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python.py
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python.py
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""" Routines for converting python objects to python. """
#***************************************************************************************************
# Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS).
# Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights
# in this software.
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0 or in the LICENSE file in the root pyGSTi directory.
#***************************************************************************************************
import collections as _collections
from ..objects.reportableqty import ReportableQty as _ReportableQty
'''
table() and cell() functions are used by table.py in table creation
everything else is used in creating formatters in formatters.py
'''
def table(customHeadings, colHeadingsFormatted, rows, spec):
'''
Create a "Python table" - really a pandas DataFrame
Parameters
----------
customHeadings : None, dict
optional dictionary of custom table headings
colHeadingsFormatted : list
formatted column headings
rows : list of lists of cell-strings
Data in the table, pre-formatted
spec : dict
options for the formatter
Returns
-------
dict : contains key 'python', which corresponds to a
pandas.DataFrame object representing the table
'''
try:
import pandas as _pd
except ImportError:
raise ValueError(("You must have the optional 'pandas' package "
"installed to render tables in the 'python' format"))
def getval(lbl):
return lbl.value if isinstance(lbl, _ReportableQty) else lbl
if customHeadings is not None \
and "python" in customHeadings:
colLabels = customHeadings['python']
else:
colLabels = [getval(x) for x in colHeadingsFormatted]
nCols = len(colLabels)
if nCols == 0: return {'python': _pd.DataFrame()}
#Remove duplicate in colLabels (otherwise these cols get merged weirdly below)
for i in range(len(colLabels)):
if colLabels[i] in colLabels[0:i]:
k = 1
while colLabels[i] + str(k) in colLabels[0:i]: k += 1
colLabels[i] = colLabels[i] + str(k)
#Add addition error-bar columns for any columns that have error bar info
cols_containing_ebs = set()
for formatted_rowData in rows:
assert(len(formatted_rowData) == nCols)
for i, formatted_cellData in enumerate(formatted_rowData):
if isinstance(formatted_cellData, _ReportableQty) and \
formatted_cellData.has_eb():
cols_containing_ebs.add(i)
n = 0 # number of cols inserted
for iCol in sorted(cols_containing_ebs):
origLbl = colLabels[iCol + n]
colLabels.insert(iCol + n + 1, origLbl + " Error Bar")
n += 1
rowLabels = []
rowIndexName = getval(colLabels[0])
if len(rowIndexName.strip()) == 0:
rowIndexName = None
dict_of_columns = _collections.OrderedDict()
for colLabel in colLabels[1:]:
dict_of_columns[colLabel] = []
for formatted_rowData in rows:
rowLabels.append(getval(formatted_rowData[0])); n = 0
for i, formatted_cellData in enumerate(formatted_rowData[1:], start=1):
if i in cols_containing_ebs:
if isinstance(formatted_cellData, _ReportableQty):
val, eb = formatted_cellData.get_value_and_err_bar()
else:
val, eb = formatted_cellData, None
dict_of_columns[colLabels[i + n]].append(val)
dict_of_columns[colLabels[i + n + 1]].append(eb)
n += 1
else:
dict_of_columns[colLabels[i + n]].append(getval(formatted_cellData))
indx = _pd.Index(rowLabels, name=rowIndexName)
#print("DB PANDAS: headings=",colLabels) #DEBUG
#print("col_dict(cnt) = ", [(k,len(v)) for k,v in dict_of_columns.items()]) #DEBUG
df = _pd.DataFrame(dict_of_columns,
columns=dict_of_columns.keys(),
index=indx)
return {'python': df}
def cell(data, label, spec):
'''
Format the cell of a python table
Parameters
----------
data : string
string representation of cell content
label : string
optional cell label, used for tooltips
spec : dict
options for the formatters
Returns
-------
string
'''
return data
def list(l, specs):
"""
Stub for conversion that isn't needed in python case.
(Convert a python list to python.)
Parameters
----------
l : list
list to convert into latex. sub-items pre formatted
specs : dictionary
Dictionary of user-specified and default parameters to formatting
Returns
-------
list
"""
return l
def vector(v, specs):
"""
Stub for conversion that isn't needed in python case.
(Convert a 1D numpy array to python.)
Parameters
----------
v : numpy array
1D array to convert.
specs : dictionary
Dictionary of user-specified and default parameters to formatting
Returns
-------
numpy array
"""
return v
def matrix(m, specs):
"""
Stub for conversion that isn't needed in python case.
Convert a 2D numpy array to python.
Parameters
----------
m : numpy array
2D array to convert.
specs : dictionary
Dictionary of user-specified and default parameters to formatting
Returns
-------
numpy array
"""
return m
def value(el, specs):
"""
Stub for conversion that isn't needed in python case.
(this function would be for converting python to python).
Parameters
----------
el : float or complex
Value to convert into latex.
specs : dictionary
Dictionary of user-specified and default parameters to formatting
Returns
-------
float or complex
"""
return el
def escaped(txt, specs):
"""
Stub for conversion that isn't needed in python case.
(Escape txt so it is python safe.)
Parameters
----------
txt : string
value to escape
specs : dictionary
Dictionary of user-specified and default parameters to formatting
Returns
-------
string
"""
return txt