Merge pull request #71 from henryiii/henryiii/fix/modern

fix: modernize and correct some parts

_episodes/02-uproot.md

@@ -75,12 +75,12 @@ Uproot doesn't do any plotting or histogram manipulation, so the most useful met
 h.to_hist().plot()
 ```
 
-Uproot histograms also satisfy the [UHI plotting protocol](https://uhi.readthedocs.io/en/latest/plotting.html), so they have methods like `values` (bin contents), `variances` (errors squared), and `edges`.
+Uproot histograms also satisfy the [UHI plotting protocol](https://uhi.readthedocs.io/en/latest/plotting.html), so they have methods like `values` (bin contents), `variances` (errors squared), and `axes`.
 
 ```python
 h.values()
 h.variances()
-h.axis("x").edges()  # "x", "y", "z" or 0, 1, 2
+list(h.axes[0])  # "x", "y", "z" or 0, 1, 2
 ```
 
 ## Reading a TTree

_episodes/03-ttree-details.md

@@ -72,7 +72,7 @@ However, it takes a long time because a lot of data have to be sent over the net
 To limit the amount of data read, set `entry_start` and `entry_stop` to the range you want. The `entry_start` is inclusive, `entry_stop` exclusive, and the first entry would be indexed by `0`, just like slices in an array interface (first lesson). Uproot only reads as many TBaskets as are needed to provide these entries.
 
 ```python
-tree["nMuon"].array(entry_start=1000, entry_stop=2000)
+tree["nMuon"].array(entry_start=1_000, entry_stop=2_000)
 ```
 
 These are the building blocks of a parallel data reader: each is responsible for a different slice. (See also [uproot.TTree.num_entries_for](https://uproot.readthedocs.io/en/latest/uproot.behaviors.TTree.TTree.html#num-entries-for) and [uproot.TTree.common_entry_offsets](https://uproot.readthedocs.io/en/latest/uproot.behaviors.TTree.TTree.html#common-entry-offsets), which can be used to pick `entry_start`/`entry_stop` in optimal ways.)
@@ -83,7 +83,7 @@ Suppose you know that you will need all of the muon TBranches. Asking for them i
 
 ```python
 muons = tree.arrays(
-    ["Muon_pt", "Muon_eta", "Muon_phi", "Muon_mass", "Muon_charge"], entry_stop=1000
+    ["Muon_pt", "Muon_eta", "Muon_phi", "Muon_mass", "Muon_charge"], entry_stop=1_000
 )
 muons
 ```
@@ -114,7 +114,7 @@ Suppose you have many muon TBranches and you don't want to list them all. The [u
 ```python
 tree.keys(filter_name="Muon_*")
 
-tree.arrays(filter_name="Muon_*", entry_stop=1000)
+tree.arrays(filter_name="Muon_*", entry_stop=1_000)
 ```
 
 (There are also `filter_typename` and `filter_branch` for more options.)
@@ -124,7 +124,7 @@ tree.arrays(filter_name="Muon_*", entry_stop=1000)
 The best way to figure out what you're doing is to tinker with small datasets, and then scale them up. Here, we take 1000 events and compute dimuon masses.
 
 ```python
-muons = tree.arrays(entry_stop=1000)
+muons = tree.arrays(entry_stop=1_000)
 cut = muons["nMuon"] == 2
 
 pt0 = muons["Muon_pt", cut, 0]
@@ -178,11 +178,11 @@ In all of the above examples, the `array`, `arrays`, and `iterate` methods retur
 Use `library="np"` or `library="pd"` to get NumPy or Pandas, respectively.
 
 ```python
-tree["nMuon"].array(library="np", entry_stop=10000)
+tree["nMuon"].array(library="np", entry_stop=10_000)
 
-tree.arrays(library="np", entry_stop=10000)
+tree.arrays(library="np", entry_stop=10_000)
 
-tree.arrays(library="pd", entry_stop=10000)
+tree.arrays(library="pd", entry_stop=10_000)
 ```
 
 NumPy is great for non-jagged data like the `"nMuon"` branch, but it has to represent an unknown number of muons per event as an array of NumPy arrays (i.e. Python objects).

_episodes/05-histograms.md

@@ -82,19 +82,31 @@ h[10:110].plot()
 but often you want to select bins by coordinate value
 
 ```python
+# Explicit version
 h[hist.loc(90) :].plot()
+
+# Short version
+h[90j:].plot()
 ```
 
 or rebin by a factor,
 
 ```python
+# Explicit version
 h[:: hist.rebin(2)].plot()
+
+# Short version
+h[::2j].plot()
 ```
 
 or sum over a range.
 
 ```python
+# Explicit version
 h[hist.loc(80) : hist.loc(100) : sum]
+
+# Short version
+h[90j:100j:sum]
 ```
 
 Things get more interesting when a histogram has multiple dimensions.
@@ -123,13 +135,9 @@ vertexhist.fill(
 )
 
 vertexhist[:, :, sum].plot2d_full()
-vertexhist[
-    hist.loc(-0.25) : hist.loc(0.25), hist.loc(-0.25) : hist.loc(0.25), sum
-].plot2d_full()
+vertexhist[-0.25j:0.25j, -0.25j:0.25j, sum].plot2d_full()
 vertexhist[sum, sum, :].plot()
-vertexhist[
-    hist.loc(-0.25) : hist.loc(0.25) : sum, hist.loc(-0.25) : hist.loc(0.25) : sum, :
-].plot()
+vertexhist[-0.25j:0.25j:sum, -0.25j:0.25j:sum, :].plot()
 ```
 
 A histogram object can have more dimensions than you can reasonably visualize—you can slice, rebin, and project it into something visual later.

_episodes/06-lorentz-vectors.md

@@ -92,7 +92,7 @@ from hepunits import GeV
 
 particle.Particle.findall("pi")
 
-z_boson = particle.Particle.from_name("Z0")
+z_boson = particle.Particle.from_string("Z0")
 z_boson.mass / GeV, z_boson.width / GeV
 
 print(z_boson.describe())

_episodes/07-scaling-up.md

@@ -46,6 +46,6 @@ However, the [Coffea project](https://github.com/CoffeaTeam) ([documentation](ht
 
 and finally
 
-- cookie [GitHub](https://github.com/scikit-hep/cookie), [documentation](https://scikit-hep.org/developer), a template for making your own...
+- cookie [GitHub](https://github.com/scientific-python/cookie), [documentation](https://learn.scientific-python.org/development), a template for making your own...
 
 {% include links.md %}