معرفی شرکت ها
NDBC-1.1.1
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
A package to automate the loading of NDBC data to a custom object.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | NDBC-1.1.1 |
| نام | NDBC |
| نسخه کتابخانه | 1.1.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | C. Ryan Manzer |
| ایمیل نویسنده | ryan@gensci.org |
| آدرس صفحه اصلی | https://github.com/GenSci/NDBC |
| آدرس اینترنتی | https://pypi.org/project/NDBC/ |
| مجوز | - |
# NDBC
 
This repository represents my attempts to build out Python class(es)
to facilitate the acquisition, analysis, and visualization of National
Data Buoy Center (NDBC) data. The goal is to develop a set of APIs to
facilitate 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 to
allow a user to define a specific data buoy they wish to gather data
from 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
```
pip install NDBC
```
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
```
from NDBC.NDBC import DataBuoy
DB = DataBuoy()
DB.set_station_id('46042') # <- Either strings or numbers are acceptable
```
`.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 using
the `set_station_id` method).
```
from NDBC.NDBC import DataBuoy
DB = DataBuoy(46042)
DB.get_station_metadata()
DB.station_info
{ 'Air temp height': '4 m above site elevation',
'Anemometer height': '5 m above site elevation',
'Barometer elevation': 'sea level',
'Sea temp depth': '0.6 m below water line',
'Site elevation': 'sea level',
'Watch circle radius': '1789 yards',
'Water depth': '1645.9 m',
'lat': '36.785 N',
'lon': '122.398 W'}
```
- `.get_data(datetime_index=False)`
After importing, the DataBuoy class is instantiated with the ID of the
station from which historical data is sought. Then data may be gathered for
the years and months specified. If no time period is specified, the most recent
full 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.
```
from NDBC.NDBC import DataBuoy
n42 = DataBuoy(46042) # <- String or numeric station ids are valid
n42.get_data(datetime_index=True) # <- no year, month argumets so latest full month is retrieved. Default data type is 'stdmet'
Oct not available. # <- Where data is missing, messages are returned to the terminal via a logger.warning() call
Sep not available.
n42.data # <- anticipating additional data collection methods, the .data property returns a dictionary. Indiviudual
data products are returned as pandas DataFrame objects
# Datetime objects are compiled from individual year, month, day, hour, minute columns and used as the index to support
# slicing data by time frames.
{'stdmet': WDIR WSPD GST WVHT DPD APD MWD PRES ATMP WTMP DEWP VIS TIDE
2019-07-31 23:50:00 298 3.6 5.2 1.25 7.69 5.37 303 1015.1 13.4 15.2 999.0 99.0 99.00
2019-08-01 00:50:00 301 5.7 7.2 1.26 7.14 5.42 306 1014.8 13.4 15.3 999.0 99.0 99.00
2019-08-01 01:50:00 323 6.6 8.3 1.33 7.14 5.47 312 1014.5 13.2 15.1 999.0 99.0 99.00
2019-08-01 02:50:00 347 5.8 7.7 1.32 7.69 5.15 319 1014.5 12.7 15.1 999.0 99.0 99.00
2019-08-01 03:50:00 353 5.6 7.2 1.26 7.69 5.31 325 1014.9 12.6 15.0 999.0 99.0 99.00
... ... ... ... ... ... ... ... ... ... ... ... ... ...
2019-08-31 18:50:00 999 6.2 7.4 0.87 13.79 4.67 186 1014.6 17.0 17.2 999.0 99.0 99.00
2019-08-31 19:50:00 999 6.8 8.3 0.83 13.79 4.56 178 1014.2 17.2 17.3 999.0 99.0 99.00
2019-08-31 20:50:00 999 6.5 7.8 0.89 13.79 4.38 195 1013.8 17.5 17.4 999.0 99.0 99.00
2019-08-31 21:50:00 999 7.5 8.9 0.95 13.79 4.52 190 1013.1 17.5 17.3 999.0 99.0 99.00
2019-08-31 22:50:00 999 8.0 9.4 0.95 13.79 4.09 171 1012.7 17.7 17.1 999.0 99.0 99.00
[741 rows x 13 columns]}
```
By default the `get_data()` function will fetch the most current month's data. However, the function can take lists of years & months ([int]) to specify a timeframe.
```
>>> n42 = NDBC.DataBuoy('46042')
>>> n42.get_data(months=[1,2], years=range(2019, 2020), datetime_index=True, data_type='swden)
Year 2019 not available.
Year 2020 not available.
>>> n42.data
{'swden': {'data': .0200 .0325 .0375 .0425 .0475 .0525 .0575 .0625 .0675 .0725 .0775 .0825 .0875 ... .3000 .3100 .3200 .3300 .3400 .3500 .3650 .3850 .4050 .4250 .4450 .4650 .4850
2021-01-01 00:40:00 0.0 0.0 0.0 0.00 1.17 9.11 24.25 24.95 15.84 20.44 26.48 20.63 12.72 ... 0.28 0.31 0.19 0.20 0.13 0.07 0.06 0.05 0.03 0.01 0.01 0.00 0.0
2021-01-01 01:40:00 0.0 0.0 0.0 0.00 0.00 13.76 26.55 22.40 24.12 30.09 23.41 15.74 14.95 ... 0.25 0.16 0.12 0.16 0.06 0.16 0.06 0.03 0.05 0.02 0.01 0.00 0.0
2021-01-01 02:40:00 0.0 0.0 0.0 0.00 0.93 4.40 16.03 33.95 41.48 38.02 31.47 18.88 14.59 ... 0.21 0.15 0.18 0.14 0.14 0.10 0.07 0.05 0.03 0.02 0.01 0.00 0.0
2021-01-01 03:40:00 0.0 0.0 0.0 0.07 1.14 6.95 27.94 45.68 41.92 30.11 25.03 19.52 10.93 ... 0.22 0.20 0.16 0.09 0.08 0.15 0.09 0.04 0.02 0.01 0.00 0.01 0.0
2021-01-01 04:40:00 0.0 0.0 0.0 0.00 0.76 3.64 11.23 18.23 29.84 27.19 12.85 11.20 9.77 ... 0.13 0.17 0.14 0.16 0.08 0.08 0.07 0.08 0.05 0.01 0.01 0.00 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2021-02-28 19:40:00 0.0 0.0 0.0 0.00 0.00 0.00 0.06 0.25 1.42 2.50 9.48 11.48 8.46 ... 0.21 0.13 0.11 0.08 0.10 0.04 0.02 0.02 0.03 0.01 0.00 0.00 0.0
2021-02-28 20:40:00 0.0 0.0 0.0 0.02 0.05 0.08 0.24 1.02 3.97 4.97 4.99 8.31 10.09 ... 0.21 0.07 0.09 0.06 0.05 0.10 0.04 0.03 0.01 0.01 0.00 0.00 0.0
2021-02-28 21:40:00 0.0 0.0 0.0 0.00 0.00 0.15 0.30 0.36 1.63 4.18 6.85 7.82 7.98 ... 0.12 0.11 0.09 0.08 0.04 0.05 0.06 0.02 0.01 0.01 0.00 0.00 0.0
2021-02-28 22:40:00 0.0 0.0 0.0 0.00 0.01 0.09 0.10 0.32 2.84 3.82 3.91 4.92 5.17 ... 0.17 0.09 0.13 0.05 0.05 0.08 0.06 0.03 0.01 0.01 0.00 0.00 0.0
2021-02-28 23:40:00 0.0 0.0 0.0 0.00 0.00 0.00 0.18 0.25 1.78 3.97 5.08 4.98 5.40 ... 0.07 0.10 0.11 0.08 0.08 0.06 0.03 0.02 0.01 0.01 0.00 0.00 0.0
[1413 rows x 47 columns]}}
```
Likely due to my own biases in my research interests, the `get_data()` function will default to fetching
standard meteorological data. However, users can specify different data packages like so `get_data(data_type='cwind')`. To view which data packages
are currently supported examine the `DataBuoy.DATA_PACKAGES` attribute:
```
{'cwind': {'name': 'Continous Wind Data', 'url_char': 'c'},
'srad': {'name': 'Solar radiation data', 'url_char': 'r'},
'stdmet': {'name': 'Standard meteoroligcal data', 'url_char': 'h'},
'swden': {'name': 'Spectral Wave Density data', 'url_char': 'w'},
'swdir': {'name': 'Spectral wave (alpha1) direction data', 'url_char': 'd'},
'swdir2': {'name': 'Spectral wave (alpha2) direction data', 'url_char': 'i'},
'swr1': {'name': 'Spectral wave (r1) direction data', 'url_char': 'j'},
'swr2': {'name': 'Spectral wave (r2) direction data', 'url_char': 'k'}}
```
Using the pandas DataFrame to store the returned data provides access to the wide array of methods the pandas package
provides.
- `.save(filename(optional))`
Saves an instantiated DataBuoy object as JSON to a file. If `filename` is not specified the file name will follow the
`databuoy_{station_id}.json` convention.
```
db = DataBuoy(46042)
db.save('/path/to/file/my_filename.json')
```
_classmethod_
- `.load(filename)`
Instantiate a DataBuoy object from a file, generated by the `.save()` method.
```
db = DataBuoy.load('/path/to/file.json')
```
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl NDBC-1.1.1:
pip install NDBC-1.1.1.whl
نصب پکیج tar.gz NDBC-1.1.1:
pip install NDBC-1.1.1.tar.gz