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#!/usr/bin/env python
from typing import TypeVar, Tuple
import collections
import json
import bisect
import pandas
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
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, default=vars)
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_first_until(self, ts: TimeStampType) -> Tuple[TimeStampType, DataType]:
"""Pop all item until a given timestamped value and return the last poped item."""
# 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_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 as_dataframe(self, exclude=[], split={}) -> pandas.DataFrame:
"""Convert as [pandas dataframe](https://pandas.pydata.org/docs/reference/frame.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
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
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