DjF(dZddlZddlmZ ddZy)zE Bland-Altman mean-difference plots Author: Joses Ho License: BSD-3 N)utilscvtj|\}}t|t|k7r td|dkrtd|dt j ||gd}||z } t j | } t j | d} |xsi}d|vrd|d<|xsi}|xsi}||fD]} d| vrd | d<d | vsd | d <d |vrd  d <d |vrd d <|j|| fi||j| fi||jdt j| dddddd|dkDrd|z| z} |j| | z | | z|| z}| |z }| |z}t||gD]\}}|j|fi||jd|d|dddddd|jd|d|dddddn"|dk(rd | z} |j| | z | | z|jd!d"#|jd$d"#|jd%&|j!|S)'a Construct a Tukey/Bland-Altman Mean Difference Plot. Tukey's Mean Difference Plot (also known as a Bland-Altman plot) is a graphical method to analyze the differences between two methods of measurement. The mean of the measures is plotted against their difference. For more information see https://en.wikipedia.org/wiki/Bland-Altman_plot Parameters ---------- m1 : array_like A 1-d array. m2 : array_like A 1-d array. sd_limit : float The limit of agreements expressed in terms of the standard deviation of the differences. If `md` is the mean of the differences, and `sd` is the standard deviation of those differences, then the limits of agreement that will be plotted are md +/- sd_limit * sd. The default of 1.96 will produce 95% confidence intervals for the means of the differences. If sd_limit = 0, no limits will be plotted, and the ylimit of the plot defaults to 3 standard deviations on either side of the mean. ax : AxesSubplot If `ax` is None, then a figure is created. If an axis instance is given, the mean difference plot is drawn on the axis. scatter_kwds : dict Options to to style the scatter plot. Accepts any keywords for the matplotlib Axes.scatter plotting method mean_line_kwds : dict Options to to style the scatter plot. Accepts any keywords for the matplotlib Axes.axhline plotting method limit_lines_kwds : dict Options to to style the scatter plot. Accepts any keywords for the matplotlib Axes.axhline plotting method Returns ------- Figure If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. References ---------- Bland JM, Altman DG (1986). "Statistical methods for assessing agreement between two methods of clinical measurement" Examples -------- Load relevant libraries. >>> import statsmodels.api as sm >>> import numpy as np >>> import matplotlib.pyplot as plt Making a mean difference plot. >>> # Seed the random number generator. >>> # This ensures that the results below are reproducible. >>> np.random.seed(9999) >>> m1 = np.random.random(20) >>> m2 = np.random.random(20) >>> f, ax = plt.subplots(1, figsize = (8,5)) >>> sm.graphics.mean_diff_plot(m1, m2, ax = ax) >>> plt.show() .. plot:: plots/graphics-mean_diff_plot.py z'm1 does not have the same length as m2.rz sd_limit (z) is less than 0.)axisscolorgray linewidthr linestylez--:z mean diff: )Gz?g?rightcenterz axes fraction)xyhorizontalalignmentverticalalignmentfontsizexycoordsg?-z SD: z0.2g)rgQ?bottom+)rgq= ףp?)rrrr Difference)rMeans ) labelsize)r create_mpl_axlen ValueErrornpmeanstdscatteraxhlineannotateroundset_ylim enumerate set_ylabel set_xlabel tick_params tight_layout)m1m2sd_limitax scatter_kwdsmean_line_kwdslimit_lines_kwdsfigmeansdiffs mean_diffstd_diffkwds half_ylimlimit_of_agreementlowerupperjlims bC:\Crop_Prediction\Backend\crop-ai-system\venv\Lib\site-packages\statsmodels/graphics/agreement.pymean_diff_plotrE sR!!"%GC 2w#b'BCC!|:hZ/@ABB GGRH1 %E GEIvve!$H%2L , S#)rN'-2!12" $ "DM d " !D  " .( [**[BJJue,|,BJJy+N+KK,rxx 1567$+"*( *!|8^x/  I ) ) +%0..../ 0FAs BJJs /. / 0 azuTl3#(/&.,  . azuTl3#(/,  . QL  I ) ) +MM,M,MM'BM'NNRN  J)g\(\?NNNN)__doc__numpyr$rrErFrDrKs"AE9=MrF