Updated: download link to reduce bounce rate AND pulled from master

assets/js/alps-download.js

@@ -0,0 +1,44 @@
+// Download tutorial scripts without navigating away from the page.
+//
+// The scripts live in ALPSim/ALPS, so the links are cross-origin and the HTML
+// `download` attribute is ignored. raw.githubusercontent.com sends
+// `access-control-allow-origin: *`, so we can fetch the file and hand the
+// browser a same-origin blob to save instead.
+//
+// If the fetch fails (offline, rate limited, file moved) we fall back to
+// opening the raw URL in a new tab, so the link is never a dead end.
+(function () {
+  "use strict";
+
+  document.addEventListener("click", function (event) {
+    if (event.defaultPrevented || event.button !== 0) return;
+    if (event.metaKey || event.ctrlKey || event.shiftKey || event.altKey) return;
+
+    var link = event.target.closest("a.alps-download");
+    if (!link) return;
+
+    event.preventDefault();
+
+    var url = link.href;
+    var filename = link.dataset.filename || url.split("/").pop();
+
+    fetch(url)
+      .then(function (response) {
+        if (!response.ok) throw new Error("HTTP " + response.status);
+        return response.blob();
+      })
+      .then(function (blob) {
+        var objectURL = URL.createObjectURL(blob);
+        var temp = document.createElement("a");
+        temp.href = objectURL;
+        temp.download = filename;
+        document.body.appendChild(temp);
+        temp.click();
+        temp.remove();
+        URL.revokeObjectURL(objectURL);
+      })
+      .catch(function () {
+        window.open(url, "_blank", "noopener");
+      });
+  });
+})();

content/en/tutorials/dmft/dmft02.md

@@ -13,7 +13,7 @@ We start by running a continuous-time quantum Monte Carlo code: the hybridizatio
 
 The CT-HYB simulation will run for roughly 1 hour in total if you want to reproduce all 6 curves in Fig. 11 mentioned above. The files for this tutorial can be found in the directory `tutorials/dmft-02-hybridization`.
 
-All DMFT tutorials can be started using a python script. The python script generates parameter files, runs them, and plots the results. You can run the short script [`tutorial2.py`](https://github.com/ALPSim/ALPS/blob/daa73925b95389c0ec5e0d76ce592b56f3cd6738/tutorials/dmft-02-hybridization/tutorial2.py), reproducing only 2 out of the 6 curves (runtime: roughly 20 minutes), or the long version [`tutorial2_long.py`](https://github.com/ALPSim/ALPS/blob/daa73925b95389c0ec5e0d76ce592b56f3cd6738/tutorials/dmft-02-hybridization/tutorial2_long.py), reproducing all 6 curves in the figure (runtime: roughly 1 hour).
+All DMFT tutorials can be started using a python script. The python script generates parameter files, runs them, and plots the results. You can run the short script <a class="alps-download" href="https://raw.githubusercontent.com/ALPSim/ALPS/master/tutorials/dmft-02-hybridization/tutorial2.py" data-filename="tutorial2.py" target="_blank" rel="noopener">`tutorial2.py`</a>, reproducing only 2 out of the 6 curves (runtime: roughly 20 minutes), or the long version <a class="alps-download" href="https://raw.githubusercontent.com/ALPSim/ALPS/master/tutorials/dmft-02-hybridization/tutorial2_long.py" data-filename="tutorial2_long.py" target="_blank" rel="noopener">`tutorial2_long.py`</a>, reproducing all 6 curves in the figure (runtime: roughly 1 hour).
 
 The python script `tutorial2.py` automatically prepares the input files for the 2 simulations, `parm_beta_6.0` and `parm_beta_12.0`, and runs them (`/path-to-alps-installation/bin/dmft parm_beta_x`).
 
@@ -127,7 +127,7 @@ You will notice that the results are relatively noisy. This is because the expan
 
 ### Checking convergence
 
-If you want to check the convergence of your DMFT self-consistency, you can plot the Green's functions of different iterations using [`tutorial2eval.py`](https://github.com/ALPSim/ALPS/blob/daa73925b95389c0ec5e0d76ce592b56f3cd6738/tutorials/dmft-02-hybridization/tutorial2eval.py), whose code is shown here:
+If you want to check the convergence of your DMFT self-consistency, you can plot the Green's functions of different iterations using <a class="alps-download" href="https://raw.githubusercontent.com/ALPSim/ALPS/master/tutorials/dmft-02-hybridization/tutorial2eval.py" data-filename="tutorial2eval.py" target="_blank" rel="noopener">`tutorial2eval.py`</a>, whose code is shown here:
 
 ```
 listobs=['0']   # we look at a single flavor (=0) 

content/en/tutorials/dmft/dmft03.md

@@ -43,7 +43,7 @@ with nearest-neighbor hopping $t$, on-site interaction $U$, and chemical potenti
 
 ### Running the simulation
 
-The files for this tutorial can be found in the directory `tutorials/dmft-03-interaction`. As in Tutorial 02, you can run the short script [`tutorial3.py`](https://github.com/ALPSim/ALPS/blob/daa73925b95389c0ec5e0d76ce592b56f3cd6738/tutorials/dmft-03-interaction/tutorial3.py), reproducing 2 of the 6 curves (runtime: roughly 10 minutes), or the long version [`tutorial3_long.py`](https://github.com/ALPSim/ALPS/blob/daa73925b95389c0ec5e0d76ce592b56f3cd6738/tutorials/dmft-03-interaction/tutorial3_long.py), reproducing all 6 curves (runtime: roughly 30 minutes). CT-INT reaches a given statistical accuracy faster than CT-HYB in this weak-coupling regime, which is why `MAX_TIME` is set much lower here (10 seconds per iteration) than in Tutorial 02 (300 seconds).
+The files for this tutorial can be found in the directory `tutorials/dmft-03-interaction`. As in Tutorial 02, you can run the short script <a class="alps-download" href="https://raw.githubusercontent.com/ALPSim/ALPS/master/tutorials/dmft-03-interaction/tutorial3.py" data-filename="tutorial3.py" target="_blank" rel="noopener">`tutorial3.py`</a>, reproducing 2 of the 6 curves (runtime: roughly 10 minutes), or the long version <a class="alps-download" href="https://raw.githubusercontent.com/ALPSim/ALPS/master/tutorials/dmft-03-interaction/tutorial3_long.py" data-filename="tutorial3_long.py" target="_blank" rel="noopener">`tutorial3_long.py`</a>, reproducing all 6 curves (runtime: roughly 30 minutes). CT-INT reaches a given statistical accuracy faster than CT-HYB in this weak-coupling regime, which is why `MAX_TIME` is set much lower here (10 seconds per iteration) than in Tutorial 02 (300 seconds).
 
 ```
 import pyalps
@@ -151,7 +151,7 @@ CT-INT and CT-HYB solve the same impurity problem with different diagrammatic ex
 
 ### Output data and plots
 
-Evaluation proceeds exactly as in [DMFT-02 Hybridization](../dmft02), using [`tutorial3eval.py`](https://github.com/ALPSim/ALPS/blob/daa73925b95389c0ec5e0d76ce592b56f3cd6738/tutorials/dmft-03-interaction/tutorial3eval.py) (identical in structure to `tutorial2eval.py`). First, the imaginary-time Green's function for both flavors, directly appended to `tutorial3.py`:
+Evaluation proceeds exactly as in [DMFT-02 Hybridization](../dmft02), using <a class="alps-download" href="https://raw.githubusercontent.com/ALPSim/ALPS/master/tutorials/dmft-03-interaction/tutorial3eval.py" data-filename="tutorial3eval.py" target="_blank" rel="noopener">`tutorial3eval.py`</a> (identical in structure to `tutorial2eval.py`). First, the imaginary-time Green's function for both flavors, directly appended to `tutorial3.py`:
 
 ```
 listobs=['0', '1']   # we will plot both flavors 0 and 1

content/en/tutorials/dmft/dmft04.md

@@ -45,7 +45,7 @@ on the Bethe lattice, at half filling ($\mu=0$). Here the interaction $U$ is swe
 
 ### Running the simulation
 
-In order to run the simulations in python use [`tutorial4a.py`](https://github.com/ALPSim/ALPS/blob/daa73925b95389c0ec5e0d76ce592b56f3cd6738/tutorials/dmft-04-mott/tutorial4a.py):
+In order to run the simulations in python use <a class="alps-download" href="https://raw.githubusercontent.com/ALPSim/ALPS/master/tutorials/dmft-04-mott/tutorial4a.py" data-filename="tutorial4a.py" target="_blank" rel="noopener">`tutorial4a.py`</a>:
 
 ```    
 import pyalps
@@ -184,7 +184,7 @@ You should observe that at small $U$ you find a metallic solution, and an insula
 
 ### Checking convergence
 
-The convergence may be checked by [`tutorial4b.py`](https://github.com/ALPSim/ALPS/blob/daa73925b95389c0ec5e0d76ce592b56f3cd6738/tutorials/dmft-04-mott/tutorial4b.py):
+The convergence may be checked by <a class="alps-download" href="https://raw.githubusercontent.com/ALPSim/ALPS/master/tutorials/dmft-04-mott/tutorial4b.py" data-filename="tutorial4b.py" target="_blank" rel="noopener">`tutorial4b.py`</a>:
 
 ```
 import pyalps

content/en/tutorials/dmft/dmft05.md

@@ -50,7 +50,7 @@ with orbital index $m=0,1$, intra-orbital hopping $t_m$, intra-orbital (Hubbard)
 
 We choose here a case with two bandwidths, $t_0=0.5$ and $t_1=1$, and density-density-like interactions of $U'=U/2$, $J=U/4$, with $U$ between $1.8$ and $2.8$: $U=1.8$ shows a Fermi-liquid-like behavior in both orbitals, $U=2.2$ is orbitally selective, and $U=2.8$ is insulating in both orbitals.
 
-The python command lines for running the simulations are found in [`tutorial5a.py`](https://github.com/ALPSim/ALPS/blob/daa73925b95389c0ec5e0d76ce592b56f3cd6738/tutorials/dmft-05-osmt/tutorial5a.py):
+The python command lines for running the simulations are found in <a class="alps-download" href="https://raw.githubusercontent.com/ALPSim/ALPS/master/tutorials/dmft-05-osmt/tutorial5a.py" data-filename="tutorial5a.py" target="_blank" rel="noopener">`tutorial5a.py`</a>:
 
 ```
 import pyalps
@@ -190,7 +190,7 @@ Because these are stochastic Monte Carlo results, the precise numbers depend on
 
 ### Checking convergence
 
-Convergence may be checked with [`tutorial5b.py`](https://github.com/ALPSim/ALPS/blob/daa73925b95389c0ec5e0d76ce592b56f3cd6738/tutorials/dmft-05-osmt/tutorial5b.py), which plots all iterations of $G_f^{it}(\tau)$ on a logarithmic scale, for both flavor 0 and flavor 2:
+Convergence may be checked with <a class="alps-download" href="https://raw.githubusercontent.com/ALPSim/ALPS/master/tutorials/dmft-05-osmt/tutorial5b.py" data-filename="tutorial5b.py" target="_blank" rel="noopener">`tutorial5b.py`</a>, which plots all iterations of $G_f^{it}(\tau)$ on a logarithmic scale, for both flavor 0 and flavor 2:
 
 ```
 import pyalps

content/en/tutorials/dmft/dmft06.md

@@ -69,7 +69,7 @@ and (for the interaction expansion version)
 python tutorial6b.py
 ```
 
-[`tutorial6a.py`](https://github.com/ALPSim/ALPS/blob/daa73925b95389c0ec5e0d76ce592b56f3cd6738/tutorials/dmft-06-paramagnet/hyb/tutorial6a.py) (CT-HYB):
+<a class="alps-download" href="https://raw.githubusercontent.com/ALPSim/ALPS/master/tutorials/dmft-06-paramagnet/hyb/tutorial6a.py" data-filename="tutorial6a.py" target="_blank" rel="noopener">`tutorial6a.py`</a> (CT-HYB):
 
 ```
 import pyalps
@@ -113,7 +113,7 @@ input_file = pyalps.writeParameterFile('parm_hyb',parms[0])
 res = pyalps.runDMFT(input_file)
 ```
 
-[`tutorial6b.py`](https://github.com/ALPSim/ALPS/blob/daa73925b95389c0ec5e0d76ce592b56f3cd6738/tutorials/dmft-06-paramagnet/int/tutorial6b.py) (CT-INT):
+<a class="alps-download" href="https://raw.githubusercontent.com/ALPSim/ALPS/master/tutorials/dmft-06-paramagnet/int/tutorial6b.py" data-filename="tutorial6b.py" target="_blank" rel="noopener">`tutorial6b.py`</a> (CT-INT):
 
 ```
 import pyalps

content/en/tutorials/dmft/dmft07.md

@@ -43,7 +43,7 @@ on the Bethe lattice at half filling ($\mu=0$), with $t=0.707106781186547=1/\sqr
 
 ### Running the simulation
 
-The Hirsch-Fye simulation will run for about 20 seconds per iteration. The files for this tutorial can be found in the directory `tutorials/dmft-07-hirschfye`. As in Tutorials 02 and 03, you can run the short script [`tutorial7.py`](https://github.com/ALPSim/ALPS/blob/daa73925b95389c0ec5e0d76ce592b56f3cd6738/tutorials/dmft-07-hirschfye/tutorial7.py), reproducing 2 of the 6 curves (runtime: roughly 5 minutes), or the long version [`tutorial7_long.py`](https://github.com/ALPSim/ALPS/blob/daa73925b95389c0ec5e0d76ce592b56f3cd6738/tutorials/dmft-07-hirschfye/tutorial7_long.py), reproducing all 6 curves.
+The Hirsch-Fye simulation will run for about 20 seconds per iteration. The files for this tutorial can be found in the directory `tutorials/dmft-07-hirschfye`. As in Tutorials 02 and 03, you can run the short script <a class="alps-download" href="https://raw.githubusercontent.com/ALPSim/ALPS/master/tutorials/dmft-07-hirschfye/tutorial7.py" data-filename="tutorial7.py" target="_blank" rel="noopener">`tutorial7.py`</a>, reproducing 2 of the 6 curves (runtime: roughly 5 minutes), or the long version <a class="alps-download" href="https://raw.githubusercontent.com/ALPSim/ALPS/master/tutorials/dmft-07-hirschfye/tutorial7_long.py" data-filename="tutorial7_long.py" target="_blank" rel="noopener">`tutorial7_long.py`</a>, reproducing all 6 curves.
 
 ```
 import pyalps
@@ -165,7 +165,7 @@ Hirsch-Fye works very differently from CT-HYB and CT-INT: it Trotter-decomposes
 
 ### Output data and plots
 
-For evaluation you may adapt `tutorial2eval.py` as described in [DMFT-02 Hybridization](../dmft02), or use [`tutorial7eval.py`](https://github.com/ALPSim/ALPS/blob/daa73925b95389c0ec5e0d76ce592b56f3cd6738/tutorials/dmft-07-hirschfye/tutorial7eval.py), which is structurally identical to `tutorial2eval.py`: it plots the iteration-resolved $G(\tau)$, the occupation $n_0=-G_0(\tau=\beta^-)$ versus $\beta$, and the Matsubara-frequency Green's function and self-energy (via the Dyson equation), for both flavors.
+For evaluation you may adapt `tutorial2eval.py` as described in [DMFT-02 Hybridization](../dmft02), or use <a class="alps-download" href="https://raw.githubusercontent.com/ALPSim/ALPS/master/tutorials/dmft-07-hirschfye/tutorial7eval.py" data-filename="tutorial7eval.py" target="_blank" rel="noopener">`tutorial7eval.py`</a>, which is structurally identical to `tutorial2eval.py`: it plots the iteration-resolved $G(\tau)$, the occupation $n_0=-G_0(\tau=\beta^-)$ versus $\beta$, and the Matsubara-frequency Green's function and self-energy (via the Dyson equation), for both flavors.
 
 ```
 import pyalps

content/en/tutorials/dmft/dmft08.md

@@ -77,7 +77,7 @@ Two lattice-specific mechanisms are available; both feed a k-integrated density
           o       o
   ```
 
-Each DOS table was produced by a small histogram script — [`DOS_Square.py`](https://github.com/ALPSim/ALPS/blob/daa73925b95389c0ec5e0d76ce592b56f3cd6738/tutorials/dmft-08-lattices/DOS/DOS_Square.py) (`GRID=4000`), [`DOS_Cubic.py`](https://github.com/ALPSim/ALPS/blob/daa73925b95389c0ec5e0d76ce592b56f3cd6738/tutorials/dmft-08-lattices/DOS/DOS_Cubic.py) (`GRID=360`), and [`DOS_Hexagonal.py`](https://github.com/ALPSim/ALPS/blob/daa73925b95389c0ec5e0d76ce592b56f3cd6738/tutorials/dmft-08-lattices/DOS/DOS_Hexagonal.py) (`GRID=4000`) — each integrating the tight-binding dispersion over the Brillouin zone on a `GRID`$\times$`GRID` k-point mesh. You can generate a DOS table for any other lattice the same way, or use the [ALPS lattice library](../../../documentation/intro/latticehowtos) as a reference for lattice geometries and coordination numbers when building your own.
+Each DOS table was produced by a small histogram script — <a class="alps-download" href="https://raw.githubusercontent.com/ALPSim/ALPS/master/tutorials/dmft-08-lattices/DOS/DOS_Square.py" data-filename="DOS_Square.py" target="_blank" rel="noopener">`DOS_Square.py`</a> (`GRID=4000`), <a class="alps-download" href="https://raw.githubusercontent.com/ALPSim/ALPS/master/tutorials/dmft-08-lattices/DOS/DOS_Cubic.py" data-filename="DOS_Cubic.py" target="_blank" rel="noopener">`DOS_Cubic.py`</a> (`GRID=360`), and <a class="alps-download" href="https://raw.githubusercontent.com/ALPSim/ALPS/master/tutorials/dmft-08-lattices/DOS/DOS_Hexagonal.py" data-filename="DOS_Hexagonal.py" target="_blank" rel="noopener">`DOS_Hexagonal.py`</a> (`GRID=4000`) — each integrating the tight-binding dispersion over the Brillouin zone on a `GRID`$\times$`GRID` k-point mesh. You can generate a DOS table for any other lattice the same way, or use the [ALPS lattice library](../../../documentation/intro/latticehowtos) as a reference for lattice geometries and coordination numbers when building your own.
 
 **TWODBS**: for the square and hexagonal lattices specifically, ALPS can instead evaluate the Hilbert transform directly as a live k-space integral at every self-consistency step (discretized on an $L\times L$ k-point mesh), without needing a pre-tabulated DOS file at all.
 
@@ -87,7 +87,7 @@ Each DOS table was produced by a small histogram script — [`DOS_Square.py`](ht
 
 ### Option DOSFILE
 
-For a general lattice, you have to provide the density of states of your lattice. Apart from that, several other changes are necessary in order to run the simulation. A working python script [`tutorial8a.py`](https://github.com/ALPSim/ALPS/blob/daa73925b95389c0ec5e0d76ce592b56f3cd6738/tutorials/dmft-08-lattices/tutorial8a.py) setting an input file, running the simulation, and plotting the result follows:
+For a general lattice, you have to provide the density of states of your lattice. Apart from that, several other changes are necessary in order to run the simulation. A working python script <a class="alps-download" href="https://raw.githubusercontent.com/ALPSim/ALPS/master/tutorials/dmft-08-lattices/tutorial8a.py" data-filename="tutorial8a.py" target="_blank" rel="noopener">`tutorial8a.py`</a> setting an input file, running the simulation, and plotting the result follows:
 
 ```
 import pyalps
@@ -192,7 +192,7 @@ For the case of two-dimensional lattices, there is an implementation of the Hilb
 - square lattice [set TWODBS=square] with nearest-neighbor [corresponding parameter: t] and next-nearest-neighbor hoppings [corresponding parameter: tprime]; the second moment EPSSQ_i is $4(t^2 + tprime^2)$;
 - hexagonal lattice [set TWODBS=hexagonal] with nearest-neighbor hoppings [corresponding parameter: t]; the second moment EPSSQ_i is $3t^2$.
 
-A working python script [`tutorial8b.py`](https://github.com/ALPSim/ALPS/blob/daa73925b95389c0ec5e0d76ce592b56f3cd6738/tutorials/dmft-08-lattices/tutorial8b.py) to produce the input file, run the simulation, and plot the result is shown here:
+A working python script <a class="alps-download" href="https://raw.githubusercontent.com/ALPSim/ALPS/master/tutorials/dmft-08-lattices/tutorial8b.py" data-filename="tutorial8b.py" target="_blank" rel="noopener">`tutorial8b.py`</a> to produce the input file, run the simulation, and plot the result is shown here:
 
 ```
 import pyalps