\", **xlab_k14)\n",
    "\n",
    "sns.scatterplot(data=som_dims[som_dims['total weight'] < 50], x=som_dims[som_dims['total weight'] < 50]['plastic > 5mm weight']/1000, y='quantity', hue='river_bassin', palette='husl', ax=axs[2,0])\n",
    "axs[2,0].get_legend().remove()\n",
    "axs[2,0].set_title(\"Kilograms of plastic removed\", **title_k14 )\n",
    "axs[2,0].set_xlabel(\"\" )\n",
    "axs[2,0].set_ylabel(\"Number of objects identified\", **xlab_k14)\n",
    "\n",
    "\n",
    "sns.scatterplot(data=som_dims[som_dims['plastic < 5mm weight'] < 10], x=som_dims[som_dims['plastic < 5mm weight'] < 10]['plastic < 5mm weight'], y='quantity', hue='river_bassin', palette='husl', ax=axs[2,1])\n",
    "axs[2,1].set_title(\"Grams of plastics less than 5mm removed\", **title_k14 )\n",
    "axs[2,1].set_xlabel(\"\")\n",
    "axs[2,1].set_ylabel(\"Number of objects identified\", **xlab_k14)\n",
    "\n",
    "plt.subplots_adjust(hspace=.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(hspace=.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Have a great day\n",
    "\n",
    "This project was made possible by the Swiss federal office for the environment.\n",
    "\n",
    "This document originates from https://github.com/hammerdirt-analyst/iqals all copyrights apply."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Produced by: roger@hammerdirt.ch\n",
      "Date: 2021-05-19\n",
      "\n",
      "love what you do\n"
     ]
    }
   ],
   "source": [
    "author = \"roger@hammerdirt.ch\"\n",
    "my_message = \"love what you do\"\n",
    "print(F\"\\nProduced by: {author}\\nDate: {today}\\n\\n{my_message}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}