plt.style.use('ggplot')
interv = (0, 1000)
x = np.linspace(interv[0], interv[1], 1000)
x = x + (100 * np.sin(0.01 * x))
y = np.linspace(interv[0], interv[1], 1000) + np.random.random(len(x)) * 20
tot_gifs = 20
x_plot, y_plot = [], []
axes = plt.gca()
axes.set_ylim([np.min(y), np.max(y)])
axes.set_xlim([np.min(x), np.max(x)])
plot1, = axes.plot(0, 0)
for ii in range(0, tot_gifs):
x_plot.extend(x[ii * int(len(x) / tot_gifs):(ii + 1) *
int(len(x) / tot_gifs)])
y_plot.extend(y[ii * int(len(x) / tot_gifs):(ii + 1) *
int(len(x) / tot_gifs)])
plot1.set_xdata(x_plot)
plot1.set_ydata(y_plot)
gif_maker('straight_line_noise.gif',
'./gif_maker_png/',
ii,
tot_gifs,
dpi=120)
fig = plt.figure(dpi=dpi)
# set the latitude angle steady, and vary the longitude. You can also reverse this to
# create a rotating globe latitudinally as well
lat_viewing_angle = [20.0, 20.0]
lon_viewing_angle = [-180, 180]
rotation_steps = 360
lat_vec = np.linspace(lat_viewing_angle[0], lat_viewing_angle[0],
rotation_steps)
lon_vec = np.linspace(lon_viewing_angle[0], lon_viewing_angle[1],
rotation_steps)
# for making the gif animation
gif_indx = 0
# loop through the longitude vector above
for pp in range(0, len(lat_vec)):
plt.cla()
m = Basemap(projection='ortho', lat_0=lat_vec[pp], lon_0=lon_vec[pp])
m.drawcoastlines(linewidth=0.5)
# m.drawcountries()
plot_topo(m)
# iterate to create the GIF animation
gif_maker('basemap_rotating_globe.gif',
'./png_dir/',
gif_indx,
len(lat_vec) - 1,
dpi=dpi)
gif_indx += 1
print("{}/{}".format(pp, len(lat_vec)))
normalize = matplotlib.colors.Normalize(vmin = cap_min,vmax = cap_max)
loop_size = len(year_sort)
num_gifs = len(np.unique(year_sort))
for pp in range(0,loop_size):
if year_sort[pp]==curr_year:
x,y = m(lons_sort[pp],lats_sort[pp])
x_array.append(x)
y_array.append(y)
cap_array.append(np.interp(capacity_sort[pp],[cap_min,cap_max],[30,200]))
color_array.append(capacity_sort[pp])
if pp!=loop_size-1:
continue
else:
curr_year = year_sort[pp]
# recreate figure each loop
fig = plt.figure(figsize=(12,7))
m = Basemap(projection='merc',llcrnrlat=bbox[0],urcrnrlat=bbox[1],\
llcrnrlon=bbox[2],urcrnrlon=bbox[3],lat_ts=10,resolution=None)
m.bluemarble() # this plots the earth-like contour to the U.S. map
# scatter new data with the color and size changes
scat1 = plt.scatter(x_array,y_array,s=cap_array,c = color_array,edgecolors='#444444',alpha=0.5,cmap=colormap,norm=normalize)
plt.colorbar(scat1,label='Average Power [kW]')
plt.ylabel(str(year_sort[pp-1])) # updated year
gif_maker('wind_turbine_yearly_with_colors.gif',png_dir,gif_indx,num_gifs,90)
gif_indx+=1
loop_size = len(year_sort)
num_gifs = len(np.unique(year_sort))
for pp in range(0, loop_size):
if year_sort[pp] == curr_year:
x, y = m(lons_sort[pp], lats_sort[pp])
x_array.append(x)
y_array.append(y)
if pp != loop_size - 1:
continue
else:
curr_year = year_sort[pp]
# recreate figure each loop
fig = plt.figure(figsize=(12, 7))
m = Basemap(projection='merc',llcrnrlat=bbox[0],urcrnrlat=bbox[1],\
llcrnrlon=bbox[2],urcrnrlon=bbox[3],lat_ts=10,resolution=None)
m.bluemarble() # this plots the earth-like contour to the U.S. map
# scatter new data
plt.scatter(x_array,
y_array,
s=20,
c='#D5D8DC',
linewidths='0.3',
edgecolors='#34495E',
alpha=0.8)
plt.ylabel(str(year_sort[pp - 1])) # updated year
gif_maker('function_test.gif', png_dir, gif_indx, num_gifs, 90)
gif_indx += 1
map_coords_xy = [m1.llcrnrx, m1.llcrnry, m1.urcrnrx, m1.urcrnry]
map_coords_geo = [m1.llcrnrlat, m1.llcrnrlon, m1.urcrnrlat, m1.urcrnrlon]
#zoom proportion and re-plot map
zoom_prop = 2.0 # use 1.0 for full-scale map
gif_indx = 0
for pp in range(0, len(lat_vec)):
ax1.clear()
ax1.set_axis_off()
m = Basemap(projection='ortho',
resolution='l',
lat_0=lat_vec[pp],
lon_0=lon_vec[pp],
llcrnrx=-map_coords_xy[2] / zoom_prop,
llcrnry=-map_coords_xy[3] / zoom_prop,
urcrnrx=map_coords_xy[2] / zoom_prop,
urcrnry=map_coords_xy[3] / zoom_prop)
m.bluemarble(scale=0.5)
m.drawcoastlines()
plt.show()
plt.pause(0.01)
gif_maker('blue_marble_rotating_globe.gif',
'./png_dir_bluemarble/',
gif_indx,
len(lat_vec) - 1,
dpi=90)
gif_indx += 1