def read_2d_data(PATH_PKL): filename = "9_30_Fig2_" + NN_NAME + ".pkl" path = os.path.join(PATH_PKL, filename) # S = np.load(path, allow_pickle=True) # with open(path, "rb") as hf: # S = pickle.load(hf) S = common.load_pickle_from_url(path) # Coordinates coords = { "path_bins": midpoint(S["QMspace"]), "lts_bins": midpoint(S["LTSspace"]) } data_vars = {} # Histogram quantities (Figure 2) dims_hist = ["path_bins", "lts_bins"] data_vars["net_precipitation_nn"] = (dims_hist, S["PREChist"][NN_NAME]) data_vars["net_precipitation_src"] = (dims_hist, S["PREChist"][TRUTH_NAME]) data_vars["net_heating_nn"] = (dims_hist, S["HEAThist"][NN_NAME]) data_vars["net_heating_src"] = (dims_hist, S["HEAThist"][TRUTH_NAME]) data_vars["count"] = (dims_hist, S["Whist"]) # Vertical quantities Figure 3 and 4 return xr.Dataset(data_vars, coords=coords)
def download_tom_data_3(): # read in stable data lrfs = {} dataold = common.load_pickle_from_url("https://github.com/tbeucler/CBRAIN-CAM/raw/master/notebooks/tbeucler_devlog/PKL_DATA/9_13_LRF.pkl") lrfs['Stable 1%'] = { 'base_state': dataold[BASE_STATE_KEY], JACOBIAN_KEY: dataold[LRF_KEY][0]['MeanLRF_stable'] } return lrfs
def download_tom_data_2(): name = "MeanLRF_stable" title = "Stable" d = common.load_pickle_from_url(S2_URL) return {title: { "base_state": d[BASE_STATE_KEY], JACOBIAN_KEY: d[LRF_KEY][name], } }
def download_tom_data_1(): dataunstab = common.load_pickle_from_url("https://github.com/tbeucler/CBRAIN-CAM/raw/master/notebooks/tbeucler_devlog/PKL_DATA/2020_03_02_LRF_Unstable.pkl") # read in unstable data lrfs = {} for ind, name in [ (0, 'Unstable'), (1, 'Unstable 1%'), (5, 'Unstable 10%'), (9, 'Unstable 20%'), ]: lrfs[name] = { 'base_state': dataunstab[BASE_STATE_KEY], JACOBIAN_KEY: dataunstab[LRF_KEY][ind]['MeanLRF_unstable'], } return lrfs
def read_2d_data(url, nn): NN_NAME = nn S = common.load_pickle_from_url(url) # Coordinates coords = { "path_bins": midpoint(S["QMspace"]), "lts_bins": midpoint(S["LTSspace"]) } data_vars = {} # Histogram quantities (Figure 2) dims_hist = ["path_bins", "lts_bins"] data_vars["net_precipitation_nn"] = (dims_hist, S["PREChist"][NN_NAME]) data_vars["net_precipitation_src"] = (dims_hist, S["PREChist"][TRUTH_NAME]) data_vars["net_heating_nn"] = (dims_hist, S["HEAThist"][NN_NAME]) data_vars["net_heating_src"] = (dims_hist, S["HEAThist"][TRUTH_NAME]) data_vars["count"] = (dims_hist, S["Whist"]) # Vertical quantities Figure 3 and 4 return xr.Dataset(data_vars, coords=coords)
import common import wave import sys import numpy as np url = 'https://github.com/tbeucler/CBRAIN-CAM/raw/master/notebooks/tbeucler_devlog/PKL_DATA/2020_03_02_GR.pkl' S = common.load_pickle_from_url(url) S['Input_reg'] = np.array([0.01, 0.05, 0.1, 0.15, 0.2, 0.25]) # Hard-coded table of results from the 4 prognostic tests: S['maxstep'] = np.array([ [134, 590, 446, 1499, 2044, 103], # Orig IC [651, 566, 332, 363, 1686, 95], # Jan12 IC [512, 678, 337, 840, 2011, 97], # Jan18 IC [297, 504, 866, 1304, 1999, 118] ]) # Jan24 IC with open(sys.argv[1], "w") as f: wave.dump(S, f)