Create DimensionConverter() class for more flexible scaling definitions
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@ -1,146 +0,0 @@
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import pint
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# Initialise unit definitions
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UREG = pint.UnitRegistry()
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# Define Froude scaling relationships
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LENGTH_EXPONENT = 1
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TIME_EXPONENT = 1 / 2
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MASS_EXPONENT = 3
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def _convert(x, length_scale_factor, input_unit, target_unit):
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# Calculate unit conversion factor
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input_unit = UREG(input_unit)
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target_unit = UREG(target_unit)
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unit_conversion_factor = input_unit.to(target_unit).magnitude
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# Calculate Froude scaling factor
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froude_scale_factor = length_scale_factor**(
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input_unit.dimensionality['[length]'] * LENGTH_EXPONENT +
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input_unit.dimensionality['[time]'] * TIME_EXPONENT +
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input_unit.dimensionality['[mass]'] * MASS_EXPONENT)
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# Scale values
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x_scaled = x * froude_scale_factor
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# Convert to output units
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x_scaled *= unit_conversion_factor
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return x_scaled
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def proto_to_model(x_proto,
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length_scale,
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input_unit,
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target_unit,
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index_input_unit=None,
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index_target_unit=None):
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"""Convert prototype value(s) to model value(s) in specified units.
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Args:
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x_proto: prototype values (array_like, or pandas dataframe)
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length_scale: ratio between proto and model dimensions (float)
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input_unit: unit of input (string)
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target_unit: unit of output (string)
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index_input_unit: unit of input index (dataframe only)
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index_target_unit: unit of output index (dataframe only)
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Returns:
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input values in model scale
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"""
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length_scale_factor = 1 / length_scale
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# Convert values
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x_model = _convert(x_proto, length_scale_factor, input_unit, target_unit)
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# Convert index (dataframe or series only)
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if (index_input_unit is not None) and (index_target_unit is not None):
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if type(x_model).__name__ in ['DataFrame', 'Series']:
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x_model.index = _convert(x_model.index, length_scale_factor,
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index_input_unit, index_target_unit)
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else:
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raise ValueError("'index_input_unit' and 'index_target_unit' "
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"can only be used when input is dataframe")
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return x_model
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def model_to_proto(x_model,
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length_scale,
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input_unit,
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target_unit,
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index_input_unit=None,
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index_target_unit=None):
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"""Convert model value(s) to prototype value(s) in specified units.
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Args:
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x_model: model values (array_like, or pandas dataframe)
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length_scale: ratio between proto and model dimensions (float)
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input_unit: unit of input (string)
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target_unit: unit of output (string)
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index_input_unit: unit of input index (dataframe only)
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index_target_unit: unit of output index (dataframe only)
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Returns:
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input values in prototype scale
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"""
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length_scale_factor = length_scale
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# Convert values
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x_proto = _convert(x_model, length_scale_factor, input_unit, target_unit)
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# Convert index (dataframe or series only)
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if (index_input_unit is not None) and (index_target_unit is not None):
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if type(x_proto).__name__ in ['DataFrame', 'Series']:
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x_proto.index = _convert(x_proto.index, length_scale_factor,
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index_input_unit, index_target_unit)
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else:
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raise ValueError("'index_input_unit' and 'index_target_unit' "
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"can only be used when input is dataframe")
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return x_proto
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def dimensions(unit):
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"""Get unit dimensions.
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Args:
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unit: unit name or symbol (string)
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Returns:
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string containing unit dimensions
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"""
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# Get dimensions of unit
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dims = UREG(unit).dimensionality
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dim_data = {'L': '[length]', 'M': '[mass]', 'T': '[time]'}
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s = ''
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for symbol, key in dim_data.items():
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exponent = dims[key]
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if exponent != 0:
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s += '{}^{:g} '.format(symbol, exponent)
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return s.strip()
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def scaling_exponent(unit):
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"""Convert prototype value(s) to model value(s) in specified units.
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Args:
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unit: unit of quantity to be scaled (string)
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Returns:
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scaling factor
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"""
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# Calculate Froude scaling factor
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scaling_exponent = (UREG(unit).dimensionality['[length]'] * LENGTH_EXPONENT
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+ UREG(unit).dimensionality['[time]'] * TIME_EXPONENT +
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UREG(unit).dimensionality['[mass]'] * MASS_EXPONENT)
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return scaling_exponent
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@ -0,0 +1,159 @@
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import pint
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# Initialise unit definitions
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UREG = pint.UnitRegistry()
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class DimensionConverter():
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def _convert(self, x, length_scale_factor, input_unit, target_unit):
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# Calculate unit conversion factor
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input_unit = UREG(input_unit)
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target_unit = UREG(target_unit)
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unit_conversion_factor = input_unit.to(target_unit).magnitude
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# Calculate Froude scaling factor
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froude_scale_factor = length_scale_factor**(
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input_unit.dimensionality['[length]'] * self.LENGTH_EXPONENT +
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input_unit.dimensionality['[time]'] * self.TIME_EXPONENT +
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input_unit.dimensionality['[mass]'] * self.MASS_EXPONENT)
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# Scale values
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x_scaled = x * froude_scale_factor
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# Convert to output units
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x_scaled *= unit_conversion_factor
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return x_scaled
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def proto_to_model(self,
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x_proto,
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length_scale,
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input_unit,
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target_unit,
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index_input_unit=None,
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index_target_unit=None):
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"""Convert prototype value(s) to model value(s) in specified units.
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Args:
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x_proto: prototype values (array_like, or pandas dataframe)
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length_scale: ratio between proto and model dimensions (float)
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input_unit: unit of input (string)
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target_unit: unit of output (string)
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index_input_unit: unit of input index (dataframe only)
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index_target_unit: unit of output index (dataframe only)
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Returns:
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input values in model scale
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"""
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length_scale_factor = 1 / length_scale
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# Convert values
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x_model = self._convert(x_proto, length_scale_factor, input_unit,
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target_unit)
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# Convert index (dataframe or series only)
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if (index_input_unit is not None) and (index_target_unit is not None):
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if type(x_model).__name__ in ['DataFrame', 'Series']:
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x_model.index = self._convert(x_model.index, length_scale_factor,
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index_input_unit, index_target_unit)
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else:
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raise ValueError("'index_input_unit' and 'index_target_unit' "
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"can only be used when input is dataframe")
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return x_model
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def model_to_proto(self,
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x_model,
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length_scale,
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input_unit,
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target_unit,
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index_input_unit=None,
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index_target_unit=None):
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"""Convert model value(s) to prototype value(s) in specified units.
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Args:
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x_model: model values (array_like, or pandas dataframe)
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length_scale: ratio between proto and model dimensions (float)
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input_unit: unit of input (string)
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target_unit: unit of output (string)
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index_input_unit: unit of input index (dataframe only)
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index_target_unit: unit of output index (dataframe only)
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Returns:
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input values in prototype scale
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"""
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length_scale_factor = length_scale
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# Convert values
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x_proto = self._convert(x_model, length_scale_factor, input_unit,
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target_unit)
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# Convert index (dataframe or series only)
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if (index_input_unit is not None) and (index_target_unit is not None):
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if type(x_proto).__name__ in ['DataFrame', 'Series']:
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x_proto.index = self._convert(x_proto.index, length_scale_factor,
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index_input_unit, index_target_unit)
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else:
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raise ValueError("'index_input_unit' and 'index_target_unit' "
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"can only be used when input is dataframe")
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return x_proto
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def dimensions(self, unit):
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"""Get unit dimensions.
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Args:
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unit: unit name or symbol (string)
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Returns:
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string containing unit dimensions
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"""
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# Get dimensions of unit
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dims = UREG(unit).dimensionality
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dim_data = {'L': '[length]', 'M': '[mass]', 'T': '[time]'}
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s = ''
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for symbol, key in dim_data.items():
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exponent = dims[key]
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if exponent != 0:
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s += '{}^{:g} '.format(symbol, exponent)
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return s.strip()
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def scaling_exponent(self, unit):
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"""Convert prototype value(s) to model value(s) in specified units.
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Args:
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unit: unit of quantity to be scaled (string)
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Returns:
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scaling factor
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"""
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# Calculate Froude scaling factor
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scaling_exponent = (
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UREG(unit).dimensionality['[length]'] * self.LENGTH_EXPONENT +
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UREG(unit).dimensionality['[time]'] * self.TIME_EXPONENT +
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UREG(unit).dimensionality['[mass]'] * self.MASS_EXPONENT)
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return scaling_exponent
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class Froude(DimensionConverter):
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def __init__(self):
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# Define Froude scaling relationships
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self.LENGTH_EXPONENT = 1
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self.TIME_EXPONENT = 1 / 2
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self.MASS_EXPONENT = 3
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class Reynolds(DimensionConverter):
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def __init__(self):
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# Define Reynolds scaling relationships
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self.LENGTH_EXPONENT = 1
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self.TIME_EXPONENT = 2
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self.MASS_EXPONENT = 3
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