{
"cells": [
{
"cell_type": "markdown",
"id": "96c4d52c-3704-474c-baaa-8feb0fa019ab",
"metadata": {},
"source": [
"# Inspecting the File content\n",
"\n",
"To get an overview of the file content, a tree-view representation is provided. It can be called using `.dump()`. This, however, is only possible in notebooks. Outside of it (e.g. in a normal python console) call `.sdump()` (string-dump) to get a \"raw\", non-interactive, tree view.\n",
"\n",
"However, we are in a notebook environment here, so let's use `dump()`:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d0f77ed6-6668-41b2-9ceb-8487e7141cc5",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Failed to import module h5tbx\n"
]
},
{
"data": {
"text/html": [
"\n",
"
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import h5rdmtoolbox as h5tbx\n",
"from h5rdmtoolbox import tutorial\n",
"\n",
"# get some example files:\n",
"list_of_hdf_filenames = tutorial.Database.generate_test_files()\n",
"\n",
"# dump the content of the first file in the list:\n",
"with h5tbx.File(list_of_hdf_filenames[0]) as h5:\n",
" h5.dump()"
]
},
{
"cell_type": "markdown",
"id": "baa082a3-77b9-4665-8131-bcf7843d5469",
"metadata": {},
"source": [
"## User-defined representation\n",
"\n",
"Every convention that is selected may have a different way of displaying the content. For the built-in convention \"h5tbx\", the attribute \"units\" is put in square brackets behind the dataset name and thus is not listed as an attribute:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ea58d4b0-6820-4665-a4a3-874240410159",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"