def Translation_analytical_raypath(
velocity_translation=2.01 * 1221.0 / 1.0e6, direction=positions.CartesianPoint(1, 0, 0)
):
# Initialize map
m, fig = plot_data.setting_map()
cm = plt.cm.get_cmap("RdYlBu")
# seismic data set (from Lauren's file)
data_points = read_write.read_from_file(
"results.dat",
slices=[
"PKIKP-PKiKP travel time residual",
"turn lat",
"turn lon",
"turn depth",
"in lat",
"in lon",
"out lat",
"out lon",
],
)
nlines, ncolumns = data_points.shape
print data_points.info
# translate it in the correct format
dataset = []
translation_dataset = []
phi, theta, age, dt = [], [], [], []
print nlines, "points to write."
for i in range(nlines):
if i % 100 == 0: # print every 100 values
print "Writing point ", i, ". Coordinates: ", data_points.ix[i]
# because it's analytical solution, we don't need to define a grid. model is calulated exactly.
translation_dataset.append(
geodynamic.Translation(
positions.SeismoPoint(
1221.0 - data_points.ix[i, "turn depth"],
data_points.ix[i, "turn lat"],
data_points.ix[i, "turn lon"],
)
)
)
translation_dataset[i].analytical(velocity_translation, direction)
phi.append(translation_dataset[i].initial_position.phi)
theta.append(translation_dataset[i].initial_position.theta)
age.append(translation_dataset[i].exact_solution)
x, y = m(phi, theta)
m.scatter(x, y, c=age, zorder=10, cmap=plt.cm.RdYlGn)
m.colorbar()
fig, ax = plt.subplots()
ax.plot(phi, age / max(age), ".")
dt = data_points["PKIKP-PKiKP travel time residual"]
print dt.shape
ax.plot(phi, dt, ".r")
plt.show()
def find_difficulty(word):
"""A prompt asking the difficulty of each question"""
difficulty = 0
while difficulty not in range(1, 6):
try:
difficulty = input('Rate difficulty from 1(EASY) to 5(HARD): ')
difficulty = int(difficulty)
if difficulty in range(1, 6):
clear_screen()
known_words = read_from_file(KNOWN_WORDS_LOCATION)
known_words[difficulty].append(word)
save_to_file(known_words, KNOWN_WORDS_LOCATION)
except:
difficulty = 0
os.system('open ' + filename)
if __name__ == '__main__':
# matrix = [
# [None, None, None, None, 7, 2, None, None, None],
# [9, None, None, None, None, None, None, 3, None],
# [None, 6, None, 1, None, None, 4, None, None],
# [None, 8, None, None, 3, None, 5, None, None],
# [None, 7, 5, None, None, None, 2, 9, None],
# [None, None, 6, None, 4, None, None, 8, None],
# [None, None, 7, None, None, 8, None, 2, None],
# [None, 1, None, None, None, None, None, None, 9],
# [None, None, None, 9, 1, None, None, None, None]
# ]
# result = sudoku_solve(matrix)
# print "-----------"
# print read_write.sudoku_html_table(result)
filename = sys.argv[1]
if len(filename) > 3:
extension = filename[-3:]
image_arr = None
if extension == 'txt':
image_arr = rw.read_from_file(filename)
else:
image_arr = rw.read_from_image(filename)
if image_arr is not None:
solution = sudoku_solve(image_arr)
rw.sudoku_html_table(solution)
# subprocess.Popen(['out.pdf'],shell=True)
open_file('out.pdf')
rand_pair = known_words_list[rand_index]
spanish = rand_pair[0]
english = rand_pair[1]
if question == True:
if spanish not in used_words:
used_words.add(spanish)
done = True
return spanish, english
else:
continue
else:
done = True
return english
words_list = csv_to_list('100_words.csv')
known_words = read_from_file('known_words.dictionary')
known_words_set = dict2set(known_words)
known_words_translation_dict = translate_to_dict(known_words_set, words_list)
known_words_list = dict2list(known_words_translation_dict)
clear_screen()
print(rand_pair(known_words_list, False))
print(rand_pair(known_words_list))
if __name__ == '__main__':
# matrix = [
# [None, None, None, None, 7, 2, None, None, None],
# [9, None, None, None, None, None, None, 3, None],
# [None, 6, None, 1, None, None, 4, None, None],
# [None, 8, None, None, 3, None, 5, None, None],
# [None, 7, 5, None, None, None, 2, 9, None],
# [None, None, 6, None, 4, None, None, 8, None],
# [None, None, 7, None, None, 8, None, 2, None],
# [None, 1, None, None, None, None, None, None, 9],
# [None, None, None, 9, 1, None, None, None, None]
# ]
# result = sudoku_solve(matrix)
# print "-----------"
# print read_write.sudoku_html_table(result)
filename = sys.argv[1]
if len(filename) > 3:
extension = filename[-3:]
image_arr = None
if extension == 'txt':
image_arr = rw.read_from_file(filename)
else:
image_arr = rw.read_from_image(filename)
if image_arr is not None:
solution = sudoku_solve(image_arr)
rw.sudoku_html_table(solution)
# subprocess.Popen(['out.pdf'],shell=True)
open_file('out.pdf')
