def plot_inflow_of_all_main_river(title="四川当月水库蓄水量信息.html",
day_delta=30,
path="source"):
"""
绘制四川当月所有河流的蓄水量信息。
:param title:
:return:
"""
engine = init_engine(db="power_supply")
datetime_now = datetime.datetime.now()
last_month = (
datetime_now -
datetime.timedelta(days=day_delta)).strftime("%Y-%m-%d 00:00:00")
df1 = pd.read_sql(
"SELECT * FROM sc_main_river_storage WHERE official_update_time> '{last_month}'"
.format(last_month=last_month), engine)
line2 = Line("全流域蓄水量", width="1600px", height="800px")
df1_1 = df1.groupby(axis=0,
by=["official_update_time"
]).aggregate({"water_storage_capacity": "sum"})
df1_2 = df1_1.reset_index()
line2.add(
"全流域河流蓄水量图",
df1_2["official_update_time"].map(lambda x: x.strftime("%Y-%m-%d")),
df1_2["water_storage_capacity"])
line2.render(title)
def chart(lists):
bar = Line("学生成绩平均绩点折线图")
bar.add(
"平均绩点GPA", ["大一（上）", "大一（下）", "大二（上）", "大二（下）", "大三（上）", "大三（下）"],
[lists[0], lists[1], lists[2], lists[3], lists[4], lists[5], lists[6]],
is_more_utils=True)
bar.show_config()
bar.render()
#shutil.move('/home/cris/system/flask/render.html','/home/cris/system/flask/templates/html')
with open('/home/cris/system/flask/render.html', 'r') as f:
hello = f.read()
abc = hello
with open('/home/cris/system/flask/templates/student.html', 'w') as f1:
f1.write("{% extends 'base.html' %}")
f1.write('\n')
f1.write("{% block page_name %}你好,{{login_user}}{% endblock %}")
f1.write('\n')
f1.write("{% block body_part1 %}你好,{{login_user}}{% endblock %}")
f1.write('\n')
f1.write("{% block body_part2 %}")
f1.write('\n')
f1.write(abc)
f1.write('\n')
f1.write("{% endblock %}")
def test_line_type():
# line is_stack
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 10, 100]
v2 = [55, 60, 16, 20, 15, 80]
line = Line("折线图-数据堆叠示例")
line.add("商家A", attr, v1, is_stack=True, is_label_show=True)
line.add("商家B", attr, v2, is_stack=True, is_label_show=True)
line.render()
# line is_step
line = Line("折线图-阶梯图示例")
line.add("商家A", attr, v1, is_step=True, is_label_show=True)
assert '"step": true' in line._repr_html_()
# line is_fill
line = Line("折线图-面积图示例")
line.add("商家A", attr, v1, is_fill=True, line_opacity=0.2,
area_opacity=0.4, symbol=None)
line.add("商家B", attr, v2, is_fill=True, area_color='#000',
area_opacity=0.3, is_smooth=True)
assert '"step": true' not in line._repr_html_()
# line yAxis type 'log'
import math
import random
line = Line("折线图示例")
line.add("商家A", attr,
[math.log10(random.randint(1, 99999)) for _ in range(6)])
line.add("商家B", attr,
[math.log10(random.randint(1, 99999999)) for _ in range(6)],
yaxis_type="log")
line.render()
def showJson(data):
attr = []
v1 = [] # 鱼类养殖面积
v2 = [] # 海水养殖面积
print(data[0]['省(市)名称'])
for i in range(len(data)):
print(data[i])
attr.append(data[i]['采集年份'])
v1.append(data[i]['鱼类养殖面积'])
v2.append(data[i]['海水养殖面积'])
for j in data[i]:
print('-------------------' + j)
print(attr)
line = Line("折线图-面积图示例")
line.add("商家A",
attr,
v1,
is_fill=True,
line_opacity=0.2,
area_opacity=0.4,
symbol=None)
line.add("商家B",
attr,
v2,
is_fill=True,
area_color='#000',
area_opacity=0.3,
is_smooth=True)
# line.show_config()
line.render()
def mutual_info_ex(param):
params = common.parse_param(param)
path_a = params[0]
ids = []
mis = []
for i in range(5):
mis.append([])
j = 0
for path in params:
res = _mutual_info_ex(path, path_a)
if j != 0:
ids.append(j)
for i in range(5):
mis[i].append(res[i])
pprint(res)
j = j + 1
bar = Line("我的第一个图表", "这里是副标题")
num_mis = len(mis)
for index in range(num_mis):
num_ele = len(mis[index])
max_n = max(mis[index])
for ele_i in range(num_ele):
mis[index][ele_i] = mis[index][ele_i] / max_n
for i in range(5):
bar.add("mi" + str(i), ids, mis[i], is_more_utils=True)
bar.render()
pprint(mis)
return
def generate(df, flg, stockCode, stockName, is_gen_single):
Y_AXIS_NAMES = ['收盘价', '温度']
if flg is 'stock_A':
Y_AXIS_NAMES = ['前复权', '温度']
dates = df['date']
y_axises2 = []
y_axises2.append(df['absolute_temp'])
y_axises2.append(df['relative_temp'])
decimal = 3
y_axises2.append(df['avg_temp'].round(decimals=decimal))
y_axises2.append(df['pb'].round(decimals=decimal))
y_axises2.append(df['pe'].round(decimals=decimal))
y_axises2.append(df['roe'].round(decimals=decimal))
y_axises2.append(df['s'].round(decimals=decimal))
y_axises2.append(df['guozhai'].round(decimals=decimal))
y_axises = df["cp"]
line = Line(width=1200, title=stockName)
option = option_process(stockCode, stockName, CHART_NAMES, dates, y_axises,
y_axises2, Y_AXIS_NAMES)
# line.render('output/temp_line.html')
# line._option = getOption()
file = 'output/abs_temp_line_' + flg + '_' + stockCode + '.html'
line._option = option
if is_gen_single is True:
line.render(path=file,
template_name='template/temp_history.html',
object_name='line')
return line
def create_month(df):
"""
生成月份分布情况(总的)
"""
month_message = df.groupby(['month'])
month_com = month_message['month'].agg(['count'])
month_com.reset_index(inplace=True)
# 生成折线图
attr = ["{}".format(str(i) + '月') for i in month_com['month']]
v1 = month_com['count']
line = Line("公众号文章月份发布数量走势图",
title_pos='center',
title_top='18',
width=800,
height=400)
line.add("",
attr,
v1,
xaxis_interval=0,
yaxis_max=120,
is_smooth=True,
is_fill=True,
area_color="#000",
is_xaxislabel_align=True,
xaxis_min="dataMin",
area_opacity=0.3,
mark_point=["max"],
mark_point_symbol="pin",
mark_point_symbolsize=45)
line.render("公众号文章月份发布数量走势图.html")
def new_user_year_all():
result_2015 = get_user_month_line_data_of_year(2015)
result_2016 = get_user_month_line_data_of_year(2016)
result_2017 = get_user_month_line_data_of_year(2017)
result_2018 = get_user_month_line_data_of_year(2018)
line = Line("新增用户趋势")
line.add('2015新增用户趋势',
result_2015[0],
result_2015[1],
mark_line=["average"],
is_smooth=True,
mark_point=["max", "min"])
line.add('2016新增用户趋势',
result_2016[0],
result_2016[1],
mark_line=["average"],
is_smooth=True,
mark_point=["max", "min"])
line.add('2017新增用户趋势',
result_2017[0],
result_2017[1],
mark_line=["average"],
is_smooth=True,
mark_point=["max", "min"])
line.add('2018新增用户趋势',
result_2018[0],
result_2018[1],
mark_line=["average"],
is_smooth=True,
mark_point=["max", "min"])
# bar.show_config()
fileName = 'user_join_data.html'
line.render(fileName)
return send_file(fileName)
def passenger_flow(self, store_id):
"""
统计店铺的客流量
:return:
"""
self.store_id = store_id
df = pd.read_csv("./daily/2018-05-17.csv",
index_col=False,
delimiter=',')
df_time = to_datetime(df.time, format='%Y/%m/%d') # 时间格式化
# 所有店铺的客流量
count = len(
df.drop_duplicates(subset=['mac'], keep='first', inplace=False))
# 店铺一天的客流量
count_day = df.groupby('shop_id').agg({
'shop_id': 'max',
'mac': pd.Series.nunique
})
# 按小时计算店铺的客流量
count_hour = df[df['shop_id'] == store_id].groupby(['shop_id', df_time.dt.hour]) \
.agg({'mac': pd.Series.nunique, 'time': 'min'}) \
# .sort_values(['shop_id', 'mac'], ascending=[True, False])
t = count_hour.time
count = count_hour.mac
from pyecharts import Bar, Line
ts = [str(_).split(':')[0].split('2018-')[1] for _ in t]
print count_hour
bar = Line(width="100%")
bar.add("店铺客流量", ts, count, is_datazoom_show=True, is_label_show=True)
bar.render(u"客流量.html")
def chart(credit_list, time_list):
bar = Line("学生成绩平均绩点折线图\n")
bar.add("平均绩点GPA", time_list, credit_list, is_more_utils=True)
bar.show_config()
bar.render()
shutil.move('/home/fty/new-system/render.html',
'/home/fty/new-system/templates/student.html')
file = ""
with open('/home/fty/new-system/templates/student.html', 'r') as f:
hello = f.read()
file = hello
f.close()
head = '''
{% extends 'base.html' %}
{% block page_name %}你好,{{login_user}}{% endblock %}
{% block body_part3 %}
<a href="{{ url_for('student') }}" class="nav-link active">
{% endblock %}
{% block body_part1 %}
<span class="glyphicon glyphicon-stats"></span> 你好,{{login_user}}
{% endblock %}
{% block body_part2 %}
'''
with open('/home/fty/new-system/templates/student.html', 'w') as f1:
f1.write(head)
f1.write(file)
f1.write('\n')
f1.write("{% endblock %}")
def statistics_for_op_length():
'''
本函数用以确定下面函数中的support的值
fc: 非流失玩家最大动作序列长度20219 最小序列长度1， 流失玩家最大序列长度7323 ，最小序列长度 1
选取的最小支持度是16，此时剔除掉了非流失玩家用户193 流失玩家用户1497
'''
from pyecharts import Line
fc_user_ops, fc_user_label, sc_user_ops, sc_user_label, tc_user_ops, tc_user_label, action_id = get_op_features_labels_from_pickle(
)
ops_length = [[], []]
[
ops_length[fc_user_label[user]].append(len(ops))
for user, ops in fc_user_ops.items()
]
user_length = [[], []]
c = [0, 0]
temp = [list(np.sort(ops_length[0])), list(np.sort(ops_length[1]))]
for support in range(1, max(ops_length[1])):
# 在1 和 max ops_length[1] 之间集中了大部分的游戏玩家
for i in [0, 1]:
for t in temp[i][c[i]:]:
if t <= support:
c[i] += 1
user_length[i].append(c[i])
line = Line()
index = [i for i in range(0, 100)]
line.add("非流失玩家", index, user_length[0][:100])
line.add("流失玩家", index, user_length[1][:100])
line.show_config()
line.render()
def draw_city_aqi(self, time = None):
#from opendatatools import aqi
line = Line("长沙当日AQI",
width=425,
height=730)
data_dict = {}
city=local
print("getting data for %s" % city)
#df_aqi = aqi.get_daily_aqi_onecity(city)
aqi_hour=pd.read_csv('daylocal.csv')
#print(df_aqi)
#print(aqi_hour)
aqi_hour.set_index('time', inplace=True)
aqi_hour.sort_index(ascending=True, inplace=True)
if time is not None:
aqi_hour = aqi_hour[aqi_hour.index <= time]
data_dict[city] = aqi_hour
axis_x = aqi_hour.index
axis_y = aqi_hour['aqi']
line.add("%s" % (city), axis_x, axis_y, mark_point=["max","min"])
line.render('aqi.html')
#return line
self.webView.load(QUrl("file:///D:/源代码/aqi.html"))
# self.webView.reload()
self.webView.repaint()
self.webView.update()
def xing_jia_bi(self):
"""
获取性价比电影
:return:
"""
res = self.get_data()
test = res.drop_duplicates(subset=['name'], keep="first")
# 根据口碑指数排名top10
sort_wom_index = test.sort_values(by="wom_index", ascending=False)[0:11]
# 对上面根据价格进行排序
sort_price = sort_wom_index.sort_values(by="avg_price", ascending=True)
sort_price.to_csv("wom_price11.csv")
movie_list = []
price_list = []
wom_list = []
# 读取csv文件
csv_file = csv.reader(open('wom_price11.csv', 'r'))
# 生成echarts表
for wp in csv_file:
if wp[1] == "date":
continue
movie_list.append(wp[2])
price_list.append(wp[4])
wom_list.append(wp[8])
line = Line("性价比（价格/口碑指数）")
line.add("平均价格价格", movie_list, price_list)
line.add("口碑指数", movie_list, wom_list)
line.render(path="性价比111.html", )
def chart(lists):
bar = Line("学生成绩平均绩点折线图")
bar.add("平均绩点GPA", ["大一（上）", "大一（下）", "大二（上）", "大二（下）", "大三（上）", "大三（下）"],
[lists[0], lists[1], lists[2], lists[3], lists[4], lists[5]],
is_more_utils=True)
bar.show_config()
bar.render()
shutil.move('/home/cris/new_system/render.html',
'/home/cris/new_system/templates/student.html')
file = ""
with open('/home/cris/new_system/templates/student.html', 'r') as f:
hello = f.read()
file = hello
f.close()
head = '''
{% extends 'base.html' %}
{% block page_name %}你好,{{login_user}}{% endblock %}
{% block body_part3 %}
<a href="{{ url_for('student') }}" class="nav-link active">
{% endblock %}
{% block body_part1 %}
<span class="glyphicon glyphicon-stats"></span> 你好,{{login_user}}
{% endblock %}
{% block body_part2 %}
'''
with open('/home/cris/new_system/templates/student.html', 'w') as f1:
f1.write(head)
f1.write(file)
f1.write('\n')
f1.write("{% endblock %}")
def show_date(global_set):
db = global_set.db
cursor = global_set.cursor
# 使用execute方法执行SQL语句
sql = """SELECT date_of_view, COUNT(ID) FROM remarks GROUP BY date_of_view ASC;"""
try:
cursor.execute(sql)
except:
print('数据读取失败!')
db.rollback()
dates_data = cursor.fetchall()
# 获取各星级评价人数
date_list = []
date_num_list = []
for key_name, key_value in dates_data:
date_list.append(key_name)
date_num_list.append(key_value)
print(date_list)
print(date_num_list)
# 配置折线图参数
attr = date_list
v1 = date_num_list
line = Line(global_set.show_title, global_set.show_subtitle_2)
line.add("观看时间分布",
attr,
v1,
is_fill=True,
area_color='#000',
area_opacity=0.3,
mark_point=["max"],
is_smooth=True,
mark_line=["max", "average"])
line.render(global_set.save_path_2)
def bir():
print("生成js源码中……")
line = Line("{}".format(bir_name()))
line.add("{}".format(bir_quadrant_1()),
irs_a_name,
irs_a,
is_fill=True,
area_color='EED5B7',
area_opacity=0.4,
is_smooth=True)
line.add("{}".format(bir_quadrant_2()),
irs_b_name,
irs_b,
is_fill=True,
area_color='#1C86EE',
area_opacity=0.3,
is_smooth=True)
line.add("{}".format(bir_quadrant_3()),
irs_c_name,
irs_c, is_fill=True,
area_color='#FF0000',
area_opacity=0.5,
is_smooth=True,
is_more_utils=True)
line_name = input("存放图表的文件名：")
line.render("{}.html".format(line_name))
print("js源码生成完毕……\n")
def render_to_html(self):
import pandas as pd
from pyecharts import Line
df = pd.read_csv(self.out_dir + self.ok_f)
attr = [i[1] for i in df.values]
p1 = [i[2] for i in df.values]
p2 = [i[3] for i in df.values]
d3 = [i[4] for i in df.values]
line = Line("统计套利")
line.add("价差",
attr,
d3,
is_smooth=True,
mark_point=["max", "average", "min"],
mark_line=["max", "average", "min"])
# line.add("外盘内盘价差", attr2, v2, is_smooth=True, mark_line=["max", "average"])
line.render('./data/index.html')
line = Line("统计套利")
line.add("ok", attr, p1)
line.add("ok季度", attr, p2)
line.render('./data/index2.html')
def create_month_year(df):
"""
生成每年各月份分布情况
"""
v = []
# 生成每年的月份情况
for i in ['2014', '2015', '2016', '2017', '2018']:
dom = df[df['year'] == i]
dom_message = dom.groupby(['month'])
dom_com = dom_message['month'].agg(['count'])
dom_com.reset_index(inplace=True)
v1 = np.array(dom_com['count'])
v1 = ["{}".format(int(i)) for i in v1]
attr = dom_com['month']
v.append(v1)
# 生成折线图
attr = ["{}".format(str(i) + '月') for i in attr]
line = Line("公众号文章每年各月份发布数量走势图",
title_pos='center',
title_top='0',
width=800,
height=400)
line.add("2015", attr, v[1], line_color='red', legend_top='8%')
line.add("2016", attr, v[2], line_color='purple', legend_top='8%')
line.add("2017", attr, v[3], line_color='green', legend_top='8%')
line.add("2018", attr, v[4], line_color='orange', legend_top='8%')
line.render("公众号文章每年各月份发布数量走势图.html")
def time_qushi(graph_title):
result = None
attr = [
'6月19', '6月20', '6月21', '6月22', '6月23', '6月24', '6月25', '6月26', '6月27',
'6月28'
]
data = []
# pipe = [
# {'$sort': {'publish_time': -1},'allowDiskUse':True },
# {'$limit': 10}
# ]
# 1530085564
for i in range(10):
pipe = [{
'$match': {
'publish_time': {
'$lte': 1530085564 - i * 24 * 60 * 60,
'$gt': 1530085564 - (i + 1) * 24 * 60 * 60
}
}
}]
data.append(len(list(coll.aggregate(pipe))))
# from pyecharts import Line
line = Line(graph_title)
line.add("",
attr,
data,
iis_stack=True,
is_label_show=True,
width=1200,
height=600)
line.render('./' + graph_title + '.html')
def StationLine(self, data):
attr2 = []
v2 = []
total2 = 0
for row in data:
hour = row[0]
minute = row[1]
count = row[2]
total2 += count
if hour == None and minute == None:
attr2.append("无时间")
else:
attr2.append(hour + ":" + minute)
v2.append(count)
line = Line("全部数据分时间段换电站内进入车辆信息统计",
"车辆总数为" + str(total2),
width=1500,
height=600)
line.add("全部数据分时间段进入换电站车辆数量",
attr2,
v2,
mark_line=["max", "average", "min"])
line.render(path="./GUI/全部数据每隔半小时进入换电站车辆信息统计.html")
def length_distance(path_name):
sharp_ratio = []
pnl_ratio = []
max_dd = []
name_list = []
file_list = os.listdir(path_name)
for file_name in file_list:
with open(path_name + file_name, 'r', encoding='utf-8') as json_file:
date_dict = json.loads(json_file.read())
sharp_ratio.append(date_dict["sharp_ratio"])
pnl_ratio.append(date_dict["pnl_ratio"])
max_dd.append(date_dict["max_drawdown"])
file = file_name.replace('.json', '')
name_list.append(file[:2] + '_' + file[3:].replace('_', '.'))
line = Line("Bollinger Band 3")
line.add("sharp_ratio", name_list, sharp_ratio, mark_point=["max"])
line.add("pnl_ratio", name_list, [item for item in pnl_ratio])
line.add("max_dd*10",
name_list, [item * 10 for item in max_dd],
xaxis_name="length-distance",
xaxis_rotate=90,
xaxis_interval=0,
xaxis_name_pos="end")
line.render("Bollinger Band 3.html")
def nav_line(path_name, figure_name):
with open(path_name, 'r', encoding='utf-8') as json_file:
temp_dict = json.loads(json_file.read())
nav_list = []
high_level = []
time_list = []
for date in range(len(temp_dict["indicatorDuration"])):
nav_list.append(temp_dict["indicatorDuration"][date]["nav"])
time_list.append(
temp_dict["indicatorDuration"][date]["createdAt"][:10])
if len(high_level) < 1:
high_level.append(nav_list[-1])
else:
high_level.append(max(nav_list[-1], high_level[-1]))
line = Line(figure_name)
line.add("净值线", time_list, nav_list)
line.add("高水平线",
time_list,
high_level,
xaxis_name="Time",
xaxis_rotate=30,
xaxis_interval=30,
xaxis_name_pos="end",
yaxis_min=1000000)
line.render(figure_name + ".html")
def time_num_visualization(time):
from pyecharts import Line
time_list = list(set(time))
time_dict = {time_list[i]: 0 for i in range(len(time_list))}
time_num = []
for i in range(len(time_list)):
time_dict[time_list[i]] = time.count(time_list[i])
# 根据数量(字典的键值)排序
sort_dict = sorted(time_dict.items(), key=lambda d: d[0], reverse=False)
time_name = []
time_num = []
print(sort_dict)
for i in range(len(sort_dict)):
time_name.append(sort_dict[i][0])
time_num.append(sort_dict[i][1])
line = Line("评论数量日期折线图")
line.add(
"日期-评论数",
time_name,
time_num,
is_fill=True,
area_color="#000",
area_opacity=0.3,
is_smooth=True,
)
line.render("H:\PyCoding\spider_maoyan\picture\c_num_line.html")
def render_to_html(self):
import pandas as pd
from pyecharts import Line
df = pd.read_csv(self.out_dir + self.asset_csv)
attr = [i[1] for i in df.values]
price = [i[2] for i in df.values]
btc = [i[3] for i in df.values]
bch = [i[4] for i in df.values]
profit = [i[5] for i in df.values]
line = Line("利润曲线")
line.add("profit",
attr,
profit,
is_smooth=True,
mark_point=["max", "average", "min"],
mark_line=["max", "average", "min"])
# line.add("外盘内盘价差", attr2, v2, is_smooth=True, mark_line=["max", "average"])
line.render('./data/kp.html')
line = Line("资产统计")
line.add("btc", attr, btc)
line.add("bch", attr, bch)
line.render('./data/ka.html')
def create_temp_htm(city):
client = MongoClient('localhost', 27017)
db = client['tianqihoubao']
data = db[city]
max_temp_list = []
min_temp_list = []
day_list = []
results = data.find({}).sort([("date", 1)])
for d in results:
max_temp_list.append(d['max_temp'])
min_temp_list.append(d['min_temp'])
day_list.append(d['date'])
city_name = get_city_name(city)
line = Line(city_name + "2011年至今气温变化", page_title="气温变化表")
line.add(
"最高温度",
day_list,
max_temp_list,
mark_point=["max", "min"],
mark_line=["average"],
)
line.add(
"最低温度",
day_list,
min_temp_list,
mark_point=["max", "min"],
mark_line=["average"],
yaxis_formatter="°C",
)
line.render(city_name + '气温变化表.html')
def generate_trend(benchmarks_map, columns, cate):
lines = {}
max_cnt = len(columns)
for (group, benchmarks) in benchmarks_map.items():
print(benchmarks)
print(benchmarks.keys())
for (bm_name, benchmark) in benchmarks.items():
print(bm_name)
#is_label_show是设置上方数据是否显示
print(benchmark[cate])
html_name = group + "_" + bm_name.split("/")[0]
if not html_name in lines:
line = Line("性能" + cate + "趋势",
"性能" + cate + "趋势",
width=1600,
height=1000)
lines[html_name] = line
line = lines[html_name]
line.add(bm_name,
columns,
benchmark[cate][0:max_cnt],
is_label_show=True)
for (name, line) in lines.items():
render_name = "./render/" + cate + "Trend_" + name + ".html"
print(render_name)
line.render(render_name)
def echarts_plot(html_path, html2_path, col_1, row_1, col_2, row_2):
'''绘图1'''
line = Line("近十日标注量变化曲线",
datetime.datetime.now().strftime('%Y-%m-%d'),
width=1200,
height=600)
line.add("日期",
row_1,
col_1,
mark_point=["max", "min"],
mark_line=["average"],
is_datazoom_show=False,
is_symbol_show=True)
line.render(path=html_path, pixel_ratio=3)
'''绘图完成'''
'''绘图2'''
line = Line("近十日审核量变化曲线",
datetime.datetime.now().strftime('%Y-%m-%d'),
width=1200,
height=600)
line.add("日期",
row_2,
col_2,
mark_point=["max", "min"],
mark_line=["average"],
is_datazoom_show=False,
is_symbol_show=True)
line.render(path=html2_path, pixel_ratio=3)
'''绘图完成'''
def render_to_html_cross(self):
import pandas as pd
from pyecharts import Line
# df = pd.read_csv('./data/diff.csv')
df = pd.read_csv(self.out_dir + self.b_f)
attr = [i[1] for i in df.values]
p1 = [i[2] for i in df.values]
p2 = [i[3] for i in df.values]
d3 = [i[4] for i in df.values]
line = Line("统计套利")
line.add("价差",
attr,
d3,
is_smooth=True,
mark_point=["max", "average", "min"],
mark_line=["max", "average", "min"])
line.render('./data/c.html')
line = Line("统计套利")
line.add("ok", attr, p1)
line.add("bitfinex", attr, p2)
line.render('./data/c2.html')
def test_line_negative_value():
line = Line("折线图示例")
line.add("最高气温", WEEK, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", WEEK, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"], mark_line=["average"])
line.render()
def show(path):
d=os.listdir(path)
d_len=[len(os.listdir(os.path.join(path,i))) for i in d]
line = Line()
line.add("train", d, d_len, mark_point=["average", "max", "min"],xaxis_rotate=40, )
line.render(path="temp.html")
def line_test(b, num):
X = pd.Series(unique(b)).values
Y = pd.Series(num.groupby(b).sum()).values
line = Line("香飘飘2019/04到2019/05月各省区经销商分销量")
line.add("", X, Y, mark_point=["average"])
line.show_config()
line.render("E:\\py_data_html\\line_test.html")
def test_line_user_define_markpoint():
line = Line("折线图示例")
line.add("商家A", CLOTHES, clothes_v1,
mark_point=["average", {
"coord": ["裤子", 10], "name": "这是我想要的第一个标记点"}])
line.add("商家B", CLOTHES, clothes_v2, is_smooth=True,
mark_point=[{
"coord": ["袜子", 80], "name": "这是我想要的第二个标记点"}])
line.render()
def test_line_log_yaxis():
import math
import random
line = Line("折线图示例")
line.add("商家A", CLOTHES,
[math.log10(random.randint(1, 99999)) for _ in range(6)])
line.add("商家B", CLOTHES,
[math.log10(random.randint(1, 99999999)) for _ in range(6)],
yaxis_type="log")
line.render()
def test_line_user_define_marks():
line = Line("折线图示例")
line.add("商家A", CLOTHES, clothes_v1,
mark_point=["average", "max", "min"],
mark_point_symbol='diamond', mark_point_textcolor='#40ff27')
line.add("商家B", CLOTHES, clothes_v2,
mark_point=["average", "max", "min"],
mark_point_symbol='arrow', mark_point_symbolsize=40)
line.show_config()
line.render()
def draw_sentiment_pic(csv_file):
attr, val = [], []
info = count_sentiment(csv_file)
info = sorted(info.items(), key=lambda x: x[0], reverse=False) # dict的排序方法
for each in info[:-1]:
attr.append(each[0])
val.append(each[1])
line = Line(csv_file+":影评情感分析")
line.add("", attr, val, is_smooth=True, is_more_utils=True)
line.render(csv_file+"_情感分析曲线图.html")
def test_line_marks():
line = Line("折线图示例")
line.add("商家A", CLOTHES, clothes_v1, mark_point=["average"])
line.add(
"商家B",
CLOTHES,
clothes_v2,
is_smooth=True,
mark_line=["max", "average"],
)
line.render()
def test_line():
# line_0
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 10, 100]
v2 = [55, 60, 16, 20, 15, 80]
line = Line("折线图示例")
line.add("商家A", attr, v1, mark_point=["average"])
line.add("商家B", attr, v2, is_smooth=True, mark_line=["max", "average"])
line.show_config()
line.render()
# line_0_1
line = Line("折线图示例")
line.add("商家A", attr, v1, mark_point=["average", "max", "min"],
mark_point_symbol='diamond', mark_point_textcolor='#40ff27')
line.add("商家B", attr, v2, mark_point=["average", "max", "min"],
mark_point_symbol='arrow', mark_point_symbolsize=40)
line.show_config()
line.render()
# line_1
line = Line("折线图-数据堆叠示例")
line.add("商家A", attr, v1, is_stack=True, is_label_show=True)
line.add("商家B", attr, v2, is_stack=True, is_label_show=True)
line.show_config()
line.render()
# line_2
line = Line("折线图-阶梯图示例")
line.add("商家A", attr, v1, is_step=True, is_label_show=True)
line.show_config()
line.render()
# # line_3
line = Line("折线图-面积图示例")
line.add("商家A", attr, v1, is_fill=True, line_opacity=0.2, area_opacity=0.4, symbol=None)
line.add("商家B", attr, v2, is_fill=True, area_color='#000', area_opacity=0.3, is_smooth=True)
line.show_config()
line.render()
# line_4
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line = Line("折线图示例")
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"], mark_line=["average"])
line.show_config()
line.render()
def test_line_type_stack():
line = Line("折线图-数据堆叠示例")
line.add("商家A", CLOTHES, clothes_v1, is_stack=True, is_label_show=True)
line.add("商家B", CLOTHES, clothes_v2, is_stack=True, is_label_show=True)
line.render()
def test_grid():
# grid_0
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, is_grid=True)
bar.add("商家A", attr, v1, is_stack=True, grid_bottom="60%")
bar.add("商家B", attr, v2, is_stack=True, grid_bottom="60%")
line = Line("折线图示例", title_top="50%")
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"],
mark_line=["average"], legend_top="50%")
bar.grid(line.get_series(), grid_top="60%")
bar.show_config()
bar.render()
# grid_1
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter(width=1200, is_grid=True)
scatter.add("散点图示例", v1, v2, grid_left="60%", legend_pos="70%")
es = EffectScatter()
es.add("动态散点图示例", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0],
effect_scale=6, legend_pos="20%")
scatter.grid(es.get_series(), grid_right="60%")
scatter.show_config()
scatter.render()
# grid_2
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", height=720, width=1200, title_pos="65%", is_grid=True)
bar.add("商家A", attr, v1, is_stack=True, grid_bottom="60%", grid_left="60%")
bar.add("商家B", attr, v2, is_stack=True, grid_bottom="60%", grid_left="60%", legend_pos="80%")
line = Line("折线图示例")
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"],
mark_line=["average"], legend_pos="20%")
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
scatter = Scatter("散点图示例", title_top="50%", title_pos="65%")
scatter.add("scatter", v1, v2, legend_top="50%", legend_pos="80%")
es = EffectScatter("动态散点图示例", title_top="50%")
es.add("es", [11, 11, 15, 13, 12, 13, 10], [1, -2, 2, 5, 3, 2, 0], effect_scale=6,
legend_top="50%", legend_pos="20%")
bar.grid(line.get_series(), grid_bottom="60%", grid_right="60%")
bar.grid(scatter.get_series(), grid_top="60%", grid_left="60%")
bar.grid(es.get_series(), grid_top="60%", grid_right="60%")
bar.show_config()
bar.render()
# grid_3
line = Line("折线图示例", width=1200, is_grid=True)
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"],
mark_line=["average"], grid_right="65%")
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"],
mark_line=["average"], legend_pos="20%")
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [11, 12, 13, 10, 10, 10]
pie = Pie("饼图示例", title_pos="45%")
pie.add("", attr, v1, radius=[30, 55], legend_pos="65%", legend_orient='vertical')
line.grid(pie.get_series(), grid_left="60%")
line.show_config()
line.render()
# grid_4
line = Line("折线图示例", width=1200, is_grid=True)
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10], mark_point=["max", "min"],
mark_line=["average"], grid_right="60%")
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0], mark_point=["max", "min"],
mark_line=["average"], legend_pos="20%", grid_right="60%")
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
value = [20, 40, 60, 80, 100, 120]
v1 = [[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22]]
kline = Kline("K 线图示例", title_pos="60%")
kline.add("日K", ["2017/7/{}".format(i + 1) for i in range(31)], v1, legend_pos="80%")
line.grid(kline.get_series(), grid_left="55%")
line.show_config()
line.render()
# grid_5
import random
x_axis = ["12a", "1a", "2a", "3a", "4a", "5a", "6a", "7a", "8a", "9a", "10a", "11a",
"12p", "1p", "2p", "3p", "4p", "5p", "6p", "7p", "8p", "9p", "10p", "11p"]
y_aixs = ["Saturday", "Friday", "Thursday", "Wednesday", "Tuesday", "Monday", "Sunday"]
data = [[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)]
heatmap = HeatMap("热力图示例", height=700, is_grid=True)
heatmap.add("热力图直角坐标系", x_axis, y_aixs, data, is_visualmap=True, visual_top="45%",
visual_text_color="#000", visual_orient='horizontal', grid_bottom="60%")
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = Bar("柱状图示例", title_top="52%")
bar.add("商家A", attr, v1, is_stack=True)
bar.add("商家B", attr, v2, is_stack=True, legend_top="50%")
heatmap.grid(bar.get_series(), grid_top="60%")
heatmap.show_config()
heatmap.render()
import sys
import glob
import os
from pyecharts import Line
def load_js(jsfile):
x_axis = []
points = []
with open(jsfile, 'r') as fp:
while True:
l = fp.readline()
if not l:
break
deal_dict = json.loads(l)
x_axis.append(deal_dict['dealDate'][0])
points.append(deal_dict['unitPrice'][0])
return x_axis, points
if __name__ == '__main__':
dir_path = sys.argv[1]
curve_name = ''
line = Line("Deal Curve")
cwd = os.getcwd()
os.chdir(dir_path)
for js in glob.glob("*.json"):
x_axis, points = load_js(js)
curve_name = js[:js.find(".json")]
line.add(curve_name, x_axis, points, is_stack=True, is_label_show=True)
os.chdir(cwd)
line.render()
#折线图适合描述两个变量之间的函数关系，例如常用它来描述一个变量随时间的变化趋势。
from pyecharts import Line
x = ['2018-{:0>2d}'.format(s) for s in range(1,13)]
y1 = [5,10,26,30,35,30,20,26,40,46,40,50]
y2 = [8,20,24,36,40,36,40,45,50,53,48,58]
line = Line(title = "月销售总额",width = 600,height = 420)
line.add(name = "商家A", x_axis = x, y_axis = y1,
line_width = 3,line_color = 'red',
#=====设置markPoint&markLine=====
mark_point = ['min','max'], #标记点
mark_line = ['average'] #标记线
)
line.add(name = "商家B", x_axis = x, y_axis = y2,
yaxis_min = 0,yaxis_max = 100,is_xaxis_boundarygap = False,
is_datazoom_show =True,line_width = 2,line_color = 'cyan',
#=====设置markPoint&markLine=====
mark_point = [
{"coord": ['2018-09', 60], "name": "2018/09销售目标"},
{"coord": ['2018-11', 80], "name": "2018/10销售目标"}] # 自定义标记点
)
line.render('result.折线图示范.html')
line
