"""
Utility functions
"""
import os
from collections import OrderedDict
from collections.abc import Sequence
from numbers import Integral, Real
try:
import ujson as json
except ImportError:
import json
import math
import platform
import re
import subprocess
import shutil
import tempfile
import logging
import sys
from typing import Dict, TextIO, List, Union, Tuple
import numpy as np
import pandas as pd
from cmdstanpy import (
_TMPDIR,
_CMDSTAN_WARMUP,
_CMDSTAN_SAMPLING,
_CMDSTAN_THIN,
_DOT_CMDSTAN,
_DOT_CMDSTANPY,
)
EXTENSION = '.exe' if platform.system() == 'Windows' else ''
def get_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):
"""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) -> 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. 2.23-rc1 -> 2.23.-99
for i in range(len(versions)): # # pylint: disable=C0200
tmp = versions[i].split('rc')
if len(tmp) == 1:
continue
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])
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"'
)
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 cxx_toolchain_path(version: 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()
toolchain_root = ''
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:
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))
rtools_dir = os.path.expanduser(
os.path.join('~', cmdstan_dir, 'RTools40')
)
if not os.path.exists(rtools_dir):
rtools_dir = os.path.expanduser(
os.path.join('~', cmdstan_dir, 'RTools35')
)
if not os.path.exists(rtools_dir):
rtools_dir = os.path.expanduser(
os.path.join('~', cmdstan_dir, 'RTools')
)
if not os.path.exists(rtools_dir):
raise ValueError(
'no RTools installation found, '
'run command line script "install_cxx_toolchain"'
)
else:
rtools_dir = os.path.expanduser(os.path.join('~', cmdstan_dir))
else:
rtools_dir = os.path.expanduser(os.path.join('~', cmdstan_dir))
compiler_path = ''
tool_path = ''
if version not in ('35', '3.5', '3') and os.path.exists(
os.path.join(rtools_dir, 'RTools40')
):
toolchain_root = os.path.join(rtools_dir, 'RTools40')
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:
compiler_path = ''
logger.warning(
'Found invalid installation for RTools40 on %s',
toolchain_root,
)
toolchain_root = ''
if (
not toolchain_root or version in ('4', '40', '4.0')
) and os.path.exists(os.path.join(rtools_dir, 'RTools35')):
toolchain_root = os.path.join(rtools_dir, 'RTools35')
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:
compiler_path = ''
logger.warning(
'Found invalid installation for RTools35 on %s',
toolchain_root,
)
toolchain_root = ''
if not toolchain_root:
raise ValueError(
'no C++ toolchain installation found, '
'run command line script "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
def _rdump_array(key: str, val: np.ndarray) -> str:
"""Flatten numpy ndarray, format as Rdump variable declaration."""
c = 'c(' + ', '.join(map(str, val.T.flat)) + ')'
if (val.size,) == val.shape:
return '{key} <- {c}'.format(key=key, c=c)
else:
dim = '.Dim = c{}'.format(val.shape)
struct = '{key} <- structure({c}, {dim})'.format(key=key, c=c, dim=dim)
return struct
def jsondump(path: str, data: Dict) -> None:
"""Dump a dict of data to a JSON file."""
data = data.copy()
for key, val in data.items():
if isinstance(val, np.ndarray):
val = val.tolist()
data[key] = val
with open(path, 'w') as fd:
json.dump(data, fd)
def rdump(path: str, data: Dict) -> None:
"""Dump a dict of data to a R dump format file."""
with open(path, 'w') as fd:
for key, val in data.items():
if isinstance(val, (np.ndarray, Sequence)):
line = _rdump_array(key, np.asarray(val))
else:
line = '{} <- {}'.format(key, val)
print(line, file=fd)
def rload(fname: str) -> dict:
"""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.array]:
"""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 = None
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: int = None,
iter_warmup: int = None,
save_warmup: bool = False,
thin: int = None,
) -> Dict:
"""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 csv file {}, '
'config error, expected thin = {}'.format(path, thin)
)
if meta['thin'] != thin:
raise ValueError(
'bad 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 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):
print(meta)
raise ValueError(
'bad csv file {}, '
'config error, expected save_warmup = 1'.format(path)
)
if meta['draws_warmup'] != draws_warmup:
raise ValueError(
'bad 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:
"""Process sampler stan_csv output file line by line."""
dict = {}
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:
"""Process optimizer stan_csv output file line by line."""
dict = {}
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:
"""
Process standalone generated quantities stan_csv output file line by line.
"""
dict = {}
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:
"""Process advi stan_csv output file line by line."""
dict = {}
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, 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 not line.endswith('(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()
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, 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: Dict, 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'] = tuple(_rename_columns(names))
config_dict['num_params'] = len(names) - 1
return lineno
def _rename_columns(names: List) -> List:
names = [
re.sub(r',([\d,]+)$', r'[\1]', column.replace('.', ','))
for column in names
]
return names
def parse_var_dims(names: Tuple[str, ...]) -> Dict:
"""
Use Stan CSV file column names to get variable names, dimensions.
Assumes that CSV file has been validated and column names are correct.
"""
if names is None:
raise ValueError('missing argument "names"')
vars_dict = {}
idx = 0
while idx < len(names):
if names[idx].endswith('__'):
pass
elif '[' not in names[idx]:
vars_dict[names[idx]] = 1
else:
vs = names[idx].split('[')
if idx < len(names) - 1 and names[idx + 1].split('[')[0] == vs[0]:
idx += 1
continue
dims = [int(x) for x in vs[1][:-1].split(',')]
vars_dict[vs[0]] = tuple(dims)
idx += 1
return vars_dict
def scan_metric(fd: TextIO, config_dict: Dict, lineno: int) -> int:
"""
Scan stepsize, metric from stan_csv file comment lines,
set config_dict entries 'metric' and 'num_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, stepsize = line.split('=')
if not label.startswith('# Step size'):
raise ValueError(
'line {}: expecting stepsize, '
'found:\n\t "{}"'.format(lineno, line)
)
try:
float(stepsize.strip())
except ValueError as e:
raise ValueError(
'line {}: invalid stepsize: {}'.format(lineno, stepsize)
) 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_params = len(line.split(','))
config_dict['num_params'] = num_params
if metric == 'diag_e':
return lineno
else:
for _ in range(1, num_params):
line = fd.readline().lstrip(' #\t')
lineno += 1
if len(line.split(',')) != num_params:
raise ValueError(
'line {}: invalid or missing mass matrix '
'specification'.format(lineno)
)
return lineno
def scan_sampling_iters(fd: TextIO, config_dict: Dict, 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.asarray(metric_dict['inv_metric'])
return list(dims.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 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: str, cwd: str = None, logger: logging.Logger = None) -> str:
"""
Spawn process, print stdout/stderr to console.
Throws RuntimeError on non-zero returncode.
"""
if logger:
logger.debug('cmd: %s', cmd)
proc = subprocess.Popen(
cmd,
cwd=cwd,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env=os.environ,
)
stdout, stderr = proc.communicate()
if proc.returncode:
if stderr:
msg = 'ERROR\n {} '.format(stderr.decode('utf-8').strip())
raise RuntimeError(msg)
if stdout:
return stdout.decode('utf-8').strip()
return None
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
_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) -> str:
"""
Create a named unique file.
"""
fd = tempfile.NamedTemporaryFile(
mode='w+', prefix=prefix, suffix=suffix, dir=dir, delete=False
)
path = fd.name
fd.close()
return path
[docs]def install_cmdstan(
version: str = None,
dir: str = None,
overwrite: bool = False,
verbose: 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`` or ``$HOME/.cmdstanpy``, if the latter exists.
If no directory is specified and neither of the above directories
exist, directory ``$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``.
: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.extend(['--overwrite', 'TRUE'])
if verbose:
cmd.extend(['--verbose', 'TRUE'])
proc = subprocess.Popen(
cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=os.environ
)
while proc.poll() is None:
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
return True
class MaybeDictToFilePath:
"""Context manager for json files."""
def __init__(self, *objs: Union[str, dict], logger: logging.Logger = None):
self._unlink = [False] * len(objs)
self._paths = [''] * len(objs)
self._logger = logger or get_logger()
i = 0
for obj in objs:
if isinstance(obj, dict):
data_file = create_named_text_file(
dir=_TMPDIR, prefix='', suffix='.json'
)
self._logger.debug('input tempfile: %s', data_file)
if any(
not item
for item in obj
if isinstance(item, (Sequence, np.ndarray))
):
rdump(data_file, obj)
else:
jsondump(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 obj is None:
self._paths[i] = None
elif i == 1 and isinstance(obj, (Integral, Real)):
self._paths[i] = obj
else:
raise ValueError('data must be string or dict')
i += 1
def __enter__(self):
return self._paths
def __exit__(self, exc_type, exc_val, exc_tb):
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._path = None
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):
return self._path, self._tmpdir is not None
def __exit__(self, exc_type, exc_val, exc_tb):
if self._tmpdir:
shutil.rmtree(self._tmpdir, ignore_errors=True)