Merge branch 'release/v0.2.2'

usnistgov · Apr 6, 2023 · 1f28060 · 1f28060
2 parents 3d727db + 65bdcc4
commit 1f28060
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diff --git a/.cruft.json b/.cruft.json
@@ -1,15 +1,16 @@
 {
   "template": "https://github.com/wpk-nist-gov/cookiecutter-pypackage.git",
-  "commit": "54f802e17db2a2f922595fc6a9401288c18ed509",
+  "commit": "c209d4c07a6142de87d6b1600f7319ebc69729cd",
   "checkout": "feature/markdown",
   "skip": [
     "src/thermoextrap/__init__.py",
-      "environment.yaml",
-      "HISTORY.md",
-      "AUTHORS.md",
-      "MANIFEST.in",
-      "docs/spelling_wordlist.txt",
-      "environment/",
+    "environment.yaml",
+    "HISTORY.md",
+    "AUTHORS.md",
+    "MANIFEST.in",
+      "README.md",
+    "docs/spelling_wordlist.txt",
+    "environment/"
   ],
   "context": {
     "cookiecutter": {

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -29,7 +29,7 @@ repos:
   - repo: https://github.com/psf/black
     rev: 23.1.0
     hooks:
-      - id: black
+      - id: black-jupyter
   - repo: https://github.com/adamchainz/blacken-docs
     rev: "1.13.0"  # replace with latest tag on GitHub
     hooks:

diff --git a/Makefile b/Makefile
@@ -155,16 +155,16 @@ environment-files: environment/dev.yaml environment/docs.yaml environment/test.y
 .PHONY: mamba-env mamba-dev mamba-env-update mamba-dev-update
 
 mamba-env: environment.yaml ## create base environment
-	mamba env create -f environment.yaml
+	mamba env create -f $<
 
 mamba-env-update: environment.yaml ## update base environment
-	mamba env update -f environment.yml
+	mamba env update -f $<
 
 mamba-dev: environment/dev.yaml ## create development environment
-	mamba env create -f environment-dev.yaml
+	mamba env create -f $<
 
 mamba-dev-update: environment/dev.yaml ## update development environment
-	mamba env update -f environment-dev.yml
+	mamba env update -f $<
 
 ################################################################################
 # TOX

diff --git a/docs/notebooks/Customized_Derivatives.ipynb b/docs/notebooks/Customized_Derivatives.ipynb
@@ -121,7 +121,6 @@
     "\n",
     "    @staticmethod\n",
     "    def create_deriv_func(order):\n",
-    "\n",
     "        def func(W, xW, dxdq, volume, ndim=1):\n",
     "            \"\"\"\n",
     "            dxdq is <sum_{i=1}^N dy/dx_i x_i>\n",
@@ -137,7 +136,7 @@
     "                deriv_val = (-xW[0] * W[1] + xW[1] + dxdq) / (volume * ndim)\n",
     "            return deriv_val\n",
     "\n",
-    "        return func\n"
+    "        return func"
    ]
   },
   {
@@ -172,19 +171,19 @@
     "# Define our reference size to extrapolated from\n",
     "volume_ref = volumes[9]\n",
     "\n",
-    "npart = 1000 #Number of particles (in single configuration)\n",
-    "nconfig = 100_000 #Number of configurations\n",
+    "npart = 1000  # Number of particles (in single configuration)\n",
+    "nconfig = 100_000  # Number of configurations\n",
     "\n",
     "# And generate new data at this reference size, using a beta of 1.0 for simplicity\n",
     "xdatavol, udatavol = idealgas.generate_data((nconfig, npart), beta=beta, vol=volume_ref)\n",
     "\n",
     "# Wrap with xarray\n",
-    "xdatavol = xr.DataArray(xdatavol, dims=['rec'])\n",
-    "udatavol = xr.DataArray(udatavol, dims=['rec'])\n",
+    "xdatavol = xr.DataArray(xdatavol, dims=[\"rec\"])\n",
+    "udatavol = xr.DataArray(udatavol, dims=[\"rec\"])\n",
     "\n",
-    "#For the IG model, the virial W is the same as the negative of the number of particles multiplied by the average position\n",
-    "#(for a=1)\n",
-    "#And we have to multiply by beta to make the virial dimensionless\n",
+    "# For the IG model, the virial W is the same as the negative of the number of particles multiplied by the average position\n",
+    "# (for a=1)\n",
+    "# And we have to multiply by beta to make the virial dimensionless\n",
     "wdatavol = -1.0 * npart * xdatavol"
    ]
   },
@@ -218,22 +217,18 @@
     "from thermoextrap.data import DataCallbackABC\n",
     "import attrs\n",
     "\n",
+    "\n",
     "@attrs.define\n",
     "class VolumeDataCallback(DataCallbackABC):\n",
     "    \"\"\"\n",
     "    object to handle callbacks of metadata\n",
     "    \"\"\"\n",
-    "    \n",
-    "    volume: float = attrs.field(\n",
-    "        validator=attrs.validators.instance_of(float)\n",
-    "    )\n",
+    "\n",
+    "    volume: float = attrs.field(validator=attrs.validators.instance_of(float))\n",
     "    dxdqv: xr.DataArray = attrs.field(\n",
     "        validator=attrs.validators.instance_of(xr.DataArray)\n",
     "    )\n",
-    "    ndim: int = attrs.field(\n",
-    "        default=3, \n",
-    "        validator=attrs.validators.instance_of(int)\n",
-    "    )\n",
+    "    ndim: int = attrs.field(default=3, validator=attrs.validators.instance_of(int))\n",
     "\n",
     "    def check(self, data):\n",
     "        pass\n",
@@ -254,7 +249,7 @@
     "            self.dxdq(rec_dim=data.rec_dim, skipna=data.skipna),\n",
     "            self.volume,\n",
     "            self.ndim,\n",
-    "        )\n"
+    "        )"
    ]
   },
   {
@@ -296,7 +291,7 @@
     "    # dxdqv = single observation of sum(dx/dq_i q_i) where q_i is the ith coordinate\n",
     "    # for ideal gas, this is just xdata\n",
     "    dxdqv=xdatavol,\n",
-    "    ndim=1\n",
+    "    ndim=1,\n",
     ")"
    ]
   },
@@ -401,19 +396,19 @@
     }
    ],
    "source": [
-    "#Check the computation of derivatives\n",
+    "# Check the computation of derivatives\n",
     "print(\"Model parameters (derivatives):\")\n",
     "print(xemv.derivs(norm=False).values)\n",
-    "print('\\n')\n",
+    "print(\"\\n\")\n",
     "\n",
-    "#Finally, look at predictions\n",
+    "# Finally, look at predictions\n",
     "print(\"Model predictions:\")\n",
     "print(xemv.predict(volumes[:4]).values)\n",
-    "print('\\n')\n",
+    "print(\"\\n\")\n",
     "\n",
-    "#And bootstrapped uncertainties\n",
+    "# And bootstrapped uncertainties\n",
     "print(\"Bootstrapped uncertainties in predictions:\")\n",
-    "print(xemv.resample(nrep=100).predict(volumes[:4]).std('rep').values)"
+    "print(xemv.resample(nrep=100).predict(volumes[:4]).std(\"rep\").values)"
    ]
   },
   {
@@ -455,43 +450,44 @@
    "source": [
     "fig, ax = plt.subplots()\n",
     "\n",
-    "nsampvals = np.array((10.0*np.ones(5))**np.arange(1,6), dtype=int)\n",
-    "nsampcolors = plt.cm.viridis(np.arange(0.0, 1.0, float(1.0/len(nsampvals))))\n",
+    "nsampvals = np.array((10.0 * np.ones(5)) ** np.arange(1, 6), dtype=int)\n",
+    "nsampcolors = plt.cm.viridis(np.arange(0.0, 1.0, float(1.0 / len(nsampvals))))\n",
     "\n",
-    "#First plot the analytical result\n",
-    "ax.plot(volumes, idealgas.x_ave(beta, volumes), 'k--', linewidth=2.0)\n",
+    "# First plot the analytical result\n",
+    "ax.plot(volumes, idealgas.x_ave(beta, volumes), \"k--\", linewidth=2.0)\n",
     "\n",
-    "#And the infinite sampling results for first order extrapolation\n",
+    "# And the infinite sampling results for first order extrapolation\n",
     "trueExtrap, trueDerivs = idealgas.x_vol_extrap(1, volume_ref, volumes, beta=beta)\n",
-    "ax.plot(volumes, trueExtrap, 'r-', zorder=0)\n",
-    "print('True extrapolation coefficients: {}'.format(trueDerivs))\n",
+    "ax.plot(volumes, trueExtrap, \"r-\", zorder=0)\n",
+    "print(\"True extrapolation coefficients: {}\".format(trueDerivs))\n",
     "\n",
     "for i, n in enumerate(nsampvals):\n",
     "    thisinds = np.random.choice(len(xdatavol), size=n, replace=False)\n",
-    "    \n",
-    "    #Get parameters for extrapolation model with this data by training it - the parameters are the derivatives\n",
+    "\n",
+    "    # Get parameters for extrapolation model with this data by training it - the parameters are the derivatives\n",
     "    xemv = xtrap.volume.factory_extrapmodel(\n",
     "        volume=volume_ref,\n",
     "        # this is acually w\n",
     "        uv=wdatavol[thisinds],\n",
     "        xv=xdatavol[thisinds],\n",
     "        dxdqv=xdatavol[thisinds],\n",
-    "        ndim=1\n",
+    "        ndim=1,\n",
     "    )\n",
-    "    \n",
+    "\n",
     "    out = xemv.predict(volumes)\n",
-    "    print('\\t With N_configs = %6i: %s'%(n, str(xemv.derivs(norm=False).values)))\n",
-    "    \n",
-    "    out.plot(marker='s', ms=4, color=nsampcolors[i], ls='None', label='N={}'.format(n), \n",
-    "                              ax=ax)\n",
+    "    print(\"\\t With N_configs = %6i: %s\" % (n, str(xemv.derivs(norm=False).values)))\n",
+    "\n",
+    "    out.plot(\n",
+    "        marker=\"s\", ms=4, color=nsampcolors[i], ls=\"None\", label=\"N={}\".format(n), ax=ax\n",
+    "    )\n",
     "\n",
-    "ax.set_ylabel(r'$\\langle x \\rangle$')\n",
-    "ax.set_xlabel(r'$L$')\n",
+    "ax.set_ylabel(r\"$\\langle x \\rangle$\")\n",
+    "ax.set_xlabel(r\"$L$\")\n",
     "\n",
-    "ax.annotate('O(1) Extrapolation', xy=(0.4, 0.7), xycoords='axes fraction', fontsize=9)\n",
+    "ax.annotate(\"O(1) Extrapolation\", xy=(0.4, 0.7), xycoords=\"axes fraction\", fontsize=9)\n",
     "\n",
     "ax.set_ylim((-1.0, 4.0))\n",
-    "ax.yaxis.set_major_locator(mpl.ticker.MaxNLocator(nbins=4, prune='both'))\n",
+    "ax.yaxis.set_major_locator(mpl.ticker.MaxNLocator(nbins=4, prune=\"both\"))\n",
     "\n",
     "fig.tight_layout()\n",
     "fig.subplots_adjust(hspace=0.0)\n",

diff --git a/docs/notebooks/Data_Organization.ipynb b/docs/notebooks/Data_Organization.ipynb
@@ -65,19 +65,19 @@
    },
    "outputs": [],
    "source": [
-    "#Import idealgas module\n",
+    "# Import idealgas module\n",
     "from thermoextrap import idealgas\n",
     "\n",
-    "#Define reference beta\n",
+    "# Define reference beta\n",
     "beta_ref = 5.6\n",
     "\n",
-    "#And maximum order\n",
+    "# And maximum order\n",
     "order = 2\n",
     "\n",
-    "npart = 1000 #Number of particles (in single configuration)\n",
-    "nconfig = 100_000 #Number of configurations\n",
+    "npart = 1000  # Number of particles (in single configuration)\n",
+    "nconfig = 100_000  # Number of configurations\n",
     "\n",
-    "#Generate all the data we could want\n",
+    "# Generate all the data we could want\n",
     "xdata, udata = idealgas.generate_data((nconfig, npart), beta_ref)"
    ]
   },
@@ -148,8 +148,8 @@
    },
    "outputs": [],
    "source": [
-    "xdata = xr.DataArray(xdata, dims=['rec'])\n",
-    "udata = xr.DataArray(udata, dims=['rec'])"
+    "xdata = xr.DataArray(xdata, dims=[\"rec\"])\n",
+    "udata = xr.DataArray(udata, dims=[\"rec\"])"
    ]
   },
   {
@@ -176,9 +176,9 @@
    },
    "outputs": [],
    "source": [
-    "data = xtrap.DataCentralMomentsVals.from_vals(order=order, \n",
-    "                                                  rec_dim='rec',\n",
-    "                                                  xv=xdata, uv=udata, central=True)"
+    "data = xtrap.DataCentralMomentsVals.from_vals(\n",
+    "    order=order, rec_dim=\"rec\", xv=xdata, uv=udata, central=True\n",
+    ")"
    ]
   },
   {
@@ -598,7 +598,7 @@
     }
    ],
    "source": [
-    "data.xv\n"
+    "data.xv"
    ]
   },
   {
@@ -3875,7 +3875,9 @@
     }
    ],
    "source": [
-    "data_noboot = xtrap.DataCentralMoments.from_vals(xv=xdata, uv=udata, rec_dim='rec', central=True, order=order)\n",
+    "data_noboot = xtrap.DataCentralMoments.from_vals(\n",
+    "    xv=xdata, uv=udata, rec_dim=\"rec\", central=True, order=order\n",
+    ")\n",
     "xr.testing.assert_allclose(data_noboot.values, data.values)\n",
     "data_noboot.values"
    ]
@@ -3916,7 +3918,7 @@
     "try:\n",
     "    data_noboot.resample(nrep=3).values\n",
     "except ValueError as e:\n",
-    "    print('caught error!')\n",
+    "    print(\"caught error!\")\n",
     "    print(e)"
    ]
   },
@@ -4380,8 +4382,8 @@
    "outputs": [],
    "source": [
     "# Make 100 averaged observations\n",
-    "xx = xr.DataArray(xdata.values.reshape(100, -1), dims=['rec', 'block'])\n",
-    "uu = xr.DataArray(udata.values.reshape(100, -1), dims=['rec', 'block'])"
+    "xx = xr.DataArray(xdata.values.reshape(100, -1), dims=[\"rec\", \"block\"])\n",
+    "uu = xr.DataArray(udata.values.reshape(100, -1), dims=[\"rec\", \"block\"])"
    ]
   },
   {
@@ -4902,7 +4904,9 @@
     "# Create directly from values of moments - notice that this is DataCentralMoments, not DataCentralMomentsVals\n",
     "# Effectively just means that the 'rec' dim will not be collapsed when using data for extrapolation, etc.\n",
     "# Behaves similarly to the 'rep' dim when resampling\n",
-    "data_fv = xtrap.DataCentralMoments.from_vals(xv=xx, uv=uu, dim='block', order=order, central=True)\n",
+    "data_fv = xtrap.DataCentralMoments.from_vals(\n",
+    "    xv=xx, uv=uu, dim=\"block\", order=order, central=True\n",
+    ")\n",
     "# So 'rec' is for each separate block average\n",
     "data_fv.values"
    ]
@@ -5361,10 +5365,12 @@
    "outputs": [],
    "source": [
     "# Compute moments of U, i.e., averages to integer powers up to maximum order desired\n",
-    "mom_u = xr.DataArray(np.arange(order + 1), dims=['umom'])\n",
-    "uave = (uu ** mom_u).mean('block')\n",
-    "xuave = (xx * uu ** mom_u).mean('block')\n",
-    "data_fa = xtrap.DataCentralMoments.from_ave_raw(u=uave, xu=xuave, central=True, w=xx.sizes['block'])\n",
+    "mom_u = xr.DataArray(np.arange(order + 1), dims=[\"umom\"])\n",
+    "uave = (uu**mom_u).mean(\"block\")\n",
+    "xuave = (xx * uu**mom_u).mean(\"block\")\n",
+    "data_fa = xtrap.DataCentralMoments.from_ave_raw(\n",
+    "    u=uave, xu=xuave, central=True, w=xx.sizes[\"block\"]\n",
+    ")\n",
     "\n",
     "xr.testing.assert_allclose(data_fv.values, data_fa.values)"
    ]
@@ -5404,10 +5410,14 @@
    "outputs": [],
    "source": [
     "# Extrapolate both average x and average x**2\n",
-    "x_xsq_data = xr.DataArray(np.vstack([xdata.values, xdata.values**2]).T, dims=['rec', 'vals'], coords={'vals': ['x','xsq']})\n",
-    "data_vec = xtrap.DataCentralMomentsVals.from_vals(order=order, \n",
-    "                                                      rec_dim='rec',\n",
-    "                                                      xv=x_xsq_data, uv=udata, central=True)"
+    "x_xsq_data = xr.DataArray(\n",
+    "    np.vstack([xdata.values, xdata.values**2]).T,\n",
+    "    dims=[\"rec\", \"vals\"],\n",
+    "    coords={\"vals\": [\"x\", \"xsq\"]},\n",
+    ")\n",
+    "data_vec = xtrap.DataCentralMomentsVals.from_vals(\n",
+    "    order=order, rec_dim=\"rec\", xv=x_xsq_data, uv=udata, central=True\n",
+    ")"
    ]
   },
   {
@@ -6302,9 +6312,15 @@
    },
    "outputs": [],
    "source": [
-    "xx_xsqxsq = xr.DataArray(x_xsq_data.values.reshape(100, -1, 2), dims=['rec', 'block', 'vals'], coords={'vals': ['x','xsq']})\n",
-    "x_xsq_uave = (xx_xsqxsq * uu ** mom_u).mean('block')\n",
-    "data_fa_vec = xtrap.DataCentralMoments.from_ave_raw(u=uave, xu=x_xsq_uave, central=True, w = xx_xsqxsq.sizes['block'])"
+    "xx_xsqxsq = xr.DataArray(\n",
+    "    x_xsq_data.values.reshape(100, -1, 2),\n",
+    "    dims=[\"rec\", \"block\", \"vals\"],\n",
+    "    coords={\"vals\": [\"x\", \"xsq\"]},\n",
+    ")\n",
+    "x_xsq_uave = (xx_xsqxsq * uu**mom_u).mean(\"block\")\n",
+    "data_fa_vec = xtrap.DataCentralMoments.from_ave_raw(\n",
+    "    u=uave, xu=x_xsq_uave, central=True, w=xx_xsqxsq.sizes[\"block\"]\n",
+    ")"
    ]
   },
   {
@@ -6711,7 +6727,7 @@
     }
    ],
    "source": [
-    "data_fa_vec.reduce('rec').values"
+    "data_fa_vec.reduce(\"rec\").values"
    ]
   },
   {