Fix typos and minor corrections in dataset PIG
@@ -13,11 +13,11 @@ PIG 8 - Datasets
* Discussion:
-Introduction
-============
+Abstract
+========
An essential feature of Gammapy for modeling gamma-ray data will be the possibility
of joint-likelihood analyses. This includes the joint-likelhood fitting of a model
-across multiple observations, joint-likelihood fitting of IACT and Fermi-LAT
+across multiple IACT observations, joint-likelihood fitting of IACT and Fermi-LAT
data, combined analysis of gamma-ray data with flux points or a combined spectral
and cube analysis. Joint-likelihood also allows for combining different event types
in a single analysis and timing analyses. For this reason we propose to introduce
@@ -158,14 +158,13 @@ For spectral analysis we propose to introduce a `SpectrumDataset`:
dataset.likelihood(mask)
The `SpectrumDatasets` with a parametric background model will be introduced first.
-The existing `SpectrumObservation` can be refactored later in a `SpectrumOnOffDataset`
-or `PHASpectrumDatasets`.
+The existing `SpectrumObservation` can be refactored later in a `SpectrumDatasetsOnOff`.
-`FluxPointDataset`
-------------------
+`FluxPointsDataset`
+-------------------
-For modeling of flux points we propose to introduce a `FluxPointDataset`:
+For modeling of flux points we propose to introduce a `FluxPointsDataset`:
.. code::
@@ -182,11 +181,11 @@ For modeling of flux points we propose to introduce a `FluxPointDataset`:
fit = Fit(dataset)
fit.optimize()
-The `FluxPointDataset` should define at least the following methods:
+The `FluxPointsDataset` should define at least the following methods:
.. code::
- dataset = FluxPointDataset()
+ dataset = FluxPointsDataset()
dataset.likelihood_per_bin()
dataset.likelihood(mask)
@@ -195,10 +194,10 @@ The `FluxPointDataset` should define at least the following methods:
dataset.flux_pred()
The `FluxPoint` class also supports the `likelihood` format, which has to be implemented
-in a special way in the `FluxPointDataset`. The `likelihood` format stores the
+in a special way in the `FluxPointsDataset`. The `likelihood` format stores the
likelihood of the flux point, depending on energy and amplitude. Given a predicted
flux by the spectral model the likelihood can be directly interpolated. This could
-be supported by a specific option `FluxPointDataset(likelihood="template")` or similar.
+be supported by a specific option `FluxPointsDataset(likelihood="template")` or similar.
`Datasets`
@@ -269,7 +268,7 @@ Combined spectral / flux points analysis:
spectrum_dataset = SpectrumDataset(model=model)
flux_points = FluxPoints.read()
- flux_point_dataset = FluxPointDataset(flux_points=flux_points, model=model, likelihood="chi2")
+ flux_point_dataset = FluxPointsDataset(flux_points=flux_points, model=model, likelihood="chi2")
datasets = Datasets([flux_point_dataset, spectrum_dataset])
@@ -355,9 +354,8 @@ Lazy loading of Datasets
For the use-case of inspecting individual datasets (e.g. MapDataset per observation)
it could be advantegeous to implement a lazy-loading or generator interface for
`Datasets.read()`. Such that the individual datasets are only read from disk on
-requests and are not loaded in memory when calling `.read()`. We propose to handle
-this with a generic lazy-loading mechanism for `Map` objects, where the data is loaded
-on access of the `.data` attribute.
+requests and are not loaded in memory when calling `.read()`. We leave this question
+un-addressed in this PR but mention it for completeness.
Simulation of `MapDataset`
@@ -378,7 +376,7 @@ as following:
dataset.counts = dataset.simulate_counts(random_seed=0)
fit = Fit(dataset)
- fit.optmimize()
+ fit.optimize()
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