02_data_prep.ipynb

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    "# MLOps for RSEs - 2. Data Preparation and Exploration\n",
    "\n",
    "The first part of a machine learning project is to produce a dataset that can be used to train and optimise one or more machine learning models. In many ways this part is the most familiar for RSEs as datasets are gathered and processed routinely for almost all research projects rather than being a particular requirement for ML projects. There are some challenges in creating a *good* dataset for use in an ML project. This notebook will look at what the requirements for the data pipeline are that RSEs will need to support and some tools and practices that can make this process easier. \n",
    "\n",
    "![Met Office Logo](https://www.metoffice.gov.uk/webfiles/1661941781161/images/icons/social-icons/default_card_315.jpg)\n"
   ]
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    "## Building a data preparation pipeline\n",
    "\n",
    "Exactly what a data preparation pipeline will look like is of course very problem and domain dependant. Of all the parts of a machine learning project, this is the part that is most likely to include \"domain specific\" tools, that is tools that are specific to the particular area of research covered by the project, rather than generic data science or machine learning tools that will be used in most of the rest of an ML project. The key parts of a pipeline include:\n",
    "\n",
    "* Data Gathering - The first step is to get the data together that will be used to create the final output. The data might be coming from public datasets, generated by a simulation, gathered from observation (either by you or some one else), scraped from the web or many other possible locations. Many ML projects involve combining data from different sources. Some times the data are easy to use together, other times a lot of subsequent processing will be required for the data to be *analysis-ready*, or more specifically *machine learning-ready*.\n",
    "  * Distributed processing - In some cases you may like to do some of the subsequent processing steps in a distributed fashion wherever the data is located rather than first gathering the data in a single location, particular where the processing involves subsetting and reduction that result in a much smaller output than input. Generally data transfer is expensive, so a good practice is to *take the compute to the data*. For example if data is located on a particular server and you need to extract data for a particular temporal and spatial location, it would be preferable to do that on the server if possible and then only copy across the data to be used to the central processing location for the project.\n",
    "* Cleaning - Removing bad data or infilling missing data. Homogenising representations of data e.g. names of categories.\n",
    "* Subsetting - Select the subset of interest for training your machine learning model.\n",
    "* Transforming - Change the representation of your data to be consistent across data sources e.g. representing geospatial data on the same projection and grid.\n",
    "* Calculating derived fields - A feature that combines various fields from raw data may be a better predictor, so such fields are typically calculated as part of the data preparation.\n",
    "* Merging - Many machine learning projects bring together data from several sources which once the previous steps have been performed are merged into a single coherent dataset.\n",
    "* Storing - Once the dataset has been created, it needs to be stored somewhere for reuse, ideally according to FAIR principles and somewhere access if efficient.\n",
    "* Cataloging and Documenting - Add your dataset to a catalog or other collection of datasets to enable ease of find and (re)use.\n",
    "* Exploring and Visualising - Enable researchers to explore the dataset through visualiations and summary statistics."
   ]
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    "### Key Principles\n",
    "* *reusability* - Create data processing components that are well engineered and can be reused in multiple pipelines.\n",
    "* *scalability* - Be able to easily run processing workflows with whatever size dataset is required by the research problem.\n",
    "* *interactivity* -  Enable research to interact with and explore"
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    "### Key Tasks for RSEs\n",
    "* Setting up scalable infrastructure for running pipeline on large data\n",
    "* Ensuring datasets created through pipeline are producible\n",
    "* Informing design to ensure processing components are reusable\n",
    "* Ensuring good management, documentation and testing of pipeline code\n",
    "* Setting up infrastructure for sharing data"
   ]
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    "### Key Terms\n",
    "* *FAIR* or *Findable, Accessible, Interoperable, Reusable* - Refers to key principles for sharing assets, egenrally, origininally data, but now also encompassing code, machine learning models and research objects generally. To effectively There amany definitions but a useful one from USGS is that analaysis ready data is *share them they follow these principles (taken from [go-fair.org](https://www.go-fair.org/fair-principles/)) \n",
    "  * Findable - The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the process of making assets FAIR.\n",
    "  * Accessible - Once the user finds the required data, she/he/they need to know how they can be accessed, possibly including authentication and authorisation.\n",
    "  * Interoperable - The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.\n",
    "  * Reusable - The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.\n",
    "* *ARCO data* or *Analysis-ready, cloud-optimised data* - A way of secribing data that is as easy to use as possible for a variety of analsysis applications and stored in such a way that access patterns typical of cloud computing can be executed effficiently for the specific dataset.\n",
    "    * Analysis-ready data - There amany definitions but a useful one from USGS is that aim of analaysis ready data is to make data *more accessible and easier to analyze, and reduce the amount of time users spend on data processing for ... analysis.* [source - USGS](https://www.usgs.gov/faqs/what-are-us-landsat-analysis-ready-data-ard).\n",
    "    * Cloud optmised - to take advantage of the compute available on demand on the cloud, generally requires a workflow that includes parallel execution. To enable this for data loading, the data eeds to be stored on disk in such a way that parallel reads and writes are not constrained by the way the data is stored.\n",
    "* pipeline - A series of tasks or operations, preferably based on reusable components, where the input of one step in the pipeline is the output of the previous step.\n",
    "* workflow - similar to a pipeline as a series of tasks, but there may be multiple consecutive or parallel pipelines in a workflow. (Definitions may differ!)\n",
    "* catalog - A machine readbale document describing a dataset or collection of datasets that enable one to load a dataset or subset directly into memory, abstracting away the machanics of storage such as handling of files."
   ]
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    "### Key Tools\n",
    "\n",
    "Many of the steps will be making use of the domain knowledge of researchers in project (e.g. ecologists, doctors, economists, psychologists etc.) and will need to make use of domain specific tools to handle the particular sorts of data. There are also many generic components, particularly around infrastructure, that can support the use of the domain specific tools:\n",
    "* workflow management and scaling (dask, ray)\n",
    "* environment management (conda, docker)\n",
    "* visualisation (matplotlib, holoviz)\n",
    "* catalog (intake)\n"
   ]
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    "### Running this notebook\n",
    "This notebook should run from a conda environment created with the [requirements_data_prep.yml file](requirements_data_prep.yml). See the [readme file](https://github.com/informatics-lab/ukrse_2022_mlops_walkthrough/blob/main/README.md) for info on how to set up a conda environment for using this notebook."
   ]
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    "## Example Problem -  Predicting impactful turbulent wind gusts\n",
    "\n",
    "The first dataset we will explore comes from a challenge by a Met Office Chief Operational Meteorologist. In certain locations around the world turbulent lee wave gusts called *rotors* can cause problems for aviation. These are localised, topographically-driven, turbulent wind gusts and where these occur at an airfield make conditions sufficiently hazardous so as to cause operations to be suspended. Rotors therefore impact the aviation industry, in particular where flights have to be diverted for considerable distances from isolated airfields.  \n",
    "\n",
    "What is needed is a prediction of whether such rotors are likely in a given time window, so that flight can be postponed or cancelled rather than risking the flight and turning back wasting fuel. As these are small scale features, they are not explicitly resolved in coarse resolution global models. Human OpMets are able to look at this model data and determine whether rotors are likely, but this is error-prone and time consuming. Can we train a machine learning algorithm to act as a tool to help OpMets with this process and provide a second opinion? In particular, a machine learning solution that can easily look at multiple model outputs (ensemble output, different forecast centres e.g. ECMWF).\n",
    "\n",
    "The target is a human-created dataset produced by Operational Meteorologists at the airfield, as to whether a rotor occurred in any given three hour period.\n",
    "\n",
    "![Lee Wave Rotors Diagram](rotors_diagram.png)\n",
    "\n",
    "[Met Office Youtube video explaining rotors](https://www.youtube.com/watch?v=jgSZG9SqN_s)"
   ]
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    "## Example 1 - Exploring and preparing Wind Rotors data\n",
    "\n",
    "We will start by exploring the wind rotors data set, and cleaning and preparing the data for machine learning as described above. First we will load in the \"raw\" dataset into a data frame. In this particular instance we won't use the standard library for dataframes *pandas*, instead we will use the dataframe implementation supplied dask. This supports *lazy loading* natively, so instead of loading our dataset that may be too large to fit in memory, or may just require waiting a long time for it to load, it reads only the *metadata* which tells it enough to construct a stub data structure and plan the computations. Any subsequent calculation will similarly not actually be executed when called, but rather continue the *lazy execution* paradigm. This creates a *task graph* which represents the chain of tasks in our workflow. \n",
    "\n",
    "When we have constructed the graph representing the workflow, we can then trigger execution of all the steps. An additional advantage is that we can send the workflow to another, potentially shared remote, platform from our interactive compute (e.g. local laptop, notebooks server) which may have more substantial resources (more memory, many CPUs, specialised processors like GPUs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "32d81711-60f1-4996-b808-f24760cbc982",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib\n",
    "import os\n",
    "import functools\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "72aa9221-d29c-4ee4-81d9-1304537d3354",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ddef7864-09de-4d14-8a0e-aa66b1cac6d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5fb8d33b-be41-4a20-b636-9395f6c3606d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask\n",
    "import dask.dataframe\n",
    "import dask.distributed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28f4e509-8033-4373-b78b-d4dfdb29a524",
   "metadata": {},
   "source": [
    "The data is available from Zenodo. See the [readme file](README.md) file for details. You can download the data into the default location of `~/data/ukrse2022` or you can specify the location in the environment variable `${RSE22_ROOT_DATA_DIR}`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "73805561-5820-4b3b-b856-cb359b84ffc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using default path\n",
      "True\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/h01/shaddad/data/ukrse2022')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "try:\n",
    "    rse_root_data_dir = pathlib.Path(os.environ['RSE22_ROOT_DATA_DIR'])\n",
    "    print('reading from environment variable')\n",
    "except KeyError as ke1:\n",
    "    rse_root_data_dir = pathlib.Path(os.environ['HOME'])  / 'data' / 'ukrse2022'\n",
    "    print('using default path')\n",
    "print(rse_root_data_dir.is_dir())\n",
    "rse_root_data_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3b8688c7-7db6-4b4d-94bd-fd0c504e6815",
   "metadata": {},
   "outputs": [],
   "source": [
    "load_from_zenodo=False\n",
    "zenodo_record_root = 'https://zenodo.org/record/7022648/files/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "63754eaa-add1-4269-9100-0abe550ada99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('https:/zenodo.org/record/7022648/files/2021_met_office_aviation_rotors.csv')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pathlib.Path(f'{zenodo_record_root}') / '2021_met_office_aviation_rotors.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f67317ad-7819-406e-ba53-e9cbbd0388c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/h01/shaddad/data/ukrse2022/2021_met_office_aviation_rotors.csv')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rotors_fname = '2021_met_office_aviation_rotors.csv'\n",
    "if load_from_zenodo:\n",
    "    rotors_path = f'{zenodo_record_root}/{rotors_fname}'\n",
    "else:\n",
    "    rotors_path = rse_root_data_dir / rotors_fname\n",
    "    print(rotors_path.is_file())\n",
    "rotors_path"
   ]
  },
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   "cell_type": "markdown",
   "id": "30a0c6a8-de8b-4483-a3f4-b0decb2ba4fb",
   "metadata": {
    "tags": []
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   "source": [
    "### Set up the cluster - Dask\n",
    "\n",
    "For this example we are using a simple local cluster. This is the only part of the code that would need to change to execute the subsequent code on a large cluster or cloud based cluster (in theory at least, there are often additional steps needed around python environments, which are getting easier as the tools mature).\n",
    "\n",
    "There are two steps in. this part, firstly initiating the cluster, then creating a client to access the cluster. In a complicated separate, the cluster set up step is likely to happen elsewhere through a command line interface, or together with a container or cloud orchestration software like [kubernetes](https://kubernetes.io/) or [docker swarm](https://docs.docker.com/engine/swarm/)."
   ]
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   "cell_type": "code",
   "execution_count": 9,
   "id": "706bb929-93ee-4789-ab90-3fc276b6f5ac",
   "metadata": {},
   "outputs": [
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       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">53caabff</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Workers:</strong> 4\n",
       "                </td>\n",
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       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total threads:</strong> 4\n",
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       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total memory:</strong> 7.62 GiB\n",
       "                </td>\n",
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       "            \n",
       "            <tr>\n",
       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
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       "        <details>\n",
       "            <summary style=\"margin-bottom: 20px;\">\n",
       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
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       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
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       "            <table style=\"width: 100%; text-align: left;\">\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Comm:</strong> tcp://127.0.0.1:44328\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Workers:</strong> 4\n",
       "                    </td>\n",
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       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total threads:</strong> 4\n",
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       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Started:</strong> Just now\n",
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       "                        <strong>Total memory:</strong> 7.62 GiB\n",
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       "\n",
       "    <details style=\"margin-left: 48px;\">\n",
       "        <summary style=\"margin-bottom: 20px;\">\n",
       "            <h3 style=\"display: inline;\">Workers</h3>\n",
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       "\n",
       "        \n",
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       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:41050\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:35084/status\" target=\"_blank\">http://127.0.0.1:35084/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 1.90 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:38778\n",
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       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /var/tmp/dask-worker-space/worker-t1wkncfh\n",
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       "\n",
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       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:38295\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:42920/status\" target=\"_blank\">http://127.0.0.1:42920/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 1.90 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:44150\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /var/tmp/dask-worker-space/worker-kb1is369\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:41309\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:44843/status\" target=\"_blank\">http://127.0.0.1:44843/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 1.90 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41971\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /var/tmp/dask-worker-space/worker-98vnmxkp\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:35068\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:34008/status\" target=\"_blank\">http://127.0.0.1:34008/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 1.90 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34546\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /var/tmp/dask-worker-space/worker-0on38yip\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "\n",
       "    </details>\n",
       "</div>\n",
       "\n",
       "        </details>\n",
       "    </div>\n",
       "</div>"
      ],
      "text/plain": [
       "LocalCluster(53caabff, 'tcp://127.0.0.1:44328', workers=4, threads=4, memory=7.62 GiB)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "local_cluster = dask.distributed.LocalCluster(n_workers=4)\n",
    "local_cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bdb88a58-70a8-4f0d-af8e-cbbc40e0e63f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-8f340975-9daa-11ed-bd06-0050569aca66</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "\n",
       "        <tr>\n",
       "        \n",
       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
       "        \n",
       "        </tr>\n",
       "\n",
       "        \n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\"></td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        </table>\n",
       "\n",
       "        \n",
       "            <button style=\"margin-bottom: 12px;\" data-commandlinker-command=\"dask:populate-and-launch-layout\" data-commandlinker-args='{\"url\": \"http://127.0.0.1:8787/status\" }'>\n",
       "                Launch dashboard in JupyterLab\n",
       "            </button>\n",
       "        \n",
       "\n",
       "        \n",
       "            <details>\n",
       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
       "    </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">53caabff</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Workers:</strong> 4\n",
       "                </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total threads:</strong> 4\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total memory:</strong> 7.62 GiB\n",
       "                </td>\n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
       "</tr>\n",
       "\n",
       "            \n",
       "        </table>\n",
       "\n",
       "        <details>\n",
       "            <summary style=\"margin-bottom: 20px;\">\n",
       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
       "            </summary>\n",
       "\n",
       "            <div style=\"\">\n",
       "    <div>\n",
       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
       "        <div style=\"margin-left: 48px;\">\n",
       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-d4f1cd9b-b1f1-4049-ad6f-11583a8e1777</p>\n",
       "            <table style=\"width: 100%; text-align: left;\">\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Comm:</strong> tcp://127.0.0.1:44328\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Workers:</strong> 4\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total threads:</strong> 4\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Started:</strong> Just now\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total memory:</strong> 7.62 GiB\n",
       "                    </td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "\n",
       "    <details style=\"margin-left: 48px;\">\n",
       "        <summary style=\"margin-bottom: 20px;\">\n",
       "            <h3 style=\"display: inline;\">Workers</h3>\n",
       "        </summary>\n",
       "\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:41050\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:35084/status\" target=\"_blank\">http://127.0.0.1:35084/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 1.90 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:38778\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /var/tmp/dask-worker-space/worker-t1wkncfh\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:38295\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:42920/status\" target=\"_blank\">http://127.0.0.1:42920/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 1.90 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:44150\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /var/tmp/dask-worker-space/worker-kb1is369\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:41309\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:44843/status\" target=\"_blank\">http://127.0.0.1:44843/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 1.90 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41971\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /var/tmp/dask-worker-space/worker-98vnmxkp\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:35068\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:34008/status\" target=\"_blank\">http://127.0.0.1:34008/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 1.90 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:34546\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /var/tmp/dask-worker-space/worker-0on38yip\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "\n",
       "    </details>\n",
       "</div>\n",
       "\n",
       "        </details>\n",
       "    </div>\n",
       "</div>\n",
       "            </details>\n",
       "        \n",
       "\n",
       "    </div>\n",
       "</div>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:44328' processes=4 threads=4, memory=7.62 GiB>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dask_client = dask.distributed.Client(local_cluster)\n",
    "dask_client"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53b696fa-949a-4129-82ee-d74bb684043f",
   "metadata": {},
   "source": [
    "### Prepare and execute data reading tasks\n",
    "\n",
    "Now that we have prepared the cluster for our tasks, we can set up workflow through a series lazy execution function calls. The code looks exactly the same as if the computation were to happen immediately, but instead if we look at the returned objects, we don't see the actual data objects, but *delayed* proxy objects which contain the data required for executing the workflow tasks when requested to do so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9e7f575f-8fe0-4e5e-8ad0-79563c67fa4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/h01/shaddad/data/ukrse2022/2021_met_office_aviation_rotors.csv')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rotors_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fc3954b1-5f68-4a2c-bff1-c65831d98a66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><strong>Dask DataFrame Structure:</strong></div>\n",
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DTG</th>\n",
       "      <th>air_temp_obs</th>\n",
       "      <th>dewpoint_obs</th>\n",
       "      <th>wind_direction_obs</th>\n",
       "      <th>wind_speed_obs</th>\n",
       "      <th>wind_gust_obs</th>\n",
       "      <th>air_temp_1</th>\n",
       "      <th>air_temp_2</th>\n",
       "      <th>air_temp_3</th>\n",
       "      <th>air_temp_4</th>\n",
       "      <th>air_temp_5</th>\n",
       "      <th>air_temp_6</th>\n",
       "      <th>air_temp_7</th>\n",
       "      <th>air_temp_8</th>\n",
       "      <th>air_temp_9</th>\n",
       "      <th>air_temp_10</th>\n",
       "      <th>air_temp_11</th>\n",
       "      <th>air_temp_12</th>\n",
       "      <th>air_temp_13</th>\n",
       "      <th>air_temp_14</th>\n",
       "      <th>air_temp_15</th>\n",
       "      <th>air_temp_16</th>\n",
       "      <th>air_temp_17</th>\n",
       "      <th>air_temp_18</th>\n",
       "      <th>air_temp_19</th>\n",
       "      <th>air_temp_20</th>\n",
       "      <th>air_temp_21</th>\n",
       "      <th>air_temp_22</th>\n",
       "      <th>sh_1</th>\n",
       "      <th>sh_2</th>\n",
       "      <th>sh_3</th>\n",
       "      <th>sh_4</th>\n",
       "      <th>sh_5</th>\n",
       "      <th>sh_6</th>\n",
       "      <th>sh_7</th>\n",
       "      <th>sh_8</th>\n",
       "      <th>sh_9</th>\n",
       "      <th>sh_10</th>\n",
       "      <th>sh_11</th>\n",
       "      <th>sh_12</th>\n",
       "      <th>sh_13</th>\n",
       "      <th>sh_14</th>\n",
       "      <th>sh_15</th>\n",
       "      <th>sh_16</th>\n",
       "      <th>sh_17</th>\n",
       "      <th>sh_18</th>\n",
       "      <th>sh_19</th>\n",
       "      <th>sh_20</th>\n",
       "      <th>sh_21</th>\n",
       "      <th>sh_22</th>\n",
       "      <th>winddir_1</th>\n",
       "      <th>windspd_1</th>\n",
       "      <th>winddir_2</th>\n",
       "      <th>windspd_2</th>\n",
       "      <th>winddir_3</th>\n",
       "      <th>windspd_3</th>\n",
       "      <th>winddir_4</th>\n",
       "      <th>windspd_4</th>\n",
       "      <th>winddir_5</th>\n",
       "      <th>windspd_5</th>\n",
       "      <th>winddir_6</th>\n",
       "      <th>windspd_6</th>\n",
       "      <th>winddir_7</th>\n",
       "      <th>windspd_7</th>\n",
       "      <th>winddir_8</th>\n",
       "      <th>windspd_8</th>\n",
       "      <th>winddir_9</th>\n",
       "      <th>windspd_9</th>\n",
       "      <th>winddir_10</th>\n",
       "      <th>windspd_10</th>\n",
       "      <th>winddir_11</th>\n",
       "      <th>windspd_11</th>\n",
       "      <th>winddir_12</th>\n",
       "      <th>windspd_12</th>\n",
       "      <th>winddir_13</th>\n",
       "      <th>windspd_13</th>\n",
       "      <th>winddir_14</th>\n",
       "      <th>windspd_14</th>\n",
       "      <th>winddir_15</th>\n",
       "      <th>windspd_15</th>\n",
       "      <th>winddir_16</th>\n",
       "      <th>windspd_16</th>\n",
       "      <th>winddir_17</th>\n",
       "      <th>windspd_17</th>\n",
       "      <th>winddir_18</th>\n",
       "      <th>windspd_18</th>\n",
       "      <th>winddir_19</th>\n",
       "      <th>windspd_19</th>\n",
       "      <th>winddir_20</th>\n",
       "      <th>windspd_20</th>\n",
       "      <th>winddir_21</th>\n",
       "      <th>windspd_21</th>\n",
       "      <th>winddir_22</th>\n",
       "      <th>windspd_22</th>\n",
       "      <th>Rotors 1 is true</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npartitions=1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<div>Dask Name: read-csv, 1 graph layer</div>"
      ],
      "text/plain": [
       "Dask DataFrame Structure:\n",
       "                  DTG air_temp_obs dewpoint_obs wind_direction_obs wind_speed_obs wind_gust_obs air_temp_1 air_temp_2 air_temp_3 air_temp_4 air_temp_5 air_temp_6 air_temp_7 air_temp_8 air_temp_9 air_temp_10 air_temp_11 air_temp_12 air_temp_13 air_temp_14 air_temp_15 air_temp_16 air_temp_17 air_temp_18 air_temp_19 air_temp_20 air_temp_21 air_temp_22     sh_1     sh_2     sh_3     sh_4     sh_5     sh_6     sh_7     sh_8     sh_9    sh_10    sh_11    sh_12    sh_13    sh_14    sh_15    sh_16    sh_17    sh_18    sh_19    sh_20    sh_21    sh_22 winddir_1 windspd_1 winddir_2 windspd_2 winddir_3 windspd_3 winddir_4 windspd_4 winddir_5 windspd_5 winddir_6 windspd_6 winddir_7 windspd_7 winddir_8 windspd_8 winddir_9 windspd_9 winddir_10 windspd_10 winddir_11 windspd_11 winddir_12 windspd_12 winddir_13 windspd_13 winddir_14 windspd_14 winddir_15 windspd_15 winddir_16 windspd_16 winddir_17 windspd_17 winddir_18 windspd_18 winddir_19 windspd_19 winddir_20 windspd_20 winddir_21 windspd_21 winddir_22 windspd_22 Rotors 1 is true\n",
       "npartitions=1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          \n",
       "               object      float64      float64            float64        float64       float64    float64    float64    float64    float64    float64    float64    float64    float64    float64     float64     float64     float64     float64     float64     float64     float64     float64     float64     float64     float64     float64     float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64   float64   float64   float64   float64   float64   float64   float64   float64   float64   float64   float64   float64   float64   float64   float64   float64   float64   float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64          float64\n",
       "                  ...          ...          ...                ...            ...           ...        ...        ...        ...        ...        ...        ...        ...        ...        ...         ...         ...         ...         ...         ...         ...         ...         ...         ...         ...         ...         ...         ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...              ...\n",
       "Dask Name: read-csv, 1 graph layer"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rotors_df = dask.dataframe.read_csv(rotors_path)\n",
    "rotors_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e6082abb-89d4-43cf-a1ad-ae005cbdcffe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DTG', 'air_temp_obs', 'dewpoint_obs', 'wind_direction_obs',\n",
       "       'wind_speed_obs', 'wind_gust_obs', 'air_temp_1', 'air_temp_2',\n",
       "       'air_temp_3', 'air_temp_4', 'air_temp_5', 'air_temp_6', 'air_temp_7',\n",
       "       'air_temp_8', 'air_temp_9', 'air_temp_10', 'air_temp_11', 'air_temp_12',\n",
       "       'air_temp_13', 'air_temp_14', 'air_temp_15', 'air_temp_16',\n",
       "       'air_temp_17', 'air_temp_18', 'air_temp_19', 'air_temp_20',\n",
       "       'air_temp_21', 'air_temp_22', 'sh_1', 'sh_2', 'sh_3', 'sh_4', 'sh_5',\n",
       "       'sh_6', 'sh_7', 'sh_8', 'sh_9', 'sh_10', 'sh_11', 'sh_12', 'sh_13',\n",
       "       'sh_14', 'sh_15', 'sh_16', 'sh_17', 'sh_18', 'sh_19', 'sh_20', 'sh_21',\n",
       "       'sh_22', 'winddir_1', 'windspd_1', 'winddir_2', 'windspd_2',\n",
       "       'winddir_3', 'windspd_3', 'winddir_4', 'windspd_4', 'winddir_5',\n",
       "       'windspd_5', 'winddir_6', 'windspd_6', 'winddir_7', 'windspd_7',\n",
       "       'winddir_8', 'windspd_8', 'winddir_9', 'windspd_9', 'winddir_10',\n",
       "       'windspd_10', 'winddir_11', 'windspd_11', 'winddir_12', 'windspd_12',\n",
       "       'winddir_13', 'windspd_13', 'winddir_14', 'windspd_14', 'winddir_15',\n",
       "       'windspd_15', 'winddir_16', 'windspd_16', 'winddir_17', 'windspd_17',\n",
       "       'winddir_18', 'windspd_18', 'winddir_19', 'windspd_19', 'winddir_20',\n",
       "       'windspd_20', 'winddir_21', 'windspd_21', 'winddir_22', 'windspd_22',\n",
       "       'Rotors 1 is true'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rotors_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a40d8bf1-b2ca-498e-a1a5-399dce6d382b",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    x = rotors_df['rotors_present']\n",
    "except KeyError:\n",
    "    rot_df = rotors_df.compute()\n",
    "    rot_df = rot_df.rename(columns={'Rotors 1 is true' : 'rotors_present'})\n",
    "    rot_df['rotors_present'].fillna(value=0.0, inplace=True)\n",
    "    os.remove(rotors_path)\n",
    "    rot_df.to_csv(rotors_path)\n",
    "    rotors_df = dask.dataframe.read_csv(rotors_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5945b6c3-a60d-4c3c-b44b-97e50882a431",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_feature_name = 'rotors_present'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f56234e9-1062-40fc-893d-861ed205d63d",
   "metadata": {},
   "outputs": [],
   "source": [
    "rotors_df  = rotors_df [(rotors_df ['wind_speed_obs'] >= 0.0) &\n",
    "                            (rotors_df ['air_temp_obs'] >= 0.0) &\n",
    "                            (rotors_df ['wind_direction_obs'] >= 0.0) &\n",
    "                            (rotors_df ['dewpoint_obs'] >= 0.0) \n",
    "                           ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "55c94c1b-1d8a-45cf-b9c9-065e023bb268",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <div>\n",
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       "            <svg width=\"76\" height=\"71\" viewBox=\"0 0 76 71\" fill=\"none\" xmlns=\"http://www.w3.org/2000/svg\">\n",
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       "            </svg>\n",
       "        </div>\n",
       "        <div style=\"margin-left: 64px;\">\n",
       "            <h3 style=\"margin-bottom: 0px;\">HighLevelGraph</h3>\n",
       "            <p style=\"color: var(--jp-ui-font-color2, #5D5851); margin-bottom:0px;\">\n",
       "                HighLevelGraph with 13 layers and 13 keys from all layers.\n",
       "            </p>\n",
       "            \n",
       "            <div style=\"\">\n",
       "    <svg width=\"24\" height=\"24\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http://www.w3.org/2000/svg\" style=\"position: absolute;\">\n",
       "        \n",
       "        <circle cx=\"16\" cy=\"16\" r=\"14\" style=\"stroke: var(--jp-ui-font-color2, #1D1D1D); fill: var(--jp-layout-color1, #F2F2F2);\" stroke-width=\"2\" />\n",
       "        \n",
       "    </svg>\n",
       "\n",
       "    <details style=\"margin-left: 32px;\">\n",
       "        <summary style=\"margin-bottom: 10px; margin-top: 10px;\">\n",
       "            <h4 style=\"display: inline;\">Layer1: read-csv</h4>\n",
       "        </summary>\n",
       "        <p style=\"color: var(--jp-ui-font-color2, #5D5851); margin: -0.25em 0px 0px 0px;\">\n",
       "            read-csv-43f4e8a9dd0c97420d7f2e17366e685a\n",
       "        </p>\n",
       "\n",
       "        <table>\n",
       "        <tr>\n",
       "            <td>\n",
       "                <table>\n",
       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">layer_type</th>\n",
       "                        <td style=\"text-align: left;\">DataFrameIOLayer</td>\n",
       "                    </tr>\n",
       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">is_materialized</th>\n",
       "                        <td style=\"text-align: left;\">False</td>\n",
       "                    </tr>\n",
       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">number of outputs</th>\n",
       "                        <td style=\"text-align: left;\">1</td>\n",
       "                    </tr>\n",
       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">npartitions</th>\n",
       "                        <td style=\"text-align: left;\">1</td>\n",
       "                    </tr>\n",
       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">columns</th>\n",
       "                        <td style=\"text-align: left;\">[&#x27;Unnamed: 0&#x27;, &#x27;DTG&#x27;, &#x27;air_temp_obs&#x27;, &#x27;dewpoint_obs&#x27;, &#x27;wind_direction_obs&#x27;, &#x27;wind_speed_obs&#x27;, &#x27;wind_gust_obs&#x27;, &#x27;air_temp_1&#x27;, &#x27;air_temp_2&#x27;, &#x27;air_temp_3&#x27;, &#x27;air_temp_4&#x27;, &#x27;air_temp_5&#x27;, &#x27;air_temp_6&#x27;, &#x27;air_temp_7&#x27;, &#x27;air_temp_8&#x27;, &#x27;air_temp_9&#x27;, &#x27;air_temp_10&#x27;, &#x27;air_temp_11&#x27;, &#x27;air_temp_12&#x27;, &#x27;air_temp_13&#x27;, &#x27;air_temp_14&#x27;, &#x27;air_temp_15&#x27;, &#x27;air_temp_16&#x27;, &#x27;air_temp_17&#x27;, &#x27;air_temp_18&#x27;, &#x27;air_temp_19&#x27;, &#x27;air_temp_20&#x27;, &#x27;air_temp_21&#x27;, &#x27;air_temp_22&#x27;, &#x27;sh_1&#x27;, &#x27;sh_2&#x27;, &#x27;sh_3&#x27;, &#x27;sh_4&#x27;, &#x27;sh_5&#x27;, &#x27;sh_6&#x27;, &#x27;sh_7&#x27;, &#x27;sh_8&#x27;, &#x27;sh_9&#x27;, &#x27;sh_10&#x27;, &#x27;sh_11&#x27;, &#x27;sh_12&#x27;, &#x27;sh_13&#x27;, &#x27;sh_14&#x27;, &#x27;sh_15&#x27;, &#x27;sh_16&#x27;, &#x27;sh_17&#x27;, &#x27;sh_18&#x27;, &#x27;sh_19&#x27;, &#x27;sh_20&#x27;, &#x27;sh_21&#x27;, &#x27;sh_22&#x27;, &#x27;winddir_1&#x27;, &#x27;windspd_1&#x27;, &#x27;winddir_2&#x27;, &#x27;windspd_2&#x27;, &#x27;winddir_3&#x27;, &#x27;windspd_3&#x27;, &#x27;winddir_4&#x27;, &#x27;windspd_4&#x27;, &#x27;winddir_5&#x27;, &#x27;windspd_5&#x27;, &#x27;winddir_6&#x27;, &#x27;windspd_6&#x27;, &#x27;winddir_7&#x27;, &#x27;windspd_7&#x27;, &#x27;winddir_8&#x27;, &#x27;windspd_8&#x27;, &#x27;winddir_9&#x27;, &#x27;windspd_9&#x27;, &#x27;winddir_10&#x27;, &#x27;windspd_10&#x27;, &#x27;winddir_11&#x27;, &#x27;windspd_11&#x27;, &#x27;winddir_12&#x27;, &#x27;windspd_12&#x27;, &#x27;winddir_13&#x27;, &#x27;windspd_13&#x27;, &#x27;winddir_14&#x27;, &#x27;windspd_14&#x27;, &#x27;winddir_15&#x27;, &#x27;windspd_15&#x27;, &#x27;winddir_16&#x27;, &#x27;windspd_16&#x27;, &#x27;winddir_17&#x27;, &#x27;windspd_17&#x27;, &#x27;winddir_18&#x27;, &#x27;windspd_18&#x27;, &#x27;winddir_19&#x27;, &#x27;windspd_19&#x27;, &#x27;winddir_20&#x27;, &#x27;windspd_20&#x27;, &#x27;winddir_21&#x27;, &#x27;windspd_21&#x27;, &#x27;winddir_22&#x27;, &#x27;windspd_22&#x27;, &#x27;rotors_present&#x27;]</td>\n",
       "                    </tr>\n",
       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">type</th>\n",
       "                        <td style=\"text-align: left;\">dask.dataframe.core.DataFrame</td>\n",
       "                    </tr>\n",
       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">dataframe_type</th>\n",
       "                        <td style=\"text-align: left;\">pandas.core.frame.DataFrame</td>\n",
       "                    </tr>\n",
       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">series_dtypes</th>\n",
       "                        <td style=\"text-align: left;\">{&#x27;Unnamed: 0&#x27;: dtype(&#x27;int64&#x27;), &#x27;DTG&#x27;: dtype(&#x27;O&#x27;), &#x27;air_temp_obs&#x27;: dtype(&#x27;float64&#x27;), &#x27;dewpoint_obs&#x27;: dtype(&#x27;float64&#x27;), &#x27;wind_direction_obs&#x27;: dtype(&#x27;float64&#x27;), &#x27;wind_speed_obs&#x27;: dtype(&#x27;float64&#x27;), &#x27;wind_gust_obs&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_1&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_2&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_3&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_4&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_5&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_6&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_7&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_8&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_9&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_10&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_11&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_12&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_13&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_14&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_15&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_16&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_17&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_18&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_19&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_20&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_21&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_22&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_1&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_2&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_3&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_4&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_5&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_6&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_7&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_8&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_9&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_10&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_11&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_12&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_13&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_14&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_15&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_16&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_17&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_18&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_19&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_20&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_21&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_22&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_1&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_1&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_2&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_2&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_3&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_3&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_4&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_4&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_5&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_5&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_6&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_6&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_7&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_7&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_8&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_8&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_9&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_9&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_10&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_10&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_11&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_11&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_12&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_12&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_13&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_13&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_14&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_14&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_15&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_15&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_16&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_16&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_17&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_17&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_18&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_18&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_19&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_19&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_20&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_20&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_21&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_21&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_22&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_22&#x27;: dtype(&#x27;float64&#x27;), &#x27;rotors_present&#x27;: dtype(&#x27;float64&#x27;)}</td>\n",
       "                    </tr>\n",
       "                \n",
       "                \n",
       "                </table>\n",
       "            </td>\n",
       "            <td>\n",
       "                \n",
       "            </td>\n",
       "        </tr>\n",
       "        </table>\n",
       "\n",
       "    </details>\n",
       "</div>\n",
       "            \n",
       "            <div style=\"\">\n",
       "    <svg width=\"24\" height=\"24\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http://www.w3.org/2000/svg\" style=\"position: absolute;\">\n",
       "        \n",
       "        <circle cx=\"16\" cy=\"16\" r=\"14\" style=\"stroke: var(--jp-ui-font-color2, #1D1D1D); fill: var(--jp-layout-color1, #F2F2F2);\" stroke-width=\"2\" />\n",
       "        \n",
       "    </svg>\n",
       "\n",
       "    <details style=\"margin-left: 32px;\">\n",
       "        <summary style=\"margin-bottom: 10px; margin-top: 10px;\">\n",
       "            <h4 style=\"display: inline;\">Layer2: getitem</h4>\n",
       "        </summary>\n",
       "        <p style=\"color: var(--jp-ui-font-color2, #5D5851); margin: -0.25em 0px 0px 0px;\">\n",
       "            getitem-e8f95bef5b7dd61d9d8202ca275763a1\n",
       "        </p>\n",
       "\n",
       "        <table>\n",
       "        <tr>\n",
       "            <td>\n",
       "                <table>\n",
       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">layer_type</th>\n",
       "                        <td style=\"text-align: left;\">Blockwise</td>\n",
       "                    </tr>\n",
       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">is_materialized</th>\n",
       "                        <td style=\"text-align: left;\">False</td>\n",
       "                    </tr>\n",
       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">number of outputs</th>\n",
       "                        <td style=\"text-align: left;\">1</td>\n",
       "                    </tr>\n",
       "                \n",
       "                \n",
       "                    \n",
       "                        <tr>\n",
       "                            <th style=\"text-align: left; width: 150px;\"> depends on </th>\n",
       "                            <td style=\"text-align: left;\">read-csv-43f4e8a9dd0c97420d7f2e17366e685a</td>\n",
       "                        </tr>\n",
       "                    \n",
       "                \n",
       "                </table>\n",
       "            </td>\n",
       "            <td>\n",
       "                \n",
       "            </td>\n",
       "        </tr>\n",
       "        </table>\n",
       "\n",
       "    </details>\n",
       "</div>\n",
       "            \n",
       "            <div style=\"\">\n",
       "    <svg width=\"24\" height=\"24\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http://www.w3.org/2000/svg\" style=\"position: absolute;\">\n",
       "        \n",
       "        <circle cx=\"16\" cy=\"16\" r=\"14\" style=\"stroke: var(--jp-ui-font-color2, #1D1D1D); fill: var(--jp-layout-color1, #F2F2F2);\" stroke-width=\"2\" />\n",
       "        \n",
       "    </svg>\n",
       "\n",
       "    <details style=\"margin-left: 32px;\">\n",
       "        <summary style=\"margin-bottom: 10px; margin-top: 10px;\">\n",
       "            <h4 style=\"display: inline;\">Layer3: ge</h4>\n",
       "        </summary>\n",
       "        <p style=\"color: var(--jp-ui-font-color2, #5D5851); margin: -0.25em 0px 0px 0px;\">\n",
       "            ge-0116a63e7a5660c3ba6f4fb4b9d35098\n",
       "        </p>\n",
       "\n",
       "        <table>\n",
       "        <tr>\n",
       "            <td>\n",
       "                <table>\n",
       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">layer_type</th>\n",
       "                        <td style=\"text-align: left;\">Blockwise</td>\n",
       "                    </tr>\n",
       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">is_materialized</th>\n",
       "                        <td style=\"text-align: left;\">False</td>\n",
       "                    </tr>\n",
       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">number of outputs</th>\n",
       "                        <td style=\"text-align: left;\">1</td>\n",
       "                    </tr>\n",
       "                \n",
       "                \n",
       "                    \n",
       "                        <tr>\n",
       "                            <th style=\"text-align: left; width: 150px;\"> depends on </th>\n",
       "                            <td style=\"text-align: left;\">getitem-e8f95bef5b7dd61d9d8202ca275763a1</td>\n",
       "                        </tr>\n",
       "                    \n",
       "                \n",
       "                </table>\n",
       "            </td>\n",
       "            <td>\n",
       "                \n",
       "            </td>\n",
       "        </tr>\n",
       "        </table>\n",
       "\n",
       "    </details>\n",
       "</div>\n",
       "            \n",
       "            <div style=\"\">\n",
       "    <svg width=\"24\" height=\"24\" viewBox=\"0 0 32 32\" fill=\"none\" xmlns=\"http://www.w3.org/2000/svg\" style=\"position: absolute;\">\n",
       "        \n",
       "        <circle cx=\"16\" cy=\"16\" r=\"14\" style=\"stroke: var(--jp-ui-font-color2, #1D1D1D); fill: var(--jp-layout-color1, #F2F2F2);\" stroke-width=\"2\" />\n",
       "        \n",
       "    </svg>\n",
       "\n",
       "    <details style=\"margin-left: 32px;\">\n",
       "        <summary style=\"margin-bottom: 10px; margin-top: 10px;\">\n",
       "            <h4 style=\"display: inline;\">Layer4: getitem</h4>\n",
       "        </summary>\n",
       "        <p style=\"color: var(--jp-ui-font-color2, #5D5851); margin: -0.25em 0px 0px 0px;\">\n",
       "            getitem-fe143e5dbc6c569d19a864e2b3a29fcd\n",
       "        </p>\n",
       "\n",
       "        <table>\n",
       "        <tr>\n",
       "            <td>\n",
       "                <table>\n",
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       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">is_materialized</th>\n",
       "                        <td style=\"text-align: left;\">False</td>\n",
       "                    </tr>\n",
       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">number of outputs</th>\n",
       "                        <td style=\"text-align: left;\">1</td>\n",
       "                    </tr>\n",
       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">npartitions</th>\n",
       "                        <td style=\"text-align: left;\">1</td>\n",
       "                    </tr>\n",
       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">columns</th>\n",
       "                        <td style=\"text-align: left;\">[&#x27;Unnamed: 0&#x27;, &#x27;DTG&#x27;, &#x27;air_temp_obs&#x27;, &#x27;dewpoint_obs&#x27;, &#x27;wind_direction_obs&#x27;, &#x27;wind_speed_obs&#x27;, &#x27;wind_gust_obs&#x27;, &#x27;air_temp_1&#x27;, &#x27;air_temp_2&#x27;, &#x27;air_temp_3&#x27;, &#x27;air_temp_4&#x27;, &#x27;air_temp_5&#x27;, &#x27;air_temp_6&#x27;, &#x27;air_temp_7&#x27;, &#x27;air_temp_8&#x27;, &#x27;air_temp_9&#x27;, &#x27;air_temp_10&#x27;, &#x27;air_temp_11&#x27;, &#x27;air_temp_12&#x27;, &#x27;air_temp_13&#x27;, &#x27;air_temp_14&#x27;, &#x27;air_temp_15&#x27;, &#x27;air_temp_16&#x27;, &#x27;air_temp_17&#x27;, &#x27;air_temp_18&#x27;, &#x27;air_temp_19&#x27;, &#x27;air_temp_20&#x27;, &#x27;air_temp_21&#x27;, &#x27;air_temp_22&#x27;, &#x27;sh_1&#x27;, &#x27;sh_2&#x27;, &#x27;sh_3&#x27;, &#x27;sh_4&#x27;, &#x27;sh_5&#x27;, &#x27;sh_6&#x27;, &#x27;sh_7&#x27;, &#x27;sh_8&#x27;, &#x27;sh_9&#x27;, &#x27;sh_10&#x27;, &#x27;sh_11&#x27;, &#x27;sh_12&#x27;, &#x27;sh_13&#x27;, &#x27;sh_14&#x27;, &#x27;sh_15&#x27;, &#x27;sh_16&#x27;, &#x27;sh_17&#x27;, &#x27;sh_18&#x27;, &#x27;sh_19&#x27;, &#x27;sh_20&#x27;, &#x27;sh_21&#x27;, &#x27;sh_22&#x27;, &#x27;winddir_1&#x27;, &#x27;windspd_1&#x27;, &#x27;winddir_2&#x27;, &#x27;windspd_2&#x27;, &#x27;winddir_3&#x27;, &#x27;windspd_3&#x27;, &#x27;winddir_4&#x27;, &#x27;windspd_4&#x27;, &#x27;winddir_5&#x27;, &#x27;windspd_5&#x27;, &#x27;winddir_6&#x27;, &#x27;windspd_6&#x27;, &#x27;winddir_7&#x27;, &#x27;windspd_7&#x27;, &#x27;winddir_8&#x27;, &#x27;windspd_8&#x27;, &#x27;winddir_9&#x27;, &#x27;windspd_9&#x27;, &#x27;winddir_10&#x27;, &#x27;windspd_10&#x27;, &#x27;winddir_11&#x27;, &#x27;windspd_11&#x27;, &#x27;winddir_12&#x27;, &#x27;windspd_12&#x27;, &#x27;winddir_13&#x27;, &#x27;windspd_13&#x27;, &#x27;winddir_14&#x27;, &#x27;windspd_14&#x27;, &#x27;winddir_15&#x27;, &#x27;windspd_15&#x27;, &#x27;winddir_16&#x27;, &#x27;windspd_16&#x27;, &#x27;winddir_17&#x27;, &#x27;windspd_17&#x27;, &#x27;winddir_18&#x27;, &#x27;windspd_18&#x27;, &#x27;winddir_19&#x27;, &#x27;windspd_19&#x27;, &#x27;winddir_20&#x27;, &#x27;windspd_20&#x27;, &#x27;winddir_21&#x27;, &#x27;windspd_21&#x27;, &#x27;winddir_22&#x27;, &#x27;windspd_22&#x27;, &#x27;rotors_present&#x27;]</td>\n",
       "                    </tr>\n",
       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">type</th>\n",
       "                        <td style=\"text-align: left;\">dask.dataframe.core.DataFrame</td>\n",
       "                    </tr>\n",
       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">dataframe_type</th>\n",
       "                        <td style=\"text-align: left;\">pandas.core.frame.DataFrame</td>\n",
       "                    </tr>\n",
       "                \n",
       "                    <tr>\n",
       "                        <th style=\"text-align: left; width: 150px;\">series_dtypes</th>\n",
       "                        <td style=\"text-align: left;\">{&#x27;Unnamed: 0&#x27;: dtype(&#x27;int64&#x27;), &#x27;DTG&#x27;: dtype(&#x27;O&#x27;), &#x27;air_temp_obs&#x27;: dtype(&#x27;float64&#x27;), &#x27;dewpoint_obs&#x27;: dtype(&#x27;float64&#x27;), &#x27;wind_direction_obs&#x27;: dtype(&#x27;float64&#x27;), &#x27;wind_speed_obs&#x27;: dtype(&#x27;float64&#x27;), &#x27;wind_gust_obs&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_1&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_2&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_3&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_4&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_5&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_6&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_7&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_8&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_9&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_10&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_11&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_12&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_13&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_14&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_15&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_16&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_17&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_18&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_19&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_20&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_21&#x27;: dtype(&#x27;float64&#x27;), &#x27;air_temp_22&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_1&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_2&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_3&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_4&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_5&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_6&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_7&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_8&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_9&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_10&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_11&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_12&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_13&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_14&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_15&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_16&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_17&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_18&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_19&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_20&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_21&#x27;: dtype(&#x27;float64&#x27;), &#x27;sh_22&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_1&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_1&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_2&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_2&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_3&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_3&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_4&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_4&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_5&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_5&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_6&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_6&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_7&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_7&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_8&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_8&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_9&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_9&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_10&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_10&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_11&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_11&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_12&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_12&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_13&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_13&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_14&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_14&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_15&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_15&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_16&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_16&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_17&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_17&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_18&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_18&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_19&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_19&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_20&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_20&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_21&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_21&#x27;: dtype(&#x27;float64&#x27;), &#x27;winddir_22&#x27;: dtype(&#x27;float64&#x27;), &#x27;windspd_22&#x27;: dtype(&#x27;float64&#x27;), &#x27;rotors_present&#x27;: dtype(&#x27;float64&#x27;)}</td>\n",
       "                    </tr>\n",
       "                \n",
       "                \n",
       "                    \n",
       "                        <tr>\n",
       "                            <th style=\"text-align: left; width: 150px;\"> depends on </th>\n",
       "                            <td style=\"text-align: left;\">and_-7cb83bc9715c7321e7a68ce25681f91e</td>\n",
       "                        </tr>\n",
       "                    \n",
       "                \n",
       "                    \n",
       "                        <tr>\n",
       "                            <th style=\"text-align: left; width: 150px;\"></th>\n",
       "                            <td style=\"text-align: left;\">read-csv-43f4e8a9dd0c97420d7f2e17366e685a</td>\n",
       "                        </tr>\n",
       "                    \n",
       "                \n",
       "                </table>\n",
       "            </td>\n",
       "            <td>\n",
       "                \n",
       "            </td>\n",
       "        </tr>\n",
       "        </table>\n",
       "\n",
       "    </details>\n",
       "</div>\n",
       "            \n",
       "        </div>\n",
       "    </div>\n",
       "</div>"
      ],
      "text/plain": [
       "HighLevelGraph with 13 layers.\n",
       "<dask.highlevelgraph.HighLevelGraph object at 0x7fc4a410ca30>\n",
       " 0. read-csv-43f4e8a9dd0c97420d7f2e17366e685a\n",
       " 1. getitem-e8f95bef5b7dd61d9d8202ca275763a1\n",
       " 2. ge-0116a63e7a5660c3ba6f4fb4b9d35098\n",
       " 3. getitem-fe143e5dbc6c569d19a864e2b3a29fcd\n",
       " 4. ge-1237f621e0b19ba55765dbd23c6bb23c\n",
       " 5. getitem-5d6531e64dc21a605df2712e3677e1c4\n",
       " 6. ge-ecb9cd34dd879db3e3bb95dd329e6063\n",
       " 7. getitem-4249bad91a24ee162f88371aa8ae0c09\n",
       " 8. ge-a5b0dd23fe03cdbf33fcb3e72c0db5f2\n",
       " 9. and_-68e25bb6873185ce59d54dde9dd8c679\n",
       " 10. and_-be3969578951599553cb79cd6a8fb773\n",
       " 11. and_-7cb83bc9715c7321e7a68ce25681f91e\n",
       " 12. getitem-1b75be27ffbae1e67ca270f0feefa7fd"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rotors_df.dask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "66406cce-2550-4c77-a725-41bffc03f275",
   "metadata": {},
   "outputs": [],
   "source": [
    "rotors_df ['DTG'] = dask.dataframe.to_datetime(rotors_df['DTG'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "823b81d5-062e-475b-a44d-95fb760e891e",
   "metadata": {},
   "outputs": [],
   "source": [
    "rotors_df  = rotors_df .drop_duplicates(subset=['DTG'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "980a89fc-1cbf-4ba2-89cd-b46e38f4c692",
   "metadata": {},
   "outputs": [],
   "source": [
    "rotors_df  = rotors_df [~rotors_df['DTG'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "02c1c70b-b84b-4f99-b899-166475c5a781",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><strong>Dask DataFrame Structure:</strong></div>\n",
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>DTG</th>\n",
       "      <th>air_temp_obs</th>\n",
       "      <th>dewpoint_obs</th>\n",
       "      <th>wind_direction_obs</th>\n",
       "      <th>wind_speed_obs</th>\n",
       "      <th>wind_gust_obs</th>\n",
       "      <th>air_temp_1</th>\n",
       "      <th>air_temp_2</th>\n",
       "      <th>air_temp_3</th>\n",
       "      <th>air_temp_4</th>\n",
       "      <th>air_temp_5</th>\n",
       "      <th>air_temp_6</th>\n",
       "      <th>air_temp_7</th>\n",
       "      <th>air_temp_8</th>\n",
       "      <th>air_temp_9</th>\n",
       "      <th>air_temp_10</th>\n",
       "      <th>air_temp_11</th>\n",
       "      <th>air_temp_12</th>\n",
       "      <th>air_temp_13</th>\n",
       "      <th>air_temp_14</th>\n",
       "      <th>air_temp_15</th>\n",
       "      <th>air_temp_16</th>\n",
       "      <th>air_temp_17</th>\n",
       "      <th>air_temp_18</th>\n",
       "      <th>air_temp_19</th>\n",
       "      <th>air_temp_20</th>\n",
       "      <th>air_temp_21</th>\n",
       "      <th>air_temp_22</th>\n",
       "      <th>sh_1</th>\n",
       "      <th>sh_2</th>\n",
       "      <th>sh_3</th>\n",
       "      <th>sh_4</th>\n",
       "      <th>sh_5</th>\n",
       "      <th>sh_6</th>\n",
       "      <th>sh_7</th>\n",
       "      <th>sh_8</th>\n",
       "      <th>sh_9</th>\n",
       "      <th>sh_10</th>\n",
       "      <th>sh_11</th>\n",
       "      <th>sh_12</th>\n",
       "      <th>sh_13</th>\n",
       "      <th>sh_14</th>\n",
       "      <th>sh_15</th>\n",
       "      <th>sh_16</th>\n",
       "      <th>sh_17</th>\n",
       "      <th>sh_18</th>\n",
       "      <th>sh_19</th>\n",
       "      <th>sh_20</th>\n",
       "      <th>sh_21</th>\n",
       "      <th>sh_22</th>\n",
       "      <th>winddir_1</th>\n",
       "      <th>windspd_1</th>\n",
       "      <th>winddir_2</th>\n",
       "      <th>windspd_2</th>\n",
       "      <th>winddir_3</th>\n",
       "      <th>windspd_3</th>\n",
       "      <th>winddir_4</th>\n",
       "      <th>windspd_4</th>\n",
       "      <th>winddir_5</th>\n",
       "      <th>windspd_5</th>\n",
       "      <th>winddir_6</th>\n",
       "      <th>windspd_6</th>\n",
       "      <th>winddir_7</th>\n",
       "      <th>windspd_7</th>\n",
       "      <th>winddir_8</th>\n",
       "      <th>windspd_8</th>\n",
       "      <th>winddir_9</th>\n",
       "      <th>windspd_9</th>\n",
       "      <th>winddir_10</th>\n",
       "      <th>windspd_10</th>\n",
       "      <th>winddir_11</th>\n",
       "      <th>windspd_11</th>\n",
       "      <th>winddir_12</th>\n",
       "      <th>windspd_12</th>\n",
       "      <th>winddir_13</th>\n",
       "      <th>windspd_13</th>\n",
       "      <th>winddir_14</th>\n",
       "      <th>windspd_14</th>\n",
       "      <th>winddir_15</th>\n",
       "      <th>windspd_15</th>\n",
       "      <th>winddir_16</th>\n",
       "      <th>windspd_16</th>\n",
       "      <th>winddir_17</th>\n",
       "      <th>windspd_17</th>\n",
       "      <th>winddir_18</th>\n",
       "      <th>windspd_18</th>\n",
       "      <th>winddir_19</th>\n",
       "      <th>windspd_19</th>\n",
       "      <th>winddir_20</th>\n",
       "      <th>windspd_20</th>\n",
       "      <th>winddir_21</th>\n",
       "      <th>windspd_21</th>\n",
       "      <th>winddir_22</th>\n",
       "      <th>windspd_22</th>\n",
       "      <th>rotors_present</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npartitions=1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<div>Dask Name: getitem, 22 graph layers</div>"
      ],
      "text/plain": [
       "Dask DataFrame Structure:\n",
       "              Unnamed: 0             DTG air_temp_obs dewpoint_obs wind_direction_obs wind_speed_obs wind_gust_obs air_temp_1 air_temp_2 air_temp_3 air_temp_4 air_temp_5 air_temp_6 air_temp_7 air_temp_8 air_temp_9 air_temp_10 air_temp_11 air_temp_12 air_temp_13 air_temp_14 air_temp_15 air_temp_16 air_temp_17 air_temp_18 air_temp_19 air_temp_20 air_temp_21 air_temp_22     sh_1     sh_2     sh_3     sh_4     sh_5     sh_6     sh_7     sh_8     sh_9    sh_10    sh_11    sh_12    sh_13    sh_14    sh_15    sh_16    sh_17    sh_18    sh_19    sh_20    sh_21    sh_22 winddir_1 windspd_1 winddir_2 windspd_2 winddir_3 windspd_3 winddir_4 windspd_4 winddir_5 windspd_5 winddir_6 windspd_6 winddir_7 windspd_7 winddir_8 windspd_8 winddir_9 windspd_9 winddir_10 windspd_10 winddir_11 windspd_11 winddir_12 windspd_12 winddir_13 windspd_13 winddir_14 windspd_14 winddir_15 windspd_15 winddir_16 windspd_16 winddir_17 windspd_17 winddir_18 windspd_18 winddir_19 windspd_19 winddir_20 windspd_20 winddir_21 windspd_21 winddir_22 windspd_22 rotors_present\n",
       "npartitions=1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           \n",
       "                   int64  datetime64[ns]      float64      float64            float64        float64       float64    float64    float64    float64    float64    float64    float64    float64    float64    float64     float64     float64     float64     float64     float64     float64     float64     float64     float64     float64     float64     float64     float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64  float64   float64   float64   float64   float64   float64   float64   float64   float64   float64   float64   float64   float64   float64   float64   float64   float64   float64   float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64    float64        float64\n",
       "                     ...             ...          ...          ...                ...            ...           ...        ...        ...        ...        ...        ...        ...        ...        ...        ...         ...         ...         ...         ...         ...         ...         ...         ...         ...         ...         ...         ...         ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...      ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...       ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...        ...            ...\n",
       "Dask Name: getitem, 22 graph layers"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rotors_df_delayed = rotors_df\n",
    "rotors_df_delayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "990d8924-f554-4db8-b6b3-e8bbef59a1ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "no_rotors_df = rotors_df[rotors_df[target_feature_name] == False]\n",
    "rotors_present_df = rotors_df[rotors_df[target_feature_name] == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "772aff86-bddd-4a81-8339-1c003e3ffb0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((Delayed('int-1f2afb82-9c6d-4d57-8e34-533aeead8f39'), 96),\n",
       " (Delayed('int-223a45ea-7ba0-42b1-af4e-d4ef90720932'), 96))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_rotors_df.shape, rotors_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3208ed34-33ad-4f01-a905-64887daefe6c",
   "metadata": {},
   "source": [
    "To get the data, we have to execute the task graph on the cluster. We should now see something happen on the dask dashboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "322f873d-8089-466b-bf13-ef5e07f31174",
   "metadata": {},
   "outputs": [],
   "source": [
    "no_rotors_df = no_rotors_df.compute()\n",
    "rotors_present_df = rotors_present_df.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fcf48ad8-c8b4-4c9b-a83f-030d7c615835",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17037, 96), (449, 96))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_rotors_df.shape, rotors_present_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d53d7c9f-1984-4f84-abee-dfa14a058573",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>DTG</th>\n",
       "      <th>air_temp_obs</th>\n",
       "      <th>dewpoint_obs</th>\n",
       "      <th>wind_direction_obs</th>\n",
       "      <th>wind_speed_obs</th>\n",
       "      <th>wind_gust_obs</th>\n",
       "      <th>air_temp_1</th>\n",
       "      <th>air_temp_2</th>\n",
       "      <th>air_temp_3</th>\n",
       "      <th>...</th>\n",
       "      <th>windspd_18</th>\n",
       "      <th>winddir_19</th>\n",
       "      <th>windspd_19</th>\n",
       "      <th>winddir_20</th>\n",
       "      <th>windspd_20</th>\n",
       "      <th>winddir_21</th>\n",
       "      <th>windspd_21</th>\n",
       "      <th>winddir_22</th>\n",
       "      <th>windspd_22</th>\n",
       "      <th>rotors_present</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01 00:00:00</td>\n",
       "      <td>283.9</td>\n",
       "      <td>280.7</td>\n",
       "      <td>110.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.000</td>\n",
       "      <td>283.625</td>\n",
       "      <td>283.250</td>\n",
       "      <td>...</td>\n",
       "      <td>5.8</td>\n",
       "      <td>341.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>330.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-01-01 03:00:00</td>\n",
       "      <td>280.7</td>\n",
       "      <td>279.7</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>281.500</td>\n",
       "      <td>281.250</td>\n",
       "      <td>280.750</td>\n",
       "      <td>...</td>\n",
       "      <td>6.8</td>\n",
       "      <td>344.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>348.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2015-01-01 06:00:00</td>\n",
       "      <td>279.8</td>\n",
       "      <td>278.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.875</td>\n",
       "      <td>279.625</td>\n",
       "      <td>279.125</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>358.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2015-01-01 09:00:00</td>\n",
       "      <td>279.9</td>\n",
       "      <td>277.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.625</td>\n",
       "      <td>279.250</td>\n",
       "      <td>278.875</td>\n",
       "      <td>...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>338.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>354.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2015-01-01 12:00:00</td>\n",
       "      <td>279.9</td>\n",
       "      <td>277.4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.250</td>\n",
       "      <td>278.875</td>\n",
       "      <td>278.375</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>273.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>329.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>338.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20101</th>\n",
       "      <td>20101</td>\n",
       "      <td>2020-12-31 06:00:00</td>\n",
       "      <td>276.7</td>\n",
       "      <td>275.5</td>\n",
       "      <td>270.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.750</td>\n",
       "      <td>277.625</td>\n",
       "      <td>...</td>\n",
       "      <td>12.1</td>\n",
       "      <td>223.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>221.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>219.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>215.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20102</th>\n",
       "      <td>20102</td>\n",
       "      <td>2020-12-31 09:00:00</td>\n",
       "      <td>277.9</td>\n",
       "      <td>276.9</td>\n",
       "      <td>270.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.625</td>\n",
       "      <td>277.875</td>\n",
       "      <td>...</td>\n",
       "      <td>10.2</td>\n",
       "      <td>230.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>230.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>227.0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20103</th>\n",
       "      <td>20103</td>\n",
       "      <td>2020-12-31 12:00:00</td>\n",
       "      <td>283.5</td>\n",
       "      <td>277.1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>281.125</td>\n",
       "      <td>280.625</td>\n",
       "      <td>280.125</td>\n",
       "      <td>...</td>\n",
       "      <td>10.3</td>\n",
       "      <td>218.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>221.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>222.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>225.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20104</th>\n",
       "      <td>20104</td>\n",
       "      <td>2020-12-31 15:00:00</td>\n",
       "      <td>286.1</td>\n",
       "      <td>276.9</td>\n",
       "      <td>250.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.625</td>\n",
       "      <td>284.125</td>\n",
       "      <td>283.625</td>\n",
       "      <td>...</td>\n",
       "      <td>9.4</td>\n",
       "      <td>218.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>212.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>218.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>226.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20105</th>\n",
       "      <td>20105</td>\n",
       "      <td>2021-01-01 00:00:00</td>\n",
       "      <td>285.1</td>\n",
       "      <td>279.3</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.250</td>\n",
       "      <td>284.000</td>\n",
       "      <td>283.750</td>\n",
       "      <td>...</td>\n",
       "      <td>8.6</td>\n",
       "      <td>241.0</td>\n",
       "      <td>10.2</td>\n",
       "      <td>236.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>232.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>227.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17037 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0                 DTG  air_temp_obs  dewpoint_obs  \\\n",
       "1               1 2015-01-01 00:00:00         283.9         280.7   \n",
       "2               2 2015-01-01 03:00:00         280.7         279.7   \n",
       "3               3 2015-01-01 06:00:00         279.8         278.1   \n",
       "4               4 2015-01-01 09:00:00         279.9         277.0   \n",
       "5               5 2015-01-01 12:00:00         279.9         277.4   \n",
       "...           ...                 ...           ...           ...   \n",
       "20101       20101 2020-12-31 06:00:00         276.7         275.5   \n",
       "20102       20102 2020-12-31 09:00:00         277.9         276.9   \n",
       "20103       20103 2020-12-31 12:00:00         283.5         277.1   \n",
       "20104       20104 2020-12-31 15:00:00         286.1         276.9   \n",
       "20105       20105 2021-01-01 00:00:00         285.1         279.3   \n",
       "\n",
       "       wind_direction_obs  wind_speed_obs  wind_gust_obs  air_temp_1  \\\n",
       "1                   110.0             4.1     -9999999.0     284.000   \n",
       "2                    90.0             7.7     -9999999.0     281.500   \n",
       "3                   100.0             7.7     -9999999.0     279.875   \n",
       "4                   120.0             7.2     -9999999.0     279.625   \n",
       "5                   120.0             8.7     -9999999.0     279.250   \n",
       "...                   ...             ...            ...         ...   \n",
       "20101               270.0             3.6     -9999999.0     277.875   \n",
       "20102               270.0             3.1     -9999999.0     277.875   \n",
       "20103               220.0             3.6     -9999999.0     281.125   \n",
       "20104               250.0             3.6     -9999999.0     284.625   \n",
       "20105               300.0             6.2     -9999999.0     284.250   \n",
       "\n",
       "       air_temp_2  air_temp_3  ...  windspd_18  winddir_19  windspd_19  \\\n",
       "1         283.625     283.250  ...         5.8       341.0         6.0   \n",
       "2         281.250     280.750  ...         6.8       344.0         5.3   \n",
       "3         279.625     279.125  ...         6.0       345.0         5.5   \n",
       "4         279.250     278.875  ...         3.1       338.0         3.5   \n",
       "5         278.875     278.375  ...         1.6       273.0         2.0   \n",
       "...           ...         ...  ...         ...         ...         ...   \n",
       "20101     277.750     277.625  ...        12.1       223.0        11.8   \n",
       "20102     277.625     277.875  ...        10.2       230.0        10.8   \n",
       "20103     280.625     280.125  ...        10.3       218.0        11.9   \n",
       "20104     284.125     283.625  ...         9.4       218.0         8.6   \n",
       "20105     284.000     283.750  ...         8.6       241.0        10.2   \n",
       "\n",
       "       winddir_20  windspd_20  winddir_21  windspd_21  winddir_22  windspd_22  \\\n",
       "1           334.0         6.1       330.0         6.0       329.0         5.8   \n",
       "2           348.0         3.8       360.0         3.2        12.0         3.5   \n",
       "3           358.0         5.0        10.0         4.2        38.0         4.0   \n",
       "4           354.0         3.9         9.0         4.4        22.0         4.6   \n",
       "5           303.0         2.3       329.0         2.5       338.0         2.4   \n",
       "...           ...         ...         ...         ...         ...         ...   \n",
       "20101       221.0        11.4       219.0        11.3       215.0        11.4   \n",
       "20102       230.0        11.6       227.0        12.3       222.0        12.0   \n",
       "20103       221.0        12.8       222.0        11.9       225.0        10.6   \n",
       "20104       212.0         8.3       218.0         8.7       226.0        10.1   \n",
       "20105       236.0        10.5       232.0        10.5       227.0        11.3   \n",
       "\n",
       "       rotors_present  \n",
       "1                 0.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  \n",
       "5                 0.0  \n",
       "...               ...  \n",
       "20101             0.0  \n",
       "20102             0.0  \n",
       "20103             0.0  \n",
       "20104             0.0  \n",
       "20105             0.0  \n",
       "\n",
       "[17037 rows x 96 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_rotors_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a34399a0-cd53-425e-81b0-122e949106e4",
   "metadata": {},
   "source": [
    "Part of the data preparation process is often to create derived features that will be more useful as input for the machine learning algorithm. This is one way to include the knowledge of researchers into a machine learning pipeline through *feature engineering* which embed the known relationships and important values in the data in the way the dataset is presented for training. \n",
    "\n",
    "Here we want to present wind as two vectors in the u direction (east-west) and the v-direction (north-south), as our research meteorologists know this is likely to be more useful in predicting these wind rotors events."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5123c72c-7eae-44c2-b909-0be1cfe5acb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_feature_names = [f'air_temp_{i1}' for i1 in range(1,23)]\n",
    "humidity_feature_names = [f'sh_{i1}' for i1 in range(1,23)]\n",
    "wind_direction_feature_names = [f'winddir_{i1}' for i1 in range(1,23)]\n",
    "wind_speed_feature_names = [f'windspd_{i1}' for i1 in range(1,23)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "475b350f-23fa-4a66-acd6-5c77311293d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_v_wind(wind_dir_name, wind_speed_name, row1):\n",
    "    return math.cos(math.radians(row1[wind_dir_name])) * row1[wind_speed_name]\n",
    "\n",
    "def get_u_wind(wind_dir_name, wind_speed_name, row1):\n",
    "    return math.sin(math.radians(row1[wind_dir_name])) * row1[wind_speed_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8049d9c1-6b31-46c6-968b-e1334468f7d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 827 ms, sys: 23.3 ms, total: 851 ms\n",
      "Wall time: 889 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "u_feature_template = 'u_wind_{level_ix}'\n",
    "v_feature_template = 'v_wind_{level_ix}'\n",
    "u_wind_feature_names = []\n",
    "v_wind_features_names = []\n",
    "for wsn1, wdn1 in zip(wind_speed_feature_names, wind_direction_feature_names):\n",
    "    level_ix = int( wsn1.split('_')[1])\n",
    "    u_feature = u_feature_template.format(level_ix=level_ix)\n",
    "    u_wind_feature_names += [u_feature]\n",
    "    rotors_df[u_feature] = rotors_df.apply(functools.partial(get_u_wind, wdn1, wsn1), axis='columns', meta=(None, 'float64'))\n",
    "    v_feature = v_feature_template.format(level_ix=level_ix)\n",
    "    v_wind_features_names += [v_feature]\n",
    "    rotors_df[v_feature] = rotors_df.apply(functools.partial(get_v_wind, wdn1, wsn1), axis='columns', meta=(None, 'float64'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e8866baa-3f46-4061-b5ac-ae6481bcd27e",
   "metadata": {},
   "outputs": [],
   "source": [
    "rotors_df = rotors_df.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3dbc5924-5c6b-4f05-95a1-dc4349908783",
   "metadata": {},
   "outputs": [],
   "source": [
    "rotors_df['time'] = rotors_df['DTG']\n",
    "rotors_df = rotors_df.drop(['DTG'], axis='columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84623144-1844-4a33-97e1-a68904a03135",
   "metadata": {},
   "source": [
    "Our data is now cleaned up and ready to be saved our as a preprocessed, analysis ready dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f4dd943b-472c-492a-b65f-87c6a62fde97",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>air_temp_obs</th>\n",
       "      <th>dewpoint_obs</th>\n",
       "      <th>wind_direction_obs</th>\n",
       "      <th>wind_speed_obs</th>\n",
       "      <th>wind_gust_obs</th>\n",
       "      <th>air_temp_1</th>\n",
       "      <th>air_temp_2</th>\n",
       "      <th>air_temp_3</th>\n",
       "      <th>air_temp_4</th>\n",
       "      <th>...</th>\n",
       "      <th>v_wind_18</th>\n",
       "      <th>u_wind_19</th>\n",
       "      <th>v_wind_19</th>\n",
       "      <th>u_wind_20</th>\n",
       "      <th>v_wind_20</th>\n",
       "      <th>u_wind_21</th>\n",
       "      <th>v_wind_21</th>\n",
       "      <th>u_wind_22</th>\n",
       "      <th>v_wind_22</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>283.9</td>\n",
       "      <td>280.7</td>\n",
       "      <td>110.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.000</td>\n",
       "      <td>283.625</td>\n",
       "      <td>283.250</td>\n",
       "      <td>282.625</td>\n",
       "      <td>...</td>\n",
       "      <td>5.756768</td>\n",
       "      <td>-1.953409</td>\n",
       "      <td>5.673111</td>\n",
       "      <td>-2.674064</td>\n",
       "      <td>5.482644</td>\n",
       "      <td>-3.000000e+00</td>\n",
       "      <td>5.196152</td>\n",
       "      <td>-2.987221</td>\n",
       "      <td>4.971570</td>\n",
       "      <td>2015-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>280.7</td>\n",
       "      <td>279.7</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>281.500</td>\n",
       "      <td>281.250</td>\n",
       "      <td>280.750</td>\n",
       "      <td>280.250</td>\n",
       "      <td>...</td>\n",
       "      <td>6.502872</td>\n",
       "      <td>-1.460878</td>\n",
       "      <td>5.094687</td>\n",
       "      <td>-0.790064</td>\n",
       "      <td>3.716961</td>\n",
       "      <td>-7.837740e-16</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>0.727691</td>\n",
       "      <td>3.423517</td>\n",
       "      <td>2015-01-01 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>279.8</td>\n",
       "      <td>278.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.875</td>\n",
       "      <td>279.625</td>\n",
       "      <td>279.125</td>\n",
       "      <td>278.625</td>\n",
       "      <td>...</td>\n",
       "      <td>5.481273</td>\n",
       "      <td>-1.423505</td>\n",
       "      <td>5.312592</td>\n",
       "      <td>-0.174497</td>\n",
       "      <td>4.996954</td>\n",
       "      <td>7.293223e-01</td>\n",
       "      <td>4.136193</td>\n",
       "      <td>2.462646</td>\n",
       "      <td>3.152043</td>\n",
       "      <td>2015-01-01 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>279.9</td>\n",
       "      <td>277.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.625</td>\n",
       "      <td>279.250</td>\n",
       "      <td>278.875</td>\n",
       "      <td>278.250</td>\n",
       "      <td>...</td>\n",
       "      <td>2.475770</td>\n",
       "      <td>-1.311123</td>\n",
       "      <td>3.245143</td>\n",
       "      <td>-0.407661</td>\n",
       "      <td>3.878635</td>\n",
       "      <td>6.883116e-01</td>\n",
       "      <td>4.345829</td>\n",
       "      <td>1.723190</td>\n",
       "      <td>4.265046</td>\n",
       "      <td>2015-01-01 09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>279.9</td>\n",
       "      <td>277.4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.250</td>\n",
       "      <td>278.875</td>\n",
       "      <td>278.375</td>\n",
       "      <td>277.875</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.775695</td>\n",
       "      <td>-1.997259</td>\n",
       "      <td>0.104672</td>\n",
       "      <td>-1.928942</td>\n",
       "      <td>1.252670</td>\n",
       "      <td>-1.287595e+00</td>\n",
       "      <td>2.142918</td>\n",
       "      <td>-0.899056</td>\n",
       "      <td>2.225241</td>\n",
       "      <td>2015-01-01 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20101</th>\n",
       "      <td>20101</td>\n",
       "      <td>276.7</td>\n",
       "      <td>275.5</td>\n",
       "      <td>270.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.750</td>\n",
       "      <td>277.625</td>\n",
       "      <td>277.500</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.555992</td>\n",
       "      <td>-8.047581</td>\n",
       "      <td>-8.629974</td>\n",
       "      <td>-7.479073</td>\n",
       "      <td>-8.603689</td>\n",
       "      <td>-7.111320e+00</td>\n",
       "      <td>-8.781749</td>\n",
       "      <td>-6.538771</td>\n",
       "      <td>-9.338333</td>\n",
       "      <td>2020-12-31 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20102</th>\n",
       "      <td>20102</td>\n",
       "      <td>277.9</td>\n",
       "      <td>276.9</td>\n",
       "      <td>270.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.625</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.875</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.956383</td>\n",
       "      <td>-8.273280</td>\n",
       "      <td>-6.942106</td>\n",
       "      <td>-8.886116</td>\n",
       "      <td>-7.456336</td>\n",
       "      <td>-8.995651e+00</td>\n",
       "      <td>-8.388580</td>\n",
       "      <td>-8.029567</td>\n",
       "      <td>-8.917738</td>\n",
       "      <td>2020-12-31 09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20103</th>\n",
       "      <td>20103</td>\n",
       "      <td>283.5</td>\n",
       "      <td>277.1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>281.125</td>\n",
       "      <td>280.625</td>\n",
       "      <td>280.125</td>\n",
       "      <td>279.625</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.332875</td>\n",
       "      <td>-7.326372</td>\n",
       "      <td>-9.377328</td>\n",
       "      <td>-8.397556</td>\n",
       "      <td>-9.660283</td>\n",
       "      <td>-7.962654e+00</td>\n",
       "      <td>-8.843423</td>\n",
       "      <td>-7.495332</td>\n",
       "      <td>-7.495332</td>\n",
       "      <td>2020-12-31 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20104</th>\n",
       "      <td>20104</td>\n",
       "      <td>286.1</td>\n",
       "      <td>276.9</td>\n",
       "      <td>250.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.625</td>\n",
       "      <td>284.125</td>\n",
       "      <td>283.625</td>\n",
       "      <td>283.000</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.646804</td>\n",
       "      <td>-5.294689</td>\n",
       "      <td>-6.776892</td>\n",
       "      <td>-4.398330</td>\n",
       "      <td>-7.038799</td>\n",
       "      <td>-5.356255e+00</td>\n",
       "      <td>-6.855694</td>\n",
       "      <td>-7.265332</td>\n",
       "      <td>-7.016050</td>\n",
       "      <td>2020-12-31 15:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20105</th>\n",
       "      <td>20105</td>\n",
       "      <td>285.1</td>\n",
       "      <td>279.3</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.250</td>\n",
       "      <td>284.000</td>\n",
       "      <td>283.750</td>\n",
       "      <td>283.250</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.514397</td>\n",
       "      <td>-8.921121</td>\n",
       "      <td>-4.945058</td>\n",
       "      <td>-8.704895</td>\n",
       "      <td>-5.871525</td>\n",
       "      <td>-8.274113e+00</td>\n",
       "      <td>-6.464445</td>\n",
       "      <td>-8.264297</td>\n",
       "      <td>-7.706581</td>\n",
       "      <td>2021-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17486 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  air_temp_obs  dewpoint_obs  wind_direction_obs  \\\n",
       "1               1         283.9         280.7               110.0   \n",
       "2               2         280.7         279.7                90.0   \n",
       "3               3         279.8         278.1               100.0   \n",
       "4               4         279.9         277.0               120.0   \n",
       "5               5         279.9         277.4               120.0   \n",
       "...           ...           ...           ...                 ...   \n",
       "20101       20101         276.7         275.5               270.0   \n",
       "20102       20102         277.9         276.9               270.0   \n",
       "20103       20103         283.5         277.1               220.0   \n",
       "20104       20104         286.1         276.9               250.0   \n",
       "20105       20105         285.1         279.3               300.0   \n",
       "\n",
       "       wind_speed_obs  wind_gust_obs  air_temp_1  air_temp_2  air_temp_3  \\\n",
       "1                 4.1     -9999999.0     284.000     283.625     283.250   \n",
       "2                 7.7     -9999999.0     281.500     281.250     280.750   \n",
       "3                 7.7     -9999999.0     279.875     279.625     279.125   \n",
       "4                 7.2     -9999999.0     279.625     279.250     278.875   \n",
       "5                 8.7     -9999999.0     279.250     278.875     278.375   \n",
       "...               ...            ...         ...         ...         ...   \n",
       "20101             3.6     -9999999.0     277.875     277.750     277.625   \n",
       "20102             3.1     -9999999.0     277.875     277.625     277.875   \n",
       "20103             3.6     -9999999.0     281.125     280.625     280.125   \n",
       "20104             3.6     -9999999.0     284.625     284.125     283.625   \n",
       "20105             6.2     -9999999.0     284.250     284.000     283.750   \n",
       "\n",
       "       air_temp_4  ...  v_wind_18  u_wind_19  v_wind_19  u_wind_20  v_wind_20  \\\n",
       "1         282.625  ...   5.756768  -1.953409   5.673111  -2.674064   5.482644   \n",
       "2         280.250  ...   6.502872  -1.460878   5.094687  -0.790064   3.716961   \n",
       "3         278.625  ...   5.481273  -1.423505   5.312592  -0.174497   4.996954   \n",
       "4         278.250  ...   2.475770  -1.311123   3.245143  -0.407661   3.878635   \n",
       "5         277.875  ...  -0.775695  -1.997259   0.104672  -1.928942   1.252670   \n",
       "...           ...  ...        ...        ...        ...        ...        ...   \n",
       "20101     277.500  ...  -8.555992  -8.047581  -8.629974  -7.479073  -8.603689   \n",
       "20102     277.875  ...  -6.956383  -8.273280  -6.942106  -8.886116  -7.456336   \n",
       "20103     279.625  ...  -8.332875  -7.326372  -9.377328  -8.397556  -9.660283   \n",
       "20104     283.000  ...  -6.646804  -5.294689  -6.776892  -4.398330  -7.038799   \n",
       "20105     283.250  ...  -2.514397  -8.921121  -4.945058  -8.704895  -5.871525   \n",
       "\n",
       "          u_wind_21  v_wind_21  u_wind_22  v_wind_22                time  \n",
       "1     -3.000000e+00   5.196152  -2.987221   4.971570 2015-01-01 00:00:00  \n",
       "2     -7.837740e-16   3.200000   0.727691   3.423517 2015-01-01 03:00:00  \n",
       "3      7.293223e-01   4.136193   2.462646   3.152043 2015-01-01 06:00:00  \n",
       "4      6.883116e-01   4.345829   1.723190   4.265046 2015-01-01 09:00:00  \n",
       "5     -1.287595e+00   2.142918  -0.899056   2.225241 2015-01-01 12:00:00  \n",
       "...             ...        ...        ...        ...                 ...  \n",
       "20101 -7.111320e+00  -8.781749  -6.538771  -9.338333 2020-12-31 06:00:00  \n",
       "20102 -8.995651e+00  -8.388580  -8.029567  -8.917738 2020-12-31 09:00:00  \n",
       "20103 -7.962654e+00  -8.843423  -7.495332  -7.495332 2020-12-31 12:00:00  \n",
       "20104 -5.356255e+00  -6.855694  -7.265332  -7.016050 2020-12-31 15:00:00  \n",
       "20105 -8.274113e+00  -6.464445  -8.264297  -7.706581 2021-01-01 00:00:00  \n",
       "\n",
       "[17486 rows x 140 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rotors_df                                                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a468910d-6454-41a9-bc27-c2ceb0c3720a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/h01/shaddad/data/ukrse2022/2021_met_office_aviation_rotors_preprocessed.csv')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rotors_preprocessed_path = rse_root_data_dir / (pathlib.Path(rotors_fname).stem + '_preprocessed.csv')\n",
    "rotors_preprocessed_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cb7a566a-7597-4d9c-92c7-f87910b2611a",
   "metadata": {},
   "outputs": [],
   "source": [
    "rotors_df.to_csv(rotors_preprocessed_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7d8f777-ca9f-4191-aab7-fd3cf76eec4e",
   "metadata": {},
   "source": [
    "### Create an interactive dashboard for data exploration\n",
    "Part of the initial process of a machine learning project is to explore the contents of the dataset, especially where it is unfamiliar to those researchers working with it. You may wish to get a sense of the values, structures and relationships in the data. There are a variety of ways to do this, through visualising the \"raw\" data directly, or summary statistics and derived metrics and indices which describe the data. Often it will be helpful to be able to share both the results of the *exploratory data analysis* and access to exploratory code itself. This can be done without having to run code directly by quickly creating a *dashboard* use one of the many python, R, javascript or other language tools to do this. This allows a range of experts in different areas to explore the data to inform subsequent data analysis and modelling choices use in the machine learning pipeline.\n",
    "\n",
    "Here we demonstrate the use of tools in the [holoviz ecosystem](https://holoviz.org/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bd911ecd-bdd3-4f97-8717-b47e98cb662f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
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       "\n",
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       "      window.requirejs.config({'packages': {}, 'paths': {'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@4.2.5/dist/gridstack-h5', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'gridstack': {'exports': 'GridStack'}}});\n",
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       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    for (var i = 0; i < js_modules.length; i++) {\n",
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       "      element.type = \"module\";\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    if (!js_urls.length && !js_modules.length) {\n",
       "      on_load()\n",
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       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
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       "      inject_raw_css(\"\\n    .bk.pn-loading.arc:before {\\n      background-image: url(\\\"data:image/svg+xml;base64,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\\\");\\n      background-size: auto calc(min(50%, 400px));\\n    }\\n    \");\n",
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       "      Bokeh.set_log_level(\"info\");\n",
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       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
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       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    }\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, js_modules, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
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i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/0.14.2/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) >= 0) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) >= 0) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\", \"https://unpkg.com/@holoviz/panel@0.14.2/dist/panel.min.js\"];\n  var js_modules = [];\n  var css_urls = [\"https://cdn.holoviz.org/panel/0.14.2/dist/css/alerts.css\", \"https://cdn.holoviz.org/panel/0.14.2/dist/css/card.css\", \"https://cdn.holoviz.org/panel/0.14.2/dist/css/dataframe.css\", \"https://cdn.holoviz.org/panel/0.14.2/dist/css/debugger.css\", \"https://cdn.holoviz.org/panel/0.14.2/dist/css/json.css\", \"https://cdn.holoviz.org/panel/0.14.2/dist/css/loading.css\", \"https://cdn.holoviz.org/panel/0.14.2/dist/css/markdown.css\", \"https://cdn.holoviz.org/panel/0.14.2/dist/css/widgets.css\"];\n  var inline_js = [    function(Bokeh) {\n      inject_raw_css(\"\\n    .bk.pn-loading.arc:before {\\n      background-image: url(\\\"data:image/svg+xml;base64,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\\\");\\n      background-size: auto calc(min(50%, 400px));\\n    }\\n    \");\n    },    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, js_modules, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
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google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        }) \n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>.bk-root, .bk-root .bk:before, .bk-root .bk:after {\n",
       "  font-family: var(--jp-ui-font-size1);\n",
       "  font-size: var(--jp-ui-font-size1);\n",
       "  color: var(--jp-ui-font-color1);\n",
       "}\n",
       "</style>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import panel\n",
    "panel.extension()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b14e01c6-9f01-4a54-a3d5-2914997c278f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'air_temp_obs', 'dewpoint_obs', 'wind_direction_obs',\n",
       "       'wind_speed_obs', 'wind_gust_obs', 'air_temp_1', 'air_temp_2',\n",
       "       'air_temp_3', 'air_temp_4',\n",
       "       ...\n",
       "       'v_wind_18', 'u_wind_19', 'v_wind_19', 'u_wind_20', 'v_wind_20',\n",
       "       'u_wind_21', 'v_wind_21', 'u_wind_22', 'v_wind_22', 'time'],\n",
       "      dtype='object', length=140)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rotors_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "749428e9-7cba-4327-9b72-c0f25ac1d71e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_vars_dict = {\n",
    "    'Temperature': 'air_temp',\n",
    "    'Specific Humidity': 'sh',\n",
    "    'Wind Direction': 'winddir' ,\n",
    "    'Wind Speed': 'windspd' ,\n",
    "           }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5a66447a-244a-4686-8b10-eba2f0cd75f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_dict = {\n",
    "    'Temperature': 'air_temp_obs',\n",
    "    'Dew Point Temperature': 'dewpoint_obs',\n",
    "    'Wind Direction': 'wind_direction_obs' ,\n",
    "    'Wind Speed': 'wind_speed_obs' ,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b1859bbe-dfad-4674-a151-ce9e698d2834",
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_histogram(plotting_df, rotors_feature_name, selected_var, subset):\n",
    "    fig1 = matplotlib.pyplot.figure(f'histogram of {selected_var}')\n",
    "    ax1 = fig1.add_subplot(1,1,1,title=f'histogram of {selected_var}')\n",
    "    if subset == 'all':\n",
    "        data_to_plot = plotting_df\n",
    "    elif subset == 'no_rotors':\n",
    "        data_to_plot = plotting_df[plotting_df[rotors_feature_name] == 0.0]\n",
    "    elif subset == 'rotors_present':\n",
    "        data_to_plot = plotting_df[plotting_df[rotors_feature_name] == 1.0]\n",
    "    _ = data_to_plot[selected_var].plot.hist(bins=20, ax=ax1)\n",
    "    return fig1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "fda48027-d3dc-4d9a-b295-401b4b6f14c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "hist_var_select = panel.widgets.Select(\n",
    "    options=obs_dict,\n",
    "    name='selected_var',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b73b2288-a329-4c51-b0e6-647a9925751e",
   "metadata": {},
   "outputs": [],
   "source": [
    "subset_select = panel.widgets.Select(\n",
    "    options = ['all', 'no_rotors', 'rotors_present'],\n",
    "    name='subset',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "58f8f948-da65-49b1-9fb2-128e3a92880c",
   "metadata": {},
   "outputs": [],
   "source": [
    "hist_plotter = panel.bind(\n",
    "    functools.partial(\n",
    "        do_histogram,\n",
    "        plotting_df=rotors_df,\n",
    "        rotors_feature_name=target_feature_name,\n",
    "        ),\n",
    "    selected_var=hist_var_select,\n",
    "    subset=subset_select,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5e9a5dfa-16ee-47c0-9902-d260b95422f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rotors_dash = panel.Column(\n",
    "    panel.widgets.StaticText(value='Rotors Data Exploration Dashboard'),\n",
    "    panel.Row(subset_select, hist_var_select),\n",
    "    hist_plotter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "391151c3-3781-4824-9a43-5aa5dd6578ee",
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
       "<div id='1002'>\n",
       "  <div class=\"bk-root\" id=\"96c670ef-a6f7-4849-82f0-b0eb105777d7\" data-root-id=\"1002\"></div>\n",
       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
       "  function embed_document(root) {\n",
       "    var docs_json = 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&quot; width=&quot;460px&quot; height=&quot;345px&quot; alt=&quot;&quot;&gt;&lt;/img&gt;\",\"width\":460},\"id\":\"1008\",\"type\":\"panel.models.markup.HTML\"},{\"attributes\":{\"children\":[{\"id\":\"1003\"},{\"id\":\"1004\"},{\"id\":\"1007\"}],\"margin\":[0,0,0,0],\"name\":\"Column00112\"},\"id\":\"1002\",\"type\":\"Column\"},{\"attributes\":{\"margin\":[5,10,5,10],\"name\":\"\",\"text\":\"Rotors Data Exploration Dashboard\"},\"id\":\"1003\",\"type\":\"Div\"},{\"attributes\":{\"margin\":[5,10,5,10],\"options\":[\"all\",\"no_rotors\",\"rotors_present\"],\"title\":\"subset\",\"value\":\"all\"},\"id\":\"1005\",\"type\":\"panel.models.widgets.CustomSelect\"}],\"root_ids\":[\"1002\",\"1009\",\"1010\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.3\"}};\n",
       "    var render_items = [{\"docid\":\"92f3c5ca-6baa-4b63-beb4-aaa2d528b3d0\",\"root_ids\":[\"1002\"],\"roots\":{\"1002\":\"96c670ef-a6f7-4849-82f0-b0eb105777d7\"}}];\n",
       "    root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       "Column\n",
       "    [0] StaticText(value='Rotors Data E...)\n",
       "    [1] Row\n",
       "        [0] Select(name='subset', options=['all', 'no_rotors', ...], value='all')\n",
       "        [1] Select(name='selected_var', options={'Temperature': 'air_temp_...}, value='air_temp_obs')\n",
       "    [2] ParamFunction(function)"
      ]
     },
     "execution_count": 45,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "1002"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rotors_dash.servable()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7cf425-6592-4e16-bad7-1692dee2508e",
   "metadata": {},
   "source": [
    "In the above example, we see a very simple dashboard to visualise some statistics about the data. This could be extended to any particular plots that are of interest to the researchers, which can easily be created in a standard script or notebook and then subsequently included in dashboard code. \n",
    "\n",
    "This code could also be put in it own script of course, or run on a website directly from the notebook using the panel library from the command line:\n",
    "```\n",
    "panel serve 02_data_prep.ipynb\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2c3a8ae-5fea-43ab-8063-a97d9b2dd6ff",
   "metadata": {},
   "source": [
    "## Example 2 - Weather Types from ERA5 Reanalysis Dataset\n",
    "\n",
    "Our next example further explores processing at scale on a different sort of data often called *gridded data* in environmental science. This is essentially data that has a similar structure to image or more generally *raster* data. The difference here is that our grid may extend in n dimensions, such as latitude, longitude, height above the surface, time, ensemble realisation etc. Here we use the ERA5 reanalysis dataset to create \"weather regime clusters\", that is find groups of air pressure patterns that are similar and are likely to be associated with similar sorts of weather. One could use experts to describe what they think such patterns would be, or one can use a data driven approach like clustering to find what patterns are actually present in the data and may reveal patterns that had not been previously considered.\n",
    "\n",
    "Further information on weather regimes:\n",
    "* https://www.metoffice.gov.uk/services/business-industry/energy/decider\n",
    "* https://www.metoffice.gov.uk/research/news/2016/new-weather-patterns-for-uk-and-europe\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9af61e77-6b71-4df8-aeeb-f345906e07a1",
   "metadata": {},
   "source": [
    "The particular challenges in this problem are again around the scale of the data requiring processing, but also around the use of domain specific libraries for processing. We will often need to use various domain specific tools and techniques in the data preparation part of the pipeline to prepare the data for use with more generic machine learning and data science tools. In this example we are using the Met Office python library [Iris](https://scitools-iris.readthedocs.io/en/stable/), which handles gridded meteorological (i.e. climate and weather) data and metadata and includes various meteorology specific functionality and handles specific data storage formats such as *NetCDF* and *Grib2*. Other research domains will have similar packages for handling their specific needs, which will similarly need to be supported as part of a machine learning projects in those research domains.\n",
    "\n",
    "Docs:\n",
    "* [Iris](https://scitools-iris.readthedocs.io/en/stable/)\n",
    "* [Xarray](https://docs.xarray.dev/en/stable/) (similar package) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "681bdb50-4e64-458c-b901-b26c1cdfc641",
   "metadata": {},
   "outputs": [],
   "source": [
    "import iris\n",
    "import iris.coord_categorisation\n",
    "import iris.quickplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a24f0ce6-0cf7-44bf-8865-27d26637fc0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cartopy\n",
    "import cartopy.crs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "856cc8f6-2ec3-4791-8861-6ad4529bc8d9",
   "metadata": {},
   "source": [
    "### Accessing the raw data\n",
    "The full global ERA5 files can most easily be accessed through a [public bucket on s3](https://registry.opendata.aws/ecmwf-era5/)\n",
    "You can either follow the instructions to access it directly from python using the instructions on the above link, or you can download a local copy using the aws s3 command line tool as follows:\n",
    "\n",
    "`aws s3 cp --no-sign-request s3://era5-pds/2019/01/data/air_pressure_at_mean_sea_level.nc ~/data/ukrse2022/`\n",
    "\n",
    "The data files are for one month at a time, so you would need to do this for all months from January 2017 to December 2019 to execute the code below. The total size for that data is 35GB, so you may wish to rather just used the pre-prepared subset available through Zenodo as described in the readme.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f0ee36a9-356d-4cbe-b3a7-b411aab36835",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using default path\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/h01/shaddad/data/ukrse2022')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "try:\n",
    "    era5_data_dir = pathlib.Path(os.environ['RSE22_ERA5_DATA_DIR'])\n",
    "    print('reading from environment variable')\n",
    "except KeyError as ke1:\n",
    "    era5_data_dir = pathlib.Path(os.environ['HOME'])  / 'data' / 'ukrse2022'\n",
    "    print('using default path')\n",
    "era5_data_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e5bce62b-b473-4acb-9f16-93261437efbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "era5_global_paths = sorted([p1 for p1 in era5_data_dir.iterdir() if '.nc' in str(p1) and 'air_pressure' in str(p1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "18486bcf-c1a7-4d49-be63-00950ad7c30d",
   "metadata": {},
   "outputs": [],
   "source": [
    "uk_na_bounds = {'latitude': (40,65), 'longitude': (-10,10)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5a154744-c15b-496f-830d-c46c06a83195",
   "metadata": {},
   "outputs": [],
   "source": [
    "do_global_extract = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2e30e6ef-f6ad-4e60-b975-9b98911e7089",
   "metadata": {},
   "outputs": [],
   "source": [
    "era5_mslp_uk_subset_fname = 'era5_mslp_UK_2017_2020.nc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "bcee8066-ed13-400f-be36-04b0ec69d6fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63b758b9-bdc7-42e5-8abc-0786a808f215",
   "metadata": {},
   "source": [
    "In the initial extraction code below, we load all the downloaded data files, tidy up some of the metadata using the `equalise_attributes` function, then join the data from each month into a single *cube*, which is what iris calls a multidimensional gridded dataset describing a single physical phenomenon or feature (in ML jargon) e.g. air temperature or air pressure. We then extract a subset of that data covering the United Kingdom and parts of the North Atlantic and North Sea. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1a0318f-95ea-494c-ac41-cdfa684f3114",
   "metadata": {},
   "source": [
    "The UK subset of ERA5 data is available from Zenodo. See the [readme file](README.md) file for details. You can download the data into the default location of `~/data/ukrse2022` or you can specify the location in the environment variable `${RSE22_ROOT_DATA_DIR}`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "54da23b2-a3d7-44f2-ad38-b67d9f1e7f62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading cached data\n"
     ]
    }
   ],
   "source": [
    "if do_global_extract:\n",
    "    print('do global_extract')\n",
    "    era5_global_cubeList = iris.load(map(str, era5_global_paths))\n",
    "    iris.util.equalise_attributes(era5_global_cubeList)\n",
    "    mslp_era5_cube = iris.cube.CubeList.concatenate_cube(era5_global_cubeList)\n",
    "    mslp_era5_uk_cube = mslp_era5_cube.intersection(latitude=uk_na_bounds['latitude'], \n",
    "                                                longitude=uk_na_bounds['longitude'])\n",
    "else:\n",
    "    print('loading cached data')\n",
    "    if load_from_zenodo:\n",
    "        mslp_era5_uk_cube = xarray.load_dataarray(zenodo_record_root  + era5_mslp_uk_subset_fname).to_iris()\n",
    "    else:\n",
    "        mslp_era5_uk_cube = iris.load_cube(str(era5_data_dir / era5_mslp_uk_subset_fname))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "661e11e8-21ca-423f-9007-8674210dea1c",
   "metadata": {},
   "outputs": [
    {
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       "<iris 'Cube' of air_pressure_at_mean_sea_level / (Pa) (time: 35064; latitude: 101; longitude: 81)>"
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   ],
   "source": [
    "print(type(mslp_era5_uk_cube.core_data()))\n",
    "mslp_era5_uk_cube.core_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0c86932-7a3c-4b49-9d87-421ba8c1f103",
   "metadata": {},
   "source": [
    "The calculation we want to do here is remove differences between seasons. What we're interested in is pressure anomalies, that is how the pressure pattern differs from what is normal for the time of year. So we want to calculate the average pressure at each point for each season. There is a lot of data so this is a large calculation, so the large compute resources we can get access to through an appropriately resourced dask cluster, especially if we were doing this on the full ERA5 data of several decades and for a larger area."
   ]
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   "execution_count": 58,
   "id": "9c3fd214-9978-4a91-8120-0bc6d37f7de1",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris.iris.coord_categorisation.add_season_number(mslp_era5_uk_cube,'time')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9f978c34-ad8d-4a46-aaab-c8e8266e3861",
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       "      background: #d5dcdf;\n",
       "      border-top: 1px solid #9c9c9c;\n",
       "      font-weight: bold;\n",
       "  }\n",
       "  .iris-word-cell {\n",
       "      text-align: left !important;\n",
       "      white-space: pre;\n",
       "  }\n",
       "  .iris-subheading-cell {\n",
       "      padding-left: 2em !important;\n",
       "  }\n",
       "  .iris-inclusion-cell {\n",
       "      padding-right: 1em !important;\n",
       "  }\n",
       "  .iris-panel-body {\n",
       "      padding-top: 0px;\n",
       "  }\n",
       "  .iris-panel-title {\n",
       "      padding-left: 3em;\n",
       "  }\n",
       "  .iris-panel-title {\n",
       "      margin-top: 7px;\n",
       "  }\n",
       "</style>\n",
       "<table class=\"iris\" id=\"140481382136176\">\n",
       "    <tr class=\"iris\">\n",
       "<th class=\"iris iris-word-cell\">Air Pressure At Mean Sea Level (Pa)</th>\n",
       "<th class=\"iris iris-word-cell\">--</th>\n",
       "<th class=\"iris iris-word-cell\">latitude</th>\n",
       "<th class=\"iris iris-word-cell\">longitude</th>\n",
       "</tr>\n",
       "    <tr class=\"iris\">\n",
       "<td class=\"iris-word-cell iris-subheading-cell\">Shape</td>\n",
       "<td class=\"iris iris-inclusion-cell\">4</td>\n",
       "<td class=\"iris iris-inclusion-cell\">101</td>\n",
       "<td class=\"iris iris-inclusion-cell\">81</td>\n",
       "</tr>\n",
       "    <tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Dimension coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tlatitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tlongitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Auxiliary coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tseason_number</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\ttime</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Cell methods</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tmean                          season_number</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Attributes</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"><br>Conventions                   CF-1.7<br>institution                   ECMWF<br>nameCDM                       Mean_sea_level_pressure_surface<br>nameECMWF                     Mean sea level pressure<br>product_type                  analysis<br>shortNameECMWF                msl<br>source                        Reanalysis</td>\n",
       "</tr>\n",
       "</table>\n",
       "        "
      ],
      "text/plain": [
       "<iris 'Cube' of air_pressure_at_mean_sea_level / (Pa) (-- : 4; latitude: 101; longitude: 81)>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mslp_uk_seasonal_mean = mslp_era5_uk_cube.aggregated_by(['season_number'],iris.analysis.MEAN)\n",
    "mslp_uk_seasonal_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7c0e96ed-9100-418b-901a-c63f87fe5a2f",
   "metadata": {},
   "outputs": [
    {
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       "                        <td> 127.83 kiB </td>\n",
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       "                        <td colspan=\"2\"> 4 chunks in 19 graph layers </td>\n",
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   ],
   "source": [
    "mslp_uk_seasonal_mean.core_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5869d214-0910-4034-98ec-f440a994c777",
   "metadata": {},
   "outputs": [],
   "source": [
    "uk_season_dict = {\n",
    "    'NH Winter': 0,\n",
    "    'NH Spring': 1,\n",
    "    'NH Summer': 2,\n",
    "    'NH Autumun': 3,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc549936-6a7f-4f1f-8d01-928c8fa7d34f",
   "metadata": {},
   "source": [
    "Next researcher may again want to explore the content of the dataset using a nice dashboard created with holoviz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "042d1d6f-ca6a-4780-9027-e79f15ce4de4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_seasonal_mean(plotting_cube, season):\n",
    "    print(f'season {season}')\n",
    "    fig1 = matplotlib.figure.Figure()\n",
    "    fig1.clf()\n",
    "    ax1 = fig1.add_subplot(1,1,1,projection=cartopy.crs.PlateCarree())\n",
    "    _ = iris.quickplot.contourf(plotting_cube[season],axes=ax1)\n",
    "    ax1.coastlines()    \n",
    "    return fig1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2105bfa1-a47c-47e8-9d71-bcdbfde2309b",
   "metadata": {},
   "outputs": [],
   "source": [
    "season_select = panel.widgets.Select(options=uk_season_dict, \n",
    "                                     value=2,\n",
    "                                     name='season'\n",
    "                                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "17852158-7186-4506-aae4-dcc8a2fa5aac",
   "metadata": {},
   "outputs": [],
   "source": [
    "season_plotter = panel.bind(\n",
    "    functools.partial(plot_seasonal_mean, mslp_uk_seasonal_mean),\n",
    "    season=season_select,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4912427b-a826-4d56-8e20-781598b5253f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "season 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seasons_dash = panel.Column(season_select, season_plotter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "020e0a4b-3759-45bb-aa6d-d12a767a9116",
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
       "<div id='1011'>\n",
       "  <div class=\"bk-root\" id=\"8b0e6411-af2f-4779-bb48-593c370d9d96\" data-root-id=\"1011\"></div>\n",
       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
       "  function embed_document(root) {\n",
       "    var docs_json = 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&quot; width=&quot;460px&quot; height=&quot;345px&quot; alt=&quot;&quot;&gt;&lt;/img&gt;\",\"width\":460},\"id\":\"1014\",\"type\":\"panel.models.markup.HTML\"}],\"root_ids\":[\"1011\",\"1015\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.3\"}};\n",
       "    var render_items = [{\"docid\":\"e27fd0d5-8092-48f6-9e64-d1fe843226b9\",\"root_ids\":[\"1011\"],\"roots\":{\"1011\":\"8b0e6411-af2f-4779-bb48-593c370d9d96\"}}];\n",
       "    root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       "Column\n",
       "    [0] Select(name='season', options={'NH Winter': 0, ...}, value=2)\n",
       "    [1] ParamFunction(function)"
      ]
     },
     "execution_count": 66,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "1011"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seasons_dash.servable()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e54fad76-b0a8-4516-9f83-3dcbe48c2db8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x1600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = matplotlib.pyplot.figure(figsize=(9,16))\n",
    "for ix1 in range(mslp_uk_seasonal_mean.shape[0]):\n",
    "    ax1 = fig1.add_subplot(1,4,ix1+1,projection=cartopy.crs.PlateCarree())\n",
    "    iris.quickplot.contourf(mslp_uk_seasonal_mean[ix1],axes=ax1)\n",
    "    ax1.coastlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "9aecfd1e-3d10-4b2f-9be6-f162e2cfe2dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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> 1.07 GiB </td>\n",
       "                        <td> 68.91 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (35064, 8181) </td>\n",
       "                        <td> (2208, 8181) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 25 chunks in 32 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=\"92\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "dask.array<concatenate, shape=(35064, 8181), dtype=float32, chunksize=(2208, 8181), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "era5_flat_deseasoned = dask.array.concatenate(\n",
    "    [(mslp_era5_uk_cube.extract(iris.Constraint(season_number=sn1)).core_data() - mslp_uk_seasonal_mean[sn1].core_data()).reshape(\n",
    "    (-1, mslp_era5_uk_cube.shape[1] * mslp_era5_uk_cube.shape[2])) for sn1 in range(4)],\n",
    "    axis=0\n",
    ")\n",
    "era5_flat_deseasoned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "561c8031-6a29-4492-9543-f8788229c412",
   "metadata": {},
   "outputs": [],
   "source": [
    "era5_flat_deseasoned = era5_flat_deseasoned.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ee55a13-592d-4423-b5ea-7e174231c623",
   "metadata": {},
   "source": [
    "## Example 3 - Creating a catalog with Intake\n",
    "\n",
    "When you think of a dataset, you probably think of a series of files in some format (often NetCDF files for weather and climate data, maybe [FITS](https://en.wikipedia.org/wiki/FITS) in astronomy or [BAM files](https://samtools.github.io/hts-specs/SAMv1.pdf) in genetics. Researchers in general don't care about the files themselves, but rather getting the data they contained loaded in memory. There are now better options for presenting data than just a unorder collection of files. One such option is a *catalog*. The idea here is the curated data files are presented logically in terms of the different datasets, which each logical dataset is presented as a single object, irrespective of how the data is actually stored on disk. There could be one file or many files, or it could be coming from a database or an API, the researcher sees a dataset, which can then be requested and loaded into memory (either actually loading the data or setting up a stub for subsequent lazy loading and lazy execution).\n",
    "\n",
    "Here we create a very basic catalog of the data we would like to make available to researchers using the data catalogue tool called *intake*. This is a very lightweight framework for creating catalog. There are two parts to such a catalog:\n",
    "\n",
    "* Catalog description - A yml file describing what is in the dataset available through the catalog\n",
    "* Data Driver - A script which knows how to load the data in question and return an in memory data object. This is generally provided for many existing popular data formats. It is also fairly easy to write one's own driver for new data formats (for example environmental data formats are well supported, but perhaps medical data formats are less well supported). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "5b4969f9-8c14-42c9-9c5e-b6fec5f893c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import intake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c2f0325c-6f84-4527-89ba-fec74e232f66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/yaml": "rotors_catalog:\n  args:\n    path: /home/h01/shaddad/data/ukrse2022/rotors_catalog.yml\n  description: ''\n  driver: intake.catalog.local.YAMLFileCatalog\n  metadata: {}\n",
      "text/plain": [
       "rotors_catalog:\n",
       "  args:\n",
       "    path: /home/h01/shaddad/data/ukrse2022/rotors_catalog.yml\n",
       "  description: ''\n",
       "  driver: intake.catalog.local.YAMLFileCatalog\n",
       "  metadata: {}\n"
      ]
     },
     "metadata": {
      "application/json": {
       "root": "rotors_catalog"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rotors_catalog = intake.open_catalog(rse_root_data_dir / 'rotors_catalog.yml')\n",
    "rotors_catalog "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b94d55b3-f698-4449-9620-e1313f613a7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rotors', 'rotors_preprocessed']"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(rotors_catalog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a19b3021-4b10-4f30-a0e3-56611968a2d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>DTG</th>\n",
       "      <th>air_temp_obs</th>\n",
       "      <th>dewpoint_obs</th>\n",
       "      <th>wind_direction_obs</th>\n",
       "      <th>wind_speed_obs</th>\n",
       "      <th>wind_gust_obs</th>\n",
       "      <th>air_temp_1</th>\n",
       "      <th>air_temp_2</th>\n",
       "      <th>air_temp_3</th>\n",
       "      <th>...</th>\n",
       "      <th>windspd_18</th>\n",
       "      <th>winddir_19</th>\n",
       "      <th>windspd_19</th>\n",
       "      <th>winddir_20</th>\n",
       "      <th>windspd_20</th>\n",
       "      <th>winddir_21</th>\n",
       "      <th>windspd_21</th>\n",
       "      <th>winddir_22</th>\n",
       "      <th>windspd_22</th>\n",
       "      <th>rotors_present</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>01/01/2015 00:00</td>\n",
       "      <td>283.9</td>\n",
       "      <td>280.7</td>\n",
       "      <td>110.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.000</td>\n",
       "      <td>283.625</td>\n",
       "      <td>283.250</td>\n",
       "      <td>...</td>\n",
       "      <td>5.8</td>\n",
       "      <td>341.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>6.1</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>329.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>01/01/2015 03:00</td>\n",
       "      <td>280.7</td>\n",
       "      <td>279.7</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>281.500</td>\n",
       "      <td>281.250</td>\n",
       "      <td>280.750</td>\n",
       "      <td>...</td>\n",
       "      <td>6.8</td>\n",
       "      <td>344.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>348.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>01/01/2015 06:00</td>\n",
       "      <td>279.8</td>\n",
       "      <td>278.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.875</td>\n",
       "      <td>279.625</td>\n",
       "      <td>279.125</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>358.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>01/01/2015 09:00</td>\n",
       "      <td>279.9</td>\n",
       "      <td>277.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.625</td>\n",
       "      <td>279.250</td>\n",
       "      <td>278.875</td>\n",
       "      <td>...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>338.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>354.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>20101</th>\n",
       "      <td>20101</td>\n",
       "      <td>31/12/2020 06:00</td>\n",
       "      <td>276.7</td>\n",
       "      <td>275.5</td>\n",
       "      <td>270.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.750</td>\n",
       "      <td>277.625</td>\n",
       "      <td>...</td>\n",
       "      <td>12.1</td>\n",
       "      <td>223.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>221.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>219.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>215.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20102</th>\n",
       "      <td>20102</td>\n",
       "      <td>31/12/2020 09:00</td>\n",
       "      <td>277.9</td>\n",
       "      <td>276.9</td>\n",
       "      <td>270.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.625</td>\n",
       "      <td>277.875</td>\n",
       "      <td>...</td>\n",
       "      <td>10.2</td>\n",
       "      <td>230.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>230.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>227.0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20103</th>\n",
       "      <td>20103</td>\n",
       "      <td>31/12/2020 12:00</td>\n",
       "      <td>283.5</td>\n",
       "      <td>277.1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>281.125</td>\n",
       "      <td>280.625</td>\n",
       "      <td>280.125</td>\n",
       "      <td>...</td>\n",
       "      <td>10.3</td>\n",
       "      <td>218.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>221.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>222.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>225.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20104</th>\n",
       "      <td>20104</td>\n",
       "      <td>31/12/2020 15:00</td>\n",
       "      <td>286.1</td>\n",
       "      <td>276.9</td>\n",
       "      <td>250.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.625</td>\n",
       "      <td>284.125</td>\n",
       "      <td>283.625</td>\n",
       "      <td>...</td>\n",
       "      <td>9.4</td>\n",
       "      <td>218.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>212.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>218.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>226.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20105</th>\n",
       "      <td>20105</td>\n",
       "      <td>01/01/2021 00:00</td>\n",
       "      <td>285.1</td>\n",
       "      <td>279.3</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.250</td>\n",
       "      <td>284.000</td>\n",
       "      <td>283.750</td>\n",
       "      <td>...</td>\n",
       "      <td>8.6</td>\n",
       "      <td>241.0</td>\n",
       "      <td>10.2</td>\n",
       "      <td>236.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>232.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>227.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20106 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0               DTG  air_temp_obs  dewpoint_obs  \\\n",
       "0               0               NaN           NaN           NaN   \n",
       "1               1  01/01/2015 00:00         283.9         280.7   \n",
       "2               2  01/01/2015 03:00         280.7         279.7   \n",
       "3               3  01/01/2015 06:00         279.8         278.1   \n",
       "4               4  01/01/2015 09:00         279.9         277.0   \n",
       "...           ...               ...           ...           ...   \n",
       "20101       20101  31/12/2020 06:00         276.7         275.5   \n",
       "20102       20102  31/12/2020 09:00         277.9         276.9   \n",
       "20103       20103  31/12/2020 12:00         283.5         277.1   \n",
       "20104       20104  31/12/2020 15:00         286.1         276.9   \n",
       "20105       20105  01/01/2021 00:00         285.1         279.3   \n",
       "\n",
       "       wind_direction_obs  wind_speed_obs  wind_gust_obs  air_temp_1  \\\n",
       "0                     NaN             NaN            NaN         NaN   \n",
       "1                   110.0             4.1     -9999999.0     284.000   \n",
       "2                    90.0             7.7     -9999999.0     281.500   \n",
       "3                   100.0             7.7     -9999999.0     279.875   \n",
       "4                   120.0             7.2     -9999999.0     279.625   \n",
       "...                   ...             ...            ...         ...   \n",
       "20101               270.0             3.6     -9999999.0     277.875   \n",
       "20102               270.0             3.1     -9999999.0     277.875   \n",
       "20103               220.0             3.6     -9999999.0     281.125   \n",
       "20104               250.0             3.6     -9999999.0     284.625   \n",
       "20105               300.0             6.2     -9999999.0     284.250   \n",
       "\n",
       "       air_temp_2  air_temp_3  ...  windspd_18  winddir_19  windspd_19  \\\n",
       "0             NaN         NaN  ...         NaN         NaN         NaN   \n",
       "1         283.625     283.250  ...         5.8       341.0         6.0   \n",
       "2         281.250     280.750  ...         6.8       344.0         5.3   \n",
       "3         279.625     279.125  ...         6.0       345.0         5.5   \n",
       "4         279.250     278.875  ...         3.1       338.0         3.5   \n",
       "...           ...         ...  ...         ...         ...         ...   \n",
       "20101     277.750     277.625  ...        12.1       223.0        11.8   \n",
       "20102     277.625     277.875  ...        10.2       230.0        10.8   \n",
       "20103     280.625     280.125  ...        10.3       218.0        11.9   \n",
       "20104     284.125     283.625  ...         9.4       218.0         8.6   \n",
       "20105     284.000     283.750  ...         8.6       241.0        10.2   \n",
       "\n",
       "       winddir_20  windspd_20  winddir_21  windspd_21  winddir_22  windspd_22  \\\n",
       "0             NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1           334.0         6.1       330.0         6.0       329.0         5.8   \n",
       "2           348.0         3.8       360.0         3.2        12.0         3.5   \n",
       "3           358.0         5.0        10.0         4.2        38.0         4.0   \n",
       "4           354.0         3.9         9.0         4.4        22.0         4.6   \n",
       "...           ...         ...         ...         ...         ...         ...   \n",
       "20101       221.0        11.4       219.0        11.3       215.0        11.4   \n",
       "20102       230.0        11.6       227.0        12.3       222.0        12.0   \n",
       "20103       221.0        12.8       222.0        11.9       225.0        10.6   \n",
       "20104       212.0         8.3       218.0         8.7       226.0        10.1   \n",
       "20105       236.0        10.5       232.0        10.5       227.0        11.3   \n",
       "\n",
       "       rotors_present  \n",
       "0                 0.0  \n",
       "1                 0.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  \n",
       "...               ...  \n",
       "20101             0.0  \n",
       "20102             0.0  \n",
       "20103             0.0  \n",
       "20104             0.0  \n",
       "20105             0.0  \n",
       "\n",
       "[20106 rows x 96 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rotors_catalog['rotors'].read()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37676962-5b80-4504-902f-8180710df111",
   "metadata": {},
   "source": [
    "Our catalog could have been pointing to any source of data, the details are abstracted by the catalog software, in this Intake. Now we will take quick look at programmatically creating a catalog from one or more data sources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f49fc329-be5f-4881-b31b-8596e381e6cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import fsspec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "35464e52-6af1-4876-bd47-28004fec7936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>air_temp_obs</th>\n",
       "      <th>dewpoint_obs</th>\n",
       "      <th>wind_direction_obs</th>\n",
       "      <th>wind_speed_obs</th>\n",
       "      <th>wind_gust_obs</th>\n",
       "      <th>air_temp_1</th>\n",
       "      <th>air_temp_2</th>\n",
       "      <th>air_temp_3</th>\n",
       "      <th>air_temp_4</th>\n",
       "      <th>...</th>\n",
       "      <th>v_wind_18</th>\n",
       "      <th>u_wind_19</th>\n",
       "      <th>v_wind_19</th>\n",
       "      <th>u_wind_20</th>\n",
       "      <th>v_wind_20</th>\n",
       "      <th>u_wind_21</th>\n",
       "      <th>v_wind_21</th>\n",
       "      <th>u_wind_22</th>\n",
       "      <th>v_wind_22</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>283.9</td>\n",
       "      <td>280.7</td>\n",
       "      <td>110.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.000</td>\n",
       "      <td>283.625</td>\n",
       "      <td>283.250</td>\n",
       "      <td>282.625</td>\n",
       "      <td>...</td>\n",
       "      <td>5.756768</td>\n",
       "      <td>-1.953409</td>\n",
       "      <td>5.673111</td>\n",
       "      <td>-2.674064</td>\n",
       "      <td>5.482644</td>\n",
       "      <td>-3.000000e+00</td>\n",
       "      <td>5.196152</td>\n",
       "      <td>-2.987221</td>\n",
       "      <td>4.971570</td>\n",
       "      <td>2015-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>280.7</td>\n",
       "      <td>279.7</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>281.500</td>\n",
       "      <td>281.250</td>\n",
       "      <td>280.750</td>\n",
       "      <td>280.250</td>\n",
       "      <td>...</td>\n",
       "      <td>6.502872</td>\n",
       "      <td>-1.460878</td>\n",
       "      <td>5.094687</td>\n",
       "      <td>-0.790064</td>\n",
       "      <td>3.716961</td>\n",
       "      <td>-7.837740e-16</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>0.727691</td>\n",
       "      <td>3.423517</td>\n",
       "      <td>2015-01-01 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>279.8</td>\n",
       "      <td>278.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.875</td>\n",
       "      <td>279.625</td>\n",
       "      <td>279.125</td>\n",
       "      <td>278.625</td>\n",
       "      <td>...</td>\n",
       "      <td>5.481273</td>\n",
       "      <td>-1.423505</td>\n",
       "      <td>5.312592</td>\n",
       "      <td>-0.174497</td>\n",
       "      <td>4.996954</td>\n",
       "      <td>7.293223e-01</td>\n",
       "      <td>4.136193</td>\n",
       "      <td>2.462646</td>\n",
       "      <td>3.152043</td>\n",
       "      <td>2015-01-01 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>279.9</td>\n",
       "      <td>277.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.625</td>\n",
       "      <td>279.250</td>\n",
       "      <td>278.875</td>\n",
       "      <td>278.250</td>\n",
       "      <td>...</td>\n",
       "      <td>2.475770</td>\n",
       "      <td>-1.311123</td>\n",
       "      <td>3.245143</td>\n",
       "      <td>-0.407661</td>\n",
       "      <td>3.878635</td>\n",
       "      <td>6.883116e-01</td>\n",
       "      <td>4.345829</td>\n",
       "      <td>1.723190</td>\n",
       "      <td>4.265046</td>\n",
       "      <td>2015-01-01 09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>279.9</td>\n",
       "      <td>277.4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.250</td>\n",
       "      <td>278.875</td>\n",
       "      <td>278.375</td>\n",
       "      <td>277.875</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.775695</td>\n",
       "      <td>-1.997259</td>\n",
       "      <td>0.104672</td>\n",
       "      <td>-1.928942</td>\n",
       "      <td>1.252670</td>\n",
       "      <td>-1.287595e+00</td>\n",
       "      <td>2.142918</td>\n",
       "      <td>-0.899056</td>\n",
       "      <td>2.225241</td>\n",
       "      <td>2015-01-01 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20101</th>\n",
       "      <td>20101</td>\n",
       "      <td>276.7</td>\n",
       "      <td>275.5</td>\n",
       "      <td>270.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.750</td>\n",
       "      <td>277.625</td>\n",
       "      <td>277.500</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.555992</td>\n",
       "      <td>-8.047581</td>\n",
       "      <td>-8.629974</td>\n",
       "      <td>-7.479073</td>\n",
       "      <td>-8.603689</td>\n",
       "      <td>-7.111320e+00</td>\n",
       "      <td>-8.781749</td>\n",
       "      <td>-6.538771</td>\n",
       "      <td>-9.338333</td>\n",
       "      <td>2020-12-31 06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20102</th>\n",
       "      <td>20102</td>\n",
       "      <td>277.9</td>\n",
       "      <td>276.9</td>\n",
       "      <td>270.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.625</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.875</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.956383</td>\n",
       "      <td>-8.273280</td>\n",
       "      <td>-6.942106</td>\n",
       "      <td>-8.886116</td>\n",
       "      <td>-7.456336</td>\n",
       "      <td>-8.995651e+00</td>\n",
       "      <td>-8.388580</td>\n",
       "      <td>-8.029567</td>\n",
       "      <td>-8.917738</td>\n",
       "      <td>2020-12-31 09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20103</th>\n",
       "      <td>20103</td>\n",
       "      <td>283.5</td>\n",
       "      <td>277.1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>281.125</td>\n",
       "      <td>280.625</td>\n",
       "      <td>280.125</td>\n",
       "      <td>279.625</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.332875</td>\n",
       "      <td>-7.326372</td>\n",
       "      <td>-9.377328</td>\n",
       "      <td>-8.397556</td>\n",
       "      <td>-9.660283</td>\n",
       "      <td>-7.962654e+00</td>\n",
       "      <td>-8.843423</td>\n",
       "      <td>-7.495332</td>\n",
       "      <td>-7.495332</td>\n",
       "      <td>2020-12-31 12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20104</th>\n",
       "      <td>20104</td>\n",
       "      <td>286.1</td>\n",
       "      <td>276.9</td>\n",
       "      <td>250.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.625</td>\n",
       "      <td>284.125</td>\n",
       "      <td>283.625</td>\n",
       "      <td>283.000</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.646804</td>\n",
       "      <td>-5.294689</td>\n",
       "      <td>-6.776892</td>\n",
       "      <td>-4.398330</td>\n",
       "      <td>-7.038799</td>\n",
       "      <td>-5.356255e+00</td>\n",
       "      <td>-6.855694</td>\n",
       "      <td>-7.265332</td>\n",
       "      <td>-7.016050</td>\n",
       "      <td>2020-12-31 15:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20105</th>\n",
       "      <td>20105</td>\n",
       "      <td>285.1</td>\n",
       "      <td>279.3</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.250</td>\n",
       "      <td>284.000</td>\n",
       "      <td>283.750</td>\n",
       "      <td>283.250</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.514397</td>\n",
       "      <td>-8.921121</td>\n",
       "      <td>-4.945058</td>\n",
       "      <td>-8.704895</td>\n",
       "      <td>-5.871525</td>\n",
       "      <td>-8.274113e+00</td>\n",
       "      <td>-6.464445</td>\n",
       "      <td>-8.264297</td>\n",
       "      <td>-7.706581</td>\n",
       "      <td>2021-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17486 rows × 140 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  air_temp_obs  dewpoint_obs  wind_direction_obs  \\\n",
       "1               1         283.9         280.7               110.0   \n",
       "2               2         280.7         279.7                90.0   \n",
       "3               3         279.8         278.1               100.0   \n",
       "4               4         279.9         277.0               120.0   \n",
       "5               5         279.9         277.4               120.0   \n",
       "...           ...           ...           ...                 ...   \n",
       "20101       20101         276.7         275.5               270.0   \n",
       "20102       20102         277.9         276.9               270.0   \n",
       "20103       20103         283.5         277.1               220.0   \n",
       "20104       20104         286.1         276.9               250.0   \n",
       "20105       20105         285.1         279.3               300.0   \n",
       "\n",
       "       wind_speed_obs  wind_gust_obs  air_temp_1  air_temp_2  air_temp_3  \\\n",
       "1                 4.1     -9999999.0     284.000     283.625     283.250   \n",
       "2                 7.7     -9999999.0     281.500     281.250     280.750   \n",
       "3                 7.7     -9999999.0     279.875     279.625     279.125   \n",
       "4                 7.2     -9999999.0     279.625     279.250     278.875   \n",
       "5                 8.7     -9999999.0     279.250     278.875     278.375   \n",
       "...               ...            ...         ...         ...         ...   \n",
       "20101             3.6     -9999999.0     277.875     277.750     277.625   \n",
       "20102             3.1     -9999999.0     277.875     277.625     277.875   \n",
       "20103             3.6     -9999999.0     281.125     280.625     280.125   \n",
       "20104             3.6     -9999999.0     284.625     284.125     283.625   \n",
       "20105             6.2     -9999999.0     284.250     284.000     283.750   \n",
       "\n",
       "       air_temp_4  ...  v_wind_18  u_wind_19  v_wind_19  u_wind_20  v_wind_20  \\\n",
       "1         282.625  ...   5.756768  -1.953409   5.673111  -2.674064   5.482644   \n",
       "2         280.250  ...   6.502872  -1.460878   5.094687  -0.790064   3.716961   \n",
       "3         278.625  ...   5.481273  -1.423505   5.312592  -0.174497   4.996954   \n",
       "4         278.250  ...   2.475770  -1.311123   3.245143  -0.407661   3.878635   \n",
       "5         277.875  ...  -0.775695  -1.997259   0.104672  -1.928942   1.252670   \n",
       "...           ...  ...        ...        ...        ...        ...        ...   \n",
       "20101     277.500  ...  -8.555992  -8.047581  -8.629974  -7.479073  -8.603689   \n",
       "20102     277.875  ...  -6.956383  -8.273280  -6.942106  -8.886116  -7.456336   \n",
       "20103     279.625  ...  -8.332875  -7.326372  -9.377328  -8.397556  -9.660283   \n",
       "20104     283.000  ...  -6.646804  -5.294689  -6.776892  -4.398330  -7.038799   \n",
       "20105     283.250  ...  -2.514397  -8.921121  -4.945058  -8.704895  -5.871525   \n",
       "\n",
       "          u_wind_21  v_wind_21  u_wind_22  v_wind_22                time  \n",
       "1     -3.000000e+00   5.196152  -2.987221   4.971570 2015-01-01 00:00:00  \n",
       "2     -7.837740e-16   3.200000   0.727691   3.423517 2015-01-01 03:00:00  \n",
       "3      7.293223e-01   4.136193   2.462646   3.152043 2015-01-01 06:00:00  \n",
       "4      6.883116e-01   4.345829   1.723190   4.265046 2015-01-01 09:00:00  \n",
       "5     -1.287595e+00   2.142918  -0.899056   2.225241 2015-01-01 12:00:00  \n",
       "...             ...        ...        ...        ...                 ...  \n",
       "20101 -7.111320e+00  -8.781749  -6.538771  -9.338333 2020-12-31 06:00:00  \n",
       "20102 -8.995651e+00  -8.388580  -8.029567  -8.917738 2020-12-31 09:00:00  \n",
       "20103 -7.962654e+00  -8.843423  -7.495332  -7.495332 2020-12-31 12:00:00  \n",
       "20104 -5.356255e+00  -6.855694  -7.265332  -7.016050 2020-12-31 15:00:00  \n",
       "20105 -8.274113e+00  -6.464445  -8.264297  -7.706581 2021-01-01 00:00:00  \n",
       "\n",
       "[17486 rows x 140 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rotors_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "90bc1b70-135a-4601-a85c-49a8fa5723cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_cat_fname = 'my_catalog.yml'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2754b9b3-ca5c-4749-b5a3-63527bdbcef4",
   "metadata": {},
   "source": [
    "Here we use the fsspec library to open our file. fsspec abstracts away the details of the underlying file system, so you can use the same code to read/write file on a local storage system as on a cloud object store such as [AWS S3](https://aws.amazon.com/s3), [Google Cloud storage](https://cloud.google.com/products/storage) or [Azure Blob Storage](https://azure.microsoft.com/en-gb/services/storage/blobs). The details of how to load the file are taken care of by fsspec. This combined with a catalog makes it much easier for researchers to access datasets without worrying about the technical details of storage and loading."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "49690bf4-ece7-44ab-89c1-dfb91a653982",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>DTG</th>\n",
       "      <th>air_temp_obs</th>\n",
       "      <th>dewpoint_obs</th>\n",
       "      <th>wind_direction_obs</th>\n",
       "      <th>wind_speed_obs</th>\n",
       "      <th>wind_gust_obs</th>\n",
       "      <th>air_temp_1</th>\n",
       "      <th>air_temp_2</th>\n",
       "      <th>air_temp_3</th>\n",
       "      <th>...</th>\n",
       "      <th>windspd_18</th>\n",
       "      <th>winddir_19</th>\n",
       "      <th>windspd_19</th>\n",
       "      <th>winddir_20</th>\n",
       "      <th>windspd_20</th>\n",
       "      <th>winddir_21</th>\n",
       "      <th>windspd_21</th>\n",
       "      <th>winddir_22</th>\n",
       "      <th>windspd_22</th>\n",
       "      <th>rotors_present</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>01/01/2015 00:00</td>\n",
       "      <td>283.9</td>\n",
       "      <td>280.7</td>\n",
       "      <td>110.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.000</td>\n",
       "      <td>283.625</td>\n",
       "      <td>283.250</td>\n",
       "      <td>...</td>\n",
       "      <td>5.8</td>\n",
       "      <td>341.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>330.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>01/01/2015 03:00</td>\n",
       "      <td>280.7</td>\n",
       "      <td>279.7</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>281.500</td>\n",
       "      <td>281.250</td>\n",
       "      <td>280.750</td>\n",
       "      <td>...</td>\n",
       "      <td>6.8</td>\n",
       "      <td>344.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>348.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>01/01/2015 06:00</td>\n",
       "      <td>279.8</td>\n",
       "      <td>278.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.875</td>\n",
       "      <td>279.625</td>\n",
       "      <td>279.125</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>358.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>01/01/2015 09:00</td>\n",
       "      <td>279.9</td>\n",
       "      <td>277.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.625</td>\n",
       "      <td>279.250</td>\n",
       "      <td>278.875</td>\n",
       "      <td>...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>338.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>354.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20101</th>\n",
       "      <td>20101</td>\n",
       "      <td>31/12/2020 06:00</td>\n",
       "      <td>276.7</td>\n",
       "      <td>275.5</td>\n",
       "      <td>270.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.750</td>\n",
       "      <td>277.625</td>\n",
       "      <td>...</td>\n",
       "      <td>12.1</td>\n",
       "      <td>223.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>221.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>219.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>215.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20102</th>\n",
       "      <td>20102</td>\n",
       "      <td>31/12/2020 09:00</td>\n",
       "      <td>277.9</td>\n",
       "      <td>276.9</td>\n",
       "      <td>270.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.625</td>\n",
       "      <td>277.875</td>\n",
       "      <td>...</td>\n",
       "      <td>10.2</td>\n",
       "      <td>230.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>230.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>227.0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20103</th>\n",
       "      <td>20103</td>\n",
       "      <td>31/12/2020 12:00</td>\n",
       "      <td>283.5</td>\n",
       "      <td>277.1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>281.125</td>\n",
       "      <td>280.625</td>\n",
       "      <td>280.125</td>\n",
       "      <td>...</td>\n",
       "      <td>10.3</td>\n",
       "      <td>218.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>221.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>222.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>225.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20104</th>\n",
       "      <td>20104</td>\n",
       "      <td>31/12/2020 15:00</td>\n",
       "      <td>286.1</td>\n",
       "      <td>276.9</td>\n",
       "      <td>250.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.625</td>\n",
       "      <td>284.125</td>\n",
       "      <td>283.625</td>\n",
       "      <td>...</td>\n",
       "      <td>9.4</td>\n",
       "      <td>218.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>212.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>218.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>226.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20105</th>\n",
       "      <td>20105</td>\n",
       "      <td>01/01/2021 00:00</td>\n",
       "      <td>285.1</td>\n",
       "      <td>279.3</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.250</td>\n",
       "      <td>284.000</td>\n",
       "      <td>283.750</td>\n",
       "      <td>...</td>\n",
       "      <td>8.6</td>\n",
       "      <td>241.0</td>\n",
       "      <td>10.2</td>\n",
       "      <td>236.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>232.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>227.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20106 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0               DTG  air_temp_obs  dewpoint_obs  \\\n",
       "0               0               NaN           NaN           NaN   \n",
       "1               1  01/01/2015 00:00         283.9         280.7   \n",
       "2               2  01/01/2015 03:00         280.7         279.7   \n",
       "3               3  01/01/2015 06:00         279.8         278.1   \n",
       "4               4  01/01/2015 09:00         279.9         277.0   \n",
       "...           ...               ...           ...           ...   \n",
       "20101       20101  31/12/2020 06:00         276.7         275.5   \n",
       "20102       20102  31/12/2020 09:00         277.9         276.9   \n",
       "20103       20103  31/12/2020 12:00         283.5         277.1   \n",
       "20104       20104  31/12/2020 15:00         286.1         276.9   \n",
       "20105       20105  01/01/2021 00:00         285.1         279.3   \n",
       "\n",
       "       wind_direction_obs  wind_speed_obs  wind_gust_obs  air_temp_1  \\\n",
       "0                     NaN             NaN            NaN         NaN   \n",
       "1                   110.0             4.1     -9999999.0     284.000   \n",
       "2                    90.0             7.7     -9999999.0     281.500   \n",
       "3                   100.0             7.7     -9999999.0     279.875   \n",
       "4                   120.0             7.2     -9999999.0     279.625   \n",
       "...                   ...             ...            ...         ...   \n",
       "20101               270.0             3.6     -9999999.0     277.875   \n",
       "20102               270.0             3.1     -9999999.0     277.875   \n",
       "20103               220.0             3.6     -9999999.0     281.125   \n",
       "20104               250.0             3.6     -9999999.0     284.625   \n",
       "20105               300.0             6.2     -9999999.0     284.250   \n",
       "\n",
       "       air_temp_2  air_temp_3  ...  windspd_18  winddir_19  windspd_19  \\\n",
       "0             NaN         NaN  ...         NaN         NaN         NaN   \n",
       "1         283.625     283.250  ...         5.8       341.0         6.0   \n",
       "2         281.250     280.750  ...         6.8       344.0         5.3   \n",
       "3         279.625     279.125  ...         6.0       345.0         5.5   \n",
       "4         279.250     278.875  ...         3.1       338.0         3.5   \n",
       "...           ...         ...  ...         ...         ...         ...   \n",
       "20101     277.750     277.625  ...        12.1       223.0        11.8   \n",
       "20102     277.625     277.875  ...        10.2       230.0        10.8   \n",
       "20103     280.625     280.125  ...        10.3       218.0        11.9   \n",
       "20104     284.125     283.625  ...         9.4       218.0         8.6   \n",
       "20105     284.000     283.750  ...         8.6       241.0        10.2   \n",
       "\n",
       "       winddir_20  windspd_20  winddir_21  windspd_21  winddir_22  windspd_22  \\\n",
       "0             NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1           334.0         6.1       330.0         6.0       329.0         5.8   \n",
       "2           348.0         3.8       360.0         3.2        12.0         3.5   \n",
       "3           358.0         5.0        10.0         4.2        38.0         4.0   \n",
       "4           354.0         3.9         9.0         4.4        22.0         4.6   \n",
       "...           ...         ...         ...         ...         ...         ...   \n",
       "20101       221.0        11.4       219.0        11.3       215.0        11.4   \n",
       "20102       230.0        11.6       227.0        12.3       222.0        12.0   \n",
       "20103       221.0        12.8       222.0        11.9       225.0        10.6   \n",
       "20104       212.0         8.3       218.0         8.7       226.0        10.1   \n",
       "20105       236.0        10.5       232.0        10.5       227.0        11.3   \n",
       "\n",
       "       rotors_present  \n",
       "0                 0.0  \n",
       "1                 0.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  \n",
       "...               ...  \n",
       "20101             0.0  \n",
       "20102             0.0  \n",
       "20103             0.0  \n",
       "20104             0.0  \n",
       "20105             0.0  \n",
       "\n",
       "[20106 rows x 96 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intake.open_csv(fsspec.open_local(rotors_path)).read()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edaaac56-cd20-41ce-bcf7-4c82ab87fdaa",
   "metadata": {},
   "source": [
    "Now we can try to programmatically create a catalog "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "b2d12385-743f-4297-9b4b-c3afccef6a76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"sources:\\n  csv:\\n    args:\\n      urlpath:\\n      - /home/h01/shaddad/data/ukrse2022/2021_met_office_aviation_rotors.csv\\n    description: ''\\n    driver: intake.source.csv.CSVSource\\n    metadata: {}\\n\""
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intake.source.csv.CSVSource(fsspec.open_local(rotors_path)).yaml()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0da242f4-cd39-461b-bff2-f5ad748f5b6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sources:\n",
      "  csv:\n",
      "    args:\n",
      "      urlpath:\n",
      "      - /home/h01/shaddad/data/ukrse2022/2021_met_office_aviation_rotors.csv\n",
      "    description: \"Tabular dataset with observed and simulated weather data, \\n   \\\n",
      "      \\ intended for use training a machine learning model predicting turbulent wind\\\n",
      "      \\ gust events.\"\n",
      "    driver: intake.source.csv.CSVSource\n",
      "    metadata: {}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open(new_cat_fname , 'w') as cat_out:\n",
    "    constructed_catalog = intake.open_csv(fsspec.open_local(rotors_path))\n",
    "    constructed_catalog.description = '''Tabular dataset with observed and simulated weather data, \n",
    "    intended for use training a machine learning model predicting turbulent wind gust events.'''\n",
    "    catalog_txt = constructed_catalog.yaml()\n",
    "    print(catalog_txt)\n",
    "    cat_out.write(catalog_txt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a9b92c4f-bd0d-462d-9071-43ef5a81b503",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/yaml": "my_catalog:\n  args:\n    path: my_catalog.yml\n  description: ''\n  driver: intake.catalog.local.YAMLFileCatalog\n  metadata: {}\n",
      "text/plain": [
       "my_catalog:\n",
       "  args:\n",
       "    path: my_catalog.yml\n",
       "  description: ''\n",
       "  driver: intake.catalog.local.YAMLFileCatalog\n",
       "  metadata: {}\n"
      ]
     },
     "metadata": {
      "application/json": {
       "root": "my_catalog"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_catalog = intake.open_catalog(new_cat_fname)\n",
    "new_catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "164ddf43-f404-41a7-8cb1-1084c17e53d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>DTG</th>\n",
       "      <th>air_temp_obs</th>\n",
       "      <th>dewpoint_obs</th>\n",
       "      <th>wind_direction_obs</th>\n",
       "      <th>wind_speed_obs</th>\n",
       "      <th>wind_gust_obs</th>\n",
       "      <th>air_temp_1</th>\n",
       "      <th>air_temp_2</th>\n",
       "      <th>air_temp_3</th>\n",
       "      <th>...</th>\n",
       "      <th>windspd_18</th>\n",
       "      <th>winddir_19</th>\n",
       "      <th>windspd_19</th>\n",
       "      <th>winddir_20</th>\n",
       "      <th>windspd_20</th>\n",
       "      <th>winddir_21</th>\n",
       "      <th>windspd_21</th>\n",
       "      <th>winddir_22</th>\n",
       "      <th>windspd_22</th>\n",
       "      <th>rotors_present</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>01/01/2015 00:00</td>\n",
       "      <td>283.9</td>\n",
       "      <td>280.7</td>\n",
       "      <td>110.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.000</td>\n",
       "      <td>283.625</td>\n",
       "      <td>283.250</td>\n",
       "      <td>...</td>\n",
       "      <td>5.8</td>\n",
       "      <td>341.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>330.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>01/01/2015 03:00</td>\n",
       "      <td>280.7</td>\n",
       "      <td>279.7</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>281.500</td>\n",
       "      <td>281.250</td>\n",
       "      <td>280.750</td>\n",
       "      <td>...</td>\n",
       "      <td>6.8</td>\n",
       "      <td>344.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>348.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>01/01/2015 06:00</td>\n",
       "      <td>279.8</td>\n",
       "      <td>278.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.875</td>\n",
       "      <td>279.625</td>\n",
       "      <td>279.125</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>358.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>01/01/2015 09:00</td>\n",
       "      <td>279.9</td>\n",
       "      <td>277.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.625</td>\n",
       "      <td>279.250</td>\n",
       "      <td>278.875</td>\n",
       "      <td>...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>338.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>354.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20101</th>\n",
       "      <td>20101</td>\n",
       "      <td>31/12/2020 06:00</td>\n",
       "      <td>276.7</td>\n",
       "      <td>275.5</td>\n",
       "      <td>270.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.750</td>\n",
       "      <td>277.625</td>\n",
       "      <td>...</td>\n",
       "      <td>12.1</td>\n",
       "      <td>223.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>221.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>219.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>215.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20102</th>\n",
       "      <td>20102</td>\n",
       "      <td>31/12/2020 09:00</td>\n",
       "      <td>277.9</td>\n",
       "      <td>276.9</td>\n",
       "      <td>270.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.625</td>\n",
       "      <td>277.875</td>\n",
       "      <td>...</td>\n",
       "      <td>10.2</td>\n",
       "      <td>230.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>230.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>227.0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20103</th>\n",
       "      <td>20103</td>\n",
       "      <td>31/12/2020 12:00</td>\n",
       "      <td>283.5</td>\n",
       "      <td>277.1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>281.125</td>\n",
       "      <td>280.625</td>\n",
       "      <td>280.125</td>\n",
       "      <td>...</td>\n",
       "      <td>10.3</td>\n",
       "      <td>218.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>221.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>222.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>225.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20104</th>\n",
       "      <td>20104</td>\n",
       "      <td>31/12/2020 15:00</td>\n",
       "      <td>286.1</td>\n",
       "      <td>276.9</td>\n",
       "      <td>250.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.625</td>\n",
       "      <td>284.125</td>\n",
       "      <td>283.625</td>\n",
       "      <td>...</td>\n",
       "      <td>9.4</td>\n",
       "      <td>218.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>212.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>218.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>226.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20105</th>\n",
       "      <td>20105</td>\n",
       "      <td>01/01/2021 00:00</td>\n",
       "      <td>285.1</td>\n",
       "      <td>279.3</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.250</td>\n",
       "      <td>284.000</td>\n",
       "      <td>283.750</td>\n",
       "      <td>...</td>\n",
       "      <td>8.6</td>\n",
       "      <td>241.0</td>\n",
       "      <td>10.2</td>\n",
       "      <td>236.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>232.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>227.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20106 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0               DTG  air_temp_obs  dewpoint_obs  \\\n",
       "0               0               NaN           NaN           NaN   \n",
       "1               1  01/01/2015 00:00         283.9         280.7   \n",
       "2               2  01/01/2015 03:00         280.7         279.7   \n",
       "3               3  01/01/2015 06:00         279.8         278.1   \n",
       "4               4  01/01/2015 09:00         279.9         277.0   \n",
       "...           ...               ...           ...           ...   \n",
       "20101       20101  31/12/2020 06:00         276.7         275.5   \n",
       "20102       20102  31/12/2020 09:00         277.9         276.9   \n",
       "20103       20103  31/12/2020 12:00         283.5         277.1   \n",
       "20104       20104  31/12/2020 15:00         286.1         276.9   \n",
       "20105       20105  01/01/2021 00:00         285.1         279.3   \n",
       "\n",
       "       wind_direction_obs  wind_speed_obs  wind_gust_obs  air_temp_1  \\\n",
       "0                     NaN             NaN            NaN         NaN   \n",
       "1                   110.0             4.1     -9999999.0     284.000   \n",
       "2                    90.0             7.7     -9999999.0     281.500   \n",
       "3                   100.0             7.7     -9999999.0     279.875   \n",
       "4                   120.0             7.2     -9999999.0     279.625   \n",
       "...                   ...             ...            ...         ...   \n",
       "20101               270.0             3.6     -9999999.0     277.875   \n",
       "20102               270.0             3.1     -9999999.0     277.875   \n",
       "20103               220.0             3.6     -9999999.0     281.125   \n",
       "20104               250.0             3.6     -9999999.0     284.625   \n",
       "20105               300.0             6.2     -9999999.0     284.250   \n",
       "\n",
       "       air_temp_2  air_temp_3  ...  windspd_18  winddir_19  windspd_19  \\\n",
       "0             NaN         NaN  ...         NaN         NaN         NaN   \n",
       "1         283.625     283.250  ...         5.8       341.0         6.0   \n",
       "2         281.250     280.750  ...         6.8       344.0         5.3   \n",
       "3         279.625     279.125  ...         6.0       345.0         5.5   \n",
       "4         279.250     278.875  ...         3.1       338.0         3.5   \n",
       "...           ...         ...  ...         ...         ...         ...   \n",
       "20101     277.750     277.625  ...        12.1       223.0        11.8   \n",
       "20102     277.625     277.875  ...        10.2       230.0        10.8   \n",
       "20103     280.625     280.125  ...        10.3       218.0        11.9   \n",
       "20104     284.125     283.625  ...         9.4       218.0         8.6   \n",
       "20105     284.000     283.750  ...         8.6       241.0        10.2   \n",
       "\n",
       "       winddir_20  windspd_20  winddir_21  windspd_21  winddir_22  windspd_22  \\\n",
       "0             NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1           334.0         6.1       330.0         6.0       329.0         5.8   \n",
       "2           348.0         3.8       360.0         3.2        12.0         3.5   \n",
       "3           358.0         5.0        10.0         4.2        38.0         4.0   \n",
       "4           354.0         3.9         9.0         4.4        22.0         4.6   \n",
       "...           ...         ...         ...         ...         ...         ...   \n",
       "20101       221.0        11.4       219.0        11.3       215.0        11.4   \n",
       "20102       230.0        11.6       227.0        12.3       222.0        12.0   \n",
       "20103       221.0        12.8       222.0        11.9       225.0        10.6   \n",
       "20104       212.0         8.3       218.0         8.7       226.0        10.1   \n",
       "20105       236.0        10.5       232.0        10.5       227.0        11.3   \n",
       "\n",
       "       rotors_present  \n",
       "0                 0.0  \n",
       "1                 0.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  \n",
       "...               ...  \n",
       "20101             0.0  \n",
       "20102             0.0  \n",
       "20103             0.0  \n",
       "20104             0.0  \n",
       "20105             0.0  \n",
       "\n",
       "[20106 rows x 96 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_catalog['csv'].read()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32b79ceb-5d87-4de8-8c57-c1509507ff12",
   "metadata": {},
   "source": [
    "* https://intake.readthedocs.io/en/latest/quickstart.html\n",
    "* http://gallery.pangeo.io/repos/pangeo-data/pangeo-tutorial-gallery/intake.html \n",
    "* https://medium.com/informatics-lab/archive-how-to-build-an-intake-catalog-c49182297572"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b557aad3-d2f0-4fef-86c5-93d7b6f67bc4",
   "metadata": {},
   "source": [
    "### Next Steps and further reading\n",
    "\n",
    "Here we have seen how we can use assorted tools an practices to make the data preparation part of a machine learning project more scalable and  reproducible, and also make the datasets created more FAIR through the use of catalogs to abstract away the details of data handling from a researcher who doesn't and shouldn't need to care about the technical details. We have used these tools in quite basic way, there are many more advanced options and tools that implement the same principles. Similarly there examples are very weather and climate focused as well as python centric. Different tools or languages will be used for different research areas (e.g. R and R Shiny may be popular)\n",
    "\n",
    "* Scaling data pipelines with dask - [docs](https://www.udemy.com/course/scalable-data-analysis-in-python-with-dask/) [Udemy course](https://www.udemy.com/course/scalable-data-analysis-in-python-with-dask/)\n",
    "  * Machine Learning with dask - [docs](https://ml.dask.org/)\n",
    "* Building a dashboard with Holoviz - [tutorial](https://holoviz.org/tutorial/exercises/Building_a_Dashboard.html)\n",
    " * Building a dashboard in R with Shiny - [docs](https://shiny.rstudio.com/articles/dashboards.html)\n",
    "* Supporting FAIR data - [European Commission report](https://ec.europa.eu/info/sites/default/files/turning_fair_into_reality_0.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6df79eeb-4638-4b97-b3ea-082b52b9092a",
   "metadata": {},
   "source": [
    "### References\n",
    "\n",
    "* [Pandas](https://pandas.pydata.org/docs/)\n",
    "* [dask](https://docs.dask.org/en/stable/)\n",
    "* [Iris](https://scitools-iris.readthedocs.io/en/stable/)\n",
    "* [Holoviz](https://holoviz.org/)\n",
    "* [intake](https://intake.readthedocs.io/en/latest/)\n",
    "* [fsspec](https://filesystem-spec.readthedocs.io/en/latest/)\n"
   ]
  }
 ],
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