control-lab-ly 0.0.1b0

Lab Equipment Automation Package

Control.lab.ly

Lab Equipment Automation Package

Description

User-friendly package that enables flexible automation an reconfigurable setups for high-throughput experimentation and machine learning.

Dependencies

• Dash (>=2.7.1)
• Impedance (>=1.4.1)
• Imutils (>=0.5.4)
• Matplotlib (>=3.3.4)
• Nest-asyncio (>=1.5.1)
• Numpy (>=1.19.5)
• Opencv-python (>=4.5.4.58)
• Pandas (>=1.2.4)
• Plotly (>=5.3.1)
• PyModbusTCP (>=0.2.0)
• Pyserial (>=3.5)
• PySimpleGUI (>=4.60)
• PyVISA (>=1.12.0)
• PyYAML (>=6.0)
• Scipy (>=1.6.2)

Device support

• Make
  • Multi-channel spin-coater [Arduino]
• Measure
  • (Keithley) 2450 Source Measure Unit (SMU) Instrument
  • (PiezoRobotics) Dynamic Mechanical Analyser (DMA)
  • Precision mass balance [Arduino]
• Move
  • (Creality) Ender-3
  • (Dobot) M1 Pro
  • (Dobot) MG400
  • Primitiv [Arduino]
• Transfer
  • (Sartorius) rLINE® dispensing modules
  • Peristaltic pump and syringe system [Arduino]
• View
  • (FLIR) AX8 thermal imaging camera - full functionality in development
  • Web cameras [General]

Installation

$ pip install control-lab-ly

Usage

Import package

import controllably as lab

Import desired class

from controllably.Move.Cartesian import Ender
mover = Ender(...)
mover.safeMoveTo((x,y,z))

Create new project

Create a /configs folder in the base folder of your project repository to store all configuration related files from which the package will read from.

This only has to be done once when you first set up the project folder.

lab.create_configs()

A different address may be used by different machines for the same device. To manage the different addresses used by different machines, you first need your machine's unique identifier.

# Get your machine's ID
print(lab.Helper.get_node())

A template of registry.yaml has also been added to the folder to hold the machine-specific addresses of your connected devices (i.e. COM ports).
Populate the YAML file in the format shown below.

### registry.yaml ###

'0123456789ABCDE':              # insert your machine's ID here (from the above step)
    cam_index:                  # camera index of the connected imaging devices
      __cam_01__: 1             # keep the leading and trailing double underscores
      __cam_02__: 0
    port:                       # addresses of serial COM ports
      __device_01__: COM3       # keep the leading and trailing double underscores
      __device_02__: COM16

To find the COM port address(es) of the device(s) that is/are currently connected to your machine, use

lab.Helper.get_ports()

Create new setup

Create a new folder for the configuration files of your new setup.

lab.create_setup(setup_name = "Setup01")
# replace "Setup01" with the desired name for your setup

If you had skipped the previous step of creating a project, calling lab.create_setup will also generate the required file structure. However, be sure to populate your machine ID and device addresses in the registry.yaml file.

This creates a /Setup01 folder that holds the configuration files for the setup, which includes config.yaml, layout.json, and program.py.

config.yaml

Configuration and calibration values for your devices is stored in config.yaml.
Each configuration starts with the name of your device, then its module, class, and settings.

### config.yaml ###

Device01:                                         # name of simple device (user-defined)
  module: __module_name_01__                      # device module
  class: __submodule_1A__.__class_1A__            # device class
  settings:
    port: __device_01__                           # port addresses defined in registry.yaml
    __setting_A__: {'tuple': [300,0,200]}         # use keys to define the type of iterable
    __setting_B__: {'array': [[0,1,0],[-1,0,0]]}  # only tuple and np.array supported

Compound devices are similarly configured. The configuration values for its component devices are defined under the component_config setting. The structure of the configuration values for the component devices are similar to that shown above, except indented to fall under the indentation of the component_config setting.

### config.yaml ###

Device02:                                     # name of 'Compound' device (user-defined)
  module: Compound                            
  class: __submodule_2A__.__class_2A__
  settings:
    __setting_C__: 1                          # other settings for your 'Compound' device
    component_config:                         # nest component configuration settings here
      Component01: 
        module: __module_name_03__
        class: __submodule_3A__.__class_3A__
        settings:
          ip_address: '192.0.0.1'             # IP addresses do not vary between machines
      Component02: 
        module: __module_name_04__
        class: __submodule_4A__.__class_4A__
        settings:
          __setting_D__: 2                    # settings for your component device

layout.json

Layout configuration of your physical workspace (Deck) will be stored in layout.json. This package uses the same Labware files as those provided by Opentrons, which can be found here, and custom Labware files can be created here. Labware files are JSON files that specifies the external and internal dimensions of a Labware block/module.

In reference_points, the bottom-left coordinates of each slot in the workspace are defined. Slots are positions where Labware blocks may be placed.

In slots, the name of each slot and the file reference for Lawbware block that occupies that slot are defined. The filepath starts with the repository's base folder name.

{
  "reference_points":{
    "1": ["_x01_","_y01_","_z01_"],
    "2": ["_x02_","_y02_","_z02_"]
  },
  "slots":{
    "1": {
      "name": "Labware01",
      "filepath": "REPO/.../Labware01.json"
    },
    "2": {
      "name": "Labware02",
      "filepath": "REPO/.../Labware02.json"
    }
  }
}

This file is optional if your setup does not involve moving objects around in a pre-defined workspace, and hence a layout configuration may not be required.

program.py

Lastly, you can define how your setup is initialised in program.py.

There are 3 objects that can be changed to suit your setup.

1. BINDING
   Here, you can define shortcuts for components in your Compound devices.

BINDINGS = {'__name__': '__device_name__.__component__'}
# User-defined 'device_name' in config.yaml

For example,

BINDINGS = {'robot_arm': 'myCompoundDevice.mover'}

2. REPO
   Here, you define REPO as the folder name that contains the /configs folder created above (i.e. repository base folder name).

3. modify_setup()
   Here, you are free to change the contents of the function to modify the setup created upon initialisation. One typical modification step is to load the Deck during initialisation, using

setup['myCompoundDevice'].loadDeck(layout_dict = layout_dict)
# layout_dict is read from file a few lines before modify_setup()

Load setup

The initialisation of the setup occurs during the import of the program.py module.

# Add the configs folder to sys.path
import os
import sys
REPO = '__repo_name__'
here = '/'.join(os.path.abspath('').split('\\')[:-1])
root = here.split(REPO)[0]
sys.path.append(f'{root}{REPO}')

# Import the previously defined program.py
from configs.SynthesisB1 import program
this = program.SETUP

With this, you can access all the devices that you have defined in configs.yaml, as well as in BINDINGS.

this.myCompoundDevice
this.robot_arm

Package Structure

1. Analyse
2. Compound
3. Control
4. Make
5. Measure
6. Move
7. Transfer
8. View

Contributors

@kylejeanlewis
@mat-fox
@Quijanove

How to Contribute

Issues and feature requests are welcome!

License

This project is distributed under the MIT License.