Tutorial 17 - extract_sequence_overview#

Step 0: Setup the project and prepare the data#

[1]:

from pathlib import Path

import pydpeet as eet

We will use “ERROR” as the logging style for better readability of the notebook

[2]:

eet.set_logging_style("ERROR")

[3]:

standardized_data = eet.read(
    input_path=str(Path.cwd().parent.parent / "res" / "raw_data_from_cyclers" / "Cal_Ageing_Checkup1.xlsx"),
    config=eet.ReadConfig.Neware_8_0_0_516,
)

[4]:

segmented_data = eet.add_primitive_segments(df=standardized_data, config=eet.PrimitiveConfig.DEFAULT)

Step 1: extract_sequence_overview via a SequenceOverviewConfig#

[5]:

config = eet.SequenceOverviewConfig.DEFAULT
config.segment_sequence_config.keys()

[5]:

dict_keys(['Discharge_iOCV', 'Charge_iOCV', 'CCCV_Charge', 'Pause', 'CC_Charge', 'CV_Charge', 'CP_Charge', 'CC_Discharge', 'CV_Discharge', 'CP_Discharge', 'CRamp_Charge', 'VRamp_Charge', 'PRamp_Charge', 'CRamp_Discharge', 'VRamp_Discharge', 'PRamp_Discharge', 'I', 'V', 'P', 'Charging', 'Discharging'])

[6]:

eet.extract_sequence_overview(
    df_primitives=segmented_data,
    config=config,
)

[6]:
ID Sequence Discharge_iOCV Charge_iOCV CCCV_Charge Pause CC_Charge CV_Charge CP_Charge CC_Discharge ... VRamp_Charge PRamp_Charge CRamp_Discharge VRamp_Discharge PRamp_Discharge I V P Charging Discharging
0.0 0 1_VRamp_Charge 0 0 0 0 0 0 0 0 ... 1 0 0 0 0 0 1 0 0 0
9112.0 1 1_CV_Charge 0 0 0 0 0 1 0 0 ... 0 0 0 0 0 0 2 0 1 0
11585.0 2 1_CC_Discharge 0 0 0 0 0 0 0 1 ... 0 0 0 0 0 1 0 0 0 1
29405.0 3 1_CV_Discharge 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 3 0 0 2
29915.0 4 2_VRamp_Charge 0 0 0 0 0 0 0 0 ... 2 0 0 0 0 0 4 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
161714.0 488 1_Charge_iOCV 0 1 0 240 0 0 0 0 ... 0 0 0 0 0 483 0 0 0 0
162075.0 489 1_Charge_iOCV 0 1 0 0 120 0 0 0 ... 0 0 0 0 0 484 0 0 121 0
162174.0 490 1_Charge_iOCV 0 1 0 241 0 0 0 0 ... 0 0 0 0 0 485 0 0 0 0
162535.0 491 1_Charge_iOCV 0 1 0 0 121 0 0 0 ... 0 0 0 0 0 486 0 0 122 0
162598.0 492 2_VRamp_Discharge 0 0 0 0 0 0 0 0 ... 0 0 0 2 0 0 7 0 0 0

493 rows × 23 columns

Step 2: use a custom config via sequence_overview_config_wrapper#

[7]:

custom_config = eet.sequence_overview_config_wrapper(
    segment_sequence_config={
        "CC_Discharge": {
            "rules": {
                "variable": "I",
                "type": "Constant",
                "direction": "Discharge",
            }
        },
        "Pause": {
            "rules": {
                "type": "Rest",
            }
        },
        "Discharge_iOCV": {
            "loop": True,
            "min_loops": 2,
            "sequence": ["CC_Discharge", "Pause"],
        },
    },
    show_runtime=True,
)

[8]:

segments_and_sequences_custom = eet.extract_sequence_overview(
    df_primitives=segmented_data,
    config=custom_config,
)
segments_and_sequences_custom.head(6)

[8]:
ID Sequence CC_Discharge Pause Discharge_iOCV
0.0 0 None 0 0 0
9112.0 1 None 0 0 0
11585.0 2 1_CC_Discharge 1 0 0
29405.0 3 None 0 0 0
29915.0 4 None 0 0 0
29976.0 5 None 0 0 0

Step 3: Filter the data to matched IDs#

[9]:

standardized_data.plot(x="Test_Time[s]", y=["Voltage[V]", "Current[A]"], subplots=True)

[9]:

array([<Axes: xlabel='Test_Time[s]'>, <Axes: xlabel='Test_Time[s]'>],
      dtype=object)
../../_images/examples_notebooks_Tutorial_17_extract_sequence_overview%26SequenceOverviewConfig%26sequence_overview_config_wrapper_14_1.png 
[10]:

filtered = segments_and_sequences_custom[segments_and_sequences_custom["Discharge_iOCV"] == 1]
filtered

[10]:
ID Sequence CC_Discharge Pause Discharge_iOCV
44502.0 7 1_Discharge_iOCV 2 0 1
44649.0 8 1_Discharge_iOCV 0 1 1
45010.0 9 1_Discharge_iOCV 3 0 1
45157.0 10 1_Discharge_iOCV 0 2 1
45518.0 11 1_Discharge_iOCV 4 0 1
... ... ... ... ... ...
105101.0 246 1_Discharge_iOCV 0 120 1
105462.0 247 1_Discharge_iOCV 122 0 1
105609.0 248 1_Discharge_iOCV 0 121 1
105970.0 249 1_Discharge_iOCV 123 0 1
106117.0 250 1_Discharge_iOCV 0 122 1

244 rows × 5 columns

[11]:

filtered_data = segmented_data[segmented_data["ID"].isin(filtered["ID"])]
filtered_data.plot(x="Test_Time[s]", y=["Voltage[V]", "Current[A]"], subplots=True)

[11]:

array([<Axes: xlabel='Test_Time[s]'>, <Axes: xlabel='Test_Time[s]'>],
      dtype=object)
../../_images/examples_notebooks_Tutorial_17_extract_sequence_overview%26SequenceOverviewConfig%26sequence_overview_config_wrapper_16_1.png