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Purpose:

NumPy offers the save method for easy saving of arrays into .npy and savez for zipping multiple .npy arrays together into a .npz file.

cnpy lets you read and write to these formats in C++.

The motivation comes from scientific programming where large amounts of data are generated in C++ and analyzed in Python.

Writing to .npy has the advantage of using low-level C++ I/O (fread and fwrite) for speed and binary format for size. The .npy file header takes care of specifying the size, shape, and data type of the array, so specifying the format of the data is unnecessary.

Loading data written in numpy formats into C++ is equally simple, but requires you to type-cast the loaded data to the type of your choice.

Installation:

Default installation directory is /usr/local. To specify a different directory, add -DCMAKE_INSTALL_PREFIX=/path/to/install/dir to the cmake invocation in step 4.

  1. get cmake
  2. create a build directory, say $HOME/build
  3. cd $HOME/build
  4. cmake /path/to/cnpy
  5. make
  6. make install

Using:

To use, #include"cnpy.h" in your source code. Compile the source code mycode.cpp as

g++ -o mycode mycode.cpp -L/path/to/install/dir -lcnpy -lz --std=c++11

Description:

There are two functions for writing data: npy_save and npz_save.

There are 3 functions for reading:

  • npy_load will load a .npy file.
  • npz_load(fname) will load a .npz and return a dictionary of NpyArray structues.
  • npz_load(fname,varname) will load and return the NpyArray for data varname from the specified .npz file.

The data structure for loaded data is below. Data is accessed via the data<T>()-method, which returns a pointer of the specified type (which must match the underlying datatype of the data). The array shape and word size are read from the npy header.

structNpyArray {
std::vector<size_t> shape;
size_t word_size;
template<typename T> T* data();
};

See example1.cpp for examples of how to use the library. example1 will also be build during cmake installation.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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This repository was archived by the owner on Jul 8, 2020. It is now read-only.

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Purpose:
NumPy offers the save method for easy saving of arrays into .npy and savez for zipping multiple .npy arrays together into a .npz file.
cnpy lets you read and write to these formats in C++.
The motivation comes from scientific programming where large amounts of data are generated in C++ and analyzed in Python.
Writing to .npy has the advantage of using low-level C++ I/O (fread and fwrite) for speed and binary format for size.
The .npy file header takes care of specifying the size, shape, and data type of the array, so specifying the format of the data is unnecessary.
Loading data written in numpy formats into C++ is equally simple, but requires you to type-cast the loaded data to the type of your choice.
Installation:
Default installation directory is /usr/local.
To specify a different directory, add -DCMAKE_INSTALL_PREFIX=/path/to/install/dir to the cmake invocation in step 4.
  1. get cmake
  2. create a build directory, say $HOME/build
  3. cd $HOME/build
  4. cmake /path/to/cnpy
  5. make
  6. make install
Using:
To use, #include"cnpy.h" in your source code. Compile the source code mycode.cpp as
g++ -o mycode mycode.cpp -L/path/to/install/dir -lcnpy -lz --std=c++11
Description:
There are two functions for writing data: npy_save and npz_save.
There are 3 functions for reading:
  • npy_load will load a .npy file.
  • npz_load(fname) will load a .npz and return a dictionary of NpyArray structues.
  • npz_load(fname,varname) will load and return the NpyArray for data varname from the specified .npz file.
The data structure for loaded data is below.
Data is accessed via the data<T>()-method, which returns a pointer of the specified type (which must match the underlying datatype of the data).
The array shape and word size are read from the npy header.
structNpyArray {
std::vector<size_t> shape;
size_t word_size;
template<typename T> T* data();
};
See example1.cpp for examples of how to use the library. example1 will also be build during cmake installation.
About
library to read/write .npy and .npz files in C/C++
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MIT license
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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This repository was archived by the owner on Jul 8, 2020. It is now read-only.

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Purpose:
NumPy offers the save method for easy saving of arrays into .npy and savez for zipping multiple .npy arrays together into a .npz file.
cnpy lets you read and write to these formats in C++.
The motivation comes from scientific programming where large amounts of data are generated in C++ and analyzed in Python.
Writing to .npy has the advantage of using low-level C++ I/O (fread and fwrite) for speed and binary format for size.
The .npy file header takes care of specifying the size, shape, and data type of the array, so specifying the format of the data is unnecessary.
Loading data written in numpy formats into C++ is equally simple, but requires you to type-cast the loaded data to the type of your choice.
Installation:
Default installation directory is /usr/local.
To specify a different directory, add -DCMAKE_INSTALL_PREFIX=/path/to/install/dir to the cmake invocation in step 4.
  1. get cmake
  2. create a build directory, say $HOME/build
  3. cd $HOME/build
  4. cmake /path/to/cnpy
  5. make
  6. make install
Using:
To use, #include"cnpy.h" in your source code. Compile the source code mycode.cpp as
g++ -o mycode mycode.cpp -L/path/to/install/dir -lcnpy -lz --std=c++11
Description:
There are two functions for writing data: npy_save and npz_save.
There are 3 functions for reading:
  • npy_load will load a .npy file.
  • npz_load(fname) will load a .npz and return a dictionary of NpyArray structues.
  • npz_load(fname,varname) will load and return the NpyArray for data varname from the specified .npz file.
The data structure for loaded data is below.
Data is accessed via the data<T>()-method, which returns a pointer of the specified type (which must match the underlying datatype of the data).
The array shape and word size are read from the npy header.
structNpyArray {
std::vector<size_t> shape;
size_t word_size;
template<typename T> T* data();
};
See example1.cpp for examples of how to use the library. example1 will also be build during cmake installation.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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This repository was archived by the owner on Jul 8, 2020. It is now read-only.

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Purpose:
NumPy offers the save method for easy saving of arrays into .npy and savez for zipping multiple .npy arrays together into a .npz file.
cnpy lets you read and write to these formats in C++.
The motivation comes from scientific programming where large amounts of data are generated in C++ and analyzed in Python.
Writing to .npy has the advantage of using low-level C++ I/O (fread and fwrite) for speed and binary format for size.
The .npy file header takes care of specifying the size, shape, and data type of the array, so specifying the format of the data is unnecessary.
Loading data written in numpy formats into C++ is equally simple, but requires you to type-cast the loaded data to the type of your choice.
Installation:
Default installation directory is /usr/local.
To specify a different directory, add -DCMAKE_INSTALL_PREFIX=/path/to/install/dir to the cmake invocation in step 4.
  1. get cmake
  2. create a build directory, say $HOME/build
  3. cd $HOME/build
  4. cmake /path/to/cnpy
  5. make
  6. make install
Using:
To use, #include"cnpy.h" in your source code. Compile the source code mycode.cpp as
g++ -o mycode mycode.cpp -L/path/to/install/dir -lcnpy -lz --std=c++11
Description:
There are two functions for writing data: npy_save and npz_save.
There are 3 functions for reading:
  • npy_load will load a .npy file.
  • npz_load(fname) will load a .npz and return a dictionary of NpyArray structues.
  • npz_load(fname,varname) will load and return the NpyArray for data varname from the specified .npz file.
The data structure for loaded data is below.
Data is accessed via the data<T>()-method, which returns a pointer of the specified type (which must match the underlying datatype of the data).
The array shape and word size are read from the npy header.
structNpyArray {
std::vector<size_t> shape;
size_t word_size;
template<typename T> T* data();
};
See example1.cpp for examples of how to use the library. example1 will also be build during cmake installation.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content

This repository was archived by the owner on Jul 8, 2020. It is now read-only.

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Purpose:
NumPy offers the save method for easy saving of arrays into .npy and savez for zipping multiple .npy arrays together into a .npz file.
cnpy lets you read and write to these formats in C++.
The motivation comes from scientific programming where large amounts of data are generated in C++ and analyzed in Python.
Writing to .npy has the advantage of using low-level C++ I/O (fread and fwrite) for speed and binary format for size.
The .npy file header takes care of specifying the size, shape, and data type of the array, so specifying the format of the data is unnecessary.
Loading data written in numpy formats into C++ is equally simple, but requires you to type-cast the loaded data to the type of your choice.
Installation:
Default installation directory is /usr/local.
To specify a different directory, add -DCMAKE_INSTALL_PREFIX=/path/to/install/dir to the cmake invocation in step 4.
  1. get cmake
  2. create a build directory, say $HOME/build
  3. cd $HOME/build
  4. cmake /path/to/cnpy
  5. make
  6. make install
Using:
To use, #include"cnpy.h" in your source code. Compile the source code mycode.cpp as
g++ -o mycode mycode.cpp -L/path/to/install/dir -lcnpy -lz --std=c++11
Description:
There are two functions for writing data: npy_save and npz_save.
There are 3 functions for reading:
  • npy_load will load a .npy file.
  • npz_load(fname) will load a .npz and return a dictionary of NpyArray structues.
  • npz_load(fname,varname) will load and return the NpyArray for data varname from the specified .npz file.
The data structure for loaded data is below.
Data is accessed via the data<T>()-method, which returns a pointer of the specified type (which must match the underlying datatype of the data).
The array shape and word size are read from the npy header.
structNpyArray {
std::vector<size_t> shape;
size_t word_size;
template<typename T> T* data();
};
See example1.cpp for examples of how to use the library. example1 will also be build during cmake installation.
About
library to read/write .npy and .npz files in C/C++
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MIT license
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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This repository was archived by the owner on Jul 8, 2020. It is now read-only.

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Purpose:
NumPy offers the save method for easy saving of arrays into .npy and savez for zipping multiple .npy arrays together into a .npz file.
cnpy lets you read and write to these formats in C++.
The motivation comes from scientific programming where large amounts of data are generated in C++ and analyzed in Python.
Writing to .npy has the advantage of using low-level C++ I/O (fread and fwrite) for speed and binary format for size.
The .npy file header takes care of specifying the size, shape, and data type of the array, so specifying the format of the data is unnecessary.
Loading data written in numpy formats into C++ is equally simple, but requires you to type-cast the loaded data to the type of your choice.
Installation:
Default installation directory is /usr/local.
To specify a different directory, add -DCMAKE_INSTALL_PREFIX=/path/to/install/dir to the cmake invocation in step 4.
  1. get cmake
  2. create a build directory, say $HOME/build
  3. cd $HOME/build
  4. cmake /path/to/cnpy
  5. make
  6. make install
Using:
To use, #include"cnpy.h" in your source code. Compile the source code mycode.cpp as
g++ -o mycode mycode.cpp -L/path/to/install/dir -lcnpy -lz --std=c++11
Description:
There are two functions for writing data: npy_save and npz_save.
There are 3 functions for reading:
  • npy_load will load a .npy file.
  • npz_load(fname) will load a .npz and return a dictionary of NpyArray structues.
  • npz_load(fname,varname) will load and return the NpyArray for data varname from the specified .npz file.
The data structure for loaded data is below.
Data is accessed via the data<T>()-method, which returns a pointer of the specified type (which must match the underlying datatype of the data).
The array shape and word size are read from the npy header.
structNpyArray {
std::vector<size_t> shape;
size_t word_size;
template<typename T> T* data();
};
See example1.cpp for examples of how to use the library. example1 will also be build during cmake installation.
About
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MIT license
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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This repository was archived by the owner on Jul 8, 2020. It is now read-only.

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Purpose:
NumPy offers the save method for easy saving of arrays into .npy and savez for zipping multiple .npy arrays together into a .npz file.
cnpy lets you read and write to these formats in C++.
The motivation comes from scientific programming where large amounts of data are generated in C++ and analyzed in Python.
Writing to .npy has the advantage of using low-level C++ I/O (fread and fwrite) for speed and binary format for size.
The .npy file header takes care of specifying the size, shape, and data type of the array, so specifying the format of the data is unnecessary.
Loading data written in numpy formats into C++ is equally simple, but requires you to type-cast the loaded data to the type of your choice.
Installation:
Default installation directory is /usr/local.
To specify a different directory, add -DCMAKE_INSTALL_PREFIX=/path/to/install/dir to the cmake invocation in step 4.
  1. get cmake
  2. create a build directory, say $HOME/build
  3. cd $HOME/build
  4. cmake /path/to/cnpy
  5. make
  6. make install
Using:
To use, #include"cnpy.h" in your source code. Compile the source code mycode.cpp as
g++ -o mycode mycode.cpp -L/path/to/install/dir -lcnpy -lz --std=c++11
Description:
There are two functions for writing data: npy_save and npz_save.
There are 3 functions for reading:
  • npy_load will load a .npy file.
  • npz_load(fname) will load a .npz and return a dictionary of NpyArray structues.
  • npz_load(fname,varname) will load and return the NpyArray for data varname from the specified .npz file.
The data structure for loaded data is below.
Data is accessed via the data<T>()-method, which returns a pointer of the specified type (which must match the underlying datatype of the data).
The array shape and word size are read from the npy header.
structNpyArray {
std::vector<size_t> shape;
size_t word_size;
template<typename T> T* data();
};
See example1.cpp for examples of how to use the library. example1 will also be build during cmake installation.
About
library to read/write .npy and .npz files in C/C++
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MIT license
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Purpose:
NumPy offers the save method for easy saving of arrays into .npy and savez for zipping multiple .npy arrays together into a .npz file.
cnpy lets you read and write to these formats in C++.
The motivation comes from scientific programming where large amounts of data are generated in C++ and analyzed in Python.
Writing to .npy has the advantage of using low-level C++ I/O (fread and fwrite) for speed and binary format for size.
The .npy file header takes care of specifying the size, shape, and data type of the array, so specifying the format of the data is unnecessary.
Loading data written in numpy formats into C++ is equally simple, but requires you to type-cast the loaded data to the type of your choice.
Installation:
Default installation directory is /usr/local.
To specify a different directory, add -DCMAKE_INSTALL_PREFIX=/path/to/install/dir to the cmake invocation in step 4.
  1. get cmake
  2. create a build directory, say $HOME/build
  3. cd $HOME/build
  4. cmake /path/to/cnpy
  5. make
  6. make install
Using:
To use, #include"cnpy.h" in your source code. Compile the source code mycode.cpp as
g++ -o mycode mycode.cpp -L/path/to/install/dir -lcnpy -lz --std=c++11
Description:
There are two functions for writing data: npy_save and npz_save.
There are 3 functions for reading:
  • npy_load will load a .npy file.
  • npz_load(fname) will load a .npz and return a dictionary of NpyArray structues.
  • npz_load(fname,varname) will load and return the NpyArray for data varname from the specified .npz file.
The data structure for loaded data is below.
Data is accessed via the data<T>()-method, which returns a pointer of the specified type (which must match the underlying datatype of the data).
The array shape and word size are read from the npy header.
structNpyArray {
std::vector<size_t> shape;
size_t word_size;
template<typename T> T* data();
};
See example1.cpp for examples of how to use the library. example1 will also be build during cmake installation.
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library to read/write .npy and .npz files in C/C++
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