动态地将两个 y 轴与相同数量的代码对齐为 0
Aligning two y-axes at 0 with the same number of tickers dynamically
我能够在同一个图上绘制一个 seaborn barplot 和 lineplot，轴在 0 处对齐，并且代码数量相同。但是，我无法动态切断任何空白区域。该图的代码可以在下面找到。我想要结束的是我附上的同一张图，但代码以 -49.3 和 -13.63 结尾（以去除任何空格）。我不想对其进行硬编码，它需要是动态的，以便能够针对任何输入数据进行调整。此外，两个轴仍然需要在 0 处对齐并具有相同数量的代码。
import numpy as np
import pandas as pd
import math
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.dates as md
import matplotlib.ticker as ticker
from datetime import datetime, timedelta
# create dataframes that will be used
date_today = datetime.now()
days = pd.date_range(date_today, date_today + timedelta(1000), freq='D')
np.random.seed(seed=1111)
data_a = np.random.randint(-10, high=20, size=len(days))
data_b = np.random.randint(-30, high=70, size=len(days))
a = pd.DataFrame({'date': days, 'a': data_a})
a = a.set_index('date')
b = pd.DataFrame({'date': days, 'b': data_b})
b = b.set_index('date')
# result dataframe which will be used for the plotting
result = pd.concat([a, b], axis=1)
# make sure only the dates are being used
result = result.reset_index()
result['date'] = result['date'].dt.date
result = result.set_index('date')
# set- up for the plot
matplotlib.rc_file_defaults()
ax1 = sns.set_style(style=None, rc=None)
fig, ax1 = plt.subplots(figsize=(12,6))
ax2 = ax1.twinx()
# bar plot
result_date = result.copy()
result_date = result_date.reset_index()
b_plot = sns.barplot(data = result_date, x=result_date.iloc[:, 0], y=result_date.iloc[:, 2], ax=ax1)
# pointplot
a_plot = sns.pointplot(data=result, x=result.index, y=result.iloc[:, 0], color="black", ax=ax2, markers = 'o', scale=0.4)
# set the x tickers to be those of the bar plot
ax1.set_xticks(np.arange(len(result_date)))
ax1.set_xticklabels(result_date.date.apply(lambda x: str(x.year)))
ax1.xaxis.set_major_locator(ticker.AutoLocator())
# to align the axes and make them start at 0
max1 = np.nanmax(np.abs(ax1.get_ybound())) # in case you have nan values
max2 = np.nanmax(np.abs(ax2.get_ybound()))
nticks = 7 #or other odd number
ax1.set_yticks(np.linspace(-max1, max1, nticks))
ax2.set_yticks(np.linspace(-max2, max2, nticks))
编辑： 这是我尝试使用其他帖子的答案的另一种方法，但它仍然没有达到我想要的效果：
import numpy as np
import pandas as pd
import math
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.dates as md
import matplotlib.ticker as ticker
from datetime import datetime, timedelta
# create dataframes that will be used
date_today = datetime.now()
days = pd.date_range(date_today, date_today + timedelta(1000), freq='D')
np.random.seed(seed=1111)
data_a = np.random.randint(-10, high=20, size=len(days))
data_b = np.random.randint(-30, high=70, size=len(days))
a = pd.DataFrame({'date': days, 'a': data_a})
a = a.set_index('date')
b = pd.DataFrame({'date': days, 'b': data_b})
b = b.set_index('date')
# result dataframe which will be used for the plotting
result = pd.concat([a, b], axis=1)
# make sure only the dates are being used
result = result.reset_index()
result['date'] = result['date'].dt.date
result = result.set_index('date')
# set- up for the plot
matplotlib.rc_file_defaults()
ax1 = sns.set_style(style=None, rc=None)
fig, ax1 = plt.subplots(figsize=(12,6))
ax2 = ax1.twinx()
# bar plot
result_date = result.copy()
result_date = result_date.reset_index()
b_plot = sns.barplot(data = result_date, x=result_date.iloc[:, 0], y=result_date.iloc[:, 2], ax=ax1)
# pointplot
a_plot = sns.pointplot(data=result, x=result.index, y=result.iloc[:, 0], color="black", ax=ax2, markers = 'o', scale=0.4)
# set the x tickers to be those of the bar plot
ax1.set_xticks(np.arange(len(result_date)))
ax1.set_xticklabels(result_date.date.apply(lambda x: str(x.year)))
ax1.xaxis.set_major_locator(ticker.AutoLocator())
ax1_ylims = ax1.axes.get_ylim() # Find y-axis limits set by the plotter
ax1_yratio = ax1_ylims[0] / ax1_ylims[1] # Calculate ratio of lowest limit to highest limit
ax2_ylims = ax2.axes.get_ylim() # Find y-axis limits set by the plotter
ax2_yratio = ax2_ylims[0] / ax2_ylims[1] # Calculate ratio of lowest limit to highest limit
# If the plot limits ratio of plot 1 is smaller than plot 2, the first data set has
# a wider range range than the second data set. Calculate a new low limit for the
# second data set to obtain a similar ratio to the first data set.
# Else, do it the other way around
if ax1_yratio < ax2_yratio:
ax2.set_ylim(bottom = ax2_ylims[1]*ax1_yratio)
nticks = len(ax1.yaxis.get_ticklabels()) # number of ticks for the wider axis
ax2.set_yticks(np.linspace(ax2.get_ylim()[0], ax2.get_ylim()[-1], nticks))
else:
ax1.set_ylim(bottom = ax1_ylims[1]*ax2_yratio)
nticks = len(ax2.yaxis.get_ticklabels()) # number of ticks for the wider axis
ax1.set_yticks(np.linspace(ax1.get_ylim()[0], ax1.get_ylim()[-1], nticks))
怎么样：
import numpy as np
import pandas as pd
import math
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.dates as md
import matplotlib.ticker as ticker
from datetime import datetime, timedelta
# create dataframes that will be used
date_today = datetime.now()
days = pd.date_range(date_today, date_today + timedelta(1000), freq='D')
np.random.seed(seed=1111)
data_a = np.random.randint(-10, high=20, size=len(days))
data_b = np.random.randint(-30, high=70, size=len(days))
a = pd.DataFrame({'date': days, 'a': data_a})
a = a.set_index('date')
b = pd.DataFrame({'date': days, 'b': data_b})
b = b.set_index('date')
# result dataframe which will be used for the plotting
result = pd.concat([a, b], axis=1)
# make sure only the dates are being used
result = result.reset_index()
result['date'] = result['date'].dt.date
result = result.set_index('date')
# set- up for the plot
matplotlib.rc_file_defaults()
sns.set_style(style=None, rc=None)
fig, ax1 = plt.subplots(figsize=(12,6))
ax2 = ax1.twinx()
# bar plot
result_date = result.reset_index()
b_plot = sns.barplot(data = result_date, x=result_date.iloc[:, 0], y=result_date.iloc[:, 2], ax=ax1)
# pointplot
a_plot = sns.pointplot(data=result, x=result.index, y=result.iloc[:, 0], color="black", ax=ax2, markers = 'o', scale=0.4)
# set the x tickers to be those of the bar plot
ax1.set_xticks(np.arange(len(result_date)))
ax1.set_xticklabels(result_date.date.apply(lambda x: str(x.year)))
ax1.xaxis.set_major_locator(ticker.AutoLocator())
# get the original ylims (before setting the yticks)
ax1_ylims = ax1.get_ylim()
ax1_yratio = ax1_ylims[0] / ax1_ylims[1] # Calculate ratio of lowest limit to highest limit
ax2_ylims = ax2.get_ylim()
ax2_yratio = ax2_ylims[0] / ax2_ylims[1] # Calculate ratio of lowest limit to highest limit
# to align the axes and make them start at 0
max1 = np.nanmax(np.abs(ax1.get_ybound())) # in case you have nan values
max2 = np.nanmax(np.abs(ax2.get_ybound()))
nticks = 7 #or other odd number
ax1.set_yticks(np.linspace(-max1, max1, nticks))
ax2.set_yticks(np.linspace(-max2, max2, nticks))
# If the plot limits ratio of plot 1 is smaller than plot 2, the first data set has
# a wider range range than the second data set. Calculate a new low limit for the
# second data set to obtain a similar ratio to the first data set.
# Else, do it the other way around
if ax1_yratio < ax2_yratio:
ax2.set_ylim(bottom=ax2_ylims[1] * ax1_yratio)
ax1.set_ylim(ax1_ylims)
else:
ax1.set_ylim(bottom=ax1_ylims[1] * ax2_yratio)
ax2.set_ylim(ax2_ylims)
plt.show()
这是我提出的解决方案，适用于不同范围的数据集。它还确保代码之间的间距相等。有什么办法可以更简洁一点，请告诉我。
import numpy as np
import pandas as pd
import math
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.dates as md
import matplotlib.ticker as ticker
from datetime import datetime, timedelta
# create dataframes that will be used
date_today = datetime.now()
days = pd.date_range(date_today, date_today + timedelta(1000), freq='D')
np.random.seed(seed=1111)
data_a = np.random.randint(-9, high=30, size=len(days))
data_b = np.random.randint(-20, high=70, size=len(days))
a = pd.DataFrame({'date': days, 'a': data_a})
a = a.set_index('date')
b = pd.DataFrame({'date': days, 'b': data_b})
b = b.set_index('date')
# result dataframe which will be used for the plotting
result = pd.concat([a, b], axis=1)
# make sure only the dates are being used
result = result.reset_index()
result['date'] = result['date'].dt.date
result = result.set_index('date')
# set- up for the plot
matplotlib.rc_file_defaults()
sns.set_style(style=None, rc=None)
fig, ax1 = plt.subplots(figsize=(12,6))
ax2 = ax1.twinx()
# bar plot
result_date = result.reset_index()
b_plot = sns.barplot(data = result_date, x=result_date.iloc[:, 0], y=result_date.iloc[:, 2], ax=ax1)
# pointplot
a_plot = sns.pointplot(data=result, x=result.index, y=result.iloc[:, 0], color="black", ax=ax2, markers = 'o', scale=0.4)
# set the x tickers to be those of the bar plot
ax1.set_xticks(np.arange(len(result_date)))
ax1.set_xticklabels(result_date.date.apply(lambda x: str(x.year)))
ax1.xaxis.set_major_locator(ticker.AutoLocator())
# get the max and min values for both plot datasets
max_value_1 = np.max(result_date.iloc[:, 2])
min_value_1 = np.min(result_date.iloc[:, 2])
max_value_2 = np.max(result.iloc[:, 0])
min_value_2 = np.min(result.iloc[:, 0])
# to align the axes and make them start at 0
max1 = np.nanmax(np.abs(ax1.get_ybound())) # in case you have nan values
max2 = np.nanmax(np.abs(ax2.get_ybound()))
nticks = 7 #or other odd number
ax1.set_yticks(np.linspace(-max1, max1, nticks))
ax2.set_yticks(np.linspace(-max2, max2, nticks))
# y axis value arrays going from -ve to +ve values
ax1_y_ticks = ax1.get_yticks()
ax2_y_ticks = ax2.get_yticks()
# create empty lists for the indexes of the upper and lower elements of the two y-ranges
ax1_lower_elements = [] # pick last value (first ticker just below the lowest value in the range)
ax1_upper_elements = [] # pick first value (first ticker just above the highest value in the range)
ax2_lower_elements = [] # pick last value
ax2_upper_elements = [] # pick first value
for i in range(len(ax1_y_ticks)):
if ax1_y_ticks[i] <= min_value_1:
ax1_lower_elements.append(i)
if ax1_y_ticks[i] >= max_value_1:
ax1_upper_elements.append(i)
for i in range(len(ax2_y_ticks)):
if ax2_y_ticks[i] <= min_value_2:
ax2_lower_elements.append(i)
if ax2_y_ticks[i] >= max_value_2:
ax2_upper_elements.append(i)
# get the indexs for the upper and lower limits of the y-axes
ax1_lower_ticker_element = ax1_lower_elements[-1]
ax1_upper_ticker_element = ax1_upper_elements[0]
ax2_lower_ticker_element = ax2_lower_elements[-1]
ax2_upper_ticker_element = ax2_upper_elements[0]
# determine which upper and lower indexes should be used
if ax1_lower_ticker_element <= ax2_lower_ticker_element:
lower_ticker_element = ax1_lower_ticker_element
else:
lower_ticker_element = ax2_lower_ticker_element
if ax1_upper_ticker_element <= ax2_upper_ticker_element:
upper_ticker_element = ax2_upper_ticker_element
else:
upper_ticker_element = ax1_upper_ticker_element
# the indexes to be used as a list
new_y_ticker_elements = []
for i in range (len(ax1_y_ticks)):
if ((i >= lower_ticker_element) & (i <= upper_ticker_element)):
new_y_ticker_elements.append(i)
# setting y-axis for ax1
ax1_rng = []
for i in range(len(new_y_ticker_elements)):
ax1_rng.append(ax1_y_ticks[new_y_ticker_elements[i]])
ax1.set_yticks(ax1_rng)
ax1.set_ylim(bottom=ax1_rng[0], top=ax1_rng[-1])
# setting y-axis for ax2
ax2_rng = []
for i in range(len(new_y_ticker_elements)):
ax2_rng.append(ax2_y_ticks[new_y_ticker_elements[i]])
ax2.set_yticks(ax2_rng)
ax2.set_ylim(bottom=ax2_rng[0], top=ax2_rng[-1])
这是最后的情节
我能够在同一个图上绘制一个 seaborn barplot 和 lineplot，轴在 0 处对齐，并且代码数量相同。但是，我无法动态切断任何空白区域。该图的代码可以在下面找到。我想要结束的是我附上的同一张图，但代码以 -49.3 和 -13.63 结尾（以去除任何空格）。我不想对其进行硬编码，它需要是动态的，以便能够针对任何输入数据进行调整。此外，两个轴仍然需要在 0 处对齐并具有相同数量的代码。
import numpy as np
import pandas as pd
import math
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.dates as md
import matplotlib.ticker as ticker
from datetime import datetime, timedelta
# create dataframes that will be used
date_today = datetime.now()
days = pd.date_range(date_today, date_today + timedelta(1000), freq='D')
np.random.seed(seed=1111)
data_a = np.random.randint(-10, high=20, size=len(days))
data_b = np.random.randint(-30, high=70, size=len(days))
a = pd.DataFrame({'date': days, 'a': data_a})
a = a.set_index('date')
b = pd.DataFrame({'date': days, 'b': data_b})
b = b.set_index('date')
# result dataframe which will be used for the plotting
result = pd.concat([a, b], axis=1)
# make sure only the dates are being used
result = result.reset_index()
result['date'] = result['date'].dt.date
result = result.set_index('date')
# set- up for the plot
matplotlib.rc_file_defaults()
ax1 = sns.set_style(style=None, rc=None)
fig, ax1 = plt.subplots(figsize=(12,6))
ax2 = ax1.twinx()
# bar plot
result_date = result.copy()
result_date = result_date.reset_index()
b_plot = sns.barplot(data = result_date, x=result_date.iloc[:, 0], y=result_date.iloc[:, 2], ax=ax1)
# pointplot
a_plot = sns.pointplot(data=result, x=result.index, y=result.iloc[:, 0], color="black", ax=ax2, markers = 'o', scale=0.4)
# set the x tickers to be those of the bar plot
ax1.set_xticks(np.arange(len(result_date)))
ax1.set_xticklabels(result_date.date.apply(lambda x: str(x.year)))
ax1.xaxis.set_major_locator(ticker.AutoLocator())
# to align the axes and make them start at 0
max1 = np.nanmax(np.abs(ax1.get_ybound())) # in case you have nan values
max2 = np.nanmax(np.abs(ax2.get_ybound()))
nticks = 7 #or other odd number
ax1.set_yticks(np.linspace(-max1, max1, nticks))
ax2.set_yticks(np.linspace(-max2, max2, nticks))
编辑： 这是我尝试使用其他帖子的答案的另一种方法，但它仍然没有达到我想要的效果：
import numpy as np
import pandas as pd
import math
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.dates as md
import matplotlib.ticker as ticker
from datetime import datetime, timedelta
# create dataframes that will be used
date_today = datetime.now()
days = pd.date_range(date_today, date_today + timedelta(1000), freq='D')
np.random.seed(seed=1111)
data_a = np.random.randint(-10, high=20, size=len(days))
data_b = np.random.randint(-30, high=70, size=len(days))
a = pd.DataFrame({'date': days, 'a': data_a})
a = a.set_index('date')
b = pd.DataFrame({'date': days, 'b': data_b})
b = b.set_index('date')
# result dataframe which will be used for the plotting
result = pd.concat([a, b], axis=1)
# make sure only the dates are being used
result = result.reset_index()
result['date'] = result['date'].dt.date
result = result.set_index('date')
# set- up for the plot
matplotlib.rc_file_defaults()
ax1 = sns.set_style(style=None, rc=None)
fig, ax1 = plt.subplots(figsize=(12,6))
ax2 = ax1.twinx()
# bar plot
result_date = result.copy()
result_date = result_date.reset_index()
b_plot = sns.barplot(data = result_date, x=result_date.iloc[:, 0], y=result_date.iloc[:, 2], ax=ax1)
# pointplot
a_plot = sns.pointplot(data=result, x=result.index, y=result.iloc[:, 0], color="black", ax=ax2, markers = 'o', scale=0.4)
# set the x tickers to be those of the bar plot
ax1.set_xticks(np.arange(len(result_date)))
ax1.set_xticklabels(result_date.date.apply(lambda x: str(x.year)))
ax1.xaxis.set_major_locator(ticker.AutoLocator())
ax1_ylims = ax1.axes.get_ylim() # Find y-axis limits set by the plotter
ax1_yratio = ax1_ylims[0] / ax1_ylims[1] # Calculate ratio of lowest limit to highest limit
ax2_ylims = ax2.axes.get_ylim() # Find y-axis limits set by the plotter
ax2_yratio = ax2_ylims[0] / ax2_ylims[1] # Calculate ratio of lowest limit to highest limit
# If the plot limits ratio of plot 1 is smaller than plot 2, the first data set has
# a wider range range than the second data set. Calculate a new low limit for the
# second data set to obtain a similar ratio to the first data set.
# Else, do it the other way around
if ax1_yratio < ax2_yratio:
ax2.set_ylim(bottom = ax2_ylims[1]*ax1_yratio)
nticks = len(ax1.yaxis.get_ticklabels()) # number of ticks for the wider axis
ax2.set_yticks(np.linspace(ax2.get_ylim()[0], ax2.get_ylim()[-1], nticks))
else:
ax1.set_ylim(bottom = ax1_ylims[1]*ax2_yratio)
nticks = len(ax2.yaxis.get_ticklabels()) # number of ticks for the wider axis
ax1.set_yticks(np.linspace(ax1.get_ylim()[0], ax1.get_ylim()[-1], nticks))
怎么样：
import numpy as np
import pandas as pd
import math
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.dates as md
import matplotlib.ticker as ticker
from datetime import datetime, timedelta
# create dataframes that will be used
date_today = datetime.now()
days = pd.date_range(date_today, date_today + timedelta(1000), freq='D')
np.random.seed(seed=1111)
data_a = np.random.randint(-10, high=20, size=len(days))
data_b = np.random.randint(-30, high=70, size=len(days))
a = pd.DataFrame({'date': days, 'a': data_a})
a = a.set_index('date')
b = pd.DataFrame({'date': days, 'b': data_b})
b = b.set_index('date')
# result dataframe which will be used for the plotting
result = pd.concat([a, b], axis=1)
# make sure only the dates are being used
result = result.reset_index()
result['date'] = result['date'].dt.date
result = result.set_index('date')
# set- up for the plot
matplotlib.rc_file_defaults()
sns.set_style(style=None, rc=None)
fig, ax1 = plt.subplots(figsize=(12,6))
ax2 = ax1.twinx()
# bar plot
result_date = result.reset_index()
b_plot = sns.barplot(data = result_date, x=result_date.iloc[:, 0], y=result_date.iloc[:, 2], ax=ax1)
# pointplot
a_plot = sns.pointplot(data=result, x=result.index, y=result.iloc[:, 0], color="black", ax=ax2, markers = 'o', scale=0.4)
# set the x tickers to be those of the bar plot
ax1.set_xticks(np.arange(len(result_date)))
ax1.set_xticklabels(result_date.date.apply(lambda x: str(x.year)))
ax1.xaxis.set_major_locator(ticker.AutoLocator())
# get the original ylims (before setting the yticks)
ax1_ylims = ax1.get_ylim()
ax1_yratio = ax1_ylims[0] / ax1_ylims[1] # Calculate ratio of lowest limit to highest limit
ax2_ylims = ax2.get_ylim()
ax2_yratio = ax2_ylims[0] / ax2_ylims[1] # Calculate ratio of lowest limit to highest limit
# to align the axes and make them start at 0
max1 = np.nanmax(np.abs(ax1.get_ybound())) # in case you have nan values
max2 = np.nanmax(np.abs(ax2.get_ybound()))
nticks = 7 #or other odd number
ax1.set_yticks(np.linspace(-max1, max1, nticks))
ax2.set_yticks(np.linspace(-max2, max2, nticks))
# If the plot limits ratio of plot 1 is smaller than plot 2, the first data set has
# a wider range range than the second data set. Calculate a new low limit for the
# second data set to obtain a similar ratio to the first data set.
# Else, do it the other way around
if ax1_yratio < ax2_yratio:
ax2.set_ylim(bottom=ax2_ylims[1] * ax1_yratio)
ax1.set_ylim(ax1_ylims)
else:
ax1.set_ylim(bottom=ax1_ylims[1] * ax2_yratio)
ax2.set_ylim(ax2_ylims)
plt.show()
这是我提出的解决方案，适用于不同范围的数据集。它还确保代码之间的间距相等。有什么办法可以更简洁一点，请告诉我。
import numpy as np
import pandas as pd
import math
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.dates as md
import matplotlib.ticker as ticker
from datetime import datetime, timedelta
# create dataframes that will be used
date_today = datetime.now()
days = pd.date_range(date_today, date_today + timedelta(1000), freq='D')
np.random.seed(seed=1111)
data_a = np.random.randint(-9, high=30, size=len(days))
data_b = np.random.randint(-20, high=70, size=len(days))
a = pd.DataFrame({'date': days, 'a': data_a})
a = a.set_index('date')
b = pd.DataFrame({'date': days, 'b': data_b})
b = b.set_index('date')
# result dataframe which will be used for the plotting
result = pd.concat([a, b], axis=1)
# make sure only the dates are being used
result = result.reset_index()
result['date'] = result['date'].dt.date
result = result.set_index('date')
# set- up for the plot
matplotlib.rc_file_defaults()
sns.set_style(style=None, rc=None)
fig, ax1 = plt.subplots(figsize=(12,6))
ax2 = ax1.twinx()
# bar plot
result_date = result.reset_index()
b_plot = sns.barplot(data = result_date, x=result_date.iloc[:, 0], y=result_date.iloc[:, 2], ax=ax1)
# pointplot
a_plot = sns.pointplot(data=result, x=result.index, y=result.iloc[:, 0], color="black", ax=ax2, markers = 'o', scale=0.4)
# set the x tickers to be those of the bar plot
ax1.set_xticks(np.arange(len(result_date)))
ax1.set_xticklabels(result_date.date.apply(lambda x: str(x.year)))
ax1.xaxis.set_major_locator(ticker.AutoLocator())
# get the max and min values for both plot datasets
max_value_1 = np.max(result_date.iloc[:, 2])
min_value_1 = np.min(result_date.iloc[:, 2])
max_value_2 = np.max(result.iloc[:, 0])
min_value_2 = np.min(result.iloc[:, 0])
# to align the axes and make them start at 0
max1 = np.nanmax(np.abs(ax1.get_ybound())) # in case you have nan values
max2 = np.nanmax(np.abs(ax2.get_ybound()))
nticks = 7 #or other odd number
ax1.set_yticks(np.linspace(-max1, max1, nticks))
ax2.set_yticks(np.linspace(-max2, max2, nticks))
# y axis value arrays going from -ve to +ve values
ax1_y_ticks = ax1.get_yticks()
ax2_y_ticks = ax2.get_yticks()
# create empty lists for the indexes of the upper and lower elements of the two y-ranges
ax1_lower_elements = [] # pick last value (first ticker just below the lowest value in the range)
ax1_upper_elements = [] # pick first value (first ticker just above the highest value in the range)
ax2_lower_elements = [] # pick last value
ax2_upper_elements = [] # pick first value
for i in range(len(ax1_y_ticks)):
if ax1_y_ticks[i] <= min_value_1:
ax1_lower_elements.append(i)
if ax1_y_ticks[i] >= max_value_1:
ax1_upper_elements.append(i)
for i in range(len(ax2_y_ticks)):
if ax2_y_ticks[i] <= min_value_2:
ax2_lower_elements.append(i)
if ax2_y_ticks[i] >= max_value_2:
ax2_upper_elements.append(i)
# get the indexs for the upper and lower limits of the y-axes
ax1_lower_ticker_element = ax1_lower_elements[-1]
ax1_upper_ticker_element = ax1_upper_elements[0]
ax2_lower_ticker_element = ax2_lower_elements[-1]
ax2_upper_ticker_element = ax2_upper_elements[0]
# determine which upper and lower indexes should be used
if ax1_lower_ticker_element <= ax2_lower_ticker_element:
lower_ticker_element = ax1_lower_ticker_element
else:
lower_ticker_element = ax2_lower_ticker_element
if ax1_upper_ticker_element <= ax2_upper_ticker_element:
upper_ticker_element = ax2_upper_ticker_element
else:
upper_ticker_element = ax1_upper_ticker_element
# the indexes to be used as a list
new_y_ticker_elements = []
for i in range (len(ax1_y_ticks)):
if ((i >= lower_ticker_element) & (i <= upper_ticker_element)):
new_y_ticker_elements.append(i)
# setting y-axis for ax1
ax1_rng = []
for i in range(len(new_y_ticker_elements)):
ax1_rng.append(ax1_y_ticks[new_y_ticker_elements[i]])
ax1.set_yticks(ax1_rng)
ax1.set_ylim(bottom=ax1_rng[0], top=ax1_rng[-1])
# setting y-axis for ax2
ax2_rng = []
for i in range(len(new_y_ticker_elements)):
ax2_rng.append(ax2_y_ticks[new_y_ticker_elements[i]])
ax2.set_yticks(ax2_rng)
ax2.set_ylim(bottom=ax2_rng[0], top=ax2_rng[-1])
这是最后的情节