# Simple plot

# import necessary libraries
import matplotlib.pyplot as plt
import numpy as np

# define number of samples to generate
n = 300
# create linear space from -x to x
X = np.linspace(-5, 5, n, endpoint=True)
# create vectors
A, B = np.cos(X), np.sin(X)
# plot cosine
plt.plot(X, A)
# plot sine
plt.plot(X, B)

# show finished plot
plt.show()

# plot cosine
plt.plot(X, A, color="green", linewidth=2.1, linestyle=":")
# plot sine
plt.plot(X, B, color="violet",  linewidth=2.3, linestyle="--")
#set x and y limits to 120% of extremes
plt.xlim(-5, 5)
plt.ylim(-1.1, 1.1)

# create latex-style labels for ticks
plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
          [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$'])

plt.yticks([-1, 0, +1],
          [r'min', r'0', r'max'])

labels = ['I', 'II', 'III', 'IV', 'V', 'VI', 'VII']
women_median_weight_PL = [44, 59, 68, 76, 88, 100, 120]
men_median_weight_PL = [69, 88, 90, 110, 132, 148, 155]

x = np.arange(len(labels))  # the label locations
width = 0.5  # the width of the bars

fig, ax = plt.subplots()
A = ax.bar(x + width/2, women_median_weight_PL, width, label='Women weight in Poland')
B = ax.bar(x - width/2, men_median_weight_PL, width, label='Men weight in Poland')

# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_ylabel('Weight [kg]')
ax.set_title('Scores for man and woman in Poland')
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.legend()

def autolabel(rects):
    """Attach a text label above each bar in *rects*, displaying its height."""
    for rect in rects:
        height = rect.get_height()
        ax.annotate('{}'.format(height),
                    xy=(rect.get_x() + rect.get_width() / 2, height),
                    xytext=(0, 3),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')

autolabel(A)
autolabel(B)
fig.tight_layout()
plt.show()

df = pd.read_csv('sunsetgirlsql_countryviews_12312020.csv', sep=',')
df = df.sort_values('Views', ascending=False)
print(df)

#Visualisation
# Filtering Views Value:between 10 to 1000.
df = df[(df['Views'] >= 10) & (df['Views'] <= 1000)]
# X-axis:
x = df['Views']
# Y-axis:
y = df['Country']
# Size of the figure (in inches).
plt.figure(figsize=(20,7))

#Scatter
plt.scatter(x, y,s = 100, color = 'violet',
            alpha = 0.8, marker = '.', 
            edgecolors='green')
# X-axis label.
plt.xlabel('Views', fontsize = 16)
# Y-axis label.
plt.ylabel('Country', fontsize = 16)

# Title of the plot.
plt.title('Heart4DataScience Mostly View\nby Country in 2020', 
          fontsize = 20)
# Grid
plt.grid(axis='y')

# Displays the plot.
plt.show()
# Clears the current figure contents.
plt.clf()