def tell_time_dtype(col_name, arr):
if not np.issubdtype(arr.dtype, np.datetime64):
htype = np.typename(np.sctype2char(arr.dtype)) # Human readable type
# The content is in a datetime format but not in datetime type
ledger.tell(
f"columns '{col_name}' looks like a datetime but the type is '{htype}'. "
f"Consider using:<br>"
f"<code>df['{col_name}'] = pd.to_datetime(df.{col_name})</code>")
def main():
fname = ""
if len(sys.argv) < 2:
usage()
sys.exit(1)
else:
fname = sys.argv[1]
f = ad.file(fname)
print "File info:"
print " %-18s %d" % ("of variables:", f.nvars)
print " %-18s %d - %d" % ("time steps:", f.current_step, f.last_step)
print " %-18s %d" % ("file size:", f.file_size)
print " %-18s %d" % ("bp version:", f.version)
print ""
for k in sorted(f.var.keys()):
v = f.var[k]
print " %-17s %-12s %d*%s" % (np.typename(np.sctype2char(v.dtype)), v.name, v.nsteps, v.dims)
def main():
fname = ""
if len(sys.argv) < 2:
usage()
sys.exit(1)
else:
fname = sys.argv[1]
f = ad.file(fname)
print "File info:"
print " %-18s %d" % ("of variables:", f.nvars)
print " %-18s %d - %d" % ("time steps:", f.current_step, f.last_step)
print " %-18s %d" % ("file size:", f.file_size)
print " %-18s %d" % ("bp version:", f.version)
print ""
for k in sorted(f.var.keys()):
v = f.var[k]
print " %-17s %-12s %d*%s" % (np.typename(np.sctype2char(v.dtype)), v.name, v.nsteps, v.dims)
import numpy as np
typechars = ['S1', '?', 'B', 'D', 'G', 'F', 'I', 'H', 'L', 'O', 'Q',
'S', 'U', 'V', 'b', 'd', 'g', 'f', 'i', 'h', 'l', 'q']
for typechar in typechars:
print
typechar, ' : ', np.typename(typechar)
import numpy as np
typechars = [
'S1', '?', 'B', 'D', 'G', 'F', 'I', 'H', 'L', 'O', 'Q', 'S', 'U', 'V', 'b',
'd', 'g', 'f', 'i', 'h', 'l', 'q'
]
for typechar in typechars:
print
typechar, ' : ', np.typename(typechar)
reveal_type(np.nan_to_num(f8)) # E: {float64}
reveal_type(np.nan_to_num(f, copy=True)) # E: Any
reveal_type(np.nan_to_num(
AR_f8, nan=1.5)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]]
reveal_type(np.nan_to_num(
AR_LIKE_f, posinf=9999)) # E: numpy.ndarray[Any, numpy.dtype[Any]]
reveal_type(
np.real_if_close(AR_f8)) # E: numpy.ndarray[Any, numpy.dtype[{float64}]]
reveal_type(
np.real_if_close(AR_c16)
) # E: Union[numpy.ndarray[Any, numpy.dtype[{float64}]], numpy.ndarray[Any, numpy.dtype[{complex128}]]]
reveal_type(
np.real_if_close(AR_c8)
) # E: Union[numpy.ndarray[Any, numpy.dtype[{float32}]], numpy.ndarray[Any, numpy.dtype[{complex64}]]]
reveal_type(
np.real_if_close(AR_LIKE_f)) # E: numpy.ndarray[Any, numpy.dtype[Any]]
reveal_type(np.typename("h")) # E: Literal['short']
reveal_type(np.typename("B")) # E: Literal['unsigned char']
reveal_type(np.typename("V")) # E: Literal['void']
reveal_type(np.typename("S1")) # E: Literal['character']
reveal_type(np.common_type(AR_i4)) # E: Type[{float64}]
reveal_type(np.common_type(AR_f2)) # E: Type[{float16}]
reveal_type(np.common_type(AR_f2, AR_i4)) # E: Type[{float64}]
reveal_type(np.common_type(AR_f16, AR_i4)) # E: Type[{float128}]
reveal_type(np.common_type(AR_c8, AR_f2)) # E: Type[{complex64}]
reveal_type(np.common_type(AR_f2, AR_c8, AR_i4)) # E: Type[{complex128}]
import numpy as np
import numpy.typing as npt
DTYPE_i8: np.dtype[np.int64]
np.mintypecode(DTYPE_i8) # E: incompatible type
np.iscomplexobj(DTYPE_i8) # E: incompatible type
np.isrealobj(DTYPE_i8) # E: incompatible type
np.typename(DTYPE_i8) # E: No overload variant
np.typename("invalid") # E: No overload variant
np.common_type(np.timedelta64()) # E: incompatible type