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split pretty print from value calculations #615

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merged 2 commits into from
Nov 26, 2024
Merged

split pretty print from value calculations #615

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300bc90
split pretty print from value calculations
bobmyhill Nov 26, 2024
6296a16
add value(uncertainty) string generation
bobmyhill Nov 26, 2024
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58 changes: 47 additions & 11 deletions burnman/utils/misc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -70,15 +70,31 @@ def flatten(arr):
)


def pretty_print_values(popt, pcov, params, extra_decimal_places=0):
def pretty_string_values(
popt, pcov, extra_decimal_places=0, combine_value_and_sigma=False
):
"""
Takes a numpy array of parameters, the corresponding covariance matrix
and a set of parameter names and prints the parameters and
principal 1-s.d.uncertainties (np.sqrt(pcov[i][i]))
in a nice text based format.
and a set of parameter names and returns the scaled variables and
principal 1-s.d.uncertainties (np.sqrt(pcov[i][i])) and scaling factor
as three separate lists of strings.

:param popt: Parameter values
:type popt: numpy array
:param pcov: Variance-covariance matrix
:type pcov: 2D numpy array
:param extra_decimal_places: extra precision for values, defaults to 0
:type extra_decimal_places: int, optional
:param combine_value_and_sigma: give values in value(sigma) format, defaults to False
:type combine_value_and_sigma: bool, optional
:return: values, uncertainties and the scaling factors
:rtype: tuple of 3 lists
"""
for i, p in enumerate(params):
p_rnd = round_to_n(popt[i], np.sqrt(pcov[i][i]), 1 + extra_decimal_places)
pval = []
psig = []
pscale = []
for i, p in enumerate(popt):
p_rnd = round_to_n(p, np.sqrt(pcov[i][i]), 1 + extra_decimal_places)
c_rnd = round_to_n(
np.sqrt(pcov[i][i]), np.sqrt(pcov[i][i]), 1 + extra_decimal_places
)
Expand All @@ -90,11 +106,31 @@ def pretty_print_values(popt, pcov, params, extra_decimal_places=0):

scale = np.power(10.0, p_expnt)
nd = p_expnt - np.floor(np.log10(np.abs(c_rnd))) + extra_decimal_places
print(
"{0:s}: ({1:{4}{5}f} +/- {2:{4}{5}f}) x {3:.0e}".format(
p, p_rnd / scale, c_rnd / scale, scale, 0, (nd) / 10.0
)
)
pval.append(f"{p_rnd / scale:0{nd / 10.0}f}")
if combine_value_and_sigma:
pval[-1] = f"{pval[-1]}({int(c_rnd / scale * np.power(10, nd))})"
psig.append(f"{c_rnd / scale:0{nd / 10.0}f}")
pscale.append(f"{scale:.0e}")
return (pval, psig, pscale)


def pretty_print_values(popt, pcov, params, extra_decimal_places=0):
"""
Takes a numpy array of parameters, the corresponding covariance matrix
and a set of parameter names and prints the scaled variables and
principal 1-s.d.uncertainties (np.sqrt(pcov[i][i])) and scaling factor
in an easy to read format.

:param popt: Parameter values
:type popt: numpy array
:param pcov: Variance-covariance matrix
:type pcov: 2D numpy array
:param extra_decimal_places: extra precision for values, defaults to 0
:type extra_decimal_places: int, optional
"""
pval, psig, pscale = pretty_string_values(popt, pcov, extra_decimal_places)
for i, p in enumerate(params):
print(f"{p:s}: ({pval[i]} +/- {psig[i]}) x {pscale[i]}")


def pretty_print_table(table, use_tabs=False):
Expand Down
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