from rackio_AI import RackioAI
from rackio import Rackio
app = Rackio()
RackioAI(app)
df = RackioAI.load_test_data('Leak')
df.info()
df2 = RackioAI.load_test_data('Leak', 'Leak111.tpl')
df2.info()
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from rackio_AI import Preprocessing, RackioAI
from rackio import Rackio
app = Rackio()
RackioAI(app)
"Filename definition where is the data"
os.chdir('../..')
cwd = os.getcwd()
filename = os.path.join(cwd, 'rackio_AI', 'data', 'pkl_files', 'test_data.pkl')
"Load Data to RackioAI"
RackioAI.load(filename)
"Definition of instrument parameters"
error = [0.0025, 0.0025, 0.0025, 0.0025]
repeteability = [0.001, 0.001, 0.001, 0.001]
lower_limit = [0, 0, 400000, 100000]
upper_limit = [500, 500, 1200000, 600000]
dead_band = [0.001, 0.001, 0.001, 0.001]
"Set Options"
RackioAI.synthetic_data.set_options(error=error,
repeteability=repeteability,
lower_limit=lower_limit,
upper_limit=upper_limit,
dead_band=dead_band)
df = pd.concat(self._df_)
return df
@ProgressBar(desc="Reading .pkl files...", unit="file")
def __read(self, pathname, **pkl_options):
"""
Read (pkl) file into DataFrame.
"""
with open(pathname, 'rb') as f:
_df = pickle.load(f)
if 'remove_initial_points' in pkl_options:
_rip = pkl_options['remove_initial_points']
_df.drop(index=_df.iloc[0:_rip, :].index.tolist(), inplace=True)
self._df_.append(_df)
return
if __name__ == "__main__":
# import doctest
# doctest.testmod()
import os
from rackio_AI import RackioAI, get_directory
filename = os.path.join(get_directory('Leak'), 'Leak01.tpl')
df = RackioAI.load(filename)
from rackio import Rackio
from rackio_AI import RackioAI
app = Rackio()
RackioAI(app)
"Rackio"
print(RackioAI.app.__class__.__name__)
print('===================')
"RackioAI"
print(RackioAI.__class__.__name__)
from rackio_AI import RackioAI, Preprocessing
from rackio import Rackio
app = Rackio()
RackioAI(app)
preprocess1 = Preprocessing(name='Preprocess1',
description='preprocess for data',
problem_type='regression')
preprocess2 = Preprocessing(name='Preprocess2',
description='preprocess for data',
problem_type='classification')
RackioAI.append_preprocessing_model(preprocess1)
RackioAI.append_preprocessing_model(preprocess2)
print(RackioAI.summary())
import os
from rackio_AI import RackioAI
from rackio import Rackio
app = Rackio()
RackioAI(app)
os.chdir('../..')
cwd = os.getcwd()
filename = os.path.join(cwd, 'rackio_AI', 'data', 'Leak', 'Leak112.tpl')
RackioAI.load(filename)
df = RackioAI.reader.tpl.to('dataframe')
print(' ')
print(
'----------------------------------------------------------------------------------------'
)
print('DATAFRAME BEFORE PERSISTING')
print(
'----------------------------------------------------------------------------------------'
)
print(' ')
df.info()
filename = 'test'
# Save pkl object
RackioAI.save_obj(df, filename)
# Load pkl object
import os
from rackio_AI import RackioAI
from rackio import Rackio
app = Rackio()
RackioAI(app)
base_path = os.path.join('..', 'data')
filename = os.path.join(base_path, 'Leak')
RackioAI.load(filename)
url_to_save = os.path.join(base_path, 'name.csv')
RackioAI.reader.tpl.to('csv', filename=url_to_save)
import os
import pandas as pd
from rackio_AI import RackioAI
from rackio import Rackio
app = Rackio()
RackioAI(app)
"Data load"
os.chdir('../..')
cwd = os.getcwd()
filename = os.path.join(cwd, 'rackio_AI', 'data', 'pkl_files', 'test_data.pkl')
RackioAI.load(filename)
variable_names = RackioAI.data.columns.to_list()
"Definition of instrument parameters"
error = [0.0025, 0.0025, 0.0025, 0.0025]
repeteability = [0.001, 0.001, 0.001, 0.001]
lower_limit = [0, 0, 400000, 100000]
upper_limit = [500, 500, 1200000, 600000]
dead_band = [0.001, 0.001, 0.001, 0.001]
"Set Options"
RackioAI.synthetic_data.set_options(error=error,
repeteability=repeteability,
lower_limit=lower_limit,
upper_limit=upper_limit,
dead_band=dead_band)
import os
from rackio_AI import RackioAI
from rackio import Rackio
app = Rackio()
RackioAI(app)
filename = os.path.join('..', 'data')
data = RackioAI.load(filename)
print(data)