使用自定义颜色渐变填充两条线之间的区域
Filling In the Area Between Two Lines with a Custom Color Gradient
我正在做一个我几乎已经完成的作业，但我想对它做一点改动，尝试用基于温度的颜色图而不是简单的颜色图来填充两条线之间的区域颜色。绘制线条的方式使它们本质上是独立的实体，因此我知道我可能需要两个相互相遇或重叠的颜色图来完成此操作，但我不太确定如何完成此操作。非常感谢任何帮助。
from datetime import datetime
import pandas as pd
import numpy as np
import matplotlib.colors as mcol
import matplotlib.cm as cm
bin = 400
hash = 'fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89'
Temp = pd.read_csv('fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv'.format(bin, hash))
Temp['Date'] = pd.to_datetime(Temp['Date'])
#Only doing this here because the mplleaflet in my personal jupyter notebook is bugged
#will take longer to execute, will take more lines of code for conversions and ultimately is less efficient than simply doing it with pandas.
#print(datetime.strptime(Temp['Date'].to_json(), '%y-%m-%d')) = datetime.strptime(Temp['Date'], format)
Temp['Y'] = Temp['Date'].dt.year
Temp['M'] = Temp['Date'].dt.month
Temp['D'] = Temp['Date'].dt.day
Temp['DV'] = Temp['Data_Value'].div(10)
Temp['E'] = Temp['Element']
Temp = Temp[~((Temp['M']==2) & (Temp['D']==29))]
GrMin = Temp[(Temp['E']=='TMIN') & (Temp['Y']>=2005) & (Temp['Y']<2015)].groupby(['M','D']).agg({'DV':np.min})
FinMin = Temp[(Temp['E']=='TMIN') & (Temp['Y']==2015)].groupby(['M','D']).agg({'DV':np.min})
GrMax = Temp[(Temp['E']=='TMAX') & (Temp['Y']>=2005) & (Temp['Y']<2015)].groupby(['M','D']).agg({'DV':np.max})
FinMax = Temp[(Temp['E']=='TMIN') & (Temp['Y']==2015)].groupby(['M','D']).agg({'DV':np.max})
#x = GrMax
#y = GrMin
#X, Y = np.meshgrid(x,y)
#Z = f(X, Y)
AnomMin = FinMin[FinMin['DV'] < GrMin['DV']]
AnomMax = FinMax[FinMax['DV'] > GrMax['DV']]
#temps = range(-30,40)
plt.figure(figsize=(18, 10), dpi = 80)
red = '#FF0000'
blue = '#0800FF'
cm1 = mcol.LinearSegmentedColormap.from_list('Temperature Map',[blue, red])
cnorm = mcol.Normalize(vmin=min(GrMin['DV']),vmax=max(GrMax['DV']))
cpick = cm.ScalarMappable(norm=cnorm,cmap=cm1)
cpick.set_array([])
plt.title('Historical Temperature Analysis In Ann Arbor Michigan')
plt.xlabel('Month')
plt.ylabel('Temperature in Celsius')
plt.plot(GrMax.values, c = red, linestyle = '-', label = 'Highest Temperatures (2005-2014)')
#plt.scatter(AnomMax, FinMax.iloc[AnomMax], c = red, s=5, label = 'Anomolous High Readings (2015)')
plt.plot(GrMin.values, c = blue, linestyle = '-', label = 'Lowest Temperatures (2005-2014)')
#plt.scatter(AnomMin, FinMin.iloc[AnomMin], c = blue, s=5, label = 'Anomolous Low Readings (2015)')
plt.xticks(np.linspace(0,60 + 60*11, num=12),(r'January',r'February',r'March',r'April',r'May',r'June',r'July',r'August',r'September',r'October',r'November',r'December'))
#Failed Attempt
#plt.contourf(X, Y, Z, 20, cmap = cm1)
#for i in temps
# plt.fill_between(len(GrMin['DV']), GrMin['DV'], i ,cmap = cm1)
#for i in temps
# plt.fill_between(len(GrMin['DV']), i ,GrMax['DV'], cmap = cm1)
#Kind of Close but doesn't exactly create the colormap
plt.gca().fill_between(range(len(GrMin.values)), GrMin['DV'], GrMax['DV'], cmap = cm1)
plt.legend(loc = '0', title='Temperature Guide')
plt.colorbar(cpick, label='Temperature in Celsius')
plt.show()
当前结果：
您可以绘制一个覆盖曲线的彩色矩形。并使用 fill_between 创建的多边形来裁剪该矩形：
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import numpy as np
x = np.linspace(0, 10, 200)
y1 = np.random.normal(0.02, 1, 200).cumsum() + 20
y2 = np.random.normal(0.05, 1, 200).cumsum() + 50
cm1 = LinearSegmentedColormap.from_list('Temperature Map', ['blue', 'red'])
polygon = plt.fill_between(x, y1, y2, lw=0, color='none')
xlim = (x.min(), x.max())
ylim = plt.ylim()
verts = np.vstack([p.vertices for p in polygon.get_paths()])
gradient = plt.imshow(np.linspace(0, 1, 256).reshape(-1, 1), cmap=cm1, aspect='auto', origin='lower',
extent=[verts[:, 0].min(), verts[:, 0].max(), verts[:, 1].min(), verts[:, 1].max()])
gradient.set_clip_path(polygon.get_paths()[0], transform=plt.gca().transData)
plt.xlim(xlim)
plt.ylim(ylim)
plt.show()
一个更复杂的替代方案，将颜色设置为上曲线对应红色，下曲线对应蓝色：
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 200)
y1 = np.random.normal(0.02, 1, 200).cumsum() + 20
y2 = np.random.normal(0.05, 1, 200).cumsum() + 50
polygon = plt.fill_between(x, y1, y2, lw=0, color='none')
ylim = plt.ylim()
verts = np.vstack([p.vertices for p in polygon.get_paths()])
ymin, ymax = verts[:, 1].min(), verts[:, 1].max()
gradient = plt.imshow(np.array([np.interp(np.linspace(ymin, ymax, 200), [y1i, y2i], np.arange(2))
for y1i, y2i in zip(y1, y2)]).T,
cmap='turbo', aspect='auto', origin='lower', extent=[x.min(), x.max(), ymin, ymax])
gradient.set_clip_path(polygon.get_paths()[0], transform=plt.gca().transData)
plt.ylim(ylim)
plt.show()
一个变体可能是平滑水平方向的颜色值（但仍然使用原始曲线进行剪辑）：
from scipy.ndimage import gaussian_filter
gradient = plt.imshow(np.array([np.interp(np.linspace(ymin, ymax, 200), [y1i, y2i], np.arange(2))
for y1i, y2i in zip(gaussian_filter(y1, 4, mode='nearest'),
gaussian_filter(y2, 4, mode='nearest'))]).T,
cmap='turbo', aspect='auto', origin='lower', extent=[x.min(), x.max(), ymin, ymax])
在更正我的代码中的一些功能错误然后应用 JohanC 提供的代码以及寻求其他一些 much-needed 指导后，我能够成功完成颜色映射。如果上线图和下线图的颜色不同，可能会更具视觉吸引力，但就颜色图而言，任务已完成。再次感谢您的帮助！
from datetime import datetime
import pandas as pd
import numpy as np
import matplotlib.colors as mcol
import matplotlib.cm as cm
bin = 400
hash = 'fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89'
Temp = pd.read_csv('fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv'.format(bin, hash))
Temp['Date'] = pd.to_datetime(Temp['Date'])
#Only doing this here because the mplleaflet in my personal jupyter notebook is bugged
#will take longer to execute, will take more lines of code for conversions and ultimately is less efficient than simply doing it with pandas.
#print(datetime.strptime(Temp['Date'].to_json(), '%y-%m-%d')) = datetime.strptime(Temp['Date'], format)
Temp['Y'] = Temp['Date'].dt.year
Temp['M'] = Temp['Date'].dt.month
Temp['D'] = Temp['Date'].dt.day
Temp['DV'] = Temp['Data_Value'].div(10)
Temp['E'] = Temp['Element']
Temp = Temp[~((Temp['M']==2) & (Temp['D']==29))]
GrMin = Temp[(Temp['E']=='TMIN') & (Temp['Y']>=2005) & (Temp['Y']<2015)].groupby(['M','D']).agg({'DV':np.min})
FinMin = Temp[(Temp['E']=='TMIN') & (Temp['Y']==2015)].groupby(['M','D']).agg({'DV':np.min})
GrMax = Temp[(Temp['E']=='TMAX') & (Temp['Y']>=2005) & (Temp['Y']<2015)].groupby(['M','D']).agg({'DV':np.max})
FinMax = Temp[(Temp['E']=='TMAX') & (Temp['Y']==2015)].groupby(['M','D']).agg({'DV':np.max})
GrMax = GrMax.reset_index()
GrMin = GrMin.reset_index()
FinMax = FinMax.reset_index()
FinMin = FinMin.reset_index()
#x = GrMax
#y = GrMin
#X, Y = np.meshgrid(x,y)
#Z = f(X, Y)
AnomMin = FinMin[FinMin['DV'] < GrMin['DV']]
AnomMax = FinMax[FinMax['DV'] > GrMax['DV']]
#temps = range(-30,40)
plt.figure(figsize=(18, 10), dpi = 160)
red = '#FF0000'
blue = '#0800FF'
cm1 = mcol.LinearSegmentedColormap.from_list('Temperature Map',[blue, red])
cnorm = mcol.Normalize(vmin=min(GrMin['DV']),vmax=max(GrMax['DV']))
cpick = cm.ScalarMappable(norm=cnorm,cmap=cm1)
cpick.set_array([])
plt.title('Historical Temperature Analysis In Ann Arbor Michigan')
plt.xlabel('Month')
plt.ylabel('Temperature in Celsius')
plt.plot(GrMax['DV'], c = red, linestyle = '-', label = 'Highest Temperatures (2005-2014)')
plt.scatter(AnomMax.index, AnomMax['DV'], c = red, s=2, label = 'Anomolous High Readings (2015)')
plt.plot(GrMin['DV'], c = blue, linestyle = '-', label = 'Lowest Temperatures (2005-2014)')
plt.scatter(AnomMin.index, AnomMin['DV'], c = blue, s=2, label = 'Anomolous Low Readings (2015)')
plt.xticks(np.linspace(0,365,12, endpoint = True),(r'January',r'February',r'March',r'April',r'May',r'June',r'July',r'August',r'September',r'October',r'November',r'December'))
#Start: Assisted from Whosebug user JohanC v
x = np.arange(len(GrMin['DV'].fillna(0).astype('float64').ravel()))
y1 = GrMax['DV'].fillna(0).astype('float64').ravel()
y2 = GrMin['DV'].fillna(0).astype('float64').ravel()
polygon = plt.fill_between(x, y1, y2, lw=0, color='none')
xlim = (x.min(), x.max())
ylim = plt.ylim()
verts = np.vstack([p.vertices for p in polygon.get_paths()])
gradient = plt.imshow(np.linspace(1, 0, 256).reshape(-1, 1), cmap=cm1, aspect='auto',
extent=[verts[:, 0].min(), verts[:, 0].max(), verts[:, 1].min(), verts[:, 1].max()])
gradient.set_clip_path(polygon.get_paths()[0], transform=plt.gca().transData)
plt.xlim(xlim)
plt.ylim(ylim)
#Finish: Assisted from Whosebug user JohanC ^
#Failed Attempt at gradient fill with colormap
#plt.contourf(X, Y, Z, 20, cmap = cm1)
#for i in temps
# plt.fill_between(len(GrMin['DV']), GrMin['DV'], i ,cmap = cm1)
#for i in temps
# plt.fill_between(len(GrMin['DV']), i ,GrMax['DV'], cmap = cm1)
#Kind of Close but doesn't exactly create the colormap
#plt.gca().fill_between(range(len(GrMin.values)), GrMin['DV'], GrMax['DV'], facecolor = 'grey', alpha = 0.10)
plt.legend(loc = 'lower center', title='Temperature Guide')
plt.colorbar(cpick, label='Temperature in Celsius')
plt.show()
enter image description here
我正在做一个我几乎已经完成的作业，但我想对它做一点改动，尝试用基于温度的颜色图而不是简单的颜色图来填充两条线之间的区域颜色。绘制线条的方式使它们本质上是独立的实体，因此我知道我可能需要两个相互相遇或重叠的颜色图来完成此操作，但我不太确定如何完成此操作。非常感谢任何帮助。
from datetime import datetime
import pandas as pd
import numpy as np
import matplotlib.colors as mcol
import matplotlib.cm as cm
bin = 400
hash = 'fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89'
Temp = pd.read_csv('fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv'.format(bin, hash))
Temp['Date'] = pd.to_datetime(Temp['Date'])
#Only doing this here because the mplleaflet in my personal jupyter notebook is bugged
#will take longer to execute, will take more lines of code for conversions and ultimately is less efficient than simply doing it with pandas.
#print(datetime.strptime(Temp['Date'].to_json(), '%y-%m-%d')) = datetime.strptime(Temp['Date'], format)
Temp['Y'] = Temp['Date'].dt.year
Temp['M'] = Temp['Date'].dt.month
Temp['D'] = Temp['Date'].dt.day
Temp['DV'] = Temp['Data_Value'].div(10)
Temp['E'] = Temp['Element']
Temp = Temp[~((Temp['M']==2) & (Temp['D']==29))]
GrMin = Temp[(Temp['E']=='TMIN') & (Temp['Y']>=2005) & (Temp['Y']<2015)].groupby(['M','D']).agg({'DV':np.min})
FinMin = Temp[(Temp['E']=='TMIN') & (Temp['Y']==2015)].groupby(['M','D']).agg({'DV':np.min})
GrMax = Temp[(Temp['E']=='TMAX') & (Temp['Y']>=2005) & (Temp['Y']<2015)].groupby(['M','D']).agg({'DV':np.max})
FinMax = Temp[(Temp['E']=='TMIN') & (Temp['Y']==2015)].groupby(['M','D']).agg({'DV':np.max})
#x = GrMax
#y = GrMin
#X, Y = np.meshgrid(x,y)
#Z = f(X, Y)
AnomMin = FinMin[FinMin['DV'] < GrMin['DV']]
AnomMax = FinMax[FinMax['DV'] > GrMax['DV']]
#temps = range(-30,40)
plt.figure(figsize=(18, 10), dpi = 80)
red = '#FF0000'
blue = '#0800FF'
cm1 = mcol.LinearSegmentedColormap.from_list('Temperature Map',[blue, red])
cnorm = mcol.Normalize(vmin=min(GrMin['DV']),vmax=max(GrMax['DV']))
cpick = cm.ScalarMappable(norm=cnorm,cmap=cm1)
cpick.set_array([])
plt.title('Historical Temperature Analysis In Ann Arbor Michigan')
plt.xlabel('Month')
plt.ylabel('Temperature in Celsius')
plt.plot(GrMax.values, c = red, linestyle = '-', label = 'Highest Temperatures (2005-2014)')
#plt.scatter(AnomMax, FinMax.iloc[AnomMax], c = red, s=5, label = 'Anomolous High Readings (2015)')
plt.plot(GrMin.values, c = blue, linestyle = '-', label = 'Lowest Temperatures (2005-2014)')
#plt.scatter(AnomMin, FinMin.iloc[AnomMin], c = blue, s=5, label = 'Anomolous Low Readings (2015)')
plt.xticks(np.linspace(0,60 + 60*11, num=12),(r'January',r'February',r'March',r'April',r'May',r'June',r'July',r'August',r'September',r'October',r'November',r'December'))
#Failed Attempt
#plt.contourf(X, Y, Z, 20, cmap = cm1)
#for i in temps
# plt.fill_between(len(GrMin['DV']), GrMin['DV'], i ,cmap = cm1)
#for i in temps
# plt.fill_between(len(GrMin['DV']), i ,GrMax['DV'], cmap = cm1)
#Kind of Close but doesn't exactly create the colormap
plt.gca().fill_between(range(len(GrMin.values)), GrMin['DV'], GrMax['DV'], cmap = cm1)
plt.legend(loc = '0', title='Temperature Guide')
plt.colorbar(cpick, label='Temperature in Celsius')
plt.show()
当前结果：
您可以绘制一个覆盖曲线的彩色矩形。并使用 fill_between 创建的多边形来裁剪该矩形：
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import numpy as np
x = np.linspace(0, 10, 200)
y1 = np.random.normal(0.02, 1, 200).cumsum() + 20
y2 = np.random.normal(0.05, 1, 200).cumsum() + 50
cm1 = LinearSegmentedColormap.from_list('Temperature Map', ['blue', 'red'])
polygon = plt.fill_between(x, y1, y2, lw=0, color='none')
xlim = (x.min(), x.max())
ylim = plt.ylim()
verts = np.vstack([p.vertices for p in polygon.get_paths()])
gradient = plt.imshow(np.linspace(0, 1, 256).reshape(-1, 1), cmap=cm1, aspect='auto', origin='lower',
extent=[verts[:, 0].min(), verts[:, 0].max(), verts[:, 1].min(), verts[:, 1].max()])
gradient.set_clip_path(polygon.get_paths()[0], transform=plt.gca().transData)
plt.xlim(xlim)
plt.ylim(ylim)
plt.show()
一个更复杂的替代方案，将颜色设置为上曲线对应红色，下曲线对应蓝色：
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 200)
y1 = np.random.normal(0.02, 1, 200).cumsum() + 20
y2 = np.random.normal(0.05, 1, 200).cumsum() + 50
polygon = plt.fill_between(x, y1, y2, lw=0, color='none')
ylim = plt.ylim()
verts = np.vstack([p.vertices for p in polygon.get_paths()])
ymin, ymax = verts[:, 1].min(), verts[:, 1].max()
gradient = plt.imshow(np.array([np.interp(np.linspace(ymin, ymax, 200), [y1i, y2i], np.arange(2))
for y1i, y2i in zip(y1, y2)]).T,
cmap='turbo', aspect='auto', origin='lower', extent=[x.min(), x.max(), ymin, ymax])
gradient.set_clip_path(polygon.get_paths()[0], transform=plt.gca().transData)
plt.ylim(ylim)
plt.show()
一个变体可能是平滑水平方向的颜色值（但仍然使用原始曲线进行剪辑）：
from scipy.ndimage import gaussian_filter
gradient = plt.imshow(np.array([np.interp(np.linspace(ymin, ymax, 200), [y1i, y2i], np.arange(2))
for y1i, y2i in zip(gaussian_filter(y1, 4, mode='nearest'),
gaussian_filter(y2, 4, mode='nearest'))]).T,
cmap='turbo', aspect='auto', origin='lower', extent=[x.min(), x.max(), ymin, ymax])
在更正我的代码中的一些功能错误然后应用 JohanC 提供的代码以及寻求其他一些 much-needed 指导后，我能够成功完成颜色映射。如果上线图和下线图的颜色不同，可能会更具视觉吸引力，但就颜色图而言，任务已完成。再次感谢您的帮助！
from datetime import datetime
import pandas as pd
import numpy as np
import matplotlib.colors as mcol
import matplotlib.cm as cm
bin = 400
hash = 'fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89'
Temp = pd.read_csv('fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv'.format(bin, hash))
Temp['Date'] = pd.to_datetime(Temp['Date'])
#Only doing this here because the mplleaflet in my personal jupyter notebook is bugged
#will take longer to execute, will take more lines of code for conversions and ultimately is less efficient than simply doing it with pandas.
#print(datetime.strptime(Temp['Date'].to_json(), '%y-%m-%d')) = datetime.strptime(Temp['Date'], format)
Temp['Y'] = Temp['Date'].dt.year
Temp['M'] = Temp['Date'].dt.month
Temp['D'] = Temp['Date'].dt.day
Temp['DV'] = Temp['Data_Value'].div(10)
Temp['E'] = Temp['Element']
Temp = Temp[~((Temp['M']==2) & (Temp['D']==29))]
GrMin = Temp[(Temp['E']=='TMIN') & (Temp['Y']>=2005) & (Temp['Y']<2015)].groupby(['M','D']).agg({'DV':np.min})
FinMin = Temp[(Temp['E']=='TMIN') & (Temp['Y']==2015)].groupby(['M','D']).agg({'DV':np.min})
GrMax = Temp[(Temp['E']=='TMAX') & (Temp['Y']>=2005) & (Temp['Y']<2015)].groupby(['M','D']).agg({'DV':np.max})
FinMax = Temp[(Temp['E']=='TMAX') & (Temp['Y']==2015)].groupby(['M','D']).agg({'DV':np.max})
GrMax = GrMax.reset_index()
GrMin = GrMin.reset_index()
FinMax = FinMax.reset_index()
FinMin = FinMin.reset_index()
#x = GrMax
#y = GrMin
#X, Y = np.meshgrid(x,y)
#Z = f(X, Y)
AnomMin = FinMin[FinMin['DV'] < GrMin['DV']]
AnomMax = FinMax[FinMax['DV'] > GrMax['DV']]
#temps = range(-30,40)
plt.figure(figsize=(18, 10), dpi = 160)
red = '#FF0000'
blue = '#0800FF'
cm1 = mcol.LinearSegmentedColormap.from_list('Temperature Map',[blue, red])
cnorm = mcol.Normalize(vmin=min(GrMin['DV']),vmax=max(GrMax['DV']))
cpick = cm.ScalarMappable(norm=cnorm,cmap=cm1)
cpick.set_array([])
plt.title('Historical Temperature Analysis In Ann Arbor Michigan')
plt.xlabel('Month')
plt.ylabel('Temperature in Celsius')
plt.plot(GrMax['DV'], c = red, linestyle = '-', label = 'Highest Temperatures (2005-2014)')
plt.scatter(AnomMax.index, AnomMax['DV'], c = red, s=2, label = 'Anomolous High Readings (2015)')
plt.plot(GrMin['DV'], c = blue, linestyle = '-', label = 'Lowest Temperatures (2005-2014)')
plt.scatter(AnomMin.index, AnomMin['DV'], c = blue, s=2, label = 'Anomolous Low Readings (2015)')
plt.xticks(np.linspace(0,365,12, endpoint = True),(r'January',r'February',r'March',r'April',r'May',r'June',r'July',r'August',r'September',r'October',r'November',r'December'))
#Start: Assisted from Whosebug user JohanC v
x = np.arange(len(GrMin['DV'].fillna(0).astype('float64').ravel()))
y1 = GrMax['DV'].fillna(0).astype('float64').ravel()
y2 = GrMin['DV'].fillna(0).astype('float64').ravel()
polygon = plt.fill_between(x, y1, y2, lw=0, color='none')
xlim = (x.min(), x.max())
ylim = plt.ylim()
verts = np.vstack([p.vertices for p in polygon.get_paths()])
gradient = plt.imshow(np.linspace(1, 0, 256).reshape(-1, 1), cmap=cm1, aspect='auto',
extent=[verts[:, 0].min(), verts[:, 0].max(), verts[:, 1].min(), verts[:, 1].max()])
gradient.set_clip_path(polygon.get_paths()[0], transform=plt.gca().transData)
plt.xlim(xlim)
plt.ylim(ylim)
#Finish: Assisted from Whosebug user JohanC ^
#Failed Attempt at gradient fill with colormap
#plt.contourf(X, Y, Z, 20, cmap = cm1)
#for i in temps
# plt.fill_between(len(GrMin['DV']), GrMin['DV'], i ,cmap = cm1)
#for i in temps
# plt.fill_between(len(GrMin['DV']), i ,GrMax['DV'], cmap = cm1)
#Kind of Close but doesn't exactly create the colormap
#plt.gca().fill_between(range(len(GrMin.values)), GrMin['DV'], GrMax['DV'], facecolor = 'grey', alpha = 0.10)
plt.legend(loc = 'lower center', title='Temperature Guide')
plt.colorbar(cpick, label='Temperature in Celsius')
plt.show()
enter image description here