def findCountriesLang():
"""Find names of countries and their official languages for all countries in “North America”"""
officialLang_df = countryLang_df[countryLang_df['IsOfficial'] == 'T']
merged_df = country_df.merge(officialLang_df,
how='left',
left_on="Code",
right_on="CountryCode")
merged_df = merged_df[(merged_df['Continent'] == 'North America'
)].loc[:, ['Name', 'Language']].sort_values(
by=['Name'])
merged_df['Language'] = merged_df['Language'].fillna('None')
langaugeLists = merged_df.groupby(['Name'])['Language'].apply(list)
formatted_df = pd.DataFrame({
'Name': np.unique(merged_df['Name']),
'languages': langaugeLists
})
vals = formatted_df.values
#For loop for sake of printing:
for country in vals:
str1 = ''
str1 += country[0]
str1 += ', '
str1 += listToString(country[1])
print(str1)
return None
def findCountriesLang():
"""Find names of countries and their official languages for all countries in “North America”"""
url = 'https://hw1-dsci551-31309.firebaseio.com/world/country_nested.json?orderBy="Continent"&equalTo="North America"&print=pretty'
response = requests.get(url)
perf_file = open("performance.txt","a")
perf_file.write("firebase-B-nested: Requests Made - 1, Size of Download (Bytes) - ")
perf_file.write(str(len(response.content)))
perf_file.write("\n\n")
perf_file.close()
countryNested_data = response.json()
data = {}
for code in countryNested_data.keys():
lang_d = countryNested_data[code]['languages']
official = []
for lang in lang_d.keys():
if lang_d[lang]['IsOfficial'] == "T":
official.append(lang)
data[countryNested_data[code]['Name']] = listToString(official)
for k,v in data.items():
if len(v) == 0:
data[k] = 'None'
data = dict(sorted(data.items()))
for k,v in data.items():
str1 = k
str1 += ', '
str1 += v
print(str1)
return None
def findCountriesLang():
"""Find names of countries and their official languages for all countries in “North America”"""
data = {}
for country in country_data:
if country['Continent'] == 'North America':
country_code = country['Code']
name = country['Name']
lang_true = []
for countryLang in countryLang_data:
if countryLang['CountryCode'] == country_code and countryLang[
'IsOfficial'] == 'T':
lang_true.append(countryLang['Language'])
data[name] = lang_true
#Change empty to 'None':
for k, v in data.items():
if len(v) == 0:
data[k] = ['None']
data = dict(sorted(data.items()))
for k, v in data.items():
str1 = k
str1 += ', '
str1 += listToString(v)
print(str1)
return None
def findCountriesCapitals():
"""Find names of countries and their capital cities for all countries in “North America”"""
merged_df = country_df.merge(city_df, how = 'left', left_on = "Capital", right_on = "ID").rename(columns = {'Name_x': 'CountryName', 'Name_y': 'CapitalName'})
merged_df = merged_df.loc[:, ['CountryName', 'CapitalName', 'Continent']]
vals = merged_df[merged_df['Continent'] == 'North America'][['CountryName', 'CapitalName']].sort_values(by=['CountryName']).values
#For loop for sake of printing:
for country in vals:
print(listToString(country))
return None
zPresp = resptf.root.zPresp.read()
mask = resptf.root.mask.read()
# Print matrix shapes
print("zRresp shape (num time points, num voxels): ", zRresp.shape)
print("zPresp shape (num time points, num voxels): ", zPresp.shape)
print("mask shape (Z, Y, X): ", mask.shape)
R_texts = []
P_texts = []
R_voxels_to_be_removed = []
P_voxels_to_be_removed = []
for index in range(len(Rstim)):
sList = Rstim[index]
if len(sList) > 0:
R_texts.append(listToString(sList))
else:
R_voxels_to_be_removed.append(index)
#Not sure if we need to add held-out set stimuli
for index in range(len(Pstim)):
sList = Pstim[index]
if len(sList) > 0:
P_texts.append(listToString(sList))
else:
P_voxels_to_be_removed.append(index)
print("Number of R sentences: ")
print(len(R_texts))
print("Number of P sentences: ")
print(len(P_texts))