def runtest(lmaManager=None, lma_view=None, HDFmanagers=None):
# colormap = get_cmap('gist_yarg_r')
colormap = get_cmap('gist_earth')
density_maxes = []
total_counts = []
all_t = []
for delta_minutes in minute_intervals:
time_delta = DateTimeDelta(0, 0, delta_minutes, 0)
n_frames = int(ceil((end_time - start_time) / time_delta))
n_cols = 6
n_rows = int(ceil( float(n_frames) / n_cols ))
w, h = figaspect(float(n_rows)/n_cols)
xedge=np.arange(b.x[0], b.x[1]+dx, dx)
yedge=np.arange(b.y[0], b.y[1]+dy, dy)
x_range = b.x[1] - b.x[0]
y_range = b.y[1] - b.y[0]
min_count, max_count = 1, max_count_baseline*delta_minutes
f = figure(figsize=(w,h))
p = small_multiples_plot(fig=f, rows=n_rows, columns=n_cols)
p.label_edges(True)
for ax in p.multiples.flat:
ax.yaxis.set_major_formatter(kilo_formatter)
ax.xaxis.set_major_formatter(kilo_formatter)
for i in range(n_frames):
frame_start = start_time + i*time_delta
frame_end = frame_start + time_delta
b.sec_of_day = (frame_start.abstime, frame_end.abstime)
b.t = (frame_start, frame_end)
do_plot = False
flash_extent_density = True
density = None
if source_density==True:
lmaManager.refresh(b)
lma_view.transformed.cache_is_old()
x,y,t=lma_view.transformed['x','y','t']
density,edges = np.histogramdd((x,y), bins=(xedge,yedge))
do_plot=True
else:
for lmaManager in HDFmanagers:
# yes, loop through every file every time and reselect data.
# so wrong, yet so convenient.
h5 = lmaManager.h5file
if flash_extent_density == False:
lmaManager.refresh(b)
lma_view = AcuityView(DataSelection(lmaManager.data, b), mapProj, bounds=b)
# lma_view.transformed.cache_is_old()
x,y,t=lma_view.transformed['x','y','t']
if x.shape[0] > 1: do_plot = True
break
else:
# assume here that the bounds sec_of_day day is the same as
# the dataset day
t0, t1 = b.sec_of_day
# events = getattr(h5.root.events, lmaManager.table.name)[:]
# flashes = getattr(h5.root.flashes, lmaManager.table.name)[:]
event_dtype = getattr(h5.root.events, lmaManager.table.name)[0].dtype
events_all = getattr(h5.root.events, lmaManager.table.name)[:]
flashes = getattr(h5.root.flashes, lmaManager.table.name)
def event_yielder(evs, fls):
these_events = []
for fl in fls:
if ( (fl['n_points']>9) &
(t0 < fl['start']) &
(fl['start'] <= t1)
):
these_events = evs[evs['flash_id'] == fl['flash_id']]
if len(these_events) <> fl['n_points']:
print 'not giving all ', fl['n_points'], ' events? ', these_events.shape
for an_ev in these_events:
yield an_ev
# events = np.fromiter((an_ev for an_ev in ( events_all[events_all['flash_id'] == fl['flash_id']]
# for fl in flashes if (
# (fl['n_points']>9) & (t0 < fl['start']) & (fl['start'] <= t1)
# )
# ) ), dtype=event_dtype)
events = np.fromiter(event_yielder(events_all, flashes), dtype=event_dtype)
# print events['flash_id'].shape
### Flash extent density ###
x,y,z = mapProj.fromECEF(
*geoProj.toECEF(events['lon'], events['lat'], events['alt'])
)
# Convert to integer grid coordinate bins
# 0 1 2 3
# | | | | |
# -1.5 0.0 1.5 3.0 4.5
if x.shape[0] > 1:
density, edges = extent_density(x,y,events['flash_id'].astype('int32'),
b.x[0], b.y[0], dx, dy, xedge, yedge)
do_plot = True
break
# print 'density values: ', density.min(), density.max()
if do_plot == True: # need some data
# density,edges = np.histogramdd((x,y), bins=(xedge,yedge))
density_plot = p.multiples.flat[i].pcolormesh(xedge,yedge,
np.log10(density.transpose()),
vmin=-0.2,
vmax=np.log10(max_count),
cmap=colormap)
label_string = frame_start.strftime('%H%M:%S')
text_label = p.multiples.flat[i].text(b.x[0]-pad+x_range*.01, b.y[0]-pad+y_range*.01, label_string, color=(0.5,)*3, size=6)
density_plot.set_rasterized(True)
density_maxes.append(density.max())
total_counts.append(density.sum())
all_t.append(frame_start)
print label_string, x.shape, density.max(), density.sum()
color_scale = ColorbarBase(p.colorbar_ax, cmap=density_plot.cmap,
norm=density_plot.norm,
orientation='horizontal')
# color_scale.set_label('count per pixel')
color_scale.set_label('log10(count per pixel)')
# moving reference frame correction. all panels will have same limits, based on time of last frame
view_dt = 0.0 # (frame_start - t0).seconds
x_ctr = x0 + view_dt*u
y_ctr = y0 + view_dt*v
view_x = (x_ctr - view_dx/2.0 - pad, x_ctr + view_dx/2.0 + pad)
view_y = (y_ctr - view_dy/2.0 - pad, y_ctr + view_dy/2.0 + pad)
# view_x = (b.x[0]+view_dt*u, b.x[1]+view_dt*u)
# view_y = (b.y[0]+view_dt*v, b.y[1]+view_dt*v)
# print 'making timeseries',
# time_series = figure(figsize=(16,9))
# ts_ax = time_series.add_subplot(111)
# ts_ax.plot_date(mx2num(all_t),total_counts,'-', label='total sources', tz=tz)
# ts_ax.plot_date(mx2num(all_t),density_maxes,'-', label='max pixel', tz=tz)
# ts_ax.xaxis.set_major_formatter(time_series_x_fmt)
# ts_ax.legend()
# time_filename = 'out/LMA-timeseries_%s_%5.2fkm_%5.1fs.pdf' % (start_time.strftime('%Y%m%d_%H%M%S'), dx/1000.0, time_delta.seconds)
# time_series.savefig(time_filename)
# print ' ... done'
print 'making multiples',
p.multiples.flat[0].axis(view_x+view_y)
filename = 'out/LMA-density_%s_%5.2fkm_%5.1fs.pdf' % (start_time.strftime('%Y%m%d_%H%M%S'), dx/1000.0, time_delta.seconds)
f.savefig(filename, dpi=150)
print ' ... done'
f.clf()
return events
def make_plot(filename, grid_name, x_name='x', y_name='y', t_name='time',
n_cols=6, outpath='', filename_prefix='LMA',
do_save=True, image_type='pdf', colormap='gist_earth'):
""" colormap: a string giving the name of a matplotlib built-in colormap,
or a matplotlib.colors.Colormap instance
"""
f = nc.NetCDFFile(filename)
data = f.variables # dictionary of variable names to nc_var objects
dims = f.dimensions # dictionary of dimension names to sizes
x = data[x_name]
y = data[y_name]
t = data[t_name]
grid = data[grid_name]
assert len(x.shape) == 1
assert len(y.shape) == 1
assert len(t.shape) == 1
grid_dims = grid.dimensions # tuple of dimension names
name_to_idx = dict((k, i) for i, k in enumerate(grid_dims))
grid_t_idx = name_to_idx[t.dimensions[0]]
grid_x_idx = name_to_idx[x.dimensions[0]]
grid_y_idx = name_to_idx[y.dimensions[0]]
n_frames = t.shape[0]
# n_cols = 6
n_rows = int(ceil( float(n_frames) / n_cols ))
if type(colormap) == type(''):
colormap = get_cmap(colormap)
grey_color = (0.5,)*3
frame_color = (0.2,)*3
density_maxes = []
total_counts = []
all_t = []
xedge = centers_to_edges(x)
x_range = xedge.max() - xedge.min()
yedge = centers_to_edges(y)
y_range = yedge.max() - yedge.min()
dx = (xedge[1]-xedge[0])
# w, h = figaspect(float(n_rows)/n_cols) # breaks for large numbers of frames - has a hard-coded max figure size
w, h, n_rows_perpage, n_pages = multiples_figaspect(n_rows, n_cols, x_range, y_range, fig_width=8.5, max_height=None)
# count_scale_factor = dx # / 1000.0
# max_count_baseline = 450 * count_scale_factor #/ 10.0
min_count, max_count = 1, grid[:].max() #max_count_baseline*(t[1]-t[0])
if (max_count == 0) | (max_count == 1 ):
max_count = min_count+1
f.close()
default_vmin = -0.2
if np.log10(max_count) <= default_vmin:
vmin_count = np.log10(max_count) + default_vmin
else:
vmin_count = default_vmin
fig = Figure(figsize=(w,h))
canvas = FigureCanvasAgg(fig)
fig.set_canvas(canvas)
p = small_multiples_plot(fig=fig, rows=n_rows, columns=n_cols)
p.label_edges(True)
pad = 0.0 # for time labels in each frame
for ax in p.multiples.flat:
ax.set_axis_bgcolor('black')
ax.spines['top'].set_edgecolor(frame_color)
ax.spines['bottom'].set_edgecolor(frame_color)
ax.spines['left'].set_edgecolor(frame_color)
ax.spines['right'].set_edgecolor(frame_color)
# ax.yaxis.set_major_formatter(kilo_formatter)
# ax.xaxis.set_major_formatter(kilo_formatter)
base_date = datetime.strptime(t.units, "seconds since %Y-%m-%d %H:%M:%S")
time_delta = timedelta(0,float(t[0]),0)
start_time = base_date + time_delta
indexer = [slice(None),]*len(grid.shape)
frame_start_times = []
for i in range(n_frames):
frame_start = base_date + timedelta(0,float(t[i]),0)
frame_start_times.append(frame_start)
indexer[grid_t_idx] = i
density = grid[indexer]
# density,edges = np.histogramdd((x,y), bins=(xedge,yedge))
density_plot = p.multiples.flat[i].pcolormesh(xedge,yedge,
np.log10(density.transpose()),
vmin=vmin_count,
vmax=np.log10(max_count),
cmap=colormap)
label_string = frame_start.strftime('%H%M:%S')
text_label = p.multiples.flat[i].text(xedge[0]-pad+x_range*.015, yedge[0]-pad+y_range*.015, label_string, color=grey_color, size=6)
density_plot.set_rasterized(True)
density_maxes.append(density.max())
total_counts.append(density.sum())
all_t.append(frame_start)
print label_string, x.shape, density.max(), density.sum()
color_scale = ColorbarBase(p.colorbar_ax, cmap=density_plot.cmap,
norm=density_plot.norm,
orientation='horizontal')
# color_scale.set_label('count per pixel')
color_scale.set_label('log10(count per pixel)')
view_x = (xedge.min(), xedge.max())
view_y = (yedge.min(), yedge.max())
print 'making multiples',
p.multiples.flat[0].axis(view_x+view_y)
filename = '%s-%s_%s_%05.2fkm_%05.1fs.%s' % (filename_prefix, grid_name, start_time.strftime('%Y%m%d_%H%M%S'), dx, time_delta.seconds, image_type)
filename = os.path.join(outpath, filename)
if do_save:
fig.savefig(filename, dpi=150)
return fig, p, frame_start_times, filename
print ' ... done'
def make_plot(filename,
grid_name,
x_name='x',
y_name='y',
t_name='time',
n_cols=6,
outpath='',
filename_prefix='LMA',
do_save=True,
image_type='pdf',
colormap='gist_earth'):
""" colormap: a string giving the name of a matplotlib built-in colormap,
or a matplotlib.colors.Colormap instance
"""
f = nc.NetCDFFile(filename)
data = f.variables # dictionary of variable names to nc_var objects
dims = f.dimensions # dictionary of dimension names to sizes
x = data[x_name]
y = data[y_name]
t = data[t_name]
grid = data[grid_name]
assert len(x.shape) == 1
assert len(y.shape) == 1
assert len(t.shape) == 1
grid_dims = grid.dimensions # tuple of dimension names
name_to_idx = dict((k, i) for i, k in enumerate(grid_dims))
grid_t_idx = name_to_idx[t.dimensions[0]]
grid_x_idx = name_to_idx[x.dimensions[0]]
grid_y_idx = name_to_idx[y.dimensions[0]]
n_frames = t.shape[0]
# n_cols = 6
n_rows = int(ceil(float(n_frames) / n_cols))
if type(colormap) == type(''):
colormap = get_cmap(colormap)
grey_color = (0.5, ) * 3
frame_color = (0.2, ) * 3
density_maxes = []
total_counts = []
all_t = []
xedge = centers_to_edges(x)
x_range = xedge.max() - xedge.min()
yedge = centers_to_edges(y)
y_range = yedge.max() - yedge.min()
dx = (xedge[1] - xedge[0])
# w, h = figaspect(float(n_rows)/n_cols) # breaks for large numbers of frames - has a hard-coded max figure size
w, h, n_rows_perpage, n_pages = multiples_figaspect(n_rows,
n_cols,
x_range,
y_range,
fig_width=8.5,
max_height=None)
# count_scale_factor = dx # / 1000.0
# max_count_baseline = 450 * count_scale_factor #/ 10.0
min_count, max_count = 1, grid[:].max() #max_count_baseline*(t[1]-t[0])
if (max_count == 0) | (max_count == 1):
max_count = min_count + 1
f.close()
default_vmin = -0.2
if np.log10(max_count) <= default_vmin:
vmin_count = np.log10(max_count) + default_vmin
else:
vmin_count = default_vmin
fig = Figure(figsize=(w, h))
canvas = FigureCanvasAgg(fig)
fig.set_canvas(canvas)
p = small_multiples_plot(fig=fig, rows=n_rows, columns=n_cols)
p.label_edges(True)
pad = 0.0 # for time labels in each frame
for ax in p.multiples.flat:
ax.set_axis_bgcolor('black')
ax.spines['top'].set_edgecolor(frame_color)
ax.spines['bottom'].set_edgecolor(frame_color)
ax.spines['left'].set_edgecolor(frame_color)
ax.spines['right'].set_edgecolor(frame_color)
# ax.yaxis.set_major_formatter(kilo_formatter)
# ax.xaxis.set_major_formatter(kilo_formatter)
base_date = datetime.strptime(t.units, "seconds since %Y-%m-%d %H:%M:%S")
time_delta = timedelta(0, float(t[0]), 0)
start_time = base_date + time_delta
indexer = [
slice(None),
] * len(grid.shape)
frame_start_times = []
for i in range(n_frames):
frame_start = base_date + timedelta(0, float(t[i]), 0)
frame_start_times.append(frame_start)
indexer[grid_t_idx] = i
density = grid[indexer]
# density,edges = np.histogramdd((x,y), bins=(xedge,yedge))
density_plot = p.multiples.flat[i].pcolormesh(xedge,
yedge,
np.log10(
density.transpose()),
vmin=vmin_count,
vmax=np.log10(max_count),
cmap=colormap)
label_string = frame_start.strftime('%H%M:%S')
text_label = p.multiples.flat[i].text(xedge[0] - pad + x_range * .015,
yedge[0] - pad + y_range * .015,
label_string,
color=grey_color,
size=6)
density_plot.set_rasterized(True)
density_maxes.append(density.max())
total_counts.append(density.sum())
all_t.append(frame_start)
print label_string, x.shape, density.max(), density.sum()
color_scale = ColorbarBase(p.colorbar_ax,
cmap=density_plot.cmap,
norm=density_plot.norm,
orientation='horizontal')
# color_scale.set_label('count per pixel')
color_scale.set_label('log10(count per pixel)')
view_x = (xedge.min(), xedge.max())
view_y = (yedge.min(), yedge.max())
print 'making multiples',
p.multiples.flat[0].axis(view_x + view_y)
filename = '%s-%s_%s_%05.2fkm_%05.1fs.%s' % (
filename_prefix, grid_name, start_time.strftime('%Y%m%d_%H%M%S'), dx,
time_delta.seconds, image_type)
filename = os.path.join(outpath, filename)
if do_save:
fig.savefig(filename, dpi=150)
return fig, p, frame_start_times, filename
print ' ... done'
def runtest(lmaManager=None, lma_view=None, HDFmanagers=None):
# colormap = get_cmap('gist_yarg_r')
colormap = get_cmap('gist_earth')
density_maxes = []
total_counts = []
all_t = []
for delta_minutes in minute_intervals:
time_delta = DateTimeDelta(0, 0, delta_minutes, 0)
n_frames = int(ceil((end_time - start_time) / time_delta))
n_cols = 6
n_rows = int(ceil(float(n_frames) / n_cols))
w, h = figaspect(float(n_rows) / n_cols)
xedge = np.arange(b.x[0], b.x[1] + dx, dx)
yedge = np.arange(b.y[0], b.y[1] + dy, dy)
x_range = b.x[1] - b.x[0]
y_range = b.y[1] - b.y[0]
min_count, max_count = 1, max_count_baseline * delta_minutes
f = figure(figsize=(w, h))
p = small_multiples_plot(fig=f, rows=n_rows, columns=n_cols)
p.label_edges(True)
for ax in p.multiples.flat:
ax.yaxis.set_major_formatter(kilo_formatter)
ax.xaxis.set_major_formatter(kilo_formatter)
for i in range(n_frames):
frame_start = start_time + i * time_delta
frame_end = frame_start + time_delta
b.sec_of_day = (frame_start.abstime, frame_end.abstime)
b.t = (frame_start, frame_end)
do_plot = False
flash_extent_density = True
density = None
if source_density == True:
lmaManager.refresh(b)
lma_view.transformed.cache_is_old()
x, y, t = lma_view.transformed['x', 'y', 't']
density, edges = np.histogramdd((x, y), bins=(xedge, yedge))
do_plot = True
else:
for lmaManager in HDFmanagers:
# yes, loop through every file every time and reselect data.
# so wrong, yet so convenient.
h5 = lmaManager.h5file
if flash_extent_density == False:
lmaManager.refresh(b)
lma_view = AcuityView(DataSelection(
lmaManager.data, b),
mapProj,
bounds=b)
# lma_view.transformed.cache_is_old()
x, y, t = lma_view.transformed['x', 'y', 't']
if x.shape[0] > 1: do_plot = True
break
else:
# assume here that the bounds sec_of_day day is the same as
# the dataset day
t0, t1 = b.sec_of_day
# events = getattr(h5.root.events, lmaManager.table.name)[:]
# flashes = getattr(h5.root.flashes, lmaManager.table.name)[:]
event_dtype = getattr(h5.root.events,
lmaManager.table.name)[0].dtype
events_all = getattr(h5.root.events,
lmaManager.table.name)[:]
flashes = getattr(h5.root.flashes,
lmaManager.table.name)
def event_yielder(evs, fls):
these_events = []
for fl in fls:
if ((fl['n_points'] > 9) & (t0 < fl['start']) &
(fl['start'] <= t1)):
these_events = evs[evs['flash_id'] ==
fl['flash_id']]
if len(these_events) <> fl['n_points']:
print 'not giving all ', fl[
'n_points'], ' events? ', these_events.shape
for an_ev in these_events:
yield an_ev
# events = np.fromiter((an_ev for an_ev in ( events_all[events_all['flash_id'] == fl['flash_id']]
# for fl in flashes if (
# (fl['n_points']>9) & (t0 < fl['start']) & (fl['start'] <= t1)
# )
# ) ), dtype=event_dtype)
events = np.fromiter(event_yielder(
events_all, flashes),
dtype=event_dtype)
# print events['flash_id'].shape
### Flash extent density ###
x, y, z = mapProj.fromECEF(*geoProj.toECEF(
events['lon'], events['lat'], events['alt']))
# Convert to integer grid coordinate bins
# 0 1 2 3
# | | | | |
# -1.5 0.0 1.5 3.0 4.5
if x.shape[0] > 1:
density, edges = extent_density(
x, y, events['flash_id'].astype('int32'),
b.x[0], b.y[0], dx, dy, xedge, yedge)
do_plot = True
break
# print 'density values: ', density.min(), density.max()
if do_plot == True: # need some data
# density,edges = np.histogramdd((x,y), bins=(xedge,yedge))
density_plot = p.multiples.flat[i].pcolormesh(
xedge,
yedge,
np.log10(density.transpose()),
vmin=-0.2,
vmax=np.log10(max_count),
cmap=colormap)
label_string = frame_start.strftime('%H%M:%S')
text_label = p.multiples.flat[i].text(
b.x[0] - pad + x_range * .01,
b.y[0] - pad + y_range * .01,
label_string,
color=(0.5, ) * 3,
size=6)
density_plot.set_rasterized(True)
density_maxes.append(density.max())
total_counts.append(density.sum())
all_t.append(frame_start)
print label_string, x.shape, density.max(), density.sum()
color_scale = ColorbarBase(p.colorbar_ax,
cmap=density_plot.cmap,
norm=density_plot.norm,
orientation='horizontal')
# color_scale.set_label('count per pixel')
color_scale.set_label('log10(count per pixel)')
# moving reference frame correction. all panels will have same limits, based on time of last frame
view_dt = 0.0 # (frame_start - t0).seconds
x_ctr = x0 + view_dt * u
y_ctr = y0 + view_dt * v
view_x = (x_ctr - view_dx / 2.0 - pad, x_ctr + view_dx / 2.0 + pad)
view_y = (y_ctr - view_dy / 2.0 - pad, y_ctr + view_dy / 2.0 + pad)
# view_x = (b.x[0]+view_dt*u, b.x[1]+view_dt*u)
# view_y = (b.y[0]+view_dt*v, b.y[1]+view_dt*v)
# print 'making timeseries',
# time_series = figure(figsize=(16,9))
# ts_ax = time_series.add_subplot(111)
# ts_ax.plot_date(mx2num(all_t),total_counts,'-', label='total sources', tz=tz)
# ts_ax.plot_date(mx2num(all_t),density_maxes,'-', label='max pixel', tz=tz)
# ts_ax.xaxis.set_major_formatter(time_series_x_fmt)
# ts_ax.legend()
# time_filename = 'out/LMA-timeseries_%s_%5.2fkm_%5.1fs.pdf' % (start_time.strftime('%Y%m%d_%H%M%S'), dx/1000.0, time_delta.seconds)
# time_series.savefig(time_filename)
# print ' ... done'
print 'making multiples',
p.multiples.flat[0].axis(view_x + view_y)
filename = 'out/LMA-density_%s_%5.2fkm_%5.1fs.pdf' % (
start_time.strftime('%Y%m%d_%H%M%S'), dx / 1000.0,
time_delta.seconds)
f.savefig(filename, dpi=150)
print ' ... done'
f.clf()
return events
