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Make scipy and pandas required dependencies
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Paul Romano
parent
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commit
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7 changed files with 18 additions and 47 deletions
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@ -401,14 +401,6 @@ distributions.
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NumPy is used extensively within the Python API for its powerful
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N-dimensional array.
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`h5py <http://www.h5py.org/>`_
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h5py provides Python bindings to the HDF5 library. Since OpenMC outputs
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various HDF5 files, h5py is needed to provide access to data within these
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files from Python.
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.. admonition:: Optional
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:class: note
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`SciPy <https://www.scipy.org/>`_
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SciPy's special functions, sparse matrices, and spatial data structures
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are used for several optional features in the API.
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@ -417,6 +409,14 @@ distributions.
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Pandas is used to generate tally DataFrames as demonstrated in
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:ref:`examples_pandas` example notebook.
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`h5py <http://www.h5py.org/>`_
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h5py provides Python bindings to the HDF5 library. Since OpenMC outputs
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various HDF5 files, h5py is needed to provide access to data within these
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files from Python.
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.. admonition:: Optional
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:class: note
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`Matplotlib <http://matplotlib.org/>`_
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Matplotlib is used to providing plotting functionality in the API like the
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:meth:`Universe.plot` method and the :func:`openmc.plot_xs` function.
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@ -4,6 +4,7 @@ from collections import Iterable
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from six import string_types
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import numpy as np
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import pandas as pd
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import openmc
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from openmc.filter import _FILTER_TYPES
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@ -786,9 +787,6 @@ class AggregateFilter(object):
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CrossFilter.get_pandas_dataframe()
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"""
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import pandas as pd
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# Create NumPy array of the bin tuples for repeating / tiling
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filter_bins = np.empty(self.num_bins, dtype=tuple)
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for i, bin in enumerate(self.bins):
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@ -8,6 +8,7 @@ from xml.etree import ElementTree as ET
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from six import add_metaclass
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import numpy as np
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import pandas as pd
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import openmc
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import openmc.checkvalue as cv
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@ -460,9 +461,7 @@ class Filter(object):
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Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
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"""
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# Initialize Pandas DataFrame
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import pandas as pd
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df = pd.DataFrame()
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filter_bins = np.repeat(self.bins, self.stride)
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@ -715,9 +714,7 @@ class SurfaceFilter(Filter):
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Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
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"""
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# Initialize Pandas DataFrame
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import pandas as pd
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df = pd.DataFrame()
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filter_bins = np.repeat(self.bins, self.stride)
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@ -875,9 +872,7 @@ class MeshFilter(Filter):
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Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
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"""
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# Initialize Pandas DataFrame
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import pandas as pd
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df = pd.DataFrame()
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# Initialize dictionary to build Pandas Multi-index column
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@ -1123,9 +1118,7 @@ class EnergyFilter(RealFilter):
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Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
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"""
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# Initialize Pandas DataFrame
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import pandas as pd
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df = pd.DataFrame()
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# Extract the lower and upper energy bounds, then repeat and tile
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@ -1335,9 +1328,7 @@ class DistribcellFilter(Filter):
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Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
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"""
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# Initialize Pandas DataFrame
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import pandas as pd
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df = pd.DataFrame()
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level_df = None
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@ -1531,9 +1522,7 @@ class MuFilter(RealFilter):
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Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
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"""
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# Initialize Pandas DataFrame
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import pandas as pd
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df = pd.DataFrame()
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# Extract the lower and upper energy bounds, then repeat and tile
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@ -1638,9 +1627,7 @@ class PolarFilter(RealFilter):
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Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
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"""
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# Initialize Pandas DataFrame
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import pandas as pd
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df = pd.DataFrame()
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# Extract the lower and upper angle bounds, then repeat and tile
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@ -1745,9 +1732,7 @@ class AzimuthalFilter(RealFilter):
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Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
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"""
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# Initialize Pandas DataFrame
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import pandas as pd
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df = pd.DataFrame()
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# Extract the lower and upper angle bounds, then repeat and tile
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@ -2039,8 +2024,6 @@ class EnergyFunctionFilter(Filter):
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Tally.get_pandas_dataframe(), CrossFilter.get_pandas_dataframe()
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"""
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import pandas as pd
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df = pd.DataFrame()
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# There is no clean way of sticking all the energy, y data into a
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@ -5,6 +5,9 @@ import os
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from six import string_types
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import numpy as np
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import h5py
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from scipy.interpolate import interp1d
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from scipy.integrate import simps
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from scipy.special import eval_legendre
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import openmc
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import openmc.mgxs
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@ -1808,10 +1811,6 @@ class XSdata(object):
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"""
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from scipy.interpolate import interp1d
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from scipy.integrate import simps
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from scipy.special import eval_legendre
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check_value('target_format', target_format, _SCATTER_TYPES)
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check_type('target_order', target_order, Integral)
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if target_format == 'legendre':
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@ -1,6 +1,8 @@
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from collections import Callable
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from numbers import Real
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import scipy.optimize as sopt
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import openmc
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import openmc.model
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import openmc.checkvalue as cv
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@ -144,8 +146,6 @@ def search_for_keff(model_builder, initial_guess=None, target=1.0,
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model = model_builder(initial_guess, **model_args)
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cv.check_type('model_builder return', model, openmc.model.Model)
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import scipy.optimize as sopt
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# Set the iteration data storage variables
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guesses = []
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results = []
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@ -11,6 +11,8 @@ from xml.etree import ElementTree as ET
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from six import string_types
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import numpy as np
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import pandas as pd
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import scipy.sparse as sps
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import h5py
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import openmc
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@ -295,8 +297,6 @@ class Tally(object):
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# Convert NumPy arrays to SciPy sparse LIL matrices
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if self.sparse:
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import scipy.sparse as sps
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self._sum = \
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sps.lil_matrix(self._sum.flatten(), self._sum.shape)
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self._sum_sq = \
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@ -337,8 +337,6 @@ class Tally(object):
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# Convert NumPy array to SciPy sparse LIL matrix
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if self.sparse:
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import scipy.sparse as sps
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self._mean = \
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sps.lil_matrix(self._mean.flatten(), self._mean.shape)
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@ -361,8 +359,6 @@ class Tally(object):
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# Convert NumPy array to SciPy sparse LIL matrix
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if self.sparse:
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import scipy.sparse as sps
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self._std_dev = \
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sps.lil_matrix(self._std_dev.flatten(), self._std_dev.shape)
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@ -608,8 +604,6 @@ class Tally(object):
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# Convert NumPy arrays to SciPy sparse LIL matrices
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if sparse and not self.sparse:
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import scipy.sparse as sps
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if self._sum is not None:
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self._sum = \
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sps.lil_matrix(self._sum.flatten(), self._sum.shape)
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@ -1610,7 +1604,6 @@ class Tally(object):
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raise KeyError(msg)
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# Initialize a pandas dataframe for the tally data
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import pandas as pd
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df = pd.DataFrame()
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# Find the total length of the tally data array
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4
setup.py
4
setup.py
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@ -45,14 +45,12 @@ kwargs = {'name': 'openmc',
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if have_setuptools:
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kwargs.update({
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# Required dependencies
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'install_requires': ['six', 'numpy>=1.9', 'h5py'],
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'install_requires': ['six', 'numpy>=1.9', 'h5py', 'scipy', 'pandas>=0.17.0'],
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# Optional dependencies
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'extras_require': {
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'decay': ['uncertainties'],
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'pandas': ['pandas>=0.17.0'],
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'plot': ['matplotlib', 'ipython'],
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'sparse' : ['scipy'],
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'vtk': ['vtk', 'silomesh'],
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'validate': ['lxml']
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},
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