def is_sci_integer(value, min=None, max=None):
"""Function for the validate module to support scientific notation"""
value = validate_convert(value, sci_int)
minval = validate_convert(min, sci_int, "min")
maxval = validate_convert(max, sci_int, "max")
if min is not None and value < minval:
raise validate.VdtValueTooSmallError(value)
if max is not None and value > maxval:
raise validate.VdtValueTooBigError(value)
return value
def real_numpy_array_check(value, min=None, max=None):
"""
Parse and validate `value` as a numpy array (of floats).
Value can be either a single number, a range expression in the form of
min:max or min:step:max, or even a list containing numbers and range
expressions.
Parameters
----------
value : str
The string to be converted. This can be either a single number, a
range expression in the form of min:max or min:step:max, or even a
list containing numbers and range expressions.
min : int
The minimum allowed value. If the converted value is (or have)
lower than `min` then the VdtValueTooSmallError exception will be
raised.
max : int
The maximum allowed value. If the converted value is (or have)
greater than `man` then the VdtValueTooSmallError exception will be
raised.
Returns
-------
np.ndarray
The parsed numpy array.
Notes
-----
You can either separate the values with commas or spaces (any comma
will have the same effect as a space). However, if you separate with
spaces the values should be in brackets, while if you separate with
commands there should be no brackets.
>> SNR = 0,5,10:20
>> SNR = [0 5 10:20]
"""
if isinstance(value, str):
# Remove '[' and ']' if they exist.
if value[0] == '[' and value[-1] == ']':
value = value[1:-1].strip()
value = value.replace(',', ' ') # Replace commas with spaces
value = value.split() # Split based on spaces
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
# Test if it is a list or not
if isinstance(value, list):
# If it is a list, each element can be either a number of a 'range
# expression' that can be parsed with _parse_float_range_expr. We
# simple apply real_numpy_array_check on each element in the list
# to do the work and stack horizontally all the results.
value = [real_numpy_array_check(a, min, max) for a in value]
out = np.hstack(value)
else:
# It its not a list, it can be either a single number of a 'range
# expression' that can be parsed with _parse_float_range_expr
try:
value = validate.is_float(value)
out = np.array([value])
except validate.VdtTypeError:
out = _parse_float_range_expr(value)
# xxxxxxxxxx Validate if minimum and maximum allowed values xxxxxxxxxxx
if min is not None:
# maybe "min" was passed as a string and thus we need to convert it
# to a float
min = float(min)
if out.min() < min:
raise validate.VdtValueTooSmallError(out.min())
if max is not None:
# maybe "min" was passed as a string and thus we need to convert it
# to a float
max = float(max)
if out.max() > max:
raise validate.VdtValueTooBigError(out.max())
# xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
return out