101 lines
3.7 KiB
Python
101 lines
3.7 KiB
Python
from typing import Any, Dict
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from matplotlib import pyplot as plt
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import seaborn as sns
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import pandas as pd
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from .common import common_input_types
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from .util import plot_post_steps
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from .util import set_up_sns_style_palette
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from .util import process_y_columns_and_labels
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from ..pandas_wrapper.utils import column_label_string_to_target_type
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class SNSLine:
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"""
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SNS Line Plot:
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Generates a line plot from a pandas DataFrame using Seaborn.
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For plotting multiple lines, enter a comma-separated column labels in y_column_name.
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If you want to display a legend for each line, enter a comma-separated legend label
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in legend label.
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The `style` parameter can be chosen from:
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* darkgrid (default) – Grid with a dark background.
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* whitegrid – Grid with a white background.
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* dark – Dark background, no grid.
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* white – White background, no grid.
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* ticks – White background with tick marks.
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The `palette` parameter can be chosen from:
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* deep (default) – High contrast, good for multiple categories.
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* muted – Softer colors, easier on the eyes.
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* pastel – Light colors, suitable for minimalistic styles.
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* bright – Vibrant and highly saturated colors.
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* dark – Darker, high-contrast colors.
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* colorblind – Optimized for color vision deficiencies.
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category: Plot
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"""
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@classmethod
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def INPUT_TYPES(cls) -> Dict[str, Any]:
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"""
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Defines the input types for the `line_plot` function.
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Returns:
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Dict[str, Any]: A dictionary specifying required input types.
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"""
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return common_input_types()
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RETURN_TYPES: tuple = ("IMAGE",)
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FUNCTION: str = "line_plot"
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CATEGORY: str = "Data Analysis"
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def line_plot(self,
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dataframe: pd.DataFrame,
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x_column_name: str,
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y_column_name: str,
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title: str,
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x_axis_label: str,
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y_axis_label: str,
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legend_label: str,
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x_tick_as_int: bool,
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style: str=None,
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palette: str=None) -> tuple:
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"""
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Generates a line plot from a pandas DataFrame.
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Args:
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dataframe (DataFrame): The DataFrame.
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x_column_name (str): The column name for the x-axis.
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y_column_name (str): The column name for the y-axis.
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title (str): The title of the plot.
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x_axis_label (str): The label for the x-axis data.
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y_axis_label (str): The label for the y-axis data.
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legend_label (str): The legend label of the plot.
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x_tick_as_int (bool): If set, use integers for x ticks. For example,
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if ticks show floating point numbers for years, e.g. 2002.5,
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setting this flag will show integer values instead.
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style (str): Style of the plot
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palette (str): Palette of the plot
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Returns:
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tuple: A tuple containing the image tensor representation of the plot.
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"""
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df = dataframe
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set_up_sns_style_palette(style, palette)
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x_column_name = column_label_string_to_target_type(df, x_column_name)
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y_columns, labels = process_y_columns_and_labels(df, y_column_name, legend_label)
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fig, ax = plt.subplots()
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for i in range(len(y_columns)):
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sns.lineplot(x=df[x_column_name], y=df[y_columns[i]], ax=ax, label=labels[i])
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return plot_post_steps(
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fig,
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ax,
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plt,
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title,
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x_axis_label,
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y_axis_label,
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x_tick_as_int)
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