<jupyter_start><jupyter_text>intake_informaticslab`intake_informaticslab` is an [intake](https://intake.readthedocs.io/en/latest/) catalogue and driver to access hundreds of terabytes of Met Office data presented as coherent [Analysis Ready Cloud Optimised](https://medium.com/informatics-lab/analysis-ready-data-47f7e80cba42) datasets.For details on the datasets and the licences associated with them see the [README](https://github.com/informatics-lab/intake_informaticslabreadme)These notebooks demonstrate some of the functionality available through this library and datasets. Many examples have been truncated so they consume less memory so they work on a free [MyBinder](https://mybinder.org/) instance.  These datasets are kindly hosted by Microsoft Azure as part of the [AI For Earth initiative](https://www.microsoft.com/en-us/ai/ai-for-earth).These datasets are hosted in the Azure East US 2 region. The performance will greatly depend on your connectivity to this region. For best performance, work in the Azure East US 2 region.  Import some things we'll need and set some plot options<jupyter_code># fix for display issue
import xarray as xr
import numpy as np
import intake
import matplotlib
matplotlib.rcParams['figure.figsize'] = (15.0, 15.0)<jupyter_output><empty_output><jupyter_text>Data access is as easy as installing a package<jupyter_code>## Un-comment to use your prefered package manager. If launched via MyBinder this step can be skipped.
# %pip install intake_informaticslab --upgrade
# %conda install -c conda-forge -c informaticslab intake_informaticslab --upgrade<jupyter_output><empty_output><jupyter_text>Explore the datasets available<jupyter_code>def print_cat_items(cat, indent=0):
    if(isinstance(cat, intake.catalog.Catalog)):
        for cat_item in list(cat):
            print(" "*indent + cat_item)
            print_cat_items(cat[cat_item], indent+2)

print_cat_items(intake.cat)<jupyter_output>met_office
  air_quality
    air_quality_hourly
    air_quality_daily
  weather_forecasts
    mogreps_uk
      single_level
      height_level
      pressure_level
      depth_level
    mogreps_g
      single_level
      height_level
      pressure_level
      depth_level
  weather_continuous_timeseries
    ukv_daily_timeseries
    ukv_hourly_timeseries<jupyter_text>Some datasets might require accepting a licence  A `LicenseNotExceptedError` will be raised if a licence needs accepting<jupyter_code>## This line would raise a LicenseNotExceptedError
intake.cat.met_office.weather_forecasts.mogreps_g.single_level.to_dask()<jupyter_output><empty_output><jupyter_text>View the dataset's license<jupyter_code>intake.cat.met_office.weather_forecasts.mogreps_g.single_level.license<jupyter_output><empty_output><jupyter_text>Accept the license with the `license_accepted` flag<jupyter_code>intake.cat.met_office.weather_forecasts.mogreps_g.single_level(license_accepted=True).to_dask()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.07179139857771757, 'fraction_numerical': 0.005756857433118863, 'mean_word_length': 5.623318385650224, 'pattern_counts': {'":': 0, '<': 24, '<?xml version=': 0, '>': 24, 'https://': 5, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '6', 'dir': 'jupyter-structured-clean-dedup', 'id': '508538', 'license': 'MIT', 'n_tokens_mistral': 943, 'n_tokens_neox': 875, 'n_words': 246, 'path': 'binder/Introduction.ipynb', 'repo_name': 'informatics-lab/met_office_datasets', 'size': '2953'}