Python package for linear and non-linear fitting of multidimensional data.
PySurfaceFit was initially planned as a narrow-scope tool for fitting the molecular potential energy surface (PES) models to the ab initio data computed via quantum chemistry methods. Currently, PySurfaceFit is a flexible general-purpose least squares fitting tool supporting a number of global and local optimization methods.
PySurfaceFit supports fit constraints and Tikhonov regularization for the data points and models parameters. For the Sympy models, based on the Sympy symbolic math library, is is possible to use analytic Jacobians and Hessians if required by the particular optimization method.
Currently, PySurfaceFit heavily relies on the Scipy optimization and root finding library (scipy.optimize).
The basic supported local optimization methods:
- Levenberg-Marquardt
- Trust Region Reflective
- Nelder-Mead
- Powell
- CG (Conjugate Gradient)
- Newton-CG
- Broyden-Fletcher-Goldfarb-Shanno (BFGS)
- L-BFGS-B
The basic supported global optimization methods:
- Simulated Annealing
- Basin Hopping Optimization
Other custom optimization method backends are fairly easy to implement (see the Notebook tutorials on the optimization methods integration).
- git clone https://github.com/RomanKochanov/pysurfacefit.git
- cd pysurfacefit
- pip install .
pip install pysurfacefit
PySurfaceFit provides two options of usage: as a CLI tool, and as a Python library. The CLI tool provides almost all basic functionality for managing the fit data points, creating fit models, and displaying the results of a fit. For beginners, it is recommended using the CLI tool to avoid most of the programming fuss.
Once installed, PySurfaceFit can be run from the console:
pysurfacefit
To obtain initial help, use the --help flag:
pysurfacefit --help
OUTPUT:
usage: pysurfacefit [-h] [--config CONFIG] [--startproject STARTPROJECT]
[--template TEMPLATE [TEMPLATE ...]] [--merge MERGE]
[--set SET] [--init] [--split] [--fit] [--stat]
[--codegen] [--plot [PLOT [PLOT ...]]] [--calc]
Python package for linear and non-linear fitting of multidimensional data.
optional arguments:
-h, --help show this help message and exit
--config CONFIG Configuration file (mandatory for all steps except
--startproject)
--startproject STARTPROJECT
Stage 1: start empty project, create dummy config
--template TEMPLATE [TEMPLATE ...]
_________1a: us a template for new project
--merge MERGE _________1b: take defaults from a config file
--set SET _________1c: explicitly set project parameters
--init Stage 2: create model package
--split Stage 3: split initial datafile with weighting scheme
--fit Stage 4: start fitting model to data
--stat Stage 5: calculate statistics
--codegen Stage 6: generate Fortran code for fitted model
--plot [PLOT [PLOT ...]]
Stage 7: plot sections of the model and compare to
data
--calc Stage 8: calculate model values on grid
This will generate an emply project with the sample config file, which will look something like this:
#################### # GENERAL SETTINGS # #################### [GENERAL] # Name of the project. project: myproj ############################## # FITTING DATA SPECIFICATION # ############################## [DATA] # CSV file containing fitting data. datafile: # Define rules to split the datafile to dataspec. # Should be ignored if dataspec is empty. split_column: split_values: split_weights: # Resulting data specification split-file. # N.B.: if empty, then the raw datafile is used. # Data specification *.txt file has the following format: # alias path wht_mul type include # ------- --------- -------- ------ ------- # test1 test1.csv 1 0 1 # test2 test2.csv 1 0 1 # test3 test3.csv 1 0 1 dataspec: dataspec.txt # Names and units of the input data columns. # Input names must be separated by semicolon # and should not contain dots. # E.g.: X;Y;Z for names, X:X_UNIT;Y:Y_UNIT for units input_columns: input_units: # Name of the output data column. See explanation above. output_column: output_units: # Weight function. wht_fun: lambda v: 1 # Global data cutoff global_cutoff_max: global_cutoff_min: ################################ # FITTING MODEL SPECIFICATIONS # ################################ [MODEL] # Model package name. model: fitmodel # Model arguments. Argument names must be in the same order # as the data column names from the DATA section. # E.g.: X;Y;Z arguments: ################## # FITTER OPTIONS # ################## [FIT] # Weighted / unweighted fit mode. weighted_fit: True # Use "rubber" regularization. rubber_on: True # Fitting method (trf, lm, basinhopping). # Available "local" methods: # "trf" -> Trust Region Reflective (least squares, bounded). # "lm" -> Levenberg Marquardt (least squares, unbounded). # 'Nelder-Mead' -> Gradient-free descent Nelder-Mead method. # 'Powell' -> modified Powell algorithm. # 'CG' -> conjugate gradient algorithm. # 'BFGS' -> BFGS algorithm. # 'Newton-CG' -> Newton-CG algorithm. # 'L-BFGS-B' -> L-BFGS-B algorithm. # 'TNC' -> truncated Newton (TNC) algorithm. # 'COBYLA' -> Constrained Optimization BY Linear Approximation. # 'SLSQP' -> Sequential Least Squares Programming. # 'trust-constr' -> minimize a scalar function subject to constraints. # 'dogleg' -> dog-leg trust-region algorithm. # 'trust-ncg' -> Newton conjugate gradient trust-region algorithm. # 'trust-exact' -> "nearly exact trust-region algorithm" # 'trust-krylov' -> "early exact trust-region algorithm" # Available "global" methods: # "basinhopping" -> local descents from a random starting point (bounded) # "anneal" -> Simulated Annealing method fitting_method: trf # Calculate analytic jacobian (only if ModelSympy is used). analytic_jacobian: True # Fit options. Must be given as a list separated by semicolon. # The valid options for most of the supported fitting methods # can be found in the corresponding scipy.optimize documentation. fit_options: max_nfev=200; ################################## # STATISTICS CALCULATION OPTIONS # ################################## [STAT] # Fit statistics file. stat_file: stat.out # Calculate outlier statistics. outlier_stats_flag: True # Type of statistics: cook, dffits, leverage ,student. outlier_stats_type: cook # Output generated symbolic function, for debug purposes only. output_symbolic_func: ########################## # CODE GENERATOR OPTIONS # ########################## [CODEGEN] # Create Fortran code. create_fortran: False # Compare original Python code with the generated Fortran. compare_fortran: False # Fortran compiler executable compiler_fortran: ifort # Grid specifications to calculate on. # Format of the specification must be as follows: # X=XMIN:XSTEP:XMAX; Y=YMIN:YSTEP:YMAX; ... gridspec: #################### # PLOTTING OPTIONS # #################### [PLOTTING] # ATTENTION: names of the plot coordinates correspond to the # model argumen names given in the MODEL section. # Type of the plot. # Available plot modes: # => "residuals": # plot unweighted fit residuals. # => "sections": # plot sections/cuts of the fitted model vs datapoints. plot_mode: residuals # Plot coordinate grid specifications. # Format of the specification must be as follows: # X=XVAL; Y=YMIN:YSTEP:YMAX; ... # In case of the fixed coordinate, variable would only have a value, e.g. X=XVAL. # In case of unfixed coordinate, there should be a full grid, e.g. Y=YMIN:YSTEP:YMAX # N.B.: 1) order of the unfixed coords affects the order of the plot axes. # 2) order of the binding MUST correspond to the argument of the model's # __func__ method. gridspec: # Model components to plot. model_components: # Calculate model's components. calculate_components: False # Plot outlier statistics in color. If False, each datagroup has its own color. plot_outlier_stats: False # Plot weighted residuals. resids_weighted: False # X axes to plot the resuduals versus. # If empty, defaults to [DATA][output_column]. resids_x_axes: # Scatter settings (2D, 3D case) scatter_opacity: 1 marker_size: 20 resize_by_weights: False # Surface settings (3D case) surface_opacity: 0.4 ####################### # CALCULATION OPTIONS # ####################### [CALC] # Output file to output calculated model. # Column names are defined in DATA section. output_file: calc.csv # Grid specifications to calculate on. # Format of the specification must be as follows: # X=XMIN:XSTEP:XMAX; Y=YMIN:YSTEP:YMAX; ... gridspec:
Now, let's perform a test fitting case step by step. First, let's generate a three-dimensional data we will fit our model to.
import numpy as np
from pysurfacefit.grids import Grid
# set 1D grids for each coordinate
x = np.arange(-10,10,1.0)
y = np.arange(-15,-5,0.1)
z = np.arange(0,20,5.0)
# Create a 3D grid
grid = Grid('x',x,'y',y,'z',z)
# Define a test function
func = lambda x,y,z: x**2 + 0.1*y**3 + 10*z**4 + 10
# Calculate a function on the 3D grid
v = grid.calculate(func).flatten()
# Save the resulting data to CSV file
x, y, z = grid.get_meshes(flat=True)
np.savetxt('sampledata.csv',list(zip(x,y,z,v)),delimiter=';',header='x;y;z;v',comments='')After the test data file is generated, we can create an empty project named "test" from it.
pysurfacefit --startproject test
To generate a ready-to-go configuration file, one must provide additional data such as names for the argument columns (or "inputs"), and the name of the function column (or "output"). The entered names must correspond to the column names in the data file:
Enter the name of the data points CSV file: sampledata.csv Enter semicolon-separated names for model inputs: x; y; z Enter name for model output: v
If everything went fine, we should have the new fitting project created in a nested subfolder.
Created new project: test New config file has been added: test/config.ini Sample data specification file has been added: dataspec.txt
Now the project folder "test" inlcudes only three files:
cd test; ls config.ini dataspec.txt sampledata.csv
The file "config.ini" is the main configuration file containing the project settings (see the example of the file above). This file is separated into the following sections:
- GENERAL Basic settings: contains only the name of the project so far.
- DATA Settings for the data to fit: defines the source file for the fitted data, names and units of the input and output columns, weight function and cutoff. Also contains settings for data splitting to apply more advanced weighting schemes which are defined in the dataspec.txt file.
- MODEL Containes the model name and names for model arguments. Usually, the argument names are the same as the names of the data inputs defined in the DATA section.
- FIT Settings for the fitter. Containes the switches for turning on/off weights and regularization, name of the optimization method, switch for analytic Jacobian, and number of iterations setting.
- STAT Additional settings to display the fit statistics.
- CODEGEN Settings for the code generation from the fitted model. Allows choosing the compiler. Currently only the Fortan code generation is supported.
- PLOTTING Settings for plotting the fit residuals and 1D/2D sections of the fitted model. 3D volume visualization is planned to be included.
- CALC Defines the output file and grid to calculate the fited model on.
To fit the data in the sampledata.csv file, let's generate a model file.
pysurfacefit --config config.ini --init
Note, that for all tasks except --startproject, we must supply the configuraion file using the --config key.
The model is created and stored in a separate Python module file. The name of this file is defined in the MODEL section using the "model" parameter.
ls config.ini dataspec.txt fitmodel.py sampledata.csv
The default model has just one fitting parameter which is a constant. Here is what a default model file looks like:
import os
from pysurfacefit.models.sympy import ModelSympy
from pysurfacefit.fitpars import Par, Parameters
class fitmodel(ModelSympy):
"""
Test model for fitting data with just one parameter.
"""
def __init__(self,calc_switch='numbified'):
self.__check_symbolic__ = False
self.__calc_switch__ = calc_switch # symbolic, lambdified, numbified
self.__components__ = {}
# Initialize empty parameters.
self.__params__ = Parameters()
# Add constant parameter.
self.__params__.append(group='constant', pars=[
Par(name='constant', value=0.0, flag=True),
])
def __units__(self):
return {"input": {}, "output": "None"}
def __func__(self,params,x,y,z):
# constant
constant = params['constant'].get_value()
# final result
res = constant
# components
self.__components__['constant'] = constant
return res
model = fitmodel()
if os.path.exists('fitmodel.csv'):
model.load_params('fitmodel.csv')
else:
model.save_params('fitmodel.csv')
So far, we will use this default constant model to fit the three-dimensional data we have generated earlier. To do that, use the "fit" command:
pysurfacefit --config config.ini --fit
This will start the fitting process and will print the intermediate fit statistics to the STDOUT:
Treating sampledata fitgroup as FitPoints
BEGIN FIT
USING SCIPY.OPTIMIZE.LEAST_SQUARES: METHOD= trf
METHOD OPTIONS: {'max_nfev': 200, 'bounds': [(-inf,), (inf,)], 'jac': <function Fitter.fit_least_squares.<locals>.<lambda> at 0x7f84462e54d0>}
==========================================
<<<< calling __sympy_initialize_func__ >>>>
==========================================
Progress:
- creating sympy objects for inputs
- creating sympy objects for parameters
- get the Sympy expression by calling function with the Sympy objects
- create lambdified Python function from sympy expression
- create compiled (numbified) code from the lambdified function
==========================================
CALC FUN 1>>> DIST:0.000000000e+00 SSE_RUB:0.000000000e+00 SSE_TOT:5.324527156e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 5.325e+14 UNWHT_SSE: 5.325e+14 WHT_SD: 2.580e+05 UNWHT_SD: 2.580e+05
===============================
==========================================
<<<< calling __sympy_initialize_jac__ >>>>
==========================================
Progress:
- get the Sympy expression by calling function with the Sympy objects
- create lambdified Python function from sympy expression
- create compiled (numbified) code from the lambdified function
==========================================
CALC JAC 1>>> DIST:0.000000000e+00
CALC FUN 2>>> DIST:1.000000000e+00 SSE_RUB:0.000000000e+00 SSE_TOT:5.324502669e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 5.325e+14 UNWHT_SSE: 5.325e+14 WHT_SD: 2.580e+05 UNWHT_SD: 2.580e+05
===============================
CALC JAC 2>>> DIST:1.000000000e+00
CALC FUN 3>>> DIST:3.000000000e+00 SSE_RUB:0.000000000e+00 SSE_TOT:5.324453696e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 5.324e+14 UNWHT_SSE: 5.324e+14 WHT_SD: 2.580e+05 UNWHT_SD: 2.580e+05
===============================
CALC JAC 3>>> DIST:3.000000000e+00
CALC FUN 4>>> DIST:7.000000000e+00 SSE_RUB:0.000000000e+00 SSE_TOT:5.324355753e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 5.324e+14 UNWHT_SSE: 5.324e+14 WHT_SD: 2.580e+05 UNWHT_SD: 2.580e+05
===============================
CALC JAC 4>>> DIST:7.000000000e+00
CALC FUN 5>>> DIST:1.500000000e+01 SSE_RUB:0.000000000e+00 SSE_TOT:5.324159873e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 5.324e+14 UNWHT_SSE: 5.324e+14 WHT_SD: 2.580e+05 UNWHT_SD: 2.580e+05
===============================
CALC JAC 5>>> DIST:1.500000000e+01
CALC FUN 6>>> DIST:3.100000000e+01 SSE_RUB:0.000000000e+00 SSE_TOT:5.323768145e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 5.324e+14 UNWHT_SSE: 5.324e+14 WHT_SD: 2.580e+05 UNWHT_SD: 2.580e+05
===============================
CALC JAC 6>>> DIST:3.100000000e+01
CALC FUN 7>>> DIST:6.300000000e+01 SSE_RUB:0.000000000e+00 SSE_TOT:5.322984811e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 5.323e+14 UNWHT_SSE: 5.323e+14 WHT_SD: 2.579e+05 UNWHT_SD: 2.579e+05
===============================
CALC JAC 7>>> DIST:6.300000000e+01
CALC FUN 8>>> DIST:1.270000000e+02 SSE_RUB:0.000000000e+00 SSE_TOT:5.321418635e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 5.321e+14 UNWHT_SSE: 5.321e+14 WHT_SD: 2.579e+05 UNWHT_SD: 2.579e+05
===============================
CALC JAC 8>>> DIST:1.270000000e+02
CALC FUN 9>>> DIST:2.550000000e+02 SSE_RUB:0.000000000e+00 SSE_TOT:5.318288249e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 5.318e+14 UNWHT_SSE: 5.318e+14 WHT_SD: 2.578e+05 UNWHT_SD: 2.578e+05
===============================
CALC JAC 9>>> DIST:2.550000000e+02
CALC FUN 10>>> DIST:5.110000000e+02 SSE_RUB:0.000000000e+00 SSE_TOT:5.312035342e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 5.312e+14 UNWHT_SSE: 5.312e+14 WHT_SD: 2.577e+05 UNWHT_SD: 2.577e+05
===============================
CALC JAC 10>>> DIST:5.110000000e+02
CALC FUN 11>>> DIST:1.023000000e+03 SSE_RUB:0.000000000e+00 SSE_TOT:5.299560985e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 5.300e+14 UNWHT_SSE: 5.300e+14 WHT_SD: 2.574e+05 UNWHT_SD: 2.574e+05
===============================
CALC JAC 11>>> DIST:1.023000000e+03
CALC FUN 12>>> DIST:2.047000000e+03 SSE_RUB:0.000000000e+00 SSE_TOT:5.274738099e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 5.275e+14 UNWHT_SSE: 5.275e+14 WHT_SD: 2.568e+05 UNWHT_SD: 2.568e+05
===============================
CALC JAC 12>>> DIST:2.047000000e+03
CALC FUN 13>>> DIST:4.095000000e+03 SSE_RUB:0.000000000e+00 SSE_TOT:5.225595644e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 5.226e+14 UNWHT_SSE: 5.226e+14 WHT_SD: 2.556e+05 UNWHT_SD: 2.556e+05
===============================
CALC JAC 13>>> DIST:4.095000000e+03
CALC FUN 14>>> DIST:8.191000000e+03 SSE_RUB:0.000000000e+00 SSE_TOT:5.129324001e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 5.129e+14 UNWHT_SSE: 5.129e+14 WHT_SD: 2.532e+05 UNWHT_SD: 2.532e+05
===============================
CALC JAC 14>>> DIST:8.191000000e+03
CALC FUN 15>>> DIST:1.638300000e+04 SSE_RUB:0.000000000e+00 SSE_TOT:4.944833778e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 4.945e+14 UNWHT_SSE: 4.945e+14 WHT_SD: 2.486e+05 UNWHT_SD: 2.486e+05
===============================
CALC JAC 15>>> DIST:1.638300000e+04
CALC FUN 16>>> DIST:3.276700000e+04 SSE_RUB:0.000000000e+00 SSE_TOT:4.608065586e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 4.608e+14 UNWHT_SSE: 4.608e+14 WHT_SD: 2.400e+05 UNWHT_SD: 2.400e+05
===============================
CALC JAC 16>>> DIST:3.276700000e+04
CALC FUN 17>>> DIST:6.553500000e+04 SSE_RUB:0.000000000e+00 SSE_TOT:4.063378221e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 4.063e+14 UNWHT_SSE: 4.063e+14 WHT_SD: 2.254e+05 UNWHT_SD: 2.254e+05
===============================
CALC JAC 17>>> DIST:6.553500000e+04
CALC FUN 18>>> DIST:1.310710000e+05 SSE_RUB:0.000000000e+00 SSE_TOT:3.489399568e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 3.489e+14 UNWHT_SSE: 3.489e+14 WHT_SD: 2.088e+05 UNWHT_SD: 2.088e+05
===============================
CALC JAC 18>>> DIST:1.310710000e+05
CALC FUN 19>>> DIST:1.530418700e+05 SSE_RUB:0.000000000e+00 SSE_TOT:3.450782038e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 3.451e+14 UNWHT_SSE: 3.451e+14 WHT_SD: 2.077e+05 UNWHT_SD: 2.077e+05
===============================
CALC JAC 19>>> DIST:1.530418700e+05
CALC FUN 20>>> DIST:1.530418700e+05 SSE_RUB:0.000000000e+00 SSE_TOT:3.450782038e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 3.451e+14 UNWHT_SSE: 3.451e+14 WHT_SD: 2.077e+05 UNWHT_SD: 2.077e+05
===============================
END FIT
9.095534 seconds elapsed, 20 func evals, 19 jac evals
As it can be seen from the fit, the result is not good since the generated data is not described by a constaint value. To improve the accuracy of the fit, let's change the code contained in the fitmodel.py by adding the three-dimensional polynomial:
import os
from functools import reduce
from pysurfacefit.models.sympy import ModelSympy
from pysurfacefit.fitpars import Par, Parameters
from pysurfacefit.models.library import generate_powers_layer_3d, poly3d
class fitmodel(ModelSympy):
"""
Test model for fitting data with just one parameter.
"""
def __init__(self,calc_switch='numbified'):
self.__check_symbolic__ = False
self.__calc_switch__ = calc_switch # symbolic, lambdified, numbified
self.__components__ = {}
# Initialize empty parameters.
self.__params__ = Parameters()
# Add polynomial parameters
self.__powers__ = reduce(lambda a,b:a+b,
[generate_powers_layer_3d(n) for n in range(1,4)]
)
for i,j,k in self.__powers__:
self.__params__.append(group='poly', pars=[
Par(name='poly_%d_%d_%d'%(i,j,k), value=0.0, flag=True),
])
# Add constant parameter.
self.__params__.append(group='constant', pars=[
Par(name='constant', value=0.0, flag=True),
])
def __units__(self):
return {"input": {}, "output": "None"}
def __func__(self,params,x,y,z):
# polynomial
p = params.get_values(group='poly')
poly = poly3d(p,x,y,z,self.__powers__)
# constant
constant = params['constant'].get_value()
# final result
res = poly + constant
# components
self.__components__['constant'] = constant
return res
model = fitmodel()
if os.path.exists('fitmodel.csv'):
model.load_params('fitmodel.csv')
else:
model.save_params('fitmodel.csv')
Let's run the fit again with more advanced model:
pysurfacefit --config config.ini --fit
This will start the fitting process and will print the intermediate fit statistics to the STDOUT:
Treating sampledata fitgroup as FitPoints
BEGIN FIT
USING SCIPY.OPTIMIZE.LEAST_SQUARES: METHOD= trf
METHOD OPTIONS: {'max_nfev': 200, 'bounds': [(-inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf), (inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf)], 'jac': <function Fitter.fit_least_squares.<locals>.<lambda> at 0x7f5def410560>}
==========================================
<<<< calling __sympy_initialize_func__ >>>>
==========================================
Progress:
- creating sympy objects for inputs
- creating sympy objects for parameters
- get the Sympy expression by calling function with the Sympy objects
- create lambdified Python function from sympy expression
- create compiled (numbified) code from the lambdified function
==========================================
CALC FUN 1>>> DIST:0.000000000e+00 SSE_RUB:0.000000000e+00 SSE_TOT:5.324527156e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 5.325e+14 UNWHT_SSE: 5.325e+14 WHT_SD: 2.580e+05 UNWHT_SD: 2.580e+05
===============================
==========================================
<<<< calling __sympy_initialize_jac__ >>>>
==========================================
Progress:
- get the Sympy expression by calling function with the Sympy objects
- create lambdified Python function from sympy expression
- create compiled (numbified) code from the lambdified function
==========================================
CALC JAC 1>>> DIST:0.000000000e+00
CALC FUN 2>>> DIST:1.000000000e+00 SSE_RUB:0.000000000e+00 SSE_TOT:5.223291702e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 5.223e+14 UNWHT_SSE: 5.223e+14 WHT_SD: 2.555e+05 UNWHT_SD: 2.555e+05
===============================
CALC JAC 2>>> DIST:1.000000000e+00
CALC FUN 3>>> DIST:2.999995352e+00 SSE_RUB:0.000000000e+00 SSE_TOT:5.024183860e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 5.024e+14 UNWHT_SSE: 5.024e+14 WHT_SD: 2.506e+05 UNWHT_SD: 2.506e+05
===============================
CALC JAC 3>>> DIST:2.999995352e+00
CALC FUN 4>>> DIST:6.999923483e+00 SSE_RUB:0.000000000e+00 SSE_TOT:4.639397348e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 4.639e+14 UNWHT_SSE: 4.639e+14 WHT_SD: 2.408e+05 UNWHT_SD: 2.408e+05
===============================
CALC JAC 4>>> DIST:6.999923483e+00
CALC FUN 5>>> DIST:1.499903800e+01 SSE_RUB:0.000000000e+00 SSE_TOT:3.923332008e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 3.923e+14 UNWHT_SSE: 3.923e+14 WHT_SD: 2.215e+05 UNWHT_SD: 2.215e+05
===============================
CALC JAC 5>>> DIST:1.499903800e+01
CALC FUN 6>>> DIST:3.098657439e+01 SSE_RUB:0.000000000e+00 SSE_TOT:2.703038473e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 2.703e+14 UNWHT_SSE: 2.703e+14 WHT_SD: 1.838e+05 UNWHT_SD: 1.838e+05
===============================
CALC JAC 6>>> DIST:3.098657439e+01
CALC FUN 7>>> DIST:6.266232927e+01 SSE_RUB:0.000000000e+00 SSE_TOT:1.075696361e+14 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 1.076e+14 UNWHT_SSE: 1.076e+14 WHT_SD: 1.160e+05 UNWHT_SD: 1.160e+05
===============================
CALC JAC 7>>> DIST:6.266232927e+01
CALC FUN 8>>> DIST:1.160883053e+02 SSE_RUB:0.000000000e+00 SSE_TOT:1.093359850e+13 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 1.093e+13 UNWHT_SSE: 1.093e+13 WHT_SD: 3.697e+04 UNWHT_SD: 3.697e+04
===============================
CALC JAC 8>>> DIST:1.160883053e+02
CALC FUN 9>>> DIST:1.900605403e+02 SSE_RUB:0.000000000e+00 SSE_TOT:2.602526806e+12 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 2.603e+12 UNWHT_SSE: 2.603e+12 WHT_SD: 1.804e+04 UNWHT_SD: 1.804e+04
===============================
CALC JAC 9>>> DIST:1.900605403e+02
CALC FUN 10>>> DIST:3.975523481e+02 SSE_RUB:0.000000000e+00 SSE_TOT:1.507914353e+12 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 1.508e+12 UNWHT_SSE: 1.508e+12 WHT_SD: 1.373e+04 UNWHT_SD: 1.373e+04
===============================
CALC JAC 10>>> DIST:3.975523481e+02
CALC FUN 11>>> DIST:8.545961426e+02 SSE_RUB:0.000000000e+00 SSE_TOT:5.250210120e+11 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 5.250e+11 UNWHT_SSE: 5.250e+11 WHT_SD: 8.101e+03 UNWHT_SD: 8.101e+03
===============================
CALC JAC 11>>> DIST:8.545961426e+02
CALC FUN 12>>> DIST:1.639588009e+03 SSE_RUB:0.000000000e+00 SSE_TOT:1.170270161e+11 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 1.170e+11 UNWHT_SSE: 1.170e+11 WHT_SD: 3.825e+03 UNWHT_SD: 3.825e+03
===============================
CALC JAC 12>>> DIST:1.639588009e+03
CALC FUN 13>>> DIST:3.039288277e+03 SSE_RUB:0.000000000e+00 SSE_TOT:3.211110377e+10 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 3.211e+10 UNWHT_SSE: 3.211e+10 WHT_SD: 2.003e+03 UNWHT_SD: 2.003e+03
===============================
CALC JAC 13>>> DIST:3.039288277e+03
CALC FUN 14>>> DIST:6.369248010e+03 SSE_RUB:0.000000000e+00 SSE_TOT:2.288170913e+09 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 2.288e+09 UNWHT_SSE: 2.288e+09 WHT_SD: 5.348e+02 UNWHT_SD: 5.348e+02
===============================
CALC JAC 14>>> DIST:6.369248010e+03
CALC FUN 15>>> DIST:7.993910245e+03 SSE_RUB:0.000000000e+00 SSE_TOT:9.174559605e-16 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 9.175e-16 UNWHT_SSE: 9.175e-16 WHT_SD: 3.386e-10 UNWHT_SD: 3.386e-10
===============================
CALC JAC 15>>> DIST:7.993910245e+03
CALC FUN 16>>> DIST:7.993910245e+03 SSE_RUB:0.000000000e+00 SSE_TOT:1.549934571e-17 ==> WEIGHTED_FIT
=====data group statistics=====
sampledata: N: 8000 MIN_WHT: 1.0e+00 MAX_WHT: 1.0e+00 WHT_SSE: 1.550e-17 UNWHT_SSE: 1.550e-17 WHT_SD: 4.402e-11 UNWHT_SD: 4.402e-11
===============================
CALC JAC 16>>> DIST:7.993910245e+03
END FIT
11.802427 seconds elapsed, 16 func evals, 16 jac evals
%cd test
from fitmodel import model
model/home/roman/work/python/PyDev/PySurfaceFit/git-repo/pysurfacefit-master/showcase/test
jeanny, Ver.3.0
# group names values bounds weights flags
--- -------- ---------- --------------- ----------- --------- -------
0 poly poly_0_0_1 7500 (-inf, inf) 0 1
1 poly poly_0_1_0 -2.92517e-11 (-inf, inf) 0 1
2 poly poly_1_0_0 1.48667e-12 (-inf, inf) 0 1
3 poly poly_0_0_2 -2750 (-inf, inf) 0 1
4 poly poly_0_1_1 -3.5252e-13 (-inf, inf) 0 1
5 poly poly_0_2_0 -3.17951e-12 (-inf, inf) 0 1
6 poly poly_1_0_1 -1.55127e-13 (-inf, inf) 0 1
7 poly poly_1_1_0 2.3229e-13 (-inf, inf) 0 1
8 poly poly_2_0_0 1 (-inf, inf) 0 1
9 poly poly_0_0_3 300 (-inf, inf) 0 1
10 poly poly_0_1_2 7.33912e-15 (-inf, inf) 0 1
11 poly poly_0_2_1 -1.30851e-14 (-inf, inf) 0 1
12 poly poly_0_3_0 0.1 (-inf, inf) 0 1
13 poly poly_1_0_2 3.78018e-14 (-inf, inf) 0 1
14 poly poly_1_1_1 1.25425e-14 (-inf, inf) 0 1
15 poly poly_1_2_0 1.44824e-14 (-inf, inf) 0 1
16 poly poly_2_0_1 4.30556e-14 (-inf, inf) 0 1
17 poly poly_2_1_0 -1.16635e-14 (-inf, inf) 0 1
18 poly poly_3_0_0 -1.22386e-14 (-inf, inf) 0 1
19 constant constant 10 (-inf, inf) 0 1
The code above lists the fitted parameters for the updated model.