Python 嵌套的 for 循环没有在两个循环中正确迭代
Python nested for loop is not iterating properly through both loops
我正在尝试遍历 'datasets' 的集合以及 'domains' 的集合。这个想法是将第一个数据集与第一个域配对，将第二个数据集与第二个域配对，依此类推。最终我想在地图上绘制这些数据集中的变量。我尝试使用嵌套的 for 循环，但它拒绝循环遍历数据集列表或域列表。这最终导致程序一遍又一遍地在同一个域上绘制同一个数据集。相关代码在这里：
# This extracts files and directories out of the path
arr = os.listdir(path)
# Create list for WRF output files (since files in case directories are separated by time; i.e. 12Z, 13Z, 14Z)
wrf_out = []
# For loop to fill list with WRF output files from case directory
for file in arr:
# os.path.join is REQUIRED because it joins the file to the path; otherwise you'll get the file name with NO data
filepath = os.path.join(path, file)
wrf_out.append(filepath)
# Sort WRF output files chronologically
wrf_out.sort()
# Test to make sure you have the right case/mpscheme/res/vert
#ds = Dataset(wrf_out[0])
#print(ds)
# Basic NWS colors for reflectivity
dbz_contour_levels = np.arange(0,80.,5.)
# Taken from UCAR MMM/COD NEXLAB Archive Mosiac's table
dbz_colors = ("#00ffff", "#0080ff", "#0000ff", "#00ff00", "#00c000", "#00800a", "#ffff00", "#ffc000", "#ff8000",
"#ff0000", "#c00000", "#800000", "#ff00ff", "#8d67cd", "#000000")
domain_2015_06_03 = [[-108.714, -101.222, 40.776, 45.348], [-108.714, -101.222, 40.776, 45.348], [-108.714, -101.222, 40.776, 45.348], [-108.714, -101.222, 40.776, 45.348],
[-108.714, -101.222, 40.776, 45.348], [-108.714, -101.222, 40.776, 45.348], [-108.714, -101.222, 40.776, 45.348], [-108.714, -101.222, 40.776, 45.348],
[-108.714, -101.222, 40.776, 45.348], [-108.714, -101.222, 40.776, 45.348], [-108.714, -101.222, 40.776, 45.348], [-108.714, -101.222, 40.776, 45.348],
[-108.714, -101.222, 40.776, 45.348], [-105.858, -98.827, 41.373, 45.840], [-105.858, -98.827, 41.373, 45.840], [-104.012, -95.882, 40.843, 45.902],
[-104.012, -95.882, 40.843, 45.902], [-104.012, -95.882, 40.843, 45.902], [-104.012, -95.882, 40.843, 45.902], [-104.012, -95.882, 40.843, 45.902],
[-101.222, -94.564, 40.007, 46.389], [-101.222, -94.564, 40.007, 46.389], [-100.738, -92.081, 39.972, 46.721], [-100.738, -92.081, 39.972, 46.721],
[-100.738, -92.081, 39.972, 46.721]]
# For loop to get variables, create levels, plot
for i in range(len(wrf_out)):
# For loop to create a shifting domain for higher resolution of variables
for j in domain_2015_06_03:
lonmin = j[0]
lonmax = j[1]
latmin = j[2]
latmax = j[3]
zoom_domain = [lonmin, lonmax, latmin, latmax]
# Get dataset
ds = Dataset(wrf_out[i])
# Get WRF Variables
theta_2m = getvar(ds, 'TH2')
t_pert = getvar(ds, 'T')
# wspd_10m is for divergence and arrows
wspd_10m = getvar(ds, 'uvmet10', units='m s-1')
# wind_10m is for contour
wind_10m = getvar(ds, 'uvmet10_wspd_wdir', units='m s-1')[0,:]
lat = getvar(ds, 'lat')
lon = getvar(ds, 'lon')
# mdbz is for reflectivity
mdbz = getvar(ds, 'mdbz')
# Calculate components and wrf lat lons
wrf_lat, wrf_lon = latlon_coords(mdbz)
wrf_lons = to_np(wrf_lon)
wrf_lats = to_np(wrf_lat)
x = wrf_lon.values
y = wrf_lat.values
# u_comp and v_comp are for divergence
u_comp = wspd_10m[0,:,:]
v_comp = wspd_10m[1,:,:]
# u and v are for arrows
u = u_comp.values
v = v_comp.values
# Get grid deltas
#dx, dy = mpcalc.lat_lon_grid_deltas(lon, lat)
# Calculate divergence
#div = mpcalc.divergence(u_comp, v_comp, dx=dx, dy=dy)
# Just need values for calculating contour levels; otherwise error thrown for values with dimensions
#div_vals = div.values
# Create theta levels
max_level_theta = np.max(theta_2m)
min_level_theta = np.min(theta_2m)
step_level_theta = 1.0
theta_levels = np.arange(min_level_theta, max_level_theta + step_level_theta, step_level_theta)
# Create wind levels and ticks
max_level = np.round(np.max(wind_10m))
min_level = np.round(np.min(wind_10m))
step_level = 0.5
wind_levels = np.arange(min_level, max_level + step_level, step_level)
wind_ticks = wind_levels
# Normalize u and v components for quiver plot if desired
u_norm = u / np.sqrt(u**2 + v**2)
v_norm = v / np.sqrt(u**2 + v**2)
# Timestamp
timestamp = to_np(mdbz.Time).astype('M8[s]').astype('O').isoformat()
time = mdbz.Time
time_str = str(time.values)
# Get CRS
crs = get_cartopy(wrfin=ds)
# Create a 4 panel image with four colorbars
fig = plt.figure(figsize=(30,15))
#gs = gridspec.GridSpec(2, 4, width_ratios = [0.05, 1, 1, 0.05], height_ratios = [1, 1], hspace=0.10, wspace=0.20)
#ax1 = plt.subplot(gs[0, 1], projection=crs)
#ax2 = plt.subplot(gs[0, 2], projection=crs)
#ax3 = plt.subplot(gs[1, 1], projection=crs)
#ax4 = plt.subplot(gs[1, 2], projection=crs)
#cax1 = plt.subplot(gs[0,0])
#cax2 = plt.subplot(gs[0,3])
#cax3 = plt.subplot(gs[1,0])
#cax4 = plt.subplot(gs[1,3])
ax1 = fig.add_subplot(2,2,1, projection=crs)
ax2 = fig.add_subplot(2,2,2, projection=crs)
ax3 = fig.add_subplot(2,2,3, projection=crs)
ax4 = fig.add_subplot(2,2,4, projection=crs)
################################################################################################################################################
# Top Left Plot - Reflectivity
dbz_contour = ax1.contourf(wrf_lons, wrf_lats, mdbz, colors=dbz_colors, levels=dbz_contour_levels, zorder=3,
transform=ccrs.PlateCarree())
#ax1.set_xlim(cartopy_xlim(mdbz))
#ax1.set_ylim(cartopy_ylim(mdbz))
ax1.set_extent(zoom_domain)
plot_background(ax1)
colorbar_dbz = fig.colorbar(dbz_contour, ax=ax1, orientation='vertical')
colorbar_dbz.set_label('dBZ', fontsize=12)
#cax1.yaxis.set_ticks_position('left')
#cax1.yaxis.set_label_position('left')
ax1.set_title('Composite Reflectivity (dBZ) '+ time_str[:19])
################################################################################################################################################
# Top Right Plot - 2m Theta
theta_contour = ax2.contour(wrf_lons, wrf_lats, theta_2m, levels=theta_levels, linewidths=0.9, colors='black', transform=ccrs.PlateCarree())
theta_contourf = ax2.contourf(wrf_lons, wrf_lats, theta_2m, levels=theta_levels, transform=ccrs.PlateCarree(), cmap=get_cmap('coolwarm'))
#ax2.set_xlim(cartopy_xlim(theta_2m))
#ax2.set_ylim(cartopy_ylim(theta_2m))
ax2.set_extent(zoom_domain)
plot_background(ax2)
colorbar_theta = fig.colorbar(theta_contourf, ax=ax2, orientation='vertical')
colorbar_theta.set_label(r"$\theta$ (K)", fontsize=12)
#cax2.yaxis.set_ticks_position('right')
#cax2.yaxis.set_label_position('right')
ax2.set_title('2-meter Potential Temperature (K) '+ time_str[:19])
################################################################################################################################################
# Bottom Left Plot - Divergence
#div_contour = ax3.contour(wrf_lons, wrf_lats, div, colors='black', transform=ccrs.PlateCarree())
#div_contourf = ax3.contourf(wrf_lons, wrf_lats, div, cmap=get_cmap('jet'), transform=ccrs.PlateCarree())
#ax3.set_xlim(cartopy_xlim(theta_2m))
#ax3.set_ylim(cartopy_ylim(theta_2m))
ax3.set_extent(zoom_domain)
plot_background(ax3)
#colorbar_div = fig.colorbar(div_contourf, ax=ax3, orientation='vertical')
#colorbar_div.set_label('Divergence (1/s)', fontsize=12)
#cax3.yaxis.set_ticks_position('left')
#cax3.yaxis.set_label_position('left')
ax3.set_title('Surface Divergence (1/s) ' + time_str[:19])
################################################################################################################################################
# Bottom Right Plot - 10m Wind Speed and Direction
wind_contour = ax4.contour(wrf_lons, wrf_lats, wind_10m, levels=wind_levels, colors='black', transform=ccrs.PlateCarree())
wind_contourf = ax4.contourf(wrf_lons, wrf_lats, wind_10m, levels=wind_levels, cmap=get_cmap('rainbow'), transform=ccrs.PlateCarree())
wind_quiver = ax4.quiver(x, y, u_norm, v_norm, pivot='middle', scale=25, width=0.005, color='black', regrid_shape=42.5, transform=ccrs.PlateCarree())
#ax4.set_xlim(cartopy_xlim(theta_2m))
#ax4.set_ylim(cartopy_ylim(theta_2m))
ax4.set_extent(zoom_domain)
plot_background(ax4)
colorbar_wind = fig.colorbar(wind_contourf, ax=ax4, orientation='vertical')
colorbar_wind.set_label('Wind Speed (m/s)', fontsize=12)
#cax4.yaxis.set_ticks_position('right')
#cax4.yaxis.set_label_position('right')
ax4.set_title('Wind Speed and Direction ' + time_str[:19])
plt.show()
这是它生成重复图像的情节：
不要担心左下角的坐标轴，或者某些图像左侧的白色 space。
我该怎么做才能解决这个问题？除了嵌套的 for 循环之外，还有更好的方法吗？感谢您的帮助！
两个嵌套的 for 循环不会完成您描述的目标。您说您想“将第一个数据集与第一个域配对，第二个数据集与第二个域配对，依此类推”，但是对于嵌套循环，它会改为尝试第一个数据集 every域，然后对每个域尝试第二个 datsaset，依此类推。如果有 10 个数据集和 10 个域，这将产生 100 个图。
听起来您想使用单个循环。您的 for i in range(len(wrf_out)): 循环看起来不错。但是，然后您想使用 i 作为域列表的索引，这样当 i 为 0 时，您将使用数据集 0 和域 0，然后当 i为 1，您将使用数据集 1 和域 1，依此类推。
例如：
for i in range(len(wrf_out)):
domain = domain_2015_06_03[i]
lonmin = domain[0]
lonmax = domain[1]
latmin = domain[2]
latmax = domain[3]
...
我正在尝试遍历 'datasets' 的集合以及 'domains' 的集合。这个想法是将第一个数据集与第一个域配对，将第二个数据集与第二个域配对，依此类推。最终我想在地图上绘制这些数据集中的变量。我尝试使用嵌套的 for 循环，但它拒绝循环遍历数据集列表或域列表。这最终导致程序一遍又一遍地在同一个域上绘制同一个数据集。相关代码在这里：
# This extracts files and directories out of the path
arr = os.listdir(path)
# Create list for WRF output files (since files in case directories are separated by time; i.e. 12Z, 13Z, 14Z)
wrf_out = []
# For loop to fill list with WRF output files from case directory
for file in arr:
# os.path.join is REQUIRED because it joins the file to the path; otherwise you'll get the file name with NO data
filepath = os.path.join(path, file)
wrf_out.append(filepath)
# Sort WRF output files chronologically
wrf_out.sort()
# Test to make sure you have the right case/mpscheme/res/vert
#ds = Dataset(wrf_out[0])
#print(ds)
# Basic NWS colors for reflectivity
dbz_contour_levels = np.arange(0,80.,5.)
# Taken from UCAR MMM/COD NEXLAB Archive Mosiac's table
dbz_colors = ("#00ffff", "#0080ff", "#0000ff", "#00ff00", "#00c000", "#00800a", "#ffff00", "#ffc000", "#ff8000",
"#ff0000", "#c00000", "#800000", "#ff00ff", "#8d67cd", "#000000")
domain_2015_06_03 = [[-108.714, -101.222, 40.776, 45.348], [-108.714, -101.222, 40.776, 45.348], [-108.714, -101.222, 40.776, 45.348], [-108.714, -101.222, 40.776, 45.348],
[-108.714, -101.222, 40.776, 45.348], [-108.714, -101.222, 40.776, 45.348], [-108.714, -101.222, 40.776, 45.348], [-108.714, -101.222, 40.776, 45.348],
[-108.714, -101.222, 40.776, 45.348], [-108.714, -101.222, 40.776, 45.348], [-108.714, -101.222, 40.776, 45.348], [-108.714, -101.222, 40.776, 45.348],
[-108.714, -101.222, 40.776, 45.348], [-105.858, -98.827, 41.373, 45.840], [-105.858, -98.827, 41.373, 45.840], [-104.012, -95.882, 40.843, 45.902],
[-104.012, -95.882, 40.843, 45.902], [-104.012, -95.882, 40.843, 45.902], [-104.012, -95.882, 40.843, 45.902], [-104.012, -95.882, 40.843, 45.902],
[-101.222, -94.564, 40.007, 46.389], [-101.222, -94.564, 40.007, 46.389], [-100.738, -92.081, 39.972, 46.721], [-100.738, -92.081, 39.972, 46.721],
[-100.738, -92.081, 39.972, 46.721]]
# For loop to get variables, create levels, plot
for i in range(len(wrf_out)):
# For loop to create a shifting domain for higher resolution of variables
for j in domain_2015_06_03:
lonmin = j[0]
lonmax = j[1]
latmin = j[2]
latmax = j[3]
zoom_domain = [lonmin, lonmax, latmin, latmax]
# Get dataset
ds = Dataset(wrf_out[i])
# Get WRF Variables
theta_2m = getvar(ds, 'TH2')
t_pert = getvar(ds, 'T')
# wspd_10m is for divergence and arrows
wspd_10m = getvar(ds, 'uvmet10', units='m s-1')
# wind_10m is for contour
wind_10m = getvar(ds, 'uvmet10_wspd_wdir', units='m s-1')[0,:]
lat = getvar(ds, 'lat')
lon = getvar(ds, 'lon')
# mdbz is for reflectivity
mdbz = getvar(ds, 'mdbz')
# Calculate components and wrf lat lons
wrf_lat, wrf_lon = latlon_coords(mdbz)
wrf_lons = to_np(wrf_lon)
wrf_lats = to_np(wrf_lat)
x = wrf_lon.values
y = wrf_lat.values
# u_comp and v_comp are for divergence
u_comp = wspd_10m[0,:,:]
v_comp = wspd_10m[1,:,:]
# u and v are for arrows
u = u_comp.values
v = v_comp.values
# Get grid deltas
#dx, dy = mpcalc.lat_lon_grid_deltas(lon, lat)
# Calculate divergence
#div = mpcalc.divergence(u_comp, v_comp, dx=dx, dy=dy)
# Just need values for calculating contour levels; otherwise error thrown for values with dimensions
#div_vals = div.values
# Create theta levels
max_level_theta = np.max(theta_2m)
min_level_theta = np.min(theta_2m)
step_level_theta = 1.0
theta_levels = np.arange(min_level_theta, max_level_theta + step_level_theta, step_level_theta)
# Create wind levels and ticks
max_level = np.round(np.max(wind_10m))
min_level = np.round(np.min(wind_10m))
step_level = 0.5
wind_levels = np.arange(min_level, max_level + step_level, step_level)
wind_ticks = wind_levels
# Normalize u and v components for quiver plot if desired
u_norm = u / np.sqrt(u**2 + v**2)
v_norm = v / np.sqrt(u**2 + v**2)
# Timestamp
timestamp = to_np(mdbz.Time).astype('M8[s]').astype('O').isoformat()
time = mdbz.Time
time_str = str(time.values)
# Get CRS
crs = get_cartopy(wrfin=ds)
# Create a 4 panel image with four colorbars
fig = plt.figure(figsize=(30,15))
#gs = gridspec.GridSpec(2, 4, width_ratios = [0.05, 1, 1, 0.05], height_ratios = [1, 1], hspace=0.10, wspace=0.20)
#ax1 = plt.subplot(gs[0, 1], projection=crs)
#ax2 = plt.subplot(gs[0, 2], projection=crs)
#ax3 = plt.subplot(gs[1, 1], projection=crs)
#ax4 = plt.subplot(gs[1, 2], projection=crs)
#cax1 = plt.subplot(gs[0,0])
#cax2 = plt.subplot(gs[0,3])
#cax3 = plt.subplot(gs[1,0])
#cax4 = plt.subplot(gs[1,3])
ax1 = fig.add_subplot(2,2,1, projection=crs)
ax2 = fig.add_subplot(2,2,2, projection=crs)
ax3 = fig.add_subplot(2,2,3, projection=crs)
ax4 = fig.add_subplot(2,2,4, projection=crs)
################################################################################################################################################
# Top Left Plot - Reflectivity
dbz_contour = ax1.contourf(wrf_lons, wrf_lats, mdbz, colors=dbz_colors, levels=dbz_contour_levels, zorder=3,
transform=ccrs.PlateCarree())
#ax1.set_xlim(cartopy_xlim(mdbz))
#ax1.set_ylim(cartopy_ylim(mdbz))
ax1.set_extent(zoom_domain)
plot_background(ax1)
colorbar_dbz = fig.colorbar(dbz_contour, ax=ax1, orientation='vertical')
colorbar_dbz.set_label('dBZ', fontsize=12)
#cax1.yaxis.set_ticks_position('left')
#cax1.yaxis.set_label_position('left')
ax1.set_title('Composite Reflectivity (dBZ) '+ time_str[:19])
################################################################################################################################################
# Top Right Plot - 2m Theta
theta_contour = ax2.contour(wrf_lons, wrf_lats, theta_2m, levels=theta_levels, linewidths=0.9, colors='black', transform=ccrs.PlateCarree())
theta_contourf = ax2.contourf(wrf_lons, wrf_lats, theta_2m, levels=theta_levels, transform=ccrs.PlateCarree(), cmap=get_cmap('coolwarm'))
#ax2.set_xlim(cartopy_xlim(theta_2m))
#ax2.set_ylim(cartopy_ylim(theta_2m))
ax2.set_extent(zoom_domain)
plot_background(ax2)
colorbar_theta = fig.colorbar(theta_contourf, ax=ax2, orientation='vertical')
colorbar_theta.set_label(r"$\theta$ (K)", fontsize=12)
#cax2.yaxis.set_ticks_position('right')
#cax2.yaxis.set_label_position('right')
ax2.set_title('2-meter Potential Temperature (K) '+ time_str[:19])
################################################################################################################################################
# Bottom Left Plot - Divergence
#div_contour = ax3.contour(wrf_lons, wrf_lats, div, colors='black', transform=ccrs.PlateCarree())
#div_contourf = ax3.contourf(wrf_lons, wrf_lats, div, cmap=get_cmap('jet'), transform=ccrs.PlateCarree())
#ax3.set_xlim(cartopy_xlim(theta_2m))
#ax3.set_ylim(cartopy_ylim(theta_2m))
ax3.set_extent(zoom_domain)
plot_background(ax3)
#colorbar_div = fig.colorbar(div_contourf, ax=ax3, orientation='vertical')
#colorbar_div.set_label('Divergence (1/s)', fontsize=12)
#cax3.yaxis.set_ticks_position('left')
#cax3.yaxis.set_label_position('left')
ax3.set_title('Surface Divergence (1/s) ' + time_str[:19])
################################################################################################################################################
# Bottom Right Plot - 10m Wind Speed and Direction
wind_contour = ax4.contour(wrf_lons, wrf_lats, wind_10m, levels=wind_levels, colors='black', transform=ccrs.PlateCarree())
wind_contourf = ax4.contourf(wrf_lons, wrf_lats, wind_10m, levels=wind_levels, cmap=get_cmap('rainbow'), transform=ccrs.PlateCarree())
wind_quiver = ax4.quiver(x, y, u_norm, v_norm, pivot='middle', scale=25, width=0.005, color='black', regrid_shape=42.5, transform=ccrs.PlateCarree())
#ax4.set_xlim(cartopy_xlim(theta_2m))
#ax4.set_ylim(cartopy_ylim(theta_2m))
ax4.set_extent(zoom_domain)
plot_background(ax4)
colorbar_wind = fig.colorbar(wind_contourf, ax=ax4, orientation='vertical')
colorbar_wind.set_label('Wind Speed (m/s)', fontsize=12)
#cax4.yaxis.set_ticks_position('right')
#cax4.yaxis.set_label_position('right')
ax4.set_title('Wind Speed and Direction ' + time_str[:19])
plt.show()
这是它生成重复图像的情节：
不要担心左下角的坐标轴，或者某些图像左侧的白色 space。
我该怎么做才能解决这个问题？除了嵌套的 for 循环之外，还有更好的方法吗？感谢您的帮助！
两个嵌套的 for 循环不会完成您描述的目标。您说您想“将第一个数据集与第一个域配对，第二个数据集与第二个域配对，依此类推”，但是对于嵌套循环，它会改为尝试第一个数据集 every域，然后对每个域尝试第二个 datsaset，依此类推。如果有 10 个数据集和 10 个域，这将产生 100 个图。
听起来您想使用单个循环。您的 for i in range(len(wrf_out)): 循环看起来不错。但是，然后您想使用 i 作为域列表的索引，这样当 i 为 0 时，您将使用数据集 0 和域 0，然后当 i为 1，您将使用数据集 1 和域 1，依此类推。
例如：
for i in range(len(wrf_out)):
domain = domain_2015_06_03[i]
lonmin = domain[0]
lonmax = domain[1]
latmin = domain[2]
latmax = domain[3]
...