abipy/flowtk/flows.py

# coding: utf-8
"""
A Flow is a container for Works, and works consist of tasks.
Flows are the final objects that can be dumped directly to a pickle file on disk
Flows are executed using abirun (abipy).
"""
import os
import sys
import time
import collections
import warnings
import shutil
import tempfile
import numpy as np

from io import StringIO
from pprint import pprint
from tabulate import tabulate
from pydispatch import dispatcher
from collections import OrderedDict
from monty.collections import dict2namedtuple
from monty.string import list_strings, is_string, make_banner
from monty.operator import operator_from_str
from monty.io import FileLock
from monty.pprint import draw_tree
from monty.termcolor import cprint, colored, cprint_map, get_terminal_size
from monty.inspect import find_top_pyfile
from monty.json import MSONable
from pymatgen.util.serialization import pmg_pickle_load, pmg_pickle_dump, pmg_serialize
from pymatgen.core.units import Memory
from pymatgen.util.io_utils import AtomicFile
from abipy.tools.plotting import add_fig_kwargs, get_ax_fig_plt
from abipy.tools.printing import print_dataframe
from abipy.flowtk import wrappers
from .nodes import Status, Node, NodeError, NodeResults, Dependency, GarbageCollector, check_spectator
from .tasks import ScfTask, TaskManager, FixQueueCriticalError
from .utils import File, Directory, Editor
from .works import NodeContainer, Work, BandStructureWork, PhononWork, BecWork, G0W0Work, QptdmWork, DteWork
from .events import EventsParser

__author__ = "Matteo Giantomassi"
__copyright__ = "Copyright 2013, The Materials Project"
__version__ = "0.1"
__maintainer__ = "Matteo Giantomassi"


__all__ = [
    "Flow",
    "G0W0WithQptdmFlow",
    "bandstructure_flow",
    "g0w0_flow",
]


def as_set(obj):
    """
    Convert obj into a set, returns None if obj is None.

    >>> assert as_set(None) is None and as_set(1) == set([1]) and as_set(range(1,3)) == set([1, 2])
    """
    if obj is None or isinstance(obj, collections.abc.Set):
        return obj

    if not isinstance(obj, collections.abc.Iterable):
        return set((obj,))
    else:
        return set(obj)


class FlowResults(NodeResults):

    JSON_SCHEMA = NodeResults.JSON_SCHEMA.copy()
    #JSON_SCHEMA["properties"] = {
    #    "queries": {"type": "string", "required": True},
    #}

    @classmethod
    def from_node(cls, flow):
        """Initialize an instance from a Work instance."""
        new = super().from_node(flow)

        # Will put all files found in outdir in GridFs
        d = {os.path.basename(f): f for f in flow.outdir.list_filepaths()}

        # Add the pickle file.
        d["pickle"] = flow.pickle_file if flow.pickle_protocol != 0 else (flow.pickle_file, "t")
        new.add_gridfs_files(**d)

        return new


class FlowError(NodeError):
    """Base Exception for :class:`Node` methods"""


class Flow(Node, NodeContainer, MSONable):
    """
    This object is a container of work. Its main task is managing the
    possible inter-dependencies among the work and the creation of
    dynamic workflows that are generated by callbacks registered by the user.

    Attributes:

        creation_date: String with the creation_date
        pickle_protocol: Protocol for Pickle database (default: -1 i.e. latest protocol)

    Important methods for constructing flows:

        register_work: register (add) a work to the flow
        resister_task: register a work that contains only this task returns the work
        allocate: propagate the workdir and manager of the flow to all the registered tasks
        build:
        build_and_pickle_dump:
    """
    VERSION = "0.1"
    PICKLE_FNAME = "__AbinitFlow__.pickle"

    Error = FlowError

    Results = FlowResults

    @classmethod
    def from_inputs(cls, workdir, inputs, manager=None, pickle_protocol=-1, task_class=ScfTask,
                    work_class=Work, remove=False):

        """
        Construct a simple flow from a list of inputs. The flow contains a single Work with
        tasks whose class is given by task_class.

        .. warning::

            Don't use this interface if you have dependencies among the tasks.

        Args:
            workdir: String specifying the directory where the works will be produced.
            inputs: List of inputs.
            manager: |TaskManager| object responsible for the submission of the jobs.
                If manager is None, the object is initialized from the yaml file
                located either in the working directory or in the user configuration dir.
            pickle_protocol: Pickle protocol version used for saving the status of the object.
                -1 denotes the latest version supported by the python interpreter.
            task_class: The class of the |Task|.
            work_class: The class of the |Work|.
            remove: attempt to remove working directory `workdir` if directory already exists.
        """
        if not isinstance(inputs, (list, tuple)): inputs = [inputs]

        flow = cls(workdir, manager=manager, pickle_protocol=pickle_protocol, remove=remove)
        work = work_class()
        for inp in inputs:
            work.register(inp, task_class=task_class)
        flow.register_work(work)

        return flow.allocate()

    @classmethod
    def as_flow(cls, obj):
        """Convert obj into a Flow. Accepts filepath, dict, or Flow object."""
        if isinstance(obj, cls): return obj
        if is_string(obj):
            return cls.pickle_load(obj)
        elif isinstance(obj, collections.abc.Mapping):
            return cls.from_dict(obj)
        else:
            raise TypeError("Don't know how to convert type %s into a Flow" % type(obj))

    def __init__(self, workdir, manager=None, pickle_protocol=-1, remove=False):
        """
        Args:
            workdir: String specifying the directory where the works will be produced.
                     if workdir is None, the initialization of the working directory
                     is performed by flow.allocate(workdir).
            manager: |TaskManager| object responsible for the submission of the jobs.
                     If manager is None, the object is initialized from the yaml file
                     located either in the working directory or in the user configuration dir.
            pickle_protocol: Pickle protocol version used for saving the status of the object.
                          -1 denotes the latest version supported by the python interpreter.
            remove: attempt to remove working directory `workdir` if directory already exists.
        """
        super().__init__()

        if workdir is not None:
            if remove and os.path.exists(workdir): shutil.rmtree(workdir)
            self.set_workdir(workdir)

        self.creation_date = time.asctime()

        if manager is None: manager = TaskManager.from_user_config()
        self.manager = manager.deepcopy()

        # List of works.
        self._works = []

        self._waited = 0

        # List of callbacks that must be executed when the dependencies reach S_OK
        self._callbacks = []

        # Install default list of handlers at the flow level.
        # Users can override the default list by calling flow.install_event_handlers in the script.
        # Example:
        #
        #    # flow level (common case)
        #    flow.install_event_handlers(handlers=my_handlers)
        #
        #    # task level (advanced mode)
        #    flow[0][0].install_event_handlers(handlers=my_handlers)
        #
        self.install_event_handlers()

        self.pickle_protocol = int(pickle_protocol)

        # ID used to access mongodb
        self._mongo_id = None

        # Save the location of the script used to generate the flow.
        # This trick won't work if we are running with nosetests, py.test etc
        pyfile = find_top_pyfile()
        if "python" in pyfile or "ipython" in pyfile: pyfile = "<" + pyfile + ">"
        self.set_pyfile(pyfile)

        # TODO
        # Signal slots: a dictionary with the list
        # of callbacks indexed by node_id and SIGNAL_TYPE.
        # When the node changes its status, it broadcast a signal.
        # The flow is listening to all the nodes of the calculation
        # [node_id][SIGNAL] = list_of_signal_handlers
        #self._sig_slots =  slots = {}
        #for work in self:
        #    slots[work] = {s: [] for s in work.S_ALL}

        #for task in self.iflat_tasks():
        #    slots[task] = {s: [] for s in work.S_ALL}

        self.on_all_ok_num_calls = 0

    @pmg_serialize
    def as_dict(self, **kwargs):
        """
        JSON serialization, note that we only need to save
        a string with the working directory since the object will be
        reconstructed from the pickle file located in workdir
        """
        return {"workdir": self.workdir}

    # This is needed for fireworks.
    to_dict = as_dict

    @classmethod
    def from_dict(cls, d, **kwargs):
        """Reconstruct the flow from the pickle file."""
        return cls.pickle_load(d["workdir"], **kwargs)

    @classmethod
    def temporary_flow(cls, manager=None):
        """Return a Flow in a temporary directory. Useful for unit tests."""
        return cls(workdir=tempfile.mkdtemp(), manager=manager)

    def set_workdir(self, workdir, chroot=False):
        """
        Set the working directory. Cannot be set more than once unless chroot is True
        """
        if not chroot and hasattr(self, "workdir") and self.workdir != workdir:
            raise ValueError("self.workdir != workdir: %s, %s" % (self.workdir,  workdir))

        # Directories with (input|output|temporary) data.
        self.workdir = os.path.abspath(workdir)
        self.indir = Directory(os.path.join(self.workdir, "indata"))
        self.outdir = Directory(os.path.join(self.workdir, "outdata"))
        self.tmpdir = Directory(os.path.join(self.workdir, "tmpdata"))
        self.wdir = Directory(self.workdir)

    def reload(self):
        """
        Reload the flow from the pickle file. Used when we are monitoring the flow
        executed by the scheduler. In this case, indeed, the flow might have been changed
        by the scheduler and we have to reload the new flow in memory.
        """
        new = self.__class__.pickle_load(self.workdir)
        self = new

    @classmethod
    def pickle_load(cls, filepath, spectator_mode=True, remove_lock=False):
        """
        Loads the object from a pickle file and performs initial setup.

        Args:
            filepath: Filename or directory name. It filepath is a directory, we
                scan the directory tree starting from filepath and we
                read the first pickle database. Raise RuntimeError if multiple
                databases are found.
            spectator_mode: If True, the nodes of the flow are not connected by signals.
                This option is usually used when we want to read a flow
                in read-only mode and we want to avoid callbacks that can change the flow.
            remove_lock:
                True to remove the file lock if any (use it carefully).
        """
        if os.path.isdir(filepath):
            # Walk through each directory inside path and find the pickle database.
            for dirpath, dirnames, filenames in os.walk(filepath):
                fnames = [f for f in filenames if f == cls.PICKLE_FNAME]
                if fnames:
                    if len(fnames) == 1:
                        filepath = os.path.join(dirpath, fnames[0])
                        break  # Exit os.walk
                    else:
                        err_msg = "Found multiple databases:\n %s" % str(fnames)
                        raise RuntimeError(err_msg)
            else:
                err_msg = "Cannot find %s inside directory %s" % (cls.PICKLE_FNAME, filepath)
                raise ValueError(err_msg)

        if remove_lock and os.path.exists(filepath + ".lock"):
            try:
                os.remove(filepath + ".lock")
            except Exception:
                pass

        with FileLock(filepath):
            with open(filepath, "rb") as fh:
                flow = pmg_pickle_load(fh)

        # Check if versions match.
        if flow.VERSION != cls.VERSION:
            msg = ("File flow version %s != latest version %s\n."
                   "Regenerate the flow to solve the problem " % (flow.VERSION, cls.VERSION))
            warnings.warn(msg)

        flow.set_spectator_mode(spectator_mode)

        # Recompute the status of each task since tasks that
        # have been submitted previously might be completed.
        flow.check_status()
        return flow

    # Handy alias
    from_file = pickle_load

    @classmethod
    def pickle_loads(cls, s):
        """Reconstruct the flow from a string."""
        strio = StringIO()
        strio.write(s)
        strio.seek(0)
        flow = pmg_pickle_load(strio)
        return flow

    def get_panel(self):
        """Build panel with widgets to interact with the |Flow| either in a notebook or in panel app."""
        from abipy.panels.flows import FlowPanel
        return FlowPanel(self).get_panel()

    def __len__(self):
        return len(self.works)

    def __iter__(self):
        return self.works.__iter__()

    def __getitem__(self, slice):
        return self.works[slice]

    def set_pyfile(self, pyfile):
        """
        Set the path of the python script used to generate the flow.

        .. Example:

            flow.set_pyfile(__file__)
        """
        # TODO: Could use a frame hack to get the caller outside abinit
        # so that pyfile is automatically set when we __init__ it!
        self._pyfile = os.path.abspath(pyfile)

    @property
    def pyfile(self):
        """
        Absolute path of the python script used to generate the flow. Set by `set_pyfile`
        """
        try:
            return self._pyfile
        except AttributeError:
            return None

    @property
    def pid_file(self):
        """The path of the pid file created by PyFlowScheduler."""
        return os.path.join(self.workdir, "_PyFlowScheduler.pid")

    @property
    def has_scheduler(self):
        """True if there's a scheduler running the flow."""
        return os.path.exists(self.pid_file)

    def check_pid_file(self):
        """
        This function checks if we are already running the |Flow| with a :class:`PyFlowScheduler`.
        Raises: Flow.Error if the pid file of the scheduler exists.
        """
        if not os.path.exists(self.pid_file):
            return 0

        self.show_status()
        raise self.Error("""\n\
            pid_file
            %s
            already exists. There are two possibilities:

               1) There's an another instance of PyFlowScheduler running
               2) The previous scheduler didn't exit in a clean way

            To solve case 1:
               Kill the previous scheduler (use 'kill pid' where pid is the number reported in the file)
               Then you can restart the new scheduler.

            To solve case 2:
               Remove the pid_file and restart the scheduler.

            Exiting""" % self.pid_file)

    @property
    def pickle_file(self):
        """The path of the pickle file."""
        return os.path.join(self.workdir, self.PICKLE_FNAME)

    @property
    def mongo_id(self):
        return self._mongo_id

    @mongo_id.setter
    def mongo_id(self, value):
        if self.mongo_id is not None:
            raise RuntimeError("Cannot change mongo_id %s" % self.mongo_id)
        self._mongo_id = value

    #def mongodb_upload(self, **kwargs):
    #    from abiflows.core.scheduler import FlowUploader
    #    FlowUploader().upload(self, **kwargs)

    def validate_json_schema(self):
        """Validate the JSON schema. Return list of errors."""
        errors = []

        for work in self:
            for task in work:
                if not task.get_results().validate_json_schema():
                    errors.append(task)
            if not work.get_results().validate_json_schema():
                errors.append(work)
        if not self.get_results().validate_json_schema():
            errors.append(self)

        return errors

    def get_mongo_info(self):
        """
        Return a JSON dictionary with information on the flow.
        Mainly used for constructing the info section in `FlowEntry`.
        The default implementation is empty. Subclasses must implement it
        """
        return {}

    def mongo_assimilate(self):
        """
        This function is called by client code when the flow is completed
        Return a JSON dictionary with the most important results produced
        by the flow. The default implementation is empty. Subclasses must implement it
        """
        return {}

    @property
    def works(self):
        """List of |Work| objects contained in self.."""
        return self._works

    @property
    def all_ok(self):
        """True if all the tasks in works have reached `S_OK`."""
        all_ok = all(work.all_ok for work in self)
        if all_ok:
            all_ok = self.on_all_ok()
        return all_ok

    @property
    def num_tasks(self):
        """Total number of tasks"""
        return len(list(self.iflat_tasks()))

    @property
    def errored_tasks(self):
        """List of errored tasks."""
        etasks = []
        for status in [self.S_ERROR, self.S_QCRITICAL, self.S_ABICRITICAL]:
            etasks.extend(list(self.iflat_tasks(status=status)))

        return set(etasks)

    @property
    def num_errored_tasks(self):
        """The number of tasks whose status is `S_ERROR`."""
        return len(self.errored_tasks)

    @property
    def unconverged_tasks(self):
        """List of unconverged tasks."""
        return list(self.iflat_tasks(status=self.S_UNCONVERGED))

    @property
    def num_unconverged_tasks(self):
        """The number of tasks whose status is `S_UNCONVERGED`."""
        return len(self.unconverged_tasks)

    @property
    def status_counter(self):
        """
        Returns a :class:`Counter` object that counts the number of tasks with
        given status (use the string representation of the status as key).
        """
        # Count the number of tasks with given status in each work.
        counter = self[0].status_counter
        for work in self[1:]:
            counter += work.status_counter

        return counter

    @property
    def ncores_reserved(self):
        """
        Returns the number of cores reserved in this moment.
        A core is reserved if the task is not running but
        we have submitted the task to the queue manager.
        """
        return sum(work.ncores_reserved for work in self)

    @property
    def ncores_allocated(self):
        """
        Returns the number of cores allocated in this moment.
        A core is allocated if it's running a task or if we have
        submitted a task to the queue manager but the job is still pending.
        """
        return sum(work.ncores_allocated for work in self)

    @property
    def ncores_used(self):
        """
        Returns the number of cores used in this moment.
        A core is used if there's a job that is running on it.
        """
        return sum(work.ncores_used for work in self)

    @property
    def has_chrooted(self):
        """
        Returns a string that evaluates to True if we have changed
        the workdir for visualization purposes e.g. we are using sshfs.
        to mount the remote directory where the `Flow` is located.
        The string gives the previous workdir of the flow.
        """
        try:
            return self._chrooted_from
        except AttributeError:
            return ""

    def chroot(self, new_workdir):
        """
        Change the workir of the |Flow|. Mainly used for
        allowing the user to open the GUI on the local host
        and access the flow from remote via sshfs.

        .. note::
            Calling this method will make the flow go in read-only mode.
        """
        self._chrooted_from = self.workdir
        self.set_workdir(new_workdir, chroot=True)

        for i, work in enumerate(self):
            new_wdir = os.path.join(self.workdir, "w" + str(i))
            work.chroot(new_wdir)

    def groupby_status(self):
        """
        Returns a ordered dictionary mapping the task status to
        the list of named tuples (task, work_index, task_index).
        """
        Entry = collections.namedtuple("Entry", "task wi ti")
        d = collections.defaultdict(list)

        for task, wi, ti in self.iflat_tasks_wti():
            d[task.status].append(Entry(task, wi, ti))

        # Sort keys according to their status.
        return OrderedDict([(k, d[k]) for k in sorted(list(d.keys()))])

    def groupby_task_class(self):
        """
        Returns a dictionary mapping the task class to the list of tasks in the flow
        """
        # Find all Task classes
        class2tasks = OrderedDict()
        for task in self.iflat_tasks():
            cls = task.__class__
            if cls not in class2tasks: class2tasks[cls] = []
            class2tasks[cls].append(task)

        return class2tasks

    def iflat_nodes(self, status=None, op="==", nids=None):
        """
        Generators that produces a flat sequence of nodes.
        if status is not None, only the tasks with the specified status are selected.
        nids is an optional list of node identifiers used to filter the nodes.
        """
        nids = as_set(nids)

        if status is None:
            if not (nids and self.node_id not in nids):
                yield self

            for work in self:
                if nids and work.node_id not in nids: continue
                yield work
                for task in work:
                    if nids and task.node_id not in nids: continue
                    yield task
        else:
            # Get the operator from the string.
            op = operator_from_str(op)

            # Accept Task.S_FLAG or string.
            status = Status.as_status(status)

            if not (nids and self.node_id not in nids):
                if op(self.status, status): yield self

            for wi, work in enumerate(self):
                if nids and work.node_id not in nids: continue
                if op(work.status, status): yield work

                for ti, task in enumerate(work):
                    if nids and task.node_id not in nids: continue
                    if op(task.status, status): yield task

    def node_from_nid(self, nid):
        """Return the node in the `Flow` with the given `nid` identifier"""
        for node in self.iflat_nodes():
            if node.node_id == nid: return node
        raise ValueError("Cannot find node with node id: %s" % nid)

    def iflat_tasks_wti(self, status=None, op="==", nids=None):
        """
        Generator to iterate over all the tasks of the `Flow`.
        Yields:

            (task, work_index, task_index)

        If status is not None, only the tasks whose status satisfies
        the condition (task.status op status) are selected
        status can be either one of the flags defined in the |Task| class
        (e.g Task.S_OK) or a string e.g "S_OK"
        nids is an optional list of node identifiers used to filter the tasks.
        """
        return self._iflat_tasks_wti(status=status, op=op, nids=nids, with_wti=True)

    def iflat_tasks(self, status=None, op="==", nids=None):
        """
        Generator to iterate over all the tasks of the |Flow|.

        If status is not None, only the tasks whose status satisfies
        the condition (task.status op status) are selected
        status can be either one of the flags defined in the |Task| class
        (e.g Task.S_OK) or a string e.g "S_OK"
        nids is an optional list of node identifiers used to filter the tasks.
        """
        return self._iflat_tasks_wti(status=status, op=op, nids=nids, with_wti=False)

    def _iflat_tasks_wti(self, status=None, op="==", nids=None, with_wti=True):
        """
        Generators that produces a flat sequence of task.
        if status is not None, only the tasks with the specified status are selected.
        nids is an optional list of node identifiers used to filter the tasks.

        Returns:
            (task, work_index, task_index) if with_wti is True else task
        """
        nids = as_set(nids)

        if status is None:
            for wi, work in enumerate(self):
                for ti, task in enumerate(work):
                    if nids and task.node_id not in nids: continue
                    if with_wti:
                        yield task, wi, ti
                    else:
                        yield task

        else:
            # Get the operator from the string.
            op = operator_from_str(op)

            # Accept Task.S_FLAG or string.
            status = Status.as_status(status)

            for wi, work in enumerate(self):
                for ti, task in enumerate(work):
                    if nids and task.node_id not in nids: continue
                    if op(task.status, status):
                        if with_wti:
                            yield task, wi, ti
                        else:
                            yield task

    def abivalidate_inputs(self):
        """
        Run ABINIT in dry mode to validate all the inputs of the flow.

        Return:
            (isok, tuples)

            isok is True if all inputs are ok.
            tuples is List of `namedtuple` objects, one for each task in the flow.
            Each namedtuple has the following attributes:

                retcode: Return code. 0 if OK.
                log_file:  log file of the Abinit run, use log_file.read() to access its content.
                stderr_file: stderr file of the Abinit run. use stderr_file.read() to access its content.

        Raises:
            `RuntimeError` if executable is not in $PATH.
        """
        if not self.allocated:
            self.allocate()

        isok, tuples = True, []
        for task in self.iflat_tasks():
            t = task.input.abivalidate()
            if t.retcode != 0: isok = False
            tuples.append(t)

        return isok, tuples

    def check_dependencies(self):
        """Test the dependencies of the nodes for possible deadlocks."""
        deadlocks = []

        for task in self.iflat_tasks():
            for dep in task.deps:
                if dep.node.depends_on(task):
                    deadlocks.append((task, dep.node))

        if deadlocks:
            lines = ["Detect wrong list of dependecies that will lead to a deadlock:"]
            lines.extend(["%s <--> %s" % nodes for nodes in deadlocks])
            raise RuntimeError("\n".join(lines))

    def find_deadlocks(self):
        """
        This function detects deadlocks

        Return:
            named tuple with the tasks grouped in: deadlocks, runnables, running
        """
        # Find jobs that can be submitted and and the jobs that are already in the queue.
        runnables = []
        for work in self:
            runnables.extend(work.fetch_alltasks_to_run())
        runnables.extend(list(self.iflat_tasks(status=self.S_SUB)))

        # Running jobs.
        running = list(self.iflat_tasks(status=self.S_RUN))

        # Find deadlocks.
        err_tasks = self.errored_tasks
        deadlocked = []
        if err_tasks:
            for task in self.iflat_tasks():
                if any(task.depends_on(err_task) for err_task in err_tasks):
                    deadlocked.append(task)

        return dict2namedtuple(deadlocked=deadlocked, runnables=runnables, running=running)

    def check_status(self, **kwargs):
        """
        Check the status of the works in self.

        Args:
            show: True to show the status of the flow.
            kwargs: keyword arguments passed to show_status
        """
        for work in self:
            work.check_status()

        if kwargs.pop("show", False):
            self.show_status(**kwargs)

    @property
    def status(self):
        """The status of the |Flow| i.e. the minimum of the status of its tasks and its works"""
        return min(work.get_all_status(only_min=True) for work in self)

    #def restart_unconverged_tasks(self, max_nlauch, excs):
    #    nlaunch = 0
    #    for task in self.unconverged_tasks:
    #        try:
    #            self.history.info("Flow will try restart task %s" % task)
    #            fired = task.restart()
    #            if fired:
    #                nlaunch += 1
    #                max_nlaunch -= 1

    #                if max_nlaunch == 0:
    #                    self.history.info("Restart: too many jobs in the queue, returning")
    #                    self.pickle_dump()
    #                    return nlaunch, max_nlaunch

    #        except task.RestartError:
    #            excs.append(straceback())

    #    return nlaunch, max_nlaunch

    def fix_abicritical(self):
        """
        This function tries to fix critical events originating from ABINIT.
        Returns the number of tasks that have been fixed.
        """
        count = 0
        for task in self.iflat_tasks(status=self.S_ABICRITICAL):
            count += task.fix_abicritical()

        return count

    def fix_queue_critical(self):
        """
        This function tries to fix critical events originating from the queue submission system.

        Returns the number of tasks that have been fixed.
        """
        count = 0
        for task in self.iflat_tasks(status=self.S_QCRITICAL):
            self.history.info("Will try to fix task %s" % str(task))
            try:
                print(task.fix_queue_critical())
                count += 1
            except FixQueueCriticalError:
                self.history.info("Not able to fix task %s" % task)

        return count

    def show_info(self, **kwargs):
        """
        Print info on the flow i.e. total number of tasks, works, tasks grouped by class.

        Example:

            Task Class      Number
            ------------  --------
            ScfTask              1
            NscfTask             1
            ScrTask              2
            SigmaTask            6
        """
        stream = kwargs.pop("stream", sys.stdout)

        lines = [str(self)]
        app = lines.append

        app("Number of works: %d, total number of tasks: %s" % (len(self), self.num_tasks))
        app("Number of tasks with a given class:\n")

        # Build Table
        data = [[cls.__name__, len(tasks)]
                for cls, tasks in self.groupby_task_class().items()]
        app(str(tabulate(data, headers=["Task Class", "Number"])))

        stream.write("\n".join(lines))

    def compare_abivars(self, varnames, nids=None, wslice=None, printout=False, with_colors=False):
        """
        Print the input of the tasks to the given stream.

        Args:
            varnames:
                List of Abinit variables. If not None, only the variable in varnames
                are selected and printed.
            nids: List of node identifiers. By defaults all nodes are shown
            wslice: Slice object used to select works.
            printout: True to print dataframe.
            with_colors: True if task status should be colored.
        """
        varnames = [s.strip() for s in list_strings(varnames)]
        index, rows = [], []
        for task in self.select_tasks(nids=nids, wslice=wslice):
            index.append(task.pos_str)
            dstruct = task.input.structure.as_dict(fmt="abivars")

            od = OrderedDict()
            for vname in varnames:
                value = task.input.get(vname, None)
                if value is None: # maybe in structure?
                    value = dstruct.get(vname, None)
                od[vname] = value

            od["task_class"] = task.__class__.__name__
            od["status"] = task.status.colored if with_colors else str(task.status)
            rows.append(od)

        import pandas as pd
        df = pd.DataFrame(rows, index=index)
        if printout:
            print_dataframe(df, title="Input variables:")

        return df

    def get_dims_dataframe(self, nids=None, printout=False, with_colors=False):
        """
        Analyze output files produced by the tasks. Print pandas DataFrame with dimensions.

        Args:
            nids: List of node identifiers. By defaults all nodes are shown
            printout: True to print dataframe.
            with_colors: True if task status should be colored.
        """
        abo_paths, index, status, abo_relpaths, task_classes, task_nids = [], [], [], [], [], []

        for task in self.iflat_tasks(nids=nids):
            if task.status not in (self.S_OK, self.S_RUN): continue
            if not task.is_abinit_task: continue

            abo_paths.append(task.output_file.path)
            index.append(task.pos_str)
            status.append(task.status.colored if with_colors else str(task.status))
            abo_relpaths.append(os.path.relpath(task.output_file.relpath))
            task_classes.append(task.__class__.__name__)
            task_nids.append(task.node_id)

        if not abo_paths: return

        # Get dimensions from output files as well as walltime/cputime
        from abipy.abio.outputs import AboRobot
        robot = AboRobot.from_files(abo_paths)
        df = robot.get_dims_dataframe(with_time=True, index=index)

        # Add columns to the dataframe.
        status = [str(s) for s in status]
        df["task_class"] = task_classes
        df["relpath"] = abo_relpaths
        df["node_id"] = task_nids
        df["status"] = status

        if printout:
            print_dataframe(df, title="Table with Abinit dimensions:\n")

        return df

    def compare_structures(self, nids=None, with_spglib=False, what="io", verbose=0,
                           precision=3, printout=False, with_colors=False):
        """
        Analyze structures of the tasks (input and output structures if it's a relaxation task.
        Print pandas DataFrame

        Args:
            nids: List of node identifiers. By defaults all nodes are shown
            with_spglib: If True, spglib is invoked to get the spacegroup symbol and number
            what (str): "i" for input structures, "o" for output structures.
            precision: Floating point output precision (number of significant digits).
                This is only a suggestion
            printout: True to print dataframe.
            with_colors: True if task status should be colored.
        """
        from abipy.core.structure import dataframes_from_structures
        structures, index, status, max_forces, pressures, task_classes = [], [], [], [], [], []

        def push_data(post, task, structure, cart_forces, pressure):
            """Helper function to fill lists"""
            index.append(task.pos_str + post)
            structures.append(structure)
            status.append(task.status.colored if with_colors else str(task.status))
            if cart_forces is not None:
                fmods = np.sqrt([np.dot(f, f) for f in cart_forces])
                max_forces.append(fmods.max())
            else:
                max_forces.append(None)
            pressures.append(pressure)
            task_classes.append(task.__class__.__name__)

        for task in self.iflat_tasks(nids=nids):
            if "i" in what:
                push_data("_in", task, task.input.structure, cart_forces=None, pressure=None)

            if "o" not in what:
                continue

            # Add final structure, pressure and max force if relaxation task or GS task
            if task.status in (task.S_RUN, task.S_OK):
                if hasattr(task, "open_hist"):
                    # Structural relaxations produce HIST.nc and we can get
                    # the final structure or the structure of the last relaxation step.
                    try:
                        with task.open_hist() as hist:
                            final_structure = hist.final_structure
                            stress_cart_tensors, pressures_hist = hist.reader.read_cart_stress_tensors()
                            forces = hist.reader.read_cart_forces(unit="eV ang^-1")[-1]
                            push_data("_out", task, final_structure, forces, pressures_hist[-1])
                    except Exception as exc:
                        cprint("Exception while opening HIST.nc file of task: %s\n%s" % (task, str(exc)), "red")

                elif hasattr(task, "open_gsr") and task.status == task.S_OK and task.input.get("iscf", 7) >= 0:
                    with task.open_gsr() as gsr:
                        forces = gsr.reader.read_cart_forces(unit="eV ang^-1")
                        push_data("_out", task, gsr.structure, forces, gsr.pressure)

        dfs = dataframes_from_structures(structures, index=index, with_spglib=with_spglib, cart_coords=False)

        if any(f is not None for f in max_forces):
            # Add pressure and forces to the dataframe
            dfs.lattice["P [GPa]"] = pressures
            dfs.lattice["Max|F| eV/ang"] = max_forces

        # Add columns to the dataframe.
        status = [str(s) for s in status]
        dfs.lattice["task_class"] = task_classes
        dfs.lattice["status"] = dfs.coords["status"] = status

        if printout:
            print_dataframe(dfs.lattice, title="Lattice parameters:", precision=precision)
            if verbose:
                print_dataframe(dfs.coords, title="Atomic positions (columns give the site index):")
            else:
                print("Use `--verbose` to print atoms.")

        return dfs

    def compare_ebands(self, nids=None, with_path=True, with_ibz=True, with_spglib=False, verbose=0,
                       precision=3, printout=False, with_colors=False):
        """
        Analyze electron bands produced by the tasks.
        Return pandas DataFrame and |ElectronBandsPlotter|.

        Args:
            nids: List of node identifiers. By default, all nodes are shown
            with_path: Select files with ebands along k-path.
            with_ibz: Select files with ebands in the IBZ.
            with_spglib: If True, spglib is invoked to get the spacegroup symbol and number
            precision: Floating point output precision (number of significant digits).
                This is only a suggestion
            printout: True to print dataframe.
            with_colors: True if task status should be colored.

        Return: (df, ebands_plotter)
        """
        ebands_list, index, status, ncfiles, task_classes, task_nids = [], [], [], [], [], []

        # Cannot use robots because ElectronBands can be found in different filetypes
        for task in self.iflat_tasks(nids=nids, status=self.S_OK):
            # Read ebands from GSR or SIGRES files.
            for ext in ("gsr", "sigres"):
                task_open_ncfile = getattr(task, "open_%s" % ext, None)
                if task_open_ncfile is not None: break
            else:
                continue

            try:
                with task_open_ncfile() as ncfile:
                    if not with_path and ncfile.ebands.kpoints.is_path: continue
                    if not with_ibz and ncfile.ebands.kpoints.is_ibz: continue
                    ebands_list.append(ncfile.ebands)
                    index.append(task.pos_str)
                    status.append(task.status.colored if with_colors else str(task.status))
                    ncfiles.append(os.path.relpath(ncfile.filepath))
                    task_classes.append(task.__class__.__name__)
                    task_nids.append(task.node_id)
            except Exception as exc:
                cprint("Exception while opening nc file of task: %s\n%s" % (task, str(exc)), "red")

        if not ebands_list: return (None, None)

        from abipy.electrons.ebands import dataframe_from_ebands
        df = dataframe_from_ebands(ebands_list, index=index, with_spglib=with_spglib)
        ncfiles = [os.path.relpath(p, self.workdir) for p in ncfiles]

        # Add columns to the dataframe.
        status = [str(s) for s in status]
        df["task_class"] = task_classes
        df["ncfile"] = ncfiles
        df["node_id"] = task_nids
        df["status"] = status

        if printout:
            from abipy.tools.printing import print_dataframe
            print_dataframe(df, title="KS electronic bands:", precision=precision)

        from abipy.electrons.ebands import ElectronBandsPlotter
        ebands_plotter = ElectronBandsPlotter(key_ebands=zip(ncfiles, ebands_list))

        return df, ebands_plotter

    def compare_hist(self, nids=None, with_spglib=False, verbose=0,
                     precision=3, printout=False, with_colors=False):
        """
        Analyze HIST nc files produced by the tasks. Print pandas DataFrame with final results.
        Return: (df, hist_plotter)

        Args:
            nids: List of node identifiers. By defaults all nodes are shown
            with_spglib: If True, spglib is invoked to get the spacegroup symbol and number
            precision: Floating point output precision (number of significant digits).
                This is only a suggestion
            printout: True to print dataframe.
            with_colors: True if task status should be colored.
        """
        hist_paths, index, status, ncfiles, task_classes, task_nids = [], [], [], [], [], []

        for task in self.iflat_tasks(nids=nids):
            if task.status not in (self.S_OK, self.S_RUN): continue
            hist_path = task.outdir.has_abiext("HIST")
            if not hist_path: continue

            hist_paths.append(hist_path)
            index.append(task.pos_str)
            status.append(task.status.colored if with_colors else str(task.status))
            ncfiles.append(os.path.relpath(hist_path))
            task_classes.append(task.__class__.__name__)
            task_nids.append(task.node_id)

        if not hist_paths: return (None, None)
        from abipy.dynamics.hist import HistRobot
        robot = HistRobot.from_files(hist_paths, labels=hist_paths)
        df = robot.get_dataframe(index=index, with_spglib=with_spglib)
        ncfiles = [os.path.relpath(p, self.workdir) for p in ncfiles]

        # Add columns to the dataframe.
        status = [str(s) for s in status]
        df["task_class"] = task_classes
        df["ncfile"] = ncfiles
        df["node_id"] = task_nids
        df["status"] = status

        if printout:
            title = "Table with final structures, pressures in GPa and force stats in eV/Ang:\n"
            from abipy.tools.printing import print_dataframe
            print_dataframe(df, title=title, precision=precision)

        return df, robot

    def show_summary(self, **kwargs):
        """
        Print a short summary with the status of the flow and a counter task_status --> number_of_tasks

        Args:
            stream: File-like object, Default: sys.stdout

        Example:

            Status       Count
            ---------  -------
            Completed       10

            <Flow, node_id=27163, workdir=flow_gwconv_ecuteps>, num_tasks=10, all_ok=True
        """
        stream = kwargs.pop("stream", sys.stdout)
        stream.write("\n")
        table = list(self.status_counter.items())
        s = tabulate(table, headers=["Status", "Count"])
        stream.write(s + "\n")
        stream.write("\n")
        stream.write("%s, num_tasks=%s, all_ok=%s\n" % (str(self), self.num_tasks, self.all_ok))
        stream.write("\n")

    def show_status(self, **kwargs):
        """
        Report the status of the works and the status of the different tasks on the specified stream.

        Args:
            stream: File-like object, Default: sys.stdout
            nids:  List of node identifiers. By defaults all nodes are shown
            wslice: Slice object used to select works.
            verbose: Verbosity level (default 0). > 0 to show only the works that are not finalized.
        """
        stream = kwargs.pop("stream", sys.stdout)
        nids = as_set(kwargs.pop("nids", None))
        wslice = kwargs.pop("wslice", None)
        verbose = kwargs.pop("verbose", 0)
        wlist = None
        if wslice is not None:
            # Convert range to list of work indices.
            wlist = list(range(wslice.start, wslice.step, wslice.stop))

        #has_colours = stream_has_colours(stream)
        has_colours = True
        red = "red" if has_colours else None

        for i, work in enumerate(self):
            if nids and work.node_id not in nids: continue
            print("", file=stream)
            cprint_map("Work #%d: %s, Finalized=%s" % (i, work, work.finalized), cmap={"True": "green"}, file=stream)
            if wlist is not None and i in wlist: continue
            if verbose == 0 and work.finalized:
                print("  Finalized works are not shown. Use verbose > 0 to force output.", file=stream)
                continue

            headers = ["Task", "Status", "Queue", "MPI|Omp|Gb",
                       "Warn|Com", "Class", "Sub|Rest|Corr", "Time",
                       "Node_ID"]
            table = []
            tot_num_errors = 0
            for task in work:
                if nids and task.node_id not in nids: continue
                task_name = os.path.basename(task.name)

                # FIXME: This should not be done here.
                # get_event_report should be called only in check_status
                # Parse the events in the main output.
                report = task.get_event_report()

                # Get time info (run-time or time in queue or None)
                stime = None
                timedelta = task.datetimes.get_runtime()
                if timedelta is not None:
                    stime = str(timedelta) + "R"
                else:
                    timedelta = task.datetimes.get_time_inqueue()
                    if timedelta is not None:
                        stime = str(timedelta) + "Q"

                events = "|".join(2*["NA"])
                if report is not None:
                    events = '{:>4}|{:>3}'.format(*map(str, (
                       report.num_warnings, report.num_comments)))

                para_info = '{:>4}|{:>3}|{:>3}'.format(*map(str, (
                   task.mpi_procs, task.omp_threads, "%.1f" % task.mem_per_proc.to("Gb"))))

                task_info = list(map(str, [task.__class__.__name__,
                                 (task.num_launches, task.num_restarts, task.num_corrections), stime, task.node_id]))

                qinfo = "None"
                if task.queue_id is not None:
                    qname = str(task.qname)
                    if not verbose:
                        qname = qname[:min(5, len(qname))]
                    qinfo = str(task.queue_id) + "@" + qname

                if task.status.is_critical:
                    tot_num_errors += 1
                    task_name = colored(task_name, red)

                if has_colours:
                    table.append([task_name, task.status.colored, qinfo,
                                  para_info, events] + task_info)
                else:
                    table.append([task_name, str(task.status), qinfo, events,
                                  para_info] + task_info)

            # Print table and write colorized line with the total number of errors.
            print(tabulate(table, headers=headers, tablefmt="grid"), file=stream)
            if tot_num_errors:
                cprint("Total number of errors: %d" % tot_num_errors, "red", file=stream)
            print("", file=stream)

        if self.all_ok:
            cprint("\nall_ok reached\n", "green", file=stream)

    def show_events(self, status=None, nids=None, stream=sys.stdout):
        """
        Print the Abinit events (ERRORS, WARNIING, COMMENTS) to stdout

        Args:
            status: if not None, only the tasks with this status are select
            nids: optional list of node identifiers used to filter the tasks.
            stream: File-like object, Default: sys.stdout
        """
        nrows, ncols = get_terminal_size()

        for task in self.iflat_tasks(status=status, nids=nids):
            report = task.get_event_report()
            if report:
                print(make_banner(str(task), width=ncols, mark="="), file=stream)
                print(report, file=stream)
                #report = report.filter_types()

    def show_corrections(self, status=None, nids=None, stream=sys.stdout):
        """
        Show the corrections applied to the flow at run-time.

        Args:
            status: if not None, only the tasks with this status are select.
            nids: optional list of node identifiers used to filter the tasks.
            stream: File-like object, Default: sys.stdout

        Return: The number of corrections found.
        """
        nrows, ncols = get_terminal_size()
        count = 0
        for task in self.iflat_tasks(status=status, nids=nids):
            if task.num_corrections == 0: continue
            count += 1
            print(make_banner(str(task), width=ncols, mark="="), file=stream)
            for corr in task.corrections:
                pprint(corr, stream=stream)

        if not count: print("No correction found.", file=stream)
        return count

    def show_history(self, status=None, nids=None, full_history=False, metadata=False, stream=sys.stdout):
        """
        Print the history of the flow to stream

        Args:
            status: if not None, only the tasks with this status are select
            full_history: Print full info set, including nodes with an empty history.
            nids: optional list of node identifiers used to filter the tasks.
            metadata: print history metadata (experimental)
            stream: File-like object, Default: sys.stdout
        """
        nrows, ncols = get_terminal_size()

        works_done = []
        # Loop on the tasks and show the history of the work is not in works_done
        for task in self.iflat_tasks(status=status, nids=nids):
            work = task.work

            if work not in works_done:
                works_done.append(work)
                if work.history or full_history:
                    cprint(make_banner(str(work), width=ncols, mark="="), file=stream, **work.status.color_opts)
                    print(work.history.to_string(metadata=metadata), file=stream)

            if task.history or full_history:
                cprint(make_banner(str(task), width=ncols, mark="="), file=stream, **task.status.color_opts)
                print(task.history.to_string(metadata=metadata), file=stream)

        # Print the history of the flow.
        if self.history or full_history:
            cprint(make_banner(str(self), width=ncols, mark="="), file=stream, **self.status.color_opts)
            print(self.history.to_string(metadata=metadata), file=stream)

    def show_inputs(self, varnames=None, nids=None, wslice=None, stream=sys.stdout):
        """
        Print the input of the tasks to the given stream.

        Args:
            varnames: List of Abinit variables. If not None, only the variable in varnames
                are selected and printed.
            nids: List of node identifiers. By defaults all nodes are shown
            wslice: Slice object used to select works.
            stream: File-like object, Default: sys.stdout
        """
        if varnames is not None:
            # Build dictionary varname --> [(task1, value1), (task2, value2), ...]
            varnames = [s.strip() for s in list_strings(varnames)]
            dlist = collections.defaultdict(list)
            for task in self.select_tasks(nids=nids, wslice=wslice):
                dstruct = task.input.structure.as_dict(fmt="abivars")

                for vname in varnames:
                    value = task.input.get(vname, None)
                    if value is None: # maybe in structure?
                        value = dstruct.get(vname, None)
                    if value is not None:
                        dlist[vname].append((task, value))

            for vname in varnames:
                tv_list = dlist[vname]
                if not tv_list:
                    stream.write("[%s]: Found 0 tasks with this variable\n" % vname)
                else:
                    stream.write("[%s]: Found %s tasks with this variable\n" % (vname, len(tv_list)))
                    for i, (task, value) in enumerate(tv_list):
                        stream.write("   %s --> %s\n" % (str(value), task))
                stream.write("\n")

        else:
            lines = []
            for task in self.select_tasks(nids=nids, wslice=wslice):
                s = task.make_input(with_header=True)

                # Add info on dependencies.
                if task.deps:
                    s += "\n\nDependencies:\n" + "\n".join(str(dep) for dep in task.deps)
                else:
                    s += "\n\nDependencies: None"

                lines.append(2*"\n" + 80 * "=" + "\n" + s + 2*"\n")

            stream.writelines(lines)

    def listext(self, ext, stream=sys.stdout):
        """
        Print to the given `stream` a table with the list of the output files
        with the given `ext` produced by the flow.
        """
        nodes_files = []
        for node in self.iflat_nodes():
            filepath = node.outdir.has_abiext(ext)
            if filepath:
                nodes_files.append((node, File(filepath)))

        if nodes_files:
            print("Found %s files with extension `%s` produced by the flow" % (len(nodes_files), ext), file=stream)

            table = [[f.relpath, "%.2f" % (f.get_stat().st_size / 1024**2),
                      node.node_id, node.__class__.__name__]
                     for node, f in nodes_files]
            print(tabulate(table, headers=["File", "Size [Mb]", "Node_ID", "Node Class"]), file=stream)

        else:
            print("No output file with extension %s has been produced by the flow" % ext, file=stream)

    def select_tasks(self, nids=None, wslice=None, task_class=None):
        """
        Return a list with a subset of tasks.

        Args:
            nids: List of node identifiers.
            wslice: Slice object used to select works.
            task_class: String or class used to select tasks. Ignored if None.

        .. note::

            nids and wslice are mutually exclusive.
            If no argument is provided, the full list of tasks is returned.
        """
        if nids is not None:
            assert wslice is None
            tasks = self.tasks_from_nids(nids)

        elif wslice is not None:
            tasks = []
            for work in self[wslice]:
                tasks.extend([t for t in work])
        else:
            # All tasks selected if no option is provided.
            tasks = list(self.iflat_tasks())

        # Filter by task class
        if task_class is not None:
            tasks = [t for t in tasks if t.isinstance(task_class)]

        return tasks

    def get_task_scfcycles(self, nids=None, wslice=None, task_class=None, exclude_ok_tasks=False):
        """
        Return list of (taks, scfcycle) tuples for all the tasks in the flow with a SCF algorithm
        e.g. electronic GS-SCF iteration, DFPT-SCF iterations etc.

        Args:
            nids: List of node identifiers.
            wslice: Slice object used to select works.
            task_class: String or class used to select tasks. Ignored if None.
            exclude_ok_tasks: True if only running tasks should be considered.

        Returns:
            List of `ScfCycle` subclass instances.
        """
        select_status = [self.S_RUN] if exclude_ok_tasks else [self.S_RUN, self.S_OK]
        tasks_cycles = []

        for task in self.select_tasks(nids=nids, wslice=wslice):
            # Fileter
            if task.status not in select_status or task.cycle_class is None:
                continue
            if task_class is not None and not task.isinstance(task_class):
                continue
            try:
                cycle = task.cycle_class.from_file(task.output_file.path)
                if cycle is not None:
                    tasks_cycles.append((task, cycle))
            except Exception:
                # This is intentionally ignored because from_file can fail for several reasons.
                pass

        return tasks_cycles

    def show_tricky_tasks(self, verbose=0, stream=sys.stdout):
        """
        Print list of tricky tasks i.e. tasks that have been restarted or
        launched more than once or tasks with corrections.

        Args:
            verbose: Verbosity level. If > 0, task history and corrections (if any) are printed.
            stream: File-like object. Default: sys.stdout
        """
        nids, tasks = [], []
        for task in self.iflat_tasks():
            if task.num_launches > 1 or any(n > 0 for n in (task.num_restarts, task.num_corrections)):
                nids.append(task.node_id)
                tasks.append(task)

        if not nids:
            cprint("Everything's fine, no tricky tasks found", color="green", file=stream)
        else:
            self.show_status(nids=nids, stream=stream)
            if not verbose:
                print("Use --verbose to print task history.", file=stream)
                return

            for nid, task in zip(nids, tasks):
                cprint(repr(task), **task.status.color_opts, stream=stream)
                self.show_history(nids=[nid], full_history=False, metadata=False, stream=stream)
                #if task.num_restarts:
                #    self.show_restarts(nids=[nid])
                if task.num_corrections:
                    self.show_corrections(nids=[nid], stream=stream)

    def inspect(self, nids=None, wslice=None, **kwargs):
        """
        Inspect the tasks (SCF iterations, Structural relaxation ...) and
        produces matplotlib plots.

        Args:
            nids: List of node identifiers.
            wslice: Slice object used to select works.
            kwargs: keyword arguments passed to `task.inspect` method.

        .. note::

            nids and wslice are mutually exclusive.
            If nids and wslice are both None, all tasks in self are inspected.

        Returns:
            List of `matplotlib` figures.
        """
        figs = []
        for task in self.select_tasks(nids=nids, wslice=wslice):
            if hasattr(task, "inspect"):
                fig = task.inspect(**kwargs)
                if fig is None:
                    cprint("Cannot inspect Task %s" % task, color="blue")
                else:
                    figs.append(fig)
            else:
                cprint("Task %s does not provide an inspect method" % task, color="blue")

        return figs

    def get_results(self, **kwargs):
        results = self.Results.from_node(self)
        results.update(self.get_dict_for_mongodb_queries())
        return results

    def get_dict_for_mongodb_queries(self):
        """
        This function returns a dictionary with the attributes that will be
        put in the mongodb document to facilitate the query.
        Subclasses may want to replace or extend the default behaviour.
        """
        d = {}
        return d
        # TODO
        all_structures = [task.input.structure for task in self.iflat_tasks()]
        all_pseudos = [task.input.pseudos for task in self.iflat_tasks()]

    def look_before_you_leap(self):
        """
        This method should be called before running the calculation to make
        sure that the most important requirements are satisfied.

        Return:
            List of strings with inconsistencies/errors.
        """
        errors = []

        try:
            self.check_dependencies()
        except self.Error as exc:
            errors.append(str(exc))

        if self.has_db:
            try:
                self.manager.db_connector.get_collection()
            except Exception as exc:
                errors.append("""
                    ERROR while trying to connect to the MongoDB database:
                        Exception:
                            %s
                        Connector:
                            %s
                    """ % (exc, self.manager.db_connector))

        return "\n".join(errors)

    @property
    def has_db(self):
        """True if flow uses `MongoDB` to store the results."""
        return self.manager.has_db

    def db_insert(self):
        """
        Insert results in the `MongDB` database.
        """
        assert self.has_db
        # Connect to MongoDb and get the collection.
        coll = self.manager.db_connector.get_collection()
        print("Mongodb collection %s with count %d", coll, coll.count())

        start = time.time()
        for work in self:
            for task in work:
                results = task.get_results()
                pprint(results)
                results.update_collection(coll)
            results = work.get_results()
            pprint(results)
            results.update_collection(coll)
        print("MongoDb update done in %s [s]" % time.time() - start)

        results = self.get_results()
        pprint(results)
        results.update_collection(coll)

        # Update the pickle file to save the mongo ids.
        self.pickle_dump()

        for d in coll.find():
            pprint(d)

    def tasks_from_nids(self, nids):
        """
        Return the list of tasks associated to the given list of node identifiers (nids).

        .. note::

            Invalid ids are ignored
        """
        if not isinstance(nids, collections.abc.Iterable): nids = [nids]

        n2task = {task.node_id: task for task in self.iflat_tasks()}
        return [n2task[n] for n in nids if n in n2task]

    def wti_from_nids(self, nids):
        """Return the list of (w, t) indices from the list of node identifiers nids."""
        return [task.pos for task in self.tasks_from_nids(nids)]

    def open_files(self, what="o", status=None, op="==", nids=None, editor=None):
        """
        Open the files of the flow inside an editor (command line interface).

        Args:
            what: string with the list of characters selecting the file type
                  Possible choices:

                    i ==> input_file,
                    o ==> output_file,
                    f ==> files_file,
                    j ==> job_file,
                    l ==> log_file,
                    e ==> stderr_file,
                    q ==> qout_file,
                    all ==> all files.

            status: if not None, only the tasks with this status are select
            op: status operator. Requires status. A task is selected
                if task.status op status evaluates to true.
            nids: optional list of node identifiers used to filter the tasks.
            editor: Select the editor. None to use the default editor ($EDITOR shell env var)
        """
        # Build list of files to analyze.
        files = []
        for task in self.iflat_tasks(status=status, op=op, nids=nids):
            lst = task.select_files(what)
            if lst:
                files.extend(lst)

        return Editor(editor=editor).edit_files(files)

    def parse_timing(self, nids=None):
        """
        Parse the timer data in the main output file(s) of Abinit.
        Requires timopt /= 0 in the input file (usually timopt = -1)

        Args:
            nids: optional list of node identifiers used to filter the tasks.

        Return: :class:`AbinitTimerParser` instance, None if error.
        """
        # Get the list of output files according to nids.
        paths = [task.output_file.path for task in self.iflat_tasks(nids=nids)]

        # Parse data.
        from abipy.flowtk.abitimer import AbinitTimerParser
        parser = AbinitTimerParser()
        read_ok = parser.parse(paths)
        if read_ok:
            return parser
        return None

    def show_abierrors(self, nids=None, stream=sys.stdout):
        """
        Write to the given stream the list of ABINIT errors for all tasks whose status is S_ABICRITICAL.

        Args:
            nids: optional list of node identifiers used to filter the tasks.
            stream: File-like object. Default: sys.stdout
        """
        lines = []
        app = lines.append

        for task in self.iflat_tasks(status=self.S_ABICRITICAL, nids=nids):
            header = "=== " + task.qout_file.path + "==="
            app(header)
            report = task.get_event_report()

            if report is not None:
                app("num_errors: %s, num_warnings: %s, num_comments: %s" % (
                    report.num_errors, report.num_warnings, report.num_comments))
                app("*** ERRORS ***")
                app("\n".join(str(e) for e in report.errors))
                app("*** BUGS ***")
                app("\n".join(str(b) for b in report.bugs))

            else:
                app("get_envent_report returned None!")

            app("=" * len(header) + 2*"\n")

        return stream.writelines(lines)

    def show_qouts(self, nids=None, stream=sys.stdout):
        """
        Write to the given stream the content of the queue output file for all tasks whose status is S_QCRITICAL.

        Args:
            nids: optional list of node identifiers used to filter the tasks.
            stream: File-like object. Default: sys.stdout
        """
        lines = []

        for task in self.iflat_tasks(status=self.S_QCRITICAL, nids=nids):
            header = "=== " + task.qout_file.path + "==="
            lines.append(header)
            if task.qout_file.exists:
                with open(task.qout_file.path, "rt") as fh:
                    lines += fh.readlines()
            else:
                lines.append("File does not exist!")

            lines.append("=" * len(header) + 2*"\n")

        return stream.writelines(lines)

    def debug(self, status=None, nids=None, stream=sys.stdout):
        """
        This method is usually used when the flow didn't completed succesfully
        It analyzes the files produced the tasks to facilitate debugging.
        Info are printed to stdout.

        Args:
            status: If not None, only the tasks with this status are selected
            nids: optional list of node identifiers used to filter the tasks.
            stream: File-like object. Default: sys.stdout
        """
        nrows, ncols = get_terminal_size()

        # Test for scheduler exceptions first.
        sched_excfile = os.path.join(self.workdir, "_exceptions")
        if os.path.exists(sched_excfile):
            with open(sched_excfile, "r") as fh:
                cprint("Found exceptions raised by the scheduler", "red", file=stream)
                cprint(fh.read(), color="red", file=stream)
                return

        if status is not None:
            tasks = list(self.iflat_tasks(status=status, nids=nids))
        else:
            errors = list(self.iflat_tasks(status=self.S_ERROR, nids=nids))
            qcriticals = list(self.iflat_tasks(status=self.S_QCRITICAL, nids=nids))
            abicriticals = list(self.iflat_tasks(status=self.S_ABICRITICAL, nids=nids))
            tasks = errors + qcriticals + abicriticals

        # For each task selected:
        #     1) Check the error files of the task. If not empty, print the content to stdout and we are done.
        #     2) If error files are empty, look at the master log file for possible errors
        #     3) If also this check failes, scan all the process log files.
        #        TODO: This check is not needed if we introduce a new __abinit_error__ file
        #        that is created by the first MPI process that invokes MPI abort!
        #
        ntasks = 0
        for task in tasks:
            print(make_banner(str(task), width=ncols, mark="="), file=stream)
            ntasks += 1

            #  Start with error files.
            for efname in ["qerr_file", "stderr_file",]:
                err_file = getattr(task, efname)
                if err_file.exists:
                    s = err_file.read()
                    if not s: continue
                    print(make_banner(str(err_file), width=ncols, mark="="), file=stream)
                    cprint(s, color="red", file=stream)
                    #count += 1

            # Check main log file.
            try:
                report = task.get_event_report()
                if report and report.num_errors:
                    print(make_banner(os.path.basename(report.filename), width=ncols, mark="="), file=stream)
                    s = "\n".join(str(e) for e in report.errors)
                else:
                    s = None
            except Exception as exc:
                s = str(exc)

            count = 0 # count > 0 means we found some useful info that could explain the failures.
            if s is not None:
                cprint(s, color="red", file=stream)
                count += 1

            if not count:
                # Inspect all log files produced by the other nodes.
                log_files = task.tmpdir.list_filepaths(wildcard="*LOG_*")
                if not log_files:
                    cprint("No *LOG_* file in tmpdir. This usually happens if you are running with many CPUs",
                           color="magenta", file=stream)

                for log_file in log_files:
                    try:
                        report = EventsParser().parse(log_file)
                        if report.errors:
                            print(report, file=stream)
                            count += 1
                            break
                    except Exception as exc:
                        cprint(str(exc), color="red", file=stream)
                        count += 1
                        break

            if not count:
                cprint("Houston, we could not find any error message that can explain the problem",
                        color="magenta", file=stream)

        print("Number of tasks analyzed: %d" % ntasks, file=stream)

    def cancel(self, nids=None):
        """
        Cancel all the tasks that are in the queue.
        nids is an optional list of node identifiers used to filter the tasks.

        Returns:
            Number of jobs cancelled, negative value if error
        """
        if self.has_chrooted:
            # TODO: Use paramiko to kill the job?
            warnings.warn("Cannot cancel the flow via sshfs!")
            return -1

        # If we are running with the scheduler, we must send a SIGKILL signal.
        if os.path.exists(self.pid_file):
            cprint("Found scheduler attached to this flow.", "yellow")
            cprint("Sending SIGKILL to the scheduler before cancelling the tasks!", "yellow")

            with open(self.pid_file, "rt") as fh:
                pid = int(fh.readline())

            retcode = os.system("kill -9 %d" % pid)
            self.history.info("Sent SIGKILL to the scheduler, retcode: %s" % retcode)
            try:
                os.remove(self.pid_file)
            except IOError:
                pass

        num_cancelled = 0
        for task in self.iflat_tasks(nids=nids):
            num_cancelled += task.cancel()

        return num_cancelled

    def get_njobs_in_queue(self, username=None):
        """
        returns the number of jobs in the queue, None when the number of jobs cannot be determined.

        Args:
            username: (str) the username of the jobs to count (default is to autodetect)
        """
        return self.manager.qadapter.get_njobs_in_queue(username=username)

    def rmtree(self, ignore_errors=False, onerror=None):
        """Remove workdir (same API as shutil.rmtree)."""
        if not os.path.exists(self.workdir): return
        shutil.rmtree(self.workdir, ignore_errors=ignore_errors, onerror=onerror)

    def rm_and_build(self):
        """Remove the workdir and rebuild the flow."""
        self.rmtree()
        self.build()

    def build(self, *args, **kwargs):
        """Make directories and files of the `Flow`."""
        # Allocate here if not done yet!
        if not self.allocated: self.allocate()

        self.indir.makedirs()
        self.outdir.makedirs()
        self.tmpdir.makedirs()

        # Check the nodeid file in workdir
        nodeid_path = os.path.join(self.workdir, ".nodeid")

        if os.path.exists(nodeid_path):
            with open(nodeid_path, "rt") as fh:
                node_id = int(fh.read())

            if self.node_id != node_id:
                msg = ("\nFound node_id %s in file:\n\n  %s\n\nwhile the node_id of the present flow is %d.\n"
                       "This means that you are trying to build a new flow in a directory already used by another flow.\n"
                       "Possible solutions:\n"
                       "   1) Change the workdir of the new flow.\n"
                       "   2) remove the old directory either with `rm -r` or by calling the method flow.rmtree()\n"
                       % (node_id, nodeid_path, self.node_id))
                raise RuntimeError(msg)

        else:
            with open(nodeid_path, "wt") as fh:
                fh.write(str(self.node_id))

        if self.pyfile and os.path.isfile(self.pyfile):
            shutil.copy(self.pyfile, self.workdir)

        for work in self:
            work.build(*args, **kwargs)

    def build_and_pickle_dump(self, abivalidate=False):
        """
        Build dirs and file of the `Flow` and save the object in pickle format.
        Returns 0 if success

        Args:
            abivalidate: If True, all the input files are validate by calling
                the abinit parser. If the validation fails, ValueError is raise.
        """
        self.build()
        if not abivalidate: return self.pickle_dump()

        # Validation with Abinit.
        isok, errors = self.abivalidate_inputs()
        if isok: return self.pickle_dump()
        errlines = []
        for i, e in enumerate(errors):
            errlines.append("[%d] %s" % (i, e))
        raise ValueError("\n".join(errlines))

    @check_spectator
    def pickle_dump(self):
        """
        Save the status of the object in pickle format.
        Returns 0 if success
        """
        if self.has_chrooted:
            warnings.warn("Cannot pickle_dump since we have chrooted from %s" % self.has_chrooted)
            return -1

        #if self.in_spectator_mode:
        #    warnings.warn("Cannot pickle_dump since flow is in_spectator_mode")
        #    return -2

        protocol = self.pickle_protocol

        # Atomic transaction with FileLock.
        with FileLock(self.pickle_file):
            with AtomicFile(self.pickle_file, mode="wb") as fh:
                pmg_pickle_dump(self, fh, protocol=protocol)

        return 0

    def pickle_dumps(self, protocol=None):
        """
        Return a string with the pickle representation.
        `protocol` selects the pickle protocol. self.pickle_protocol is used if `protocol` is None
        """
        strio = StringIO()
        pmg_pickle_dump(self, strio,
                        protocol=self.pickle_protocol if protocol is None
                        else protocol)
        return strio.getvalue()

    def register_task(self, input, deps=None, manager=None, task_class=None, append=False):
        """
        Utility function that generates a `Work` made of a single task

        Args:
            input: |AbinitInput|
            deps: List of :class:`Dependency` objects specifying the dependency of this node.
                  An empy list of deps implies that this node has no dependencies.
            manager: The |TaskManager| responsible for the submission of the task.
                     If manager is None, we use the |TaskManager| specified during the creation of the work.
            task_class: Task subclass to instantiate. Default: |AbinitTask|
            append: If true, the task is added to the last work (a new Work is created if flow is empty)

        Returns:
            The generated |Work| for the task, work[0] is the actual task.
        """
        # append True is much easier to use. In principle should be the default behaviour
        # but this would break the previous API so ...
        if not append:
            work = Work(manager=manager)
        else:
            if not self.works:
                work = Work(manager=manager)
                append = False
            else:
                work = self.works[-1]

        task = work.register(input, deps=deps, task_class=task_class)
        if not append: self.register_work(work)

        return work

    def register_work(self, work, deps=None, manager=None, workdir=None):
        """
        Register a new |Work| and add it to the internal list, taking into account possible dependencies.

        Args:
            work: |Work| object.
            deps: List of :class:`Dependency` objects specifying the dependency of this node.
                  An empy list of deps implies that this node has no dependencies.
            manager: The |TaskManager| responsible for the submission of the task.
                     If manager is None, we use the `TaskManager` specified during the creation of the work.
            workdir: The name of the directory used for the |Work|.

        Returns:
            The registered |Work|.
        """
        if getattr(self, "workdir", None) is not None:
            # The flow has a directory, build the name of the directory of the work.
            work_workdir = None
            if workdir is None:
                work_workdir = os.path.join(self.workdir, "w" + str(len(self)))
            else:
                work_workdir = os.path.join(self.workdir, os.path.basename(workdir))

            work.set_workdir(work_workdir)

        if manager is not None:
            work.set_manager(manager)

        self.works.append(work)

        if deps:
            deps = [Dependency(node, exts) for node, exts in deps.items()]
            work.add_deps(deps)

        return work

    def register_work_from_cbk(self, cbk_name, cbk_data, deps, work_class, manager=None):
        """
        Registers a callback function that will generate the :class:`Task` of the :class:`Work`.

        Args:
            cbk_name: Name of the callback function (must be a bound method of self)
            cbk_data: Additional data passed to the callback function.
            deps: List of :class:`Dependency` objects specifying the dependency of the work.
            work_class: |Work| class to instantiate.
            manager: The |TaskManager| responsible for the submission of the task.
                    If manager is None, we use the `TaskManager` specified during the creation of the |Flow|.

        Returns:
            The |Work| that will be finalized by the callback.
        """
        # TODO: pass a Work factory instead of a class
        # Directory of the Work.
        work_workdir = os.path.join(self.workdir, "w" + str(len(self)))

        # Create an empty work and register the callback
        work = work_class(workdir=work_workdir, manager=manager)

        self._works.append(work)

        deps = [Dependency(node, exts) for node, exts in deps.items()]
        if not deps:
            raise ValueError("A callback must have deps!")

        work.add_deps(deps)

        # Wrap the callable in a Callback object and save
        # useful info such as the index of the work and the callback data.
        cbk = FlowCallback(cbk_name, self, deps=deps, cbk_data=cbk_data)
        self._callbacks.append(cbk)

        return work

    @property
    def allocated(self):
        """Numer of allocations. Set by `allocate`."""
        try:
            return self._allocated
        except AttributeError:
            return 0

    def allocate(self, workdir=None, use_smartio=False):
        """
        Allocate the `Flow` i.e. assign the `workdir` and (optionally)
        the |TaskManager| to the different tasks in the Flow.

        Args:
            workdir: Working directory of the flow. Must be specified here
                if we haven't initialized the workdir in the __init__.

        Return:
            self
        """
        if workdir is not None:
            # We set the workdir of the flow here
            self.set_workdir(workdir)
            for i, work in enumerate(self):
                work.set_workdir(os.path.join(self.workdir, "w" + str(i)))

        if not hasattr(self, "workdir"):
            raise RuntimeError("You must call flow.allocate(workdir) if the workdir is not passed to __init__")

        for work in self:
            # Each work has a reference to its flow.
            work.allocate(manager=self.manager)
            work.set_flow(self)
            # Each task has a reference to its work.
            for task in work:
                task.set_work(work)

        self.check_dependencies()

        if not hasattr(self, "_allocated"): self._allocated = 0
        self._allocated += 1

        if use_smartio:
            self.use_smartio()

        return self

    def use_smartio(self):
        """
        This function should be called when the entire `Flow` has been built.
        It tries to reduce the pressure on the hard disk by using Abinit smart-io
        capabilities for those files that are not needed by other nodes.
        Smart-io means that big files (e.g. WFK) are written only if the calculation
        is unconverged so that we can restart from it. No output is produced if
        convergence is achieved.

        Return: self
        """
        if not self.allocated:
            #raise RuntimeError("You must call flow.allocate() before invoking flow.use_smartio()")
            self.allocate()

        for task in self.iflat_tasks():
            children = task.get_children()
            if not children:
                # Change the input so that output files are produced
                # only if the calculation is not converged.
                task.history.info("Will disable IO for task")
                task.set_vars(prtwf=-1, prtden=0) # TODO: prt1wf=-1,
            else:
                must_produce_abiexts = []
                for child in children:
                    # Get the list of dependencies. Find that task
                    for d in child.deps:
                        must_produce_abiexts.extend(d.exts)

                must_produce_abiexts = set(must_produce_abiexts)
                #print("must_produce_abiexts", must_produce_abiexts)

                # Variables supporting smart-io.
                smart_prtvars = {
                    "prtwf": "WFK",
                }

                # Set the variable to -1 to disable the output
                for varname, abiext in smart_prtvars.items():
                    if abiext not in must_produce_abiexts:
                        print("%s: setting %s to -1" % (task, varname))
                        task.set_vars({varname: -1})

        return self

    def show_dependencies(self, stream=sys.stdout):
        """
        Writes to the given stream the ASCII representation of the dependency tree.
        """
        def child_iter(node):
            return [d.node for d in node.deps]

        def text_str(node):
            return colored(str(node), color=node.status.color_opts["color"])

        for task in self.iflat_tasks():
            print(draw_tree(task, child_iter, text_str), file=stream)

    def on_all_ok(self):
        """
        This method is called when all the works in the flow have reached S_OK.
        This method shall return True if the calculation is completed or
        False if the execution should continue due to side-effects such as adding a new work to the flow.

        This methods allows subclasses to implement customized logic such as extending the flow by adding new works.
        The flow has an internal counter: on_all_ok_num_calls
        that shall be incremented by client code when subclassing this method.
        This counter can be used to decide if futher actions are needed or not.

        An example of flow that adds a new work (only once) when all_ok is reached for the first time:

        def on_all_ok(self):
            if self.on_all_ok_num_calls > 0: return True
            self.on_all_ok_num_calls += 1

            `implement_logic_to_create_new_work`

            self.register_work(work)
            self.allocate()
            self.build_and_pickle_dump()

            return False # The scheduler will keep on running the flow.
        """
        return True

    def on_dep_ok(self, signal, sender):
        # TODO
        # Replace this callback with dynamic dispatch
        # on_all_S_OK for work
        # on_S_OK for task
        self.history.info("on_dep_ok with sender %s, signal %s" % (str(sender), signal))

        for i, cbk in enumerate(self._callbacks):
            if not cbk.handle_sender(sender):
                self.history.info("%s does not handle sender %s" % (cbk, sender))
                continue

            if not cbk.can_execute():
                self.history.info("Cannot execute %s" % cbk)
                continue

            # Execute the callback and disable it
            self.history.info("flow in on_dep_ok: about to execute callback %s" % str(cbk))
            cbk()
            cbk.disable()

            # Update the database.
            self.pickle_dump()

    @check_spectator
    def finalize(self):
        """
        This method is called when the flow is completed. Return 0 if success
        """
        self.history.info("Calling flow.finalize.")
        if self.finalized:
            self.history.warning("Calling finalize on an already finalized flow. Returning 1")
            return 1

        self.finalized = True

        if self.has_db:
            self.history.info("Saving results in database.")
            try:
                self.flow.db_insert()
                self.finalized = True
            except Exception:
                self.history.critical("MongoDb insertion failed.")
                return 2

        # Here we remove the big output files if we have the garbage collector
        # and the policy is set to "flow."
        if self.gc is not None and self.gc.policy == "flow":
            self.history.info("gc.policy set to flow. Will clean task output files.")
            for task in self.iflat_tasks():
                task.clean_output_files()

        return 0

    def set_garbage_collector(self, exts=None, policy="task"):
        """
        Enable the garbage collector that will remove the big output files that are not needed.

        Args:
            exts: string or list with the Abinit file extensions to be removed. A default is
                provided if exts is None
            policy: Either `flow` or `task`. If policy is set to 'task', we remove the output
                files as soon as the task reaches S_OK. If 'flow', the files are removed
                only when the flow is finalized. This option should be used when we are dealing
                with a dynamic flow with callbacks generating other tasks since a |Task|
                might not be aware of its children when it reached S_OK.
        """
        assert policy in ("task", "flow")
        exts = list_strings(exts) if exts is not None else ("WFK", "SUS", "SCR", "BSR", "BSC")

        gc = GarbageCollector(exts=set(exts), policy=policy)

        self.set_gc(gc)
        for work in self:
            #work.set_gc(gc) # TODO Add support for Works and flow policy
            for task in work:
                task.set_gc(gc)

    def connect_signals(self):
        """
        Connect the signals within the `Flow`.
        The `Flow` is responsible for catching the important signals raised from its works.
        """
        # Connect the signals inside each Work.
        for work in self:
            work.connect_signals()

        # Observe the nodes that must reach S_OK in order to call the callbacks.
        for cbk in self._callbacks:
            #cbk.enable()
            for dep in cbk.deps:
                self.history.info("Connecting %s \nwith sender %s, signal %s" % (str(cbk), dep.node, dep.node.S_OK))
                dispatcher.connect(self.on_dep_ok, signal=dep.node.S_OK, sender=dep.node, weak=False)

        # Associate to each signal the callback _on_signal
        # (bound method of the node that will be called by `Flow`
        # Each node will set its attribute _done_signal to True to tell
        # the flow that this callback should be disabled.

        # Register the callbacks for the Work.
        #for work in self:
        #    slot = self._sig_slots[work]
        #    for signal in S_ALL:
        #        done_signal = getattr(work, "_done_ " + signal, False)
        #        if not done_sig:
        #            cbk_name = "_on_" + str(signal)
        #            cbk = getattr(work, cbk_name, None)
        #            if cbk is None: continue
        #            slot[work][signal].append(cbk)
        #            print("connecting %s\nwith sender %s, signal %s" % (str(cbk), dep.node, dep.node.S_OK))
        #            dispatcher.connect(self.on_dep_ok, signal=signal, sender=dep.node, weak=False)

        # Register the callbacks for the Tasks.
        #self.show_receivers()

    def disconnect_signals(self):
        """Disable the signals within the `Flow`."""
        # Disconnect the signals inside each Work.
        for work in self:
            work.disconnect_signals()

        # Disable callbacks.
        for cbk in self._callbacks:
            cbk.disable()

    def show_receivers(self, sender=None, signal=None):
        sender = sender if sender is not None else dispatcher.Any
        signal = signal if signal is not None else dispatcher.Any
        print("*** live receivers ***")
        for rec in dispatcher.liveReceivers(dispatcher.getReceivers(sender, signal)):
            print("receiver -->", rec)
        print("*** end live receivers ***")

    def set_spectator_mode(self, mode=True):
        """
        When the flow is in spectator_mode, we have to disable signals, pickle dump and possible callbacks
        A spectator can still operate on the flow but the new status of the flow won't be saved in
        the pickle file. Usually the flow is in spectator mode when we are already running it via
        the scheduler or other means and we should not interfere with its evolution.
        This is the reason why signals and callbacks must be disabled.
        Unfortunately preventing client-code from calling methods with side-effects when
        the flow is in spectator mode is not easy (e.g. flow.cancel will cancel the tasks submitted to the
        queue and the flow used by the scheduler won't see this change!
        """
        # Set the flags of all the nodes in the flow.
        mode = bool(mode)
        self.in_spectator_mode = mode
        for node in self.iflat_nodes():
            node.in_spectator_mode = mode

        # connect/disconnect signals depending on mode.
        if not mode:
            self.connect_signals()
        else:
            self.disconnect_signals()

    #def get_results(self, **kwargs)

    def rapidfire(self, check_status=True, max_nlaunch=-1, max_loops=1, sleep_time=5, **kwargs):
        """
        Use :class:`PyLauncher` to submits tasks in rapidfire mode.
        kwargs contains the options passed to the launcher.

        Args:
            check_status:
            max_nlaunch: Maximum number of launches. default: no limit.
            max_loops: Maximum number of loops
            sleep_time: seconds to sleep between rapidfire loop iterations

        Return: Number of tasks submitted.
        """
        self.check_pid_file()
        self.set_spectator_mode(False)
        if check_status: self.check_status()
        from .launcher import PyLauncher
        return PyLauncher(self, **kwargs).rapidfire(max_nlaunch=max_nlaunch, max_loops=max_loops, sleep_time=sleep_time)

    def single_shot(self, check_status=True, **kwargs):
        """
        Use :class:`PyLauncher` to submits one task.
        kwargs contains the options passed to the launcher.

        Return: Number of tasks submitted.
        """
        self.check_pid_file()
        self.set_spectator_mode(False)
        if check_status: self.check_status()
        from .launcher import PyLauncher
        return PyLauncher(self, **kwargs).single_shot()

    def make_scheduler(self, **kwargs):
        """
        Build and return a :class:`PyFlowScheduler` to run the flow.

        Args:
            kwargs: if empty we use the user configuration file.
                    if `filepath` in kwargs we init the scheduler from filepath.
                    else pass kwargs to :class:`PyFlowScheduler` __init__ method.
        """
        from .launcher import PyFlowScheduler
        if not kwargs:
            # User config if kwargs is empty
            sched = PyFlowScheduler.from_user_config()
        else:
            # Use from_file if filepath if present, else call __init__
            filepath = kwargs.pop("filepath", None)
            if filepath is not None:
                assert not kwargs
                sched = PyFlowScheduler.from_file(filepath)
            else:
                sched = PyFlowScheduler(**kwargs)

        sched.add_flow(self)
        return sched

    def batch(self, timelimit=None):
        """
        Run the flow in batch mode, return exit status of the job script.
        Requires a manager.yml file and a batch_adapter adapter.

        Args:
            timelimit: Time limit (int with seconds or string with time given with the slurm convention:
            "days-hours:minutes:seconds"). If timelimit is None, the default value specified in the
            `batch_adapter` entry of `manager.yml` is used.
        """
        from .launcher import BatchLauncher
        # Create a batch dir from the flow.workdir.
        prev_dir = os.path.join(*self.workdir.split(os.path.sep)[:-1])
        prev_dir = os.path.join(os.path.sep, prev_dir)
        workdir = os.path.join(prev_dir, os.path.basename(self.workdir) + "_batch")

        return BatchLauncher(workdir=workdir, flows=self).submit(timelimit=timelimit)

    def make_light_tarfile(self, name=None):
        """Lightweight tarball file. Mainly used for debugging. Return the name of the tarball file."""
        name = os.path.basename(self.workdir) + "-light.tar.gz" if name is None else name
        return self.make_tarfile(name=name, exclude_dirs=["outdata", "indata", "tmpdata"])

    def make_tarfile(self, name=None, max_filesize=None, exclude_exts=None, exclude_dirs=None, verbose=0, **kwargs):
        """
        Create a tarball file.

        Args:
            name: Name of the tarball file. Set to os.path.basename(`flow.workdir`) + "tar.gz"` if name is None.
            max_filesize (int or string with unit): a file is included in the tar file if its size <= max_filesize
                Can be specified in bytes e.g. `max_files=1024` or with a string with unit e.g. `max_filesize="1 Mb"`.
                No check is done if max_filesize is None.
            exclude_exts: List of file extensions to be excluded from the tar file.
            exclude_dirs: List of directory basenames to be excluded.
            verbose (int): Verbosity level.
            kwargs: keyword arguments passed to the :class:`TarFile` constructor.

        Returns: The name of the tarfile.
        """
        def any2bytes(s):
            """Convert string or number to memory in bytes."""
            if is_string(s):
                return int(Memory.from_string(s).to("b"))
            else:
                return int(s)

        if max_filesize is not None:
            max_filesize = any2bytes(max_filesize)

        if exclude_exts:
            # Add/remove ".nc" so that we can simply pass "GSR" instead of "GSR.nc"
            # Moreover this trick allows one to treat WFK.nc and WFK file on the same footing.
            exts = []
            for e in list_strings(exclude_exts):
                exts.append(e)
                if e.endswith(".nc"):
                    exts.append(e.replace(".nc", ""))
                else:
                    exts.append(e + ".nc")
            exclude_exts = exts

        def filter(tarinfo):
            """
            Function that takes a TarInfo object argument and returns the changed TarInfo object.
            If it instead returns None the TarInfo object will be excluded from the archive.
            """
            # Skip links.
            if tarinfo.issym() or tarinfo.islnk():
                if verbose: print("Excluding link: %s" % tarinfo.name)
                return None

            # Check size in bytes
            if max_filesize is not None and tarinfo.size > max_filesize:
                if verbose: print("Excluding %s due to max_filesize" % tarinfo.name)
                return None

            # Filter filenames.
            if exclude_exts and any(tarinfo.name.endswith(ext) for ext in exclude_exts):
                if verbose: print("Excluding %s due to extension" % tarinfo.name)
                return None

            # Exlude directories (use dir basenames).
            if exclude_dirs and any(dir_name in exclude_dirs for dir_name in tarinfo.name.split(os.path.sep)):
                if verbose: print("Excluding %s due to exclude_dirs" % tarinfo.name)
                return None

            return tarinfo

        back = os.getcwd()
        os.chdir(os.path.join(self.workdir, ".."))

        import tarfile
        name = os.path.basename(self.workdir) + ".tar.gz" if name is None else name
        with tarfile.open(name=name, mode='w:gz', **kwargs) as tar:
            tar.add(os.path.basename(self.workdir), arcname=None, recursive=True, filter=filter)

            # Add the script used to generate the flow.
            if self.pyfile is not None and os.path.exists(self.pyfile):
                tar.add(self.pyfile)

        os.chdir(back)
        return name

    def get_graphviz(self, engine="automatic", graph_attr=None, node_attr=None, edge_attr=None):
        """
        Generate flow graph in the DOT language.

        Args:
            engine: Layout command used. ['dot', 'neato', 'twopi', 'circo', 'fdp', 'sfdp', 'patchwork', 'osage']
            graph_attr: Mapping of (attribute, value) pairs for the graph.
            node_attr: Mapping of (attribute, value) pairs set for all nodes.
            edge_attr: Mapping of (attribute, value) pairs set for all edges.

        Returns: graphviz.Digraph <https://graphviz.readthedocs.io/en/stable/api.html#digraph>
        """
        self.allocate()

        from graphviz import Digraph
        fg = Digraph("flow", #filename="flow_%s.gv" % os.path.basename(self.relworkdir),
            engine="fdp" if engine == "automatic" else engine)

        # Set graph attributes. https://www.graphviz.org/doc/info/
        fg.attr(label=repr(self))
        fg.attr(rankdir="LR", pagedir="BL")
        fg.node_attr.update(color='lightblue2', style='filled')

        # Add input attributes.
        if graph_attr is not None: fg.graph_attr.update(**graph_attr)
        if node_attr is not None: fg.node_attr.update(**node_attr)
        if edge_attr is not None: fg.edge_attr.update(**edge_attr)

        def node_kwargs(node):
            return dict(
                #shape="circle",
                color=node.color_hex,
                fontsize="8.0",
                label=(str(node) if not hasattr(node, "pos_str") else
                    node.pos_str + "\n" + node.__class__.__name__),
            )

        edge_kwargs = dict(arrowType="vee", style="solid")
        cluster_kwargs = dict(rankdir="LR", pagedir="BL", style="rounded", bgcolor="azure2")

        for work in self:
            # Build cluster with tasks.
            cluster_name = "cluster%s" % work.name
            with fg.subgraph(name=cluster_name) as wg:
                wg.attr(**cluster_kwargs)
                wg.attr(label="%s (%s)" % (work.__class__.__name__, work.name))
                for task in work:
                    wg.node(task.name, **node_kwargs(task))
                    # Connect children to task.
                    for child in task.get_children():
                        # Find file extensions required by this task
                        i = [dep.node for dep in child.deps].index(task)
                        edge_label = "+".join(child.deps[i].exts)
                        fg.edge(task.name, child.name, label=edge_label, color=task.color_hex,
                                **edge_kwargs)

        # Treat the case in which we have a work producing output for other tasks.
        for work in self:
            children = work.get_children()
            if not children: continue
            cluster_name = "cluster%s" % work.name
            seen = set()
            for child in children:
                # This is not needed, too much confusing
                #fg.edge(cluster_name, child.name, color=work.color_hex, **edge_kwargs)
                # Find file extensions required by work
                i = [dep.node for dep in child.deps].index(work)
                for ext in child.deps[i].exts:
                    out = "%s (%s)" % (ext, work.name)
                    fg.node(out)
                    fg.edge(out, child.name, **edge_kwargs)
                    key = (cluster_name, out)
                    if key not in seen:
                        seen.add(key)
                        fg.edge(cluster_name, out, color=work.color_hex, **edge_kwargs)

        # Treat the case in which we have a task that depends on external files.
        seen = set()
        for task in self.iflat_tasks():
            #print(task.get_parents())
            for node in (p for p in task.get_parents() if p.is_file):
                #print("parent file node", node)
                #infile = "%s (%s)" % (ext, work.name)
                infile = node.filepath
                if infile not in seen:
                    seen.add(infile)
                    fg.node(infile, **node_kwargs(node))

                fg.edge(infile, task.name, color=node.color_hex, **edge_kwargs)

        return fg

    @add_fig_kwargs
    def graphviz_imshow(self, ax=None, figsize=None, dpi=300, fmt="png", **kwargs):
        """
        Generate flow graph in the DOT language and plot it with matplotlib.

        Args:
            ax: |matplotlib-Axes| or None if a new figure should be created.
            figsize: matplotlib figure size (None to use default)
            dpi: DPI value.
            fmt: Select format for output image

        Return: |matplotlib-Figure|
        """
        graph = self.get_graphviz(**kwargs)
        graph.format = fmt
        graph.attr(dpi=str(dpi))
        _, tmpname = tempfile.mkstemp()
        path = graph.render(tmpname, view=False, cleanup=True)
        ax, fig, _ = get_ax_fig_plt(ax=ax, figsize=figsize, dpi=dpi)
        import matplotlib.image as mpimg
        ax.imshow(mpimg.imread(path, format="png")) #, interpolation="none")
        ax.axis("off")

        return fig

    @add_fig_kwargs
    def plot_networkx(self, mode="network", with_edge_labels=False, ax=None, arrows=False,
                      node_size="num_cores", node_label="name_class", layout_type="spring", **kwargs):
        """
        Use networkx to draw the flow with the connections among the nodes and
        the status of the tasks.

        Args:
            mode: `networkx` to show connections, `status` to group tasks by status.
            with_edge_labels: True to draw edge labels.
            ax: |matplotlib-Axes| or None if a new figure should be created.
            arrows: if True draw arrowheads.
            node_size: By default, the size of the node is proportional to the number of cores used.
            node_label: By default, the task class is used to label node.
            layout_type: Get positions for all nodes using `layout_type`. e.g. pos = nx.spring_layout(g)

        .. warning::

            Requires networkx package.
        """
        self.allocate()

        # Build the graph
        import networkx as nx
        g = nx.Graph() if not arrows else nx.DiGraph()
        edge_labels = {}
        for task in self.iflat_tasks():
            g.add_node(task, name=task.name)
            for child in task.get_children():
                g.add_edge(task, child)
                # TODO: Add getters! What about locked nodes!
                i = [dep.node for dep in child.deps].index(task)
                edge_labels[(task, child)] = " ".join(child.deps[i].exts)

            filedeps = [d for d in task.deps if d.node.is_file]
            for d in filedeps:
                #print(d.node, d.exts)
                g.add_node(d.node, name="%s (%s)" % (d.node.basename, d.node.node_id))
                g.add_edge(d.node, task)
                edge_labels[(d.node, task)] = "+".join(d.exts)

        # This part is needed if we have a work that produces output used by other nodes.
        for work in self:
            children = work.get_children()
            if not children:
                continue
            g.add_node(work, name=work.name)
            for task in work:
                g.add_edge(task, work)
                edge_labels[(task, work)] = "all_ok "
            for child in children:
                g.add_edge(work, child)
                i = [dep.node for dep in child.deps].index(work)
                edge_labels[(work, child)] = "+".join(child.deps[i].exts)

        # Get positions for all nodes using layout_type.
        # e.g. pos = nx.spring_layout(g)
        pos = getattr(nx, layout_type + "_layout")(g)

        # Select function used to compute the size of the node
        def make_node_size(node):
            if node.is_task:
                return 300 * node.manager.num_cores
            else:
                return 600

        # Function used to build the label
        def make_node_label(node):
            if node_label == "name_class":
                if node.is_file:
                    return "%s\n(%s)" % (node.basename, node.node_id)
                else:
                    return (node.pos_str + "\n" + node.__class__.__name__
                            if hasattr(node, "pos_str") else str(node))
            else:
                raise NotImplementedError("node_label: %s" % str(node_label))

        labels = {node: make_node_label(node) for node in g.nodes()}
        ax, fig, plt = get_ax_fig_plt(ax=ax)

        # Select plot type.
        if mode == "network":
            nx.draw_networkx(g, pos, labels=labels,
                             node_color=[node.color_rgb for node in g.nodes()],
                             node_size=[make_node_size(node) for node in g.nodes()],
                             width=1, style="dotted", with_labels=True, arrows=arrows, ax=ax)

            # Draw edge labels
            if with_edge_labels:
                nx.draw_networkx_edge_labels(g, pos, edge_labels=edge_labels, ax=ax)

        elif mode == "status":
            # Group tasks by status (only tasks are show here).
            for status in self.ALL_STATUS:
                tasks = list(self.iflat_tasks(status=status))

                # Draw nodes (color is given by status)
                node_color = status.color_opts["color"]
                if node_color is None: node_color = "black"
                #print("num nodes %s with node_color %s" % (len(tasks), node_color))

                nx.draw_networkx_nodes(g, pos,
                                       nodelist=tasks,
                                       node_color=node_color,
                                       node_size=[make_node_size(task) for task in tasks],
                                       alpha=0.5, ax=ax
                                       #label=str(status),
                                       )
            # Draw edges.
            nx.draw_networkx_edges(g, pos, width=2.0, alpha=0.5, arrows=arrows, ax=ax) # edge_color='r')

            # Draw labels
            nx.draw_networkx_labels(g, pos, labels, font_size=12, ax=ax)

            # Draw edge labels
            if with_edge_labels:
                nx.draw_networkx_edge_labels(g, pos, edge_labels=edge_labels, ax=ax)
                #label_pos=0.5, font_size=10, font_color='k', font_family='sans-serif', font_weight='normal',
                # alpha=1.0, bbox=None, ax=None, rotate=True, **kwds)

        else:
            raise ValueError("Unknown value for mode: %s" % str(mode))

        ax.axis("off")
        return fig

    def write_open_notebook(flow, foreground):
        """
        Generate an ipython notebook and open it in the browser.
        Return system exit code.
        """
        import nbformat
        nbf = nbformat.v4
        nb = nbf.new_notebook()

        nb.cells.extend([
            #nbf.new_markdown_cell("This is an auto-generated notebook for %s" % os.path.basename(pseudopath)),
            nbf.new_code_cell("""\
    import sys, os
    import numpy as np

    %matplotlib notebook
    from IPython.display import display

    # This to render pandas DataFrames with https://github.com/quantopian/qgrid
    #import qgrid
    #qgrid.nbinstall(overwrite=True)  # copies javascript dependencies to your /nbextensions folder

    from abipy import abilab

    # Tell AbiPy we are inside a notebook and use seaborn settings for plots.
    # See https://seaborn.pydata.org/generated/seaborn.set.html#seaborn.set
    abilab.enable_notebook(with_seaborn=True)
    """),

            nbf.new_code_cell("flow = abilab.Flow.pickle_load('%s')" % flow.workdir),
            nbf.new_code_cell("if flow.num_errored_tasks: flow.debug()"),
            nbf.new_code_cell("flow.check_status(show=True, verbose=0)"),
            nbf.new_code_cell("flow.show_dependencies()"),
            nbf.new_code_cell("flow.plot_networkx();"),
            nbf.new_code_cell("#flow.get_graphviz();"),
            nbf.new_code_cell("flow.show_inputs(nids=None, wslice=None)"),
            nbf.new_code_cell("flow.show_history()"),
            nbf.new_code_cell("flow.show_corrections()"),
            nbf.new_code_cell("flow.show_event_handlers()"),
            nbf.new_code_cell("flow.inspect(nids=None, wslice=None)"),
            nbf.new_code_cell("flow.show_abierrors()"),
            nbf.new_code_cell("flow.show_qouts()"),
        ])

        import tempfile, io
        _, nbpath = tempfile.mkstemp(suffix='.ipynb', text=True)

        with io.open(nbpath, 'wt', encoding="utf8") as fh:
            nbformat.write(nb, fh)

        from monty.os.path import which
        has_jupyterlab = which("jupyter-lab") is not None
        appname = "jupyter-lab" if has_jupyterlab else "jupyter notebook"
        if not has_jupyterlab:
            if which("jupyter") is None:
                raise RuntimeError("Cannot find jupyter in $PATH. Install it with `pip install`")

        appname = "jupyter-lab" if has_jupyterlab else "jupyter notebook"

        if foreground:
            return os.system("%s %s" % (appname, nbpath))
        else:
            fd, tmpname = tempfile.mkstemp(text=True)
            print(tmpname)
            cmd = "%s %s" % (appname, nbpath)
            print("Executing:", cmd, "\nstdout and stderr redirected to %s" % tmpname)
            import subprocess
            process = subprocess.Popen(cmd.split(), shell=False, stdout=fd, stderr=fd)
            cprint("pid: %s" % str(process.pid), "yellow")
            return process.returncode


class G0W0WithQptdmFlow(Flow):

    def __init__(self, workdir, scf_input, nscf_input, scr_input, sigma_inputs, manager=None):
        """
        Build a :class:`Flow` for one-shot G0W0 calculations.
        The computation of the q-points for the screening is parallelized with qptdm
        i.e. we run independent calculations for each q-point and then we merge the final results.

        Args:
            workdir: Working directory.
            scf_input: Input for the GS SCF run.
            nscf_input: Input for the NSCF run (band structure run).
            scr_input: Input for the SCR run.
            sigma_inputs: Input(s) for the SIGMA run(s).
            manager: |TaskManager| object used to submit the jobs. Initialized from manager.yml if manager is None.
        """
        super().__init__(workdir, manager=manager)

        # Register the first work (GS + NSCF calculation)
        bands_work = self.register_work(BandStructureWork(scf_input, nscf_input))

        # Register the callback that will be executed the work for the SCR with qptdm.
        scr_work = self.register_work_from_cbk(cbk_name="cbk_qptdm_workflow", cbk_data={"input": scr_input},
                                               deps={bands_work.nscf_task: "WFK"}, work_class=QptdmWork)

        # The last work contains a list of SIGMA tasks
        # that will use the data produced in the previous two works.
        if not isinstance(sigma_inputs, (list, tuple)):
            sigma_inputs = [sigma_inputs]

        sigma_work = Work()
        for sigma_input in sigma_inputs:
            sigma_work.register_sigma_task(sigma_input, deps={bands_work.nscf_task: "WFK", scr_work: "SCR"})
        self.register_work(sigma_work)

        self.allocate()

    def cbk_qptdm_workflow(self, cbk):
        """
        This callback is executed by the flow when bands_work.nscf_task reaches S_OK.

        It computes the list of q-points for the W(q,G,G'), creates nqpt tasks
        in the second work (QptdmWork), and connect the signals.
        """
        scr_input = cbk.data["input"]
        # Use the WFK file produced by the second
        # Task in the first Work (NSCF step).
        nscf_task = self[0][1]
        wfk_file = nscf_task.outdir.has_abiext("WFK")

        work = self[1]
        work.set_manager(self.manager)
        work.create_tasks(wfk_file, scr_input)
        work.add_deps(cbk.deps)

        work.set_flow(self)
        # Each task has a reference to its work.
        for task in work:
            task.set_work(work)
            # Add the garbage collector.
            if self.gc is not None: task.set_gc(self.gc)

        work.connect_signals()
        work.build()

        return work


class FlowCallbackError(Exception):
    """Exceptions raised by FlowCallback."""


class FlowCallback(object):
    """
    This object implements the callbacks executed by the |Flow| when
    particular conditions are fulfilled. See on_dep_ok method of |Flow|.

    .. note::

        I decided to implement callbacks via this object instead of a standard
        approach based on bound methods because:

            1) pickle (v<=3) does not support the pickling/unplickling of bound methods

            2) There's some extra logic and extra data needed for the proper functioning
               of a callback at the flow level and this object provides an easy-to-use interface.
    """
    Error = FlowCallbackError

    def __init__(self, func_name, flow, deps, cbk_data):
        """
        Args:
            func_name: String with the name of the callback to execute.
                       func_name must be a bound method of flow with signature:

                            func_name(self, cbk)

                       where self is the Flow instance and cbk is the callback
            flow: Reference to the |Flow|.
            deps: List of dependencies associated to the callback
                  The callback is executed when all dependencies reach S_OK.
            cbk_data: Dictionary with additional data that will be passed to the callback via self.
        """
        self.func_name = func_name
        self.flow = flow
        self.deps = deps
        self.data = cbk_data or {}
        self._disabled = False

    def __str__(self):
        return "%s: %s bound to %s" % (self.__class__.__name__, self.func_name, self.flow)

    def __call__(self):
        """Execute the callback."""
        if self.can_execute():
            # Get the bound method of the flow from func_name.
            # We use this trick because pickle (format <=3) does not support bound methods.
            try:
                func = getattr(self.flow, self.func_name)
            except AttributeError as exc:
                raise self.Error(str(exc))

            return func(self)

        else:
            raise self.Error("You tried to __call_ a callback that cannot be executed!")

    def can_execute(self):
        """True if we can execute the callback."""
        return not self._disabled and all(dep.status == dep.node.S_OK for dep in self.deps)

    def disable(self):
        """
        True if the callback has been disabled. This usually happens when the callback has been executed.
        """
        self._disabled = True

    def enable(self):
        """Enable the callback"""
        self._disabled = False

    def handle_sender(self, sender):
        """
        True if the callback is associated to the sender
        i.e. if the node who sent the signal appears in the
        dependencies of the callback.
        """
        return sender in [d.node for d in self.deps]


# Factory functions.
def bandstructure_flow(workdir, scf_input, nscf_input, dos_inputs=None, manager=None, flow_class=Flow, allocate=True):
    """
    Build a |Flow| for band structure calculations.

    Args:
        workdir: Working directory.
        scf_input: Input for the GS SCF run.
        nscf_input: Input for the NSCF run (band structure run).
        dos_inputs: Input(s) for the NSCF run (dos run).
        manager: |TaskManager| object used to submit the jobs. Initialized from manager.yml if manager is None.
        flow_class: Flow subclass
        allocate: True if the flow should be allocated before returning.

    Returns: |Flow| object
    """
    flow = flow_class(workdir, manager=manager)
    work = BandStructureWork(scf_input, nscf_input, dos_inputs=dos_inputs)
    flow.register_work(work)

    # Handy aliases
    flow.scf_task, flow.nscf_task, flow.dos_tasks = work.scf_task, work.nscf_task, work.dos_tasks

    if allocate: flow.allocate()
    return flow


def g0w0_flow(workdir, scf_input, nscf_input, scr_input, sigma_inputs, manager=None, flow_class=Flow, allocate=True):
    """
    Build a |Flow| for one-shot $G_0W_0$ calculations.

    Args:
        workdir: Working directory.
        scf_input: Input for the GS SCF run.
        nscf_input: Input for the NSCF run (band structure run).
        scr_input: Input for the SCR run.
        sigma_inputs: List of inputs for the SIGMA run.
        flow_class: Flow class
        manager: |TaskManager| object used to submit the jobs. Initialized from manager.yml if manager is None.
        allocate: True if the flow should be allocated before returning.

    Returns: |Flow| object
    """
    flow = flow_class(workdir, manager=manager)
    work = G0W0Work(scf_input, nscf_input, scr_input, sigma_inputs)
    flow.register_work(work)
    if allocate: flow.allocate()
    return flow


class PhononFlow(Flow):
    """
    This Flow provides a high-level interface to compute phonons with DFPT
    The flow consists of

    1) One workflow for the GS run.

    2) nqpt works for phonon calculations. Each work contains
       nirred tasks where nirred is the number of irreducible phonon perturbations
       for that particular q-point.

    .. note:

        For a much more flexible interface, use the DFPT works defined in works.py
        For instance, EPH calculations are much easier to implement by connecting a single
        work that computes all the q-points with the EPH tasks instead of using PhononFlow.
    """
    @classmethod
    def from_scf_input(cls, workdir, scf_input, ph_ngqpt, with_becs=True, manager=None, allocate=True):
        """
        Create a `PhononFlow` for phonon calculations from an `AbinitInput` defining a ground-state run.

        Args:
            workdir: Working directory of the flow.
            scf_input: |AbinitInput| object with the parameters for the GS-SCF run.
            ph_ngqpt: q-mesh for phonons. Must be a sub-mesh of the k-mesh used for
                electrons. e.g if ngkpt = (8, 8, 8). ph_ngqpt = (4, 4, 4) is a valid choice
                whereas ph_ngqpt = (3, 3, 3) is not!
            with_becs: True if Born effective charges are wanted.
            manager: |TaskManager| object. Read from `manager.yml` if None.
            allocate: True if the flow should be allocated before returning.

        Return:
            :class:`PhononFlow` object.
        """
        flow = cls(workdir, manager=manager)

        # Register the SCF task
        flow.register_scf_task(scf_input)
        scf_task = flow[0][0]

        # Make sure k-mesh and q-mesh are compatible.
        scf_ngkpt, ph_ngqpt = np.array(scf_input["ngkpt"]), np.array(ph_ngqpt)

        if any(scf_ngkpt % ph_ngqpt != 0):
            raise ValueError("ph_ngqpt %s should be a sub-mesh of scf_ngkpt %s" % (ph_ngqpt, scf_ngkpt))

        # Get the q-points in the IBZ from Abinit
        qpoints = scf_input.abiget_ibz(ngkpt=ph_ngqpt, shiftk=(0, 0, 0), kptopt=1).points

        # Create a PhononWork for each q-point. Add DDK and E-field if q == Gamma and with_becs.
        for qpt in qpoints:
            if np.allclose(qpt, 0) and with_becs:
                ph_work = BecWork.from_scf_task(scf_task)
            else:
                ph_work = PhononWork.from_scf_task(scf_task, qpoints=qpt)

            flow.register_work(ph_work)

        if allocate: flow.allocate()

        return flow

    def open_final_ddb(self):
        """
        Open the DDB file located in the output directory of the flow.

        Return:
            :class:`DdbFile` object, None if file could not be found or file is not readable.
        """
        ddb_path = self.outdir.has_abiext("DDB")
        if not ddb_path:
            if self.status == self.S_OK:
                self.history.critical("%s reached S_OK but didn't produce a GSR file in %s" % (self, self.outdir))
            return None

        from abipy.dfpt.ddb import DdbFile
        try:
            return DdbFile(ddb_path)
        except Exception as exc:
            self.history.critical("Exception while reading DDB file at %s:\n%s" % (ddb_path, str(exc)))
            return None

    def finalize(self):
        """This method is called when the flow is completed."""
        # Merge all the out_DDB files found in work.outdir.
        ddb_files = list(filter(None, [work.outdir.has_abiext("DDB") for work in self]))

        # Final DDB file will be produced in the outdir of the work.
        out_ddb = self.outdir.path_in("out_DDB")
        desc = "DDB file merged by %s on %s" % (self.__class__.__name__, time.asctime())

        mrgddb = wrappers.Mrgddb(manager=self.manager, verbose=0)
        mrgddb.merge(self.outdir.path, ddb_files, out_ddb=out_ddb, description=desc)
        print("Final DDB file available at %s" % out_ddb)

        # Call the method of the super class.
        retcode = super().finalize()
        return retcode


class NonLinearCoeffFlow(Flow):
    """
    1) One workflow for the GS run.

    2) nqpt works for electric field calculations. Each work contains
       nirred tasks where nirred is the number of irreducible perturbations
       for that particular q-point.
    """
    @classmethod
    def from_scf_input(cls, workdir, scf_input, manager=None, allocate=True):
        """
        Create a `NonlinearFlow` for second order susceptibility calculations from
        an `AbinitInput` defining a ground-state run.

        Args:
            workdir: Working directory of the flow.
            scf_input: |AbinitInput| object with the parameters for the GS-SCF run.
            manager: |TaskManager| object. Read from `manager.yml` if None.
            allocate: True if the flow should be allocated before returning.

        Return:
            :class:`NonlinearFlow` object.
        """
        flow = cls(workdir, manager=manager)

        flow.register_scf_task(scf_input)
        scf_task = flow[0][0]

        nl_work = DteWork.from_scf_task(scf_task)

        flow.register_work(nl_work)

        if allocate: flow.allocate()

        return flow

    def open_final_ddb(self):
        """
        Open the DDB file located in the output directory of the flow.

        Return:
            |DdbFile| object, None if file could not be found or file is not readable.
        """
        ddb_path = self.outdir.has_abiext("DDB")
        if not ddb_path:
            if self.status == self.S_OK:
                self.history.critical("%s reached S_OK but didn't produce a GSR file in %s" % (self, self.outdir))
            return None

        from abipy.dfpt.ddb import DdbFile
        try:
            return DdbFile(ddb_path)
        except Exception as exc:
            self.history.critical("Exception while reading DDB file at %s:\n%s" % (ddb_path, str(exc)))
            return None

    def finalize(self):
        """This method is called when the flow is completed."""
        # Merge all the out_DDB files found in work.outdir.
        ddb_files = list(filter(None, [work.outdir.has_abiext("DDB") for work in self]))

        # Final DDB file will be produced in the outdir of the work.
        out_ddb = self.outdir.path_in("out_DDB")
        desc = "DDB file merged by %s on %s" % (self.__class__.__name__, time.asctime())

        mrgddb = wrappers.Mrgddb(manager=self.manager, verbose=0)
        mrgddb.merge(self.outdir.path, ddb_files, out_ddb=out_ddb, description=desc)

        print("Final DDB file available at %s" % out_ddb)

        # Call the method of the super class.
        retcode = super().finalize()
        print("retcode", retcode)
        #if retcode != 0: return retcode
        return retcode


def phonon_conv_flow(workdir, scf_input, qpoints, params, manager=None, allocate=True):
    """
    Create a |Flow| to perform convergence studies for phonon calculations.

    Args:
        workdir: Working directory of the flow.
        scf_input: |AbinitInput| object defining a GS-SCF calculation.
        qpoints: List of list of lists with the reduced coordinates of the q-point(s).
        params:
            To perform a converge study wrt ecut: params=["ecut", [2, 4, 6]]
        manager: |TaskManager| object responsible for the submission of the jobs.
            If manager is None, the object is initialized from the yaml file
            located either in the working directory or in the user configuration dir.
        allocate: True if the flow should be allocated before returning.

    Return: |Flow| object.
    """
    qpoints = np.reshape(qpoints, (-1, 3))

    flow = Flow(workdir=workdir, manager=manager)

    for qpt in qpoints:
        for gs_inp in scf_input.product(*params):
            # Register the SCF task
            work = flow.register_scf_task(gs_inp)

            # Add the PhononWork connected to this scf_task.
            flow.register_work(PhononWork.from_scf_task(work[0], qpoints=qpt))

    if allocate: flow.allocate()
    return flow