lstid_processing.model.analysis

Analyse concatonated SAMI3 files.

Functions

get_default_indices([time_array, lat_array])	Get the default indices for the C/NOFS and DMSP F16 field lines.
get_time_index(time_array, dtime)	Retrieve the index corresponding to the closest time value.
get_f2_peaks(nlind, nfind, sami)	Get the F2 peak locations for a specified field line.
get_dominant_acceleration(sami)	Get the dominant acceleration variation term indices.
get_topside_peaks(sami, nzinds, dat_keys, dat_scale, ...)	Get the variation peaks across a range of altitudes.
find_linear_breaks(sami, nlind, nfind, nzinds, peak_ind)	Identify the breaks in peaks that indicate a change in wavefront.
fit_lines_to_peaks(peak_lat, peak_sec, lat_break[, ...])	Calculate linear fits to the maxima and minima for each pass.

Module Contents

lstid_processing.model.analysis.get_default_indices(time_array=None, lat_array=None)

Get the default indices for the C/NOFS and DMSP F16 field lines.

Parameters:

time_arrayarray-like or NoneType

Array of times, from which the conjugate time index may be selected.

lat_arrayarray-like or NoneType

Array of latitudes with dimensions of time x nl x nf x nz.

Returns:

nlindint

SAMI3 ‘nl’ index for the C/NOFS and DMSP F16 location

nfindcint

SAMI3 ‘nf’ index for the C/NOFS location

nfindcint

SAMI3 ‘nf’ index for the DMSP F16 location

ntint or NoneType

None if time_array is None, time index for the satelite conjuction otherwise

nzindcint

None if lat_array or time_array is None; otherwise the altitude index for the C/NOFS location

nzinddint

None if lat_array is None or time_array is None; otherwise altitude index for the DMSP F16 location

Notes

C/NOFS is at: 11.3 N, 33.2 E, 479.4 km DMSP F16 is at: 11.3 N, 33.2 E, 845.8 km Conjunction at: 26 March 2014 at 02:49:59 UT

lstid_processing.model.analysis.get_time_index(time_array, dtime)

Retrieve the index corresponding to the closest time value.

Parameters:

time_arrayarray-like

Array of times, from which the desired time index may be selected.

dtimedt.datetime

Desired time as a datetime object

Returns:

ntint

Time index

lstid_processing.model.analysis.get_f2_peaks(nlind, nfind, sami)

Get the F2 peak locations for a specified field line.

Parameters:

nlindint

SAMI3 nl index for the field line

nfindint

SAMI3 nf index for the field line

samixr.Dataset

Dataset with SAMI3 values

Returns:

f2_indsdict

Dict with keys ‘south’ and ‘north’ that contain arrays of indicies corresponding to the altitude indices for the F2 peak across all times. Note that these will only be valid if the field line reaches into the topside ionosphere.

lstid_processing.model.analysis.get_dominant_acceleration(sami)

Get the dominant acceleration variation term indices.

Parameters:

samixr.Dataset

SAMI3 concatonated data with acceleration variation terms

Returns:

dom_accarray-like

Array of numbers indicating which acceleration term has the greatest variability; has values of 1, 2, and 3

Notes

1 is the plasma pressure gradient, 2 is the ion-neutral collisions, and 3 is the ion-ion collisions

lstid_processing.model.analysis.get_topside_peaks(sami, nzinds, dat_keys, dat_scale, peak_height)

Get the variation peaks across a range of altitudes.

Parameters:

samixr.Dataset

Dataset with SAMI3 values for the desired time period

nzindslist-like

Lists of altitude indices at which peaks will be calculated

dat_keyslist-like

List of data keys to evaluate, expects relative variation keys like [‘rel_dene_d’]

dat_scalelist-like

List of scaling parameters to divide from the data

peak_heightlist-like

List of heights that peaks must meet in order to qualify in the detection, can be None to include all peaks regardless of significance

Returns:

min_inddict

Dict with keys corresponding to data keys and values contianing a list of indices that locate significant peak minima

max_inddict

Dict with keys corresponding to data keys and values contianing a list of indices that locate significant peak maxima

lstid_processing.model.analysis.find_linear_breaks(sami, nlind, nfind, nzinds, peak_ind, lat_break=6.0, sec_break=900, max_lat=20.0)

Identify the breaks in peaks that indicate a change in wavefront.

Parameters:

samixr.Dataset

Dataset with SAMI3 values for the desired time period

nlindint

‘nl’ index for the relative data included in the minima and maxima data keys

nfindint

‘nf’ index for the relative data included in the minima and maxima data keys

nzindslist-like

Lists of altitude indices at which peaks were calculated

peak_inddict

Dict with keys corresponding to data keys and values contianing a list of indices that locate significant peak minima or maxima

lat_breakfloat

Change in latitude that indicates a break (default=6.0)

sec_breakint

Change in time (seconds) that indicates a break (default=900)

max_latfloat

Maximum latitude for data, starting point for breaks (default=20.0)

Returns:

peak_latdict

Latitudes corresponding to the peak indices with keys corresponding to data variables

peak_secdict

Seconds from the starting time corresponding to the peak indices with keys corresponding to data variables

lat_break_indsdict

Indices of the latitude breaks with keys corresponding to data variables

Notes

Only identifies North-to-South trends automatically

lstid_processing.model.analysis.fit_lines_to_peaks(peak_lat, peak_sec, lat_break, min_pnts=40)

Calculate linear fits to the maxima and minima for each pass.

Parameters:

peak_latdict

Latitudes corresponding to the peak indices with keys corresponding to data variables

peak_secdict

Seconds from the starting time corresponding to the peak indices with keys corresponding to data variables

lat_breakdict

Indices of the latitude breaks with keys corresponding to data variables

min_pntsint

Minimum number of points needed to calculate a linear fit (default=40)

Returns:

lin_fitdict

Output from scipy.stats.linregress for each valid fit period