lstid_processing.model.plots ============================ .. py:module:: lstid_processing.model.plots .. autoapi-nested-parse:: Create some useful plots for the SAMI3 concatonated data. .. !! processed by numpydoc !! Functions --------- .. autoapisummary:: lstid_processing.model.plots.plot_f2_peak_char_and_diff lstid_processing.model.plots.plot_dom_acceleration lstid_processing.model.plots.plot_dens_var lstid_processing.model.plots.plot_field_lines_w_linear_var lstid_processing.model.plots.get_plot_tid_peaks Module Contents --------------- .. py:function:: plot_f2_peak_char_and_diff(sami, f2_inds, nt=None, dat_keys=None, start=dt.datetime(2014, 3, 25, 22), stop=dt.datetime(2014, 3, 26, 6)) Plot the F2 peak characteristics and hemispheric differences. :Parameters: **sami** : xr.Dataset Dataset with concatonated SAMI3 data **f2_inds** : dict Dict with north and south indices for the F2 peaks along a field line **nt** : int or NoneType Event time index or None to not include a vertical line marking this point (default=None) **dat_keys** : list-like List of data keys to plot, if None uses altitude and field-aligned neutral wind (default=None) **start** : dt.datetime Starting time for plot (default=dt.datetime(2014, 3, 25, 22)) **stop** : dt.datetime Ending time for plot (default=dt.datetime(2014, 3, 26, 6)) :Returns: **fig** : plt.Figure Figure handle .. !! processed by numpydoc !! .. py:function:: plot_dom_acceleration(sami, nt, nlind, dom_acc=None, nfinds=None, nz_sat=None, nz_north=None, nz_south=None) Plot the dominate acceleration terms for a single time and meridian. :Parameters: **sami** : xr.Dataset Dataset with concatonated SAMI3 data **nt** : int Time index **nlind** : int 'nl' meridian index **dom_acc** : array-like or NoneType Array of values specifying the dominant acceleration term or None to calculate (default=None) **nfinds** : list-like or None List of 'nf' indices for field lines to be plotted (default=None) **nz_sat** : list-like or None List of 'nz' indices corresponding to the `nfinds` indices for satellite locations (default=None) **nz_north** : list-like or None List of 'nz' indices corresponding to the `nfinds` indices for northern F2 peak locations (default=None) **nz_south** : list-like or None List of 'nz' indices corresponding to the `nfinds` indices for southern F2 peak locations (default=None) :Returns: **fig** : plt.Figure Figure handle .. !! processed by numpydoc !! .. py:function:: plot_dens_var(sami, nlind, nfind, nzind, sat_key, nt=None, nt_color='k', title='', start=dt.datetime(2014, 3, 25, 22), stop=dt.datetime(2014, 3, 26, 6)) Plot the electron, O+, and H+ density variations at a desired location. :Parameters: **sami** : xr.Dataset Dataset with concatonated SAMI3 data **nlind** : int 'nl' index **nfind** : int 'nf' index **nzind** : int 'nf' index **nt** : int or None Time index to plot a vertical line marking time or None (default=None) **nt_color** : str Color for vertical line (default='k') **sat_key** : str Single character string used to specify the satellite/meridian for the data variations (e.g., 'c' or 'd') **title ; str** Figure title string (default='') **start** : dt.datetime Starting time for plot (default=dt.datetime(2014, 3, 25, 22)) **stop** : dt.datetime Ending time for plot (default=dt.datetime(2014, 3, 26, 6)) :Returns: **fig** : plt.Figure Figure handle .. !! processed by numpydoc !! .. py:function:: plot_field_lines_w_linear_var(sami, nt, nlind, nfinds, nz_sat, nz_north, nz_south, colors, sat_labels) Plot the 6-panel conjunction figure. :Parameters: **sami** : xr.Dataset SAMI3 standard or concatonated data set **nt** : int Time index **nlind** : int 'nl' index **nfinds** : list-like List of two 'nl' indexes **nz_sat** : list-like List of 'nz' indices corresponding to the `nfinds` indices for satellite locations **nz_north** : list-like List of 'nz' indices corresponding to the `nfinds` indices for northern F2 peak locations **nz_south** : list-like or None List of 'nz' indices corresponding to the `nfinds` indices for southern F2 peak locations **colors** : list-like This that contains two colors for each satellite as a list, for example [['orange', 'tan'], ['k', 'grey']] **sat_labels** : list-like List of satellite labels :Returns: **fig** : plt.Figure Figure handle .. !! processed by numpydoc !! .. py:function:: get_plot_tid_peaks(sami, nt_start, nt_stop, nlind, nfind, nzinds, dat_keys, dat_labels=None, dat_scale=None, peak_height=None, add_lines=True, add_line_labels=False, min_lat=None, min_sec=None, min_lat_break=None, min_lin_fit=None, max_lat=None, max_sec=None, max_lat_break=None, max_lin_fit=None) Calculate and plot the TID peaks for a given time and altitude range. :Parameters: **sami** : xr.Dataset SAMI3 concatonated data set **nt_start: int** Starting time index **nt_stop** : int Ending time index **nlind** : int 'nl' index **nfind** : int 'nf' index **nzinds** : list-like List of 'nz' indices corresponding to, e.g., the topside ionosphere **dat_keys** : list-like or str List of data keys to plot or a satellite string to use defaults **dat_labels** : list-like or NoneType List of data labels, will be overwritten if `dat_keys` is a satellite string (default=None) **dat_scale** : list-like or NoneType List of data scales for keys, if a satellite string is used for `dat_keys` this will be reset (default=None) **peak_height** : list-like or NoneType List of peak height minima for keys, if a satellite string is used for `dat_keys` this will be reset (default=None) **add_lines** : bool Add linear fits to plot (default=True) **add_line_labels** : bool Add the linear fits to the legend (default=False) **min_lat** : dict or NoneType Latitudes corresponding to the minima indices with keys corresponding to data variables or None to calculate (default=None) **min_sec** : dict or NoneType Seconds from the starting time corresponding to the minima indices with keys corresponding to data variables or None to calculate (default=None) **min_lat_break** : dict or NoneType Indices of the latitude breaks with keys corresponding to data variables for the minima or None to calculate (default=None) **min_lin_fit** : dict or NoneType Output from scipy.stats.linregress for each valid fit period or None to calculate (default=None) **max_lat** : dict or NoneType Latitudes corresponding to the maxima indices with keys corresponding to data variables or None to calculate (default=None) **max_sec** : dict or NoneType Seconds from the starting time corresponding to the maxima indices with keys corresponding to data variables or None to calculate (default=None) **max_lat_break** : dict or NoneType Indices of the latitude breaks with keys corresponding to data variables for the maxim aor None to calculate (default=None) **max_lin_fit** : dict or NoneType Output from scipy.stats.linregress for each valid fit period or None to calculate (default=None) :Returns: **min_lat** : dict Latitudes corresponding to the minima indices with keys corresponding to data variables **min_sec** : dict Seconds from the starting time corresponding to the minima indices with keys corresponding to data variables **min_lat_break** : dict Indices of the latitude breaks with keys corresponding to data variables for the minima **min_lin_fit** : dict Output from scipy.stats.linregress for each valid fit period **max_lat** : dict Latitudes corresponding to the maxima indices with keys corresponding to data variables **max_sec** : dict Seconds from the starting time corresponding to the maxima indices with keys corresponding to data variables **max_lat_break** : dict Indices of the latitude breaks with keys corresponding to data variables for the maxima **max_lin_fit** : dict Output from scipy.stats.linregress for each valid fit period **fig** : plt.Figure Figure handle .. !! 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