8000 fixed jvm error by bradmiro · Pull Request #8360 · GoogleCloudPlatform/python-docs-samples · GitHub
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fixed jvm error #8360

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Sep 27, 2022
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fixed jvm error #8360

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65 changes: 33 additions & 32 deletions composer/2022_airflow_summit/data_analytics_process_expansion.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,38 +23,6 @@
from pyspark.sql import SparkSession
import pyspark.sql.functions as f

# Inverse Distance Weighting algorithm (DWA)
@f.pandas_udf("YEAR integer, VALUE double", f.PandasUDFType.GROUPED_MAP)
def phx_dw_compute(year, df) -> pd.DataFrame:
# This adjusts the rainfall / snowfall in Phoenix for a given year using Inverse Distance Weighting
# based on each weather station's distance to Phoenix. The closer a station is to Phoenix, the higher
# its measurement is weighed.
#
# This function combines the distance equation and inverse distance factor since the distance equation is:
#
# d = sqrt((x1-x2)^2 + (y1-y2)^2))
#
# and the inverse distance factor is:
#
# idf = 1 / d^2
#
# so we negate the square and square root to combine this into:
#
# idf = 1 / ((x1-x2)^2 + (y1-y2)^2))

# Latitude and longitude of Phoenix
PHX_LATITUDE = 33.4484
PHX_LONGITUDE = -112.0740

inverse_distance_factors = 1.0 / (
(PHX_LATITUDE - df.LATITUDE) ** 2 + (PHX_LONGITUDE - df.LONGITUDE) ** 2
)

# Calculate each station's weight
weights = inverse_distance_factors / inverse_distance_factors.sum()

return pd.DataFrame({"YEAR": year, "VALUE": (weights * df.ANNUAL_AMOUNT).sum()})


if __name__ == "__main__":
# read in the input argument
Expand Down Expand Up @@ -133,6 +101,39 @@ def phx_dw_compute(year, df) -> pd.DataFrame:
states_near_phx = ["AZ", "CA", "CO", "NM", "NV", "UT"]
annual_df = df.where(df.STATE.isin(states_near_phx))

# Inverse Distance Weighting algorithm (DWA)
@f.pandas_udf("YEAR integer, VALUE double", f.PandasUDFType.GROUPED_MAP)
def phx_dw_compute(year, df) -> pd.DataFrame:
# This adjusts the rainfall / snowfall in Phoenix for a given year using Inverse Distance Weighting
# based on each weather station's distance to Phoenix. The closer a station is to Phoenix, the higher
# its measurement is weighed.
#
# This function combines the distance equation and inverse distance factor since the distance equation is:
#
# d = sqrt((x1-x2)^2 + (y1-y2)^2))
#
# and the inverse distance factor is:
#
# idf = 1 / d^2
#
# so we negate the square and square root to combine this into:
#
# idf = 1 / ((x1-x2)^2 + (y1-y2)^2))

# Latitude and longitude of Phoenix
PHX_LATITUDE = 33.4484
PHX_LONGITUDE = -112.0740

inverse_distance_factors = 1.0 / (
(PHX_LATITUDE - df.LATITUDE) ** 2 +
(PHX_LONGITUDE - df.LONGITUDE) ** 2
)

# Calculate each station's weight
weights = inverse_distance_factors / inverse_distance_factors.sum()

return pd.DataFrame({"YEAR": year, "VALUE": (weights * df.ANNUAL_AMOUNT).sum()})

# Calculate the distance-weighted precipitation amount
phx_annual_prcp_df = (
annual_df.where((annual_df.ELEMENT == "PRCP"))
Expand Down
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