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https://github.com/janishutz/BiogasControllerApp.git
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Janis Hutz
7905cb851a
Added a config validator and documented code that was previously undocumented, for the plot_generator scripts, documented them.
93 lines
3.0 KiB
Python
93 lines
3.0 KiB
Python
import csv
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import numpy as np
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import matplotlib.pyplot as plt
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n = int(input("Sensor number to be printed: "))
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file = ""
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def generate_plot():
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# Read data using the CSV library
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reader = csv.reader(file, delimiter=",")
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# Create a list from the data
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data = list(reader)
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# Sort the list using a lambda sort descriptor
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# A lambda function is an anonymous function (= an unnamed function),
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# which makes it convenient. A sort descriptor is a function that
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# (usually, but not here) returns a value indicating which of two values
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# come before or after in the ordering.
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# Here, instead we simply return a floating point value for each data point
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data.sort(key=lambda data_point: float(data_point[2]))
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# Store the x and y coordinates in two arrays
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x = []
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y = []
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for _ in range(len(data)):
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# Extract the data point
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data_point = data.pop(0)
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sensor = int(data_point.pop(0))
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if sensor == n:
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y.append(float(data_point.pop(0)))
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x.append(float(data_point.pop(0)))
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# Use Numpy's polyfit function to fit a 2nd degree polynomial to the points using quadratic regression
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# This function returns an array with the coefficients
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fit = np.polyfit(x, y, 2)
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# The formula to output to the plot
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formula = f"F(U) = {round(float(fit[0]), 4)}U^2+{round(float(fit[1]), 4)}U+{round(float(fit[2]), 4)}"
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# Create a fit function from the previously determined coefficients
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fit_fn = np.poly1d(fit) # Returns a function that takes a list of x-coordinate as argument
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# Plot the line on the graph
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plt.plot(x, fit_fn(x), color="BLUE", label="T(U)")
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# Scatter Plot the data points that we have
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plt.scatter(x, y, color="MAGENTA", marker="o", label="Data")
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# Label the graph
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plt.ylabel("Temperature")
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plt.xlabel("Voltage")
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plt.title("Sensor MCP9701A #{}".format(n))
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# Scale the axis appropriately
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plt.axis((0.6, 2.0, 15.0, 70.0))
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# Print a legend and set the graph to be annotated
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plt.legend(loc="lower right")
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plt.annotate(formula, xy=(0.85, 60))
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# Enable the background grid
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plt.grid(True)
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# Finally, show the graph
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plt.show()
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# Get user input whether to save the plot or not
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saveit = input("Do you wish to save the plot? (y/N) ").lower()
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if saveit == "y":
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# Save the plot as Sensor[Number] (e.g. Sensor9) as png, pdf and svg
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plt.savefig("Sensor" + str(n) + ".png")
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plt.savefig("Sensor" + str(n) + ".pdf", format="pdf")
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plt.savefig("Sensor" + str(n) + ".svg", format="svg")
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print("==> Images saved")
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else:
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print("==> Images discarded")
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# Since we have defined a function above as a function, this here is executed first
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filename = input("Please enter a file path to the csv file to be plotted: ")
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# Try to open the file
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try:
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file = open(filename, "r")
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generate_plot()
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except FileNotFoundError:
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print("Failed to open file (non-existent or corrupted?)")
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