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#!/usr/bin/env python
"""Miscellaneous data features."""
__author__ = "Théo de la Hogue"
__credits__ = []
__copyright__ = "Copyright 2023, Ecole Nationale de l'Aviation Civile (ENAC)"
__license__ = "BSD"
from typing import TypeVar, Tuple, Any
from dataclasses import dataclass, field
import importlib
from inspect import getmembers, getmodule
import collections
import json
import ast
import bisect
import threading
import math
import time
import pandas
import numpy
import matplotlib.pyplot as mpyplot
import matplotlib.patches as mpatches
TimeStampType = TypeVar('TimeStamp', int, float)
"""Type definition for timestamp as integer or float values."""
DataType = TypeVar('Data')
"""Type definition for data to store anything in time."""
TimeStampedBufferType = TypeVar('TimeStampedBuffer', bound="TimeStampedBuffer")
# Type definition for type annotation convenience
def as_dict(dataclass_object) -> dict:
"""
Get dataclass object fields's values as a dictionary.
Returns:
values: dictionary of dataclass fields's values
"""
# Get data class fields names
fields_names = []
for member_name, member_value in getmembers(dataclass_object):
if member_name == '__dataclass_fields__':
fields_names = member_value.keys()
# Copy fields values
return {name: vars(dataclass_object)[name] for name in fields_names}
def module_path(obj) -> str:
"""
Get object module path.
Returns:
module path
"""
return obj.__class__.__module__
class JsonEncoder(json.JSONEncoder):
"""Specific ArGaze JSON Encoder."""
def default(self, obj):
"""default implementation to serialize object."""
# numpy cases
if isinstance(obj, numpy.integer):
return int(obj)
elif isinstance(obj, numpy.floating):
return float(obj)
elif isinstance(obj, numpy.ndarray):
return obj.tolist()
# default case
try:
return json.JSONEncoder.default(self, obj)
# class case
except:
# ignore attribute starting with _
public_dict = {}
for k, v in vars(obj).items():
if not k.startswith('_'):
# numpy cases
if isinstance(v, numpy.integer):
v = int(v)
elif isinstance(v, numpy.floating):
v = float(v)
elif isinstance(v, numpy.ndarray):
v = v.tolist()
public_dict[k] = v
return public_dict
class TimeStampedBuffer(collections.OrderedDict):
"""Ordered dictionary to handle timestamped data.
```
{
timestamp1: data1,
timestamp2: data2,
...
}
```
!!! warning
Timestamps must be numbers.
!!! warning "Timestamps are not sorted internally"
Data are considered to be stored according at their coming time.
"""
def __new__(cls, args = None):
"""Inheritance"""
return super(TimeStampedBuffer, cls).__new__(cls)
def __setitem__(self, ts: TimeStampType, data: DataType):
"""Store data at given timestamp."""
assert(type(ts) == int or type(ts) == float)
super().__setitem__(ts, data)
def __repr__(self):
"""String representation"""
return json.dumps(self, ensure_ascii=False, cls=JsonEncoder)
def __str__(self):
"""String representation"""
return json.dumps(self, ensure_ascii=False, cls=JsonEncoder)
def append(self, timestamped_buffer: TimeStampedBufferType) -> TimeStampedBufferType:
"""Append a timestamped buffer."""
for ts, value in timestamped_buffer.items():
self[ts] = value
return self
@property
def first(self) -> Tuple[TimeStampType, DataType]:
"""Easing access to first item."""
return list(self.items())[0]
def pop_first(self) -> Tuple[TimeStampType, DataType]:
"""Easing FIFO access mode."""
return self.popitem(last=False)
def pop_last_until(self, ts: TimeStampType) -> Tuple[TimeStampType, DataType]:
"""Pop all item until a given timestamped value and return the first after."""
# get last item before given timestamp
earliest_ts, earliest_value = self.get_last_until(ts)
first_ts, first_value = self.first
while first_ts < earliest_ts:
self.pop_first()
first_ts, first_value = self.first
return first_ts, first_value
def pop_last_before(self, ts: TimeStampType) -> Tuple[TimeStampType, DataType]:
"""Pop all item before a given timestamped value and return the last one."""
# get last item before given timestamp
earliest_ts, earliest_value = self.get_last_before(ts)
popep_ts, poped_value = self.pop_first()
while popep_ts != earliest_ts:
popep_ts, poped_value = self.pop_first()
return popep_ts, poped_value
@property
def last(self) -> Tuple[TimeStampType, DataType]:
"""Easing access to last item."""
return list(self.items())[-1]
def pop_last(self) -> Tuple[TimeStampType, DataType]:
"""Easing FIFO access mode."""
return self.popitem(last=True)
def get_first_from(self, ts) -> Tuple[TimeStampType, DataType]:
"""Retreive first item timestamp from a given timestamp value."""
ts_list = list(self.keys())
first_from_index = bisect.bisect_left(ts_list, ts)
if first_from_index < len(self):
first_from_ts = ts_list[first_from_index]
return first_from_ts, self[first_from_ts]
else:
raise KeyError(f'No data stored after {ts} timestamp.')
def get_last_before(self, ts) -> Tuple[TimeStampType, DataType]:
"""Retreive last item timestamp before a given timestamp value."""
ts_list = list(self.keys())
last_before_index = bisect.bisect_left(ts_list, ts) - 1
if last_before_index >= 0:
last_before_ts = ts_list[last_before_index]
return last_before_ts, self[last_before_ts]
else:
raise KeyError(f'No data stored before {ts} timestamp.')
def get_last_until(self, ts) -> Tuple[TimeStampType, DataType]:
"""Retreive last item timestamp until a given timestamp value."""
ts_list = list(self.keys())
last_until_index = bisect.bisect_right(ts_list, ts) - 1
if last_until_index >= 0:
last_until_ts = ts_list[last_until_index]
return last_until_ts, self[last_until_ts]
else:
raise KeyError(f'No data stored until {ts} timestamp.')
@classmethod
def from_json(self, json_filepath: str) -> TimeStampedBufferType:
"""Create a TimeStampedBuffer from .json file."""
with open(json_filepath, encoding='utf-8') as ts_buffer_file:
json_buffer = json.load(ts_buffer_file)
return TimeStampedBuffer({ast.literal_eval(ts_str): json_buffer[ts_str] for ts_str in json_buffer})
def to_json(self, json_filepath: str):
"""Save a TimeStampedBuffer to .json file."""
with open(json_filepath, 'w', encoding='utf-8') as ts_buffer_file:
json.dump(self, ts_buffer_file, ensure_ascii=False, cls=JsonEncoder)
@classmethod
def from_dataframe(self, dataframe: pandas.DataFrame, exclude=[]) -> TimeStampedBufferType:
"""Create a TimeStampedBuffer from [Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html)."""
dataframe.drop(exclude, inplace=True, axis=True)
assert(dataframe.index.name == 'timestamp')
return TimeStampedBuffer(dataframe.to_dict('index'))
def as_dataframe(self, exclude=[], split={}) -> pandas.DataFrame:
"""Convert as [Pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html).
The optional *split* argument allows tuple values to be stored in dedicated columns.
For example: to convert {"point": (0, 0)} data as two separated "x" and "y" columns, use split={"point": ["x", "y"]}
!!! warning "Values must be dictionaries"
Each key is stored as a column name.
!!! note
Timestamps are stored as index column called 'timestamp'.
"""
df = pandas.DataFrame.from_dict(self.values())
# Exclude columns
df.drop(exclude, inplace=True, axis=True)
# Split columns
if len(split) > 0:
splited_columns = []
for column in df.columns:
if column in split.keys():
df[split[column]] = pandas.DataFrame(df[column].tolist(), index=df.index)
df.drop(column, inplace=True, axis=True)
for new_column in split[column]:
splited_columns.append(new_column)
else:
splited_columns.append(column)
# Reorder splited columns
df = df[splited_columns]
# Append timestamps as index column
df['timestamp'] = self.keys()
df.set_index('timestamp', inplace=True)
return df
def plot(self, names=[], colors=[], split={}, samples=None) -> list:
"""Plot as [matplotlib](https://matplotlib.org/) time chart."""
df = self.as_dataframe(split=split)
legend_patches = []
# decimate data
if samples != None:
if samples < len(df):
step = int(len(df) / samples) + 1
df = df.iloc[::step, :]
for name, color in zip(names, colors):
markerline, stemlines, baseline = mpyplot.stem(df.index, df[name])
mpyplot.setp(markerline, color=color, linewidth=1, markersize = 1)
mpyplot.setp(stemlines, color=color, linewidth=1)
mpyplot.setp(baseline, color=color, linewidth=1)
legend_patches.append(mpatches.Patch(color=color, label=name.upper()))
return legend_patches
class DataDictionary(dict):
"""Enable dot.notation access to dictionary attributes"""
__getattr__ = dict.get
__setattr__ = dict.__setitem__
__delattr__ = dict.__delitem__
class SharedObject():
"""Abstract class to enable multiple threads sharing and timestamp management."""
def __init__(self):
self._lock = threading.Lock()
self._timestamp = math.nan
self._execution_times = {}
self._exceptions = {}
@property
def locker(self) -> threading.Lock:
return self._lock
def acquire(self):
self._lock.acquire()
def release(self):
self._lock.release()
@property
def locked(self) -> bool:
return self._lock.locked()
@property
def timestamp(self) -> int|float:
"""Get timestamp"""
self._lock.acquire()
timestamp = self._timestamp
self._lock.release()
return timestamp
@timestamp.setter
def timestamp(self, timestamp: int|float):
"""Set timestamp"""
self._lock.acquire()
self._timestamp = timestamp
self._lock.release()
def untimestamp(self):
"""Reset timestamp"""
self._lock.acquire()
self._timestamp = math.nan
self._lock.release()
@property
def timestamped(self) -> bool:
"""Is the object timestamped?"""
self._lock.acquire()
timestamped = not math.isnan(self._timestamp)
self._lock.release()
return timestamped
class PipelineStepObject():
"""Abstract class to assess pipeline step methods execution time.
Parameters:
execution_times: dictionary with each PipelineStepMethod execution time in ms.
"""
execution_times: dict = {}
observers: dict = {}
def PipelineStepMethod(method):
"""Define a decorator use into PipelineStepObject class to declare pipeline method.
!!! danger
PipelineStepMethod must have a timestamp as first argument.
"""
def wrapper(self, timestamp, *args, **kw):
"""Wrap pipeline step method to measure execution time."""
# Initialize execution time assessment
start = time.perf_counter()
try:
# Execute wrapped method
result = method(self, timestamp, *args, **kw)
finally:
# Measure execution time
self.execution_times[method.__name__] = (time.perf_counter() - start) * 1e3
# Notify observers that method has been called
subscription_name = f'on_{method.__name__}'
for observer_name, observer in self.observers.items():
# Does the observer cares about this method?
try:
subscription = getattr(observer, subscription_name)
# Call subscription
subscription(timestamp, self)
except AttributeError:
pass
return result
return wrapper
class PipelineStepObserver():
"""Define abstract class to observe pipeline step object use.
!!! note
To subscribe to a method call, the inherited class simply needs to define 'on_<method_name>' functions with timestamp and object argument.
"""
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