def index():
""" Very simple embedding of a lightcurve chart
"""
# FLASK
# Grab the inputs arguments from the URL
# This is automated by the button
args = flask.request.args
_from = str(args.get('_from', str(DEFAULT_TR.start)))
_to = str(args.get('_to', str(DEFAULT_TR.end)))
tr = TimeRange(parse_time(_from), parse_time(_to))
if 'next' in args:
tr = tr.next()
if 'prev' in args:
tr = tr.previous()
if 'next_hour' in args:
tr = TimeRange(tr.start + ONE_HOUR, tr.end + ONE_HOUR)
if 'next_day' in args:
tr = TimeRange(tr.start + ONE_DAY, tr.end + ONE_DAY)
if 'prev_hour' in args:
tr = TimeRange(tr.start - ONE_HOUR, tr.end - ONE_HOUR)
if 'prev_day' in args:
tr = TimeRange(tr.start - ONE_DAY, tr.end - ONE_DAY)
_from = str(tr.start)
_to = str(tr.end)
# get the data
goes = lc.GOESLightCurve.create(tr)
# resample to reduce the number of points for debugging
goes.data = goes.data.resample("1T").mean()
# add time string for display of hover tool
goes.data['time_str'] = goes.data.index.strftime('%F %H:%M:%S')
source = ColumnDataSource(data=goes.data)
source_static = ColumnDataSource(data=goes.data)
# now create the bokeh plots
# XRS-B Plot
fig1 = figure(title="GOES",
tools=TOOLS,
plot_height=PLOT_HEIGHT,
width=PLOT_WIDTH,
x_axis_type='datetime',
y_axis_type="log",
y_range=(10**-9, 10**-2),
toolbar_location="right")
fig1.xaxis.formatter = formatter
fig1.line('index',
'xrsb',
source=source_static,
color='red',
line_width=2,
legend="xrsa 1-8 Angstrom")
fig2 = figure(title="GOES",
tools=TOOLS,
plot_height=PLOT_HEIGHT,
width=PLOT_WIDTH,
x_axis_type='datetime',
y_axis_type="log",
y_range=(10**-9, 10**-2))
fig2.xaxis.formatter = formatter
fig2.line('index',
'xrsa',
source=source_static,
color='blue',
line_width=2,
legend="xrsa 0.5-4.0 Angstrom")
# link the x-range for common panning
fig2.x_range = fig1.x_range
fig = Column(fig1, fig2)
source_static.callback = CustomJS(code="""
var inds = cb_obj.selected['1d'].indices;
var d1 = cb_obj.data;
var m = 0;
if (inds.length == 0) { return; }
for (i = 0; i < inds.length; i++) {
d1['color'][inds[i]] = "red"
if (d1['y'][inds[i]] > m) { m = d1['y'][inds[i]] }
}
console.log(m);
cb_obj.trigger('change');
""")
hover = HoverTool()
hover.tooltips = [("time", "@time_str"), ("xrsb", "@xrsb"),
("xrsa", "@xrsa")]
fig1.add_tools(hover)
hover2 = HoverTool()
hover2.tooltips = [("time", "@time_str"), ("xrsb", "@xrsb"),
("xrsa", "@xrsa")]
fig2.add_tools(hover2)
# Configure resources to include BokehJS inline in the document.
# For more details see:
# http://bokeh.pydata.org/en/latest/docs/reference/resources_embedding.html#bokeh-embed
js_resources = INLINE.render_js()
css_resources = INLINE.render_css()
# For more details see:
# http://bokeh.pydata.org/en/latest/docs/user_guide/embedding.html#components
script, div = components(fig, INLINE)
html = flask.render_template(
'embed.html',
plot_script=script,
plot_div=div,
js_resources=js_resources,
css_resources=css_resources,
_from=_from,
_to=_to,
)
return encode_utf8(html)
def main(date_start, date_finish, image_size_output, time_window, flag,
home_dir, bases):
date_time_pre_start = date_start + '-0000'
date_time_start = parser.parse(date_time_pre_start)
print('date_time_start:', date_time_start)
date_time_pre_end = date_finish + '-2359'
date_time_end = parser.parse(date_time_pre_end)
print('date_time_end:', date_time_end)
time_increment = 60
print('image_size_output:', image_size_output)
print('flag:', flag)
print('home_dir:', home_dir)
url_prefix = 'https://seal.nascom.nasa.gov/'
print('url_prefix:', url_prefix)
look_ahead = int(
np.ceil(time_window * 60 / 10.)
) #should sufficiently cover all 7 products based on their cadence.
print('look_ahead:', look_ahead)
diff_start_finish_total_sec = (date_time_end -
date_time_start).total_seconds()
print('diff_start_finish_total_sec:', diff_start_finish_total_sec)
total_sec = timedelta(days=time_increment).total_seconds()
print('total_sec:', total_sec)
num_loops = np.ceil(
diff_start_finish_total_sec / total_sec
) + 1 #num_loops would be equal to 94 + 1 for 19960101-0000' - '20110501-0000'
print('num_loops:', num_loops)
base_list = bases.split(',')
for base in tqdm(base_list):
start_process_time = process_time() #initialize clock per product type
base = base.strip(' ')
holes_list = []
unreadable_file_ids_product_list_global = []
print(f'{base}')
base_dir = home_dir + base
if not os.path.exists(base_dir):
os.makedirs(base_dir)
time_range = TimeRange(
date_time_start, timedelta(days=time_increment)
) #time_range re-initialized here for each new base name
prev_time, time_range_modified = prev_time_resumer(
home_dir, base, time_range, date_time_end)
for t_value in tqdm(np.arange(
num_loops)): #this is the main workhorse loop of the program
print('t_value:', t_value)
print('prev_time:', prev_time)
if time_range_modified.end > date_time_end:
time_range_modified = TimeRange(time_range_modified.start,
date_time_end)
product_results = product_search(base, time_range_modified,
date_time_start)
product_results_number = product_results.file_num
if product_results_number != 0:
ind = index_of_sizes(base, product_results)
all_size_sieved_times_pre, fetch_indices_product_orig = fetch_indices(
base, ind, product_results, time_window, look_ahead,
prev_time)
if len(fetch_indices_product_orig) != 0:
all_time_window_sieved_times_product_times_modified, holes_product_list, unreadable_file_ids_product_list_local = product_distiller(
fetch_indices_product_orig, base,
all_size_sieved_times_pre, ind, product_results,
look_ahead, time_window, url_prefix, flag,
image_size_output, home_dir)
if holes_product_list: #if image had missing regions (e.g., arising from telemetry errors)
holes_list.append(holes_product_list)
if unreadable_file_ids_product_list_local:
unreadable_file_ids_product_list_global.append(
unreadable_file_ids_product_list_local)
all_time_window_sieved_times_sorted = np.unique(
all_time_window_sieved_times_product_times_modified)
#print(f'{base} np.unique(all_size_sieved_times_pre):', np.unique(all_size_sieved_times_pre), len(np.unique(all_size_sieved_times_pre)))
#print(f'{base} list(all_time_window_sieved_times_sorted):', list(all_time_window_sieved_times_sorted), len(all_time_window_sieved_times_sorted))
prev_time = [] #reset to empty list
if len(all_time_window_sieved_times_sorted) != 0:
prev_time.append(
all_time_window_sieved_times_sorted[-1]
) #append the last good time entry from the previous loop
csv_writer(base, home_dir, date_start, date_finish, flag,
time_window, image_size_output,
all_time_window_sieved_times_sorted)
time_range_modified.next(
) #Sunpy iterator to go for the next time increment in number of days. There is also time_range_modified.previous() to go backwards in time.
#print('time_range_modified next:', time_range_modified)
print(f'{base} holes_list', holes_list)
print(f'{base} unreadable_file_ids_product_list_global:',
unreadable_file_ids_product_list_global)
data_cuber(home_dir, base, date_start, date_finish, flag, time_window,
image_size_output)
end_process_time = process_time()
time_of_process = end_process_time - start_process_time
print(f'{base} time of process in seconds:', time_of_process)
def returnDataRange(centralDate, delta_min):
date_rangeUp = TimeRange(centralDate, delta_min * u.min).previous()
date_rangeFinal = TimeRange(date_rangeUp.start, date_rangeUp.next().end)
return date_rangeFinal
delta_t = t1 - t0
print()
print('-----------------------')
print(t0)
print(t1)
print(delta_t.sec)
# sunpy time ranges
from sunpy.time import TimeRange
time_range = TimeRange(t0, t1)
print(time_range.center)
tc = Time(time_range.center, scale='utc')
print(tc.mjd)
print(tc.datetime)
print(time_range.seconds)
print(time_range.next())
# example of windowing time ranges at a candence in sunpy (potentially very useful!)
import astropy.units as u
w = time_range.window(600 * u.second, 60 * u.second)
for win in w:
print(win.center)
"""
illustration of how I think I will use Astropy and Sunpy time objects
- start with a set of dates that spans your range of interest
- convert these to astropy time objects
- generate a sunpy time range that spans the whole interval
- divide up this interval into entries
"""
print('\n---------------------\n')
date_start = '2012-07-01T00:00:00.000'
def main(date_start, date_finish, image_size_output, time_window, flag,
home_dir, bases, fits_headers, lev1_LASCO, email, mission):
warnings.simplefilter(action='ignore', category=RuntimeWarning)
client = drms.Client(email=email, verbose=False) #True
date_time_pre_start = date_start + '-0000'
date_time_start = parser.parse(date_time_pre_start)
print('date_time_start:', date_time_start)
date_time_pre_end = date_finish + '-2359'
date_time_end = parser.parse(date_time_pre_end)
print('date_time_end:', date_time_end)
time_increment = 60
print(f'time increment {time_increment} days')
print('image_size_output:', image_size_output)
print('flag:', flag)
print('home_dir:', home_dir)
url_prefix = 'https://seal.nascom.nasa.gov/'
print('url_prefix:', url_prefix)
look_ahead = int(
np.ceil(time_window * 60 / 10.)
) #should sufficiently cover all 7 products based on their cadence.
print('look_ahead:', look_ahead)
diff_start_finish_total_sec = (date_time_end -
date_time_start).total_seconds()
print('diff_start_finish_total_sec:', diff_start_finish_total_sec)
total_sec = timedelta(days=time_increment).total_seconds()
print('total_sec:', total_sec)
num_loops = np.ceil(
diff_start_finish_total_sec / total_sec
) + 1 #num_loops would be equal to 94 + 1 for 19960101-0000' - '20110501-0000'
print('num_loops:', num_loops)
base_list = bases.split(',')
for base in tqdm(base_list):
start_process_time = process_time() #initialize clock per product type
base = base.strip(' ')
holes_list = []
transients_list = []
blobs_list = []
unreadable_file_ids_product_list_global = []
print(f'{base}')
base_dir = home_dir + base + f'_{mission}'
if not os.path.exists(base_dir):
os.makedirs(base_dir)
'''
if ('AIA' in base) and (int(base.split('AIA')[1]) != 4500): #AIA4500 is once an hour. So if not AIA4500, then have AIA cadences at 12s and 24s -> time_increment has to be reduced from 60d -> 1d
time_increment = 1
print(f'new time increment {time_increment} days:')
total_sec = timedelta(days = time_increment).total_seconds()
print('new total_sec:', total_sec)
num_loops = np.ceil(diff_start_finish_total_sec/total_sec) + 1 #num_loops would be equal to 94 + 1 for 19960101-0000' - '20110501-0000'
print('new num_loops:', num_loops)
time_range = TimeRange(date_time_start, timedelta(days = time_increment))
else:
time_range = TimeRange(date_time_start, timedelta(days = time_increment)) #time_range re-initialized here for each new base name
'''
time_range = TimeRange(date_time_start, timedelta(days=time_increment))
prev_time, time_range_modified = prev_time_resumer(
home_dir, base, time_range, date_time_end, mission
) #time_range_modified.next() is the workshorse that advances time at the end of the time for-loop
for t_value in tqdm(np.arange(
num_loops)): #this is the main workhorse loop of the program
print('t_value:', t_value)
print('prev_time:', prev_time)
if time_range_modified.end > date_time_end:
time_range_modified = TimeRange(time_range_modified.start,
date_time_end)
product_results, client = product_search(base, time_range_modified,
client, mission,
time_window)
if ('MDI' in base) or (mission == 'SDO'):
client_export_failed = ghost_file_check(product_results)
if client_export_failed == True:
product_results_number = 0
elif client_export_failed == False:
product_results_number = product_results.data.count(
)['record'] #### old when this object was a query and not an export: product_results.count()['T_REC']
else:
product_results_number = product_results.file_num
client_export_failed = False
if (product_results_number !=
0) and (client_export_failed
== False): #product_results.has_failed()
ind, fits_headers = index_of_sizes(
base, product_results, fits_headers, lev1_LASCO, mission
) #putting fits_headers here to insert it into __init__.py
all_size_sieved_times_pre, fetch_indices_product_orig = fetch_indices(
base, ind, product_results, time_window, look_ahead,
prev_time, mission)
if len(fetch_indices_product_orig) != 0:
all_time_window_sieved_times_product_times_modified, holes_product_list, transients_product_list, blobs_product_list, unreadable_file_ids_product_list_local = product_distiller(
fetch_indices_product_orig, base,
all_size_sieved_times_pre, ind, product_results,
look_ahead, time_window, url_prefix, flag,
image_size_output, home_dir, email, fits_headers,
lev1_LASCO, client, mission)
if holes_product_list: #if image had missing regions (e.g., arising from telemetry errors)
holes_list.append(holes_product_list)
if transients_product_list:
transients_list.append(transients_product_list)
if blobs_product_list:
blobs_list.append(blobs_product_list)
if unreadable_file_ids_product_list_local:
unreadable_file_ids_product_list_global.append(
unreadable_file_ids_product_list_local)
all_time_window_sieved_times_sorted = np.unique(
all_time_window_sieved_times_product_times_modified)
#print(f'{base} np.unique(all_size_sieved_times_pre):', np.unique(all_size_sieved_times_pre), len(np.unique(all_size_sieved_times_pre)))
#print(f'{base} list(all_time_window_sieved_times_sorted):', list(all_time_window_sieved_times_sorted), len(all_time_window_sieved_times_sorted))
prev_time = [] #reset to empty list
if len(all_time_window_sieved_times_sorted) != 0:
prev_time.append(
all_time_window_sieved_times_sorted[-1]
) #append the last good time entry from the previous loop
csv_writer(base, home_dir, date_start, date_finish, flag,
time_window, image_size_output,
all_time_window_sieved_times_sorted, lev1_LASCO,
mission)
time_range_modified.next(
) #Sunpy iterator to go for the next time increment in number of days. There is also time_range_modified.previous() to go backwards in time.
#print('time_range_modified next:', time_range_modified)
print(f'{base} holes_list', holes_list)
print(f'{base} transients_list', transients_list)
print(f'{base} blobs_list', blobs_list)
print(f'{base} unreadable_file_ids_product_list_global:',
unreadable_file_ids_product_list_global)
data_cuber(home_dir, base, date_start, date_finish, flag, time_window,
image_size_output, lev1_LASCO, mission, fits_headers)
end_process_time = process_time()
time_of_process = end_process_time - start_process_time
print(f'{base} time of process in seconds:', time_of_process)
