Table of Contents
Our eyes are drawn to colors and patterns. Data visualization is a form of visual art that grabs our interest and keeps our eyes on the message. When we see a chart, we quickly see trends and outliers.
This projects consists of sending DHT11 sensor data acquired on a esp32 board and visualizing the data with a python script using matplotlib
- ESP32 board ( or any board compatible with arduino IDE)
- DHT11 temperature and humidity sensor
PS : if You dont have the entire DHT11 module then you would need a 10k ohm resistor
- Python 3
- A python IDE , in my case I used PyCharm.
- Arduino IDE.
- pySerial : It will allows access for the serial port.
- Matplotlib : Matplot libs allows to make all types of plots and charts in order to properly and easily visualize data.
- DHTNEW : Arduino library for DHT11 and DHT22 with automatic sensor type recognition.
NB: All these packages need to be installed properly.
- The first phase of this project is collecting data from our sensor. This is done using an Esp32 board and the code snippets below :
- First let's properly setup our variables:
#include <dhtnew.h> // the dhtnew library to interface with our sensor
DHTNEW mySensor(15); // attach the sensor data pin to the D15 pin of the esp32 board
byte H ; // byte variable to store the humidity value read fro the sensor
float T; // float variable to store the temperature value read fro the sensor
void setup()
{
Serial.begin(115200); // set communication rate with the serial port to 115200 bauds
mySensor.setSuppressError(true);
}-
The humidity value was stored as byte ( values from 0 to 255) since in my case I didn't need the entire precision. the integer value was enough.
-
the
mySensor.setSuppressError(true)line serves a very important role . thegetHumidity()andgetTemperature()functions returns last read value (float) or -999 in case of error. This might cause huge negative spikes in case we want to properly visualize the data , hencemySensor.setSuppressError(true)suppresses the error and replaces the -999 value with the last correctly read value. -
Now let's get to the loop function :
void loop()
{
if (millis() - mySensor.lastRead() > 2000) // get values from the sensor every 2 seconds
{
int errcode = mySensor.read(); //begin reading
T = mySensor.getTemperature(); // getting the data
H = mySensor.getHumidity();
Serial.print(uint16_t(T*1000 + H)) ; // sending the data via the serial port
}
}- explaining the
Serial.print(uint16_t(T*1000 + H))line : - the humidity value is generally under 100 which means it has 2 digits.
- the temperature value is a float with one decimal number . so we multiply it by 1000 and add to it the humidity value and convert t to a 2-bytes-integer uint16_t
- example : say wa have a humidity value of 40 and a temperature value of 22.3 the value sent by the arduino is 223040. this 2-bytes value will be later converted to a string to be easily manipulated and to extract data with the python code.
Now let's get to our code:
Let's begin with importing the required packages
import matplotlib.pyplot as plt # plotting
from matplotlib.animation import FuncAnimation # animating the plots since there are new values added in real-time
from datetime import date
from datetime import datetime
import serial # For serial communicationLet's establish a serial communication on COM4 port with 115200 baud rate and 0.1 sec timeout:
esp_board = serial.Serial(port='COM4', baudrate=115200, timeout=.1)Let's setup our plotting variables :
#get the current date in the OCT - 17 - 2020 format
today = date.today()
date = today.strftime("%b-%d-%Y")
print("date = ", date)
#setup a figure containing 2 plots (for humidity and temperature respactively) over one row and 2 columns
fig , (hum_plt, temp_plt) = plt.subplots(1,2,constrained_layout=False)
#The lists that contains the data
dates = list() # list of time frames for the x-axis
Hum = list()
m_Hum= list() # mean humidity values
Temp = list()
m_Temp= list() # mean temperatures values
# thresholds that will be represented by one dashed line each.
Temp_thresh_high_lst =list()
Hum_thresh_high_lst = list()
Temp_thresh_low_lst = list()
Hum_thresh_low_lst = list()the below function to calculate the mean value of a list
def mean(ls):
moy=0
if len(ls)>0:
for i in ls:
moy += i
moy = moy / len(ls)
return moyNow to the most important function that adds new data and plots.
def add_new_data(x):
data = esp_board.readline() # return data sent by the board as bytes
str_data = data.decode('utf-8').rstrip() # reformats the bytes into a string of characters
if (len(str_data) > 0) and (str_data.isnumeric()):
Temp_val = float(str_data[0:3]) / 10 # the first 3 chars represent the temperature value
Hum_val = int(str_data[3:]) # the last 2 chars represent the Humidity value
print("Temp val = ", Temp_val, "Hum val = ", Hum_val)
# Get the current time and append it to the list
now = datetime.now()
dates.append(now)
# append data to the lists
Hum.append(Hum_val)
Temp.append(Temp_val)
# Set the thresholds values
Temp_thresh_high_lst.append(30)
Hum_thresh_high_lst.append(60)
Temp_thresh_low_lst.append(20)
Hum_thresh_low_lst.append(30)
# Calculate the means of data and append them to the means lists
m_hum = mean(Hum)
m_Hum.append(m_hum)
m_temp = mean(Temp)
m_Temp.append(m_temp)
hum_plt.cla()
temp_plt.cla()
# Setting plot titles ans axis labels
hum_plt.set_title('Humdity')
hum_plt.set_xlabel('Time')
hum_plt.set_ylabel('Value')
temp_plt.set_title('Temperature')
temp_plt.set_xlabel('Time')
temp_plt.set_ylabel('Value')
# Plotting the data
hum_plt.plot(dates,m_Hum, color='#444444', linestyle=':', label = 'Mean Humidity')
temp_plt.plot(dates, m_Temp, color='#dc00d6', linestyle=':', label='Mean Temperature')
temp_plt.plot_date(dates, Temp,'r-', label= 'Temperature')
hum_plt.plot_date(dates, Hum, 'b-', label='Humidity')
# Plotting the threshold lines
temp_plt.plot(dates, Temp_thresh_high_lst, color='#f10d0d', linestyle='--', label='High temperature threshold')
temp_plt.plot(dates, Temp_thresh_low_lst, color='#f10d0d', linestyle='--', label='Low Temperature threshold')
hum_plt.plot(dates, Hum_thresh_high_lst, color='#f10d0d', linestyle='--', label='High Humidity threshold')
hum_plt.plot(dates, Hum_thresh_low_lst, color='#f10d0d', linestyle='--', label='Low Humidity threshold')
# Adding legends and the title of the figure which is the current date
temp_plt.legend(loc = "upper left",ncol = 1 )
hum_plt.legend(loc = "best",ncol = 1)
fig.suptitle('DATE : ' + date, fontsize=16)
fig.autofmt_xdate() # to have a nice time format Let's actually animate and display the plots:
anim = FuncAnimation(fig,add_new_data,interval=1700)
plt.show()
In this project, we successfullty collected data from DHT11 sensor send them to the pc via the serial communication and displayed the data ,using the matplotlib module.