如何使用Seaborn誤差帶顯示純矩陣[Samples,X

我存盤了一些資料[samples, x_values_toconsider]，然后將其提供給 seaborn。我已經嘗試了很多組合來繪制誤差帶，但它似乎并沒有這樣做，它只是針對 x 的特定特征/值繪制我的每個樣本。所以它最終有samples很多曲線，這不是我想要的。我也嘗試將其安排為資料框，但這也無濟于事。

為什么 seaborn 將每個單獨的樣本繪制為它自己的曲線？我希望它用通常的置信區間聚合它。

自包含的可復制代碼：

#%%
"""
https://seaborn.pydata.org/tutorial/relational.html#relational-tutorial

https://seaborn.pydata.org/examples/errorband_lineplots.html
https://www.youtube.com/watch?v=G3F0EZcW9Ew
https://github.com/knathanieltucker/seaborn-weird-parts/commit/3e571fd8e211ea04b6c9577fd548e7e532507acf
https://github.com/knathanieltucker/seaborn-weird-parts/blob/3e571fd8e211ea04b6c9577fd548e7e532507acf/tsplot.ipynb
"""
from collections import OrderedDict

import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
from pandas import DataFrame
import pandas as pd

print(sns)

np.random.seed(22)
sns.set(color_codes=True)

# the number of x values to consider in a given range e.g. [0,1] will sample 10 raw features x sampled at in [0,1] interval
num_x: int = 10
# the repetitions for each x feature value e.g. multiple measurements for sample x=0.0 up to x=1.0 at the end
rep_per_x: int = 5
total_size_data_set: int = num_x * rep_per_x
print(f'{total_size_data_set=}')
# - create fake data set
# only consider 10 features from 0 to 1
x = np.linspace(start=0.0, stop=1.0, num=num_x)
# to introduce fake variation add uniform noise to each feature and pretend each one is a new observation for that feature
noise_uniform: np.ndarray = np.random.rand(rep_per_x, num_x)
# same as above but have the noise be the same for each x (thats what the 1 means)
noise_normal: np.ndarray = np.random.randn(rep_per_x, 1)
# signal function
sin_signal: np.ndarray = np.sin(x)
# [rep_per_x, num_x]
data: np.ndarray = sin_signal   noise_uniform   noise_normal

# data_od: OrderedDict = OrderedDict()
# for idx_x in range(num_x):
#     # [rep_per_x, 1]
#     samples_for_x: np.ndarray = data[:, idx_x]
#     data_od[str(x[idx_x])] = samples_for_x
#
# data_df = pd.DataFrame(data_od)
# data = data_df

print(data)
ax = sns.lineplot(data=data)
# ax = sns.lineplot(data=data, err_style='band')
# ax = sns.lineplot(data=data, err_style='bars')
# ax = sns.lineplot(data=data, ci='sd', err_style='band')
# ax = sns.lineplot(data=data, ci='sd', err_style='bars')
 
# ax = sns.relplot(data=data)

plt.show()

#%%
"""
https://seaborn.pydata.org/examples/errorband_lineplots.html
"""

# import numpy as np
# import seaborn as sns
# from matplotlib import pyplot as plt
# from pandas import DataFrame
#
# fmri: DataFrame = sns.load_dataset("fmri")
# print(fmri)
# sns.lineplot(x="timepoint", y="signal",  hue="region", style="event", data=fmri)
# plt.show()

錯誤的情節產生

如何使用 Seaborn 誤差帶顯示純矩陣 [Samples, X_Range] 的誤差帶？

但希望有這樣的事情（并且帶有自己的錯誤帶的附加行會更好，因為我有很多矩陣！）

如何使用 Seaborn 誤差帶顯示純矩陣 [Samples, X_Range] 的誤差帶？

喜歡：

如何使用 Seaborn 誤差帶顯示純矩陣 [Samples, X_Range] 的誤差帶？

請注意，我不想預先計算 stds 并繪制波段，因此大多數問題/答案對我不起作用。

What puzzles me is that when I pass it the fmri data it works but not when I pass it my matrix of observations for each x value...

請注意，在沒有明確x給出的教程示例中，使用 Pandas 資料幀形式的資料或資料幀中的一列資料。在這種情況下，資料幀的索引可用于 x 值。

如何使用 Seaborn 誤差帶顯示純矩陣 [Samples, X_Range] 的誤差帶？

或者您可以連接所有值。使用資料框可能會更容易查看正在發生的事情，并且還會自動使用列名稱來標記軸和圖例。

ax = sns.lineplot(x=np.tile(x, rep_per_x1   rep_per_x2),
                  y=np.concatenate([data1.ravel(), data2.ravel()]),
                  hue=np.concatenate([np.repeat(1, num_x * rep_per_x1), np.repeat(2, num_x * rep_per_x2)]),
                  palette=['crimson', 'dodgerblue'])

uj5u.com熱心網友回復：

為了為未來的用戶提供一個帶有可重用代碼的完整示例：

def plot_seaborn_curve_with_x_values_y_values(x: np.ndarray, y: np.ndarray,
                                              xlabel: str, ylabel: str,
                                              title: str,
                                              curve_label: Optional[str] = None,
                                              err_style: str = 'band',
                                              marker: Optional[str] = 'x',
                                              dashes: bool = False,
                                              show: bool = False
                                              ):
    """
    Given a list of x values in a range with num_x_values number of x values in that range and the corresponding samples
    for each specific x value (so [samples_per_x] for each value of x giving in total a matrix of size
    [samples_per_x, num_x_values]), plot aggregates of them with error bands.
    Note that the main assumption is that each x value has a number of y values corresponding to it (likely due to noise
    for example).


    Note:
        - if you want string in the specific x axis point do
        sns.lineplot(x=np.tile([f'Layer{i}' for i in range(1, num_x 1)], rep_per_x),...) assuming the x values are the
        layers. https://stackoverflow.com/questions/69888181/how-to-show-error-bands-for-pure-matrices-samples-x-range-with-seaborn-error/69889619?noredirect=1#comment123544763_69889619
        - note you can all this function multiple times to insert different curves to your plot.
        - note its recommended call show only for if you have one or at the final curve you want to add.
        - if you want bands and bars it might work if you call this function twice but using the bar and band argument
        for each call.

    ref:
        - https://stackoverflow.com/questions/69888181/how-to-show-error-bands-for-pure-matrices-samples-x-range-with-seaborn-error/69889619?noredirect=1#comment123544763_69889619

    :param x: [num_x_values]
    :param y: [samples_per_x, num_x_values]
    :param xlabel:
    :param ylabel:
    :param title:
    :param curve_label:
    :param err_style:
    :param marker:
    :param dashes:
    :param show:
    :return:
    """
    import seaborn as sns
    samples_per_x: int = y.shape[0]
    num_x_values: int = x.shape[0]
    assert(num_x_values == y.shape[1]), f'We are plotting aggreagates for one specific value of x multple values of y,' \
                                        f'thus we need to have the same number of x values match in the x axis.'

    # - since seaborn expects a an x value paired with it's y value, let's flatten the y's and make sure the corresponding
    # x value is aligned with it's y value [num_x_values * samples_per_x]
    x: np.ndarray = np.tile(x, samples_per_x)  # np.tile = Construct an array by repeating A the number of times given by reps.
    assert (x.shape == (num_x_values * samples_per_x,))
    y: np.ndarray = np.ravel(y)  # flatten the y's to match the x values to have the x to it's corresponding y
    assert (y.shape == (num_x_values * samples_per_x,))
    assert (x.shape == y.shape)

    # - plot
    ax = sns.lineplot(x=x, y=y, err_style=err_style, label=curve_label, marker=marker, dashes=dashes)
    plt.title(title)
    plt.xlabel(xlabel)
    plt.ylabel(ylabel)
    if show:
        plt.show()

例如

def plot_seaborn_curve_with_x_values_y_values_test():
    # the number of x values to consider in a given range e.g. [0,1] will sample 10 raw features x sampled at in [0,1] interval
    num_x: int = 10
    # the repetitions for each x feature value e.g. multiple measurements for sample x=0.0 up to x=1.0 at the end
    rep_per_x: int = 5
    total_size_data_set: int = num_x * rep_per_x
    print(f'{total_size_data_set=}')
    # - create fake data set
    # only consider 10 features from 0 to 1
    x = np.linspace(start=0.0, stop=1.0, num=num_x)

    # to introduce fake variation add uniform noise to each feature and pretend each one is a new observation for that feature
    noise_uniform: np.ndarray = np.random.rand(rep_per_x, num_x)
    # same as above but have the noise be the same for each x (thats what the 1 means)
    noise_normal: np.ndarray = np.random.randn(rep_per_x, 1)
    # signal function
    sin_signal: np.ndarray = np.sin(x)
    cos_signal: np.ndarray = np.cos(x)
    # [rep_per_x, num_x]
    y1: np.ndarray = sin_signal   noise_uniform   noise_normal
    y2: np.ndarray = cos_signal   noise_uniform   noise_normal

    plot_seaborn_curve_with_x_values_y_values(x=x, y=y1, xlabel='x', ylabel='y', title='Sin vs Cos')
    plot_seaborn_curve_with_x_values_y_values(x=x, y=y2, xlabel='x', ylabel='y', title='Sin vs Cos')
    plt.show()

輸出：

如何使用 Seaborn 誤差帶顯示純矩陣 [Samples, X_Range] 的誤差帶？

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標籤：python matplotlib seaborn

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如何使用Seaborn誤差帶顯示純矩陣[Samples,X_Range]的誤差帶？