\n",
"Note that we have not used `getden2 = -1` in the second dataset \n",
"since AbiPy knows how to connect the two Tasks.\n",
"So no need for `get*` or `ird*` variables with Abipy. \n",
"Just specify the correct dependency and python will do the rest!\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To have useful information on the status of the flow, one uses:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Work #0: \n",
"`__AbinitFlow__.pickle` is the pickle file used to save the status of `Flow`. **Don't touch it!** \n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An Abinit Task *has* an [AbinitInput](abinit_input.ipynb) which in turn has a [Structure](structure.ipynb):"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"################################################## SECTION: basic
##############################################
ecut 6
nband 8
ngkpt 8 8 8
kptopt 1
nshiftk 1
shiftk 0 0 0
tolvrs 1e-06
##############################################
#### STRUCTURE
##############################################
natom 2
ntypat 1
typat 1 1
znucl 14
xred
0.0000000000 0.0000000000 0.0000000000
0.2500000000 0.2500000000 0.2500000000
acell 1.0 1.0 1.0
rprim
6.3285005272 0.0000000000 3.6537614829
2.1095001757 5.9665675167 3.6537614829
0.0000000000 0.0000000000 7.3075229659" ], "text/plain": [ "
Reduced Formula: Si
abc : 3.866975 3.866975 3.866975
angles: 60.000000 60.000000 60.000000
Sites (2)
# SP a b c
--- ---- ---- ---- ----
0 Si 0 0 0
1 Si 0.25 0.25 0.25" ], "text/plain": [ "Structure Summary\n", "Lattice\n", " abc : 3.86697462 3.86697462 3.86697462\n", " angles : 59.99999999999999 59.99999999999999 59.99999999999999\n", " volume : 40.88829179346891\n", " A : 3.3488982567096763 0.0 1.9334873100000005\n", " B : 1.1162994189032256 3.1573715557642927 1.9334873100000005\n", " C : 0.0 0.0 3.86697462\n", "PeriodicSite: Si (0.0000, 0.0000, 0.0000) [0.0000, 0.0000, 0.0000]\n", "PeriodicSite: Si (1.1163, 0.7893, 1.9335) [0.2500, 0.2500, 0.2500]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flow[0][0].input.structure" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "
![\"\"](\"https://github.com/abinit/abipy_assets/blob/master/Dont-Try-This-At-Home.jpg?raw=true\")
"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"nband in the first NscfTask: 1000\n",
"nband in the new input: 1000\n"
]
}
],
"source": [
"# Just copy the previous input and change nband, it's super-easy, the FORTRAN guru said!\n",
"new_input = t1.input\n",
"new_input[\"nband\"] = 1000\n",
"\n",
"print(\"nband in the first NscfTask:\", t1.input[\"nband\"])\n",
"print(\"nband in the new input:\", new_input[\"nband\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tada! Thanks to the trick of our beloved FORTRAN guru, we ended up with *two* NscfTaks\n",
"with the **same number** of bands (1000!). Why?\n",
"\n",
"Because `AbinitInput` is implemented internally with a dictionary, python dictionaries are **mutable**\n",
"and python variables are essentially references (they do not store data, actually they store the address of the data)."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a dict: {'foo': 'bar', 'hello': 'world'}\n",
"b dict: {'foo': 'bar', 'hello': 'world'}\n",
"c dict: {'foo': 'bar'}\n"
]
}
],
"source": [
"a = {\"foo\": \"bar\"}\n",
"b = a \n",
"c = a.copy()\n",
"a[\"hello\"] = \"world\"\n",
"print(\"a dict:\", a)\n",
"print(\"b dict:\", b)\n",
"print(\"c dict:\", c)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For a more techical explanation see [here](http://docs.python-guide.org/en/latest/writing/gotchas/)\n",
"\n",
"To avoid this mistake, we need to *copy* the object before changing it"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"nband in the first NscfTask: 8\n",
"nband in the new input: 1000\n"
]
}
],
"source": [
"t1.input[\"nband\"] = 8 # back to the old value\n",
"\n",
"new_input = t1.input.new_with_vars(nband=1000) # Copy and change nband\n",
"\n",
"print(\"nband in the first NscfTask:\", t1.input[\"nband\"])\n",
"print(\"nband in the new input:\", new_input[\"nband\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can finally add the second `NscfTask` with 1000 bands:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
"class PhononWork(Work, MergeDdb):\n",
" """\n",
" This work consists of nirred Phonon tasks where nirred is\n",
" the number of irreducible atomic perturbations for a given set of q-points.\n",
" It provides the callback method (on_all_ok) that calls mrgddb (mrgdv) to merge\n",
" all the partial DDB (POT) files produced. The two files are available in the\n",
" output directory of the Work.\n",
"\n",
" .. rubric:: Inheritance Diagram\n",
" .. inheritance-diagram:: PhononWork\n",
" """\n",
" @classmethod\n",
" def from_scf_task(cls, scf_task, qpoints, is_ngqpt=False, tolerance=None, with_becs=False,\n",
" ddk_tolerance=None, manager=None):\n",
" """\n",
" Construct a `PhononWork` from a |ScfTask| object.\n",
" The input file for phonons is automatically generated from the input of the ScfTask.\n",
" Each phonon task depends on the WFK file produced by the `scf_task`.\n",
"\n",
" Args:\n",
" scf_task: |ScfTask| object.\n",
" qpoints: q-points in reduced coordinates. Accepts single q-point, list of q-points\n",
" or three integers defining the q-mesh if `is_ngqpt`.\n",
" is_ngqpt: True if `qpoints` should be interpreted as divisions instead of q-points.\n",
" tolerance: dict {"varname": value} with the tolerance to be used in the phonon run.\n",
" None to use AbiPy default.\n",
" with_becs: Activate calculation of Electric field and Born effective charges.\n",
" ddk_tolerance: dict {"varname": value} with the tolerance used in the DDK run if with_becs.\n",
" None to use AbiPy default.\n",
" manager: |TaskManager| object.\n",
" """\n",
"\n",
"`nohup` is a standard Unix tool. The command make the scheduler immune \n",
"to hangups so that you can close the shell session without killing the scheduler.\n",
"
\n",
"\n",
"This brings us to the last and most crucial question. \n",
"How do we configure AbiPy to run Abinit workflows on different architectures ranging from \n",
"standard laptops to high-performance supercomputers?\n",
"\n",
"Unfortunately this notebook is already quite long and these details are best covered \n",
"in a technical documentation.\n",
"What should be stressed here is that the behaviour can be customized with two Yaml files.\n",
"All the information related to your environment (Abinit build, modules, resource managers, shell environment)\n",
"are read from the `manager.yml` configuration file, that is usually located in the directory `~/.abinit/abipy/`\n",
"The options for the python scheduler responsible for job submission are given in `scheduler.yml`.\n",
"\n",
"For a more complete description of these configuration options, \n",
"please consult the [TaskManager documentation](http://abinit.github.io/abipy/workflows/taskmanager.html).\n",
"A list of configuration files for different machines and clusters is available \n",
"[here](http://abinit.github.io/abipy/workflows/manager_examples.html)\n",
"while the [Flows HOWTO](http://abinit.github.io/abipy/flows_howto.html)\n",
"gathers answers to frequently asked questions.\n",
"\n",
"Last but not least, check out our \n",
"[gallery of AbiPy Flows](http://abinit.github.io/abipy/flow_gallery/index.html) for inspiration."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Back to the main [Index](index.ipynb)"
]
}
],
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