data and analysis for PRL 130, 207001 (2023) (#5)

* data and analysis for PRL 130, 207001 (2023)

* update noxfile

* Pin matplotlib to prevent bug

Prevents matplotlib/matplotlib#26287

* Pin sklearn

---------

Co-authored-by: Andrey Antipov <[email protected]>
Co-authored-by: Bas Nijholt <[email protected]>

notebooks/PRL130_207001.ipynb

@@ -0,0 +1,113 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc9b387c-fb17-44ef-b8bc-6b8996d2ae93",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tgp\n",
+    "from tgp.plot import paper\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ebf07338-e9cb-4795-9dd1-d9ab4afd85e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_T0 = xr.load_dataset('../data/PRL130_207001.nc')\n",
+    "ds_T0 = tgp.prepare.prepare(ds_T0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d099e77-db27-4812-b880-4194a913b9d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_T0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30c6c010-bd14-4aea-8a07-9beca5aae77b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Perform temperature broadening\n",
+    "ds = tgp.prepare.broaden_with_temperature(ds_T0, T_mK=40)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da44ef29-bc39-4630-bb85-6182159b867a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Inspect data\n",
+    "ds_left = ds.rename({\"bias\": \"left_bias\"})\n",
+    "ds_right = ds.rename({\"bias\": \"right_bias\"})\n",
+    "tgp.plot.two.plot_data_interactive(ds_left, ds_right)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23f4b3bb-e322-4097-9515-6bc35ea3fe01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Identify regions of zero bias peaks and finite gap\n",
+    "ds_left, ds_right = tgp.two.extract_gap(ds_left, ds_right)\n",
+    "zbp_ds = tgp.two.zbp_dataset_derivative(ds_left, ds_right, average_over_cutter=False)\n",
+    "tgp.two.set_gap_threshold(zbp_ds, threshold_high=None)\n",
+    "zbp_ds = tgp.two.cluster_and_score(zbp_ds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab58f1b3-89b3-4b9d-bd7b-ecd58db2bf58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selected_cutter = int(ds.where(ds[\"μ_L\"] == 0.0, drop =True).cutter_pair_index)\n",
+    "fig, axs = tgp.plot.paper.plot_stage2_diagram(zbp_ds, cutter_value=selected_cutter, zbp_cluster_numbers=\"all\")\n",
+    "axs[0].set_ylabel(r'$\\mu$ [meV]')\n",
+    "axs[0].set_xlabel(r'$B$ [meV]')\n",
+    "axs[1].set_xlabel(r'$B$ [meV]')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe262954-88e1-43c1-83c0-aa6018661d63",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

noxfile.py

@@ -10,6 +10,7 @@
     "notebooks/stage-one-analysis.ipynb",
     "notebooks/stage-two-analysis.ipynb",
     "notebooks/fridge-calibration.ipynb",
+    "notebooks/PRL130_207001.ipynb",
 ]
 
 

requirements.txt

@@ -2,13 +2,13 @@ h5netcdf
 ipython
 ipywidgets
 jinja2
-matplotlib
+matplotlib==3.7.1
 numpy
 pandas
 pyyaml
 rich
 ruamel.yaml
-scikit-learn
+scikit-learn==1.2.2
 scipy
 toolz
 tqdm