Plan your shoot!

Biggest and best full moon for a while will occur on 2nd January 2018 at 02:24 GMT. The moon's angular radius will be a whopping 9.25% larger than average. Bet it'll be overcast here...

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Generated in python with pyephem and matplotlib.

Needs to be viewed at 2048 or above to see the text clearly on my little notebook.

# Trying to figure out why the log of this function

# (previous photo) is used as the logistic regression

# error function

import numpy as np

import matplotlib.pyplot as plt

# predicted probabilities

a = np.linspace(0.001, 0.999, num=100);

# actual label

y = 0.999;

loss = -(pow(a,y)*pow((1-a),(1-y)))+1;

plt.plot(a, loss, 'o');

plt.xlabel('Predictions');

plt.ylabel('f(a,y)');

plt.title('f(a,y) for label = 0.999')

import numpy as np

import matplotlib.pyplot as plt

def plot_frequency_sine_waves(sine_wave_tuples, time):

plt.figure(figsize=(24, 6))

for sine_wave, note_name, frequency in sine_wave_tuples:

label = f"{note_name} ({frequency} Hz)"

plt.plot(time, sine_wave, alpha=0.7, label=label)

# Add the summed sine wave for the C major chord

c_major_wave = np.sum(sine_waves, axis=0)

plt.plot(time, c_major_wave, alpha=0.7, label="C major", linestyle="--")

plt.xlabel("Time (s)")

plt.ylabel("Amplitude")

plt.title("C Major Frequencies")

plt.legend(loc="upper right")

plt.grid()

plt.show()

# Example usage

duration = 0.008

sample_rate = 44100

notes = [C4, E4, G4] # Frequencies for a C major chord

note_names = ["C4", "E4", "G4"]

time = np.linspace(0, duration, int(duration * sample_rate), False)

sine_waves = [np.sin(2 * np.pi * freq * time) for freq in notes]

sine_wave_tuples = list(zip(sine_waves, note_names, notes))

plot_frequency_sine_waves(sine_wave_tuples, time)

Where y is are the true labels (probabilities) and y hat is the predicted probabilitiy.

import numpy as np

import matplotlib.pyplot as plt

# predicted probabilities

yhat = np.linspace(0.001, 0.999, num=100);

y = 0.001;

logloss = -y*np.log(yhat)-(1-y)*np.log(1-yhat);

plt.plot(yhat, logloss, 'o');

plt.xlabel('Predictions');

plt.ylabel('Log Loss');

plt.title('Log loss for ground truth = 0')

# Steering angle predictions by model 20201107210627_nvidia1.h5

def plotSteering(p,g,n):

"""

Plot

Inputs

p: array of floats, steering angle prediction

g: array of floats, steering angle ground truth.

n: float, normalization constant

Output

"""

import matplotlib.pyplot as plt

plt.rcParams["figure.figsize"] = (18,3)

plt.plot(p*n, label="model")

plt.plot(g*n, label="simulator")

plt.ylabel('Steering angle')

# Set a title of the current axes.

plt.title('Predicted and actual steering angles: SDSandbox simulator and model 20201107210627_nvidia1.h5')

# show a legend on the plot

plt.legend()

# Display a figure.

# horizontal grid only

plt.grid(axis='y')

# set limit

plt.xlim([-5,len(sarr)+5])

# invert axis so if seen sideways plot corresponds to direction of steering wheel

plt.gca().invert_yaxis()

plt.show()

p = sarr[:,0]

g = sarr[:,1]

n = 25 # 25 frames per second

plotSteering(p,g,n)

Matplotlib and Seaborn form the core of Python-based data visualization. They facilitate data analysis and forecasting by breaking down a large dataset into manageable graphs. Both are crucial components of data science that simplify and expand the accessibility of complex datahttps://www.datascienceverse.com/data-visualization/python-seaborn-vs-matplotlib-comparison

A bar plot (or bar chart) is a graph that presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally.

The matplotlib.pyplot.bar() functions makes a bar plot. The bars are positioned at x with the given alignment. Their dimensions are given by height and width. The vertical baseline is bottom (default 0). Many parameters can take either a single value applying to all bars or a sequence of values, one for each bar.

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“ImportError: No module named matplotlib.pyplot”

Did you get this error “ImportError: No module named matplotlib.pyplot” while working? This is a very basic error and easy to handle. Here are the ways to fix this error. Let’s find out together!

#ittutoria #program #python #informationtechnology

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“ImportError: No module named matplotlib.pyplot“

Did you execute your code when the following error occurred: “ImportError: No module named matplotlib.pyplot“

Please refer to our article to fix the above error.

#itprospt #matplotlib #pyplot #module #solve

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Contour plots also called level plots are a way to show a three-dimensional surface on a two-dimensional plane. It graphs two predictor variables X and Y on the y-axis and a response variable Z as contours. These contours are sometimes called z-slices or iso-response values.

Contour plots are widely used to visualize the density, altitudes, or heights of the mountain as well as in the meteorological department. Due to such wide usage matplotlib.pyplot provides a method contour to make it easy to draw contour plots.