wps_climdex_rmm¶
WPS wrapper for climdex.pcic's functions which return the annual count of days where daily precipitation is more than some threshold per day
Precipitation Exceeding 10mm Per Day (climdex.r10mm): This function takes a climdexInput object as input and computes the climdex index R10mm: theannual count of days where daily precipitation is more than 10mm per day.
Precipitation Exceeding 20mm Per Day (climdex.r20mm): This function takes a climdexInput object as input and computes the climdex index R20mm: theannual count of days where daily precipitation is more than 20mm per day.
Precipitation Exceeding A Specified Amount Per Day (climdex.rnnmm): This function takes a climdexInput object as input and computes the climdex index Rnnmm: theannual count of days where daily precipitation is more thannnmm per day
In [1]:
import os
import requests
from birdy import WPSClient
from rpy2 import robjects
from urllib.request import urlretrieve
from importlib.resources import files
from tempfile import NamedTemporaryFile
from wps_tools.testing import get_target_url
from wps_tools.R import rda_to_vector, construct_r_out, test_rda_output
In [2]:
# Ensure we are in the working directory with access to the data
while os.path.basename(os.getcwd()) != "quail":
os.chdir('../')
In [3]:
# NBVAL_IGNORE_OUTPUT
url = get_target_url("quail")
print(f"Using quail on {url}")
Using quail on https://marble-dev01.pcic.uvic.ca/twitcher/ows/proxy/quail/wps
In [4]:
quail = WPSClient(url)
Help for individual processes can be diplayed using the ? command (ex/ bird.process?)¶
In [5]:
# NBVAL_IGNORE_OUTPUT
quail.climdex_rmm?
Signature: quail.climdex_rmm( climdex_input, threshold=None, loglevel='INFO', output_file='output.rda', output_formats=None, ) Docstring: The annual count of days where daily precipitation is more than [threshold] mm per day Parameters ---------- climdex_input : ComplexData:mimetype:`application/x-gzip` RDS or Rdata (.rds, .rda, .rdata) file containing R Object of type climdexInput output_file : string Filename to store the output Rdata (extension .rda) threshold : float mm threshold for daily precipitation loglevel : {'CRITICAL', 'ERROR', 'WARNING', 'INFO', 'DEBUG', 'NOTSET'}string Logging level Returns ------- rda_output : ComplexData:mimetype:`application/x-gzip` Rda file containing R output data File: ~/github/quail/</tmp/quail-venv/lib/python3.8/site-packages/birdy/client/base.py-5> Type: method
Run wps_climdex_rmm with climdex.r10mm¶
In [6]:
with NamedTemporaryFile(suffix=".rda", prefix="r10mm_", dir="/tmp", delete=True) as output_file:
output = quail.climdex_rmm(
climdex_input=(files("tests") / "data/climdexInput.rda").resolve(),
threshold=10.0,
output_file=output_file.name,
)
r10mm_url = output.get()[0]
You can also use rds input
Run wps_climdex_rmm with climdex.r20mm with rds input¶
In [7]:
with NamedTemporaryFile(suffix=".rda", prefix="r20mm_", dir="/tmp", delete=True) as output_file:
output = quail.climdex_rmm(
climdex_input=(files("tests") / "data/climdexInput.rds").resolve(),
threshold=20.0,
output_file=output_file.name,
)
r20mm_url = output.get()[0]
You can use multiple inputs
Run wps_climdex_rmm with climdex.rnnmm with rds input with multiple inputs¶
In [8]:
climdex_inputs = [
(files("tests") / "data/climdexInput.rds").resolve(),
(files("tests") / "data/climdexInput.rda").resolve(),
(files("tests") / "data/climdex_input_multiple.rda").resolve(),
]
with NamedTemporaryFile(suffix=".rda", prefix="rm=nnmm_", dir="/tmp", delete=True) as output_file:
output = quail.climdex_rmm(
climdex_input=climdex_inputs,
threshold=9.7,
output_file=output_file.name,
)
rnnmm_url = output.get()[0]
Access the output with rda_to_vector or construct_r_out from wps_tools.R
In [9]:
# NBVAL_IGNORE_OUTPUT
# use print() to see whole vector
r10mm = rda_to_vector(r10mm_url, "r10.0mm1_ci")
print(f"r10mm\n{r10mm}")
r20mm = rda_to_vector(r20mm_url, "r20.0mm1_ci")
print(f"r20mm\n{r20mm}")
rnnmm = rda_to_vector(rnnmm_url, "r9.7mm1_ci")
print(f"rnnmm\n{rnnmm}")
r10mm 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 NA 24 NA NA NA 26 27 22 34 30 16 25 26 28 19 26 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 33 NA NA 15 18 27 26 NA 33 NA NA NA NA NA 22 44 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 NA 22 NA NA 31 NA 41 34 42 16 21 NA 35 NA r20mm 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 NA 7 NA NA NA 6 10 9 13 7 4 2 12 14 3 6 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 11 NA NA 2 7 8 8 NA 7 NA NA NA NA NA 9 11 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 NA 7 NA NA 10 NA 9 13 13 2 7 NA 12 NA rnnmm 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 NA 24 NA NA NA 28 30 23 41 35 19 27 30 30 21 28 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 35 NA NA 16 19 28 28 NA 34 NA NA NA NA NA 23 45 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 NA 25 NA NA 32 NA 41 36 43 16 22 NA 36 NA
In [10]:
# NBVAL_IGNORE_OUTPUT
construct_r_out([r10mm_url, r20mm_url, rnnmm_url])
Out[10]:
[[R object with classes: ('numeric',) mapped to:
[ nan, 24.000000, nan, nan, ..., 21.000000, nan, 35.000000, nan]],
[R object with classes: ('numeric',) mapped to:
[ nan, 7.000000, nan, nan, ..., 7.000000, nan, 12.000000, nan]],
[R object with classes: ('numeric',) mapped to:
[ nan, 24.000000, nan, nan, ..., 22.000000, nan, 36.000000, nan],
R object with classes: ('numeric',) mapped to:
[ nan, 24.000000, nan, nan, ..., 22.000000, nan, 36.000000, nan],
R object with classes: ('numeric',) mapped to:
[ nan, 24.000000, nan, nan, ..., 22.000000, nan, 36.000000, nan],
R object with classes: ('numeric',) mapped to:
[ nan, 24.000000, nan, nan, ..., 22.000000, nan, 36.000000, nan]]]
In [11]:
test_rda_output(
r10mm_url, "r10.0mm1_ci", "expected_rmm.rda", "expected_r10mm"
)
test_rda_output(
r20mm_url, "r20.0mm1_ci", "expected_rmm.rda", "expected_r20mm"
)
test_rda_output(
rnnmm_url, "r9.7mm1_ci", "expected_rmm.rda", "expected_rnnmm"
)