Note
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Use of Custom Readers that support Pandas DataFrames
This is a demonstration of using customised readers that
support data contained within Pandas DataFrames, rather than
loading directly from a .csv file using CSVGroundTruthReader or
CSVDetectionReader.
The benefit is that this allows us to use the versatile data loading capabilities of pandas to read from many different data source types as needed, including .csv, JSON, XML, Parquet, HDF5, .txt, .zip and more. The resulting DataFrame can then simply be fed into the defined DataFrameGroundTruthReader or DataFrameDetectionReader for further processing in Stone Soup as required.
Software dependencies
Before beginning this example, you need to ensure that Pandas is installed, which is a fast, powerful and flexible open-source data analysis tool in Python. The easiest way to install pandas (if not done so already), is to run pip install from a terminal window within the desired environment:
pip install pandas
The main dependencies and imports for this example are included below:
import numpy as np
import pandas as pd
from math import modf
from stonesoup.base import Property
from stonesoup.buffered_generator import BufferedGenerator
from stonesoup.reader.base import GroundTruthReader, DetectionReader, Reader
from stonesoup.types.detection import Detection
from stonesoup.types.groundtruth import GroundTruthPath, GroundTruthState
from typing import Sequence, Collection
from datetime import datetime, timedelta, timezone
from dateutil.parser import parse
Data Frame Reader
Similarly to Stone Soup’s _CSVFrameReader, we’ll define a _DataFrameReader
class that inherits from the base Reader class to read a DataFrame containing
state vector fields, a time field, and additional metadata fields (all other columns
by default). The only difference between this class and the _CSVFrameReader
class is that we have no path attribute (the DataFrame is already loaded in memory).
class _DataFrameReader(Reader):
state_vector_fields: Sequence[str] = Property(
doc='List of columns names to be used in state vector')
time_field: str = Property(
doc='Name of column to be used as time field')
time_field_format: str = Property(
default=None, doc='Optional datetime format')
timestamp: bool = Property(
default=False, doc='Treat time field as a timestamp from epoch')
metadata_fields: Collection[str] = Property(
default=None, doc='List of columns to be saved as metadata, default all')
def _get_metadata(self, row):
if self.metadata_fields is None:
local_metadata = dict(row)
for key in list(local_metadata):
if key == self.time_field or key in self.state_vector_fields:
del local_metadata[key]
else:
local_metadata = {field: row[field]
for field in self.metadata_fields
if field in row}
return local_metadata
def _get_time(self, row):
if self.time_field_format is not None:
time_field_value = datetime.strptime(row[self.time_field], self.time_field_format)
elif self.timestamp:
fractional, timestamp = modf(float(row[self.time_field]))
time_field_value = datetime.fromtimestamp(
int(timestamp), timezone.utc).replace(tzinfo=None)
time_field_value += timedelta(microseconds=fractional * 1E6)
else:
time_field_value = row[self.time_field]
if not isinstance(time_field_value, datetime):
time_field_value = parse(time_field_value, ignoretz=True)
return time_field_value
Data Ground Truth Reader
With the help of our _DataFrameReader class, we can now define a custom
DataFrameGroundTruthReader. This is similar to CSVGroundTruthReader and
inherits from the base GroundTruthReader class. A key difference is that we
include an instance attribute for the dataframe containing our data.
We also define a custom generator function (groundtruth_paths_gen) that uses the decorator @BufferedGenerator.generator_method. The generator needs to return a time and a set of detections, like so:
class DataFrameGroundTruthReader(GroundTruthReader, _DataFrameReader):
"""A custom reader for pandas DataFrames containing truth data.
The DataFrame must have columns containing all fields needed to generate the
ground truth state. Those states with the same ID will be put into
a :class:`~.GroundTruthPath` in sequence, and all paths that are updated at the same time
are yielded together, and such assumes file is in time order.
Parameters
----------
"""
dataframe: pd.DataFrame = Property(doc="DataFrame containing the ground truth data.")
path_id_field: str = Property(doc='Name of column to be used as path ID')
@BufferedGenerator.generator_method
def groundtruth_paths_gen(self):
""" Generator method for providing each row of ground truth data. """
groundtruth_dict = {}
updated_paths = set()
previous_time = None
for row in self.dataframe.to_dict(orient="records"):
time = self._get_time(row)
if previous_time is not None and previous_time != time:
yield previous_time, updated_paths
updated_paths = set()
previous_time = time
state = GroundTruthState(np.array([[row[col_name]] for col_name
in self.state_vector_fields],
dtype=np.float64),
timestamp=time,
metadata=self._get_metadata(row))
id_ = row[self.path_id_field]
if id_ not in groundtruth_dict:
groundtruth_dict[id_] = GroundTruthPath(id=id_)
groundtruth_path = groundtruth_dict[id_]
groundtruth_path.append(state)
updated_paths.add(groundtruth_path)
# Yield remaining
yield previous_time, updated_paths
With our DataFrameGroundTruthReader defined, we can test it on a simple example. Let’s do a basic 3D simulation to create an example dataframe, from which we can test our class:
from stonesoup.models.transition.linear import CombinedLinearGaussianTransitionModel, \
ConstantVelocity
q_x = 0.05
q_y = 0.05
q_z = 0.05
start_time = datetime.now()
transition_model = CombinedLinearGaussianTransitionModel([ConstantVelocity(q_x),
ConstantVelocity(q_y),
ConstantVelocity(q_z)])
truth = GroundTruthPath([GroundTruthState([0, 1, 0, 1, 0, 1], timestamp=start_time)])
times = []
x, y, z = [], [], []
vel_x, vel_y, vel_z = [], [], []
num_steps = 25
for k in range(1, num_steps + 1):
time = start_time+timedelta(seconds=k)
next_state = GroundTruthState(
transition_model.function(truth[k-1], noise=True,
time_interval=timedelta(seconds=1)),
timestamp=time)
truth.append(next_state)
times.append(time)
x.append(next_state.state_vector[0])
vel_x.append(next_state.state_vector[1])
y.append(next_state.state_vector[2])
vel_y.append(next_state.state_vector[3])
z.append(next_state.state_vector[4])
vel_z.append(next_state.state_vector[5])
truth_df = pd.DataFrame({'time': times,
'x': x,
'y': y,
'z': z,
'vel_x': vel_x,
'vel_y': vel_y,
'vel_z': vel_z,
'track_id': 0})
truth_df.head(5)
Note that the process above is just an example for providing a simple dataframe to use,
and is not generally something we would need to do (since we already have the GroundTruthPath).
The dataframe above is just used to show the workings of our newly defined
DataFrameGroundTruthReader. In practice, we can use any dataframe containing
our Cartesian positions or longitude and latitude co-ordinates. Note that if we
are using longitude and latitude inputs, we would also need to transform these
using LongLatToUTMConverter or equivalent.
We can now initialise our DataFrameGroundTruthReader using this example DataFrame like so:
# read ground truth data from pandas dataframe
ground_truth_reader = DataFrameGroundTruthReader(
dataframe=truth_df,
state_vector_fields=['x', 'vel_x', 'y', 'vel_y', 'z', 'vel_z'],
time_field='time',
path_id_field='track_id')
Let’s demonstrate the ground truth reader generating output for one iteration:
next(iter(ground_truth_reader))
(Timestamp('2024-07-25 11:43:18.664939'), {GroundTruthPath(
states=[GroundTruthState(
state_vector=StateVector([[1.03278316],
[1.02845193],
[1.37291711],
[1.62611509],
[0.91331851],
[0.80787661]]),
timestamp=2024-07-25 11:43:18.664939,
metadata={'track_id': 0})],
id=0)})
Another benefit of this ground truth reader is that we now have convenient access to the original dataframe, using the .dataframe attribute, like so:
ground_truth_reader.dataframe.head(3)
DataFrame Detection Reader
Similarly to our DataFrameGroundTruthReader, we can develop a custom DataFrameDetectionReader that can read in DataFrames containing detections through subclassing from Stone Soup’s DetectionReader class, along with our custom _DataFrameReader class above. Again, this closely resembles the existing CSVDetectionReader class within the Stone Soup library, except we include an instance attribute ‘dataframe’, and modify our detections_gen function to work with dataframes rather than .csv files. This can be seen below:
class DataFrameDetectionReader(DetectionReader, _DataFrameReader):
"""A custom detection reader for DataFrames containing detections.
DataFrame must have headers with the appropriate fields needed to generate
the detection. Detections at the same time are yielded together, and such assume file is in
time order.
Parameters
----------
"""
dataframe: pd.DataFrame = Property(doc="DataFrame containing the detection data.")
@BufferedGenerator.generator_method
def detections_gen(self):
detections = set()
previous_time = None
for row in self.dataframe.to_dict(orient="records"):
time = self._get_time(row)
if previous_time is not None and previous_time != time:
yield previous_time, detections
detections = set()
previous_time = time
detections.add(Detection(
np.array([[row[col_name]] for col_name in self.state_vector_fields],
dtype=np.float64),
timestamp=time,
metadata=self._get_metadata(row)))
# Yield remaining
yield previous_time, detections
We can instantiate this using our example DataFrame above like so:
detection_reader = DataFrameDetectionReader(
dataframe=truth_df,
state_vector_fields=['x', 'vel_x', 'y', 'vel_y', 'z', 'vel_z'],
time_field='time')
Following this, we can now perform any desired follow-up task such as simulation or tracking as covered in the other Stone Soup examples, tutorials and demonstrations. As discussed previously, the huge benefits of using a custom DataFrame reader like this is that we can read any type of data supported by the pandas library, which gives us a huge range of options. This strategy also saves us the overhead of manually specifying custom Stone Soup Reader classes for each format of data.
Total running time of the script: (0 minutes 0.229 seconds)
