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

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

Author: pre-commit-ci[bot]

.github/dependabot.yml

@@ -9,4 +9,3 @@ updates:
       actions:
         patterns:
           - "*"
-

.github/workflows/ci.yml

@@ -41,6 +41,3 @@ jobs:
 
       - name: Test package
         run:  pytest
-
-
-

.pre-commit-config.yaml

@@ -22,4 +22,3 @@ repos:
       - id: ruff-format
       - id: ruff-check
         args: ["--fix", "--show-fixes"]
-

README.md

@@ -2,12 +2,12 @@
 
 This is the lightcurves repository. Check it out: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1OqafFK4FQA_tBwTTnYMG-1D5uhTQ5X0D#scrollTo=european-mechanism) <br>
 See here for scientific application of this code:
-https://pos.sissa.it/395/868 
+https://pos.sissa.it/395/868
 
 ## LC.py
-Initialize a LightCurve object based on time, flux and flux_error. 
+Initialize a LightCurve object based on time, flux and flux_error.
 Study its Bayesian block representation (based on Scargle et al. 2013  https://ui.adsabs.harvard.edu/abs/2013arXiv1304.2818S/abstract ).<br>
-Characterize flares (start, peak, end time) with the HOP algorithm (following Meyer et al. 2019 https://ui.adsabs.harvard.edu/abs/2019ApJ...877...39M/abstract ). There are four different methods to define flares (baseline, half, flip, sharp) as illustrated in the Jupyter Notebook. 
+Characterize flares (start, peak, end time) with the HOP algorithm (following Meyer et al. 2019 https://ui.adsabs.harvard.edu/abs/2019ApJ...877...39M/abstract ). There are four different methods to define flares (baseline, half, flip, sharp) as illustrated in the Jupyter Notebook.
 
 ## HOP.py
 Initialize a Hopject to consider parameters of an individual flare.

illustration_lightcurve.ipynb

@@ -26,17 +26,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
     "import numpy as np\n",
-    "from matplotlib import pyplot as plt\n",
     "from IPython.core.pylabtools import figsize\n",
-    "figsize(10,3)\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "figsize(10, 3)\n",
     "\n",
     "try:\n",
-    "    from LC import LightCurve\n",
     "    from HOP import Hopject\n",
+    "    from LC import LightCurve\n",
     "    from LC_Set import LC_Set\n",
     "except ModuleNotFoundError:\n",
-    "    print('If you installed the zipped version from GitHub, you have to delete \"lightcurves.\" in the import statements of the above files')"
+    "    print(\n",
+    "        'If you installed the zipped version from GitHub, you have to delete \"lightcurves.\" in the import statements of the above files'\n",
+    "    )"
    ]
   },
   {
@@ -77,8 +82,8 @@
     }
    ],
    "source": [
-    "time, flux, flux_error = np.load('./lc.npy') #mock lc with gaussian flares\n",
-    "time += 55000 #e.g. MJD\n",
+    "time, flux, flux_error = np.load(\"./lc.npy\")  # mock lc with gaussian flares\n",
+    "time += 55000  # e.g. MJD\n",
     "lc = LightCurve(time, flux, flux_error)\n",
     "lc.plot_lc()\n",
     "plt.legend()"
@@ -122,8 +127,10 @@
     }
    ],
    "source": [
-    "block_pbin, block_val, block_val_error, edge_index, edges = lc.get_bblocks(p0_value=0.05)\n",
-    "#for more information on edges and edge_index see Appendix A2\n",
+    "block_pbin, block_val, block_val_error, edge_index, edges = lc.get_bblocks(\n",
+    "    p0_value=0.05\n",
+    ")\n",
+    "# for more information on edges and edge_index see Appendix A2\n",
     "lc.plot_bblocks()\n",
     "plt.legend()"
    ]
@@ -173,8 +180,8 @@
     }
    ],
    "source": [
-    "hops_bl = lc.find_hop('baseline')\n",
-    "print('hops_bl is a list of Hopjects with interesting attributes, see HOP.py')\n",
+    "hops_bl = lc.find_hop(\"baseline\")\n",
+    "print(\"hops_bl is a list of Hopjects with interesting attributes, see HOP.py\")\n",
     "print(hops_bl)\n",
     "\n",
     "hop = hops_bl[0]\n",
@@ -220,8 +227,8 @@
     }
    ],
    "source": [
-    "hops_half = lc.find_hop('half', lc_edges='add')\n",
-    "#one could add the edges of the light curve as start and end of HOP if there is a peak\n",
+    "hops_half = lc.find_hop(\"half\", lc_edges=\"add\")\n",
+    "# one could add the edges of the light curve as start and end of HOP if there is a peak\n",
     "\n",
     "hop = hops_half[0]\n",
     "print(hop.start_time, hop.peak_time, hop.end_time)\n",
@@ -266,9 +273,9 @@
     }
    ],
    "source": [
-    "hops_flip = lc.find_hop('flip', lc_edges='neglect') \n",
-    "#or one could conservatively neglect the edges of the light curve \n",
-    "#because it is uncertain whether there would be more decrease/increase of flux\n",
+    "hops_flip = lc.find_hop(\"flip\", lc_edges=\"neglect\")\n",
+    "# or one could conservatively neglect the edges of the light curve\n",
+    "# because it is uncertain whether there would be more decrease/increase of flux\n",
     "\n",
     "hop = hops_flip[0]\n",
     "print(hop.start_time, hop.peak_time, hop.end_time)\n",
@@ -323,7 +330,7 @@
     }
    ],
    "source": [
-    "hops_sharp = lc.find_hop('sharp', lc_edges='neglect') \n",
+    "hops_sharp = lc.find_hop(\"sharp\", lc_edges=\"neglect\")\n",
     "\n",
     "hop = hops_sharp[0]\n",
     "print(hop.start_time, hop.peak_time, hop.end_time)\n",
@@ -361,7 +368,7 @@
     }
    ],
    "source": [
-    "lc.plot_all_hop(lc_edges='neglect')"
+    "lc.plot_all_hop(lc_edges=\"neglect\")"
    ]
   },
   {
@@ -400,7 +407,7 @@
    ],
    "source": [
     "hop = hops_flip[3]\n",
-    "print('asymmetry measure: ', hop.asym)\n",
+    "print(\"asymmetry measure: \", hop.asym)\n",
     "hop.plot_hop()"
    ]
   },
@@ -549,30 +556,35 @@
     }
    ],
    "source": [
-    "time = np.arange(0, 2000, 1)        # e.g. daily binning for 12 years = 4380 days of observation\n",
-    "N_b=len(time)         # number of bins\n",
+    "time = np.arange(\n",
+    "    0, 2000, 1\n",
+    ")  # e.g. daily binning for 12 years = 4380 days of observation\n",
+    "N_b = len(time)  # number of bins\n",
     "flux = np.ones(N_b)\n",
-    "rel_flux_error = 0.1       # flux error ~ 5% \n",
-    "noise = np.random.normal(0, 0.07, N_b)        # white noise backround with strength \n",
+    "rel_flux_error = 0.1  # flux error ~ 5%\n",
+    "noise = np.random.normal(0, 0.07, N_b)  # white noise backround with strength\n",
     "\n",
     "# maximum values for gaussian flares\n",
     "amp_max = 100\n",
     "cen_max = 4380\n",
     "wid_max = 40\n",
     "\n",
+    "\n",
     "def gaussian(x, amp, cen, wid):\n",
     "    \"1-d Gaussian: gaussian(x=data, amp, cen, wid)\"\n",
-    "    return (amp / (np.sqrt(2*np.pi) * wid)) * np.exp(-(x-cen)**2 / (2*wid**2))\n",
+    "    return (amp / (np.sqrt(2 * np.pi) * wid)) * np.exp(-((x - cen) ** 2) / (2 * wid**2))\n",
+    "\n",
     "\n",
     "def rand_gauss(x, amp_max, cen_max, wid_max):\n",
     "    \"random 1-d Gaussian\"\n",
     "    \"takes maximum values for parameters and multiplies each with uniform random number [0,1) ... 0 aba kee1\"\n",
     "    \"https://numpy.org/doc/stable/reference/random/generated/numpy.random.random.html\"\n",
     "    amp = np.random.random(1) * amp_max\n",
     "    cen = np.random.random(1) * cen_max\n",
-    "    #wid = np.random.random(1) * wid_max\n",
-    "    wid = amp*0.25       # assume that flares are self similar; always same shape\n",
-    "    return(gaussian(x, amp, cen, wid))\n",
+    "    # wid = np.random.random(1) * wid_max\n",
+    "    wid = amp * 0.25  # assume that flares are self similar; always same shape\n",
+    "    return gaussian(x, amp, cen, wid)\n",
+    "\n",
     "\n",
     "lc_array = np.zeros(10, dtype=object)\n",
     "for l in range(10):\n",
@@ -584,15 +596,15 @@
     "    flux_error = flux * rel_flux_error\n",
     "    lc = LightCurve(time, flux, flux_error)\n",
     "    lc.get_bblocks()\n",
-    "    lc.find_hop('flip')\n",
+    "    lc.find_hop(\"flip\")\n",
     "    lc_array[l] = lc\n",
-    "    \n",
+    "\n",
     "    plt.figure(l)\n",
     "    lc.plot_lc()\n",
     "    lc.plot_bblocks()\n",
     "    lc.plot_hop()\n",
-    "    plt.xlabel('Time')\n",
-    "    plt.ylabel('Flux')"
+    "    plt.xlabel(\"Time\")\n",
+    "    plt.ylabel(\"Flux\")"
    ]
   },
   {
@@ -633,11 +645,11 @@
     }
    ],
    "source": [
-    "lcs = LC_Set(lc_array, block_min = 2)       #single block flares do not resolve asymmetry\n",
+    "lcs = LC_Set(lc_array, block_min=2)  # single block flares do not resolve asymmetry\n",
     "lcs.plot_asym()\n",
     "plt.legend()\n",
-    "plt.ylabel('probability density')\n",
-    "plt.xlabel('asymmetry measure')\n",
+    "plt.ylabel(\"probability density\")\n",
+    "plt.xlabel(\"asymmetry measure\")\n",
     "# for histogram binning see https://docs.astropy.org/en/stable/api/astropy.visualization.hist.html"
    ]
   },
@@ -712,27 +724,29 @@
     }
    ],
    "source": [
-    "time, flux, flux_error = np.load('./lc.npy') #mock lc with gaussian flares\n",
-    "time += 55000 #e.g. MJD\n",
-    "fig = plt.figure(0,(10,3))\n",
-    "time_one = np.ones(len(time)) #used for plotting\n",
+    "time, flux, flux_error = np.load(\"./lc.npy\")  # mock lc with gaussian flares\n",
+    "time += 55000  # e.g. MJD\n",
+    "fig = plt.figure(0, (10, 3))\n",
+    "time_one = np.ones(len(time))  # used for plotting\n",
     "ax0 = fig.add_subplot(211)\n",
-    "plt.plot(time,time_one,linewidth=0,label='data bin',marker='+',c = 'blue', markersize=10)\n",
-    "plt.axvline(edges[0],color='red',label='edges',linewidth=1)\n",
-    "for i in range(1,len(edges)):\n",
-    "    plt.axvline(edges[i],color='red',linewidth=1)\n",
-    "plt.xlabel('Time/ MJD', fontsize = 14)\n",
+    "plt.plot(\n",
+    "    time, time_one, linewidth=0, label=\"data bin\", marker=\"+\", c=\"blue\", markersize=10\n",
+    ")\n",
+    "plt.axvline(edges[0], color=\"red\", label=\"edges\", linewidth=1)\n",
+    "for i in range(1, len(edges)):\n",
+    "    plt.axvline(edges[i], color=\"red\", linewidth=1)\n",
+    "plt.xlabel(\"Time/ MJD\", fontsize=14)\n",
     "plt.yticks([])\n",
     "ax0.xaxis.tick_top()\n",
-    "ax0.xaxis.set_label_position('top') \n",
+    "ax0.xaxis.set_label_position(\"top\")\n",
     "plt.legend()\n",
     "\n",
     "ax1 = fig.add_subplot(212)\n",
-    "plt.plot(time_one,linewidth=0,label='data bin',marker='+',c = 'blue', markersize=10)\n",
-    "plt.axvline(edge_index[0],color='green',label='edge_index',linewidth=1)\n",
-    "for i in range(1,len(edge_index)):\n",
-    "    plt.axvline(edge_index[i],color='green',linewidth=1)\n",
-    "plt.xlabel('Time array index', fontsize = 14)\n",
+    "plt.plot(time_one, linewidth=0, label=\"data bin\", marker=\"+\", c=\"blue\", markersize=10)\n",
+    "plt.axvline(edge_index[0], color=\"green\", label=\"edge_index\", linewidth=1)\n",
+    "for i in range(1, len(edge_index)):\n",
+    "    plt.axvline(edge_index[i], color=\"green\", linewidth=1)\n",
+    "plt.xlabel(\"Time array index\", fontsize=14)\n",
     "plt.yticks([])\n",
     "plt.legend()\n",
     "plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.2, hspace=0)"

pyproject.toml

@@ -27,7 +27,7 @@ classifiers = [
 
 requires-python = ">=3.10"
 
-#list of dependencies. 
+#list of dependencies.
 #do not list imports from standard library like logging or pickle
 dependencies = [
   "astropy",
@@ -87,8 +87,3 @@ isort.required-imports = ["from __future__ import annotations"]
 
 [tool.ruff.lint.per-file-ignores]
 "tests/**" = ["T20"]
-
-
-
-
-