[Thermo] improve error messages
Incorporate reviewer suggestions for error messages (user feedback) in PureFluid.TPX setter and SolutionArray.restore_data
This commit is contained in:
Ingmar Schoegl
Ray Speth
parent
074fd2cd74
commit
fe7be79552
3 changed files with 51 additions and 34 deletions
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@ -299,7 +299,7 @@ class SolutionArray:
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states using the same `Solution` object and computing properties for that
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array of states.
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SolutionArray can represent both 1D and multi-dimensional arrays of states,
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`SolutionArray` can represent both 1D and multi-dimensional arrays of states,
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with shapes described in the same way as Numpy arrays. All of the states
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can be set in a single call::
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@ -311,7 +311,7 @@ class SolutionArray:
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>>> states.TPX = T, P, X
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Similar to Numpy arrays, input with fewer non-singleton dimensions than the
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SolutionArray is 'broadcast' to generate input of the appropriate shape. In
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`SolutionArray` is 'broadcast' to generate input of the appropriate shape. In
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the above example, the single value for the mole fraction input is applied
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to each input, while each row has a constant temperature and each column has
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a constant pressure.
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@ -337,7 +337,7 @@ class SolutionArray:
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>>> states.equilibrate('HP')
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>>> states.T # -> adiabatic flame temperature at various equivalence ratios
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SolutionArray objects can also be 'sliced' like Numpy arrays, which can be
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`SolutionArray` objects can also be 'sliced' like Numpy arrays, which can be
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used both for accessing and setting properties::
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>>> states = ct.SolutionArray(gas, (6, 10))
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@ -347,7 +347,7 @@ class SolutionArray:
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If many slices or elements of a property are going to be accessed (i.e.
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within a loop), it is generally more efficient to compute the property array
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once and access this directly, rather than repeatedly slicing the
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SolutionArray object, e.g.::
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`SolutionArray` object, e.g.::
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>>> mu = states.viscosity
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>>> for i,j in np.ndindex(mu.shape):
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@ -368,19 +368,19 @@ class SolutionArray:
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>>> s.reaction_equation(10)
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'CH4 + O <=> CH3 + OH'
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Data represented by a SolutionArray can be extracted and saved to a CSV file
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Data represented by a `SolutionArray` can be extracted and saved to a CSV file
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using the `write_csv` method::
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>>> states.write_csv('somefile.csv', cols=('T', 'P', 'X', 'net_rates_of_progress'))
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As long as stored columns specify a valid thermodynamic state, the contents of
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a SolutionArray can be restored using the `read_csv` method::
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a `SolutionArray` can be restored using the `read_csv` method::
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>>> states = ct.SolutionArray(gas)
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>>> states.read_csv('somefile.csv')
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As an alternative to comma separated export and import, data extracted from
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SolutionArray objects can also be saved to and restored from a pandas compatible
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`SolutionArray` objects can also be saved to and restored from a pandas compatible
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HDF container file using the `write_hdf`::
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>>> states.write_hdf('somefile.h5', cols=('T', 'P', 'X'), key='some_key')
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@ -399,7 +399,7 @@ class SolutionArray:
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:param phase: The `Solution` object used to compute the thermodynamic,
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kinetic, and transport properties
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:param shape: A tuple or integer indicating the dimensions of the
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SolutionArray. If the shape is 1D, the array may be extended using the
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`SolutionArray`. If the shape is 1D, the array may be extended using the
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`append` method. Otherwise, the shape is fixed.
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:param states: The initial array of states. Used internally to provide
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slicing support.
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@ -597,35 +597,40 @@ class SolutionArray:
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def restore_data(self, data, labels):
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"""
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Restores a SolutionArray based on *data* specified in a single
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Restores a `SolutionArray` based on *data* specified in a single
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2D Numpy array and a list of corresponding column *labels*. Thus,
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this method allows to restore data exported by `collect_data`.
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:param data: a 2D Numpy array holding data to be restored.
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:param labels: a list of labels corresponding to SolutionArray entries.
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:param labels: a list of labels corresponding to `SolutionArray` entries.
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The receiving SolutionArray either has to be empty or should have
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The receiving `SolutionArray` either has to be empty or should have
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matching dimensions. Essential state properties and extra entries
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are detected automatically whereas stored information of calculated
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properties is omitted. If the receiving SolutionArray has extra
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entries already specified, only those will be imported; if *labels* does
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not contain those entries, an error is raised.
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properties is omitted. If the receiving `SolutionArray` has extra
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entries already specified, only those will be imported; if *labels*
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does not contain those entries, an error is raised.
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"""
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# check arguments
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if not isinstance(data, np.ndarray) or data.ndim != 2:
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raise TypeError("restore_data only works for 2D ndarrays")
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elif len(labels) != data.shape[1]:
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raise ValueError("inconsistent data and label dimensions: "
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raise ValueError("inconsistent data and label dimensions "
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"({} vs. {})".format(len(labels), data.shape[1]))
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rows = data.shape[0]
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if self._shape != (0,) and self._shape != (rows,):
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raise ValueError('incompatible dimensions.')
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raise ValueError(
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"incompatible dimensions ({} vs. {}): the receiving "
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"SolutionArray object either needs to be empty "
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"or have a length that matches data rows "
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"to be restored".format(self._shape[0], rows)
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)
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# get full state information (may differ depending on type of ThermoPhase)
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# get full state information (may differ depending on ThermoPhase type)
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full_states = self._phase._full_states.values()
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if isinstance(self._phase, PureFluid):
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# make sure that potentially non-unique state definitions are checked last
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# ensure that potentially non-unique state definitions are checked last
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last = ['TP', 'TX', 'PX']
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full_states = [fs for fs in full_states
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if fs not in last] + ['TPX'] + last
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@ -633,14 +638,13 @@ class SolutionArray:
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# determine whether complete concentration is available (mass or mole)
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# assumes that `X` or `Y` is always in last place
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mode = ''
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has_species = False
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for prefix in ['X_', 'Y_']:
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spc = ['{}{}'.format(prefix, s) for s in self.species_names]
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# solution species names also found in labels
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valid_species = {s[2:]: labels.index(s) for s in spc
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if s in labels}
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# labels that start with prefix (indicating concentration)
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all_species = [l for l in labels if l[:2] == prefix]
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all_species = [l for l in labels if l.startswith(prefix)]
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if valid_species:
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# save species mode and remaining full_state candidates
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mode = prefix[0]
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@ -655,7 +659,7 @@ class SolutionArray:
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full_states = {v[:2] for v in full_states}
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# determine suitable thermo properties for reconstruction
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basis = {'molar': 'mole', 'mass': 'mass'}[self.basis]
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basis = 'mass' if self.basis == 'mass' else 'mole'
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prop = {'T': ('T'), 'P': ('P'),
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'D': ('density', 'density_{}'.format(basis)),
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'U': ('u', 'int_energy_{}'.format(basis)),
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@ -671,7 +675,10 @@ class SolutionArray:
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mode = fs + mode
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break
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if len(mode) == 1:
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raise ValueError('invalid/incomplete state information.')
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raise ValueError(
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"invalid/incomplete state information (detected "
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"partial information as mode='{}')".format(mode)
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)
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# assemble and restore state information
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state_data = tuple([data[:, state[i][mode[i]]] for i in range(len(state))])
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@ -734,8 +741,8 @@ class SolutionArray:
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are specified, then either the mass or mole fraction of that species
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will be taken, depending on the value of *species*. *cols* may also
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include any arrays which were specified as 'extra' variables when
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defining the SolutionArray object. The special value 'extra' can be
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used to include all 'extra' variables.
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defining the `SolutionArray` object. The special value 'extra' can
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be used to include all 'extra' variables.
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:param threshold: Relative tolerance for including a particular column.
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The tolerance is applied by comparing the maximum absolute value for
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a particular column to the maximum absolute value in all columns for
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@ -802,7 +809,7 @@ class SolutionArray:
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*cols*. The first row of the CSV file will contain column labels.
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Additional arguments are passed on to `collect_data`. This method works
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only with 1D SolutionArray objects.
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only with 1D `SolutionArray` objects.
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"""
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data, labels = self.collect_data(cols, *args, **kwargs)
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with open(filename, 'w') as outfile:
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@ -813,7 +820,7 @@ class SolutionArray:
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def read_csv(self, filename):
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"""
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Read a CSV file named *filename* and restore data to the SolutionArray
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Read a CSV file named *filename* and restore data to the `SolutionArray`
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using `restore_data`. This method allows for recreation of data
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previously exported by `write_csv`.
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"""
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@ -829,7 +836,7 @@ class SolutionArray:
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Returns the data specified by *cols* in a single pandas DataFrame.
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Additional arguments are passed on to `collect_data`. This method works
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only with 1D SolutionArray objects and requires a working pandas
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only with 1D `SolutionArray` objects and requires a working pandas
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installation. Use pip or conda to install `pandas` to enable this method.
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"""
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@ -841,7 +848,7 @@ class SolutionArray:
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def from_pandas(self, df):
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"""
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Restores SolutionArray data from a pandas DataFrame *df*.
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Restores `SolutionArray` data from a pandas DataFrame *df*.
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This method is intendend for loading of data that were previously
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exported by `to_pandas`. The method requires a working pandas
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@ -880,7 +887,7 @@ class SolutionArray:
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Additional arguments (i.e. *args* and *kwargs*) are passed on to
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`collect_data` via `to_pandas`; see `collect_data` for further
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information. This method works only with 1D SolutionArray objects
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information. This method works only with 1D `SolutionArray` objects
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and requires working installations of pandas and PyTables. These
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packages can be installed using pip (`pandas` and `tables`) or conda
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(`pandas` and `pytables`).
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@ -895,7 +902,7 @@ class SolutionArray:
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def read_hdf(self, filename, key=None):
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"""
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Read a dataset identified by *key* from a HDF file named *filename*
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and restore data to the SolutionArray object. This method allows for
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and restore data to the `SolutionArray` object. This method allows for
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recreation of data previously exported by `write_hdf`.
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The method imports data using `restore_data` via `from_pandas` and
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@ -155,10 +155,12 @@ class TestPureFluid(utilities.CanteraTest):
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self.water.TPX = T, P, X
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self.assertNear(X, 0.8)
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with self.assertRaises(ValueError):
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with self.assertRaisesRegex(ValueError, 'invalid thermodynamic'):
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self.water.TPX = T, .999*P, X
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with self.assertRaises(ValueError):
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with self.assertRaisesRegex(ValueError, 'invalid thermodynamic'):
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self.water.TPX = T, 1.001*P, X
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with self.assertRaisesRegex(ValueError, 'numeric value is required'):
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self.water.TPX = T, P, 'spam'
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# To minimize errors when transcribing tabulated data, the input units here are:
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@ -3,6 +3,7 @@
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import warnings
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import weakref
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import numbers as _numbers
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cdef enum ThermoBasis:
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mass_basis = 0
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@ -1565,7 +1566,7 @@ cdef class PureFluid(ThermoPhase):
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property TPX:
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"""
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Get/Set the temperature [K], pressure [Pa], and vapor fraction of a
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Get/Set the temperature [K], pressure [Pa], and vapor fraction of a
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PureFluid.
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An Exception is raised if the thermodynamic state is not consistent.
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@ -1576,6 +1577,11 @@ cdef class PureFluid(ThermoPhase):
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T = values[0] if values[0] is not None else self.T
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P = values[1] if values[1] is not None else self.P
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X = values[2] if values[2] is not None else self.X
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if not isinstance(X, (np.ndarray, _numbers.Number)):
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raise ValueError(
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'a numeric value is required to quanitify '
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'the vapor fraction (X)'
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)
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if np.isclose(P, self.thermo.satPressure(T)):
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self.TX = T, X
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elif np.isclose(X, 0.) or np.isclose(X, 1.):
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@ -1586,7 +1592,9 @@ cdef class PureFluid(ThermoPhase):
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'received a combination of property values that '
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'do not represent a valid state. As an alternative, '
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'specify the state using two fully independent '
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'properties (e.g. TD)')
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'properties (e.g. TD) instead of: '
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'T={}, P={}, X={}'.format(T, P, X)
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)
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property UVX:
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"""
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