end_hour) + ":00:00"
start_epoch = calendar.timegm(time.strptime(start_date_time, pattern))
end_epoch = calendar.timegm(time.strptime(end_date_time, pattern))
# Read in SuperDARN data
loc_root = str(((
pathlib.Path().parent.absolute()).parent.absolute()).parent.absolute())
in_dir = loc_root + "/DataReading/SD/data/" + station + "/" + station + year + month + day
in_file = in_dir + "/" + station + year + month + day + ".pkl"
df = pd.read_pickle(in_file)
# We are only interested in 15 km resolution data (from the multi-freq analysis)
# This double filter should be redundant but better to be safe
df = df.loc[(df['firstRang'] == 90) & (df['rangeSep'] == 15)]
df = basic_SD_df_filter(df)
# Filter for a gate range and time
df = df.loc[(df['gate'] >= gate_range[0]) & (df['gate'] <= gate_range[1])]
df = df.loc[(df['epoch'] >= start_epoch) & (df['epoch'] <= end_epoch)]
df = df.loc[df['vel'].notna(
)] # There should not be any nan velocities, but just to be safe
# Compute adjusted elevation angle and use it to compute virtual height
df.reset_index(drop=True, inplace=True)
elevation_v2(df, t_diff)
slant_range = 90 + 15 * df['gate'] + 15 / 2 # Slant range [km]
df['height'] = np.sqrt(Re * Re + slant_range * slant_range +
2 * Re * slant_range *
np.sin(np.radians(np.asarray(df['adjElv'])))) - Re
def SD_velocity_histogram(file_name, flag, plot_out):
"""
Create a velocity histogram
:param plot_out: str: show or save, what to do with the build plot
:param file_name: str: The name of the pickled data frame you would like to use. e.g. "event_summary"
:param flag: str: All events in the event summary that have this flag will be considered
:return:
"""
if plot_out != 'show' and plot_out != 'save':
raise Exception("Invalid plot_out, options are 'show' and 'save'.")
vel_lim = [-600, 600]
occ_lim = [-100, 8600]
n_bins = int((vel_lim[1] - vel_lim[0]) / 40) # 50 m/s bins
# Check to make sure inputs are okay
if flag is None or file_name is None:
raise Exception(
"Error: you must pass SDvelocityHistogram() a file_name and flag "
"so it knows which events to consider.")
if not isinstance(flag, str) or not isinstance(file_name, str):
raise Exception("Error: both file_name and flag must be strings.")
df = get_df_multi_event(file_name, flag)
df = basic_SD_df_filter(df)
# Restrict to the overlapping data
# At RKN the overlap is beam 5 and gates 25-32
# At INV the overlap is beam 12 and gates 30-38
starting_gate = np.where(df['stationId'] == 'rkn', 25, 30)
ending_gate = np.where(df['stationId'] == 'rkn', 32, 38)
overlapping_beam = np.where(df['stationId'] == 'rkn', 5, 12)
df = df.loc[(df['gate'] >= starting_gate) & (df['gate'] <= ending_gate) &
(df['bmnum'] == overlapping_beam)]
df = df.loc[df['vel'].notna(
)] # There should not be any nan velocities, but just to be safe
df.reset_index(drop=True, inplace=True)
# Build an event summary that can be printed onto the plot
df['eventString'] = df['stationId'] + " " + \
df['year'].astype(str) + "." + df['month'].astype(str) + "." + df['day'].astype(str)
list_of_included_events = [i for i in df['eventString'].unique()]
fig, ax = plt.subplots(figsize=(8, 9),
sharex='col',
dpi=300,
nrows=1,
ncols=1)
plt.subplots_adjust(hspace=0.05)
fig.suptitle("One-And-One-Half-Hop: Multi-Event Occurrence Distributions" +
"\n Events: " + str(list_of_included_events) +
"\nProduced by " + str(os.path.basename(__file__)),
fontsize=12)
# start by just plotting the first event
first_event = list_of_included_events[0]
event_df = df[df['eventString'] == first_event]
ax.hist(event_df['vel'],
bins=n_bins,
range=vel_lim,
align='mid',
histtype='step',
zorder=3,
label=first_event)
# Loop thorough all the other events and add them on one at a time
for event in list_of_included_events:
if event == first_event:
continue # we have already done this one
# add in the next event
event_df = pd.concat([event_df, df[df['eventString'] == event]])
# Plot the histogram
ax.hist(event_df['vel'],
bins=n_bins,
range=vel_lim,
align='mid',
histtype='step',
zorder=3,
label=event)
# Format the plot
ax.set_xlim(vel_lim)
ax.xaxis.set_minor_locator(MultipleLocator(50))
ax.grid(b=True, which='major', axis='x', linestyle='--', linewidth=0.5)
ax.grid(b=True, which='minor', axis='x', linestyle='--', linewidth=0.1)
# ax.plot([0, 0], ax.get_ylim(), linestyle='-', linewidth=0.5, color='black')
# Add a legend to the plot
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles[::-1], labels[::-1], loc='upper left')
if plot_out == "show":
plt.show()
if plot_out == "save":
# Save to file
loc_root = str(
((pathlib.Path().parent.absolute()).parent.absolute()).absolute())
out_dir = loc_root + "/VelocityAnalysis/out/"
fig.savefig(out_dir + "/multi_event_vel_hist_" + ".pdf",
format='pdf',
dpi=300)