How to use the qcodes.config function in qcodes

print('Estimated number of clusters: %d' % n_clusters_)
    print("Silhouette Coefficient: %0.3f" % metrics.silhouette_score(X, labels))

# plt.rcParams.update(pd.tools.plotting.mpl_stylesheet)
plt.figure(301)
plt.clf()
plt.jet()
ax = plt.gca()
nvtools.nvtools.add_attraction_grid(ax, attractmV, attractFreq)
if 0:
    df.plot(kind='scatter', x='gate jump', y='yellow jump', ax=plt.gca(), c=0 * labels, cmap=cm.jet, linewidths=0, colorbar=False, grid=False, zorder=3)
    plt.savefig(os.path.join(qcodes.config['user']['nvDataDir'], 'results', 'clustering0.png'))

df.plot(kind='scatter', x='gate jump', y='yellow jump', ax=plt.gca(), c=labels, cmap=cm.jet, linewidths=0, colorbar=False, grid=False, zorder=3)
plt.title('Clustering of jumps', fontsize=15)
plt.savefig(os.path.join(qcodes.config['user']['nvDataDir'], 'results', 'clustering.png'))
np.save(os.path.join(qcodes.config['user']['nvDataDir'], 'labels.npy'), labels)


#%% Find dense 0 cluster
densityKern = KernelDensity().fit(X)
s = densityKern.score_samples(X)
plt.figure()
plt.subplot(121)
plt.scatter(df['gate jump'], s)
plt.subplot(122)
plt.scatter(df['yellow jump'], s)

X = X[s < -2.5, :]
#%%
# translate by mean and scale with std

dfS[:] = datascaler.transform(df)

Xbase = dataS[:, 4:]  # base data
datascalerBase = StandardScaler().fit(data[:, 4:])
x = dataS[:, 4]
y = dataS[:, 5]

#%% Create data set with 100 data points -> 1 label
lag = 100
ran = range(0, len(dfS[['gate jump']]))
lagSquare = np.concatenate([dfS[['gate jump']].shift(i) for i in ran], axis=1)
gateSet = lagSquare[lag:, :lag]
lagSquare = np.concatenate([dfS[['yellow jump']].shift(i) for i in ran], axis=1)
yellowSet = lagSquare[lag:, :lag]
#%%
labels = np.load(os.path.join(qcodes.config['user']['nvDataDir'], 'labels.npy'))
dataSet = np.dstack((gateSet, yellowSet))[:-1, :, :]  # I don't know the label for the final sequence, so drop it
lbls = labels[lag + 1:]
lbls[lbls == -1] = 5  # Setting this class to 5 so it can be one hot encoded and more easily be cut off
if 1:  # to make training a little bit easier for now
    dataSet = dataSet[lbls < 5, :, :]  # Remove all the points that do not belong to a class
    lbls = lbls[lbls < 5]

if 0:  # Throw out the 0 cluster
    dataSet = dataSet[lbls > 0, :, :]
    lbls = lbls[lbls > 0]

if 0:  # Only classify 0 cluster vs not 0 cluster
    lbls[lbls > 0] = 1

lbls = OneHotEncoder(sparse=False).fit_transform(lbls.reshape(-1, 1))

from sklearn.preprocessing import StandardScaler
from statsmodels.graphics.gofplots import qqplot
from scipy.interpolate import interp1d

interpolated = False
rmvZeroClust = False

#%%
print('Generating Data')
data = np.load(os.path.join(qcodes.config['user']['nvDataDir'], 'jdata.npy')).T

df = pd.DataFrame(data, columns=['time', 'gate', 'yellow', 'new', 'gate jump', 'yellow jump', 'jump index'])
#plt.figure(300); plt.clf()
#df.plot(kind='scatter', x='gate jump', y='yellow jump', ax=plt.gca(), linewidths=0)

labels = np.load(os.path.join(qcodes.config['user']['nvDataDir'], 'labels.npy'))

#%% Remove the 0 cluster (optional)
if rmvZeroClust:
    strippedLabels = labels[labels != 0]
    df = df.iloc[labels != 0]

#%% Data needs to be scaled for almost any machine learning algorithm to work

# translate by mean and scale with std
datascaler = StandardScaler()
dataS = datascaler.fit_transform(data)
dfS = df.copy()
dfS[:] = datascaler.transform(df)

Xbase = dataS[:, 4:]  # base data
datascalerBase = StandardScaler().fit(data[:, 4:])

import qcodes
import pandas as pd
from pynufft import pynufft  # pip3 install pynufft --user
from scipy.interpolate import interp1d
import matplotlib
import nufftpy

#%%
print('Generating Data')
data = np.load(os.path.join(qcodes.config['user']['nvDataDir'], 'jdata.npy')).T

df = pd.DataFrame(data, columns=['time', 'gate', 'yellow', 'new', 'gate jump', 'yellow jump'])
#plt.figure(300); plt.clf()
#df.plot(kind='scatter', x='gate jump', y='yellow jump', ax=plt.gca(), linewidths=0)

labels = np.load(os.path.join(qcodes.config['user']['nvDataDir'], 'labels.npy'))

#%% Check the time steps
time = df[['time']].values.ravel()
dTime = [time[i + 1] - time[i] for i in range(time.size - 1)]

#%% interpolation test
interp = interp1d(df[['time']].values.ravel(), df[['gate']].values.ravel(), kind='nearest')
x = range(8000, 10000, 10)

ind = np.where((time >= 8000) & (time <= 10000))

plt.figure()
plt.scatter(time[ind], df[['gate']].values.ravel()[ind])
plt.plot(x, interp(x))

#%% fft as is

Args:
        station (qcodes station)
        tag (str or None)
        overwrite (bool): If True overwrite existing data, otherwise raise error
        data (None or object): optional extra data
        verbose (int)

    Example:
        >>> save_state(station, tag='tripledot1')    

    The data is written to an HDF5 file. The default location is the user
    home directory with name qtt_statefile.hdf5.
    
    To install hickle: pip install git+https://github.com/telegraphic/hickle.git@dev
    """
    statefile = qcodes.config.get('statefile', None)
    if statefile is None:
        statefile = os.path.join(os.path.expanduser('~'), 'qtt_statefile.hdf5')

    snapshot = station.snapshot()
    gv = station.gates.allvalues()

    datestring = "{:%Y%m%d-%H%M%S}".format(datetime.now());

    if verbose >= 2:
        print(datestring)
    if tag is None:
        tag = datestring

    obj = {'gatevalues': gv, 'snapshot': snapshot,
           'datestring': datestring, 'data': data}

def datasetFromFile(path, runId):
    qc.config['core']['db_location'] = path
    ds = DataSet(path)
    ds.run_id = runId
    return ds

def list_states(verbose=1):
    """ List available states of the system

    Args:
        verbose (int)

    Returns:
        states (list): List of string tags

    See also:
        load_state
    """
    statefile = qcodes.config.get('statefile', None)
    if statefile is None:
        statefile = os.path.join(os.path.expanduser('~'), 'qtt_statefile.hdf5')
    if not os.path.exists(statefile):
        return []
    tags = []
    with h5py.File(statefile, 'r') as h5group:
        tags = list(h5group.keys())
    if verbose:
        print('states on system from file %s: ' % (statefile, ), end='')
        print(', '.join([str(x) for x in tags]))
    return tags

#%% Global settings
dataSelection = ['yellow jump', 'gate jump', 'yellow', 'gate']
lblsAsInput = True
lag = 100
keepNoClass = True


#%%
print('Generating Data')
data = np.load(os.path.join(qcodes.config['user']['nvDataDir'], 'jdata.npy')).T
data = data[:, 0:6]
df = pd.DataFrame(data, columns=['time', 'gate', 'yellow', 'new', 'gate jump', 'yellow jump'])
jumps = df[['gate jump', 'yellow jump']]

labels = np.load(os.path.join(qcodes.config['user']['nvDataDir'], 'labels.npy'))
labels[labels == -1] = 5  # this makes it a bit nicer to handle

#%% Data needs to be scaled for almost any machine learning algorithm to work

# translate by mean and scale with std
datascaler = StandardScaler()
dataS = datascaler.fit_transform(data)
dfS = df.copy()
dfS[:] = datascaler.transform(df)

#%% Select the subset of data to use:
selectedData = dfS[dataSelection]
ran = range(0, selectedData.shape[0])

laggedData = np.zeros((len(dataSelection), selectedData.shape[0] - lag, lag))
for i in range(len(dataSelection)):

lblsAsInput = True
lag = 100
keepNoClass = False
keepZeroCluster = True
zeroOrNotZero = False  # Classify only zero cluster vs not-zero cluster (so don't remove that zero cluster)
sequentialTesting = False  # The stateful LSTM will likely work better with this set to True
LSTMtype = 3
doPCA = True

batchSize = 1
nbEpochs = 300
learningRate = 0.00001

#%%
print('Generating Data')
data = np.load(os.path.join(qcodes.config['user']['nvDataDir'], 'jdata.npy')).T
data = data[:, 0:6]
df = pd.DataFrame(data, columns=['time', 'gate', 'yellow', 'new', 'gate jump', 'yellow jump'])
jumps = df[['gate jump', 'yellow jump']]

labels = np.load(os.path.join(qcodes.config['user']['nvDataDir'], 'labels.npy'))
labels[labels == -1] = 5  # this makes it a bit nicer to handle

#%% Data needs to be scaled for almost any machine learning algorithm to work

# translate by mean and scale with std
datascaler = StandardScaler()
dataS = datascaler.fit_transform(data)
dfS = df.copy()
dfS[:] = datascaler.transform(df)

#%% Select the subset of data to use:

#%% Global settings
dataSelection = ['yellow jump', 'gate jump', 'yellow', 'gate']
lblsAsInput = True
lag = 100
keepNoClass = True
colors = ['r', 'b', 'g', 'y', 'c', 'm']

#%%
print('Generating Data')
data = np.load(os.path.join(qcodes.config['user']['nvDataDir'], 'jdata.npy')).T
data = data[:, 0:6]
df = pd.DataFrame(data, columns=['time', 'gate', 'yellow', 'new', 'gate jump', 'yellow jump'])
jumps = df[['gate jump', 'yellow jump']]

labels = np.load(os.path.join(qcodes.config['user']['nvDataDir'], 'labels.npy'))
labels[labels == -1] = 5  # this makes it a bit nicer to handle

#%% Data needs to be scaled for almost any machine learning algorithm to work

# translate by mean and scale with std
datascaler = StandardScaler()
dataS = datascaler.fit_transform(data)
dfS = df.copy()
dfS[:] = datascaler.transform(df)

#%% Select the subset of data to use:
selectedData = dfS[dataSelection]
ran = range(0, selectedData.shape[0])

laggedData = np.zeros((len(dataSelection), selectedData.shape[0] - lag, lag))
for i in range(len(dataSelection)):