Ndbc

Completed in 1994 by the PMEL research laboratory, and transferred to an operational status at NDBC in 2005, the array provides real-time high quality oceanographic and surface meteorological data for monitoring, forecasting and understanding climate swings associated with El Nino La Nina.

• ‎The first and still the best, NOAA Buoy and Tide Data allows you to retrieve weather data from NOAA's National Data Buoy Center. It also provides tide predictions for the US and Moon phase information. The perfect tool to help plan your time on the water.For a map of buoys locations, go here.
• Ards & North Down Borough Council has 40 councillors representing 7 district electoral areas. Members provide leadership and oversee the day to day running.

We are in business to improve our patients’ quality of life

Not just provide a medical service…

National Dizzy and Balance Center (NDBC) is a unique outpatient clinic system. We combine Physicians, Audiologists, Physical Therapists, and Occupational Therapists, all within each facility to offer a true multidisciplinary approach to vertigo, dizziness, balance disorders, and concussions.

We utilize State-of-The-Art Diagnostic Balance Testing Labs to more accurately diagnose our patients when entering our programs here at NDBC. We utilize this data to develop individualized rehabilitation programs for each patient at NDBC, which increases outcomes, and lowers costs.

Released:

A package to automate the loading of NDBC data to a custom object.

Project description

This repository represents my attempts to build out Python class(es)to facilitate the acquisition, analysis, and visualization of NationalData Buoy Center (NDBC) data. The goal is to develop a set of APIs tofacilitate rapid discovery of data resources, exploratory data analysis,and allow integration into automated data workflows.

NDBC.py

This file defines the DataBuoy class. The purpose of this class is toallow a user to define a specific data buoy they wish to gather datafrom and provide the user with methods to collect and analyze this data.

Dependencies are listed in requirements.txt

Usage

Installation

Install using pip from PyPI

Then you are ready to start using this module in exploratory data analyses and scripted workflows.

Methods of DataBuoy Class

.set_station_id

If a DataBuoy class has been instantiated without any station_id argument, this method allows for setting a station id

.get_station_metadata()

Perform a scrape of the public webpage for a specified data station and save a dictionary of available metadata to the .station_info property. This is only available if a DataBuoy has a valid station_id set (either during class instantiation or usingthe set_station_id method).

• .get_stdmet(datetime_index=False)

After importing, the DataBuoy class is instantiated with the ID of thestation from which historical data is sought. Then data may be gathered forthe years and months specified. If no time period is specified, the most recentfull month available is retrieved.

The default behavior is to append datetime values built from date part columns (YY, MM, DD, etc.) to a column 'datetime'. If value True is passed as the datetime_index argument, the datetime values will be used as index values for the returned dataframe. In some cases this is advantageous for time series analyses.

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By default the get_stdmet function will fetch the most current month's data. However, the function can take lists of years & months ([int]) to specify a timeframe.

Using the pandas DataFrame to store the returned data provides access to the wide array of methods the pandas packageprovides.

• .save(filename(optional))

Saves an instantiated DataBuoy object as JSON to a file. If filename is not specified the file name will follow thedatabuoy_{station_id}.json convention.

classmethod

• .load(filename)Instantiate a DataBuoy object from a file, generated by the .save() method.

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1.0.1

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0.1.1

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