Read a binary or ascii DMAT file and return a numpy array.
Parameters:
| Name |
Type |
Description |
Default |
file_path |
str
|
Path to the DMAT file. |
required
|
Returns:
| Name | Type |
Description |
data |
numpy.ndarray
|
The data in the DMAT file. |
dmat files are described well here
Examples:
# Read a DMAT file
data = gpytoolbox.read_dmat('data.dmat')
print(data)
Source code in src/gpytoolbox/read_dmat.py
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63 | def read_dmat(file_path):
"""
Read a binary or ascii DMAT file and return a numpy array.
Parameters
----------
file_path : str
Path to the DMAT file.
Returns
-------
data : numpy.ndarray
The data in the DMAT file.
Notes
-----
dmat files are described well [here](https://libigl.github.io/file-formats/dmat/)
Examples
--------
```python
# Read a DMAT file
data = gpytoolbox.read_dmat('data.dmat')
print(data)
```
"""
with open(file_path, 'rb') as f:
# Read the first line to determine if it's an ASCII or binary file
header = f.readline().decode('ascii').strip()
cols, rows = map(int, header.split())
if cols == 0 and rows == 0: # Binary file
# Read the binary header
binary_header = f.readline().decode('ascii').strip()
cols, rows = map(int, binary_header.split())
# Read binary data
data = np.empty((rows, cols))
for j in range(cols):
for i in range(rows):
# Read 8-byte double precision floating point
float_bytes = f.read(8)
float_value = struct.unpack('<d', float_bytes)[0]
data[i, j] = float_value
else: # ASCII file
# Read ASCII data
data = np.empty((rows, cols))
for j in range(cols):
for i in range(rows):
while True:
# Read the next coefficient
c = f.read(0)
if c == b'' or c == b'\n':
break
# Read the next float value
float_value = float(f.readline().decode('ascii').strip())
data[i, j] = float_value
return data
|