def merge_json():
final_dict = {}
dept_dict = read_json("year_dept.json")
category_dict = read_json("year_category.json")
for each in dept_dict:
each["CREATION YEAR"] = str(each["CREATION YEAR"])
each["ZIP CODE"] = str(int(each["ZIP CODE"]))
if each["CREATION YEAR"] not in final_dict.keys():
final_dict[each["CREATION YEAR"]] = {}
if each["ZIP CODE"] not in final_dict[each["CREATION YEAR"]].keys():
final_dict[each["CREATION YEAR"]][each["ZIP CODE"]] = {}
if "DEPARTMENT" not in final_dict[each["CREATION YEAR"]][each["ZIP CODE"]].keys():
final_dict[each["CREATION YEAR"]][each["ZIP CODE"]]["DEPARTMENT"] = []
dept = {"name": each["DEPARTMENT"], "count": each["COUNT"]}
final_dict[each["CREATION YEAR"]][each["ZIP CODE"]]["DEPARTMENT"].append(dept)
for each in category_dict:
each["CREATION YEAR"] = str(each["CREATION YEAR"])
each["ZIP CODE"] = str(int(each["ZIP CODE"]))
if each["CREATION YEAR"] not in final_dict.keys():
final_dict[each["CREATION YEAR"]] = {}
if each["ZIP CODE"] not in final_dict[each["CREATION YEAR"]].keys():
final_dict[each["CREATION YEAR"]][each["ZIP CODE"]] = {}
if "CATEGORY" not in final_dict[each["CREATION YEAR"]][each["ZIP CODE"]].keys():
final_dict[each["CREATION YEAR"]][each["ZIP CODE"]]["CATEGORY"] = []
dept = {"name": each["CATEGORY"], "count": each["COUNT"]}
final_dict[each["CREATION YEAR"]][each["ZIP CODE"]]["CATEGORY"].append(dept)
write_json(final_dict, "final.json")
def process_time_series_data():
print("values.")
data_311 = pd.read_csv(final_data)
data_311 = data_311[data_311['CREATION YEAR'] >= 2015]
data_311['DAYS TO CLOSE'] = data_311['DAYS TO CLOSE'].apply(lambda x: str(x).replace(",", ""))
data_311['DAYS TO CLOSE'] = data_311['DAYS TO CLOSE'].astype("float64")
time_series = data_311.groupby(by=['CREATION YEAR', 'CREATION MONTH', 'ZIP CODE', 'DEPARTMENT'])[
'DAYS TO CLOSE'].mean().reset_index().to_dict('records')
write_json(time_series, "time_series.json")
def process_final_data():
data_311 = pd.read_csv(final_data)
data_311 = data_311[data_311['CREATION YEAR'] >= 2015]
list_records_dept = data_311.groupby(by=['CREATION YEAR', 'ZIP CODE', 'DEPARTMENT'])['CASE ID'].count().reset_index(
name='COUNT').to_dict('records')
list_records_category = data_311.groupby(by=['CREATION YEAR', 'ZIP CODE', 'CATEGORY'])[
'CASE ID'].count().reset_index(
name='COUNT').to_dict('records')
write_json(list_records_dept, "year_dept.json")
write_json(list_records_category, "year_category.json")
def save_result_json(output_path, imgname, res):
"""
把生成的坐标和图片保存
"""
img_op = os.path.join(output_path, "images")
file_utils.check_path(img_op)
msg_op = os.path.join(output_path, "message")
file_utils.check_path(msg_op)
txt_op = os.path.join(output_path, "labels")
file_utils.check_path(txt_op)
ninstance = len(res)
for i in range(ninstance):
print(colorize(Color.GREEN, 'added into the db %s ' % res[i]['txt']))
temp = res[i]
img = temp['img']
word_bb = temp['wordBB']
char_bb = temp['charBB']
txt = temp['txt']
# TODO 有换行符的是两个box
new_text = []
for line in txt:
arr = line.split("\n")
new_text.extend(arr)
# (n,4,2) 把顺序调整为正常
word_boxes = np.transpose(word_bb).astype('uint8')
#
dname = "%s_%d" % (imgname, i) + ".jpg"
print(dname, new_text)
img_file = os.path.join(img_op, dname)
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
cv2.imwrite(img_file, img)
result_json = {
'text': new_text,
'word_pos': word_bb.tolist(),
'char_pos': char_bb.tolist()
}
json_utils.write_json(msg_op, dname, result_json)
# TODO 统一格式
# 写 labels 四点坐标+ 文本，逗号隔开
txt_name = os.path.splitext(dname)[0] + ".txt"
f2 = open(os.path.join(txt_op, txt_name), 'w', encoding='utf-8')
writer = csv.writer(f2)
for j in range(len(txt)):
box = word_boxes[j]
word = new_text[j]
line = np.append(box.reshape(-1), word)
writer.writerow(line)
f2.close()
def process_dept_df():
counter = 0
dept_df = final_data[['CASE ID', 'DESCRIPTION', 'DEPARTMENT']]
for each in list(dept_df.DEPARTMENT.unique()):
dept_category[str(counter)] = each
counter += 1
write_json(dept_category, dept_json)
print('finished')
dept_df['label'] = dept_df.DEPARTMENT.apply(apply_dept_label)
dept_df.rename(columns={
'CASE ID': 'u_id',
'DESCRIPTION': 'desc'
},
inplace=True)
write_to_csv(dept_df, dept_file)
def process_prob_df():
counter = 0
print(list(final_data['REQUEST TYPE'].unique()))
final_data['label'] = final_data['REQUEST TYPE'].apply(apply_prob)
print(list(final_data.label.unique()))
for each in list(final_data.label.unique()):
prob_category[str(counter)] = each
counter += 1
write_json(prob_category, prob_json)
final_data['label'] = final_data['label'].apply(apply_prob_label)
prob_df = final_data[['CASE ID', 'DESCRIPTION', 'label']]
prob_df.rename(columns={
'CASE ID': 'u_id',
'DESCRIPTION': 'desc'
},
inplace=True)
write_to_csv(prob_df, prob_file)
print('finished')
def save_log(self, filename, env_to_file=None):
assert not filename is None, "filename not supplied. save_log failed"
lg = deepcopy(self.log)
for _, drone_log in lg["drones"].items():
drone_log["info"] = drone_log["info"].to_JSONable()
for step, step_log in drone_log["trajectory"].items():
drone_log["trajectory"][step] = step_log.to_JSONable()
if isinstance(self.log["environment"], str):
lg["environment"] = {"path": self.log["environment"]}
else:
if env_to_file is None:
lg["environment"] = self.log["environment"].to_JSONable()
else:
lg["environment"] = {"path": env_to_file}
write_json(env_to_file, self.log["environment"].to_JSONable())
write_json(filename, lg)
def get_movie_info():
headers = {'User-Agent': 'Mozilla/5.0 xxxxxx'}
basel = 'https://movie.douban.com/subject/1292213/'
html = requests.get(basel,
headers=headers).content.decode('utf-8', 'ignore')
url_content = re.search(
r'"@context": "http://schema.org",(.*?)"ratingValue": "9.2"', html,
re.S)
texts = url_content.group() # 获取匹配正则表达式的整体结果
texts = str("{" + texts + "}}")
# important
data = json.loads(texts, strict=False)
movie_info = {
'name': data['name'],
'author': data['author'],
'actor': data['actor'],
'director': data['director']
}
print(movie_info)
ju.write_json(data, r'data/data.json')
def process_json():
time_series_dict = read_json("time_series.json")
final_dict = {}
for each in time_series_dict:
each["CREATION YEAR"] = str(each["CREATION YEAR"])
each["ZIP CODE"] = str(int(each["ZIP CODE"]))
each["CREATION MONTH"] = str(each["CREATION MONTH"])
if each["CREATION YEAR"] not in final_dict.keys():
final_dict[each["CREATION YEAR"]] = {}
if each["ZIP CODE"] not in final_dict[each["CREATION YEAR"]].keys():
final_dict[each["CREATION YEAR"]][each["ZIP CODE"]] = {}
if "DEPARTMENT" not in final_dict[each["CREATION YEAR"]][each["ZIP CODE"]].keys():
final_dict[each["CREATION YEAR"]][each["ZIP CODE"]]["DEPARTMENT"] = []
dept = {
"CREATION MONTH": each["CREATION MONTH"],
"NAME": each["DEPARTMENT"],
"COUNT": each["DAYS TO CLOSE"]
}
final_dict[each["CREATION YEAR"]][each["ZIP CODE"]]["DEPARTMENT"].append(dept)
write_json(final_dict, "time_series_final.json")
if DBG:
print "Item satisfies auto_contact_sms for filter but already contacted", f
if f.satisfies_auto_contact_email(item):
if DBG:
print "Item satisfies auto_contact_email for filter", f
if not already_auto_contacted_email(item):
# Auto contact
if not auto_contact_email(item, f):
printe("ERROR, something went wrong while trying to auto_contact_email")
else:
if DBG:
print "Item satisfies auto_contact_email for filter but already contacted", f
f_existing_items = open(FILTERED_ITEMS_FILEPATH, 'w+')
total_items = already_existing_items.values() + new_items
# While debugging, use pretty-printed JSON, when not debugging anymore, use compact notation for space efficiency
if DBG:
json_repr = json.dumps(total_items, indent=4, separators=(',', ': '))
else:
json_repr = json.dumps(total_items)
f_existing_items.write(json_repr)
f_existing_items.close()
write_json(passed_alerts, PASSED_ALERTS_FILEPATH)
write_json(passed_mail_contacts, PASSED_MAILS_FILEPATH)
write_json(passed_sms_contacts, PASSED_SMS_FILEPATH)
def dump_data(self):
print "Dumping data to %s" % self.fname_log_items
write_json(self.log_items, self.fname_log_items)
print "Dumping data to %s" % self.fname_serp_items
write_json(self.serp_items, self.fname_serp_items)
for word in segment:
if word.strip() not in stopwords:
if len(word) > 1:
if word != '\t':
if word != '\r\n':
# 计算词频
if word in word_:
word_[word] += 1
else:
word_[word] = 1
print(word_)
len(word_)
del word_['nbsp']
ju.write_json(word_, os.getcwd() + r"/test/text_data.json")
word_ = sorted(word_.items(), key=lambda x: x[1], reverse=True)
dic_temp = {}
for word in word_:
dic_temp[word[0]] = word[1]
ju.write_json(dic_temp, os.getcwd() + r"/test/text_data.json")
dic_temp["姜明"]
for word in word_:
name.append(word[0])
value.append(word[1])
name.index("雪艳姐")
value[184]
generatepath = os.getcwd() + r"/test/test_cloud.html"
name[:200]
value[:200]
def close_spider(self, spider):
print "Dumping data to %s" % self.fname
write_json(self.items, self.fname)
