Creating an Installer for PSLab Desktop App

PSLab device is made useful with applications running on two platforms. One is Android and the other one is a desktop application developed using Python frameworks. Desktop application uses half a dozen of dependent libraries and they are required to be installed prior to installing the application itself.

For someone with zero or less knowledge on how to install packages in a Linux environment, this task will be quite difficult. To ease up the process of installing the desktop application in a computer, we can use a script to run specific commands which will install the dependencies and the application.

Dependencies required by PSLab  Desktop app

  • PyQt 4.7
  • Python 2.6, 2.7 or 3.x
  • NumPy, Scipy
  • pyqt4-dev-tools
  • Pyqtgraph
  • pyopengl and qt-opengl
  • iPython-qtconsole

These dependencies can be made installed using a bash script running with root permission. A bash script will have the file extension “.sh” and a header line;

#!/bin/bash

A bash script needs to be made executable by the user himself. To do this, user needs to type a one line command in the terminal as follows and enter his password;

sudo chmod +x <Name_of_the_script>.sh

The keyword “sudo” interprets as “Super User DO” and the line follows will be executed with root permission. In other words with administrative privileges to modify system settings such as copying content to system folders.

The keyword “chmod” stands for “Change Mode” which will alter the mode of a file. In current context, the file is made executable by adding the executable property to the bash script using “+x” syntax.

Once the script is made executable, it can be executed using;

sudo ./<Name_of_the_script>.sh

An installer can be made attractive by using different colors rather than the plain old text outputs. For this purpose we can use color syntax in bash script. They are represented using ANSI escape codes and following is a list of commonly used colors;

Black        0;30     Dark Gray     1;30
Red          0;31     Light Red     1;31
Green        0;32     Light Green   1;32
Brown/Orange 0;33     Yellow        1;33
Blue         0;34     Light Blue    1;34
Purple       0;35     Light Purple  1;35
Cyan         0;36     Light Cyan    1;36
Light Gray   0;37     White         1;37

As in any programming language, rather than using the same line in many places, we can define variables in a bash script. The syntax will be the variable name followed by an equal sign with the value. There cannot be spaces around the equal sign or it will generate an error.

GREEN='\033[0;32m'

These variables can be accessed using a special syntax as follows;

${GREEN}

Finally we can output a message to the console using the “echo” command

echo -e "${GREEN}Welcome to PSLab Desktop app installer${NOCOLOR}"

Note that the keyword “-e” is used to enable interpretation of the following backslash escapes.

In order to install the packages and libraries, we use two package management tools. One is “apt” which stands for “Advanced Packaging Tool” and the second is “pip” which is used to download python related packages from “Python Package Index”. The following two lines illustrates how the two commands can be accessed.

apt-get install python-pip python-dev build-essential -y

pip install pyqtgraph

The keyword “-y” avoids the confirmation prompt in console to allow installation by pressing “Y” key every time it installs a package from “apt”.

Resources:

I2C Communication in PSLab

PSLab supports communication using the I2C protocol and both the Desktop App and the Android App have the framework set-up to use the I2C protocol. I2C protocol is mainly used by sensors which can be connected to PSLab. For supporting I2C communication, PSLab board has a separate block for I2C communication and has pins named 3.3V, GND, SCL and SDA. A brief overview of how I2C communication works and its advantages & limitations compared to SPI communication can be found here.

The PSLab Python and Java communication libraries have a class dedicated for I2C communication with numerous methods defined in them. The methods required for a particular I2C sensor may differ, however, in general most sensors utilise a certain common set of methods. The set of methods that are commonly used are listed below with their functions. For utilising the methods, the I2C bus is first notified using the HEADER byte (it is common to all the methods) and then a byte to uniquely determine the method in use.

The send method is used to send the data over the I2C bus. First the I2C bus is initialised and set to the correct slave address using I2C.start(address) followed by this method. The method takes the data to be sent as the argument.

def send(self, data):
    try:
        self.H.__sendByte__(CP.I2C_HEADER)
        self.H.__sendByte__(CP.I2C_SEND)
        self.H.__sendByte__(data)  # data byte
        return self.H.__get_ack__() >> 4
    except Exception as ex:
        self.raiseException(ex, "Communication Error , Function : " + inspect.currentframe().f_code.co_name)

 

The read method reads a fixed number of bytes from the I2C slave. One can also use I2C.simpleRead(address,  numbytes) instead to read from the I2C slave. This method takes the length of the data to be read as argument.  It fetches length-1 bytes with acknowledge bits for each.

def read(self, length):
     data = []
     try:
        for a in range(length - 1):
             self.H.__sendByte__(CP.I2C_HEADER)
             self.H.__sendByte__(CP.I2C_READ_MORE)
             data.append(self.H.__getByte__())
             self.H.__get_ack__()
       self.H.__sendByte__(CP.I2C_HEADER)
       self.H.__sendByte__(CP.I2C_READ_END)
       data.append(self.H.__getByte__())
       self.H.__get_ack__()
    except Exception as ex:
       self.raiseException(ex, "Communication Error , Function : " + inspect.currentframe().f_code.co_name)
   return data

 

The readBulk method reads the data from the I2C slave. This takes the I2C slave device address, the address of the device from which the data is to be read and the length of the data to be read as argument and the returns the bytes read in the form of a list.

def readBulk(self, device_address, register_address, bytes_to_read):
        try:
            self.H.__sendByte__(CP.I2C_HEADER)
            self.H.__sendByte__(CP.I2C_READ_BULK)
            self.H.__sendByte__(device_address)
            self.H.__sendByte__(register_address)
            self.H.__sendByte__(bytes_to_read)
            data = self.H.fd.read(bytes_to_read)
            self.H.__get_ack__()
            try:
                return [ord(a) for a in data]
            except:
                print('Transaction failed')
                return False
        except Exception as ex:
           self.raiseException(ex, "Communication Error , Function : " + inspect.currentframe().f_code.co_name)

 

The writeBulk method writes the data to the I2C slave. It takes address of the particular I2C slave for which the data is to be written and the data to be written as arguments.

def writeBulk(self, device_address, bytestream):
        try:
            self.H.__sendByte__(CP.I2C_HEADER)
            self.H.__sendByte__(CP.I2C_WRITE_BULK)
            self.H.__sendByte__(device_address)
            self.H.__sendByte__(len(bytestream))
            for a in bytestream:
                self.H.__sendByte__(a)
            self.H.__get_ack__()
        except Exception as ex:
  self.raiseException(ex, "Communication Error , Function : " + inspect.currentframe().f_code.co_name)

 

The scan method scans the I2C port for connected devices which utilise I2C as a communication mode. It takes frequency as an argument to set the frequency of the communication and is by default set to 100000. An array containing the addresses of the connected devices (which are integers) is returned.

def scan(self, frequency=100000, verbose=False):
        self.config(frequency, verbose)
        addrs = []
        n = 0
        if verbose:
            print('Scanning addresses 0-127...')
            print('Address', '\t', 'Possible Devices')
        for a in range(0, 128):
            x = self.start(a, 0)
            if x & 1 == 0:  # ACK received
                addrs.append(a)
                if verbose: print(hex(a), '\t\t', self.SENSORS.get(a, 'None'))
                n += 1
            self.stop()
       return addrs

 

Additional Sources

  1. Learn more about the principles behind i2c communication https://learn.sparkfun.com/tutorials/i2c
  2. A simple experiment to demonstrate use of i2c communication with Arduino http://howtomechatronics.com/tutorials/arduino/how-i2c-communication-works-and-how-to-use-it-with-arduino/
  3. Java counterpart of the PSLab I2C library https://github.com/fossasia/pslab-android/blob/master/app/src/main/java/org/fossasia/pslab/communication/peripherals/I2C.java

Real time Sensor Data Analysis on PSLab Android

PSLab device has the capacity to connect plug and play sensors through the I2C bus. The sensors are capable of providing data in real time. So, the PSLab Android App and the Desktop app need to have the feature to fetch real time sensor values and display the same in the user interface along with plotting the values on a simple graph.

The UI was made following the guidelines of Google’s Material Design and incorporating some ideas from the Science Journal app. Cards are used for making each section of the UI. There are segregated sections for real time updates and plotting where the real time data can be visualised. A methods for fetching the data are run continuously in the background which receive the data from the sensor and then update the screen.

The following section denotes a small portion of the UI responsible for displaying the data on the screen continuously and are quite simple enough. There are a number of TextViews which are being constantly updated on the screen. Their number depends on the type and volume of data sent by the sensor.

<TextView
       android:layout_width="wrap_content"
       android:layout_height="30dp"
       android:layout_gravity="start"
       android:text="@string/ax"
       android:textAlignment="textStart"
       android:textColor="@color/black"
       android:textSize="@dimen/textsize_edittext"
       android:textStyle="bold" />

<TextView
       android:id="@+id/tv_sensor_mpu6050_ax"
       android:layout_width="wrap_content"
       android:layout_height="30dp"
       android:layout_gravity="start"
       android:textAlignment="textStart"
       android:textColor="@color/black"
       android:textSize="@dimen/textsize_edittext"
       android:textStyle="bold" />

 

The section here represents the portion of the UI responsible for displaying the graph. Like all other parts of the UI of PSLab Android, MPAndroidChart is being used here for plotting the graph.

<LinearLayout
       android:layout_width="match_parent"
       android:layout_height="160dp"
       android:layout_marginTop="40dp">

       <com.github.mikephil.charting.charts.LineChart
               android:id="@+id/chart_sensor_mpu6050"
               android:layout_width="match_parent"
               android:layout_height="match_parent"
               android:background="#000" />
</LinearLayout>

 

Since the updates needs to continuous, a process should be continuously run for updating the display of the data and the graph. There are a variety of options available in Android in this regard like using a Timer on the UI thread and keep updating the data continuously, using ASyncTask to run a process in the background etc.

The issue with the former is that since all the processes i.e. fetching the data and updating the textviews & graph will run on the UI thread, the UI will become laggy. So, the developer team chose to use ASyncTask and make all the processes run in the background so that the UI thread functions smoothly.

A new class SensorDataFetch which extends AsyncTask is defined and its object is created in a runnable and the use of runnable ensures that the thread is run continuously till the time the fragment is used by the user.

scienceLab = ScienceLabCommon.scienceLab;
i2c = scienceLab.i2c;
try {
    MPU6050 = new MPU6050(i2c);
} catch (IOException e) {
    e.printStackTrace();
}
Runnable runnable = new Runnable() {
    @Override
    public void run() {
        while (true) {
            if (scienceLab.isConnected()) {
                try {
                    sensorDataFetch = new SensorDataFetch();
                } catch (IOException e) {
                    e.printStackTrace();
                }
                sensorDataFetch.execute();
            }
        }
    }
};
new Thread(runnable).start();

 

The following is the code for the ASyncTask created. There are two methods defined here doInBackground and onPostExecute which are responsible for fetching the data and updating the display respectively.

The raw data is fetched using the getRaw method of the MPU6050 object and stored in an ArrayList. The data type responsible for storing the data will depend on the return type of the getRaw method of each sensor class and might be different for other sensors. The data returned by getRaw is semi-processed and the data just needs to be split in sections before presenting it for display.

The PSLab Android app’s sensor files can be viewed here and they can give a better idea about how the sensors are calibrated, how the intrinsic nonlinearity is taken care of, how the communication actually works etc.

After the data is stored, the control moves to the onPostExecute method, here the textviews on the display and the chart are updated. The updation is slowed down a bit so that the user can visualize the data received.

private class SensorDataFetch extends AsyncTask<Void, Void, Void> {
   MPU6050 MPU6050 = new MPU6050(i2c);
   ArrayList<Double> dataMPU6050 = new ArrayList<Double>();

   private SensorDataFetch(MPU6050 MPU6050) throws IOException {
   }

   @Override
   protected Void doInBackground(Void... params) {
       try {
           if (MPU6050 != null) {
               dataMPU6050 = MPU6050.getRaw();
           }
       } catch (IOException e) {
           e.printStackTrace();
       }
           return null;
   }

   protected void onPostExecute(Void aVoid) {
       super.onPostExecute(aVoid);
       tvSensorMPU6050ax.setText(String.valueOf(dataMPU6050.get(0)));
       tvSensorMPU6050ay.setText(String.valueOf(dataMPU6050.get(1)));
       tvSensorMPU6050az.setText(String.valueOf(dataMPU6050.get(2)));
       tvSensorMPU6050gx.setText(String.valueOf(dataMPU6050.get(3)));
       tvSensorMPU6050gy.setText(String.valueOf(dataMPU6050.get(4)));
       tvSensorMPU6050gz.setText(String.valueOf(dataMPU6050.get(5)));
       tvSensorMPU6050temp.setText(String.valueOf(dataMPU6050.get(6)));
   }
}

The detailed implementation of the same can be found here.

Additional Resources

  1. Learn more about how real time sensor data analysis can be used in various fields like IOT http://ieeexplore.ieee.org/document/7248401/
  2. Google Fit guide on how to use native built-in sensors on phones, smart watches etc. https://developers.google.com/fit/android/sensors
  3. A simple starter guide to build an app capable of real time sensor data analysis http://developer.telerik.com/products/building-an-android-app-that-displays-live-accelerometer-data/
  4. Learn more about using AsyncTask https://developer.android.com/reference/android/os/AsyncTask.html

Analyzing Sensor Data on PSLab

PSLab Android App and Desktop app have the functionality of reading data from the sensors. The raw sensor data received is in the form of a long string and needs to parsed to understand what the data actually conveys.

The sensor data is unique in terms of volume of data sent, the units of measurement of the data etc., however none of this is reflected in the raw data. The blog describes how the sensor data received by the Android/Desktop app is parsed, interpreted and finally presented to the user for viewing.

The image below displays the raw data sent by the sensors

blog_post_9_2

Fig: Raw Sensor data displayed below the Get Raw button

  • In order to understand the data sent from the sensor, we need to understand what the sensor does.
    • For example, HMC5883L is a 3-axis magnetometer and it returns the value of the magnetic field in the x, y & z axes in the order of nanoTeslas.
    • Similarly, the DAC of PSLab – MCP4728 can also be used like other sensors, it returns the values of channels in millivolts.
    • The sensor MPU6050 being 3-axes accelerometer & gyroscope which returns the values of acceleration & angular momentum of the x, y & z axes in their SI units respectively.
  • Each sensor has a sensitivity value. The sensitivity of the sensor can be modified to adjust the accuracy of the data received. For PSLab, the data returned is a float number with each data point having 4 bytes of memory with the highest sensitivity. Although sensitivity is not a reliable indicator of the accuracy of the data. Each value received has a lot of trailing values after the decimal and it is evident that no sensor can possibly achieve accuracy that high, so the data after 2-3 decimal places is garbage and not taken into consideration.
  • Some sensors are configurable up to a great extent like MPU6050 where limits can also be set on the range of data, volume of data sent etc. whereas some are not configurable and are just meant for sending the data at regular intervals.
  • In order to parse the above data, if the sensor returns a single value, then the data is ready to be used. However, in most cases like above where the sensors return multiple values, the data stream can be divided into equal parts since each value occupies equal space and each value can be stored in different variables.
  • The stored data has to be presented to the user in a better understandable format where it is clear that what each value represents. For example, in case of the 3 axes sensors, the data of each axis must be distinctly represented to the user.

Shown below are the mock-ups of the sensor UIs in which each value has been distinctly represented.

         

Fig: Mock-ups for the sensor UIs (a) – HMC5883L (b) – MPU6050

Each UI has a card to display those values. These values are updated in real time and there are additional options to plot the data received in real time and in some cases also configure the sensor. In addition to that there are features for data logging where the data is recorded for a given time interval specified by the user and on completion of recording, calculations like the mean, standard deviation etc. are presented to the user.

Additional Resources

  1. Analyzing sensor data using Arduino, similar to method for PSLab – http://tronixstuff.com/2014/01/21/online-data-analysis-arduino-plotly/
  2. YouTube video to understand analysis of data from MPU6050 in Arduino – https://www.youtube.com/watch?v=taZHl4Mr-Pk