{ "cells": [ { "cell_type": "markdown", "id": "4888e800", "metadata": {}, "source": [ "# WolfArray, what is it?" ] }, { "cell_type": "markdown", "id": "74e6f297", "metadata": {}, "source": [ "## Basics\n", "\n", "A `WolfArray` is a data structure that allows you to store and manipulate arrays of data more efficiently. It is designed to be easy to use and flexible, enabling you to work with different types of data without worrying about the underlying implementation details.\n", "\n", "Internally, a `WolfArray` contains a masked numpy array (see [numpy.ma](https://numpy.org/doc/stable/reference/maskedarray.html)) in the `.array` attribute. This allows you to store data in a way that is both efficient and easy to work with. The masked array enables you to handle missing or invalid data easily while still providing the performance benefits of a regular numpy array.\n", "\n", "Georeferencing capabilities are provided by these attributes (stored in the `header_wolf` class, from which this class inherits):\n", "\n", "- `origx`, `origy`: The local coordinates of the lower-left corner of the array.\n", "- `dx`, `dy`: The pixel size in the x and y directions, respectively.\n", "- `nbx`, `nby`: The number of pixels in the x and y directions, respectively.\n", "- `translx`, `transly`: The translation in the x and y directions, respectively—equal to `(0., 0.)` by default but can be set to any value.\n", "\n", "`(origx, origy)` and `(translx, transly)` are summed to give the global coordinates of the lower-left corner of the array.\n", "\n", "Maybe you known the `Rasterio` package? In a certain way, the `WolfArray` is *similar* to a `rasterio` dataset, but it is designed to be more flexible and fully compatible with WOLF. \n", "\n", "## Numpy Considerations\n", "\n", "The masked numpy array is:\n", "\n", "- Fortran contiguous (F-order), so the first index evolves the fastest.\n", "- `nbx` is the number of rows (first index).\n", "- `nby` is the number of columns (second index).\n", "- The first element of the array `(0, 0)` is at the `(origx + dx / 2., origy + dy / 2.)` coordinates, eventually translated by `(translx, transly)`.\n", "- The element `(i, j)` is at the `(origx + dx / 2. + i * dx, origy + dy / 2. + j * dy)` coordinates, eventually translated by `(translx, transly)`.\n", "\n", "So, the X-axis corresponds to the first index, and the Y-axis corresponds to the second index. This is important to remember when working with the `WolfArray`, as it can affect how you access and manipulate the data.\n", "\n", "This is not the **classical** way of storing 2D data in Python, where the first index is the row and the second index is the column. However, this design choice was made for Fortran compatibility, and it is important to keep this in mind when working with the `WolfArray`. Retrocompatibility is important!\n", "\n", "You can use the `transpose` method to convert the array to the classical way of storing 2D data in Python, but this is not recommended unless absolutely necessary.\n", "\n", "Based on this design, the indices of the array `[i, j]` can be viewed as the `[x, y]` position. `i` varies along the X-axis, and `j` varies along the Y-axis.\n", "\n", "## Viewer Compatibility\n", "\n", "The `WolfArray` is designed to be compatible with the `WolfMapviewer`.\n", "\n", "The class inherits from the `Element_to_Draw` class, which unifies the minimal interface of objects that can be drawn in the viewer.\n", "\n", "## Plotting\n", "\n", "A `WolfArray` can be plotted in the viewer using the `plot` method (OpenGL accelerated).\n", "\n", "In a script, you can use the `plot_matplotlib` method to plot the data using Matplotlib.\n", "\n", "## Python/Fortran\n", "\n", "As Wolf uses both Python and Fortran, the `WolfArray` is designed to be compatible with both languages.\n", "\n", "You will find in some routines an `aswolf` parameter. If `True`, the routine will use/return a Fortran 1-based numeration, and if `False`, it will use a Python 0-based numeration.\n", "\n", "**This is important to remember when working with the `WolfArray`, as it can affect how you access and manipulate the data.**\n", "\n", "## Data types\n", "\n", "A `WolfArray` can store different types of data, including:\n", "\n", "- `int`: Integer (int8, uint8, int16, int32)\n", "- `float`: Floating-point (**float32 - by default**, float64)\n", "- `bool`: Boolean/Logical\n", "\n", "Informations can be obtained using the `dtype` attribute or `wolftype`, which returns the data type of the array.\n", "\n", "`dtype` is a numpy dtype object, while `wolftype` is an integer representation of the data type.\t\n" ] }, { "cell_type": "markdown", "id": "e5cae381", "metadata": {}, "source": [ "## How to create a WolfArray?" ] }, { "cell_type": "code", "execution_count": 49, "id": "8765acbd", "metadata": {}, "outputs": [], "source": [ "# import the module\n", "from wolfhece.wolf_array import WolfArray, header_wolf\n", "\n", "# create a header object\n", "h = header_wolf()\n", "# Define some parameters\n", "h.set_origin(10., 50.) # (origx, origy)\n", "h.set_resolution(0.5, 0.5) # (dx, dy)\n", "h.shape = (100, 100) # (nbx, nby)\n", "\n", "wa = WolfArray(srcheader=h)" ] }, { "cell_type": "code", "execution_count": 50, "id": "eb339593", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape : 100 x 100 \n", "Resolution : 0.5 x 0.5 \n", "Spatial extent : \n", " - Origin : (10.0 ; 50.0) \n", " - End : (60.0 ; 100.0) \n", " - Width x Height : 50.0 x 50.0 \n", " - Translation : (0.0 ; 0.0)\n", "Null value : 0.0\n", "\n", "\n" ] } ], "source": [ "# print infos\n", "print(wa)" ] }, { "cell_type": "code", "execution_count": 51, "id": "d810c517", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0\n", "1.0\n" ] } ], "source": [ "# By default, the Numpay array is initialized with ones, not zeros.\n", "#\n", "# A the .array attribute is a numpy array, so you can use all the numpy functions on it.\n", "print(wa.array.max())\n", "print(wa.array.min())" ] }, { "cell_type": "code", "execution_count": 52, "id": "6791f90e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Bounds: [xmin, xmax], [ymin, ymax] ([10.0, 60.0], [50.0, 100.0])\n" ] } ], "source": [ "# find bounds\n", "bounds = wa.get_bounds()\n", "print(\"Bounds: [xmin, xmax], [ymin, ymax]\", bounds)\n", "\n", "(xmin, xmax), (ymin, ymax) = bounds" ] }, { "cell_type": "code", "execution_count": 53, "id": "eb37ad34", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "wa.plot_matplotlib() # The plot routine uses the header informations to set the axes limits and ticks\n", "\n", "# colormap/palette is available in the .mypal attribute\n", "# print the colormap\n", "import matplotlib.pyplot as plt\n", "fig, ax = plt.subplots()\n", "wa.mypal.plot(fig, ax)\n", "fig.set_size_inches(8,1)" ] }, { "cell_type": "code", "execution_count": 54, "id": "f2374613", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
, )" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# You can mask cells\n", "wa.array[20:30, 20:30] = 0.\n", "wa.mask_data(0.)\n", "wa.plot_matplotlib()" ] }, { "cell_type": "code", "execution_count": 55, "id": "5de9805f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
, )" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "wa.array.mask[60:80, 70:90] = True\n", "wa.set_nullvalue_in_mask()\n", "wa.plot_matplotlib()" ] }, { "cell_type": "code", "execution_count": 56, "id": "7995f24d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0\n", "0.0\n" ] } ], "source": [ "# The Class has a NoData/NullValue attribute\n", "# .nodata and .nullvalue are the same information (aliases)\n", "\n", "print(wa.nullvalue)\n", "print(wa.nodata)" ] }, { "cell_type": "code", "execution_count": 57, "id": "d2831f72", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Fill the array with linear values\n", "import numpy as np\n", "wa.array.data[:,:] = np.linspace(0, 1, wa.array.size).reshape(wa.array.shape) # We use the .data attribute to access the array data -- the mask is intact\n", "wa.set_nullvalue_in_mask() # We set the nullvalue in the mask again, because we changed the data\n", "fig, ax = wa.plot_matplotlib() # we can get the figure and axes objects from the plot function" ] }, { "cell_type": "markdown", "id": "f72450fb", "metadata": {}, "source": [ "`WolfArray` has numerous routines to manipulate the data. " ] }, { "cell_type": "code", "execution_count": 58, "id": "d7555ae6", "metadata": {}, "outputs": [], "source": [ "new_wa = WolfArray(mold=wa) # We can create a new WolfArray object from an existing one, using the mold parameter" ] }, { "cell_type": "code", "execution_count": 59, "id": "3dcb4a7b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
, )" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "crop_wa = WolfArray(mold=wa, crop=[[20,30],[60,70]]) # We can crop the array using the mold parameter and the crop parameter\n", "crop_wa.plot_matplotlib() # We can plot the cropped array" ] }, { "cell_type": "code", "execution_count": null, "id": "5cfff01a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape : 50 x 50 \n", "Resolution : 1.0 x 1.0 \n", "Spatial extent : \n", " - Origin : (10.0 ; 50.0) \n", " - End : (60.0 ; 100.0) \n", " - Width x Height : 50.0 x 50.0 \n", " - Translation : (0.0 ; 0.0)\n", "Null value : 0.0\n", "\n", "\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# We can rebin (aggregation/disaggregation) the array using the rebin method.\n", "\n", "# if\n", "new_wa.rebin(factor = 2) # We can rebin the array using the rebin method, with a factor of 2. An operator can be set in the rebin method, default is 'mean'.\n", "new_wa.plot_matplotlib()\n", "print(new_wa)" ] }, { "cell_type": "code", "execution_count": null, "id": "698d6f06", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape : 400 x 400 \n", "Resolution : 0.125 x 0.125 \n", "Spatial extent : \n", " - Origin : (10.0 ; 50.0) \n", " - End : (60.0 ; 100.0) \n", " - Width x Height : 50.0 x 50.0 \n", " - Translation : (0.0 ; 0.0)\n", "Null value : 0.0\n", "\n", "\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "new_wa2 = WolfArray(mold=wa)\n", "new_wa2.rebin(factor = .25)\n", "new_wa2.plot_matplotlib()\n", "print(new_wa2)" ] }, { "cell_type": "markdown", "id": "22a45586", "metadata": {}, "source": [ "A `WolfArray` can read and write data to and from various file formats, including `.bin` (Wolf Binary), `.tif`/`.tiff`, `.vrt`, `.npy`, and `.npz`.\n", "\n", "Some formats include georeferencing information, while others do not. The `WolfArray` automatically detects the format and processes the data accordingly.\n", "\n", "Internally, GDAL can be used to handle reading and writing data to and from files." ] } ], "metadata": { "kernelspec": { "display_name": "python3.10", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.11" } }, "nbformat": 4, "nbformat_minor": 5 }