Merge pull request #244 from MIT-AI-Accelerator/gnadt

Gnadt

LICENSE.md

@@ -2,7 +2,7 @@ This work is created by the Department of the Air Force under 17 U.S.C. 105. Thi
 
 When reproducing, copying, modifying, or creating derivative works, the portion(s) of the work attributable under 17 U.S.C. 105 may be designated with the above notice where appropriate. Improperly claiming ownership of this work could be punishable by law under 17 U.S.C. 506(c).
 
-Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
 
 The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 

README.md

@@ -1,7 +1,7 @@
 # MagNav: airborne Magnetic anomaly Navigation
 
 [![CI](https://github.com/MIT-AI-Accelerator/MagNav.jl/workflows/CI/badge.svg)](https://github.com/MIT-AI-Accelerator/MagNav.jl/actions/workflows/ci.yml)
-[![Codecov](https://codecov.io/gh/MIT-AI-Accelerator/MagNav.jl/branch/master/graph/badge.svg)](https://app.codecov.io/gh/MIT-AI-Accelerator/MagNav.jl)
+[![Codecov](https://codecov.io/gh/MIT-AI-Accelerator/MagNav.jl/graph/badge.svg)](https://app.codecov.io/gh/MIT-AI-Accelerator/MagNav.jl)
 [![docs-stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://mit-ai-accelerator.github.io/MagNav.jl/stable/)
 
 MagNav.jl contains a full suite of tools for airborne **Mag**netic anomaly **Nav**igation (MagNav), including flight path & INS data import or simulation, mapping, aeromagnetic compensation, and navigation. This package was developed as part of the [DAF-MIT Artificial Intelligence Accelerator](https://aia.mit.edu/). More information on this effort, including a list of relevant publications, is provided on the [challenge problem website](https://magnav.mit.edu/). The package has been tested on the long-term support (LTS) and latest stable versions of Julia, which may be downloaded from [here](https://julialang.org/downloads/). The recommended IDE for Julia is [Visual Studio Code](https://code.visualstudio.com/) (with the Julia extension).

examples/dataframes_setup.jl

@@ -1,9 +1,9 @@
 ## setup DataFrames for use with examples
 
 ## SGL calibration flight lines
-df_comp = DataFrame(CSV.File("dataframes/df_comp.csv"))
-df_comp[!,:flight]   = Symbol.(df_comp[!,:flight])
-df_comp[!,:map_name] = Symbol.(df_comp[!,:map_name]) # not ideal, but ok
+df_cal = DataFrame(CSV.File("dataframes/df_cal.csv"))
+df_cal[!,:flight]   = Symbol.(df_cal[!,:flight])
+df_cal[!,:map_name] = Symbol.(df_cal[!,:map_name]) # not ideal, but ok
 
 ## SGL flight data HDF5 files
 df_flight = DataFrame(CSV.File("dataframes/df_flight.csv"))

examples/jupyter_demo.ipynb

@@ -26,7 +26,7 @@
     "Dataframe  | Description\n",
     ":--------- | :----------\n",
     "`df_map`   | map files relevant for SGL flights\n",
-    "`df_comp`  | SGL calibration flight lines\n",
+    "`df_cal`   | SGL calibration flight lines\n",
     "`df_flight`| SGL flight files\n",
     "`df_all`   | all flight lines\n",
     "`df_nav`   | all *navigation-capable* flight lines\n",
@@ -152,7 +152,7 @@
    "id": "741742c1-007c-43cc-abde-1860ce4c5d4c",
    "metadata": {},
    "source": [
-    "For the Tolles-Lawson calibration, we will select flight line 1006.04, which occurred at a higher altitude (see [readme](https://github.com/MIT-AI-Accelerator/MagNav.jl/blob/master/readmes/Flt1006_readme.txt)). This is the first calibration box of this flight line. `TL_ind` holds the Boolean indices (mask) just for this portion of the calibration flight line. The full list of calibration flight line options is in `df_comp`."
+    "For the Tolles-Lawson calibration, we will select flight line 1006.04, which occurred at a higher altitude (see [readme](https://github.com/MIT-AI-Accelerator/MagNav.jl/blob/master/readmes/Flt1006_readme.txt)). This is the first calibration box of this flight line. `TL_ind` holds the Boolean indices (mask) just for this portion of the calibration flight line. The full list of calibration flight line options is in `df_cal`."
    ]
   },
   {
@@ -163,7 +163,7 @@
    "outputs": [],
    "source": [
     "TL_i   = 6 # select first calibration box of 1006.04\n",
-    "TL_ind = get_ind(xyz;tt_lim=[df_comp.t_start[TL_i],df_comp.t_end[TL_i]]);\n"
+    "TL_ind = get_ind(xyz;tt_lim=[df_cal.t_start[TL_i],df_cal.t_end[TL_i]]);\n"
    ]
   },
   {
@@ -750,15 +750,15 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Julia 1.9.3",
+   "display_name": "Julia 1.10.2",
    "language": "julia",
-   "name": "julia-1.9"
+   "name": "julia-1.10"
   },
   "language_info": {
    "file_extension": ".jl",
    "mimetype": "application/julia",
    "name": "julia",
-   "version": "1.9.3"
+   "version": "1.10.2"
   }
  },
  "nbformat": 4,

examples/jupyter_end-to-end_ML.ipynb

@@ -37,7 +37,7 @@
     "Dataframe  | Description\n",
     ":--------- | :----------\n",
     "`df_map`   | map files relevant for SGL flights\n",
-    "`df_comp`  | SGL calibration flight lines\n",
+    "`df_cal`   | SGL calibration flight lines\n",
     "`df_flight`| SGL flight files\n",
     "`df_all`   | all flight lines\n",
     "`df_nav`   | all *navigation-capable* flight lines\n",
@@ -142,7 +142,7 @@
    "id": "3f19688f",
    "metadata": {},
    "source": [
-    "For the Tolles-Lawson calibration, we will select flight line 1006.04, which occurred at a higher altitude (see [readme](https://github.com/MIT-AI-Accelerator/MagNav.jl/blob/master/readmes/Flt1006_readme.txt)). This is the first calibration box of this flight line. `TL_ind` holds the Boolean indices (mask) just for this portion of the calibration flight line. The full list of calibration flight line options is in `df_comp`."
+    "For the Tolles-Lawson calibration, we will select flight line 1006.04, which occurred at a higher altitude (see [readme](https://github.com/MIT-AI-Accelerator/MagNav.jl/blob/master/readmes/Flt1006_readme.txt)). This is the first calibration box of this flight line. `TL_ind` holds the Boolean indices (mask) just for this portion of the calibration flight line. The full list of calibration flight line options is in `df_cal`."
    ]
   },
   {
@@ -153,7 +153,7 @@
    "outputs": [],
    "source": [
     "TL_i   = 6 # select first calibration box of 1006.04\n",
-    "TL_ind = get_ind(xyz;tt_lim=[df_comp.t_start[TL_i],df_comp.t_end[TL_i]]);\n"
+    "TL_ind = get_ind(xyz;tt_lim=[df_cal.t_start[TL_i],df_cal.t_end[TL_i]]);\n"
    ]
   },
   {
@@ -834,15 +834,15 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Julia 1.9.3",
+   "display_name": "Julia 1.10.2",
    "language": "julia",
-   "name": "julia-1.9"
+   "name": "julia-1.10"
   },
   "language_info": {
    "file_extension": ".jl",
    "mimetype": "application/julia",
    "name": "julia",
-   "version": "1.9.3"
+   "version": "1.10.2"
   }
  },
  "nbformat": 4,

examples/pluto_fi.jl

@@ -37,7 +37,7 @@ The DataFrames listed below provide useful information about the flight data (co
 Dataframe  | Description
 :--------- | :----------
 `df_map`   | map files relevant for SGL flights
-`df_comp`  | SGL calibration flight lines
+`df_cal`   | SGL calibration flight lines
 `df_flight`| SGL flight files
 `df_all`   | all flight lines
 `df_nav`   | all *navigation-capable* flight lines

examples/pluto_linear.jl

@@ -44,7 +44,7 @@ The DataFrames listed below provide useful information about the flight data (co
 Dataframe  | Description
 :--------- | :----------
 `df_map`   | map files relevant for SGL flights
-`df_comp`  | SGL calibration flight lines
+`df_cal`   | SGL calibration flight lines
 `df_flight`| SGL flight files
 `df_all`   | all flight lines
 `df_nav`   | all *navigation-capable* flight lines
@@ -101,14 +101,14 @@ md"Perform PLSR-based compensation on testing data. The full list of navigation-
 comp_test(comp_params_1,lines_test,df_nav,df_flight,df_map);
 
 # ╔═╡ 800fbc33-2c64-496a-8a6e-f2c9bf463692
-md"Setup data for Tolles-Lawson. `TL_ind` holds the Boolean indices (mask) just for the selected flight data. The full list of calibration flight line options is in `df_comp`.
+md"Setup data for Tolles-Lawson. `TL_ind` holds the Boolean indices (mask) just for the selected flight data. The full list of calibration flight line options is in `df_cal`.
 "
 
 # ╔═╡ 8eebdfaa-22d2-4466-8092-d62a6392c49b
 begin
 	TL_i   = 6 # select first calibration box of 1006.04
-	TL_xyz = get_XYZ(df_comp.flight[TL_i],df_flight;silent=true) # load flight data
-	TL_ind = get_ind(TL_xyz;tt_lim=[df_comp.t_start[TL_i],df_comp.t_end[TL_i]])
+	TL_xyz = get_XYZ(df_cal.flight[TL_i],df_flight;silent=true) # load flight data
+	TL_ind = get_ind(TL_xyz;tt_lim=[df_cal.t_start[TL_i],df_cal.t_end[TL_i]])
 end;
 
 # ╔═╡ d6efdf23-1a67-4e3e-b8d8-162b706032f6

examples/pluto_maps.jl

@@ -37,7 +37,7 @@ The DataFrames listed below provide useful information about the flight data (co
 Dataframe  | Description
 :--------- | :----------
 `df_map`   | map files relevant for SGL flights
-`df_comp`  | SGL calibration flight lines
+`df_cal`   | SGL calibration flight lines
 `df_flight`| SGL flight files
 `df_all`   | all flight lines
 `df_nav`   | all *navigation-capable* flight lines
@@ -170,7 +170,7 @@ begin
 	dy        = MagNav.get_step(e_mapS_395.yy)
 	xx        = [(e_mapS_395.xx[1]-dx*px):dx:(e_mapS_395.xx[end]+dx*px_end);]
 	yy        = [(e_mapS_395.yy[1]-dy*py):dy:(e_mapS_395.yy[end]+dy*py_end);]
-	(lon,lat) = map_border(e_mapS_plot;sort_border=true) # get map border
+	(lat,lon) = map_border(e_mapS_plot;sort_border=true) # get map border
 	p5 = plot_map(map_map,xx,yy;dpi=dpi)
 	plot_path!(p5,lat,lon;path_color=:black)
 end
@@ -213,8 +213,8 @@ md" First the `map_interpolate` function is used to create map interpolation obj
 begin
 	e_itp_mapS = map_interpolate(e_mapS_plot)
 	n_itp_mapS = map_interpolate(n_mapS_395)
-	e_mapS_val = e_itp_mapS.(lon_trim,lat_trim)
-	n_mapS_val = n_itp_mapS.(lon_trim,lat_trim)
+	e_mapS_val = e_itp_mapS.(lat_trim,lon_trim)
+	n_mapS_val = n_itp_mapS.(lat_trim,lon_trim)
 	mapS_err   = round(Int,std(n_mapS_val - e_mapS_val)) # error
 end
 

examples/pluto_model3.jl

@@ -37,7 +37,7 @@ The DataFrames listed below provide useful information about the flight data (co
 Dataframe  | Description
 :--------- | :----------
 `df_map`   | map files relevant for SGL flights
-`df_comp`  | SGL calibration flight lines
+`df_cal`   | SGL calibration flight lines
 `df_flight`| SGL flight files
 `df_all`   | all flight lines
 `df_nav`   | all *navigation-capable* flight lines
@@ -47,7 +47,7 @@ Dataframe  | Description
 # ╔═╡ 3a55962c-bd1b-410c-b98a-3130fc11ee11
 md"## Train a (linear) Tolles-Lawson model
 
-Select Flight 1006 (see [readme](https://github.com/MIT-AI-Accelerator/MagNav.jl/blob/master/readmes/Flt1006_readme.txt)), load the flight data, & get the Boolean indices for a specific calibration flight line that will be used to fit the Tolles-Lawson coefficients. The full list of SGL flights is in `df_flight`, & the full list of calibration flight line options is in `df_comp`.
+Select Flight 1006 (see [readme](https://github.com/MIT-AI-Accelerator/MagNav.jl/blob/master/readmes/Flt1006_readme.txt)), load the flight data, & get the Boolean indices for a specific calibration flight line that will be used to fit the Tolles-Lawson coefficients. The full list of SGL flights is in `df_flight`, & the full list of calibration flight line options is in `df_cal`.
 "
 
 # ╔═╡ bf9f72f0-c351-48d3-a811-418ee965073c
@@ -62,9 +62,9 @@ xyz_train = get_XYZ(flight_train, df_flight;
 
 # ╔═╡ 99405ae1-580a-4fd3-a860-13cc5b22b045
 begin # select a calibration flight line to train Tolles-Lawson on
-	println(df_comp[df_comp.flight .== flight_train, :])
+	println(df_cal[df_cal.flight .== flight_train, :])
 	TL_i   = 6 # select first calibration box of 1006.04
-	TL_ind = get_ind(xyz_train;tt_lim=[df_comp.t_start[TL_i],df_comp.t_end[TL_i]])
+	TL_ind = get_ind(xyz_train;tt_lim=[df_cal.t_start[TL_i],df_cal.t_end[TL_i]])
 end;
 
 # ╔═╡ 3d438be1-537b-4c13-a422-7f5f48479e62

examples/pluto_sgl.jl

@@ -37,7 +37,7 @@ The DataFrames listed below provide useful information about the flight data (co
 Dataframe  | Description
 :--------- | :----------
 `df_map`   | map files relevant for SGL flights
-`df_comp`  | SGL calibration flight lines
+`df_cal`   | SGL calibration flight lines
 `df_flight`| SGL flight files
 `df_all`   | all flight lines
 `df_nav`   | all *navigation-capable* flight lines
@@ -92,13 +92,13 @@ begin
 end;
 
 # ╔═╡ f665ea95-dac3-4823-94af-c7ba58cd4401
-md"Select a flight line (row of `df_comp`) & get the flight data Boolean indices (mask) for Tolles-Lawson calibration. The full list of calibration flight line options is in `df_comp`.
+md"Select a flight line (row of `df_cal`) & get the flight data Boolean indices (mask) for Tolles-Lawson calibration. The full list of calibration flight line options is in `df_cal`.
 "
 
 # ╔═╡ 5346a989-cf32-436c-9181-d7aff6dd44a1
 begin
 	TL_i   = 6 # select first calibration box of 1006.04
-	TL_ind = get_ind(xyz;tt_lim=[df_comp.t_start[TL_i],df_comp.t_end[TL_i]])
+	TL_ind = get_ind(xyz;tt_lim=[df_cal.t_start[TL_i],df_cal.t_end[TL_i]])
 end;
 
 # ╔═╡ db7dc866-b889-4fb7-81d4-97cd8435636e

examples/pluto_sim.jl

@@ -37,7 +37,7 @@ The DataFrames listed below provide useful information about the flight data (co
 Dataframe  | Description
 :--------- | :----------
 `df_map`   | map files relevant for SGL flights
-`df_comp`  | SGL calibration flight lines
+`df_cal`   | SGL calibration flight lines
 `df_flight`| SGL flight files
 `df_all`   | all flight lines
 `df_nav`   | all *navigation-capable* flight lines