Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
4 changed files
with
145 additions
and
149 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,38 +1,17 @@ | ||
| #!/usr/bin/env python | ||
| """ Height Profile Example """ | ||
| import pyiri2016 | ||
| import pyiri2016 as iri | ||
| import pyiri2016.plots as piri | ||
| # | ||
| import numpy as np | ||
| from matplotlib.pyplot import figure, show | ||
| from matplotlib.pyplot import show | ||
|
|
||
| glat, glon = -11.95, -76.77 | ||
|
|
||
| alt_km = np.arange(80,1000,20.) | ||
|
|
||
| iri = pyiri2016.IRI('2012-08-21T12', alt_km, glat, glon) | ||
| iono = iri.IRI('2012-08-21T12', alt_km, glat, glon) | ||
|
|
||
| fig = figure(figsize=(16,6)) | ||
| axs = fig.subplots(1,2) | ||
|
|
||
| fig.suptitle(f'{str(iri.time[0].values)[:-13]}\n Glat, Glon: {iri.glat}, {iri.attrs["glon"]}') | ||
|
|
||
|
|
||
| pn = axs[0] | ||
| pn.plot(iri['ne'].squeeze(), iri.alt_km, label='N$_e$') | ||
| #pn.set_title(iri2016Obj.title1) | ||
| pn.set_xlabel('Density (m$^{-3}$)') | ||
| pn.set_ylabel('Altitude (km)') | ||
| pn.set_xscale('log') | ||
| pn.legend(loc='best') | ||
| pn.grid(True) | ||
|
|
||
| pn = axs[1] | ||
| pn.plot(iri['Ti'].squeeze(), iri.alt_km, label='T$_i$') | ||
| pn.plot(iri['Te'].squeeze(), iri.alt_km, label='T$_e$') | ||
| #pn.set_title(iri2016Obj.title2) | ||
| pn.set_xlabel('Temperature (K)') | ||
| pn.set_ylabel('Altitude (km)') | ||
| pn.legend(loc='best') | ||
| pn.grid(True) | ||
| piri.altprofile(iono) | ||
|
|
||
| show() |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,45 +1,23 @@ | ||
| #!/usr/bin/env python | ||
| import pyiri2016 | ||
| from numpy import arange | ||
| from matplotlib.pyplot import figure, show | ||
| import pyiri2016 as iri | ||
| import pyiri2016.plots as piri | ||
| from matplotlib.pyplot import show | ||
|
|
||
| """ Geog. Latitude Profile Example """ | ||
|
|
||
| latlim = [-60, 60] | ||
| latstp = 2. | ||
| sim = pyiri2016.geoprofile(altkm=600, latlim=latlim, dlat=latstp, \ | ||
| glon=-76.77, time='2004-01-01T17') | ||
|
|
||
| latbins = arange(latlim[0], latlim[1], latstp) | ||
| if __name__ == '__main__': | ||
| from argparse import ArgumentParser | ||
| p = ArgumentParser() | ||
| p.add_argument('-o','--outfn',help='write data to file') | ||
| p = p.parse_args() | ||
|
|
||
| latlim = [-60, 60] | ||
| latstp = 2. | ||
| iono = iri.geoprofile(altkm=600, latlim=latlim, dlat=latstp, | ||
| glon=-76.77, time='2004-01-01T17') | ||
|
|
||
| fig = figure(figsize=(8,12)) | ||
| axs = fig.subplots(2,1, sharex=True) | ||
| piri.latprofile(iono) | ||
|
|
||
| pn = axs[0] | ||
|
|
||
| pn.plot(latbins, sim['NmF2'].squeeze(), label='N$_m$F$_2$') | ||
| pn.plot(latbins, sim['NmF1'].squeeze(), label='N$_m$F$_1$') | ||
| pn.plot(latbins, sim['NmE'].squeeze(), label='N$_m$E') | ||
| pn.set_title(str(sim.time[0].values)[:-13] + ' latitude'+str(latlim)) | ||
| pn.set_xlim(latbins[[0, -1]]) | ||
| pn.set_xlabel('Geog. Lat. ($^\circ$)') | ||
| pn.set_ylabel('(m$^{-3}$)') | ||
| pn.set_yscale('log') | ||
|
|
||
|
|
||
| pn = axs[1] | ||
| pn.plot(latbins, sim['hmF2'].squeeze(), label='h$_m$F$_2$') | ||
| pn.plot(latbins, sim['hmF1'].squeeze(), label='h$_m$F$_1$') | ||
| pn.plot(latbins, sim['hmE'].squeeze(), label='h$_m$E') | ||
| pn.set_xlim(latbins[[0, -1]]) | ||
| pn.set_title(str(sim.time[0].values)[:-13] + ' latitude'+str(latlim)) | ||
| pn.set_xlabel('Geog. Lat. ($^\circ$)') | ||
| pn.set_ylabel('(km)') | ||
|
|
||
| for a in axs: | ||
| a.legend(loc='best') | ||
| a.grid(True) | ||
|
|
||
|
|
||
| show() | ||
| show() |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,121 @@ | ||
| import xarray | ||
| from matplotlib.pyplot import figure | ||
|
|
||
| def timeprofile(iono:xarray.Dataset): | ||
| # %% Plots | ||
| Nplot=3 | ||
|
|
||
| if Nplot>2: | ||
| fig = figure(figsize=(16,12)) | ||
| axs = fig.subplots(3,1, sharex=True).ravel() | ||
| else: | ||
| fig = figure(figsize=(16,6)) | ||
| axs = fig.subplots(1,2).ravel() | ||
|
|
||
| fig.suptitle(f'{str(iono.time[0].values)[:-13]} to {str(iono.time[-1].values)[:-13]}\n Glat, Glon: {iono.glat}, {iono.glon}') | ||
|
|
||
| ax = axs[0] | ||
|
|
||
| ax.plot(iono.time, iono['NmF2'].squeeze(), label='N$_m$F$_2$') | ||
| ax.plot(iono.time, iono['NmF1'].squeeze(), label='N$_m$F$_1$') | ||
| ax.plot(iono.time, iono['NmE'].squeeze(), label='N$_m$E') | ||
| ax.set_title('Maximum number densities vs. ionospheric layer') | ||
| ax.set_xlabel('Hour (UT)') | ||
| ax.set_ylabel('(m$^{-3}$)') | ||
| ax.set_yscale('log') | ||
| ax.legend(loc='best') | ||
|
|
||
| ax = axs[1] | ||
| ax.plot(iono.time, iono['hmF2'].squeeze(), label='h$_m$F$_2$') | ||
| ax.plot(iono.time, iono['hmF1'].squeeze(), label='h$_m$F$_1$') | ||
| ax.plot(iono.time, iono['hmE'].squeeze(), label='h$_m$E') | ||
| ax.set_title('Height of maximum density vs. ionospheric layer') | ||
| ax.set_xlabel('Hour (UT)') | ||
| ax.set_ylabel('(km)') | ||
| ax.legend(loc='best') | ||
| # %% | ||
| if Nplot > 2: | ||
| ax = axs[2] | ||
|
|
||
| for a in iono.alt_km: | ||
| ax.plot(iono.time, iono['ne'].squeeze(), marker='.', label=f'{a.item()} km') | ||
| ax.set_xlabel('time UTC (hours)') | ||
| ax.set_ylabel('[m$^{-3}$]') | ||
| ax.set_title(f'$N_e$ vs. altitude and time') | ||
| ax.set_yscale('log') | ||
| ax.legend(loc='best') | ||
| # %% | ||
| if Nplot > 4: | ||
| ax = axs[4] | ||
| tec = iono.b[36, :] | ||
| ax.plot(iono.time, tec, label=r'TEC') | ||
| ax.set_xlabel('Hour (UT)') | ||
| ax.set_ylabel('(m$^{-2}$)') | ||
| #ax.set_yscale('log') | ||
| ax.legend(loc='best') | ||
|
|
||
| ax = axs[5] | ||
| vy = iono.b[43, :] | ||
| ax.plot(iono.time, vy, label=r'V$_y$') | ||
| ax.set_xlabel('Hour (UT)') | ||
| ax.set_ylabel('(m/s)') | ||
| ax.legend(loc='best') | ||
|
|
||
| for a in axs.ravel(): | ||
| a.grid(True) | ||
|
|
||
|
|
||
| def altprofile(iono:xarray.Dataset): | ||
| fig = figure(figsize=(16,6)) | ||
| axs = fig.subplots(1,2) | ||
|
|
||
| fig.suptitle(f'{str(iono.time[0].values)[:-13]}\n Glat, Glon: {iono.glat}, {iono.attrs["glon"]}') | ||
|
|
||
|
|
||
| pn = axs[0] | ||
| pn.plot(iono['ne'].squeeze(), iono.alt_km, label='N$_e$') | ||
| #pn.set_title(iri2016Obj.title1) | ||
| pn.set_xlabel('Density (m$^{-3}$)') | ||
| pn.set_ylabel('Altitude (km)') | ||
| pn.set_xscale('log') | ||
| pn.legend(loc='best') | ||
| pn.grid(True) | ||
|
|
||
| pn = axs[1] | ||
| pn.plot(iono['Ti'].squeeze(), iono.alt_km, label='T$_i$') | ||
| pn.plot(iono['Te'].squeeze(), iono.alt_km, label='T$_e$') | ||
| #pn.set_title(iri2016Obj.title2) | ||
| pn.set_xlabel('Temperature (K)') | ||
| pn.set_ylabel('Altitude (km)') | ||
| pn.legend(loc='best') | ||
| pn.grid(True) | ||
|
|
||
| def latprofile(iono:xarray.Dataset): | ||
|
|
||
| fig = figure(figsize=(8,12)) | ||
| axs = fig.subplots(2,1, sharex=True) | ||
|
|
||
| ax = axs[0] | ||
|
|
||
| ax.plot(iono.lat, iono['NmF2'].squeeze(), label='N$_m$F$_2$') | ||
| ax.plot(iono.lat, iono['NmF1'].squeeze(), label='N$_m$F$_1$') | ||
| ax.plot(iono.lat, iono['NmE'].squeeze(), label='N$_m$E') | ||
| ax.set_title(str(iono.time[0].values)[:-13] + f' latitude {iono.lat[[0, -1]].values}') | ||
| #ax.set_xlim(iono.lat[[0, -1]]) | ||
| ax.set_xlabel('Geog. Lat. ($^\circ$)') | ||
| ax.set_ylabel('(m$^{-3}$)') | ||
| ax.set_yscale('log') | ||
|
|
||
|
|
||
| ax = axs[1] | ||
| ax.plot(iono.lat, iono['hmF2'].squeeze(), label='h$_m$F$_2$') | ||
| ax.plot(iono.lat, iono['hmF1'].squeeze(), label='h$_m$F$_1$') | ||
| ax.plot(iono.lat, iono['hmE'].squeeze(), label='h$_m$E') | ||
| ax.set_xlim(iono.lat[[0, -1]]) | ||
| ax.set_title(str(iono.time[0].values)[:-13] + f' latitude {iono.lat[[0, -1]].values}') | ||
| ax.set_xlabel('Geog. Lat. ($^\circ$)') | ||
| ax.set_ylabel('(km)') | ||
|
|
||
| for a in axs: | ||
| a.legend(loc='best') | ||
| a.grid(True) |