<jupyter_start><jupyter_text>**IceFlow** Point Cloud Data Access Accessing Data with the IceFlow Widget NASA's Earthdata CredentialsTo access data using the *IceFlow* library it is necessary to log into [Earthdata Login](https://urs.earthdata.nasa.gov/). To do this, enter your NASA Earthdata credentials in the next step after executing the following code cell.**Note**: If you don't have NASA Earthdata credentials you have to register first at the link above. You don't need to be a NASA employee to register with NASA Earthdata!<jupyter_code># Importing IceFlow client library
from iceflow.ui import IceFlowUI
# Instantiateing the client
client = IceFlowUI()
# You need to use your NASA Earthdata Credentials and verify that they work.
# Please click on set credentials and then see if authentication is successful by executing the next cell.
client.display_credentials()
# This cell will verify if your credentials are valid. 
# This may take a little while, if it fails for some reason try again.
# NOTE: Wednesday mornings are usually downtime for NSIDC services and you might experience difficulties accessing data.
authorized = client.authenticate()
if authorized is None:
    print('Earthdata Login not successful')
else:
    print('Earthdata Login successful!')<jupyter_output><empty_output><jupyter_text>**Note:** To view the widget output you need to enable Jupyter's log console. You can activate the console by clicking the right-most icon at the bottom of your browser  or by slecting “View” >> “Show log console” on the menu bar.<jupyter_code># Let's start loading the user interface. Next, we will explain all options and user interface components.
# vertical = Sidecar widget, horizontal = render the widget in this notebook.
# Note that depending on your screen size and resolution the 'vertical' display option may not work correctly.
# This is a current bug in the jupyter-widget that can not be solved within the scope of IceFlow.
client.display_map('horizontal', extra_layers=True)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.06044226044226044, 'fraction_numerical': 0.0, 'mean_word_length': 5.264615384615385, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 11, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '436646', 'license': 'MIT', 'n_tokens_mistral': 524, 'n_tokens_neox': 503, 'n_words': 274, 'path': 'notebooks/iceflow/1_widget.ipynb', 'repo_name': 'mikala-nsidc/NSIDC-Data-Tutorials', 'size': '2035'}