Fix git weirdness

Examples/Inputs/2016gkg/2016gkg.toml

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+[ global ]
+base_path = "../"
+output_path = "../Outputs/2016gkg" 
+logging = true
+
+[ data ]
+data_path = "2016gkg_data.toml" # Can be absolute, or relative to base_path
+max_time = 3 # Can specify either a value, min, or max. If min or max are specified, you canalso give a range of max_times, and it will find where the min / max flux within that range is. Can do the same with min_time
+
+[[ model ]]
+name = "P15"
+constraints.R.unit = "Rsun"
+constraints.R.prior = "Normal"
+constraints.R.values = [25.0, 15.0]
+constraints.R.min = 0 # Truncates distribution. Defaults to -Inf.
+constraints.R.max = 500
+
+constraints.M.unit = "Msun"
+constraints.M.prior = "Normal"
+constraints.M.values = [1.0, 0.1]
+constraints.M.min = 0
+constraints.M.max = 10
+
+constraints.v.unit = "km / s"
+constraints.v.prior = "Normal"
+constraints.v.values = [3e4, 1e4]
+constraints.v.min = 1e4
+constraints.v.max = 5e4
+
+constraints.t.unit = "d"
+constraints.t.prior = "Normal"
+constraints.t.values = [-23, 3]
+constraints.t.min = -30
+constraints.t.max = -20
+
+[ fitting ]
+numwalkers = 100
+thinning = 1
+burnin = 100
+numsamples_perwalker = 200
+bestfit = "median" # How to choose the best fitting value
+
+[ plot.prior ] # show the distribution of prior values
+
+[ plot.burnin ] # show how the walkers moved over the burn in phase
+
+[ plot.comparison ] # compare the best fit to the data
+
+[ plot.contour ] # show a contour plot of the posterior

Examples/Inputs/2016gkg/2016gkg_data.toml

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+[ global ]
+base_path = "../" # Optional, defaults to the directory containing this input file. Can be relative (to this input file's directory) or absolute. All relative paths will be assumed to be relative to base_path
+filter_path = "Filters" # Defaults to base_path / Filters
+output_path = "../Output" # Defaults to base_path / Output
+
+# Data
+[ data ]
+# First include information about the supernova
+name = "2016gkg" # Required
+peak_time = true # Default false. Can either be true, in which case all times will become relative to the peak data point. Alternatively, give a value, and all times will be relative to that value
+zeropoint = 23.00
+zeropoint_unit = "AB_mag" # Optional, default to AB_mag
+redshift = 0.0049
+
+[[ data.observations ]] # Now load in different observations of the supernova. This can either be one file with all observations, or you can load in multiple files
+name = "2016gkg"
+path = "2016gkg/2016gkg.csv"
+delimiter = ","
+header.upperlimit.col = "upperlimit"
+header.time.col = "time"
+header.time.unit = "d"
+
+header.magnitude.col = "magnitude"
+header.magnitude.unit = "AB_mag"
+
+header.magnitude_err.col = "e_magnitude"
+header.magnitude_err.unit = "AB_mag"
+
+header.filter.col = "band"
+header.instrument.col = "instrument"
+header.facility.col = "telescope"

Examples/Inputs/2017jgh/2017jgh.toml

@@ -0,0 +1,45 @@
+[ global ]
+output_path = "../../Outputs/2017jgh"
+data_path = "../Data"
+logging = true
+
+<include 2017jgh_data.toml>
+
+[ data.modifications ]
+max_time = 58117.5 # Can specify either a value, min, or max. If min or max are specified, you canalso give a range of max_times, and it will find where the min / max flux within that range is. Can do the same with min_time
+max_time_unit = "d" # Defaults to d
+
+[[ model ]]
+name = "P15"
+constraints.R.unit = "Rsun"
+constraints.R.prior = "Normal"
+constraints.R.values = [25.0, 15.0]
+constraints.R.min = 0 # Truncates distribution. Defaults to -Inf.
+constraints.R.max = 500
+
+constraints.M.unit = "Msun"
+constraints.M.prior = "Normal"
+constraints.M.values = [1.0, 0.1]
+constraints.M.min = 0
+constraints.M.max = 10
+
+constraints.v.unit = "km / s"
+constraints.v.prior = "Normal"
+constraints.v.values = [3e4, 1e4]
+constraints.v.min = 1e4
+constraints.v.max = 5e4
+
+constraints.t.unit = "d"
+constraints.t.prior = "Normal"
+constraints.t.values = [-23, 3]
+constraints.t.min = -30
+constraints.t.max = -20
+
+[ fitting ]
+numwalkers = 1e1
+burnin = 1e2
+iterations = 1e3
+
+[ plot.comparison ] # compare the best fit to the data
+
+[ plot.contour ] # show a contour plot of the posterior

Examples/Inputs/2017jgh/2017jgh_data.toml

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+# Data
+[ data ]
+# First include information about the supernova
+name = "2017jgh" # Required
+zeropoint = 25.3 
+redshift = 0.079
+#max_flux_err = 2.5e2 # Optional, set's the maximum allowed value for the uncertainty in the flux
+#max_flux_err_unit = "µJy" # Optional, default to µJy
+peak_time = 58127.0 # Default false. Can either be true, in which case all times will become relative to the peak data point. Alternatively, give a value, and all times will be relative to that value
+peak_time_unit = "d"
+
+[[ data.observations ]] # Now load in different observations of the supernova. This can either be one file with all observations, or you can load in multiple files
+name = "kepler" # Required, Human readable name to distinguish observations
+path = "2017jgh/2017jghCleaned2.csv" # Required, Accepts either relative (to Supernova) or absolute path
+delimiter = "," # Optional, defaults to comma
+header.time.col = "time"
+header.time.unit = "d"
+
+header.flux.col = "flux"
+header.flux.unit = "erg / s / cm^2 / Hz"
+
+header.flux_err.col = "e_flux"
+header.flux_err.unit = "erg / s / cm^2 / Hz"
+
+header.upperlimit.col = "upperlimit"
+upperlimit_false = ["False"]
+header.facility.col = "facility"
+header.instrument.col = "instrument"
+header.passband.col = "band"
+
+[[ plot.lightcurve ]]
+legend = true