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Merge pull request #1 from htjb/terminal_output
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Edited terminal print output options
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@htjb
htjb committed Sep 23, 2021
2 parents da090cb + d4184bc commit 6187675
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2 changes: 1 addition & 1 deletion README.rst
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Expand Up @@ -7,7 +7,7 @@ Introduction

:maxsmooth: Derivative Constrained Function Fitting
:Author: Harry Thomas Jones Bevins
:Version: 1.2.0
:Version: 1.2.1
:Homepage: https://github.com/htjb/maxsmooth
:Documentation: https://maxsmooth.readthedocs.io/

Expand Down
235 changes: 100 additions & 135 deletions maxsmooth/DCF.py
View file
Expand Up @@ -74,10 +74,15 @@ class smooth(object):
be over written on repeated runs but they are needed to run the
'chidist_plotter'.
all_output: **Default = False**
| If set to True this outputs to the
print_output: **Default = 1**
| The parameter can take a value of 0, 1, 2.
If set to 2 this outputs to the
terminal every fit performed by the algorithm. By default the
parameter has a value of 1 and the
only output is the optimal solution once the code is finished.
Setting this to 0 will prevent any output to the terminal. This
is useful when running the code inside a nested sampling loop
for example.
cvxopt_maxiter: **Default = 10000 else integer**
| This shouldn't need
Expand Down Expand Up @@ -193,7 +198,7 @@ def __init__(self, x, y, N, **kwargs):
for keys, values in kwargs.items():
if keys not in set(
['fit_type', 'model_type', 'base_dir',
'all_output', 'cvxopt_maxiter', 'zero_crossings',
'print_output', 'cvxopt_maxiter', 'zero_crossings',
'data_save',
'constraints', 'chi_squared_limit', 'cap',
'initial_params', 'basis_functions',
Expand Down Expand Up @@ -235,15 +240,16 @@ def __init__(self, x, y, N, **kwargs):
if type(self.cvxopt_maxiter) is not int:
raise ValueError("'cvxopt_maxiter' is not integer.")

self.all_output = kwargs.pop('all_output', False)
self.print_output = kwargs.pop('print_output', 1)
if self.print_output not in [0, 1, 2]:
raise ValueError("'print_output' must have a value in the set" +
" [0, 1, 2]. See documentation for details.")

self.data_save = kwargs.pop('data_save', False)
boolean_kwargs = [self.data_save, self.all_output]
for i in range(len(boolean_kwargs)):
if type(boolean_kwargs[i]) is not bool:
raise TypeError(
"Boolean keyword argument with value "
+ str(boolean_kwargs[i]) +
" is not True or False.")
if type(self.data_save) is not bool:
raise TypeError(
"Boolean keyword argument with value 'data_save'" +
" is not True or False.")

self.constraints = kwargs.pop('constraints', 2)
if type(self.constraints) is not int:
Expand Down Expand Up @@ -350,9 +356,7 @@ def signs_array(nums):
shutil.rmtree(self.base_dir+'Output_Evaluation/')

def qp(x, y, pivot_point): # Testing all signs
print(
'######################################################' +
'#######')

start = time.time()

if self.zero_crossings is not None:
Expand All @@ -369,13 +373,11 @@ def qp(x, y, pivot_point): # Testing all signs
fit = qp_class(
x, y, self.N, signs[j, :], pivot_point,
self.model_type, self.cvxopt_maxiter,
self.all_output, self.zero_crossings,
self.zero_crossings,
self.initial_params, self.constraints, self.new_basis)

if self.all_output is True:
print(
'-----------------------------------------------' +
'-----')
if self.print_output == 2:
print('-'*50)
print('Polynomial Order:', self.N)
if self.zero_crossings is not None:
print(
Expand Down Expand Up @@ -403,9 +405,7 @@ def qp(x, y, pivot_point): # Testing all signs
'Zero Crossings Used?' +
' (0 signifies Yes\n in derivative order "i"):',
fit.zc_dict)
print(
'-----------------------------------------------' +
'-----')
print('-'*50)

append_params(fit.parameters)
append_chi(fit.chi_squared)
Expand Down Expand Up @@ -437,45 +437,38 @@ def qp(x, y, pivot_point): # Testing all signs

end = time.time()

print(
'######################################################' +
'#######')
print(
'----------------------OPTIMUM RESULT--------------------' +
'-----')
print('Time:', end-start)
print('Polynomial Order:', self.N)
if self.zero_crossings is not None:
print(
'Number of Constrained Derivatives:',
self.N-self.constraints-len(self.zero_crossings))
else:
print(
'Number of Constrained Derivatives:',
self.N-self.constraints)
print('Signs :', Optimum_sign_combination)
print('Objective Function Value:', Optimum_chi_squared)
print('Parameters:', Optimum_params.T)
print('Method:', self.fit_type)
print('Model:', self.model_type)
print('Constraints: m >=', self.constraints)
if self.zero_crossings is None:
print(
'Zero Crossings Used?' +
' (0 signifies Yes\n in derivative order "i"):',
Optimum_zc_dict)
if self.zero_crossings is not None:
print('Zero Crossing Derivatives:', self.zero_crossings)
print(
'Zero Crossings Used?' +
' (0 signifies Yes\n in derivative order "i"):',
Optimum_zc_dict)
print(
'-------------------------------------------------------' +
'------')
print(
'######################################################' +
'#######')
if self.print_output in [1, 2]:
print('#'*50)
print('-'*20 + 'OPTIMUM RESULT' + '-'*20)
print('Time:', end-start)
print('Polynomial Order:', self.N)
if self.zero_crossings is not None:
print(
'Number of Constrained Derivatives:',
self.N-self.constraints-len(self.zero_crossings))
else:
print(
'Number of Constrained Derivatives:',
self.N-self.constraints)
print('Signs :', Optimum_sign_combination)
print('Objective Function Value:', Optimum_chi_squared)
print('Parameters:', Optimum_params.T)
print('Method:', self.fit_type)
print('Model:', self.model_type)
print('Constraints: m >=', self.constraints)
if self.zero_crossings is None:
print(
'Zero Crossings Used?' +
' (0 signifies Yes\n in derivative order "i"):',
Optimum_zc_dict)
if self.zero_crossings is not None:
print('Zero Crossing Derivatives:', self.zero_crossings)
print(
'Zero Crossings Used?' +
' (0 signifies Yes\n in derivative order "i"):',
Optimum_zc_dict)
print('-'*50)
print('#'*50)

save_optimum(
self.base_dir, end-start, self.N,
Expand All @@ -487,9 +480,7 @@ def qp(x, y, pivot_point): # Testing all signs
Optimum_sign_combination, Optimum_zc_dict

def qp_sign_flipping(x, y, pivot_point): # Sign Sampling
print(
'######################################################' +
'#######')

start = time.time()

if self.zero_crossings is not None:
Expand All @@ -511,18 +502,16 @@ def qp_sign_flipping(x, y, pivot_point): # Sign Sampling
fit = qp_class(
x, y, self.N, signs, pivot_point,
self.model_type, self.cvxopt_maxiter,
self.all_output, self.zero_crossings,
self.zero_crossings,
self.initial_params, self.constraints, self.new_basis)
chi_squared.append(fit.chi_squared)
tested_signs.append(signs)
parameters.append(fit.parameters)
chi_squared_old = fit.chi_squared
previous_signs = signs

if self.all_output is True:
print(
'--------------------------------------' +
'--------------')
if self.print_output == 2:
print('-'*50)
print('Polynomial Order:', self.N)
if self.zero_crossings is not None:
print(
Expand Down Expand Up @@ -550,9 +539,7 @@ def qp_sign_flipping(x, y, pivot_point): # Sign Sampling
print(
'Zero Crossings Used? (0 signifies' +
'Yes\n in derivative order "i"):', fit.zc_dict)
print(
'--------------------------------------' +
'--------------')
print('-'*50)
if self.data_save is True:
save(
self.base_dir, fit.parameters, fit.chi_squared,
Expand Down Expand Up @@ -582,7 +569,6 @@ def qp_sign_flipping(x, y, pivot_point): # Sign Sampling
fit = qp_class(
x, y, self.N, signs, pivot_point,
self.model_type, self.cvxopt_maxiter,
self.all_output,
self.zero_crossings, self.initial_params,
self.constraints, self.new_basis)
if fit.chi_squared < chi_squared_old:
Expand All @@ -592,10 +578,8 @@ def qp_sign_flipping(x, y, pivot_point): # Sign Sampling
tested_signs.append(signs)
parameters.append(fit.parameters)

if self.all_output is True:
print(
'--------------------------------------' +
'--------------')
if self.print_output == 2:
print('-'*50)
print('Polynomial Order:', self.N)
if self.zero_crossings is not None:
print(
Expand Down Expand Up @@ -629,9 +613,7 @@ def qp_sign_flipping(x, y, pivot_point): # Sign Sampling
' (0 signifies' +
'Yes\n in derivative order "i"):',
fit.zc_dict)
print(
'--------------------------------------' +
'--------------')
print('-'*50)
if self.data_save is True:
save(
self.base_dir, fit.parameters,
Expand Down Expand Up @@ -676,18 +658,15 @@ def qp_sign_flipping(x, y, pivot_point): # Sign Sampling
fit = qp_class(
x, y, self.N, signs, pivot_point,
self.model_type, self.cvxopt_maxiter,
self.all_output,
self.zero_crossings, self.initial_params,
self.constraints, self.new_basis)
chi_down = fit.chi_squared
chi_squared.append(fit.chi_squared)
tested_signs.append(signs)
parameters.append(fit.parameters)

if self.all_output is True:
print(
'--------------------------------------' +
'--------------')
if self.print_output == 2:
print('-'*50)
print('Polynomial Order:', self.N)
if self.zero_crossings is not None:
print(
Expand Down Expand Up @@ -718,9 +697,7 @@ def qp_sign_flipping(x, y, pivot_point): # Sign Sampling
'Zero Crossings Used? (0 signifies' +
'Yes\n in derivative order "i"):',
fit.zc_dict)
print(
'--------------------------------------' +
'--------------')
print('-'*50)
if self.data_save is True:
save(
self.base_dir, fit.parameters, fit.chi_squared,
Expand All @@ -743,18 +720,15 @@ def qp_sign_flipping(x, y, pivot_point): # Sign Sampling
fit = qp_class(
x, y, self.N, signs, pivot_point,
self.model_type, self.cvxopt_maxiter,
self.all_output,
self.zero_crossings, self.initial_params,
self.constraints, self.new_basis)
chi_up = fit.chi_squared
chi_squared.append(fit.chi_squared)
tested_signs.append(signs)
parameters.append(fit.parameters)

if self.all_output is True:
print(
'--------------------------------------' +
'--------------')
if self.print_output == 2:
print('-'*50)
print('Polynomial Order:', self.N)
if self.zero_crossings is not None:
print(
Expand Down Expand Up @@ -785,9 +759,7 @@ def qp_sign_flipping(x, y, pivot_point): # Sign Sampling
'Zero Crossings Used? (0 signifies' +
'Yes\n in derivative order "i"):',
fit.zc_dict)
print(
'--------------------------------------' +
'--------------')
print('-'*50)
if self.data_save is True:
save(
self.base_dir, fit.parameters, fit.chi_squared,
Expand All @@ -814,45 +786,38 @@ def qp_sign_flipping(x, y, pivot_point): # Sign Sampling

end = time.time()

print(
'######################################################' +
'#######')
print(
'----------------------OPTIMUM RESULT--------------------' +
'-----')
print('Time:', end-start)
print('Polynomial Order:', self.N)
if self.zero_crossings is not None:
print(
'Number of Constrained Derivatives:',
self.N-self.constraints-len(self.zero_crossings))
else:
print(
'Number of Constrained Derivatives:',
self.N-self.constraints)
print('Signs :', Optimum_sign_combination)
print('Objective Function Value:', Optimum_chi_squared)
print('Parameters:', Optimum_params.T)
print('Method:', self.fit_type)
print('Model:', self.model_type)
print('Constraints: m >=', self.constraints)
if self.zero_crossings is None:
print(
'Zero Crossings Used?' +
' (0 signifies Yes\n in derivative order "i"):',
Optimum_zc_dict)
if self.zero_crossings is not None:
print('Zero Crossing Derivatives:', self.zero_crossings)
print(
'Zero Crossings Used?' +
' (0 signifies Yes\n in derivative order "i"):',
Optimum_zc_dict)
print(
'----------------------------------------------------' +
'---------')
print(
'####################################################' +
'#########')
if self.print_output in [1, 2]:
print('#'*50)
print('-'*20 + 'OPTIMUM RESULT' + '-'*20)
print('Time:', end-start)
print('Polynomial Order:', self.N)
if self.zero_crossings is not None:
print(
'Number of Constrained Derivatives:',
self.N-self.constraints-len(self.zero_crossings))
else:
print(
'Number of Constrained Derivatives:',
self.N-self.constraints)
print('Signs :', Optimum_sign_combination)
print('Objective Function Value:', Optimum_chi_squared)
print('Parameters:', Optimum_params.T)
print('Method:', self.fit_type)
print('Model:', self.model_type)
print('Constraints: m >=', self.constraints)
if self.zero_crossings is None:
print(
'Zero Crossings Used?' +
' (0 signifies Yes\n in derivative order "i"):',
Optimum_zc_dict)
if self.zero_crossings is not None:
print('Zero Crossing Derivatives:', self.zero_crossings)
print(
'Zero Crossings Used?' +
' (0 signifies Yes\n in derivative order "i"):',
Optimum_zc_dict)
print('-'*50)
print('#'*50)

save_optimum(
self.base_dir, end-start, self.N,
Expand Down

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