[Thermo] add write_hdf to SolutionArray objects
* The commit implements saving of data extracted from SolutionArrays to HDF containers using pandas infrastructure. * Two methods are introduced: `write_hdf` and `to_pandas`. * Both methods only work if the pandas module can be imported; an exception is raised only if the method is called without a working pandas installation.
This commit is contained in:
Ingmar Schoegl
Ray Speth
parent
5184ebccba
commit
eea04255fd
1 changed files with 74 additions and 8 deletions
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@ -1,10 +1,11 @@
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# This file is part of Cantera. See License.txt in the top-level directory or
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# at http://www.cantera.org/license.txt for license and copyright information.
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# at https://cantera.org/license.txt for license and copyright information.
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from ._cantera import *
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import numpy as np
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import csv as _csv
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class Solution(ThermoPhase, Kinetics, Transport):
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"""
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A class for chemically-reacting solutions. Instances can be created to
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@ -294,7 +295,7 @@ class SolutionArray:
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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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can be set in a single call::
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>>> gas = ct.Solution('gri30.cti')
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>>> states = ct.SolutionArray(gas, (6, 10))
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@ -347,7 +348,7 @@ class SolutionArray:
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... # do something with mu[i,j]
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Information about a subset of species may also be accessed, using
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parentheses to specify the species:
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parentheses to specify the species::
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>>> states('CH4').Y # -> mass fraction of CH4 in each state
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>>> states('H2','O2').partial_molar_cp # -> cp for H2 and O2
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@ -361,10 +362,20 @@ class SolutionArray:
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>>> s.reaction_equation(10)
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'CH4 + O <=> CH3 + OH'
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Data represnted 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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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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>>> states.write_csv('somefile.csv', cols=('T', 'P', 'X', 'net_rates_of_progress'))
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As an alternative, data extracted from SolutionArray objects can be saved
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to a pandas compatible HDF container file using the `write_hdf` method::
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>>> states.write_hdf('somefile.h5', cols=('T', 'P', 'X'), key='some_key')
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In this case, the (optional) key argument allows for saving and accessing
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multiple solutions in a single container file. Note that `write_hdf` requires
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working installations of pandas and PyTables. These packages can be installed
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using pip (`pandas` and `tables`) or conda (`pandas` and `pytables`).
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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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@ -582,7 +593,7 @@ class SolutionArray:
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self._phase.set_equivalence_ratio(phi[index], *args, **kwargs)
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self._states[index][:] = self._phase.state
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def collect_data(self, cols=('extra','T','density','Y'), threshold=0,
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def collect_data(self, cols=('extra', 'T', 'density', 'Y'), threshold=0,
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species='Y'):
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"""
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Returns the data specified by *cols* in a single 2D Numpy array, along
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@ -654,7 +665,7 @@ class SolutionArray:
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return np.hstack(data), labels
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def write_csv(self, filename, cols=('extra','T','density','Y'),
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def write_csv(self, filename, cols=('extra', 'T', 'density', 'Y'),
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*args, **kwargs):
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"""
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Write a CSV file named *filename* containing the data specified by
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@ -670,6 +681,61 @@ class SolutionArray:
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for row in data:
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writer.writerow(row)
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def to_pandas(self, cols=('extra', 'T', 'density', 'Y'),
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*args, **kwargs):
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"""
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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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installation. Use pip or conda to install `pandas` to enable this method.
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"""
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# local import avoids explicit dependence of cantera on pandas
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import pandas as pd
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data, labels = self.collect_data(cols, *args, **kwargs)
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return pd.DataFrame(data=data, columns=labels)
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def write_hdf(self, filename, cols=('extra', 'T', 'density', 'Y'),
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key='df', mode=None, append=None, complevel=None,
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*args, **kwargs):
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"""
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Write data specified by *cols* to a HDF container file named *filename*.
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Note that it is possible to write multiple data entries to a single HDF
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container file, where *key* is used to differentiate data.
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Internally, every HDF data entry is a `pandas.DataFrame` generated by
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the `to_pandas` method.
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:param filename: name of the HDF container file; typical file extensions
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are `.hdf`, `.hdf5` or `.h5`.
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:param cols: A list of any properties of the solution being exported.
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:param key: Identifier for the group in the container file.
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:param mode: Mode to open the file {None, 'a', 'w', 'r+}.
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:param append: If True, a less efficient structure is used that makes
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HDF entries appendable {None, True, False}.
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:param complevel: Specifies a compression level for data {None, 0-9}.
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A value of 0 disables compression.
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Arguments *key*, *mode*, *append*, and *complevel* are mapped to
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parameters for `pandas.DataFrame.to_hdf`; the choice `None` for *mode*,
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*append*, and *complevel* results in default values set by pandas.
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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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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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"""
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# create pandas DataFame and write to file
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df = self.to_pandas(cols, *args, **kwargs)
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pd_kwargs = {'mode': mode, 'append': append, 'complevel': complevel}
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pd_kwargs = {k: v for k, v in pd_kwargs.items() if v is not None}
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df.to_hdf(filename, key, **pd_kwargs)
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def _make_functions():
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# this is wrapped in a function to avoid polluting the module namespace
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