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docs/Chapter1.qmd

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+<a href="https://colab.research.google.com/github/tuomaseerola/emr/blob/master/nb/Chapter1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
+
+# Ch. 1 – Notebook basics
+
+This first notebook is just a demonstration of running R in notebook calculates the correlation between ratings of energy and tension from an existing dataset.
+
+## Preliminaries
+
+To install the `MusicScienceData` package that contains several example datasets used in this book, run the following command.
+
+```{r}
+#| eval: false
+#if (!require(devtools)) install.packages("devtools",quiet=TRUE)
+devtools::install_github("tuomaseerola/MusicScienceData",quiet=TRUE)
+```
+
+## Code 1.1
+
+This is the first R code example, which demonstrates loading package that contains datasets, choosing one dataset, and then calculating correlation between two rated concepts (energy and tension).
+
+```{r}
+# Code 1.1
+library(MusicScienceData)             # loads library w data
+data <- MusicScienceData::soundtrack  # pick data
+cor.test(data$Energy,                 # calc. correlation
+         data$Tension)
+```

docs/Chapter10.1.qmd

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+
+# Ch. 10 – Basics (sines)
+
+## Figure 10.1. Illustration of basic representations and transformations of audio using a 400 Hz sine wave and complex tone consisting of 400, 600 and 1600 Hz sine waves.
+
+```{python}
+#| echo: true
+#| eval: true
+#| label: libraries
+#| code-fold: true
+#| code-summary: "Show the code"
+import numpy as np
+from matplotlib import pyplot as plt 
+```
+
+## Create sine waves
+
+```{python}
+#| echo: true
+#| eval: true
+#| label: onesine
+#| warning: false
+
+### Define the properties of a sine wave
+
+frequency = 400   # Frequency
+duration = 0.01   # Duration of sound
+amplitude = 1.0   # Amplitude
+phase = 0.75      # Phase
+Fs = 22050        # Sampling rate (per second)
+
+# This code creates the sine wave with the properties you detailed above
+num_samples = int(Fs * duration) 
+t = np.arange(num_samples) / Fs
+x = amplitude * np.sin(2 * np.pi * (frequency * t - phase))
+fig, ax = plt.subplots(figsize=(7.5, 2.75))
+ax.plot(t, x, color='red')
+ax.set_xlabel('Time (s)')
+ax.set_title("Sine (400 Hz)")
+ax.set_ylabel('Air pressure deviation')
+ax.set_ylim([-1.05, 1.05])
+ax.set_yticks(np.arange(-1, 1.5, 1.0)) 
+ax.set_xlim([0.0, 0.01])
+ax.set_xticks(np.arange(0, 0.0125, 0.0025)) 
+ax.grid()
+ax.annotate('', xy=(0.0025, 0), xytext=(0.0025, 1), 
+            arrowprops=dict(arrowstyle='<->', mutation_scale=15, 
+            color='0.3'), size=2)
+ax.text(0.0025, 0.5, "Amplitude", size=12, 
+        color='0.3', ha="center", va="center")
+ax.annotate('', xy=(0, 1), xytext=(0.0025, 1), 
+            arrowprops=dict(arrowstyle='<->', mutation_scale=19, 
+            color='0.3'), size=2)
+ax.text(0.00125, 0.85, "Period", size=12, 
+        color='0.3', ha="center", va="center")
+
+plt.show()
+
+
+```
+
+## Complex sounds
+
+Let's combine sine waves of different frequency (400, 600, 1600 Hz).
+
+```{python}
+#| echo: true
+#| eval: true
+#| label: threesines
+#| warning: false
+#| code-fold: true
+#| code-summary: "Show the code"
+
+import numpy as np
+from matplotlib import pyplot as plt 
+
+fig = plt.figure()
+fig.set_figheight(6)
+fig.set_figwidth(10)
+
+ax1 = plt.subplot2grid(shape=(6, 3), loc=(0, 1), colspan=2, rowspan=3)
+ax3 = plt.subplot2grid(shape=(6, 3), loc=(3, 1), colspan=2, rowspan=3)
+ax2 = plt.subplot2grid(shape=(6, 3), loc=(3, 0), colspan=1)
+ax4 = plt.subplot2grid(shape=(6, 3), loc=(4, 0), colspan=1)
+ax5 = plt.subplot2grid(shape=(6, 3), loc=(5, 0), colspan=1)
+
+frequency = 400   # Frequency
+duration = 0.01   # Duration of sound
+amplitude = 1.0   # Amplitude
+phase = 0.75      # Phase
+Fs = 22050        # Sampling rate (per second)
+
+num_samples = int(Fs * duration) 
+t = np.arange(num_samples) / Fs
+x = amplitude * np.sin(2 * np.pi * (frequency * t - phase))
+
+ax1.plot(t, x, color='red', linewidth=2.0, linestyle='-')
+ax1.set_xlabel('Time (s)')
+ax1.set_title("Sine (400 Hz)")
+ax1.set_ylabel('Air pressure deviation')
+ax1.set_ylim([-1.05, 1.05])
+ax1.set_yticks(np.arange(-1, 1.5, 1.0)) 
+ax1.set_xlim([0.0, 0.01])
+ax1.set_xticks(np.arange(0, 0.0125, 0.0025)) 
+ax1.grid()
+
+ax1.annotate('', xy=(0.0025, 0), xytext=(0.0025, 1), 
+             arrowprops=dict(arrowstyle='<->', 
+             mutation_scale=15, color='0.3'), size=2)
+ax1.text(0.0025, 0.5, "Amplitude", size=12, color='0.3', 
+         ha="center", va="center")
+ax1.annotate('', xy=(0, 1), xytext=(0.0025, 1), 
+             arrowprops=dict(arrowstyle='<->', mutation_scale=19, 
+             color='0.3'), size=2)
+ax1.text(0.00125, 0.85, "Period", size=12, 
+         color='0.3', ha="center", va="center")
+
+# Combine several sine waves (here are three frequencies)
+frequency1 = 400  
+frequency2 = 600
+frequency3 = 1600
+duration = 0.01
+amplitude = 1.0
+phase = 0.75
+Fs = 20050
+
+num_samples = int(Fs * duration)
+t = np.arange(num_samples) / Fs
+x1 = amplitude * np.sin(2 * np.pi * (frequency1 * t - phase))  # 1st sine
+x2 = amplitude * np.sin(2 * np.pi * (frequency2 * t - phase))  # 2nd sine
+x3 = amplitude * np.sin(2 * np.pi * (frequency3 * t - phase))  # 3rd sine
+
+ax2.plot(t, x1, color='red')
+ax4.plot(t, x2, color='red')
+ax5.plot(t, x3, color='red')
+
+ax2.set_title("400 Hz")
+ax4.set_title("600 Hz")
+ax5.set_title("1600 Hz")
+
+ax2.set_xticks(np.arange(0, 0.0125, 0.0025)) 
+ax2.set_xlim([0.0, 0.01])
+ax2.set_yticks(np.arange(-1, 1.5, 1.0)) 
+
+ax4.set_xticks(np.arange(0, 0.0125, 0.0025)) 
+ax4.set_xlim([0.0, 0.01])
+ax4.set_yticks(np.arange(-1, 1.5, 1.0)) 
+
+ax5.set_xticks(np.arange(0, 0.0125, 0.0025)) 
+ax5.set_xlim([0.0, 0.01])
+ax5.set_yticks(np.arange(-1, 1.5, 1.0)) 
+
+fig.subplots_adjust(hspace=.001, wspace=0.5)
+
+# Combine all three (sum and divide by 3 to keep the amplitude as original)
+x123 = (x1+x2+x3)/3
+
+ax3.plot(t, x123, color='blue', linewidth=2.0, linestyle='-')
+ax3.set_xlabel('Time (s)')
+ax3.set_title("Complex tone (sines of 400 Hz + 600 Hz + 1600 Hz)")
+ax3.set_ylabel('')
+ax3.set_ylim([-1.01, 1.01])
+ax3.set_xlim([0, 0.01])
+ax3.set_xticks(np.arange(0, 0.0125, 0.0025)) 
+ax3.set_yticks(np.arange(-1, 1.5, 1.0)) 
+ax3.grid()
+fig.tight_layout()
+
+
+ax2.annotate('', xy=(1.11/100, -9.3), xytext=(1.01/100, 0), 
+             arrowprops=dict(width=0.5, headlength=3, headwidth=3, 
+             color='0.3'), size=2, annotation_clip=False)
+ax4.annotate('', xy=(1.063/100, 0), xytext=(1.01/100, 0), 
+             arrowprops=dict(width=0.5, headlength=3, headwidth=3, 
+             color='0.3'), size=2, annotation_clip=False)
+ax5.annotate('', xy=(1.11/100, 9.3), xytext=(1.01/100, 0),
+             arrowprops=dict(width=0.5, headlength=3, headwidth=3, 
+             color='0.3'), size=2, annotation_clip=False)
+ax4.text(1.09/100, -0.6, r'$\sum$', size=9, backgroundcolor='0.8')
+
+plt.show()
+
+```