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metedraw.py
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#coding=utf-8
import math
import numpy as np
import pandas as pd
from pandas import DataFrame
import matplotlib.pyplot as plt
from dateutil.parser import parse
def preprocessing(data,**keywords):
data = DataFrame(data)
index_list = data.keys()
time_index = str(index_list[0])
data[time_index] = data[time_index].map(lambda x:parse(str(x)))
data.index = data[time_index]
try:
print_timeindex = keywords['print_timeindex']
print('start time:',data[time_index][0],'end time:',data[time_index][len(data[time_index])-1])
except:
print_timeindex = False
try:
lack_values = keywords['lack_values']
data = data.replace(lack_values,np.nan)
data = data.dropna()
except:
lack_values = False
try:
start_time = keywords['start_time']
end_time = keywords['end_time']
data = data[start_time:end_time]
except:
keywords['start_time'] = False
return data
def get_num(index_list):
num = len(index_list)-1
sub_num1 = int(num**0.5)
sub_num2 = math.ceil(num/sub_num1)
return num,sub_num1,sub_num2
def draw_ax(df,index_list,fig,i,num,sub_num1,sub_num2,keywords):
try:
scheme = keywords['scheme']
except:
keywords['scheme']=0;
try:
range_adjustment = keywords['range_adjustment']
except:
keywords['range_adjustment']=[];
try:
color_list = keywords['color_list']
except:
keywords['color_list'] = ['k']*num
ax = fig.add_subplot(sub_num1,sub_num2,i)
if(scheme == 'bar'):
ax.bar(df[index_list[0]],height=df[index_list[i]],color = color_list[i-1])
elif(scheme == 'linesc'):
ax.plot(df[index_list[0]],df[index_list[i]],color = color_list[i-1])
ax.scatter(df[index_list[0]],df[index_list[i]],color = color_list[i-1])
else:
ax.plot(df[index_list[0]],df[index_list[i]],color = color_list[i-1])
ax.set_xlabel('time')
ax.set_ylabel(index_list[i])
if(i in range_adjustment):
k_y0 = (max(df[index_list[i]])-min(df[index_list[i]]))/max(df[index_list[i]])
y0 = min(df[index_list[i]])*(1-abs(k_y0))
y1 = max(df[index_list[i]])*(1+abs(k_y0))
plt.ylim(ymin=y0,ymax=y1)
#plt.xticks(rotation=30) #坐标轴倾斜，解决坐标轴标签重叠问题
#plt.ticklabel_format(axis='y',style='sci', scilimits=(1,0)) #科学计数法,解决某些版本不能自动显示科学计数法的问题
return ax
def plt_adjust():
plt.subplots_adjust(hspace=0.3,wspace=0.3)
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
def time_series(df,**keywords):
index_list = df.keys()
num,sub_num1,sub_num2 = get_num(index_list)
fig=plt.figure(figsize=(sub_num1*5,sub_num2*5),dpi=200)
for i in range(1,num+1):
draw_ax(df,index_list,fig,i,num,sub_num1,sub_num2,keywords)
plt_adjust()
def year_mean(df,**keywords):
index_list = df.keys()
num,sub_num1,sub_num2 = get_num(index_list)
time_index = str(index_list[0])
start_year = df[time_index][0].year
end_year = df[time_index][len(df)-1].year
year_list = [str(year) for year in range(start_year,end_year+1)]
dict1 = {time_index:year_list}
df1 = DataFrame(dict1)
for i in range(1,num+1):
year_average = []
data = df[str(index_list[i])]
for year in year_list:
year_average.append(data[year].mean());
df1[str(index_list[i])] = year_average
fig=plt.figure(figsize=(sub_num1*5,sub_num2*5),dpi=200)
for i in range(1,num+1):
draw_ax(df1,index_list,fig,i,num,sub_num1,sub_num2,keywords)
plt_adjust()
def month_mean(df,**keywords):
index_list = df.keys()
num,sub_num1,sub_num2 = get_num(index_list)
time_index = str(index_list[0])
month_list = [str(month) for month in range(1,13)]
dict1 = {time_index:month_list}
df1 = DataFrame(dict1)
for i in range(1,num+1):
data = df[str(index_list[i])].resample('m').mean()
data.index = data.index.strftime('%m')
average_list = data.groupby(data.index).mean()
average_list = average_list.values
df1[str(index_list[i])] = average_list
fig=plt.figure(figsize=(sub_num1*5,sub_num2*5),dpi=200)
for i in range(1,num+1):
draw_ax(df1,index_list,fig,i,num,sub_num1,sub_num2,keywords)
plt_adjust()
def hour_mean(df,**keywords):
index_list = df.keys()
num,sub_num1,sub_num2 = get_num(index_list)
time_index = str(index_list[0])
hour_num = [i for i in range(24)]
df['hour'] = df[str(index_list[0])].map(lambda x:x.hour)
x = []
time_str = ':00'
for i in hour_num:
i_str = str(i)
x.append(i_str+time_str);
dict1 = {time_index:x}
df1 = DataFrame(dict1)
for i in range(1,num+1):
out_mean = []
for hour in hour_num:
hour_df = df[df['hour'] == hour]
out_mean.append(hour_df[str(index_list[i])].mean())
df1[str(index_list[i])] = out_mean
fig=plt.figure(figsize=(sub_num1*5,sub_num2*5),dpi=200)
for i in range(1,num+1):
draw_ax(df1,index_list,fig,i,num,sub_num1,sub_num2,keywords)
plt_adjust()