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chore: remove debug artifacts, unused imports, and fix docstring typos #214

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Merged
ChrisMarsden833 merged 2 commits into from
Jul 25, 2026
Merged

chore: remove debug artifacts, unused imports, and fix docstring typos #214

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c4cb005
fix: remove debug artifacts, unused imports, and fix docstring typos
Jul 9, 2026
48f3ed7
Merge branch 'nodates' into fix/cleanup-imports-and-artifacts
RussellSB Jul 9, 2026
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5 changes: 1 addition & 4 deletions pytrendy/detect_trends.py
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Expand Up @@ -3,15 +3,12 @@
import warnings
import pandas as pd
import numpy as np
import warnings
from difflib import get_close_matches
from .process_signals import process_signals
from .post_processing.segments_get import get_segments
from .post_processing.segments_refine import refine_segments
from .post_processing.segments_analyse import analyse_segments
from .io.plot_pytrendy import plot_pytrendy
from .io.results_pytrendy import PyTrendyResults
from datetime import date

def detect_trends(df: pd.DataFrame,
value_col: str,
Expand Down Expand Up @@ -43,7 +40,7 @@ def detect_trends(df: pd.DataFrame,
value_col (str):
Name of the column containing the primary signal to analyse for trend detection.
date_col (str|None):
Historically, this represents the name of the column containing timestamps, but pytrendy now allows for indexes of any type to be used. In general, this column represents a human readable reference to the x-position of the sequence. Normally this would be a date or timestamp, but any unique set of values could be used. Default is 'None', in which case an integer sequence will be generated and used to idenify segmenets.
Historically, this represents the name of the column containing dates, but pytrendy now allows for indexes of any type to be used. In general, this column represents a human readable reference to the x-position of the sequence. Normally this would be a date or timestamp, but any unique set of values could be used. Default is 'None', in which case an integer sequence will be generated and used to identify segments.
plot (bool, optional):
If `True`, generates a matplotlib plot showing the detected trend segments over the original signal.
Defaults to `True`.
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1 change: 0 additions & 1 deletion tests/conftest.py
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Expand Up @@ -6,7 +6,6 @@
"""

import pandas as pd
import math

def assert_segments_match(detected_segments, expected_segments):
"""
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1 change: 0 additions & 1 deletion tests/test_core_cases.py
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Expand Up @@ -8,7 +8,6 @@

import pytest
import pytrendy as pt
import pandas as pd
from conftest import assert_segments_match


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