{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ESA CCI Toolbox Data Access with Parameters\n",
    "\n",
    "This notebook shows how to open data cubes from the ESA Open Data Portal for a given time range and region:\n",
    "\n",
    "A **temporarily regular cube** with aggregated CCI data that fall into equal-size time periods;\n",
    "\n",
    "We start with necessary imports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from xcube.core.store import new_data_store\n",
    "import IPython.display\n",
    "import shapely"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create the store."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<frozen abc>:106: FutureWarning: xarray subclass VectorDataCube should explicitly define __slots__\n"
     ]
    }
   ],
   "source": [
    "cci_store = new_data_store('esa-cci')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this demo, we are using the dataset with the id 'esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1'. Here is its metadata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/json": {
       "attrs": {
        "abstract": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.   This dataset comprises Level 3 Absorbing Aerosol Index (AAI) products, using the Multi-Sensor UVAI algorithm, Version 1.7.  L3 products are provided as daily and monthly gridded products as well as a monthly climatology. \n\nFor further details about these data products please see the linked documentation.",
        "catalog_url": "https://catalogue.ceda.ac.uk/uuid/2e656d34d016414c8d6bced18634772c",
        "cci_project": "AEROSOL",
        "comment": "Sun glint and solar eclipse events were filtered out.",
        "data_type": "AAI",
        "description": "Multi-Sensor AAI field for January 1979",
        "ecv": "AEROSOL",
        "history": "2017-07-24 09:13:47 - Product generated using create_msaai_monthly_products.pro",
        "institute": "Royal Netherlands Meteorological Institute",
        "platform_id": "multi-platform",
        "processing_level": "L3",
        "product_string": "MSAAI",
        "product_version": "1-7",
        "project": "Climate Change Initiative - European Space Agency",
        "publication_date": "2020-08-27T16:02:30",
        "references": "http://www.esa-aerosol-cci.org",
        "sensor_id": "multi-sensor",
        "source": "TOMS AAI version 8, GOME-1 AAI version 5.0, SCIAMACHY AAI version 5.1, OMI AAI version 1.1.1, GOME-2 AAI version 1.35",
        "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products  from the Multi-Sensor UV Absorbing Aerosol Index (MS UVAI) algorithm, Version 1.7",
        "uuid": "2e656d34d016414c8d6bced18634772c"
       },
       "bbox": [
        -180,
        -90,
        180,
        90
       ],
       "coords": {
        "latitude": {
         "attrs": {
          "chunk_sizes": [
           180
          ],
          "data_type": "float32",
          "dimensions": [
           "latitude"
          ],
          "file_chunk_sizes": [
           180
          ],
          "file_dimensions": [
           "latitude"
          ],
          "fill_value": null,
          "long_name": "Latitudes of the centre of the grid cells",
          "orig_data_type": "float32",
          "shape": [
           180
          ],
          "size": 180,
          "units": "degrees"
         },
         "dims": [
          "latitude"
         ],
         "dtype": "float32",
         "name": "latitude"
        },
        "longitude": {
         "attrs": {
          "chunk_sizes": [
           360
          ],
          "data_type": "float32",
          "dimensions": [
           "longitude"
          ],
          "file_chunk_sizes": [
           360
          ],
          "file_dimensions": [
           "longitude"
          ],
          "fill_value": null,
          "long_name": "Longitudes of the centre of the grid cells",
          "orig_data_type": "float32",
          "shape": [
           360
          ],
          "size": 360,
          "units": "degrees"
         },
         "dims": [
          "longitude"
         ],
         "dtype": "float32",
         "name": "longitude"
        },
        "time": {
         "attrs": {
          "calendar": "proleptic_gregorian",
          "shape": [
           422
          ],
          "size": 422,
          "standard_name": "time",
          "units": "seconds since 1970-01-01T00:00:00Z"
         },
         "dims": [
          "time"
         ],
         "dtype": "int64",
         "name": "time"
        },
        "time_bnds": {
         "attrs": {
          "calendar": "proleptic_gregorian",
          "shape": [
           2,
           422
          ],
          "size": 844,
          "standard_name": "time_bnds",
          "units": "seconds since 1970-01-01T00:00:00Z"
         },
         "dims": [
          "time",
          "bnds"
         ],
         "dtype": "int64",
         "name": "time_bnds"
        }
       },
       "crs": "WGS84",
       "data_id": "esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1",
       "data_type": "dataset",
       "data_vars": {
        "absorbing_aerosol_index": {
         "attrs": {
          "chunk_sizes": [
           180,
           360
          ],
          "data_type": "float32",
          "dimensions": [
           "latitude",
           "longitude"
          ],
          "file_chunk_sizes": [
           180,
           360
          ],
          "file_dimensions": [
           "latitude",
           "longitude"
          ],
          "fill_value": null,
          "long_name": "Absorbing aerosol index averaged for each grid cell",
          "orig_data_type": "float32",
          "shape": [
           180,
           360
          ],
          "size": 64800,
          "units": "1"
         },
         "dims": [
          "time",
          "lat",
          "lon"
         ],
         "dtype": "float32",
         "name": "absorbing_aerosol_index"
        },
        "number_of_observations": {
         "attrs": {
          "chunk_sizes": [
           180,
           360
          ],
          "data_type": "int64",
          "dimensions": [
           "latitude",
           "longitude"
          ],
          "file_chunk_sizes": [
           180,
           360
          ],
          "file_dimensions": [
           "latitude",
           "longitude"
          ],
          "fill_value": 9223372036854776000,
          "long_name": "Number of observations for each grid cell",
          "orig_data_type": "int32",
          "shape": [
           180,
           360
          ],
          "size": 64800,
          "units": "1"
         },
         "dims": [
          "time",
          "lat",
          "lon"
         ],
         "dtype": "int64",
         "name": "number_of_observations"
        },
        "number_of_positive_observations": {
         "attrs": {
          "chunk_sizes": [
           180,
           360
          ],
          "data_type": "int64",
          "dimensions": [
           "latitude",
           "longitude"
          ],
          "file_chunk_sizes": [
           180,
           360
          ],
          "file_dimensions": [
           "latitude",
           "longitude"
          ],
          "fill_value": 9223372036854776000,
          "long_name": "Number of positive observations for each grid cell",
          "orig_data_type": "int32",
          "shape": [
           180,
           360
          ],
          "size": 64800,
          "units": "1"
         },
         "dims": [
          "time",
          "lat",
          "lon"
         ],
         "dtype": "int64",
         "name": "number_of_positive_observations"
        },
        "positive_absorbing_aerosol_index": {
         "attrs": {
          "chunk_sizes": [
           180,
           360
          ],
          "data_type": "float32",
          "dimensions": [
           "latitude",
           "longitude"
          ],
          "file_chunk_sizes": [
           180,
           360
          ],
          "file_dimensions": [
           "latitude",
           "longitude"
          ],
          "fill_value": null,
          "long_name": "Positive absorbing aerosol index averaged for each grid cell",
          "orig_data_type": "float32",
          "shape": [
           180,
           360
          ],
          "size": 64800,
          "units": "1"
         },
         "dims": [
          "time",
          "lat",
          "lon"
         ],
         "dtype": "float32",
         "name": "positive_absorbing_aerosol_index"
        },
        "solar_zenith_angle": {
         "attrs": {
          "chunk_sizes": [
           180,
           360
          ],
          "data_type": "float32",
          "dimensions": [
           "latitude",
           "longitude"
          ],
          "file_chunk_sizes": [
           180,
           360
          ],
          "file_dimensions": [
           "latitude",
           "longitude"
          ],
          "fill_value": null,
          "long_name": "Solar zenith angle averaged for each grid cell",
          "orig_data_type": "float32",
          "shape": [
           180,
           360
          ],
          "size": 64800,
          "units": "degrees"
         },
         "dims": [
          "time",
          "lat",
          "lon"
         ],
         "dtype": "float32",
         "name": "solar_zenith_angle"
        }
       },
       "dims": {
        "bnds": 2,
        "lat": 180,
        "lon": 360,
        "time": 422
       },
       "open_params_schema": {
        "additionalProperties": false,
        "properties": {
         "bbox": {
          "items": [
           {
            "maximum": 180,
            "minimum": -180,
            "type": "number"
           },
           {
            "maximum": 90,
            "minimum": -90,
            "type": "number"
           },
           {
            "maximum": 180,
            "minimum": -180,
            "type": "number"
           },
           {
            "maximum": 90,
            "minimum": -90,
            "type": "number"
           }
          ],
          "type": "array"
         },
         "normalize_data": {
          "default": true,
          "type": "boolean"
         },
         "time_range": {
          "items": [
           {
            "format": "date",
            "maxDate": "2015-12-31",
            "minDate": "1978-11-01",
            "type": "string"
           },
           {
            "format": "date",
            "maxDate": "2015-12-31",
            "minDate": "1978-11-01",
            "type": "string"
           }
          ],
          "type": "array"
         },
         "variable_names": {
          "items": {
           "enum": [
            "absorbing_aerosol_index",
            "number_of_observations",
            "solar_zenith_angle",
            "positive_absorbing_aerosol_index",
            "number_of_positive_observations"
           ],
           "type": "string"
          },
          "type": "array"
         }
        },
        "type": "object"
       },
       "spatial_res": 1,
       "time_period": "1M",
       "time_range": [
        "1978-11-01",
        "2015-12-31"
       ]
      },
      "text/plain": [
       "<xcube.core.store.descriptor.DatasetDescriptor at 0x7fa76c6d29f0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cci_store.describe_data('esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are focussing on Africa, so we define a bounding box accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x1 = -23.40  # degree\n",
    "y1 = -40.40  # degree\n",
    "x2 = 57.40  # degree\n",
    "y2 = 40.40  # degree\n",
    "\n",
    "bbox = (x1, y1, x2, y2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the bounding box. If you don't see anything, please refer to [Ex0-DCFS-Setup](./Ex0-DCFS-Setup.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/geo+json": {
       "coordinates": [
        [
         [
          57.4,
          -40.4
         ],
         [
          57.4,
          40.4
         ],
         [
          -23.4,
          40.4
         ],
         [
          -23.4,
          -40.4
         ],
         [
          57.4,
          -40.4
         ]
        ]
       ],
       "type": "Polygon"
      },
      "text/plain": [
       "<IPython.display.GeoJSON object>"
      ]
     },
     "metadata": {
      "application/geo+json": {
       "expanded": false,
       "root": "root"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "IPython.display.GeoJSON(shapely.geometry.box(*bbox).__geo_interface__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our time range covers data from 1997: 1997-01-01 to 1997-12-01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Now to open the dataset. You may check its metadata first to make sure that your parameters are valid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/json": {
       "additionalProperties": false,
       "properties": {
        "bbox": {
         "items": [
          {
           "maximum": 180,
           "minimum": -180,
           "type": "number"
          },
          {
           "maximum": 90,
           "minimum": -90,
           "type": "number"
          },
          {
           "maximum": 180,
           "minimum": -180,
           "type": "number"
          },
          {
           "maximum": 90,
           "minimum": -90,
           "type": "number"
          }
         ],
         "type": "array"
        },
        "normalize_data": {
         "default": true,
         "type": "boolean"
        },
        "time_range": {
         "items": [
          {
           "format": "date",
           "maxDate": "2015-12-31",
           "minDate": "1978-11-01",
           "type": "string"
          },
          {
           "format": "date",
           "maxDate": "2015-12-31",
           "minDate": "1978-11-01",
           "type": "string"
          }
         ],
         "type": "array"
        },
        "variable_names": {
         "items": {
          "enum": [
           "absorbing_aerosol_index",
           "number_of_observations",
           "solar_zenith_angle",
           "positive_absorbing_aerosol_index",
           "number_of_positive_observations"
          ],
          "type": "string"
         },
         "type": "array"
        }
       },
       "type": "object"
      },
      "text/plain": [
       "<xcube.util.jsonschema.JsonObjectSchema at 0x7fa76c49c1a0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cci_store.get_open_data_params_schema(data_id='esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1', opener_id='dataset:zarr:cciodp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select any valid combination from the paramater in *properties* above. The only mandatory parameter is the data_id. If you leave any parameter out, all available values will be considered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "html[data-theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 308kB\n",
       "Dimensions:                  (time: 12, lat: 80, lon: 80, bnds: 2)\n",
       "Coordinates:\n",
       "  * lat                      (lat) float32 320B -39.5 -38.5 -37.5 ... 38.5 39.5\n",
       "  * lon                      (lon) float32 320B -22.5 -21.5 -20.5 ... 55.5 56.5\n",
       "  * time                     (time) datetime64[ns] 96B 2005-01-16T12:00:00 .....\n",
       "    time_bnds                (time, bnds) datetime64[ns] 192B dask.array&lt;chunksize=(12, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    absorbing_aerosol_index  (time, lat, lon) float32 307kB dask.array&lt;chunksize=(1, 80, 80), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:             CF-1.7\n",
       "    title:                   esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-pla...\n",
       "    date_created:            2024-11-05T17:45:16.060471\n",
       "    processing_level:        L3\n",
       "    time_coverage_start:     2005-01-01T00:00:00\n",
       "    time_coverage_end:       2006-01-01T00:00:00\n",
       "    time_coverage_duration:  P365DT0H0M0S\n",
       "    history:                 [{&#x27;program&#x27;: &#x27;xcube_cci.chunkstore.CciChunkStore...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-15058a61-0fe0-4a37-8ffb-f473acdef92a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-15058a61-0fe0-4a37-8ffb-f473acdef92a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 12</li><li><span class='xr-has-index'>lat</span>: 80</li><li><span class='xr-has-index'>lon</span>: 80</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e03f0024-bc1b-46c7-9671-5a26cbd321e1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e03f0024-bc1b-46c7-9671-5a26cbd321e1' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-39.5 -38.5 -37.5 ... 38.5 39.5</div><input id='attrs-ad2f4a7c-01de-435e-8310-e2b7072c7196' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad2f4a7c-01de-435e-8310-e2b7072c7196' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44c37e41-b67a-450a-bb56-e06f8f6e13bc' class='xr-var-data-in' type='checkbox'><label for='data-44c37e41-b67a-450a-bb56-e06f8f6e13bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Latitudes of the centre of the grid cells</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>orig_data_type :</span></dt><dd>float32</dd><dt><span>fill_value :</span></dt><dd>nan</dd><dt><span>size :</span></dt><dd>80</dd><dt><span>shape :</span></dt><dd>[80]</dd><dt><span>chunk_sizes :</span></dt><dd>[80]</dd><dt><span>file_chunk_sizes :</span></dt><dd>[80]</dd><dt><span>data_type :</span></dt><dd>float32</dd><dt><span>dimensions :</span></dt><dd>[&#x27;latitude&#x27;]</dd><dt><span>file_dimensions :</span></dt><dd>[&#x27;latitude&#x27;]</dd></dl></div><div class='xr-var-data'><pre>array([-39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-22.5 -21.5 -20.5 ... 55.5 56.5</div><input id='attrs-b5ff126c-3668-435b-9889-93347418a557' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5ff126c-3668-435b-9889-93347418a557' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-620145c6-2120-4cc3-945d-ac7c5d8b3fb2' class='xr-var-data-in' type='checkbox'><label for='data-620145c6-2120-4cc3-945d-ac7c5d8b3fb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Longitudes of the centre of the grid cells</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>orig_data_type :</span></dt><dd>float32</dd><dt><span>fill_value :</span></dt><dd>nan</dd><dt><span>size :</span></dt><dd>80</dd><dt><span>shape :</span></dt><dd>[80]</dd><dt><span>chunk_sizes :</span></dt><dd>[80]</dd><dt><span>file_chunk_sizes :</span></dt><dd>[80]</dd><dt><span>data_type :</span></dt><dd>float32</dd><dt><span>dimensions :</span></dt><dd>[&#x27;longitude&#x27;]</dd><dt><span>file_dimensions :</span></dt><dd>[&#x27;longitude&#x27;]</dd></dl></div><div class='xr-var-data'><pre>array([-22.5, -21.5, -20.5, -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5,\n",
       "       -12.5, -11.5, -10.5,  -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,\n",
       "        -2.5,  -1.5,  -0.5,   0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,\n",
       "         7.5,   8.5,   9.5,  10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,\n",
       "        17.5,  18.5,  19.5,  20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,\n",
       "        27.5,  28.5,  29.5,  30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,\n",
       "        37.5,  38.5,  39.5,  40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,\n",
       "        47.5,  48.5,  49.5,  50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2005-01-16T12:00:00 ... 2005-12-...</div><input id='attrs-f372caca-4baa-4b01-a679-bde4c9657408' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f372caca-4baa-4b01-a679-bde4c9657408' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-320ff912-7bcc-4b32-9bd4-27e08d82b174' class='xr-var-data-in' type='checkbox'><label for='data-320ff912-7bcc-4b32-9bd4-27e08d82b174' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2005-01-16T12:00:00.000000000&#x27;, &#x27;2005-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-03-16T12:00:00.000000000&#x27;, &#x27;2005-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-05-16T12:00:00.000000000&#x27;, &#x27;2005-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-07-16T12:00:00.000000000&#x27;, &#x27;2005-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-09-16T00:00:00.000000000&#x27;, &#x27;2005-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-11-16T00:00:00.000000000&#x27;, &#x27;2005-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 2), meta=np.ndarray&gt;</div><input id='attrs-37175d61-aace-42d2-9e74-5d846f3a7fe9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37175d61-aace-42d2-9e74-5d846f3a7fe9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa2407a0-d166-4f91-86fc-291d1b409e54' class='xr-var-data-in' type='checkbox'><label for='data-aa2407a0-d166-4f91-86fc-291d1b409e54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 192 B </td>\n",
       "                        <td> 192 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (12, 2) </td>\n",
       "                        <td> (12, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"91\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"41\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"41\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 41.20382942136741,0.0 41.20382942136741,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"20.601915\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"61.203829\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,61.203829,60.000000)\">12</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8bc7254b-d83d-48c7-a19f-5f89750f8210' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8bc7254b-d83d-48c7-a19f-5f89750f8210' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>absorbing_aerosol_index</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 80, 80), meta=np.ndarray&gt;</div><input id='attrs-f83ad2a1-7860-4542-9531-71a21d4b4be0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f83ad2a1-7860-4542-9531-71a21d4b4be0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25793749-02fa-48bb-869c-ba3521047f03' class='xr-var-data-in' type='checkbox'><label for='data-25793749-02fa-48bb-869c-ba3521047f03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Absorbing aerosol index averaged for each grid cell</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>orig_data_type :</span></dt><dd>float32</dd><dt><span>fill_value :</span></dt><dd>nan</dd><dt><span>size :</span></dt><dd>76800</dd><dt><span>shape :</span></dt><dd>[12, 80, 80]</dd><dt><span>chunk_sizes :</span></dt><dd>[1, 80, 80]</dd><dt><span>file_chunk_sizes :</span></dt><dd>[80, 80]</dd><dt><span>data_type :</span></dt><dd>float32</dd><dt><span>dimensions :</span></dt><dd>[&#x27;time&#x27;, &#x27;latitude&#x27;, &#x27;longitude&#x27;]</dd><dt><span>file_dimensions :</span></dt><dd>[&#x27;latitude&#x27;, &#x27;longitude&#x27;]</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 300.00 kiB </td>\n",
       "                        <td> 25.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (12, 80, 80) </td>\n",
       "                        <td> (1, 80, 80) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"203\" height=\"193\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"120\" x2=\"33\" y2=\"143\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"121\" />\n",
       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"123\" />\n",
       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"125\" />\n",
       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"127\" />\n",
       "  <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"129\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"131\" />\n",
       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"133\" />\n",
       "  <line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"135\" />\n",
       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"137\" />\n",
       "  <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"139\" />\n",
       "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"141\" />\n",
       "  <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"143\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 33.94492314957306,23.94492314957306 33.94492314957306,143.94492314957307 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"11\" y1=\"1\" x2=\"131\" y2=\"1\" />\n",
       "  <line x1=\"13\" y1=\"3\" x2=\"133\" y2=\"3\" />\n",
       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
       "  <line x1=\"17\" y1=\"7\" x2=\"137\" y2=\"7\" />\n",
       "  <line x1=\"19\" y1=\"9\" x2=\"139\" y2=\"9\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
       "  <line x1=\"25\" y1=\"15\" x2=\"145\" y2=\"15\" />\n",
       "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" />\n",
       "  <line x1=\"29\" y1=\"19\" x2=\"149\" y2=\"19\" />\n",
       "  <line x1=\"31\" y1=\"21\" x2=\"151\" y2=\"21\" />\n",
       "  <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"130\" y1=\"0\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 130.0,0.0 153.94492314957307,23.94492314957306 33.94492314957306,23.94492314957306\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"33\" y1=\"143\" x2=\"153\" y2=\"143\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"143\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"153\" y1=\"23\" x2=\"153\" y2=\"143\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"33.94492314957306,23.94492314957306 153.94492314957307,23.94492314957306 153.94492314957307,143.94492314957307 33.94492314957306,143.94492314957307\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"93.944923\" y=\"163.944923\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >80</text>\n",
       "  <text x=\"173.944923\" y=\"83.944923\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,173.944923,83.944923)\">80</text>\n",
       "  <text x=\"11.972462\" y=\"151.972462\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.972462,151.972462)\">12</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-572a518c-1b3f-4d56-9689-5a3bc76e9d28' class='xr-section-summary-in' type='checkbox'  ><label for='section-572a518c-1b3f-4d56-9689-5a3bc76e9d28' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-33826649-d585-4031-9dce-1ae70ae43e78' class='xr-index-data-in' type='checkbox'/><label for='index-33826649-d585-4031-9dce-1ae70ae43e78' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7e583255-e628-43ac-8ec3-a44a444d5241' class='xr-index-data-in' type='checkbox'/><label for='index-7e583255-e628-43ac-8ec3-a44a444d5241' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-22.5, -21.5, -20.5, -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5,\n",
       "       -12.5, -11.5, -10.5,  -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,\n",
       "        -2.5,  -1.5,  -0.5,   0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,\n",
       "         7.5,   8.5,   9.5,  10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,\n",
       "        17.5,  18.5,  19.5,  20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,\n",
       "        27.5,  28.5,  29.5,  30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,\n",
       "        37.5,  38.5,  39.5,  40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,\n",
       "        47.5,  48.5,  49.5,  50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f3003050-1182-4fa2-abf3-1f411a4ddc8f' class='xr-index-data-in' type='checkbox'/><label for='index-f3003050-1182-4fa2-abf3-1f411a4ddc8f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2005-01-16 12:00:00&#x27;, &#x27;2005-02-15 00:00:00&#x27;,\n",
       "               &#x27;2005-03-16 12:00:00&#x27;, &#x27;2005-04-16 00:00:00&#x27;,\n",
       "               &#x27;2005-05-16 12:00:00&#x27;, &#x27;2005-06-16 00:00:00&#x27;,\n",
       "               &#x27;2005-07-16 12:00:00&#x27;, &#x27;2005-08-16 12:00:00&#x27;,\n",
       "               &#x27;2005-09-16 00:00:00&#x27;, &#x27;2005-10-16 12:00:00&#x27;,\n",
       "               &#x27;2005-11-16 00:00:00&#x27;, &#x27;2005-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b6dfabd8-4954-4c04-9e77-e988cac7d10e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b6dfabd8-4954-4c04-9e77-e988cac7d10e' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>title :</span></dt><dd>esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1</dd><dt><span>date_created :</span></dt><dd>2024-11-05T17:45:16.060471</dd><dt><span>processing_level :</span></dt><dd>L3</dd><dt><span>time_coverage_start :</span></dt><dd>2005-01-01T00:00:00</dd><dt><span>time_coverage_end :</span></dt><dd>2006-01-01T00:00:00</dd><dt><span>time_coverage_duration :</span></dt><dd>P365DT0H0M0S</dd><dt><span>history :</span></dt><dd>[{&#x27;program&#x27;: &#x27;xcube_cci.chunkstore.CciChunkStore&#x27;, &#x27;cube_params&#x27;: {&#x27;variable_names&#x27;: [&#x27;absorbing_aerosol_index&#x27;], &#x27;time_range&#x27;: [&#x27;2005-01-01T00:00:00&#x27;, &#x27;2005-12-31T00:00:00&#x27;], &#x27;bbox&#x27;: [-23.4, -40.4, 57.4, 40.4]}}]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 308kB\n",
       "Dimensions:                  (time: 12, lat: 80, lon: 80, bnds: 2)\n",
       "Coordinates:\n",
       "  * lat                      (lat) float32 320B -39.5 -38.5 -37.5 ... 38.5 39.5\n",
       "  * lon                      (lon) float32 320B -22.5 -21.5 -20.5 ... 55.5 56.5\n",
       "  * time                     (time) datetime64[ns] 96B 2005-01-16T12:00:00 .....\n",
       "    time_bnds                (time, bnds) datetime64[ns] 192B dask.array<chunksize=(12, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    absorbing_aerosol_index  (time, lat, lon) float32 307kB dask.array<chunksize=(1, 80, 80), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:             CF-1.7\n",
       "    title:                   esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-pla...\n",
       "    date_created:            2024-11-05T17:45:16.060471\n",
       "    processing_level:        L3\n",
       "    time_coverage_start:     2005-01-01T00:00:00\n",
       "    time_coverage_end:       2006-01-01T00:00:00\n",
       "    time_coverage_duration:  P365DT0H0M0S\n",
       "    history:                 [{'program': 'xcube_cci.chunkstore.CciChunkStore..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cube = cci_store.open_data('esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1',\n",
    "                           variable_names=['absorbing_aerosol_index'],\n",
    "                           bbox=bbox,\n",
    "                           time_range=['2005-01-01', '2005-12-31'])\n",
    "cube"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We may visualize the cube's time coordinates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa76ac354c0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cube.time.diff(dim='time').plot.line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally, display the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa76aca72f0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cube.absorbing_aerosol_index.sel(time='2005-04-15 12:00:00', method='nearest').plot.imshow(cmap='Greys_r', figsize=(8, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}