getgfs package
Submodules
getgfs.decode module
Decodes the downloaded files to extract the variables and their coordinates
- class getgfs.decode.Coordinate(name, values)
Bases:
objectHolds the information and values describing a coordinate
- class getgfs.decode.File(text)
Bases:
objectHolds the variables and information from a text file returned by the forecast site
- class getgfs.decode.Variable(name, coords, data)
Bases:
objectHolds the information and data for an extracted variable
- getgfs.decode.replace_val(arr, val, position)
Inserts a value into a 1 to 4 dimensional numpy array
Note
I am sure there are better ways todo this but I couldn’t find any after quite a search
- Args:
arr (numpy array): Array to insert into val (float/int): Value to insert position (tuple): Coordinate position in array
- Raises:
TypeError: Position invalid ValueError: Dimensionality of array too high
- Returns:
[type]: [description]
getgfs.getgfs module
getgfs - a library for extracting weather forecast variables from the NOAA GFS forecast in a pure python, no obscure dependencies way
- class getgfs.getgfs.Forecast(resolution='0p25', timestep='')
Bases:
objectObject that can be manipulated to get forecast information
- check_avail(forecast_date, forecast_time)
- datetime_to_forecast(date_time)
Works out which forecast date/run/time is required for the latest values for a chosen time
- Parameters
date_time (string) – The date and time of the desired forecast, parser is used so any format is valid e.g. 20210205 11pm
- Raises
ValueError – The date time requested is not available from the NOAA at this time
- Returns
forecast date string: forecast run string: forecast query time (the appropriate timestep within the forecast)
- Return type
string
- get(variables, date_time, lat, lon)
Returns the latest forecast available for the requested date and time
Note
“raw” since you have to put indexes in rather than coordinates and it returns a file object rather than a processed file
If a variable has level dependance, you get all the levels - it seems extremely hard to impliment otherwise
- Args:
variables (list): list of required variables by short name date_time (string): datetime requested (parser used so any format fine) lat (string or number): latitude in the format “[min:max]” or a single value lon (string or number): longitude in the format “[min:max]” or a single value
- Raises:
ValueError: Invalid variable choice ValueError: Level dependance needs to be specified for chosen variable Exception: Unknown failure to download the file
- Returns:
File Object: File object with the downloaded variable data (see File documentation)
- get_windprofile(date_time, lat, lon)
Finds the verticle wind profile for a location. I wrote this since it is what I require in another program. The U/V compoents of wind with sigma do not go down to the surface so the surface components are also included as well as a pressure altitude change of x- variable.
- Parameters
date_time (string) – datetime requested (parser used so any format fine)
lat (string or number) – Latitude for data
lon (string or number) – Longitude for data
- Returns
U component of wind interpolater by altitude interpolation object: V component of wind interpolater by altitude
- Return type
interpolation object
- search(variable, sensetivity=80)
The short names of the forecast variables are nonsence so this can be used to find the short name (used for the rest of the forecast) that you are looking for
Note
Will not work if fuzzywuzzy is not installed
- Args:
variable (string): Search terms for the variable (e.g. U-Component wind) sensetivity (int, optional): The search sensitivity, for common words a higher sensitivity may be required. Defaults to 80.
- Raises:
RuntimeError: Fuzzywuzzy not installed
- Returns:
list: List of possible matches sorted by ratio, short name (what you need) and long name also given
- value_input_to_index(coord, inpt)
Turns a chosen value of a coordinate/coordinate range to the index in the forecast array
- Parameters
coord (string) – The short name of the coordinate to convert
inpt (float/str) – The value or range requested, for a range a string in the format [min_val:max_val] is required
- Raises
ValueError – Incorrect inpt format
- Returns
Index of coordinate value(s) as the forecast requires it (i.e. within [])
- Return type
str
- value_to_index(coord, value)
Turns a coordinate value into the index in the forecast array
- Parameters
coord (string) – The short name of the coordinate to convert
inpt (float/str) – The value requested
- Returns
Index in array
- Return type
int
- getgfs.getgfs.extract_line(possibles, line)
Works out what is being refered to by a line in the das and dds pages for the forecast
- Parameters
possibles (list) – Possible titles
line (string) – Line to search
- Returns
Index of the attribute
- Return type
int
- getgfs.getgfs.get_attributes(res, step)
Finds the available variables and coordinates for a given forecast
- Parameters
res (str, optional) – The forecast resulution, choices are 1p00, 0p50 and 0p25. Defaults to “0p25”.
step (str, optional) – The timestep of the forecast to use, most do not have a choice but 0p25 can be 3hr (default) or 1hr. Defaults to “”.
- Raises
Exception – Failed to download the requested resolution and forecast
RuntimeError – Failed to download the other attributes
- Returns
Time attributes (the number of timesteps and the size of the timesteps) dict: Coordinates for the forecast with their short name, number of steps, min, max, resolution dict: Variables with all the information about them
- Return type
dict
- getgfs.getgfs.hour_round(t)
Rounds to the nearest hour for a datetime object
- Parameters
t (datetime) – Datetime to round
- Returns
Rounded datetime
- Return type
datetime