def test_get_clean_sets():
test_db = 'my_great_db.db'
test_filename = 'my_great_data.xlsx'
test_decoded_df = pd.DataFrame({
'Cycle_Index': [1, 1, 2, 2, 2],
'Data_Point': [0, 1, 2, 3, 4],
'Voltage(V)': [4, 8, 16, 8, 4],
'Current(A)': [2, 4, 6, 8, 12],
'Discharge_Capacity(Ah)': [10, 0, 30, 0, 10],
'Charge_Capacity(Ah)': [0, 20, 0, 10, 0],
'Step_Index': [1, 0, 1, 0, 1]
})
test_datatype = 'ARBIN'
# initialize database:
init_master_table(test_db)
core_test_filename = get_filename_pref(test_filename)
parse_update_master(core_test_filename, test_db, test_datatype,
test_decoded_df)
# set up by adding raw data frame to database
cycle_dict = load_sep_cycles(core_test_filename, test_db, test_datatype)
clean_cycle_dict = get_clean_cycles(cycle_dict,
core_test_filename,
test_db,
test_datatype,
windowlength=9,
polyorder=3)
result = get_clean_sets(clean_cycle_dict, core_test_filename, test_db)
assert type(result) == pd.DataFrame
assert list(result['Cycle_Index'].unique()) == [1, 2]
os.remove(test_db)
def test_update_master_table():
init_master_table('amazing_database2.db')
update_dict = {
'Dataset_Name': 'my_dataset',
'Raw_Data_Prefix': 'raw',
'Cleaned_Data_Prefix': 'clean',
'Cleaned_Cycles_Prefix': 'cycles',
'Descriptors_Prefix': 'desc'
}
update_master_table(update_dict, 'amazing_database2.db')
test_df = get_file_from_database('master_table', 'amazing_database2.db')
expected = pd.DataFrame({
'Dataset_Name': ['my_dataset'],
'Raw_Data_Prefix': ['raw'],
'Cleaned_Data_Prefix': ['clean'],
'Cleaned_Cycles_Prefix': ['cycles'],
'Descriptors_Prefix': ['desc']
})
assert pd.DataFrame.equals(test_df, expected)
neg_result = update_master_table(
None,
'amazing_database2.db',
)
assert neg_result == [{}]
os.remove('amazing_database2.db')
return
def process_data(file_name,
database_name,
decoded_dataframe,
datatype,
windowlength=9,
polyorder=3):
"""Takes raw file, separates cycles, cleans cycles,
gets the descriptors, saves descriptors for each cycle
into database, puts cycles back together, and then saves
resulting cleaned data. """
if not os.path.exists(database_name):
init_master_table(database_name)
names_list = get_table_names(database_name)
core_file_name = get_filename_pref(file_name)
if core_file_name + 'CleanSet' in names_list:
return
else:
parse_update_master(core_file_name, database_name, datatype,
decoded_dataframe)
cycle_dict = load_sep_cycles(core_file_name, database_name, datatype)
clean_cycle_dict = get_clean_cycles(cycle_dict, core_file_name,
database_name, datatype,
windowlength, polyorder)
clean_set_df = get_clean_sets(clean_cycle_dict, core_file_name,
database_name)
return
def test_init_master_table():
init_master_table('new_database.db')
assert os.path.exists('new_database.db')
init_table = get_file_from_database('master_table', 'new_database.db')
expected_cols = [
'Dataset_Name', 'Raw_Data_Prefix', 'Cleaned_Data_Prefix',
'Cleaned_Cycles_Prefix', 'Descriptors_Prefix'
]
assert init_table.empty
assert set(expected_cols) == set(init_table.columns)
os.remove('new_database.db')
return
def test_load_sep_cycles():
test_db = 'my_great_db.db'
test_filename = 'my_great_data.xlsx'
test_decoded_df = pd.DataFrame({
'Cycle_Index': [1, 1, 2, 2, 2],
'Data_Point': [0, 1, 2, 3, 4],
'Voltage(V)': [4, 8, 16, 8, 4],
'Current(A)': [2, 4, 6, 8, 12],
'Discharge_Capacity(Ah)': [10, 0, 30, 0, 10],
'Charge_Capacity(Ah)': [0, 20, 0, 10, 0],
'Step_Index': [1, 0, 1, 0, 1]
})
test_datatype = 'ARBIN'
# initialize database:
init_master_table(test_db)
core_test_filename = get_filename_pref(test_filename)
parse_update_master(core_test_filename, test_db, test_datatype,
test_decoded_df)
# set up by adding raw data frame to database
result = load_sep_cycles(core_test_filename, test_db, test_datatype)
# there are two cycles in this test data:
assert list(result.keys()) == [1, 2]
os.remove(test_db)
return
from diffcapanalyzer.app_helper_functions import parse_contents
from diffcapanalyzer.app_helper_functions import pop_with_db
from diffcapanalyzer.chachifuncs import col_variables
from diffcapanalyzer.databasewrappers import get_db_filenames, get_filename_pref
from diffcapanalyzer.databasefuncs import get_file_from_database
from diffcapanalyzer.databasefuncs import init_master_table
from diffcapanalyzer.descriptors import generate_model
from diffcapanalyzer.descriptors import get_model_dfs
database = "data/databases/dQdV.db"
init_db = "data/databases/init_database.db"
assert os.path.exists(init_db)
if not os.path.exists(database):
init_master_table(database)
app = dash.Dash(__name__,
external_stylesheets=[
"https://codepen.io/chriddyp/pen/bWLwgP.css", {
'href': "https://codepen.io/chriddyp/pen/bWLwgP.css",
'rel': 'stylesheet'
}
])
##########################################
# App Layout
##########################################
Introduction = dcc.Markdown('''
# dQ/dV