Histogram drawing using matplotlib by python code.

 Refer to code:

first one is draw histogram

second one is for drawing simple graph

drawing histogram

.

import matplotlib.pyplot as plt

# Data to plot

data = [(40, 1.646054384000001), (233, 3.0769193350000013), (221, 2.6460548819999996),

(214, 2.3542021680000005), (322, 2.726835301999998), (94, 1.201160183999999),

(193, 2.501363478000002), (171, 1.3009034040000031), (595, 5.574669749999998),

(248, 2.455411452)]

# Separate the data into two lists for word length and processing time

word_lengths = [d[0] for d in data]

processing_times = [d[1] for d in data]

# Plot the histogram

plt.hist(processing_times, bins=5)

# Add labels and title

plt.xlabel("Processing Time")

plt.ylabel("Frequency")

plt.title("Histogram of Processing Time")

# Show the plot

plt.show()

..

drawing graph

.

import matplotlib.pyplot as plt

# Data to plot

data = [(40, 1.646054384000001), (233, 3.0769193350000013), ..]

word_lengths = [d[0] for d in data]

processing_times = [d[1] for d in data]

plt.bar(word_lengths, processing_times)

plt.xlabel("Word Length")

plt.ylabel("Processing Time")

plt.title("Histogram of Word Lengths vs. Processing Times")

plt.show()

..

Thank you.🙇🏻‍♂️

www.marearts.com

matplotlib plt to cv2

This example code is based on plt.pie drawing.

But you can apply any drawing way, just refer to how to be converted plt.fig 2 Numpy(cv2).

..

    import matplotlib.pyplot as plt

fig = plt.figure()

plt.pie(ratio, labels = mylabels, colors = mycolors) #, radius=180)

def get_img_from_fig(fig, dpi=180):

import io

buf = io.BytesIO()

fig.savefig(buf, format="png", dpi=dpi)

buf.seek(0)

img_arr = np.frombuffer(buf.getvalue(), dtype=np.uint8)

buf.close()

img = cv2.imdecode(img_arr, 1)

img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

return img

plot_img_np = get_img_from_fig(fig)

cv2.namedWindow('palette')

cv2.imshow('palette', plot_img_np)

cv2.waitKey(0)

..

Thank you.

🙇🏻‍♂️