Answers for "programs made with python"

games made with python

Video games uses or made by using python python
1. Battlefield 2 uses Python for all of its add-ons and a lot of its functionality.
2. Bridge Commander
3. Civilization IV uses Python for most of its tasks
4. Disney's Toontown Online is written in Python and uses Panda3D for graphics.
5. Doki Doki Literature Club!, a psychological horror visual novel using the Ren'Py engine
6. Eve Online uses Stackless Python.
7. Freedom Force
8. Frets on Fire is written in Python and uses Pygame
9. Mount & Blade is written in Python.
10. Pirates of the Caribbean Online is written in Python and uses Panda3D for graphics.
11. The Sims 4 uses Python
12. The Temple of Elemental Evil, a computer role-playing game based on the classic Greyhawk Dungeons & Dragons campaign setting
13. Unity of Command (video game) is an operational-level wargame about the 1942/43 Stalingrad Campaign on the Eastern Front.
14. Vampire: The Masquerade – Bloodlines, a computer role-playing game based on the World of Darkness campaign setting
15. Vega Strike, an open source space simulator, uses Python for internal scripting
16. World of Tanks uses Python for most of its tasks.

how to python program

>>>time.sleep(1) #Python Sleep command

best python programs

theta = 2 * np.pi * np.random.random(1000)
r = 6 * np.random.random(1000)
x = np.ravel(r * np.sin(theta))
y = np.ravel(r * np.cos(theta))
z = f(x, y)
ax = plt.axes(projection=’3d’)
ax.plot_trisurf(x, y, z,cmap=’viridis’, edgecolor=’none’);

best python programs

ax = plt.axes(projection=’3d’)# Data for a three-dimensional line
zline = np.linspace(0, 15, 1000)
xline = np.sin(zline)
yline = np.cos(zline)
ax.plot3D(xline, yline, zline, ‘gray’)# Data for three-dimensional scattered points
zdata = 15 * np.random.random(100)
xdata = np.sin(zdata) + 0.1 * np.random.randn(100)
ydata = np.cos(zdata) + 0.1 * np.random.randn(100)
ax.scatter3D(xdata, ydata, zdata, c=zdata, cmap=’Greens’);

best python programs

import numpy as np
 
import tensorflow as tf
 
 
 
from include.data import get_data_set
 
from include.model import model
 
 
 
 
 
test_x, test_y = get_data_set("test")
 
x, y, output, y_pred_cls, global_step, learning_rate = model()
 
 
 
 
 
_BATCH_SIZE = 128
 
_CLASS_SIZE = 10
 
_SAVE_PATH = "./tensorboard/cifar-10-v1.0.0/"
 
 
 
 
 
saver = tf.train.Saver()
 
sess = tf.Session()
 
 
 
 
 
try:
 
    print("
Trying to restore last checkpoint ...")
 
    last_chk_path = tf.train.latest_checkpoint(checkpoint_dir=_SAVE_PATH)
 
    saver.restore(sess, save_path=last_chk_path)
 
    print("Restored checkpoint from:", last_chk_path)
 
except ValueError:
 
    print("
Failed to restore checkpoint. Initializing variables instead.")
 
    sess.run(tf.global_variables_initializer())
 
 
 
 
 
def main():
 
    i = 0
 
    predicted_class = np.zeros(shape=len(test_x), dtype=np.int)
 
    while i < len(test_x):
 
        j = min(i + _BATCH_SIZE, len(test_x))
 
        batch_xs = test_x[i:j, :]
 
        batch_ys = test_y[i:j, :]
 
        predicted_class[i:j] = sess.run(y_pred_cls, feed_dict={x: batch_xs, y: batch_ys})
 
        i = j
 
 
 
    correct = (np.argmax(test_y, axis=1) == predicted_class)
 
    acc = correct.mean() * 100
 
    correct_numbers = correct.sum()
 
    print()
 
    print("Accuracy on Test-Set: {0:.2f}% ({1} / {2})".format(acc, correct_numbers, len(test_x)))
 
if __name__ == "__main__":
 
    main()
 
sess.close()