我正在编写循环遍历多个 netcdf 文件(大~28G)的代码。 netcdf 文件在整个域中具有多个 4D 变量 [时间、东西、南北、高度]。目标是遍历这些文件并遍历域中所有这些变量的每个位置，并将某些变量提取到一个大数组中。当文件丢失或不完整时，我用 99.99 填充这些值。现在我只是通过循环超过 2 个每日 netcdf 文件来进行测试，但由于某种原因它会永远(~14 小时)。我不确定是否有优化此代码的方法。我不认为 python 应该花这么长时间来完成这项任务，但也许这是 python 或我的代码的问题。下面是我的代码，希望它是可读的，并且非常感谢任何关于如何使它更快的建议:
#Domain to loop over
k_space = np.arange(0,37)
j_space = np.arange(80,170)
i_space = np.arange(200,307)
predictors_wrf=[]
names_wrf=[]
counter = 0
cdate = start_date
while cdate <= end_date:
if cdate.month not in month_keep:
cdate+=inc
continue
yy = cdate.strftime('%Y')
mm = cdate.strftime('%m')
dd = cdate.strftime('%d')
filename = wrf_path+'\wrfoutRED_d01_'+yy+'-'+mm+'-'+dd+'_'+hour_str+'_00_00'
for i in i_space:
for j in j_space:
for k in k_space:
if os.path.isfile(filename):
f = nc.Dataset(filename,'r')
times = f.variables['Times'][1:]
num_lines = times.shape[0]
if num_lines == 144:
u = f.variables['U'][1:,k,j,i]
v = f.variables['V'][1:,k,j,i]
wspd = np.sqrt(u**2.+v**2.)
w = f.variables['W'][1:,k,j,i]
p = f.variables['P'][1:,k,j,i]
t = f.variables['T'][1:,k,j,i]
if num_lines < 144:
print "partial files for WRF: "+ filename
u = np.ones((144,))*99.99
v = np.ones((144,))*99.99
wspd = np.ones((144,))*99.99
w = np.ones((144,))*99.99
p = np.ones((144,))*99.99
t = np.ones((144,))*99.99
else:
u = np.ones((144,))*99.99
v = np.ones((144,))*99.99
wspd = np.ones((144,))*99.99
w = np.ones((144,))*99.99
p = np.ones((144,))*99.99
t = np.ones((144,))*99.99
counter=counter+1
predictors_wrf.append(u)
predictors_wrf.append(v)
predictors_wrf.append(wspd)
predictors_wrf.append(w)
predictors_wrf.append(p)
predictors_wrf.append(t)
u_names = 'u_'+str(k)+'_'+str(j)+'_'+str(i)
v_names = 'v_'+str(k)+'_'+str(j)+'_'+str(i)
wspd_names = 'wspd_'+str(k)+'_'+str(j)+'_'+str(i)
w_names = 'w_'+str(k)+'_'+str(j)+'_'+str(i)
p_names = 'p_'+str(k)+'_'+str(j)+'_'+str(i)
t_names = 't_'+str(k)+'_'+str(j)+'_'+str(i)
names_wrf.append(u_names)
names_wrf.append(v_names)
names_wrf.append(wspd_names)
names_wrf.append(w_names)
names_wrf.append(p_names)
names_wrf.append(t_names)
cdate+=inc
最佳答案
这是收紧您的 forloop 的第一关。由于每个文件只使用一次文件形状，因此可以将处理移到循环之外，这将减少中断处理中的数据加载量。我仍然不明白 counter 和 inc 做了什么，因为它们似乎没有在循环中更新。您肯定想研究重复的字符串连接性能，或者将附加到 predictors_wrf 和 names_wrf 的性能作为起点
k_space = np.arange(0,37)
j_space = np.arange(80,170)
i_space = np.arange(200,307)
predictors_wrf=[]
names_wrf=[]
counter = 0
cdate = start_date
while cdate <= end_date:
if cdate.month not in month_keep:
cdate+=inc
continue
yy = cdate.strftime('%Y')
mm = cdate.strftime('%m')
dd = cdate.strftime('%d')
filename = wrf_path+'\wrfoutRED_d01_'+yy+'-'+mm+'-'+dd+'_'+hour_str+'_00_00'
file_exists = os.path.isfile(filename)
if file_exists:
f = nc.Dataset(filename,'r')
times = f.variables['Times'][1:]
num_lines = times.shape[0]
for i in i_space:
for j in j_space:
for k in k_space:
if file_exists:
if num_lines == 144:
u = f.variables['U'][1:,k,j,i]
v = f.variables['V'][1:,k,j,i]
wspd = np.sqrt(u**2.+v**2.)
w = f.variables['W'][1:,k,j,i]
p = f.variables['P'][1:,k,j,i]
t = f.variables['T'][1:,k,j,i]
if num_lines < 144:
print "partial files for WRF: "+ filename
u = np.ones((144,))*99.99
v = np.ones((144,))*99.99
wspd = np.ones((144,))*99.99
w = np.ones((144,))*99.99
p = np.ones((144,))*99.99
t = np.ones((144,))*99.99
else:
u = np.ones((144,))*99.99
v = np.ones((144,))*99.99
wspd = np.ones((144,))*99.99
w = np.ones((144,))*99.99
p = np.ones((144,))*99.99
t = np.ones((144,))*99.99
counter=counter+1
predictors_wrf.append(u)
predictors_wrf.append(v)
predictors_wrf.append(wspd)
predictors_wrf.append(w)
predictors_wrf.append(p)
predictors_wrf.append(t)
u_names = 'u_'+str(k)+'_'+str(j)+'_'+str(i)
v_names = 'v_'+str(k)+'_'+str(j)+'_'+str(i)
wspd_names = 'wspd_'+str(k)+'_'+str(j)+'_'+str(i)
w_names = 'w_'+str(k)+'_'+str(j)+'_'+str(i)
p_names = 'p_'+str(k)+'_'+str(j)+'_'+str(i)
t_names = 't_'+str(k)+'_'+str(j)+'_'+str(i)
names_wrf.append(u_names)
names_wrf.append(v_names)
names_wrf.append(wspd_names)
names_wrf.append(w_names)
names_wrf.append(p_names)
names_wrf.append(t_names)
cdate+=inc
关于python - 如何让我的 python 代码运行得更快，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/42382666/