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Hovmollor diagramは，縦横軸の一方に時間，もう一方に距離に関する座標（緯度または経度）で描く２次元の図です．緯度経度を使うということは，緯度経度にふさわしい軸の表示が必要になります．そこで，縦(y)軸または横(x)軸を，緯度または経度表示に変更する関数を作りました．これを使えば，Hovmollor diagramを作るのが大分楽になります．
その関数とサンプルプログラムを下に貼っておきます．
A Hovmollor diagram is a two-dimensional diagram with time on one of the horizontal and vertical axes and distance coordinates (latitude or longitude) on the other. The use of latitude and longitude means that the appropriate axis for latitude and longitude must be displayed. So, I have created a function to change the vertical (y) or horizontal (x) axis to display latitude or longitude. This makes it much easier to create a Hovmollor diagram.
The function and a sample program are posted below.
import numpy as npimport matplotlib.pyplot as pltfrom cartopy.mpl.ticker import LatitudeFormatter,LongitudeFormatterimport pandas as pd
def set_lat_or_lon_label(ax, lat_or_lon, x_or_y, delta_deg=None): """ parameter ------------- ax : matplotlib.axes (not geaxes) delta_deg : float, interval of longitude or latitude label x_or_y : 'x' or 'y' for x-axis (horizontal) and y-axis (vertial), respectively. lon_or_lat : 'lon' or 'lat'
In this function "xy" means "x or y". Made by Shoshiro Minobe """
if x_or_y=='x': xy_lim=ax.get_xlim() if x_or_y=='y': xy_lim=ax.get_ylim() xy_wid=xy_lim[1]-xy_lim[0]
xy_ticks=[] if lat_or_lon=='lat': for _lat in np.arange(0., 90.1, delta_deg): if xy_lim[0]<=_lat and _lat<=xy_lim[1]: xy_ticks.append(_lat) if xy_lim[0]<=-_lat and -_lat<=xy_lim[1] and _lat!=0: xy_ticks.append(-_lat) if lat_or_lon=='lon': for _lon in np.arange(0., 360.1, delta_deg): if xy_lim[0]<=_lon and _lon<=xy_lim[1]: xy_ticks.append(_lon) if xy_lim[0]<=-_lon and -_lon<=xy_lim[1] and _lon!=0: xy_ticks.append(-_lon)
xy_ticks.sort() print('xy_lim=',xy_lim) print(xy_ticks)
if lat_or_lon=='lon': lonfmt=LongitudeFormatter(zero_direction_label=True) if x_or_y=='x': ax.set_xticks(xy_ticks) ax.xaxis.set_major_formatter(lonfmt) if x_or_y=='y': ax.set_yticks(xy_ticks) ax.yaxis.set_major_formatter(lonfmt)
if lat_or_lon=='lat': latfmt=LatitudeFormatter() if x_or_y=='x': ax.set_xticks(xy_ticks) ax.xaxis.set_major_formatter(latfmt) if x_or_y=='y': ax.set_yticks(xy_ticks) ax.yaxis.set_major_formatter(latfmt)
ax.axes.tick_params(labelsize=10) return xy_ticks
xs1=np.arange(0, 360, 1)ts1=pd.date_range('2001-01-01',end='2001-12-31',freq='1D')ys1=np.arange(len(ts1))xsz=len(xs1)ysz=len(ys1)xs2d=np.ones([ysz,1]) @ xs1.reshape(1,xsz)ys2d=ys1.reshape(ysz,1) @ np.ones([1,xsz])zs1=np.cos(xs2d/180*np.pi) * np.cos(ys2d/90*np.pi)
ys2=np.arange(-90, 90.1, 1)ts2=pd.date_range('2001-01-01',end='2001-12-31',freq='1D')xs2=np.arange(len(ts2))xsz=len(xs2)ysz=len(ys2)xs2d=np.ones([ysz,1]) @ xs2.reshape(1,xsz)ys2d=ys2.reshape(ysz,1) @ np.ones([1,xsz])zs2=np.cos(xs2d/180*np.pi) * np.cos(ys2d/90*np.pi)
plt.ion()plt.clf()
ax=plt.subplot(2,2,1)ax.contourf(xs1,ts1,zs1)cartopy_helper.set_lat_or_lon_label(ax,'lon','x',delta_deg=60)
ax=plt.subplot(2,2,2)ax.contourf(ts1,xs1,zs1.T)cartopy_helper.set_lat_or_lon_label(ax,'lon','y',delta_deg=60)
ax=plt.subplot(2,2,3)ax.contourf(ys2,ts2,zs2.T)cartopy_helper.set_lat_or_lon_label(ax,'lat','x',delta_deg=30)
ax=plt.subplot(2,2,4)ax.contourf(ts2,ys2,zs2)cartopy_helper.set_lat_or_lon_label(ax,'lat','y',delta_deg=30)
def set_lat_or_lon_label(ax, lat_or_lon, x_or_y, delta_deg=None): """ parameter ------------- ax : matplotlib.axes (not geaxes) delta_deg : float, interval of longitude or latitude label x_or_y : 'x' or 'y' for x-axis (horizontal) and y-axis (vertial), respectively. lon_or_lat : 'lon' or 'lat'
In this function "xy" means "x or y". Made by Shoshiro Minobe """
if x_or_y=='x': xy_lim=ax.get_xlim() if x_or_y=='y': xy_lim=ax.get_ylim() xy_wid=xy_lim[1]-xy_lim[0]
xy_ticks=[] if lat_or_lon=='lat': for _lat in np.arange(0., 90.1, delta_deg): if xy_lim[0]<=_lat and _lat<=xy_lim[1]: xy_ticks.append(_lat) if xy_lim[0]<=-_lat and -_lat<=xy_lim[1] and _lat!=0: xy_ticks.append(-_lat) if lat_or_lon=='lon': for _lon in np.arange(0., 360.1, delta_deg): if xy_lim[0]<=_lon and _lon<=xy_lim[1]: xy_ticks.append(_lon) if xy_lim[0]<=-_lon and -_lon<=xy_lim[1] and _lon!=0: xy_ticks.append(-_lon)
xy_ticks.sort() print('xy_lim=',xy_lim) print(xy_ticks)
if lat_or_lon=='lon': lonfmt=LongitudeFormatter(zero_direction_label=True) if x_or_y=='x': ax.set_xticks(xy_ticks) ax.xaxis.set_major_formatter(lonfmt) if x_or_y=='y': ax.set_yticks(xy_ticks) ax.yaxis.set_major_formatter(lonfmt)
if lat_or_lon=='lat': latfmt=LatitudeFormatter() if x_or_y=='x': ax.set_xticks(xy_ticks) ax.xaxis.set_major_formatter(latfmt) if x_or_y=='y': ax.set_yticks(xy_ticks) ax.yaxis.set_major_formatter(latfmt)
ax.axes.tick_params(labelsize=10) return xy_ticks
xs1=np.arange(0, 360, 1)ts1=pd.date_range('2001-01-01',end='2001-12-31',freq='1D')ys1=np.arange(len(ts1))xsz=len(xs1)ysz=len(ys1)xs2d=np.ones([ysz,1]) @ xs1.reshape(1,xsz)ys2d=ys1.reshape(ysz,1) @ np.ones([1,xsz])zs1=np.cos(xs2d/180*np.pi) * np.cos(ys2d/90*np.pi)
ys2=np.arange(-90, 90.1, 1)ts2=pd.date_range('2001-01-01',end='2001-12-31',freq='1D')xs2=np.arange(len(ts2))xsz=len(xs2)ysz=len(ys2)xs2d=np.ones([ysz,1]) @ xs2.reshape(1,xsz)ys2d=ys2.reshape(ysz,1) @ np.ones([1,xsz])zs2=np.cos(xs2d/180*np.pi) * np.cos(ys2d/90*np.pi)
plt.ion()plt.clf()
ax=plt.subplot(2,2,1)ax.contourf(xs1,ts1,zs1)cartopy_helper.set_lat_or_lon_label(ax,'lon','x',delta_deg=60)
ax=plt.subplot(2,2,2)ax.contourf(ts1,xs1,zs1.T)cartopy_helper.set_lat_or_lon_label(ax,'lon','y',delta_deg=60)
ax=plt.subplot(2,2,3)ax.contourf(ys2,ts2,zs2.T)cartopy_helper.set_lat_or_lon_label(ax,'lat','x',delta_deg=30)
ax=plt.subplot(2,2,4)ax.contourf(ts2,ys2,zs2)cartopy_helper.set_lat_or_lon_label(ax,'lat','y',delta_deg=30)
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