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MakiaSat Land Station Software 💻

This software makes it possible to save and display data, transmitted by CanSat, received from an antenna via the serial port in real time.

Requirements

The following dependencies must be installed to use the code:

You can install all the python libraries you need using the command:

pip install -r requirements.txt

Usage

First of all, it is neessary that redis is running

redis-server

To start the data collection program, simply run

python main.py

Open another shell to plot the data

python worker.py

Documentation

Redis

RedisClient is an object designed to establish a connection with a redis server and that provides methods for adding or removing items from a queue

RedisClient(key: str, host: str, port: int)

  initial_del(self) -> None:
  push_list(self, element: list) -> Union[Awaitable[int], int]:
  pop_list(self) -> None or list:
  • Clear redis queue on start
  • Push element to queue
  • Pop element from queue
# Initialize client
r = RedisClient(host=REDIS_HOST, port=REDIS_PORT, key=REDIS_KEY)

# Delate previus data
r.initial_del()

# Push list to ram
r.push_list([1, 2, 3, 4])

# Pop list from ram
print(r.pop_list())

>>> [1, 2, 3, 4]

Data Object

Data objects are designed to make data plotting simple; each object holds its data within itself and is responsible for plotting it

DataObject(name: str, key: int, initial_value: float)
    update_data(self, new_data: list[float] = None) -> None:

PlottableDataObject(DataObject):
    update_graph(self, xdata: list) -> None:

MultiPlotDataObject(
                 ax: plt.axes,
                 name: str,
                 keys: list[int],
                 xlim: list[int],
                 ylim: list[int] = None,
                 delta: int = 0,
                 sources: list[str] = None,
                 initial_values: list[float] = None,
                 colors: list[str] = None
                ):
    update_y_limits():
    update_data(self, new_data: list[float] = None) -> None:
  • Update data parsing redis data
  • Update plot data
  • Calculate viewport
# Matplotlib figure    
fig = plt.figure()

# Create the axes
temp_axes = fig.add_subplot()

plt.ion()

# Create the plot
temp = MultiPlotDataObject(
  ax=temp_axes,
  colors=["red", "orange"],
  initial_values=[0, 0],
  keys=[5, 8],
  name="Temperature",
  sources=["Pressure", "Humidity"],
  xlim=view_section,
  ylim=[0, 20],
  delta=1
)

# Update the data
temp.update_data(new_data)

# Update graph with new data
temp.update_graph()

Warning

Sorry, documentation is planned ...

How does it works ?

The dispatcher handles receiving the data its saving and adding it to a queue hosted on a redis database that is iterated by a worker that parses the data in order to display it in real time in an animated graph

graph
S(Serila Port)
A([Dispatcher])
B([Worker])
c[(Redis Queue)]
D[data.csv]

B --> P[Real time plot dashboard]
S --> A
A <--> c
B <--> c
A --> D
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