import colorcet
import matplotlib.animation as animation
from collections import Counter
import sys
import tristan_tools.analysis as analysis
import signal
signal.signal(signal.SIGINT, signal.SIG_DFL)
from mpl_toolkits.axes_grid1 import make_axes_locatable
import gc
analyzer = analysis.TristanDataAnalyzer(analyzer_path + 'output')
os.makedirs(analyzer_path + 'plots/', exist_ok=1)
def plot_gamma_spectra2(analyzer, axarr, timesteps):
print(timesteps)
max_gammas = np.zeros((2, len(timesteps)))
mean_gammas = np.zeros((2, len(timesteps)))
times = np.zeros(len(timesteps))
for j in range(len(timesteps)):
timestep = timesteps[j]
print(timestep)
import matplotlib.pyplot as plt
import tristan_tools.analysis as analysis
import os
output_path_3d = '../test_data/user_beam_on_background_3d/output/'
output_path = output_path_3d
os.path.expanduser(output_path)
analyzer = analysis.TristanDataAnalyzer(output_path)
plot_time = 0
analyzer.load_indices(plot_time)
analyzer.compute_indices(plot_time)
analyzer.print_shapes(plot_time)
plotter = analysis.TristanDataPlotter(analyzer,
plot_type='hist1d',
keys=['PP_e_spec'])
print(plotter.plotter.data)
ax = plt.axes()
plotter.set_plotter_canvas(ax)
plotter.plot_timestep(plot_time)
plotter.timestep = plot_time + 1
plotter.plot_timestep(plot_time)
import tristan_tools.analysis as analysis
import signal
signal.signal(signal.SIGINT, signal.SIG_DFL)
from mpl_toolkits.axes_grid1 import make_axes_locatable
import gc
os.makedirs( './plots/', exist_ok = 1 )
analyzer = analysis.TristanDataAnalyzer( './output' )
downsample = 1
if len( sys.argv ) > 1 :
downsample = int( sys.argv[1] )
load = 1
if len( sys.argv ) > 2 :
load = int( sys.argv[2] )
# check if there's a directory called 'output' in cwd
else:
tmp = os.path.join(cwd, 'output')
if os.path.exists(tmp):
data_path = tmp
else:
data_path = None
if data_path:
print('INFO: found data at path: %s' % data_path)
else:
print('ERROR: did not find tristan output data path')
sys.exit(0)
# set the type of analyzer to be used in the gui_config.py
analyzer = analysis.TristanDataAnalyzer(data_path)
timestep = 1
analyzer.load_indices([timestep], keys='dens')
print([x for x in analyzer.data])
print(type(analyzer.data))
# source = mlab.pipeline.vector_field( * data )
print(analyzer.data.dens[timestep])
plot = mlab.pipeline.scalar_cut_plane(analyzer.data.dens[1],
plane_orientation='z_axes')
