# npy-cpp-patches
Read N-Dimensional patches from `.npy` files. This module is built using `C++` and has `Python3` bindings.
## Data Specifications
- Arrays must be saved in `C-contiguous` format, i.e. **NOT** `Fortran-contiguous`.
- First dimension is indexed using in a non-contiguous manner. For example, this can be used to extract specific channels within a natural image.
- Next dimensions are specified by a patch shape `C++` vector or `Python` tuple. To extract patches of lower dimensionality than that of the data, set the corresponding dimensions to `1`.
## Python Usage
### Install
```pip install npy-patcher```
### Usage
```python
from npy_patcher import PatcherDouble, PatcherFloat, PatcherInt, PatcherLong
# In this example lets say our data has shape (10, 90, 90), and is therefore 3D.
data_fpath = '/my/numpy/file.npy'
patcher = PatcherFloat() # Use PatcherFloat for np.float32 datatype
nc_index = [0, 1, 3, 5, 7] # Non-contiguous index
patch_shape = (30, 30) # Contiguous patch shape.
patch_stride = (30, 30) # Contiguous patch stride, set a smaller value than `patch_stride` for overlapping patches.
patch_num = 2
# The patch number indexes the patches (starting from 0). So in our example the index 2
# would be equivalent to data[nc_index, 0:30, 60:90]. The variable indexes the patches
# in C-contiguous manner, i.e. the last dimension has the smallest stride.
extra_padding = (0, 0) # Optionally apply extra padding, this is applied after initial padding calculation.
patch_num_offset = (0, 0) # Optionally provide an offset to extract patches after.
patch = patcher.get_patch(
data_fpath, nc_index, patch_shape, patch_stride, patch_num, extra_padding, patch_num_offset
)
patch = patch.reshape((5, 30, 30)) # PatcherFloat returns a list, therefore we need to reshape.
```
## C++ Usage
Below is an example written in `C++`, equivalent to the `Python` usage above.
```cpp
// test.cpp
#include "src/patcher.hpp"
#include <vector>
#include <string>
int main() {
std::string fpath = "data.npy";
std::vector<size_t> nc_index {0, 1, 3, 5, 7};
std::vector<size_t> patch_shape {30, 30};
std::vector<size_t> patch_stride{30, 30};
size_t patch_num = 2;
std::vector<size_t> extra_padding = {0, 0};
std::vector<size_t> patch_num_offset = {0, 0};
Patcher<float> patcher;
// Here the patch object is a contiguous 1D vector
std::vector<float> patch = patcher.get_patch(
fpath, nc_index, patch_shape, patch_stride, patch_num, extra_padding, patch_num_offset
);
return 0;
}
```
You can then build the package, for example using `g++`:
```bash
$ cd npy-cpp-patches/
$ g++ -std=c++17 -I ./ -g test.cpp src/npy_header.cpp src/pyparse.cpp -o test
```
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"description": "# npy-cpp-patches\nRead N-Dimensional patches from `.npy` files. This module is built using `C++` and has `Python3` bindings.\n\n## Data Specifications\n\n- Arrays must be saved in `C-contiguous` format, i.e. **NOT** `Fortran-contiguous`.\n- First dimension is indexed using in a non-contiguous manner. For example, this can be used to extract specific channels within a natural image.\n- Next dimensions are specified by a patch shape `C++` vector or `Python` tuple. To extract patches of lower dimensionality than that of the data, set the corresponding dimensions to `1`.\n\n\n## Python Usage\n\n### Install\n```pip install npy-patcher```\n\n### Usage\n```python\n\nfrom npy_patcher import PatcherDouble, PatcherFloat, PatcherInt, PatcherLong\n\n# In this example lets say our data has shape (10, 90, 90), and is therefore 3D.\ndata_fpath = '/my/numpy/file.npy'\npatcher = PatcherFloat() # Use PatcherFloat for np.float32 datatype\nnc_index = [0, 1, 3, 5, 7] # Non-contiguous index\npatch_shape = (30, 30) # Contiguous patch shape.\npatch_stride = (30, 30) # Contiguous patch stride, set a smaller value than `patch_stride` for overlapping patches.\npatch_num = 2\n# The patch number indexes the patches (starting from 0). So in our example the index 2\n# would be equivalent to data[nc_index, 0:30, 60:90]. The variable indexes the patches\n# in C-contiguous manner, i.e. the last dimension has the smallest stride.\nextra_padding = (0, 0) # Optionally apply extra padding, this is applied after initial padding calculation.\npatch_num_offset = (0, 0) # Optionally provide an offset to extract patches after.\n\npatch = patcher.get_patch(\n data_fpath, nc_index, patch_shape, patch_stride, patch_num, extra_padding, patch_num_offset\n)\npatch = patch.reshape((5, 30, 30)) # PatcherFloat returns a list, therefore we need to reshape.\n```\n\n## C++ Usage\n\nBelow is an example written in `C++`, equivalent to the `Python` usage above.\n\n```cpp\n// test.cpp\n#include \"src/patcher.hpp\"\n#include <vector>\n#include <string>\n\nint main() {\n std::string fpath = \"data.npy\";\n std::vector<size_t> nc_index {0, 1, 3, 5, 7};\n std::vector<size_t> patch_shape {30, 30};\n std::vector<size_t> patch_stride{30, 30};\n size_t patch_num = 2;\n std::vector<size_t> extra_padding = {0, 0};\n std::vector<size_t> patch_num_offset = {0, 0};\n\n Patcher<float> patcher;\n\n // Here the patch object is a contiguous 1D vector\n std::vector<float> patch = patcher.get_patch(\n fpath, nc_index, patch_shape, patch_stride, patch_num, extra_padding, patch_num_offset\n );\n\n return 0;\n}\n```\n\nYou can then build the package, for example using `g++`:\n\n```bash\n$ cd npy-cpp-patches/\n$ g++ -std=c++17 -I ./ -g test.cpp src/npy_header.cpp src/pyparse.cpp -o test\n```\n",
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