# Calculation plugin - Integer summation¶

In this chapter we will give you some examples and a brief guide on how to write a plugin to support a new code. We will focus here on a very simple code (that simply adds two numbers), so that we can focus only on how AiiDA manages the calculation. At the end, you will have an overview of how a plugin is developed. You will be able then to proceed to more complex plugin guides like the guide for the Quantum Espresso plugin, or you can directly jump in and develop your own plugin!

## Overview¶

Before analysing the different components of the plugin, it is important to understand which are these and their interaction.

We should keep in mind that AiiDA is a tool allowing us to perform easily calculations and to maintain data provenance. That said, it should be clear that AiiDA doesn’t perform the calculations but orchestrates the calculation procedure following the user’s directives. Therefore, AiiDA executes (external) codes and it needs to know:

• where the code is;
• how to prepare the input for the code. This is called an input plugin or a Calculation subclass;
• how to parse the output of the code. This is called an output plugin or a Parser subclass.

It is also useful, but not necessary, to have a script that prepares the calculation for AiiDA with the necessary parameters and submits it. Let’s start to see how to prepare these components.

## Code¶

The code is an external program that does a useful calculation for us. For detailed information on how to setup the new codes, you can have a look at the respective documentation page.

Imagine that we have the following python code that we want to install. It does the simple task of adding two numbers that are found in a JSON file, whose name is given as a command-line parameter:

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import json
import sys

in_file = sys.argv[1]
out_file = sys.argv[2]

with open(in_file) as f:

out_dict = { 'sum':in_dict['x1']+in_dict['x2'] }

with open(out_file,'w') as f:
json.dump(out_dict,f)

The result will be stored in JSON format in a file which name is also passed as parameter. The resulting file from the script will be handled by AiiDA. The code can be downloaded from here. We will now proceed to prepare an AiiDA input plugin for this code.

## Input plugin¶

In abstract term, this plugin must contain the following two pieces of information:

• what are the input data objects of the calculation;
• how to convert the input data object in the actual input file required by the external code.

Let’s have a look at the input plugin developed for the aforementioned summation code (a detailed description of the different sections follows):

# -*- coding: utf-8 -*-

from aiida.orm import JobCalculation
from aiida.orm.data.parameter import ParameterData
from aiida.common.utils import classproperty
from aiida.common.exceptions import InputValidationError
from aiida.common.exceptions import ValidationError
from aiida.common.datastructures import CalcInfo, CodeInfo
import json

class SumCalculation(JobCalculation):
"""
A generic plugin for adding two numbers.
"""

def _init_internal_params(self):
super(SumCalculation, self)._init_internal_params()

self._DEFAULT_INPUT_FILE = 'in.json'
self._DEFAULT_OUTPUT_FILE = 'out.json'
self._default_parser = 'sum'

@classproperty
def _use_methods(cls):
"""
Additional use_* methods for the namelists class.
"""
retdict = JobCalculation._use_methods
retdict.update({
"parameters": {
'valid_types': ParameterData,
'docstring': ("Use a node that specifies the input parameters "
"for the namelists"),
},
})
return retdict

def _prepare_for_submission(self,tempfolder, inputdict):
"""
This is the routine to be called when you want to create
the input files and related stuff with a plugin.

:param tempfolder: a aiida.common.folders.Folder subclass where
the plugin should put all its files.
:param inputdict: a dictionary with the input nodes, as they would
be returned by get_inputs_dict (with the Code!)
"""
try:
except KeyError:
raise InputValidationError("No parameters specified for this "
"calculation")
if not isinstance(parameters, ParameterData):
raise InputValidationError("parameters is not of type "
"ParameterData")
try:
except KeyError:
raise InputValidationError("No code specified for this "
"calculation")
if inputdict:
raise ValidationError("Cannot add other nodes beside parameters")

##############################
# END OF INITIAL INPUT CHECK #
##############################

input_json = parameters.get_dict()

# write all the input to a file
input_filename = tempfolder.get_abs_path(self._DEFAULT_INPUT_FILE)
with open(input_filename, 'w') as infile:
json.dump(input_json, infile)

# ============================ calcinfo ================================

calcinfo = CalcInfo()
calcinfo.uuid = self.uuid
calcinfo.local_copy_list = []
calcinfo.remote_copy_list = []
calcinfo.retrieve_list = [self._DEFAULT_OUTPUT_FILE]
calcinfo.retrieve_temporary_list = [['path/hugefiles*[0-9].xml', '.', '1']]

codeinfo = CodeInfo()
codeinfo.cmdline_params = [self._DEFAULT_INPUT_FILE,self._DEFAULT_OUTPUT_FILE]
codeinfo.code_uuid = code.uuid
calcinfo.codes_info = [codeinfo]

return calcinfo

The above input plugin can be downloaded from (here) and should be placed at aiida/orm/calculation/job/sum.py.

In order the plugin to be automatically discoverable by AiiDA, it is important to:

• give the right name to the file. This should be the name of your input plugin (all lowercase);
• place the plugin under aiida/orm/calculation/job;
• name the class inside the plugin as PluginnameCalculation. For example, the class name of the summation input plugin is, as you see above, SumCalculation. The first letter must be capitalized, the other letters must be lowercase;
• inherit the class from JobCalculation.

By doing the above, your plugin will be discoverable and loadable using CalculationFactory.

Note

The base Calculation class should only be used as the abstract base class. Any calculation that needs to run on a remote scheduler must inherit from AbstractJobCalculation, that contains all the methods to run on a remote scheduler, get the calculation state, copy files remotely and retrieve them, …

### Defining the accepted input Data nodes¶

The input data nodes that the input plugin expects are those returned by the _use_methods class property. It is important to always extend the dictionary returned by the parent class, starting this method with:

retdict = JobCalculation._use_methods

(or the correct parent class, instead of JobCalculation, if you are inheriting from a subclass).

The specific parameters needed by the plugin are defined by the following code snippet:

retdict.update({
"parameters": {
'valid_types': ParameterData,
'docstring': ("Use a node that specifies the input parameters "
"for the namelists"),
},
})

This means that this specific summation plugin expects only one input data node, which is of the type ParameterData and with link name parameters.

### The main plugin logic¶

The main logic of the plugin (called by AiiDA just before submission, in order to read the AiiDA input data nodes and create the actual input files for the extenal code) must be defined inside a method _prepare_for_submission, that will receive (beside self) two parameters, a temporary folder tempfolder in which content can be written, and a dictionary containing all the input nodes that AiiDA will retrieve from the database (in this way, the plugin does not need to browse the database).

The input data node with the parameter is retrieved using its link name parameters specified above:

A few additional checks are performed to retrieve also the input code (the AiiDA node representing the code executable, that we are going to setup in the next section) and verify that there are no unexpected additional input nodes.

The following lines do the actual job, and prepare the input file for the external code, creating a suitable JSON file:

input_json = parameters.get_dict()

# write all the input to a file
input_filename = tempfolder.get_abs_path(self._DEFAULT_INPUT_FILE)
with open(input_filename, 'w') as infile:
json.dump(input_json, infile)

### The last step: the calcinfo¶

We can now create the calculation info: an object containing some additional information that AiiDA needs (beside the files you generated in the folder) in order to submit the claculation. In the calcinfo object, you need to store the calculation UUID:

calcinfo.uuid = self.uuid

You should also define a list of output files that will be retrieved automatically after the code execution, and that will be stored permanently into the AiiDA database:

calcinfo.retrieve_list = [self._DEFAULT_OUTPUT_FILE]

The entries of the list should either be a string, which corresponds to the full filepath of the file on the remote, or if you want to specify a group of files with wildcards, it should be another list containing the following three items

• Remote path with wildcards e.g. some/path/bigfiles*[0-9].xml
• Local path, which should always be '.' in this case of using wildcards
• Depth, which is an integer that indicates to what level the nested subtree structure should be kept. For example in this example, with a depth of 1, the matched files will be copied to the root directory as bigfiles*[0-9].xml. For depth=1, the sub path path will be included and the files will be copied as path/bigfiles*[0-9].xml

There is another field that follows exactly the same syntax as the retrieve_list but behaves a little differently.

calcinfo.retrieve_temporary_list = [[‘some/path/bigfiles*[0-9].xml’, ‘.’, 0]]

The difference is that these files will be retrieved and stored in a temporary folder, that will only be available during the parsing of the calculation. After the parsing is completed, successfully or not, the files will be deleted. This is useful if during parsing, one wants to analyze the contents of big files and parse a small subset of the data to keep permanently, but does not want to have the store the raw files themselves which would unnecessarily increase the size of the repository. The files that are retrieved will be stored in a temporary FolderData and be passed as an argument to the parse_with_retrieved method of the Parser class, which is implemented by the specific plugin. It will be passed under the key retrieved_temporary_folder.

For the time being, just define also the following variables as empty lists (we will describe them in the next sections):

calcinfo.local_copy_list = []
calcinfo.remote_copy_list = []

Finally, you need to specify which code executable(s) need to be called link the code to the codeinfo object. For each code, you need to create a CodeInfo object, specify the code UUID, and define the command line parameters that should be passed to the code as a list of strings (only paramters after the executable name must be specified. Moreover, AiiDA takes care of escaping spaces and other symbols). In our case, our code requires the name of the input file, followed by the name of the output file, so we write:

codeinfo.cmdline_params = [self._DEFAULT_INPUT_FILE,self._DEFAULT_OUTPUT_FILE]

Finally, we link the just created codeinfo to the calcinfo, and return it:

calcinfo.codes_info = [codeinfo]

return calcinfo

Note

calcinfo.codes_info is a list of CodeInfo objects. This allows to support the execution of more than one code, and will be described later.

Note

All content stored in the tempfolder will be then stored into the AiiDA database, potentially forever. Therefore, before generating huge files, you should carefully think at how to design your plugin interface. In particular, give a look to the local_copy_list and remote_copy_list attributes of calcinfo, described in more detail in the Quantum ESPRESSO developer plugin tutorial.

By doing all the above, we have clarified what parameters should be passed to which code, we have prepared the input file that the code will access and we let also AiiDA know the name of the output file: our first input plugin is ready!

Note

A few class internal parameters can (or should) be defined inside the _init_internal_params method:

def _init_internal_params(self):
super(SumCalculation, self)._init_internal_params()

self._DEFAULT_INPUT_FILE = 'in.json'
self._DEFAULT_OUTPUT_FILE = 'out.json'
self._default_parser = 'sum'

In particular, it is good practice to define a _DEFAULT_INPUT_FILE and _DEFAULT_OUTPUT_FILE attributes (pointing to the default input and output file name – these variables are then used by some verdi commands, such as verdi calculation outputcat). Also, you need to define the name of the default parser that will be invoked when the calculation completes in _default_parser. In the example above, we choose the ‘sum’ plugin (that we are going to define later on). If you don’t want to call any parser, set this variable to None.

As a final step, after copying the file in the location specified above, we can check if AiiDA recognised the plugin, by running the command verdi calculation plugins and veryfing that our new sum plugin is now listed.

## Setup of the code¶

Now that we know the executable that we want to run, and we have setup the input plugin, we can proceed to configure AiiDA by setting up a new code to execute:

verdi code setup

During the setup phase, you can either configure a remote code (meaning that you are going to place the python executable in the right folder of the remote computer, and then just instruct AiiDA on the location), or as a local folder, meaning that you are going to store (during the setup phase) the python executable into the AiiDA DB, and AiiDA will copy it to the remote computer when needed. In this second case, put the sum_executable.py in an empty folder and pass this folder in the setup phase.

Note

In both cases, remember to set the executable flag to the code by running chmod +x sum_executable.py.

After defining the code, we should be able to see it in the list of our installed codes by typing:

verdi code list

A typical output of the above command is:

\$ verdi code list
# List of configured codes:
# (use 'verdi code show CODEID' to see the details)
* Id 73: sum

Where we can see the already installed summation code. We can further see the specific parameters that we gave when we set-up the code by typing:

verdi code show 73

Which will give us an output similar to the following:

\$ verdi code show 73
* PK:             73
* Label:          sum
* Description:    A simple sum executable
* Default plugin: sum
* Used by:        0 calculations
* Type:           local
* Exec name:      ./sum_executable.py
* List of files/folders:
* [file] sum_executable.py
* prepend text:
# No prepend text.
* append text:
# No append text.

What is important to keep from the above is that we have informed AiiDA for the existence of a code that resides at a specific location and we have also specified the default (input) plugin that will be used.

## Output plugin: the parser¶

In general, it is useful to parse files generated by the code to import relevant data into the database. This has two advantages:

• we can store information in specific data classes to facilitate their use (e.g. crystal structures, parameters, …)
• we can then make use of efficient database queries if, e.g., output quantities are stored as integers or floats rather than as strings in a long text file.

The following is a sample output plugin for the summation code, described in detail later:

# -*- coding: utf-8 -*-

from aiida.orm.calculation.job.sum import SumCalculation
from aiida.parsers.parser import Parser
from aiida.parsers.exceptions import OutputParsingError
from aiida.orm.data.parameter import ParameterData

import json

class SumParser(Parser):
"""
This class is the implementation of the Parser class for Sum.
"""
def parse_with_retrieved(self, retrieved):
"""
Parses the datafolder, stores results.
This parser for this simple code does simply store in the DB a node
representing the file of forces in real space
"""

successful = True
# select the folder object
# Check that the retrieved folder is there
try:
except KeyError:
self.logger.error("No retrieved folder found")
return False, ()

# check what is inside the folder
list_of_files = out_folder.get_folder_list()
# at least the stdout should exist
if self._calc._DEFAULT_OUTPUT_FILE not in list_of_files:
successful = False
return successful,()

try:
with open( out_folder.get_abs_path(self._calc._DEFAULT_OUTPUT_FILE) ) as f:
except ValueError:
successful=False
self.logger.error("Error parsing the output json")
return successful,()

# save the arrays
output_data = ParameterData(dict=out_dict)

return successful,new_nodes_list

As mentioned above the output plugin will parse the output of the executed code at the remote computer and it will store the results to the AiiDA database.

All the parsing code is enclosed in a single method parse_with_retrieved, that will receive as a single parameter retrieved, a dictionary of retrieved nodes. The default behavior is to create a single FolderData node, that can be retrieved using:

We then read and parse the output file that will contain the result:

with open( out_folder.get_abs_path(self._calc._DEFAULT_OUTPUT_FILE) ) as f:

Note

all parsers have a self._calc attribute that points to the calculation being parsed. This is automatically set in the parent Parser class.

After loading the code result data to the dictionary out_dict, we construct a ParameterData object (ParameterData(dict=out_dict)) that will be linked to the calculation in the AiiDA graph to be later in the database:

output_data = ParameterData(dict=out_dict)

return successful,new_nodes_list

Note

Parsers should not store nodes manually. Instead, they should return a list of output unstored nodes (together with a link name string, as shown above). AiiDA will then take care of storing the node, and creating the appropriate links in the DB.

Note

the self.get_linkname_outparams() is a string automatically defined in all Parser classes and subclasses. In general, you can have multiple output nodes with any name, but it is good pratice so have also one of the output nodes with link name self.get_linkname_outparams() and of type ParameterData. The reason is that this node is the one exposed with the calc.res interface (for instance, later we will be able to get the results using print calc.res.sum.

The above output plugin can be downloaded from here and should be placed at aiida/parsers/plugins/sum.py.

Note

Before continuing, it is important to restart the daemon, so that it can recognize the new files added into the aiida code and use the new plugins. To do so, run now:

verdi daemon restart

## Submission script¶

It’s time to calculate how much 2+3 is! We need to submit a new calculation. To this aim, we don’t necessarily need a submission script, but it definitely facilitates the calculation submission. A very minimal sample script follows (other examples can be found in the aiida/examples/submission folder):

#!/usr/bin/env runaiida
# -*- coding: utf-8 -*-
import sys
import os

from aiida.common.exceptions import NotExistent
ParameterData = DataFactory('parameter')

# The name of the code setup in AiiDA
codename = 'sum'
computer_name = 'localhost'

################################################################
try:
dontsend = sys.argv[1]
if dontsend == "--dont-send":
submit_test = True
elif dontsend == "--send":
submit_test = False
else:
raise IndexError
except IndexError:
print >> sys.stderr, ("The first parameter can only be either "
"--send or --dont-send")
sys.exit(1)

code = Code.get_from_string(codename)
# The following line is only needed for local codes, otherwise the
# computer is automatically set from the code
computer = Computer.get(computer_name)

# These are the two numbers to sum
parameters = ParameterData(dict={'x1':2,'x2':3})

calc = code.new_calc()
calc.label = "Test sum"
calc.description = "Test calculation with the sum code"
calc.set_max_wallclock_seconds(30*60) # 30 min
calc.set_computer(computer)
calc.set_withmpi(False)
calc.set_resources({"num_machines": 1})

calc.use_parameters(parameters)

if submit_test:
subfolder, script_filename = calc.submit_test()
print "Test submit file in {}".format(os.path.join(
os.path.relpath(subfolder.abspath),
script_filename
))
else:
calc.store_all()
calc.submit()
print "submitted calculation; calc=Calculation(uuid='{}') # ID={}".format(
calc.uuid,calc.dbnode.pk)

What is important to note in the script above is the definition of the code to be used:

codename = 'sum'
code = Code.get_from_string(codename)

and the definition of the parameters:

parameters = ParameterData(dict={'x1':2,'x2':3})
calc.use_parameters(parameters)

If everything is done correctly, by running the script a new calculation will be generated and submitted to AiiDA (to run the script, remember to change its permissions with chmod +x filename first, and then run it with ./scriptname.py). When the code finishes its execution, AiiDA will retrieve the results, parse and store them back to the AiiDA database using the output plugin. You can download the submission script from here.

## Conclusion¶

We have just managed to write our first AiiDA plugin! What is important to remember is that:

• AiiDA doesn’t know how to execute your code. Therefore, you have to setup your code (with verdi code setup) and let AiiDA know how to prepare the data that will be given to the code (input plugin or calculation) and how to handle the result of the code (output plugin or parser).
• you need to do pass the actual data for the calculation you want to submit, either in the interactive shell, or via a submission script.

As usual, we can see the executed calculations by doing a verdi calculation list. To see the calculations of the last day:

\$ verdi calculation list -a -p1
# Last daemon state_updater check: 0h:00m:06s ago (at 20:10:31 on 2015-10-20)
# Pk|State        |Creation|Sched. state|Computer   |Type
327 |FINISHED     |4h ago  |DONE        |localhost  |sum

and we can see the result of the sum by running in the verdi shell the following commands (change 327 with the correct calculation PK):