def freq_shooting_plot(player_name, gridNum=25):
plot_size = (12, 8)
shot_df = df[df.name == player_name]
from matplotlib.patches import Circle
x = shot_df.x[shot_df['y'] < 425.1]
y = shot_df.y[shot_df['y'] < 425.1]
#compute shooting percentage and # of shots
(ShootingPctLocs, shotNumber) = find_shootingPcts(shot_df, gridNum)
#draw figure and court
fig = plt.figure(figsize=plot_size) #(12,7)
cmap = mymap #my modified colormap
ax = plt.axes([0.1, 0.1, 0.8,
0.8]) #where to place the plot within the figure
draw_court(outer_lines=False)
plt.xlim(-250, 250)
plt.ylim(400, -25)
#draw player image
zoom = np.float(plot_size[0]) / (
12.0 * 2
) #how much to zoom the player's pic. I have this hackily dependent on figure size
img = acquire_playerPic(shot_df.player_id, zoom)
ax.add_artist(img)
#draw circles
for i, shots in enumerate(ShootingPctLocs):
restricted = Circle(shotNumber.get_offsets()[i],
radius=shotNumber.get_array()[i],
color=cmap(shots),
alpha=1,
fill=True)
if restricted.radius > 240 / gridNum: restricted.radius = 240 / gridNum
ax.add_patch(restricted)
#draw color bar
ax2 = fig.add_axes([0.95, 0.1, 0.02, 0.8])
cb = matplotlib.colorbar.ColorbarBase(ax2,
cmap=cmap,
orientation='vertical')
cb.set_label('Field Goal %')
cb.set_ticks([0.0, 0.25, 0.5, 0.75, 1.0])
cb.set_ticklabels(['0%', '25%', '50%', '75%', '100%'])
ax.set_title(shot_df.name.unique()[0] + ' - Shot Chart 2014-15')
#plot season stats
ax.text(135, 395, get_season_stats(player_name)[1])
plt.show()
return ax
def shooting_plot(shot_df, plot_size=(12,11), gridNum=30, mymap=plt.get_cmap('Spectral')):
from matplotlib.patches import Circle
x = shot_df.LOC_X[shot_df['LOC_Y']<425.1]
y = shot_df.LOC_Y[shot_df['LOC_Y']<425.1]
(shotVals, shotNumber) = find_shot_vals(shot_df, gridNum)
fig = plt.figure(figsize=plot_size)
cmap = mymap
ax = plt.axes([0.1, 0.1, 0.8, 0.8])
draw_court(outer_lines=False)
plt.xlim(-250,250)
plt.ylim(422.5, -47.5)
norm = mpl.colors.Normalize(vmin=-0.5, vmax = -0.1)
import math
def logistic(x):
return 20 / (1 + math.exp(-0.18*(x-np.median(shotNumber.get_array()))))
for i, shots in enumerate(shotVals):
restricted = Circle(shotNumber.get_offsets()[i], radius=shotNumber.get_array()[i],
color=cmap(norm(shots)),alpha=0.8, fill=True)
restricted.radius = logistic( np.sqrt(restricted.radius) )
if restricted.radius > 240/gridNum: restricted.radius=240/gridNum
ax.add_patch(restricted)
ax2 = fig.add_axes([0.92, 0.1, 0.02, 0.8])
cb = mpl.colorbar.ColorbarBase(ax2,cmap=cmap, orientation='vertical')
cb.set_label('next-possession value')
cb.set_ticks([0.0, 0.25, 0.5, 0.75, 1.0])
cb.set_ticklabels(['-0.5','-0.4', '-0.3','-0.2', '-0.1'])
plt.show()
return ax
def team_freq_plot(team, gridNum=25):
plot_size = (12, 8)
team_df = df[df.team_name == team]
from matplotlib.patches import Circle
#compute shooting percentage and # of shots
(ShootingPctLocs, shotNumber) = find_shootingPcts(team_df, gridNum)
#draw figure and court
fig = plt.figure(figsize=plot_size)
cmap = mymap #my modified colormap
ax = plt.axes([0.1, 0.1, 0.8,
0.8]) #where to place the plot within the figure
draw_court(outer_lines=False)
plt.xlim(-250, 250)
plt.ylim(400, -25)
#draw team image
team_ac = team_df.htm[team_df.is_home == 1].unique()[0]
zoom = 1 #np.float(plot_size[0])/(8.0)
img = get_team_logo(team_ac, zoom)
ax.add_artist(img)
#draw circles
for i, shots in enumerate(ShootingPctLocs):
restricted = Circle(shotNumber.get_offsets()[i],
radius=shotNumber.get_array()[i],
color=cmap(shots),
alpha=.95,
fill=True)
if restricted.radius > 240 / gridNum: restricted.radius = 240 / gridNum
ax.add_patch(restricted)
#draw color bar
ax2 = fig.add_axes([0.95, 0.1, 0.02, 0.8])
cb = matplotlib.colorbar.ColorbarBase(ax2,
cmap=cmap,
orientation='vertical')
cb.set_label('Field Goal %')
cb.set_ticks([0.0, 0.25, 0.5, 0.75, 1.0])
cb.set_ticklabels(['0%', '25%', '50%', '75%', '100%'])
ax.set_title(team_df.team_name.unique()[0] + ' - Shot Chart 2014-15')
#plot season stats
ax.text(150, 395, get_team_stats(team)[1])
plt.show()
def kobe_shooting_plot(kobe_df, kobeyear, gridNum, isClutch):
#compute shooting percentage and # of shots
(ShootingPctLocs, shotNumber,
shootingFreqLocs) = find_shootingPcts(kobe_df, kobeyear, gridNum,
isClutch)
#draw figure and court
fig = plt.figure() #(12,7)
ax = plt.axes([0.1, 0.1, 0.8,
0.8]) #where to place the plot within the figure
draw_court(outer_lines=False)
ax.set_xlabel('')
ax.set_ylabel('')
ax.tick_params(labelbottom='off', labelleft='off')
plt.xlim(-250, 250)
plt.ylim(400, -25)
#cdict = {'red': (
#}
cmap = plt.cm.RdYlBu
#mymap = mpl.colors.LinearSegmentedColormap('my_colormap', cdict, 1024)
#cmap = mymap
#draw circles
for i, shots in enumerate(ShootingPctLocs):
restricted = Circle(shotNumber.get_offsets()[i],
radius=shootingFreqLocs[i] * 50,
color=cmap(shots),
alpha=0.8,
fill=True)
if restricted.radius > 240 / gridNum: restricted.radius = 240 / gridNum
ax.add_patch(restricted)
#draw color bar
ax2 = fig.add_axes([0.92, 0.1, 0.02, 0.8])
cb = mpl.colorbar.ColorbarBase(ax2, cmap=cmap, orientation='vertical')
cb.set_label('Efficiency Compared to Career Avg.')
cb.set_ticks([0.0, 1.0])
cb.set_ticklabels(['Low %', 'High %'])
if isClutch:
plt.suptitle('Kobe Bryant: ' + str(kobeyear) + ' Clutch Shot Chart')
else:
plt.suptitle('Kobe Bryant: ' + str(kobeyear) + ' Shot Chart')
plt.show()
return ax
def shooting_plot(shot_df, plot_size=(12, 8), gridNum=30):
from matplotlib.patches import Circle
x = shot_df.loc_x[shot_df['loc_y'] < 425.1]
y = shot_df.loc_y[shot_df['loc_y'] < 425.1]
#compute shooting percentage and # of shots
(ShootingPctLocs, shotNumber) = find_shootingPcts(shot_df, gridNum)
#draw figure and court
fig = plt.figure(figsize=plot_size) #(12,7)
cmap = mymap #my modified colormap
ax = plt.axes([0.1, 0.1, 0.8,
0.8]) #where to place the plot within the figure
draw_court(outer_lines=False)
plt.xlim(-250, 250)
plt.ylim(400, -25)
#draw player image
#zoom = np.float(plot_size[0])/(12.0*2) #how much to zoom the player's pic. I have this hackily dependent on figure size
#img = acquire_playerPic(PlayerID, zoom)
#ax.add_artist(img)
#draw circles
for i, shots in enumerate(ShootingPctLocs):
restricted = Circle(shotNumber.get_offsets()[i],
radius=shotNumber.get_array()[i],
color=cmap(shots),
alpha=0.8,
fill=True)
if restricted.radius > 240 / gridNum: restricted.radius = 240 / gridNum
ax.add_patch(restricted)
#draw color bar
ax2 = fig.add_axes([0.92, 0.1, 0.02, 0.8])
cb = mpl.colorbar.ColorbarBase(ax2, cmap=cmap, orientation='vertical')
cb.set_label('Shooting %')
cb.set_ticks([0.0, 0.25, 0.5, 0.75, 1.0])
cb.set_ticklabels(['0%', '25%', '50%', '75%', '100%'])
ax.set_xticks([])
ax.set_yticks([])
plt.show()
return ax
def create_shot_chart_plot(self,
shooting_pct,
shot_num,
plot_size=(12, 8),
gridnum=30):
"""Show plot that has X and Y coordinate values for court."""
# Draw figure and court
fig = plt.figure(figsize=plot_size)
# Where the plot places within the figure
ax = plt.axes([0.1, 0.1, 0.8, 0.8])
self.draw_court(outer_lines=False)
# Add main title to graph
plt.title("Shot chart for{} {} for the last {} games".format(
self.player_info.player_firstname,
self.player_info.player_lastname, self.player_info.num_games))
# Set court limits
plt.xlim(-250, 250)
plt.ylim(400, -25)
for i, shots in enumerate(shooting_pct):
restricted = Circle(shot_num.get_offsets()[i],
radius=shot_num.get_array()[i],
color=mymap(shots),
alpha=0.8,
fill=True)
if restricted.radius > 240 / gridnum:
restricted.radius = 240 / gridnum
ax.add_patch(restricted)
# Draw color bar to indicate percentage as heatmap
ax2 = fig.add_axes([0.92, 0.1, 0.02, 0.8])
cb = colorbar.ColorbarBase(ax2, cmap=mymap, orientation='vertical')
cb.set_label('Shooting %')
cb.set_ticks([0.0, 0.25, 0.5, 0.75, 1.0])
cb.set_ticklabels(['0%', '25%', '50%', '75%', '100%'])
plt.show()
return ax
def team_freq_plot(team, gridNum=25):
plot_size = (10, 8)
team_df = df[df.team_name == team]
from matplotlib.patches import Circle
#compute shooting percentage and # of shots
(ShootingPctLocs2, shotNumber2) = find_shootingPcts(team_df, gridNum)[0:2]
(ShootingPctLocs3, shotNumber3) = find_shootingPcts(team_df, gridNum)[2:]
#draw figure and court
fig = plt.figure(figsize=plot_size)
ax = plt.axes([0.1, 0.1, 0.8,
0.8]) #where to place the plot within the figure
draw_court(outer_lines=False)
plt.xlim(-250, 250)
plt.ylim(400, -25)
#draw team image
team_ac = team_df.htm[team_df.is_home == 1].unique()[0]
zoom = 1 #np.float(plot_size[0])/(8.0)
img = get_team_logo(team_ac, zoom)
ax.add_artist(img)
############################################ TWO POINTERS #################################################
cmap = mymap.from_list('Color Map', [(0, '#ff0000'), (.45, '#ffff00'),
(.6, '#00ff00'), (1, '#004d00')])
#draw circles
for i, shots in enumerate(ShootingPctLocs2):
restricted2 = Circle(shotNumber2.get_offsets()[i],
radius=shotNumber2.get_array()[i],
color=cmap(shots),
alpha=.9,
fill=True)
if restricted2.radius > 240 / gridNum:
restricted2.radius = 240 / gridNum
ax.add_patch(restricted2)
#draw color bar
ax2 = fig.add_axes([0.95, 0.1, 0.02, 0.8])
cb = matplotlib.colorbar.ColorbarBase(ax2,
cmap=cmap,
orientation='vertical')
cb.set_label('Field Goal %', labelpad=20)
cb.set_ticks([0.0, 0.25, .485, 0.75, 1.0])
cb.set_ticklabels(['0%', '25%', '48.5%\nLg Avg', '75%', '100%'])
########################################### THREE POINTERS ################################################
#plotting 3 pointers separately to account for expected lower fg% from deep
cmap3 = mymap.from_list('Color Map', [(0, '#ff0000'), (.35, '#ffff00'),
(.6, '#00ff00'), (1, '#004d00')])
#draw circles
for i, shots in enumerate(ShootingPctLocs3):
restricted3 = Circle(shotNumber3.get_offsets()[i],
radius=shotNumber3.get_array()[i],
color=cmap3(shots),
alpha=.9,
fill=True)
if restricted3.radius > 240 / gridNum:
restricted3.radius = 240 / gridNum
ax.add_patch(restricted3)
#draw color bar
ax3 = fig.add_axes([1.1, 0.1, 0.02, 0.8])
cb3 = matplotlib.colorbar.ColorbarBase(ax3,
cmap=cmap3,
orientation='vertical')
cb3.set_label('Three Point %', labelpad=-8)
cb3.set_ticks([0.0, 0.25, .35, 0.5, 0.75, 1.0])
cb3.set_ticklabels(['0%', '25%', '35% - Lg Avg', '50%', '75%', '100%'])
ax.set_title(team_df.team_name.unique()[0] + ' - Shot Chart 2014-15')
#plot season stats
ax.text(150, 395, get_team_stats(team)[1])
plt.show()
def Visualize(Date,
PlotID=1,
FPS=15,
Steps=20,
MP4_quality=300,
Name="LSST Scheduler Simulator.mp4",
showClouds=True):
# Import data
All_Fields = np.loadtxt("NightDataInLIS/Constants/fieldID.lis",
unpack=True)
N_Fields = len(All_Fields[0])
if showClouds:
Time_slots = np.loadtxt("NightDataInLIS/TimeSlots{}.lis".format(
int(ephem.julian_date(Date))),
unpack=True)
All_Cloud_cover = np.loadtxt("NightDataInLIS/Clouds{}.lis".format(
int(ephem.julian_date(Date))),
unpack=True)
Site = ephem.Observer()
Site.lon = -1.2320792
Site.lat = -0.517781017
Site.elevation = 2650
Site.pressure = 0.
Site.horizon = 0.
#Initialize date and time
lastN_start = float(Date) - 1
lastN_end = float(Date)
toN_start = float(Date)
toN_end = float(Date) + 1
#Connect to the History data base
con = lite.connect('FBDE.db')
cur = con.cursor()
# Prepare to save in MP4 format
FFMpegWriter = animation.writers['ffmpeg']
metadata = dict(title='LSST Simulation', artist='Elahe', comment='Test')
writer = FFMpegWriter(fps=FPS, metadata=metadata)
#Progress bar initialization
pbar = ProgressBar()
# Initialize plot
Fig = plt.figure()
if PlotID == 1:
ax = plt.subplot(111, axisbg='black')
if PlotID == 2:
ax = plt.subplot(211, axisbg='black')
unobserved, Observed_lastN, Obseved_toN,\
ToN_History_line, last_10_History_line,\
Horizon, airmass_horizon, S_Pole,\
LSST,\
Clouds\
= ax.plot([], [], '*',[], [], '*',[], [], '*',
[], [], '-',[], [], '*',
[], [], '-',[], [], '-',[], [], 'D',
[], [], 'o',
[], [], 'o')
ax.set_xlim(-1.5, 1.5)
ax.set_ylim(-1.5, 1.5)
ax.set_aspect('equal', adjustable='box')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# Coloring
Horizon.set_color('white')
airmass_horizon.set_color('red')
S_Pole.set_markersize(3)
S_Pole.set_markerfacecolor('red')
star_size = 4
unobserved.set_color('dimgray')
unobserved.set_markersize(star_size)
Observed_lastN.set_color('blue')
Observed_lastN.set_markersize(star_size)
Obseved_toN.set_color('chartreuse')
Obseved_toN.set_markersize(star_size + 2)
Clouds.set_color('white')
Clouds.set_markersize(10)
ToN_History_line.set_color('orange')
ToN_History_line.set_lw(.5)
last_10_History_line.set_color('red')
last_10_History_line.set_lw(.5)
LSST.set_color('red')
LSST.set_markersize(8)
if PlotID == 2:
freqAX = plt.subplot(212)
cur.execute(
'SELECT N_visit, Last_visit, Second_last_visit, Third_last_visit, Fourth_last_visit From FieldsStatistics'
)
row = cur.fetchall()
N_visit = [x[0] for x in row]
Last_visit = [x[1] for x in row]
Second_last_visit = [x[2] for x in row]
Third_last_visit = [x[3] for x in row]
Fourth_last_visit = [x[4] for x in row]
initHistoricalcoverage = N_visit
for index, id in enumerate(All_Fields):
if Last_visit[index] > toN_start:
initHistoricalcoverage[index] -= 1
if Second_last_visit[index] > toN_start:
initHistoricalcoverage[index] -= 1
if Third_last_visit > toN_start:
initHistoricalcoverage[index] -= 1
covering, current_cover = freqAX.plot(All_Fields[0],
initHistoricalcoverage, '-', [],
[], 'o')
freqAX.set_xlim(0, N_Fields)
freqAX.set_ylim(0, np.max(initHistoricalcoverage) + 5)
covering.set_color('chartreuse')
covering.set_markersize(2)
current_cover.set_color('red')
current_cover.set_markersize(6)
cur.execute(
'SELECT Night_count, T_start, T_end FROM NightSummary WHERE T_start BETWEEN (?) AND (?)',
(toN_start, toN_end))
row = cur.fetchone()
vID = row[0]
t_start = row[1]
t_end = row[2]
t = t_start
# Figure labels and fixed elements
Phi = np.arange(0, 2 * np.pi, 0.05)
Horizon.set_data(1.01 * np.cos(Phi), 1.01 * np.sin(Phi))
ax.text(-1.3, 0, 'West', color='white', fontsize=7)
ax.text(1.15, 0, 'East', color='white', fontsize=7)
ax.text(0, 1.1, 'North', color='white', fontsize=7)
airmass_horizon.set_data(
np.cos(np.pi / 4) * np.cos(Phi),
np.cos(np.pi / 4) * np.sin(Phi))
ax.text(-.3,
0.6,
'Acceptable airmass horizon',
color='white',
fontsize=5,
fontweight='bold')
Alt, Az = Fields_local_coordinate(180, -90, t, Site)
x, y = AltAz2XY(Alt, Az)
S_Pole.set_data(x, y)
ax.text(x + .05, y, 'S-Pole', color='white', fontsize=7)
# Observed last night fields
cur.execute(
'SELECT Field_id FROM Schedule WHERE ephemDate BETWEEN (?) AND (?)',
(lastN_start, lastN_end))
row = cur.fetchall()
if row is not None:
F1 = [x[0] for x in row]
else:
F1 = []
# Tonight observation path
cur.execute(
'SELECT Field_id, ephemDate FROM Schedule WHERE ephemDate BETWEEN (?) AND (?)',
(toN_start, toN_end))
row = cur.fetchall()
if row[0][0] is not None:
F2 = [x[0] for x in row]
F2_timing = [x[1] for x in row]
else:
F2 = []
F2_timing = []
# Sky elements
Moon = Circle((0, 0), 0, color='silver', zorder=3)
ax.add_patch(Moon)
Moon_text = ax.text([], [], 'Moon', color='white', fontsize=7)
with writer.saving(Fig, Name, MP4_quality):
for t in pbar(np.linspace(t_start, t_end, num=Steps)):
# Find the index of the current time
time_index = 0
while t > F2_timing[time_index]:
time_index += 1
if showClouds:
Slot_n = 0
while t > Time_slots[Slot_n]:
Slot_n += 1
visit_index = 0
visited_field = 0
# Object fields: F1)Observed last night F2)Observed tonight F3)Unobserved F4)Covered by clouds
F1_X = []
F1_Y = []
F2_X = []
F2_Y = []
F3_X = []
F3_Y = []
F4_X = []
F4_Y = []
# F1 coordinate:
for i in F1:
Alt, Az = Fields_local_coordinate(All_Fields[1, i - 1],
All_Fields[2,
i - 1], t, Site)
if Alt > 0:
X, Y = AltAz2XY(Alt, Az)
F1_X.append(X)
F1_Y.append(Y)
# F2 coordinate:
for i, tau in zip(F2, F2_timing):
Alt, Az = Fields_local_coordinate(All_Fields[1, i - 1],
All_Fields[2,
i - 1], t, Site)
if Alt > 0:
X, Y = AltAz2XY(Alt, Az)
F2_X.append(X)
F2_Y.append(Y)
if t >= tau:
visit_index = len(F2_X) - 1
visited_field = i
# F3 coordinate:
for i in range(0, N_Fields):
if True:
Alt, Az = Fields_local_coordinate(All_Fields[1, i],
All_Fields[2,
i], t, Site)
if Alt > 0:
X, Y = AltAz2XY(Alt, Az)
F3_X.append(X)
F3_Y.append(Y)
# F4 coordinates
if showClouds:
for i in range(0, N_Fields):
if All_Cloud_cover[i, Slot_n] == 1:
Alt, Az = Fields_local_coordinate(
All_Fields[1, i], All_Fields[2, i], t, Site)
if Alt > 0:
X, Y = AltAz2XY(Alt, Az)
F4_X.append(X)
F4_Y.append(Y)
# Update plot
unobserved.set_data([F3_X, F3_Y])
Observed_lastN.set_data([F1_X, F1_Y])
Obseved_toN.set_data([F2_X[0:visit_index], F2_Y[0:visit_index]])
ToN_History_line.set_data(
[F2_X[0:visit_index], F2_Y[0:visit_index]])
last_10_History_line.set_data([
F2_X[visit_index - 10:visit_index],
F2_Y[visit_index - 10:visit_index]
])
LSST.set_data([F2_X[visit_index], F2_Y[visit_index]])
Clouds.set_data([F4_X, F4_Y])
# Update Moon
X, Y, r, alt = update_moon(t, Site)
Moon.center = X, Y
Moon.radius = r
if alt > 0:
#Moon.set_visible(True)
Moon_text.set_visible(True)
Moon_text.set_x(X + .002)
Moon_text.set_y(Y + .002)
else:
Moon.set_visible(False)
Moon_text.set_visible(False)
#Update coverage
if PlotID == 2:
Historicalcoverage = np.zeros(N_Fields)
for i, tau in zip(F2, F2_timing):
if tau <= t:
Historicalcoverage[i - 1] += 1
else:
break
tot = Historicalcoverage + initHistoricalcoverage
current_cover.set_data(visited_field - 1,
tot[visited_field - 1])
covering.set_data(All_Fields[0], tot)
#Observation statistics
leg = plt.legend([Observed_lastN, Obseved_toN],
['Visited last night', time_index])
for l in leg.get_texts():
l.set_fontsize(6)
date = ephem.date(t)
Fig.suptitle('Top view of the LSST site on {}, GMT'.format(date))
'''
# progress
perc= int(100*(t - t_start)/(t_end - t_start))
if perc <= 100:
print('{} %'.format(perc))
else:
print('100 %')
'''
#Save current frame
writer.grab_frame()
def visualize(night,
file_name,
PlotID=1,
FPS=15,
Steps=20,
MP4_quality=300,
Name="Visualization.mp4",
showClouds=False,
nside=64,
fancy_slow=True):
Site = ephem.Observer()
Site.lon = -1.2320792
Site.lat = -0.517781017
Site.elevation = 2650
Site.pressure = 0.
Site.horizon = 0.
# create pix
hpid = np.arange(hp.nside2npix(16))
ra, dec = _hpid2RaDec(16, hpid)
SCP_indx, NES_indx, GP_indx, WFD_indx = mutually_exclusive_regions(
nside=16)
DD_indx = []
#Initialize date and time
last_night = night - 1
#Connect to the History data base
con = lite.connect(file_name)
cur = con.cursor()
# Prepare to save in MP4 format
FFMpegWriter = animation.writers['ffmpeg']
metadata = dict(title='LSST Simulation', artist='Elahe', comment='Test')
writer = FFMpegWriter(fps=FPS, metadata=metadata)
# Initialize plot
Fig = plt.figure()
if PlotID == 1:
ax = plt.subplot(111, axisbg='black')
if PlotID == 2:
ax = plt.subplot(211, axisbg='black')
unobserved, Observed_lastN, Obseved_toN,\
WFD, GP, NE, SE, DD,\
ToN_History_line,\
uu,gg,rr,ii,zz,yy,\
last_10_History_line,\
Horizon, airmass_horizon, S_Pole,\
LSST,\
Clouds\
= ax.plot([], [], '*',[], [], '*',[], [], '*',
[], [], '*',[], [], '*',[], [], '*', [], [], '*', [], [], '*', # regions
[], [], '*',
[], [], '*',[], [], '*',[], [], '*',
[], [], '*',[], [], '*',[], [], '*',
[], [], '-',
[], [], '-',[], [], '-',[], [], 'D',
[], [], 'o',
[], [], 'o')
ax.set_xlim(-1.5, 1.5)
ax.set_ylim(-1.5, 1.5)
ax.set_aspect('equal', adjustable='box')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# Coloring
Horizon.set_color('white')
airmass_horizon.set_color('red')
S_Pole.set_markersize(3)
S_Pole.set_markerfacecolor('red')
star_size = 4
unobserved.set_color('dimgray')
unobserved.set_markersize(star_size)
Observed_lastN.set_color('blue')
Observed_lastN.set_markersize(star_size)
Obseved_toN.set_color('chartreuse')
Obseved_toN.set_markersize(0)
# regions
WFD.set_color('black')
WFD.set_alpha(0.3)
SE.set_color('orange')
SE.set_alpha(0.3)
NE.set_color('orange')
NE.set_alpha(0.3)
GP.set_color('orange')
GP.set_alpha(0.3)
DD.set_color('orange')
DD.set_alpha(0.3)
DD.set_markersize(7)
# filters
uu.set_color('purple')
gg.set_color('green')
rr.set_color('red')
ii.set_color('orange')
zz.set_color('pink')
yy.set_color('deeppink')
# clouds
Clouds.set_color('white')
Clouds.set_markersize(10)
Clouds.set_alpha(0.2)
Clouds.set_markeredgecolor(None)
ToN_History_line.set_color('orange')
ToN_History_line.set_lw(.5)
last_10_History_line.set_color('gray')
last_10_History_line.set_lw(.5)
LSST.set_color('red')
LSST.set_markersize(8)
if PlotID == 2:
freqAX = plt.subplot(212)
cur.execute(
'SELECT N_visit, Last_visit, Second_last_visit, Third_last_visit, Fourth_last_visit From FieldsStatistics'
)
row = cur.fetchall()
N_visit = [x[0] for x in row]
Last_visit = [x[1] for x in row]
Second_last_visit = [x[2] for x in row]
Third_last_visit = [x[3] for x in row]
Fourth_last_visit = [x[4] for x in row]
initHistoricalcoverage = N_visit
for index, id in enumerate(All_Fields):
if Last_visit[index] > toN_start:
initHistoricalcoverage[index] -= 1
if Second_last_visit[index] > toN_start:
initHistoricalcoverage[index] -= 1
if Third_last_visit > toN_start:
initHistoricalcoverage[index] -= 1
covering, current_cover = freqAX.plot(All_Fields[0],
initHistoricalcoverage, '-', [],
[], 'o')
freqAX.set_xlim(0, N_Fields)
freqAX.set_ylim(0, np.max(initHistoricalcoverage) + 5)
covering.set_color('chartreuse')
covering.set_markersize(2)
current_cover.set_color('red')
current_cover.set_markersize(6)
# Figure labels and fixed elements
Phi = np.arange(0, 2 * np.pi, 0.05)
Horizon.set_data(1.01 * np.cos(Phi), 1.01 * np.sin(Phi))
ax.text(-1.3, 0, 'East', color='white', fontsize=7)
ax.text(1.15, 0, 'West', color='white', fontsize=7)
ax.text(0, 1.1, 'North', color='white', fontsize=7)
airmass_horizon.set_data(
np.cos(np.pi / 4) * np.cos(Phi),
np.cos(np.pi / 4) * np.sin(Phi))
ax.text(-.3,
0.6,
'airmass horizon',
color='white',
fontsize=5,
fontweight='bold')
Alt, Az = Fields_local_coordinate(180, -90, 59581.0381944435, Site)
x, y = AltAz2XY(Alt, Az)
S_Pole.set_data(x, y)
ax.text(x + .05, y, 'S-Pole', color='white', fontsize=7)
DD_indicator = ax.text(-1.4,
1.3,
'Deep Drilling Observation',
color='red',
fontsize=9,
visible=False)
WFD_indicator = ax.text(-1.4,
1.3,
'Wide Fast Deep Observation',
color='white',
fontsize=9,
visible=False)
GP_indicator = ax.text(-1.4,
1.3,
'Galactic Plane Observation',
color='white',
fontsize=9,
visible=False)
NES_indicator = ax.text(-1.4,
1.3,
'Notrh Ecliptic Spur Observation',
color='white',
fontsize=9,
visible=False)
SCP_indicator = ax.text(-1.4,
1.3,
'South Celestial Pole Observation',
color='white',
fontsize=9,
visible=False)
# Observed last night fields
cur.execute(
'SELECT RA, dec, mjd, filter FROM SummaryAllProps WHERE night=%i' %
(last_night))
row = cur.fetchall()
if row is not None:
F1 = [x[0:2] for x in row]
else:
F1 = []
# Tonight observation path
cur.execute(
'SELECT RA, dec, mjd, filter FROM SummaryAllProps WHERE night=%i' %
(night))
row = cur.fetchall()
if row[0][0] is not None:
F2 = [x[0:2] for x in row]
RA = np.asanyarray([x[0] for x in row])
DEC = np.asarray([x[1] for x in row])
F2_timing = [x[2] for x in row]
F2_filtering = [x[3] for x in row]
F2_region = RaDec2region(RA, DEC, nside)
else:
F2 = []
F2_timing = []
F2_filtering = []
F2_region = []
# Sky elements
Moon = Circle((0, 0), 0, color='silver', zorder=3)
ax.add_patch(Moon)
Moon_text = ax.text([], [], 'Moon', color='white', fontsize=7)
doff = ephem.Date(0) - ephem.Date('1858/11/17')
with writer.saving(Fig, Name, MP4_quality):
for t in np.linspace(F2_timing[0], F2_timing[-1], num=Steps):
# Find the index of the current time
time_index = 0
while t > F2_timing[time_index]:
time_index += 1
if showClouds:
Slot_n = 0
while t > Time_slots[Slot_n]:
Slot_n += 1
visit_index = 0
visited_field = 0
visit_index_u = 0
visit_index_g = 0
visit_index_r = 0
visit_index_i = 0
visit_index_z = 0
visit_index_y = 0
visit_filter = 'r'
# Object fields: F1)Observed last night F2)Observed tonight F3)Unobserved F4)Covered by clouds
F1_X = []
F1_Y = []
F2_X = []
F2_Y = []
F3_X = []
F3_Y = []
F4_X = []
F4_Y = []
# Filter coloring for tonight observation
U_X = []
U_Y = []
G_X = []
G_Y = []
R_X = []
R_Y = []
I_X = []
I_Y = []
Z_X = []
Z_Y = []
Y_X = []
Y_Y = []
# Coloring different proposals
WFD_X = []
WFD_Y = []
NE_X = []
NE_Y = []
SE_X = []
SE_Y = []
GP_X = []
GP_Y = []
DD_X = []
DD_Y = []
# F1 coordinate:
for i in F1:
Alt, Az = Fields_local_coordinate(i[0], i[1], t - doff, Site)
if Alt > 0:
X, Y = AltAz2XY(Alt, Az)
F1_X.append(X)
F1_Y.append(Y)
# F2 coordinate:
for i, tau, filter in zip(F2, F2_timing, F2_filtering):
Alt, Az = Fields_local_coordinate(i[0], i[1], t - doff, Site)
if Alt > 0:
X, Y = AltAz2XY(Alt, Az)
F2_X.append(X)
F2_Y.append(Y)
if filter == 'u':
U_X.append(X)
U_Y.append(Y)
if t >= tau:
visit_index_u = len(U_X) - 1
elif filter == 'g':
G_X.append(X)
G_Y.append(Y)
if t >= tau:
visit_index_g = len(G_Y) - 1
elif filter == 'r':
R_X.append(X)
R_Y.append(Y)
if t >= tau:
visit_index_r = len(R_Y) - 1
elif filter == 'i':
I_X.append(X)
I_Y.append(Y)
if t >= tau:
visit_index_i = len(I_Y) - 1
elif filter == 'z':
Z_X.append(X)
Z_Y.append(Y)
if t >= tau:
visit_index_z = len(Z_Y) - 1
elif filter == 'y':
Y_X.append(X)
Y_Y.append(Y)
if t >= tau:
visit_index_y = len(Y_Y) - 1
if t >= tau:
visit_index = len(F2_X) - 1
visited_field = i
visit_filter = filter
# F3 coordinate:
if fancy_slow:
Alt, Az = stupidFast_RaDec2AltAz(ra, dec, t)
for al, az, i in zip(Alt, Az, hpid):
if al > 0:
X, Y = AltAz2XY(al, az)
F3_X.append(X)
F3_Y.append(Y)
if i in DD_indx:
DD_X.append(X)
DD_Y.append(Y)
elif i in WFD_indx:
WFD_X.append(X)
WFD_Y.append(Y)
elif i in GP_indx:
GP_X.append(X)
GP_Y.append(Y)
elif i in NES_indx:
NE_X.append(X)
NE_Y.append(Y)
elif i in SCP_indx:
SE_X.append(X)
SE_Y.append(Y)
# F4 coordinates
if showClouds:
for i in range(0, N_Fields):
if All_Cloud_cover[Slot_n, i] == 2 or All_Cloud_cover[
Slot_n, i] == 1 or All_Cloud_cover[Slot_n,
i] == -1:
Alt, Az = Fields_local_coordinate(
i[0], i[1], t - doff, Site)
if Alt > 0:
X, Y = AltAz2XY(Alt, Az)
F4_X.append(X)
F4_Y.append(Y)
# Update plot
unobserved.set_data([F3_X, F3_Y])
Observed_lastN.set_data([F1_X, F1_Y])
Obseved_toN.set_data([F2_X[0:visit_index], F2_Y[0:visit_index]])
# filters
uu.set_data([U_X[0:visit_index_u], U_Y[0:visit_index_u]])
gg.set_data([G_X[0:visit_index_g], G_Y[0:visit_index_g]])
rr.set_data([R_X[0:visit_index_r], R_Y[0:visit_index_r]])
ii.set_data([I_X[0:visit_index_i], I_Y[0:visit_index_i]])
zz.set_data([Z_X[0:visit_index_z], Z_Y[0:visit_index_z]])
yy.set_data([Y_X[0:visit_index_y], Y_Y[0:visit_index_y]])
ToN_History_line.set_data(
[F2_X[0:visit_index], F2_Y[0:visit_index]])
last_10_History_line.set_data([
F2_X[visit_index - 10:visit_index],
F2_Y[visit_index - 10:visit_index]
])
# telescope position and color
LSST.set_data([F2_X[visit_index], F2_Y[visit_index]])
if visit_filter == 'u':
LSST.set_color('purple')
if visit_filter == 'g':
LSST.set_color('green')
if visit_filter == 'r':
LSST.set_color('red')
if visit_filter == 'i':
LSST.set_color('orange')
if visit_filter == 'z':
LSST.set_color('pink')
if visit_filter == 'y':
LSST.set_color('deeppink')
Clouds.set_data([F4_X, F4_Y])
# regions
WFD.set_data([WFD_X, WFD_Y])
DD.set_data([DD_X, DD_Y])
NE.set_data([NE_X, NE_Y])
SE.set_data([SE_X, SE_Y])
GP.set_data([GP_X, GP_Y])
# Update Moon
X, Y, r, alt = update_moon(t, Site)
Moon.center = X, Y
Moon.radius = r
if alt > 0:
#Moon.set_visible(True)
Moon_text.set_visible(True)
Moon_text.set_x(X + .002)
Moon_text.set_y(Y + .002)
else:
Moon.set_visible(False)
Moon_text.set_visible(False)
#Update coverage
if PlotID == 2:
Historicalcoverage = np.zeros(N_Fields)
for i, tau in zip(F2, F2_timing):
if tau <= t:
Historicalcoverage[i - 1] += 1
else:
break
tot = Historicalcoverage + initHistoricalcoverage
current_cover.set_data(visited_field - 1,
tot[visited_field - 1])
covering.set_data(All_Fields[0], tot)
#Update indicators of the proposal
if F2_region[time_index] == 'DD':
DD_indicator.set_visible(True)
else:
DD_indicator.set_visible(False)
if F2_region[time_index] == 'WFD':
WFD_indicator.set_visible(True)
else:
WFD_indicator.set_visible(False)
if F2_region[time_index] == 'GP':
GP_indicator.set_visible(True)
else:
GP_indicator.set_visible(False)
if F2_region[time_index] == 'NES':
NES_indicator.set_visible(True)
else:
NES_indicator.set_visible(False)
if F2_region[time_index] == 'SCP':
SCP_indicator.set_visible(True)
else:
SCP_indicator.set_visible(False)
#Observation statistics
leg = plt.legend([Observed_lastN, Obseved_toN],
['Visited last night', time_index])
for l in leg.get_texts():
l.set_fontsize(6)
date = t
Fig.suptitle('Top view of the LSST site on {}, GMT'.format(date))
'''
# progress
perc= int(100*(t - t_start)/(t_end - t_start))
if perc <= 100:
print('{} %'.format(perc))
else:
print('100 %')
'''
#Save current frame
writer.grab_frame()
