yt.data_objects.time_series module

Time series analysis functions.

class yt.data_objects.time_series.AnalysisTaskProxy(time_series)[source]

Bases: object

keys()[source]
class yt.data_objects.time_series.DatasetSeries(outputs, parallel=True, setup_function=None, mixed_dataset_types=False, **kwargs)[source]

Bases: object

The DatasetSeries object is a container of multiple datasets, allowing easy iteration and computation on them.

DatasetSeries objects are designed to provide easy ways to access, analyze, parallelize and visualize multiple datasets sequentially. This is primarily expressed through iteration, but can also be constructed via analysis tasks (see Time Series Analysis).

Parameters:
  • filenames (list or pattern) – This can either be a list of filenames (such as [“DD0001/DD0001”, “DD0002/DD0002”]) or a pattern to match, such as “DD*/DD*.index”). If it’s the former, they will be loaded in order. The latter will be identified with the glob module and then sorted.
  • parallel (True, False or int) – This parameter governs the behavior when .piter() is called on the resultant DatasetSeries object. If this is set to False, the time series will not iterate in parallel when .piter() is called. If this is set to either True, one processor will be allocated for each iteration of the loop. If this is set to an integer, the loop will be parallelized over this many workgroups. It the integer value is less than the total number of available processors, more than one processor will be allocated to a given loop iteration, causing the functionality within the loop to be run in parallel.
  • setup_function (callable, accepts a ds) – This function will be called whenever a dataset is loaded.
  • mixed_dataset_types (True or False, default False) – Set to True if the DatasetSeries will load different dataset types, set to False if loading dataset of a single type as this will result in a considerable speed up from not having to figure out the dataset type.

Examples

>>> ts = DatasetSeries(
        "GasSloshingLowRes/sloshing_low_res_hdf5_plt_cnt_0[0-6][0-9]0")
>>> for ds in ts:
...     SlicePlot(ds, "x", "Density").save()
...
>>> def print_time(ds):
...     print ds.current_time
...
>>> ts = DatasetSeries(
...     "GasSloshingLowRes/sloshing_low_res_hdf5_plt_cnt_0[0-6][0-9]0",
...      setup_function = print_time)
...
>>> for ds in ts:
...     SlicePlot(ds, "x", "Density").save()
eval(tasks, obj=None)[source]
classmethod from_filenames(filenames, parallel=True, setup_function=None, **kwargs)[source]

Create a time series from either a filename pattern or a list of filenames.

This method provides an easy way to create a DatasetSeries, given a set of filenames or a pattern that matches them. Additionally, it can set the parallelism strategy.

Parameters:
  • filenames (list or pattern) – This can either be a list of filenames (such as [“DD0001/DD0001”, “DD0002/DD0002”]) or a pattern to match, such as “DD*/DD*.index”). If it’s the former, they will be loaded in order. The latter will be identified with the glob module and then sorted.
  • parallel (True, False or int) – This parameter governs the behavior when .piter() is called on the resultant DatasetSeries object. If this is set to False, the time series will not iterate in parallel when .piter() is called. If this is set to either True or an integer, it will be iterated with 1 or that integer number of processors assigned to each parameter file provided to the loop.
  • setup_function (callable, accepts a ds) – This function will be called whenever a dataset is loaded.

Examples

>>> def print_time(ds):
...     print ds.current_time
...
>>> ts = DatasetSeries.from_filenames(
...     "GasSloshingLowRes/sloshing_low_res_hdf5_plt_cnt_0[0-6][0-9]0",
...      setup_function = print_time)
...
>>> for ds in ts:
...     SlicePlot(ds, "x", "Density").save()
classmethod from_output_log(output_log, line_prefix='DATASET WRITTEN', parallel=True)[source]
outputs
particle_trajectories(indices, fields=None, suppress_logging=False, ptype=None)[source]

Create a collection of particle trajectories in time over a series of datasets.

Parameters:
  • indices (array_like) – An integer array of particle indices whose trajectories we want to track. If they are not sorted they will be sorted.
  • fields (list of strings, optional) – A set of fields that is retrieved when the trajectory collection is instantiated. Default: None (will default to the fields ‘particle_position_x’, ‘particle_position_y’, ‘particle_position_z’)
  • suppress_logging (boolean) – Suppress yt’s logging when iterating over the simulation time series. Default: False
  • ptype (str, optional) – Only use this particle type. Default: None, which uses all particle type.

Examples

>>> my_fns = glob.glob("orbit_hdf5_chk_00[0-9][0-9]")
>>> my_fns.sort()
>>> fields = ["particle_position_x", "particle_position_y",
>>>           "particle_position_z", "particle_velocity_x",
>>>           "particle_velocity_y", "particle_velocity_z"]
>>> ds = load(my_fns[0])
>>> init_sphere = ds.sphere(ds.domain_center, (.5, "unitary"))
>>> indices = init_sphere["particle_index"].astype("int")
>>> ts = DatasetSeries(my_fns)
>>> trajs = ts.particle_trajectories(indices, fields=fields)
>>> for t in trajs :
>>>     print t["particle_velocity_x"].max(), t["particle_velocity_x"].min()

Note

This function will fail if there are duplicate particle ids or if some of the particle disappear.

piter(storage=None)[source]

Iterate over time series components in parallel.

This allows you to iterate over a time series while dispatching individual components of that time series to different processors or processor groups. If the parallelism strategy was set to be multi-processor (by “parallel = N” where N is an integer when the DatasetSeries was created) this will issue each dataset to an N-processor group. For instance, this would allow you to start a 1024 processor job, loading up 100 datasets in a time series and creating 8 processor groups of 128 processors each, each of which would be assigned a different dataset. This could be accomplished as shown in the examples below. The storage option is as seen in parallel_objects() which is a mechanism for storing results of analysis on an individual dataset and then combining the results at the end, so that the entire set of processors have access to those results.

Note that supplying a store changes the iteration mechanism; see below.

Parameters:storage (dict) – This is a dictionary, which will be filled with results during the course of the iteration. The keys will be the dataset indices and the values will be whatever is assigned to the result attribute on the storage during iteration.

Examples

Here is an example of iteration when the results do not need to be stored. One processor will be assigned to each dataset.

>>> ts = DatasetSeries("DD*/DD*.index")
>>> for ds in ts.piter():
...    SlicePlot(ds, "x", "Density").save()
...

This demonstrates how one might store results:

>>> def print_time(ds):
...     print ds.current_time
...
>>> ts = DatasetSeries("DD*/DD*.index",
...             setup_function = print_time )
...
>>> my_storage = {}
>>> for sto, ds in ts.piter(storage=my_storage):
...     v, c = ds.find_max("density")
...     sto.result = (v, c)
...
>>> for i, (v, c) in sorted(my_storage.items()):
...     print "% 4i  %0.3e" % (i, v)
...

This shows how to dispatch 4 processors to each dataset:

>>> ts = DatasetSeries("DD*/DD*.index",
...                     parallel = 4)
>>> for ds in ts.piter():
...     ProjectionPlot(ds, "x", "Density").save()
...
class yt.data_objects.time_series.DatasetSeriesObject(time_series, data_object_name, *args, **kwargs)[source]

Bases: object

eval(tasks)[source]
get(ds)[source]
class yt.data_objects.time_series.RegisteredSimulationTimeSeries(name, b, d)[source]

Bases: type

mro() → list

return a type’s method resolution order

class yt.data_objects.time_series.SimulationTimeSeries(parameter_filename, find_outputs=False)[source]

Bases: yt.data_objects.time_series.DatasetSeries

arr
eval(tasks, obj=None)
from_filenames(filenames, parallel=True, setup_function=None, **kwargs)

Create a time series from either a filename pattern or a list of filenames.

This method provides an easy way to create a DatasetSeries, given a set of filenames or a pattern that matches them. Additionally, it can set the parallelism strategy.

Parameters:
  • filenames (list or pattern) – This can either be a list of filenames (such as [“DD0001/DD0001”, “DD0002/DD0002”]) or a pattern to match, such as “DD*/DD*.index”). If it’s the former, they will be loaded in order. The latter will be identified with the glob module and then sorted.
  • parallel (True, False or int) – This parameter governs the behavior when .piter() is called on the resultant DatasetSeries object. If this is set to False, the time series will not iterate in parallel when .piter() is called. If this is set to either True or an integer, it will be iterated with 1 or that integer number of processors assigned to each parameter file provided to the loop.
  • setup_function (callable, accepts a ds) – This function will be called whenever a dataset is loaded.

Examples

>>> def print_time(ds):
...     print ds.current_time
...
>>> ts = DatasetSeries.from_filenames(
...     "GasSloshingLowRes/sloshing_low_res_hdf5_plt_cnt_0[0-6][0-9]0",
...      setup_function = print_time)
...
>>> for ds in ts:
...     SlicePlot(ds, "x", "Density").save()
from_output_log(output_log, line_prefix='DATASET WRITTEN', parallel=True)
outputs
particle_trajectories(indices, fields=None, suppress_logging=False, ptype=None)

Create a collection of particle trajectories in time over a series of datasets.

Parameters:
  • indices (array_like) – An integer array of particle indices whose trajectories we want to track. If they are not sorted they will be sorted.
  • fields (list of strings, optional) – A set of fields that is retrieved when the trajectory collection is instantiated. Default: None (will default to the fields ‘particle_position_x’, ‘particle_position_y’, ‘particle_position_z’)
  • suppress_logging (boolean) – Suppress yt’s logging when iterating over the simulation time series. Default: False
  • ptype (str, optional) – Only use this particle type. Default: None, which uses all particle type.

Examples

>>> my_fns = glob.glob("orbit_hdf5_chk_00[0-9][0-9]")
>>> my_fns.sort()
>>> fields = ["particle_position_x", "particle_position_y",
>>>           "particle_position_z", "particle_velocity_x",
>>>           "particle_velocity_y", "particle_velocity_z"]
>>> ds = load(my_fns[0])
>>> init_sphere = ds.sphere(ds.domain_center, (.5, "unitary"))
>>> indices = init_sphere["particle_index"].astype("int")
>>> ts = DatasetSeries(my_fns)
>>> trajs = ts.particle_trajectories(indices, fields=fields)
>>> for t in trajs :
>>>     print t["particle_velocity_x"].max(), t["particle_velocity_x"].min()

Note

This function will fail if there are duplicate particle ids or if some of the particle disappear.

piter(storage=None)

Iterate over time series components in parallel.

This allows you to iterate over a time series while dispatching individual components of that time series to different processors or processor groups. If the parallelism strategy was set to be multi-processor (by “parallel = N” where N is an integer when the DatasetSeries was created) this will issue each dataset to an N-processor group. For instance, this would allow you to start a 1024 processor job, loading up 100 datasets in a time series and creating 8 processor groups of 128 processors each, each of which would be assigned a different dataset. This could be accomplished as shown in the examples below. The storage option is as seen in parallel_objects() which is a mechanism for storing results of analysis on an individual dataset and then combining the results at the end, so that the entire set of processors have access to those results.

Note that supplying a store changes the iteration mechanism; see below.

Parameters:storage (dict) – This is a dictionary, which will be filled with results during the course of the iteration. The keys will be the dataset indices and the values will be whatever is assigned to the result attribute on the storage during iteration.

Examples

Here is an example of iteration when the results do not need to be stored. One processor will be assigned to each dataset.

>>> ts = DatasetSeries("DD*/DD*.index")
>>> for ds in ts.piter():
...    SlicePlot(ds, "x", "Density").save()
...

This demonstrates how one might store results:

>>> def print_time(ds):
...     print ds.current_time
...
>>> ts = DatasetSeries("DD*/DD*.index",
...             setup_function = print_time )
...
>>> my_storage = {}
>>> for sto, ds in ts.piter(storage=my_storage):
...     v, c = ds.find_max("density")
...     sto.result = (v, c)
...
>>> for i, (v, c) in sorted(my_storage.items()):
...     print "% 4i  %0.3e" % (i, v)
...

This shows how to dispatch 4 processors to each dataset:

>>> ts = DatasetSeries("DD*/DD*.index",
...                     parallel = 4)
>>> for ds in ts.piter():
...     ProjectionPlot(ds, "x", "Density").save()
...
print_key_parameters()[source]

Print out some key parameters for the simulation.

quan
class yt.data_objects.time_series.TimeSeriesParametersContainer(data_object)[source]

Bases: object

class yt.data_objects.time_series.TimeSeriesQuantitiesContainer(data_object, quantities)[source]

Bases: object

yt.data_objects.time_series.get_ds_prop(propname)[source]
yt.data_objects.time_series.get_filenames_from_glob_pattern(filenames)[source]