gnssrefl.subdaily module
- gnssrefl.subdaily.apply_new_constraints(tv, azim1, azim2, ampl, peak2noise, d1, d2, h1, h2)
cleaning up the main code. this sorts data and applies various “commandline” constraints tv is the full set of results from gnssrefl
- Parameters:
tv (numpy array) – lsp results
azim1 (float) – min azimuth (deg)
azim2 (float) – max azimuth (deg)
ampl (float) – required amplitude for periodogram
peak2noise (float) – require peak2noise criterion
d1 (int) – min day of year
d2 (int) – max day of year
h1 (float) – min reflector height (m)
h2 (float) – max reflector height (m)
- Returns:
tv (numpy array) – edited from input
t (numpy of floats) – crude time for obs, in fractional days
rh (numpy of floats) – reflector heights (m)
firstdoy (int) – first day of year
lastdoy (int) – last day of year
- gnssrefl.subdaily.flipit(tvd, col)
take RH values from the first and last day and attaches them as fake data to make the spline fit stable. Also fill the temporal gaps with fake data
- Parameters:
tvd (numpy array of floats) – output of LSP runs.
col (integer) – column number (in normal speak) of the RH results in python-speak, this has one subtracted
- Returns:
tnew (numpy array of floats) – time in days of year
ynew (numpy array) – RH in meters
- gnssrefl.subdaily.flipit2(tvd, col)
take RH values from the first and last day and attaches them as fake data to make the spline fit stable. Also fill the temporal gaps with fake data
This version uses MJD rather than day of year for x-axis
- Parameters:
tvd (numpy array of floats) – output of LSP runs.
col (integer) – column number (in normal speak) of the RH results in python-speak, this has one subtracted
- Returns:
tnew (numpy array of floats) – time in days of year
ynew (numpy array) – RH in meters
- gnssrefl.subdaily.fract_to_obstimes(spl_x)
this does not seem to be used
- Parameters:
spl_x (numpy array) – fractional time
obstimes (numpy array) – datetime format
- gnssrefl.subdaily.my_percentile(rh, p1, p2)
numpy percentile was crashing docker build this is a quick work around
- Parameters:
rh (numpy array) – reflector heights, but could be anything really
p1 (float) – low percentage (from 0-1)
p2 (float) – high percentage (from 0-1)
- Returns:
low (float) – low value (using input percentile)
highv (float) – high value (using input percentile)
- gnssrefl.subdaily.output_names(txtdir, txtfile, csvfile, jsonfile)
figures out what the names of the outputs are going to be
I have modified this so it always returns plain txt. csv will simply be written out in addition.
this function no longer has much point.
- Parameters:
txtdir (str) – the directory where the results should be written out
txtfile (str) – name of the output file
csvfile (bool) – cl input whether the output file should be csv format
jsonfile (bool) – cl input for whether the output file should be in the json format
- Returns:
writetxt (bool) – whether output should be plain txt
writecsv (bool) – whether output should be csv format
writejson (bool) – whether output should be json format
outfile (str) – output filename
- gnssrefl.subdaily.readin_and_plot(station, year, d1, d2, plt2screen, extension, sigma, writecsv, azim1, azim2, ampl, peak2noise, txtfile, h1, h2, kplt, txtdir, default_usage, hires_figs, fs, **kwargs)
Reads in RH results and makes various plots to help users assess the quality of the solution
This is basically “section 1” of the code
- Parameters:
station (str) – 4 character station name
year (int) – full year
d1 (int) – first day of year evaluated
d2 (int) – last day of year evaluated
plt2screen (bool) – if True plots are displayed to the screen
extension (str) – allow user to specify an extension for results (i.e. gnssir was run using extension string)
sigma (float) – how many standard deviations away from mean you allow for the crude outlier detector.
writecsv (bool) – whether output is written in csv format.
azim1 (float) – minimum azimuth value (degrees)
azim2 (float) – maximum azimuth value (degrees)
ampl (float) – minimum LSP amplitude allowed
peak2noise (float) – minim peak2noise value to set solution good
txtfile (str) – name of plain text output file
h1 (float) – minimum reflector height (m)
h2 (float) – maximum reflector height (m)
kplt (bool) – special plot made
txtdir (str) – directory where the results will be written
default_usage (bool) – flag as to whether you are using this code for subdaily or for rh_plot. this changes the plots a bit.
hires_figs (bool) – whether to switch from png to eps
fs (int) – fontsize for figure axes
- Returns:
tv (numpy array) – LSP results (augmented)
otimes (datetime object) – times of observations
fname (str) – initial result file - colated
fname_new (str) – result file with outliers removed
- gnssrefl.subdaily.rhdot_correction2(station, fname, fname_new, pltit, outlierV, outlierV2, **kwargs)
Part two of subdaily. It computes rhdot correction and interfrequency bias correction for RH time series. This code assumes you have at least removed crude outliers in the previous section of the subdaily code.
- Parameters:
station (str) – 4 char station name
fname (list of str) – input filename(s)
fname_new (str) – output filename for results
pltit (bool) – whether you want plots to the screen
outlierV (float) – outlier criterion, in meters used in first go thru if None, then use 3 sigma (which is the default)
outlierV2 (float) – outlier criterion, in meters used in second go thru if None, then use 3 sigma (which is the default)
delta_out (float, optional) – seconds for smooth output
txtdir (str) – if wanting to set your own output directory
apply_if_corr (bool, optional) – whether you want to apply the IF correction default is true
apply_rhdot (bool, optional) – whether you want to apply the rhdot correction default is true
gap_min_val (float, optional) – gap allowed in last spline, in hours
knots2 (int, optional) – a secondary knot value if you want the final output to use a different one than the one used for outliers and RH dot
gap_flag (bool, option) – whether gaps are written as 999 in final output
- gnssrefl.subdaily.spline_in_out(x, y, knots_per_day)
- Parameters:
x (numpy of floats) – time of observations in fractional days
y (numpy of floats) – reflector heights in meters
knots_per_day (int) – number of knots per day
- Returns:
xx (numpy of floats) – regularly spaced observations
spline(xx) (numpy of floats) – spline value at those times
- gnssrefl.subdaily.write_out_header(fout, station, extraline, **kwargs)
writes out header for results file …
- Parameters:
fout (fileID) –
station (str) – 4 character station name
extraline (bool) – not sure why this is here
- gnssrefl.subdaily.write_subdaily(outfile, station, ntv, csv, extraline, **kwargs)
writes out the subdaily results. currently only works for plain txt
>> this code should be moved to the library
- Parameters:
input (str) – output filename
station (str) – 4 character station name, lowercase
nvt (numpy multi-dimensional) – the variable with the LSP results read via np.loadtxt
csv (bool) – whether both csv and txt file should be written
extraline (bool) – whether the header has an extra line