{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example Usage of HDFWriter"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If properties of a class needs to be saved in a hdf file, then the class should inherit from `HDFWriterMixin` as demonstrated below.\n",
"\n",
"`hdf_properties (list)` : Contains names of all the properties that needs to be saved.
\n",
"`hdf_name (str)` : Specifies the default name of the group under which the properties will be saved."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from tardis.io.util import HDFWriterMixin\n",
"\n",
"class ExampleClass(HDFWriterMixin):\n",
" hdf_properties = ['property1', 'property2']\n",
" hdf_name = 'mock_setup'\n",
" def __init__(self, property1, property2):\n",
" self.property1 = property1\n",
" self.property2 = property2\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"#Instantiating Object\n",
"property1 = np.array([4.0e14, 2, 2e14, 27.5])\n",
"property2 = pd.DataFrame({'one': pd.Series([1., 2., 3.], index=['a', 'b', 'c']),\n",
" 'two': pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])})\n",
"obj = ExampleClass(property1, property2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can now save properties using `to_hdf` method.\n",
"\n",
"#### Parameters\n",
"`file_path` : Path where the HDF file will be saved
\n",
"`path` : Path inside the HDF store to store the `elements`
\n",
"`name` : Name of the group inside HDF store, under which properties will be saved.
\n",
"If not specified , then it uses the value specified in `hdf_name` attribute.
\n",
"If `hdf_name` is also not defined , then it converts the Class name into Snake Case, and uses this value.
\n",
"Like for example , if `name` is not passed as an argument , and `hdf_name` is also not defined for `ExampleClass` above, then , it will save properties under `example_class` group.\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"obj.to_hdf(file_path='test.hdf', path='test')\n",
"#obj.to_hdf(file_path='test.hdf', path='test', name='hdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can now read hdf file using `pd.HDFStore` , or `pd.read_hdf`"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"File path: test.hdf\n",
"/test/mock_setup/property1 series (shape->[4]) \n",
"/test/mock_setup/property2 frame (shape->[4,2])\n"
]
}
],
"source": [
"#Read HDF file\n",
"with pd.HDFStore('test.hdf','r') as data:\n",
" print data\n",
" #print data['/test/mock_setup/property1']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Saving nested class objects.\n",
"\n",
"Just extend `hdf_properties` list to include that class object.
"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class NestedExampleClass(HDFWriterMixin):\n",
" hdf_properties = ['property1', 'nested_object']\n",
" def __init__(self, property1, nested_obj):\n",
" self.property1 = property1\n",
" self.nested_object = nested_obj"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"obj2 = NestedExampleClass(property1, obj)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"obj2.to_hdf(file_path='nested_test.hdf')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"File path: nested_test.hdf\n",
"/nested_example_class/nested_object/property1 series (shape->[4]) \n",
"/nested_example_class/nested_object/property2 frame (shape->[4,2])\n",
"/nested_example_class/property1 series (shape->[4]) \n"
]
}
],
"source": [
"#Read HDF file\n",
"with pd.HDFStore('nested_test.hdf','r') as data:\n",
" print data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Modifed Usage\n",
"\n",
"In `BasePlasma` class, the way properties of object are collected is different. It does not uses `hdf_properties` attribute.
\n",
"That\\`s why , `PlasmaWriterMixin` (which extends `HDFWriterMixin`) changes how the properties of `BasePlasma` class will be collected, by changing `get_properties` function.
\n",
"\n",
"Here is a quick demonstration, if behaviour of default `get_properties` function inside `HDFWriterMixin` needs to be changed, by subclassing it to create a new `Mixin`."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class ModifiedWriterMixin(HDFWriterMixin):\n",
" def get_properties(self):\n",
" #Change behaviour here, how properties will be collected from Class\n",
" data = {name: getattr(self, name) for name in self.outputs}\n",
" return data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A demo class , using this modified mixin."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class DemoClass(ModifiedWriterMixin):\n",
" outputs = ['property1']\n",
" hdf_name = 'demo'\n",
" def __init__(self, property1):\n",
" self.property1 = property1"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"File path: demo_class.hdf\n",
"/demo/scalars series (shape->[1])\n"
]
}
],
"source": [
"obj3 = DemoClass('random_string')\n",
"obj3.to_hdf('demo_class.hdf')\n",
"with pd.HDFStore('demo_class.hdf','r') as data:\n",
" print data"
]
}
],
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