{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detailed example\n",
    "\n",
    "This overview of the most important functions repeats the previous 30-seconds-example, but in more detail and shows additional functionality and alternative steps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authentificate & access project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import up42\n",
    "up42.authenticate(project_id=\"12345\", project_api_key=\"12345\")\n",
    "#up42.authenticate(cfg_file=\"config.json\")\n",
    "\n",
    "project = up42.initialize_project()\n",
    "project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get information about the available blocks to later construct your workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "up42.get_blocks(basic=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create or access the workflow\n",
    "You can either create a new workflow, use project.get_workflows() to get all existing workflows within the project, or access an exisiting workflow directly via its workflow_id."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A new workflow is created and filled with tasks ([Sentinel-2 data](https://marketplace.up42.com/block/018dfb34-fc19-4334-8125-14fd7535f979), \n",
    "[Land-Surface-Temperature](https://marketplace.up42.com/block/34767300-5caf-472b-a684-a351212b5c14)). \n",
    "The area of interest and workflow parameters are defined. After running the job, \n",
    "the results are downloaded and visualized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new, empty workflow.\n",
    "\n",
    "workflow = project.create_workflow(name=\"30-seconds-workflow\", use_existing=False)\n",
    "workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add workflow tasks\n",
    "\n",
    "input_tasks = [\"Sentinel-2 Level 2 (BOA) AOI clipped\", \"Land Surface Temperature Estimation\"]\n",
    "workflow.add_workflow_tasks(input_tasks=input_tasks)\n",
    "\n",
    "# Check the added tasks.\n",
    "workflow.get_workflow_tasks(basic=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alternative: Get all existing workflows within the project.\n",
    "\n",
    "all_workflows = project.get_workflows()\n",
    "workflow = all_workflows[0]\n",
    "workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alternative: Directly access the existing workflow the id (has to exist within the accessed project).\n",
    "\n",
    "UP42_WORKFLOW_ID=\"7fb2ec8a-45be-41ad-a50f-98ba6b528b98\"\n",
    "workflow = up42.initialize_workflow(workflow_id=UP42_WORKFLOW_ID)\n",
    "\n",
    "workflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select the aoi\n",
    "\n",
    "There are multiple ways to select an aoi, you can:\n",
    "- Provide aoi the geometry directly in code as a FeatureCollection, Feature, GeoDataFrame, shapely Polygon or list of bounds coordinates.\n",
    "- Use up42.draw_aoi() to draw the aoi and export it as a geojson.\n",
    "- Use up42.read_vector_file() to read a geojson, json, shapefile, kml or wkt file.\n",
    "- Use up42.get_example_aoi() to read multiple provided sample aois."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aoi = [13.375966, 52.515068, 13.378314, 52.516639]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aoi = up42.read_vector_file(\"data/aoi_berlin.geojson\", as_dataframe=True)\n",
    "aoi.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#aoi = up42.get_example_aoi(location=\"Berlin\")\n",
    "#aoi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#up42.draw_aoi()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select the workflow parameters\n",
    "\n",
    "There are also multiple ways to construct the workflow input parameters, you can:\n",
    "- Provide the parameters directly in code as a json string.\n",
    "- Use .get_parameters_info() to get a an overview of all potential parameters for the selected workflow and information about the parameter defaults and ranges.\n",
    "- Use .get_input_parameters(aoi_type=\"bbox\", aoi_geometry=aoi) to construct the parameters with the provided aoi and all default parameters. Selecting the aoi_type is independent from the provided aoi, you can e.g. provide a irregular Polygon and still select aoi_type=\"bbox\", then the bounding box of the polygon will be selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "workflow.get_parameters_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_parameters = workflow.construct_parameters(geometry=aoi, geometry_operation=\"bbox\", limit=1)\n",
    "\n",
    "# Further update the input_parameters manually\n",
    "input_parameters[\"sentinelhub-s2-aoiclipped:1\"].update({\"max_cloud_cover\":10})\n",
    "input_parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Price estimation & Test Job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "workflow.estimate_job(input_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run a test job to query data availability and check the configuration.\n",
    "test_job = workflow.test_job(input_parameters=input_parameters, track_status=True)\n",
    "test_results = test_job.get_results_json()\n",
    "print(test_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the workflow & download results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run the actual job.\n",
    "job = workflow.run_job(input_parameters=input_parameters, track_status=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p align=\"center\">\n",
    "    <img src=\"_assets/job_running.png\" width=\"700\">\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download & Display results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download job result (default downloads to Desktop). Only works after download is finished.\n",
    "results_fp = job.download_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "job.plot_results(figsize=(6,6), bands=[1], cmap=\"YlOrBr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#job.map_results(bands=[1])"
   ]
  }
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