Application Programming Interface

This library contains components for PyIRI software.

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather, ESS Open Archive, September 28, 2023, doi:10.22541/essoar.169592556.61105365/v1.

Bilitza et al. (2022), The International Reference Ionosphere model: A review and description of an ionospheric benchmark, Reviews of Geophysics, 60.

Nava et al. (2008). A new version of the nequick ionosphere electron density model. J. Atmos. Sol. Terr. Phys., 70 (15), doi:10.1016/j.jastp.2008.01.015.

Jones, W. B., Graham, R. P., & Leftin, M. (1966). Advances in ionospheric mapping by numerical methods.

PyIRI.main_library.EDP_builder(x, aalt)[source]

Construct vertical EDP.

Parameters:

xarray-like: Array where 1st dimention indicates the parameter (total 11 parameters), second dimension is time, and third is horizontal grid [11, N_T, N_G].
aaltarray-like: 1-D array of altitudes [N_V] in km.

Returns:

density_outarray-like: 3-D electron density [N_T, N_V, N_G] in m-3.

Notes

This function builds the EDP from the provided parameters for all time frames, all vertical and all horizontal points.

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

PyIRI.main_library.F107_2_IG12(F107)[source]

Convert F10.7 to IG12 coefficients.

Parameters:

F107float or array-like: Solar flux F10.7 voefficient in SFU.

Returns:

IG12float or array-like: Ionosonde Gloabal coeffcient.

Notes

This function converts F10.7 to IG12.

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

Bilitza et al. (2022), The International Reference Ionosphere model: A review and description of an ionospheric benchmark, Reviews of Geophysics, 60.

PyIRI.main_library.F107_2_R12(F107)[source]

Convert F10.7 to R12 coefficients.

Parameters:

F107float or array-like: Solar flux at 10.7 in SFU.

Returns:

R12float or array-like: 12-month sunspot number.

Notes

This function converts F10.7 to R12.

PyIRI.main_library.IG12_2_F107(IG12)[source]

Convert IG12 to F10.7 coefficients.

Parameters:

IG12float or array-like: Ionosonde Gloabal coeffcient.

Returns:

F107float or array-like: Solar flux F10.7 voefficient in SFU.

Notes

This function converts IG12 to F10.7.

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

Bilitza et al. (2022), The International Reference Ionosphere model: A review and description of an ionospheric benchmark, Reviews of Geophysics, 60.

PyIRI.main_library.IG12_2_R12(IG12)[source]

Convert IG12 to R12 coefficients.

Parameters:

IG12float or array-like: Ionosonde Gloabal coeffcient.

Returns:

R12float or array-like: Sunspot number coefficient R12.

Notes

This function converts IG12 to R12.

References

Bilitza et al. (2022), The International Reference Ionosphere model: A review and description of an ionospheric benchmark, Reviews of Geophysics, 60.

PyIRI.main_library.IRI_density_1day(year, mth, day, aUT, alon, alat, aalt, F107, coeff_dir, ccir_or_ursi=0)[source]

Output ionospheric parameters for a particular day.

Parameters:

yearint: Year.
mthint: Month of year.
dayint: Day of month.
aUTarray-like: Array of universal time (UT) in hours. Must be Numpy array of any size [N_T].
alonarray-like: Flattened array of geographic longitudes in degrees. Must be Numpy array of any size [N_G].
alatarray-like: Flattened array of geographic latitudes in degrees. Must be Numpy array of any size [N_G].
aaltarray-like: Array of altitudes in km. Must be Numpy array of any size [N_V].
F107float: User provided F10.7 solar flux index in SFU.
coeff_dirstr: Place where coefficients are located.
ccir_or_ursiint: If 0 is given CCIR will be used for F2 critical frequency. If 1 then URSI coefficients. (default=0)

Returns:

F2dict: ‘Nm’ is peak density of F2 region in m-3. ‘fo’ is critical frequency of F2 region in MHz. ‘M3000’ is the obliquity factor for a distance of 3,000 km. Defined as refracted in the ionosphere, can be received at a distance of 3,000 km, unitless. ‘hm’ is height of the F2 peak in km. ‘B_topi is top thickness of the F2 region in km. ‘B_bot’ is bottom thickness of the F2 region in km. Shape [N_T, N_G, 2].
F1dict: ‘Nm’ is peak density of F1 region in m-3. ‘fo’ is critical frequency of F1 region in MHz. ‘P’ is the probability occurrence of F1 region, unitless. ‘hm’ is height of the F1 peak in km. ‘B_bot’ is bottom thickness of the F1 region in km. Shape [N_T, N_G, 2].
Edict: ‘Nm’ is peak density of E region in m-3. ‘fo’ is critical frequency of E region in MHz. ‘hm’ is height of the E peak in km. ‘B_top’ is bottom thickness of the E region in km. ‘B_bot’ is bottom thickness of the E region in km. Shape [N_T, N_G, 2].
Esdict: ‘Nm’ is peak density of Es region in m-3. ‘fo’ is critical frequency of Es region in MHz. ‘hm’ is height of the Es peak in km. ‘B_top’ is bottom thickness of the Es region in km. ‘B_bot’ is bottom thickness of the Es region in km. Shape [N_T, N_G, 2].
sundict: ‘lon’ is longitude of subsolar point in degrees. ‘lat’ is latitude of subsolar point in degrees. Shape [N_G].
magdict: ‘inc’ is inclination of the magnetic field in degrees. ‘modip’ is modified dip angle in degrees. ‘mag_dip_lat’ is magnetic dip latitude in degrees. Shape [N_G].
EDParray-like: Electron density profiles in m-3 with shape [N_T, N_V, N_G]

Notes

This function returns ionospheric parameters and 3-D electron density for a given day and provided F10.7 solar flux index.

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

PyIRI.main_library.IRI_monthly_mean_par(year, mth, aUT, alon, alat, coeff_dir, ccir_or_ursi=0)[source]

Output monthly mean ionospheric parameters.

Parameters:

yearint: Year.
mthint: Month of year.
aUTarray-like: Array of universal time (UT) in hours. Must be Numpy array of any size [N_T].
alonarray-like: Flattened array of geographic longitudes in degrees. Must be Numpy array of any size [N_G].
alatarray-like: Flattened array of geographic latitudes in degrees. Must be Numpy array of any size [N_G].
coeff_dirstr: Place where coefficients are.
ccir_or_ursiint: If 0 is given CCIR will be used for F2 critical frequency, if 1 then URSI. (default=0)

Returns:

F2dict: ‘Nm’ is peak density of F2 region in m-3. ‘fo’ is critical frequency of F2 region in MHz. ‘M3000’ is the obliquity factor for a distance of 3,000 km. Defined as refracted in the ionosphere, can be received at a distance of 3,000 km, unitless. ‘hm’ is height of the F2 peak in km. ‘B_topi is top thickness of the F2 region in km. ‘B_bot’ is bottom thickness of the F2 region in km. Shape [N_T, N_G, 2].
F1dict: ‘Nm’ is peak density of F1 region in m-3. ‘fo’ is critical frequency of F1 region in MHz. ‘P’ is the probability occurrence of F1 region, unitless. ‘hm’ is height of the F1 peak in km. ‘B_bot’ is bottom thickness of the F1 region in km. Shape [N_T, N_G, 2].
Edict: ‘Nm’ is peak density of E region in m-3. ‘fo’ is critical frequency of E region in MHz. ‘hm’ is height of the E peak in km. ‘B_top’ is bottom thickness of the E region in km. ‘B_bot’ is bottom thickness of the E region in km. Shape [N_T, N_G, 2].
Esdict: ‘Nm’ is peak density of Es region in m-3. ‘fo’ is critical frequency of Es region in MHz. ‘hm’ is height of the Es peak in km. ‘B_top’ is bottom thickness of the Es region in km. ‘B_bot’ is bottom thickness of the Es region in km. Shape [N_T, N_G, 2].
sundict: ‘lon’ is longitude of subsolar point in degrees. ‘lat’ is latitude of subsolar point in degrees. Shape [N_G]
magdict: ‘inc’ is inclination of the magnetic field in degrees. ‘modip’ is modified dip angle in degrees. ‘mag_dip_lat’ is magnetic dip latitude in degrees. Shape [N_G]

Notes

This function returns monthly mean ionospheric parameters for min and max levels of solar activity that correspond to the Ionosonde Index IG12 of 0 and 100.

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

PyIRI.main_library.Probability_F1(year, mth, utime, alon, alat, mag_dip_lat, aIG)[source]

Calculate probability occurence of F1 layer.

Parameters:

yearint: Year.
mthint: Month.
timearray-like: Array of UTs in hours.
alonarray-like: Flattened array of longitudes in degrees.
alatarray-like: Flattened array of latitudes in degrees.
mag_dip_latarray-like: Flattened array of magnetic dip latitudes in degrees.
aIGarray-like: Min and Max of IG12.

Returns:

a_Parray-like: Probability occurrence of F1 layer.
a_foF1array-like: Critical freqeuncy of F1 layer in MHz.

Notes

This function caclulates numerical maps probability of F1 layer.

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

Bilitza et al. (2022), The International Reference Ionosphere model: A review and description of an ionospheric benchmark, Reviews of Geophysics, 60.

PyIRI.main_library.R12_2_F107(R12)[source]

Convert R12 to F10.7 coefficients.

Parameters:

R12float or array-like: 12-month sunspot number.

Returns:

F107float or array-like: Solar flux at 10.7 in SFU.

Notes

This function converts R12 to F10.7.

PyIRI.main_library.R12_2_IG12(R12)[source]

Convert R12 to IG12 coefficients.

Parameters:

R12float or array-like: Sunspot number coefficient R12.

Returns:

IG12float or array-like: Ionosonde Gloabal coeffcient.

Notes

This function converts R12 to IG12.

PyIRI.main_library.adjust_longitude(lon, type)[source]

Adjust longitudes from 180 to 360 and back.

Parameters:

lonarray-like: Longitudes in degrees.
typestr: Indicates the type of adjustment.

Returns:

lonarray-like: Adjusted longitude.

Notes

This function adjustst the array of longitudes to go from -180-180 or from 0-360.

PyIRI.main_library.create_reg_grid(hr_res=1, lat_res=1, lon_res=1, alt_res=10, alt_min=0, alt_max=700)[source]

Run IRI for a single day on a regular grid.

Parameters:

hr_resint or float: Time resolution in hours (default=1)
lat_resint or float: Latitude resolution in degrees (default=1)
lon_resint or float: Longitude resolution in degrees (default=1)
alt_resint or float: Altitude resolution in km (default=10)
alt_minint or float: Altitude minimum in km (default=0)
alt_maxint or float: Altitude maximum in km (default=700)

Returns:

alonarray-like: 1D longitude grid
alatarray-like: 1D latitude grid
alon_2darray-like: 2D longitude grid
alat_2darray-like: 2D latitude grid
aaltarray-like: Altitude grid
ahrarray-like: UT grid

Notes

This function creates a regular global grid in Geographic coordinates using given spatial resolution lon_res, lat_res. In case you want to run IRI in magnetic coordinates, you can obtain a regular grid in magnetic coordinates, then convert it to geographic coordinates using your own methods, and then use these 1-D alon and alat arrays. Similarly, if you are interested in regional grid, then use your own alon and alat 1-D arrays. alon and alat can also be irregular arrays. In case you need to run IRI for 1 grid point, define alon and alat as NumPy arrays that have only 1 element. Size of alon and alat is [N_G]. This function creates creates an array of altitudes for the veritical dimension of electron density profiles. Any 1-D Numpy array in [km] would work, regularly or irregularly spaced. This function also creates time array aUT using a given temporal resolution hr_res in hours. E.g. if hr_res = 0.25 it will lead to 15-minutes time resolution. You can define aUT your own way, just keep it 1-D and expressed in hours. It can be regularly or irregularly spaced array. If you want to run PyIRI for just 1 time frame, then define aUT as NumPy arrays that have only 1 element. Size of aUT is [N_T].

PyIRI.main_library.day_of_the_month_corr(year, month, day)[source]

Calculate ftactions of influence of monthes “before” and “afer”.

Parameters:

yearint: Given year.
monthint: Given month.
dayday: Given day.

Returns:

t_beforeclass:dt.datetime: Consider mean values from this month as month “before”.
t_afterclass:dt.datetime: Consider mean values from this month as month “after”.
fraction1float: Fractional influence of month “before”.
fraction2float: Fractional influence of month “after”.

Notes

This function finds two months around the given day and calculates fractions of influence for previous and following monthes.

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

Bilitza et al. (2022), The International Reference Ionosphere model: A review and description of an ionospheric benchmark, Reviews of Geophysics, 60.

PyIRI.main_library.decimal_year(dtime)[source]

Determine the decimal year.

Parameters:

dtimeclass:dt.datetime: Given datetime.

Returns:

date_decimalfloat: Decimal year.

Notes

This function returns decimal year. For example, middle of the year is 2020.5.

PyIRI.main_library.diurnal_functions(time_array)[source]

Set diurnal functions for F2, M3000, and Es.

Parameters:

time_arrayarray-like: Array of UTs in hours.

Returns:

D_f0f2array-like: Diurnal functions for foF2.
D_M3000array-like: Diurnal functions for M3000.
D_Es_medianarray-like: Diurnal functions for Es.

Notes

This function calculates diurnal functions for F0F2, M3000, and Es coefficients

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

Jones, W. B., Graham, R. P., & Leftin, M. (1966). Advances in ionospheric mapping 476 by numerical methods.

PyIRI.main_library.drop_function(x)[source]

Calculate drop function from a simple family of curve.

Parameters:

xarray-like: Portion of the altitude array.

Returns:

yarray-like: Function that can be multiplied with x to reduce the influence of x.

Notes

This is a drop function from a simple family of curve. It is used to reduce the F1_top contribution for the F2_bot region, so that when the summation of epsein functions is performed, the presence of F1 region would not mess up with the value of NmF2.

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

PyIRI.main_library.epstein(Nm, hm, B, alt)[source]

Calculate Epstein function for given parameters.

Parameters:

Nmarray-like: Peak density in m-3.
hmarray-like: Height of peak density in km.
Barray-like: Thickness of the layer in km.
altarray-like: Altitude array in km.

Returns:

resarray-like: Constructed Epstein profile in m-3.

Notes

This function returns epstein function for given parameters. In a typical Epstein function: X = Nm, Y = hm, Z = B, and W = alt.

PyIRI.main_library.epstein_function_array(A1, hm, B, x)[source]

Construct density epstein profile for any layer (except topside of F2).

Parameters:

A1array-like: Amplitude of layer in m-3.
hmarray-like: Height of layer in km.
Barray-like: Thickness in km.
xarray-like: Altitude in km.

Returns:

densityarray-like: Constructed density in m-3.

Notes

This function constructs density Epstein profile for any layer.

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

PyIRI.main_library.epstein_function_top_array(A1, hmF2, B_F2_top, x)[source]

Construct density epstein profile for the topside of F2 layer.

Parameters:

A1array-like: Amplitude of F2 layer in m-3.
hmF2array-like: Height of F2 layer in km.
B_F2_toparray-like: Thickness of topside F2 layer in km.
xarray-like: Altitude in km.

Returns:

densityarray-like: Constructed density in m-3.

Notes

This function constructs density epstein profile for the topside of F2 layer.

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

PyIRI.main_library.fexp(x)[source]

Calculate exponent without overflow.

Parameters:

xarray-like: Any input.

Returns:

yarray-like: Exponent of x.

Notes

This function function caclulates exp(x) with restrictions to not cause overflow.

PyIRI.main_library.find_B_F1_bot(hmF1, hmE, P_F1)[source]

Determine the thickness of F1 layer.

Parameters:

hmF1array-like: Height of F1 layer in km.
hmEarray-like: Height of E layer in km.
P_F1array-like: Probability of observing F1 layer.

Returns:

B_F1_botarray-like: Thickness of F1 layer in km.

Notes

This function returns thickness of F1 layer in km. This is done using hmF1 and hmE, as described in NeQuick Eq 87

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

PyIRI.main_library.foE(mth, solzen_effective, alat, f107)[source]

Calculate critical freqeuency of E region.

Parameters:

mthint: Month.
solzen_effectivearray-like: Effective solar zenith angle with shape [ntime].
alatarray-like: Flattened array of latitudes in degrees with shape [ngrid].
f107float: F10.7 solar flux in SFU.

Returns:

foEarray-like: Critical frequency of E region in MHz with shape [ntime, ngrid].

Notes

This function caclulates foE for a given effective solar zenith angle and level of solar activity. This routine is based on the Ionospheric Correction Algorithm for Galileo Single Frequency Users that describes the NeQuick Model.

Result is: foE = critical frequency of the E region (MHz), np.array,

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

Nava et al. (2008). A new version of the nequick ionosphere electron density model. J. Atmos. Sol. Terr. Phys., 70 (15), 490 doi: 10.1016/j.jastp.2008.01.015

PyIRI.main_library.fractional_correction_of_dictionary(fraction1, fraction2, F_before, F_after)[source]

Interpolate btw 2 middles of consequent months to the given day.

Parameters:

fraction1float: Fractional influence of month “before”.
fraction2float: Fractional influence of month “after”.
F_beforedict: Dictionary of mean parametrs for month “before”.
F_afterdict: Dictionary of mean parametrs for month “after”.

Returns:

F_newdict: Parameters interpolated according to given fractions.

Notes

This function interpolates between 2 middles of consequent months to the specified day by using provided fractions previousely calculated by function “day_of_the_month_corr”.

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

Bilitza et al. (2022), The International Reference Ionosphere model: A review and description of an ionospheric benchmark, Reviews of Geophysics, 60.

PyIRI.main_library.freq2den(freq)[source]

Convert ionospheric frequency to plasma density.

Parameters:

freqarray-like: ionospheric freqeuncy in MHz.

Returns:

densarray-like: plasma density in m-3.

Notes

This function converts ionospheric frequency to plasma density.

PyIRI.main_library.freq_to_Nm(foF2, foF1, foE, foEs)[source]

Convert critical frequency to plasma density.

Parameters:

foF2array-like: Critical frequency of F2 layer in MHz.
foF1array-like: Critical frequency of F1 layer in MHz.
foEarray-like: Critical frequency of E layer in MHz.
foEsarray-like: Critical frequency of Es layer in MHz.

Returns:

NmF2array-like: Peak density of F2 layer in m-3.
NmF1array-like: Peak density of F1 layer in m-3.
NmEarray-like: Peak density of E layer in m-3.
NmEsarray-like: Peak density of Es layer in m-3.

Notes

This function returns maximum density for the given critical frequency and limits it to 1 if it is below zero.

PyIRI.main_library.gamma(D_f0f2, D_M3000, D_Es_median, G_fof2, G_M3000, G_Es_median, F_fof2_coeff, F_M3000_coeff, F_Es_median)[source]

Calculate foF2, M3000 propagetion parameter, and foEs.

Parameters:

D_f0f2array-like: Diurnal functions for F2 region.
D_M3000array-like: Diurnal functions for M3000 propagation parameter.
D_Es_medianarray-like: Diurnal functions for Es region.
G_fof2array-like: Global functions for F2 region.
G_M3000array-like: Global functions for M3000 propagation parameter.
G_Es_medianarray-like: Global functions for Es region.
F_fof2_coeffarray-like: CCIR or URCI coefficients.
F_M3000_coeffarray-like: CCIR coefficients.
F_Es_medianarray-like: Bradley Es coefficients.

Returns:

gamma_f0f2array-like: Critical frequency of F2 layer.
gamma_M3000array-like: M3000 propagation parameter.
gamma_Es_medianarray-like: Critical frequency of Es layer.

Notes

This function caclulates numerical maps for F0F2, M3000, and Es for 2 levels of solar activity (min, max) using matrix multiplication

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

PyIRI.main_library.gammaE(year, mth, utime, alon, alat, aIG)[source]

Calculate numerical maps for critical freqeuency of E region.

Parameters:

yearint: Year.
mthint: Month.
utimearray-like: Array of UTs in hours.
alonarray-like: Flattened array of longitudes in degrees.
alatarray-like: Flattened array of latitudes in degrees.
aIGarray-like: Min and max of IG12 index.

Returns:

gamma_Earray-like: critical frequency of E region in MHz.
slonarray-like: Longitude of subsolar point in degrees.
slatarray-like: Latitude of subsolar point in degrees.

Notes

This function caclulates numerical maps for FoE for 2 levels of solar activity.

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

PyIRI.main_library.highest_power_of_extension()[source]

Provide the highest power of extension.

Returns:

constdict: Dictionary that has QM, nk, and nj parameters.

Notes

This function sets a common set of constants that define the power of etensions. QM = array of highest power of sin(x). nk = highest order of geographic extension. e.g. there are 76 functions in Table 3 on page 18 in Jones & Graham 1965. nj = highest order in diurnal variation.

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

Jones, W. B., & Gallet, R. M. (1965). Representation of diurnal and geographic variations of ionospheric data by numerical methods, control of instability, ITU Telecommunication Journal , 32 (1), 18–28.

PyIRI.main_library.hmF1_from_F2(NmF2, NmF1, hmF2, B_F2_bot)[source]

Determine the height of F1 layer.

Parameters:

NmF2array-like: Peak density of F2 layer in m-3.
NmF1array-like: Peak density of F1 layer in m-3.
hmF2array-like: Height of F2 layer in km.
B_F2_botarray-like: Thickness of F2 bottom layer in km.

Returns:

hmF1array-like: Height of F1 layer in km.

Notes

This function calculates hmF1 from known shape of F2 bottom side, where it drops to NmF1.

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

PyIRI.main_library.hm_IRI(M3000, foE, foF2, modip, aIG)[source]

Return height of the ionospheric layers.

Parameters:

M3000array-like: Propagation parameter for F2 region related to hmF2.
foEarray-like: Critical frequency of E region in MHz.
foF2array-like: Critical frequency of F2 region in MHz.
modiparray-like: Modified dip angle in degrees.
aIGarray-like: Min and max of IG12 index.

Returns:

hmF2array-like: Height of F2 layer in km.
hmEarray-like: Height of E layer in km.
hmEsarray-like: Height of Es layer in km.

Notes

This function returns height of ionospheric layers like it is done in IRI.

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

Bilitza et al. (2022), The International Reference Ionosphere model: A review and description of an ionospheric benchmark, Reviews of Geophysics, 60.

PyIRI.main_library.juldat(times)[source]

Calculate the Julian time given calendar date and time.

Parameters:

timesclass:dt.datetime: Julian time in days.

Returns:

julian_datetimefloat: Julian date.

Raises:

ValueError: For input of the wrong type.

Notes

This function calculates the Julian time given calendar date and time in np.datetime64 array. This function is consistent with NOVAS, The US Navy Observatory Astromony Software Library and algorythm by Fliegel and Van Flander.

PyIRI.main_library.quadratic(coeff)[source]

Solve quadratic equation for given coefficients a, b, c.

Parameters:

coeffarray-like: Array with a, b, and c coefficients as the first three elements, which may be floats or arrays.

Returns:

[root1, root2]list: List of two root solutions. The first solution, uses addition and the second solution uses subtraction.

Notes

This function solves quadratic equation and returns 2 roots.

PyIRI.main_library.read_ccir_ursi_coeff(mth, coeff_dir, output_quartiles=False)[source]

Read coefficients from CCIR, URSI, and Es.

Parameters:

mthint: Month.
coeff_dirstr: Place where the coefficint files are.
output_quartilesbool: Return an additional output, the upper and lower quartiles of the Bradley coefficients for Es (default=False)

Returns:

F_fof2_CCIRarray-like: CCIR coefficients for F2 frequency.
F_fof2_URSIarray-like: URSI coefficients for F2 frequency.
F_M3000array-like: CCIR coefficients for M3000.
F_Es_medianarray-like: Bradley coefficients for Es.
F_Es_lowarray-like: Optional output only included if output_quartiles is True.
F_Es_upperarray-like: Optional output only included if output_quartiles is True.

Notes

This function sets the combination of sin and cos functions that define the diurnal destribution of the parameters and that can be further multiplied by the coefficients U_jk (from CCIR, URSI, and Es maps). The desired eequation can be found in the Technical Note Advances in Ionospheric Mapping by Numerical Methods, Jones & Graham 1966. Equation (c) page 38. Acknowledgement for Es coefficients: Mrs. Estelle D. Powell and Mrs. Gladys I. Waggoner in supervising the collection, keypunching and processing of the foEs data. This work was sponsored by U.S. Navy as part of the SS-267 program. The final development work and production of the foEs maps was supported by the U.S Information Agency. Acknowledgemets to Doug Drob (NRL) for giving me these coefficients.

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

Jones, W. B., Graham, R. P., & Leftin, M. (1966). Advances in ionospheric mapping 476 by numerical methods.

Bradley, P. A. (2003). Ingesting a sporadic-e model to iri. Adv. Space Res., 31(3), 577-588.

PyIRI.main_library.reconstruct_density_from_parameters(F2, F1, E, alt)[source]

Construct vertical EDP for 2 levels of solar activity.

Parameters:

F2dict: Dictionary of parameters for F2 layer.
F1dict: Dictionary of parameters for F1 layer.
Edict: Dictionary of parameters for E layer.
altarray-like: 1-D array of altitudes [N_V] in km.

Returns:

x_outarray-like: Electron density for two levels of solar activity [2, N_T, N_V, N_G] in m-3.

Notes

This function calculates 3-D density from given dictionaries of the parameters for 2 levels of solar activity.

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

PyIRI.main_library.reconstruct_density_from_parameters_1level(F2, F1, E, alt)[source]

Construct vertical EDP for 1 level of solar activity.

Parameters:

F2dict: Dictionary of parameters for F2 layer.
F1dict: Dictionary of parameters for F1 layer.
Edict: Dictionary of parameters for E layer.
altarray-like: 1-D array of altitudes [N_V] in km.

Returns:

x_outarray-like: Electron density for two levels of solar activity [N_T, N_V, N_G] in m-3.

Notes

This function calculates 3-D density from given dictionaries of the parameters for 1 level of solar activity.

References

Forsythe et al. (2023), PyIRI: Whole-Globe Approach to the International Reference Ionosphere Modeling Implemented in Python, Space Weather.

PyIRI.main_library.run_iri_reg_grid(year, month, day, f107, hr_res=1, lat_res=1, lon_res=1, alt_res=10, alt_min=0, alt_max=700, ccir_or_ursi=0)[source]

Run IRI for a single day on a regular grid.

Parameters:

yearint: Four digit year in C.E.
monthint: Integer month (range 1-12)
dayint: Integer day of month (range 1-31)
f107int or float: F10.7 index for the given day
hr_resint or float: Time resolution in hours (default=1)
lat_resint or float: Latitude resolution in degrees (default=1)
lon_resint or float: Longitude resolution in degrees (default=1)
alt_resint or float: Altitude resolution in km (default=10)
alt_minint or float: Altitude minimum in km (default=0)
alt_maxint or float: Altitude maximum in km (default=700)
ccir_or_ursiint: If 0 use CCIR coefficients, if 1 use URSI coefficients

Returns:

alonarray-like: 1D longitude grid
alatarray-like: 1D latitude grid
alon_2darray-like: 2D longitude grid
alat_2darray-like: 2D latitude grid
aaltarray-like: Altitude grid
ahrarray-like: UT grid
f2array-like: F2 peak
f1array-like: F1 peak
epeakarray-like: E peak
es_peakarray-like: Sporadic E (Es) peak
sunarray-like: Solar zenith angle in degrees
magarray-like: Magnetic inclination in degrees
edens_profarray-like: Electron density profile in per cubic m

References

Alken et al. (2021). International geomagnetic reference field: the thirteenth generation. Earth, Planets and Space, 73(49), doi:10.1186/s40623-020-01288-x.

PyIRI.igrf_library.geo_to_gg(radius, theta)[source]

Compute geodetic colatitude and vertical height above the ellipsoid.

Parameters:

radiusarray-like: Geocentric radius in kilometers.
thetaarray-like: Geocentric colatitude in degrees.

Returns:

heightarray-like: Altitude in kilometers.
betaarray-like: Geodetic colatitude.

Notes

IGRF-13 Alken et al., 2021. Compute geodetic colatitude and vertical height above the ellipsoid from geocentric radius and colatitude. Round-off errors might lead to a failure of the algorithm especially but not exclusively for points close to the geographic poles. Corresponding geodetic coordinates are returned as NaN.

References

Zhu, J., “Conversion of Earth-centered Earth-fixed coordinates to geodetic coordinates”, IEEE Transactions on Aerospace and Electronic Systems}, 1994, vol. 30, num. 3, pp. 957-961.

PyIRI.igrf_library.gg_to_geo(h, gdcolat)[source]

Compute geocentric colatitude and radius from geodetic colat and height.

Parameters:

harray-like: Altitude in kilometers.
gdcolatarray-like: Geodetic colatitude in degrees.

Returns:

radarray-like: Geocentric radius in kilometers.
thcarray-like: Geocentric colatitude in degrees.
sdarray-like: Rotate B_X to gd_lat.
cdarray-like: Rotate B_Z to gd_lat.

Notes

IGRF-13 Alken et al., 2021.

References

Equations (51)-(53) from “The main field” (chapter 4) by Langel, R. A. in: “Geomagnetism”, Volume 1, Jacobs, J. A., Academic Press, 1987.

Malin, S.R.C. and Barraclough, D.R., 1981. An algorithm for synthesizing the geomagnetic field. Computers & Geosciences, 7(4), pp. 401-405.

PyIRI.igrf_library.inc2magnetic_dip_latitude(inc)[source]

Calculate magnetic dip latitude from magnetic inclination.

Parameters:

incarray-like: Magnetic inclination in degrees.

Returns:

magnetic_dip_latitudearray-like: Magnetic dip latitude in degrees.

Notes

This function calculates magnetic dip latitude.

PyIRI.igrf_library.inc2modip(inc, alat)[source]

Calculate modified dip angle from magnetic inclination.

Parameters:

incarray-like: Magnetic inclination in degrees.
alatarray-like: Flattened array of latitudes in degrees.

Returns:

modip_degarray-like: Modified dip angle in degrees.

Notes

This function calculates modified dip angle for a given inclination and geographic latitude.

PyIRI.igrf_library.inclination(coeff_dir, date_decimal, alon, alat)[source]

Calculate magnetic inclination using IGRF13.

Parameters:

coeff_dirstr: Where IGRF13 coefficients are located.
date_decimalfloat: Decimal year
alonarray-like: Flattened array of geographic longitudes in degrees.
alatarray-like: Flattened array of geographic latitudes in degrees.

Returns:

incarray-like: Magnetic inclination in degrees.

Notes

This code is a slight modification of the IGRF13 pyIGRF release, https://www.ngdc.noaa.gov/IAGA/vmod/igrf.html The reading of the IGRF coefficient file was modified to speded up the process, and the main code was simlified to oly focus on the iclination of magnetic field output for the given grid.

PyIRI.igrf_library.legendre_poly(nmax, theta)[source]

Calculate associated Legendre polynomials P(n,m).

Parameters:

nmaxint: Maximum degree up to which expansion.
thetaarray-like: Array containing the colatitude in degrees.

Returns:

Pnmarray-like: Evaluated values and derivatives.

Notes

by IGRF-13 Alken et al., 2021 Returns associated Legendre polynomials P(n,m) (Schmidt quasi-normalized), and the derivative \(dP(n,m)/d\\theta\) evaluated at \(\\theta\).

References

Zhu, J., “Conversion of Earth-centered Earth-fixed coordinates to geodetic coordinates”, IEEE Transactions on Aerospace and Electronic Systems}, 1994, vol. 30, num. 3, pp. 957-961.

PyIRI.igrf_library.synth_values(coeffs, radius, theta, phi, nmax=None, nmin=1, grid=False)[source]

Compute radial, colatitude and azimuthal field components.

Parameters:

coeffsarray-like: Coefficients of the spherical harmonic expansion. The last dimension is equal to the number of coefficients, N at the grid points.
radiusarray-like: Array containing the radius in km.
thetaarray-like: Array containing the colatitude in degrees.
phiarray-like: Array containing the longitude in degrees.
nmaxint or NoneType: Maximum degree up to which expansion is to be used, if None it will be specified by the coeffs variable. However, smaller values are also valid if specified. (default=None)
nminint: Minimum degree from which expansion is to be used. Note that it will just skip the degrees smaller than nmin, the whole sequence of coefficients 1 through nmax must still be given in coeffs. (default=1)
gridbool: If True, field components are computed on a regular grid. Arrays theta and phi must have one dimension less than the output grid since the grid will be created as their outer product (default=False).

Returns:

B_radiusarray-like: Radial field components in km.
B_thetaarray-like: Colatitude field components in degrees.
B_phiarray-like: Azimuthal field components in degrees.

Raises:

ValueError: If an inappropriate input value is supplied

Notes

by IGRF-13 Alken et al., 2021 Based on chaosmagpy from Clemens Kloss (DTU Space, Copenhagen) Computes radial, colatitude and azimuthal field components from the magnetic potential field in terms of spherical harmonic coefficients. A reduced version of the DTU synth_values chaosmagpy code.

References

Zhu, J., “Conversion of Earth-centered Earth-fixed coordinates to geodetic coordinates”, IEEE Transactions on Aerospace and Electronic Systems}, 1994, vol. 30, num. 3, pp. 957-961.

PyIRI.igrf_library.xyz2dhif(x, y, z)[source]

Calculate declination, intensity, inclination of mag field.

Parameters:

xarray-like: North component of the magnetic field in nT.
yarray-like: East component of the magnetic field in nT.
yarray-like: Vertical component of the magnetic field in nT.

Returns:

decarray-like: Declination of the magnetic field in degrees.
hozarray-like: Horizontal intensity of the magnetic field in nT.
incarray-like: Inclination of the magnetic field in degrees.
effarray-like: Total intensity of the magnetic filed in nT.

Notes

by IGRF-13 Alken et al., 2021 Calculate D, H, I and F from (X, Y, Z) Based on code from D. Kerridge, 2019.

This library contains components visualisation routines for PyIRI.

PyIRI.plotting.PyIRI_EDP_sample(EDP, aUT, alon, alat, lon_plot, lat_plot, aalt, UT, plot_dir, plot_name='PyIRI_EDP_sample.pdf')[source]

Plot EDP for one location for solar min and max.

Parameters:

EDParray-like: 3-D electron density array output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
lon_plotfloat: Longitude location for EDP.
lat_plotarray-like: Latitude location for EDP.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_EDP_sample.pdf’)

PyIRI.plotting.PyIRI_EDP_sample_1day(EDP, aUT, alon, alat, lon_plot, lat_plot, aalt, UT, plot_dir, plot_name='PyIRI_EDP_sample_1day.pdf')[source]

Plot EDP for one location.

Parameters:

EDParray-like: 3-D electron density array output of IRI_density_1day.
aUTarray-like: Array of universal times in hours used in PyIRI
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
lon_plotfloat: Longitude location for EDP.
lat_plotarray-like: Latitude location for EDP.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_EDP_sample_1day.pdf’)

PyIRI.plotting.PyIRI_plot_1location_diurnal_density(EDP, alon, alat, lon_plot, lat_plot, aalt, aUT, plot_dir, plot_name='PyIRI_EDP_diurnal.pdf')[source]

Plot diurnal parameters for one location.

Parameters:

EDParray-like: 3-D electron density array output of IRI_density_1day with shape [N_T, N_V, N_H].
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
lon_plotfloat: Longitude location for EDP.
lat_plotarray-like: Latitude location for EDP.
aaltarray-like: Flattened array of altitudes in km.
aUTarray-like: Array of universal times in hours used in PyIRI.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_foF1_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_1location_diurnal_par(F2, F1, E, Es, alon, alat, lon_plot, lat_plot, aUT, plot_dir, plot_name='PyIRI_diurnal.pdf')[source]

Plot diurnal parameters for one location.

Parameters:

F2dict: Dictionary output of IRI_monthly_mean_parameters.
F1dict: Dictionary output of IRI_monthly_mean_parameters.
Edict: Dictionary output of IRI_monthly_mean_parameters.
Esdict: Dictionary output of IRI_monthly_mean_parameters.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
lon_plotfloat: Longitude location for EDP.
lat_plotarray-like: Latitude location for EDP.
aUTarray-like: Array of universal times in hours used in PyIRI.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_foF1_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_B_F1_bot(F1, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_B_F1_bot.pdf')[source]

Plot thickness of F1 bottom side.

Parameters:

F1dict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_B_F1_bot.pdf’)

PyIRI.plotting.PyIRI_plot_B_F1_bot_min_max(F1, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_B_F1_bot_min_max.pdf')[source]

Plot thickness of F1 bottom side for solar min and max.

Parameters:

F1dict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_B_F1_bot_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_B_F2_bot(F2, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_B_F2_bot.pdf')[source]

Plot thickness of F2 bottom side.

Parameters:

F2dict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_B_F2_bot.pdf’)

PyIRI.plotting.PyIRI_plot_B_F2_bot_min_max(F2, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_B_F2_bot_min_max.pdf')[source]

Plot thickness of F2 bottom side for solar min and max.

Parameters:

F2dict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_B_F2_bot_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_B_F2_top(F2, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_B_F2_top.pdf')[source]

Plot thickness of F2 topside.

Parameters:

F2dict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_B_F2_top.pdf’)

PyIRI.plotting.PyIRI_plot_B_F2_top_min_max(F2, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_B_F2_top_min_max.pdf')[source]

Plot thickness of F2 topside for solar min and max.

Parameters:

F2dict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_B_F2_top_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_M3000(F2, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_M3000.pdf')[source]

Plot M3000 propagation parameter.

Parameters:

F2dict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_M3000.pdf’)

PyIRI.plotting.PyIRI_plot_M3000_min_max(F2, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_M3000_min_max.pdf')[source]

Plot M3000 propagation parameter for solar min and max.

Parameters:

F2dict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_M3000_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_NmF1(F1, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_NmF1.pdf')[source]

Plot NmF1.

Parameters:

F1dict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_NmF1_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_NmF1_min_max(F1, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_NmF1_min_max.pdf')[source]

Plot NmF1 for solar min and max.

Parameters:

F1dict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_NmF1_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_NmF2(F2, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_NmF2.pdf')[source]

Plot NmF2.

Parameters:

F2dict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_NmF2_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_NmF2_min_max(F2, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_NmF2_min_max.pdf')[source]

Plot NmF2 for solar min and max.

Parameters:

F2dict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_NmF2_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_foE(E, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_foE.pdf')[source]

Plot foE.

Parameters:

Edict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_foE_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_foE_min_max(E, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_foE_min_max.pdf')[source]

Plot foE for solar min and max.

Parameters:

Edict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_foE_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_foEs(Es, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_foEs.pdf')[source]

Plot foEs.

Parameters:

Esdict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_foEs_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_foEs_min_max(Es, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_foEs_min_max.pdf')[source]

Plot foEs for solar min and max.

Parameters:

Esdict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_foEs_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_foF1(F1, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_foF1.pdf')[source]

Plot foF1.

Parameters:

F1dict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_foF1_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_foF1_min_max(F1, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_foF1_min_max.pdf')[source]

Plot foF1 for solar min and max.

Parameters:

F1dict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_foF1_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_foF2(F2, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_foF2.pdf')[source]

Plot foF2.

Parameters:

F2dict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_foF2_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_foF2_min_max(F2, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_foF2_min_max.pdf')[source]

Plot foF2 for solar min and max.

Parameters:

F2dict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_foF2_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_hmF1(F1, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_hmF1.pdf')[source]

Plot hmF1 for solar min and max.

Parameters:

F1dict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_hmF1_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_hmF1_min_max(F1, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_hmF1_min_max.pdf')[source]

Plot hmF1 for solar min and max.

Parameters:

F1dict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_hmF1_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_hmF2(F2, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_hmF2.pdf')[source]

Plot hmF2.

Parameters:

F2dict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_hmF2.pdf’)

PyIRI.plotting.PyIRI_plot_hmF2_min_max(F2, aUT, alon, alat, alon_2d, alat_2d, sun, UT, plot_dir, plot_name='PyIRI_hmF2_min_max.pdf')[source]

Plot hmF2 for solar min and max.

Parameters:

F2dict: Dictionary output of IRI_monthly_mean_parameters.
aUTarray-like: Array of universal times in hours used in PyIRI.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
sundict: Dictionary output of IRI_monthly_mean_parameters.
UTfloat: UT time frame from array aUT to plot.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_hmF2_min_max.pdf’)

PyIRI.plotting.PyIRI_plot_inc(mag, alon, alat, alon_2d, alat_2d, plot_dir, plot_name='PyIRI_inc.pdf')[source]

Plot magnetic inclination.

Parameters:

magdict: Dictionary output of IRI_monthly_mean_parameters.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_inc.pdf’)

PyIRI.plotting.PyIRI_plot_mag_dip_lat(mag, alon, alat, alon_2d, alat_2d, plot_dir, plot_name='PyIRI_mag_dip_lat.pdf')[source]

Plot magnetic dip latitude.

Parameters:

magdict: Dictionary output of IRI_monthly_mean_parameters.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
plot_dirstr: Direction where to save the figure.
plot_namestr: Output name, without directory, for the saved figure (default=’PyIRI_mag_dip_lat.pdf’)

PyIRI.plotting.PyIRI_plot_modip(mag, alon, alat, alon_2d, alat_2d, plot_dir, plot_name='PyIRI_modip.pdf')[source]

Plot modified dip angle.

Parameters:

magdict: Dictionary output of IRI_monthly_mean_parameters.
alonarray-like: Flattened array of geo longitudes in degrees.
alatarray-like: Flattened array of geo latitudes in degrees.
alon_2darray-like: 2-D array of geo longitudes in degrees.
alat_2darray-like: 2-D array of geo latitudes in degrees.
plot_dirstr: Direction where to save the figure.
plot_namestr: Name for the output figure, without directory (default=’PyIRI_modip.pdf’)