"""
Utility functions
"""
import contextlib
import functools
import logging
import math
import os
import platform
import re
import shutil
import subprocess
import sys
import tempfile
from collections import OrderedDict
from collections.abc import Collection
from typing import (
Any,
Callable,
Dict,
Iterator,
List,
Mapping,
MutableMapping,
Optional,
TextIO,
Tuple,
Union,
)
import numpy as np
import pandas as pd
import ujson as json
from cmdstanpy import (
_CMDSTAN_SAMPLING,
_CMDSTAN_THIN,
_CMDSTAN_WARMUP,
_DOT_CMDSTAN,
_DOT_CMDSTANPY,
_TMPDIR,
)
EXTENSION = '.exe' if platform.system() == 'Windows' else ''
@functools.lru_cache(maxsize=None)
def get_logger() -> logging.Logger:
"""cmdstanpy logger"""
logger = logging.getLogger('cmdstanpy')
if len(logger.handlers) == 0:
logging.basicConfig(level=logging.INFO)
return logger
def validate_dir(install_dir: str) -> None:
"""Check that specified install directory exists, can write."""
if not os.path.exists(install_dir):
try:
os.makedirs(install_dir)
except (IOError, OSError, PermissionError) as e:
raise ValueError(
'Cannot create directory: {}'.format(install_dir)
) from e
else:
if not os.path.isdir(install_dir):
raise ValueError(
'File exists, should be a directory: {}'.format(install_dir)
)
try:
with open('tmp_test_w', 'w'):
pass
os.remove('tmp_test_w') # cleanup
except OSError as e:
raise ValueError(
'Cannot write files to directory {}'.format(install_dir)
) from e
def get_latest_cmdstan(cmdstan_dir: str) -> Optional[str]:
"""
Given a valid directory path, find all installed CmdStan versions
and return highest (i.e., latest) version number.
Assumes directory populated via script `install_cmdstan`.
"""
versions = [
''.join(name.split('-')[1:]) # name may contain '-rc'
for name in os.listdir(cmdstan_dir)
if os.path.isdir(os.path.join(cmdstan_dir, name))
and name.startswith('cmdstan-')
and name[8].isdigit()
]
# munge rc for sort, e.g. 2.25.0-rc1 -> 2.25.0.-99
for i in range(len(versions)): # # pylint: disable=C0200
tmp = versions[i].split('rc')
if len(tmp) == 1:
versions[i] = '.'.join([tmp[0], '0'])
else:
rc_sortable = str(int(tmp[1]) - 100)
versions[i] = '.'.join([tmp[0], rc_sortable])
versions.sort(key=lambda s: list(map(int, s.split('.'))))
if len(versions) == 0:
return None
latest = 'cmdstan-{}'.format(versions[len(versions) - 1])
# unmunge
tmp = latest.split('.')
prefix = '.'.join(tmp[0:3])
if int(tmp[3]) == 0:
latest = prefix
else:
tmp[3] = 'rc' + str(int(tmp[3]) + 100)
latest = '-'.join([prefix, tmp[3]])
return latest
def validate_cmdstan_path(path: str) -> None:
"""
Validate that CmdStan directory exists and binaries have been built.
Throws exception if specified path is invalid.
"""
if not os.path.isdir(path):
raise ValueError('no such CmdStan directory {}'.format(path))
if not os.path.exists(os.path.join(path, 'bin', 'stanc' + EXTENSION)):
raise ValueError(
'no CmdStan binaries found, '
'run command line script "install_cmdstan"'
)
[docs]def set_cmdstan_path(path: str) -> None:
"""
Validate, then set CmdStan directory path.
"""
validate_cmdstan_path(path)
os.environ['CMDSTAN'] = path
[docs]def set_make_env(make: str) -> None:
"""
set MAKE environmental variable.
"""
os.environ['MAKE'] = make
[docs]def cmdstan_path() -> str:
"""
Validate, then return CmdStan directory path.
"""
cmdstan = ''
if 'CMDSTAN' in os.environ:
cmdstan = os.environ['CMDSTAN']
else:
cmdstan_dir = os.path.expanduser(os.path.join('~', _DOT_CMDSTAN))
if not os.path.exists(cmdstan_dir):
cmdstan_dir = os.path.expanduser(os.path.join('~', _DOT_CMDSTANPY))
if not os.path.exists(cmdstan_dir):
raise ValueError(
'no CmdStan installation found, '
'run command line script "install_cmdstan"'
)
get_logger().warning(
"Using ~/.cmdstanpy is deprecated and"
" will not be automatically detected in version 1.0!\n"
" Please rename to ~/.cmdstan"
)
latest_cmdstan = get_latest_cmdstan(cmdstan_dir)
if latest_cmdstan is None:
raise ValueError(
'no CmdStan installation found, '
'run command line script "install_cmdstan"'
)
cmdstan = os.path.join(cmdstan_dir, latest_cmdstan)
os.environ['CMDSTAN'] = cmdstan
validate_cmdstan_path(cmdstan)
return cmdstan
def cmdstan_version_at(maj: int, min: int) -> bool:
"""
Check that CmdStan version is at or above Maj.min version.
Parses version string out of CmdStan makefile in CmdStan path dir.
:param maj: Major version number
:param min: Minor version number
:return: True if version at or above, else False
"""
# pylint:disable=bare-except
try:
path = cmdstan_path()
makefile = os.path.join(path, 'makefile')
if not os.path.exists(makefile):
raise ValueError(
'CmdStan installation {}: missing makefile'.format(path)
)
version = None
with open(makefile, 'r') as fd:
contents = fd.read()
start_idx = contents.find('CMDSTAN_VERSION := ') + len(
'CMDSTAN_VERSION := '
)
end_idx = contents.find('\n', start_idx)
version = contents[start_idx:end_idx]
if version is None:
raise ValueError(
'Cannot parse version from makefile: {}'.format(makefile)
)
splits = version.split('.')
if len(splits) < 2:
raise ValueError(
'Cannot parse version from makefile: {}'.format(makefile)
)
cur_maj = int(splits[0])
cur_min = int(splits[1])
if cur_maj > maj or (cur_maj == maj and cur_min >= min):
return True
except: # noqa
pass
return False
def cxx_toolchain_path(
version: Optional[str] = None, install_dir: Optional[str] = None
) -> Tuple[str, ...]:
"""
Validate, then activate C++ toolchain directory path.
"""
if platform.system() != 'Windows':
raise RuntimeError(
'Functionality is currently only supported on Windows'
)
if version is not None and not isinstance(version, str):
raise TypeError('Format version number as a string')
logger = get_logger()
if 'CMDSTAN_TOOLCHAIN' in os.environ:
toolchain_root = os.environ['CMDSTAN_TOOLCHAIN']
if os.path.exists(os.path.join(toolchain_root, 'mingw64')):
compiler_path = os.path.join(
toolchain_root,
'mingw64' if (sys.maxsize > 2 ** 32) else 'mingw32',
'bin',
)
if os.path.exists(compiler_path):
tool_path = os.path.join(toolchain_root, 'usr', 'bin')
if not os.path.exists(tool_path):
tool_path = ''
compiler_path = ''
logger.warning(
'Found invalid installion for RTools40 on %s',
toolchain_root,
)
toolchain_root = ''
else:
compiler_path = ''
logger.warning(
'Found invalid installion for RTools40 on %s',
toolchain_root,
)
toolchain_root = ''
elif os.path.exists(os.path.join(toolchain_root, 'mingw_64')):
compiler_path = os.path.join(
toolchain_root,
'mingw_64' if (sys.maxsize > 2 ** 32) else 'mingw_32',
'bin',
)
if os.path.exists(compiler_path):
tool_path = os.path.join(toolchain_root, 'bin')
if not os.path.exists(tool_path):
tool_path = ''
compiler_path = ''
logger.warning(
'Found invalid installion for RTools35 on %s',
toolchain_root,
)
toolchain_root = ''
else:
compiler_path = ''
logger.warning(
'Found invalid installion for RTools35 on %s',
toolchain_root,
)
toolchain_root = ''
else:
rtools40_home = os.environ.get('RTOOLS40_HOME')
cmdstan_dir = os.path.expanduser(os.path.join('~', _DOT_CMDSTAN))
cmdstan_dir_old = os.path.expanduser(os.path.join('~', _DOT_CMDSTANPY))
for toolchain_root in (
([rtools40_home] if rtools40_home is not None else [])
+ (
[
os.path.join(install_dir, 'RTools40'),
os.path.join(install_dir, 'RTools35'),
os.path.join(install_dir, 'RTools30'),
os.path.join(install_dir, 'RTools'),
]
if install_dir is not None
else []
)
+ [
os.path.join(cmdstan_dir, 'RTools40'),
os.path.join(cmdstan_dir_old, 'RTools40'),
os.path.join(os.path.abspath("/"), "RTools40"),
os.path.join(cmdstan_dir, 'RTools35'),
os.path.join(cmdstan_dir_old, 'RTools35'),
os.path.join(os.path.abspath("/"), "RTools35"),
os.path.join(cmdstan_dir, 'RTools'),
os.path.join(cmdstan_dir_old, 'RTools'),
os.path.join(os.path.abspath("/"), "RTools"),
os.path.join(os.path.abspath("/"), "RBuildTools"),
]
):
compiler_path = ''
tool_path = ''
if os.path.exists(toolchain_root):
if version not in ('35', '3.5', '3'):
compiler_path = os.path.join(
toolchain_root,
'mingw64' if (sys.maxsize > 2 ** 32) else 'mingw32',
'bin',
)
if os.path.exists(compiler_path):
tool_path = os.path.join(toolchain_root, 'usr', 'bin')
if not os.path.exists(tool_path):
tool_path = ''
compiler_path = ''
logger.warning(
'Found invalid installation for RTools40 on %s',
toolchain_root,
)
toolchain_root = ''
else:
break
else:
compiler_path = ''
logger.warning(
'Found invalid installation for RTools40 on %s',
toolchain_root,
)
toolchain_root = ''
else:
compiler_path = os.path.join(
toolchain_root,
'mingw_64' if (sys.maxsize > 2 ** 32) else 'mingw_32',
'bin',
)
if os.path.exists(compiler_path):
tool_path = os.path.join(toolchain_root, 'bin')
if not os.path.exists(tool_path):
tool_path = ''
compiler_path = ''
logger.warning(
'Found invalid installation for RTools35 on %s',
toolchain_root,
)
toolchain_root = ''
else:
break
else:
compiler_path = ''
logger.warning(
'Found invalid installation for RTools35 on %s',
toolchain_root,
)
toolchain_root = ''
else:
toolchain_root = ''
if not toolchain_root:
raise ValueError(
'no RTools toolchain installation found, '
'run command line script '
'"python -m cmdstanpy.install_cxx_toolchain"'
)
logger.info('Add C++ toolchain to $PATH: %s', toolchain_root)
os.environ['PATH'] = ';'.join(
list(
OrderedDict.fromkeys(
[compiler_path, tool_path] + os.getenv('PATH', '').split(';')
)
)
)
return compiler_path, tool_path
[docs]def write_stan_json(path: str, data: Mapping[str, Any]) -> None:
"""
Dump a mapping of strings to data to a JSON file.
Values can be any numeric type, a boolean (converted to int),
or any collection compatible with :func:`numpy.asarray`, e.g a
:class:`pandas.Series`.
Produces a file compatible with the
`Json Format for Cmdstan
<https://mc-stan.org/docs/2_27/cmdstan-guide/json.html>`__
:param path: File path for the created json. Will be overwritten if
already in existence.
:param data: A mapping from strings to values. This can be a dictionary
or something more exotic like an :class:`xarray.Dataset`. This will be
copied before type conversion, not modified
"""
data_out = {}
for key, val in data.items():
if val is not None:
if isinstance(val, (str, bytes)) or (
type(val).__module__ != 'numpy'
and not isinstance(val, (Collection, bool, int, float))
):
raise TypeError(
f"Invalid type '{type(val)}' provided to "
+ f"write_stan_json for key '{key}'"
)
try:
if not np.all(np.isfinite(val)):
raise ValueError(
"Input to write_stan_json has nan or infinite "
+ f"values for key '{key}'"
)
except TypeError:
# handles cases like val == ['hello']
# pylint: disable=raise-missing-from
raise ValueError(
"Invalid type provided to "
+ f"write_stan_json for key '{key}' "
+ f"as part of collection {type(val)}"
)
if type(val).__module__ == 'numpy':
data_out[key] = val.tolist()
elif isinstance(val, Collection):
data_out[key] = np.asarray(val).tolist()
elif isinstance(val, bool):
data_out[key] = int(val)
else:
data_out[key] = val
with open(path, 'w') as fd:
json.dump(data_out, fd)
def rload(fname: str) -> Optional[Dict[str, Union[int, float, np.ndarray]]]:
"""Parse data and parameter variable values from an R dump format file.
This parser only supports the subset of R dump data as described
in the "Dump Data Format" section of the CmdStan manual, i.e.,
scalar, vector, matrix, and array data types.
"""
data_dict = {}
with open(fname, 'r') as fd:
lines = fd.readlines()
# Variable data may span multiple lines, parse accordingly
idx = 0
while idx < len(lines) and '<-' not in lines[idx]:
idx += 1
if idx == len(lines):
return None
start_idx = idx
idx += 1
while True:
while idx < len(lines) and '<-' not in lines[idx]:
idx += 1
next_var = idx
var_data = ''.join(lines[start_idx:next_var]).replace('\n', '')
lhs, rhs = [item.strip() for item in var_data.split('<-')]
lhs = lhs.replace('"', '') # strip optional Jags double quotes
rhs = rhs.replace('L', '') # strip R long int qualifier
data_dict[lhs] = parse_rdump_value(rhs)
if idx == len(lines):
break
start_idx = next_var
idx += 1
return data_dict
def parse_rdump_value(rhs: str) -> Union[int, float, np.ndarray]:
"""Process right hand side of Rdump variable assignment statement.
Value is either scalar, vector, or multi-dim structure.
Use regex to capture structure values, dimensions.
"""
pat = re.compile(
r'structure\(\s*c\((?P<vals>[^)]*)\)'
r'(,\s*\.Dim\s*=\s*c\s*\((?P<dims>[^)]*)\s*\))?\)'
)
val: Union[int, float, np.ndarray]
try:
if rhs.startswith('structure'):
parse = pat.match(rhs)
if parse is None or parse.group('vals') is None:
raise ValueError(rhs)
vals = [float(v) for v in parse.group('vals').split(',')]
val = np.array(vals, order='F')
if parse.group('dims') is not None:
dims = [int(v) for v in parse.group('dims').split(',')]
val = np.array(vals).reshape(dims, order='F')
elif rhs.startswith('c(') and rhs.endswith(')'):
val = np.array([float(item) for item in rhs[2:-1].split(',')])
elif '.' in rhs or 'e' in rhs:
val = float(rhs)
else:
val = int(rhs)
except TypeError as e:
raise ValueError('bad value in Rdump file: {}'.format(rhs)) from e
return val
def check_sampler_csv(
path: str,
is_fixed_param: bool = False,
iter_sampling: Optional[int] = None,
iter_warmup: Optional[int] = None,
save_warmup: bool = False,
thin: Optional[int] = None,
) -> Dict[str, Any]:
"""Capture essential config, shape from stan_csv file."""
meta = scan_sampler_csv(path, is_fixed_param)
if thin is None:
thin = _CMDSTAN_THIN
elif thin > _CMDSTAN_THIN:
if 'thin' not in meta:
raise ValueError(
'bad Stan CSV file {}, '
'config error, expected thin = {}'.format(path, thin)
)
if meta['thin'] != thin:
raise ValueError(
'bad Stan CSV file {}, '
'config error, expected thin = {}, found {}'.format(
path, thin, meta['thin']
)
)
draws_sampling = iter_sampling
if draws_sampling is None:
draws_sampling = _CMDSTAN_SAMPLING
draws_warmup = iter_warmup
if draws_warmup is None:
draws_warmup = _CMDSTAN_WARMUP
draws_warmup = int(math.ceil(draws_warmup / thin))
draws_sampling = int(math.ceil(draws_sampling / thin))
if meta['draws_sampling'] != draws_sampling:
raise ValueError(
'bad Stan CSV file {}, expected {} draws, found {}'.format(
path, draws_sampling, meta['draws_sampling']
)
)
if save_warmup:
if not ('save_warmup' in meta and meta['save_warmup'] == 1):
raise ValueError(
'bad Stan CSV file {}, '
'config error, expected save_warmup = 1'.format(path)
)
if meta['draws_warmup'] != draws_warmup:
raise ValueError(
'bad Stan CSV file {}, '
'expected {} warmup draws, found {}'.format(
path, draws_warmup, meta['draws_warmup']
)
)
return meta
def scan_sampler_csv(path: str, is_fixed_param: bool = False) -> Dict[str, Any]:
"""Process sampler stan_csv output file line by line."""
dict: Dict[str, Any] = {}
lineno = 0
with open(path, 'r') as fd:
lineno = scan_config(fd, dict, lineno)
lineno = scan_column_names(fd, dict, lineno)
if not is_fixed_param:
lineno = scan_warmup_iters(fd, dict, lineno)
lineno = scan_metric(fd, dict, lineno)
lineno = scan_sampling_iters(fd, dict, lineno)
return dict
def scan_optimize_csv(path: str) -> Dict[str, Any]:
"""Process optimizer stan_csv output file line by line."""
dict: Dict[str, Any] = {}
lineno = 0
with open(path, 'r') as fd:
lineno = scan_config(fd, dict, lineno)
lineno = scan_column_names(fd, dict, lineno)
line = fd.readline().lstrip(' #\t').rstrip()
xs = line.split(',')
dict['mle'] = [float(x) for x in xs]
return dict
def scan_generated_quantities_csv(path: str) -> Dict[str, Any]:
"""
Process standalone generated quantities stan_csv output file line by line.
"""
dict: Dict[str, Any] = {}
lineno = 0
with open(path, 'r') as fd:
lineno = scan_config(fd, dict, lineno)
lineno = scan_column_names(fd, dict, lineno)
return dict
def scan_variational_csv(path: str) -> Dict[str, Any]:
"""Process advi stan_csv output file line by line."""
dict: Dict[str, Any] = {}
lineno = 0
with open(path, 'r') as fd:
lineno = scan_config(fd, dict, lineno)
lineno = scan_column_names(fd, dict, lineno)
line = fd.readline().lstrip(' #\t').rstrip()
lineno += 1
if line.startswith('Stepsize adaptation complete.'):
line = fd.readline().lstrip(' #\t\n')
lineno += 1
if not line.startswith('eta'):
raise ValueError(
'line {}: expecting eta, found:\n\t "{}"'.format(
lineno, line
)
)
line = fd.readline().lstrip(' #\t\n')
lineno += 1
xs = line.split(',')
variational_mean = [float(x) for x in xs]
dict['variational_mean'] = variational_mean
dict['variational_sample'] = pd.read_csv(
path,
comment='#',
skiprows=lineno,
header=None,
float_precision='high',
)
return dict
def scan_config(fd: TextIO, config_dict: Dict[str, Any], lineno: int) -> int:
"""
Scan initial stan_csv file comments lines and
save non-default configuration information to config_dict.
"""
cur_pos = fd.tell()
line = fd.readline().strip()
while len(line) > 0 and line.startswith('#'):
lineno += 1
if line.endswith('(Default)'):
line = line.replace('(Default)', '')
line = line.lstrip(' #\t')
key_val = line.split('=')
if len(key_val) == 2:
if key_val[0].strip() == 'file' and not key_val[1].endswith('csv'):
config_dict['data_file'] = key_val[1].strip()
elif key_val[0].strip() != 'file':
raw_val = key_val[1].strip()
val: Union[int, float, str]
try:
val = int(raw_val)
except ValueError:
try:
val = float(raw_val)
except ValueError:
val = raw_val
config_dict[key_val[0].strip()] = val
cur_pos = fd.tell()
line = fd.readline().strip()
fd.seek(cur_pos)
return lineno
def scan_warmup_iters(
fd: TextIO, config_dict: Dict[str, Any], lineno: int
) -> int:
"""
Check warmup iterations, if any.
"""
if 'save_warmup' not in config_dict:
return lineno
cur_pos = fd.tell()
line = fd.readline().strip()
draws_found = 0
while len(line) > 0 and not line.startswith('#'):
lineno += 1
draws_found += 1
cur_pos = fd.tell()
line = fd.readline().strip()
fd.seek(cur_pos)
config_dict['draws_warmup'] = draws_found
return lineno
def scan_column_names(
fd: TextIO, config_dict: MutableMapping[str, Any], lineno: int
) -> int:
"""
Process columns header, add to config_dict as 'column_names'
"""
line = fd.readline().strip()
lineno += 1
names = line.split(',')
config_dict['column_names_raw'] = tuple(names)
config_dict['column_names'] = tuple(munge_varnames(names))
return lineno
def munge_varnames(names: List[str]) -> List[str]:
"""
Change formatting for indices of container var elements
from use of dot separator to array-like notation, e.g.,
rewrite label ``y_forecast.2.4`` to ``y_forecast[2,4]``.
"""
if names is None:
raise ValueError('missing argument "names"')
return [
re.sub(r',([\d,]+)$', r'[\1]', column.replace('.', ','))
for column in names
]
def parse_method_vars(names: Tuple[str, ...]) -> Dict[str, Tuple[int, ...]]:
"""
Parses out names ending in `__` from list of CSV file column names.
Return a dict mapping sampler variable name to Stan CSV file column, using
zero-based column indexing.
Currently, (Stan 2.X) all CmdStan inference method vars are scalar,
the map entries are tuples of int to allow for structured variables.
"""
if names is None:
raise ValueError('missing argument "names"')
# note: method vars are currently all scalar so not checking for structure
return {v: tuple([k]) for (k, v) in enumerate(names) if v.endswith('__')}
def parse_stan_vars(
names: Tuple[str, ...]
) -> Tuple[Dict[str, Tuple[int, ...]], Dict[str, Tuple[int, ...]]]:
"""
Parses out Stan variable names (i.e., names not ending in `__`)
from list of CSV file column names.
Returns a pair of dicts which map variable names to dimensions and
variable names to columns, respectively, using zero-based column indexing.
Note: assumes: (a) munged varnames and (b) container vars are non-ragged
and dense; no checks size, indices.
"""
if names is None:
raise ValueError('missing argument "names"')
dims_map: Dict[str, Tuple[int, ...]] = {}
cols_map: Dict[str, Tuple[int, ...]] = {}
idxs = []
dims: Union[List[str], List[int]]
for (idx, name) in enumerate(names):
idxs.append(idx)
var, *dims = name.split('[')
if var.endswith('__'):
idxs = []
elif len(dims) == 0:
dims_map[var] = ()
cols_map[var] = tuple(idxs)
idxs = []
else:
if idx < len(names) - 1 and names[idx + 1].split('[')[0] == var:
continue
dims = [int(x) for x in dims[0][:-1].split(',')]
dims_map[var] = tuple(dims)
cols_map[var] = tuple(idxs)
idxs = []
return (dims_map, cols_map)
def scan_metric(fd: TextIO, config_dict: Dict[str, Any], lineno: int) -> int:
"""
Scan step size, metric from stan_csv file comment lines,
set config_dict entries 'metric' and 'num_unconstrained_params'
"""
if 'metric' not in config_dict:
config_dict['metric'] = 'diag_e'
metric = config_dict['metric']
line = fd.readline().strip()
lineno += 1
if not line == '# Adaptation terminated':
raise ValueError(
'line {}: expecting metric, found:\n\t "{}"'.format(lineno, line)
)
line = fd.readline().strip()
lineno += 1
label, step_size = line.split('=')
if not label.startswith('# Step size'):
raise ValueError(
'line {}: expecting step size, '
'found:\n\t "{}"'.format(lineno, line)
)
try:
float(step_size.strip())
except ValueError as e:
raise ValueError(
'line {}: invalid step size: {}'.format(lineno, step_size)
) from e
line = fd.readline().strip()
lineno += 1
if not (
(
metric == 'diag_e'
and line == '# Diagonal elements of inverse mass matrix:'
)
or (
metric == 'dense_e' and line == '# Elements of inverse mass matrix:'
)
):
raise ValueError(
'line {}: invalid or missing mass matrix '
'specification'.format(lineno)
)
line = fd.readline().lstrip(' #\t')
lineno += 1
num_unconstrained_params = len(line.split(','))
config_dict['num_unconstrained_params'] = num_unconstrained_params
if metric == 'diag_e':
return lineno
else:
for _ in range(1, num_unconstrained_params):
line = fd.readline().lstrip(' #\t')
lineno += 1
if len(line.split(',')) != num_unconstrained_params:
raise ValueError(
'line {}: invalid or missing mass matrix '
'specification'.format(lineno)
)
return lineno
def scan_sampling_iters(
fd: TextIO, config_dict: Dict[str, Any], lineno: int
) -> int:
"""
Parse sampling iteration, save number of iterations to config_dict.
"""
draws_found = 0
num_cols = len(config_dict['column_names'])
cur_pos = fd.tell()
line = fd.readline().strip()
while len(line) > 0 and not line.startswith('#'):
lineno += 1
draws_found += 1
data = line.split(',')
if len(data) != num_cols:
raise ValueError(
'line {}: bad draw, expecting {} items, found {}'.format(
lineno, num_cols, len(line.split(','))
)
)
cur_pos = fd.tell()
line = fd.readline().strip()
config_dict['draws_sampling'] = draws_found
fd.seek(cur_pos)
return lineno
def read_metric(path: str) -> List[int]:
"""
Read metric file in JSON or Rdump format.
Return dimensions of entry "inv_metric".
"""
if path.endswith('.json'):
with open(path, 'r') as fd:
metric_dict = json.load(fd)
if 'inv_metric' in metric_dict:
dims_np = np.asarray(metric_dict['inv_metric'])
return list(dims_np.shape)
else:
raise ValueError(
'metric file {}, bad or missing'
' entry "inv_metric"'.format(path)
)
else:
dims = list(read_rdump_metric(path))
if dims is None:
raise ValueError(
'metric file {}, bad or missing'
' entry "inv_metric"'.format(path)
)
return dims
def read_rdump_metric(path: str) -> List[int]:
"""
Find dimensions of variable named 'inv_metric' in Rdump data file.
"""
metric_dict = rload(path)
if metric_dict is None or not (
'inv_metric' in metric_dict
and isinstance(metric_dict['inv_metric'], np.ndarray)
):
raise ValueError(
'metric file {}, bad or missing entry "inv_metric"'.format(path)
)
return list(metric_dict['inv_metric'].shape)
def do_command(
cmd: List[str],
cwd: Optional[str] = None,
) -> Optional[str]:
"""
Spawn process, print stdout/stderr to console.
Throws RuntimeError on non-zero returncode.
"""
get_logger().debug('cmd: %s', cmd)
try:
proc = subprocess.Popen(
cmd,
cwd=cwd,
stdin=subprocess.DEVNULL,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env=os.environ,
)
stdout, stderr = proc.communicate()
if proc.returncode != 0: # problem, throw RuntimeError with msg
try:
serror = os.strerror(proc.returncode)
except ValueError as e:
pass
if proc.returncode < 0:
msg = 'Command: {}\nterminated by signal'.format(cmd)
elif proc.returncode <= 125:
msg = 'Command: {}\nfailed'.format(cmd)
elif proc.returncode == 127:
msg = 'Command: {}\nfailed, program not found'.format(cmd)
else:
msg = 'Command: {}\nmost likely crashed'.format(cmd)
msg = '{}, returncode: {}'.format(msg, proc.returncode)
if serror:
msg = '{}, error: {}'.format(msg, serror)
if stderr:
msg = '{}, stderr: {} '.format(
msg, stderr.decode('utf-8').strip()
)
raise RuntimeError(msg)
if stdout or stderr: # success, return stdout, stderr, if any
msg = ''
if stdout:
msg = '{}'.format(stdout.decode('utf-8').strip())
if stderr:
msg = '{}\nWarning or error:\t{}'.format(
msg, stderr.decode('utf-8').strip()
)
return msg
except OSError as e:
msg = 'Command: {}\nfailed with error {}\n'.format(cmd, str(e))
raise RuntimeError(msg) from e
return None # success
def windows_short_path(path: str) -> str:
"""
Gets the short path name of a given long path.
http://stackoverflow.com/a/23598461/200291
On non-Windows platforms, returns the path
If (base)path does not exist, function raises RuntimeError
"""
if platform.system() != 'Windows':
return path
if os.path.isfile(path) or (
not os.path.isdir(path) and os.path.splitext(path)[1] != ''
):
base_path, file_name = os.path.split(path)
else:
base_path, file_name = path, ''
if not os.path.exists(base_path):
raise RuntimeError(
'Windows short path function needs a valid directory. '
'Base directory does not exist: "{}"'.format(base_path)
)
import ctypes
from ctypes import wintypes
# pylint: disable=invalid-name
_GetShortPathNameW = (
ctypes.windll.kernel32.GetShortPathNameW # type: ignore
)
_GetShortPathNameW.argtypes = [
wintypes.LPCWSTR,
wintypes.LPWSTR,
wintypes.DWORD,
]
_GetShortPathNameW.restype = wintypes.DWORD
output_buf_size = 0
while True:
output_buf = ctypes.create_unicode_buffer(output_buf_size)
needed = _GetShortPathNameW(base_path, output_buf, output_buf_size)
if output_buf_size >= needed:
short_base_path = output_buf.value
break
else:
output_buf_size = needed
short_path = (
os.path.join(short_base_path, file_name)
if file_name
else short_base_path
)
return short_path
def create_named_text_file(
dir: str, prefix: str, suffix: str, name_only: bool = False
) -> str:
"""
Create a named unique file, return filename.
Flag 'name_only' will create then delete the tmp file;
this lets us create filename args for commands which
disallow overwriting existing files (e.g., 'stansummary').
"""
fd = tempfile.NamedTemporaryFile(
mode='w+', prefix=prefix, suffix=suffix, dir=dir, delete=name_only
)
path = fd.name
fd.close()
return path
[docs]def install_cmdstan(
version: Optional[str] = None,
dir: Optional[str] = None,
overwrite: bool = False,
verbose: bool = False,
compiler: bool = False,
) -> bool:
"""
Download and install a CmdStan release from GitHub by running
script ``install_cmdstan`` as a subprocess. Downloads the release
tar.gz file to temporary storage. Retries GitHub requests in order
to allow for transient network outages. Builds CmdStan executables
and tests the compiler by building example model ``bernoulli.stan``.
:param version: CmdStan version string, e.g. "2.24.1".
Defaults to latest CmdStan release.
:param dir: Path to install directory. Defaults to hidden directory
``$HOME/.cmdstan``.
If no directory is specified and the above directory does not
exist, directory ``$HOME/.cmdstan`` will be created and populated.
:param overwrite: Boolean value; when ``True``, will overwrite and
rebuild an existing CmdStan installation. Default is ``False``.
:param verbose: Boolean value; when ``True``, output from CmdStan build
processes will be streamed to the console. Default is ``False``.
:param compiler: Boolean value; when ``True`` on WINDOWS ONLY, use the
C++ compiler from the ``install_cxx_toolchain`` command or install
one if none is found.
:return: Boolean value; ``True`` for success.
"""
logger = get_logger()
python = sys.executable
here = os.path.dirname(os.path.abspath(__file__))
path = os.path.join(here, 'install_cmdstan.py')
cmd = [python, '-u', path]
if version is not None:
cmd.extend(['--version', version])
if dir is not None:
cmd.extend(['--dir', dir])
if overwrite:
cmd.append('--overwrite')
if verbose:
cmd.append('--verbose')
if compiler:
cmd.append('--compiler')
proc = subprocess.Popen(
cmd,
stdin=subprocess.DEVNULL,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env=os.environ,
)
while proc.poll() is None and proc.stdout:
print(proc.stdout.readline().decode('utf-8').strip())
_, stderr = proc.communicate()
if proc.returncode:
logger.warning('CmdStan installation failed.')
if stderr:
logger.warning(stderr.decode('utf-8').strip())
return False
if dir is not None:
if version is not None:
set_cmdstan_path(os.path.join(dir, 'cmdstan-' + version))
else:
set_cmdstan_path(
os.path.join(dir, get_latest_cmdstan(dir)) # type: ignore
)
return True
def flatten_chains(draws_array: np.ndarray) -> np.ndarray:
"""
Flatten a 3D array of draws X chains X variable into 2D array
where all chains are concatenated into a single column.
:param draws_array: 3D array of draws
"""
if len(draws_array.shape) != 3:
raise ValueError(
'Expecting 3D array, found array with {} dims'.format(
len(draws_array.shape)
)
)
num_rows = draws_array.shape[0] * draws_array.shape[1]
num_cols = draws_array.shape[2]
return draws_array.reshape((num_rows, num_cols), order='F')
@contextlib.contextmanager
def pushd(new_dir: str) -> Iterator[None]:
"""Acts like pushd/popd."""
previous_dir = os.getcwd()
os.chdir(new_dir)
yield
os.chdir(previous_dir)
def wrap_progress_hook() -> Optional[Callable[[int, int, int], None]]:
try:
from tqdm import tqdm
pbar = tqdm(
unit='B',
unit_scale=True,
unit_divisor=1024,
)
def download_progress_hook(
count: int, block_size: int, total_size: int
) -> None:
if pbar.total is None:
pbar.total = total_size
pbar.reset()
downloaded_size = count * block_size
pbar.update(downloaded_size - pbar.n)
if pbar.n >= total_size:
pbar.close()
except (ImportError, ModuleNotFoundError):
print("tqdm was not downloaded, progressbar not shown")
return None
return download_progress_hook
class MaybeDictToFilePath:
"""Context manager for json files."""
def __init__(
self,
*objs: Union[str, Mapping[str, Any], List[Any], int, float, None],
):
self._unlink = [False] * len(objs)
self._paths: List[Any] = [''] * len(objs)
i = 0
for obj in objs:
if isinstance(obj, Mapping):
data_file = create_named_text_file(
dir=_TMPDIR, prefix='', suffix='.json'
)
get_logger().debug('input tempfile: %s', data_file)
write_stan_json(data_file, obj)
self._paths[i] = data_file
self._unlink[i] = True
elif isinstance(obj, str):
if not os.path.exists(obj):
raise ValueError("File doesn't exist {}".format(obj))
self._paths[i] = obj
elif isinstance(obj, list):
err_msgs = []
missing_obj_items = []
for j, obj_item in enumerate(obj):
if not isinstance(obj_item, str):
err_msgs.append(
(
'List element {} must be a filename string,'
' found {}'
).format(j, obj_item)
)
elif not os.path.exists(obj_item):
missing_obj_items.append(
"File doesn't exist: {}".format(obj_item)
)
if err_msgs:
raise ValueError('\n'.join(err_msgs))
if missing_obj_items:
raise ValueError('\n'.join(missing_obj_items))
self._paths[i] = obj
elif obj is None:
self._paths[i] = None
elif i == 1 and isinstance(obj, (int, float)):
self._paths[i] = obj
else:
raise ValueError('data must be string or dict')
i += 1
def __enter__(self) -> List[str]:
return self._paths
def __exit__(self, exc_type, exc_val, exc_tb) -> None: # type: ignore
for can_unlink, path in zip(self._unlink, self._paths):
if can_unlink and path:
try:
os.remove(path)
except PermissionError:
pass
class TemporaryCopiedFile:
"""Context manager for tmpfiles, handles spaces in filepath."""
def __init__(self, file_path: str):
self._tmpdir = None
if ' ' in os.path.abspath(file_path) and platform.system() == 'Windows':
base_path, file_name = os.path.split(os.path.abspath(file_path))
os.makedirs(base_path, exist_ok=True)
try:
short_base_path = windows_short_path(base_path)
if os.path.exists(short_base_path):
file_path = os.path.join(short_base_path, file_name)
except RuntimeError:
pass
if ' ' in os.path.abspath(file_path):
tmpdir = tempfile.mkdtemp()
if ' ' in tmpdir:
raise RuntimeError(
'Unable to generate temporary path without spaces! \n'
+ 'Please move your stan file to location without spaces.'
)
_, path = tempfile.mkstemp(suffix='.stan', dir=tmpdir)
shutil.copy(file_path, path)
self._path = path
self._tmpdir = tmpdir
else:
self._path = file_path
def __enter__(self) -> Tuple[str, bool]:
return self._path, self._tmpdir is not None
def __exit__(self, exc_type, exc_val, exc_tb) -> None: # type: ignore
if self._tmpdir:
shutil.rmtree(self._tmpdir, ignore_errors=True)