def test_jellyroll_subplot():
data = ecm.load_cases(root)
case_index = 0
amp_index = 0
var_index = 0
case_folder = children[case_index]
amps = ecm.get_amp_cases(case_folder)
case = list(data.keys())[case_index]
ecm.jellyroll_subplot(data,
case,
amps[amp_index],
var=var_index,
soc_list=[[1.0, 0.99], [0.98, 0.97]],
global_range=False,
dp=1)
if close_plots:
plt.close('all')
wrk.clear()
assert 1 == 1
def load_all_data():
config = configparser.ConfigParser()
net = ecm.get_net()
weights = ecm.get_weights(net)
cases = get_cases()
amps = ecm.get_amp_cases()
variables = ecm.get_saved_var_names()
data = {}
for ci in range(len(cases.keys())):
case_folder = os.path.join(root, cases[ci]['file'])
data[ci] = {}
config.read(os.path.join(case_folder, 'config.txt'))
data[ci]['config'] = ecm.config2dict(config)
for amp in amps:
amp_folder = os.path.join(case_folder, str(amp) + 'A')
data[ci][amp] = {}
for vi, v in enumerate(variables):
data[ci][amp][vi] = {}
temp = ecm.load_and_amalgamate(amp_folder, v)
if temp is not None:
if vi == 0:
check_nans = np.any(np.isnan(temp), axis=1)
if np.any(check_nans):
print('Nans removed from', amp_folder)
if np.any(check_nans):
temp = temp[~check_nans, :]
data[ci][amp][vi]['data'] = temp
means = np.zeros(temp.shape[0])
for t in range(temp.shape[0]):
(mean, std_dev) = ecm.weighted_avg_and_std(
temp[t, :], weights)
means[t] = mean
data[ci][amp][vi]['mean'] = means
data[ci][amp][vi]['min'] = np.min(temp, axis=1)
data[ci][amp][vi]['max'] = np.max(temp, axis=1)
if temp is not None:
t_hrs = data[ci][amp][10]['data'][:, 0]
cap = t_hrs * amp
data[ci][amp]['capacity'] = cap
return data
plt.style.use('seaborn-dark')
cmap = sns.color_palette("rocket")
warnings.filterwarnings("ignore")
root = 'D:\\pybamm_pnm_results\\46800'
save_im_path = 'D:\\pybamm_pnm_results\\46800\\figures'
plt.close('all')
savefigs = True
tab_1 = [0, 1, 2, 3, ]
tab_tesla = [4, 5, 6, 7, ]
amps = ecm.get_amp_cases()
d = ecm.load_all_data()
cases = ecm.get_cases()
soc_list = [[0.9, 0.8, 0.7],
[0.6, 0.5, 0.4],
[0.3, 0.2, 0.1]]
mini_soc_list = [[0.8, 0.6],
[0.4, 0.2]]
grp = 'neg'
data = d[0][17.5][0]['data']
def find_best_fit(y, report_results=False):
# Set up list of candidate distributions to use
# See https://docs.scipy.org/doc/scipy/reference/stats.html for more
#y = data_spm.copy()
def _load(filepath):
data = ecm.load_cases(filepath)
cases = list(data.keys())
amps = ecm.get_amp_cases(os.path.join(filepath, cases[0]))
return data, amps, cases