MatSense 0.4.1

A toolkit for matrix sensor data processing 阵列传感器数据处理工具库

MatSense

A toolkit that supports both real-time and off-line matrix sensor data processing and 3D visualization.

A typical real-time data flow would be in a client-server manner:

• Matrix sensor data: collected (e.g. by Arduino) and transmitted via a serial port to the computer.
• Data processing: the series of matrix data frames are processed and served by the server.
• Applications: clients connect to server to get processed data and do further work.

Data can also be recorded to and processed from files.

3D visualization tools are provided to play real-time stream or recorded data.

Installation

From PyPI:

pip install MatSense

This will install Matplotlib to implement 3D visualization tools.

If you want to further try PyQtGraph as visualization method:

pip install MatSense[pyqtgraph]

Usage

Off-the-shelf tools

3 handy tools are provided. Pass -h to get detailed information.

• matserver / python -m matsense.server
  • functions:
    • receive data from serial port, process and serve
    • process data from file(s) and output to file
    • other helpful functions
  • supported processing methods:
    • voltage-pressure conversion (optional for pressure data)
    • spatial filter (in-frame denoising): none, ideal, butterworth, gaussian
    • temporal filter (pixel-wise between-frame denoising): none, moving average, rectangular window
    • calibration: static or dynamic
• matclient / python -m matsense.client: receive server data, process and visualize; or control server via interactive commands
  • supported processing methods:
    • interpolation
    • blob parsing
• matdata / python -m matsense.data: visualize file data, or process off-line data

Configuration

All 3 tools can be totally configured by a YAML configuration file:

## server console
matserver --config your_config.yaml

## client console
matclient --config your_config.yaml

## off-line data processing
matdata --config your_config.yaml

Priority: commandline arguments > config file > program defaults.

A template YAML configuration (unused options can be set to ~ or removed):

## template configurations
## ~ for defaults

## configurations for matserver mode
server_mode:
  ## enable backend service
  service: ~
  ## enable visualization or not (suppress service)
  visualize: ~
  ## enumerate all serial ports
  enumerate: ~

  ## (suppress serial) simulated data source without actual serial connection
  ## debug mode: true, false
  debug: ~

  ## (suppress serial) use file as data source or not: true, false
  use_file: ~

## configurations for matclient mode
client_mode:
  ## make client present raw data
  raw: ~
  ## interactive command line mode
  interactive: ~

## configurations for matdata mode
data_mode:
  ## process file data instead of visualization
  process: ~

## configurations for file data
data:
  ## input filename(s), filename or a list of filenames: [a.csv, b.csv, ...]
  in_filenames: ~
  ## output filename, default filename is used when not provided
  out_filename: ~

## configurations for matrix sensor
sensor:
  ## sensor shape: [16, 16], [8, 8], [6, 24]
  shape: ~
  ## total points, can be set to ~
  total: ~
  ## 0/1 mask to exclude non-existent points
  ## |- for multiline without a newline in the end
  mask: ~

## configurations for serial port
serial:
  ## baudrate: 9600, 250000, 500000, 1000000
  baudrate: ~
  ## serial port timeout, in seconds
  timeout: ~
  ## serial port
  port: ~
  ## data transmission protocol: simple, secure
  protocol: ~
  ## support IMU data
  imu: ~

## configurations for client-server connections
connection:
  ## use UDP or UNIX domain socket
  udp: ~
  ## udp address format: 127.0.0.1:20503
  ## UNIX deomain socket address format: /var/tmp/unix.socket.server
  server_address: ~
  client_address: ~

## configurations for data processing
process:
  ### voltage to the reciprocal of resistance
  ## reference voltage: 255, 255/3.6*3.3
  V0: ~
  ## constant factor: 1
  R0_RECI: ~
  ## convert voltage to resistance: true
  convert: ~

  ### server data processing
  ## no filtering and calibration
  raw: ~
  ## time of warming up in seconds: 1
  warm_up: ~
  ## spatial filter: none, ideal, butterworth, gaussian
  filter_spatial: ~
  ## spatial filter cut-off freq: 3.5
  filter_spatial_cutoff: ~
  ## Butterworth filter order: 2
  butterworth_order: ~
  ## temporal filter: none, moving average, rectangular window
  filter_temporal: ~
  ## temporal filter size: 15
  filter_temporal_size: ~
  ## rectangular window filter cut-off frequency: 0.04
  rw_cutoff: ~
  ## calibrative frames, 0 for no calibration: 0, 200
  cali_frames: ~
  ## calibration frame window size, 0 for static and >0 for dynamic: 0, 10000
  cali_win_size: ~
  ## intermediate result: 0, 1, 2
    ## 0: convert voltage to reciprocal resistance
    ## 1: convert & spatial filter
    ## 2: convert & spatial filter & temporal filter
  intermediate: ~

  ### (optional) client data processing
  ## interpolation shape, default to sensor.shape
  interp: ~
  ## interpolation order: 3
  interp_order: ~
  ## filter out blobs: true
  blob: ~
  ## total blob number: 3
  blob_num: ~
  ## blob filter threshole: 0.1, 0.15
  threshold: ~
  ## special check for certain hardwares: false
  special_check: ~

pointing:
  ## value bound for checking cursor moving state: 0
  bound: ~
  ## directly map coordinates or relatively (suppress trackpoint)
  direct_map: ~
  ## use ThinkPad's TrackPoint (red dot) control style
  trackpoint: ~
  ## smoothing
  alpha: ~

## configurations for visualization
visual:
  ## using pyqtgraph or matplotlib
  pyqtgraph: ~
  ## z-axis limit: 3, 5
  zlim: ~
  ## frame rate: 100
  fps: ~
  ## scatter plot: false
  scatter: ~
  ## show text value: false
  show_value: ~

Useful modules

• matsense.uclient

  • Uclient: interface to receive data from server

• matsense.process: data processing tools

  • DataHandlerPressure: process pressure data (conversion & filtering & calibration)
  • BlobParser
  • Interpolator
  • PointSmoother
  • CursorController
  • PressureSelector

• matsense.datasetter: data setter, using serial port or file data

  • DataSetterSerial
  • DataSetterFile

• matense.tools: configuration and other helpful tools

• matsense.filemanager: file I/O tools

• matsense.visual: visualization tools

  • from matsense.visual.player_matplot import Player3DMatplot: 3D player using Matplotlib
  • from matsense.visual.player_pyqtgraph import Player3DPyqtgraph: 3D player using PyQtGraph

Server-Client Protocol

Use matclient -i to control server.

The underlying server-client communication protocol is：

Name	meaning	Value	Format	Return	Return format
CLOSE	close server	0	1byte	status	1byte
DATA	get a data frame	1	1byte	frame+index	256double+1int
RAW	get a raw data frame	2	1byte	frame+index	256double+1int
REC_DATA	ask server to record data to file	3(+filename)	1byte+string	status+filename	1byte+string
REC_RAW	ask server to record raw data to file	4(+filename)	1byte+string	status+filename	1byte+string
REC_STOP	ask server to stop recording	5	1byte	status	1byte
RESTART	restart server with config string	6(+config_str)	1byte+string	status+config_string	1byte+string
RESTART_FILE	restart server with config filename	10+config_filename	1byte+string	status+config_string	1byte+string
CONFIG	get server config	7	1byte	status+config_string	1byte+string
DATA_IMU	get IMU data	9	1byte	IMU_frame+index	6double+1int

• status (1 byte): 0 for success and 255 for failure

Author

Atomie CHEN: [e-mail]