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    "## Exercise 9: Analysing data for an area of interest\n",
    "\n",
    "In previous exercises we've learned how to load and display satellite data using a `Scene`-object. In this exercise we will extract a piece of data from that scene and do simple analysis on it. Here we learn how to play with `dask`-arrays. Furthermore, we will touch some Python basics, i.e., string formatting, use of `glob`-module, and using slices."
   ]
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   "source": [
    "### Exercise 9.1: Find the file\n",
    "\n",
    "Use string formatting and `glob`-module ot get get a list of files in directory `/tcenas/scratch/pytroll/ex9`. Example of string formatting is given below"
   ]
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    "import glob\n",
    "from satpy import Scene\n",
    "\n",
    "# String formatting example\n",
    "str = '{} world!'\n",
    "print (str.format('Hello'))\n",
    "\n",
    "# Using glob, find *.nat files in the given directory\n",
    "base_dir = '/tcenas/scratch/pytroll/ex9'\n",
    "files = \n",
    "\n",
    "# print found files\n",
    "print (files)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 9.2: Create a `Scene`-object and load some data\n",
    "\n",
    "The resulting `files`-variable should only contain one file. Use the variable as a list to initiate a `Scene`-object and load `day_microphysics`-composite and channel data for 0.6um and 1.6um. _Hint:_ `Scene`-object has a convenience method `available_dataset_names` to check how the different channels are named in the reader."
   ]
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   "source": [
    "# Create a Scene-object\n",
    "scn = \n",
    "# Let's define a composite variable for convenience\n",
    "composite = 'day_microphysics'\n",
    "# Make a list with the composite, and 0.6um and 1.6um channel data\n",
    "datasets = \n",
    "# Load datasets to the Scene-object\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 9.3: Resampling the `Scene`\n",
    "\n",
    "Resample the created `Scene`-object to `EastEurope` area. Show the `day_microphysics`-composite."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Resample the scene to the new area\n",
    "ee = \n",
    "# Show composite\n",
    "ee.show(composite)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 9.4: Data slicing and statistics\n",
    "\n",
    "Ńow we're goint to work with the resampled image. The left hand side shows a big thunderstorm system over East Europe. Take a data slice of `[245:275, 80:120]` from both 0.6um and 1.6um channels and calculate the mean reflectance. _Hint_: The extracted data slice is a `dask`-array. These arrays are _lazy_. Also, we're using `slice`-objects here for convenient ROI definition."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Area of interest\n",
    "rows = slice()\n",
    "cols = slice()\n",
    "# Slice the area of interest to a variable\n",
    "vis06 = \n",
    "ir16 = \n",
    "# Compute and print the mean reflectances\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 9.5: Utilizing the data loaded for the composite\n",
    "\n",
    "The `day_microphysics` composite is a three component data array. Here's how the composite is build: http://www.eumetrain.org/rgb_quick_guides/quick_guides/DaymicroRGB.pdf\n",
    "Calculate and print the mean of the 0.8um channel using the data loaded for the `day_microphysics` composite. Furthermore, calculate and print the minimum, maximum, and standard deviation of the 10.8um channel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Slice the area of interest to variables\n",
    "vis08 = \n",
    "ir108 =\n",
    "# Compute and print the required statistics\n"
   ]
  }
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