|
| str | dir1 = "./sampledata/scale-5.4.5/scale-rm/test/tutorial/real/experiment/run/" |
| |
| int | PRC_NUM_X = 2 |
| |
| int | PRC_NUM_Y = 2 |
| |
| str | ftype = "history" |
| |
| str | domainlabel = "_d01" |
| |
| str | timelabel = "" |
| |
| str | varname1 = "PREC" |
| | Value (e.g., PREC) 2D (spatial) + time.
|
| |
| int | var_scaling_const = 3600 |
| |
| str | var_unit = "(mm/h)" |
| |
| str | weight_type = "cell_area" |
| |
| int | weight_scaling_const = 1 |
| |
| | args = sys.argv |
| |
| | PRC_NUM_X_ANL = int(args[1]) |
| |
| | PRC_NUM_Y_ANL = int(args[2]) |
| |
| str | dir_out = "./fig/" |
| |
| | comm |
| |
| | size |
| |
| | rank |
| |
| | PRC_NUM_X_PER_ANL |
| |
| | PRC_NUM_Y_PER_ANL |
| |
| | fpathlist = get_fpathlist_mpi(dir1,ftype,domainlabel,timelabel,PRC_NUM_X,PRC_NUM_Y,PRC_NUM_X_ANL,PRC_NUM_Y_ANL,size,rank) |
| |
| | ds = get_xrvar(fpathlist) |
| |
| | value_ = np.array(ds[varname1].values) |
| |
| | weight_ = np.ones_like(value_) |
| |
| | weight_sum_ = np.array( np.sum( weight_, axis=(1,2)), 'd') |
| |
| tuple | weighted_value_ = ( value_ * weight_ ) |
| |
| | weighted_value_sum_ = np.array( np.sum( weighted_value_, axis=(1,2) ), 'd') |
| |
| | weighted_value_sum = np.array(np.zeros(weighted_value_.shape[0]),'d') |
| |
| | weight_sum = np.array(np.zeros(weight_sum_.shape[0]),'d') |
| |
| | op |
| |
| | SUM |
| |
| | root |
| |
| | value_max_ = np.array( np.max( value_, axis=(1,2) ), 'd') |
| |
| | value_min_ = np.array( np.min( value_, axis=(1,2) ), 'd') |
| |
| | value_max = np.array( np.zeros(value_max_.shape[0]), 'd') |
| |
| | value_min = np.array( np.zeros(value_min_.shape[0]), 'd') |
| |
| | MAX |
| |
| | MIN |
| |
| | time1 = ds["time"].values |
| |
| | fig |
| |
| | ax |
| |
| | marker |
| |
