OverflowError: cannot convert float infinity to integer in reading `case_RTS_GMLC`

[yasirroni]

In reading case_RTS_GMLC.m,

from matpowercaseframes import CaseFrames


case_name = "case_RTS_GMLC"
cf = CaseFrames(case_name)

But, it works using load_case_engine.

cf = CaseFrames(case_name, load_case_engine=m)

But, using .infer_numpy() cause warning

cf.infer_numpy()
# RuntimeWarning: invalid value encountered in cast
#   if (arr.astype(int) == arr).all():
# RuntimeWarning: invalid value encountered in cast
#   if (arr.astype(int) == arr).all():

Also, I think it would be better to use C..., X..., and Y... as described in here.

header = {"MODEL", "STARTUP", "SHUTDOWN", "NCOST"};
if mpc.gencost(1, MODEL) == PW_LINEAR
  N = (size(mpc.gencost, 2) - NCOST) / 2;
  for i = 1:N
    header{end+1} = sprintf('X%d', i);
    header{end+1} = sprintf('Y%d', i);
  end
else
  N = size(mpc.gencost, 2) - NCOST;
  for i = N-1:-1:0
    header{end+1} = sprintf('C%d', i);
  end
endif

[yasirroni]

The cf.infer_numpy() gives warning due to dcline contains -inf and inf. It can be ignored.

import warnings
import pandas as pd

from matpower import start_instance
from matpowercaseframes import CaseFrames

warnings.filterwarnings("error")

m = start_instance()

CASE_NAME = "case_RTS_GMLC.m"
cf_RTS_GMLC = CaseFrames(CASE_NAME, load_case_engine=m)
cf_RTS_GMLC.infer_numpy()

for attribute in cf_RTS_GMLC._attributes:
    df = getattr(cf_RTS_GMLC, attribute)
    if not isinstance(df, pd.DataFrame):
        continue
    print(attribute)
    df = df.convert_dtypes()

    print('integer')
    columns = df.select_dtypes(include=["integer"]).columns
    df[columns] = df[columns].astype(int, errors="ignore")

    print('float')
    columns = df.select_dtypes(include=["float"]).columns
    df[columns] = df[columns].astype(float, errors="ignore")

    print('string')
    columns = df.select_dtypes(include=["string"]).columns
    df[columns] = df[columns].astype(str)

    print('boolean')
    columns = df.select_dtypes(include=["boolean"]).columns
    df[columns] = df[columns].astype(bool)

Solution if you don't want to see it:

warnings.filterwarnings("ignore", category=RuntimeWarning, message=".*invalid value.*")

[yasirroni]

Closed with #31