#average map consumption_var = np.sum(np.diff(consumption, axis = 1),axis = 1) consumption_var = ma.masked_where(isnan(consumption_var) == 1, consumption_var) consumption_ref = np.mean(consumption, axis = 1) consumption_ref = ma.masked_where(isnan(consumption_ref) == 1, consumption_ref) consumption_change = consumption_var / consumption_ref np.average(consumption_change, weights = weights) np.average(consumption_change[mask==0], weights = weights[mask==0]) map_consumption = module.make_map(consumption_change, filter_array, idlist, spatial_unit) module.new_map_netcdf(Input.outputDir + '/'+ 'map_consumption_change_1960to2010', map_consumption, 'consumption_change', '%', latitude, longitude) plt.imshow(map_consumption) plt.hist(consumption_change) np.max(consumption_change) np.min(consumption_change)
"""
33. サ変名詞
サ変接続の名詞をすべて抽出せよ．
"""
from module import make_map
lines = make_map()
st = set()
for line in lines:
for data in line:
if data["pos"] == "名詞" and data["pos1"] == "サ変接続":
st.add(data["base"])
print(st)
"""
34. 「AのB」
2つの名詞が「の」で連結されている名詞句を抽出せよ．
"""
from module import make_map
lines = make_map()
st = set()
for line in make_map():
for i in range(len(line)):
if line[i]["surface"] == "の":
if i == 0 or i == len(line) - 1:
continue
if line[i - 1]["pos"] == "名詞" and line[i + 1]["pos"] == "名詞":
st.add(line[i - 1]["surface"] + line[i]["surface"] + line[i + 1]["surface"])
print(st)