Source code for kuibit.cactus_scalars

#!/usr/bin/env python3

# Copyright (C) 2020-2022 Gabriele Bozzola
#
# Based on code originally developed by Wolfgang Kastaun. This file may contain
# algorithms and/or structures first implemented in
# GitHub:wokast/PyCactus/PostCactus/cactus_scalars.py
#
# This program is free software; you can redistribute it and/or modify it under
# the terms of the GNU General Public License as published by the Free Software
# Foundation; either version 3 of the License, or (at your option) any later
# version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
# details.
#
# You should have received a copy of the GNU General Public License along with
# this program; if not, see <https://www.gnu.org/licenses/>.

"""The :py:mod:`~.cactus_scalars` module provides simple interfaces to access
time series data as output by CarpetASCII, including all the reductions.

There are multiple classes defined in this module:

- :py:class`~.ScalarsDir` interfaces with :py:class:`~.SimDir` and organizes the
  data according to their reduction. This is a dictionary-like object with keys
  the possible reduction (e.g., ``max``, ``average``, ``norm2``). -
- :py:class`~.AllScalars` takes all the files that correspond to a given reduction
  and organize them according to the variables they contain.
- :py:class`~.OneScalar` represents one single scalar variable, with data that
  is represented as :py:class:`~.TimeSeries` objects. :py:class`~.AllScalars` contains
  many :py:class`~.OneScalar` objects.

These are hierarchical classes, one containing the others, so one typically ends
up with a series of brackets or dots to access the actual data. For example, if
``sim`` is a :py:class:`~.SimDir`, ``sim.ts.max['rho_b']`` is maximum of
``rho_b`` represented as :py:class:`~.TimeSeries`.

"""

import os
import re
from bz2 import open as bopen
from functools import lru_cache
from gzip import open as gopen

import numpy as np

from kuibit import simdir
from kuibit import timeseries as ts
from kuibit.attr_dict import pythonize_name_dict
from kuibit.cactus_ascii_utils import scan_header


[docs]class OneScalar: """Read scalar data produced by CarpetASCII. :py:class:`~.OneScalar` is a dictionary-like object with keys the variables and values the :py:class:`~.TimeSeries`. Single variable per file or single file per group are supported. In the latter case, the header is inspected to understand the content of the file. Compressed files (gz and bz2) are supported too. :py:class:`~.OneScalar` represents one scalar file, there can be multiple variables inside, (if it was output with ``one_file_per_group = yes``). :ivar path: Path of the file. :type path: str :ivar folder: Path of the folder that contains the file. :type folder: str :ivar reduction_type: Type of reduction. :type reduction_type: str """ # What is this pattern? # Let's understand it. We have ^ and $, so we match the entire string and # we have seven capturing groups. # 1: (\w+) matches any number of characters greater than 0 (w = word) # 2: ((-(\w+))|(\[\d+\]))? optionally match one of the two # 3: Matches - with followed by 4: any word # 5: Matches brackets with a number inside # In between match a dot (\.) # 6: (minimum|maximum|norm1|norm2|norm_inf|average|scalars)? optionally match one # of those # In between match .asc (\.asc) # 7: (\.(gz|bz2))? optionally match .gz or .bz2 # We want to match file names like hydrobase-press.maximum.asc or # hydrobase-vel[0].maximum.asc # # The .scalars. file is the one generated with the option # all_reductions_in_one_file _pattern_filename = r""" ^(\w+) ((-(\w+))|(\[\d+\]))? \.(minimum|maximum|norm1|norm2|norm_inf|average|scalars)? \.asc (\.(gz|bz2))?$""" _rx_filename = re.compile(_pattern_filename, re.VERBOSE) _reduction_types = { "minimum": "min", "maximum": "max", "norm1": "norm1", "norm2": "norm2", "norm_inf": "infnorm", "average": "average", None: "scalar", } # What function to use to open the file? # What mode? _decompressor = { None: (open, "r"), "gz": (gopen, "rt"), "bz2": (bopen, "rt"), } def __init__(self, path): """Constructor. Here we understand what the file contains. :param path: Path of the file. :type path: str """ self.path = str(path) # The _vars_columns dictionary contains a mapping between the various variables # and the column numbers in which they are stored. self._vars_columns = {} # The _vars dictionary maps variables with the TimeSeries. It is used to # cached loaded data. self._vars = {} self.folder, filename = os.path.split(self.path) filename_match = self._rx_filename.match(filename) if filename_match is None: raise RuntimeError(f"Name scheme not recognized for {filename}") # variable_name1 may be a thorn name (e.g. hydrobase-press) # Or the actual variable name (e.g. H) ( variable_name1, _0, _1, variable_name2, index_in_brackets, reduction_type, _2, compression_method, ) = filename_match.groups() self._compression_method = compression_method self.reduction_type = ( reduction_type if reduction_type is not None else "scalar" ) # If the file contains multiple variables, we will scan the header # immediately to understand the content. If not, we scan the header # only when needed self._is_one_file_per_group = variable_name2 is not None self._was_header_scanned = False if self._is_one_file_per_group: # We need the variable names self._scan_header() else: variable_name = variable_name1 if index_in_brackets is not None: variable_name += index_in_brackets self._vars_columns = {variable_name: None} def _scan_header(self): # Call scan_header with the right argument extended_format = self.reduction_type == "scalar" # What method to we need to use to open the file? # opener can be open, gopen, or bopen depending on the extension # of the file opener, opener_mode = self._decompressor[self._compression_method] self._time_column, columns_info = scan_header( self.path, self._is_one_file_per_group, extended_format, opener=opener, opener_mode=opener_mode, ) if self._is_one_file_per_group: self._vars_columns.update(columns_info) else: # There is only one data_column self._vars_columns = { list(self._vars_columns.keys())[0]: columns_info } self._was_header_scanned = True
[docs] @lru_cache(128) def load(self, variable): """Read file and return a TimeSeries with the requested variable. :param variable: Requested variable. :type variable: str :returns: :py:class:`~.TimeSeries` with requested variable as read from file :rtype: :py:class:`~.TimeSeries` """ if not self._was_header_scanned: self._scan_header() if variable not in self: raise KeyError(f"{variable} not available") column_number = self._vars_columns[variable] t, y = np.loadtxt( self.path, unpack=True, ndmin=2, usecols=(self._time_column, column_number), ) return ts.remove_duplicated_iters(t, y)
def __getitem__(self, key): if key not in self._vars: # Error checking is done in __load__ self._vars[key] = self.load(key) return self._vars[key] def __contains__(self, key): return key in self._vars_columns
[docs] def keys(self): """Return the list of variables available. :returns: Variables in the file :rtype: dict_keys """ return self._vars_columns.keys()
[docs]class AllScalars: """Helper class to read various types of scalar data in a list of files and properly order them. The core of this object is the ``_vars`` dictionary which contains the location of all the files for a specific variable and reduction (as :py:class:`~.OneScalar`). :py:class:`~.AllScalars` is a dictionary-like object, using the bracket notation you can access values with as TimeSeries. Alternatively, you can access the data as attributes of the ``fields`` attribute. :ivar reduction_type: Type of reduction. :type reduction_type: str """ def __init__(self, allfiles, reduction_type): """Constructor. :param allfiles: List of all the files :type allfiles: list of str :param reduction_type: Type of reduction. :type reduction_type: str """ self.reduction_type = str(reduction_type) # TODO: Is it necessary to have the folder level? # Probably not, so remove it # _vars_readers is like _vars_readers['variable']['folder'] -> # OneScalar(f) _vars_readers['variable'] is a dictionary with as keys # the folders where to find the files associated to the variable and the # reduction reduction_type # # OneScalar objects act as readers. self._vars_readers = {} for file_ in allfiles: # We only save those that variables are well-behaved try: cactusascii_file = OneScalar(file_) if cactusascii_file.reduction_type == reduction_type: for var in list(cactusascii_file.keys()): # We add to the _vars_readers dictionary the mapping: # [var][folder] to OneScalar(f) folder = cactusascii_file.folder self._vars_readers.setdefault(var, {})[ folder ] = cactusascii_file except RuntimeError: pass # We cache the results in _vars, a dictionary with keys the variables # and values the timeseries. self._vars = {} # What pythonize_name_dict does is to make the various variables # accessible as attributes, e.g. self.fields.rho self.fields = pythonize_name_dict(list(self.keys()), self.__getitem__) def __getitem__(self, key): if key not in self: raise KeyError(f"{key} not available") if key not in self._vars: # We read all the files associated to variable key folders = self._vars_readers[key] series = [f[key] for f in folders.values()] self._vars[key] = ts.combine_ts(series) return self._vars[key] def __contains__(self, key): return key in self._vars_readers
[docs] def keys(self): """Return the available variables corresponding to the given reduction. :returns: Variables with given reduction :rtype: dict_keys """ return self._vars_readers.keys()
[docs] def get(self, key, default=None): """Return variable if available, else return the default value. :param key: Requested variable. :type key: str :param default: Returned value if ``variable`` is not available. :type default: any :returns: :py:class:`~.TimeSeries` of the requested variable :rtype: :py:class:`~.TimeSeries` """ if key in self: return self[key] return default
def __str__(self): ret = f"Available {self.reduction_type} timeseries:\n" ret += f"{list(self.keys())}\n" return ret
[docs]class ScalarsDir: """This class provides acces to various types of scalar data in a given simulation directory. Typically used from a :py:class:`~.SimDir` instance. The different scalars are available as attributes: :ivar scalar: access to grid scalars. :ivar minimum: access to minimum reduction. :ivar maximum: access to maximum reduction. :ivar norm1: access to norm1 reduction. :ivar norm2: access to norm2 reduction. :ivar average: access to average reduction. :ivar infnorm: access to inf-norm reduction. Each of those works as a dictionary mapping variable names to :py:class:`~.TimeSeries` instances. """ # TODO: Implement the following, possibly in a clean way # .. note:: # infnorm is reconstructed from min and max if infnorm # itself is not available. def __init__(self, sd): """The constructor is not intended for direct use. :param sd: Simulation directory :type sd: :py:class:`~.SimDir` instance. """ if not isinstance(sd, simdir.SimDir): raise TypeError("Input is not SimDir") self.path = sd.path self.point = AllScalars(sd.allfiles, "scalar") self.scalar = AllScalars(sd.allfiles, "scalar") self.minimum = AllScalars(sd.allfiles, "minimum") self.maximum = AllScalars(sd.allfiles, "maximum") self.norm1 = AllScalars(sd.allfiles, "norm1") self.norm2 = AllScalars(sd.allfiles, "norm2") self.average = AllScalars(sd.allfiles, "average") self.infnorm = AllScalars(sd.allfiles, "infnorm") # Aliases self.max = self.maximum self.min = self.minimum def __getitem__(self, reduction): return getattr(self, reduction)
[docs] def get(self, key, default=None): """Return a reduction if available, else return the default value. :param key: Requested reduction. :type key: str :param default: Returned value if ``reduction`` is not available. :type default: any :returns: Collection of all the variables with a given reduction. :rtype: :py:class:`~.AllScalars` """ if key in [ "point", "scalar", "minimum", "maximum", "norm1", "norm2", "average", "infnorm", ]: return self[key] return default
def __str__(self): str_ = [f"Folder {self.path}"] for key in ( self.path, self.scalar, self.minimum, self.maximum, self.norm1, self.norm2, self.average, ): str_.append(str(key)) return "\n".join(str_)