{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# When is Brandenburg Covered in its Entirety?\n",
    "\n",
    "When trying to generate mosaics covering the entire federal state of Brandenburg it was discovered that even over relatively long time frames (i.e. four weeks) uncovered areas could be detected.\n",
    "This discovery stands in contrast with the claim that the revisit time in that given latitude would be approximately one week.\n",
    "This notebook contains the research that was undertaken to find out over which time frame exactly an image of the entire federal state can be generated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sentinel_helpers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first fetch the geometry of the area we are interested in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "brandenburg = sentinel_helpers.search_osm('Brandenburg, Germany')[:1]\n",
    "brandenburg.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we fetch all available Sentinel-2 level 2a products on SciHub in the last year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from sentinelsat import SentinelAPI\n",
    "\n",
    "api = SentinelAPI(os.getenv('SCIHUB_USERNAME'), os.getenv('SCIHUB_PASSWORD'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We simplify the geometry and convert it to wkt we can run intersection queries against the api:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "footprint = brandenburg.convex_hull[0].wkt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The time frame we are interested in spans 4 weeks (i.e. approximately one month in the Gregorian calendar):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from datetime import date\n",
    "import datetime\n",
    "\n",
    "end_date = date(2020, 7, 1)\n",
    "start_date = end_date - datetime.timedelta(weeks=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Which Area Exactly is Covered by the Products?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Querying products: 100%|██████████| 181/181 [00:03<00:00, 24.77 products/s]\n",
      "/opt/conda/lib/python3.8/site-packages/pyproj/crs/crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    }
   ],
   "source": [
    "products = api.query(footprint,\n",
    "                     platformname='Sentinel-2',\n",
    "                     processinglevel='Level-2A',\n",
    "                     date=(start_date, end_date))\n",
    "products = api.to_geodataframe(products)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's verify visually that we have enough products to cover the area we are interested in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's get an overview of all products\n",
    "ax = products.plot(color='None', edgecolor='blue', alpha=0.1, figsize=(16, 9))\n",
    "brandenburg.plot(ax=ax, color='None', edgecolor='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to be able to conviently reference the products by UTM tile we extract that information from the product identifier and save it in a separate column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "eb1fec8e-d933-48c3-99f2-c2b3731dc5a1    T33UUS\n",
       "a87c7ff5-8664-4002-9258-7b1508a1de78    T33UUT\n",
       "f07d56ae-df3c-4402-a63d-e3879e4e671d    T33UVV\n",
       "6c1721a4-3628-49dd-a312-788111bf2ce9    T33UVU\n",
       "8c3b2e01-7c8b-43d4-b3e0-1a6a376cdd65    T33UVT\n",
       "Name: tile, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products['tile'] = products['identifier'].map(lambda s: s.split('_')[5])\n",
    "products['tile'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some UTM tiles start with `T32` while others start with `T33`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(75, 106)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products['tile'].str.startswith('T32').sum(), products['tile'].str.startswith('T33').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which tile exactly a product is covering depends on the exact position of the satellite. It also determines the angle of the cutoff of the pixel image. In the plot below `T32…` tiles are plotted in blue and `T33…` tiles are plotted in green:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = products[products['tile'].str.startswith('T32')].plot(color='None', edgecolor='blue', alpha=0.1, figsize=(16, 9))\n",
    "products[products['tile'].str.startswith('T33')].plot(ax=ax, color='None', edgecolor='green', alpha=0.1)\n",
    "brandenburg.plot(ax=ax, color='None', edgecolor='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that because the satellites are crossing the area more frequently than their repeat frequency, the area covered per orbit is slightly different. This can be made more complicated when dealing with hardware failures. \n",
    "\n",
    "Each orbit gets a designated `orbitnumber` which allows us to plot all products in our area per orbit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "orbits = products['orbitnumber'].unique()\n",
    "\n",
    "ncols = 5\n",
    "fig, axs = plt.subplots(nrows=math.ceil(len(orbits) / ncols), ncols=ncols, figsize=(12, 12))\n",
    "fig.tight_layout(h_pad=2) # h_pad=2 works better if we have titles in our subplots\n",
    "axs = axs.flatten() # lets us address a subplot with a 1d index\n",
    "\n",
    "for ax, orbit in zip(axs, sorted(orbits)):\n",
    "    ax.set_title('Orbit #{}'.format(orbit))\n",
    "    per_orbit = products[products['orbitnumber'] == orbit]\n",
    "    per_orbit.plot(ax=ax, color='None', edgecolor='blue', alpha=0.2)\n",
    "    brandenburg.plot(ax=ax, color='None', edgecolor='gray', alpha=0.5)\n",
    "    \n",
    "# hide empty subplots\n",
    "for ax in axs[len(orbits):]:\n",
    "    ax.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can nicely see a repeating pattern. The covered area shifts slightly to the left in each pass before re-appearing on the right. The exact same area is covered every 143 orbits, which is one repeat cycle.\n",
    "\n",
    "The oribt numbers are counted per Sentinel-2 satellite. There are currently two satellites in the Sentinel-2 program, S2A and S2B. These can be identified using the product `title`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['S2A', 'S2B'], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products['title'].apply(lambda t: t.split('_')[0]).unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "S2A has been launched earlier, which is why the orbit numbers are higher:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([26231, 26188, 26145, 26131, 26088, 26045, 26002, 25988, 25945,\n",
       "       25902, 25859, 25845])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products[products['title'].str.startswith('S2A')]['orbitnumber'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([17308, 17294, 17251, 17208, 17165, 17151, 17108, 17065, 17022,\n",
       "       17008, 16965])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products[products['title'].str.startswith('S2B')]['orbitnumber'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensuring Complete Coverage\n",
    "\n",
    "What is the minimum time span in those four weeks to ensure a coverage of all of Brandenburg?\n",
    "To find out we iterate through the returned products, for each iteration $i$ unifying the associated product's geometry $P_i$ with all products we already iterated through:\n",
    "\n",
    "\\begin{align}\n",
    "P &= \\{P_1, P_2, \\cdots, P_n\\} \\\\\n",
    "U_1 &= \\{\\} \\\\\n",
    "U_{n+1} &= U_{n} \\cup P_n\n",
    "\\end{align}\n",
    "\n",
    "Given the shape of Brandenburg $B$ the condition for termination is when $B$ is contained entirely in the unified shape $U_n$:\n",
    "\n",
    "$$\n",
    "B = B \\cap U_n\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "brandenburg_geometry = brandenburg.iloc[0].geometry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we continue we do a sanity check: Is the total area covered by the returned products big enough to cover the entirety of Brandenburg?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are the products sorted in the order of their capture or do we need to sort them?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 42.1 ms, sys: 3.37 ms, total: 45.5 ms\n",
      "Wall time: 44.1 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time products['geometry'].unary_union.contains(brandenburg_geometry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is!\n",
    "The `unary_union` operation is also sufficiently small for the algorithm to terminate in a reasonable amount of time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Coverage Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas  as pd\n",
    "import geopandas as gpd\n",
    "\n",
    "# we use tqdm to display progress bars\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would like "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(np.unique(products['beginposition'].values)).is_monotonic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No need to sort.\n",
    "If we iterate through them in the order they are returned from the API we can get a set of products covering our area of interest that is…\n",
    "\n",
    "1. …as close to the start of our observation time span as possible\n",
    "2. …captured over a time span that is as small as possible\n",
    "\n",
    "The code for the algorithm is very short thanks to the `shapely` geometry operators:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a2b7752b986d486f88994d75860a28e5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=181.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for idx, product in tqdm(products.iterrows(), total=len(products)):\n",
    "    union = products.loc[:idx].unary_union\n",
    "    if union.contains(brandenburg_geometry):\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The blue line represents the outline of the area we selected to cover the geometry shown in pink."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = gpd.GeoSeries(union).plot(color='None', edgecolor='b', alpha=0.2)\n",
    "brandenburg.plot(ax=ax, color='None', edgecolor='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that our algorithm worked.\n",
    "\n",
    "What is the delta between the capture date of the first and the last product contained in our union?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('3 days 00:10:00')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_in_union = products.loc[:idx]\n",
    "products_in_union.iloc[0]['beginposition'] - products_in_union.iloc[-1]['beginposition']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This small delta suggests that only few orbits were needed to capture the entire area:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([26231, 17308, 17294, 26188])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_in_union['orbitnumber'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Considering Cloud Coverage\n",
    "\n",
    "For the union above we did not consider cloud coverage at all.\n",
    "A plot of the cloud coverage can give us an estimate of how useful the combined image would be without plotting it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "products_in_union['cloudcoverpercentage'].plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not good.\n",
    "A cloud coverage of 100% is not of much use for us because it amounts to an image that does not contain any of the surface features we are interested in.\n",
    "\n",
    "We might have to make a compromise between recency and cloud coverage in some cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2020-06-30 10:20:31.024000'),\n",
       " Timestamp('2020-06-27 10:10:31.024000'))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products_in_union.iloc[0]['beginposition'], products_in_union.iloc[-1]['beginposition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8cc92fa09d2e46cf865326b9af4df694",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=76.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "less_cloudy_products = products[products['cloudcoverpercentage'] < 50]\n",
    "for idx, product in tqdm(less_cloudy_products.iterrows(), total=len(less_cloudy_products)):\n",
    "    union = less_cloudy_products.loc[:idx].unary_union\n",
    "    if union.contains(brandenburg_geometry):\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/geopandas/plotting.py:146: MatplotlibDeprecationWarning: Using a string of single character colors as a color sequence is deprecated since 3.2 and will be removed two minor releases later. Use an explicit list instead.\n",
      "  collection = PatchCollection([PolygonPatch(poly) for poly in geoms], **kwargs)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Expected 2-dimensional array, got 1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-00523162cdd3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGeoSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munion\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medgecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'b'\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[0mbrandenburg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medgecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'pink'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/geopandas/geoseries.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    440\u001b[0m         \u001b[0mthere\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m         \"\"\"\n\u001b[0;32m--> 442\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    444\u001b[0m     \u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/geopandas/plotting.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(s, cmap, color, ax, figsize, aspect, **style_kwds)\u001b[0m\n\u001b[1;32m    431\u001b[0m         )\n\u001b[1;32m    432\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 433\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\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    434\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    435\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m()\u001b[0m\n\u001b[1;32m    851\u001b[0m     \u001b[0mthe\u001b[0m \u001b[0mcurrent\u001b[0m \u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    852\u001b[0m     \"\"\"\n\u001b[0;32m--> 853\u001b[0;31m     \u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\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    854\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mdraw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2010\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2011\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\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[0;32m-> 2012\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   2013\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2014\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeprecated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"3.2\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    405\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[1;32m    406\u001b[0m               else nullcontext()):\n\u001b[0;32m--> 407\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\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    408\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1861\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1862\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1863\u001b[0;31m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   1864\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[1;32m   1865\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[1;32m    409\u001b[0m                          \u001b[0;32melse\u001b[0m \u001b[0mdeprecation_addendum\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m                 **kwargs)\n\u001b[0;32m--> 411\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minner_kwargs\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    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2745\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\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   2746\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2747\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\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   2748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2749\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\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    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    404\u001b[0m                 mpath.Path(offsets), transOffset, tuple(facecolors[0]))\n\u001b[1;32m    405\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 406\u001b[0;31m             renderer.draw_path_collection(\n\u001b[0m\u001b[1;32m    407\u001b[0m                 \u001b[0mgc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrozen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpaths\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transforms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffsets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransOffset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw_path_collection\u001b[0;34m(self, gc, master_transform, paths, all_transforms, offsets, offsetTrans, facecolors, edgecolors, linewidths, linestyles, antialiaseds, urls, offset_position)\u001b[0m\n\u001b[1;32m    170\u001b[0m                 \u001b[0;34m\"3.3\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Support for offset_position='data' is \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m                 \"deprecated since %(since)s and will be removed %(removal)s.\")\n\u001b[0;32m--> 172\u001b[0;31m         return self._renderer.draw_path_collection(\n\u001b[0m\u001b[1;32m    173\u001b[0m             \u001b[0mgc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaster_transform\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpaths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mall_transforms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffsets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffsetTrans\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m             \u001b[0mfacecolors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medgecolors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinestyles\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mantialiaseds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murls\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Expected 2-dimensional array, got 1"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Expected 2-dimensional array, got 1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\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    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 248\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    249\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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/opt/conda/lib/python3.8/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\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    133\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\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[0m\n\u001b[1;32m    134\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2209\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2210\u001b[0;31m                 result = print_method(\n\u001b[0m\u001b[1;32m   2211\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[1;32m   2212\u001b[0m                     \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1637\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1638\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1639\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1641\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs, *args)\u001b[0m\n\u001b[1;32m    507\u001b[0m             \u001b[0;34m*\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mincluding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[0;34m'Software'\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m         \"\"\"\n\u001b[0;32m--> 509\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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    510\u001b[0m         mpl.image.imsave(\n\u001b[1;32m    511\u001b[0m             \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\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[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"upper\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    405\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[1;32m    406\u001b[0m               else nullcontext()):\n\u001b[0;32m--> 407\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\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    408\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1861\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1862\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1863\u001b[0;31m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   1864\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[1;32m   1865\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\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    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[1;32m    409\u001b[0m                          \u001b[0;32melse\u001b[0m \u001b[0mdeprecation_addendum\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m                 **kwargs)\n\u001b[0;32m--> 411\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minner_kwargs\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    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2745\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\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   2746\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2747\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\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   2748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2749\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\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    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    404\u001b[0m                 mpath.Path(offsets), transOffset, tuple(facecolors[0]))\n\u001b[1;32m    405\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 406\u001b[0;31m             renderer.draw_path_collection(\n\u001b[0m\u001b[1;32m    407\u001b[0m                 \u001b[0mgc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrozen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpaths\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transforms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffsets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransOffset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw_path_collection\u001b[0;34m(self, gc, master_transform, paths, all_transforms, offsets, offsetTrans, facecolors, edgecolors, linewidths, linestyles, antialiaseds, urls, offset_position)\u001b[0m\n\u001b[1;32m    170\u001b[0m                 \u001b[0;34m\"3.3\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Support for offset_position='data' is \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m                 \"deprecated since %(since)s and will be removed %(removal)s.\")\n\u001b[0;32m--> 172\u001b[0;31m         return self._renderer.draw_path_collection(\n\u001b[0m\u001b[1;32m    173\u001b[0m             \u001b[0mgc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaster_transform\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpaths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mall_transforms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffsets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffsetTrans\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m             \u001b[0mfacecolors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medgecolors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinestyles\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mantialiaseds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murls\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Expected 2-dimensional array, got 1"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = gpd.GeoSeries(union).plot(color='', edgecolor='b')\n",
    "brandenburg.plot(ax=ax, color='', edgecolor='pink')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "products_in_union = less_cloudy_products.loc[:idx]\n",
    "products_in_union.iloc[0]['beginposition'] - products_in_union.iloc[-1]['beginposition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "products_in_union['cloudcoverpercentage'].plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can find less cloudy products if we increase the timespan to 15 days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "products_in_union.iloc[0]['beginposition'], products_in_union.iloc[-1]['beginposition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "products_in_union"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try to further reduce the amount of products we need to download by dropping identical geometries, keeping the one with the smallest cloud cover:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "deduplicated = products_in_union.sort_values(by='cloudcoverpercentage').drop_duplicates(subset=['geometry'])\n",
    "deduplicated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "deduplicated['cloudcoverpercentage'].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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