Exercise for spectrograms

D2Dbook/3_Data_sources/3_APIs/downloads/weather.json

@@ -8,36 +8,36 @@
             "id": 804,
             "main": "Clouds",
             "description": "overcast clouds",
-            "icon": "04d"
+            "icon": "04n"
         }
     ],
     "base": "stations",
     "main": {
-        "temp": 276.3,
-        "feels_like": 275.22,
-        "temp_min": 275.5,
-        "temp_max": 277.64,
-        "pressure": 1011,
-        "humidity": 100,
-        "sea_level": 1011,
-        "grnd_level": 994
+        "temp": 283.55,
+        "feels_like": 282.94,
+        "temp_min": 282.16,
+        "temp_max": 284.53,
+        "pressure": 1021,
+        "humidity": 88,
+        "sea_level": 1021,
+        "grnd_level": 1004
     },
     "visibility": 10000,
     "wind": {
-        "speed": 1.34,
-        "deg": 357,
-        "gust": 2.51
+        "speed": 4.36,
+        "deg": 210,
+        "gust": 11
     },
     "clouds": {
-        "all": 95
+        "all": 100
     },
-    "dt": 1728888498,
+    "dt": 1729708149,
     "sys": {
         "type": 2,
         "id": 2006772,
         "country": "NO",
-        "sunrise": 1728885203,
-        "sunset": 1728922287
+        "sunrise": 1729664132,
+        "sunset": 1729698363
     },
     "timezone": 7200,
     "id": 3139081,

D2Dbook/4_Data_quality/3_Preprocessing/3_Time_formats.ipynb

@@ -22,19 +22,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-17T11:09:26.933980Z",
-     "iopub.status.busy": "2024-08-17T11:09:26.933628Z",
-     "iopub.status.idle": "2024-08-17T11:09:26.940994Z",
-     "shell.execute_reply": "2024-08-17T11:09:26.940411Z"
-    },
     "slideshow": {
      "slide_type": "slide"
-    }
+    },
+    "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date and time is: 2024-10-21 11:54:01.076687\n",
+      "Timestamp is: 1729504441.076687\n"
+     ]
+    }
+   ],
    "source": [
     "from datetime import datetime\n",
     "\n",
@@ -98,19 +102,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-08-17T11:09:26.974670Z",
-     "iopub.status.busy": "2024-08-17T11:09:26.974535Z",
-     "iopub.status.idle": "2024-08-17T11:09:26.977022Z",
-     "shell.execute_reply": "2024-08-17T11:09:26.976746Z"
-    },
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "An arbitrary format: 21/10-2024 11:57:40\n",
+      "Reformatted to the standard: 2024-10-21 11:57:40\n"
+     ]
+    }
+   ],
    "source": [
     "now = datetime.now()\n",
     "formatted = now.strftime(\"%d/%m-%Y %H:%M:%S\")\n",
@@ -175,5 +183,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

D2Dbook/4_Data_quality/3_Preprocessing/4_Synchronisation.ipynb

@@ -74,7 +74,7 @@
     "      - This coresponds to the different uses of filters in the [Noise reduction section](../1_Signal/2_Noise_reduction.ipynb).\n",
     "      - ... which also means that it is possible to echange simple averages with other smoothers.\n",
     "\n",
-    "<img src=\"../../images/Accumulation.png\" width=\"700px\">"
+    "<img src=\"https://github.com/khliland/IND320/blob/main/D2Dbook/images/Accumulation.png?raw=TRUE\" width=\"700px\" />"
    ]
   },
   {

D2Dbook/4_Data_quality/3_Preprocessing/5_Spectrograms.ipynb

@@ -92,8 +92,8 @@
    "outputs": [],
    "source": [
     "# Produce a spectrogram using scipy.signal.stft (short-time Fourier transform)\n",
-    "from scipy.signal import stft\n",
-    "f_stft, t_stft, Zxx = stft(y_approx, fs, nperseg=576)"
+    "from scipy.signal import stft # Legacy function stft. Anyone want to update this to ShortTimeFFT?\n",
+    "f_stft, t_stft, Zxx = stft(y_approx, fs, nperseg=1152)"
    ]
   },
   {
@@ -127,7 +127,7 @@
    "source": [
     "# Plot the spectrogram without pre-computed frequency and time axes\n",
     "import matplotlib.pyplot as plt\n",
-    "plt.specgram(y_approx, Fs=fs, NFFT=576, noverlap=400, scale='linear')\n",
+    "plt.specgram(y_approx, Fs=fs, NFFT=1152, noverlap=400, scale='linear')\n",
     "plt.xlabel('Time (s)')\n",
     "plt.ylabel('Frequency (Hz)')\n",
     "plt.colorbar()\n",
@@ -186,7 +186,7 @@
    "outputs": [],
    "source": [
     "# Produce a spectrogram using scipy.signal.stft (short-time Fourier transform)\n",
-    "f, t, Zxx = stft(samples, song.frame_rate, nperseg=576)"
+    "f, t, Zxx = stft(samples, song.frame_rate, nperseg=1152)"
    ]
   },
   {
@@ -261,6 +261,17 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise\n",
+    "- Revisit the sunspot data from the STL decomposition.\n",
+    "- Create a spectrogram.\n",
+    "- See how changing parameters alters the output.\n",
+    "- Try to get an intuition for the role of the sampling rate (test values)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -280,7 +291,7 @@
  "metadata": {
   "celltoolbar": "Slideshow",
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "IND320_2024",
    "language": "python",
    "name": "python3"
   },