def uwpt_plot(handles, uwpt, color_dict):
'''
plot unique word count
wc is list
'''
x_offset = .1 # offsets wrt x & y position for bar legends
y_offset = .5
fontsize = 10
x_pos = np.arange(len(handles))
title = 'Tweet diversity'
word_count = np.zeros(len(handles))
for i, handle in enumerate(handles):
word_count[i] = uwpt[i]
print(f'[uwpt_plot]: word count {handles[i]}={int(word_count[i]):.2f}')
fig, ax = build_base_barplot(handles, word_count, x_pos, title, '', 'unique words / tweet', color_dict)
for j in range(3):
ax.text(x_pos[j] - x_offset,
word_count[j] - y_offset,
f'{word_count[j]:.2f}',
rotation=0,
color='black',
fontsize=fontsize)
plt.savefig(clean_filename(title , 'png', plot_directory))
plt.show()
def unique_word_volume_plot(handles, wc, color_dict):
'''
plot unique word count
wc is list of dictionaries
'''
x_offset = .125 # offsets wrt x & y position for bar legends
y_offset = 500
fontsize = 10
x_pos = np.arange(len(handles))
title = 'Unique word volume'
word_count = np.zeros(len(handles))
for i, handle in enumerate(handles):
word_count[i] = int(wc[i])
print(f'word count {handles[i]}={int(word_count[i]):d}')
fig, ax = build_base_barplot(handles, word_count, x_pos, title, '', '# words', color_dict)
for j in range(3):
ax.text(x_pos[j] - x_offset,
word_count[j] - y_offset,
f'{int(word_count[j]):d}',
rotation=0,
color='black',
fontsize=fontsize)
plt.savefig(clean_filename(title , 'png', plot_directory))
plt.show()
def tweet_count_plot(bars1, bars2, handles):
barWidth = 0.4 # set width of bar
colors = ['tan', 'yellowgreen']
labels = ['Raw tweets', 'Processed']
bars = [bars1, bars2]
arr = np.array(bars)
# Set position of bar on X axis
r = list()
r.append(np.arange(len(bars[0])) + barWidth/2)
r.append([x + barWidth for x in r[0]])
# Make the plot
for i in range(2):
plt.bar(r[i],
bars[i],
color=colors[i],
width=barWidth,
edgecolor='white',
label=labels[i])
text_x_offset = .06
text_y_offset = 250
for i in range(2):
for j in range(3):
plt.text(r[i][j]-barWidth/2 + text_x_offset,
bars[i][j] - text_y_offset,
f'{bars[i][j]/np.sum(arr, axis=1)[i]*100:.1f}%',
rotation=0,
color='black',
fontsize=10)
# Add ticks on the middle of the group bars
plt.ylabel('# tweets', fontweight='bold')
plt.xticks([r + barWidth for r in range(len(bars1))], handles)
title = 'Tweet count'
plt.title(title)
# Create legend show graphic & save to file
plt.legend()
title = 'Tweet pre-processing'
plt.savefig(clean_filename(title , 'png', plot_directory))
plt.show()
def tweeting_period_plot(handles, start, end, color_dict):
'''Plot tweeting period from start and end dates'''
fontsize = 14
names = get_names()
ndays = get_ndays(start, end)
fig, ax = plt.subplots(figsize=(18,4.875))
y_pos = np.arange(len(handles))
barlist=ax.barh(y_pos, ndays, .75)
for i, handle in enumerate(handles):
barlist[i].set_color(color_dict[handle])
# Annotate bars
end_x = .89
start_x = [.05, .275, .55]
y_val = [.815, .485, .15]
for i, handle in enumerate(handles):
ax.annotate('<-- ' + start[i],
xy=(start_x[i], y_val[i]),
xycoords = 'axes fraction',
fontsize = fontsize)
ax.annotate(end[i] + ' -->',
xy=(end_x, y_val[i]),
xycoords = 'axes fraction',
fontsize = fontsize)
plt.gca().invert_xaxis() # right to left
ax.set_yticks(y_pos)
ax.set_yticklabels(names)
ax.invert_yaxis() # labels read top-to-bottom
ax.set_xlabel('# of days')
title = 'Tweeting time period'
ax.set_title(title, fontsize = 18)
plt.savefig(clean_filename(title , 'png', plot_directory))
plt.show()
def plot_distribution(dic, name, prefix, xlabel, max_x, max_y, color, mu, sigma):
width = .85
annot_color = 'saddlebrown'
X = dic.keys()
Y = dic.values()
plt.bar(X, Y, width, color=color)
plt.axis([0, max_x, 0, max_y])
# build vertical bar for mean with legend
plt.axvline(x=mu, color = annot_color, linestyle='--')
annot = rf'$\mu={mu:.1f}$ $\sigma={sigma:.1f}$'
plt.text(mu+.25, 0.95*max_y, annot, rotation=0, color=annot_color)
# build title and axes labels
title = build_title(prefix, name, '')
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel('count')
plt.savefig(clean_filename(title , 'png', plot_directory))
plt.show()
def tweet_frequency_plot(handles, start, end, ntweets, color_dict):
x_offset = .075 # offsets wrt x & y position for bar legends
y_offset = .5
annot_color = 'saddlebrown'
fontsize = 12
x_pos = np.arange(len(handles))
ndays = get_ndays(start, end)
title = 'Tweet frequency'
freq = list()
mu = 0 # compute mean frequency
for i, handle in enumerate(handles):
freq.append(ntweets[i]/ndays[i])
mu += ntweets[i]
mu /= np.sum(ndays)
print(f'Mean frequency={mu}')
fig, ax = build_base_barplot(handles, freq, x_pos, title, '', '# tweets/day', color_dict)
for j in range(3):
ax.text(x_pos[j] - x_offset,
freq[j] - y_offset,
f'{freq[j]:.1f}',
rotation=0,
color='black',
fontsize=fontsize)
# build vertical bar for mean with legend
plt.axhline(y=mu, color = annot_color, linestyle='--')
annot = rf'$\mu$=' + str(f"{mu:.1f}")
plt.text(-.4, mu+.2, annot, rotation=0, color=annot_color)
plt.savefig(clean_filename(title , 'png', plot_directory))
plt.show()