Use of wtk data with a python interface
Create a conda environment and install pywtk
module purge; module load conda
conda env update -n pywtk
source activate pywtk
python setup.py installSet an environment variable named PYWTK_CACHE_DIR to the directory where the data will reside and use the API as normal. To find a directory where a specific site data resides use pywtk.wtk_api.site_from_cache:
from pywtk.wtk_api import site_from_cache, WIND_FCST_DIR
files = site_from_cache(12345, WIND_FCST_DIR)The complete data set is 2.4 TB
aws s3 sync --dryrun --no-sign-request s3://nrel-pds-wtk/wtk-techno-economic/pywtk-data /local-data-dir/- Modify pywtk/wtk_api.py in your source then run python setup.py install
WIND_FCST_DIR = "/local-data-dir/fcst_data"
WIND_MET_NC_DIR = "/local-data-dir/met_data"On peregrine run the start_notebook.sh in the notebooks/ directory. When accessing the notebook for the first time, look for the token parameter in the URL that shows the server you have access. Otherwise you will be prompted for a password that won't work.
To view the notebook locally, run:
ssh -L 8888:n0290:8888 hpc-login1.hpc.nrel.gov
Then open your browser to http://localhost:8888/
[I 09:47:27.987 NotebookApp] [nb_conda_kernels] enabled, 13 kernels found
[I 09:47:35.463 NotebookApp] [nb_anacondacloud] enabled
[I 09:47:35.506 NotebookApp] [nb_conda] enabled
[I 09:47:36.242 NotebookApp] ✓ nbpresent HTML export ENABLED
[W 09:47:36.242 NotebookApp] ✗ nbpresent PDF export DISABLED: No module named nbbrowserpdf.exporters.pdf
[I 09:47:36.250 NotebookApp] Serving notebooks from local directory: /home/hsorense/projects/wind2plexos
[I 09:47:36.250 NotebookApp] 0 active kernels
[I 09:47:36.250 NotebookApp] The Jupyter Notebook is running at: http://0.0.0.0:8888/?token=c7c42d73e5e7954688873ab27c79207977c335829385b30d- Wind site metadata
$ python -c "import pywtk.site_lookup; print(pywtk.site_lookup.sites.ix[11222])"
gid 11223
fraction_of_usable_area 1
power_curve 3
capacity 16
wind_speed 7.34
capacity_factor 0.411
the_geom 0101000020E61000009414580053BA59C069FD2D01F857...
city NaN
state Texas
country United States
elevation NaN
point POINT (-102.911316 32.687256)
Name: 11222, dtype: object- Wind site timezones
$ python -c "import pywtk.site_lookup; print(pywtk.site_lookup.timezones.ix[11222])"
abbreviation CDT
countryCode US
nextAbbreviation CST
timestamp 1470316655
dst 1
dstStart 1457856000
countryName United States
gmtOffset -18000
dstEnd 1478415600
zoneName America/Chicago
Name: 11222, dtype: object- Available met data attributes
$ python -c "import pywtk.wtk_api; print pywtk.wtk_api.MET_ATTRS"
['density', 'power', 'pressure', 'temperature', 'wind_direction', 'wind_speed']- Available forecast data attributes
$ python -c "import pywtk.wtk_api; print pywtk.wtk_api.FORECAST_ATTRS"
['day_ahead_power', 'hour_ahead_power', '4_hour_ahead_power', '6_hour_ahead_power', 'day_ahead_power_p90', 'hour_ahead_power_p90', '4_hour_ahead_power_p90', '6_hour_ahead_power_p90', 'day_ahead_power_p10', 'hour_ahead_power_p10', '4_hour_ahead_power_p10', '6_hour_ahead_power_p10']- Lookup sites within a Well Known Text rectangle descriptor
$ python -c "import pywtk.site_lookup; in_sites = pywtk.site_lookup.get_3tiersites_from_wkt('POLYGON((-120.82 34.4,-119.19 34.4,-119.19 33.92,-120.82 33.92,-120.82 34.4))'); print in_sites.index.values"
[29375 29733 29872 30019 30190 30539 30712 30713 30874 31032 31033 31189
31192 31320 31321 31322 31323 31324 31563 32060 32834 33203 30873 31034
31190 31191]- Lookup the three nearest sites to a Well Known Text point
python -c "import pywtk.site_lookup; sorted_sites = pywtk.site_lookup.get_3tiersites_from_wkt('POINT(-103.12 40.24)'); print sorted_sites.index.values[:3]"
[53252 52873 54322]- Retrieval of met data for multiple sites for a Well Known Text descriptor for specified attributes and year
import pandas
import pywtk.wtk_api
wkt = 'POLYGON((-120.82 34.4,-119.19 34.4,-119.19 33.92,-120.82 33.92,-120.82 34.4))'
years = ['2008']
attributes = ['power', 'wind_speed']
wind_data = pywtk.wtk_api.get_wind_data_by_wkt(wkt, years, attributes=attributes)
print(wind_data.keys())
print(wind_data[31563].info())[30539, 31320, 30873, 30874, 29733, 29872, 33203, 31032, 31033, 31034, 32060, 29375, 32834, 30019, 31563, 31189, 31190, 31191, 31192, 31321, 31322, 31323, 31324, 30190, 30712, 30713]
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 105120 entries, 2008-01-01 00:00:00-08:00 to 2008-12-31 23:55:00-08:00
Data columns (total 2 columns):
power 105120 non-null float64
wind_speed 105120 non-null float64
dtypes: float64(2)
memory usage: 1.6 MB
None
- Retrieval of met data for a single site for specified attributes and timespan
import pandas
import pywtk.wtk_api
start = pandas.Timestamp('2007-08-01', tz='utc')
end = pandas.Timestamp('2007-08-15', tz='utc')
attributes = ['power', 'wind_speed']
wind_data = pywtk.wtk_api.get_wind_data(102445, start, end, attributes=attributes)
print(wind_data.info())<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 4033 entries, 2007-07-31 20:00:00-04:00 to 2007-08-14 20:00:00-04:00
Data columns (total 2 columns):
power 4033 non-null float64
wind_speed 4033 non-null float64
dtypes: float64(2)
memory usage: 94.5 KB
None
- Retrieval of forecast data for multiple sites for a Well Known Text descriptor for specified attributes and years
import pandas
import pywtk.wtk_api
wkt = 'POLYGON((-120.82 34.4,-119.19 34.4,-119.19 33.92,-120.82 33.92,-120.82 34.4))'
years = ['2008']
attributes = ['hour_ahead_power', 'day_ahead_power']
wind_data = pywtk.wtk_api.get_wind_data_by_wkt(wkt, years, attributes=attributes, dataset="forecast", utc=True)
print(wind_data.keys())
print(wind_data[31563].info())[30539, 31320, 30873, 30874, 29733, 29872, 33203, 31032, 31033, 31034, 32060, 29375, 32834, 30019, 31563, 31189, 31190, 31191, 31192, 31321, 31322, 31323, 31324, 30190, 30712, 30713]
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 8784 entries, 2008-01-01 00:00:00+00:00 to 2008-12-31 23:00:00+00:00
Data columns (total 2 columns):
hour_ahead_power 8784 non-null float32
day_ahead_power 8784 non-null float32
dtypes: float32(2)
memory usage: 137.2 KB
None
- Retrieval of forecast data for a single site for specified attributes and timespan
import pandas
import pywtk.wtk_api
start = pandas.Timestamp('2007-08-01', tz='utc')
end = pandas.Timestamp('2007-08-15', tz='utc')
attributes = ['hour_ahead_power', 'day_ahead_power']
wind_data = pywtk.wtk_api.get_forecast_data(102445, start, end, attributes=attributes)
print(wind_data.info())<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 337 entries, 2007-07-31 20:00:00-04:00 to 2007-08-14 20:00:00-04:00
Data columns (total 2 columns):
hour_ahead_power 337 non-null float32
day_ahead_power 337 non-null float32
dtypes: float32(2)
memory usage: 5.3 KB
None
The WKT descriptor should follow the SQL spec. The following are examples of a polygon and point definitions:
min_lng = -120.82
max_lng = -119.19
min_lat = 33.92
max_lat = 34.4
wkt = "POLYGON(({0} {3},{1} {3},{1} {2},{0} {2},{0} {3}))".format(min_lng, max_lng, min_lat, max_lat)pt_lng = -103.12
pt_lat = 40.24
wkt = "POINT({0} {1})".format(pt_lng, pt_lat)
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