def setUp(self):
        env = Environment(trajectory='Test_'+repr(time.time()).replace('.','_'),
                          filename=make_temp_dir(os.path.join(
                              'experiments',
                              'tests',
                              'briantests',
                              'HDF5',
                               'briantest.hdf5')),
                          file_title='test',
                          log_config=get_log_config(),
                          dynamic_imports=['pypet.brian.parameter.BrianParameter',
                                                        BrianMonitorResult],
                          multiproc=False)

        traj = env.v_trajectory

        #env._set_standard_storage()
        #env._hdf5_queue_writer._hdf5storageservice = LazyStorageService()
        traj = env.v_trajectory
        #traj.set_storage_service(LazyStorageService())

        add_params(traj)
        #traj.mode='Parallel'


        traj.f_explore(cartesian_product({traj.f_get('N').v_full_name:[50,60],
                               traj.f_get('tauw').v_full_name:[30*ms,40*ms]}))

        self.traj = traj

        self.env = env
        self.traj = traj

def main():
    # Let's be very verbose!
    logging.basicConfig(level = logging.INFO)


    # Let's do multiprocessing this time with a lock (which is default)
    filename = os.path.join('hdf5', 'example_07.hdf5')
    env = Environment(trajectory='Example_07_BRIAN',
                      filename=filename,
                      file_title='Example_07_Brian',
                      comment = 'Go Brian!',
                      dynamically_imported_classes=[BrianMonitorResult, BrianParameter],
                      multiproc=True,
                      wrap_mode='QUEUE',
                      ncores=2)

    traj = env.trajectory

    # 1st a) add the parameters
    add_params(traj)

    # 1st b) prepare, we want to explore the different network sizes and different tauw time scales
    traj.f_explore(cartesian_product({traj.f_get('N').v_full_name:[50,60],
                           traj.f_get('tauw').v_full_name:[30*ms,40*ms]}))

    # 2nd let's run our experiment
    env.run(run_net)

    # You can take a look at the results in the hdf5 file if you want!

    # Finally disable logging and close all log-files
    env.disable_logging()

    def explore(self):

        matrices = []


        for irun in range(3):

            spsparse_lil = spsp.lil_matrix((111,111))
            spsparse_lil[3,2] = 44.5*irun

            matrices.append(spsparse_lil)


        self.explore_dict=cartesian_product({'npstr':[np.array(['Uno', 'Dos', 'Tres']),
                               np.array(['Cinco', 'Seis', 'Siette']),
                            np.array(['Ocho', 'Nueve', 'Diez'])],
                           'val0':[1,2,3],
                           'spsparse_lil' : matrices}, (('npstr','val0'),'spsparse_lil'))




        ## Explore the parameter:
        for key, vallist in self.explore_dict.items():
            self.param[key]._explore(vallist)

    def setUp(self):
        logging.basicConfig(level = logging.DEBUG)


        env = Environment(trajectory='Test_'+repr(time.time()).replace('.','_'),
                          filename=make_temp_file('experiments/tests/briantests/HDF5/briantest.hdf5'),
                          file_title='test',
                          log_folder=make_temp_file('experiments/tests/briantests/log'),
                          dynamically_imported_classes=['pypet.brian.parameter.BrianParameter',
                                                        BrianMonitorResult],
                          multiproc=True,
                          use_pool=True,
                          complib='blosc',
                          wrap_mode='QUEUE',
                          ncores=2)

        traj = env.v_trajectory

        #env._set_standard_storage()
        #env._hdf5_queue_writer._hdf5storageservice = LazyStorageService()
        traj = env.v_trajectory
        #traj.set_storage_service(LazyStorageService())

        add_params(traj)
        #traj.mode='Parallel'


        traj.f_explore(cartesian_product({traj.f_get('N').v_full_name:[50,60],
                               traj.f_get('tauw').v_full_name:[30*ms,40*ms]}))

        self.traj = traj

        self.env = env
        self.traj = traj

def main():
    # Let's be very verbose!
    logging.basicConfig(level = logging.INFO)


    # Let's do multiprocessing this time with a lock (which is default)
    env = Environment(trajectory='Example_07_BRIAN',
                      filename='experiments/example_07/HDF5/example_07.hdf5',
                      file_title='Example_07_Euler_Integration',
                      log_folder='experiments/example_07/LOGS/',
                      comment = 'Go Brian!',
                      dynamically_imported_classes=[BrianMonitorResult, BrianParameter],
                      multiproc=True,
                      wrap_mode='QUEUE',
                      ncores=2)

    traj = env.v_trajectory

    # 1st a) add the parameters
    add_params(traj)

    # 1st b) prepare, we want to explore the different network sizes and different tauw time scales
    traj.f_explore(cartesian_product({traj.f_get('N').v_full_name:[50,60],
                           traj.f_get('tauw').v_full_name:[30*ms,40*ms]}))

    # 2nd let's run our experiment
    env.f_run(run_net)

def main():
    try:
        # Create an environment that handles running
        env = Environment(trajectory='Example1_Quick_And_Not_So_Dirty',filename='experiments/example_01/HDF5/',
                          file_title='Example1_Quick_And_Not_So_Dirty', log_folder='experiments/example_01/LOGS/',
                          comment='The first example!',
                          complib='blosc',
                          small_overview_tables=False,
                          git_repository='./', git_message='Im a message!',
                          sumatra_project='./', sumatra_reason='Testing!')

        # Get the trajectory from the environment
        traj = env.v_trajectory

        # Add both parameters
        traj.f_add_parameter('x', 1, comment='Im the first dimension!')
        traj.f_add_parameter('y', 1, comment='Im the second dimension!')

        # Explore the parameters with a cartesian product:
        traj.f_explore(cartesian_product({'x':[1,2,3], 'y':[6,7,8]}))

        # Run the simulation
        env.f_run(multiply)

        print("Python git test successful")

        # traj.f_expand({'x':[3,3],'y':[42,43]})
        #
        # env.f_run(multiply)
    except Exception as e:
        print(repr(e))
        sys.exit(1)

    def test_cartesian_product(self):

        cartesian_dict=cartesian_product({'param1':[1,2,3], 'param2':[42.0, 52.5]},
                                          ('param1','param2'))
        result_dict = {'param1':[1,1,2,2,3,3],'param2': [42.0,52.5,42.0,52.5,42.0,52.5]}

        self.assertTrue(nested_equal(cartesian_dict,result_dict), '%s != %s' %
                                                        (str(cartesian_dict),str(result_dict)))

    def expand(self):
        self.expanded ={'Normal.trial': [1],
            'Numpy.double': [np.array([1.0,2.0,3.0,4.0]), np.array([-1.0,3.0,5.0,7.0])],
            'csr_mat' :[spsp.csr_matrix((2222,22)), spsp.csr_matrix((2222,22))]}

        self.expanded['csr_mat'][0][1,2]=44.0
        self.expanded['csr_mat'][1][2,2]=33

        self.traj.f_expand(cartesian_product(self.expanded))

    def test_cartesian_product_combined_params(self):
        cartesian_dict=cartesian_product( {'param1': [42.0, 52.5], 'param2':['a', 'b'],\
            'param3' : [1,2,3]}, (('param3',),('param1', 'param2')))

        result_dict={'param3':[1,1,2,2,3,3],'param1' : [42.0,52.5,42.0,52.5,42.0,52.5],
                      'param2':['a','b','a','b','a','b']}

        self.assertTrue(nested_equal(cartesian_dict,result_dict), '%s != %s' %
                                                    (str(cartesian_dict),str(result_dict)))

    def explore(self):
        self.explore_dict=cartesian_product({'npstr':[np.array(['Uno', 'Dos', 'Tres']),
                               np.array(['Cinco', 'Seis', 'Siette']),
                            np.array(['Ocho', 'Nueve', 'Diez'])],
                           'val0':[1,2,3]})

        ## Explore the parameter:
        for key, vallist in self.explore_dict.items():
            self.param[key]._explore(vallist)

    def explore(self, traj):
        self.explored ={'Normal.trial': [0],
            'Numpy.double': [np.array([1.0,2.0,3.0,4.0]), np.array([-1.0,3.0,5.0,7.0])],
            'csr_mat' :[spsp.lil_matrix((2222,22)), spsp.lil_matrix((2222,22))]}

        self.explored['csr_mat'][0][1,2]=44.0
        self.explored['csr_mat'][1][2,2]=33

        self.explored['csr_mat'][0] = self.explored['csr_mat'][0].tocsr()
        self.explored['csr_mat'][1] = self.explored['csr_mat'][0].tocsr()

        traj.f_explore(cartesian_product(self.explored))

    def explore(self):
        self.explore_dict = cartesian_product({#'brian2_array_a': [np.array([1., 2.]) * mV],
                                               # 'brian2_array_b': [2 * mV],
                                               # Arrays need to be of the same size!
                                               'brian2_array_c': [np.array([5., 8.]) * mV, np.array([7., 8.]) * mV],
                                               })


        ## Explore the parameter:
        for key, vallist in self.explore_dict.items():
            self.param[key]._explore(vallist)
            self.assertTrue(self.param[key].v_explored and self.param[key].f_has_range())

def main():
    # Create an environment that handles running
    filename = os.path.join('hdf5','example_18.hdf5')
    env = Environment(trajectory='Multiplication',
                      filename=filename,
                      file_title='Example_18_Many_Runs',
                      overwrite_file=True,
                      comment='Contains many runs',
                      multiproc=True,
                      use_pool=True,
                      freeze_input=True,
                      ncores=2,
                      wrap_mode='QUEUE')

    # The environment has created a trajectory container for us
    traj = env.trajectory

    # Add both parameters
    traj.f_add_parameter('x', 1, comment='I am the first dimension!')
    traj.f_add_parameter('y', 1, comment='I am the second dimension!')

    # Explore the parameters with a cartesian product, yielding 2500 runs
    traj.f_explore(cartesian_product({'x': range(50), 'y': range(50)}))

    # Run the simulation
    env.run(multiply)

    # Disable logging
    env.disable_logging()

    # turn auto loading on, since results have not been loaded, yet
    traj.v_auto_load = True
    # Use the `v_idx` functionality
    traj.v_idx = 2042
    print('The result of run %d is: ' % traj.v_idx)
    # Now we can rely on the wildcards
    print(traj.res.crunset.crun.z)
    traj.v_idx = -1
    # Or we can use the shortcuts `rts_X` (run to set) and `r_X` to get particular results
    print('The result of run %d is: ' % 2044)
    print(traj.res.rts_2044.r_2044.z)

def main(inputargs):
    args = docopt(__doc__, argv=inputargs)
    wavpath = path.join(modulePath, "resources", "tone_in_noise")
    stimuli = [path.join(wavpath, i) for i in glob.glob(path.join(wavpath, "*.wav"))]
    outfile = path.realpath(path.expanduser(args["--out"]))
    env = Environment(trajectory='tone-in-noise',
                      filename=outfile,
                      overwrite_file=True,
                      file_title="Tone in noise at different SNR",
                      comment="some comment",
                      large_overview_tables="False",
                      # freeze_input=True,
                      # use_pool=True,
                      multiproc=True,
                      ncores=3,
                      graceful_exit=True,
                      #wrap_mode=pypetconstants.WRAP_MODE_QUEUE,
                      )

    traj = env.trajectory
    traj.f_add_parameter('periphery', 'verhulst', comment="which periphery was used")
    traj.f_add_parameter('brainstem', 'nelsoncarney04', comment="which brainstem model was used")
    traj.f_add_parameter('weighting', "--no-cf-weighting ", comment="weighted CFs")
    traj.f_add_parameter('wavfile', '', comment="Which wav file to run")
    traj.f_add_parameter('level', 80, comment="stimulus level, spl")
    traj.f_add_parameter('neuropathy', "none", comment="")

    parameter_dict = {
        "periphery" : ['verhulst', 'zilany'],
        "brainstem" : ['nelsoncarney04', 'carney2015'],
        "weighting" : [cf_weighting, ""],
        "wavfile"   : stimuli,
        "level"     : [80],
        "neuropathy": ["none", "moderate", "severe", "ls-moderate", "ls-severe"]
    }

    traj.f_explore(cartesian_product(parameter_dict))
    env.run(tone_in_noise)
    return 0

    def explore(self, traj):
        self.explored =cartesian_product({'Normal.trial': [0,1],
            'Numpy.double': [np.array([1.0,2.0,3.0,4.0]), np.array([-1.0,3.0,5.0,7.0])]})


        traj.f_explore(self.explored)

 def explore_cartesian(self,traj):
     self.explore_dict=cartesian_product({'x':[-1,1,2,3,4, 5, 6],'y':[1,1,2,2,3,4,4]})
     traj.f_explore(self.explore_dict)

    """Sophisticated simulation of multiplication"""
    z=traj.x*traj.y
    traj.f_add_result('z',z=z, comment='I am the product of two reals!')



# Create an environment that handles running.
# Let's enable multiprocessing with 2 workers.
env = Environment(trajectory='Example_04_MP',
                  filename='experiments/example_04/HDF5/example_04.hdf5',
                  file_title='Example_04_MP',
                  log_folder='experiments/example_04/LOGS/',
                  comment = 'Multiprocessing example!',
                  multiproc=True,
                  ncores=2,
                  use_pool=True,
                  wrap_mode=pypetconstants.WRAP_MODE_LOCK)

# Get the trajectory from the environment
traj = env.v_trajectory

# Add both parameters
traj.f_add_parameter('x', 1.0, comment='I am the first dimension!')
traj.f_add_parameter('y', 1.0, comment='I am the second dimension!')

# Explore the parameters with a cartesian product, but we want to explore a bit more
traj.f_explore(cartesian_product({'x':[float(x) for x in range(15)],
                                  'y':[float(y) for y in range(15)]}))

# Run the simulation
env.f_run(multiply)

    def explore2(self, traj):
        self.explored2 ={'Normal.trial': [0,1],
            'Normal.int': [44, 45]}

        traj.f_explore(cartesian_product(self.explored2,  ('Normal.int','Normal.trial') ))

    def explore_trials_differently(self, traj):
        self.explored ={'Normal.trial': [0,1],
            'Numpy.double': [np.array([-1.0,2.0,3.0,5.0]), np.array([-1.0,3.0,5.0,7.0])]}

        traj.f_explore(cartesian_product(self.explored, ('Numpy.double','Normal.trial')))

env2 = Environment(trajectory='Traj2',filename='experiments/example_03/HDF5/example_03.hdf5',
                  file_title='Example_03', log_folder='experiments/example_03/LOGS/',
                  comment = 'I am going to be merged into some other trajectory!')

# Get the trajectories from the environment
traj1 = env1.v_trajectory
traj2 = env2.v_trajectory

# Add both parameters
traj1.f_add_parameter('x', 1.0, comment='I am the first dimension!')
traj1.f_add_parameter('y', 1.0, comment='I am the second dimension!')
traj2.f_add_parameter('x', 1.0, comment='I am the first dimension!')
traj2.f_add_parameter('y', 1.0, comment='I am the second dimension!')

# Explore the parameters with a cartesian product for the first trajectory:
traj1.f_explore(cartesian_product({'x':[1.0,2.0,3.0,4.0], 'y':[6.0,7.0,8.0]}))
# Let's explore slightly differently for the second:
traj2.f_explore(cartesian_product({'x':[3.0,4.0,5.0,6.0], 'y':[7.0,8.0,9.0]}))


# Run the simulations with all parameter combinations
env1.f_run(multiply)
env2.f_run(multiply)

# Now we merge them together into traj1
# We want to remove duplicate entries
# like the parameter space point x=3.0, y=7.0.
# Several points have been explored by both trajectories and we need them only once.
# Therefore, we set remove_duplicates=True (Note this takes O(N1*N2)!).
# We also want to backup both trajectories, but we let the system choose the filename.
# Accordingly we choose backup_filename=True instead of providing a filename.