def df_ctr(Country):
'''
Parameter
- Country: name of a country or "The Whole World" | str
Return
a DataFrame with selected features
'''
if Country == 'The Whole World':
df_ctr = data.load_df()
else:
df_ctr = data.load_df()[data.load_df().country == Country]
return df_ctr.drop(['eventid', 'latitude', 'longitude'], 1)
def df_occur_by_ctr(ctr_name):
'''
Parameter
- ctr_name: country name or "The Whole World" | str
Return
- number of attacks in the chosen country
across the whole time series
(non-attack years not included) | DataFrame
'''
df = data.load_df()
if ctr_name == 'The Whole World':
df_all_c = df
# It is necessary to convert the variable "df_call_c"
# to the transposing DataFrame format,
# for countries with only one time attack occurrence.
# Otherwise will cause bugs
else:
df_all_c = set_as_index(df, 'country').ix[ctr_name]
if type(df_all_c
) == pd.Series: # for those countries with only one attack
df_all_c = pd.DataFrame(df_all_c).T
df_yr = pd.DataFrame(df_all_c.groupby('year').count().eventid)
# rename the 'count' column as 'occurrences' to avoid unexpected potential misusage
df_yr.columns = ['occurrences']
return df_yr.reset_index()
def main():
tweetfile = "data/tweets/clean/clean.csv"
# df = load_df(tweetfile)
# some shitty CLI
args = sys.argv[1:]
if len(args) < 2:
print(
"LOL. Please input in the format <loopback value> <word1> <word2> ..."
)
print("Example: tweetgen2.py 2 my life")
return
n = int(args[0])
initial_words = args[1:]
mc = MarkovChain(lookback=n)
mc.train(load_df(tweetfile)['text'].values.tolist())
# initial_words = ['we', 'tend', 'to']
# initial_words = ['life', 'is']
tweet = mc.generate(initial_words)
print("Generated tweet::\n{}".format(tweet))
print('-' * 30)
print("After preprocessing <SENTENCE>::\n{}".format(
preprocess_sentence(tweet)))
def gtd_country_names():
'''
Return a list of:
- all country names in alphabetical order, plus
- 'The Whole World'
'''
all_ctr = sorted(data.load_df().country.unique())
all_ctr.insert(0, 'The Whole World')
return all_ctr
def df_sel_btw_years(year_interval):
'''
Parameter
- year_interval: Time Interval | tuple
Return
- all data in the chosen time interval | DataFrame
'''
gt_df = data.load_df()
df_intv = gt_df[(gt_df.year <= year_interval[1])
& (gt_df.year >= year_interval[0])]
return df_intv
def main():
tweetfile = "data/tweets/clean/clean.csv"
df = load_df(tweetfile)
text = "\n".join(df['text'].values.tolist()).strip()
pairs = create_pairs(text)
trie = build_trie(pairs)
generated_words = generate1(trie,
initial_word='i',
max_len=15,
verbose=False)
generated_text = ' '.join(generated_words)
print("Generated tweet::\n{}".format(generated_text))
print('-' * 30)
print("After preprocessing <SENTENCE>::\n{}".format(
preprocess_sentence(generated_text)))
def plot_2D_density(Year, MapStyle):
'''
Parameters
- Year : between 1970-2015 | str
- MapStyle : style palette | str
Return
A 2D Geo Map: The denser the red marker in a country,
the more severe damages had taken place.
'''
# use regular expression to check the format
if not re.match(r'[\[|\(][0-9]{4}\,\s?[0-9]{4}[\]|\)]$', str(Year)):
raise NotIntervalError
# the starting year should be less than the ending yer
elif Year[0] >= Year[1]:
raise IntervalReverseError
# catch the missing value exceptions in 1993
elif Year == (1993, 1993):
print('Data of 1993 is not available in Global Terrorism Database.\n\
Click the link to learn why.\nhttps://www.start.umd.edu/gtd/faq/')
# catch the out of range yer interval input errors
elif (Year[0] < 1970) or (Year[1] > 2015):
raise IntervalLeakError
else:
if Year[0] == Year[
1]: # catch the excetion the starting year and the ending year converge
df_gt = data.load_df()
df = df_gt[df_gt.year == Year[0]]
else:
df = ut.df_sel_btw_years(Year)
plt.figure(figsize=(18, 10), frameon=False)
m = Basemap('mill')
m.drawcountries(linewidth=0.5,
linestyle='solid',
color='white',
antialiased=1,
ax=None,
zorder=None)
# Background settings
if MapStyle == 'Blue Marble':
m.drawcoastlines()
m.bluemarble()
elif MapStyle == 'Etopo':
m.etopo()
else:
m.drawcoastlines(color='w')
m.drawcountries(color='w')
m.drawstates(color='w')
m.fillcontinents(color='lightblue', lake_color='w')
m.drawmapboundary(fill_color='w', color='w')
# get latitude and longitude
lat = ut.make_array(df, 'latitude')
lon = ut.make_array(df, 'longitude')
x, y = m(lon, lat)
m.plot(x, y, 'r^', marker='o', markersize=4, alpha=.3)
plt.title('Global Attack Density Plot: {}-{}'.format(Year[0], Year[1]),
size=16)
plt.show()
