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HowToSD-ComfyUI-Data-Analysis/modules/matplotlib/mpl_line.py
T

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2.3 KiB
Python

from typing import Any, Dict
from matplotlib import pyplot as plt
from io import StringIO
import pandas as pd
from .common import common_input_types
from .util import plot_post_steps
from ..pandas_wrapper.utils import column_label_string_to_target_type
class MPLLine:
"""
MPL Line Plot:
Generates a line plot from a pandas DataFrame.
category: Plot
"""
@classmethod
def INPUT_TYPES(cls) -> Dict[str, Any]:
"""
Defines the input types for the `line_plot` function.
Returns:
Dict[str, Any]: A dictionary specifying required input types.
"""
return common_input_types()
RETURN_TYPES: tuple = ("IMAGE",)
FUNCTION: str = "line_plot"
CATEGORY: str = "Data Analysis"
def line_plot(self,
dataframe: pd.DataFrame,
x_column_name: str,
y_column_name: str,
title: str,
x_axis_label: str,
y_axis_label: str,
x_tick_as_int: bool) -> tuple:
"""
Generates a line plot from a pandas DataFrame.
Args:
dataframe (DataFrame): The DataFrame.
x_column_name (str): The column name for the x-axis.
y_column_name (str): The column name for the y-axis.
title (str): The title of the plot.
x_axis_label (str): The label for the x-axis data.
y_axis_label (str): The label for the y-axis data.
x_tick_as_int (bool): If set, use integers for x ticks. For example,
if ticks show floating point numbers for years, e.g. 2002.5,
setting this flag will show integer values instead.
Returns:
tuple: A tuple containing the image tensor representation of the plot.
"""
df = dataframe
x_column_name = column_label_string_to_target_type(df, x_column_name)
y_column_name = column_label_string_to_target_type(df, y_column_name)
# Create the plot
fig, ax = plt.subplots()
ax.plot(df[x_column_name], df[y_column_name])
return plot_post_steps(
fig,
ax,
plt,
title,
x_axis_label,
y_axis_label,
x_tick_as_int)