Skip to content

j-tenny/NFDRS4py

BranchesTags
 
 

Repository files navigation

nfdrs4py - Simple Python Interface for the National Fire Danger Rating System 4.0

Calculate fuel moisture and fire danger indices using this python interface to the NFDRS4 C++ library.

This library offers a simple interface to NFDRS4 and easier installation for Python users. Compilation was tested with Microsoft Visual Studio 2022 with C++ developer workload. Create a github issue if you have problems with installation or otherwise.

Core code forked from https://github.com/firelab/NFDRS4

See also: https://github.com/j-tenny/pyrothermel

Installation

Ensure that you have a C++ compiler installed and configured, then run

pip install nfdrs4py

Or clone this repository, cd to the project root, then run pip install .

Example

import nfdrs4py
import pandas as pd
import requests
import io

Run NFDRS4 Example With Config File

wx = pd.read_csv('data/241513_2001_2017.fw21')
wx
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
DateTime Temperature(F) RelativeHumidity(%) Precipitation(in) WindSpeed(mph) WindAzimuth(degrees) SolarRadiation(W/m2) SnowFlag GustSpeed(mph) GustAzimuth(degrees)
0 2001-03-22T15:00:00-07:00 62.0 16.0 0.0 4.0 6.0 650.0 1.0 8.0 34.0
1 2001-03-22T16:00:00-07:00 58.0 15.0 0.0 2.0 69.0 188.0 1.0 7.0 60.0
2 2001-03-22T17:00:00-07:00 60.0 12.0 0.0 5.0 270.0 93.0 1.0 16.0 243.0
3 2001-03-22T18:00:00-07:00 58.0 12.0 0.0 7.0 278.0 178.0 1.0 19.0 258.0
4 2001-03-22T19:00:00-07:00 57.0 10.0 0.0 8.0 300.0 26.0 1.0 13.0 289.0
... ... ... ... ... ... ... ... ... ... ...
143019 2017-12-31T19:00:00-07:00 18.0 93.0 0.0 0.0 196.0 0.0 NaN 0.0 186.0
143020 2017-12-31T20:00:00-07:00 18.0 93.0 0.0 0.0 241.0 0.0 NaN 0.0 340.0
143021 2017-12-31T21:00:00-07:00 17.0 90.0 0.0 0.0 215.0 0.0 NaN 0.0 254.0
143022 2017-12-31T22:00:00-07:00 17.0 92.0 0.0 0.0 196.0 0.0 NaN 0.0 224.0
143023 2017-12-31T23:00:00-07:00 17.0 92.0 0.0 0.0 220.0 0.0 NaN 0.0 206.0

143024 rows × 10 columns

interface = nfdrs4py.NFDRS4py.init_from_config('data/NFDRSInitSample.txt')
results = interface.process_df(wx,0,1,2,3,6,4,7)
results
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
DateTime Temperature(F) RelativeHumidity(%) Precipitation(in) WindSpeed(mph) WindAzimuth(degrees) SolarRadiation(W/m2) SnowFlag GustSpeed(mph) GustAzimuth(degrees) ... dmc_100_hr dmc_1000_hr lmc_herb lmc_woody burning_index energy_release_component spread_component ignition_component growing_season_index kb_drought_index
0 2001-03-22T15:00:00-07:00 62.0 16.0 0.0 4.0 6.0 650.0 1.0 8.0 34.0 ... 29.999996 20.0 30.0 60.0 0.0 3.308081 0.0 0.0 0.0 100.0
1 2001-03-22T16:00:00-07:00 58.0 15.0 0.0 2.0 69.0 188.0 1.0 7.0 60.0 ... 29.999966 20.0 30.0 60.0 0.0 1.858711 0.0 0.0 0.0 100.0
2 2001-03-22T17:00:00-07:00 60.0 12.0 0.0 5.0 270.0 93.0 1.0 16.0 243.0 ... 29.999914 20.0 30.0 60.0 0.0 1.060471 0.0 0.0 0.0 100.0
3 2001-03-22T18:00:00-07:00 58.0 12.0 0.0 7.0 278.0 178.0 1.0 19.0 258.0 ... 29.999834 20.0 30.0 60.0 0.0 0.824433 0.0 0.0 0.0 100.0
4 2001-03-22T19:00:00-07:00 57.0 10.0 0.0 8.0 300.0 26.0 1.0 13.0 289.0 ... 29.999721 20.0 30.0 60.0 0.0 0.782769 0.0 0.0 0.0 100.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
143019 2017-12-31T19:00:00-07:00 18.0 93.0 0.0 0.0 196.0 0.0 NaN 0.0 186.0 ... NaN NaN 30.0 50.0 NaN NaN NaN NaN 0.0 100.0
143020 2017-12-31T20:00:00-07:00 18.0 93.0 0.0 0.0 241.0 0.0 NaN 0.0 340.0 ... NaN NaN 30.0 50.0 NaN NaN NaN NaN 0.0 100.0
143021 2017-12-31T21:00:00-07:00 17.0 90.0 0.0 0.0 215.0 0.0 NaN 0.0 254.0 ... NaN NaN 30.0 50.0 NaN NaN NaN NaN 0.0 100.0
143022 2017-12-31T22:00:00-07:00 17.0 92.0 0.0 0.0 196.0 0.0 NaN 0.0 224.0 ... NaN NaN 30.0 50.0 NaN NaN NaN NaN 0.0 100.0
143023 2017-12-31T23:00:00-07:00 17.0 92.0 0.0 0.0 220.0 0.0 NaN 0.0 206.0 ... NaN NaN 30.0 50.0 NaN NaN NaN NaN 0.0 100.0

143024 rows × 22 columns

Run on RAWS data imported from web API

station_id = '20284'
start_date = '2018-05-23T23:30:00Z'
end_date = '2018-06-03T23:29:59Z'
url = f'https://fems.fs2c.usda.gov/api/climatology/download-weather?stationIds={station_id}&startDate={start_date}&endDate={end_date}&dataset=observation&dataFormat=fw21&dataIncrement=hourly&stationtypes=RAWS(SATNFDRS)'
request = requests.get(url)
wx = pd.read_csv(io.BytesIO(request.content))
wx['SnowFlag'] = wx['SnowFlag'].astype(bool)
wx
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
StationId DateTime Temperature(F) RelativeHumidity(%) Precipitation(in) WindSpeed(mph) WindAzimuth(degrees) GustSpeed(mph) GustAzimuth(degrees) SnowFlag SolarRadiation(W/m2) Tflag RHflag PCPflag WSflag WAflag SRflag GSflag GAflag
0 20284 2018-05-24T00:00:00-07:00 34 39 0.0 2 322 4 217 False 0.0 0 0 0 0 0 0 0 0
1 20284 2018-05-24T01:00:00-07:00 32 42 0.0 2 60 3 52 False 0.0 0 0 0 0 0 0 0 0
2 20284 2018-05-24T02:00:00-07:00 29 46 0.0 1 212 2 50 False 0.0 0 0 0 0 0 0 0 0
3 20284 2018-05-24T03:00:00-07:00 28 48 0.0 1 189 3 12 False 0.0 0 0 0 0 0 0 0 0
4 20284 2018-05-24T04:00:00-07:00 26 56 0.0 1 182 4 223 False 0.0 0 0 0 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
259 20284 2018-06-03T19:00:00-07:00 79 9 0.0 7 216 15 270 False 113.0 0 0 0 0 0 0 0 0
260 20284 2018-06-03T20:00:00-07:00 68 13 0.0 3 223 8 216 False 4.0 0 0 0 0 0 0 0 0
261 20284 2018-06-03T21:00:00-07:00 59 18 0.0 5 225 6 232 False 0.0 0 0 0 0 0 0 0 0
262 20284 2018-06-03T22:00:00-07:00 52 22 0.0 2 355 5 224 False 0.0 0 0 0 0 0 0 0 0
263 20284 2018-06-03T23:00:00-07:00 47 25 0.0 2 7 3 52 False 0.0 0 0 0 0 0 0 0 0

264 rows × 19 columns

interface = nfdrs4py.NFDRS4py(Lat=35,FuelModel='W',SlopeClass=1,AvgAnnPrecip=30)
results = interface.process_df(wx)
results
<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } 
.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
StationId DateTime Temperature(F) RelativeHumidity(%) Precipitation(in) WindSpeed(mph) WindAzimuth(degrees) GustSpeed(mph) GustAzimuth(degrees) SnowFlag ... dmc_100_hr dmc_1000_hr lmc_herb lmc_woody burning_index energy_release_component spread_component ignition_component growing_season_index kb_drought_index
0 20284 2018-05-24T00:00:00-07:00 34 39 0.0 2 322 4 217 False ... 19.999734 19.999999 30.0 60.0 0.000000 0.000000 0.000000 0.000000 0.000000 100.0
1 20284 2018-05-24T01:00:00-07:00 32 42 0.0 2 60 3 52 False ... 19.998554 19.999989 30.0 60.0 0.000000 0.000000 0.000000 0.000000 0.000000 100.0
2 20284 2018-05-24T02:00:00-07:00 29 46 0.0 1 212 2 50 False ... 19.996832 19.999972 30.0 60.0 0.000000 0.000000 0.528339 0.539606 0.000000 100.0
3 20284 2018-05-24T03:00:00-07:00 28 48 0.0 1 189 3 12 False ... 19.994536 19.999947 30.0 60.0 0.000000 0.000000 0.821775 0.747045 0.000000 100.0
4 20284 2018-05-24T04:00:00-07:00 26 56 0.0 1 182 4 223 False ... 19.991556 19.999915 30.0 60.0 0.000000 0.000000 0.842248 0.747889 0.000000 100.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
259 20284 2018-06-03T19:00:00-07:00 79 9 0.0 7 216 15 270 False ... 8.521227 13.391623 30.0 60.0 43.918018 13.209191 25.687646 50.040193 0.074623 161.0
260 20284 2018-06-03T20:00:00-07:00 68 13 0.0 3 223 8 216 False ... 8.497583 13.370282 30.0 60.0 29.707420 12.736459 11.388582 29.923758 0.074623 161.0
261 20284 2018-06-03T21:00:00-07:00 59 18 0.0 5 225 6 232 False ... 8.481125 13.355573 30.0 60.0 34.591961 12.025729 16.792868 31.345662 0.074623 161.0
262 20284 2018-06-03T22:00:00-07:00 52 22 0.0 2 355 5 224 False ... 8.639911 13.345286 30.0 60.0 23.865936 11.142063 8.088035 18.152894 0.074623 161.0
263 20284 2018-06-03T23:00:00-07:00 47 25 0.0 2 7 3 52 False ... 8.630694 13.337515 30.0 60.0 22.471078 10.204840 7.747199 14.670750 0.074623 161.0

264 rows × 31 columns

results['DateTime'] = pd.to_datetime(results['DateTime'])
results.plot(x='DateTime',y=['dmc_1_hr','dmc_10_hr','dmc_100_hr','dmc_1000_hr'])
<Axes: xlabel='DateTime'>

png

About

National Fire Danger Rating System 4.0

Resources

Readme
Activity

Stars

1 star

Watchers

0 watching

Forks

1 fork
Report repository

Releases

No releases published

Packages

No packages published

Languages

  • C++ 80.7%
  • Jupyter Notebook 7.6%
  • C 6.6%
  • Python 4.7%
  • Other 0.4%