{"id":25905344,"url":"https://github.com/datumbrain/npy","last_synced_at":"2026-04-29T06:38:00.086Z","repository":{"id":280042188,"uuid":"940810381","full_name":"datumbrain/npy","owner":"datumbrain","description":"Numpy file reader/writer library in 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returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["go","go-library","golang","library","numpy"],"created_at":"2025-03-03T05:14:53.321Z","updated_at":"2026-04-29T06:38:00.069Z","avatar_url":"https://github.com/datumbrain.png","language":"Go","funding_links":[],"categories":[],"sub_categories":[],"readme":"# npy\n\nA Go library for reading and writing NumPy's `.npy` and `.npz` file formats with support for mixed types and Go generics.\n\n## Features\n\n- Type-safe API using Go generics\n- Support for all common NumPy data types (bool, int8/16/32/64, uint8/16/32/64, float32/64)\n- Read/write single arrays (`.npy` files)\n- Read/write multiple arrays (`.npz` files)\n- Support for multi-dimensional arrays\n- Support for both row-major (C order) and column-major (Fortran order) arrays\n\n## Installation\n\n```bash\ngo get github.com/datumbrain/npy\n```\n\n## Import\n\n```go\nimport \"github.com/datumbrain/npy\"\n```\n\n## Usage Examples\n\n### Working with `.npy` Files\n\n#### Creating and Writing a NumPy Array\n\n```go\npackage main\n\nimport (\n    \"fmt\"\n    \"log\"\n\n    \"github.com/datumbrain/npy\"\n)\n\nfunc main() {\n    // Create a 2x3 float64 matrix\n    data := []float64{1.0, 2.0, 3.0, 4.0, 5.0, 6.0}\n    shape := []int{2, 3}\n\n    // Create a NumPy array\n    arr := \u0026npy.Array[float64]{\n        Data:    data,\n        Shape:   shape,\n        DType:   npy.Float64,\n        Fortran: false, // Use row-major (C) order\n    }\n\n    // Write to a .npy file\n    err := npy.WriteFile(\"matrix.npy\", arr)\n    if err != nil {\n        log.Fatalf(\"Failed to write array: %v\", err)\n    }\n\n    fmt.Println(\"Array successfully written to matrix.npy\")\n}\n```\n\n#### Reading a NumPy Array\n\n```go\npackage main\n\nimport (\n    \"fmt\"\n    \"log\"\n\n    \"github.com/datumbrain/npy\"\n)\n\nfunc main() {\n    // Read a .npy file with float64 data\n    arr, err := npy.ReadFile[float64](\"matrix.npy\")\n    if err != nil {\n        log.Fatalf(\"Failed to read array: %v\", err)\n    }\n\n    // Print shape\n    fmt.Printf(\"Array shape: %v\\n\", arr.Shape)\n\n    // Access data\n    fmt.Printf(\"Element at (0,0): %f\\n\", arr.Data[0])\n\n    // Calculate index for position [1,2] in a 2x3 matrix\n    // Index = row*width + col = 1*3 + 2 = 5\n    fmt.Printf(\"Element at (1,2): %f\\n\", arr.Data[5])\n}\n```\n\n### Working with `.npz` Files\n\n#### Creating and Writing Multiple Arrays\n\n```go\npackage main\n\nimport (\n    \"fmt\"\n    \"log\"\n\n    \"github.com/datumbrain/npy\"\n)\n\nfunc main() {\n    // Create first array (float64)\n    arr1 := \u0026npy.Array[float64]{\n        Data:    []float64{1.0, 2.0, 3.0, 4.0},\n        Shape:   []int{2, 2},\n        DType:   npy.Float64,\n        Fortran: false,\n    }\n\n    // Create second array (int32)\n    arr2 := \u0026npy.Array[int32]{\n        Data:    []int32{5, 6, 7, 8, 9},\n        Shape:   []int{5},\n        DType:   npy.Int32,\n        Fortran: false,\n    }\n\n    // Create NPZ file\n    npzFile := npy.NewNPZFile()\n\n    // Add arrays to NPZ file\n    npy.Add(npzFile, \"matrix\", arr1)\n    npy.Add(npzFile, \"vector\", arr2)\n\n    // Write NPZ file\n    err := npy.WriteNPZFile(\"data.npz\", npzFile)\n    if err != nil {\n        log.Fatalf(\"Failed to write NPZ file: %v\", err)\n    }\n\n    fmt.Println(\"NPZ file successfully written to data.npz\")\n}\n```\n\n#### Reading Multiple Arrays\n\n```go\npackage main\n\nimport (\n    \"fmt\"\n    \"log\"\n\n    \"github.com/datumbrain/npy\"\n)\n\nfunc main() {\n    // Read NPZ file\n    npzFile, err := npy.ReadNPZFile(\"data.npz\")\n    if err != nil {\n        log.Fatalf(\"Failed to read NPZ file: %v\", err)\n    }\n\n    // List all arrays in the file\n    fmt.Printf(\"Arrays in NPZ file: %v\\n\", npy.Keys(npzFile))\n\n    // Get float64 array\n    matrix, ok := npy.Get[float64](npzFile, \"matrix\")\n    if !ok {\n        log.Fatal(\"Matrix not found in NPZ file\")\n    }\n\n    // Get int32 array\n    vector, ok := npy.Get[int32](npzFile, \"vector\")\n    if !ok {\n        log.Fatal(\"Vector not found in NPZ file\")\n    }\n\n    // Print data\n    fmt.Printf(\"Matrix: %v\\n\", matrix.Data)\n    fmt.Printf(\"Vector: %v\\n\", vector.Data)\n}\n```\n\n## Working with Different Types\n\nThe library supports all common NumPy data types:\n\n```go\n// Create arrays with different types\nboolArr := \u0026npy.Array[bool]{\n    Data:  []bool{true, false, true},\n    Shape: []int{3},\n    DType: npy.Bool,\n}\n\nint8Arr := \u0026npy.Array[int8]{\n    Data:  []int8{-1, 0, 1},\n    Shape: []int{3},\n    DType: npy.Int8,\n}\n\nuint16Arr := \u0026npy.Array[uint16]{\n    Data:  []uint16{100, 200, 300},\n    Shape: []int{3},\n    DType: npy.Uint16,\n}\n\nfloat32Arr := \u0026npy.Array[float32]{\n    Data:  []float32{1.1, 2.2, 3.3},\n    Shape: []int{3},\n    DType: npy.Float32,\n}\n```\n\n## Multi-dimensional Arrays\n\nWhen working with multi-dimensional arrays, remember that NumPy arrays are stored in either:\n\n- C order (row-major, default): last dimension varies fastest\n- Fortran order (column-major): first dimension varies fastest\n\nFor example, a 2x3 array in C order would have elements in this sequence:\n\n```raw\n[0,0], [0,1], [0,2], [1,0], [1,1], [1,2]\n```\n\nWhen specifying multi-dimensional data, ensure your Go slice follows this ordering based on your `Fortran` flag.\n\n## CSV Export\n\nThe library also provides functionality to export NumPy arrays to CSV format:\n\n### Exporting a Single Array to CSV\n\n```go\npackage main\n\nimport (\n    \"fmt\"\n    \"log\"\n\n    \"github.com/datumbrain/npy\"\n)\n\nfunc main() {\n    // Read a .npy file with float64 data\n    arr, err := npy.ReadFile[float64](\"matrix.npy\")\n    if err != nil {\n        log.Fatalf(\"Failed to read array: %v\", err)\n    }\n\n    // Export to CSV\n    err = npy.ToCSV(arr, \"matrix.csv\")\n    if err != nil {\n        log.Fatalf(\"Failed to export to CSV: %v\", err)\n    }\n\n    fmt.Println(\"Successfully exported to CSV\")\n}\n```\n\n### Exporting All Arrays from an NPZ File\n\n```go\npackage main\n\nimport (\n    \"fmt\"\n    \"log\"\n\n    \"github.com/datumbrain/npy\"\n)\n\nfunc main() {\n    // Export all arrays in an NPZ file to individual CSV files\n    err := npy.NPZToCSVDir(\"data.npz\", \"./csv_output\")\n    if err != nil {\n        log.Fatalf(\"Failed to export NPZ to CSV: %v\", err)\n    }\n\n    fmt.Println(\"Successfully exported all arrays to CSV files\")\n    // Creates files like:\n    // - ./csv_output/array1.csv\n    // - ./csv_output/array2.csv\n}\n```\n\nThe CSV export supports:\n\n- 1D arrays (exported as a single row)\n- 2D arrays (exported as rows and columns)\n- Both row-major (C order) and column-major (Fortran order) arrays\n- All NumPy data types supported by the library\n\n## License\n\nMIT\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatumbrain%2Fnpy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdatumbrain%2Fnpy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatumbrain%2Fnpy/lists"}