def _open_file_checks(self, bdf_filename, basename=False):
# type: (str, bool) -> None
"""
Verifies that the BDF about to be opened:
1. Exists
2. Is Unique
3. Isn't an OP2
4. Is a File
Parameters
----------
bdf_filename : str
the bdf filename to open
basename : bool; default=False
only take the basename of the bdf
"""
if basename:
bdf_filename_inc = os.path.join(self.include_dir,
os.path.basename(bdf_filename))
else:
bdf_filename_inc = os.path.join(self.include_dir, bdf_filename)
if not os.path.exists(_filename(bdf_filename_inc)):
msg = 'No such bdf_filename: %r\n' % bdf_filename_inc
msg += 'cwd: %r\n' % os.getcwd()
msg += 'include_dir: %r\n' % self.include_dir
msg += print_bad_path(bdf_filename_inc)
print(msg)
raise IOError(msg)
elif bdf_filename_inc.endswith('.op2'):
msg = 'Invalid filetype: bdf_filename=%r' % bdf_filename_inc
print(msg)
raise IOError(msg)
bdf_filename = bdf_filename_inc
if bdf_filename in self.active_filenames:
msg = 'bdf_filename=%s is already active.\nactive_filenames=%s' \
% (bdf_filename, self.active_filenames)
print(msg)
raise RuntimeError(msg)
elif os.path.isdir(_filename(bdf_filename)):
current_filename = self.active_filename if len(
self.active_filenames) > 0 else 'None'
msg = 'Found a directory: bdf_filename=%r\ncurrent_file=%s' % (
bdf_filename_inc, current_filename)
print(msg)
raise IOError(msg)
elif not os.path.isfile(_filename(bdf_filename)):
msg = 'Not a file: bdf_filename=%r' % bdf_filename
print(msg)
raise IOError(msg)
def _open_file(self,
bdf_filename,
basename=False,
check=True,
encoding=None):
"""
Opens a new bdf_filename with the proper encoding and include directory
Parameters
----------
bdf_filename : str
the filename to open
basename : bool (default=False)
should the basename of bdf_filename be appended to the include directory
check : bool; default=True
you can disable the checks
"""
if encoding is None:
encoding = self.encoding
if basename:
bdf_filename_inc = os.path.join(self.include_dir,
os.path.basename(bdf_filename))
else:
bdf_filename_inc = os.path.join(self.include_dir, bdf_filename)
self._validate_open_file(bdf_filename, bdf_filename_inc, check)
self.log.debug('opening %r' % bdf_filename_inc)
self.active_filenames.append(bdf_filename_inc)
#print('ENCODING - _open_file=%r' % self.encoding)
#self._check_pynastran_header(lines)
bdf_file = open(_filename(bdf_filename_inc), 'r', encoding=encoding)
return bdf_file
def load_csv(out_filename, encoding='latin1'):
"""
The GUI CSV loading function.
Considers:
- extension in determining how to load a file (e.g. commas or not)
- header line of file for information regarding data types
"""
ext = os.path.splitext(out_filename)[1].lower()
if ext not in ['.csv', '.dat', '.txt']:
raise NotImplementedError(
'extension=%r is not supported (use .dat, .txt, or .csv)' % ext)
with open(_filename(out_filename), 'r', encoding=encoding) as file_obj:
names, fmt_dict, dtype, delimiter = _load_format_header(
file_obj, ext, force_float=False)
try:
#A = loadtxt(file_obj, dtype=dtype, delimiter=delimiter)
A = loadtxt_nice(file_obj,
dtype=dtype,
comments='#',
delimiter=delimiter)
except:
traceback.print_exc(file=sys.stdout)
msg = 'extension=%r nheaders=%s delimiter=%r dtype=%s' % (
ext, len(names), delimiter, dtype)
raise RuntimeError(msg)
return A, fmt_dict, names
def _open_file(self, bdf_filename, basename=False, check=True):
"""
Opens a new bdf_filename with the proper encoding and include directory
Parameters
----------
bdf_filename : str
the filename to open
basename : bool (default=False)
should the basename of bdf_filename be appended to the include directory
"""
if basename:
bdf_filename_inc = os.path.join(self.include_dir,
os.path.basename(bdf_filename))
else:
bdf_filename_inc = os.path.join(self.include_dir, bdf_filename)
self._validate_open_file(bdf_filename, bdf_filename_inc, check)
self.log.debug('opening %r' % bdf_filename_inc)
self.active_filenames.append(bdf_filename_inc)
#print('ENCODING - _open_file=%r' % self.encoding)
bdf_file = codec_open(_filename(bdf_filename_inc),
'r',
encoding=self.encoding)
return bdf_file
def read_cart3d(self, infilename, result_names=None):
"""extracts the points, elements, and Cp"""
self.infilename = infilename
self.log.info("---starting reading cart3d file...%r---" % self.infilename)
self.infilename = infilename
if is_binary_file(infilename):
with open(infilename, 'rb') as self.infile:
npoints, nelements, nresults = self._read_header_binary()
self.points = self._read_points_binary(npoints)
self.elements = self._read_elements_binary(nelements)
self.regions = self._read_regions_binary(nelements)
# TODO: loads
else:
with codec_open(_filename(infilename), 'r', encoding=self._encoding) as self.infile:
npoints, nelements, nresults = self._read_header_ascii()
self.points = self._read_points_ascii(npoints)
self.elements = self._read_elements_ascii(nelements)
self.regions = self._read_regions_ascii(nelements)
self._read_results_ascii(0, self.infile, nresults, result_names=result_names)
self.log.debug("npoints=%s nelements=%s" % (self.npoints, self.nelements))
self.log.info("---finished reading cart3d file...%r---" % self.infilename)
assert self.npoints > 0, 'npoints=%s' % self.npoints
assert self.nelements > 0, 'nelements=%s' % self.nelements
def load_deflection_csv(out_filename, encoding='latin1'):
"""
The GUI deflection CSV loading function.
Considers:
- extension in determining how to load a file (e.g. commas or not)
- header line of file for information regarding data types
"""
ext = os.path.splitext(out_filename)[1].lower()
if ext not in ['.csv', '.dat', '.txt']:
raise NotImplementedError(
'extension=%r is not supported (use .dat, .txt, or .csv)' % ext)
with open(_filename(out_filename), 'r', encoding=encoding) as file_obj:
names, fmt_dict, dtype, delimiter = _load_format_header(
file_obj, ext, force_float=False)
try:
#A = np.loadtxt(file_obj, dtype=dtype, delimiter=delimiter)
A = loadtxt_nice(file_obj, comments='#', delimiter=delimiter)
except:
traceback.print_exc(file=sys.stdout)
msg = 'extension=%r nheaders=%s delimiter=%r dtype=%s' % (
ext, len(names), delimiter, dtype)
raise RuntimeError(msg)
names_without_index = names[1:]
fmt_dict_without_index = {
key: fmt_dict[key]
for key in names_without_index
}
nnames_without_index = len(names_without_index)
nexpected_results = 1 + 3 * nnames_without_index
try:
_nrows, ncols = A.shape
except ValueError:
msg = ('A should be (nnodes, 1+ndeflection_results); '
'A.shape=%s nexpected_results=%s names=%s' %
(str(A.shape), nexpected_results, names))
raise ValueError(msg)
if ncols != nexpected_results:
msg = 'A.shape=%s ncols=%s nexpected_results=%s names=%s nnames_without_index=%s' % (
str(A.shape), ncols, nexpected_results, names,
nnames_without_index)
raise ValueError(msg)
B = {}
for i, name in enumerate(names_without_index):
B[name] = A[:, 1 + 3 * i:1 + 3 * i + 3]
assert len(B) == len(
fmt_dict_without_index), 'B.keys()=%s fmt_dict.keys()=%s' % (list(
B.keys()), list(fmt_dict_without_index.keys()))
assert len(B) == len(
names_without_index), 'B.keys()=%s names.keys()=%s' % (list(
B.keys()), names_without_index)
return B, fmt_dict_without_index, names_without_index
def _validate_open_file(self, bdf_filename, bdf_filename_inc, check=True):
"""
checks that the file doesn't have obvious errors
- hasn't been used
- not a directory
- is a file
Parameters
----------
bdf_filename : str
the current bdf filename
bdf_filename_inc : str
the next bdf filename
check : bool; default=True
you can disable the checks
Raises
------
RuntimeError : file is active
IOError : Invalid file type
"""
if check:
if not os.path.exists(_filename(bdf_filename_inc)):
msg = 'No such bdf_filename: %r\n' % bdf_filename_inc
msg += 'cwd: %r\n' % os.getcwd()
msg += 'include_dir: %r\n' % self.include_dir
msg += print_bad_path(bdf_filename_inc)
raise IOError(msg)
elif bdf_filename_inc.endswith('.op2'):
raise IOError('Invalid filetype: bdf_filename=%r' %
bdf_filename_inc)
bdf_filename = bdf_filename_inc
if bdf_filename in self.active_filenames:
msg = 'bdf_filename=%s is already active.\nactive_filenames=%s' \
% (bdf_filename, self.active_filenames)
raise RuntimeError(msg)
elif os.path.isdir(_filename(bdf_filename)):
current_fname = self.active_filename if len(
self.active_filenames) > 0 else 'None'
raise IOError(
'Found a directory: bdf_filename=%r\ncurrent_file=%s' %
(bdf_filename_inc, current_fname))
elif not os.path.isfile(_filename(bdf_filename)):
raise IOError('Not a file: bdf_filename=%r' % bdf_filename)
def _dump_file(self, bdf_dump_filename, lines, i):
# type: (str, List[str], int) -> None
"""
Writes a BDF up to some failed line index
Parameters
----------
bdf_dump_filename : str
the bdf filename to dump
lines : List[str]
the entire list of lines
i : int
the last index to write
"""
with open(_filename(bdf_dump_filename), 'w',
encoding=self.encoding) as crash_file:
for line in lines[:i]:
crash_file.write(line)
def load_deflection_csv(out_filename, encoding='latin1'):
"""
The GUI deflection CSV loading function.
Considers:
- extension in determining how to load a file (e.g. commas or not)
- header line of file for information regarding data types
"""
ext = os.path.splitext(out_filename)[1].lower()
if ext not in ['.csv', '.dat', '.txt']:
raise NotImplementedError(
'extension=%r is not supported (use .dat, .txt, or .csv)' % ext)
with codec_open(_filename(out_filename), 'r',
encoding=encoding) as file_obj:
names, fmt_dict, dtype, delimiter = _load_format_header(
file_obj, ext, force_float=False)
nnames = len(names)
try:
#A = np.loadtxt(file_obj, dtype=dtype, delimiter=delimiter)
A = loadtxt_nice(file_obj, delimiter=delimiter)
except:
traceback.print_exc(file=sys.stdout)
msg = 'extension=%r nheaders=%s delimiter=%r dtype=%s' % (
ext, len(names), delimiter, dtype)
raise RuntimeError(msg)
try:
nrows, ncols = A.shape
except ValueError:
msg = 'A should be (nnodes, 3); A.shape=%s nnames*3=%s names=%s' % (
str(A.shape), nnames * 3, names)
raise ValueError(msg)
if ncols != (nnames * 3):
msg = 'A.shape=%s ncols=%s nnames*3=%s names=%s' % (str(
A.shape), ncols, nnames * 3, names)
raise RuntimeError(msg)
B = {}
for i, name in enumerate(names):
B[name] = A[:, 3 * i:3 * i + 3]
return B, fmt_dict, names
def read_cart3d(self, infilename, result_names=None):
"""extracts the points, elements, and Cp"""
self.infilename = infilename
self.log.info("---reading cart3d...%r---" % self.infilename)
self.infilename = infilename
if is_binary_file(infilename):
with open(infilename, 'rb') as self.infile:
try:
npoints, nelements, nresults = self._read_header_binary()
self.points = self._read_points_binary(npoints)
self.elements = self._read_elements_binary(nelements)
self.regions = self._read_regions_binary(nelements)
# TODO: loads
except:
msg = 'failed reading %r' % infilename
self.log.error(msg)
raise
else:
with codec_open(_filename(infilename),
'r',
encoding=self._encoding) as self.infile:
try:
npoints, nelements, nresults = self._read_header_ascii()
self.points = self._read_points_ascii(npoints)
self.elements = self._read_elements_ascii(nelements)
self.regions = self._read_regions_ascii(nelements)
self._read_results_ascii(0,
self.infile,
nresults,
result_names=result_names)
except:
msg = 'failed reading %r' % infilename
self.log.error(msg)
raise
self.log.debug("npoints=%s nelements=%s" %
(self.npoints, self.nelements))
assert self.npoints > 0, 'npoints=%s' % self.npoints
assert self.nelements > 0, 'nelements=%s' % self.nelements
def load_deflection_csv(out_filename, encoding='latin1'):
"""
The GUI deflection CSV loading function.
Considers:
- extension in determining how to load a file (e.g. commas or not)
- header line of file for information regarding data types
"""
ext = os.path.splitext(out_filename)[1].lower()
if ext not in ['.csv', '.dat', '.txt']:
raise NotImplementedError('extension=%r is not supported (use .dat, .txt, or .csv)' % ext)
with codec_open(_filename(out_filename), 'r', encoding=encoding) as file_obj:
names, fmt_dict, dtype, delimiter = _load_format_header(file_obj, ext, force_float=False)
nnames = len(names)
try:
#A = np.loadtxt(file_obj, dtype=dtype, delimiter=delimiter)
A = loadtxt_nice(file_obj, delimiter=delimiter)
except:
traceback.print_exc(file=sys.stdout)
msg = 'extension=%r nheaders=%s delimiter=%r dtype=%s' % (
ext, len(names), delimiter, dtype)
raise RuntimeError(msg)
try:
nrows, ncols = A.shape
except ValueError:
msg = 'A should be (nnodes, 3); A.shape=%s nnames*3=%s names=%s' % (
str(A.shape), nnames*3, names)
raise ValueError(msg)
if ncols != (nnames * 3):
msg = 'A.shape=%s ncols=%s nnames*3=%s names=%s' % (
str(A.shape), ncols, nnames*3, names)
raise RuntimeError(msg)
B = {}
for i, name in enumerate(names):
B[name] = A[:, 3*i:3*i+3]
return B, fmt_dict, names
def _show_bad_file(self, bdf_filename, encoding, nlines_previous=10):
# type: (Union[str, StringIO]) -> None
"""
Prints the 10 lines before the UnicodeDecodeError occurred.
Parameters
----------
bdf_filename : str
the filename to print the lines of
encoding : str
the file encoding
nlines_previous : int; default=10
the number of lines to show
"""
lines = [] # type: List[str]
print('ENCODING - show_bad_file=%r' % encoding)
with codec_open(_filename(bdf_filename), 'r',
encoding=encoding) as bdf_file:
iline = 0
nblank = 0
while 1:
try:
line = bdf_file.readline().rstrip()
except UnicodeDecodeError:
iline0 = max([iline - nlines_previous, 0])
self.log.error('filename=%s' % bdf_filename)
for iline1, line in enumerate(lines[iline0:iline]):
self.log.error('lines[%i]=%r' % (iline0 + iline1, line))
msg = "\n%s encoding error on line=%s of %s; not '%s'" % (
encoding, iline, bdf_filename, encoding)
raise RuntimeError(msg)
if line:
nblank = 0
else:
nblank += 1
if nblank == 20:
raise RuntimeError('20 blank lines')
iline += 1
lines.append(line)
def load_csv(out_filename, encoding='latin1'):
"""
The GUI CSV loading function.
Considers:
- extension in determining how to load a file (e.g. commas or not)
- header line of file for information regarding data types
"""
ext = os.path.splitext(out_filename)[1].lower()
if ext not in ['.csv', '.dat', '.txt']:
raise NotImplementedError('extension=%r is not supported (use .dat, .txt, or .csv)' % ext)
with codec_open(_filename(out_filename), 'r', encoding=encoding) as file_obj:
names, fmt_dict, dtype, delimiter = _load_format_header(file_obj, ext, force_float=False)
try:
#A = loadtxt(file_obj, dtype=dtype, delimiter=delimiter)
A = loadtxt_nice(file_obj, dtype=dtype, delimiter=delimiter)
except:
traceback.print_exc(file=sys.stdout)
msg = 'extension=%r nheaders=%s delimiter=%r dtype=%s' % (
ext, len(names), delimiter, dtype)
raise RuntimeError(msg)
return A, fmt_dict, names
def load_user_geom(fname, encoding='latin1'):
"""
Loads a file of the form:
# all supported cards
# - GRID
# - BAR
# - TRI
# - QUAD
#
# doesn't support:
# - solid elements
# - element properties
# - custom colors
# - coordinate systems
# - materials
# - loads
# - results
# id x y z
GRID, 1, 0.2, 0.3, 0.3
GRID, 2, 1.2, 0.3, 0.3
GRID, 3, 2.2, 0.3, 0.3
GRID, 4, 5.2, 0.3, 0.3
grid, 5, 5.2, 1.3, 2.3 # case insensitive
# ID, nodes
BAR, 1, 1, 2
# eid, n1,n2,n3,n4
TRI, 2, 1, 2, 3
# this is a comment
# eid, n1,n2,n3,n4
QUAD, 3, 1, 5, 3, 4
QUAD, 4, 1, 2, 3, 4 # this is after a blank line
"""
with codec_open(_filename(fname), 'r', encoding=encoding) as user_geom:
lines = user_geom.readlines()
grid_ids = []
xyz = []
bars = []
tris = []
quads = []
#lines2 = []
for line in lines:
line2 = line.strip().split('#')[0].upper()
if line2:
sline = line2.split(',')
if line2.startswith('GRID'):
assert len(sline) == 5, sline
grid_ids.append(sline[1])
xyz.append(sline[2:])
elif line2.startswith('BAR'):
assert len(sline) == 4, sline
bars.append(sline[1:])
elif line2.startswith('TRI'):
assert len(sline) == 5, sline
tris.append(sline[1:])
elif line2.startswith('QUAD'):
assert len(sline) == 6, sline
quads.append(sline[1:])
else:
print(sline)
grid_ids = np.array(grid_ids, dtype='int32')
xyz = np.array(xyz, dtype='float32')
tris = np.array(tris, dtype='int32')
quads = np.array(quads, dtype='int32')
bars = np.array(bars, dtype='int32')
return grid_ids, xyz, bars, tris, quads
def load_csv(out_filename, encoding='latin1'):
"""
The GUI CSV loading function.
Considers:
- extension in determining how to load a file (e.g. commas or not)
- header line of file for information regarding data types
"""
ext = os.path.splitext(out_filename)[1].lower()
if ext not in ['.csv', '.dat', '.txt']:
raise NotImplementedError('extension=%r is not supported (use .dat, .txt, or .csv)' % ext)
with codec_open(_filename(out_filename), 'r', encoding=encoding) as file_obj:
header_line = file_obj.readline().strip()
if not header_line.startswith('#'):
msg = 'Expected file of the form:\n'
if ext in ['.dat', '.txt']:
msg += '# var1 var2\n'
msg += '1 2\n'
msg += '3 4\n'
msg += '\nor:\n'
msg += '# var1(%i) var2(%f)\n'
msg += '1 2.1\n'
msg += '3 4.2\n'
elif ext == '.csv':
msg += '# var1, var2\n'
msg += '1, 2\n'
msg += '3, 4\n'
msg += '\nor:\n'
msg += '# var1(%i), var2(%f)\n'
msg += '1, 2.1\n'
msg += '3, 4.2\n'
else:
msg = 'extension=%r is not supported (use .dat, .txt, or .csv)' % ext
raise NotImplementedError(msg)
raise SyntaxError(msg)
header_line = header_line.lstrip('# \t').strip()
if ext in ['.dat', '.txt']:
headers = header_line.split()
elif ext == '.csv':
headers = header_line.split(',')
else:
msg = 'extension=%r is not supported (use .dat, .txt, or .csv)' % ext
raise NotImplementedError(msg)
headers = [header.strip() for header in headers if header.strip()]
fmt_dict = {}
names = []
dtype_fmts = []
for iheader, header in enumerate(headers):
# TODO: works for making a \n, but screws up the sidebar
# and scale
header2 = header.strip()#.replace('\\n', '\n')
dtype_fmt = 'float'
str_fmt = '%.3f'
if header2.endswith(')') and '%' in header2:
header2_temp, fmt_temp = header2[:-1].rsplit('(', 1)
header2_temp = header2_temp.strip()
fmt = fmt_temp.strip()
#('S1', 'i4', 'f4')
if '%' in fmt:
#fmt_temp = fmt_temp.replace('%', '%%')
if 'i' in fmt:
fmt % 5
dtype_fmt = 'int32'
str_fmt = '%i'
elif 'g' in fmt or 'e' in fmt or 'f' in fmt or 's' in fmt:
dtype_fmt = 'float32'
str_fmt = fmt
else:
raise TypeError('iheader=%s header=%r fmt=%r' % (iheader, header2, fmt))
else:
# default to float32
dtype_fmt = 'float32'
else:
dtype_fmt = 'float32'
header2_temp = header2
names.append(header2_temp)
dtype_fmts.append(dtype_fmt)
fmt_dict[header2_temp] = str_fmt
if ext in ['.dat', '.txt']:
delimiter = None
elif ext == '.csv':
delimiter = ','
else:
raise NotImplementedError('extension=%r is not supported (use .dat, .txt, or .csv)' % ext)
dtype = {
'names': tuple(names),
'formats': tuple(dtype_fmts),
}
try:
#A = loadtxt(file_obj, dtype=dtype, delimiter=delimiter)
A = loadtxt_nice(file_obj, dtype=dtype, delimiter=delimiter)
except:
traceback.print_exc(file=sys.stdout)
msg = 'extension=%r nheaders=%s delimiter=%r dtype=%s' % (ext, len(names), delimiter, dtype)
raise RuntimeError(msg)
return A, fmt_dict, names
def loadtxt_nice(filename, delimiter=None, skiprows=0, comment='#', dtype=np.float64,
converters=None, usecols=None, unpack=False,
ndmin=0,):
"""
Reimplmenentation of numpy's loadtxt that doesn't complain about
training commas (or other delimiter) that vary from one line to
the other. It also provides better error messages.
Parameters
----------
filename : varies
str : the filename to load
file : the file object to load
cStringIO/StringIO : a file-like object
delimiter : str; default=None (any whitespace)
the field splitter (e.g. comma or tab)
skiprows : int; default=1
the number of rows to skip
comment : str, default='#'
the comment line
dtype : numpy.dtype; default=None (float)
allows for alternate casting
int32, float32, ...
dtype = {
names : ('A', 'B', 'C'),
formats : ('int32', 'float32', 'float64'),
}
usecols : sequence; default=None
Which columns to read, with 0 being the first. For example,
``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.
The default, None, results in all columns being read.
unpack : bool, optional
If True, the returned array is transposed, so that arguments may be
unpacked using ``x, y, z = loadtxt(...)``. When used with a structured
data-type, arrays are returned for each field. Default is False.
converters : dict; default=None
not supported
crashes if not None
A dictionary mapping column number to a function that will convert
that column to a float. E.g., if column 0 is a date string:
``converters = {0: datestr2num}``. Converters can also be used to
provide a default value for missing data (but see also `genfromtxt`):
``converters = {3: lambda s: float(s.strip() or 0)}``. Default: None.
ndmin : int, optional
crashes if not 0
The returned array will have at least `ndmin` dimensions.
Otherwise mono-dimensional axes will be squeezed.
Legal values: 0 (default), 1 or 2.
Returns
-------
data : (nrows, ncols) ndarray
the data object
"""
if converters is not None:
raise NotImplementedError('converters=%r must be None' % converters)
#if ndmin is not [0, 2]: ## TODO: remove 2
#raise NotImplementedError('ndmin=%r must be 0' % ndmin)
if delimiter is None:
ending_characters = '\n\r \t'
else:
ending_characters = '\n\r \t' + delimiter
data = []
if isinstance(filename, StringIO):
lines = filename.getvalue().split('\n')[skiprows:]
filename = None
elif is_file_obj(filename):
lines = filename.readlines()[skiprows:]
filename = filename.name
else:
with codec_open(_filename(filename), 'r') as file_obj:
if skiprows:
lines = file_obj.readlines()[skiprows:]
else:
lines = file_obj.readlines()
if usecols:
for usecol in usecols:
assert isinstance(usecol, int), 'usecol=%s usecols=%s' % (usecol, usecols)
assert len(np.unique(usecols)), 'usecols=%s must be unique' % (usecols)
for line in lines:
if line.startswith(comment):
continue
sline = line.strip(delimiter).split(delimiter)
data.append([sline[i] for i in usecols])
else:
for line in lines:
if line.startswith(comment):
continue
sline = line.strip(delimiter).split(delimiter)
data.append(sline)
del lines
#print(data)
allowed_dtypes = ['float32', 'float64', 'float128', np.float64, 'int32', 'int64', 'int128']
if dtype in allowed_dtypes:
assert dtype in allowed_dtypes, 'dtype=%r allowed_dtypes=[%s]' % (dtype, ', '.join(allowed_dtypes))
X = np.array(data, dtype=dtype)
elif isinstance(dtype, dict):
a = np.array(data, dtype=object)
X = {}
names = dtype['names']
nnames = len(names)
assert len(set(names)) == nnames, 'non-unique headers in %s' % str(names)
for icol, name, dtypei in zip(count(), dtype['names'], dtype['formats']):
assert dtypei in allowed_dtypes, 'dtype=%r allowed_dtypes=[%s]' % (dtypei, ', '.join(allowed_dtypes))
try:
X[name] = np.asarray(a[:, icol], dtype=dtypei)
except IndexError:
# the number of columns in A is not consistent
ncols = [len(datai) for datai in data]
ucols = np.unique(ncols)
msg = 'The number of columns is not consistent; expected=%s; actual=%s' % (nnames, ucols)
raise IndexError(msg)
except ValueError:
print(a)
# we only allow floats
msg = ''
if dtypei in ['float32', 'float64', 'float128', np.float64]:
for irow, val in zip(count(), a[:, icol]):
try:
float(val)
except:
msg += 'for name=%r, row=%s -> val=%r (expected float)\n' % (name, irow, val)
is_failed = True
elif dtypei in ['int32', 'int64', 'int128']:
for irow, val in zip(count(), a[:, icol]):
try:
int(val)
except:
msg += 'for name=%r, row=%s -> val=%r (expected int)\n' % (name, irow, val)
is_failed = True
else:
raise NotImplementedError(dtype)
if is_failed:
raise RuntimeError(msg)
#print('A =', A)
else:
raise NotImplementedError('dtype_else=%s' % dtype)
#return np.array(data, dtype=dtype)
#if usecols is not None:
#raise NotImplementedError('usecols=%s must be None' % str(usecols))
#if unpack is not False:
#raise NotImplementedError('unpack=%r must be False' % unpack)
if not isinstance(dtype, dict):
# Tweak the size and shape of the arrays - remove extraneous dimensions
if X.ndim > ndmin:
X = np.squeeze(X)
# and ensure we have the minimum number of dimensions asked for
# - has to be in this order for the odd case ndmin=1, X.squeeze().ndim=0
if X.ndim < ndmin:
if ndmin == 1:
X = np.atleast_1d(X)
elif ndmin == 2:
X = np.atleast_2d(X).T
if unpack:
#print(X)
if isinstance(dtype, dict) > 1:
if ndmin == 0:
# For structured arrays, return an array for each field.
return (np.squeeze(X[name]) for name in dtype.names)
else:
raise RuntimeError('I think this can never happen...type(dtype)=dict; ndmin=%s' % ndmin)
else:
#print('X = ', X)
#raise NotImplementedError('unpack=%s dtypes=%s' % (unpack, dtype))
#if ndmin == 0: # and A.shape[0] == 1
#out = (np.squeeze(X[:, i]) for i in range(X.shape[1]))
#return out
#else:
#return (X[:, i] for i in range(X.shape[1]))
#return (X[:, i] for i in range(X.shape[1]))
return X.T
else:
return X
def load_user_geom(fname, encoding='latin1'):
"""
Loads a file of the form:
# all supported cards
# - GRID
# - BAR
# - TRI
# - QUAD
#
# doesn't support:
# - solid elements
# - element properties
# - custom colors
# - coordinate systems
# - materials
# - loads
# - results
# id x y z
GRID, 1, 0.2, 0.3, 0.3
GRID, 2, 1.2, 0.3, 0.3
GRID, 3, 2.2, 0.3, 0.3
GRID, 4, 5.2, 0.3, 0.3
grid, 5, 5.2, 1.3, 2.3 # case insensitive
# ID, nodes
BAR, 1, 1, 2
# eid, n1,n2,n3,n4
TRI, 2, 1, 2, 3
# this is a comment
# eid, n1,n2,n3,n4
QUAD, 3, 1, 5, 3, 4
QUAD, 4, 1, 2, 3, 4 # this is after a blank line
"""
if fname.lower().endswith('.stl'):
stl_filename = fname
stl = read_stl(stl_filename)
nnodes = stl.nodes.shape[0]
ntris = stl.elements.shape[0]
grid_ids = np.arange(1, nnodes + 1, dtype='int32')
xyz = stl.nodes
eids = np.arange(1, ntris + 1, dtype='int32')
tris = np.vstack([eids, stl.elements.T + 1]).T
#tris = stl.elements + 1
#print(tris)
quads = np.array([], dtype='int32')
bars = np.array([], dtype='int32')
return grid_ids, xyz, bars, tris, quads
with open(_filename(fname), 'r', encoding=encoding) as user_geom:
lines = user_geom.readlines()
grid_ids = []
xyz = []
bars = []
tris = []
quads = []
#lines2 = []
for line in lines:
line2 = line.strip().split('#')[0].upper()
if line2:
sline = line2.split(',')
if line2.startswith('GRID'):
assert len(sline) == 5, sline
grid_ids.append(sline[1])
xyz.append(sline[2:])
elif line2.startswith('BAR'):
assert len(sline) == 4, sline
bars.append(sline[1:])
elif line2.startswith('TRI'):
assert len(sline) == 5, sline
tris.append(sline[1:])
elif line2.startswith('QUAD'):
assert len(sline) == 6, sline
quads.append(sline[1:])
else:
self.log.warning(sline)
grid_ids = np.array(grid_ids, dtype='int32')
xyz = np.array(xyz, dtype='float32')
tris = np.array(tris, dtype='int32')
quads = np.array(quads, dtype='int32')
bars = np.array(bars, dtype='int32')
return grid_ids, xyz, bars, tris, quads
def loadtxt_nice(
filename,
delimiter=None,
skiprows=0,
comment='#',
dtype=np.float64,
converters=None,
usecols=None,
unpack=False,
ndmin=0,
):
"""
Reimplmenentation of numpy's loadtxt that doesn't complain about
training commas (or other delimiter) that vary from one line to
the other. It also provides better error messages.
Parameters
----------
filename : varies
str : the filename to load
file : the file object to load
cStringIO/StringIO : a file-like object
delimiter : str; default=None (any whitespace)
the field splitter (e.g. comma or tab)
skiprows : int; default=1
the number of rows to skip
comment : str, default='#'
the comment line
dtype : numpy.dtype; default=None (float)
allows for alternate casting
int32, float32, ...
dtype = {
names : ('A', 'B', 'C'),
formats : ('int32', 'float32', 'float64'),
}
usecols : sequence; default=None
Which columns to read, with 0 being the first. For example,
``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.
The default, None, results in all columns being read.
unpack : bool, optional
If True, the returned array is transposed, so that arguments may be
unpacked using ``x, y, z = loadtxt(...)``. When used with a structured
data-type, arrays are returned for each field. Default is False.
converters : dict; default=None
not supported
crashes if not None
A dictionary mapping column number to a function that will convert
that column to a float. E.g., if column 0 is a date string:
``converters = {0: datestr2num}``. Converters can also be used to
provide a default value for missing data (but see also `genfromtxt`):
``converters = {3: lambda s: float(s.strip() or 0)}``. Default: None.
ndmin : int, optional
crashes if not 0
The returned array will have at least `ndmin` dimensions.
Otherwise mono-dimensional axes will be squeezed.
Legal values: 0 (default), 1 or 2.
Returns
-------
data : (nrows, ncols) ndarray
the data object
"""
if converters is not None:
raise NotImplementedError('converters=%r must be None' % converters)
#if ndmin is not [0, 2]: ## TODO: remove 2
#raise NotImplementedError('ndmin=%r must be 0' % ndmin)
#if delimiter is None:
#ending_characters = '\n\r \t'
#else:
#ending_characters = '\n\r \t' + delimiter
data = []
if isinstance(filename, StringIO):
lines = filename.getvalue().split('\n')[skiprows:]
filename = None
elif is_file_obj(filename):
lines = filename.readlines()[skiprows:]
filename = filename.name
else:
with codec_open(_filename(filename), 'r') as file_obj:
if skiprows:
lines = file_obj.readlines()[skiprows:]
else:
lines = file_obj.readlines()
if usecols:
for usecol in usecols:
assert isinstance(usecol,
int), 'usecol=%s usecols=%s' % (usecol, usecols)
assert len(np.unique(usecols)), 'usecols=%s must be unique' % (usecols)
for line in lines:
if line.startswith(comment):
continue
sline = line.strip(delimiter).split(delimiter)
data.append([sline[i] for i in usecols])
else:
for line in lines:
if line.startswith(comment):
continue
sline = line.strip(delimiter).split(delimiter)
data.append(sline)
del lines
#print(data)
allowed_dtypes = [
'float32', 'float64', 'float128', np.float64, 'int32', 'int64',
'int128'
]
if dtype in allowed_dtypes:
if dtype not in allowed_dtypes:
raise RuntimeError('dtype=%r allowed_dtypes=[%s]' %
(dtype, ', '.join(allowed_dtypes)))
X = np.array(data, dtype=dtype)
elif isinstance(dtype, dict):
a = np.array(data, dtype=object)
X = {}
names = dtype['names']
nnames = len(names)
assert len(
set(names)) == nnames, 'non-unique headers in %s' % str(names)
for icol, name, dtypei in zip(count(), dtype['names'],
dtype['formats']):
if dtypei not in allowed_dtypes:
raise RuntimeError('dtype=%r allowed_dtypes=[%s]' %
(dtypei, ', '.join(allowed_dtypes)))
try:
X[name] = np.asarray(a[:, icol], dtype=dtypei)
except IndexError:
# the number of columns in A is not consistent
ncols = [len(datai) for datai in data]
ucols = np.unique(ncols)
msg = 'The number of columns is not consistent; expected=%s; actual=%s' % (
nnames, ucols)
raise IndexError(msg)
except ValueError:
print(a)
# we only allow floats
msg = ''
if dtypei in ['float32', 'float64', 'float128', np.float64]:
for irow, val in zip(count(), a[:, icol]):
try:
float(val)
except ValueError:
msg += 'for name=%r, row=%s -> val=%r (expected float)\n' % (
name, irow, val)
is_failed = True
elif dtypei in ['int32', 'int64', 'int128']:
for irow, val in zip(count(), a[:, icol]):
try:
int(val)
except ValueError:
msg += 'for name=%r, row=%s -> val=%r (expected int)\n' % (
name, irow, val)
is_failed = True
else:
raise NotImplementedError(dtype)
if is_failed:
raise RuntimeError(msg)
#print('A =', A)
else:
raise NotImplementedError('dtype_else=%s' % dtype)
#return np.array(data, dtype=dtype)
#if usecols is not None:
#raise NotImplementedError('usecols=%s must be None' % str(usecols))
#if unpack is not False:
#raise NotImplementedError('unpack=%r must be False' % unpack)
if not isinstance(dtype, dict):
# Tweak the size and shape of the arrays - remove extraneous dimensions
if X.ndim > ndmin:
X = np.squeeze(X)
# and ensure we have the minimum number of dimensions asked for
# - has o be in this order for the odd case ndmin=1, X.squeeze().ndim=0
if X.ndim < ndmin:
if ndmin == 1:
X = np.atleast_1d(X)
elif ndmin == 2:
X = np.atleast_2d(X).T
if unpack:
#print(X)
if isinstance(dtype, dict) > 1:
if ndmin == 0:
# For structured arrays, return an array for each field.
return (np.squeeze(X[name]) for name in dtype.names)
else:
msg = 'I think this can never happen...type(dtype)=dict; ndmin=%s' % ndmin
raise RuntimeError(msg)
else:
#print('X = ', X)
#raise NotImplementedError('unpack=%s dtypes=%s' % (unpack, dtype))
#if ndmin == 0: # and A.shape[0] == 1
#out = (np.squeeze(X[:, i]) for i in range(X.shape[1]))
#return out
#else:
#return (X[:, i] for i in range(X.shape[1]))
#return (X[:, i] for i in range(X.shape[1]))
return X.T
else:
return X
def loadtxt_nice(filename, delimiter=None, skiprows=0, comments='#', dtype=np.float64,
converters=None, usecols=None, unpack=False,
ndmin=0,):
"""
Reimplementation of numpy's loadtxt that doesn't complain about
training commas (or other delimiter) that vary from one line to
the other. It also provides better error messages when failing to
load files.
Parameters
----------
filename : varies
str : the filename to load
file : the file object to load
cStringIO/StringIO : a file-like object
delimiter : str; default=None (any whitespace)
the field splitter (e.g. comma or tab)
skiprows : int; default=1
the number of rows to skip
comments : str, default='#'
the comment line
dtype : numpy.dtype; default=None (float)
allows for alternate casting
int32, float32, ...
dtype = {
names : ('A', 'B', 'C'),
formats : ('int32', 'float32', 'float64'),
}
usecols : sequence; default=None
Which columns to read, with 0 being the first. For example,
``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.
The default, None, results in all columns being read.
unpack : bool, optional
If True, the returned array is transposed, so that arguments may be
unpacked using ``x, y, z = loadtxt(...)``. When used with a structured
data-type, arrays are returned for each field. Default is False.
converters : dict; default=None
not supported
crashes if not None
A dictionary mapping column number to a function that will convert
that column to a float. E.g., if column 0 is a date string:
``converters = {0: datestr2num}``. Converters can also be used to
provide a default value for missing data (but see also `genfromtxt`):
``converters = {3: lambda s: float(s.strip() or 0)}``. Default: None.
ndmin : int, optional
crashes if not 0
The returned array will have at least `ndmin` dimensions.
Otherwise mono-dimensional axes will be squeezed.
Legal values: 0 (default), 1 or 2.
Returns
-------
data : (nrows, ncols) ndarray
the data object
"""
complex_dtypes = [
'complex64', 'complex128', np.complex128, np.complex64,
]
if dtype in complex_dtypes:
return np.loadtxt(
filename, dtype=dtype, comments=comments, delimiter=delimiter,
converters=converters,
skiprows=skiprows, usecols=usecols,
unpack=unpack, ndmin=ndmin,
encoding='bytes')
if converters is not None:
raise NotImplementedError('converters=%r must be None' % converters)
#if ndmin is not [0, 2]: ## TODO: remove 2
#raise NotImplementedError('ndmin=%r must be 0' % ndmin)
#if delimiter is None:
#ending_characters = '\n\r \t'
#else:
#ending_characters = '\n\r \t' + delimiter
data = []
if isinstance(filename, StringIO):
lines = filename.getvalue().split('\n')[skiprows:]
filename = None
elif isinstance(filename, str):
with open(_filename(filename), 'r') as file_obj:
if skiprows:
lines = file_obj.readlines()[skiprows:]
else:
lines = file_obj.readlines()
elif is_file_obj(filename):
lines = filename.readlines()[skiprows:]
filename = filename.name
else: # pragma: no cover
raise TypeError('filename=%s is not a file-like object; type=%s' % (filename, type(filename)))
if usecols:
for usecol in usecols:
assert isinstance(usecol, int), 'usecol=%s usecols=%s' % (usecol, usecols)
assert len(np.unique(usecols)), 'usecols=%s must be unique' % (usecols)
for line in lines:
if line.startswith(comments):
continue
sline = line.strip(delimiter).split(delimiter)
data.append([sline[i] for i in usecols])
else:
for line in lines:
if line.startswith(comments):
continue
sline = line.strip(delimiter).split(delimiter)
data.append(sline)
del lines
allowed_float_dtypes = [
np.float64, np.float32, 'float32', 'float64', 'f4',
np.int32, np.int64, 'int32', 'int64', 'i4',
#'float128', np.float128,
#'int128', np.int128,
]
#if dtype not in allowed_float_dtypes: # pragma: no cover
#'dtype=%r allowed_float_dtypes=[%s]' % (
#dtype, ', '.join(allowed_float_dtypes))
#raise RuntimeError(msg)
if dtype in allowed_float_dtypes:
X = np.array(data, dtype=dtype)
#elif dtype in complex_dtypes:
#return np.loadtxt(fname, dtype=dtype, comments=comments, delimiter=delimiter,
#converters=converters,
#skiprows=skiprows, usecols=usecols,
#unpack=unpack, ndmin=ndmin,
#encoding='bytes')
#if dtype not in allowed_complex_dtypes: # pragma: no cover
#'dtype=%r allowed_complex_dtypes=[%s]' % (
#dtype, ', '.join(allowed_complex_dtypes))
#raise RuntimeError(msg)
#data2 = (d.strip('()'))
elif isinstance(dtype, dict):
X = _loadtxt_as_dict(data, dtype, allowed_float_dtypes)
#print('A =', A)
elif isinstance(dtype, list): # tuple
assert len(data) == len(dtype)
X = []
for name_dtypei, datai in zip(dtype, data):
unused_name, dtypei = name_dtypei
xi = np.array(datai, dtype=dtypei)
X.append(xi)
unpack = False
dtype = {name_dtypei[0] : name_dtypei[1] for name_dtypei in dtype}
#X = np.vstack(X)#.astype(dtype)
#X = X.astype(dtype)
#print('X =', X)
#dtype = np.dtype(dtype)
#X = np.array(data, dtype=dtype)
else:
raise NotImplementedError('dtype_else=%s' % dtype)
#return np.array(data, dtype=dtype)
#if usecols is not None:
#raise NotImplementedError('usecols=%s must be None' % str(usecols))
#if unpack is not False:
#raise NotImplementedError('unpack=%r must be False' % unpack)
if not isinstance(dtype, dict):
# Tweak the size and shape of the arrays - remove extraneous dimensions
if X.ndim > ndmin:
X = np.squeeze(X)
# and ensure we have the minimum number of dimensions asked for
# - has o be in this order for the odd case ndmin=1, X.squeeze().ndim=0
if X.ndim < ndmin:
if ndmin == 1:
X = np.atleast_1d(X)
elif ndmin == 2:
X = np.atleast_2d(X).T
if unpack:
#print(X)
if isinstance(dtype, dict) > 1:
if ndmin == 0:
# For structured arrays, return an array for each field.
return (np.squeeze(X[name]) for name in dtype.names)
else:
msg = 'I think this can never happen...type(dtype)=dict; ndmin=%s' % ndmin
raise RuntimeError(msg)
else:
#print('X = ', X)
#raise NotImplementedError('unpack=%s dtypes=%s' % (unpack, dtype))
#if ndmin == 0: # and A.shape[0] == 1
#out = (np.squeeze(X[:, i]) for i in range(X.shape[1]))
#return out
#else:
#return (X[:, i] for i in range(X.shape[1]))
#return (X[:, i] for i in range(X.shape[1]))
return X.T
return X