def predict(self, filename, input):
dill.load_session(filename)
self.batch_size = 1
self.forward_pass(input)
a = self.layers[self.num_layers-1].activations
a[np.where(a==np.max(a))] = 1
a[np.where(a!=np.max(a))] = 0
return a
def dump_session(file_path): """Pickle the current python session to be used in the worker. Note: Due to the inconsistency in the first dump of dill dump_session we create and load the dump twice to have consistent results in the worker and the running session. Check: https://github.com/uqfoundation/dill/issues/195 """ dill.dump_session(file_path) dill.load_session(file_path) return dill.dump_session(file_path)
def load_dill(self):
fname = fdialog.askopenfilename(filetypes=['dill {.pkl}'],
title='Open dill PKL file',
defaultextension='PKL')
if fname:
dill.load_session(fname)
self.set_dataset_name(fname)
curdir = Path(fname).parent
os.chdir(curdir)
messagebox.showinfo("Success", "Data loaded.")
def load_game():
if os.path.exists('savefile.pkl') == True:
dill.load_session('savefile.pkl')
print('Game succesfully loaded!')
input('<Continue (Press Enter)>')
print_location()
else:
os.system('cls')
print("There\'s no saved game to load!")
input('<Return (Press Enter)>')
title_screen()
def restore_session(file):
"""Resore a session from file.
:return: Returns True if session is restored
:rtype: bool
"""
if not os.path.isfile(file):
print(file + " doesn't exist")
return False
#load the session
dill.load_session(file)
print("Session restored.")
return True
def dump_session(file_path):
"""For internal use only; no backwards-compatibility guarantees.
Pickle the current python session to be used in the worker.
Note: Due to the inconsistency in the first dump of dill dump_session we
create and load the dump twice to have consistent results in the worker and
the running session. Check: https://github.com/uqfoundation/dill/issues/195
"""
with _pickle_lock_unless_py2:
dill.dump_session(file_path)
dill.load_session(file_path)
return dill.dump_session(file_path)
def check_accuracy(self, filename, inputs, labels):
dill.load_session(filename)
self.batch_size = len(inputs)
self.forward_pass(inputs)
a = self.layers[self.num_layers - 1].activations
a[np.where(a == np.max(a))] = 1
a[np.where(a != np.max(a))] = 0
total = 0
correct = 0
for i in range(len(a)):
total += 1
if np.equal(a[i], labels[i]).all():
correct += 1
print("Accuracy: ", correct * 100 / total)
def checkAccuracy(self, dataFile, data, flags):
dill.load_session(dataFile)
self.batchSize = len(data)
self.passForward(data)
a = self.layers[self.numLayers - 1].activations
a[np.where(a == np.max(a))] = 1
a[np.where(a != np.max(a))] = 0
total = 0
correct = 0
for i in range(len(a)):
total += 1
if np.equal(a[i], flags[i]).all():
correct += 1
print("Accuracy: ", correct * 100 / total)
def predict(self, filename, input):
'''
It takes 2 inputs:
1. filename: The file from which trained model is to be loaded.
2. input: The input for which we want the prediction.
It does a forward pass, and then converts the output into one-hot encoding i.e. the maximum element of array is 1 and all others are 0.
'''
dill.load_session(filename)
self.batch_size = 1
self.forward_pass(input)
a = self.layers[self.num_layers - 1].activations
a[np.where(a == np.max(a))] = 1
a[np.where(a != np.max(a))] = 0
return a
def check_accuracy(self, filename, inputs, labels):
dill.load_session(filename)
self.batch_size = len(inputs)
self.forward_pass(inputs)
a = self.layers[self.num_layers-1].activations
num_classes = 10
targets = np.array([a]).reshape(-1)
a = np.asarray(a)
one_hot_labels = np.eye(num_classes)[a.astype(int)]
total=0
correct=0
for i in range(len(a)):
total += 1
if np.equal(one_hot_labels[i], labels[i]).all():
correct += 1
print("Accuracy: ", correct*100/total)
def check_accuracy(self, filename, inputs, labels):
dill.load_session(filename)
self.batch_size = len(inputs)
self.forward_pass(inputs)
a = self.layers[self.num_layers-1].activations
num_classes = 10
targets = np.array([a]).reshape(-1)
a = np.asarray(a)
one_hot_labels = np.eye(num_classes)[a.astype(int)]
total=0
correct=0
for i in range(len(a)):
total += 1
if np.equal(one_hot_labels[i], labels[i]).all():
correct += 1
print("Accuracy: ", correct*100/total)
def WorkspaceBasedCheckPt(CheckPtPosition = 0, AccessRecentOrNewWorkSpaceImage = False, Force = {'access':False, 'task':'load'}, CheckPoint_Dir = "NotebookCheckpoints/"):
if not os.path.exists(CheckPoint_Dir):
os.makedirs(CheckPoint_Dir)
file= open((CheckPoint_Dir + '0__CheckPt.db'),"w+")
file.close()
LastCheckPt = max([int(CheckPoint.split('/')[len(CheckPoint.split('/')) -1].split('__')[0]) for CheckPoint in glob.glob(CheckPoint_Dir + '*{}'.format('CheckPt.db'))])
## Force can be used when proceding in non serialized manner
if Force['access']: ## could have been compressed with chunk below using boolean algebra to make code more smaller
AccessRecentOrNewWorkSpaceImage = False
if Force['task'] == 'load':
print("Checkpoint ", CheckPtPosition, "is to be loaded")
if os.path.exists(CheckPoint_Dir + str(CheckPtPosition) + '__CheckPt.db'):
dill.load_session(CheckPoint_Dir + str(CheckPtPosition) + '__CheckPt.db') # To restore a session
else:
print("This checkpoint doesn't exist, hence won't be loaded.")
elif Force['task'] == 'save':
print("Checkpoint ", CheckPtPosition, "is to be Saved")
dill.dump_session(CheckPoint_Dir + str(CheckPtPosition) + '__CheckPt.db') # To Save a session
return "Force used to {} workspace checkpoint_{}.".format(Force['task'], CheckPtPosition) ## exit here only
## This Code below is used to handle the actions on returning the value to run/not run the cell
### = is set so that the current check point cell code are able to run
if ((AccessRecentOrNewWorkSpaceImage == False) and (CheckPtPosition <= LastCheckPt)):
print('Most Recent Checkpoint is : {} \nHence, cells won\'t be running content untill most recent checkpoint is crossed.'.format(LastCheckPt))
return False
elif ((AccessRecentOrNewWorkSpaceImage == False) and (CheckPtPosition == (LastCheckPt +1))):
print('Running this cell')
return True
elif ((AccessRecentOrNewWorkSpaceImage == False) and (CheckPtPosition > (LastCheckPt +1))):
print("You have skipped over a checkpoint. Still running this cell")
return True
## This Code below is used to handle the actions on saving/loading the workspace images
if (AccessRecentOrNewWorkSpaceImage and (CheckPtPosition == 0)):
print("Initial Phase, hence not saving workspace.")
elif (AccessRecentOrNewWorkSpaceImage and (LastCheckPt > CheckPtPosition)):
print("This is not the most recent checkpoint, hence not loading it. [Use Force to force load a checkpoint]")
elif (AccessRecentOrNewWorkSpaceImage and (LastCheckPt == CheckPtPosition)):
dill.load_session(CheckPoint_Dir + str(CheckPtPosition) + '__CheckPt.db') # To restore a session
print("This is the most recent checkpoint, hence loading it.")
elif (AccessRecentOrNewWorkSpaceImage and ((LastCheckPt +1) == CheckPtPosition)):
dill.dump_session(CheckPoint_Dir + str(CheckPtPosition) + '__CheckPt.db') # To Save a session
print("Congrats, on reaching a new checkpoint, saving it.")
elif (AccessRecentOrNewWorkSpaceImage and ((LastCheckPt +1) < CheckPtPosition)):
print("You have skipped over a checkpoint. Hence not Saving anything.")
def main():
plt.close('all')
file_path = './result'
if not os.path.isdir(file_path):
os.makedirs(file_path)
dill.load_session('result/compare.pkl')
S_list = main.S_list
err = main.err
err_pca = main.err_pca
err_svd = main.err_svd
err_mds = main.err_mds
pr.plot_compare_mean_std(S_list, err, err_pca, err_svd, err_mds, False)
pr.plot_compare_mean_std(S_list, err, err_pca, err_svd, err_mds, True)
pr.plot_compare_scatter(S_list, err, err_pca, err_svd, err_mds, False)
pr.plot_compare_scatter(S_list, err, err_pca, err_svd, err_mds, True)
plt.close('all')
def check_accuracy(self, filename, inputs, targets):
dill.load_session(filename)
# self.batch_size = len(inputs)
self.forward_pass(inputs)
a = self.layers[self.num_layers - 1].activations
# a[np.where(a == np.max(a))] = 1
# a[np.where(a != np.max(a))] = 0
a = np.rint(a)
total = 0
correct = 0
for i in range(len(a)):
total = total + 1
# print(self.layers[self.num_layers - 1].activations[i], a[i], labels[i])
# print(a[i], targets[i])
if np.equal(a[i], targets[i]).all():
correct = correct + 1
print("Accuracy on test: " + str(correct) + "/" + str(total))
def _run(func_fn):
"""Executes dill-ed function/the calculator of an ASE Atoms object using args/kwargs from dill-ed args/kwargs files"""
session_fn = func_fn.replace('.pkl', '_session.pkl')
try:
dill.load_session(session_fn)
except IOError:
pass
with open(func_fn) as func_f:
func, args, kwargs = dill.load(func_f)
if func.__class__ == gausspy.gaussian.Gaussian:
func.start(*args, **kwargs)
elif func.__class__ == ase.atoms.Atoms:
func.calc.start(*args, **kwargs)
else:
func(*args, **kwargs)
def plot_alpha(filename=(), prename=(), name=()):
dill.load_session(filename)
k = 0
it_alpha_fixed_ratio = 0
p.z = p.isolver.save_alpha[:, it_alpha_fixed_ratio]
p.plot_pre()
fig = plt.figure()
plt.contour(p.x, p.y, p.z.reshape(p.y.size, p.x.size), 30)
p.ld.plot(lw=0.8, ms=1)
p.so.plot(lw=0.8, ms=1)
plt.colorbar()
plt.title('alpha')
plt.axis('equal')
plt.axis('square')
plt.show(block=False)
if savefig:
plt.savefig('runs/fig-thesis/%s_lsm_%s%s_alpha_it_alpha_%s.eps' %
(prename, name, k, it_alpha_fixed_ratio),
bbox_inches='tight')
def _run(func_fn):
"""Executes dill-ed function/the calculator of an ASE Atoms object using args/kwargs from dill-ed args/kwargs files"""
session_fn = func_fn.replace('.pkl', '_session.pkl')
try:
dill.load_session(session_fn)
except IOError:
pass
with open(func_fn) as func_f:
func, args, kwargs = dill.load(func_f)
if func.__class__ == gausspy.gaussian.Gaussian:
func.start(*args, **kwargs)
elif func.__class__ == ase.atoms.Atoms:
func.calc.start(*args, **kwargs)
else:
func(*args, **kwargs)
def test_loadRuntimeDataAndRun(self):
"""
Tests the pickled function and data can be loaded and run correctly.
"""
# ensure the 'add' function from the test's __main__ session is removed and
# not available to this test case.
del globals()['add']
self.assertFalse('add' in globals().keys())
# load session data and test if the 'add' function is again avaliable after
# data is loaded.
dill.load_session(self.__class__.sesPathPkl)
self.assertTrue('add' in globals().keys())
# load data for the 'add' function, and test if we get expected result after
# running the 'add' function with the loaded data.
with open(self.__class__.jobPathPkl, 'rb') as f:
data = dill.load(f)
self.assertTrue(add(*data) == 3)
def show_num_feat_stat():
axs = {}
ds = ['NSL-KDD', 'UCI-BCW']
r = []
for t in [1, 2]:
app.mText.insert('end-1c', 'type: %s\n' % t)
with os.scandir() as li:
for entry in li:
if entry.is_file() and ".pkl" in entry.name:
dill.load_session(entry.name)
s = 1
if (t == 1 and ld[0][0] in ['src_bytes', '\'src_bytes\''])\
or (t == 2 and ld[0][0] not in ['src_bytes', '\'src_bytes\'']):
app.mText.insert('end-1c', '%s\n' % entry.name)
for l in ld:
if ld[0][0][0] == '\'':
r.append(
[ds[t - 1], entry.name, s, l[1], l[0]])
elif isinstance(ld[0][2], type({})):
r.append([
ds[t - 1], entry.name, s, l[2][l[0]], l[0]
])
else:
r.append(
[ds[t - 1], entry.name, s, l[2], l[0]])
s += 1
rd = pd.DataFrame(r, columns=['dataset', 'name', 'step', 'acc', 'feature'])
rds = rd.groupby(by=['dataset', 'name']).agg('max')['step']
rd2 = pd.DataFrame({
'dataset':
np.array(rds.index.get_level_values(0).values),
'step':
np.array(rds.values)
})
ax: plt.Axes = rd2.boxplot(column='step', by='dataset')
ax.get_figure().suptitle('')
ax.set_title('Number of selected features')
ax.get_figure().canvas.set_window_title('num_feat')
plt.show()
def show_growth_stat():
axs = {}
for t in [1, 2]:
app.mText.insert('end-1c', 'type: %s\n' % t)
r = []
with os.scandir() as li:
for entry in li:
if entry.is_file() and ".pkl" in entry.name:
dill.load_session(entry.name)
s = 1
if (t == 1 and ld[0][0] in ['src_bytes', '\'src_bytes\''])\
or (t == 2 and ld[0][0] not in ['src_bytes', '\'src_bytes\'']):
app.mText.insert('end-1c', '%s\n' % entry.name)
for l in ld:
if ld[0][0][0] == '\'':
r.append([entry.name, s, l[1], l[0]])
elif isinstance(ld[0][2], type({})):
r.append([entry.name, s, l[2][l[0]], l[0]])
else:
r.append([entry.name, s, l[2], l[0]])
s += 1
rd = pd.DataFrame(r, columns=['name', 'step', 'acc', 'feature'])
ax: plt.Axes = rd.boxplot(column='acc', by='step')
ax.get_figure().suptitle('')
ax.set_title('Accuracy by step')
ax.get_figure().canvas.set_window_title('growth')
'''
ax = rd.boxplot(column='acc', by='step')
ax.get_figure().suptitle('')
ax.set_title('Accuracy by step')
ax.set_yscale('log')
ax.get_figure().canvas.set_window_title('growth_log')
'''
axs[t] = ax
xlim = axs[1].get_xlim()
ylim = axs[1].get_ylim()
axs[2].set_xlim(xlim[0], xlim[1])
axs[2].set_ylim(ylim[0], ylim[1])
plt.show()
def show_important_features_builtin():
axs = {}
global top_n, FI
for t in [1, 2]:
app.mText.insert('end-1c', 'type: %s\n' % t)
r = []
with os.scandir() as li:
for entry in li:
if entry.is_file() and ".pkl" in entry.name:
top_n = []
FI = []
dill.load_session(entry.name)
if (t == 1 and ld[0][0] in ['src_bytes', '\'src_bytes\''])\
or (t == 2 and ld[0][0] not in ['src_bytes', '\'src_bytes\'']):
app.mText.insert('end-1c', '%s\n' % entry.name)
if len(top_n):
app.mText.insert(
'end-1c', '%s;%s;%s;%s;%s;%s\n' %
(X_train.columns[top_n[0]],
locale.format("%g", FI[top_n[0]]),
X_train.columns[top_n[1]],
locale.format("%g", FI[top_n[1]]),
X_train.columns[top_n[2]],
locale.format("%g", FI[top_n[2]])))
def load_session(dir):
"""
The method to load a session
#------------------
Parameters:
*argv: the objects that wanted to be stored in the session
#------------------
Returns:
Loads the session
#------------------
"""
return dill.load_session(dir)
def load_data_KDD():
dill.load_session('NSL-KDD.pkl')
# In[1]:
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import r2_score, mean_squared_error
from sklearn import metrics
from sklearn import preprocessing
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
import seaborn as sns
import matplotlib.pyplot as plt
import dill
dill.load_session("tsne.db")
# In[ ]:
from sklearn.manifold import TSNE
import seaborn as sns
def run_tsne(X, y, Perplexity, Iterations, noprogresstolerance):
tsneModel = TSNE(perplexity=Perplexity,
n_iter=Iterations,
n_iter_without_progress=noprogresstolerance,
n_jobs=100)
X_embedded = tsneModel.fit_transform(X)
return sns.scatterplot(X_embedded[:, 0],
X_embedded[:, 1],
# 2018 Oct 9 - Continuing to analyze
import dill
in_filename = 'Analyzing_Kepler-76b_Using_Emcee.pkl'
dill.load_session(in_filename)
# Recover last position
pos = sampler.chain[:, -1, :]
nsteps = 5000
for i, result in enumerate(sampler.sample(pos, iterations=nsteps)):
if (i + 1) % 50 == 0:
print("{0:5.1%}".format(float(i) / nsteps))
out_filename = 'Continuing_to_Analyze_Kepler-76b_Using_Emcee.pkl'
dill.dump_session(out_filename)
print(np.mean(sampler.chain[:, :, 0]), np.std(sampler.chain[:, :, 0]))
def load_data_cancer():
dill.load_session('UCI-BCW.pkl')
from __future__ import absolute_import
import os
import dill
import __main__ as _main_module
import six
if six.PY2:
PYCALC_SESSION_NAME = os.path.expanduser("~/.pycalc_session")
elif six.PY3:
PYCALC_SESSION_NAME = os.path.expanduser("~/.pycalc3_session")
else:
print("Python version not supported")
try:
dill.load_session(PYCALC_SESSION_NAME)
except IOError:
print("Re-loading pycalc modules...")
import numpy as np
_main_module.np = np
import scipy as sc
_main_module.sc = sc
import sympy as sp
import sympy.physics
_main_module.sp = sp
_main_module.u = sp.physics.units
_main_module.u.B = sympy.physics.units.Unit('byte', 'B')
_main_module.u.b = sympy.Rational(1, 8) * sympy.physics.units.B
_main_module.u.KiB = 1024 * sympy.physics.units.B
# Broadcasting: https://docs.scipy.org/doc/numpy-1.13.0/reference/ufuncs.html#ufuncs-broadcasting
# 当多个array做element-wize function时，按下面的规则broadcasting
# - 对齐维度: 确定最大的dimension， 先prepend 长度为1的dimension来对齐所有dimension
# - 对齐维度长度： 所有维度要么长度相等，要么长度为1.
# 存储整个环境变量
# https://stackoverflow.com/a/35296032
import dill # pip install dill
filename = 'globalsave.pkl'
dill.dump_session(filename)
# and to load the session again:
dill.load_session(filename)
# Jupyter notebook 相关 --------------------------
jupyter nbconvert --to=python [YOUR_NOTEBOOK].ipynb # 会生成 [YOUR_NOTEBOOK].py 文件
jupyter nbconvert --to notebook --output OUT.ipynb --execute [YOUR_NOTEBOOK].ipynb
# 如果没有指定 --to nbtebook，默认输出类型是html
# 不加output会生成 [YOUR_NOTEBOOK].html 或者 [YOUR_NOTEBOOK].nbconvert.ipynb
# Seems below method does not work
# Configuring https://nbconvert.readthedocs.io/en/latest/config_options.html
# add below into ~/.jupyter/jupyter_nbconvert_config.json
"ExecutePreprocessor": {
uct_visit_ave_cnt_list, ocba_visit_ave_cnt_list = [], []
uct_ave_Q_list, ocba_ave_Q_list = [], []
uct_ave_std_list, ocba_ave_std_list = [], []
if opp_first_move == 0:
optimal_set = {4}
setup = 1
elif opp_first_move == 4:
optimal_set = {0, 2, 6, 8}
setup = 2
else:
raise ValueError('opp_first_move should either be 0 or 4.')
ckpt = args.checkpoint
if ckpt != '':
dill.load_session(ckpt)
# resume experiment from the last finished budget
budget_range = range(budget + step, budget_end + 1, step)
for budget in budget_range:
PCS_uct = 0
PCS_ocba = 0
uct_selection.append([])
ocba_selection.append([])
uct_visit_cnt, ocba_visit_cnt = defaultdict(int), defaultdict(int)
uct_ave_Q, ocba_ave_Q = defaultdict(int), defaultdict(int)
uct_ave_std, ocba_ave_std = defaultdict(int), defaultdict(int)
for i in range(rep):
uct_mcts, uct_root_node, uct_cur_node = play_game_uct(
budget=budget,
exploration_weight=exploration_weight,
def load_model(self, filename):
dill.load_session(filename)
d_tpp = datetime.strptime(d, '%Y-%m-%d')
df['date'][df['date']==d] = d_tpp
# Maybe then better to store session from here
# Will review tomorrow
# Save session
dnm = r'C:\Users\rghiglia\Documents\ML_ND\Yelp'
fnmO = (dnm + '\\' + 'yelp_data.pkl')
import time
import dill #pip install dill --user
start_time = time.time()
dill.dump_session(fnmO)
print("--- %s seconds ---" % (time.time() - start_time))
# It seems prohibitively long :(
# Load session
dnm = r'C:\Users\rghiglia\Documents\ML_ND\Yelp'
import time
import dill #pip install dill --user
start_time = time.time()
dill.load_session(fnmO)
print("--- %s seconds ---" % (time.time() - start_time))
def load_model(self, filename):
dill.load_session(filename)
def load_session(file_path): return dill.load_session(file_path)
import sys
import os
import dill
#----- Global variables ----------
path_globals = str(sys.argv[1])
dill.load_session(path_globals)
#--------------------------------
prefix = "abs_"
dir_plots = dir_main + "plots/"
dir_chunks = dir_main + "chunks/"
file_abs = dir_main + "Absolute_magnitudes.csv"
bands = []
errors = []
covariates = []
waves = []
for instrument in instruments:
bands.append(instrument["observables"])
errors.append(instrument["uncertainties"])
covariates.append(instrument["covariates"])
waves.append(instrument["wavelengths"])
bands = sum(bands, [])
errors = sum(errors, [])
covariates = sum(covariates, [])
waves = sum(waves, [])
observables = [prefix + band for band in bands]
uncertainties = [prefix + error for error in errors]
def load_session(file_path):
with _pickle_lock_unless_py2:
return dill.load_session(file_path)
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 13 16:53:46 2014
@author: s362khan
"""
import dill
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as ss
plt.ion()
dill.load_session('positionToleranceData.pkl')
# ---------------------------------------------------------
# Code to plot Zoccolan Data onto a figure
zoc_pos_tol = yScatterRaw * np.pi / 180.0 # in Radians
zoc_sel_af = xScatterRaw
plt.scatter(zoc_sel_af, zoc_pos_tol, label='Original Data', marker='o', s=60, color='blue')
plt.plot(xScatterRaw,
(yLineFit[0]* np.pi / 180.0 * xScatterRaw + yLineFit[1] * np.pi / 180.0),
color='blue',
linewidth=2)
#----------------------------------------------------------
plt.figure('Original Data')
# -*- coding: utf-8 -*- """ Created on Sat Apr 7 13:31:16 2018 @author: msval """ import dill filename = 'globalsave.pkl' dill.load_session(filename)
a_const = 1
a_lst = [1, 2, 3]
a_dict = {'a': 1, 'b': [1, 2]}
a_df = pandas.DataFrame({'col': [2, 4]})
a_lambda_func = lambda x: x**2
def a_func(x):
return x**2
class a_class(object):
def class_func(self, n):
return n + 1
a_class_instance = a_class()
# to save session
dill.dump_session('./backup_workspace/backup/backup_dill.db')
# ====== to load your session ======
import dill
bk_restore = dill.load_session('./backup_workspace/backup/backup_dill.db')
restored_variables = [el for el in dir() if el.startswith('a_')]
print(f'{len(restored_variables)} restored variables: ', restored_variables)
reload(msabpy)
########################################
# Play sound when a program finishes
########################################
from IPython.display import Audio, display
def allDone():
display(Audio(url="https://sound.peal.io/ps/audios/000/000/537/original/woo_vu_luvub_dub_dub.wav', autoplay=True))
########################################
# Clear notebook output
########################################
from IPython.display import clear_output
clear_output(wait=True)
########################################
# Save (& restore) variables
########################################
import dill
dill.dump_session("notebook.db")
dill.load_session("notebook.db")
########################################
# Reusing variables across notebooks
########################################
%store # List of all variables and their current values
%store var1 # Store variable `var1`
%store -r var1 # Restore variable `var1`
%store -z # Remove all variables from storage
# Reference: https://ipython.org/ipython-doc/3/config/extensions/storemagic.html
