Binout
There is a youtube tutorial available for this class:
This class is meant to read binouts from LS-Dyna
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filepath |
str
|
Path to the binout to read. May contain * (glob) for selecting multiple files. |
required |
Attributes:
| Name | Type | Description |
|---|---|---|
filelist |
List[str]
|
List of files which are opened. lsda: Lsda The underlying LS-Dyna binout reader instance from code from LSTC. lsda_root: Symbol Root lsda symbol which is like a root directory to traverse the content of the binout file. |
Notes
This class is only a utility wrapper for Lsda from LSTC.
Examples:
>>> binout = Binout("path/to/binout")
__init__(filepath)
Constructor for a binout
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filepath |
str
|
path to the binout or pattern |
required |
Notes
The class loads the file given in the filepath. By giving a
search pattern such as: "binout*", all files with that
pattern will be loaded.
Examples:
>>> # reads a single binout
>>> binout = Binout("path/to/binout0000")
>>> binout.filelist
['path/to/binout0000']
>>> # reads multiple files
>>> binout = Binout("path/to/binout*")
>>> binout.filelist
['path/to/binout0000','path/to/binout0001']
as_df(*args)
read data and convert to pandas dataframe if possible
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
*args |
internal path in the folder structure of the binout |
()
|
Returns:
| Name | Type | Description |
|---|---|---|
df |
pandas.DataFrame
|
data converted to pandas dataframe |
Raises:
| Type | Description |
|---|---|
ValueError
|
if the data cannot be converted to a pandas dataframe |
Examples:
>>> from lasso.dyna import Binout
>>> binout = Binout('path/to/binout')
Read a time-dependent array.
>>> binout.as_df('glstat', 'eroded_kinetic_energy')
time
0.00000 0.000000
0.19971 0.000000
0.39942 0.000000
0.59976 0.000000
0.79947 0.000000
...
119.19978 105.220786
119.39949 105.220786
119.59983 105.220786
119.79954 105.220786
119.99988 105.220786
Name: eroded_kinetic_energy, Length: 601, dtype: float64
Read a time and id-dependent array.
>>> binout.as_df('secforc', 'x_force')
1 2 3 ... 33 34
time .
0.00063 2.168547e-16 2.275245e-15 -3.118639e-14 ... -5.126108e-13 4.592941e-16
0.20034 3.514243e-04 3.797908e-04 -1.701294e-03 ... 2.530416e-11 2.755493e-07
0.40005 3.052490e-03 3.242951e-02 -2.699926e-02 ... 6.755315e-06 -2.608923e-03
0.60039 -1.299816e-02 4.930999e-02 -1.632376e-02 ... 8.941705e-05 -2.203455e-02
0.80010 1.178485e-02 4.904512e-02 -9.740204e-03 ... 5.648263e-05 -6.999854e-02
... ... ... ... ... ... ...
119.00007 9.737679e-01 -8.833702e+00 1.298964e+01 ... -9.977377e-02 7.883521e+00
119.20041 7.421170e-01 -8.849411e+00 1.253505e+01 ... -1.845916e-01 7.791409e+00
119.40012 9.946615e-01 -8.541475e+00 1.188757e+01 ... -3.662228e-02 7.675800e+00
119.60046 9.677638e-01 -8.566695e+00 1.130774e+01 ... 5.144208e-02 7.273052e+00
119.80017 1.035165e+00 -8.040828e+00 1.124044e+01 ... -1.213450e-02 7.188395e+00
read(*path)
Read all data from Binout (top to low level)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
path |
internal path in the folder structure of the binout |
()
|
Returns:
| Name | Type | Description |
|---|---|---|
ret |
Union[List[str], str, np.ndarray]
|
list of subdata within the folder or data itself (array or string) |
Notes
This function is used to read any data from the binout. It has been used
to make the access to the data more comfortable. The return type depends
on the given path:
- `binout.read()`: `List[str] names of directories (in binout)
- `binout.read(dir)`: `List[str]` names of variables or subdirs
- `binout.read(dir1, ..., variable)`: np.array data
If you have multiple outputs with different ids (e.g. in nodout for
multiple nodes) then don't forget to read the id array for
identification or id-labels.
Examples:
>>> from lasso.dyna import Binout
>>> binout = Binout("test/binout")
>>> # get top dirs
>>> binout.read()
['swforc']
>>> binout.read("swforc")
['title', 'failure', 'ids', 'failure_time', ...]
>>> binout.read("swforc","shear").shape
(321L, 26L)
>>> binout.read("swforc","ids").shape
(26L,)
>>> binout.read("swforc","ids")
array([52890, 52891, 52892, ...])
>>> # read a string value
>>> binout.read("swforc","date")
'11/05/2013'
save_hdf5(filepath, compression='gzip')
Save a binout as HDF5
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filepath |
path where the HDF5 shall be saved |
required | |
compression |
compression technique (see h5py docs) |
'gzip'
|
Examples:
>>> binout = Binout("path/to/binout")
>>> binout.save_hdf5("path/to/binout.h5")