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greater_tables_project/greater_tables/gtcore.py
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Stephen Mildenhall 85a11fb61b Rearranged cgtore - no coding changes
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"""
Core rendering logic for GreaterTables.
Defines the `GreaterTables` class, which formats and renders pandas DataFrames
to HTML, plain text, or LaTeX output using a validated configuration model.
This is the main entry point for rendering logic. See `gtconfig.py` for configuration schema.
"""
from collections import namedtuple
from decimal import InvalidOperation
from io import StringIO
from itertools import groupby
import logging
from pathlib import Path
import re
import sys
from textwrap import wrap
from typing import Optional, Union, Literal
import warnings
import yaml
from bs4 import BeautifulSoup
from cachetools import LRUCache
import numpy as np
import pandas as pd
from pandas.errors import IntCastingNaNError
from pandas.api.types import is_datetime64_any_dtype, is_integer_dtype, \
is_float_dtype # , is_numeric_dtype
from pydantic import ValidationError
from rich import box
from rich.table import Table
from . gtenums import Breakability, Alignment
from . gtformats import GT_Format, TableFormat, Line, DataRow
from . gtconfig import GTConfigModel
from . hasher import df_short_hash
from . tex_svg import TikzProcessor
# turn this fuck-fest off
pd.set_option('future.no_silent_downcasting', True)
# pandas complaining about casting columns eg putting object in float column
warnings.simplefilter(action='ignore', category=FutureWarning)
# GPT recommended approach
logger = logging.getLogger(__name__)
# Disable log propagation to prevent duplicates
logger.propagate = False
if logger.hasHandlers():
# Clear existing handlers
logger.handlers.clear()
# SET DEGBUGGER LEVEL
LEVEL = logging.WARNING # DEBUG or INFO, WARNING, ERROR, CRITICAL
logger.setLevel(LEVEL)
handler = logging.StreamHandler(sys.stderr)
handler.setLevel(LEVEL)
formatter = logging.Formatter(
'%(asctime)s | %(levelname)s | %(funcName)-15s | %(message)s')
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.info(f'Logger Setup; {__name__} module recompiled.')
class GT(object):
"""
Create a greater_tables formatting object.
Provides html and latex output in quarto/Jupyter accessible manner.
Wraps AND COPIES the dataframe df. WILL NOT REFLECT CHANGES TO DF.
Recommended usage is to subclass GT (or use functools.partial) and set
defaults suitable to your particular
application. In that way you can maintain a "house-style"
Process
--------
**Input transformation**
* ``pd.Series`` converted to ``DataFrame``
* ``list`` converted to ``DataFrame``, optionally using row 0 as
``config.header_row``
* A string is assumed to be a pipe-separated markdown table which is
converted to a ``DataFrame`` setting aligners per the alignment row
* All other input types are an error
The input ``df`` must have unique column names. It is then copied into
``self.df`` which will be changed and ``self.raw_df`` for reference.
The copy is hashed for the table name.
**Mangling**
* If show_index, the index is reset and kept, so that all columns are on an
config.equal footing
* The index change levels are computed to determine LaTeX hrules
* ratio year, and raw columns converted to a list (can be input as a single
string name)
* Columns, except raw columns, are cast to floats
* Column types by index determined
* default formatter function set (wrapping input, if any)
* Aligner column input decoded into aligner values
(``grt-left,grt-right,grt-center``); index aligners separated
* Formatters decoded, strings mapped to lambda functions as f-string
formatters, integers as number of decimals
* Tab values expanded into an iterable
* Dataframe at this point (index reset, cast) saved to
``df_pre_applying_formatters``
* Determine formatters (``df_formatters`` property, a list of column index
formatting functions:
* Make the default float formatter if entered (callable, string, number;
wrapped in try/except)
* Determine each column's format type and add function
* Run ``apply_formatters`` to apply all format choices to ``df``. This
function handles index columns slightly differently, but results in the
formatters being applied to each column.
* Sparsify if requested and if multiindex
* Result is a dataframe with all object column types and values that
reflect the formatting choices.
Parameters
-----------
:param df: target DataFrame or list of lists or markdown table string
:param caption: table caption, optional (GT will look for gt_caption
attribute of df and use that)
:param label: TeX label (used in \\label{} command). For markdown
tables with #tbl:... in the caption it is extracted automatically.
:param aligners: None or dict (type or colname) -> left | center |
right
:param formatters: None or dict (type or colname) -> format function
for the column; formatters trump ratio_cols
:param unbreakable: None or list of columns to be considered unbreakable
:param ratio_cols: None, or "all" or list of column names treated as
ratios. Set defaults in derived class suitable to application.
:param year_cols: None, or "all" or list of column names treated as
years (no commas, no decimals). Set defaults in derived class suitable
to application.
:param date_cols: None, or "all" or list of column names treated as
dates. Set defaults in derived class suitable to application.
:param raw_cols: None, or "all" or list of column names that are NOT
cast to floats. Set defaults in derived class suitable to application.
:param show_index: if True, show the index columns, default True
:param config.default_integer_str: format f-string for integers, default
value '{x:,d}'
:param config.default_float_str: format f-string for floats, default
value '{x:,.3f}'
:param config.default_date_str: format f-string for dates, default '%Y-%m-%d'.
NOTE: no braces or x!
:param config.default_ratio_str: format f-string for ratios, default '{x:.1%}'
:param config.table_float_format: None or format string for floats in the
table format function, applied to entire table, default None
:param config.table_hrule_width: width of the table top, botton and header
hrule, default 1
:param config.table_vrule_width: width of the table vrule, separating the
index from the body, default 1
:param config.hrule_widths: None or tuple of three ints for hrule widths
(for use with multiindexes)
:param config.vrule_widths: None or tuple of three ints for vrule widths
(for use when columns have multiindexes)
:param config.sparsify: if True, config.sparsify the index columns, you almost always
want this to be true!
:param config.sparsify_columns: if True, config.sparsify the columns, default True,
generally a better look, headings centered in colspans
:param config.spacing: 'tight', 'medium', 'wide' to quickly set cell padding.
Medium is default (2, 10, 2, 10).
:param config.padding_trbl: None or tuple of four ints for padding, in order
top, right, bottom, left.
:param config.tikz_scale: scale factor applied to tikz LaTeX tables.
:param config.font_body: font size for body text, default 0.9. Units in em.
:param config.font_head: font size for header text, default 1.0. Units in em.
:param config.font_caption: font size for caption text, default 1.1.
Units in em.
:param config.font_bold_index: if True, make the index columns bold,
default False.
:param config.pef_precision: precision (digits after period) for pandas
engineering format, default 3.
:param config.pef_lower: apply engineering format to floats with absolute
value < 10**config.pef_lower; default -3.
:param config.pef_upper: apply engineering format to floats with absolute
value > 10**config.pef_upper; default 6.
:param config.cast_to_floats: if True, try to cast all non-integer, non-date
columns to floats
:param config.header_row: True: use first row as headers; False no headings.
Default True
:param config.tabs: None or list of column widths in characters or a common
int or float width. (It is converted into em; one character is about
0.5em on average; digits are exactly 0.5em.) If None, will be calculated.
Default None.
:param config.equal: if True, set all column widths config.equal. Default False. Maybe
ignored, depending on computed ideal column widths.
:param config.caption_align: for the caption
:param config.large_ok: signal that you are intentionally applying to a large
dataframe. Sub-classes may restrict or apply .head() to df.
:param config.max_str_length: maximum displayed length of object types, that
are cast to strings. Eg if you have nested DataFrames!
:param str_table_fmt: table border format used for string output
(markdown), default mixed_grid DEPRECATED??
:param config.table_width_mode:
'explicit': set using config.max_table_width
'natural': each cell on one line (can be very wide with long strings)
'breakable': wrap breakable cells (text strings) at word boundaries
to fit longest word
'minimum': wrap breakable and ok-to-break (dates) cells
:param config.table_width_header_adjust: additional proportion of table width
used to balance header columns.
:param config.table_width_header_relax: extra spaces allowed per column heading
to facilitate better column header wrapping.
:param config.max_table_width: max table width used for markdown string output,
default 200; width is never less than minimum width. Padding (3 chars
per row plus 1) consumed out of config.max_table_width in string output mode.
:param config.debug: if True, add id to caption and use colored lines in table,
default False.
"""
# TeX control sequence display widths (heuristic)
TEX_SIMPLE_GLYPHS = {
'alpha', 'beta', 'gamma', 'delta', 'epsilon', 'zeta', 'eta', 'theta',
'iota', 'kappa', 'lambda', 'mu', 'nu', 'xi', 'omicron', 'pi', 'rho',
'sigma', 'tau', 'upsilon', 'phi', 'chi', 'psi', 'omega', 'infty',
'sum', 'prod', 'int', 'cup', 'cap', 'vee', 'wedge', 'forall', 'exists',
'neg', 'leq', 'geq', 'neq', 'approx', 'to', 'leftarrow', 'rightarrow'
}
TEX_WIDE = {'frac', 'sqrt', 'sum', 'int', 'prod'}
TEX_SPACING = {'quad', 'qquad', ',', ';', ' ', '!'}
def __init__(
self,
df,
*,
caption='',
label='',
aligners: dict[str, callable] | None = None,
formatters: dict[str, callable] | None = None,
tabs: Optional[Union[list[float], float, int]] | None = None,
unbreakable=None,
ratio_cols=None,
year_cols=None,
date_cols=None,
raw_cols=None,
show_index=True,
#
config: GTConfigModel | None = None,
config_path: Path | None = None,
**overrides,
):
if config and config_path:
raise ValueError(
"Pass either 'config' or 'config_path', not both.")
if config:
base_config = config
elif config_path:
try:
raw = yaml.safe_load(config_path.read_text(encoding="utf-8"))
base_config = GTConfigModel.model_validate(raw)
except (ValidationError, OSError) as e:
raise ValueError(
f"Failed to load config from {config_path}") from e
else:
base_config = GTConfigModel()
# access through config
# update and validate; need to merge to avoid repeated args
merged = dict(base_config.model_dump(), **overrides)
self.config = GTConfigModel(**merged)
# no validation
# self.config = base_config.model_copy(update=overrides)
# deal with alternative input modes for df: None, DataFrame, Series, markdown text table
if df is None:
# don't want None to fail
df = pd.DataFrame([])
if isinstance(df, pd.DataFrame):
# usual use case
pass
elif isinstance(df, pd.Series):
df = df.to_frame()
elif isinstance(df, list):
df = pd.DataFrame(df)
# override this selection come what may
show_index = False
if config.header_row:
# Set first row as column names
df.columns = df.iloc[0]
# Drop first row and reset index
df = df[1:].reset_index(drop=True)
elif isinstance(df, str):
df = df.strip()
if df == '':
df = pd.DataFrame([])
else:
df, aligners, caption, label = GT.md_to_df(df)
show_index = False
else:
raise ValueError(
'df must be a DataFrame, a list of lists, or a markdown table string')
if len(df) > 50 and not config.large_ok:
raise ValueError(
'Large dataframe (>50 rows) and config.large_ok not set to true...do you know what you are doing?')
if not df.columns.is_unique:
raise ValueError('df column names are not unique')
# extract value BEFORE copying, copying does not carry these attributes over
if caption != '':
self.caption = caption
else:
# used by querex etc.
self.caption = getattr(df, 'gt_caption', '')
self.label = label
self.df = df.copy(deep=True) # the object being formatted
self.raw_df = df.copy(deep=True)
# if not column_names:
# get rid of column names
# self.df.columns.names = [None] * self.df.columns.nlevels
self.df_id = df_short_hash(self.df)
# TODO: update / change
# self.str_table_fmt = str_table_fmt
# TODO: implement
# self.table_width_mode = config.table_width_mode.lower()
# if config.table_width_mode not in ('explicit', 'natural', 'breakable', 'minimum'):
# raise ValueError(f'Inadmissible options {config.table_width_mode} for config.table_width_mode.')
# self.table_width_mode = table_width_mode
# self.table_width_header_adjust = table_width_header_adjust
# self.table_width_header_relax = table_width_header_relax
# self.max_table_width = max_table_width
# self.debug = debug
if self.caption != '' and self.config.debug:
self.caption += f' (id: {self.df_id})'
# self.max_str_length = max_str_length
# before messing
self.show_index = show_index
self.nindex = self.df.index.nlevels if self.show_index else 0
self.ncolumns = self.df.columns.nlevels
self.ncols = self.df.shape[1]
self.dt = self.df.dtypes
# reset index to put all columns on an config.equal footing, but note number ofindex cols
with warnings.catch_warnings():
if self.show_index:
warnings.simplefilter(
"ignore", category=pd.errors.PerformanceWarning)
self.df = self.df.reset_index(
drop=False, col_level=self.df.columns.nlevels - 1)
# want the new index to be ints - that is not default if old was multiindex
self.df.index = np.arange(self.df.shape[0], dtype=int)
self.index_change_level = GT.changed_column(
self.df.iloc[:, :self.nindex])
if self.ncolumns > 1:
# will be empty rows above the index headers
self.index_change_level = pd.Series(
[i[-1] for i in self.index_change_level])
self.column_change_level = GT.changed_level(self.raw_df.columns)
# determine ratio columns
if ratio_cols is not None and not self.df.columns.is_unique:
logger.warning(
'Ratio cols specified with non-unique column names: ignoring request.')
self.ratio_cols = []
else:
if ratio_cols is None:
self.ratio_cols = []
elif ratio_cols == 'all':
self.ratio_cols = [i for i in self.df.columns]
elif ratio_cols is not None and not isinstance(ratio_cols, (tuple, list)):
self.ratio_cols = self.cols_from_regex(
ratio_cols) # [ratio_cols]
else:
self.ratio_cols = ratio_cols
# determine year columns
if year_cols is not None and not self.df.columns.is_unique:
logger.warning(
'Year cols specified with non-unique column names: ignoring request.')
self.year_cols = []
else:
if year_cols is None:
self.year_cols = []
elif year_cols is not None and not isinstance(year_cols, (tuple, list)):
self.year_cols = self.cols_from_regex(year_cols) # [year_cols]
else:
self.year_cols = year_cols
# determine date columns
if date_cols is not None and not self.df.columns.is_unique:
logger.warning(
'Year cols specified with non-unique column names: ignoring request.')
self.date_cols = []
else:
if date_cols is None:
self.date_cols = []
elif date_cols is not None and not isinstance(date_cols, (tuple, list)):
self.date_cols = self.cols_from_regex(date_cols) # [date_cols]
else:
self.date_cols = date_cols
# determine columns NOT to cast to floats
if raw_cols is not None and not self.df.columns.is_unique:
logger.warning(
'Year cols specified with non-unique column names: ignoring request.')
self.raw_cols = []
else:
if raw_cols is None:
self.raw_cols = []
elif raw_cols is not None and not isinstance(raw_cols, (tuple, list)):
self.raw_cols = self.cols_from_regex(raw_cols) # [raw_cols]
else:
self.raw_cols = raw_cols
# figure the default formatter (used in conjunction with raw columns)
if self.config.default_formatter is None:
self.default_formatter = self.default_formatter
else:
assert callable(
config.default_formatter), 'config.default_formatter must be callable'
def wrapped_default_formatter(x):
try:
return config.default_formatter(x)
except ValueError:
return str(x)
self.default_formatter = wrapped_default_formatter
# cast as much as possible to floats
with warnings.catch_warnings():
warnings.simplefilter(
"ignore", category=pd.errors.PerformanceWarning)
if self.config.cast_to_floats:
for i, c in enumerate(self.df.columns):
if c in self.raw_cols or c in self.date_cols:
continue
old_type = self.df.dtypes[c]
if not np.any((is_integer_dtype(self.df.iloc[:, i]),
is_datetime64_any_dtype(self.df.iloc[:, i]))):
try:
self.df.iloc[:, i] = self.df.iloc[:,
i].astype(float)
logger.debug(
f'coerce {i}={c} from {old_type} to float')
except (ValueError, TypeError):
logger.debug(
f'coercing {i}={c} from {old_type} to float FAILED')
# massage unbreakable
if unbreakable is None:
unbreakable = []
elif isinstance(unbreakable, str):
unbreakable = [unbreakable]
# now can determine types and infer the break penalties (for column sizes)
self.float_col_indices = []
self.integer_col_indices = []
self.date_col_indices = []
self.object_col_indices = [] # not actually used, but for neatness
self.break_penalties = []
# manage non-unique col names here
logger.debug('FIGURING TYPES')
for i, cn in enumerate(self.df.columns): # range(self.df.shape[1]):
ser = self.df.iloc[:, i]
if cn in self.date_cols:
logger.debug(f'col {i}/{cn} specified as date col')
self.date_col_indices.append(i)
self.break_penalties.append(
Breakability.NEVER if cn in unbreakable else Breakability.DATE)
elif is_datetime64_any_dtype(ser):
logger.debug(f'col {i} = {self.df.columns[i]} is DATE')
self.date_col_indices.append(i)
self.break_penalties.append(
Breakability.NEVER if cn in unbreakable else Breakability.DATE)
elif is_integer_dtype(ser):
logger.debug(f'col {i} = {self.df.columns[i]} is INTEGER')
self.integer_col_indices.append(i)
self.break_penalties.append(
Breakability.NEVER if cn in unbreakable else Breakability.NEVER)
elif is_float_dtype(ser):
logger.debug(f'col {i} = {self.df.columns[i]} is FLOAT')
self.float_col_indices.append(i)
self.break_penalties.append(
Breakability.NEVER if cn in unbreakable else Breakability.NEVER)
else:
logger.debug(f'col {i} = {self.df.columns[i]} is OBJECT')
self.object_col_indices.append(i)
c = ser.name
if c in self.year_cols or c in self.ratio_cols:
self.break_penalties.append(
Breakability.NEVER if cn in unbreakable else Breakability.NEVER)
else:
self.break_penalties.append(
Breakability.NEVER if cn in unbreakable else Breakability.ACCEPTABLE)
# figure out column and index alignment
if aligners is not None and np.any(self.df.columns.duplicated()):
logger.warning(
'aligners specified with non-unique column names: ignoring request.')
aligners = None
if aligners is None:
# not using
aligners = []
elif isinstance(aligners, str):
# lrc for each column
aligners = {c: a for c, a in zip(self.df.columns, aligners)}
self.df_aligners = []
lrc = {'l': 'grt-left', 'r': 'grt-right', 'c': 'grt-center'}
# TODO: index aligners
for i, c in enumerate(self.df.columns):
# test aligners BEFORE index!
if c in aligners:
self.df_aligners.append(lrc.get(aligners[c], 'grt-center'))
elif i < self.nindex:
# index -> left
self.df_aligners.append('grt-left')
elif c in self.year_cols:
self.df_aligners.append('grt-center')
elif c in self.raw_cols:
# these are strings
self.df_aligners.append('grt-left')
elif i in self.date_col_indices:
# center dates, why not!
self.df_aligners.append('grt-center')
elif c in self.ratio_cols or i in self.float_col_indices or i in self.integer_col_indices:
# number -> right
self.df_aligners.append('grt-right')
else:
# all else, left
self.df_aligners.append('grt-left')
self.df_idx_aligners = self.df_aligners[:self.nindex]
if formatters is None:
self.default_formatters = {}
else:
self.default_formatters = {}
for k, v in formatters.items():
if callable(v):
self.default_formatters[k] = v
elif type(v) == str:
self.default_formatters[k] = lambda x: v.format(x=x)
elif type(v) == int:
fmt = f'{{x:.{v}f}}'
self.default_formatters[k] = lambda x: fmt.format(x=x)
else:
raise ValueError(
'formatters must be dict of callables or ints or format strings {x:...}')
# store defaults
# self.default_integer_str = default_integer_str
# VERY rarely used; for floats in cols that are not floats
# self.default_float_str = default_float_str
# self.default_date_str = default_date_str.replace(
# '{x:', '').replace('}', '')
# self.default_ratio_str = default_ratio_str
# self.pef_precision = pef_precision
# self.pef_lower = pef_lower
# self.pef_upper = pef_upper
self._pef = None
# self.table_float_format = table_float_format
# self.default_float_formatter = None
# self.hrule_widths = hrule_widths or (0, 0, 0)
# if not isinstance(self.config.hrule_widths, (list, tuple)):
# self.config.hrule_widths = (self.config.hrule_widths,)
# self.vrule_widths = vrule_widths or (0, 0, 0)
# if not isinstance(self.config.hrule_widths, (list, tuple)):
# self.config.hrule_widths = (self.config.hrule_widths, )
# self.table_hrule_width = table_hrule_width
# self.table_vrule_width = table_vrule_width
# self.font_body = font_body
# self.font_head = font_head
# self.font_caption = font_caption
# self.tikz_scale = tikz_scale
# self.font_bold_index = font_bold_index
# self.caption_align = caption_align
# self.sparsify_columns = sparsify_columns
if tabs is None:
self.tabs = None
elif isinstance(tabs, (int, float)):
self.tabs = (tabs,)
elif isinstance(tabs, (np.ndarray, list, tuple)):
self.tabs = tabs # Already iterable, self.tabs = as is
else:
self.tabs = [tabs] # Fallback for anything else
# self.equal = equal
if self.config.padding_trbl is not None:
padding_trbl = self.config_padding_trbl
elif self.config.padding_trbl is None:
if self.config.spacing == 'tight':
padding_trbl = (0, 5, 0, 5)
elif self.config.spacing == 'medium':
padding_trbl = (2, 10, 2, 10)
elif self.config.spacing == 'wide':
padding_trbl = (4, 15, 4, 15)
else:
raise ValueError(
'config.spacing must be tight, medium, or wide or tuple of four ints.')
# pydantic will see to it this is OK
self.padt, self.padr, self.padb, self.padl = padding_trbl
# because of the problem of non-unique indexes use a list and
# not a dict to pass the formatters to to_html
self._df_formatters = None
self.df_style = ''
self.df_html = ''
self._clean_html = ''
self._clean_tex = ''
self._rich_table = None
self._string = ''
# finally config.sparsify and then apply formaters
# this radically alters the df, so keep a copy for now...
self.df_pre_applying_formatters = self.df.copy()
self.df = self.apply_formatters(self.df)
# cache for various things...
self._cache = LRUCache(20)
# config.sparsify
if self.config.sparsify and self.nindex > 1:
self.df = GT.sparsify(self.df, self.df.columns[:self.nindex])
# for c in self.df.columns[:self.nindex]:
# # config.sparsify returns some other stuff...
# self.df[c], _ = GT.config.sparsify(self.df[c])
def __repr__(self):
"""Basic representation."""
return f"GT(df_id={self.df_id})"
def __str__(self):
"""String representation, for print()."""
return self.make_string()
def _repr_html_(self):
"""
Apply format to self.df.
ratio cols like in constructor
"""
return self.html
def _repr_latex_(self):
"""Generate a LaTeX tabular representation."""
# return ''
# latex = self.df.to_latex(caption=self.caption, formatters=self._df_formatters)
if self._clean_tex == '':
self._clean_tex = self.make_tikz()
logger.info('CREATED LATEX')
return self._clean_tex
def cols_from_regex(self, regex):
"""Return columns of self.df matching regex"""
return [col for col in self.df.columns if isinstance(col, str) and re.search(regex, col)]
def cache_get(self, key):
"""Retrieve item from cache."""
return self._cache.get(key, None)
def cache_set(self, key, value):
"""Add item to cache."""
self._cache[key] = value
# define the default and easy formatters ===================================================
def default_ratio_formatter(self, x):
"""Ratio formatter."""
try:
return self.config.default_ratio_str.format(x=x)
except ValueError:
return str(x)
def default_date_formatter(self, x):
"""Date formatter that works for strings too."""
if pd.isna(x):
return ""
try:
dt = pd.to_datetime(x, errors='coerce')
if pd.isna(dt):
return str(x)
return dt.strftime(self.config.default_date_str)
except Exception:
logger.error("date error with %s", x)
return str(x)
def default_integer_formatter(self, x):
"""Integer formatter."""
try:
return self.config.default_integer_str.format(x=x)
except ValueError:
return str(x)
def default_year_formatter(self, x):
"""Year formatter."""
try:
return f'{int(x):d}'
except ValueError:
return str(x)
def default_raw_formatter(self, x):
"""Formatter for columns flagged as raw."""
return str(x)
@staticmethod
def default_float_format(x, neng=3):
"""
the endless quest for the perfect float formatter...
NOT USED AT THE MINUTE.
tester::
for x in 1.123123982398324723947 * 10.**np.arange(-23, 23):
print(default_float_format(x))
:param x:
:return:
"""
ef = pd.io.formats.format.EngFormatter(neng, True) # noqa
try:
if x == 0:
ans = '0'
elif 1e-3 <= abs(x) < 1e6:
if abs(x) <= 10:
ans = f'{x:.3g}'
elif abs(x) < 100:
ans = f'{x:,.2f}'
elif abs(x) < 1000:
ans = f'{x:,.1f}'
else:
ans = f'{x:,.0f}'
else:
ans = ef(x)
return ans
except ValueError as e:
logger.debug(f'ValueError {e}')
return str(x)
except TypeError as e:
logger.debug(f'TypeError {e}')
return str(x)
except AttributeError as e:
logger.debug(f'AttributeError {e}')
return str(x)
def default_formatter(self, x):
"""Default universal formatter for other types (GTP re-write of above cluster)."""
try:
f = float(x)
except (TypeError, ValueError):
s = str(x)
return s if self.config.max_str_length < 0 else s[:self.config.max_str_length]
if self.default_float_formatter:
return self.default_float_formatter(f)
if np.isinf(f) or np.isnan(f): # clearer handling of weird float cases
return str(x)
if f.is_integer():
return self.config.default_integer_str.format(x=int(f))
else:
return self.config.default_float_str.format(x=f)
def pef(self, x):
"""Pandas engineering format."""
if self._pef is None:
self._pef = pd.io.formats.format.EngFormatter(accuracy=self.config.pef_precision, use_eng_prefix=True) # noqa
return self._pef(x)
def make_float_formatter(self, ser):
"""
Make a float formatter suitable for the Series ser.
Obeys these rules:
* All elements in the column are formatted consistently
* ...
TODO flesh out... at some point shd use pef?!
"""
amean = ser.abs().mean()
# mean = ser.mean()
amn = ser.abs().min()
amx = ser.abs().max()
# smallest = ser.abs().min()
# sd = ser.sd()
# p10, p50, p90 = np.quantile(ser, [0.1, .5, 0.9], method='inverted_cdf')
# pl = 10. ** self.config.pef_lower
# pu = 10. ** self.config.pef_upper
pl, pu = 10. ** self.config.pef_lower, 10. ** self.config.pef_upper
if amean < 1:
precision = 5
elif amean < 10:
precision = 3
elif amean < 20000:
precision = 2
else:
precision = 0
fmt = f'{{x:,.{precision}f}}'
logger.debug(f'{ser.name=}, {amean=}, {fmt=}')
if amean < pl or amean > pu or amx / max(1, amn) > pu:
# go with eng
def ff(x):
try:
return self.pef(x)
except (ValueError, TypeError, InvalidOperation):
return str(x)
else:
def ff(x):
try:
return fmt.format(x=x)
# well and good but results in ugly differences
# by entries in a column
# if x == int(x) and np.abs(x) < pu:
# return f'{x:,.0f}.'
# else:
# return fmt.format(x=x)
except (ValueError, TypeError):
return str(x)
return ff
@ property
def df_formatters(self):
"""
Make and return the list of formatters.
Created one per column. Int, date, objects use defaults, but
for float cols the formatter is created custom to the details of
each column.
"""
if self._df_formatters is None:
# because of non-unique indexes, index by position not name
if self.config.table_float_format is not None:
if callable(self.config.table_float_format):
# wrap in error protections
def ff(x):
try:
return self.config.table_float_format(x=x)
except ValueError:
return str(x)
except Exception as e:
logger.error(f'Custom float function raised {e=}')
self.default_float_formatter = ff
else:
if type(self.config.table_float_format) != str:
raise ValueError(
'config.table_float_format must be a string or a function')
fmt = self.config.table_float_format
def ff(x):
try:
return fmt.format(x=x)
except ValueError:
return str(x)
except Exception as e:
logger.error(
f'Custom float format string raised {e=}')
self.default_float_formatter = ff
else:
self.default_float_formatter = False
self._df_formatters = []
for i, c in enumerate(self.df.columns):
# set a default, note here can have
# non-unique index so work with position i
if c in self.default_formatters:
self._df_formatters.append(self.default_formatters[c])
elif c in self.ratio_cols:
# print(f'{i} ratio')
self._df_formatters.append(self.default_ratio_formatter)
elif c in self.year_cols:
self._df_formatters.append(self.default_year_formatter)
elif c in self.raw_cols:
self._df_formatters.append(self.default_raw_formatter)
elif i in self.date_col_indices:
self._df_formatters.append(self.default_date_formatter)
elif i in self.integer_col_indices:
# print(f'{i} int')
self._df_formatters.append(self.default_integer_formatter)
elif i in self.float_col_indices:
# trickier approach...
self._df_formatters.append(
self.default_float_formatter or self.make_float_formatter(self.df.iloc[:, i]))
else:
# print(f'{i} default')
self._df_formatters.append(self.default_formatter)
# self._df_formatters is now a list of length config.equal to cols in df
if len(self._df_formatters) != self.df.shape[1]:
raise ValueError(
f'Something wrong: {len(self._df_formatters)=} != {self.df.shape=}')
return self._df_formatters
@staticmethod
def apply_formatters_work(df, formatters):
"""Apply formatters to a DataFrame."""
try:
new_df = pd.DataFrame({i: map(f, df.iloc[:, i])
for i, f in enumerate(formatters)})
except TypeError:
print('NASTY TYPE ERROR')
raise
new_df.columns = df.columns
return new_df
def apply_formatters(self, df, mode='adjusted'):
"""
Replace df (the raw df) with formatted df, including the index.
If mode is 'adjusted' operates on columns only, does not touch the
index. Otherwise, called from tikz and operating on raw_df
"""
if mode == 'adjusted':
# apply to df where the index has been reset
# number of columns = len(self.df_formatters)
return GT.apply_formatters_work(df, self.df_formatters)
elif mode == 'raw':
# work on raw_df where the index has not been reset
# because of non-unique indexes, index by position not name
# create the df and the index separately
data_formatters = self.df_formatters[self.nindex:]
new_body = GT.apply_formatters_work(df, data_formatters)
if not self.show_index:
return new_body
# else have to handle the index
index_formatters = self.df_formatters[:self.nindex]
df_index = df.reset_index(
drop=False, col_level=self.df.columns.nlevels - 1).iloc[:, :self.nindex]
new_index = GT.apply_formatters_work(df_index, index_formatters)
# put them back together
new_df = pd.concat([new_index, new_body], axis=1)
new_df = new_df.set_index(list(df_index.columns))
new_df.index.names = df.index.names
return new_df
else:
raise ValueError(f'unknown mode {mode}')
@staticmethod
def changed_column(bit):
"""Return the column that changes with each row."""
tf = bit.ne(bit.shift())
tf = tf.loc[tf.any(axis=1)]
return tf.idxmax(axis=1)
@staticmethod
def changed_level(idx):
"""
Return the level of index that changes with each row.
Very ingenious GTP code with some SM enhancements.
"""
# otherwise you alter the actual index
idx = idx.copy()
idx.names = [i for i in range(idx.nlevels)]
# Determine at which level the index changes
# Convert MultiIndex to a DataFrame
index_df = idx.to_frame(index=False)
# true / false match last row
tf = index_df.ne(index_df.shift())
# changes need at least one true
tf = tf.loc[tf.any(axis=1)]
level_changes = tf.idxmax(axis=1)
return level_changes
def make_column_width_df(self):
"""
Return dataframe of width information.
Returned dataframe has columns for
* natural width, all on one line = max len by col
* min width = max length given breaks
* break type of column
* alignment of column
* index natural width
* index min width
"""
df = self.df
n_row, n_col = df.shape
# The width if content didn't wrap (single line)
# Series=dict colname->max width of cells in column
natural_width = df.map(lambda x: len(x.strip())).max(axis=0).to_dict()
# re.split(r'(?<=[\s.,:;!?()\[\]{}\-\\/|])\s*', text)
# (?<=...) is a lookbehind to preserve the break character with the left-hand fragment.
# [\s.,:;!?()\[\]{}\-\\/|] matches common punctuation and separators:
# \s = whitespace
# . , : ; ! ? = terminal punctuation
# () [] {} = brackets
# \- = dash
# \\/| = slash, backslash, pipe
pat = r'(?<=[.,;:!?)\]}\u2014\u2013])\s+|--+\s+|\s+'
iso_date_split = r'(?<=\b\d{4})-(?=\d{2}-\d{2})'
pat = f'{pat}|{iso_date_split}'
# Calculate ideal (no wrap) and minimum possible widths for all columns
# The absolute minimum width each column can take (e.g., longest word for text)
min_acceptable_width = {}
for col_name in df.columns:
min_acceptable_width[col_name] = (
df[col_name].str
.split(pat=pat, regex=True, expand=True)
.fillna('')
.map(len)
.max(axis=1)
.max()
)
# ans will be the col_width_df
ans = pd.DataFrame({
'alignment': [i[4:] for i in self.df_aligners],
'break_penalties': self.break_penalties,
'breakability': [x.name for x in self.break_penalties],
'natural_width': natural_width.values(),
'min_acceptable_width': min_acceptable_width.values(),
}, index=df.columns)
ans['break_acceptable'] = np.where(
ans.break_penalties == Breakability.ACCEPTABLE, ans.min_acceptable_width, ans.natural_width)
# DUH - this is min_acceptable_width
# ans['break_dates'] = np.where(ans.break_penalties==Breakability.DATE, ans.min_acceptable_width, ans.break_acceptable)
natural, acceptable, min_acceptable = ans.iloc[:, 3:].sum()
PADDING = 2 # per column
if self.config.table_width_mode == 'explicit':
# target width INCLUDES padding and column marks |
target_width = self.config.max_table_width - \
(PADDING + 1) * n_col - 1
logger.info(f'Col padding effect {self.config.max_table_width=}'
f' ==> {target_width=}')
elif self.config.table_width_mode == 'natural':
target_width = natural + (PADDING + 1) * n_col + 1
elif self.config.table_width_mode == 'breakable':
target_width = acceptable + (PADDING + 1) * n_col + 1
elif self.config.table_width_mode == 'minimum':
target_width = min_acceptable + (PADDING + 1) * n_col + 1
# extra space for the headers to relax, if useful
if self.config.table_width_header_adjust > 0:
max_extra = int(
self.config.table_width_header_adjust * target_width)
else:
max_extra = 0
if target_width > natural:
# everything gets its natural width
ans['recommended'] = ans['natural_width']
space = target_width - natural
logger.info('Space for NATURAL! Spare space = %s', space)
elif target_width > acceptable:
# strings wrap
ans['recommended'] = ans['break_acceptable']
# use up extra on the ACCEPTABLE cols
space = target_width - acceptable
logger.info(
'Using breaks acceptable (dates not wrapped), spare space = %s', space)
elif target_width > min_acceptable:
# strings and dates wrap
ans['recommended'] = ans['min_acceptable_width']
# use up extra on dates first, then strings
space = target_width - min_acceptable
logger.info(
'Breaking all breakable (incl dates), spare space = %s', space)
else:
# OK severely too small
ans['recommended'] = ans['min_acceptable_width']
logger.info(
'Desired width too small for pleasant formatting, table will be too wide.')
space = target_width - min_acceptable
input_df = None
if space >= 0:
# Allocate the excess ------------------------------
# Fancy col headings currently only for 1-d index
# TODO NOTE: use config.sparsify logic you have for index applied to df.T
# to sort the columns!!
if df.columns.nlevels == 1:
# Step 1: baseline comes in from code above
ans['raw_rec'] = ans['recommended']
# Step 2: get rid of intra-line breaks
if max_extra > 0:
adj, input_df = self.header_adjustment(
df, ans['recommended'], space, max_extra)
# create new col and populate per GPT
ans['header_tweak'] = pd.Series(adj)
else:
ans['header_tweak'] = 0
ans['recommended'] = ans['recommended'] + ans['header_tweak']
ans['natural_w_header'] = ans['recommended']
else:
# avoid a failure blow
ans['raw_rec'] = np.nan
ans['header_tweak'] = np.nan
ans['natural_w_header'] = np.nan
# Step 3: distribute remaining slack proportionally
remaining = target_width - ans['recommended'].sum()
if remaining > 0:
slack = ans['natural_width'] - ans['recommended']
total_slack = slack.clip(lower=0).sum()
if total_slack > 0:
fractions = slack.clip(lower=0) / total_slack
ans['recommended'] += np.floor(fractions *
remaining).astype(int)
ans['recommended'] = np.maximum(
ans['recommended'], ans['natural_w_header'])
# Ensure final constraint
try:
ans['recommended'] = ans['recommended'].astype(int)
except IntCastingNaNError:
print('getting error')
print(ans['recommended'])
ans['recommended'] = pd.to_numeric(
ans['recommended'], errors='coerce').fillna(0).astype(int)
logger.info("Raw rec: %s\tTweaks: %s\tActual: %s\tTarget: %s\tOver/(U): %s",
ans['raw_rec'].sum(),
ans['header_tweak'].sum(),
ans['recommended'].sum(),
target_width,
ans['recommended'].sum() - target_width
)
ans = ans[[
'alignment',
'break_penalties',
'breakability',
'natural_width',
'break_acceptable',
'min_acceptable_width',
'raw_rec',
'header_tweak',
'natural_w_header',
'recommended',
]]
# in all cases...
# need recommended to be > 0
ans['recommended'] = np.maximum(ans['recommended'], 1)
self.cache_set('column_width_df', ans)
# info about the header adjustment
self.cache_set('input_df', input_df)
return ans
@staticmethod
def header_adjustment(df, min_widths, space, max_extra):
"""
Fine-adjust heading for optimal config.spacing.
Return a dict with per-column recommended width adjustments to avoid
intra-word breaks and reduce overall header height.
Parameters:
df: DataFrame with 1-level string column names
min_widths: dict of column name -> minimal acceptable width
space: amount of space available to be allocated
max_extra: max extra characters to consider allocating per column
Returns:
dict: column -> additional width to allocate
"""
colnames = list(df.columns)
adjustments = {col: 0 for col in colnames}
num_lines = 0
def has_intra_word_break(text: str, width: int) -> bool:
"""
Determine if textwrap.wrap breaks any words in the given text.
Gemini - GPT code did not work, even after seveal iterations.
This is a nice approach to the problem.
Args:
text: The input string.
width: The maximum width for wrapping.
Returns:
True if any word is broken across lines, False otherwise.
"""
nonlocal num_lines
wrapped_lines = wrap(text, width=width)
num_lines = len(wrapped_lines)
original_words = text.split()
reconstructed_text_from_wrapped = " ".join(wrapped_lines)
reconstructed_words = reconstructed_text_from_wrapped.split()
# If the number of words differs, it means some words were split.
# This catches cases where a word might be split and then later re-joined
# due to subsequent wrapping logic, leading to a different number of words.
if len(original_words) != len(reconstructed_words):
return True
# Compare word by word. If any word from the original doesn't exactly match
# a word from the reconstructed list, it implies a split.
for i in range(len(original_words)):
if original_words[i] != reconstructed_words[i]:
return True
return False
# First pass: avoid ugly intraword breaks
# make dict of col -> longest word length
min_acceptable = {c: v for c, v in
zip(colnames, map(lambda x: max(len(i) for i in re.split(r'[ \-/]', x)), colnames))}
options = []
for col in colnames:
if not isinstance(col, str):
continue
base_width = min_widths[col]
if not has_intra_word_break(col, base_width):
options.append([col, 0, num_lines])
# nothing to be gained, move to next col
continue
extra0 = max(0, min_acceptable[col] - base_width)
if extra0 > max_extra:
# ok, can't flatten word because it is too long
extra0 = 0
elif extra0 == max_extra:
# go with that
adjustments[col] = max_extra
continue
# see if col can be flattened within max_extra chars, starting
# at extra0, which is enough to avoid intraword breaks
for extra in range(extra0, max_extra + 1):
if not has_intra_word_break(col, base_width + extra):
options.append([col, extra, num_lines])
if adjustments[col] == 0:
# take first, but compute rest...
adjustments[col] = extra
# temporary diagnostic DEBUG information - comment in prod
# from IPython.display import display
# config.debug = pd.Series([col, min_acceptable[col], base_width, has_intra_word_break(col, base_width), extra0, max_extra,
# wrap(col, base_width), extra],
# index=['col name', 'min acceptable', 'base_width (from data)', 'intra word break', 'extra0', 'max_extra', 'split', 'selected extra']).to_frame('Value')
# display(config.debug)
# make df[col name, amount of extra space for col, resulting number of lines]
# this is needed as input for the optimal heading function (next)
input_df = pd.DataFrame(options, columns=['col', 'extra', 'num_lines'])
# min amount to avoid intra work breaks
avoid_intra = input_df.groupby('col').min().extra.sum()
if avoid_intra >= space:
# that's all we can do
print("NO FURTHER IMPROVEMENTS")
else:
# can try for a better solution
sol = GT.optimal_heading(input_df, space)
adjustments.update(sol[1])
logger.info('best solution: %s', sol)
# global temp
# temp = input_df
return adjustments, input_df
@staticmethod
def optimal_heading(input_df: pd.DataFrame, total_es_budget: int) -> tuple[int, dict[str, int]]:
"""
Optimize extra config.spacing for best heading.
Finds the best way to allocate extra space to minimize max_lines in heading.
Gemini solution.
Args:
input_df: DataFrame with 'col', 'extra', 'num_lines'.
total_es_budget: The total extra space to allocate.
Returns:
A tuple: (min_max_lines, optimal_extra_allocation_per_column).
.. _table_layout_optimization:
Table Layout Optimization
=========================
This document describes the algorithm implemented in the :py:func:`find_best_layout` function, which aims to optimize the allocation of a fixed amount of extra space (`ES`) among table columns to minimize the overall table height (i.e., the maximum number of lines used by any single column).
Problem Statement
-----------------
Given a set of table columns, each with a known relationship between allocated "extra space" and the resulting "number of lines" it occupies when wrapped, and a total budget of extra space, the goal is to find an allocation of this extra space to each column such that the maximum number of lines among all columns is minimized.
For example, a column named "location category (float)" might take 3 lines with 0 extra space, but perhaps only 2 lines with 2 extra space, and 1 line with 5 extra space. The relationship is provided in a Pandas DataFrame with columns `col`, `extra`, and `num_lines`.
Algorithm: Binary Search on the Answer
-------------------------------------
The problem exhibits a monotonic property: if a table layout can be achieved with a maximum height of `X` lines, it can also be achieved with any maximum height `Y > X` lines (by simply using the same or more `extra` space). This property makes binary search on the *minimum possible maximum lines* an efficient solution.
The algorithm proceeds as follows:
1. **Preprocessing the Input Data:**
The input `pandas.DataFrame` is processed to create a convenient lookup structure. For each unique column, a sorted list of `(extra_space, num_lines)` tuples is created. This allows for quick identification of the minimum `extra` space required for a given `column` to fit within a `target_max_lines`.
.. code-block:: python
unique_cols = input_df['col'].unique().tolist()
col_extra_num_lines_options = {}
for col_name in unique_cols:
col_data = input_df[input_df['col'] == col_name].sort_values(by='extra')
col_extra_num_lines_options[col_name] = list(zip(col_data['extra'], col_data['num_lines']))
2. **Defining the Search Space (Bounds for `max_lines`):**
The binary search operates on the possible values for the `optimal_max_lines`.
* **Lower Bound (`L`):** The absolute minimum number of lines observed across all columns and all `extra` space options in the input data. This represents the theoretical minimum height a column could ever achieve.
* **Upper Bound (`R`):** The absolute maximum number of lines observed across all columns and all `extra` space options in the input data. This represents the worst-case height, which is always achievable.
.. code-block:: python
all_num_lines = input_df['num_lines'].unique()
if len(all_num_lines) == 0:
return 0, {} # Handle empty DataFrame case
L = all_num_lines.min()
R = all_num_lines.max()
3. **The `check(target_max_lines)` Function:**
This is the core helper function for the binary search. Given a `target_max_lines` (a candidate for the overall maximum height), it determines if it's *possible* to achieve this height for *all* columns simultaneously, without exceeding the `total_es_budget`.
For each column:
* It iterates through its `(extra_space, num_lines)` options (which are sorted by `extra_space`).
* It finds the *smallest* `extra_space` value for which the corresponding `num_lines` is less than or config.equal to `target_max_lines`.
* If no such `extra_space` is found for a column (meaning even with the maximum available `extra` for that column, it still exceeds `target_max_lines`), then `target_max_lines` is not achievable, and the function returns `False`.
* Otherwise, it sums up these minimum required `extra_space` values across all columns.
* If the total `extra_space` required is less than or config.equal to `total_es_budget`, the function returns `True` (meaning `target_max_lines` is achievable). Otherwise, it returns `False`.
.. code-block:: python
def check(target_max_lines: int) -> bool:
current_extra_needed = 0
for col_name in unique_cols:
min_extra_for_col = float('inf')
found_suitable_extra = False
for extra_val, num_lines_val in col_extra_num_lines_options[col_name]:
if num_lines_val <= target_max_lines:
min_extra_for_col = extra_val
found_suitable_extra = True
break # Found the minimum extra for this column
if not found_suitable_extra:
return False # This target_max_lines is too low for this column
current_extra_needed += min_extra_for_col
return current_extra_needed <= total_es_budget
4. **Binary Search Loop:**
The main binary search loop iteratively narrows down the range `[L, R]`.
* In each iteration, it calculates the `mid_max_lines = L + (R - L) // 2`.
* It then calls the `check(mid_max_lines)` function.
* If `check(mid_max_lines)` returns `True` (meaning `mid_max_lines` is achievable):
* `mid_max_lines` becomes a candidate for the `optimal_max_lines`. We record the current allocation that achieved it.
* We try to achieve an even smaller `max_lines` by setting `R = mid_max_lines - 1`.
* If `check(mid_max_lines)` returns `False` (meaning `mid_max_lines` is not achievable):
* We need to allow for more lines, so we set `L = mid_max_lines + 1`.
The loop continues until `L > R`, at which point `optimal_max_lines` will hold the smallest possible maximum height, and `best_allocation` will store the corresponding `extra_space` allocation for each column.
.. code-block:: python
optimal_max_lines = R
best_allocation = {}
while L <= R:
mid_max_lines = L + (R - L) // 2
# Recalculate allocation within the loop to store the specific 'extra' values
temp_current_extra_needed = 0
temp_current_allocation = {}
possible = True
for col_name in unique_cols:
min_extra_for_col = float('inf')
found_suitable_extra = False
for extra_val, num_lines_val in col_extra_num_lines_options[col_name]:
if num_lines_val <= mid_max_lines:
min_extra_for_col = extra_val
found_suitable_extra = True
break
if not found_suitable_extra:
possible = False
break
temp_current_extra_needed += min_extra_for_col
temp_current_allocation[col_name] = min_extra_for_col
if possible and temp_current_extra_needed <= total_es_budget:
optimal_max_lines = mid_max_lines
best_allocation = temp_current_allocation.copy()
R = mid_max_lines - 1
else:
L = mid_max_lines + 1
The function returns the `optimal_max_lines` and the `best_allocation` dictionary, mapping each column name to the minimal `extra_space` it needs to achieve that optimal height.
Why this approach is effective:
------------------------------
* **Optimal Solution:** The binary search guarantees finding the absolute minimum possible `max_lines` because it systematically explores the entire solution space.
* **Efficiency:** The `check` function runs in time proportional to the number of columns times the average number of `extra` options per column. The binary search itself performs `log(range_of_num_lines)` iterations. This makes the overall complexity efficient for typical table sizes.
* **Flexibility:** It does not assume any particular mathematical function relating `extra` space to `num_lines`. It works with arbitrary discrete relationships provided in the input DataFrame, as long as `num_lines` is non-increasing as `extra` increases (which is the natural expectation for this problem).
"""
# Pre-processing
unique_cols = input_df['col'].unique().tolist()
col_extra_num_lines_options = {}
for col_name in unique_cols:
col_data = input_df[input_df['col'] ==
col_name].sort_values(by='extra')
col_extra_num_lines_options[col_name] = list(
zip(col_data['extra'], col_data['num_lines']))
def check(target_max_lines: int) -> bool:
current_extra_needed = 0
for col_name in unique_cols:
min_extra_for_col = float('inf')
found_suitable_extra = False
for extra_val, num_lines_val in col_extra_num_lines_options[col_name]:
if num_lines_val <= target_max_lines:
min_extra_for_col = extra_val
found_suitable_extra = True
break
if not found_suitable_extra:
return False
current_extra_needed += min_extra_for_col
return current_extra_needed <= total_es_budget
all_num_lines = input_df['num_lines'].unique()
# Corrected line: Check length of the numpy array
if len(all_num_lines) == 0:
return 0, {}
L = all_num_lines.min()
R = all_num_lines.max()
optimal_max_lines = R
best_allocation = {}
while L <= R:
mid_max_lines = L + (R - L) // 2
temp_current_extra_needed = 0
temp_current_allocation = {}
possible = True
for col_name in unique_cols:
min_extra_for_col = float('inf')
found_suitable_extra = False
for extra_val, num_lines_val in col_extra_num_lines_options[col_name]:
if num_lines_val <= mid_max_lines:
min_extra_for_col = extra_val
found_suitable_extra = True
break
if not found_suitable_extra:
possible = False
break
temp_current_extra_needed += min_extra_for_col
temp_current_allocation[col_name] = min_extra_for_col
if possible and temp_current_extra_needed <= total_es_budget:
optimal_max_lines = mid_max_lines
best_allocation = temp_current_allocation.copy()
R = mid_max_lines - 1
else:
L = mid_max_lines + 1
return optimal_max_lines, best_allocation
@staticmethod
def estimate_column_widths(df, target_width, nc_index, scale, equal=False):
"""
Estimate sensible column widths for the dataframe [in what units?]
Internal variables:
mxmn affects alignment: are all columns the same width?
TODO: de-TeX-ification will mess up how the tex table is printed...
but one rarely looks at that.
:param df:
:param nc_index: number of columns in the index...these are not counted as "data columns"
:param config.equal: if True, try to make all data columns the same width (hint can be rejected)
:return:
colw affects how the tex is printed to ensure it "looks neat" (actual width of data elements)
tabs affects the actual output
"""
# this
# tabs from _tabs, an estimate column widths, determines the size of the table columns as displayed
# print(f'{nc_index=}, {scale=}, {config.equal=}')
colw = dict.fromkeys(df.columns, 0)
headw = dict.fromkeys(df.columns, 0)
tabs = []
mxmn = {}
if df.empty:
return colw, tabs
nl = nc_index
for i, c in enumerate(df.columns):
# figure width of the column labels; if index c= str, if MI then c = tuple
# cw is the width of the column header/title
if type(c) == str:
if i < nl:
cw = GT.text_display_len(c)
else:
# for data columns look at words rather than whole phrase
cw = max(map(GT.text_display_len, c.split(' ')))
# logger.info(f'leng col = {len(c)}, longest word = {cw}')
else:
# column name could be float etc. or if multi index a tuple
try:
if isinstance(c, tuple):
# multiindex: join and split into words and take length of each word
words = ' '.join(c).split(' ')
cw = max(
map(lambda x: GT.text_display_len(str(x)), words))
else:
cw = max(map(lambda x: GT.text_display_len(str(x)), c))
# print(f'{c}: {cw=} no error')
except TypeError:
# not a MI, float or something
cw = GT.text_display_len(str(c))
# print(f'{c}: {cw=} WITH error')
headw[c] = cw
# now figure the width of the elements in the column
# mxmn is used to determine whether to center the column (if all the same size)
if df.dtypes.iloc[i] == object:
# weirdness here were some objects actually contain floats, str evaluates to NaN
# and picks up width zero
try:
lens = df.iloc[:, i].map(
lambda x: GT.text_display_len(str(x)))
colw[c] = lens.max()
mxmn[c] = (lens.max(), lens.min())
except Exception as e:
logger.error(
f'{c} error {e} DO SOMETHING ABOUT THIS...if it never occurs dont need the if')
colw[c] = df[c].str.len().max()
mxmn[c] = (df[c].str.len().max(), df[c].str.len().min())
else:
lens = df.iloc[:, i].map(lambda x: GT.text_display_len(str(x)))
colw[c] = lens.max()
mxmn[c] = (lens.max(), lens.min())
# print(f'{headw[c]=}, {colw[c]=}, {mxmn[c]=}, {c=}')
# now know all column widths...decide what to do
# are all the data columns about the same width?
data_cols = np.array([colw[k] for k in df.columns[nl:]])
same_size = (data_cols.std() <= 0.1 * data_cols.mean())
# print(f'same size test requires {data_cols.std()} <= {0.1 * data_cols.mean()}')
common_size = 0
if same_size:
common_size = int(data_cols.mean() + data_cols.std())
logger.info(f'data cols appear same size = {common_size}')
# print(f'data cols appear same size = {common_size}')
for i, c in enumerate(df.columns):
if i < nl or not same_size:
# index columns
tabs.append(int(max(colw[c], headw[c])))
else:
# data all seems about the same width
tabs.append(common_size)
logger.info(f'Determined tab config.spacing: {tabs}')
if equal:
# see if config.equal widths makes sense
dt = tabs[nl:]
if max(dt) / sum(dt) < 4 / 3:
tabs = tabs[:nl] + [max(dt)] * (len(tabs) - nl)
logger.info(f'Taking config.equal width hint: {tabs}')
# print(f'Taking config.equal width hint: {tabs}')
else:
logger.info(f'Rejecting config.equal width hint')
# print(f'Rejecting config.equal width hint')
# look to rescale, shoot for width of 150 on 100 scale basis
data_width = sum(tabs[nl:])
index_width = sum(tabs[:nl])
target_width = target_width * scale - index_width
if data_width and data_width / target_width < 0.9:
# don't rescale above 1:1 - don't want too large
rescale = min(1 / scale, target_width / data_width)
tabs = [w if i < nl else w * rescale for i, w in enumerate(tabs)]
logger.info(f'Rescale {rescale} applied; tabs = {tabs}')
# print(f'Rescale {rescale} applied; tabs = {tabs}')
# print(f'{colw.values()=}\n{tabs=}')
return colw, tabs
@staticmethod
def text_display_len(s: str) -> int:
"""Estimate text display length of a string allowing for TeX constructs."""
# note you DO WANT SPACES! So, no strip applied ever.
if s.find('$') < 0:
return len(s)
parts = re.split(r'(\$\$.*?\$\$)|(\$.*?\$)', s)
total = 0
for part in parts:
if part is None:
continue
if part.startswith('$$') and part.endswith('$$'):
total += GT._estimate_math_width(part[2:-2])
elif part.startswith('$') and part.endswith('$'):
total += GT._estimate_math_width(part[1:-1])
else:
total += len(part)
return total
@staticmethod
def _estimate_math_width(tex: str) -> int:
tokens = re.findall(r'\\[a-zA-Z]+|[a-zA-Z0-9]|.', tex)
width = 0
for tok in tokens:
if tok.startswith('\\'):
name = tok[1:]
if name in GT.TEX_SIMPLE_GLYPHS:
width += 1
elif name in GT.TEX_WIDE:
width += 3
elif name in GT.TEX_SPACING:
width += 1
else:
width += 2 # unknown control sequences
elif tok in '{}^_':
continue # grouping, sub/superscripts: ignore
else:
width += 1
return width
def make_style(self, tabs):
"""Write out custom CSS for the table."""
if self.config.debug:
head_tb = '#0ff'
body_b = '#f0f'
h0 = '#f00'
h1 = '#b00'
h2 = '#900'
bh0 = '#f00'
bh1 = '#b00'
v0 = '#0f0'
v1 = '#0a0'
v2 = '#090'
else:
head_tb = '#000'
body_b = '#000'
h0 = '#000'
h1 = '#000'
h2 = '#000'
bh0 = '#000'
bh1 = '#000'
v0 = '#000'
v1 = '#000'
v2 = '#000'
table_hrule = self.config.table_hrule_width
table_vrule = self.config.table_vrule_width
# for local use
padt, padr, padb, padl = self.padt, self.padr, self.padb, self.padl
style = [f'''
<style>
#{self.df_id} {{
border-collapse: collapse;
font-family: "Roboto", "Open Sans Condensed", "Arial", 'Segoe UI', sans-serif;
font-size: {self.config.font_body}em;
width: auto;
/* tb and lr
width: fit-content; */
margin: 10px auto;
border: none;
overflow: auto;
margin-left: auto;
margin-right: auto;
}}
/* center tables in quarto context
.greater-table {{
display: block;
text-align: center;
}}
.greater-table > table {{
display: inline-table;
}} */
/* tag formats */
#{self.df_id} caption {{
padding: {2 * padt}px {padr}px {padb}px {padl}px;
font-size: {self.config.font_caption}em;
text-align: {self.config.caption_align};
font-weight: normal;
caption-side: top;
}}
#{self.df_id} thead {{
/* top and bottom of header */
border-top: {table_hrule}px solid {head_tb};
border-bottom: {table_hrule}px solid {head_tb};
font-size: {self.config.font_head}em;
}}
#{self.df_id} tbody {{
/* bottom of body */
border-bottom: {table_hrule}px solid {body_b};
}}
#{self.df_id} th {{
vertical-align: bottom;
padding: {2 * padt}px {padr}px {2 * padb}px {padl}px;
}}
#{self.df_id} td {{
/* top, right, bottom left cell padding */
padding: {padt}px {padr}px {padb}px {padl}px;
vertical-align: top;
}}
/* class overrides */
#{self.df_id} .grt-hrule-0 {{
border-top: {self.config.hrule_widths[0]}px solid {h0};
}}
#{self.df_id} .grt-hrule-1 {{
border-top: {self.config.hrule_widths[1]}px solid {h1};
}}
#{self.df_id} .grt-hrule-2 {{
border-top: {self.config.hrule_widths[2]}px solid {h2};
}}
/* for the header, there if you have v lines you want h lines
hence use config.vrule_widths */
#{self.df_id} .grt-bhrule-0 {{
border-bottom: {self.config.vrule_widths[0]}px solid {bh0};
}}
#{self.df_id} .grt-bhrule-1 {{
border-bottom: {self.config.vrule_widths[1]}px solid {bh1};
}}
#{self.df_id} .grt-vrule-index {{
border-left: {table_vrule}px solid {v0};
}}
#{self.df_id} .grt-vrule-0 {{
border-left: {self.config.vrule_widths[0]}px solid {v0};
}}
#{self.df_id} .grt-vrule-1 {{
border-left: {self.config.vrule_widths[1]}px solid {v1};
}}
#{self.df_id} .grt-vrule-2 {{
border-left: {self.config.vrule_widths[2]}px solid {v2};
}}
#{self.df_id} .grt-left {{
text-align: left;
}}
#{self.df_id} .grt-center {{
text-align: center;
}}
#{self.df_id} .grt-right {{
text-align: right;
font-variant-numeric: tabular-nums;
}}
#{self.df_id} .grt-head {{
font-family: "Times New Roman", 'Courier New';
font-size: {self.config.font_head}em;
}}
#{self.df_id} .grt-bold {{
font-weight: bold;
}}
''']
for i, w in enumerate(tabs):
style.append(f' #{self.df_id} .grt-c-{i} {{ width: {w}em; }}')
style.append('</style>')
logger.info('CREATED CSS')
return '\n'.join(style)
def make_html(self):
"""Convert a pandas DataFrame to an HTML table."""
index_name_to_level = dict(
zip(self.raw_df.index.names, range(self.nindex)))
index_change_level = self.index_change_level.map(index_name_to_level)
# this is easier and computed in the init
column_change_level = self.column_change_level
# Start table
html = [f'<table id="{self.df_id}">']
if self.label != "":
pass
# TODO put in achor tag somehow!!
if self.caption != '':
html.append(f'<caption>{self.caption}</caption>')
# Process header: allow_duplicates=True means can create cols with the same name
bit = self.df.T.reset_index(drop=False, allow_duplicates=True)
idx_header = bit.iloc[:self.nindex, :self.ncolumns]
columns = bit.iloc[self.nindex:, :self.ncolumns]
colw, tabs = GT.estimate_column_widths(
self.df, self.config.max_table_width, nc_index=self.nindex, scale=1, equal=self.config.equal)
if self.config.debug:
print(f'Make html Input {self.tabs=}\nComputed {tabs=}')
if self.tabs is not None:
if len(tabs) == len(self.tabs):
tabs = self.tabs
elif len(self.tabs) == 1:
tabs = self.tabs * len(tabs)
else:
logger.error(
f'{self.tabs=} must be None, a single number, or a list of numbers of the correct length. Ignoring.')
# print('HTML ' + ', '.join([f'{c:,.2f}' for c in tabs]))
# set column widths; tabs returns lengths of strings in each column
# for proportional fonts, average char is 0.4 to 0.5 em but numbers with
# tabular-nums are fixed 0.5, so use that
# scale: want tables about 150-200 char wide, 1 char = 0.5 px size of font
# so what 75-100 em wide in total
# add the padding
# TODO FONT SIZE
# /4 works well for the tests (handles dates) but seems a bit illogical...
# guessing font size...
tabs = np.array(tabs) + (self.padl + self.padr) / 12
# em_per_char = 0.5; true exactly for tabular-nums
em_per_char = 0.6
tabs = tabs * em_per_char
# this gets stripped out by quarto, so make part of style
html.append('<colgroup>')
for w in tabs:
html.append(f'<col style="width: {w * em_per_char}em;">')
html.append('</colgroup>')
# TODO Add header aligners
# this is TRANSPOSED!!
if self.config.sparsify_columns:
html.append("<thead>")
for i in range(self.ncolumns):
# one per row of columns m index, usually only 1
html.append("<tr>")
if self.show_index:
for j, r in enumerate(idx_header.iloc[:, i]):
# columns one per level of index
html.append(f'<th class="grt-left">{r}</th>')
# if not for col span issue you could just to this:
# for j in range(self.ncols):
# hrule = f'grt-bhrule-{i}' if i < self.ncolumns - 1 else ''
# if j == 0:
# # start with the first column come what may
# vrule = f'grt-vrule-index'
# elif j >= self.column_change_level[i]:
# vrule = f'grt-vrule-{column_change_level[cum_col]}'
# else:
# vrule = ''
# html.append(f'<th colspan="{colspan}" class="grt-center {hrule} {vrule}">{nm}</th>')
# here, the groupby needs to consider all levels at and above i
# this concats all the levels
# need :i+1 to get down to the ith level
cum_col = 0 # keep track of where we are up to
for j, (nm, g) in enumerate(groupby(columns.iloc[:, :i + 1].
apply(lambda x: ':::'.join(str(i) for i in x), axis=1))):
# ::: needs to be something that does not appear in the col names
# need to combine for groupby but be able to split off the last level
# picks off the name of the bottom level
nm = nm.split(':::')[-1]
hrule = f'grt-bhrule-{i}' if i < self.ncolumns - 1 else ''
colspan = sum(1 for _ in g)
if 0 < j:
vrule = f'grt-vrule-{column_change_level[cum_col]}'
elif j == 0 and self.show_index:
# start with the first column if showing index
vrule = f'grt-vrule-index'
else:
vrule = ''
if j == 0 and not self.show_index:
# first column, no index, left align label
html.append(
f'<th colspan="{colspan}" class="grt-left {hrule} {vrule}">{nm}</th>')
else:
html.append(
f'<th colspan="{colspan}" class="grt-center {hrule} {vrule}">{nm}</th>')
cum_col += colspan
html.append("</tr>")
html.append("</thead>")
else:
html.append("<thead>")
for i in range(self.ncolumns):
# one per row of columns m index, usually only 1
html.append("<tr>")
if self.show_index:
for j, r in enumerate(idx_header.iloc[:, i]):
# columns one per level of index
html.append(f'<th class="grt-left">{r}</th>')
for j, r in enumerate(columns.iloc[:, i]):
# one per column of dataframe
# figure how high up mindex the vrules go
# all headings get hrules, it's the vrules that are tricky
hrule = f'grt-bhrule-{i}' if i < self.ncolumns - 1 else ''
if 0 < j < self.ncols and i >= column_change_level[j]:
vrule = f'grt-vrule-{column_change_level[j]}'
elif j == 0 and self.show_index:
# start with the first column come what may
vrule = f'grt-vrule-index'
else:
vrule = ''
html.append(
f'<th class="grt-center {hrule} {vrule}">{r}</th>')
html.append("</tr>")
html.append("</thead>")
bold_idx = 'grt-bold' if self.config.font_bold_index else ''
html.append("<tbody>")
for i, (n, r) in enumerate(self.df.iterrows()):
# one per row of dataframe
html.append("<tr>")
hrule = ''
if self.show_index:
for j, c in enumerate(r.iloc[:self.nindex]):
# dx = data in index
# if this is the level that changes for this row
# will use a top rule hence omit i = 0 which already has an hrule
# appears in the index change level. But if it DOES NOT appear then
# it isn't a change level so no rule required
if i > 0 and hrule == '' and i in index_change_level and j == index_change_level[i]:
hrule = f'grt-hrule-{j}'
# html.append(f'<td class="grt-dx-r-{i} grt-dx-c-{j} {self.df_aligners[j]} {hrule}">{c}</td>')
col_id = f'grt-c-{j}'
html.append(
f'<td class="{col_id} {bold_idx} {self.df_aligners[j]} {hrule}">{c}</td>')
for j, c in enumerate(r.iloc[self.nindex:]):
# first col left handled by index/body divider
if 0 < j < self.ncols:
vrule = f'grt-vrule-{column_change_level[j]}'
elif j == 0 and self.show_index:
# start with the first column come what may
vrule = f'grt-vrule-index'
else:
vrule = ''
# html.append(f'<td class="grt-data-r-{i} grt-data-c-{j} {self.df_aligners[j+self.nindex]} {hrule} {vrule}">{c}</td>')
col_id = f'grt-c-{j+self.nindex}'
html.append(
f'<td class="{col_id} {self.df_aligners[j+self.nindex]} {hrule} {vrule}">{c}</td>')
html.append("</tr>")
html.append("</tbody>")
text = '\n'.join(html)
self.df_html = GT.clean_html_tex(text)
logger.info('CREATED HTML')
self.df_style = self.make_style(tabs)
return self.df_html
def clean_style(self, soup):
"""Minify CSS inside <style> blocks and remove /* ... */ comments."""
if not self.config.debug:
for style_tag in soup.find_all("style"):
if style_tag.string:
# Remove CSS comments
cleaned_css = re.sub(
r'/\*.*?\*/', '', style_tag.string, flags=re.DOTALL)
# Minify whitespace
# cleaned_css = re.sub(r'\s+', ' ', cleaned_css).strip()
style_tag.string.replace_with(cleaned_css)
return soup
@property
def html(self):
if self._clean_html == '':
if self.df_html == '':
# makes style and html (need tabs)
self.df_html = self.make_html()
code = ["<div class='greater-table'>",
self.df_style,
self.df_html,
"</div>"]
soup = BeautifulSoup('\n'.join(code), 'html.parser')
soup = self.clean_style(soup)
self._clean_html = str(soup) # .prettify() -> too many newlines
logger.info('CREATED HTML')
return self._clean_html
def make_tikz(self,
column_sep=4 / 8, # was 3/8
row_sep=1 / 8,
container_env='table',
extra_defs='',
hrule=None,
vrule=None,
post_process='',
label='',
latex=None,
sparsify=1):
"""
Write DataFrame to custom tikz matrix to allow greater control of
formatting and insertion of horizontal and vertical divider lines
Estimates tabs from text width of fields (not so great if includes
a lot of TeX macros) with a manual override available. Tabs gives
the widths of each field in em (width of M)
Standard row height = 1.5em seems to work - set in meta.
first and last thick rules
others below (Python, zero-based) row number, excluding title row
keyword arguments : value (no newlines in value) escape back slashes!
``#keyword...`` rows ignored
passed in as a string to facilitate using them with %%pmt?
**Rules**
* hrule at i means below row i of the table. (1-based) Top, bottom and
below index lines are inserted automatically. Top and bottom lines
are thicker.
* vrule at i means to the left of table column i (1-based); there will
never be a rule to the far right...it looks plebby; remember you must
include the index columns!
config.sparsify number of cols of multi index to config.sparsify
Issue: column with floats and spaces or missing causes problems (VaR,
TVaR, EPD, mean and CV table)
From great.pres_maker.df_to_tikz
keyword args:
scale picks up self.config.tikz_scale; scale applied to whole
table - default 0.717
height row height, rec. 1 (em)
column_sep col sep in em
row_sep row sep in em
container_env table, figure or sidewaysfigure
color color for text boxes (helps config.debugging)
extra_defs TeX defintions and commands put at top of table,
e.g., \\centering
lines lines below these rows, -1 for next to last row
etc.; list of ints
post_process e.g., non-line commands put at bottom of table
latex arguments after \begin{table}[latex]
caption text for caption
Previous version see great.pres_maker
Original version see: C:\\S\\TELOS\\CAS\\AR_Min_Bias\\cvs_to_md.py
:param column_sep:
:param row_sep:
:param figure:
:param extra_defs:
:param post_process:
:param label:
:return:
"""
# local variable - with all formatters already applied
df = self.apply_formatters(self.raw_df.copy(), mode='raw')
caption = self.caption
label = self.label
# prepare label and caption
if label == '':
lt = ''
label = ''
else:
lt = label
label = f'\\label{{{label}}}'
if caption == '':
if lt != '':
logger.info(
f'You have a label but no caption; the label {label} will be ignored.')
caption = '% caption placeholder'
else:
caption = f'\\caption{{{self.caption}}}\n{label}'
if not df.columns.is_unique:
# possible index/body column interaction
raise ValueError('tikz routine requires unique column names')
# {extra_defs}
# centering handled by quarto
header = """
\\begin{{{container_env}}}{latex}
{caption}
\\centering{{
\\begin{{tikzpicture}}[
auto,
transform shape,
nosep/.style={{inner sep=0}},
table/.style={{
matrix of nodes,
row sep={row_sep}em,
column sep={column_sep}em,
nodes in empty cells,
nodes={{rectangle, scale={scale}, text badly ragged {debug}}},
"""
# put draw=blue!10 or so in nodes to see the node
footer = """
{post_process}
\\end{{tikzpicture}}
}} % close centering
\\end{{{container_env}}}
"""
# always a good idea to do this...need to deal with underscores, %
# and it handles index types that are not strings
df = GT.clean_index(df)
if not np.all([i == 'object' for i in df.dtypes]) and not df.empty:
logger.warning('cols of df not all objects (expect all obs at this '
'point): ', df.dtypes, sep='\n')
# make sure percents are escaped, but not if already escaped
df = df.replace(r"(?<!\\)%", r"\%", regex=True)
# pres_maker.df_to_tikz code line 1931
if self.show_index:
nc_index = df.index.nlevels
with warnings.catch_warnings():
warnings.simplefilter(
"ignore", category=pd.errors.PerformanceWarning)
df = df.reset_index(
drop=False, col_level=df.columns.nlevels - 1)
if sparsify:
if hrule is None:
hrule = set()
for i in range(sparsify):
# TODO update to new sparsify!!
df.iloc[:, i], rules = GT.sparsify_old(df.iloc[:, i])
# don't want lines everywhere
if len(rules) < len(df) - 1:
hrule = set(hrule).union(rules)
else:
nc_index = 0
if vrule is None:
vrule = set()
else:
vrule = set(vrule)
# to the left of... +1
vrule.add(nc_index + 1)
nr_columns = df.columns.nlevels
logger.info(
f'rows in columns {nr_columns}, columns in index {nc_index}')
# internal TeX code (same as HTML code)
matrix_name = self.df_id
# note this happens AFTER you have reset the index...need to pass
# number of index columns
# have also converted everything to formatted strings
# estimate... originally called guess_column_widths, with more parameters
colw, tabs = GT.estimate_column_widths(df, self.config.max_table_width, nc_index=nc_index, scale=self.config.tikz_scale, equal=self.config.equal) # noqa
if self.config.debug:
print(f'Make TikZ Input {self.tabs=}\nComputed {tabs=}')
if self.tabs is not None:
if len(tabs) == len(self.tabs):
tabs = self.tabs
elif len(self.tabs) == 1:
tabs = self.tabs * len(tabs)
else:
logger.error(
f'{self.tabs=} must be None, a single number, or a list of numbers of the correct length. Ignoring.')
# print('TIKZ ' + ', '.join([f'{c:,.2f}' for c in tabs]))
# print(f'TIKZ {colw=}, {tabs=}')
logger.info(f'tabs: {tabs}')
logger.info(f'colw: {colw}')
# alignment dictionaries - these are still used below
ad = {'l': 'left', 'r': 'right', 'c': 'center'}
ad2 = {'l': '<', 'r': '>', 'c': '^'}
# use df_aligners, at this point the index has been reset
align = []
for n, i in zip(df.columns, self.df_aligners):
if i == 'grt-left':
align.append('l')
elif i == 'grt-right':
align.append('r')
elif i == 'grt-center':
align.append('c')
else:
align.append('l')
# start writing
sio = StringIO()
if latex is None:
latex = ''
else:
latex = f'[{latex}]'
debug = ''
if self.config.debug:
# color all boxes
debug = ', draw=blue!10'
else:
debug = ''
sio.write(header.format(container_env=container_env,
caption=caption,
extra_defs=extra_defs,
scale=self.config.tikz_scale,
column_sep=column_sep,
row_sep=row_sep,
latex=latex,
debug=debug))
# table header
# title rows, start with the empty spacer row
i = 1
sio.write(
f'\trow {i}/.style={{nodes={{text=black, anchor=north, inner ysep=0, text height=0, text depth=0}}}},\n')
for i in range(2, nr_columns + 2):
sio.write(
f'\trow {i}/.style={{nodes={{text=black, anchor=south, inner ysep=.2em, minimum height=1.3em, font=\\bfseries}}}},\n')
# write column spec
for i, w, al in zip(range(1, len(align) + 1), tabs, align):
# average char is only 0.48 of M
# https://en.wikipedia.org/wiki/Em_(gtypography)
if i == 1:
# first column sets row height for entire row
sio.write(f'\tcolumn {i:>2d}/.style={{'
f'nodes={{align={ad[al]:<6s}}}, '
'text height=0.9em, text depth=0.2em, '
f'inner xsep={column_sep}em, inner ysep=0, '
f'text width={max(2, 0.6 * w):.2f}em}},\n')
else:
sio.write(f'\tcolumn {i:>2d}/.style={{'
f'nodes={{align={ad[al]:<6s}}}, nosep, text width={max(2, 0.6 * w):.2f}em}},\n')
# extra col to right which enforces row height
sio.write(
f'\tcolumn {i+1:>2d}/.style={{text height=0.9em, text depth=0.2em, nosep, text width=0em}}')
sio.write('\t}]\n')
sio.write("\\matrix ({matrix_name}) [table, ampersand replacement=\\&]{{\n".format(
matrix_name=matrix_name))
# body of table, starting with the column headers
# spacer row
nl = ''
for cn, al in zip(df.columns, align):
s = f'{nl} {{cell:{ad2[al]}{colw[cn]}s}} '
nl = '\\&'
sio.write(s.format(cell=' '))
# include the blank extra last column
sio.write('\\& \\\\\n')
# write header rows (again, issues with multi index)
mi_vrules = {}
sparse_columns = {}
if isinstance(df.columns, pd.MultiIndex):
for lvl in range(len(df.columns.levels)):
nl = ''
sparse_columns[lvl], mi_vrules[lvl] = GT.sparsify_mi(df.columns.get_level_values(lvl),
lvl == len(df.columns.levels) - 1)
for cn, c, al in zip(df.columns, sparse_columns[lvl], align):
# c = wfloat_format(c)
s = f'{nl} {{cell:{ad2[al]}{colw[cn]}s}} '
nl = '\\&'
sio.write(s.format(cell=c + '\\grtspacer'))
# include the blank extra last column
sio.write('\\& \\\\\n')
else:
nl = ''
for c, al in zip(df.columns, align):
# c = wfloat_format(c)
s = f'{nl} {{cell:{ad2[al]}{colw[c]}s}} '
nl = '\\&'
sio.write(s.format(cell=c + '\\grtspacer'))
sio.write('\\& \\\\\n')
# write table entries
for idx, row in df.iterrows():
nl = ''
for c, cell, al in zip(df.columns, row, align):
# cell = wfloat_format(cell)
s = f'{nl} {{cell:{ad2[al]}{colw[c]}s}} '
nl = '\\&'
sio.write(s.format(cell=cell))
# if c=='p':
# print('COLp', cell, type(cell), s, s.format(cell=cell))
sio.write('\\& \\\\\n')
sio.write(f'}};\n\n')
# decorations and post processing - horizontal and vertical lines
nr, nc = df.shape
# add for the index and the last row plus 1 for the added spacer row at the top
nr += nr_columns + 1
# always include top and bottom
# you input a table row number and get a line below it; it is implemented as a line ABOVE the next row
# function to convert row numbers to TeX table format (edge case on last row -1 is nr and is caught, -2
# is below second to last row = above last row)
# shift down extra 1 for the spacer row at the top
def python_2_tex(x): return x + nr_columns + \
2 if x >= 0 else nr + x + 3
tb_rules = [nr_columns + 1, python_2_tex(-1)]
if hrule:
hrule = set(map(python_2_tex, hrule)).union(tb_rules)
else:
hrule = list(tb_rules)
logger.debug(f'hlines: {hrule}')
# why
yshift = row_sep / 2
xshift = -column_sep / 2
descender_proportion = 0.25
# top rule is special
ls = 'thick'
ln = 1
sio.write(
f'\\path[draw, {ls}] ({matrix_name}-{ln}-1.south west) -- ({matrix_name}-{ln}-{nc+1}.south east);\n')
for ln in hrule:
ls = 'thick' if ln == nr + nr_columns + \
1 else ('semithick' if ln == 1 + nr_columns else 'very thin')
if ln < nr:
# line above TeX row ln+1 that exists
sio.write(f'\\path[draw, {ls}] ([yshift={-yshift}em]{matrix_name}-{ln}-1.south west) -- '
f'([yshift={-yshift}em]{matrix_name}-{ln}-{nc+1}.south east);\n')
else:
# line above row below bottom = line below last row
# descenders are 200 to 300 below baseline
ln = nr
sio.write(f'\\path[draw, thick] ([yshift={-descender_proportion-yshift}em]{matrix_name}-{ln}-1.base west) -- '
f'([yshift={-descender_proportion-yshift}em]{matrix_name}-{ln}-{nc+1}.base east);\n')
# if multi index put in lines within the index TODO make this better!
if nr_columns > 1:
for ln in range(2, nr_columns + 1):
sio.write(f'\\path[draw, very thin] ([xshift={xshift}em, yshift={-yshift}em]'
f'{matrix_name}-{ln}-{nc_index+1}.south west) -- '
f'([yshift={-yshift}em]{matrix_name}-{ln}-{nc+1}.south east);\n')
written = set(range(1, nc_index + 1))
if vrule and self.show_index:
# to left of col, 1 based, includes index
# write these first
# TODO fix madness vrule is to the left, mi_vrules are to the right...
ls = 'very thin'
for cn in vrule:
if cn not in written:
sio.write(f'\\path[draw, {ls}] ([xshift={xshift}em]{matrix_name}-1-{cn}.south west) -- '
f'([yshift={-descender_proportion-yshift}em, xshift={xshift}em]{matrix_name}-{nr}-{cn}.base west);\n')
written.add(cn - 1)
if len(mi_vrules) > 0:
logger.debug(
f'Generated vlines {mi_vrules}; already written {written}')
# vertical rules for the multi index
# these go to the RIGHT of the relevant column and reflect the index columns already
# mi_vrules = {level of index: [list of vrule columns]
# written keeps track of which vrules have been done already; start by cutting out the index columns
ls = 'ultra thin'
for k, cols in mi_vrules.items():
# don't write the lowest level
if k == len(mi_vrules) - 1:
break
for cn in cols:
if cn in written:
pass
else:
written.add(cn)
top = k + 1
if top == 0:
sio.write(f'\\path[draw, {ls}] ([xshift={-xshift}em]{matrix_name}-{top}-{cn}.south east) -- '
f'([yshift={-descender_proportion-yshift}em, xshift={-xshift}em]{matrix_name}-{nr}-{cn}.base east);\n')
else:
sio.write(f'\\path[draw, {ls}] ([xshift={-xshift}em, yshift={-yshift}em]{matrix_name}-{top}-{cn}.south east) -- '
f'([yshift={-descender_proportion-yshift}em, xshift={-xshift}em]{matrix_name}-{nr}-{cn}.base east);\n')
sio.write(footer.format(container_env=container_env,
post_process=post_process))
return sio.getvalue()
@staticmethod
def sparsify(df, cs):
out = df.copy()
for i, c in enumerate(cs):
mask = df[cs[:i + 1]].ne(df[cs[:i + 1]].shift()).any(axis=1)
out.loc[~mask, c] = ''
return out
@staticmethod
def sparsify_old(col):
"""
sparsify col values, col a pd.Series or dict, with items and accessor
column results from a reset_index so has index 0,1,2... this is relied upon.
TODO: this doesn't work if there is a change in a higher level but not this level
"""
# fix error with empty dfs
new_col = col.copy()
rules = []
if col.empty:
return new_col, rules
last = col[0]
for k, v in col[1:].items():
if v == last:
new_col[k] = ''
else:
last = v
rules.append(k - 1)
new_col[k] = v
return new_col, rules
@staticmethod
def sparsify_mi(mi, bottom_level=False):
"""
as above for a multi index level, without the benefit of the index...
really all should use this function
:param mi:
:param bottom_level: for the lowest level ... all values repeated, no sparsificaiton
:return:
"""
last = mi[0]
new_col = list(mi)
rules = []
for k, v in enumerate(new_col[1:]):
if v == last and not bottom_level:
new_col[k + 1] = ''
else:
last = v
rules.append(k + 1)
new_col[k + 1] = v
return new_col, rules
@staticmethod
def clean_name(n):
"""
Escape underscores for using a name in a DataFrame index
and converts to a string. Also escape %.
Called by Tikz routines.
:param n: input name, str
:return:
"""
try:
if type(n) == str:
# quote underscores that are not in dollars
return '$'.join((i if n % 2 else i.replace('_', '\\_').replace('%', '\\%')
for n, i in enumerate(n.split('$'))))
else:
# can't contain an underscore!
return str(n)
except:
return str(n)
# @staticmethod
# def clean_underscores(s):
# """
# check s for unescaped _s
# returns true if all _ escaped else false
# :param s:
# :return:
# """
# return np.all([s[x.start() - 1] == '\\' for x in re.finditer('_', s)])
@staticmethod
def clean_html_tex(text):
r"""
Clean TeX entries in HTML: $ -> \( and \) and $$ to \[ \].
Apply after all other HTML rendering steps. HTML rendering only.
"""
text = re.sub(r'\$\$(.*?)\$\$', r'\\[\1\\]', text, flags=re.DOTALL)
# Convert inline math: $...$ → \(...\)
text = re.sub(r'(?<!\$)\$(.*?)(?<!\\)\$(?!\$)', r'\\(\1\\)', text)
return text
@staticmethod
def clean_index(df):
"""
escape _ for columns and index, being careful about subscripts
in TeX formulas.
:param df:
:return:
"""
return df.rename(index=GT.clean_name, columns=GT.clean_name)
@staticmethod
def md_to_df(txt):
"""Convert markdown text string table to DataFrame."""
# extract table and optional caption part
table, caption = GT.parse_markdown_table_and_caption(txt)
m = re.search(r'\{#(tbl[:a-zA-Z0-9_-]+)\}', caption)
if m:
label = m.group(1)
if label != '':
# remove from caption
caption = caption.replace(f'{{#{label}}}', '').strip()
else:
label = ''
# print(f'{caption = } and {label = }')
if table == '':
raise ValueError('Bad markdown table')
# remove starting and ending | in each line (optional anyway)
txt = re.sub(r'^\||\|$', '', table, flags=re.MULTILINE)
txt = txt.split('\n')
# remove starting and ending *'s added by hand - but try to avoid * within headings!
txt[0] = '|'.join([re.sub(r'^\*\*?|\*\*?$', '', i.strip())
for i in txt[0].split('|')])
# remove the alignment row
alignment_row = txt.pop(1)
aligners = []
for t in alignment_row.split('|'):
if t[0] == ':' and t[-1] == ':':
aligners.append('c')
elif t[0] == ':':
aligners.append('l')
elif t[-1] == ':':
aligners.append('r')
else:
# no alignment info
pass
if len(aligners) == 0:
aligners = None
else:
aligners = ''.join(aligners)
txt = [[j.strip() for j in i.split('|')] for i in txt]
df = pd.DataFrame(txt).T
df = df.set_index(0)
df = df.T
return df, aligners, caption, label
@staticmethod
def parse_markdown_table_and_caption(txt: str) -> tuple[str, str | None]:
"""
Parses a Markdown table and an optional caption from a given string,
handling cases where only the caption is present.
Args:
txt: The input string.
Returns:
A tuple containing the table string (empty if not found) and the caption string (or None if no caption).
"""
table_match = re.search(r"((?:\|.*\|\s*(?:\n|$))+)", txt, re.DOTALL)
caption_match = re.search(
r"^(?:table)?:\s*(.+)", txt, re.MULTILINE + re.IGNORECASE)
table_part = table_match.group(1).strip() if table_match else ""
caption_part = caption_match.group(1) if caption_match else ""
return table_part.strip(), caption_part.strip()
def make_string(self):
"""Print to string using custom (i.e., not Tabulate) functionality."""
if self.df.empty:
return ""
if self._string == "":
cw_df = self.make_column_width_df()
cw = cw_df['recommended']
aligners = cw_df['alignment']
self._string = GT.make_text_table(
self.df, cw, aligners, index_levels=self.nindex)
return self._string
@staticmethod
def make_text_table(
df: pd.DataFrame,
data_col_widths: list[int],
data_col_aligns: list[str],
*,
index_levels: int = 1,
fmt: TableFormat = GT_Format
) -> str:
"""
Render self.df as a wrapped, boxed table.
Output like tabulate's mixed_grid with support for:
- Multi-level column headers (always shown, bottom-aligned, can wrap)
- Split index vs. body section with heavy vertical separator
- Per-column width and alignment
- Wrapped body cells with top alignment
Custom code to print a dataframe to text.
pd.DataFrame.to_string uses tabulate.tabulate which is hard to
control. This modoule provides similar functionality with greater
control over column widths and the ability to demark the index
columns.
Returns:
str: A fully formatted table as a string (useful for print, logs, or files).
"""
buf = StringIO()
def _write_line(line: str) -> None:
"""Writes a line to the buffer followed by a newline."""
buf.write(line + '\n')
def _format_cell(text: str, width: int, align: str) -> list[str]:
"""
Formats a single cell, wrapping text and applying padding and alignment.
Returns a list of strings, each representing a line of the cell.
"""
lines = wrap(str(text), width=width) or ['']
padded_width = width + 2 * fmt.padding
return [
(" " * fmt.padding)
+ (line.ljust(width) if align == 'left'
else line.center(width) if align == 'center'
else line.rjust(width)) +
(" " * fmt.padding)
for line in lines
]
def _make_horizontal_line(line_fmt: Line, col_widths: list[int]) -> str:
"""Constructs a full horizontal line for the table."""
parts = []
for i, w in enumerate(col_widths):
total = w + 2 * fmt.padding
if index_levels and i == index_levels:
parts.append(line_fmt.index_sep)
elif i > 0:
parts.append(line_fmt.sep)
parts.append(line_fmt.hline * total)
return f"{line_fmt.begin}{''.join(parts)}{line_fmt.end}"
def _make_data_row(row_fmt: DataRow, line_cells: list[str]) -> str:
"""Constructs a single data row from formatted cell strings."""
parts = []
for i, cell in enumerate(line_cells):
if index_levels and i == index_levels:
parts.append(row_fmt.index_sep)
elif i > 0:
parts.append(row_fmt.sep)
parts.append(cell)
return f"{row_fmt.begin}{''.join(parts)}{row_fmt.end}"
def _render_header_level(wrapped_cells: list[list[str]], level_widths: list[int]) -> list[str]:
"""
Renders a single level of the header, ensuring cells are bottom-aligned.
Returns a list of strings, each representing a line of the header.
"""
max_height = max(len(c) for c in wrapped_cells)
padded_cells = [
[' ' * (w + 2 * fmt.padding)] * (max_height - len(cell)) + cell
for cell, w in zip(wrapped_cells, level_widths)
]
return [_make_data_row(fmt.headerrow, [col[i] for col in padded_cells]) for i in range(max_height)]
col_levels = df.columns.nlevels
col_tuples = df.columns if col_levels > 1 else [
(c,) for c in df.columns]
# Step 1: format each level of the column headers (one header line per level)
# header alignment is left in index and center in body
index_col_aligns = [
'left' if i < index_levels else 'center' for i in range(len(data_col_aligns))]
_write_line(_make_horizontal_line(fmt.lineabove, data_col_widths))
# collect all wrapped + bottom-aligned rows for each level
for level in range(col_levels):
level_texts = [str(t[level] if level < len(t) else '')
for t in col_tuples]
wrapped_cells = [_format_cell(txt, w, a) for txt, w, a in zip(
level_texts, data_col_widths, index_col_aligns)]
level_rows = _render_header_level(wrapped_cells, data_col_widths)
for row in level_rows:
_write_line(row)
if level < col_levels - 1:
_write_line(_make_horizontal_line(
fmt.linebetweenrows, data_col_widths))
_write_line(_make_horizontal_line(
fmt.linebelowheader, data_col_widths))
for row_idx, (_, row) in enumerate(df.iterrows()):
data_cells = [
_format_cell(val, w, a)
for val, w, a in zip(row.values, data_col_widths, data_col_aligns)
]
max_height = max(len(c) for c in data_cells)
padded = [
c + [' ' * (w + 2 * fmt.padding)] * (max_height - len(c))
for c, w in zip(data_cells, data_col_widths)
]
for i in range(max_height):
_write_line(_make_data_row(
fmt.datarow, [col[i] for col in padded]))
if row_idx < len(df) - 1:
_write_line(_make_horizontal_line(
fmt.linebetweenrows, data_col_widths))
else:
_write_line(_make_horizontal_line(
fmt.linebelow, data_col_widths))
return buf.getvalue()
@staticmethod
def make_rich_table(
df,
column_widths,
column_alignments=None,
num_index_columns=0,
title=None,
show_lines=False,
box_style=box.SIMPLE_HEAVY,
):
"""
Render a preformatted DataFrame as a Rich table.
Parameters:
df (pd.DataFrame): DataFrame with all string values.
column_widths (dict or list): Widths by column name or position.
column_alignments (dict or list): Alignments ('left', 'center', 'right').
num_index_columns (int): Number of left-most columns to treat as index-like.
title (str): Optional title.
show_lines (bool): Add row separator lines.
box_style (rich.box.Box): Border style (see below).
"""
colnames = list(df.columns)
if isinstance(column_widths, list):
column_widths = {colnames[i]: w for i,
w in enumerate(column_widths)}
if column_alignments is None:
column_alignments = {}
elif isinstance(column_alignments, list):
column_alignments = {
colnames[i]: a for i, a in enumerate(column_alignments)}
table = Table(title=title,
box=box_style,
show_lines=show_lines,
expand=True)
for i, col in enumerate(colnames):
is_index = i < num_index_columns
table.add_column(
header=str(col),
width=column_widths.get(col, None),
justify=column_alignments.get(col, "left"),
style="dim" if is_index else None,
header_style="bold dim" if is_index else "bold",
no_wrap=False,
overflow="fold",
vertical="middle",
# divider=divider,
)
for _, row in df.iterrows():
table.add_row(*row.tolist())
return table
def rich_table(self, console, box_style=box.SQUARE):
"""Render to a rich table using Console object console."""
# hijack max table width
mtw = self.config.max_table_width
tw_mode = self.config.table_width_mode
self.config.table_width_mode = 'explicit'
self.config.max_table_width = console.width
# figure col widths and aligners
cw_df = self.make_column_width_df()
cw = cw_df['recommended']
aligners = cw_df['alignment']
show_lines = self.config.hrule_widths[0] > 0
self._rich_table = table = GT.make_rich_table(self.df,
cw,
aligners,
num_index_columns=self.nindex,
title=self.caption,
show_lines=show_lines,
box_style=box_style)
self.config.max_table_width = mtw
self.config.table_width_mode = tw_mode
return table
def make_svg(self):
"""Render tikz into svg text."""
tz = TikzProcessor(self._repr_latex_(), file_name=self.df_id)
p = tz.file_path.with_suffix('.svg')
if not p.exists():
tz.process_tikz(verbose=False)
txt = p.read_text()
return txt
def save_html(self, fn):
"""Save HTML to file."""
p = Path(fn)
p.parent.mkdir(parents=True, exist_ok=True)
p = p.with_suffix('.html')
soup = BeautifulSoup(self.html, 'html.parser')
p.write_text(soup.prettify(), encoding='utf-8')
logger.info(f'Saved to {p}')
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