Source code for pyNastran.op2.op2_interface.write_utils

"""
Defines methods for the op2 & hdf5 writer
"""
from struct import Struct, pack
from typing import List

import numpy as np
import scipy.sparse as sp


[docs]def set_table3_field(str_fields, ifield, value): """ ifield is 1 based """ return str_fields[:ifield-1] + value + str_fields[ifield:]
def _write_markers(op2_file, fascii, markers): """ writes pairs of markers Parameters ---------- op2_file : file the op2 file object markers : List[int] a set of 3 markers such as [-3, 1, 0] will write as [4, -3, 4, 4, 1, 4, 4, 0, 4] """ out = [] n = 0 for marker in markers: out += [4, marker, 4] n += 3 fascii.write(f'marker = [4, {marker:d}, 4]\n') op2_file.write(pack(b'<%ii' % n, *out))
[docs]def write_table_header(op2_file, fascii, table_name): """ Writes the beginning of an op2 table Parameters ---------- op2_file : file the op2 file object table_name : str the table name to write """ table0 = [ 4, 2, 4, 8, table_name.encode('ascii'), 8, #4, 0, 4, ] assert len(table_name) == 8, table_name table0_format = '<4i 8s i' struct_table = Struct(table0_format) op2_file.write(struct_table.pack(*table0)) fascii.write('%s header0 = %s\n' % (table_name, table0))
[docs]def to_column_bytes(data_list: List[np.ndarray], dtype_out: str, debug: bool=False) -> np.ndarray: """ Takes an stackable numpy array of mixed types (e.g., ints/strings) and casts them to the appropriate output datatype (typically float32/float64). An array is stackable if it's the same shape (e.g., ints/floats). This requirement is a bit looser for strings (4 characters per 32-bit float) """ for i, datai in enumerate(data_list): #if isinstance(datai, bytes): ##print('bytes') #data_list[i] = np.frombuffer(datai, dtype=dtype_out) if datai.dtype != dtype_out: #print(datai.dtype, dtype_out) data_list[i] = view_dtype(datai, dtype_out) # TODO: is this faster/correct? #data_list[i] = datai.view(dtype_out) # TODO: is this faster/correct? #data_list[i] = np.frombuffer(datai.tobytes(), dtype=dtype_out) elif debug: #print('floats...') print(datai.shape) if debug: print(data_list[i].shape) out = np.column_stack(data_list) return out
[docs]def get_complex_fdtype(dtype): """complex64 -> float32; complex128 -> float64""" if dtype.itemsize == 8: return np.float32(1).dtype return np.float64(1).dtype # 8
[docs]def view_idtype_as_fdtype(int_array, fdtype): """ If we're downcasting from int64 to float32, we can't directly go to float32. We need to first go to int32, then to float32. """ if int_array.dtype == np.int64: int_array = view_dtype(int_array.astype('int32'), fdtype) else: #print(f'array_obj.dtype.itemsize={nodedevice_gridtype.dtype.itemsize} dtype.itemsize={fdtype.itemsize}') int_array = view_dtype(int_array, fdtype) return int_array
[docs]def view_dtype(array_obj, dtype): """handles downcasting data""" if array_obj.dtype.itemsize == dtype.itemsize: return array_obj.view(dtype) return array_obj.astype(dtype)
[docs]def export_to_hdf5(self, group, log): """exports the object to HDF5 format""" #headers = self.get_headers() # for some reason we can't just not write the properties... names = self.object_attributes(filter_properties=False) dynamic_string = [ 'headers', 'data_names', 'words', 'gridtype_str', 'element_data_type', 'location', 'failure_theory', ] for name in names: if name in ['data_code', 'dataframe', 'data_frame', 'element_mapper', 'h5_file']: continue value = getattr(self, name) if value is None: continue elif isinstance(value, dict): log.warning(f'HDF5: skipping name={name!r} value={value:d}') continue elif isinstance(value, sp.coo.coo_matrix): # F:\work\pyNastran\pyNastran\master2\pyNastran\bdf\test\nx_spike\out_bsh111svd2.op2 # # https://stackoverflow.com/questions/43390038/storing-scipy-sparse-matrix-as-hdf5 #g = group.create_group('Mcoo') group.create_dataset('data', data=value.data) group.create_dataset('row', data=value.row) group.create_dataset('col', data=value.col) group.attrs['shape'] = value.shape continue #if name in ['dt', 'nonlinear_factor', 'element'] and value is None: #continue # h5py doesn't support unicode, so we have to turn the data into ASCII. # All these are fine, but this routine will probably fail at some point # on the subtitle/label being actual unicode. if name in ['element_names']: # grid point forces value = np.asarray(value, dtype='|S8').tolist() elif name in dynamic_string: svalue = [str(valuei) for valuei in value] # the size of the array is the |S8 or |S12 or whatever max_len = max([(len(valuei)) for valuei in svalue]) dtype = '|S%i' % max_len value = np.array(svalue, dtype=dtype) elif name in ['element', 'element_type'] and isinstance(value, np.ndarray): if value.dtype is np.dtype(np.int32): pass else: # unicode #value = value.tolist() value = np.asarray(value, dtype='|S8').tolist() #if hasattr(value, 'export_to_hdf5'): #msg = 'sub-object export_to_hdf5 not supported\nkey=%s value=%s' % (key, value) #raise NotImplementedError(msg) try: group.create_dataset(name, data=value) except TypeError: print('name = %r; type=%s' % (name, type(value))) print(value) print('------------------') raise
#continue #print('done')