def on_key_down(self, unicode, key, mod):
if mod & pygame.KMOD_CTRL:
if key == pygame.K_s:
pygame.image.save(self.grid._display, 'grids/img/latest.png')
elif key == pygame.K_g:
save_grid(self.grid)
elif key == pygame.K_l:
load_grid(self.grid)
def generate_board_medium_2():
'generate a board from sodoku app, difficulty -medium-'
data = \
'''
||||
||||
||||
-------------
||||
||||
||||
-------------
||||
||||
||||
'''
data = \
'''
| | |6 9|
|1 | 4| |
| 5|3 6|821|
-------------
| 4|67 | 5 |
| 7| |9 |
| |54 | |
-------------
|37 |4 5|2 6|
| | |51 |
| 6 | 2 | 37|
'''
return grid.load_grid(data)
def generate_board_hard_1():
'generate a board from sodoku app, difficulty -hard-'
data = \
'''
|984|5 1| 72|
| 57| 9| 3 |
|6 | 7| |
-------------
| | 2| 1 |
| | |7 |
|561| | 28|
-------------
| |4 | |
| |2 | 6|
|19 | 3|2 |
'''
return grid.load_grid(data)
def generate_board_expert_2():
'generate a board from sodoku app, difficulty -expert-'
data = \
'''
| | 1|3 |
|76 |4 |1 |
| 5| 7 | 6 |
-------------
|6 | | 3 |
| | 7| 49|
|5 | 1 | |
-------------
| | 32| |
| 9 | | 8|
| 84| | |
'''
return grid.load_grid(data)
def run(percent_to_download=-1):
globs.general.DATA_DIR_API = "./data/api/"
globs.general.PROPER_DATA_FILE_API = globs.general.DATA_DIR_API + "actual_data{}.csv".format(
percent_to_download)
globs.general.LOG_FILE_PATH_API = globs.general.DATA_DIR_API + "logs{}".format(
percent_to_download)
globs.general.PROPER_DATA_FILE_API = globs.general.DATA_DIR_API + "actual_data{}.csv".format(
percent_to_download)
globs.general.NOT_VALID_DATA_FILE_API = globs.general.DATA_DIR_API + "to_much{}.csv".format(
percent_to_download)
init_files()
# init grid
if not os.path.exists(globs.general.GRID_FILE_TO_SAVE_API):
grid.create_save_grid()
g_tmp = grid.load_grid(percent_to_download)
# capturing data
capture_whole_data(g_tmp, 300)
def generate_board_medium_1():
'generate a board from sodoku app, difficulty -medium-'
data = \
'''
|64 | 3 | 7|
|5 1| 7 |9 |
| | | 1 |
-------------
| 4|9 8| 6 |
| 8 | 3| 2 |
| |4 | |
-------------
|4 |157| 3 |
|2 8|3 | 4 |
|75 | | 96|
'''
return grid.load_grid(data)
def main(redo_cube_correlation_calculation = False,
redo_grid_correlation_calculation = False):
import grid
import numpy as np
if redo_grid_correlation_calculation:
perseus_grid = grid.load_grid('/d/bip3/ezbc/perseus/data/galfa/' + \
'perseus_galfa.138_62.10')
# define directory locations
output_dir = '/d/bip3/ezbc/perseus/data/python_output/nhi_av/'
figure_dir = '/d/bip3/ezbc/perseus/figures/'
av_dir = '/d/bip3/ezbc/perseus/data/2mass/'
hi_dir = '/d/bip3/ezbc/perseus/data/galfa/'
# load 2mass Av and GALFA HI images, on same grid
av_data = load_fits(av_dir + '2mass_av_lee12_nocal_regrid.fits')
av_SNR = load_fits(av_dir + '2mass_av_lee12_nocal_SNR_regrid.fits')
hi_data,h = load_fits(hi_dir + 'perseus.galfa.cube.bin.4arcmin.fits',
return_header=True)
# make the velocity axis
velocity_axis = (np.arange(h['NAXIS3']) - h['CRPIX3'] + 1) * h['CDELT3'] + \
h['CRVAL3']
velocity_axis /= 1000.
# define the parameters to derive NHI from the GALFA cube
velocity_centers = np.arange(-20,20,0.5)
velocity_widths = np.arange(1,120,5)
#velocity_centers = np.arange(-40,40,5)
#velocity_centers = np.array([5])
#velocity_widths = np.arange(1,100,20)
if redo_grid_correlation_calculation:
correlations = calculate_correlation(SpectralGrid=perseus_grid,
av_image=av_data,
velocity_centers=velocity_centers,
velocity_widths=velocity_widths)
else:
correlations = np.load(output_dir + 'correlations.npy')
if redo_cube_correlation_calculation:
cube_correlations = calculate_correlation(cube=hi_data,
velocity_axis=velocity_axis,av_image=av_data,
av_SNR=av_SNR,
velocity_centers=velocity_centers,
velocity_widths=velocity_widths)
else:
cube_correlations = np.load(output_dir + 'cube_correlations.npy')
# save arrays
np.save(output_dir + 'cube_correlations', cube_correlations)
np.save(output_dir + 'correlations', correlations)
np.save(output_dir + 'velocity_centers', velocity_centers)
np.save(output_dir + 'velocity_widths', velocity_widths)
# Plot heat map of correlations
cube_correlations_array = plot_correlations(cube_correlations,
velocity_centers, velocity_widths, returnimage=True,
savedir=figure_dir,
filename='perseus.nhi_av_correlation.png',
show=False)
plot_center_velocity(cube_correlations,
velocity_centers, velocity_widths,
velocity_center=5, returnimage=True,
savedir=figure_dir,
filename='perseus.nhi_av_5kms_correlation.png',
show=False)
plot_center_velocity(cube_correlations,
velocity_centers, velocity_widths,
velocity_center=10, returnimage=True,
savedir=figure_dir,
filename='perseus.nhi_av_10kms_correlation.png',
show=False)
# Plot NHI vs. Av for a given velocity range
#hi_data_corrected = np.ma.array(hi_data, mask=np.where(hi_data > -5))
nhi_image = calculate_NHI(cube=hi_data,
velocity_axis=velocity_axis,
velocityrange=[-5,15])
av_data_blk = load_fits(av_dir + '2mass_av_lee12_regrid.fits')
plot_nhi_vs_av(nhi_image,av_data_blk,
savedir=figure_dir,
filename='perseus.av_nhi_2dDensity.png',)
plot_nhi_vs_av(nhi_image,av_data,
savedir=figure_dir,
filename='perseus.av_nhi_2dDensity_2mass.png',)
# Plot heat map of correlations
if correlations is not None:
correlations_array = plot_correlations(correlations,
velocity_centers, velocity_widths, returnimage=True,show=False)
# Print best-fit characteristics
indices = np.where(cube_correlations_array == \
cube_correlations_array.max())
print('Maximum correlation values: ')
print(str(velocity_centers[indices[0]][0]) + ' km/s center')
print(str(velocity_widths[indices[1]][0]) + ' km/s width')
plt.clf()
guesses=guesses,
ncomp=len(guesses)/3,
alpha=0.001,
coords='image',
numberOfFits=1e6,
COcube=cfa,
COwidthScale=1.)
################################################################################
# Loading cfa grid of persues
################################################################################
import grid
cfa = grid.load_grid('perseus.cfa.138_62.5sigma')
grid.plot_ncompImage(cfa)
box=[0,36,180,180]
reload(grid)
perseus_galfa = grid.SpectralGrid('../galfa/'+\
'perseus.galfa.cube.bin.4arcmin.fits',
box=box,
noiseScale=10.,
noiseRange=((-110,-90),(90,110)),
basesubtract=True)
guesses = [48,5,10]
perseus_galfa.fit_profiles(
growPos = (138,62),
tileSaveFreq=100,
def main(redo_cube_correlation_calculation=False,
redo_grid_correlation_calculation=False):
import grid
import numpy as np
if redo_grid_correlation_calculation:
perseus_grid = grid.load_grid('/d/bip3/ezbc/perseus/data/galfa/' + \
'perseus_galfa.138_62.10')
# define directory locations
output_dir = '/d/bip3/ezbc/perseus/data/python_output/nhi_av/'
figure_dir = '/d/bip3/ezbc/perseus/figures/'
av_dir = '/d/bip3/ezbc/perseus/data/2mass/'
hi_dir = '/d/bip3/ezbc/perseus/data/galfa/'
# load 2mass Av and GALFA HI images, on same grid
av_data = load_fits(av_dir + '2mass_av_lee12_nocal_regrid.fits')
av_SNR = load_fits(av_dir + '2mass_av_lee12_nocal_SNR_regrid.fits')
hi_data, h = load_fits(hi_dir + 'perseus.galfa.cube.bin.4arcmin.fits',
return_header=True)
# make the velocity axis
velocity_axis = (np.arange(h['NAXIS3']) - h['CRPIX3'] + 1) * h['CDELT3'] + \
h['CRVAL3']
velocity_axis /= 1000.
# define the parameters to derive NHI from the GALFA cube
velocity_centers = np.arange(-20, 20, 0.5)
velocity_widths = np.arange(1, 120, 5)
#velocity_centers = np.arange(-40,40,5)
#velocity_centers = np.array([5])
#velocity_widths = np.arange(1,100,20)
if redo_grid_correlation_calculation:
correlations = calculate_correlation(SpectralGrid=perseus_grid,
av_image=av_data,
velocity_centers=velocity_centers,
velocity_widths=velocity_widths)
else:
correlations = np.load(output_dir + 'correlations.npy')
if redo_cube_correlation_calculation:
cube_correlations = calculate_correlation(
cube=hi_data,
velocity_axis=velocity_axis,
av_image=av_data,
av_SNR=av_SNR,
velocity_centers=velocity_centers,
velocity_widths=velocity_widths)
else:
cube_correlations = np.load(output_dir + 'cube_correlations.npy')
# save arrays
np.save(output_dir + 'cube_correlations', cube_correlations)
np.save(output_dir + 'correlations', correlations)
np.save(output_dir + 'velocity_centers', velocity_centers)
np.save(output_dir + 'velocity_widths', velocity_widths)
# Plot heat map of correlations
cube_correlations_array = plot_correlations(
cube_correlations,
velocity_centers,
velocity_widths,
returnimage=True,
savedir=figure_dir,
filename='perseus.nhi_av_correlation.png',
show=False)
plot_center_velocity(cube_correlations,
velocity_centers,
velocity_widths,
velocity_center=5,
returnimage=True,
savedir=figure_dir,
filename='perseus.nhi_av_5kms_correlation.png',
show=False)
plot_center_velocity(cube_correlations,
velocity_centers,
velocity_widths,
velocity_center=10,
returnimage=True,
savedir=figure_dir,
filename='perseus.nhi_av_10kms_correlation.png',
show=False)
# Plot NHI vs. Av for a given velocity range
#hi_data_corrected = np.ma.array(hi_data, mask=np.where(hi_data > -5))
nhi_image = calculate_NHI(cube=hi_data,
velocity_axis=velocity_axis,
velocityrange=[-5, 15])
av_data_blk = load_fits(av_dir + '2mass_av_lee12_regrid.fits')
plot_nhi_vs_av(
nhi_image,
av_data_blk,
savedir=figure_dir,
filename='perseus.av_nhi_2dDensity.png',
)
plot_nhi_vs_av(
nhi_image,
av_data,
savedir=figure_dir,
filename='perseus.av_nhi_2dDensity_2mass.png',
)
# Plot heat map of correlations
if correlations is not None:
correlations_array = plot_correlations(correlations,
velocity_centers,
velocity_widths,
returnimage=True,
show=False)
# Print best-fit characteristics
indices = np.where(cube_correlations_array == \
cube_correlations_array.max())
print('Maximum correlation values: ')
print(str(velocity_centers[indices[0]][0]) + ' km/s center')
print(str(velocity_widths[indices[1]][0]) + ' km/s width')
plt.clf()
break
fhandle.close()
return insta_coor
if __name__ == '__main__':
startTime = time.time()
#fname = 'mediumInstagram.json'
#fname = 'tinyInstagram.json'
fname = 'bigInstagram.json'
log_file = 'result.txt'
comm = MPI.COMM_WORLD
grid = G.load_grid(log_file)
print "Starting...", comm.size
if comm.rank == 0:
loghdl = open(log_file,'a')
loghdl.write("**********" + str(startTime) + "**********\n")
loghdl.flush()
loghdl.close()
#file_read_time = time.time()
insta_list = generate_fpoint(fname, comm.size)
result = []
#print "through time", time.time() - file_read_time
else:
insta_list = []
