def create_gif(filename):
anim = animation(fig,
animate,
init_func=create_legend,
frames=duration * fps,
interval=fps)
anim.save(f'animations/{filename}/example.gif')
def __init__(self,init,maskBool,saniBool,SDbool):
#init = [N,inf,rec]
self.N = init[0]
suc = self.N-init[1]-(init[2]+init[3])
self.cols = np.repeat([sus_col,inf_col,rem_col,ded_col],
[suc,init[1],init[2],init[3]])
self.low,self.high = -stage_dimension/2,stage_dimension/2
self.pos = np.random.uniform(self.low,self.high,size=(2,self.N))
self.stream = self.data_stream()
self.fig,self.ax = plt.subplots();
self.ani = animation(self.fig, self.update, interval=80,
init_func=self.setup, blit=True)
def animate(data, impl, frames, plot_size=13000):
fig, ax = plt.subplots()
lines, = plt.plot(0, 0, '.')
plt.axis([-plot_size, plot_size, -plot_size, plot_size])
_ = animation(fig=fig,
func=lambda step: impl(data, step, lines),
init_func=lambda: None,
frames=frames,
repeat=False,
interval=1)
plt.show()
def graphIt(aList):
x = 0
listY = []
fig = plt.figure()
graph, = plt.plot([], [], 'o') #(listY, aList, 'o')
for y in aList:
x += 1
listY.append(x)
plt.plot(x, y, marker='o', markersize=3)
def animate(i):
graph.set_data(listY[:i + 1], aList[:i + 1])
return graph
plt.xlabel('Number of Steps')
plt.ylabel('Value at Step')
plt.title('Collatz Conjecture')
ani = animation(fig, animate, frames=len(listY), interval=50)
ani.save("collatzConjecture2.gif", writer='pillow')
plt.show()
def begin(file_name,
x_axis,
file_path='',
selected_columns=None,
fresh_rate=100,
sample=-1,
figsize=(15, 6),
sep=';'):
fig = plt.figure(figsize=figsize)
fig.canvas.set_window_title('Liverplot')
def animate(i):
df = pd.read_csv(file_path + file_name, sep=sep)
# If user is going to plot specifc columns, he'll not pass the x_axis inside
# selected_columns again. For this convenience, we shall add to the array
columns = selected_columns + \
[x_axis] if selected_columns else df.columns
# Tail do whats suppose to do. By default, Liverplot will plot the whole
# dataframe, however, it's possible to plot only a sample
df = df[columns].tail(sample)
# Clear axis for next plot
plt.cla()
for column in df.columns:
if column != x_axis:
plt.plot(df[x_axis], df[column], label=column)
plt.legend(loc='upper left')
plt.tight_layout()
a = animation(fig, animate, interval=fresh_rate)
plt.show()
def multi_animator(self):
curves = list()
x_temp = list()
ax = list()
an = list()
row, col, fig = self.dynamicCanvas()
for n, [x, y] in enumerate(self.funcOrJustDataTargetorAddParam):
x_temp.append(np.arange(min(x), max(x), 0.05).reshape(-1, 1))
# TODO subtitle, label names, range limits, legends
ax.append(plt.subplot(row, col, n + 1))
if self.funcParams['plot' + str(n + 1)].get('name', None):
ax[n].set_xlabel(self.funcParams['plot' + str(n + 1)].get(
'name', None))
plt.plot(x, y, 'bo', markersize=3)
curves.append(
plt.plot(x_temp[n],
self.funcParams['plot' + str(n + 1)]['func'][0](
x_temp[n]),
'r',
linewidth=2))
if len(self.funcParams['plot' + str(n + 1)]) > 1:
paramStr = ''
for key in self.funcParams['plot' + str(n + 1)]:
if key != 'func':
paramStr += '{}:{}\n'.format(
key, self.funcParams['plot' + str(n + 1)][key][0])
an.append(ax[n].annotate(paramStr,
xy=(0.75, 0.9),
xycoords='axes fraction',
fontsize=10,
bbox=dict(facecolor='white',
alpha=0.8),
horizontalalignment='left',
verticalalignment='bottom'))
else:
an.append(None)
def update(i):
label = 'Time Step {0}'.format(i)
plt.suptitle(label)
for n, curve in enumerate(curves):
curve[0].set_ydata(
self.funcParams['plot' + str(n + 1)]['func'][i](x_temp[n]))
if len(self.funcParams['plot' + str(n + 1)]) > 1:
an[n].remove()
paramStr = ''
for key in self.funcParams['plot' + str(n + 1)]:
if key != 'func' and key != 'name':
paramStr += '{}:{}\n'.format(
key,
self.funcParams['plot' + str(n + 1)][key][i])
#TODO shorten to 5 or 6 digits long
an[n] = ax[n].annotate(paramStr,
xy=(0.75, 0.9),
xycoords='axes fraction',
fontsize=10,
bbox=dict(facecolor='white',
alpha=0.8),
horizontalalignment='left',
verticalalignment='bottom')
if Plotter.isIPy:
plt.rcParams["animation.html"] = "jshtml"
anim = animation(fig,
update,
frames=range(len(self.funcParams['plot1']['func'])),
interval=self.frameInterval)
if Plotter.isIPy:
self.anim = anim
else:
self.anim = anim
plt.show()
import random
from matplotlib import pyplot as plt
from matplotlib.animation import FuncAnimation as animation
from itertools import count
x = []
y = []
index = count()
def animate(i):
x.append(next(index))
y.append(random.randint(0, 100))
plt.cla()
plt.plot(x, y)
ani = animation(plt.gcf(), animate, interval=1000)
plt.xlabel = 'x'
plt.xlabel = 'y'
plt.tight_layout()
plt.show()
ax2.clear()
ax2.axhline(
3.14159265359,
color='r',
)
ax2.plot(approxdata, '-b')
#ax2.plot([it],[approximation], '-o', color = 'red')
plt.title(' n = ' + str(n) + ' PI = {0:.16f}'.format(approximation))
# print( str(it) + ". n = " + str(n) + " PI = " + str(approximation))
# print(approximation)
# print('\n')
it += 1
def animate(i):
framex, framey = [], []
for i in range(0, 10000):
x = rand.random()
y = rand.random()
framex.append(x)
framey.append(y)
ax1.scatter(framex, framey, marker='.')
xdata.extend(framex)
ydata.extend(framey)
approx_pi()
ani = animation(fig, animate, interval=1, frames=500, repeat=False)
plt.show()
for run in split_at_value(new_data, 'New_Run'):
Runs.append(run)
Current_run = Runs[-1]
container = Current_run[-1]['Container']
lats = []
longs = []
for row in Current_run:
lat = float(row['Latitude'])
long = float(row['Longitude'])
lats.append(lat)
longs.append(long)
if new_data != old_data:
print('Updating ...')
plt.cla()
if container != 'None':
m.warpimage(f"Images/{container}_sheet.png")
m.plot(longs, lats, latlon=True, color='c', marker='.', markersize=9, markeredgecolor='k', markeredgewidth=0.5, linestyle='-', linewidth=0.8, alpha=0.75)
old_data = new_data
ani = animation(fig, animate, interval=1000)
plt.show()
