{ "cells": [ { "cell_type": "markdown", "id": "66200505", "metadata": {}, "source": [ "# The NDDataset object" ] }, { "cell_type": "markdown", "id": "d60770b0", "metadata": {}, "source": [ "The NDDataset is the main object use by **SpectroChemPy**.\n", "\n", "Like numpy ndarrays, NDDataset have the capability to be sliced, sorted and subject to\n", "mathematical operations.\n", "\n", "But, in addition, NDDataset may have units, can be masked and each dimensions can have\n", "coordinates also with units.\n", "This make NDDataset aware of units compatibility, *e.g.*, for binary operation such as\n", "additions or subtraction or during the application of mathematical operations.\n", "In addition or in replacement of numerical data for coordinates,\n", "NDDataset can also have labeled coordinates where labels can be different kind of\n", "objects (strings, datetime,\n", "numpy nd.ndarray or other NDDatasets, etc...).\n", "\n", "This offers a lot of flexibility in using NDDatasets that, we hope, will be useful\n", "for applications.\n", "See the **[Examples](../../gallery/auto_examples/index.rst)** for\n", "additional information about such possible applications." ] }, { "cell_type": "markdown", "id": "ce32663e", "metadata": {}, "source": [ "**Below (and in the next sections), we try to give an almost complete view of the\n", "NDDataset features.**" ] }, { "cell_type": "code", "execution_count": 1, "id": "2b263c0d", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:03.108623Z", "iopub.status.busy": "2023-06-06T01:29:03.108364Z", "iopub.status.idle": "2023-06-06T01:29:06.245359Z", "shell.execute_reply": "2023-06-06T01:29:06.244790Z" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", "
\n", " \n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", "   SpectroChemPy's API - v.0.6.5
© Copyright 2014-2023 - A.Travert & C.Fernandez @ LCS\n", " \n", "
\n", "
\n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import spectrochempy as scp" ] }, { "cell_type": "markdown", "id": "05321f21", "metadata": {}, "source": [ "As we will make some reference to the\n", "**[numpy](https://numpy.org/doc/stable/index.html)** library, we also import it here." ] }, { "cell_type": "code", "execution_count": 2, "id": "cadcdd49", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.249794Z", "iopub.status.busy": "2023-06-06T01:29:06.248603Z", "iopub.status.idle": "2023-06-06T01:29:06.256963Z", "shell.execute_reply": "2023-06-06T01:29:06.256457Z" } }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "markdown", "id": "e41c71e4", "metadata": {}, "source": [ "We additionally import the three main SpectroChemPy objects that we will use through\n", "this tutorial" ] }, { "cell_type": "code", "execution_count": 3, "id": "a0833419", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.261042Z", "iopub.status.busy": "2023-06-06T01:29:06.259812Z", "iopub.status.idle": "2023-06-06T01:29:06.264072Z", "shell.execute_reply": "2023-06-06T01:29:06.263561Z" } }, "outputs": [], "source": [ "from spectrochempy import Coord, CoordSet, NDDataset" ] }, { "cell_type": "markdown", "id": "3560f892", "metadata": {}, "source": [ "For a convenient usage of units, we will also directly import\n", "**[ur]((../units/units.ipynb)**, the unit registry which contains all available\n", "units." ] }, { "cell_type": "code", "execution_count": 4, "id": "45e46c23", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.267801Z", "iopub.status.busy": "2023-06-06T01:29:06.266797Z", "iopub.status.idle": "2023-06-06T01:29:06.270746Z", "shell.execute_reply": "2023-06-06T01:29:06.270253Z" } }, "outputs": [], "source": [ "from spectrochempy import ur" ] }, { "cell_type": "markdown", "id": "3530e7aa", "metadata": {}, "source": [ "Multidimensional array are defined in Spectrochempy using the `NDDataset` object.\n", "\n", "`NDDataset` objects mostly behave as numpy's `numpy.ndarray`\n", "(see for instance __\n", "[numpy quickstart tutorial](https://numpy.org/doc/stable/user/quickstart.html)__)." ] }, { "cell_type": "markdown", "id": "6d641a8f", "metadata": {}, "source": [ "However, unlike raw numpy's ndarray, the presence of optional properties make\n", "them (hopefully) more appropriate for handling spectroscopic information,\n", "one of the major objectives of the SpectroChemPy package:\n", "\n", "* `mask`: Data can be partially masked at will\n", "* `units`: Data can have units, allowing units-aware operations\n", "* `CoordSet`: Data can have a set of coordinates, one or several by dimensions\n", "\n", "Additional metadata can also be added to the instances of this class\n", "through the `meta` properties." ] }, { "cell_type": "markdown", "id": "148475ed", "metadata": { "tags": [] }, "source": [ "## 1D-Dataset (unidimensional dataset)" ] }, { "cell_type": "markdown", "id": "78557de4", "metadata": {}, "source": [ "In the following example, a minimal 1D dataset is created from a simple list,\n", "to which we can add some metadata:" ] }, { "cell_type": "code", "execution_count": 5, "id": "14568f76", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.273813Z", "iopub.status.busy": "2023-06-06T01:29:06.273431Z", "iopub.status.idle": "2023-06-06T01:29:06.291588Z", "shell.execute_reply": "2023-06-06T01:29:06.291051Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name Dataset N1
author Blake and Mortimer
created 2023-06-06 01:29:06+00:00
description
A dataset from scratch
history
2023-06-06 01:29:06+00:00> Creation
DATA
title
values
[ 10 20 30]
size 3
" ], "text/plain": [ "NDDataset: [float64] unitless (size: 3)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1D = NDDataset(\n", " [10.0, 20.0, 30.0],\n", " name=\"Dataset N1\",\n", " author=\"Blake and Mortimer\",\n", " description=\"A dataset from scratch\",\n", " history=\"creation\",\n", ")\n", "d1D" ] }, { "cell_type": "code", "execution_count": 6, "id": "f9178b2e", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.294606Z", "iopub.status.busy": "2023-06-06T01:29:06.294394Z", "iopub.status.idle": "2023-06-06T01:29:06.299569Z", "shell.execute_reply": "2023-06-06T01:29:06.299078Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NDDataset: [float64] unitless (size: 3)\n" ] } ], "source": [ "print(d1D)" ] }, { "cell_type": "code", "execution_count": 7, "id": "014cf458", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.302616Z", "iopub.status.busy": "2023-06-06T01:29:06.302228Z", "iopub.status.idle": "2023-06-06T01:29:06.665610Z", "shell.execute_reply": "2023-06-06T01:29:06.665056Z" } }, "outputs": [ { "data": { "image/jpeg": 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dOuhe3MdxfX13Le3TxKVjEj4yEB52gADnk4zWP4d0rTr3xV4vnu7C1uJo9VjCSSwq7Li2hPBIyOa7auU8J/8AIxeMv+wqn/pLBQB1dFFFABRRRQAUjMqKWYgKBkkngCob29ttPtJLq7mSGCMZZ2PA/wAT7VhLa3fiZhLqEclrpGcx2TcSXHoZfRf9j8/SrjC+r0RnOdnyrVive3fiV2g0uV7bSwdst+vDzeqw+3+3+XrW3Y2NrptolrZwrFCg4Vf5n1PvU6IsaKiKFVRgKBgAUtEp3VloghCz5nqwoooqDQKKKKACiiigAooooAKKKKACiiigAooooAK5Twn/AMjF4y/7Cqf+ksFdXXKeE/8AkYvGX/YVT/0lgoA6uiiigAooooAKKKKAOQ8Xa34i0K+0+ezj0ttKmvLa1l84SNOTLIEO0AhRgEYJz9Ks63reqf8ACRWnh7Q0sxeyWz3k894rPHDEGCjCqQWZmPqMAHrWR8Sr9xBpthBperXcseoWl47WlhLMgjSYFvmVSNwCk7evT1p11eTW3i2w8Wx6Xqk2nXemvYzRpZyG4gZZd6M0ON+D8w6ccdjQBveFdcn1uwulvYI4NQsLuSyu0iJKeYmDuXPO1lZWGeRnFUfCf/IxeMv+wqn/AKSwU7wRZXcNvq+pXttJayarqUl4lvKMPHHtVEDDsxCAkds4rGk0bxrp/ifW7zShaNZX12twqterGTiJE5Bt3P8AB2Yf1oA9CorlPt/jv/oX9D/8G0n/AMYo+3+O/wDoX9D/APBtJ/8AGKAOrrP1PWbTS4gZGMs7tsit4vmkkfGdoH0IPoAcmsT7f47/AOhf0P8A8G0n/wAYrJ0/T/GVhq+o6odA0Se7vZQ/mSarJmNAiqEX9x0+XP485xVR5b+8TLmtaJ0lnpFze3cep65sedDut7NTmK29/wDaf/a7dq3a5T7f47/6F/Q//BtJ/wDGKPt/jv8A6F/Q/wDwbSf/ABiiUnLcIQUVodXSBlYsFYEqcMAehxnn8CK5X7f47/6F/Q//AAbSf/GKq2j+OrW5vphoWht9rnExH9qyDbiNEx/qefuZ/GpKO1orlPt/jv8A6F/Q/wDwbSf/ABij7f47/wChf0P/AMG0n/xigDq6QMrFgrAlThgD0OM8/gRXK/b/AB3/ANC/of8A4NpP/jFVbR/HVrc30w0LQ2+1ziYj+1ZBtxGiY/1PP3M/jQB2tFcp9v8AHf8A0L+h/wDg2k/+MUfb/Hf/AEL+h/8Ag2k/+MUAdXSBlYsFYEqcMAehxnn8CK5X7f47/wChf0P/AMG0n/xiqto/jq1ub6YaFobfa5xMR/asg24jRMf6nn7mfxoA7WiuU+3+O/8AoX9D/wDBtJ/8Yo+3+O/+hf0P/wAG0n/xigDq6QMrFgrAlThgD0OM8/gRXK/b/Hf/AEL+h/8Ag2k/+MVVtH8dWtzfTDQtDb7XOJiP7VkG3EaJj/U8/cz+NAHa0Vyn2/x3/wBC/of/AINpP/jFH2/x3/0L+h/+DaT/AOMUAdXXKeE/+Ri8Zf8AYVT/ANJYKPt/jv8A6F/Q/wDwbSf/ABipPCOm6xZ3Ou3ms29rBNqF6twkdtOZVVRFHH94qv8Ac9O9AHT0UUUAFFFFABRRRQAUUVz03gLwjczyTzeGtJklkYu7taISzE5JJx1zQBF4s1W/t7rRtG0qdba91a5aL7SyB/JjRGkdgp4LYUAZ45pnh3UdSh8R6t4d1W8+3SWkUN1b3ZiWN5IpNwIcKAuVZDyAMgjiq2seE103+xr7wtpVrHLpV49x9hhCwrOkkZjkAPADYIIJ4+Wn6RZaz/a+u+J73S/Ju7i2jt7LTjcIX2RB2AdxlQXdz0JAAFAEHjzV9Q03UtBhttSvdPsrmSf7ZPZ2QuZAqx5X5TG/8WBwO9E3ihdN+HN5ren6wdeljPlwz3EaRnzWdY1R1RVxhmGQQDWzeavrdtaWE0Phma7lmh3XMEN5CGtnwPky5UOMkjIPbpzXML4N1XVPC3ihLtYLDUNavBew26vvS3ZPL8sMw4JLRgsR696ANG2u9d0DxXo+marq/wDatvq0cyh2t0iaCaNN/wAuwDKFQ3ByRgcmtLxjrF3o+jwLp3li/vryGxt3kXcsbyNjeR32jcce1Zttaa5r/ivR9T1XSP7Kt9JjmYI9wkrTzSJs+XYThApbk4JyOBUuv2Gs+IdCEi6dHZ6lp2ppdWcMlwHW4EMmVJYD5d6547Z5oANKvdW0rxkPD2qakdThurFru2uZIUikVkdVdCEAUj51IOM9Qc1peMNbl8O+FL/VLeJZbiJVSFH+6ZHYImfbcwzWbpVlq2q+Mh4h1TTTpkNrYtaW1tJMksjs7qzuShKgfIoAznqTitLxhokviLwpf6XbyrFcSqrwu/3RIjB0z7blGaAMi2u9d0DxXo+marq/9q2+rRzKHa3SJoJo03/LsAyhUNwckYHJq78QNR1HSvBd5eaVNLDeJLbqkkUIlZVaZFYhSGBO0t2NUra01zX/ABXo+p6rpH9lW+kxzMEe4SVp5pE2fLsJwgUtycE5HArZfVNbTTZ7geHWe5junjS1W8j3SQhsLKGOFBIwdpII9aAM7wfqcF/LepH4ovdXli2h4L20S2kg687BFG2D6kEccV1ThijBGCsRwSM4Ncho2n6vqHjibxLqOmf2XEmn/YIbd5kklkzIHLuUJUAYwBk9T0rsKAOL8K3WuxeMtd0fWNZ/tNLW2tZomFqkAQyGTcAF5x8o6k1p+MdYu9H0eBdO8sX99eQ2Nu8i7ljeRsbyO+0bjj2rA0aTxIvj7UdVuvCV5b2mow21vva8tm8nyzJuZgshJHzjpk8Vpa/Yaz4h0ISLp0dnqWnaml1ZwyXAdbgQyZUlgPl3rnjtnmgA0q91bSvGQ8PapqR1OG6sWu7a5khSKRWR1V0IQBSPnUg4z1BzXWuGKMEYKxHBIzg1yWlWWrar4yHiHVNNOmQ2ti1pbW0kySyOzurO5KEqB8igDOepOK66gDi/Ct1rsXjLXdH1jWf7TS1trWaJhapAEMhk3ABecfKOpNXviBqOo6V4LvLzSppYbxJbdUkihErKrTIrEKQwJ2luxrF0aTxIvj7UdVuvCV5b2mow21vva8tm8nyzJuZgshJHzjpk8V0z6praabPcDw6z3Md08aWq3ke6SENhZQxwoJGDtJBHrQBneD9Tgv5b1I/FF7q8sW0PBe2iW0kHXnYIo2wfUgjjiumuVme1mS3lWKdkYRyMu4I2OCRkZAPbNcpo2n6vqHjibxLqOmf2XEmn/YIbd5kklkzIHLuUJUAYwBk9T0rqruWaGznlt7c3E6Rs0cIYKZGA4XJ4GTxk8UAcdJda74e8U6DY3OunV49Ulkilt5bWONowsZbzEKAYUEAENn7w5rW8Y6xd6Po8C6d5Yv768hsbd5F3LG8jY3kd9o3HHtXPeEotfj1qTVPEHhi+Or3p8uW8NxbGG0hzkRRqJSwQd8DLHk+2pr9hrPiHQhIunR2epadqaXVnDJcB1uBDJlSWA+XeueO2eaADSr3VtK8ZDw9qmpHU4bqxa7trmSFIpFZHVXQhAFI+dSDjPUHNdTcrM9rMlvKsU7IwjkZdwRscEjIyAe2a5bSrLVtV8ZDxDqmmnTIbWxa0traSZJZHZ3VnclCVA+RQBnPUnFdRdyzQ2c8tvbm4nSNmjhDBTIwHC5PAyeMnigDjpLrXfD3inQbG5106vHqkskUtvLaxxtGFjLeYhQDCggAhs/eHNaPxA1HUdK8F3l5pU0sN4ktuqSRQiVlVpkViFIYE7S3Y1h+Eotfj1qTVPEHhi+Or3p8uW8NxbGG0hzkRRqJSwQd8DLHk+3UPqmtpps9wPDrPcx3TxpareR7pIQ2FlDHCgkYO0kEetAGd4P1OC/lvUj8UXuryxbQ8F7aJbSQdedgijbB9SCOOK6uuP0bT9X1DxxN4l1HTP7LiTT/sENu8ySSyZkDl3KEqAMYAyep6V2FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRUdwJjbSi2eNJyh8tpFLKGxwSAQSM9siuK8P+MtW8Qa1FpC6fFbXWnZGutIrFI35CJCcjO/G8E5AXrkmgDuaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAIbyO4lsp47SdILhkIimePzAjY4YrkZx6ZFctpfgKDQ77Tb7TL14ryFWTUJZE3nUVYlmMnIw+87g3OMkYIrr6KACiiigAooooAKKKKACiiigAooooAKKKKACiiigD//Z", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = d1D.plot(figsize=(3, 2))" ] }, { "cell_type": "markdown", "id": "837e4c29", "metadata": {}, "source": [ "Except few additional metadata such `author` , `created` ..., there is not much\n", "difference with respect to a conventional\n", "**[numpy.array](\n", "https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array)**.\n", "For example, one\n", "can apply numpy\n", "**[ufunc](https://numpy.org/doc/stable/reference/ufuncs.html#available-ufuncs)'s**\n", "directly to a NDDataset or make basic arithmetic\n", "operation with these objects:" ] }, { "cell_type": "code", "execution_count": 8, "id": "d3f24ae5", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.669893Z", "iopub.status.busy": "2023-06-06T01:29:06.668836Z", "iopub.status.idle": "2023-06-06T01:29:06.677742Z", "shell.execute_reply": "2023-06-06T01:29:06.677250Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name Dataset N1
author Blake and Mortimer
created 2023-06-06 01:29:06+00:00
description
A dataset from scratch
history
2023-06-06 01:29:06+00:00> Creation
2023-06-06 01:29:06+00:00> Ufunc sqrt applied.
DATA
title sqrt()
values
[ 3.162 4.472 5.477]
size 3
" ], "text/plain": [ "NDDataset: [float64] unitless (size: 3)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sqrt(d1D)" ] }, { "cell_type": "code", "execution_count": 9, "id": "43a8573c", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.681711Z", "iopub.status.busy": "2023-06-06T01:29:06.680674Z", "iopub.status.idle": "2023-06-06T01:29:06.690753Z", "shell.execute_reply": "2023-06-06T01:29:06.690270Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name Dataset N1
author Blake and Mortimer
created 2023-06-06 01:29:06+00:00
description
A dataset from scratch
history
2023-06-06 01:29:06+00:00> Creation
2023-06-06 01:29:06+00:00> Inplace binary op: iadd with `Dataset N1`
DATA
title
values
[ 15 30 45]
size 3
" ], "text/plain": [ "NDDataset: [float64] unitless (size: 3)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1D += d1D / 2.0\n", "d1D" ] }, { "cell_type": "markdown", "id": "b3a98609", "metadata": {}, "source": [ "As seen above, there are some attributes that are automatically added to the dataset:\n", "\n", "* `id` : This is a unique identifier for the object.\n", "* `name`: A short and unique name for the dataset. It will beequal to the automatic\n", "`id` if it is not provided.\n", "* `author` : Author determined from the computer name if not provided.\n", "* `created` : Date and time of creation.\n", "* `modified`: Date and time of modification.\n", "\n", "These attributes can be modified by the user, but the `id` , `created` and `modified`\n", "attributes are read only.\n", "\n", "Some other attributes are defined to describe the data:\n", "* `title`: A long name that will be used in plots or in some other operations.\n", "* `history`: history of operation achieved on the object since the object creation.\n", "* `description`: A comment or a description of the objects purpose or contents.\n", "* `origin`: An optional reference to the source of the data." ] }, { "cell_type": "markdown", "id": "840725d1", "metadata": {}, "source": [ "Here is an example of the use of the NDDataset attributes:" ] }, { "cell_type": "code", "execution_count": 10, "id": "9b9747ec", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.694778Z", "iopub.status.busy": "2023-06-06T01:29:06.693750Z", "iopub.status.idle": "2023-06-06T01:29:06.700945Z", "shell.execute_reply": "2023-06-06T01:29:06.700461Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name mydataset
author Blake and Mortimer
created 2023-06-06 01:29:06+00:00
description
Some experimental measurements
history
2023-06-06 01:29:06+00:00> Creation
2023-06-06 01:29:06+00:00> Inplace binary op: iadd with `Dataset N1`
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title intensity
values
[ 15 30 45]
size 3
" ], "text/plain": [ "NDDataset: [float64] unitless (size: 3)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1D.title = \"intensity\"\n", "d1D.name = \"mydataset\"\n", "d1D.history = \"created from scratch\"\n", "d1D.description = \"Some experimental measurements\"\n", "d1D" ] }, { "cell_type": "markdown", "id": "ac1b2bc1", "metadata": {}, "source": [ "d1D is a 1D (1-dimensional) dataset with only one dimension.\n", "\n", "Some attributes are useful to check this kind of information:" ] }, { "cell_type": "code", "execution_count": 11, "id": "a63c1aab", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.704951Z", "iopub.status.busy": "2023-06-06T01:29:06.703908Z", "iopub.status.idle": "2023-06-06T01:29:06.709814Z", "shell.execute_reply": "2023-06-06T01:29:06.709344Z" } }, "outputs": [ { "data": { "text/plain": [ "(3,)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1D.shape # the shape of 1D contain only one dimension size" ] }, { "cell_type": "code", "execution_count": 12, "id": "eb2444ef", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.713707Z", "iopub.status.busy": "2023-06-06T01:29:06.712694Z", "iopub.status.idle": "2023-06-06T01:29:06.718495Z", "shell.execute_reply": "2023-06-06T01:29:06.718024Z" } }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1D.ndim # the number of dimensions" ] }, { "cell_type": "code", "execution_count": 13, "id": "db11d045", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.722385Z", "iopub.status.busy": "2023-06-06T01:29:06.721372Z", "iopub.status.idle": "2023-06-06T01:29:06.727190Z", "shell.execute_reply": "2023-06-06T01:29:06.726717Z" } }, "outputs": [ { "data": { "text/plain": [ "['x']" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1D.dims # the name of the dimension (it has been automatically attributed)" ] }, { "cell_type": "markdown", "id": "a731f1e6", "metadata": {}, "source": [ "**Note**: The names of the dimensions are set automatically. But they can be changed,\n", "with the limitation that the\n", "name must be a single letter." ] }, { "cell_type": "code", "execution_count": 14, "id": "1ad40b16", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.731126Z", "iopub.status.busy": "2023-06-06T01:29:06.730118Z", "iopub.status.idle": "2023-06-06T01:29:06.734264Z", "shell.execute_reply": "2023-06-06T01:29:06.733789Z" } }, "outputs": [], "source": [ "d1D.dims = [\"q\"] # change the list of dim names." ] }, { "cell_type": "code", "execution_count": 15, "id": "8fb4c324", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.738132Z", "iopub.status.busy": "2023-06-06T01:29:06.737105Z", "iopub.status.idle": "2023-06-06T01:29:06.742998Z", "shell.execute_reply": "2023-06-06T01:29:06.742518Z" } }, "outputs": [ { "data": { "text/plain": [ "['q']" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1D.dims" ] }, { "cell_type": "markdown", "id": "416ecdae", "metadata": {}, "source": [ "### nD-Dataset (multidimensional dataset)" ] }, { "cell_type": "markdown", "id": "51c308f1", "metadata": {}, "source": [ "To create a nD NDDataset, we can provide a nD-array like object to the NDDataset\n", "instance constructor" ] }, { "cell_type": "code", "execution_count": 16, "id": "f586a397", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.746999Z", "iopub.status.busy": "2023-06-06T01:29:06.745987Z", "iopub.status.idle": "2023-06-06T01:29:06.752377Z", "shell.execute_reply": "2023-06-06T01:29:06.751899Z" } }, "outputs": [ { "data": { "text/plain": [ "array([[[ 0.3122, 0.9475, ..., 0.6869, 0.3645],\n", " [ 0.2168, 0.9174, ..., 0.225, 0.8701],\n", " [ 0.5616, 0.9712, ..., 0.8156, 0.4228],\n", " [ 0.4389, 0.7739, ..., 0.873, 0.4794]],\n", "\n", " [[ 0.8906, 0.006258, ..., 0.9852, 0.94],\n", " [ 0.7681, 0.5764, ..., 0.7628, 0.2925],\n", " [ 0.2177, 0.07877, ..., 0.5296, 0.3631],\n", " [ 0.4269, 0.2059, ..., 0.7017, 0.798]]])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = np.random.rand(2, 4, 6)\n", "a" ] }, { "cell_type": "code", "execution_count": 17, "id": "8c556d11", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.756282Z", "iopub.status.busy": "2023-06-06T01:29:06.755272Z", "iopub.status.idle": "2023-06-06T01:29:06.764488Z", "shell.execute_reply": "2023-06-06T01:29:06.763985Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name 3D dataset creation
author Someone
created 2023-06-06 01:29:06+00:00
description
Some example
history
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title energy
values
[[[ 0.3122 0.9475 ... 0.6869 0.3645]
[ 0.2168 0.9174 ... 0.225 0.8701]
[ 0.5616 0.9712 ... 0.8156 0.4228]
[ 0.4389 0.7739 ... 0.873 0.4794]]

[[ 0.8906 0.006258 ... 0.9852 0.94]
[ 0.7681 0.5764 ... 0.7628 0.2925]
[ 0.2177 0.07877 ... 0.5296 0.3631]
[ 0.4269 0.2059 ... 0.7017 0.798]]]
shape (u:2, v:4, t:6)
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (u:2, v:4, t:6))" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D = NDDataset(a)\n", "d3D.title = \"energy\"\n", "d3D.author = \"Someone\"\n", "d3D.name = \"3D dataset creation\"\n", "d3D.history = \"created from scratch\"\n", "d3D.description = \"Some example\"\n", "d3D.dims = [\"u\", \"v\", \"t\"]\n", "d3D" ] }, { "cell_type": "markdown", "id": "57e38fa1", "metadata": {}, "source": [ "We can also add all information in a single statement" ] }, { "cell_type": "code", "execution_count": 18, "id": "7eec6d1b", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.768488Z", "iopub.status.busy": "2023-06-06T01:29:06.767458Z", "iopub.status.idle": "2023-06-06T01:29:06.776393Z", "shell.execute_reply": "2023-06-06T01:29:06.775904Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name 3D_dataset
author Someone
created 2023-06-06 01:29:06+00:00
description
a single statement creation example
history
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title Energy
values
[[[ 0.3122 0.9475 ... 0.6869 0.3645]
[ 0.2168 0.9174 ... 0.225 0.8701]
[ 0.5616 0.9712 ... 0.8156 0.4228]
[ 0.4389 0.7739 ... 0.873 0.4794]]

[[ 0.8906 0.006258 ... 0.9852 0.94]
[ 0.7681 0.5764 ... 0.7628 0.2925]
[ 0.2177 0.07877 ... 0.5296 0.3631]
[ 0.4269 0.2059 ... 0.7017 0.798]]]
shape (u:2, v:4, t:6)
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (u:2, v:4, t:6))" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D = NDDataset(\n", " a,\n", " dims=[\"u\", \"v\", \"t\"],\n", " title=\"Energy\",\n", " author=\"Someone\",\n", " name=\"3D_dataset\",\n", " history=\"created from scratch\",\n", " description=\"a single statement creation example\",\n", ")\n", "d3D" ] }, { "cell_type": "markdown", "id": "ecf23aa4", "metadata": {}, "source": [ "Three names are attributed at the creation (if they are not provided with the `dims`\n", "attribute, then the name are:\n", "'z','y','x' automatically attributed)" ] }, { "cell_type": "code", "execution_count": 19, "id": "0443d0a8", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.780378Z", "iopub.status.busy": "2023-06-06T01:29:06.779359Z", "iopub.status.idle": "2023-06-06T01:29:06.785293Z", "shell.execute_reply": "2023-06-06T01:29:06.784814Z" } }, "outputs": [ { "data": { "text/plain": [ "['u', 'v', 't']" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.dims" ] }, { "cell_type": "code", "execution_count": 20, "id": "efccb351", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.789205Z", "iopub.status.busy": "2023-06-06T01:29:06.788184Z", "iopub.status.idle": "2023-06-06T01:29:06.793988Z", "shell.execute_reply": "2023-06-06T01:29:06.793520Z" } }, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.ndim" ] }, { "cell_type": "code", "execution_count": 21, "id": "51f07e81", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.797866Z", "iopub.status.busy": "2023-06-06T01:29:06.796862Z", "iopub.status.idle": "2023-06-06T01:29:06.808989Z", "shell.execute_reply": "2023-06-06T01:29:06.808461Z" } }, "outputs": [ { "data": { "text/plain": [ "(2, 4, 6)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.shape" ] }, { "cell_type": "markdown", "id": "3836ae4f", "metadata": {}, "source": [ "## About the dates and times\n", "The dates and times are stored internally as\n", "[UTC (Coordinated_Universal_Time)](https://en.wikipedia.org/wiki/Coordinated_\n", "Universal_Time).\n", "Timezone information is stored in the timezone attribute.\n", "If not set, the default is to use the local timezone,\n", "which is probably the most common case." ] }, { "cell_type": "code", "execution_count": 22, "id": "99ddb610", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.812970Z", "iopub.status.busy": "2023-06-06T01:29:06.811936Z", "iopub.status.idle": "2023-06-06T01:29:06.818174Z", "shell.execute_reply": "2023-06-06T01:29:06.817669Z" } }, "outputs": [ { "data": { "text/plain": [ "'2023-06-06 01:29:06+00:00'" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nd = NDDataset()\n", "nd.created" ] }, { "cell_type": "markdown", "id": "28598a41", "metadata": {}, "source": [ "In this case our local timezone has been used by default for the conversion from\n", "UTC datetime." ] }, { "cell_type": "code", "execution_count": 23, "id": "8d14d19d", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.822104Z", "iopub.status.busy": "2023-06-06T01:29:06.821092Z", "iopub.status.idle": "2023-06-06T01:29:06.832995Z", "shell.execute_reply": "2023-06-06T01:29:06.832461Z" } }, "outputs": [ { "data": { "text/plain": [ "'UTC'" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nd.local_timezone" ] }, { "cell_type": "code", "execution_count": 24, "id": "7b1acdd6", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.836912Z", "iopub.status.busy": "2023-06-06T01:29:06.835887Z", "iopub.status.idle": "2023-06-06T01:29:06.863939Z", "shell.execute_reply": "2023-06-06T01:29:06.863354Z" } }, "outputs": [ { "data": { "text/plain": [ "'2023-06-05 20:29:06-05:00'" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nd.timezone = \"EST\"\n", "nd.created" ] }, { "cell_type": "markdown", "id": "fc22baa9", "metadata": {}, "source": [ "For a list of timezone code (TZ) you can have a look at\n", "[List_of_tz_database_time_zones](\n", "https://en.wikipedia.org/wiki/List_of_tz_database_time_zones)." ] }, { "cell_type": "markdown", "id": "fe32dd44", "metadata": {}, "source": [ "## About the `history` attribute" ] }, { "cell_type": "markdown", "id": "5d3aca6c", "metadata": {}, "source": [ "The history is saved internally into a list, but its has a different behaviour than\n", "the usual list.\n", "The first time a NDDataset is created, the list is empty" ] }, { "cell_type": "code", "execution_count": 25, "id": "edfaf57f", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.868200Z", "iopub.status.busy": "2023-06-06T01:29:06.867167Z", "iopub.status.idle": "2023-06-06T01:29:06.881024Z", "shell.execute_reply": "2023-06-06T01:29:06.880466Z" } }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nd = NDDataset()\n", "nd.history" ] }, { "cell_type": "markdown", "id": "8d22a371", "metadata": {}, "source": [ "Assigning a string to the history attribute has two effects. The first one is that\n", "the string is appended automatically to the previous history list, and second it is\n", "preceeded by the time it has been added." ] }, { "cell_type": "code", "execution_count": 26, "id": "e17204de", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.885130Z", "iopub.status.busy": "2023-06-06T01:29:06.884097Z", "iopub.status.idle": "2023-06-06T01:29:06.890436Z", "shell.execute_reply": "2023-06-06T01:29:06.889910Z" } }, "outputs": [ { "data": { "text/plain": [ "['2023-06-06 01:29:06+00:00> Some history',\n", " '2023-06-06 01:29:06+00:00> Another history to append',\n", " '2023-06-06 01:29:06+00:00> ...']" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nd.history = \"some history\"\n", "nd.history = \"another history to append\"\n", "nd.history = \"...\"\n", "nd.history" ] }, { "cell_type": "markdown", "id": "deb14438", "metadata": {}, "source": [ "If you want to erase the history, assign an empty list" ] }, { "cell_type": "code", "execution_count": 27, "id": "552a6f03", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.893461Z", "iopub.status.busy": "2023-06-06T01:29:06.893251Z", "iopub.status.idle": "2023-06-06T01:29:06.899000Z", "shell.execute_reply": "2023-06-06T01:29:06.898499Z" } }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nd.history = []\n", "nd.history" ] }, { "cell_type": "markdown", "id": "a1da9eb4", "metadata": {}, "source": [ "If you want to replace the full history use bracket around your history line:" ] }, { "cell_type": "code", "execution_count": 28, "id": "0cd59e40", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.902080Z", "iopub.status.busy": "2023-06-06T01:29:06.901699Z", "iopub.status.idle": "2023-06-06T01:29:06.907888Z", "shell.execute_reply": "2023-06-06T01:29:06.907422Z" } }, "outputs": [ { "data": { "text/plain": [ "['2023-06-06 01:29:06+00:00> A more interesting message']" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nd.history = \"Created form scratch\"\n", "nd.history = \"a second ligne that will be erased\"\n", "nd.history = [\"A more interesting message\"]\n", "nd.history" ] }, { "cell_type": "markdown", "id": "9403cf50", "metadata": {}, "source": [ "## Units" ] }, { "cell_type": "markdown", "id": "43e2ca4a", "metadata": {}, "source": [ "One interesting possibility for a NDDataset is to have defined units for the internal\n", "data." ] }, { "cell_type": "code", "execution_count": 29, "id": "40fbd651", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.910975Z", "iopub.status.busy": "2023-06-06T01:29:06.910595Z", "iopub.status.idle": "2023-06-06T01:29:06.914896Z", "shell.execute_reply": "2023-06-06T01:29:06.914420Z" } }, "outputs": [], "source": [ "d1D.units = ur.eV # ur is a registry containing all available units" ] }, { "cell_type": "code", "execution_count": 30, "id": "eebec195", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.917691Z", "iopub.status.busy": "2023-06-06T01:29:06.917316Z", "iopub.status.idle": "2023-06-06T01:29:06.933031Z", "shell.execute_reply": "2023-06-06T01:29:06.932478Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name mydataset
author Blake and Mortimer
created 2023-06-06 01:29:06+00:00
description
Some experimental measurements
history
2023-06-06 01:29:06+00:00> Creation
2023-06-06 01:29:06+00:00> Inplace binary op: iadd with `Dataset N1`
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title intensity
values
[ 15 30 45] eV
size 3
" ], "text/plain": [ "NDDataset: [float64] eV (size: 3)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1D # note the eV symbol of the units added to the values field below" ] }, { "cell_type": "markdown", "id": "4fb00e2c", "metadata": {}, "source": [ "This allows to make units-aware calculations:" ] }, { "cell_type": "code", "execution_count": 31, "id": "d367d905", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.937013Z", "iopub.status.busy": "2023-06-06T01:29:06.935823Z", "iopub.status.idle": "2023-06-06T01:29:06.945310Z", "shell.execute_reply": "2023-06-06T01:29:06.944797Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name mydataset
author Blake and Mortimer
created 2023-06-06 01:29:06+00:00
description
Some experimental measurements
history
2023-06-06 01:29:06+00:00> Creation
2023-06-06 01:29:06+00:00> Inplace binary op: iadd with `Dataset N1`
2023-06-06 01:29:06+00:00> Created from scratch
2023-06-06 01:29:06+00:00> Binary operation pow with `2` has been performed
DATA
title intensity
values
[ 225 900 2025] eV²
size 3
" ], "text/plain": [ "NDDataset: [float64] eV² (size: 3)" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1D**2 # note the results in eV^2" ] }, { "cell_type": "code", "execution_count": 32, "id": "49821f44", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.949149Z", "iopub.status.busy": "2023-06-06T01:29:06.948141Z", "iopub.status.idle": "2023-06-06T01:29:06.963531Z", "shell.execute_reply": "2023-06-06T01:29:06.962974Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name mydataset
author Blake and Mortimer
created 2023-06-06 01:29:06+00:00
description
Some experimental measurements
history
2023-06-06 01:29:06+00:00> Creation
2023-06-06 01:29:06+00:00> Inplace binary op: iadd with `Dataset N1`
2023-06-06 01:29:06+00:00> Created from scratch
2023-06-06 01:29:06+00:00> Ufunc sqrt applied.
DATA
title sqrt(intensity)
values
[ 3.873 5.477 6.708] eV⁰⋅⁵
size 3
" ], "text/plain": [ "NDDataset: [float64] eV⁰⋅⁵ (size: 3)" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sqrt(d1D) # note the result in e^0.5" ] }, { "cell_type": "code", "execution_count": 33, "id": "8c2c61d3", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.967453Z", "iopub.status.busy": "2023-06-06T01:29:06.966446Z", "iopub.status.idle": "2023-06-06T01:29:06.983513Z", "shell.execute_reply": "2023-06-06T01:29:06.982957Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name mydataset
author Blake and Mortimer
created 2023-06-06 01:29:06+00:00
description
Some experimental measurements
history
2023-06-06 01:29:06+00:00> Creation
2023-06-06 01:29:06+00:00> Inplace binary op: iadd with `Dataset N1`
2023-06-06 01:29:06+00:00> Created from scratch
2023-06-06 01:29:06+00:00> Binary operation truediv with `5.0 s` has been performed
DATA
title intensity
values
[ 3 6 9] eV.s⁻¹
size 3
" ], "text/plain": [ "NDDataset: [float64] eV.s⁻¹ (size: 3)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "time = 5.0 * ur.second\n", "d1D / time # here we get results in eV/s" ] }, { "cell_type": "markdown", "id": "68a3f9ec", "metadata": {}, "source": [ "Conversion can be done between different units transparently" ] }, { "cell_type": "code", "execution_count": 34, "id": "aa5f58b4", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:06.987546Z", "iopub.status.busy": "2023-06-06T01:29:06.986541Z", "iopub.status.idle": "2023-06-06T01:29:07.003360Z", "shell.execute_reply": "2023-06-06T01:29:07.002833Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name mydataset
author Blake and Mortimer
created 2023-06-06 01:29:06+00:00
description
Some experimental measurements
history
2023-06-06 01:29:06+00:00> Creation
2023-06-06 01:29:06+00:00> Inplace binary op: iadd with `Dataset N1`
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title intensity
values
[2.403e-18 4.807e-18 7.21e-18] J
size 3
" ], "text/plain": [ "NDDataset: [float64] J (size: 3)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1D.to(\"J\")" ] }, { "cell_type": "code", "execution_count": 35, "id": "4604a6fc", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.006383Z", "iopub.status.busy": "2023-06-06T01:29:07.006174Z", "iopub.status.idle": "2023-06-06T01:29:07.014704Z", "shell.execute_reply": "2023-06-06T01:29:07.014227Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name mydataset
author Blake and Mortimer
created 2023-06-06 01:29:06+00:00
description
Some experimental measurements
history
2023-06-06 01:29:06+00:00> Creation
2023-06-06 01:29:06+00:00> Inplace binary op: iadd with `Dataset N1`
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title intensity
values
[1.741e+05 3.481e+05 5.222e+05] K
size 3
" ], "text/plain": [ "NDDataset: [float64] K (size: 3)" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1D.to(\"K\")" ] }, { "cell_type": "markdown", "id": "ede21867", "metadata": {}, "source": [ "## Coordinates" ] }, { "cell_type": "markdown", "id": "7728d69d", "metadata": {}, "source": [ "The above created `d3D` dataset has 3 dimensions, but no coordinate for these\n", "dimensions. Here arises a big difference\n", "with simple `numpy`-arrays:\n", "* We can add coordinates to each dimension of a NDDataset." ] }, { "cell_type": "markdown", "id": "3a760fe9", "metadata": {}, "source": [ "To get the list of all defined coordinates, we can use the `coords` attribute:" ] }, { "cell_type": "code", "execution_count": 36, "id": "1df6baf7", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.018667Z", "iopub.status.busy": "2023-06-06T01:29:07.017679Z", "iopub.status.idle": "2023-06-06T01:29:07.021645Z", "shell.execute_reply": "2023-06-06T01:29:07.021176Z" } }, "outputs": [], "source": [ "d3D.coordset # no coordinates, so it returns nothing (None)" ] }, { "cell_type": "code", "execution_count": 37, "id": "cdcaf060", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.025400Z", "iopub.status.busy": "2023-06-06T01:29:07.024387Z", "iopub.status.idle": "2023-06-06T01:29:07.028337Z", "shell.execute_reply": "2023-06-06T01:29:07.027876Z" } }, "outputs": [], "source": [ "d3D.t # the same for coordinate t, v, u which are not yet set" ] }, { "cell_type": "markdown", "id": "679b19a7", "metadata": {}, "source": [ "To add coordinates, on way is to set them one by one:" ] }, { "cell_type": "code", "execution_count": 38, "id": "9167a041", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.032122Z", "iopub.status.busy": "2023-06-06T01:29:07.031134Z", "iopub.status.idle": "2023-06-06T01:29:07.056766Z", "shell.execute_reply": "2023-06-06T01:29:07.056233Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
DIMENSION `t`
size 6
title time
coordinates
[ 0 0.1 0.2 0.3 0.4 0.5] s
" ], "text/plain": [ "CoordSet: [t:time, u:, v:]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.t = (\n", " Coord.arange(6) * 0.1\n", ") # we need a sequence of 6 values for `t` dimension (see shape above)\n", "d3D.t.title = \"time\"\n", "d3D.t.units = ur.seconds\n", "d3D.coordset # now return a list of coordinates" ] }, { "cell_type": "code", "execution_count": 39, "id": "a5bf607a", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.061611Z", "iopub.status.busy": "2023-06-06T01:29:07.060113Z", "iopub.status.idle": "2023-06-06T01:29:07.070148Z", "shell.execute_reply": "2023-06-06T01:29:07.069653Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "
size 6
title time
coordinates
[ 0 0.1 0.2 0.3 0.4 0.5] s
" ], "text/plain": [ "Coord: [float64] s (size: 6)" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.t" ] }, { "cell_type": "code", "execution_count": 40, "id": "7103c3f9", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.074890Z", "iopub.status.busy": "2023-06-06T01:29:07.073459Z", "iopub.status.idle": "2023-06-06T01:29:07.083357Z", "shell.execute_reply": "2023-06-06T01:29:07.082872Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "
size 6
title time
coordinates
[ 0 0.1 0.2 0.3 0.4 0.5] s
" ], "text/plain": [ "Coord: [float64] s (size: 6)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.coordset(\"t\") # Alternative way to get a given coordinates" ] }, { "cell_type": "code", "execution_count": 41, "id": "668e8323", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.087978Z", "iopub.status.busy": "2023-06-06T01:29:07.086548Z", "iopub.status.idle": "2023-06-06T01:29:07.096365Z", "shell.execute_reply": "2023-06-06T01:29:07.095877Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "
size 6
title time
coordinates
[ 0 0.1 0.2 0.3 0.4 0.5] s
" ], "text/plain": [ "Coord: [float64] s (size: 6)" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D[\"t\"] # another alternative way to get a given coordinates" ] }, { "cell_type": "markdown", "id": "80ecd570", "metadata": {}, "source": [ "The two other coordinates u and v are still undefined" ] }, { "cell_type": "code", "execution_count": 42, "id": "baf9ba4a", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.101079Z", "iopub.status.busy": "2023-06-06T01:29:07.099634Z", "iopub.status.idle": "2023-06-06T01:29:07.108087Z", "shell.execute_reply": "2023-06-06T01:29:07.107599Z" } }, "outputs": [ { "data": { "text/plain": [ "(Coord: empty, Coord: empty)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.u, d3D.v" ] }, { "cell_type": "markdown", "id": "62dc939b", "metadata": {}, "source": [ "When the dataset is printed, only the information for the existing coordinates is\n", "given." ] }, { "cell_type": "code", "execution_count": 43, "id": "eb0e5d36", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.112694Z", "iopub.status.busy": "2023-06-06T01:29:07.111276Z", "iopub.status.idle": "2023-06-06T01:29:07.122516Z", "shell.execute_reply": "2023-06-06T01:29:07.122028Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name 3D_dataset
author Someone
created 2023-06-06 01:29:06+00:00
description
a single statement creation example
history
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title Energy
values
[[[ 0.3122 0.9475 ... 0.6869 0.3645]
[ 0.2168 0.9174 ... 0.225 0.8701]
[ 0.5616 0.9712 ... 0.8156 0.4228]
[ 0.4389 0.7739 ... 0.873 0.4794]]

[[ 0.8906 0.006258 ... 0.9852 0.94]
[ 0.7681 0.5764 ... 0.7628 0.2925]
[ 0.2177 0.07877 ... 0.5296 0.3631]
[ 0.4269 0.2059 ... 0.7017 0.798]]]
shape (u:2, v:4, t:6)
DIMENSION `t`
size 6
title time
coordinates
[ 0 0.1 0.2 0.3 0.4 0.5] s
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (u:2, v:4, t:6))" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D" ] }, { "cell_type": "markdown", "id": "dfc93373", "metadata": {}, "source": [ "Programmatically, we can use the attribute `is_empty` or `has_data` to check this" ] }, { "cell_type": "code", "execution_count": 44, "id": "9ef13ff9", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.126433Z", "iopub.status.busy": "2023-06-06T01:29:07.125432Z", "iopub.status.idle": "2023-06-06T01:29:07.131423Z", "shell.execute_reply": "2023-06-06T01:29:07.130916Z" } }, "outputs": [ { "data": { "text/plain": [ "(False, True)" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.v.has_data, d3D.v.is_empty" ] }, { "cell_type": "markdown", "id": "0acd126a", "metadata": {}, "source": [ "An error is raised when a coordinate doesn't exist" ] }, { "cell_type": "code", "execution_count": 45, "id": "7c3a2f5b", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.135250Z", "iopub.status.busy": "2023-06-06T01:29:07.134256Z", "iopub.status.idle": "2023-06-06T01:29:07.139951Z", "shell.execute_reply": "2023-06-06T01:29:07.139480Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " ERROR | KeyError: 'Could not find `x` in coordinates names or titles'\n" ] } ], "source": [ "try:\n", " d3D.x\n", "except KeyError as e:\n", " scp.error_(KeyError, e)" ] }, { "cell_type": "markdown", "id": "00d4d71e", "metadata": {}, "source": [ "In some case it can also be useful to get a coordinate from its title instead of its\n", "name (the limitation is that if\n", "several coordinates have the same title, then only the first ones that is found in\n", "the coordinate list, will be\n", "returned - this can be ambiguous)" ] }, { "cell_type": "code", "execution_count": 46, "id": "e3f45d07", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.143774Z", "iopub.status.busy": "2023-06-06T01:29:07.142783Z", "iopub.status.idle": "2023-06-06T01:29:07.149402Z", "shell.execute_reply": "2023-06-06T01:29:07.148923Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "
size 6
title time
coordinates
[ 0 0.1 0.2 0.3 0.4 0.5] s
" ], "text/plain": [ "Coord: [float64] s (size: 6)" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D[\"time\"]" ] }, { "cell_type": "code", "execution_count": 47, "id": "6e295cba", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.153221Z", "iopub.status.busy": "2023-06-06T01:29:07.152164Z", "iopub.status.idle": "2023-06-06T01:29:07.165005Z", "shell.execute_reply": "2023-06-06T01:29:07.164461Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "
size 6
title time
coordinates
[ 0 0.1 0.2 0.3 0.4 0.5] s
" ], "text/plain": [ "Coord: [float64] s (size: 6)" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.time" ] }, { "cell_type": "markdown", "id": "e26bea6b", "metadata": {}, "source": [ "## Labels" ] }, { "cell_type": "markdown", "id": "23f8de11", "metadata": {}, "source": [ "It is possible to use labels instead of numerical coordinates. They are sequence of\n", "objects .The length of the\n", "sequence must be equal to the size of a dimension." ] }, { "cell_type": "markdown", "id": "f30df646", "metadata": {}, "source": [ "The labels can be simple strings, *e.g.,*" ] }, { "cell_type": "code", "execution_count": 48, "id": "c88f7456", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.168995Z", "iopub.status.busy": "2023-06-06T01:29:07.167978Z", "iopub.status.idle": "2023-06-06T01:29:07.181041Z", "shell.execute_reply": "2023-06-06T01:29:07.180470Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name 3D_dataset
author Someone
created 2023-06-06 01:29:06+00:00
description
a single statement creation example
history
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title Energy
values
[[[ 0.3122 0.9475 ... 0.6869 0.3645]
[ 0.2168 0.9174 ... 0.225 0.8701]
[ 0.5616 0.9712 ... 0.8156 0.4228]
[ 0.4389 0.7739 ... 0.873 0.4794]]

[[ 0.8906 0.006258 ... 0.9852 0.94]
[ 0.7681 0.5764 ... 0.7628 0.2925]
[ 0.2177 0.07877 ... 0.5296 0.3631]
[ 0.4269 0.2059 ... 0.7017 0.798]]]
shape (u:2, v:4, t:6)
DIMENSION `t`
size 6
title time
coordinates
[ 0 0.1 0.2 0.3 0.4 0.5] s
DIMENSION `u`
size 2
title some tags
labels
[ a b]
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (u:2, v:4, t:6))" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tags = list(\"ab\")\n", "d3D.u.title = \"some tags\"\n", "d3D.u.labels = tags # TODO: avoid repetition\n", "d3D" ] }, { "cell_type": "markdown", "id": "6af0c3bd", "metadata": {}, "source": [ "or more complex objects.\n", "\n", "For instance here we use datetime.timedelta objects:" ] }, { "cell_type": "code", "execution_count": 49, "id": "9e6aafa9", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.185132Z", "iopub.status.busy": "2023-06-06T01:29:07.184080Z", "iopub.status.idle": "2023-06-06T01:29:07.203696Z", "shell.execute_reply": "2023-06-06T01:29:07.203108Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name 3D_dataset
author Someone
created 2023-06-06 01:29:06+00:00
description
a single statement creation example
history
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title Energy
values
[[[ 0.3122 0.9475 ... 0.6869 0.3645]
[ 0.2168 0.9174 ... 0.225 0.8701]
[ 0.5616 0.9712 ... 0.8156 0.4228]
[ 0.4389 0.7739 ... 0.873 0.4794]]

[[ 0.8906 0.006258 ... 0.9852 0.94]
[ 0.7681 0.5764 ... 0.7628 0.2925]
[ 0.2177 0.07877 ... 0.5296 0.3631]
[ 0.4269 0.2059 ... 0.7017 0.798]]]
shape (u:2, v:4, t:6)
DIMENSION `t`
size 6
title time
labels
[ 0:00:00 0:01:00 0:02:00 0:03:00 0:04:00 0:05:00]
DIMENSION `u`
size 2
title some tags
labels
[ a b]
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (u:2, v:4, t:6))" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from datetime import timedelta\n", "\n", "start = timedelta(0)\n", "times = [start + timedelta(seconds=x * 60) for x in range(6)]\n", "d3D.t = None\n", "d3D.t.labels = times\n", "d3D.t.title = \"time\"\n", "d3D" ] }, { "cell_type": "markdown", "id": "a02f872c", "metadata": {}, "source": [ "In this case, getting a coordinate that doesn't possess numerical data but labels,\n", "will return the labels" ] }, { "cell_type": "code", "execution_count": 50, "id": "8ac5c4f7", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.206872Z", "iopub.status.busy": "2023-06-06T01:29:07.206654Z", "iopub.status.idle": "2023-06-06T01:29:07.213100Z", "shell.execute_reply": "2023-06-06T01:29:07.212565Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "
size 6
title time
labels
[ 0:00:00 0:01:00 0:02:00 0:03:00 0:04:00 0:05:00]
" ], "text/plain": [ "Coord: [labels] [ 0:00:00 0:01:00 0:02:00 0:03:00 0:04:00 0:05:00] (size: 6)" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.time" ] }, { "cell_type": "markdown", "id": "7d1e9958", "metadata": {}, "source": [ "# More insight on coordinates" ] }, { "cell_type": "markdown", "id": "d5c2b463", "metadata": {}, "source": [ "## Sharing coordinates between dimensions" ] }, { "cell_type": "markdown", "id": "70dc5277", "metadata": {}, "source": [ "Sometimes it is not necessary to have different coordinates for each axe. Some can be\n", "shared between axes.\n", "\n", "For example, if we have a square matrix with the same coordinate in the two\n", "dimensions, the second dimension can\n", "refer to the first. Here we create a square 2D dataset, using the `diag` method:" ] }, { "cell_type": "code", "execution_count": 51, "id": "24bf3541", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.216190Z", "iopub.status.busy": "2023-06-06T01:29:07.215800Z", "iopub.status.idle": "2023-06-06T01:29:07.231460Z", "shell.execute_reply": "2023-06-06T01:29:07.230910Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name NDDataset_87a7cec8
author runner@fv-az626-878
created 2023-06-06 01:29:07+00:00
history
2023-06-06 01:29:07+00:00> Created using method : diag
DATA
title
values
[[ 3 0 0]
[ 0 3 0]
[ 0 0 2.5]]
shape (y:3, x:3)
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (y:3, x:3))" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nd = NDDataset.diag((3, 3, 2.5))\n", "nd" ] }, { "cell_type": "markdown", "id": "135e753d", "metadata": {}, "source": [ "and then we add the same coordinate for both dimensions" ] }, { "cell_type": "code", "execution_count": 52, "id": "97ba2d7b", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.234519Z", "iopub.status.busy": "2023-06-06T01:29:07.234307Z", "iopub.status.idle": "2023-06-06T01:29:07.251743Z", "shell.execute_reply": "2023-06-06T01:29:07.251134Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name NDDataset_87a7cec8
author runner@fv-az626-878
created 2023-06-06 01:29:07+00:00
history
2023-06-06 01:29:07+00:00> Created using method : diag
DATA
title
values
[[ 3 0 0]
[ 0 3 0]
[ 0 0 2.5]]
shape (y:3, x:3)
DIMENSION `x`=`y`
size 3
title
coordinates
[ 0 1 2]
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (y:3, x:3))" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coordx = Coord.arange(3)\n", "nd.set_coordset(x=coordx, y=\"x\")\n", "nd" ] }, { "cell_type": "markdown", "id": "bd522760", "metadata": {}, "source": [ "## Setting coordinates using `set_coordset`" ] }, { "cell_type": "markdown", "id": "7be7523f", "metadata": {}, "source": [ "Let's create 3 `Coord` objects to be used as coordinates for the 3 dimensions of the\n", "previous d3D dataset." ] }, { "cell_type": "code", "execution_count": 53, "id": "f7ec4e81", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.255068Z", "iopub.status.busy": "2023-06-06T01:29:07.254835Z", "iopub.status.idle": "2023-06-06T01:29:07.263110Z", "shell.execute_reply": "2023-06-06T01:29:07.262586Z" } }, "outputs": [], "source": [ "d3D.dims = [\"t\", \"v\", \"u\"]\n", "s0, s1, s2 = d3D.shape\n", "coord0 = Coord.linspace(10.0, 100.0, s0, units=\"m\", title=\"distance\")\n", "coord1 = Coord.linspace(20.0, 25.0, s1, units=\"K\", title=\"temperature\")\n", "coord2 = Coord.linspace(0.0, 1000.0, s2, units=\"hour\", title=\"elapsed time\")" ] }, { "cell_type": "markdown", "id": "f94b48d4", "metadata": {}, "source": [ "### Syntax 1" ] }, { "cell_type": "code", "execution_count": 54, "id": "cdf2403f", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.267123Z", "iopub.status.busy": "2023-06-06T01:29:07.266107Z", "iopub.status.idle": "2023-06-06T01:29:07.277543Z", "shell.execute_reply": "2023-06-06T01:29:07.277032Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name 3D_dataset
author Someone
created 2023-06-06 01:29:06+00:00
description
a single statement creation example
history
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title Energy
values
[[[ 0.3122 0.9475 ... 0.6869 0.3645]
[ 0.2168 0.9174 ... 0.225 0.8701]
[ 0.5616 0.9712 ... 0.8156 0.4228]
[ 0.4389 0.7739 ... 0.873 0.4794]]

[[ 0.8906 0.006258 ... 0.9852 0.94]
[ 0.7681 0.5764 ... 0.7628 0.2925]
[ 0.2177 0.07877 ... 0.5296 0.3631]
[ 0.4269 0.2059 ... 0.7017 0.798]]]
shape (t:2, v:4, u:6)
DIMENSION `t`
size 2
title distance
coordinates
[ 10 100] m
DIMENSION `u`
size 6
title elapsed time
coordinates
[ 0 200 400 600 800 1000] h
DIMENSION `v`
size 4
title temperature
coordinates
[ 20 21.67 23.33 25] K
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (t:2, v:4, u:6))" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.set_coordset(u=coord2, v=coord1, t=coord0)\n", "d3D" ] }, { "cell_type": "markdown", "id": "49ac15cb", "metadata": {}, "source": [ "### Syntax 2" ] }, { "cell_type": "code", "execution_count": 55, "id": "b2754559", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.281530Z", "iopub.status.busy": "2023-06-06T01:29:07.280509Z", "iopub.status.idle": "2023-06-06T01:29:07.292382Z", "shell.execute_reply": "2023-06-06T01:29:07.291841Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name 3D_dataset
author Someone
created 2023-06-06 01:29:06+00:00
description
a single statement creation example
history
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title Energy
values
[[[ 0.3122 0.9475 ... 0.6869 0.3645]
[ 0.2168 0.9174 ... 0.225 0.8701]
[ 0.5616 0.9712 ... 0.8156 0.4228]
[ 0.4389 0.7739 ... 0.873 0.4794]]

[[ 0.8906 0.006258 ... 0.9852 0.94]
[ 0.7681 0.5764 ... 0.7628 0.2925]
[ 0.2177 0.07877 ... 0.5296 0.3631]
[ 0.4269 0.2059 ... 0.7017 0.798]]]
shape (t:2, v:4, u:6)
DIMENSION `t`
size 2
title distance
coordinates
[ 10 100] m
DIMENSION `u`
size 6
title elapsed time
coordinates
[ 0 200 400 600 800 1000] h
DIMENSION `v`
size 4
title temperature
coordinates
[ 20 21.67 23.33 25] K
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (t:2, v:4, u:6))" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.set_coordset({\"u\": coord2, \"v\": coord1, \"t\": coord0})\n", "d3D" ] }, { "cell_type": "markdown", "id": "49937d73", "metadata": {}, "source": [ "## Adding several coordinates to a single dimension\n", "We can add several coordinates to the same dimension" ] }, { "cell_type": "code", "execution_count": 56, "id": "05aac17a", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.296487Z", "iopub.status.busy": "2023-06-06T01:29:07.295438Z", "iopub.status.idle": "2023-06-06T01:29:07.300573Z", "shell.execute_reply": "2023-06-06T01:29:07.300055Z" } }, "outputs": [], "source": [ "coord1b = Coord([1, 2, 3, 4], units=\"millitesla\", title=\"magnetic field\")" ] }, { "cell_type": "code", "execution_count": 57, "id": "ba03fd58", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.304413Z", "iopub.status.busy": "2023-06-06T01:29:07.303408Z", "iopub.status.idle": "2023-06-06T01:29:07.321896Z", "shell.execute_reply": "2023-06-06T01:29:07.321392Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name 3D_dataset
author Someone
created 2023-06-06 01:29:06+00:00
description
a single statement creation example
history
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title Energy
values
[[[ 0.3122 0.9475 ... 0.6869 0.3645]
[ 0.2168 0.9174 ... 0.225 0.8701]
[ 0.5616 0.9712 ... 0.8156 0.4228]
[ 0.4389 0.7739 ... 0.873 0.4794]]

[[ 0.8906 0.006258 ... 0.9852 0.94]
[ 0.7681 0.5764 ... 0.7628 0.2925]
[ 0.2177 0.07877 ... 0.5296 0.3631]
[ 0.4269 0.2059 ... 0.7017 0.798]]]
shape (t:2, v:4, u:6)
DIMENSION `t`
size 2
title distance
coordinates
[ 10 100] m
DIMENSION `u`
size 6
title elapsed time
coordinates
[ 0 200 400 600 800 1000] h
DIMENSION `v`
size 4
(_1)
title magnetic field
coordinates
[ 1 2 3 4] mT
(_2)
title temperature
coordinates
[ 20 21.67 23.33 25] K
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (t:2, v:4, u:6))" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.set_coordset(u=coord2, v=[coord1, coord1b], t=coord0)\n", "d3D" ] }, { "cell_type": "markdown", "id": "2f4b3951", "metadata": {}, "source": [ "We can retrieve the various coordinates for a single dimension easily:" ] }, { "cell_type": "code", "execution_count": 58, "id": "bb5b1da4", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.325802Z", "iopub.status.busy": "2023-06-06T01:29:07.324796Z", "iopub.status.idle": "2023-06-06T01:29:07.331355Z", "shell.execute_reply": "2023-06-06T01:29:07.330863Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "
size 4
title magnetic field
coordinates
[ 1 2 3 4] mT
" ], "text/plain": [ "Coord: [float64] mT (size: 4)" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.v_1" ] }, { "cell_type": "markdown", "id": "61d08121", "metadata": {}, "source": [ "## Summary of the coordinate setting syntax\n", "Some additional information about coordinate setting syntax" ] }, { "cell_type": "markdown", "id": "6fcc5575", "metadata": {}, "source": [ "**A.** First syntax (probably the safer because the name of the dimension is\n", "specified, so this is less prone to\n", "errors!)" ] }, { "cell_type": "code", "execution_count": 59, "id": "58d906bc", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.335212Z", "iopub.status.busy": "2023-06-06T01:29:07.334223Z", "iopub.status.idle": "2023-06-06T01:29:07.361935Z", "shell.execute_reply": "2023-06-06T01:29:07.361440Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name 3D_dataset
author Someone
created 2023-06-06 01:29:06+00:00
description
a single statement creation example
history
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title Energy
values
[[[ 0.3122 0.9475 ... 0.6869 0.3645]
[ 0.2168 0.9174 ... 0.225 0.8701]
[ 0.5616 0.9712 ... 0.8156 0.4228]
[ 0.4389 0.7739 ... 0.873 0.4794]]

[[ 0.8906 0.006258 ... 0.9852 0.94]
[ 0.7681 0.5764 ... 0.7628 0.2925]
[ 0.2177 0.07877 ... 0.5296 0.3631]
[ 0.4269 0.2059 ... 0.7017 0.798]]]
shape (t:2, v:4, u:6)
DIMENSION `t`
size 2
title distance
coordinates
[ 10 100] m
DIMENSION `u`
size 6
title elapsed time
coordinates
[ 0 200 400 600 800 1000] h
DIMENSION `v`
size 4
(_1)
title magnetic field
coordinates
[ 1 2 3 4] mT
(_2)
title temperature
coordinates
[ 20 21.67 23.33 25] K
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (t:2, v:4, u:6))" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.set_coordset(u=coord2, v=[coord1, coord1b], t=coord0)\n", "# or equivalent\n", "d3D.set_coordset(u=coord2, v=CoordSet(coord1, coord1b), t=coord0)\n", "d3D" ] }, { "cell_type": "markdown", "id": "c771761f", "metadata": {}, "source": [ "**B.** Second syntax assuming the coordinates are given in the order of the\n", "dimensions.\n", "\n", "Remember that we can check this order using the `dims` attribute of a NDDataset" ] }, { "cell_type": "code", "execution_count": 60, "id": "8521b6ee", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.365889Z", "iopub.status.busy": "2023-06-06T01:29:07.364881Z", "iopub.status.idle": "2023-06-06T01:29:07.370802Z", "shell.execute_reply": "2023-06-06T01:29:07.370266Z" } }, "outputs": [ { "data": { "text/plain": [ "['t', 'v', 'u']" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.dims" ] }, { "cell_type": "code", "execution_count": 61, "id": "1cd0fcba", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.373814Z", "iopub.status.busy": "2023-06-06T01:29:07.373605Z", "iopub.status.idle": "2023-06-06T01:29:07.405787Z", "shell.execute_reply": "2023-06-06T01:29:07.405254Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name 3D_dataset
author Someone
created 2023-06-06 01:29:06+00:00
description
a single statement creation example
history
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title Energy
values
[[[ 0.3122 0.9475 ... 0.6869 0.3645]
[ 0.2168 0.9174 ... 0.225 0.8701]
[ 0.5616 0.9712 ... 0.8156 0.4228]
[ 0.4389 0.7739 ... 0.873 0.4794]]

[[ 0.8906 0.006258 ... 0.9852 0.94]
[ 0.7681 0.5764 ... 0.7628 0.2925]
[ 0.2177 0.07877 ... 0.5296 0.3631]
[ 0.4269 0.2059 ... 0.7017 0.798]]]
shape (t:2, v:4, u:6)
DIMENSION `t`
size 2
title distance
coordinates
[ 10 100] m
DIMENSION `u`
size 6
title elapsed time
coordinates
[ 0 200 400 600 800 1000] h
DIMENSION `v`
size 4
(_1)
title magnetic field
coordinates
[ 1 2 3 4] mT
(_2)
title temperature
coordinates
[ 20 21.67 23.33 25] K
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (t:2, v:4, u:6))" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.set_coordset((coord0, [coord1, coord1b], coord2))\n", "# or equivalent\n", "d3D.set_coordset(coord0, CoordSet(coord1, coord1b), coord2)\n", "d3D" ] }, { "cell_type": "markdown", "id": "4f59e1f1", "metadata": {}, "source": [ "**C.** Third syntax (from a dictionary)" ] }, { "cell_type": "code", "execution_count": 62, "id": "6c1d9e1d", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.409807Z", "iopub.status.busy": "2023-06-06T01:29:07.408807Z", "iopub.status.idle": "2023-06-06T01:29:07.435156Z", "shell.execute_reply": "2023-06-06T01:29:07.434661Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name 3D_dataset
author Someone
created 2023-06-06 01:29:06+00:00
description
a single statement creation example
history
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title Energy
values
[[[ 0.3122 0.9475 ... 0.6869 0.3645]
[ 0.2168 0.9174 ... 0.225 0.8701]
[ 0.5616 0.9712 ... 0.8156 0.4228]
[ 0.4389 0.7739 ... 0.873 0.4794]]

[[ 0.8906 0.006258 ... 0.9852 0.94]
[ 0.7681 0.5764 ... 0.7628 0.2925]
[ 0.2177 0.07877 ... 0.5296 0.3631]
[ 0.4269 0.2059 ... 0.7017 0.798]]]
shape (t:2, v:4, u:6)
DIMENSION `t`
size 2
title distance
coordinates
[ 10 100] m
DIMENSION `u`
size 6
title elapsed time
coordinates
[ 0 200 400 600 800 1000] h
DIMENSION `v`
size 4
(_1)
title magnetic field
coordinates
[ 1 2 3 4] mT
(_2)
title temperature
coordinates
[ 20 21.67 23.33 25] K
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (t:2, v:4, u:6))" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.set_coordset({\"t\": coord0, \"u\": coord2, \"v\": [coord1, coord1b]})\n", "d3D" ] }, { "cell_type": "markdown", "id": "3d6c46df", "metadata": {}, "source": [ "**D.** It is also possible to use directly the `CoordSet` property" ] }, { "cell_type": "code", "execution_count": 63, "id": "37828926", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.439812Z", "iopub.status.busy": "2023-06-06T01:29:07.438392Z", "iopub.status.idle": "2023-06-06T01:29:07.457394Z", "shell.execute_reply": "2023-06-06T01:29:07.456907Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name 3D_dataset
author Someone
created 2023-06-06 01:29:06+00:00
description
a single statement creation example
history
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title Energy
values
[[[ 0.3122 0.9475 ... 0.6869 0.3645]
[ 0.2168 0.9174 ... 0.225 0.8701]
[ 0.5616 0.9712 ... 0.8156 0.4228]
[ 0.4389 0.7739 ... 0.873 0.4794]]

[[ 0.8906 0.006258 ... 0.9852 0.94]
[ 0.7681 0.5764 ... 0.7628 0.2925]
[ 0.2177 0.07877 ... 0.5296 0.3631]
[ 0.4269 0.2059 ... 0.7017 0.798]]]
shape (t:2, v:4, u:6)
DIMENSION `t`
size 2
title distance
coordinates
[ 10 100] m
DIMENSION `u`
size 6
title elapsed time
coordinates
[ 0 200 400 600 800 1000] h
DIMENSION `v`
size 4
(_1)
title temperature
coordinates
[ 20 21.67 23.33 25] K
(_2)
title magnetic field
coordinates
[ 1 2 3 4] mT
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (t:2, v:4, u:6))" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.coordset = coord0, [coord1, coord1b], coord2\n", "d3D" ] }, { "cell_type": "code", "execution_count": 64, "id": "d3d22b35", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.461261Z", "iopub.status.busy": "2023-06-06T01:29:07.460248Z", "iopub.status.idle": "2023-06-06T01:29:07.486465Z", "shell.execute_reply": "2023-06-06T01:29:07.485963Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name 3D_dataset
author Someone
created 2023-06-06 01:29:06+00:00
description
a single statement creation example
history
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title Energy
values
[[[ 0.3122 0.9475 ... 0.6869 0.3645]
[ 0.2168 0.9174 ... 0.225 0.8701]
[ 0.5616 0.9712 ... 0.8156 0.4228]
[ 0.4389 0.7739 ... 0.873 0.4794]]

[[ 0.8906 0.006258 ... 0.9852 0.94]
[ 0.7681 0.5764 ... 0.7628 0.2925]
[ 0.2177 0.07877 ... 0.5296 0.3631]
[ 0.4269 0.2059 ... 0.7017 0.798]]]
shape (t:2, v:4, u:6)
DIMENSION `t`
size 2
title distance
coordinates
[ 10 100] m
DIMENSION `u`
size 6
title elapsed time
coordinates
[ 0 200 400 600 800 1000] h
DIMENSION `v`
size 4
(_1)
title magnetic field
coordinates
[ 1 2 3 4] mT
(_2)
title temperature
coordinates
[ 20 21.67 23.33 25] K
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (t:2, v:4, u:6))" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.coordset = {\"t\": coord0, \"u\": coord2, \"v\": [coord1, coord1b]}\n", "d3D" ] }, { "cell_type": "code", "execution_count": 65, "id": "2069ea73", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.490425Z", "iopub.status.busy": "2023-06-06T01:29:07.489423Z", "iopub.status.idle": "2023-06-06T01:29:07.511516Z", "shell.execute_reply": "2023-06-06T01:29:07.510994Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name 3D_dataset
author Someone
created 2023-06-06 01:29:06+00:00
description
a single statement creation example
history
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title Energy
values
[[[ 0.3122 0.9475 ... 0.6869 0.3645]
[ 0.2168 0.9174 ... 0.225 0.8701]
[ 0.5616 0.9712 ... 0.8156 0.4228]
[ 0.4389 0.7739 ... 0.873 0.4794]]

[[ 0.8906 0.006258 ... 0.9852 0.94]
[ 0.7681 0.5764 ... 0.7628 0.2925]
[ 0.2177 0.07877 ... 0.5296 0.3631]
[ 0.4269 0.2059 ... 0.7017 0.798]]]
shape (t:2, v:4, u:6)
DIMENSION `t`
size 2
title distance
coordinates
[ 10 100] m
DIMENSION `u`
size 6
title elapsed time
coordinates
[ 0 200 400 600 800 1000] h
DIMENSION `v`
size 4
(_1)
title magnetic field
coordinates
[ 1 2 3 4] mT
(_2)
title temperature
coordinates
[ 20 21.67 23.33 25] K
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (t:2, v:4, u:6))" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.coordset = CoordSet(t=coord0, u=coord2, v=[coord1, coord1b])\n", "d3D" ] }, { "cell_type": "markdown", "id": "f92d9176", "metadata": {}, "source": [ "
\n", "WARNING\n", "\n", "Do not use list for setting multiples coordinates! use tuples\n", "
" ] }, { "cell_type": "markdown", "id": "030edaed", "metadata": {}, "source": [ "This raise an error (list have another signification: it's used to set a \"same dim\"\n", "CoordSet see example A or B)" ] }, { "cell_type": "code", "execution_count": 66, "id": "2001aa07", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.515558Z", "iopub.status.busy": "2023-06-06T01:29:07.514552Z", "iopub.status.idle": "2023-06-06T01:29:07.537044Z", "shell.execute_reply": "2023-06-06T01:29:07.536465Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " ERROR | ValueError: Coordinates must be of the same size for a dimension with multiple coordinates\n" ] } ], "source": [ "try:\n", " d3D.coordset = [coord0, coord1, coord2]\n", "except ValueError:\n", " scp.error_(\n", " ValueError,\n", " \"Coordinates must be of the same size for a dimension with multiple coordinates\",\n", " )" ] }, { "cell_type": "markdown", "id": "5d2e5ba6", "metadata": {}, "source": [ "This works : it uses a tuple `()` , not a list `[]`" ] }, { "cell_type": "code", "execution_count": 67, "id": "f2425831", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.541228Z", "iopub.status.busy": "2023-06-06T01:29:07.540176Z", "iopub.status.idle": "2023-06-06T01:29:07.562030Z", "shell.execute_reply": "2023-06-06T01:29:07.561488Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name 3D_dataset
author Someone
created 2023-06-06 01:29:06+00:00
description
a single statement creation example
history
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title Energy
values
[[[ 0.3122 0.9475 ... 0.6869 0.3645]
[ 0.2168 0.9174 ... 0.225 0.8701]
[ 0.5616 0.9712 ... 0.8156 0.4228]
[ 0.4389 0.7739 ... 0.873 0.4794]]

[[ 0.8906 0.006258 ... 0.9852 0.94]
[ 0.7681 0.5764 ... 0.7628 0.2925]
[ 0.2177 0.07877 ... 0.5296 0.3631]
[ 0.4269 0.2059 ... 0.7017 0.798]]]
shape (t:2, v:4, u:6)
DIMENSION `t`
size 2
title distance
coordinates
[ 10 100] m
DIMENSION `u`
size 6
title elapsed time
coordinates
[ 0 200 400 600 800 1000] h
DIMENSION `v`
size 4
title temperature
coordinates
[ 20 21.67 23.33 25] K
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (t:2, v:4, u:6))" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.coordset = (\n", " coord0,\n", " coord1,\n", " coord2,\n", ") # equivalent to d3D.coordset = coord0, coord1, coord2\n", "d3D" ] }, { "cell_type": "markdown", "id": "8ff2e17b", "metadata": {}, "source": [ "**E.** Setting the coordinates individually" ] }, { "cell_type": "markdown", "id": "c5d3f3ff", "metadata": {}, "source": [ "Either a single coordinate" ] }, { "cell_type": "code", "execution_count": 68, "id": "f86ce065", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.565180Z", "iopub.status.busy": "2023-06-06T01:29:07.564967Z", "iopub.status.idle": "2023-06-06T01:29:07.583427Z", "shell.execute_reply": "2023-06-06T01:29:07.582901Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name 3D_dataset
author Someone
created 2023-06-06 01:29:06+00:00
description
a single statement creation example
history
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title Energy
values
[[[ 0.3122 0.9475 ... 0.6869 0.3645]
[ 0.2168 0.9174 ... 0.225 0.8701]
[ 0.5616 0.9712 ... 0.8156 0.4228]
[ 0.4389 0.7739 ... 0.873 0.4794]]

[[ 0.8906 0.006258 ... 0.9852 0.94]
[ 0.7681 0.5764 ... 0.7628 0.2925]
[ 0.2177 0.07877 ... 0.5296 0.3631]
[ 0.4269 0.2059 ... 0.7017 0.798]]]
shape (t:2, v:4, u:6)
DIMENSION `t`
size 2
title distance
coordinates
[ 10 100] m
DIMENSION `u`
size 6
title elapsed time
coordinates
[ 0 200 400 600 800 1000] h
DIMENSION `v`
size 4
title temperature
coordinates
[ 20 21.67 23.33 25] K
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (t:2, v:4, u:6))" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.u = coord2\n", "d3D" ] }, { "cell_type": "markdown", "id": "0ae2e3ef", "metadata": {}, "source": [ "or multiple coordinates for a single dimension" ] }, { "cell_type": "code", "execution_count": 69, "id": "2728fe78", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.586470Z", "iopub.status.busy": "2023-06-06T01:29:07.586261Z", "iopub.status.idle": "2023-06-06T01:29:07.623972Z", "shell.execute_reply": "2023-06-06T01:29:07.623380Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name 3D_dataset
author Someone
created 2023-06-06 01:29:06+00:00
description
a single statement creation example
history
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title Energy
values
[[[ 0.3122 0.9475 ... 0.6869 0.3645]
[ 0.2168 0.9174 ... 0.225 0.8701]
[ 0.5616 0.9712 ... 0.8156 0.4228]
[ 0.4389 0.7739 ... 0.873 0.4794]]

[[ 0.8906 0.006258 ... 0.9852 0.94]
[ 0.7681 0.5764 ... 0.7628 0.2925]
[ 0.2177 0.07877 ... 0.5296 0.3631]
[ 0.4269 0.2059 ... 0.7017 0.798]]]
shape (t:2, v:4, u:6)
DIMENSION `t`
size 2
title distance
coordinates
[ 10 100] m
DIMENSION `u`
size 6
title elapsed time
coordinates
[ 0 200 400 600 800 1000] h
DIMENSION `v`
size 4
(_1)
title temperature
coordinates
[ 20 21.67 23.33 25] K
(_2)
title magnetic field
coordinates
[ 1 2 3 4] mT
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (t:2, v:4, u:6))" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.v = [coord1, coord1b]\n", "d3D" ] }, { "cell_type": "markdown", "id": "73b34296", "metadata": {}, "source": [ "or using a CoordSet object." ] }, { "cell_type": "code", "execution_count": 70, "id": "8c44e22f", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.627214Z", "iopub.status.busy": "2023-06-06T01:29:07.626990Z", "iopub.status.idle": "2023-06-06T01:29:07.650377Z", "shell.execute_reply": "2023-06-06T01:29:07.649841Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name 3D_dataset
author Someone
created 2023-06-06 01:29:06+00:00
description
a single statement creation example
history
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title Energy
values
[[[ 0.3122 0.9475 ... 0.6869 0.3645]
[ 0.2168 0.9174 ... 0.225 0.8701]
[ 0.5616 0.9712 ... 0.8156 0.4228]
[ 0.4389 0.7739 ... 0.873 0.4794]]

[[ 0.8906 0.006258 ... 0.9852 0.94]
[ 0.7681 0.5764 ... 0.7628 0.2925]
[ 0.2177 0.07877 ... 0.5296 0.3631]
[ 0.4269 0.2059 ... 0.7017 0.798]]]
shape (t:2, v:4, u:6)
DIMENSION `t`
size 2
title distance
coordinates
[ 10 100] m
DIMENSION `u`
size 6
title elapsed time
coordinates
[ 0 200 400 600 800 1000] h
DIMENSION `v`
size 4
(_1)
title magnetic field
coordinates
[ 1 2 3 4] mT
(_2)
title temperature
coordinates
[ 20 21.67 23.33 25] K
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (t:2, v:4, u:6))" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3D.v = CoordSet(coord1, coord1b)\n", "d3D" ] }, { "cell_type": "markdown", "id": "e8b4f27a", "metadata": {}, "source": [ "# Methods to create NDDataset\n", "\n", "There are many ways to create `NDDataset` objects.\n", "\n", "Let's first create 2 coordinate objects, for which we can define `labels` and `units`!\n", "Note the use of the function\n", "`linspace`to generate the data." ] }, { "cell_type": "code", "execution_count": 71, "id": "39f85bce", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.654644Z", "iopub.status.busy": "2023-06-06T01:29:07.653616Z", "iopub.status.idle": "2023-06-06T01:29:07.659233Z", "shell.execute_reply": "2023-06-06T01:29:07.658742Z" } }, "outputs": [], "source": [ "c0 = Coord.linspace(\n", " start=4000.0, stop=1000.0, num=5, labels=None, units=\"cm^-1\", title=\"wavenumber\"\n", ")" ] }, { "cell_type": "code", "execution_count": 72, "id": "6cfcd0e0", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.663105Z", "iopub.status.busy": "2023-06-06T01:29:07.662100Z", "iopub.status.idle": "2023-06-06T01:29:07.667441Z", "shell.execute_reply": "2023-06-06T01:29:07.666950Z" } }, "outputs": [], "source": [ "c1 = Coord.linspace(\n", " 10.0, 40.0, 3, labels=[\"Cold\", \"RT\", \"Hot\"], units=\"K\", title=\"temperature\"\n", ")" ] }, { "cell_type": "markdown", "id": "5dc6cc65", "metadata": {}, "source": [ "The full coordset will be the following" ] }, { "cell_type": "code", "execution_count": 73, "id": "f91a8087", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.671377Z", "iopub.status.busy": "2023-06-06T01:29:07.670366Z", "iopub.status.idle": "2023-06-06T01:29:07.680394Z", "shell.execute_reply": "2023-06-06T01:29:07.679907Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
DIMENSION `x`
size 3
title temperature
coordinates
[ 10 25 40] K
labels
[ Cold RT Hot]
DIMENSION `y`
size 5
title wavenumber
coordinates
[ 4000 3250 2500 1750 1000] cm⁻¹
" ], "text/plain": [ "CoordSet: [x:temperature, y:wavenumber]" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cs = CoordSet(c0, c1)\n", "cs" ] }, { "cell_type": "markdown", "id": "61111541", "metadata": {}, "source": [ "Now we will generate the full dataset, using a `fromfunction` method.\n", "All needed information are passed as\n", "parameter of the NDDataset instance constructor." ] }, { "cell_type": "markdown", "id": "d53ca796", "metadata": {}, "source": [ "## Create a dataset from a function" ] }, { "cell_type": "code", "execution_count": 74, "id": "3b980875", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.684324Z", "iopub.status.busy": "2023-06-06T01:29:07.683326Z", "iopub.status.idle": "2023-06-06T01:29:07.687441Z", "shell.execute_reply": "2023-06-06T01:29:07.686958Z" } }, "outputs": [], "source": [ "def func(x, y, extra):\n", " return x * y / extra" ] }, { "cell_type": "code", "execution_count": 75, "id": "0ba6f2c1", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.691257Z", "iopub.status.busy": "2023-06-06T01:29:07.690266Z", "iopub.status.idle": "2023-06-06T01:29:07.707222Z", "shell.execute_reply": "2023-06-06T01:29:07.706691Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name mydataset
author Blake & Mortimer
created 2023-06-06 01:29:07+00:00
description
Dataset example created for this tutorial.
It's a 2-D dataset
history
2023-06-06 01:29:07+00:00> Created using method : fromfunction
DATA
title absorbance
values
[[ 400 1000 1600]
[ 325 812.5 1300]
...
[ 175 437.5 700]
[ 100 250 400]] K
shape (y:5, x:3)
DIMENSION `x`
size 3
title temperature
coordinates
[ 10 25 40] K
labels
[ Cold RT Hot]
DIMENSION `y`
size 5
title wavenumber
coordinates
[ 4000 3250 2500 1750 1000] cm⁻¹
" ], "text/plain": [ "NDDataset: [float64] K (shape: (y:5, x:3))" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = NDDataset.fromfunction(\n", " func,\n", " extra=100 * ur.cm**-1, # extra arguments passed to the function\n", " coordset=cs,\n", " name=\"mydataset\",\n", " title=\"absorbance\",\n", " units=None,\n", ") # when None, units will be determined from the function results\n", "\n", "ds.description = \"\"\"Dataset example created for this tutorial.\n", "It's a 2-D dataset\"\"\"\n", "\n", "ds.author = \"Blake & Mortimer\"\n", "ds" ] }, { "cell_type": "markdown", "id": "82e3b18d", "metadata": {}, "source": [ "## Using numpy-like constructors of NDDatasets" ] }, { "cell_type": "code", "execution_count": 76, "id": "7e652563", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.711310Z", "iopub.status.busy": "2023-06-06T01:29:07.710290Z", "iopub.status.idle": "2023-06-06T01:29:07.717145Z", "shell.execute_reply": "2023-06-06T01:29:07.716644Z" } }, "outputs": [], "source": [ "dz = NDDataset.zeros(\n", " (5, 3), coordset=cs, units=\"meters\", title=\"Datasets with only zeros\"\n", ")" ] }, { "cell_type": "code", "execution_count": 77, "id": "e9b53673", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.721024Z", "iopub.status.busy": "2023-06-06T01:29:07.720002Z", "iopub.status.idle": "2023-06-06T01:29:07.726942Z", "shell.execute_reply": "2023-06-06T01:29:07.726457Z" } }, "outputs": [], "source": [ "do = NDDataset.ones(\n", " (5, 3), coordset=cs, units=\"kilograms\", title=\"Datasets with only ones\"\n", ")" ] }, { "cell_type": "code", "execution_count": 78, "id": "c51e12b7", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.730899Z", "iopub.status.busy": "2023-06-06T01:29:07.729890Z", "iopub.status.idle": "2023-06-06T01:29:07.741308Z", "shell.execute_reply": "2023-06-06T01:29:07.740829Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name NDDataset_87f68d74
author runner@fv-az626-878
created 2023-06-06 01:29:07+00:00
history
2023-06-06 01:29:07+00:00> Created using method : full
DATA
title with only float=1.25
values
[[ 1.25 1.25 1.25]
[ 1.25 1.25 1.25]
...
[ 1.25 1.25 1.25]
[ 1.25 1.25 1.25]] rad
shape (y:5, x:3)
DIMENSION `x`
size 3
title temperature
coordinates
[ 10 25 40] K
labels
[ Cold RT Hot]
DIMENSION `y`
size 5
title wavenumber
coordinates
[ 4000 3250 2500 1750 1000] cm⁻¹
" ], "text/plain": [ "NDDataset: [float64] rad (shape: (y:5, x:3))" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = NDDataset.full(\n", " (5, 3), fill_value=1.25, coordset=cs, units=\"radians\", title=\"with only float=1.25\"\n", ")\n", "df" ] }, { "cell_type": "markdown", "id": "b8f8ae8a", "metadata": {}, "source": [ "As with numpy, it is also possible to take another dataset as a template:" ] }, { "cell_type": "code", "execution_count": 79, "id": "4a63a5bf", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.745241Z", "iopub.status.busy": "2023-06-06T01:29:07.744220Z", "iopub.status.idle": "2023-06-06T01:29:07.771891Z", "shell.execute_reply": "2023-06-06T01:29:07.771358Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name 3D_dataset
author runner@fv-az626-878
created 2023-06-06 01:29:07+00:00
history
2023-06-06 01:29:06+00:00> Created from scratch
2023-06-06 01:29:07+00:00> Created using method : full_like
DATA
title Energy
values
[[[ 2 2 ... 2 2]
[ 2 2 ... 2 2]
[ 2 2 ... 2 2]
[ 2 2 ... 2 2]]

[[ 2 2 ... 2 2]
[ 2 2 ... 2 2]
[ 2 2 ... 2 2]
[ 2 2 ... 2 2]]]
shape (t:2, v:4, u:6)
DIMENSION `t`
size 2
title distance
coordinates
[ 10 100] m
DIMENSION `u`
size 6
title elapsed time
coordinates
[ 0 200 400 600 800 1000] h
DIMENSION `v`
size 4
(_1)
title magnetic field
coordinates
[ 1 2 3 4] mT
(_2)
title temperature
coordinates
[ 20 21.67 23.33 25] K
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (t:2, v:4, u:6))" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = NDDataset.full_like(d3D, dtype=\"int\", fill_value=2)\n", "df" ] }, { "cell_type": "code", "execution_count": 80, "id": "26e2d8e1", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.776011Z", "iopub.status.busy": "2023-06-06T01:29:07.774977Z", "iopub.status.idle": "2023-06-06T01:29:07.783306Z", "shell.execute_reply": "2023-06-06T01:29:07.782819Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name NDDataset_87fd23f0
author runner@fv-az626-878
created 2023-06-06 01:29:07+00:00
history
2023-06-06 01:29:07+00:00> Created using method : diag
DATA
title
values
[[ 3 0 0]
[ 0 3 0]
[ 0 0 2.5]]
shape (y:3, x:3)
" ], "text/plain": [ "NDDataset: [float64] unitless (shape: (y:3, x:3))" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nd = NDDataset.diag((3, 3, 2.5))\n", "nd" ] }, { "cell_type": "markdown", "id": "9e985b29", "metadata": {}, "source": [ "## Copying existing NDDataset\n", "\n", "To copy an existing dataset, this is as simple as:" ] }, { "cell_type": "code", "execution_count": 81, "id": "75dff115", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.787192Z", "iopub.status.busy": "2023-06-06T01:29:07.786203Z", "iopub.status.idle": "2023-06-06T01:29:07.806611Z", "shell.execute_reply": "2023-06-06T01:29:07.806102Z" } }, "outputs": [], "source": [ "d3D_copy = d3D.copy()" ] }, { "cell_type": "markdown", "id": "efbebb0e", "metadata": {}, "source": [ "or alternatively:" ] }, { "cell_type": "code", "execution_count": 82, "id": "dfa2a61b", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.810693Z", "iopub.status.busy": "2023-06-06T01:29:07.809684Z", "iopub.status.idle": "2023-06-06T01:29:07.865550Z", "shell.execute_reply": "2023-06-06T01:29:07.864964Z" } }, "outputs": [], "source": [ "d3D_copy = d3D[:]" ] }, { "cell_type": "markdown", "id": "d66fcd34", "metadata": {}, "source": [ "Finally, it is also possible to initialize a dataset using an existing one:" ] }, { "cell_type": "code", "execution_count": 83, "id": "87b19d93", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.871044Z", "iopub.status.busy": "2023-06-06T01:29:07.869485Z", "iopub.status.idle": "2023-06-06T01:29:07.887957Z", "shell.execute_reply": "2023-06-06T01:29:07.887435Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
name duplicate of 3D_dataset
author runner@fv-az626-878
created 2023-06-06 01:29:07+00:00
history
2023-06-06 01:29:06+00:00> Created from scratch
DATA
title Energy
values
[[[ 0.3122 0.9475 ... 0.6869 0.3645]
[ 0.2168 0.9174 ... 0.225 0.8701]
[ 0.5616 0.9712 ... 0.8156 0.4228]
[ 0.4389 0.7739 ... 0.873 0.4794]]

[[ 0.8906 0.006258 ... 0.9852 0.94]
[ 0.7681 0.5764 ... 0.7628 0.2925]
[ 0.2177 0.07877 ... 0.5296 0.3631]
[ 0.4269 0.2059 ... 0.7017 0.798]]] a.u.
shape (t:2, v:4, u:6)
DIMENSION `t`
size 2
title distance
coordinates
[ 10 100] m
DIMENSION `u`
size 6
title elapsed time
coordinates
[ 0 200 400 600 800 1000] h
DIMENSION `v`
size 4
(_1)
title magnetic field
coordinates
[ 1 2 3 4] mT
(_2)
title temperature
coordinates
[ 20 21.67 23.33 25] K
" ], "text/plain": [ "NDDataset: [float64] a.u. (shape: (t:2, v:4, u:6))" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d3Dduplicate = NDDataset(d3D, name=\"duplicate of %s\" % d3D.name, units=\"absorbance\")\n", "d3Dduplicate" ] }, { "cell_type": "markdown", "id": "f551af94", "metadata": {}, "source": [ "## Importing from external dataset\n", "\n", "NDDataset can be created from the importation of external data\n", "\n", "A **test**'s data folder contains some data for experimenting some features of\n", "datasets." ] }, { "cell_type": "code", "execution_count": 84, "id": "f8e8563d", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.892788Z", "iopub.status.busy": "2023-06-06T01:29:07.891337Z", "iopub.status.idle": "2023-06-06T01:29:07.898736Z", "shell.execute_reply": "2023-06-06T01:29:07.898245Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "testdata\n" ] } ], "source": [ "# let check if this directory exists and display its actual content:\n", "datadir = scp.preferences.datadir\n", "if datadir.exists():\n", " print(datadir.name)" ] }, { "cell_type": "markdown", "id": "9c606a8c", "metadata": {}, "source": [ "Let's load grouped IR spectra acquired using OMNIC:" ] }, { "cell_type": "code", "execution_count": 85, "id": "038dddae", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:07.903335Z", "iopub.status.busy": "2023-06-06T01:29:07.901923Z", "iopub.status.idle": "2023-06-06T01:29:09.161031Z", "shell.execute_reply": "2023-06-06T01:29:09.160450Z" } }, "outputs": [ { "data": { "image/jpeg": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nd = NDDataset.read_omnic(datadir / \"irdata/nh4y-activation.spg\")\n", "nd.preferences.reset()\n", "_ = nd.plot()" ] }, { "cell_type": "markdown", "id": "d3641795", "metadata": {}, "source": [ "Even if we do not specify the **datadir**, the application first look in the\n", "directory by default." ] }, { "cell_type": "markdown", "id": "278c96f7", "metadata": {}, "source": [ "Now, lets load a NMR dataset (in the Bruker format)." ] }, { "cell_type": "code", "execution_count": 86, "id": "43ed484f", "metadata": { "execution": { "iopub.execute_input": "2023-06-06T01:29:09.164526Z", "iopub.status.busy": "2023-06-06T01:29:09.164275Z", "iopub.status.idle": "2023-06-06T01:29:10.281368Z", "shell.execute_reply": "2023-06-06T01:29:10.280799Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " WARNING | (UserWarning) (196608,)cannot be shaped into(147, 1024)\n" ] }, { "data": { "image/jpeg": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "path = datadir / \"nmrdata\" / \"bruker\" / \"tests\" / \"nmr\" / \"topspin_2d\"\n", "\n", "# load the data directly (no need to create the dataset first)\n", "nd2 = NDDataset.read_topspin(path, expno=1, remove_digital_filter=True)\n", "\n", "# view it...\n", "nd2.x.to(\"s\")\n", "nd2.y.to(\"ms\")\n", "\n", "ax = nd2.plot(method=\"map\")" ] } ], "metadata": { "jupytext": { "formats": "ipynb,py:percent", "notebook_metadata_filter": "all" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.16" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 }