text: # coding: utf-8
  # Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department
  # Distributed under the terms of "New BSD License", see the LICENSE file.
  
  import codecs
  from collections import OrderedDict
  from datetime import datetime
  import dill as pickle
  import json
  import numpy as np
  import os
  import pandas
  from pandas.errors import EmptyDataError
  from tqdm import tqdm
  import types
  
  from pyiron_base.job.generic import GenericJob
  from pyiron_base.generic.hdfio import FileHDFio
  from pyiron_base.interfaces.has_groups import HasGroups
  from pyiron_base.master.generic import get_function_from_string
  
  
  __author__ = "Uday Gajera, Jan Janssen, Joerg Neugebauer"
  __copyright__ = (
      "Copyright 2020, Max-Planck-Institut für Eisenforschung GmbH - "
      "Computational Materials Design (CM) Department"
  )
  __version__ = "0.0.1"
  __maintainer__ = "Jan Janssen"
  __email__ = "janssen@mpie.de"
  __status__ = "development"
  __date__ = "Sep 1, 2018"
  
  def _to_pickle(hdf, key, value):
      hdf[key] = codecs.encode(pickle.dumps(value), "base64").decode()
  
  def _from_pickle(hdf, key):
      return pickle.loads(codecs.decode(hdf[key].encode(), "base64"))
  
  
  class FunctionContainer(object):
      """
      Class which is able to append, store and retreive a set of functions.
  
      """
      def __init__(self, system_function_lst=None):
          if system_function_lst is None:
              system_function_lst = []
          self._user_function_dict = {}
          self._system_function_lst = system_function_lst
          self._system_function_dict = {
              func.__name__: False for func in self._system_function_lst
          }
          self._system_function_dict["get_job_id"] = True
  
      @property
      def _function_lst(self):
          return [
              funct
              for funct in self._system_function_lst
              if funct.__name__ in self._system_function_dict.keys()
              and self._system_function_dict[funct.__name__]
          ] + list(self._user_function_dict.values())
  
      def _to_hdf(self, hdf):
          _to_pickle(
              hdf=hdf, key="user_function_dict", value=self._user_function_dict
          )
          _to_pickle(
              hdf=hdf, key="system_function_dict", value=self._system_function_dict
          )
  
      def _from_hdf(self, hdf):
          self._user_function_dict = _from_pickle(hdf=hdf, key="user_function_dict")
          self._system_function_dict = _from_pickle(
              hdf=hdf, key="system_function_dict"
          )
  
      def __setitem__(self, key, item):
          if isinstance(item, str):
              self._user_function_dict[key] = eval(
                  'lambda job: {"' + key + '":' + item + "}"
              )
          elif isinstance(item, types.FunctionType):
              self._user_function_dict[key] = lambda job: {key: item(job)}
          else:
              raise TypeError("unsupported function type!")
  
      def __getitem__(self, key):
          return self._user_function_dict[key]
  
      def __getattr__(self, name):
          if name in list(self._system_function_dict.keys()):
              self._system_function_dict[name] = True
              return self._system_function_dict[name]
          else:
              super(FunctionContainer, self).__getattr__(name)
  
      def __dir__(self):
          return list(self._system_function_dict.keys())
  
  class JobFilters(object):
      """
      Certain predefined job filters
  
      """
      @staticmethod
      def job_type(job_type):
          def filter_job_type(job):
              return job.__name__ == job_type
          return filter_job_type
  
      @staticmethod
      def job_name_contains(job_name_segment):
          def filter_job_name_segment(job):
              return job_name_segment in job.job_name
          return filter_job_name_segment
  
  
  class PyironTable(HasGroups):
      """
      Class for easy, efficient, and pythonic analysis of data from pyiron projects
  
      Args:
          project (pyiron.project.Project/None): The project to analyze
          name (str): Name of the pyiron table
          system_function_lst (list/ None): List of built-in functions
      """
      def __init__(self, project, name=None, system_function_lst=None):
          self._project = project
          self._df = pandas.DataFrame({})
          self.convert_to_object = False
          self._name = name
          self._db_filter_function = always_true_pandas
          self._filter_function = always_true
          self._filter = JobFilters()
          self._system_function_lst = system_function_lst
          self.add = FunctionContainer(system_function_lst=self._system_function_lst)
          self._csv_file = None
          if self._is_file():
              self.load()
  
          self.EMPTY_STR = "-"
  
      @property
      def filter(self):
          """
          Object containing pre-defined filter functions
  
          Returns:
              pyiron.table.datamining.JobFilters: The object containing the filters
  
          """
          return self._filter
  
      @property
      def _file_name_csv(self):
          if self._csv_file is None:
              return self._project.path + self.name + ".csv"
          else:
              return self._csv_file
  
      @property
      def _file_name_txt(self):
          return self._project.path + self.name + ".txt"
  
      @property
      def name(self):
          """
          Name of the table. Takes the project name if not specified
  
          Returns:
              str: Name of the table
  
          """
          if self._name is None:
              return self._project.name
          return self._name
  
      @property
      def db_filter_function(self):
          """
          Function to filter the a project database table before job specific functions are applied.
  
          The function must take a pyiron project table in the pandas.DataFrame format (project.job_table()) and return a
          boolean pandas.DataSeries with the same number of rows as the project table
  
          Example:
  
              def function(df):
                  return (df["chemicalformula"=="H2"]) & (df["hamilton"=="Vasp"])
  
          """
          return self._db_filter_function
  
      @db_filter_function.setter
      def db_filter_function(self, funct):
          self._db_filter_function = funct
  
      @property
      def filter_function(self):
          """
          Function to filter each job before more expensive functions are applied
          """
          return self._filter_function
  
      @filter_function.setter
      def filter_function(self, funct):
          self._filter_function = funct
  
      def to_hdf(self):
          file = FileHDFio(file_name=self._project.path + self.name + ".h5", h5_path="/")
          self.add._to_hdf(file)
  
      def from_hdf(self):
          file = FileHDFio(file_name=self._project.path + self.name + ".h5", h5_path="/")
          self.add._from_hdf(file)
  
      def save(self, name=None):
          self._name = name
          self.to_hdf()
          self._save_csv()
  
      def load(self, name=None):
          self._name = name
          self.from_hdf()
          self._load_csv()
  
      def create_table(self, enforce_update=False, level=3, file=None, job_status_list=None):
          skip_table_update = False
          filter_funct = self.filter_function
          if job_status_list is None:
              job_status_list = ["finished"]
          if self._is_file():
              if file is None:
                  file = FileHDFio(
                      file_name=self._project.path + self.name + ".h5", h5_path="/"
                  )
              temp_user_function_dict, temp_system_function_dict = self._get_data_from_hdf5(
                  hdf=file
              )
              job_update_lst = self._collect_job_update_lst(
                  job_status_list=job_status_list,
                  filter_funct=filter_funct,
                  job_stored_ids=self._get_job_ids()
              )
              keys_update_user_lst = [
                  key
                  for key in self.add._user_function_dict.keys()
                  if key not in temp_user_function_dict.keys()
              ]
              keys_update_system_lst = [
                  k
                  for k, v in self.add._system_function_dict.items()
                  if v and not temp_system_function_dict[k]
              ]
              if (
                  len(job_update_lst) == 0
                  and len(keys_update_user_lst) == 0
                  and keys_update_system_lst == 0
                  and not enforce_update
              ):
                  skip_table_update = True
          else:
              job_update_lst = self._collect_job_update_lst(
                  job_status_list=job_status_list,
                  filter_funct=filter_funct,
                  job_stored_ids=None
              )
              keys_update_user_lst, keys_update_system_lst = [], []
          if not skip_table_update and len(job_update_lst) != 0:
              df_new_ids = self._iterate_over_job_lst(
                  job_lst=job_update_lst, function_lst=self.add._function_lst, level=level
              )
          else:
              df_new_ids = pandas.DataFrame({})
          if not skip_table_update and (
              len(keys_update_user_lst) != 0 or len(keys_update_system_lst) != 0
          ):
              job_update_lst = self._collect_job_update_lst(
                  job_status_list=job_status_list,
                  filter_funct=filter_funct,
                  job_stored_ids=None
              )
              function_lst = [
                  v
                  for k, v in self.add._user_function_dict.items()
                  if k in keys_update_system_lst
              ] + [
                  funct
                  for funct in self.add._system_function_lst
                  if funct.__name__ in keys_update_system_lst
              ]
              df_new_keys = self._iterate_over_job_lst(
                  job_lst=job_update_lst, function_lst=function_lst, level=level
              )
          else:
              df_new_keys = pandas.DataFrame({})
          if len(self._df) > 0 and len(df_new_keys) > 0:
              self._df = pandas.concat(
                  [self._df, df_new_keys], axis=1, sort=False
              ).reset_index(drop=True)
          if len(self._df) > 0 and len(df_new_ids) > 0:
              self._df = pandas.concat([self._df, df_new_ids], sort=False).reset_index(
                  drop=True
              )
          elif len(df_new_ids) > 0:
              self._df = df_new_ids
  
      def convert_dict(self, input_dict):
          return {key: self.str_to_value(value) for key, value in input_dict.items()}
  
      def refill_dict(self, diff_dict_lst):
          total_key_lst = self.total_lst_of_keys(diff_dict_lst)
          for ind, sub_dict in enumerate(diff_dict_lst):
              for key in total_key_lst:
                  if key not in sub_dict.keys():
                      sub_dict[key] = self.EMPTY_STR
                  else:
                      sub_dict[key] = self.str_to_value(sub_dict[key])
  
      def col_to_value(self, col_name):
          val_lst, key_lst, ind_lst = [], [], []
          for ind, name in enumerate(self._df[col_name]):
              #             print ('name: ', ind, name)
              #             if name == self.EMPTY_STR:
              #                 continue
              name = name.split("_")
              ind_lst.append(ind)
              key_lst.append(name[0])
              val_lst.append(eval(".".join(name[1:])))
          if len(set(key_lst)) == 1:
              key = key_lst[0]
              self._df[key] = val_lst
          else:
              raise ValueError("key not unique: {}".format(set(key_lst)))
  
      def get_dataframe(self):
          return self._df
  
      def _list_nodes(self):
          return list(self._df.columns)
  
      def _list_groups(self):
          return list(set(self._df["col_0"]))
  
      @staticmethod
      def str_to_value(input_val):
          if not isinstance(input_val, str):
              return input_val
          else:
              try:
                  return eval(input_val)
              except (TypeError, SyntaxError, NameError):
                  return input_val
  
      @staticmethod
      def _apply_function_on_job(funct, job):
          try:
              return funct(job)
          except (ValueError, TypeError):
              return {}
  
      @staticmethod
      def total_lst_of_keys(diff_dict_lst):
          total_key_lst = []
          for sub_dict in diff_dict_lst:
              for key in sub_dict.keys():
                  total_key_lst.append(key)
          return set(total_key_lst)
  
      def __getitem__(self, item, max_level=5):
          rename_dict = OrderedDict()
          if item in self.list_groups():
              for i in range(1, max_level):
                  rename_dict["col_{}".format(i)] = "col_{}".format(i - 1)
  
              new_table = PyironTable(
                  project=self._project[item],
                  system_function_lst=self._system_function_lst
              )
              new_table._df = self._df.drop("col_0", axis=1)
              new_table._df.rename(index=str, columns=rename_dict, inplace=True)
              return new_table
          if item in self.list_nodes():
              return np.array(self._df[item])
          return None
  
      def __str__(self):
          return self._df.__str__()
  
      def __repr__(self):
          """
          Human readable string representation
  
          Returns:
              str: pandas Dataframe structure as string
          """
          return self._df.__repr__()
  
      def _is_file(self):
          return self._project is not None and os.path.isfile(self._file_name_csv)
  
      def _save_csv(self):
          self._df.to_csv(self._file_name_csv, index=False)
  
      def _load_csv(self):
          self._df = pandas.read_csv(self._file_name_csv)
  
      def _get_project_list(self, name, pr_len, level=3):
          lst = [self.EMPTY_STR for _ in range(level)]
          for i, p in enumerate(name.split("/")[pr_len - 1 : -1]):
              if len(lst) > i:
                  lst[i] = p
          return lst
  
      def _get_data_from_hdf5(self, hdf):
          temp_user_function_dict = _from_pickle(
              hdf=hdf, key="user_function_dict"
          )
          temp_system_function_dict = _from_pickle(
              hdf=hdf, key="system_function_dict"
          )
          return temp_user_function_dict, temp_system_function_dict
  
      def _get_job_ids(self):
          if len(self._df) > 0:
              return self._df.job_id.values
          else:
              return np.array([])
  
      def _get_filtered_job_ids_from_project(self, recursive=True):
          project_table = self._project.job_table(recursive=recursive)
          filter_funct = self.db_filter_function
          return project_table[filter_funct(project_table)]["id"].tolist()
  
      def _apply_list_of_functions_on_job(self, job, fucntion_lst):
          diff_dict = {}
          for funct in fucntion_lst:
              funct_dict = self._apply_function_on_job(funct, job)
              for key, value in funct_dict.items():
                  diff_dict[key] = value
          return diff_dict
  
      def _iterate_over_job_lst(self, job_lst, function_lst, level):
          pr_len = len(self._project.project_path.split("/"))
          diff_dict_lst = []
          for job_inspect in tqdm(job_lst):
              if self.convert_to_object:
                  job = job_inspect.load_object()
              else:
                  job = job_inspect
              diff_dict = self._apply_list_of_functions_on_job(
                  job=job, fucntion_lst=function_lst
              )
              pr_lst = self._get_project_list(job.project.project_path, pr_len, level)
              for ic, col in enumerate(pr_lst):
                  diff_dict["col_{}".format(ic)] = col
              diff_dict_lst.append(diff_dict)
          self.refill_dict(diff_dict_lst)
          return pandas.DataFrame(diff_dict_lst)
  
      def _collect_job_update_lst(self, job_status_list, filter_funct, job_stored_ids=None):
          """
          Collect jobs to update the pyiron table
  
          Args:
              job_status_list (list): List of job status to consider
              filter_funct (function): Filter function
              job_stored_ids (list/ None): List of already analysed job ids
  
          Returns:
              list: List of JobCore objects
          """
          if job_stored_ids is not None:
              job_id_lst = [
                  job_id
                  for job_id in self._get_filtered_job_ids_from_project()
                  if job_id not in job_stored_ids
              ]
          else:
              job_id_lst = self._get_filtered_job_ids_from_project()
  
          job_update_lst = []
          for job_id in tqdm(job_id_lst):
              try:
                  job = self._project.inspect(job_id)
              except IndexError:  # In case the job was deleted while the pyiron table is running
                  job = None
              if job is not None and job.status in job_status_list and filter_funct(job):
                  job_update_lst.append(job)
          return job_update_lst
  
      def _repr_html_(self):
          """
          Internal helper function to represent the GenericParameters object within the Jupyter Framework
  
          Returns:
              HTML: Jupyter HTML object
          """
          return self._df._repr_html_()
  
  
  class TableJob(GenericJob):
      def __init__(self, project, job_name):
          super(TableJob, self).__init__(project, job_name)
          self.__version__ = "0.1"
          self.__hdf_version__ = "0.2"
          self.__name__ = "TableJob"
          self._analysis_project = None
          self._system_function_lst = None
          self._pyiron_table = PyironTable(
              project=None,
              system_function_lst=self._system_function_lst
          )
          self._enforce_update = False
          self._project_level = 0
          self.analysis_project = project.project
  
      @property
      def filter(self):
          return self._pyiron_table.filter
  
      @property
      def project_level(self):
          return self._project_level
  
      @project_level.setter
      def project_level(self, level):
          self._project_level = level
  
      @property
      def db_filter_function(self):
          return self._pyiron_table.db_filter_function
  
      @db_filter_function.setter
      def db_filter_function(self, funct):
          self._pyiron_table.db_filter_function = funct
  
      @property
      def filter_function(self):
          return self._pyiron_table.filter_function
  
      @filter_function.setter
      def filter_function(self, funct):
          self._pyiron_table.filter_function = funct
  
      @property
      def pyiron_table(self):
          return self._pyiron_table
  
      @property
      def ref_project(self):
          return self.analysis_project
  
      @ref_project.setter
      def ref_project(self, project):
          self.analysis_project = project
  
      @property
      def analysis_project(self):
          """
          :class:`.Project`: which pyiron project should be searched for jobs
  
          WARNING: setting this resets any previously added analysis and filter functions
          """
          return self._analysis_project
  
      @analysis_project.setter
      def analysis_project(self, project):
          self._analysis_project = project
          self._pyiron_table = PyironTable(
              project=self._analysis_project,
              system_function_lst=self._system_function_lst
          )
  
      @property
      def add(self):
          return self._pyiron_table.add
  
      @property
      def convert_to_object(self):
          return self._pyiron_table.convert_to_object
  
      @convert_to_object.setter
      def convert_to_object(self, conv_to_obj):
          self._pyiron_table.convert_to_object = conv_to_obj
  
      @property
      def enforce_update(self):
          return self._enforce_update
  
      @enforce_update.setter
      def enforce_update(self, enforce):
          if isinstance(enforce, bool):
              if enforce:
                  self._enforce_update = True
                  if self.status.finished:
                      self.status.created = True
              else:
                  self._enforce_update = False
          else:
              raise TypeError()
  
      @staticmethod
      def convert_numpy_to_list(table_dict):
          for k, v in table_dict.items():
              for k1, v1 in v.items():
                  if isinstance(v1, np.ndarray):
                      v[k1] = v1.tolist()
          return table_dict
  
      def to_hdf(self, hdf=None, group_name=None):
          """
          Store pyiron table job in HDF5
  
          Args:
              hdf:
              group_name:
  
          """
          super(TableJob, self).to_hdf(hdf=hdf, group_name=group_name)
          with self.project_hdf5.open("input") as hdf5_input:
              hdf5_input["bool_dict"] = {
                  "enforce_update": self._enforce_update,
                  "convert_to_object": self._pyiron_table.convert_to_object,
              }
              self._pyiron_table.add._to_hdf(hdf5_input)
              if self._analysis_project is not None:
                  hdf5_input["project"] = {
                      "path": self._analysis_project.path,
                      "user": self._analysis_project.user,
                      "sql_query": self._analysis_project.sql_query,
                      "filter": self._analysis_project._filter,
                      "inspect_mode": self._analysis_project._inspect_mode,
                  }
              if self.pyiron_table._filter_function is not None:
                  _to_pickle(hdf5_input, "filter", self.pyiron_table._filter_function)
              if self.pyiron_table._db_filter_function is not None:
                  _to_pickle(hdf5_input, "db_filter", self.pyiron_table._db_filter_function)
          if len(self.pyiron_table._df) != 0:
              with self.project_hdf5.open("output") as hdf5_output:
                  table_dict = self.convert_numpy_to_list(self.pyiron_table._df.to_dict())
                  hdf5_output["table"] = json.dumps(table_dict)
  
      def from_hdf(self, hdf=None, group_name=None):
          """
          Restore pyiron table job from HDF5
  
          Args:
              hdf:
              group_name:
          """
          super(TableJob, self).from_hdf(hdf=hdf, group_name=group_name)
          hdf_version = self.project_hdf5.get("HDF_VERSION", "0.1.0")
          with self.project_hdf5.open("input") as hdf5_input:
              if "project" in hdf5_input.list_nodes():
                  project_dict = hdf5_input["project"]
                  project = self.project.__class__(
                      path=project_dict["path"],
                      user=project_dict["user"],
                      sql_query=project_dict["sql_query"],
                  )
                  project._filter = project_dict["filter"]
                  project._inspect_mode = project_dict["inspect_mode"]
                  self.analysis_project = project
              if "filter" in hdf5_input.list_nodes():
                  if hdf_version == "0.1.0":
                      self.pyiron_table._filter_function_str = hdf5_input["filter"]
                      self.pyiron_table.filter_function = get_function_from_string(
                          hdf5_input["filter"]
                      )
                  else:
                      self.pyiron_table.filter_function = _from_pickle(hdf5_input, "filter")
              if "db_filter" in hdf5_input.list_nodes():
                  if hdf_version == "0.1.0":
                      self.pyiron_table._db_filter_function_str = hdf5_input["db_filter"]
                      self.pyiron_table.db_filter_function = get_function_from_string(
                          hdf5_input["db_filter"]
                      )
                  else:
                      self.pyiron_table.db_filter_function = _from_pickle(hdf5_input, "db_filter")
              bool_dict = hdf5_input["bool_dict"]
              self._enforce_update = bool_dict["enforce_update"]
              self._pyiron_table.convert_to_object = bool_dict["convert_to_object"]
              self._pyiron_table.add._from_hdf(hdf5_input)
          pyiron_table = os.path.join(self.working_directory, "pyirontable.csv")
          if os.path.exists(pyiron_table):
              try:
                  self._pyiron_table._df = pandas.read_csv(pyiron_table)
                  self._pyiron_table._csv_file = pyiron_table
              except EmptyDataError:
                  pass
          else:
              with self.project_hdf5.open("output") as hdf5_output:
                  if "table" in hdf5_output.list_nodes():
                      self._pyiron_table._df = pandas.DataFrame(
                          json.loads(hdf5_output["table"])
                      )
  
      def validate_ready_to_run(self):
          if self._analysis_project is None:
              raise ValueError("Analysis project not defined!")
  
      def run_static(self):
          self._create_working_directory()
          self.status.running = True
          self.update_table()
          self.status.finished = True
  
      def update_table(self, job_status_list=None):
          """
          Update the pyiron table object, add new columns if a new function was added or add new rows for new jobs
  
          Args:
              job_status_list (list/None): List of job status which are added to the table by default ["finished"]
          """
          if job_status_list is None:
              job_status_list = ["finished"]
          self.project.db.item_update({"timestart": datetime.now()}, self.job_id)
          with self.project_hdf5.open("input") as hdf5_input:
              self._pyiron_table.create_table(
                  enforce_update=self._enforce_update,
                  file=hdf5_input,
                  level=self._project_level,
                  job_status_list=job_status_list,
              )
          self.to_hdf()
          self._pyiron_table._df.to_csv(
              os.path.join(self.working_directory, "pyirontable.csv"), index=False
          )
          with self.project_hdf5.open("output") as hdf5_output:
              table_dict = self.convert_numpy_to_list(self.pyiron_table._df.to_dict())
              hdf5_output["table"] = json.dumps(table_dict)
          self.project.db.item_update(self._runtime(), self.job_id)
  
      def write_input(self):
          pass
  
      def get_dataframe(self):
          """
  
          Returns:
              pandas.Dataframe
          """
          return self.pyiron_table._df
  
  
  def always_true_pandas(job_table):
      """
      A function which returns a pandas Series with all True values based on the size of the input pandas dataframe
      Args:
          job_table (pandas.DataFrame): Input dataframe
  
      Returns:
          pandas.Series: A series of True values
  
      """
      from pandas import Series
      return Series([True] * len(job_table), index=job_table.index)
  
  
  def always_true(_):
      """
      A function that always returns True no matter what!
  
      Returns:
          bool: True
  
      """
      return True
  