docs/segmentation.html
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<article id="content">
<header>
<h1 class="title">Module <code>trend_classifier.segmentation</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">import warnings
import numpy as np
from trend_classifier.configuration import Config
from trend_classifier.models import Metrics
from trend_classifier.segment import Segment
from trend_classifier.segment import SegmentList
from trend_classifier.types import FigSize
from trend_classifier.visuals import _plot_detrended_signal
from trend_classifier.visuals import _plot_segment
from trend_classifier.visuals import _plot_segment_with_trendlines_no_context
from trend_classifier.visuals import _plot_segments
def _error(a: float, b: float, metrics: Metrics = Metrics.ABSOLUTE_ERROR) -> float:
"""Calculate how much two parameters differ.
Used e.g. to calculate how much the slopes of linear trends in two windows differ.
Args:
a: First parameter.
b: Second parameter.
metrics: Metrics to use for the calculation.
Returns:
Measure of difference between the two parameters.
See Also:
class `Metrics`
"""
if metrics == Metrics.RELATIVE_ABSOLUTE_ERROR:
return abs(a - b) / abs(a)
if metrics == Metrics.ABSOLUTE_ERROR:
return abs(a - b)
class Segmenter:
"""Class for segmenting a time series into segments with similar trend."""
def __init__(
self,
x: list[int] | None = None,
y: list[int] | None = None,
df=None,
column: str | None = "Adj Close",
config: Config | None = None,
n: int | None = None,
):
"""Initialize the segmenter.
Args:
x: List of x values.
y: List of y values.
df: Pandas DataFrame with time series.
column: Name of the column with the time series.
config: Configuration of the segmenter.
n: Number of samples in a window.
"""
self._handle_configuration(config, n)
self._handle_input_data(column=column, df=df, x=x, y=y)
self.y_de_trended: list | None = None
self.segments: SegmentList[Segment] | None = None
self.slope: float | None = None
self.offset: float | None = None
self.slopes_std: float | None = None
self.offsets_std: float | None = None
def _handle_configuration(self, config, n):
# Handle configuration
if config is None:
# use default configuration if no configuration is provided
self.config = Config()
if n is not None:
# override default N in configuration if N is provided
self.config.N = n
if config is not None:
if n is not None:
# raise error
raise ValueError("Provide either config or N, not both.")
self.config = config
def _handle_input_data(self, column, df, x, y):
# --- Handle input data
# error - most likely pandas dataframe as argument instead of kwarg
if x is not None and not isinstance(x, list):
# TODO: KS: 2022-09-07: accept also numpy array, ndarray,np.matrix, pd.Series
raise TypeError(
"x must be a list, got {}. For pandas dataframe use 'df' keyword argument".format(
type(x)
)
)
# error - no input data provided
if x is None and y is None and df is None:
raise ValueError("Provide timeseries data: either x and y or df.")
# error - ambiguous input data provided - both x,y and df provided
if x is not None and y is not None and df is not None:
raise ValueError(
"Provide timeseries data: either (x and y) or (df), not all."
)
# input data provided as x and y
if x is not None and y is not None:
self.x = x
self.y = y
# make warning if column provided but not dataframe
if df is None and column is not None:
warnings.warn("No dataframe provided, column argument will be ignored.")
# input data provided as dataframe
if df is not None:
self.x = list(range(0, len(df.index.tolist()), 1)) # noqa: FKA01
self.y = df[column].tolist()
def calculate_segments(self) -> list[Segment]:
"""Calculate segments with similar trend for the given timeserie.
Calculates:
- boundaries of segments
- slopes and offsets of windows
"""
# check if initialized x and y
if self.x is None or self.y is None:
raise ValueError("Segmenter x and y must be initialized!")
# Read data from config to short variables
N = self.config.N
overlap_ratio = self.config.overlap_ratio
alpha = self.config.alpha
beta = self.config.beta
metrics_alpha = self.config.metrics_alpha
metrics_beta = self.config.metrics_beta
prev_fit = None
segments = SegmentList()
new_segment = {"s_start": 0, "slopes": [], "offsets": [], "starts": []}
off = self._set_offset(N, overlap_ratio)
for start in range(0, len(self.x) - N, off): # noqa: FKA01
end = start + N
fit = np.polyfit(x=self.x[start:end], y=self.y[start:end], deg=1)
new_segment["slopes"].append(fit[0])
new_segment["offsets"].append(fit[1])
new_segment["starts"].append(start)
if prev_fit is not None:
# asses if the slope is similar to the previous one
prev_slope = float(prev_fit[0])
this_slope = float(fit[0])
r0 = _error(prev_slope, this_slope, metrics=metrics_alpha)
# asses if the offset is similar to the previous one
prev_offset = float(prev_fit[1])
this_offset = float(fit[1])
r1 = _error(prev_offset, this_offset, metrics=metrics_beta)
is_slope_different = r0 >= alpha if alpha is not None else False
is_offset_different = r1 >= beta if beta is not None else False
new_segment["is_slope_different"] = is_slope_different
new_segment["is_offset_different"] = is_offset_different
new_segment = self._finish_segment_if_needed(
offset=off, new_segment=new_segment, segments=segments, start=start
)
prev_fit = fit
# add last segment
last_segment = Segment(
start=int(new_segment["s_start"]),
stop=int(len(self.x)),
slopes=new_segment["slopes"],
offsets=new_segment["offsets"],
starts=new_segment["starts"],
)
segments.append(last_segment)
self.segments = segments
# remove outstanding windows
last_segment.remove_outstanding_windows(self.config.N)
# add extra information to the segments
self._describe_segments()
return segments
def _finish_segment_if_needed(self, offset, new_segment, segments, start):
need_to_finish_segment = (
new_segment["is_slope_different"] or new_segment["is_offset_different"]
)
if need_to_finish_segment:
s_stop = _determine_trend_end_point(offset, start)
reason = self.describe_reason_for_new_segment(
new_segment["is_offset_different"], new_segment["is_slope_different"]
)
segment = Segment(
start=int(new_segment["s_start"]),
stop=int(s_stop),
slopes=new_segment["slopes"],
offsets=new_segment["offsets"],
starts=new_segment["starts"],
reason_for_new_segment=reason,
)
# remove outstanding windows
segment.remove_outstanding_windows(self.config.N)
segments.append(segment)
new_segment["s_start"] = s_stop + 1
new_segment["slopes"] = []
new_segment["offsets"] = []
new_segment["starts"] = []
return new_segment
@staticmethod
def _set_offset(n, overlap_ratio):
offset = int(n * overlap_ratio)
if offset == 0:
print("Overlap ratio is too small, setting it to 1")
print("N = ", n)
print("overlap_ratio = ", overlap_ratio)
offset = 1
return offset
@staticmethod
def describe_reason_for_new_segment(
is_offset_different: bool, is_slope_different: bool
) -> str:
reason = "slope" if is_slope_different else "offset"
if is_slope_different and is_offset_different:
reason = "slope and offset"
return reason
def _describe_segments(self) -> None:
"""Add extra information about the segments."""
y_norm = []
for idx, segment in enumerate(self.segments):
start = segment.start
stop = segment.stop
# x and y for the segment
xx = self.x[start : stop + 1]
yy = self.y[start : stop + 1]
# trend calculation
fit = np.polyfit(x=xx, y=yy, deg=1)
fit_fn = np.poly1d(fit)
# calculate point for the trend line
yt = np.array(fit_fn(xx))
# FIXME: KS: 2022-09-02: Normalize (as below?)
# normalize each point in yy by value of corresponding point in yt,
# store results in ydtn
# ydtn = yy / yt
ydt = np.array(yy) - yt
# calculate standard deviation of the values with removed trend
s = np.std(ydt)
# calculate span of the values in the segment normalized by
# the mean value of the segment
span = 1000 * (np.max(ydt) - np.min(ydt)) // np.mean(yy)
# store de-trended values
y_norm.extend(ydt)
# store volatility measures for the segment
self.segments[idx].std = s
self.segments[idx].span = span
self.y_de_trended = y_norm
self.segments[idx].slope = fit[0]
self.segments[idx].offset = fit[1]
self.segments[idx].slopes_std = np.std(self.segments[idx].slopes)
self.segments[idx].offsets_std = np.std(self.segments[idx].offsets)
def plot_segment(
self,
idx: list[int] | int,
col: str = "red",
fig_size: FigSize = (10, 5),
) -> None:
"""Plot segment with given index or multiple segments with given indices.
Args:
idx: index of the segment or list of indices of segments
col: color of the segment
fig_size: size of the figure
"""
_plot_segment(obj=self, idx=idx, col=col, fig_size=fig_size)
def plot_segment_with_trendlines_no_context(
self,
idx: int,
fig_size: FigSize = (10, 5),
) -> None:
"""Plot segment with given index.
Args:
idx: index of the segment or list of indices of segments
fig_size: size of the figure
"""
_plot_segment_with_trendlines_no_context(obj=self, idx=idx, fig_size=fig_size)
def plot_segments(self, fig_size: FigSize = (8, 4)) -> None:
"""Plot all segments and linear trend lines.
Args:
fig_size: size of the figure e.g. (8, 4)
"""
_plot_segments(self, fig_size)
def plot_detrended_signal(self, fig_size: FigSize = (10, 5)) -> None:
"""Plot de-trended signal.
Args:
fig_size: size of the figure
"""
_plot_detrended_signal(
x=self.x, y_de_trended=self.y_de_trended, fig_size=fig_size
)
def calc_area_outside_trend(self) -> float:
"""Calculate area outside trend.
Sum of absolute values of the points below/above the trend line.
Normalized by the mean value of the signal.
Normalized by the length of the signal.
Returns:
area outside trend
"""
return np.sum(np.abs(self.y_de_trended)) / np.mean(self.y) / len(self.y)
def _determine_trend_end_point(off: int, start: int) -> int:
"""Determine end point of the trend.
Args:
off: offset of the window
start: start point of the trend
"""
# TODO: KS: 2022-09-06: proper calculation of the end of the segment
s_stop = start + off / 2
return int(s_stop)
# TODO: KS: 2022-09-06: Automatically determine parameters based on history:
# see the difference e.g. between AAPL and BTC
# TODO: KS: 2022-09-06: Check how it works with different timeframes
# (e.g. 1h, 4h, 1d)
# TODO: KS: 2022-09-06: improve quality on basic data V-shape or lambda-shape
# TODO: KS: 2022-09-07: add docstrings</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="trend_classifier.segmentation.Segmenter"><code class="flex name class">
<span>class <span class="ident">Segmenter</span></span>
<span>(</span><span>x: list[int] | None = None, y: list[int] | None = None, df=None, column: str | None = 'Adj Close', config: <a title="trend_classifier.configuration.Config" href="configuration.html#trend_classifier.configuration.Config">Config</a> | None = None, n: int | None = None)</span>
</code></dt>
<dd>
<div class="desc"><p>Class for segmenting a time series into segments with similar trend.</p>
<p>Initialize the segmenter.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>x</code></strong></dt>
<dd>List of x values.</dd>
<dt><strong><code>y</code></strong></dt>
<dd>List of y values.</dd>
<dt><strong><code>df</code></strong></dt>
<dd>Pandas DataFrame with time series.</dd>
<dt><strong><code>column</code></strong></dt>
<dd>Name of the column with the time series.</dd>
<dt><strong><code>config</code></strong></dt>
<dd>Configuration of the segmenter.</dd>
<dt><strong><code>n</code></strong></dt>
<dd>Number of samples in a window.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class Segmenter:
"""Class for segmenting a time series into segments with similar trend."""
def __init__(
self,
x: list[int] | None = None,
y: list[int] | None = None,
df=None,
column: str | None = "Adj Close",
config: Config | None = None,
n: int | None = None,
):
"""Initialize the segmenter.
Args:
x: List of x values.
y: List of y values.
df: Pandas DataFrame with time series.
column: Name of the column with the time series.
config: Configuration of the segmenter.
n: Number of samples in a window.
"""
self._handle_configuration(config, n)
self._handle_input_data(column=column, df=df, x=x, y=y)
self.y_de_trended: list | None = None
self.segments: SegmentList[Segment] | None = None
self.slope: float | None = None
self.offset: float | None = None
self.slopes_std: float | None = None
self.offsets_std: float | None = None
def _handle_configuration(self, config, n):
# Handle configuration
if config is None:
# use default configuration if no configuration is provided
self.config = Config()
if n is not None:
# override default N in configuration if N is provided
self.config.N = n
if config is not None:
if n is not None:
# raise error
raise ValueError("Provide either config or N, not both.")
self.config = config
def _handle_input_data(self, column, df, x, y):
# --- Handle input data
# error - most likely pandas dataframe as argument instead of kwarg
if x is not None and not isinstance(x, list):
# TODO: KS: 2022-09-07: accept also numpy array, ndarray,np.matrix, pd.Series
raise TypeError(
"x must be a list, got {}. For pandas dataframe use 'df' keyword argument".format(
type(x)
)
)
# error - no input data provided
if x is None and y is None and df is None:
raise ValueError("Provide timeseries data: either x and y or df.")
# error - ambiguous input data provided - both x,y and df provided
if x is not None and y is not None and df is not None:
raise ValueError(
"Provide timeseries data: either (x and y) or (df), not all."
)
# input data provided as x and y
if x is not None and y is not None:
self.x = x
self.y = y
# make warning if column provided but not dataframe
if df is None and column is not None:
warnings.warn("No dataframe provided, column argument will be ignored.")
# input data provided as dataframe
if df is not None:
self.x = list(range(0, len(df.index.tolist()), 1)) # noqa: FKA01
self.y = df[column].tolist()
def calculate_segments(self) -> list[Segment]:
"""Calculate segments with similar trend for the given timeserie.
Calculates:
- boundaries of segments
- slopes and offsets of windows
"""
# check if initialized x and y
if self.x is None or self.y is None:
raise ValueError("Segmenter x and y must be initialized!")
# Read data from config to short variables
N = self.config.N
overlap_ratio = self.config.overlap_ratio
alpha = self.config.alpha
beta = self.config.beta
metrics_alpha = self.config.metrics_alpha
metrics_beta = self.config.metrics_beta
prev_fit = None
segments = SegmentList()
new_segment = {"s_start": 0, "slopes": [], "offsets": [], "starts": []}
off = self._set_offset(N, overlap_ratio)
for start in range(0, len(self.x) - N, off): # noqa: FKA01
end = start + N
fit = np.polyfit(x=self.x[start:end], y=self.y[start:end], deg=1)
new_segment["slopes"].append(fit[0])
new_segment["offsets"].append(fit[1])
new_segment["starts"].append(start)
if prev_fit is not None:
# asses if the slope is similar to the previous one
prev_slope = float(prev_fit[0])
this_slope = float(fit[0])
r0 = _error(prev_slope, this_slope, metrics=metrics_alpha)
# asses if the offset is similar to the previous one
prev_offset = float(prev_fit[1])
this_offset = float(fit[1])
r1 = _error(prev_offset, this_offset, metrics=metrics_beta)
is_slope_different = r0 >= alpha if alpha is not None else False
is_offset_different = r1 >= beta if beta is not None else False
new_segment["is_slope_different"] = is_slope_different
new_segment["is_offset_different"] = is_offset_different
new_segment = self._finish_segment_if_needed(
offset=off, new_segment=new_segment, segments=segments, start=start
)
prev_fit = fit
# add last segment
last_segment = Segment(
start=int(new_segment["s_start"]),
stop=int(len(self.x)),
slopes=new_segment["slopes"],
offsets=new_segment["offsets"],
starts=new_segment["starts"],
)
segments.append(last_segment)
self.segments = segments
# remove outstanding windows
last_segment.remove_outstanding_windows(self.config.N)
# add extra information to the segments
self._describe_segments()
return segments
def _finish_segment_if_needed(self, offset, new_segment, segments, start):
need_to_finish_segment = (
new_segment["is_slope_different"] or new_segment["is_offset_different"]
)
if need_to_finish_segment:
s_stop = _determine_trend_end_point(offset, start)
reason = self.describe_reason_for_new_segment(
new_segment["is_offset_different"], new_segment["is_slope_different"]
)
segment = Segment(
start=int(new_segment["s_start"]),
stop=int(s_stop),
slopes=new_segment["slopes"],
offsets=new_segment["offsets"],
starts=new_segment["starts"],
reason_for_new_segment=reason,
)
# remove outstanding windows
segment.remove_outstanding_windows(self.config.N)
segments.append(segment)
new_segment["s_start"] = s_stop + 1
new_segment["slopes"] = []
new_segment["offsets"] = []
new_segment["starts"] = []
return new_segment
@staticmethod
def _set_offset(n, overlap_ratio):
offset = int(n * overlap_ratio)
if offset == 0:
print("Overlap ratio is too small, setting it to 1")
print("N = ", n)
print("overlap_ratio = ", overlap_ratio)
offset = 1
return offset
@staticmethod
def describe_reason_for_new_segment(
is_offset_different: bool, is_slope_different: bool
) -> str:
reason = "slope" if is_slope_different else "offset"
if is_slope_different and is_offset_different:
reason = "slope and offset"
return reason
def _describe_segments(self) -> None:
"""Add extra information about the segments."""
y_norm = []
for idx, segment in enumerate(self.segments):
start = segment.start
stop = segment.stop
# x and y for the segment
xx = self.x[start : stop + 1]
yy = self.y[start : stop + 1]
# trend calculation
fit = np.polyfit(x=xx, y=yy, deg=1)
fit_fn = np.poly1d(fit)
# calculate point for the trend line
yt = np.array(fit_fn(xx))
# FIXME: KS: 2022-09-02: Normalize (as below?)
# normalize each point in yy by value of corresponding point in yt,
# store results in ydtn
# ydtn = yy / yt
ydt = np.array(yy) - yt
# calculate standard deviation of the values with removed trend
s = np.std(ydt)
# calculate span of the values in the segment normalized by
# the mean value of the segment
span = 1000 * (np.max(ydt) - np.min(ydt)) // np.mean(yy)
# store de-trended values
y_norm.extend(ydt)
# store volatility measures for the segment
self.segments[idx].std = s
self.segments[idx].span = span
self.y_de_trended = y_norm
self.segments[idx].slope = fit[0]
self.segments[idx].offset = fit[1]
self.segments[idx].slopes_std = np.std(self.segments[idx].slopes)
self.segments[idx].offsets_std = np.std(self.segments[idx].offsets)
def plot_segment(
self,
idx: list[int] | int,
col: str = "red",
fig_size: FigSize = (10, 5),
) -> None:
"""Plot segment with given index or multiple segments with given indices.
Args:
idx: index of the segment or list of indices of segments
col: color of the segment
fig_size: size of the figure
"""
_plot_segment(obj=self, idx=idx, col=col, fig_size=fig_size)
def plot_segment_with_trendlines_no_context(
self,
idx: int,
fig_size: FigSize = (10, 5),
) -> None:
"""Plot segment with given index.
Args:
idx: index of the segment or list of indices of segments
fig_size: size of the figure
"""
_plot_segment_with_trendlines_no_context(obj=self, idx=idx, fig_size=fig_size)
def plot_segments(self, fig_size: FigSize = (8, 4)) -> None:
"""Plot all segments and linear trend lines.
Args:
fig_size: size of the figure e.g. (8, 4)
"""
_plot_segments(self, fig_size)
def plot_detrended_signal(self, fig_size: FigSize = (10, 5)) -> None:
"""Plot de-trended signal.
Args:
fig_size: size of the figure
"""
_plot_detrended_signal(
x=self.x, y_de_trended=self.y_de_trended, fig_size=fig_size
)
def calc_area_outside_trend(self) -> float:
"""Calculate area outside trend.
Sum of absolute values of the points below/above the trend line.
Normalized by the mean value of the signal.
Normalized by the length of the signal.
Returns:
area outside trend
"""
return np.sum(np.abs(self.y_de_trended)) / np.mean(self.y) / len(self.y)</code></pre>
</details>
<h3>Static methods</h3>
<dl>
<dt id="trend_classifier.segmentation.Segmenter.describe_reason_for_new_segment"><code class="name flex">
<span>def <span class="ident">describe_reason_for_new_segment</span></span>(<span>is_offset_different: bool, is_slope_different: bool) ‑> str</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@staticmethod
def describe_reason_for_new_segment(
is_offset_different: bool, is_slope_different: bool
) -> str:
reason = "slope" if is_slope_different else "offset"
if is_slope_different and is_offset_different:
reason = "slope and offset"
return reason</code></pre>
</details>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="trend_classifier.segmentation.Segmenter.calc_area_outside_trend"><code class="name flex">
<span>def <span class="ident">calc_area_outside_trend</span></span>(<span>self) ‑> float</span>
</code></dt>
<dd>
<div class="desc"><p>Calculate area outside trend.</p>
<p>Sum of absolute values of the points below/above the trend line.
Normalized by the mean value of the signal.
Normalized by the length of the signal.</p>
<h2 id="returns">Returns</h2>
<p>area outside trend</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def calc_area_outside_trend(self) -> float:
"""Calculate area outside trend.
Sum of absolute values of the points below/above the trend line.
Normalized by the mean value of the signal.
Normalized by the length of the signal.
Returns:
area outside trend
"""
return np.sum(np.abs(self.y_de_trended)) / np.mean(self.y) / len(self.y)</code></pre>
</details>
</dd>
<dt id="trend_classifier.segmentation.Segmenter.calculate_segments"><code class="name flex">
<span>def <span class="ident">calculate_segments</span></span>(<span>self) ‑> list[<a title="trend_classifier.segment.Segment" href="segment.html#trend_classifier.segment.Segment">Segment</a>]</span>
</code></dt>
<dd>
<div class="desc"><p>Calculate segments with similar trend for the given timeserie.</p>
<p>Calculates:
- boundaries of segments
- slopes and offsets of windows</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def calculate_segments(self) -> list[Segment]:
"""Calculate segments with similar trend for the given timeserie.
Calculates:
- boundaries of segments
- slopes and offsets of windows
"""
# check if initialized x and y
if self.x is None or self.y is None:
raise ValueError("Segmenter x and y must be initialized!")
# Read data from config to short variables
N = self.config.N
overlap_ratio = self.config.overlap_ratio
alpha = self.config.alpha
beta = self.config.beta
metrics_alpha = self.config.metrics_alpha
metrics_beta = self.config.metrics_beta
prev_fit = None
segments = SegmentList()
new_segment = {"s_start": 0, "slopes": [], "offsets": [], "starts": []}
off = self._set_offset(N, overlap_ratio)
for start in range(0, len(self.x) - N, off): # noqa: FKA01
end = start + N
fit = np.polyfit(x=self.x[start:end], y=self.y[start:end], deg=1)
new_segment["slopes"].append(fit[0])
new_segment["offsets"].append(fit[1])
new_segment["starts"].append(start)
if prev_fit is not None:
# asses if the slope is similar to the previous one
prev_slope = float(prev_fit[0])
this_slope = float(fit[0])
r0 = _error(prev_slope, this_slope, metrics=metrics_alpha)
# asses if the offset is similar to the previous one
prev_offset = float(prev_fit[1])
this_offset = float(fit[1])
r1 = _error(prev_offset, this_offset, metrics=metrics_beta)
is_slope_different = r0 >= alpha if alpha is not None else False
is_offset_different = r1 >= beta if beta is not None else False
new_segment["is_slope_different"] = is_slope_different
new_segment["is_offset_different"] = is_offset_different
new_segment = self._finish_segment_if_needed(
offset=off, new_segment=new_segment, segments=segments, start=start
)
prev_fit = fit
# add last segment
last_segment = Segment(
start=int(new_segment["s_start"]),
stop=int(len(self.x)),
slopes=new_segment["slopes"],
offsets=new_segment["offsets"],
starts=new_segment["starts"],
)
segments.append(last_segment)
self.segments = segments
# remove outstanding windows
last_segment.remove_outstanding_windows(self.config.N)
# add extra information to the segments
self._describe_segments()
return segments</code></pre>
</details>
</dd>
<dt id="trend_classifier.segmentation.Segmenter.plot_detrended_signal"><code class="name flex">
<span>def <span class="ident">plot_detrended_signal</span></span>(<span>self, fig_size: tuple[typing.Union[float, int], typing.Union[float, int]] = (10, 5)) ‑> None</span>
</code></dt>
<dd>
<div class="desc"><p>Plot de-trended signal.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>fig_size</code></strong></dt>
<dd>size of the figure</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def plot_detrended_signal(self, fig_size: FigSize = (10, 5)) -> None:
"""Plot de-trended signal.
Args:
fig_size: size of the figure
"""
_plot_detrended_signal(
x=self.x, y_de_trended=self.y_de_trended, fig_size=fig_size
)</code></pre>
</details>
</dd>
<dt id="trend_classifier.segmentation.Segmenter.plot_segment"><code class="name flex">
<span>def <span class="ident">plot_segment</span></span>(<span>self, idx: list[int] | int, col: str = 'red', fig_size: tuple[typing.Union[float, int], typing.Union[float, int]] = (10, 5)) ‑> None</span>
</code></dt>
<dd>
<div class="desc"><p>Plot segment with given index or multiple segments with given indices.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>idx</code></strong></dt>
<dd>index of the segment or list of indices of segments</dd>
<dt><strong><code>col</code></strong></dt>
<dd>color of the segment</dd>
<dt><strong><code>fig_size</code></strong></dt>
<dd>size of the figure</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def plot_segment(
self,
idx: list[int] | int,
col: str = "red",
fig_size: FigSize = (10, 5),
) -> None:
"""Plot segment with given index or multiple segments with given indices.
Args:
idx: index of the segment or list of indices of segments
col: color of the segment
fig_size: size of the figure
"""
_plot_segment(obj=self, idx=idx, col=col, fig_size=fig_size)</code></pre>
</details>
</dd>
<dt id="trend_classifier.segmentation.Segmenter.plot_segment_with_trendlines_no_context"><code class="name flex">
<span>def <span class="ident">plot_segment_with_trendlines_no_context</span></span>(<span>self, idx: int, fig_size: tuple[typing.Union[float, int], typing.Union[float, int]] = (10, 5)) ‑> None</span>
</code></dt>
<dd>
<div class="desc"><p>Plot segment with given index.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>idx</code></strong></dt>
<dd>index of the segment or list of indices of segments</dd>
<dt><strong><code>fig_size</code></strong></dt>
<dd>size of the figure</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def plot_segment_with_trendlines_no_context(
self,
idx: int,
fig_size: FigSize = (10, 5),
) -> None:
"""Plot segment with given index.
Args:
idx: index of the segment or list of indices of segments
fig_size: size of the figure
"""
_plot_segment_with_trendlines_no_context(obj=self, idx=idx, fig_size=fig_size)</code></pre>
</details>
</dd>
<dt id="trend_classifier.segmentation.Segmenter.plot_segments"><code class="name flex">
<span>def <span class="ident">plot_segments</span></span>(<span>self, fig_size: tuple[typing.Union[float, int], typing.Union[float, int]] = (8, 4)) ‑> None</span>
</code></dt>
<dd>
<div class="desc"><p>Plot all segments and linear trend lines.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>fig_size</code></strong></dt>
<dd>size of the figure e.g. (8, 4)</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def plot_segments(self, fig_size: FigSize = (8, 4)) -> None:
"""Plot all segments and linear trend lines.
Args:
fig_size: size of the figure e.g. (8, 4)
"""
_plot_segments(self, fig_size)</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="trend_classifier" href="index.html">trend_classifier</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="trend_classifier.segmentation.Segmenter" href="#trend_classifier.segmentation.Segmenter">Segmenter</a></code></h4>
<ul class="">
<li><code><a title="trend_classifier.segmentation.Segmenter.calc_area_outside_trend" href="#trend_classifier.segmentation.Segmenter.calc_area_outside_trend">calc_area_outside_trend</a></code></li>
<li><code><a title="trend_classifier.segmentation.Segmenter.calculate_segments" href="#trend_classifier.segmentation.Segmenter.calculate_segments">calculate_segments</a></code></li>
<li><code><a title="trend_classifier.segmentation.Segmenter.describe_reason_for_new_segment" href="#trend_classifier.segmentation.Segmenter.describe_reason_for_new_segment">describe_reason_for_new_segment</a></code></li>
<li><code><a title="trend_classifier.segmentation.Segmenter.plot_detrended_signal" href="#trend_classifier.segmentation.Segmenter.plot_detrended_signal">plot_detrended_signal</a></code></li>
<li><code><a title="trend_classifier.segmentation.Segmenter.plot_segment" href="#trend_classifier.segmentation.Segmenter.plot_segment">plot_segment</a></code></li>
<li><code><a title="trend_classifier.segmentation.Segmenter.plot_segment_with_trendlines_no_context" href="#trend_classifier.segmentation.Segmenter.plot_segment_with_trendlines_no_context">plot_segment_with_trendlines_no_context</a></code></li>
<li><code><a title="trend_classifier.segmentation.Segmenter.plot_segments" href="#trend_classifier.segmentation.Segmenter.plot_segments">plot_segments</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
</main>
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