def griddata_from_cmadaas_json(json,time=None,dtime=None):
try:
lonS = round(json['startLon'],4)
# lonE = round(json['endLon'],3)
latS = round(json['startLat'],4)
# latE = round(json['endLat'],3)
dlon = round(json['lonStep'],4)
dlat = round(json['latStep'],4)
nlon = int(json['lonCount'])
nlat = int(json['latCount'])
lonE = lonS + (nlon-1)*dlon
latE = latS + (nlat-1)*dlat
#print(lonS,dlon,nlon,lonE)
data = np.array(json['DS'])
#data
# print(data.shape)
if time is not None:
if not isinstance(time,list):
time = [time]#确保time为列表
if dtime is not None:
if not isinstance(dtime,list):
dtime = [dtime]#确保dtime为列表
# print(time,dtime)
grid_info = meb.grid([lonS,lonE,dlon],[latS,latE,dlat]
,gtime = time
,dtime_list=dtime)
#print(grid_info)
grid = meb.grid_data(grid_info,data)
except Exception as data:
print(json)
return None
return grid
def plot_values(dataset_ob, grd_ob, grd_fo, cmap):
data_Xlabeled = grd_ob
data_Ylabeled = grd_fo
#整理无坐标信息的格点场的坐标：原始的观测场和预报场的经纬度范围应该是对应的
glon = [
dataset_ob["lon"].values[0], dataset_ob["lon"].values[-1],
dataset_ob["lon"].values[1] - dataset_ob["lon"].values[0]
] #经度的起点、终点、间隔
glat = [
dataset_ob["lat"].values[0], dataset_ob["lat"].values[-1],
dataset_ob["lat"].values[1] - dataset_ob["lat"].values[0]
] #纬度的起点、终点、间隔
grid1 = meb.grid(glon, glat)
data_Xlabeled_tr = meb.grid_data(grid1, data_Xlabeled)
data_Ylabeled_tr = meb.grid_data(grid1, data_Ylabeled)
a = data_Xlabeled_tr
b = data_Ylabeled_tr
#colorbar生成
#cmap, clev = meb.def_cmap_clevs(meb.cmaps.mode, vmax = np.max((np.max(a), np.max(b))))
#clev = meb.def_cmap_clevs(meb.cmaps.mode, vmax = np.max((np.max(a), np.max(b))))
#meb.tool.color_tools.show_cmap_clev(cmap, clev) #展示colorbar
#meb.tool.plot_tools.plot_2d_grid_list([a, b], cmap = cmap, clevs = clev)
meb.tool.plot_tools.plot_2d_grid_list([a, b],
cmap=cmap,
vmax=np.max((np.max(a), np.max(b))))
def get_grd(dataset_ob, data):
#dataset_ob参数是为了获取原始场的起始经纬度坐标和间隔
#整理无坐标信息的格点场的坐标：原始的观测场和预报场的经纬度范围应该是对应的
glon = [
dataset_ob["lon"].values[0], dataset_ob["lon"].values[-1],
dataset_ob["lon"].values[1] - dataset_ob["lon"].values[0]
] #经度的起点、终点、间隔
glat = [
dataset_ob["lat"].values[0], dataset_ob["lat"].values[-1],
dataset_ob["lat"].values[1] - dataset_ob["lat"].values[0]
] #纬度的起点、终点、间隔
grid1 = meb.grid(glon, glat)
data_tr = meb.grid_data(grid1, data)
return data_tr
def plot_ids(dataset_ob, grd_ob, grd_fo):
data_Xlabeled = grd_ob
data_Ylabeled = grd_fo
#整理无坐标信息的格点场的坐标：原始的观测场和预报场的经纬度范围应该是对应的
glon = [
dataset_ob["lon"].values[0], dataset_ob["lon"].values[-1],
dataset_ob["lon"].values[1] - dataset_ob["lon"].values[0]
] #经度的起点、终点、间隔
glat = [
dataset_ob["lat"].values[0], dataset_ob["lat"].values[-1],
dataset_ob["lat"].values[1] - dataset_ob["lat"].values[0]
] #纬度的起点、终点、间隔
grid1 = meb.grid(glon, glat)
data_Xlabeled_tr = meb.grid_data(grid1, data_Xlabeled)
data_Ylabeled_tr = meb.grid_data(grid1, data_Ylabeled)
a = data_Xlabeled_tr
b = data_Ylabeled_tr
meb.tool.plot_tools.plot_2d_grid_list([a, b],
cmap="mode",
vmax=np.max(
(np.max(a), np.max(b))) + 1)
getTimeOb.qiBaoShiJian[2:-2] + '.' + str(x).zfill(3)
for x in range(1, 25)
]
listFileSrc = [qibaoshijian + '.' + str(x).zfill(3) for x in range(13, 37)]
################从FTP下载资料
ftpOb = FtpLoad(ftpHost, ftpPort, ftpUsr, ftpPwd)
ftpConnect = ftpOb.ftpConnet
# fileList = ftpConnect.nlst('/20200915')
# print(fileList)
for eFile in listFileSrc:
try:
ftpOb.ftp_download(pathDstOrig + eFile,
'/20' + qibaoshijian[:-2] + pathFTPBase + eFile)
########################插值切片
grd = meb.read_griddata_from_micaps4(pathDstOrig + eFile)
grid1 = meb.grid([89.3, 102.95, 0.05], [31.4, 39.4, 0.05])
grd1 = meb.interp_gg_linear(grd, grid1)
save_path = pathDstOrig + eFile
meb.write_griddata_to_micaps4(grd1, save_path, effectiveNum=2)
except Exception as downloadError:
print(downloadError)
# ##############拷贝##############
try:
for ee in range(len(listFileDst)):
shutil.copy(pathDstOrig + listFileSrc[ee],
pathDst + listFileDst[ee])
except Exception as e:
print(e)
#######################选取最大最小值
try:
finalData = get_Tmost_array(listFileDst)