I have a question regarding the Weibull distributions that are being fit in PyWAsP and how they compare to the relative wind datasets.

I'm trying to find a way to reproduce this table from WAsP (see here), where the mean wind speed and wind power density are compared between the measured wind climate and the fitted (emergent) Weibull distribution, including the relative discrepancy.

This information should be obtainable from the original Observed Wind Climate (OWC) files generated by WAsP. However, I am currently working with the BWC and WWC formats in PyWAsP, where it does not seem to be directly available.

So far, I haven't found an easy way to replicate this in PyWAsP. Do you have any suggestions?

Best regards

I have a question regarding the configuration of the climate parameters in PyWAsP. I know that the T-star stability model has been introduced in WAsp 12.8, but sometimes we still prefer to model the old heat flux over land parameter (in the EWA profile model) to match measured vertical profiles.

How can I do this in PyWAsP? I want to keep the latest flow model and change the heat flux. I tried this:

config = pw.wasp.Config("WAsP_12.10")
config.climate.set_profile_model(2) #WAsP 12.7 with EWA climate model
config.climate['off_hfx_land'] = 0

I find the explanation in the user guide a bit confusing. Sometimes WAsP 12.7 is referred to as "1", but then I also see that the numbers go from -1 to 3 (see link 1 and link 2). Can you give some clarifications on this, please?

Best regards

I am having problems when running calculations in PyWAsP for my own projects. Note that I have the same issues when running the tutorial notebooks.

1) When I call pw.potential_aep I get the following error:

aep_park2 = pw.potential_aep(
    wwc,
    wtg,
    wind_farm_model="PARK2_onshore",
    turbulence_intensity=0.0
)
TypeError: Passing a Dataset as `data_vars` to the Dataset constructor is not supported. Use `ds.copy()` to create a copy of a Dataset.

This is odd as I'm running the exact same command for the gross_aep, which does work. 

2) From the same function, I noticed that if I don't specify the turbulence intensity, I get:

"turbulence_intensity is not in the WWC and no explicit value was passed"

which reading from the documentation, should be automatically retrieved from the wwc. Is the TI not calculated when using the linear model?

3) I also have a question regarding the predict_wwc input parameter adapt_gwc.  From the documentation I read: "Adapt the standard heights and roughnesses in the gwc to match  those of the input and output site_effects."

But how do I know which values are being used? I would like to be able to specify my own values, similarly to what I have found in the deprecated function: 

def generalize_and_downscale(
    bwc, topo_map, output_locs, generalize_kwargs={}, downscale_kwargs={}
)

4) Lastly, I noticed that calling predict_wwc consumes 3 runs instead of the 2 specified on your website. Did you recently make any changes to this?

Best regards

I managed to download ERA5 data with Windkit, but it does not contain enough data to be able to compute a stability metric (MOL, or Ri).

Apart from a direct ERA5 download from the ECMWF (slow), can anyone suggest any other way to download time-series of stability? Or alternatively, atmospheric stability statistics?

Thank you.

1. Is this package independent from DTU's?

2. Is there any alternative within Windkit to do something similar?

Thank you

quite surprisingly, WAsP Climate analyst 3 (ver.3.01.0049) , windographer WAsP algortithm (ver.5.3.1), windPRO (ver.4.1.263) WAsP algorithm, and python coding results with reference to "Comparison of Weibull Estimation Methods for Diverse Winds - Bingöl - 2020 - Advances in Meteorology - Wiley Online Library" and "Fitting Weibull Parameters for Wind Energy
Applications-EMD", they all shows different A,k,f, Vmean values.

therefore, I was trying to use "Windkit (ver.0.8.0).

time series fit - overall value, with from windkit.weibull import _fit_wasp_m1_m3_fgt works well.

but to check sectorwise --> combined & emergent might be tricky.

i got stuck in sector wise fit. 

the main issue is wind data without core information that are required by windkit (south_north, west_east, height, crs) linked to time series data.

wind data has typical time stamp , wind speed, wind direction at different heights or several files.

the wind data has read csv format using pandas. To use windkit, it is converted to Xrray.

for example, four information was read in csv format and try to inject. 

def run_windkit_sectorwise(df, turbine_ids, turbine_coords, epsg=xxxxx, output_dir="Windkit_output"):

...

            # Build Dataset
            ds = xr.Dataset(
                {
                    "wind_speed": (("time",), ws.values),
                    "wind_direction": (("time",), wd.values)
                },
                coords={
                    "time": ("time", ws.index),
                    "point": [0],
                    "south_north": ("point", [coords["y"]]),
                    "west_east": ("point", [coords["x"]]),
                    "height": ("point", [coords["height"]])
                }
            )

            # Add CRS before calling WindKit
            ds = add_crs(ds, epsg)
            
            # Call WindKit
            bwc = bwc_from_timeseries(ds, nsec=12)

...

based on other discussion of similar issues, this worked and i tried to do with different ways, but in the end, 

i received "Please enter your email adress." and if i put my email address, then ask me "institution", instead of error message.

i gave up now as I cannot proceed any further without any error messages.

hope your team improve the message to trace it better and easy-to-use.

or might be good idea to provide the best structured file sample, which i could not find "WindKit — windkit 0.8.0 documentation".

Regards,

Gyeongil

I am trying to use WindKit to create a .tab file via a binned wind climate. However, I get an error when I try and run the code.

The minimum working example is:

import windkit
import pandas as pd
import numpy as np
import random
import xarray as xr

# Function to make dummy dataframe
def dummy_df(size):
    df = pd.DataFrame(zip([random.uniform(0, 25) for i in range(size)], [random.uniform(0, 360) for i in range(size)]),
                      index=pd.date_range("01-01-2023 00:00:00", periods=size, freq="10T"), columns=['wind_speed', 'wind_direction'])
    df.index.name = 'time'
    df = np.round(df, 2)
    return df

# Dummy pandas dataframe
DF = dummy_df(144)

# Dataframe to xarray dataset
DS = xr.Dataset.from_dataframe(DF)

# Assign coords to dataset
DS = DS.assign_coords({"west_east":0,"south_north":0,"height":90.0})

# Add CRS to dataset
windkit.add_crs(DS, 4326)

# Create binned wind cliamte from timeseries
bwc = windkit.bwc_from_timeseries(
    ts=DS
)

# Create .tab file from binned wind climate
windkit.binned_wind_climate.bwc_to_tabfile(
    bwc=bwc
)

And I get the following error:

Traceback (most recent call last):

  File "C:\Appl\venv\lib\site-packages\spyder_kernels\py3compat.py", line 356, in compat_exec
    exec(code, globals, locals)

  File "c:\users\nga\onedrive - equinor\documents\tab_files.py", line 30, in <module>
    bwc = windkit.bwc_from_timeseries(

  File "C:\Appl\venv\lib\site-packages\windkit\_validate.py", line 66, in validate
    obj = args[0]

IndexError: tuple index out of range

Any advice on where I might be going wrong?

Thanks in advance,

Nils

I am trying to use PyWAsP to export weibull wind climates as rsf and wrg files but have been having trouble with the windkit to_rsffile and to_wrgfile functions. 
After loading in a tab file, contour map, roughness map, and turbine locations, I calculated the generalised wind climate and the predicted wind climate at each turbine. I then attempted to output the results to an rsf using the code below but I keep getting the following error.

site_effects = topo_map.get_site_effects(output_locs, nsecs=12)

pwc_rsf = pw.wasp.get_wasp_down(gwc, site_effects, genwc_interp="nearest")

wk.to_rsffile(pwc_rsf, 'test.rsf')

KeyError                                  Traceback (most recent call last)
File ~/mambaforge/envs/pywasp_env/lib/python3.8/site-packages/xarray/core/dataset.py:1340, in Dataset._construct_dataarray(self, name)
   1339 try:
-> 1340     variable = self._variables[name]
   1341 except KeyError:

KeyError: 'elevation'

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
Cell In[104], line 1
----> 1 wk.to_rsffile(pwc_rsf, 'test.rsf')

File ~/mambaforge/envs/pywasp_env/lib/python3.8/site-packages/windkit/weibull_wind_climate.py:408, in to_rsffile(wwc, rsffile, wrg_header)
    398 def to_rsffile(wwc, rsffile, wrg_header=False):
    399     """Write weibull wind climate dataset to .rsf file.
    400 
    401     Parameters
   (...)
    406         Path to .rsf file
    407     """
--> 408     return _to_resource_file(wwc, rsffile, wrg_header=wrg_header)

File ~/mambaforge/envs/pywasp_env/lib/python3.8/site-packages/windkit/_validate.py:71, in create_validator.<locals>.validator_wrapper.<locals>.validate(*args, **kwargs)
     68 # Do bwc checks
     69 validator(obj)  # Raises ValueError if errors exist
---> 71 result = func(*args, **kwargs)
     72 return result

File ~/mambaforge/envs/pywasp_env/lib/python3.8/site-packages/windkit/weibull_wind_climate.py:363, in _to_resource_file(wwc, rsffile, wrg_header)
    360 wwc_cp["k"] = (wwc_cp["k"] * 100.0).astype(np.int16)
    361 wwc_cp["wdfreq"] = (wwc_cp["wdfreq"] * 1000.0).astype(np.int16)
--> 363 df = wwc_cp["elevation"].to_dataframe().reset_index()
    364 new_columns = ["point", "Name", "west_east", "south_north", "elevation", "height"]
    365 df = df.reindex(columns=new_columns)

File ~/mambaforge/envs/pywasp_env/lib/python3.8/site-packages/xarray/core/dataset.py:1431, in Dataset.__getitem__(self, key)
   1429     return self.isel(**key)
   1430 if utils.hashable(key):
-> 1431     return self._construct_dataarray(key)
   1432 if utils.iterable_of_hashable(key):
   1433     return self._copy_listed(key)

File ~/mambaforge/envs/pywasp_env/lib/python3.8/site-packages/xarray/core/dataset.py:1342, in Dataset._construct_dataarray(self, name)
   1340     variable = self._variables[name]
   1341 except KeyError:
-> 1342     _, name, variable = _get_virtual_variable(self._variables, name, self.dims)
   1344 needed_dims = set(variable.dims)
   1346 coords: dict[Hashable, Variable] = {}

File ~/mambaforge/envs/pywasp_env/lib/python3.8/site-packages/xarray/core/dataset.py:178, in _get_virtual_variable(variables, key, dim_sizes)
    176 split_key = key.split(".", 1)
    177 if len(split_key) != 2:
--> 178     raise KeyError(key)
    180 ref_name, var_name = split_key
    181 ref_var = variables[ref_name]

KeyError: 'elevation'

Does anyone know why I'm getting this error and/or how I could fix this issue? Am I missing some steps between downscaling to a wwc and outputting the rsf?

Any help is much appreciated, thank you 

I am a master's student at Wind Power. Now, I am writing my thesis about wind resource assessment. I need to do MCP in Python, but my coding skills are limited. I am doing it at WindPRO but need to do it in open source for my thesis. I am trying to write code in Python, but it is time-consuming, and I am unsure about the results. Is there any change to apply the same WindPRO MCP routines in Python? If yes, how can I do it? I found Windkit here, but I have no idea how to implement it. I need your expertise.

Your answers are highly appreciated!

Thanks a lot!

I am just being lazy and wanted to take advantage of Windkit's histogram plots, windrose plots and weibull fitting.

 i can't remember/find if the conversion from 10min statistics (well organized in a Pandas dataframe) to a tabfile is currently part of the package or not yet.

My regards to everyone in RAM!

Andrea

one of my colleagues aske me if we can use or how to use Pywasp.

he refers to this documents: https://wes.copernicus.org/articles/5/1679/2020/wes-5-1679-2020.pdf and want to apply his project.

based on the available information: https://docs.wasp.dk/pywasp/installation.html

/

You will be prompted for the url and port number of your license server, which should be provided by your institution. From there, you will be able to access all of the pywasp_api from the pw namespace.

/

it seems like we need to first contact someone responsible for it and get and API or some information to access.

my company has already use commerical WAsP/WAsP eng. if this is necessary to use.

Could you please advise me how to use pywasp?

Regards,

Gyeongil

First of all thank you for providing these tools.

I think the windkit conversion from RSF files contains a bug: When it tries to infer the number of sectors in the source file (in _infer_resource_file_nsec), the lines are split and counted:

    return (len(line.split()) - 9) // 3

However, this doesn't seem to account for occasions where some of the columns in the RSF file adhere to the column width specs, but don't have a space in between, like here:

Point   726452.0 5781855.8   130.5149.0 9.54 2.244        633.900 12  78 116  235  24  77  207  35  72  246  99  83  278  92  80  260  36  77  244  51  77  232  93  82  238 124  87  240 140 101  247 110 115  263 118 122  272
Point   726452.0 5781880.8   130.8149.0 9.54 2.244        633.700 12  78 116  235  24  77  207  35  72  246  99  83  278  92  80  260  36  77  244  51  77  232  93  82  238 124  87  240 140 101  247 110 115  263 118 122  272

It can also be problematic when the text string in the first ten columns is an empty string.

Is there a reason not to read the number of sectors from columns 70-72?

Cheers,
Florian
 

Above channels are already in the current list