panels/lms/parser.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json, sys, os, requests\n",
    "import altair as alt\n",
    "from altair import expr, datum\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 500)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eco_git_home = (\n",
    "    \"https://raw.githubusercontent.com/EconomicsObservatory/ECOvisualisations/main/\"\n",
    ")\n",
    "vega_embed = requests.get(eco_git_home + \"guidelines/html/vega-embed.html\").text\n",
    "colors = json.loads(\n",
    "    requests.get(eco_git_home + \"guidelines/colors/eco-colors.json\").content\n",
    ")\n",
    "category_color = json.loads(\n",
    "    requests.get(eco_git_home + \"guidelines/colors/eco-category-color.json\").content\n",
    ")\n",
    "hue_color = json.loads(\n",
    "    requests.get(eco_git_home + \"guidelines/colors/eco-single-hue-color.json\").content\n",
    ")\n",
    "mhue_color = json.loads(\n",
    "    requests.get(eco_git_home + \"guidelines/colors/eco-multi-hue-color.json\").content\n",
    ")\n",
    "div_color = json.loads(\n",
    "    requests.get(eco_git_home + \"guidelines/colors/eco-diverging-color.json\").content\n",
    ")\n",
    "config = json.loads(\n",
    "    requests.get(eco_git_home + \"guidelines/charts/eco-global-config.json\").content\n",
    ")\n",
    "height = config[\"height\"]\n",
    "width = config[\"width\"]\n",
    "height, width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ons_data_ts(dataset, series, save=True, api_path=\"https://api.ons.gov.uk\"):\n",
    "    r = requests.get(\n",
    "        api_path\n",
    "        + \"/timeseries/\"\n",
    "        + series.lower()\n",
    "        + \"/dataset/\"\n",
    "        + dataset.lower()\n",
    "        + \"/data\"\n",
    "    )\n",
    "    content = json.loads(r.content)\n",
    "    if save:\n",
    "        print(\"Saving DATASET\", dataset, \"SERIES\", series, \"to local mirror...\")\n",
    "        open(\"../../datasets/ons/\" + dataset + \"/\" + series + \".json\", \"w\").write(\n",
    "            json.dumps(content)\n",
    "        )\n",
    "        print(\"OK!\")\n",
    "    return content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Employment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving DATASET lms SERIES mgsx to local mirror...\n",
      "OK!\n",
      "Saving DATASET lms SERIES lf24 to local mirror...\n",
      "OK!\n",
      "Saving DATASET lms SERIES kac3 to local mirror...\n",
      "OK!\n",
      "Saving DATASET lms SERIES lf2s to local mirror...\n",
      "OK!\n"
     ]
    }
   ],
   "source": [
    "dataset = \"lms\"\n",
    "data = {}\n",
    "series = [\"mgsx\", \"lf24\", \"kac3\", \"lf2s\"]\n",
    "series_colors = {\n",
    "    \"mgsx\": \"eco-pink\",\n",
    "    \"lf24\": \"eco-turquiose\",\n",
    "    \"kac3\": \"eco-yellow\",\n",
    "    \"lf2s\": \"eco-mid-blue\",\n",
    "}\n",
    "series_offset = {\"mgsx\": 0.8, \"lf24\": 0.5, \"kac3\": 1, \"lf2s\": 0.9}\n",
    "series_domain = {\"mgsx\": [2, 12], \"lf24\": [65, 80], \"kac3\": [-2, 6], \"lf2s\": [20, 26]}\n",
    "series_domain2 = {\n",
    "    \"mgsx\": [3.5, 5.5],\n",
    "    \"lf24\": [74, 77],\n",
    "    \"kac3\": [-2, 6],\n",
    "    \"lf2s\": [20, 21.5],\n",
    "}\n",
    "series_titles = {\n",
    "    \"mgsx\": \"Unemployment\",\n",
    "    \"lf24\": \"Employment\",\n",
    "    \"kac3\": \"Wage growth\",\n",
    "    \"lf2s\": \"Economic inactivity rate\",\n",
    "}\n",
    "series_subtitles = {\n",
    "    \"mgsx\": \"Rate (%), age 16+ seasonally adjusted\",\n",
    "    \"lf24\": \"Rate (%), age 16+ seasonally adjusted\",\n",
    "    \"kac3\": \"Weekly earnings, three month average, y-o-y growth (%)\",\n",
    "    \"lf2s\": \"UK, Aged 16-64 (%)\",\n",
    "}\n",
    "series_text3 = {\n",
    "    \"mgsx\": \"year(datum.date)>=2021\",\n",
    "    \"lf24\": \"year(datum.date)>=2021\",\n",
    "    \"kac3\": \"year(datum.date)>=2021&month(datum.date)>=1\",\n",
    "    \"lf2s\": \"year(datum.date)>=2021&month(datum.date)>=1\",\n",
    "}\n",
    "for serie in series:\n",
    "    d = get_ons_data_ts(dataset, serie)\n",
    "    data[serie] = {\n",
    "        \"title\": d[\"description\"][\"title\"].split(\")\")[0] + \")\",\n",
    "        \"data\": d[\"months\"],\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now push datasets to GitHub."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing mgsx for live ...\n",
      "Processing lf24 for live ...\n",
      "Processing kac3 for live ...\n",
      "Processing lf2s for live ...\n",
      "OK!\n",
      "Processing mgsx for eco ...\n",
      "Processing lf24 for eco ...\n",
      "Processing kac3 for eco ...\n",
      "Processing lf2s for eco ...\n",
      "OK!\n",
      "Processing mgsx for local ...\n",
      "Processing lf24 for local ...\n",
      "Processing kac3 for local ...\n",
      "Processing lf2s for local ...\n",
      "OK!\n"
     ]
    }
   ],
   "source": [
    "for l in [\"live\", \"eco\", \"local\"]:\n",
    "    layers = []\n",
    "    for serie in series:\n",
    "        if l == \"live\":\n",
    "            url = (\n",
    "                \"https://api.allorigins.win/raw?url=https://api.ons.gov.uk/timeseries/\"\n",
    "                + serie\n",
    "                + \"/dataset/\"\n",
    "                + dataset\n",
    "                + \"/data\"\n",
    "            )\n",
    "        else:\n",
    "            url = (\n",
    "                \"https://raw.githubusercontent.com/EconomicsObservatory/ECOdataHUB/main/datasets/ons/\"\n",
    "                + dataset\n",
    "                + \"/\"\n",
    "                + serie\n",
    "                + \".json\"\n",
    "            )\n",
    "        if l == \"local\":\n",
    "            data = alt.Data(\n",
    "                url=url,\n",
    "                format=alt.DataFormat(type=\"json\", property=\"months\"),\n",
    "            )\n",
    "        else:\n",
    "            data = pd.DataFrame(json.loads(requests.get(url).content)[\"months\"])\n",
    "            data[\"date\"] = pd.to_datetime(data[\"date\"])\n",
    "        print(\"Processing\", serie, \"for\", l, \"...\")\n",
    "        base = (\n",
    "            alt.Chart(data)\n",
    "            .transform_calculate(serie=repr(serie))\n",
    "            .transform_calculate(label=\"datum.year+' '+datum.month\")\n",
    "            .transform_calculate(date=\"toDate(datum.year+' '+datum.month+' '+1)\")\n",
    "            .encode(x=alt.X(\"date:T\", axis=alt.Axis(grid=True, title=\"\")))\n",
    "        )\n",
    "        area = base.mark_area(\n",
    "            interpolate=\"monotone\",\n",
    "            fillOpacity=0.7,\n",
    "            stroke=colors[\"eco-gray\"],\n",
    "            strokeWidth=0.5,\n",
    "            color=alt.Gradient(\n",
    "                gradient=\"linear\",\n",
    "                stops=[\n",
    "                    alt.GradientStop(color=\"white\", offset=0),\n",
    "                    alt.GradientStop(\n",
    "                        color=colors[series_colors[serie]], offset=series_offset[serie]\n",
    "                    ),\n",
    "                ],\n",
    "                x1=0.8,\n",
    "                x2=1,\n",
    "                y1=1,\n",
    "                y2=0,\n",
    "            ),\n",
    "        ).encode(\n",
    "            y=alt.Y(\n",
    "                \"value:Q\",\n",
    "                axis=alt.Axis(grid=True, title=\"\"),\n",
    "                # scale=alt.Scale(domain=series_domain[serie]),\n",
    "            )\n",
    "        )\n",
    "        line = base.mark_line(color=colors[series_colors[serie]]).encode(\n",
    "            y=alt.Y(\n",
    "                \"value:Q\",\n",
    "                axis=alt.Axis(grid=True, title=\"\"),\n",
    "                scale=alt.Scale(domain=series_domain2[serie]),\n",
    "            )\n",
    "        )\n",
    "        # Create a selection that chooses the nearest point & selects based on x-value\n",
    "        nearest = alt.selection(\n",
    "            type=\"single\", nearest=True, on=\"mouseover\", fields=[\"date\"], empty=\"none\"\n",
    "        )\n",
    "        # Transparent selectors across the chart. This is what tells us\n",
    "        # the x-value of the cursor\n",
    "        selectors = (\n",
    "            base.mark_point()\n",
    "            .encode(\n",
    "                opacity=alt.value(0),\n",
    "            )\n",
    "            .add_selection(nearest)\n",
    "        )\n",
    "\n",
    "        # Draw points on the line, and highlight based on selection\n",
    "        points = area.mark_point().encode(\n",
    "            opacity=alt.condition(nearest, alt.value(1), alt.value(0))\n",
    "        )\n",
    "\n",
    "        # Draw text labels near the points, and highlight based on selection\n",
    "        text = area.mark_text(align=\"left\", dx=-25, dy=35).encode(\n",
    "            text=alt.condition(nearest, \"value:N\", alt.value(\" \"))\n",
    "        )\n",
    "\n",
    "        # Draw text labels near the points, and highlight based on selection\n",
    "        text2 = area.mark_text(align=\"right\", dx=5, dy=10,).encode(\n",
    "            text=alt.condition(nearest, \"label:N\", alt.value(\" \")),\n",
    "            x=alt.value(390),\n",
    "            y=alt.value(0),\n",
    "        )\n",
    "\n",
    "        # Draw text labels near the points, and highlight based on selection\n",
    "        text3 = line.mark_text(align=\"right\", dx=5, dy=10, fontSize=13).encode(\n",
    "            text=alt.condition(series_text3[serie], \"value:Q\", alt.value(\" \")),\n",
    "            x=alt.value(92),\n",
    "            y=alt.value(3),\n",
    "        )\n",
    "\n",
    "        # Draw a rule at the location of the selection\n",
    "        rules = base.mark_rule(color=colors[\"eco-gray\"]).transform_filter(nearest)\n",
    "\n",
    "        # Put the five layers into a chart and bind the data\n",
    "        layer1 = alt.layer(area, selectors, points, rules, text, text2,).properties(\n",
    "            width=400,\n",
    "            height=150,\n",
    "            title={\"text\": series_titles[serie], \"subtitle\": series_subtitles[serie]},\n",
    "        )\n",
    "        layer2 = (\n",
    "            alt.layer(\n",
    "                line,\n",
    "                selectors,\n",
    "                points,\n",
    "                rules,\n",
    "                text,\n",
    "                text3,\n",
    "            )\n",
    "            .properties(width=100, height=150)\n",
    "            .transform_filter(\"datum.date>='2020'\")\n",
    "        )\n",
    "        layers.append(alt.hconcat(layer1, layer2, spacing=20))\n",
    "\n",
    "    vlayers = alt.vconcat(\n",
    "        layers[0], layers[1], layers[2], layers[3]\n",
    "    )  # .configure_view(stroke=None)\n",
    "    vlayers.save(dataset + \"_\" + l + \".json\")\n",
    "    print(\"OK!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vlayers"
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving DATASET lms SERIES lf2s to local mirror...\n",
      "OK!\n",
      "Saving DATASET lms SERIES ybtm to local mirror...\n",
      "OK!\n",
      "Saving DATASET lms SERIES lf2t to local mirror...\n",
      "OK!\n"
     ]
    }
   ],
   "source": [
    "dataset = \"lms\"\n",
    "data = {}\n",
    "series = [\"lf2s\", \"ybtm\", \"lf2t\"]\n",
    "series_colors = {\"lf2s\": \"eco-pink\", \"ybtm\": \"eco-turquiose\", \"lf2t\": \"eco-yellow\"}\n",
    "series_offset = {\"lf2s\": 0.8, \"ybtm\": 0.5, \"lf2t\": 1}\n",
    "series_domain = {\"lf2s\": [2, 12], \"ybtm\": [65, 80], \"lf2t\": [-2, 6]}\n",
    "series_domain2 = {\"lf2s\": [20, 22], \"ybtm\": [15, 18], \"lf2t\": [24, 25]}\n",
    "series_titles = {\n",
    "    \"lf2s\": \"Inactivity Rate\",\n",
    "    \"ybtm\": \"Inactivity Rate\",\n",
    "    \"lf2t\": \"Inactivity Rate\",\n",
    "}\n",
    "series_subtitles = {\n",
    "    \"lf2s\": \"(%), All\",\n",
    "    \"ybtm\": \"(%), Men\",\n",
    "    \"lf2t\": \"(%), Women\",\n",
    "}\n",
    "series_text3 = {\n",
    "    \"lf2s\": \"year(datum.date)>=2021\",\n",
    "    \"ybtm\": \"year(datum.date)>=2021\",\n",
    "    \"lf2t\": \"year(datum.date)>=2021&month(datum.date)>=1\",\n",
    "}\n",
    "for serie in series:\n",
    "    d = get_ons_data_ts(dataset, serie)\n",
    "    data[serie] = {\n",
    "        \"title\": d[\"description\"][\"title\"].split(\")\")[0] + \")\",\n",
    "        \"data\": d[\"months\"],\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Economic inactivity rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing lf2s for live ...\n",
      "Processing ybtm for live ...\n",
      "Processing lf2t for live ...\n",
      "OK!\n",
      "Processing lf2s for eco ...\n",
      "Processing ybtm for eco ...\n",
      "Processing lf2t for eco ...\n",
      "OK!\n",
      "Processing lf2s for local ...\n",
      "Processing ybtm for local ...\n",
      "Processing lf2t for local ...\n",
      "OK!\n"
     ]
    },
    {
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{\"calculate\": \"toDate(datum.year+' '+datum.month+' '+1)\", \"as\": \"date\"}]}, {\"mark\": {\"type\": \"rule\", \"color\": \"#676A86\"}, \"encoding\": {\"x\": {\"type\": \"temporal\", \"axis\": {\"grid\": true, \"title\": \"\"}, \"field\": \"date\"}}, \"transform\": [{\"calculate\": \"'lf2t'\", \"as\": \"serie\"}, {\"calculate\": \"datum.year+' '+datum.month\", \"as\": \"label\"}, {\"calculate\": \"toDate(datum.year+' '+datum.month+' '+1)\", \"as\": \"date\"}, {\"filter\": {\"selection\": \"selector021\"}}]}, {\"mark\": {\"type\": \"text\", \"align\": \"left\", \"dx\": -25, \"dy\": 35}, \"encoding\": {\"text\": {\"condition\": {\"type\": \"nominal\", \"field\": \"value\", \"selection\": \"selector021\"}, \"value\": \" \"}, \"x\": {\"type\": \"temporal\", \"axis\": {\"grid\": true, \"title\": \"\"}, \"field\": \"date\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"grid\": true, \"title\": \"\"}, \"field\": \"value\"}}, \"transform\": [{\"calculate\": \"'lf2t'\", \"as\": \"serie\"}, {\"calculate\": \"datum.year+' '+datum.month\", \"as\": \"label\"}, {\"calculate\": \"toDate(datum.year+' '+datum.month+' '+1)\", \"as\": \"date\"}]}, {\"mark\": {\"type\": \"text\", \"align\": \"right\", \"dx\": 5, \"dy\": 10}, \"encoding\": {\"text\": {\"condition\": {\"type\": \"nominal\", \"field\": \"label\", \"selection\": \"selector021\"}, \"value\": \" \"}, \"x\": {\"value\": 390}, \"y\": {\"value\": 0}}, \"transform\": [{\"calculate\": \"'lf2t'\", \"as\": \"serie\"}, {\"calculate\": \"datum.year+' '+datum.month\", \"as\": \"label\"}, {\"calculate\": \"toDate(datum.year+' '+datum.month+' '+1)\", \"as\": \"date\"}]}], \"height\": 150, \"title\": {\"text\": \"Inactivity Rate\", \"subtitle\": \"(%), Women\"}, \"width\": 400}, {\"layer\": [{\"mark\": {\"type\": \"line\", \"color\": \"#F4C245\"}, \"encoding\": {\"x\": {\"type\": \"temporal\", \"axis\": {\"grid\": true, \"title\": \"\"}, \"field\": \"date\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"grid\": true, \"title\": \"\"}, \"field\": \"value\", \"scale\": {\"domain\": [24, 25]}}}, \"transform\": [{\"calculate\": \"'lf2t'\", \"as\": \"serie\"}, {\"calculate\": \"datum.year+' '+datum.month\", \"as\": \"label\"}, {\"calculate\": \"toDate(datum.year+' '+datum.month+' '+1)\", \"as\": \"date\"}]}, {\"mark\": \"point\", \"encoding\": {\"opacity\": {\"value\": 0}, \"x\": {\"type\": \"temporal\", \"axis\": {\"grid\": true, \"title\": \"\"}, \"field\": \"date\"}}, \"selection\": {\"selector021\": {\"type\": \"single\", \"nearest\": true, \"on\": \"mouseover\", \"fields\": [\"date\"], \"empty\": \"none\"}}, \"transform\": [{\"calculate\": \"'lf2t'\", \"as\": \"serie\"}, {\"calculate\": \"datum.year+' '+datum.month\", \"as\": \"label\"}, {\"calculate\": \"toDate(datum.year+' '+datum.month+' '+1)\", \"as\": \"date\"}]}, {\"mark\": \"point\", \"encoding\": {\"opacity\": {\"condition\": {\"value\": 1, \"selection\": \"selector021\"}, \"value\": 0}, \"x\": {\"type\": \"temporal\", \"axis\": {\"grid\": true, \"title\": \"\"}, \"field\": \"date\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"grid\": true, \"title\": \"\"}, \"field\": \"value\"}}, \"transform\": [{\"calculate\": \"'lf2t'\", \"as\": \"serie\"}, {\"calculate\": \"datum.year+' '+datum.month\", \"as\": \"label\"}, {\"calculate\": \"toDate(datum.year+' '+datum.month+' '+1)\", \"as\": \"date\"}]}, {\"mark\": {\"type\": \"rule\", \"color\": \"#676A86\"}, \"encoding\": {\"x\": {\"type\": \"temporal\", \"axis\": {\"grid\": true, \"title\": \"\"}, \"field\": \"date\"}}, \"transform\": [{\"calculate\": \"'lf2t'\", \"as\": \"serie\"}, {\"calculate\": \"datum.year+' '+datum.month\", \"as\": \"label\"}, {\"calculate\": \"toDate(datum.year+' '+datum.month+' '+1)\", \"as\": \"date\"}, {\"filter\": {\"selection\": \"selector021\"}}]}, {\"mark\": {\"type\": \"text\", \"align\": \"left\", \"dx\": -25, \"dy\": 35}, \"encoding\": {\"text\": {\"condition\": {\"type\": \"nominal\", \"field\": \"value\", \"selection\": \"selector021\"}, \"value\": \" \"}, \"x\": {\"type\": \"temporal\", \"axis\": {\"grid\": true, \"title\": \"\"}, \"field\": \"date\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"grid\": true, \"title\": \"\"}, \"field\": \"value\"}}, \"transform\": [{\"calculate\": \"'lf2t'\", \"as\": \"serie\"}, {\"calculate\": \"datum.year+' '+datum.month\", \"as\": \"label\"}, {\"calculate\": \"toDate(datum.year+' '+datum.month+' '+1)\", \"as\": \"date\"}]}, {\"mark\": {\"type\": \"text\", \"align\": \"right\", \"dx\": 5, \"dy\": 10, \"fontSize\": 13}, \"encoding\": {\"text\": {\"condition\": {\"type\": \"quantitative\", \"field\": \"value\", \"test\": \"year(datum.date)>=2021&month(datum.date)>=1\"}, \"value\": \" \"}, \"x\": {\"value\": 92}, \"y\": {\"value\": 3}}, \"transform\": [{\"calculate\": \"'lf2t'\", \"as\": \"serie\"}, {\"calculate\": \"datum.year+' '+datum.month\", \"as\": \"label\"}, {\"calculate\": \"toDate(datum.year+' '+datum.month+' '+1)\", \"as\": \"date\"}]}], \"height\": 150, \"transform\": [{\"filter\": \"datum.date>='2020'\"}], \"width\": 100}], \"data\": {\"url\": \"https://raw.githubusercontent.com/EconomicsObservatory/ECOdataHUB/main/datasets/ons/lms/lf2t.json\", \"format\": {\"type\": \"json\", \"property\": \"months\"}}, \"spacing\": 20}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\"}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for l in [\"live\", \"eco\", \"local\"]:\n",
    "    layers = []\n",
    "    for serie in series:\n",
    "        if l == \"live\":\n",
    "            url = (\n",
    "                \"https://api.allorigins.win/raw?url=https://api.ons.gov.uk/timeseries/\"\n",
    "                + serie\n",
    "                + \"/dataset/\"\n",
    "                + dataset\n",
    "                + \"/data\"\n",
    "            )\n",
    "        else:\n",
    "            url = (\n",
    "                \"https://raw.githubusercontent.com/EconomicsObservatory/ECOdataHUB/main/datasets/ons/\"\n",
    "                + dataset\n",
    "                + \"/\"\n",
    "                + serie\n",
    "                + \".json\"\n",
    "            )\n",
    "        if l == \"local\":\n",
    "            data = alt.Data(\n",
    "                url=url,\n",
    "                format=alt.DataFormat(type=\"json\", property=\"months\"),\n",
    "            )\n",
    "        else:\n",
    "            data = pd.DataFrame(json.loads(requests.get(url).content)[\"months\"])\n",
    "            data[\"date\"] = pd.to_datetime(data[\"date\"])\n",
    "        print(\"Processing\", serie, \"for\", l, \"...\")\n",
    "        base = (\n",
    "            alt.Chart(data)\n",
    "            .transform_calculate(serie=repr(serie))\n",
    "            .transform_calculate(label=\"datum.year+' '+datum.month\")\n",
    "            .transform_calculate(date=\"toDate(datum.year+' '+datum.month+' '+1)\")\n",
    "            .encode(x=alt.X(\"date:T\", axis=alt.Axis(grid=True, title=\"\")))\n",
    "        )\n",
    "        area = base.mark_area(\n",
    "            interpolate=\"monotone\",\n",
    "            fillOpacity=0.7,\n",
    "            stroke=colors[\"eco-gray\"],\n",
    "            strokeWidth=0.5,\n",
    "            color=alt.Gradient(\n",
    "                gradient=\"linear\",\n",
    "                stops=[\n",
    "                    alt.GradientStop(color=\"white\", offset=0),\n",
    "                    alt.GradientStop(\n",
    "                        color=colors[series_colors[serie]], offset=series_offset[serie]\n",
    "                    ),\n",
    "                ],\n",
    "                x1=0.8,\n",
    "                x2=1,\n",
    "                y1=1,\n",
    "                y2=0,\n",
    "            ),\n",
    "        ).encode(\n",
    "            y=alt.Y(\n",
    "                \"value:Q\",\n",
    "                axis=alt.Axis(grid=True, title=\"\"),\n",
    "                # scale=alt.Scale(domain=series_domain[serie]),\n",
    "            )\n",
    "        )\n",
    "        line = base.mark_line(color=colors[series_colors[serie]]).encode(\n",
    "            y=alt.Y(\n",
    "                \"value:Q\",\n",
    "                axis=alt.Axis(grid=True, title=\"\"),\n",
    "                scale=alt.Scale(domain=series_domain2[serie]),\n",
    "            )\n",
    "        )\n",
    "        # Create a selection that chooses the nearest point & selects based on x-value\n",
    "        nearest = alt.selection(\n",
    "            type=\"single\", nearest=True, on=\"mouseover\", fields=[\"date\"], empty=\"none\"\n",
    "        )\n",
    "        # Transparent selectors across the chart. This is what tells us\n",
    "        # the x-value of the cursor\n",
    "        selectors = (\n",
    "            base.mark_point()\n",
    "            .encode(\n",
    "                opacity=alt.value(0),\n",
    "            )\n",
    "            .add_selection(nearest)\n",
    "        )\n",
    "\n",
    "        # Draw points on the line, and highlight based on selection\n",
    "        points = area.mark_point().encode(\n",
    "            opacity=alt.condition(nearest, alt.value(1), alt.value(0))\n",
    "        )\n",
    "\n",
    "        # Draw text labels near the points, and highlight based on selection\n",
    "        text = area.mark_text(align=\"left\", dx=-25, dy=35).encode(\n",
    "            text=alt.condition(nearest, \"value:N\", alt.value(\" \"))\n",
    "        )\n",
    "\n",
    "        # Draw text labels near the points, and highlight based on selection\n",
    "        text2 = area.mark_text(align=\"right\", dx=5, dy=10,).encode(\n",
    "            text=alt.condition(nearest, \"label:N\", alt.value(\" \")),\n",
    "            x=alt.value(390),\n",
    "            y=alt.value(0),\n",
    "        )\n",
    "\n",
    "        # Draw text labels near the points, and highlight based on selection\n",
    "        text3 = line.mark_text(align=\"right\", dx=5, dy=10, fontSize=13).encode(\n",
    "            text=alt.condition(series_text3[serie], \"value:Q\", alt.value(\" \")),\n",
    "            x=alt.value(92),\n",
    "            y=alt.value(3),\n",
    "        )\n",
    "\n",
    "        # Draw a rule at the location of the selection\n",
    "        rules = base.mark_rule(color=colors[\"eco-gray\"]).transform_filter(nearest)\n",
    "\n",
    "        # Put the five layers into a chart and bind the data\n",
    "        layer1 = alt.layer(area, selectors, points, rules, text, text2,).properties(\n",
    "            width=400,\n",
    "            height=150,\n",
    "            title={\"text\": series_titles[serie], \"subtitle\": series_subtitles[serie]},\n",
    "        )\n",
    "        layer2 = (\n",
    "            alt.layer(\n",
    "                line,\n",
    "                selectors,\n",
    "                points,\n",
    "                rules,\n",
    "                text,\n",
    "                text3,\n",
    "            )\n",
    "            .properties(width=100, height=150)\n",
    "            .transform_filter(\"datum.date>='2020'\")\n",
    "        )\n",
    "        layers.append(alt.hconcat(layer1, layer2, spacing=20))\n",
    "\n",
    "    layers = alt.vconcat(\n",
    "        layers[0], layers[1], layers[2]\n",
    "    )  # .configure_view(stroke=None)\n",
    "    layers.save(dataset + \"2_\" + l + \".json\")\n",
    "    print(\"OK!\")\n",
    "layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mgvf\n",
      "Saving DATASET lms SERIES mgvf to local mirror...\n",
      "OK!\n",
      "ycgm\n",
      "Saving DATASET lms SERIES ycgm to local mirror...\n",
      "OK!\n",
      "ycgs\n",
      "Saving DATASET lms SERIES ycgs to local mirror...\n",
      "OK!\n",
      "lf28\n",
      "Saving DATASET lms SERIES lf28 to local mirror...\n",
      "OK!\n",
      "k5hu\n",
      "Saving DATASET lms SERIES k5hu to local mirror...\n",
      "OK!\n"
     ]
    }
   ],
   "source": [
    "ages = {\n",
    "    \"mgvf\": \"16-24\",\n",
    "    \"ycgm\": \"25-34\",\n",
    "    \"ycgs\": \"35-49\",\n",
    "    \"lf28\": \"50-64\",\n",
    "    \"k5hu\": \"65+\",\n",
    "}\n",
    "dfs = []\n",
    "for s in ages:\n",
    "    print(s)\n",
    "    df = pd.DataFrame(get_ons_data_ts(\"lms\", s)[\"months\"])\n",
    "    df[\"series\"] = s\n",
    "    df[\"age\"] = ages[s]\n",
    "    df[\"date\"] = pd.to_datetime(df[\"date\"])\n",
    "    dfs.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat(dfs)\n",
    "df = (\n",
    "    df.set_index(\"date\")\n",
    "    .sort_index()\n",
    "    .loc[\"2019-10-01\":]\n",
    "    .reset_index()\n",
    "    .set_index([\"age\", \"date\"])[\"value\"]\n",
    "    .unstack()\n",
    "    .astype(int)\n",
    ")\n",
    "for c in df.columns[1:]:\n",
    "    df[c] = df[df.columns[0]] - df[c]\n",
    "df[df.columns[0]] = 0\n",
    "df = df.stack().reset_index()\n",
    "df.columns = [\"age\", \"date\", \"value\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = \"unemployment_by_age\"\n",
    "df.to_csv(\"../../datasets/eco/unemployment/\" + f + \".csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'color_scale' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-d07a7c4c5bab>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     42\u001b[0m         ),\n\u001b[1;32m     43\u001b[0m     ),\n\u001b[0;32m---> 44\u001b[0;31m     \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mColor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"age:N\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlegend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mScale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor_scale\u001b[0m\u001b[0;34m)\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[0m\u001b[1;32m     45\u001b[0m )\n\u001b[1;32m     46\u001b[0m \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmark_line\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;31mNameError\u001b[0m: name 'color_scale' is not defined"
     ]
    }
   ],
   "source": [
    "base = alt.Chart(df).encode(\n",
    "    y=alt.Y(\n",
    "        \"value:Q\",\n",
    "        axis=alt.Axis(\n",
    "            grid=True,\n",
    "            title=\"thousand\",\n",
    "            titleAnchor=\"start\",\n",
    "            labelColor=colors[\"eco-gray\"],\n",
    "            titleColor=colors[\"eco-gray\"],\n",
    "            tickColor=colors[\"eco-gray\"],\n",
    "            domainColor=colors[\"eco-gray\"],\n",
    "            titleFontSize=10,\n",
    "            titleFontWeight=\"normal\",\n",
    "            ticks=False,\n",
    "            labelAlign=\"left\",\n",
    "            labelBaseline=\"middle\",\n",
    "            labelPadding=-5,\n",
    "            labelOffset=-10,\n",
    "            titleX=30,\n",
    "            titleY=295,\n",
    "            titleBaseline=\"bottom\",\n",
    "            titleAngle=0,\n",
    "            titleAlign=\"left\",\n",
    "            tickCount=6,\n",
    "            format=\".0f\",\n",
    "        ),\n",
    "    ),\n",
    "    x=alt.X(\n",
    "        \"date:T\",\n",
    "        axis=alt.Axis(\n",
    "            grid=False,\n",
    "            title=\"\",\n",
    "            titleAnchor=\"start\",\n",
    "            labelColor=colors[\"eco-gray\"],\n",
    "            titleColor=colors[\"eco-gray\"],\n",
    "            tickColor=colors[\"eco-gray\"],\n",
    "            domainColor=colors[\"eco-gray\"],\n",
    "            titleFontSize=10,\n",
    "            titleFontWeight=\"normal\",\n",
    "            labelExpr=\"datum.value<toDate('2019-11-01')?'':month(datum.value)==0?timeFormat(datum.value,'%Y'):timeFormat(datum.value,'%B')\",\n",
    "            labelAngle=0,\n",
    "        ),\n",
    "    ),\n",
    "    color=alt.Color(\"age:N\", legend=None, scale=alt.Scale(range=color_scale)),\n",
    ")\n",
    "lines = base.mark_line()\n",
    "labels = (\n",
    "    base.mark_text(align=\"left\", dx=5)\n",
    "    .encode(text=alt.Text(\"age:N\"))\n",
    "    .transform_filter(\"datum.date>toDate('2020-12-01')\")\n",
    ")\n",
    "layer = lines + labels\n",
    "layer.configure_view(stroke=None).properties(title=\"\").properties(height=300, width=400)\n",
    "layer.save(\"../../panels/unemployment/\" + f + \".json\")\n",
    "layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ages = {\n",
    "    \"mgwy\": \"16-24\",\n",
    "    \"ycgp\": \"25-34\",\n",
    "    \"ycgv\": \"35-49\",\n",
    "    \"lf2e\": \"50-64\",\n",
    "    \"k5hw\": \"65+\",\n",
    "}\n",
    "dfs = []\n",
    "for s in ages:\n",
    "    print(s)\n",
    "    df = pd.DataFrame(get_ons_data_ts(\"lms\", s)[\"months\"])\n",
    "    df[\"series\"] = s\n",
    "    df[\"age\"] = ages[s]\n",
    "    df[\"date\"] = pd.to_datetime(df[\"date\"])\n",
    "    dfs.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat(dfs)\n",
    "df = (\n",
    "    df.set_index(\"date\")\n",
    "    .sort_index()\n",
    "    .loc[\"2019-10-01\":]\n",
    "    .reset_index()\n",
    "    .set_index([\"age\", \"date\"])[\"value\"]\n",
    "    .unstack()\n",
    "    .astype(float)\n",
    ")\n",
    "for c in df.columns[1:]:\n",
    "    df[c] = df[df.columns[0]] - df[c]\n",
    "df[df.columns[0]] = 0\n",
    "df = df.stack().reset_index()\n",
    "df.columns = [\"age\", \"date\", \"value\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = \"unemployment_rate_by_age_nov2019\"\n",
    "df.to_csv(\"../../datasets/eco/unemployment/\" + f + \".csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "base = alt.Chart(df).encode(\n",
    "    y=alt.Y(\n",
    "        \"value:Q\",\n",
    "        axis=alt.Axis(\n",
    "            grid=True,\n",
    "            title=\"change in percentage points\",\n",
    "            titleAnchor=\"start\",\n",
    "            labelColor=colors[\"eco-gray\"],\n",
    "            titleColor=colors[\"eco-gray\"],\n",
    "            tickColor=colors[\"eco-gray\"],\n",
    "            domainColor=colors[\"eco-gray\"],\n",
    "            titleFontSize=10,\n",
    "            titleFontWeight=\"normal\",\n",
    "            ticks=False,\n",
    "            labelAlign=\"left\",\n",
    "            labelBaseline=\"middle\",\n",
    "            labelPadding=-5,\n",
    "            labelOffset=-10,\n",
    "            titleX=22,\n",
    "            titleY=262,\n",
    "            titleBaseline=\"bottom\",\n",
    "            titleAngle=0,\n",
    "            titleAlign=\"left\",\n",
    "            tickCount=6,\n",
    "            format=\".0f\",\n",
    "        ),\n",
    "    ),\n",
    "    x=alt.X(\n",
    "        \"date:T\",\n",
    "        axis=alt.Axis(\n",
    "            grid=False,\n",
    "            title=\"\",\n",
    "            titleAnchor=\"start\",\n",
    "            labelColor=colors[\"eco-gray\"],\n",
    "            titleColor=colors[\"eco-gray\"],\n",
    "            tickColor=colors[\"eco-gray\"],\n",
    "            domainColor=colors[\"eco-gray\"],\n",
    "            titleFontSize=10,\n",
    "            titleFontWeight=\"normal\",\n",
    "            labelExpr=\"datum.value<toDate('2019-11-01')?'':month(datum.value)==0?timeFormat(datum.value,'%Y'):timeFormat(datum.value,'%B')\",\n",
    "            labelAngle=0,\n",
    "        ),\n",
    "    ),\n",
    "    color=alt.Color(\"age:N\", legend=None, scale=alt.Scale(range=color_scale)),\n",
    ")\n",
    "lines = base.mark_line()\n",
    "labels = (\n",
    "    base.mark_text(align=\"left\", dx=5)\n",
    "    .encode(text=alt.Text(\"age:N\"))\n",
    "    .transform_filter(\"datum.date>toDate('2020-12-01')\")\n",
    ")\n",
    "layer = lines + labels\n",
    "layer.configure_view(stroke=None).properties(title=\"\").properties(height=300, width=400)\n",
    "layer.save(\"../../panels/unemployment/\" + f + \".json\")\n",
    "layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat(dfs)\n",
    "df = (\n",
    "    df.set_index(\"date\")\n",
    "    .sort_index()\n",
    "    .loc[\"2019-10-01\":]\n",
    "    .reset_index()\n",
    "    .set_index([\"age\", \"date\"])[\"value\"]\n",
    "    .unstack()\n",
    "    .astype(float)\n",
    ")\n",
    "df = df.stack().reset_index()\n",
    "df.columns = [\"age\", \"date\", \"value\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = \"unemployment_rate_by_age\"\n",
    "df.to_csv(\"../../datasets/eco/unemployment/\" + f + \".csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "base = alt.Chart(df).encode(\n",
    "    y=alt.Y(\n",
    "        \"value:Q\",\n",
    "        axis=alt.Axis(\n",
    "            grid=True,\n",
    "            title=\"%\",\n",
    "            titleAnchor=\"start\",\n",
    "            labelColor=colors[\"eco-gray\"],\n",
    "            titleColor=colors[\"eco-gray\"],\n",
    "            tickColor=colors[\"eco-gray\"],\n",
    "            domainColor=colors[\"eco-gray\"],\n",
    "            titleFontSize=10,\n",
    "            titleFontWeight=\"normal\",\n",
    "            ticks=False,\n",
    "            labelAlign=\"left\",\n",
    "            labelBaseline=\"middle\",\n",
    "            labelPadding=-5,\n",
    "            labelOffset=-10,\n",
    "            titleX=22,\n",
    "            titleY=-5,\n",
    "            titleBaseline=\"bottom\",\n",
    "            titleAngle=0,\n",
    "            titleAlign=\"left\",\n",
    "            tickCount=6,\n",
    "            format=\".0f\",\n",
    "        ),\n",
    "    ),\n",
    "    x=alt.X(\n",
    "        \"date:T\",\n",
    "        axis=alt.Axis(\n",
    "            grid=False,\n",
    "            title=\"\",\n",
    "            titleAnchor=\"start\",\n",
    "            labelColor=colors[\"eco-gray\"],\n",
    "            titleColor=colors[\"eco-gray\"],\n",
    "            tickColor=colors[\"eco-gray\"],\n",
    "            domainColor=colors[\"eco-gray\"],\n",
    "            titleFontSize=10,\n",
    "            titleFontWeight=\"normal\",\n",
    "            labelExpr=\"datum.value<toDate('2019-11-01')?'':month(datum.value)==0?timeFormat(datum.value,'%Y'):timeFormat(datum.value,'%B')\",\n",
    "            labelAngle=0,\n",
    "        ),\n",
    "    ),\n",
    "    color=alt.Color(\"age:N\", legend=None, scale=alt.Scale(range=color_scale)),\n",
    ")\n",
    "lines = base.mark_line()\n",
    "labels = (\n",
    "    base.mark_text(align=\"left\", dx=5)\n",
    "    .encode(text=alt.Text(\"age:N\"))\n",
    "    .transform_filter(\"datum.date>toDate('2020-12-01')\")\n",
    ")\n",
    "layer = lines + labels\n",
    "layer.configure_view(stroke=None).properties(title=\"\").properties(height=300, width=400)\n",
    "layer.save(\"../../panels/unemployment/\" + f + \".json\")\n",
    "layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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