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<main>
<article id="content">
<header>
<h1 class="title">Module <code>trend_classifier.segment</code></h1>
</header>
<section id="section-intro">
<p>Module with pydantic model of Segment and helper datastructure - SegmentList.</p>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">"""Module with pydantic model of Segment and helper datastructure - SegmentList."""
import logging
from copy import deepcopy
from pydantic import BaseModel
logger = logging.getLogger(__name__)
class Segment(BaseModel):
"""Segment of a time series."""
# ---- mandatory attributes
start: int
"""Start index of the segment."""
stop: int
"""Stop index of the segment."""
slopes: list[float] = []
"""List of slopes of linear trends in windows in the segment."""
offsets: list[float] = []
"""List of offsets of linear trends in windows in the segment."""
starts: list[int] = []
"""List of start indices of windows in the segment."""
# --- optional attributes with default values
slope: float = 0
"""Slope of the segment."""
offset: float = 0
"""Offset of the segments."""
slopes_std: float = 0
"""Standard deviation of the slopes of linear trends in windows in the segment."""
offsets_std: float = 0
"""Standard deviation of the offsets of linear trends in windows in the segment."""
std: float = 0
"""Standard deviation of the samples in the segment with removed trend."""
span: float = 0
"""Span of the values in the segment normalized by the mean value of the segment.
Indicator if the volatility of the segment is high or low."""
reason_for_new_segment: str = ""
"""Reason for creating a new segment (which criterion was violated)."""
def __str__(self):
return f"Segment(start={self.start}, stop={self.stop}, slope={self.slope:.4g})"
def __repr__(self):
s1 = f"Segment(start={self.start}, stop={self.stop}, slope={self.slope}, "
s2 = f"offset={self.offset}, std={self.std}, span={self.span}, "
s3 = f"reason_for_new_segment={self.reason_for_new_segment}, "
s4 = f"slopes={self.slopes}, offsets={self.offsets}, slopes_std={self.slopes_std}, "
s5 = f"offsets_std={self.offsets_std})"
return s1 + s2 + s3 + s4 + s5
def remove_outstanding_windows(self, n):
new_slopes = deepcopy(self.slopes)
new_offsets = deepcopy(self.offsets)
new_starts = deepcopy(self.starts)
for window_start in self.starts:
n_windows = len(new_starts)
window_end = window_start + n
is_outstanding = window_end > self.stop
if n_windows > 1 and is_outstanding:
new_slopes.remove(self.slopes[self.starts.index(window_start)])
new_offsets.remove(self.offsets[self.starts.index(window_start)])
new_starts.remove(window_start)
logger.debug( # noqa: FKA01
"Removed window %f - %f.", window_start, window_start + n
)
else:
logger.debug( # noqa: FKA01
"Keeping window %f - %f.", window_start, window_start + n
)
self.slopes = new_slopes
self.offsets = new_offsets
self.starts = new_starts
class SegmentList(list):
"""List of segments. Each segment group samples with similar trend.
New methods dedicated e.g. to processing od displaying list of segments
can be added here.
"""
def to_dataframe(self):
"""Convert segments to a pandas DataFrame.
Returns:
A pandas DataFrame.
"""
try:
import pandas as pd
except ImportError:
raise ImportError(
"Pandas is not installed. Install it with `pip install pandas`."
)
df = pd.DataFrame([s.__dict__ for s in self])
# reorder columns
df = df[
[
"start",
"stop",
"slope",
"offset",
"slopes_std",
"offsets_std",
"std",
"span",
"reason_for_new_segment",
"slopes",
"offsets",
]
]
return df</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.segment.Segment"><code class="flex name class">
<span>class <span class="ident">Segment</span></span>
<span>(</span><span>**data: Any)</span>
</code></dt>
<dd>
<div class="desc"><p>Segment of a time series.</p>
<p>Create a new model by parsing and validating input data from keyword arguments.</p>
<p>Raises ValidationError if the input data cannot be parsed to form a valid model.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class Segment(BaseModel):
"""Segment of a time series."""
# ---- mandatory attributes
start: int
"""Start index of the segment."""
stop: int
"""Stop index of the segment."""
slopes: list[float] = []
"""List of slopes of linear trends in windows in the segment."""
offsets: list[float] = []
"""List of offsets of linear trends in windows in the segment."""
starts: list[int] = []
"""List of start indices of windows in the segment."""
# --- optional attributes with default values
slope: float = 0
"""Slope of the segment."""
offset: float = 0
"""Offset of the segments."""
slopes_std: float = 0
"""Standard deviation of the slopes of linear trends in windows in the segment."""
offsets_std: float = 0
"""Standard deviation of the offsets of linear trends in windows in the segment."""
std: float = 0
"""Standard deviation of the samples in the segment with removed trend."""
span: float = 0
"""Span of the values in the segment normalized by the mean value of the segment.
Indicator if the volatility of the segment is high or low."""
reason_for_new_segment: str = ""
"""Reason for creating a new segment (which criterion was violated)."""
def __str__(self):
return f"Segment(start={self.start}, stop={self.stop}, slope={self.slope:.4g})"
def __repr__(self):
s1 = f"Segment(start={self.start}, stop={self.stop}, slope={self.slope}, "
s2 = f"offset={self.offset}, std={self.std}, span={self.span}, "
s3 = f"reason_for_new_segment={self.reason_for_new_segment}, "
s4 = f"slopes={self.slopes}, offsets={self.offsets}, slopes_std={self.slopes_std}, "
s5 = f"offsets_std={self.offsets_std})"
return s1 + s2 + s3 + s4 + s5
def remove_outstanding_windows(self, n):
new_slopes = deepcopy(self.slopes)
new_offsets = deepcopy(self.offsets)
new_starts = deepcopy(self.starts)
for window_start in self.starts:
n_windows = len(new_starts)
window_end = window_start + n
is_outstanding = window_end > self.stop
if n_windows > 1 and is_outstanding:
new_slopes.remove(self.slopes[self.starts.index(window_start)])
new_offsets.remove(self.offsets[self.starts.index(window_start)])
new_starts.remove(window_start)
logger.debug( # noqa: FKA01
"Removed window %f - %f.", window_start, window_start + n
)
else:
logger.debug( # noqa: FKA01
"Keeping window %f - %f.", window_start, window_start + n
)
self.slopes = new_slopes
self.offsets = new_offsets
self.starts = new_starts</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>pydantic.main.BaseModel</li>
<li>pydantic.utils.Representation</li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="trend_classifier.segment.Segment.offset"><code class="name">var <span class="ident">offset</span> : float</code></dt>
<dd>
<div class="desc"><p>Offset of the segments.</p></div>
</dd>
<dt id="trend_classifier.segment.Segment.offsets"><code class="name">var <span class="ident">offsets</span> : list[float]</code></dt>
<dd>
<div class="desc"><p>List of offsets of linear trends in windows in the segment.</p></div>
</dd>
<dt id="trend_classifier.segment.Segment.offsets_std"><code class="name">var <span class="ident">offsets_std</span> : float</code></dt>
<dd>
<div class="desc"><p>Standard deviation of the offsets of linear trends in windows in the segment.</p></div>
</dd>
<dt id="trend_classifier.segment.Segment.reason_for_new_segment"><code class="name">var <span class="ident">reason_for_new_segment</span> : str</code></dt>
<dd>
<div class="desc"><p>Reason for creating a new segment (which criterion was violated).</p></div>
</dd>
<dt id="trend_classifier.segment.Segment.slope"><code class="name">var <span class="ident">slope</span> : float</code></dt>
<dd>
<div class="desc"><p>Slope of the segment.</p></div>
</dd>
<dt id="trend_classifier.segment.Segment.slopes"><code class="name">var <span class="ident">slopes</span> : list[float]</code></dt>
<dd>
<div class="desc"><p>List of slopes of linear trends in windows in the segment.</p></div>
</dd>
<dt id="trend_classifier.segment.Segment.slopes_std"><code class="name">var <span class="ident">slopes_std</span> : float</code></dt>
<dd>
<div class="desc"><p>Standard deviation of the slopes of linear trends in windows in the segment.</p></div>
</dd>
<dt id="trend_classifier.segment.Segment.span"><code class="name">var <span class="ident">span</span> : float</code></dt>
<dd>
<div class="desc"><p>Span of the values in the segment normalized by the mean value of the segment.
Indicator if the volatility of the segment is high or low.</p></div>
</dd>
<dt id="trend_classifier.segment.Segment.start"><code class="name">var <span class="ident">start</span> : int</code></dt>
<dd>
<div class="desc"><p>Start index of the segment.</p></div>
</dd>
<dt id="trend_classifier.segment.Segment.starts"><code class="name">var <span class="ident">starts</span> : list[int]</code></dt>
<dd>
<div class="desc"><p>List of start indices of windows in the segment.</p></div>
</dd>
<dt id="trend_classifier.segment.Segment.std"><code class="name">var <span class="ident">std</span> : float</code></dt>
<dd>
<div class="desc"><p>Standard deviation of the samples in the segment with removed trend.</p></div>
</dd>
<dt id="trend_classifier.segment.Segment.stop"><code class="name">var <span class="ident">stop</span> : int</code></dt>
<dd>
<div class="desc"><p>Stop index of the segment.</p></div>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="trend_classifier.segment.Segment.remove_outstanding_windows"><code class="name flex">
<span>def <span class="ident">remove_outstanding_windows</span></span>(<span>self, n)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def remove_outstanding_windows(self, n):
new_slopes = deepcopy(self.slopes)
new_offsets = deepcopy(self.offsets)
new_starts = deepcopy(self.starts)
for window_start in self.starts:
n_windows = len(new_starts)
window_end = window_start + n
is_outstanding = window_end > self.stop
if n_windows > 1 and is_outstanding:
new_slopes.remove(self.slopes[self.starts.index(window_start)])
new_offsets.remove(self.offsets[self.starts.index(window_start)])
new_starts.remove(window_start)
logger.debug( # noqa: FKA01
"Removed window %f - %f.", window_start, window_start + n
)
else:
logger.debug( # noqa: FKA01
"Keeping window %f - %f.", window_start, window_start + n
)
self.slopes = new_slopes
self.offsets = new_offsets
self.starts = new_starts</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="trend_classifier.segment.SegmentList"><code class="flex name class">
<span>class <span class="ident">SegmentList</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt>
<dd>
<div class="desc"><p>List of segments. Each segment group samples with similar trend.</p>
<p>New methods dedicated e.g. to processing od displaying list of segments
can be added here.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class SegmentList(list):
"""List of segments. Each segment group samples with similar trend.
New methods dedicated e.g. to processing od displaying list of segments
can be added here.
"""
def to_dataframe(self):
"""Convert segments to a pandas DataFrame.
Returns:
A pandas DataFrame.
"""
try:
import pandas as pd
except ImportError:
raise ImportError(
"Pandas is not installed. Install it with `pip install pandas`."
)
df = pd.DataFrame([s.__dict__ for s in self])
# reorder columns
df = df[
[
"start",
"stop",
"slope",
"offset",
"slopes_std",
"offsets_std",
"std",
"span",
"reason_for_new_segment",
"slopes",
"offsets",
]
]
return df</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>builtins.list</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="trend_classifier.segment.SegmentList.to_dataframe"><code class="name flex">
<span>def <span class="ident">to_dataframe</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"><p>Convert segments to a pandas DataFrame.</p>
<h2 id="returns">Returns</h2>
<p>A pandas DataFrame.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def to_dataframe(self):
"""Convert segments to a pandas DataFrame.
Returns:
A pandas DataFrame.
"""
try:
import pandas as pd
except ImportError:
raise ImportError(
"Pandas is not installed. Install it with `pip install pandas`."
)
df = pd.DataFrame([s.__dict__ for s in self])
# reorder columns
df = df[
[
"start",
"stop",
"slope",
"offset",
"slopes_std",
"offsets_std",
"std",
"span",
"reason_for_new_segment",
"slopes",
"offsets",
]
]
return df</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.segment.Segment" href="#trend_classifier.segment.Segment">Segment</a></code></h4>
<ul class="">
<li><code><a title="trend_classifier.segment.Segment.offset" href="#trend_classifier.segment.Segment.offset">offset</a></code></li>
<li><code><a title="trend_classifier.segment.Segment.offsets" href="#trend_classifier.segment.Segment.offsets">offsets</a></code></li>
<li><code><a title="trend_classifier.segment.Segment.offsets_std" href="#trend_classifier.segment.Segment.offsets_std">offsets_std</a></code></li>
<li><code><a title="trend_classifier.segment.Segment.reason_for_new_segment" href="#trend_classifier.segment.Segment.reason_for_new_segment">reason_for_new_segment</a></code></li>
<li><code><a title="trend_classifier.segment.Segment.remove_outstanding_windows" href="#trend_classifier.segment.Segment.remove_outstanding_windows">remove_outstanding_windows</a></code></li>
<li><code><a title="trend_classifier.segment.Segment.slope" href="#trend_classifier.segment.Segment.slope">slope</a></code></li>
<li><code><a title="trend_classifier.segment.Segment.slopes" href="#trend_classifier.segment.Segment.slopes">slopes</a></code></li>
<li><code><a title="trend_classifier.segment.Segment.slopes_std" href="#trend_classifier.segment.Segment.slopes_std">slopes_std</a></code></li>
<li><code><a title="trend_classifier.segment.Segment.span" href="#trend_classifier.segment.Segment.span">span</a></code></li>
<li><code><a title="trend_classifier.segment.Segment.start" href="#trend_classifier.segment.Segment.start">start</a></code></li>
<li><code><a title="trend_classifier.segment.Segment.starts" href="#trend_classifier.segment.Segment.starts">starts</a></code></li>
<li><code><a title="trend_classifier.segment.Segment.std" href="#trend_classifier.segment.Segment.std">std</a></code></li>
<li><code><a title="trend_classifier.segment.Segment.stop" href="#trend_classifier.segment.Segment.stop">stop</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="trend_classifier.segment.SegmentList" href="#trend_classifier.segment.SegmentList">SegmentList</a></code></h4>
<ul class="">
<li><code><a title="trend_classifier.segment.SegmentList.to_dataframe" href="#trend_classifier.segment.SegmentList.to_dataframe">to_dataframe</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
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