Source code for rtctools.optimization.csv_lookup_table_mixin

import configparser
import glob
import logging
import os
import pickle
from typing import Iterable, List, Tuple, Union

import casadi as ca

import numpy as np

import as csv
from rtctools._internal.caching import cached
from import BSpline1D
from import BSpline2D
from rtctools.optimization.timeseries import Timeseries

from scipy.interpolate import bisplev, bisplrep, splev
from scipy.optimize import brentq

from .optimization_problem import LookupTable as LookupTableBase
from .optimization_problem import OptimizationProblem

logger = logging.getLogger("rtctools")

[docs]class LookupTable(LookupTableBase): """ Lookup table. """ def __init__(self, inputs: List[ca.MX], function: ca.Function, tck: Tuple = None): """ Create a new lookup table object. :param inputs: List of lookup table input variables. :param function: Lookup table CasADi :class:`Function`. """ self.__inputs = inputs self.__function = function self.__t, self.__c, self.__k = [None] * 3 if tck is not None: if len(tck) == 3: self.__t, self.__c, self.__k = tck elif len(tck) == 5: self.__t = tck[:2] self.__c = tck[2] self.__k = tck[3:] @property @cached def domain(self) -> Tuple: t = self.__t if t is None: raise AttributeError('This lookup table was not instantiated with tck metadata. \ Domain/Range information is unavailable.') if type(t) == tuple and len(t) == 2: raise NotImplementedError('Domain/Range information is not yet implemented for 2D LookupTables') return np.nextafter(t[0], np.inf), np.nextafter(t[-1], -np.inf) @property @cached def range(self) -> Tuple: return self(self.domain[0]), self(self.domain[1]) @property def inputs(self) -> List[ca.MX]: """ List of lookup table input variables. """ return self.__inputs @property def function(self) -> ca.Function: """ Lookup table CasADi :class:`Function`. """ return self.__function @property @cached def __numeric_function_evaluator(self): return np.vectorize( lambda *args: np.nan if np.any(np.isnan(args)) else float(self.function(*args)) )
[docs] def __call__( self, *args: Union[float, Iterable, Timeseries] ) -> Union[float, np.ndarray, Timeseries]: """ Evaluate the lookup table. :param args: Input values. :type args: Float, iterable of floats, or :class:`.Timeseries` :returns: Lookup table evaluated at input values. Example use:: y = lookup_table(1.0) [y1, y2] = lookup_table([1.0, 2.0]) """ evaluator = self.__numeric_function_evaluator if len(args) == 1: arg = args[0] if isinstance(arg, Timeseries): return Timeseries(arg.times, self(arg.values)) else: if hasattr(arg, "__iter__"): arg = np.fromiter(arg, dtype=float) return evaluator(arg) else: arg = float(arg) return evaluator(arg).item() else: if any(isinstance(arg, Timeseries) for arg in args): raise TypeError( "Higher-order LookupTable calls do not yet support Timeseries parameters" ) elif any(hasattr(arg, "__iter__") for arg in args): raise TypeError( "Higher-order LookupTable calls do not yet support vector parameters" ) else: args = np.fromiter(args, dtype=float) return evaluator(*args)
def reverse_call( self, y: Union[float, Iterable, Timeseries], domain: Tuple[float, float] = (None, None), detect_range_error: bool = True, ) -> Union[float, np.ndarray, Timeseries]: """Do an inverted call on this LookupTable Uses SciPy brentq optimizer to simulate a reversed call. Note: Method does not work with higher-order LookupTables """ if isinstance(y, Timeseries): # Recurse and return return Timeseries(y.times, self.reverse_call(y.values)) # Get domain information l_d, u_d = domain if l_d is None: l_d = self.domain[0] if u_d is None: u_d = self.domain[1] # Cast y to array of float if hasattr(y, "__iter__"): y_array = np.fromiter(y, dtype=float) else: y_array = np.array([y], dtype=float) # Find not np.nan is_not_nan = ~np.isnan(y_array) y_array_not_nan = y_array[is_not_nan] # Detect if there is a range violation if detect_range_error: l_r, u_r = self.range lb_viol = y_array_not_nan < l_r ub_viol = y_array_not_nan > u_r all_viol = y_array_not_nan[lb_viol | ub_viol] if all_viol.size > 0: raise ValueError( "Values {} are not in lookup table range ({}, {})".format( all_viol, l_r, u_r ) ) # Construct function to do inverse evaluation evaluator = self.__numeric_function_evaluator def inv_evaluator(y_target): """inverse evaluator function""" return brentq(lambda x: evaluator(x) - y_target, l_d, u_d) inv_evaluator = np.vectorize(inv_evaluator) # Calculate x_array x_array = np.full_like(y_array, np.nan, dtype=float) if y_array_not_nan.size != 0: x_array[is_not_nan] = inv_evaluator(y_array_not_nan) # Return x if hasattr(y, "__iter__"): return x_array else: return x_array.item()
[docs]class CSVLookupTableMixin(OptimizationProblem): """ Adds lookup tables to your optimization problem. During preprocessing, the CSV files located inside the ``lookup_tables`` subfolder are read. In every CSV file, the first column contains the output of the lookup table. Subsequent columns contain the input variables. Cubic B-Splines are used to turn the data points into continuous lookup tables. Optionally, a file ``curvefit_options.ini`` may be included inside the ``lookup_tables`` folder. This file contains, grouped per lookup table, the following options: * monotonicity: * is an integer, magnitude is ignored * if positive, causes spline to be monotonically increasing * if negative, causes spline to be monotonically decreasing * if 0, leaves spline monotonicity unconstrained * curvature: * is an integer, magnitude is ignored * if positive, causes spline curvature to be positive (convex) * if negative, causes spline curvature to be negative (concave) * if 0, leaves spline curvature unconstrained .. note:: Currently only one-dimensional lookup tables are fully supported. Support for two- dimensional lookup tables is experimental. :cvar csv_delimiter: Column delimiter used in CSV files. Default is ``,``. :cvar csv_lookup_table_debug: Whether to generate plots of the spline fits. Default is ``false``. :cvar csv_lookup_table_debug_points: Number of evaluation points for plots. Default is ``100``. """ #: Column delimiter used in CSV files csv_delimiter = ',' #: Debug settings csv_lookup_table_debug = False csv_lookup_table_debug_points = 100 def __init__(self, **kwargs): # Check arguments if 'input_folder' in kwargs: assert ('lookup_table_folder' not in kwargs) self.__lookup_table_folder = os.path.join( kwargs['input_folder'], 'lookup_tables') else: self.__lookup_table_folder = kwargs['lookup_table_folder'] # Call parent super().__init__(**kwargs) def pre(self): # Call parent class first for default behaviour. super().pre() # Get curve fitting options from curvefit_options.ini file ini_path = os.path.join( self.__lookup_table_folder, 'curvefit_options.ini') try: ini_config = configparser.RawConfigParser() no_curvefit_options = False except IOError: "CSVLookupTableMixin: No curvefit_options.ini file found. Using default values.") no_curvefit_options = True def get_curvefit_options(curve_name, no_curvefit_options=no_curvefit_options): if no_curvefit_options: return 0, 0, 0 curvefit_options = [] def get_property(prop_name): try: prop = int(ini_config.get(curve_name, prop_name)) except configparser.NoSectionError: prop = 0 except configparser.NoOptionError: prop = 0 except ValueError: raise Exception( 'CSVLookupTableMixin: Invalid {0} constraint for {1}. {0} should ' 'be either -1, 0, or 1.'.format(prop_name, curve_name)) return prop for prop_name in ['monotonicity', 'monotonicity2', 'curvature']: curvefit_options.append(get_property(prop_name)) logger.debug("CSVLookupTableMixin: Curve fit option for {}:({},{},{})".format( curve_name, *curvefit_options)) return tuple(curvefit_options) def check_lookup_table(lookup_table): if lookup_table in self.__lookup_tables: raise Exception( "Cannot add lookup table {}," "since there is already one with this name.".format(lookup_table)) # Read CSV files "CSVLookupTableMixin: Generating Splines from lookup table data.") self.__lookup_tables = {} for filename in glob.glob(os.path.join(self.__lookup_table_folder, "*.csv")): logger.debug( "CSVLookupTableMixin: Reading lookup table from {}".format(filename)) csvinput = csv.load(filename, delimiter=self.csv_delimiter) output = csvinput.dtype.names[0] inputs = csvinput.dtype.names[1:] # Get monotonicity and curvature from ini file mono, mono2, curv = get_curvefit_options(output) logger.debug( "CSVLookupTableMixin: Output is {}, inputs are {}.".format(output, inputs)) tck = None function = None # If tck file is newer than the csv file, first try to load the cached values from the tck file tck_filename = filename.replace('.csv', '.tck') valid_cache = False if os.path.exists(tck_filename): if no_curvefit_options: valid_cache = os.path.getmtime(filename) < os.path.getmtime(tck_filename) else: valid_cache = (os.path.getmtime(filename) < os.path.getmtime(tck_filename)) and \ (os.path.getmtime(ini_path) < os.path.getmtime(tck_filename)) if valid_cache: logger.debug( 'CSVLookupTableMixin: Attempting to use cached tck values for {}'.format(output)) with open(tck_filename, 'rb') as f: try: tck, function = pickle.load(f) except Exception: valid_cache = False if not valid_cache: 'CSVLookupTableMixin: Recalculating tck values for {}'.format(output)) if len(csvinput.dtype.names) == 2: if not valid_cache: k = 3 # default value # 1D spline fitting needs k+1 data points if len(csvinput[output]) >= k + 1: tck =[inputs[0]], csvinput[ output], k=k, monotonicity=mono, curvature=curv) else: raise Exception( 'CSVLookupTableMixin: Too few data points in {} to do spline fitting. ' 'Need at least {} points.'.format(filename, k + 1)) if self.csv_lookup_table_debug: import pylab i = np.linspace(csvinput[inputs[0]][0], csvinput[ inputs[0]][-1], self.csv_lookup_table_debug_points) o = splev(i, tck) pylab.clf() # TODO: Figure out why cross-section B0607 in NZV does not # conform to constraints! pylab.plot(i, o) pylab.plot(csvinput[inputs[0]], csvinput[ output], linestyle='', marker='x', markersize=10) figure_filename = filename.replace('.csv', '.png') pylab.savefig(figure_filename) symbols = [ca.SX.sym(inputs[0])] if not valid_cache: function = ca.Function('f', symbols, [BSpline1D(*tck)(symbols[0])]) check_lookup_table(output) self.__lookup_tables[output] = LookupTable(symbols, function, tck) elif len(csvinput.dtype.names) == 3: if tck is None: kx = 3 # default value ky = 3 # default value # 2D spline fitting needs (kx+1)*(ky+1) data points if len(csvinput[output]) >= (kx + 1) * (ky + 1): # TODO: add curvature parameters from curvefit_options.ini # once 2d fit is implemented tck = bisplrep(csvinput[inputs[0]], csvinput[ inputs[1]], csvinput[output], kx=kx, ky=ky) else: raise Exception( 'CSVLookupTableMixin: Too few data points in {} to do spline fitting. ' 'Need at least {} points.'.format(filename, (kx + 1) * (ky + 1))) if self.csv_lookup_table_debug: import pylab i1 = np.linspace(csvinput[inputs[0]][0], csvinput[ inputs[0]][-1], self.csv_lookup_table_debug_points) i2 = np.linspace(csvinput[inputs[1]][0], csvinput[ inputs[1]][-1], self.csv_lookup_table_debug_points) i1, i2 = np.meshgrid(i1, i2) i1 = i1.flatten() i2 = i2.flatten() o = bisplev(i1, i2, tck) pylab.clf() pylab.plot_surface(i1, i2, o) figure_filename = filename.replace('.csv', '.png') pylab.savefig(figure_filename) symbols = [ca.SX.sym(inputs[0]), ca.SX.sym(inputs[1])] if not valid_cache: function = ca.Function('f', symbols, [BSpline2D(*tck)(symbols[0], symbols[1])]) check_lookup_table(output) self.__lookup_tables[output] = LookupTable(symbols, function, tck) else: raise Exception( 'CSVLookupTableMixin: {}-dimensional lookup tables not implemented yet.'.format( len(csvinput.dtype.names))) if not valid_cache: pickle.dump((tck, function), open(filename.replace('.csv', '.tck'), 'wb'), protocol=pickle.HIGHEST_PROTOCOL)
[docs] def lookup_tables(self, ensemble_member): # Call parent class first for default values. lookup_tables = super().lookup_tables(ensemble_member) # Update lookup_tables with imported csv lookup tables lookup_tables.update(self.__lookup_tables) return lookup_tables