Add R-Factor support.
The Curve comparison is possible through R-Factor analysis. Further comparison like shape analysis, similarity index... will be included later. Data export is now possible through the gui menu or as a method of the Data object.
This commit is contained in:
parent
3a20719d19
commit
cd3fb05932
|
|
@ -13,6 +13,7 @@ import numpy as np
|
|||
import ase
|
||||
from ase import Atom, Atoms
|
||||
from ase.data import chemical_symbols
|
||||
import os
|
||||
|
||||
from msspec.misc import UREG, LOGGER
|
||||
from msspec.utils import get_atom_index
|
||||
|
|
@ -1015,3 +1016,248 @@ class SpecIO(object):
|
|||
with open(filename, 'r') as fd:
|
||||
content = fd.read()
|
||||
return pat.findall(content)
|
||||
|
||||
|
||||
class CompCurveIO(object):
|
||||
def __init__(self, parameters):
|
||||
self.parameters = parameters
|
||||
|
||||
def write_input_file(self, filename='comp_curve.dat'):
|
||||
def title(t, shift=4, width=78, center=True):
|
||||
if center:
|
||||
s = ('{}*{:^%ds}*\n' % (width - shift - 2)
|
||||
).format(' ' * shift, t)
|
||||
else:
|
||||
s = ('{}*{:%ds}*\n' % (width - shift - 2)
|
||||
).format(' ' * shift, t)
|
||||
return s
|
||||
|
||||
def rule(tabs=(5, 10, 10, 10, 10), symbol='=', shift=4, width=78):
|
||||
s = ' ' * shift + '*' + symbol * (width - shift - 2) + '*\n'
|
||||
t = np.cumsum(tabs) + shift
|
||||
sep = list(s)
|
||||
for i in t:
|
||||
sep[i] = '+'
|
||||
return ''.join(sep)
|
||||
|
||||
def fillstr(a, b, index, justify='left'):
|
||||
alist = list(a)
|
||||
|
||||
if justify == 'left':
|
||||
offset = -len(b) + 1
|
||||
elif justify == 'center':
|
||||
offset = (-len(b) + 1) / 2
|
||||
elif justify == 'decimal':
|
||||
try:
|
||||
offset = -(b.index('.') - 1)
|
||||
except ValueError:
|
||||
offset = 0
|
||||
else:
|
||||
offset = 0
|
||||
|
||||
for i, _ in enumerate(b):
|
||||
alist[int(index + offset + i)] = _
|
||||
return ''.join(alist)
|
||||
|
||||
def create_line(legend='', index=49, dots=False):
|
||||
s = ' ' * 78 + '\n'
|
||||
if dots:
|
||||
s = fillstr(s, "..", 6, justify='right')
|
||||
s = fillstr(s, "*", 4)
|
||||
s = fillstr(s, "*", 77)
|
||||
s = fillstr(s, legend, index, justify='right')
|
||||
return s
|
||||
|
||||
p = self.parameters
|
||||
|
||||
content = rule(tabs=(), symbol='*')
|
||||
content += title('R-FACTOR, SIMILARITY INDEX, DISTANCE, GOODNESS OF FIT')
|
||||
content += title('KERNEL DISTANCE AND SHAPE ANALYSIS')
|
||||
content += rule(tabs=(), symbol='*')
|
||||
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
content += create_line('CALCULATION PARAMETERS : GENERAL', index=29)
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
line = create_line("N_PAR,NORM,I_SCALE,I_NORM")
|
||||
line = fillstr(line, str(p.get_parameter('general_npar')), 9, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('general_norm')), 19, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('general_iscale')), 29, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('general_inorm')), 39, 'left')
|
||||
content += line
|
||||
line = create_line("I_SYM,SYM,I_POSI")
|
||||
line = fillstr(line, str(p.get_parameter('general_isym')), 9, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('general_sym')), 19, 'decimal')
|
||||
line = fillstr(line, str(p.get_parameter('general_iposi')), 29, 'left')
|
||||
content += line
|
||||
line = create_line("I_DIST,I_CUR,I_SA,I_PRINT")
|
||||
line = fillstr(line, str(p.get_parameter('general_idist')), 9, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('general_icur')), 19, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('general_isa')), 29, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('general_iprint')), 39, 'left')
|
||||
content += line
|
||||
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
content += create_line('CALCULATION PARAMETERS : WEIGHTS', index=29)
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
line = create_line("I_WEIGHT,ALPHA,BETA,SIGMA")
|
||||
line = fillstr(line, str(p.get_parameter('weights_iweight')), 9, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('weights_alpha')), 19, 'decimal')
|
||||
line = fillstr(line, str(p.get_parameter('weights_beta')), 29, 'decimal')
|
||||
line = fillstr(line, str(p.get_parameter('weights_sigma')), 39, 'decimal')
|
||||
content += line
|
||||
line = create_line("I_SHIFT,MAXW")
|
||||
line = fillstr(line, str(p.get_parameter('weights_ishift')), 9, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('weights_maxw')), 19, 'decimal')
|
||||
content += line
|
||||
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
content += create_line('CALCULATION PARAMETERS : R-FACTORS', index=29)
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
line = create_line("V_I")
|
||||
line = fillstr(line, str(p.get_parameter('rfc_vi')), 9, 'decimal')
|
||||
content += line
|
||||
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
content += create_line('CALCULATION PARAMETERS : SIMILARITY INDICES', index=29)
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
line = create_line("ALHPAS,BETAS,N_BINS")
|
||||
line = fillstr(line, str(p.get_parameter('sim_alphas')), 9, 'decimal')
|
||||
line = fillstr(line, str(p.get_parameter('sim_betas')), 19, 'decimal')
|
||||
line = fillstr(line, str(p.get_parameter('sim_nbins')), 29, 'left')
|
||||
content += line
|
||||
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
content += create_line('CALCULATION PARAMETERS : DISTANCES', index=29)
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
line = create_line("ALHPAD,I_BETA,L,SIGMAD")
|
||||
line = fillstr(line, str(p.get_parameter('dist_alphad')), 9, 'decimal')
|
||||
line = fillstr(line, str(p.get_parameter('dist_ibeta')), 19, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('dist_l')), 29, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('dist_sigmad')), 39, 'decimal')
|
||||
content += line
|
||||
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
content += create_line('CALCULATION PARAMETERS : GOODNESS OF FIT', index=29)
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
line = create_line("N_BING,ALPHAG")
|
||||
line = fillstr(line, str(p.get_parameter('gof_nbing')), 9, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('gof_alphag')), 19, 'decimal')
|
||||
content += line
|
||||
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
content += create_line('CALCULATION PARAMETERS : KERNEL DISTANCES', index=29)
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
line = create_line("ALPHAK,L,SIGMAK")
|
||||
line = fillstr(line, str(p.get_parameter('kdist_alphak')), 9, 'decimal')
|
||||
line = fillstr(line, str(p.get_parameter('kdist_l')), 19, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('kdist_sigmak')), 29, 'decimal')
|
||||
content += line
|
||||
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
content += create_line('CALCULATION PARAMETERS : MOMENTS', index=29)
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
line = create_line("N_GRID,N_MOM,BASIS")
|
||||
line = fillstr(line, str(p.get_parameter('mom_ngrid')), 9, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('mom_nmom')), 19, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('mom_basis')), 29, 'left')
|
||||
content += line
|
||||
line = create_line("I_ALG,MU,NU")
|
||||
line = fillstr(line, str(p.get_parameter('mom_ialg')), 9, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('mom_mu')), 19, 'decimal')
|
||||
line = fillstr(line, str(p.get_parameter('mom_nu')), 29, 'decimal')
|
||||
content += line
|
||||
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
content += create_line('CALCULATION PARAMETERS : CHORDS', index=29)
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
line = create_line("I_CHORD,METHOD,VALUE,N_BINC")
|
||||
line = fillstr(line, str(p.get_parameter('chords_ichord')), 9, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('chords_method')), 19, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('chords_value')), 29, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('chords_nbinc')), 39, 'left')
|
||||
content += line
|
||||
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
content += create_line('CALCULATION PARAMETERS : CHAIN CODES', index=29)
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
line = create_line("N_CONNECT,SCALEC")
|
||||
line = fillstr(line, str(p.get_parameter('codes_nconnect')), 9, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('codes_scalec')), 19, 'decimal')
|
||||
content += line
|
||||
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
content += create_line('CALCULATION PARAMETERS : CONTOUR', index=29)
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
line = create_line("NBIN,N_LEN,SH_AN,I_FOU")
|
||||
line = fillstr(line, str(p.get_parameter('cont_nbin')), 9, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('cont_nlen')), 19, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('cont_shan')), 29, 'left')
|
||||
line = fillstr(line, str(p.get_parameter('cont_ifou')), 39, 'left')
|
||||
content += line
|
||||
line = create_line("I_NORM")
|
||||
line = fillstr(line, str(p.get_parameter('cont_ifou')), 9, 'left')
|
||||
content += line
|
||||
|
||||
content += rule(tabs=(5,10,10,10,10))
|
||||
content += create_line('EXPERIMENTAL INPUT FILE :', index=29)
|
||||
content += rule(tabs=(), symbol='-')
|
||||
line = create_line('')
|
||||
line = fillstr(line, 'NAME', 14, 'right')
|
||||
line = fillstr(line, 'TYPE', 58, 'right')
|
||||
content += line
|
||||
content += rule(tabs=(5,40))
|
||||
line = create_line('EXPERIMENT', index=49)
|
||||
line = fillstr(line, str(p.get_parameter('exp_filename')), 9, 'right')
|
||||
content += line
|
||||
|
||||
content += rule(tabs=(5,40))
|
||||
content += create_line('CALCULATED INPUT FILE :', index=29)
|
||||
content += rule(tabs=(), symbol='-')
|
||||
line = create_line('')
|
||||
line = fillstr(line, 'NAME', 14, 'right')
|
||||
line = fillstr(line, 'PARAMETER 1', 49, 'right')
|
||||
line = fillstr(line, 'PARAMETER 2', 63, 'right')
|
||||
content += line
|
||||
content += rule(tabs=(5,40,7,7,7))
|
||||
calc_fnames = p.get_parameter('calc_filename').value
|
||||
for fname in calc_fnames:
|
||||
line = create_line('')
|
||||
line = fillstr(line, fname, 9, 'right')
|
||||
line = fillstr(line, str(p.get_parameter('calc_param1')), 56, 'decimal')
|
||||
line = fillstr(line, str(p.get_parameter('calc_param2')), 70, 'decimal')
|
||||
content += line
|
||||
|
||||
content += rule(tabs=(5,40,7,7,7))
|
||||
content += create_line('END OF DATA FILE', index=31)
|
||||
content += rule(tabs=())
|
||||
content += rule(tabs=(),symbol='*')
|
||||
|
||||
# Write the content to filename
|
||||
try:
|
||||
with open(filename, 'r') as fd:
|
||||
old_content = fd.read()
|
||||
except IOError:
|
||||
old_content = ''
|
||||
|
||||
modified = False
|
||||
if content != old_content:
|
||||
with open(filename, 'w') as fd:
|
||||
fd.write(content)
|
||||
modified = True
|
||||
|
||||
return modified
|
||||
|
||||
def load_results(self, index=0, prefix='rfc/experiment_rf'):
|
||||
data = []
|
||||
for i in range(1, 13):
|
||||
#data.append(np.loadtxt(prefix + f'{i:02d}' + '.txt')[-1])
|
||||
results = np.loadtxt(prefix + f'{i:02d}' + '.txt')
|
||||
results = results.reshape((-1, 2))
|
||||
data.append(results[index,1])
|
||||
suffix = 'ren'
|
||||
exp = {'int': None, 'ren': None, 'chi': None, 'cdf': None}
|
||||
exp_ren = np.loadtxt(os.path.join('exp', 'div',
|
||||
f'experiment_{suffix}.txt'))
|
||||
calc_ren = np.loadtxt(os.path.join('calc', 'div',
|
||||
f'calculation{index:d}_{suffix}.txt'))
|
||||
return data, exp_ren, calc_ren
|
||||
|
|
|
|||
|
|
@ -42,6 +42,7 @@ For more information on MsSpec, follow this
|
|||
"""
|
||||
|
||||
import inspect
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
import shutil
|
||||
|
|
@ -63,6 +64,7 @@ from ase.calculators.calculator import Calculator
|
|||
from terminaltables.ascii_table import AsciiTable
|
||||
|
||||
from msspec import iodata
|
||||
from msspec.calcio import CompCurveIO
|
||||
from msspec.calcio import PhagenIO
|
||||
from msspec.calcio import SpecIO
|
||||
from msspec.data import electron_be
|
||||
|
|
@ -74,6 +76,8 @@ from msspec.misc import set_log_output
|
|||
from msspec.misc import UREG
|
||||
from msspec.misc import XRaySource
|
||||
from msspec.parameters import CalculationParameters
|
||||
from msspec.parameters import CompCurveParameters
|
||||
from msspec.parameters import CompCurveGeneralParameters
|
||||
from msspec.parameters import DetectorParameters
|
||||
from msspec.parameters import EIGParameters
|
||||
from msspec.parameters import GlobalParameters
|
||||
|
|
@ -91,6 +95,7 @@ from msspec.spec.fortran import _eig_mi
|
|||
from msspec.spec.fortran import _eig_pw
|
||||
from msspec.spec.fortran import _phd_mi_noso_nosp_nosym
|
||||
from msspec.spec.fortran import _phd_se_noso_nosp_nosym
|
||||
from msspec.spec.fortran import _comp_curves
|
||||
from msspec.utils import get_atom_index
|
||||
|
||||
|
||||
|
|
@ -1003,6 +1008,228 @@ def MSSPEC(spectroscopy='PED', **kwargs):
|
|||
return cls(**kwargs)
|
||||
|
||||
|
||||
class RFACTOR(object):
|
||||
def __init__(self, folder='./rfc'):
|
||||
self.iodata = iodata.Data('R-Factor analysis')
|
||||
self.folder = folder
|
||||
self._params = CompCurveParameters()
|
||||
self.general_parameters = CompCurveGeneralParameters(
|
||||
self._params)
|
||||
self.io = CompCurveIO(self._params)
|
||||
|
||||
# prepare the working area
|
||||
if not os.path.exists(self.folder):
|
||||
os.makedirs(self.folder)
|
||||
os.makedirs(os.path.join(self.folder, 'rfc'))
|
||||
os.makedirs(os.path.join(self.folder, 'exp/div'))
|
||||
os.makedirs(os.path.join(self.folder, 'calc/div'))
|
||||
os.makedirs(os.path.join(self.folder, 'plot'))
|
||||
|
||||
# store the r-factor analysis results
|
||||
# self.variables = {'variable0_name': [value0, value1, ...],
|
||||
# 'variable1_name': [value0, value1, ...],
|
||||
# ...}
|
||||
self.variables = {}
|
||||
# self.rfc = [[rf0_0, rf0_1, ... , rf0_11], <-- run 0
|
||||
# [rf1_0, rf1_1, ... , rf1_11], <-- run 1
|
||||
# ............................,
|
||||
# [rfn_0, rfn_1, ... , rfn_11]] <-- run n
|
||||
self.rfc = []
|
||||
|
||||
# The x and y array to compare to
|
||||
self.xref = self.yref = [0,]
|
||||
|
||||
# The index of the best value
|
||||
self.index = 0
|
||||
|
||||
# The best values as a dictionary
|
||||
self.best_values = {}
|
||||
|
||||
# The number of calculation files in the stack. This counter is
|
||||
# inremented each time calculation is run
|
||||
self.stack_count = 0
|
||||
|
||||
# initialize all parameters with defaults values
|
||||
self.logger = logging.getLogger("RFACTOR")
|
||||
self.logger.info("Set default values =========================================")
|
||||
for p in (list(self.general_parameters)):
|
||||
p.set(p.default)
|
||||
self.logger.info("End of default values ======================================")
|
||||
#exit()
|
||||
|
||||
|
||||
def set_references(self, X, Y):
|
||||
self.xref = X
|
||||
self.yref = Y
|
||||
|
||||
def run(self, *args, data=None, **kwargs):
|
||||
# Get the data object if provided
|
||||
#data = kwargs.pop('data', None)
|
||||
if data:
|
||||
assert isinstance(data, iodata.Data), ("Unsupported type for data"
|
||||
"keyword.")
|
||||
self.iodata = data
|
||||
|
||||
# Move to the working folder
|
||||
cwd = os.getcwd()
|
||||
os.chdir(self.folder)
|
||||
# Write the reference data
|
||||
np.savetxt('exp/experiment.txt', np.transpose([self.xref, self.yref]))
|
||||
|
||||
# Write all the input calculation files
|
||||
# Number of input files
|
||||
noif = int(len(args)/2)
|
||||
for i in range(noif):
|
||||
X, Y = args[2*i], args[2*i+1]
|
||||
fname = os.path.join('calc',
|
||||
f'calculation{self.stack_count:d}.txt')
|
||||
# And save to the working space
|
||||
np.savetxt(fname, np.transpose([X, Y]))
|
||||
self.stack_count += 1
|
||||
|
||||
# Update the list of input calculation files
|
||||
self._params.calc_filename = []
|
||||
for i in range(self.stack_count):
|
||||
fname = os.path.join('calc', f'calculation{i:d}.txt')
|
||||
self._params.calc_filename.append(fname)
|
||||
|
||||
# Write the input file
|
||||
self.io.write_input_file('comp_curves.dat')
|
||||
# And finally run
|
||||
_comp_curves.run()
|
||||
|
||||
#######################################################################
|
||||
# Load the results
|
||||
#######################################################################
|
||||
self.rfc = []
|
||||
for i in range(self.stack_count):
|
||||
# Read results files and get the R-Factors, exp data and calc
|
||||
# data for file index 'i'
|
||||
rfc, exp_data, calc_data = self.io.load_results(index=i)
|
||||
# Update the list of R-Factors results
|
||||
self.rfc.append(rfc)
|
||||
# Update the variables values
|
||||
for i in range(noif):
|
||||
for k,v in kwargs.items():
|
||||
try:
|
||||
vi = v[i]
|
||||
except (IndexError, TypeError) as err:
|
||||
vi = v
|
||||
try:
|
||||
self.variables[k].append(vi)
|
||||
except KeyError as err:
|
||||
self.variables[k] = [vi,]
|
||||
|
||||
#######################################################################
|
||||
# Analysis
|
||||
#######################################################################
|
||||
rfc = np.array(self.rfc)
|
||||
# Get the index of the minimum for each R-Factor (each column)
|
||||
all_min = np.argmin(rfc, axis=0)
|
||||
# Iterate over each run and get the number of R-Factors that are min
|
||||
# for this set
|
||||
all_counts = np.zeros(self.stack_count, dtype=int)
|
||||
for i in range(self.stack_count):
|
||||
all_counts[i] = len(np.where(all_min==i)[0])
|
||||
|
||||
# The best set of variables (ie the run index) is the one with the
|
||||
# highest number of counts:
|
||||
self.index = np.argmax(all_counts)
|
||||
|
||||
# Update the "best values" dict
|
||||
self.best_values = {k:self.variables[k][self.index] for k in
|
||||
self.variables.keys()}
|
||||
|
||||
# with np.printoptions(precision=6, linewidth=300, threshold=200):
|
||||
# print('rfc: ')
|
||||
# print(rfc)
|
||||
# print('all_min: ', all_min)
|
||||
# print('all_counts: ', all_counts)
|
||||
# print('variables: ', self.variables)
|
||||
|
||||
#######################################################################
|
||||
# Store values
|
||||
#######################################################################
|
||||
# Three datasets will be created or existing ones will be reused if
|
||||
# any.
|
||||
dset_values_title = "CurveComparison Values"
|
||||
dset_results_title = "CurveComparison Results"
|
||||
dset_rfc_title = "CurveComparison R-Factors"
|
||||
|
||||
# Create (or re-create) the datasets
|
||||
dset_values = self.iodata.add_dset(dset_values_title, overwrite=True)
|
||||
dset_values.add_columns(x=[], yref=[])
|
||||
view_values = dset_values.add_view("Comparison", xlabel="X", ylabel="Y",
|
||||
autoscale=True, overwrite=True)
|
||||
|
||||
dset_results = self.iodata.add_dset(dset_results_title, overwrite=True)
|
||||
dset_results.add_columns(variable_set=list(range(self.stack_count)),
|
||||
counts=all_counts.copy())
|
||||
dset_results.add_columns(**self.variables)
|
||||
view_results = dset_results.add_view("R-Factor analysis",
|
||||
xlabel="variable set number",
|
||||
ylabel="counts",
|
||||
title=("Number of R-Factors "
|
||||
"minima for each set of "
|
||||
"variables"))
|
||||
|
||||
dset_rfc = self.iodata.add_dset(dset_rfc_title, overwrite=True)
|
||||
dset_rfc.add_columns(rfactor_number=list(range(12)))
|
||||
view_rfc = dset_rfc.add_view("R-Factor results", xlabel="R-Factor #",
|
||||
ylabel="R-Factor value",
|
||||
title="", autoscale=True, marker="s")
|
||||
|
||||
for i in range(self.stack_count):
|
||||
rfc, exp_data, calc_data = self.io.load_results(index=i)
|
||||
# Store the experimental data
|
||||
dset_values.x, dset_values.yref = exp_data.T
|
||||
# Append the calculated values
|
||||
ycalc = calc_data[:,1]
|
||||
dset_values.add_columns(**{f"calc{i:d}": ycalc})
|
||||
dset_rfc.add_columns(**{f'variable_set{i:d}': rfc})
|
||||
|
||||
# Plot the curves
|
||||
view_values.select('x', 'yref', legend='Reference values')
|
||||
title = ''
|
||||
for k,v in self.best_values.items():
|
||||
title += f'{k}={v} '
|
||||
view_values.select('x', f"calc{self.index:d}",
|
||||
legend="Best calculated values")
|
||||
view_values.set_plot_options(title=title)
|
||||
|
||||
view_results.select('counts')
|
||||
|
||||
for i in range(self.stack_count):
|
||||
view_rfc.select('rfactor_number', f'variable_set{i:d}',
|
||||
legend=f"variables set #{i:d}")
|
||||
# Save the parameters
|
||||
for p in self.get_parameters():
|
||||
bundle = {'group': str(p.group),
|
||||
'name': str(p.name),
|
||||
'value': str(p.value),
|
||||
'unit': '' if p.unit is None else str(p.unit)}
|
||||
dset_results.add_parameter(**bundle)
|
||||
|
||||
# Move back to the initial folder
|
||||
os.chdir(cwd)
|
||||
# And return the Data object
|
||||
return self.iodata
|
||||
|
||||
def get_parameters(self):
|
||||
"""Get all the defined parameters in the calculator.
|
||||
|
||||
:return: A list of all parameters objects.
|
||||
:rtype: List of :py:class:`parameters.Parameter`
|
||||
|
||||
"""
|
||||
_ = []
|
||||
for section in ('general', ):
|
||||
parameters = getattr(self, section + '_parameters')
|
||||
for p in parameters:
|
||||
_.append(p)
|
||||
return _
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
pass
|
||||
|
|
|
|||
|
|
@ -72,6 +72,7 @@ import sys
|
|||
from distutils.version import LooseVersion
|
||||
from distutils.version import StrictVersion
|
||||
from io import StringIO
|
||||
from datetime import datetime
|
||||
|
||||
import ase.io
|
||||
import h5py
|
||||
|
|
@ -79,6 +80,7 @@ import numpy as np
|
|||
import wx.grid
|
||||
from lxml import etree
|
||||
from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg as FigureCanvas
|
||||
from matplotlib.backends.backend_agg import FigureCanvasAgg
|
||||
from matplotlib.backends.backend_wxagg import NavigationToolbar2WxAgg
|
||||
from matplotlib.figure import Figure
|
||||
from terminaltables import AsciiTable
|
||||
|
|
@ -124,6 +126,18 @@ def cols2matrix(x, y, z, nx=88*1+1, ny=360*1+1):
|
|||
return ux, uy, zz
|
||||
|
||||
|
||||
def is_title_valid(title):
|
||||
""" Ensure the string does not contain special characters:
|
||||
/\:*?"<>|
|
||||
"""
|
||||
special_chars = ('/', '\\', ':', '*', '?', '\"', '<', '>', '|')
|
||||
for char in special_chars:
|
||||
if title.find(char) > -1:
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
|
||||
class _DataPoint(dict):
|
||||
def __init__(self, *args, **kwargs):
|
||||
dict.__init__(self, *args, **kwargs)
|
||||
|
|
@ -146,6 +160,12 @@ class DataSet(object):
|
|||
|
||||
"""
|
||||
def __init__(self, title, notes=""):
|
||||
assert is_title_valid(title), '\/:*?"<>| are not allowed in the string'
|
||||
#self._col_names = []
|
||||
#self._col_arrays = []
|
||||
self.__dict__['_col_names'] = []
|
||||
self.__dict__['_col_arrays'] = []
|
||||
|
||||
self.title = title
|
||||
self.notes = notes
|
||||
self._views = []
|
||||
|
|
@ -153,12 +173,16 @@ class DataSet(object):
|
|||
self.attributes = {}
|
||||
|
||||
|
||||
self._col_names = []
|
||||
self._col_arrays = []
|
||||
self._defaults = {'bool': False, 'str': '', 'int': 0, 'float': 0.,
|
||||
'complex': complex(0)}
|
||||
self._formats = {bool: '{:s}', str: '{:s}', int: '{:<20d}',
|
||||
float: '{:<20.10e}', complex: 's'}
|
||||
self._formats = ((np.integer, '{:<20d}'),
|
||||
(np.floating, '{:<20.10e}'),
|
||||
(np.complex, '({0.real:<.10e} {0.imag:<.10e}j)'),
|
||||
(np.bool, '{:s}'),
|
||||
(str, '{:s}'))
|
||||
|
||||
|
||||
def _empty_array(self, val):
|
||||
if isinstance(val, str):
|
||||
|
|
@ -273,7 +297,7 @@ class DataSet(object):
|
|||
"""
|
||||
return self._col_names
|
||||
|
||||
def add_view(self, name, **plotopts):
|
||||
def add_view(self, name, overwrite=False, **plotopts):
|
||||
"""
|
||||
Creates a new view named *name* with specied plot options.
|
||||
|
||||
|
|
@ -283,6 +307,9 @@ class DataSet(object):
|
|||
:return: a view.
|
||||
:rtype: :py:class:`iodata._DataSetView`
|
||||
"""
|
||||
if overwrite:
|
||||
self.delete_view(name)
|
||||
|
||||
if isinstance(name, str):
|
||||
v = _DataSetView(self, name, **plotopts)
|
||||
else:
|
||||
|
|
@ -291,6 +318,14 @@ class DataSet(object):
|
|||
self._views.append(v)
|
||||
return v
|
||||
|
||||
def delete_view(self, name):
|
||||
view_titles = [_.title for _ in self._views]
|
||||
try:
|
||||
i = view_titles.index(name)
|
||||
self._views.pop(i)
|
||||
except:
|
||||
pass
|
||||
|
||||
def views(self):
|
||||
"""Returns all the defined views in the dataset.
|
||||
|
||||
|
|
@ -368,6 +403,12 @@ class DataSet(object):
|
|||
condition = kwargs.get('where', 'True')
|
||||
indices = []
|
||||
|
||||
def export_views(self, folder):
|
||||
for view in self.views():
|
||||
f = view.get_figure()
|
||||
fname = os.path.join(folder, view.title) + '.png'
|
||||
f.savefig(fname)
|
||||
|
||||
|
||||
def export(self, filename="", mode="w"):
|
||||
"""Export the DataSet to the given *filename*.
|
||||
|
|
@ -379,8 +420,48 @@ class DataSet(object):
|
|||
|
||||
Not yet implemented
|
||||
"""
|
||||
|
||||
rule = '#' * 80 + '\n'
|
||||
|
||||
def header():
|
||||
s = '# PARAMETERS:\n'
|
||||
groups = []
|
||||
for p in self.parameters():
|
||||
g = p['group']
|
||||
if g not in groups:
|
||||
groups.append(g)
|
||||
parameters = {}
|
||||
for group in groups:
|
||||
parameters[group] = self.get_parameter(group=group)
|
||||
for k, v in parameters.items():
|
||||
if k == 'Cluster':
|
||||
continue
|
||||
s += f"# {k}:\n"
|
||||
if not(isinstance(v, list)):
|
||||
v = [v,]
|
||||
for p in v:
|
||||
s += f"# {p['name']} = {p['value']} {p['unit']}\n"
|
||||
return s
|
||||
|
||||
colnames = self.columns()
|
||||
with open(filename, mode) as fd:
|
||||
# write the date and time of export
|
||||
now = datetime.now()
|
||||
fd.write(f"# Data exported on {now}\n")
|
||||
fd.write(rule)
|
||||
|
||||
# Append notes
|
||||
fd.write("# NOTES:\n")
|
||||
for line in self.notes.split('\n'):
|
||||
fd.write(f"# {line}\n")
|
||||
fd.write(rule)
|
||||
|
||||
# Append parameters
|
||||
fd.write(header())
|
||||
fd.write(rule)
|
||||
|
||||
# Append the data
|
||||
fd.write("# DATA:\n")
|
||||
fd.write("# " + ("{:<20s}" * len(colnames)).format(*colnames
|
||||
) + "\n")
|
||||
for i in range(len(self)):
|
||||
|
|
@ -389,7 +470,7 @@ class DataSet(object):
|
|||
value = row[key][0]
|
||||
fmt = '{:s}'
|
||||
#print value
|
||||
for t, f in list(self._formats.items()):
|
||||
for t, f in self._formats:
|
||||
if isinstance(value, t):
|
||||
fmt = f
|
||||
break
|
||||
|
|
@ -406,7 +487,7 @@ class DataSet(object):
|
|||
|
||||
new._col_names = self.columns()
|
||||
for arr in self._col_arrays:
|
||||
new._col_arrays.append(np.array(arr[itemspec]).flatten())
|
||||
new._col_arrays.append(np.asarray(arr)[itemspec].flatten())
|
||||
|
||||
return new
|
||||
|
||||
|
|
@ -424,6 +505,13 @@ class DataSet(object):
|
|||
raise AttributeError("'{}' object has no attribute '{}'".format(
|
||||
self.__class__.__name__, name))
|
||||
|
||||
def __setattr__(self, name, value):
|
||||
if name in self._col_names:
|
||||
i = self._col_names.index(name)
|
||||
self._col_arrays[i] = value
|
||||
else:
|
||||
self.__dict__[name] = value
|
||||
|
||||
def __iter__(self):
|
||||
for i in range(len(self)):
|
||||
_ = {k: arr[i] for k, arr in zip(self._col_names,
|
||||
|
|
@ -433,7 +521,10 @@ class DataSet(object):
|
|||
|
||||
def __len__(self):
|
||||
try:
|
||||
length = len(self._col_arrays[0])
|
||||
#length = len(self._col_arrays[0])
|
||||
length = 0
|
||||
for array in self._col_arrays:
|
||||
length = max(length, len(array))
|
||||
except IndexError:
|
||||
length = 0
|
||||
return length
|
||||
|
|
@ -482,18 +573,27 @@ class Data(object):
|
|||
|
||||
"""
|
||||
def __init__(self, title=''):
|
||||
assert is_title_valid(title), '\/:*?"<>| are not allowed in the string'
|
||||
self.title = title
|
||||
self._datasets = []
|
||||
self._dirty = False
|
||||
|
||||
def add_dset(self, title):
|
||||
def add_dset(self, title, overwrite=False):
|
||||
"""Adds a new DataSet in the Data object.
|
||||
|
||||
:param title: The name of the DataSet.
|
||||
:type title: str
|
||||
:param overwrite: Tells whether to re-create the dataset if it exists.
|
||||
:type overwrite: bool
|
||||
:return: The newly created DataSet.
|
||||
:rtype: :py:class:`iodata.DataSet`
|
||||
"""
|
||||
if overwrite:
|
||||
try:
|
||||
self.delete_dset(title)
|
||||
except Exception as err:
|
||||
pass
|
||||
|
||||
titles = [d.title for d in self._datasets]
|
||||
if not title in titles:
|
||||
dset = DataSet(title)
|
||||
|
|
@ -598,6 +698,16 @@ class Data(object):
|
|||
self._dirty = False
|
||||
LOGGER.info('Data saved in {}'.format(os.path.abspath(filename)))
|
||||
|
||||
def export(self, folder, overwrite=False):
|
||||
os.makedirs(folder, exist_ok=overwrite)
|
||||
for dset in self._datasets:
|
||||
dset_name = dset.title.replace(' ', '_')
|
||||
p = os.path.join(folder, dset_name)
|
||||
os.makedirs(p, exist_ok=overwrite)
|
||||
fname = os.path.join(p, dset_name) + '.txt'
|
||||
dset.export(fname)
|
||||
dset.export_views(p)
|
||||
|
||||
@staticmethod
|
||||
def load(filename):
|
||||
"""Loads an HDF5 file from the disc.
|
||||
|
|
@ -688,8 +798,9 @@ class Data(object):
|
|||
|
||||
class _DataSetView(object):
|
||||
def __init__(self, dset, name, **plotopts):
|
||||
self.dataset = dset
|
||||
assert is_title_valid(name), '\/:*?"<>| are not allowed in the string'
|
||||
self.title = name
|
||||
self.dataset = dset
|
||||
self._plotopts = dict(
|
||||
title='No title',
|
||||
xlabel='', ylabel='', grid=True, legend=[], colorbar=False,
|
||||
|
|
@ -706,9 +817,15 @@ class _DataSetView(object):
|
|||
def select(self, *args, **kwargs):
|
||||
condition = kwargs.get('where', 'True')
|
||||
legend = kwargs.get('legend', '')
|
||||
self._selection_conditions.append(condition)
|
||||
self._selection_tags.append(args)
|
||||
self._plotopts['legend'].append(legend)
|
||||
index = kwargs.get('index', None)
|
||||
if index is None:
|
||||
self._selection_conditions.append(condition)
|
||||
self._selection_tags.append(args)
|
||||
self._plotopts['legend'].append(legend)
|
||||
else:
|
||||
self._selection_conditions[index] = condition
|
||||
self._selection_tags[index] = args
|
||||
self._plotopts['legend'][index] = legend
|
||||
|
||||
def tags(self):
|
||||
return self._selection_tags
|
||||
|
|
@ -733,6 +850,71 @@ class _DataSetView(object):
|
|||
data.append(values)
|
||||
return data
|
||||
|
||||
def get_figure(self):
|
||||
opts = self._plotopts
|
||||
|
||||
figure = Figure()
|
||||
axes = None
|
||||
proj = opts['projection']
|
||||
scale = opts['scale']
|
||||
if proj == 'rectilinear':
|
||||
axes = figure.add_subplot(111, projection='rectilinear')
|
||||
elif proj in ('polar', 'ortho', 'stereo'):
|
||||
axes = figure.add_subplot(111, projection='polar')
|
||||
|
||||
for values, label in zip(self.get_data(), opts['legend']):
|
||||
# if we have only one column to plot, select a bar graph
|
||||
if np.shape(values)[0] == 1:
|
||||
xvalues = list(range(len(values[0])))
|
||||
axes.bar(xvalues, values[0], label=label,
|
||||
picker=5)
|
||||
axes.set_xticks(xvalues)
|
||||
else:
|
||||
if proj in ('ortho', 'stereo'):
|
||||
theta, phi, Xsec = cols2matrix(*values)
|
||||
theta_ticks = np.arange(0, 91, 15)
|
||||
if proj == 'ortho':
|
||||
R = np.sin(np.radians(theta))
|
||||
R_ticks = np.sin(np.radians(theta_ticks))
|
||||
elif proj == 'stereo':
|
||||
R = 2 * np.tan(np.radians(theta/2.))
|
||||
R_ticks = 2 * np.tan(np.radians(theta_ticks/2.))
|
||||
#R = np.tan(np.radians(theta/2.))
|
||||
X, Y = np.meshgrid(np.radians(phi), R)
|
||||
im = axes.pcolormesh(X, Y, Xsec)
|
||||
axes.set_yticks(R_ticks)
|
||||
axes.set_yticklabels(theta_ticks)
|
||||
|
||||
figure.colorbar(im)
|
||||
|
||||
elif proj == 'polar':
|
||||
values[0] = np.radians(values[0])
|
||||
axes.plot(*values, label=label, picker=5,
|
||||
marker=opts['marker'])
|
||||
else:
|
||||
if scale == 'semilogx':
|
||||
pltcmd = axes.semilogx
|
||||
elif scale == 'semilogy':
|
||||
pltcmd = axes.semilogy
|
||||
elif scale == 'log':
|
||||
pltcmd = axes.loglog
|
||||
else:
|
||||
pltcmd = axes.plot
|
||||
pltcmd(*values, label=label, picker=5,
|
||||
marker=opts['marker'])
|
||||
axes.grid(opts['grid'])
|
||||
axes.set_title(opts['title'])
|
||||
axes.set_xlabel(opts['xlabel'])
|
||||
axes.set_ylabel(opts['ylabel'])
|
||||
axes.set_xlim(*opts['xlim'])
|
||||
axes.set_ylim(*opts['ylim'])
|
||||
if label:
|
||||
axes.legend()
|
||||
axes.autoscale(enable=opts['autoscale'])
|
||||
|
||||
canvas = FigureCanvasAgg(figure)
|
||||
return figure
|
||||
|
||||
def serialize(self):
|
||||
data = {
|
||||
'name': self.title,
|
||||
|
|
@ -930,6 +1112,7 @@ class _DataWindow(wx.Frame):
|
|||
self.Bind(wx.EVT_MENU, self.on_open, id=110)
|
||||
self.Bind(wx.EVT_MENU, self.on_save, id=120)
|
||||
self.Bind(wx.EVT_MENU, self.on_saveas, id=130)
|
||||
self.Bind(wx.EVT_MENU, self.on_export, id=140)
|
||||
self.Bind(wx.EVT_MENU, self.on_close, id=199)
|
||||
|
||||
|
||||
|
|
@ -1004,6 +1187,28 @@ class _DataWindow(wx.Frame):
|
|||
dlg.Destroy()
|
||||
self.update_title()
|
||||
|
||||
def on_export(self, event):
|
||||
overwrite = True
|
||||
dlg = wx.DirDialog(
|
||||
self, message="Export data...", defaultPath=os.getcwd(),
|
||||
style=wx.DD_DEFAULT_STYLE)
|
||||
|
||||
if dlg.ShowModal() == wx.ID_OK:
|
||||
path = dlg.GetPath()
|
||||
if os.listdir(path):
|
||||
mbx = wx.MessageDialog(self,
|
||||
('This folder is not empty. '
|
||||
'Exporting tour data here may '
|
||||
'overwrite its content. Do you wish '
|
||||
'to continue ?'),
|
||||
'Warning: Folder is not empty',
|
||||
wx.YES_NO | wx.ICON_WARNING)
|
||||
if mbx.ShowModal() == wx.ID_NO:
|
||||
overwrite = False
|
||||
mbx.Destroy()
|
||||
self.data.export(path, overwrite)
|
||||
dlg.Destroy()
|
||||
|
||||
def on_viewdata(self, event):
|
||||
dset = self.data[self._current_dset]
|
||||
frame = _GridWindow(dset, parent=self)
|
||||
|
|
@ -1018,7 +1223,8 @@ class _DataWindow(wx.Frame):
|
|||
s.write(dset.get_parameter(group='Cluster', name='cluster')['value'])
|
||||
atoms = ase.io.read(s, format='xyz')
|
||||
cluster_viewer.set_atoms(atoms, rescale=True, center=True)
|
||||
cluster_viewer.rotate_atoms(45., 45.)
|
||||
cluster_viewer.rotate_atoms(0., 180.)
|
||||
cluster_viewer.rotate_atoms(-45., -45.)
|
||||
#cluster_viewer.show_emitter(True)
|
||||
win.Show()
|
||||
|
||||
|
|
@ -1054,6 +1260,36 @@ class _DataWindow(wx.Frame):
|
|||
self._current_dset = name
|
||||
|
||||
def create_page(self, nb, view):
|
||||
# Get the matplotlib figure
|
||||
figure = view.get_figure()
|
||||
|
||||
# Create a panel
|
||||
p = wx.Panel(nb, -1)
|
||||
|
||||
# Create a matplotlib canvas for the figure
|
||||
canvas = FigureCanvas(p, -1, figure)
|
||||
sizer = wx.BoxSizer(wx.VERTICAL)
|
||||
|
||||
toolbar = NavigationToolbar2WxAgg(canvas)
|
||||
toolbar.Realize()
|
||||
|
||||
sizer.Add(toolbar, 0, wx.ALL|wx.EXPAND)
|
||||
toolbar.update()
|
||||
|
||||
sizer.Add(canvas, 5, wx.ALL|wx.EXPAND)
|
||||
|
||||
p.SetSizer(sizer)
|
||||
p.Fit()
|
||||
p.Show()
|
||||
|
||||
# MPL events
|
||||
figure.canvas.mpl_connect('motion_notify_event', self.on_mpl_motion)
|
||||
figure.canvas.mpl_connect('pick_event', self.on_mpl_pick)
|
||||
|
||||
nb.AddPage(p, view.title)
|
||||
|
||||
|
||||
def OLDcreate_page(self, nb, view):
|
||||
opts = view._plotopts
|
||||
p = wx.Panel(nb, -1)
|
||||
|
||||
|
|
@ -1084,36 +1320,45 @@ class _DataWindow(wx.Frame):
|
|||
|
||||
|
||||
for values, label in zip(view.get_data(), opts['legend']):
|
||||
if proj in ('ortho', 'stereo'):
|
||||
theta, phi, Xsec = cols2matrix(*values)
|
||||
theta_ticks = np.arange(0, 91, 15)
|
||||
if proj == 'ortho':
|
||||
R = np.sin(np.radians(theta))
|
||||
R_ticks = np.sin(np.radians(theta_ticks))
|
||||
elif proj == 'stereo':
|
||||
R = 2 * np.tan(np.radians(theta/2.))
|
||||
R_ticks = 2 * np.tan(np.radians(theta_ticks/2.))
|
||||
#R = np.tan(np.radians(theta/2.))
|
||||
X, Y = np.meshgrid(np.radians(phi), R)
|
||||
im = axes.pcolormesh(X, Y, Xsec)
|
||||
axes.set_yticks(R_ticks)
|
||||
axes.set_yticklabels(theta_ticks)
|
||||
|
||||
figure.colorbar(im)
|
||||
|
||||
elif proj == 'polar':
|
||||
values[0] = np.radians(values[0])
|
||||
axes.plot(*values, label=label, picker=5, marker=opts['marker'])
|
||||
# if we have only one column to plot, select a bar graph
|
||||
if np.shape(values)[0] == 1:
|
||||
xvalues = list(range(len(values[0])))
|
||||
axes.bar(xvalues, values[0], label=label,
|
||||
picker=5)
|
||||
axes.set_xticks(xvalues)
|
||||
else:
|
||||
if scale == 'semilogx':
|
||||
pltcmd = axes.semilogx
|
||||
elif scale == 'semilogy':
|
||||
pltcmd = axes.semilogy
|
||||
elif scale == 'log':
|
||||
pltcmd = axes.loglog
|
||||
if proj in ('ortho', 'stereo'):
|
||||
theta, phi, Xsec = cols2matrix(*values)
|
||||
theta_ticks = np.arange(0, 91, 15)
|
||||
if proj == 'ortho':
|
||||
R = np.sin(np.radians(theta))
|
||||
R_ticks = np.sin(np.radians(theta_ticks))
|
||||
elif proj == 'stereo':
|
||||
R = 2 * np.tan(np.radians(theta/2.))
|
||||
R_ticks = 2 * np.tan(np.radians(theta_ticks/2.))
|
||||
#R = np.tan(np.radians(theta/2.))
|
||||
X, Y = np.meshgrid(np.radians(phi), R)
|
||||
im = axes.pcolormesh(X, Y, Xsec)
|
||||
axes.set_yticks(R_ticks)
|
||||
axes.set_yticklabels(theta_ticks)
|
||||
|
||||
figure.colorbar(im)
|
||||
|
||||
elif proj == 'polar':
|
||||
values[0] = np.radians(values[0])
|
||||
axes.plot(*values, label=label, picker=5,
|
||||
marker=opts['marker'])
|
||||
else:
|
||||
pltcmd = axes.plot
|
||||
pltcmd(*values, label=label, picker=5, marker=opts['marker'])
|
||||
if scale == 'semilogx':
|
||||
pltcmd = axes.semilogx
|
||||
elif scale == 'semilogy':
|
||||
pltcmd = axes.semilogy
|
||||
elif scale == 'log':
|
||||
pltcmd = axes.loglog
|
||||
else:
|
||||
pltcmd = axes.plot
|
||||
pltcmd(*values, label=label, picker=5,
|
||||
marker=opts['marker'])
|
||||
axes.grid(opts['grid'])
|
||||
axes.set_title(opts['title'])
|
||||
axes.set_xlabel(opts['xlabel'])
|
||||
|
|
|
|||
|
|
@ -1874,3 +1874,159 @@ class EIGParameters(BaseParameters):
|
|||
def bind_kernel_matrix_spectrum(self, p):
|
||||
value = int(p.value)
|
||||
self.spec_parameters.eigval_ispectrum_ne = value
|
||||
|
||||
|
||||
class CompCurveParameters(BaseParameters):
|
||||
def __init__(self):
|
||||
parameters = (
|
||||
Parameter('general_npar', types=int, default=1, limits=[1, 2],
|
||||
fmt='d'),
|
||||
Parameter('general_norm', types=int, default=0, limits=[0, 5],
|
||||
fmt='d'),
|
||||
Parameter('general_iscale', types=int, default=0, limits=[0, 1],
|
||||
fmt='d'),
|
||||
Parameter('general_inorm', types=int, default=1, limits=[-2, 2],
|
||||
fmt='d'),
|
||||
Parameter('general_isym', types=int, default=0, limits=[0, 2],
|
||||
fmt='d'),
|
||||
Parameter('general_sym', types=(int, float), default=180.,
|
||||
limits=[0., 360.], fmt='.2f'),
|
||||
Parameter('general_iposi', types=int, default=0, limits=[0, 1],
|
||||
fmt='d'),
|
||||
Parameter('general_idist', types=int, default=0, limits=[0, 4],
|
||||
fmt='d'),
|
||||
Parameter('general_icur', types=int, default=0, limits=[0, 4],
|
||||
fmt='d'),
|
||||
Parameter('general_isa', types=int, default=0, limits=[0, 4],
|
||||
fmt='d'),
|
||||
Parameter('general_iprint', types=int, default=1, limits=[0, 1],
|
||||
fmt='d'),
|
||||
|
||||
Parameter('weights_iweight', types=int, default=0, limits=[0, 8],
|
||||
fmt='d'),
|
||||
Parameter('weights_alpha', types=(int, float), default=1.,
|
||||
fmt='.2f'),
|
||||
Parameter('weights_beta', types=(int, float), default=0.33,
|
||||
fmt='.2f'),
|
||||
Parameter('weights_sigma', types=(int, float), default=0.5,
|
||||
fmt='.2f'),
|
||||
Parameter('weights_ishift', types=int, default=0, limits=[0, 1],
|
||||
fmt='d'),
|
||||
Parameter('weights_maxw', types=(int, float), default=30,
|
||||
fmt='.2f'),
|
||||
|
||||
Parameter('rfc_vi', types=(int, float), default=12, fmt='.2f'),
|
||||
|
||||
Parameter('sim_alphas', types=(int, float), default=1., fmt='.2f'),
|
||||
Parameter('sim_betas', types=(int, float), default=1., fmt='.2f'),
|
||||
Parameter('sim_nbins', types=int, default=30, limits=[1, None],
|
||||
fmt='d'),
|
||||
|
||||
Parameter('dist_alphad', types=(int, float), default=.5,
|
||||
fmt='.2f'),
|
||||
Parameter('dist_ibeta', types=int, default=2, fmt='d'),
|
||||
Parameter('dist_l', types=int, default=2, fmt='d'),
|
||||
Parameter('dist_sigmad', types=(int, float), default=1, fmt='.2f'),
|
||||
|
||||
Parameter('gof_nbing', types=int, default=30, limits=[1, None],
|
||||
fmt='d'),
|
||||
Parameter('gof_alphag', types=(int, float), default=1.5, fmt='.2f'),
|
||||
|
||||
Parameter('kdist_alphak', types=(int, float), default=.5,
|
||||
fmt='.2f'),
|
||||
Parameter('kdist_l', types=int, default=2, fmt='d'),
|
||||
Parameter('kdist_sigmak', types=(int, float), default=5.5,
|
||||
fmt='.2f'),
|
||||
|
||||
Parameter('mom_ngrid', types=int, default=75, fmt='d'),
|
||||
Parameter('mom_nmom', types=int, default=75, fmt='d'),
|
||||
Parameter('mom_basis', types=str,
|
||||
allowed_values=['GEOM', 'LEGE', 'CHEB', 'KRAW', 'HAHN',
|
||||
'MEIX', 'CHAR', 'SHMA'],
|
||||
default='KRAW', fmt='s'),
|
||||
Parameter('mom_ialg', types=int, limits=[1, 3], default=1, fmt='d'),
|
||||
Parameter('mom_mu', types=(int, float), default=.5, fmt='.2f'),
|
||||
Parameter('mom_nu', types=(int, float), default=.5, fmt='.2f'),
|
||||
|
||||
Parameter('chords_ichord', types=int, default=3, limits=[1, 3],
|
||||
fmt='d'),
|
||||
Parameter('chords_method', types=str,
|
||||
allowed_values=['SIN', 'HIS', 'SUM'], default='SUM',
|
||||
fmt='s'),
|
||||
Parameter('chords_value', types=int, limits=[1, 3], default=1,
|
||||
fmt='d'),
|
||||
Parameter('chords_nbinc', types=int, default=30, limits=[1, None],
|
||||
fmt='d'),
|
||||
|
||||
Parameter('codes_nconnect', types=int, allowed_values=[3, 5, 9],
|
||||
default=9, fmt='d'),
|
||||
Parameter('codes_scalec', types=(int, float), default=1,
|
||||
fmt='.2f'),
|
||||
|
||||
Parameter('cont_nbin', types=int, limits=[1, None], default=66,
|
||||
fmt='d'),
|
||||
Parameter('cont_nlen', types=int, limits=[1, None], default=4,
|
||||
fmt='d'),
|
||||
Parameter('cont_shan', types=str,
|
||||
allowed_values=['CDIS', 'TANG', 'CURV', 'TRAR', 'BEAS',
|
||||
'8CCH', 'CLEN', 'CANG', 'ACDI', 'FOUR'],
|
||||
default='TANG',
|
||||
fmt='s'),
|
||||
Parameter('cont_ifou', types=int, limits=[1, 4], default=1,
|
||||
fmt='d'),
|
||||
Parameter('cont_inorm', types=int, limits=[1, 4], default=2,
|
||||
fmt='d'),
|
||||
|
||||
Parameter('exp_filename', types=str, default='exp/experiment.txt',
|
||||
fmt='s'),
|
||||
Parameter('calc_filename', types=(list,),
|
||||
default=['calc/calculation.txt',],
|
||||
fmt='s'),
|
||||
Parameter('calc_param1', types=(float, int), default=0.,
|
||||
fmt='.2f'),
|
||||
Parameter('calc_param2', types=(float, int), default=0.,
|
||||
fmt='.2f'),
|
||||
)
|
||||
BaseParameters.__init__(self)
|
||||
self.add_parameters(*parameters)
|
||||
self.freeze()
|
||||
|
||||
class CompCurveGeneralParameters(BaseParameters):
|
||||
def __init__(self, compcurve_parameters):
|
||||
parameters = (
|
||||
Parameter('normalization', types=(type(None), str),
|
||||
allowed_values=(None, "variance", "area", "max",
|
||||
"decimal_scaling", "zero_one"),
|
||||
default="max"),
|
||||
Parameter('rescale', types=bool, default=True),
|
||||
Parameter('function', types=str, allowed_values=(
|
||||
"coordinates", "chi", "cdf", "curvature", "signature"),
|
||||
default="chi"),
|
||||
)
|
||||
BaseParameters.__init__(self)
|
||||
self.add_parameters(*parameters)
|
||||
self.compcurve_parameters = compcurve_parameters
|
||||
self.freeze()
|
||||
|
||||
def bind_normalization(self, p):
|
||||
value = p.allowed_values.index(p.value)
|
||||
self.compcurve_parameters.general_norm = value
|
||||
LOGGER.info("Curve Comparison: Normalization mode set to "
|
||||
f"\"{p.value}\"")
|
||||
|
||||
def bind_rescale(self, p):
|
||||
self.compcurve_parameters.general_iscale = int(p.value)
|
||||
state = "deactivated"
|
||||
if p.value:
|
||||
state = "activated"
|
||||
LOGGER.info(f"Curve Comparison: Rescaling of data {state}")
|
||||
|
||||
def bind_function(self, p):
|
||||
value = p.allowed_values.index(p.value)
|
||||
self.compcurve_parameters.general_icur = value
|
||||
LOGGER.info("Curve Comparison: Type of data used for comparison "
|
||||
f"set to \"{p.value}\"")
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -18,6 +18,7 @@ phd_mi_noso_nosp_nosym = env_spec.FilteredGlob('phd_mi_noso_nosp_nosym/*.f', omi
|
|||
eig_common = Glob('eig/common/*.f')
|
||||
eig_mi = env_spec.FilteredGlob('eig/mi/*.f', omit=['main.f'])
|
||||
eig_pw = env_spec.FilteredGlob('eig/pw/*.f', omit=['main.f'])
|
||||
comp_curves = ['treatment/comp_curves.f']
|
||||
|
||||
conf = Configure(env_spec, log_file='./config.log')
|
||||
if conf.CheckLib('lapack'):
|
||||
|
|
@ -38,6 +39,7 @@ phd_mi_noso_nosp_nosym_obj = env_spec.Object(phd_mi_noso_nosp_nosym)
|
|||
eig_common_obj = env_spec.Object(eig_common)
|
||||
eig_pw_obj = env_spec.Object(eig_pw)
|
||||
eig_mi_obj = env_spec.Object(eig_mi)
|
||||
comp_curves_obj = env_spec.Object(comp_curves)
|
||||
|
||||
Requires(memalloc_obj, dim_mod_obj)
|
||||
|
||||
|
|
@ -60,6 +62,10 @@ deps = common_deps + renormalization_obj + eig_common_obj + eig_pw_obj
|
|||
eig_pw_mod = env_spec.F2py('_eig_pw.so', ['eig/pw/main.f'] + deps)
|
||||
env_spec.InstallModule(eig_pw_mod)
|
||||
|
||||
deps = comp_curves_obj
|
||||
comp_curve_mod = env_spec.F2py('_comp_curves.so', ['treatment/main.f'] + deps)
|
||||
env_spec.InstallModule(comp_curve_mod)
|
||||
|
||||
# Alias
|
||||
env_spec.Alias('spec', [phd_se_mod, phd_mi_mod, eig_pw_mod, eig_mi_mod])
|
||||
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,3 @@
|
|||
SUBROUTINE RUN()
|
||||
CALL COMP_CURVES()
|
||||
END SUBROUTINE RUN
|
||||
Loading…
Reference in New Issue