[pre-commit.ci] pre-commit autoupdate (#148)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.5.6 → v0.6.3](astral-sh/ruff-pre-commit@v0.5.6...v0.6.3)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update lc_cat.ipynb

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Lehman Garrison <lgarrison@flatironinstitute.org>

Author: pre-commit-ci[bot]

.pre-commit-config.yaml

@@ -13,7 +13,7 @@ exclude: |
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.5.6"
+    rev: "v0.6.3"
     hooks:
       - id: ruff
         args: [--fix]

docs/tutorials/analysis/power_spectrum.ipynb

@@ -49,8 +49,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "power_test_data = dict(**np.load(\"../../../tests/data_power/test_pos.npz\"))\n",
-    "Lbox = 1000.\n",
+    "power_test_data = dict(**np.load('../../../tests/data_power/test_pos.npz'))\n",
+    "Lbox = 1000.0\n",
     "pos = power_test_data['pos']"
    ]
   },
@@ -73,8 +73,8 @@
     "nmesh = 72\n",
     "nbins_mu = 4\n",
     "logk = False\n",
-    "k_hMpc_max = np.pi*nmesh/Lbox + 1.e-6\n",
-    "nbins_k = nmesh//2\n",
+    "k_hMpc_max = np.pi * nmesh / Lbox + 1.0e-6\n",
+    "nbins_k = nmesh // 2\n",
     "poles = [0, 2, 4]"
    ]
   },
@@ -91,8 +91,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "power = calc_power(pos, Lbox, nbins_k, nbins_mu, k_hMpc_max, logk, \\\n",
-    "                    paste, nmesh, compensated, interlaced, poles=poles)"
+    "power = calc_power(\n",
+    "    pos,\n",
+    "    Lbox,\n",
+    "    nbins_k,\n",
+    "    nbins_mu,\n",
+    "    k_hMpc_max,\n",
+    "    logk,\n",
+    "    paste,\n",
+    "    nmesh,\n",
+    "    compensated,\n",
+    "    interlaced,\n",
+    "    poles=poles,\n",
+    ")"
    ]
   },
   {
@@ -232,9 +243,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "comp_str = \"_compensated\" if compensated else \"\"\n",
-    "int_str = \"_interlaced\" if interlaced else \"\"\n",
-    "fn = f\"../../../tests/data_power/nbody_{paste}{comp_str}{int_str}.npz\"\n",
+    "comp_str = '_compensated' if compensated else ''\n",
+    "int_str = '_interlaced' if interlaced else ''\n",
+    "fn = f'../../../tests/data_power/nbody_{paste}{comp_str}{int_str}.npz'\n",
     "data = np.load(fn)\n",
     "k_nbody = data['k']\n",
     "Pkmu_nbody = data['power'].real\n",
@@ -274,9 +285,15 @@
     "    else:\n",
     "        label1 = label2 = None\n",
     "    plt.plot(k_nbody, Pkmu_nbody[:, i] * k_nbody, c=colors[i], label=label1)\n",
-    "    plt.plot(power['k_mid'], power['power'][:, i] * power['k_mid'], c=colors[i], ls='--', label=label2)\n",
-    "plt.ylabel(r\"$k P(k)$\")\n",
-    "plt.xlabel(r\"$k \\ [h/{\\rm Mpc}]$\")\n",
+    "    plt.plot(\n",
+    "        power['k_mid'],\n",
+    "        power['power'][:, i] * power['k_mid'],\n",
+    "        c=colors[i],\n",
+    "        ls='--',\n",
+    "        label=label2,\n",
+    "    )\n",
+    "plt.ylabel(r'$k P(k)$')\n",
+    "plt.xlabel(r'$k \\ [h/{\\rm Mpc}]$')\n",
     "plt.legend();"
    ]
   },
@@ -306,9 +323,15 @@
     "    else:\n",
     "        label1 = label2 = None\n",
     "    plt.plot(k_nbody, Pell_nbody[i, :] * k_nbody, c=colors[i], label=label1)\n",
-    "    plt.plot(power['k_mid'], power['poles'][:,i] * power['k_mid'], c=colors[i], ls='--', label=label2)\n",
-    "plt.ylabel(r\"$k P_\\ell(k)$\")\n",
-    "plt.xlabel(r\"$k \\ [h/{\\rm Mpc}]$\")\n",
+    "    plt.plot(\n",
+    "        power['k_mid'],\n",
+    "        power['poles'][:, i] * power['k_mid'],\n",
+    "        c=colors[i],\n",
+    "        ls='--',\n",
+    "        label=label2,\n",
+    "    )\n",
+    "plt.ylabel(r'$k P_\\ell(k)$')\n",
+    "plt.xlabel(r'$k \\ [h/{\\rm Mpc}]$')\n",
     "plt.legend();"
    ]
   }

docs/tutorials/analysis/tsc.ipynb

@@ -50,11 +50,11 @@
    "source": [
     "# Generate some particles\n",
     "N = 10**7\n",
-    "box = 123.\n",
+    "box = 123.0\n",
     "nthread = 32\n",
     "\n",
     "rng = np.random.default_rng(123)\n",
-    "pos = rng.random((N,3), dtype=np.float32) * box"
+    "pos = rng.random((N, 3), dtype=np.float32) * box"
    ]
   },
   {
@@ -98,7 +98,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dens = np.zeros((ngrid,ngrid,ngrid), dtype=np.float32)\n",
+    "dens = np.zeros((ngrid, ngrid, ngrid), dtype=np.float32)\n",
     "dens = tsc_parallel(pos, dens, box, nthread=nthread)"
    ]
   },
@@ -202,7 +202,7 @@
    "source": [
     "fig, ax = plt.subplots(dpi=144)\n",
     "ax.set_aspect('equal')\n",
-    "ax.scatter(hpos[:,0], hpos[:,1])\n",
+    "ax.scatter(hpos[:, 0], hpos[:, 1])\n",
     "ax.set_xlabel('$x$ [Mpc/$h$]')\n",
     "ax.set_ylabel('$y$ [Mpc/$h$]')\n",
     "ax.set_xlim(0, L)\n",
@@ -247,11 +247,12 @@
     "dens = tsc_parallel(hpos, 64, L, nthread=nthread)\n",
     "\n",
     "fig, ax = plt.subplots(dpi=144)\n",
-    "ax.imshow(dens.sum(axis=2).T,\n",
+    "ax.imshow(\n",
+    "    dens.sum(axis=2).T,\n",
     "    origin='lower',\n",
     "    interpolation='none',\n",
-    "    extent=(0,L,0,L),\n",
-    "    )\n",
+    "    extent=(0, L, 0, L),\n",
+    ")\n",
     "ax.set_xlabel('$x$ [Mpc/$h$]')\n",
     "ax.set_ylabel('$y$ [Mpc/$h$]')"
    ]
@@ -323,10 +324,10 @@
     }
    ],
    "source": [
-    "dens = np.zeros((ngrid,ngrid,ngrid), dtype=np.float32)\n",
+    "dens = np.zeros((ngrid, ngrid, ngrid), dtype=np.float32)\n",
     "%timeit tsc_parallel(pos, dens, box, nthread=nthread)\n",
     "\n",
-    "dens64 = np.zeros((ngrid,ngrid,ngrid), dtype=np.float64)\n",
+    "dens64 = np.zeros((ngrid, ngrid, ngrid), dtype=np.float64)\n",
     "pos64 = pos.astype(np.float64)\n",
     "%timeit tsc_parallel(pos64, dens64, box, nthread=nthread)"
    ]
@@ -389,20 +390,25 @@
    ],
    "source": [
     "def test_precision(N, ngrid):\n",
-    "    pos = rng.random((N,3), dtype=np.float32) * box\n",
-    "    dens = np.zeros((ngrid,ngrid,ngrid), dtype=np.float32)\n",
+    "    pos = rng.random((N, 3), dtype=np.float32) * box\n",
+    "    dens = np.zeros((ngrid, ngrid, ngrid), dtype=np.float32)\n",
     "    tsc_parallel(pos, dens, box, nthread=nthread)\n",
     "    print(f'Total dens, 32-bit computation, 32-bit summation type: {dens.sum()}')\n",
-    "    print(f'Total dens, 32-bit computation, 64-bit summation type: {dens.sum(dtype=np.float64)}')\n",
+    "    print(\n",
+    "        f'Total dens, 32-bit computation, 64-bit summation type: {dens.sum(dtype=np.float64)}'\n",
+    "    )\n",
     "\n",
-    "    dens = np.zeros((ngrid,ngrid,ngrid), dtype=np.float64)\n",
+    "    dens = np.zeros((ngrid, ngrid, ngrid), dtype=np.float64)\n",
     "    tsc_parallel(pos, dens, box, nthread=nthread)\n",
-    "    print(f'Total dens, 64-bit computation, 64-bit summation type: {dens.sum(dtype=np.float64)}')\n",
+    "    print(\n",
+    "        f'Total dens, 64-bit computation, 64-bit summation type: {dens.sum(dtype=np.float64)}'\n",
+    "    )\n",
+    "\n",
     "\n",
     "print('Case 1:')\n",
     "test_precision(10**7, 256)\n",
     "print('Case 2:')\n",
-    "test_precision(5*10**9, 2048)"
+    "test_precision(5 * 10**9, 2048)"
    ]
   },
   {
@@ -429,7 +435,7 @@
     }
    ],
    "source": [
-    "pos = rng.random((5 * 10**9,3), dtype=np.float32) * box\n",
+    "pos = rng.random((5 * 10**9, 3), dtype=np.float32) * box\n",
     "dens = tsc_parallel(pos, 2048, box, nthread=nthread)\n",
     "\n",
     "delta = dens / dens.mean(dtype=np.float64) - 1\n",

docs/tutorials/analysis/zcv.ipynb

@@ -67,7 +67,6 @@
    "outputs": [],
    "source": [
     "# Load necessary packages\n",
-    "import os\n",
     "\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -93,7 +92,7 @@
    "outputs": [],
    "source": [
     "# Load the config file and parse in relevant parameters\n",
-    "path2config = \"config/lrg_hod_base_z0.500_nmesh576.yaml\"\n",
+    "path2config = 'config/lrg_hod_base_z0.500_nmesh576.yaml'\n",
     "\n",
     "# Read the parameters from the yaml file\n",
     "config = yaml.safe_load(open(path2config))\n",
@@ -163,7 +162,7 @@
     "newBall = AbacusHOD(sim_params, HOD_params, clustering_params)\n",
     "mock_dict = newBall.run_hod(newBall.tracers, want_rsd, write_to_disk, Nthread=16)\n",
     "nobj = mock_dict['LRG']['mass'].size\n",
-    "print(\"number of galaxies\", nobj)"
+    "print('number of galaxies', nobj)"
    ]
   },
   {
@@ -311,9 +310,9 @@
     "zcv_dict = newBall.apply_zcv(mock_dict, config, load_presaved=load_presaved)\n",
     "print(zcv_dict.keys())\n",
     "for key in zcv_dict.keys():\n",
-    "    if \"Pk\" in key:\n",
+    "    if 'Pk' in key:\n",
     "        print(key, zcv_dict[key][:, :10])\n",
-    "        print(\"-----------------\")"
+    "        print('-----------------')"
    ]
   },
   {
@@ -385,9 +384,9 @@
     "zcv_dict_xi = newBall.apply_zcv_xi(mock_dict, config, load_presaved=load_presaved)\n",
     "print(zcv_dict_xi.keys())\n",
     "for key in zcv_dict_xi.keys():\n",
-    "    if \"Xi\" in key:\n",
+    "    if 'Xi' in key:\n",
     "        print(key, zcv_dict_xi[key][:, :10])\n",
-    "        print(\"-----------------\")"
+    "        print('-----------------')"
    ]
   },
   {
@@ -454,10 +453,9 @@
     "f, ax = plt.subplots(1, n_ell, sharex=True, sharey=True, figsize=figsize)\n",
     "if not want_rsd:\n",
     "    ax = [ax]\n",
-    "    \n",
+    "\n",
     "# Loop over all multipoles\n",
     "for ell in range(n_ell):\n",
-    "    \n",
     "    if want_rsd:\n",
     "        pk_zz_ell = pk_zz[ell, :].flatten()\n",
     "        pk_zenbu_ell = pk_zenbu[ell, :].flatten()\n",
@@ -468,17 +466,21 @@
     "        pk_zenbu_ell = pk_zenbu.flatten()\n",
     "        pk_tt_poles_ell = pk_tt_poles.flatten()\n",
     "        pk_nn_betasmooth_ell = pk_nn_betasmooth.flatten()\n",
-    "        \n",
-    "    ax[ell].plot(k_binc, k_binc * pk_tt_poles_ell, c=cs[0], label='Measured tracer power')\n",
+    "\n",
+    "    ax[ell].plot(\n",
+    "        k_binc, k_binc * pk_tt_poles_ell, c=cs[0], label='Measured tracer power'\n",
+    "    )\n",
     "    ax[ell].plot(k_binc, k_binc * pk_zz_ell, c=cs[1], label='IC rescaled power')\n",
-    "    ax[ell].plot(k_binc, k_binc * pk_nn_betasmooth_ell, c=cs[2], label='Reduced tracer power')\n",
+    "    ax[ell].plot(\n",
+    "        k_binc, k_binc * pk_nn_betasmooth_ell, c=cs[2], label='Reduced tracer power'\n",
+    "    )\n",
     "    ax[ell].plot(k_binc, k_binc * pk_zenbu_ell, c=cs[3], label='ZeNBu fitted power')\n",
-    "    \n",
+    "\n",
     "    if ell == 0:\n",
-    "        ax[ell].set_ylabel(r\"$k P(k)$\")\n",
-    "    ax[ell].set_xlabel(r\"$k \\ [h/{\\rm Mpc}]$\")\n",
+    "        ax[ell].set_ylabel(r'$k P(k)$')\n",
+    "    ax[ell].set_xlabel(r'$k \\ [h/{\\rm Mpc}]$')\n",
     "plt.xscale('log')\n",
-    "plt.legend()\n"
+    "plt.legend()"
    ]
   },
   {
@@ -520,11 +522,11 @@
     "figsize = (14, 6)\n",
     "f, ax = plt.subplots(1, n_ell, sharex=True, sharey=True, figsize=figsize)\n",
     "for ell in range(n_ell):\n",
-    "    ax[ell].plot(r_binc, xi[ell]*r_binc**2, cs[1], label='IFT[P(k)] CV')\n",
-    "    ax[ell].plot(r_binc, xi_raw[ell]*r_binc**2, cs[2], label='IFT[P(k)] Raw')\n",
-    "    ax[ell].set_xlabel(r\"$r \\ [{\\rm Mpc}/h]$\")\n",
+    "    ax[ell].plot(r_binc, xi[ell] * r_binc**2, cs[1], label='IFT[P(k)] CV')\n",
+    "    ax[ell].plot(r_binc, xi_raw[ell] * r_binc**2, cs[2], label='IFT[P(k)] Raw')\n",
+    "    ax[ell].set_xlabel(r'$r \\ [{\\rm Mpc}/h]$')\n",
     "    if ell == 0:\n",
-    "        ax[ell].set_ylabel(r\"$\\xi_\\ell(r) \\  r^2 \\ [{\\rm Mpc}/h]$\")\n",
+    "        ax[ell].set_ylabel(r'$\\xi_\\ell(r) \\  r^2 \\ [{\\rm Mpc}/h]$')\n",
     "plt.legend(fontsize=16)\n",
     "plt.ylim([-100, 120])\n",
     "plt.xlim([1, 200])"
@@ -567,7 +569,7 @@
    ],
    "source": [
     "# sqrt(1-rho_xc^2) gives the ratio between the ZCV and the Raw power spectrum uncertainty for each Legendre multipole\n",
-    "print(np.sqrt(1.-r_zt[:, :30]**2))"
+    "print(np.sqrt(1.0 - r_zt[:, :30] ** 2))"
    ]
   },
   {