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)