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Result table tabulate #1

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Result table tabulate #1

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f7789ae
.gitignore
jagerber48 Jan 30, 2024
2124f45
refactor fit_report and correl_table to use tabulate
jagerber48 Jan 31, 2024
cdb3a7d
keep gformat
jagerber48 Jan 31, 2024
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1 change: 1 addition & 0 deletions .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -28,3 +28,4 @@ examples/*.sav
*htmlcov/*
.eggs
lmfit/version.py
.venv/
142 changes: 86 additions & 56 deletions lmfit/printfuncs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,8 @@
import re

import numpy as np
from tabulate import tabulate
from uncertainties import ufloat

try:
import numdifftools # noqa: F401
Expand Down Expand Up @@ -81,6 +83,13 @@ def gformat(val, length=11):
return f'{val:{length}.{prec}{form}}'


def indent_string_block(string_block, depth=4):
indent = " "*depth
string_block = f"{indent}{string_block}"
string_block = string_block.replace("\n", f"\n{indent}")
return string_block


def fit_report(inpars, modelpars=None, show_correl=True, min_correl=0.1,
sort_pars=False, correl_mode='list'):
"""Generate a report of the fitting results.
Expand Down Expand Up @@ -139,16 +148,23 @@ def fit_report(inpars, modelpars=None, show_correl=True, min_correl=0.1,
namelen = max(len(n) for n in parnames)
if result is not None:
add("[[Fit Statistics]]")
add(f" # fitting method = {result.method}")
add(f" # function evals = {getfloat_attr(result, 'nfev')}")
add(f" # data points = {getfloat_attr(result, 'ndata')}")
add(f" # variables = {getfloat_attr(result, 'nvarys')}")
add(f" chi-square = {getfloat_attr(result, 'chisqr')}")
add(f" reduced chi-square = {getfloat_attr(result, 'redchi')}")
add(f" Akaike info crit = {getfloat_attr(result, 'aic')}")
add(f" Bayesian info crit = {getfloat_attr(result, 'bic')}")
fit_stats_data = [
["# fitting method", result.method],
["# function evals", getfloat_attr(result, 'nfev')],
["# data points", getfloat_attr(result, 'ndata')],
["# variables", getfloat_attr(result, 'nvarys')],
["chi-square", getfloat_attr(result, 'chisqr')],
["reduced chi-square", getfloat_attr(result, 'redchi')],
["Akaike info crit", getfloat_attr(result, 'aic')],
["Bayesian info crit", getfloat_attr(result, 'bic')],
]
if hasattr(result, 'rsquared'):
add(f" R-squared = {getfloat_attr(result, 'rsquared')}")
fit_stats_data.append(["R-squared", getfloat_attr(result, 'rsquared')])

fit_stats_table = tabulate(fit_stats_data, disable_numparse=True)
fit_stats_table = indent_string_block(fit_stats_table)
add(fit_stats_table)

if not result.errorbars:
add("## Warning: uncertainties could not be estimated:")
if result.method in ('leastsq', 'least_squares') or HAS_NUMDIFFTOOLS:
Expand All @@ -167,39 +183,65 @@ def fit_report(inpars, modelpars=None, show_correl=True, min_correl=0.1,
add(" `pip install numdifftools` for lmfit to estimate uncertainties")
add(" with this fitting method.")

var_data = []
add("[[Variables]]")
for name in parnames:
single_var_data = {
"Name": None,
"Value": None,
"Percent Uncertainty": None,
"Constraint": None,
"Init Val": None,
"Model Val": None,
}
par = params[name]
space = ' '*(namelen-len(name))
nout = f"{name}:{space}"
inval = '(init = ?)'
single_var_data["Name"] = name

if par.init_value is not None:
inval = f'(init = {par.init_value:.7g})'
single_var_data["Init Val"] = f"{par.init_value:.7g}"
else:
single_var_data["Init Val"] = ""

if modelpars is not None and name in modelpars:
inval = f'{inval}, model_value = {modelpars[name].value:.7g}'
try:
sval = gformat(par.value)
except (TypeError, ValueError):
sval = ' Non Numeric Value?'
if par.stderr is not None:
serr = gformat(par.stderr)
try:
spercent = f'({abs(par.stderr/par.value):.2%})'
except ZeroDivisionError:
spercent = ''
sval = f'{sval} +/-{serr} {spercent}'
single_var_data["Model Val"] = f"{modelpars[name].value:.7g}"
else:
single_var_data["Model Val"] = ""

val = par.value
if not isinstance(val, (int, float)):
single_var_data["Value"] = "Non Numeric Value?"
else:
stderr = par.stderr
if stderr is not None:
single_var_data["Value"] = f"{ufloat(val, stderr):.2ue}"
try:
single_var_data[
"Percent Uncertainty"] = f'{abs(par.stderr / par.value):.2%}'
except ZeroDivisionError:
single_var_data["Percent Uncertainty"] = ""
else:
single_var_data["Value"] = f"{val:.7g}"
single_var_data["Percent Uncertainty"] = ""

if par.vary:
add(f" {nout} {sval} {inval}")
single_var_data["Constraint"] = "Vary"
elif par.expr is not None:
add(f" {nout} {sval} == '{par.expr}'")
single_var_data["Constraint"] = par.expr
else:
add(f" {nout} {par.value: .7g} (fixed)")
single_var_data["Constraint"] = "Fixed"

var_data.append(single_var_data)

var_table = tabulate(var_data, headers="keys", tablefmt="simple", numalign="center",
stralign="center", disable_numparse=True)
var_table = indent_string_block(var_table)
add(var_table)

if show_correl and correl_mode.startswith('tab'):
add('[[Correlations]] ')
for line in correl_table(params).split('\n'):
buff.append(' %s' % line)
correl_table_str = correl_table(params)
correl_table_str = indent_string_block(correl_table_str)
add(correl_table_str)
elif show_correl:
correls = {}
for i, name in enumerate(parnames):
Expand Down Expand Up @@ -313,39 +355,27 @@ def stat_row(label, val, val2=None, cat='td'):


def correl_table(params):
"""Return a printable correlation table for a Parameters object."""
varnames = [vname for vname in params if params[vname].vary]
nwid = max(8, max([len(vname) for vname in varnames])) + 1

def sfmt(a):
return f" {a:{nwid}s}"

def ffmt(a):
return sfmt(f"{a:+.4f}")

title = ['', sfmt('Variable')]
title.extend([sfmt(vname) for vname in varnames])
correl_data = []

title = '|'.join(title) + '|'
bar = [''] + ['-'*(nwid+1) for i in range(len(varnames)+1)] + ['']
bar = '+'.join(bar)

buff = [bar, title, bar]

for vname, par in params.items():
if not par.vary:
continue
line = ['', sfmt(vname)]
for vname in varnames:
var_data_dict = dict.fromkeys([""] + varnames)
var_data_dict[""] = vname
par = params[vname]
for vother in varnames:

if vother == vname:
line.append(ffmt(1))
var_data_dict[vother] = f"{1.0:+.4f}"
elif vother in par.correl:
line.append(ffmt(par.correl[vother]))
var_data_dict[vother] = f"{par.correl[vother]:+.4f}"
else:
line.append('unknown')
buff.append('|'.join(line) + '|')
buff.append(bar)
return '\n'.join(buff)
var_data_dict[vother] = "unknown"
correl_data.append(var_data_dict)

correl_table_str = tabulate(correl_data, headers="keys", tablefmt="simple_grid",
disable_numparse=True)
return correl_table_str


def params_html_table(params):
Expand Down