fixed last commit.

-removed obsolete seaborn dependencies
- reverted changes that might be a (?bug)

LombScargle/LombScargleCrossspectrum_tutorial.ipynb

@@ -9,16 +9,6 @@
     "This tutorial shows how to make and manipulate a Lomb Scargle cross spectrum of two light curves using Stingray."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# we can see the new Valid seaborn Styles by running: \n",
-    "#plt.style.available "
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -31,9 +21,9 @@
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib.font_manager as font_manager\n",
-    "plt.style.use('seaborn-v0_8-talk')# removed the earlier seaborn-talk dependency\"\n",
+    "plt.style.use('seaborn-v0_8-talk')\n",
     "%matplotlib inline\n",
-    "from matplotlib.font_manager import FontProperties #Not importing this was causing Import error\n",
+    "from matplotlib.font_manager import FontProperties\n",
     "font_prop = font_manager.FontProperties(size=16)"
    ]
   },

LombScargle/LombScarglePowerspectrum_tutorial.ipynb

@@ -23,6 +23,7 @@
     "import matplotlib.font_manager as font_manager\n",
     "%matplotlib inline\n",
     "plt.style.use('seaborn-v0_8-talk')\n",
+    "\n",
     "font_prop = font_manager.FontProperties(size=16)"
    ]
   },

Modeling/ModelingExamples.ipynb

@@ -2035,11 +2035,9 @@
    ],
    "source": [
     "plt.figure()\n",
-    "#ps.freq and ps.power have different lengths\n",
-    "#Truncating one of the arrays to match the length of the other array\n",
-    "plt.loglog(ps.freq[:1000], ps.power, ds=\"steps-mid\", lw=2, color=\"black\")\n",
     "plt.figure()\n",
-    "plt.plot(ps.freq[:1000], res.mfit, lw=3, color=\"red\")"
+    "plt.loglog(ps.freq, ps.power, ds=\"steps-mid\", lw=2, color=\"black\")\n",
+    "plt.plot(ps.freq, res.mfit, lw=3, color=\"red\")"
    ]
   },
   {

Window Functions/window_functions.ipynb

@@ -149,7 +149,7 @@
    ],
    "source": [
     "nfft = 2048\n",
-    "A = fft(window,nfft ) / (len(window)/2.0)\n",
+    "A = fft(uniform_window,nfft ) / (len(uniform_window)/2.0)\n",
     "freq = fftfreq(nfft)\n",
     "response = 20 * np.log10(np.abs(fftshift(A/(abs(A).max()))))\n",
     "plt.plot(freq, response)\n",