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Commit 5dd1b131 authored by banashree.p's avatar banashree.p
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Assignment-1,task-2

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task2/.idea/.gitignore 0 → 100644
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# Default ignored files
/shelf/
/workspace.xml
task2/.idea/inspectionProfiles/Project_Default.xml 0 → 100644
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<component name="InspectionProjectProfileManager">
<profile version="1.0">
<option name="myName" value="Project Default" />
<inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
<option name="ignoredIdentifiers">
<list>
<option value="main.device" />
<option value="main.Dr_Bana" />
<option value="str.__setitem__" />
</list>
</option>
</inspection_tool>
</profile>
</component>
\ No newline at end of file
task2/.idea/inspectionProfiles/profiles_settings.xml 0 → 100644
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<component name="InspectionProjectProfileManager">
<settings>
<option name="USE_PROJECT_PROFILE" value="false" />
<version value="1.0" />
</settings>
</component>
\ No newline at end of file
task2/.idea/misc.xml 0 → 100644
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (pythonProjecttask2)" project-jdk-type="Python SDK" />
</project>
\ No newline at end of file
task2/.idea/modules.xml 0 → 100644
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectModuleManager">
<modules>
<module fileurl="file://$PROJECT_DIR$/.idea/pythonProjecttask2.iml" filepath="$PROJECT_DIR$/.idea/pythonProjecttask2.iml" />
</modules>
</component>
</project>
\ No newline at end of file
task2/.idea/pythonProjecttask2.iml 0 → 100644
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<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$" />
<orderEntry type="inheritedJdk" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
</module>
\ No newline at end of file
task2/.idea/vcs.xml 0 → 100644
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="VcsDirectoryMappings">
<mapping directory="$PROJECT_DIR$/.." vcs="Git" />
</component>
</project>
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task2/airrrr1-AirQualityUCI-_1_ - Worksheet.csv 0 → 100644
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task2/appi.py 0 → 100644
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from starlette.responses import FileResponse
import uvicorn
#from service import minm
from fastapi import FastAPI
#from service import monthlytempdata
from code import weeklytempdata, dailytempdata, monthlytempdata
from qu import *
app = FastAPI()
@app.get("/getData1")
def monthlytempdata1():
fpath,fname = monthlytempdata()
return FileResponse(fpath, media_type='application/octet-stream', filename=fname)
@app.get("/getData2")
def dailytempdata1():
fpath,fname = dailytempdata()
return FileResponse(fpath, media_type='application/octet-stream', filename=fname)
@app.get("/getData3")
def weeklytempdata1():
fpath, fname = weeklytempdata()
return FileResponse(fpath, media_type='application/octet-stream',filename=fname)
if __name__ == '__main__':
uvicorn.run('appi:app')
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task2/code.py 0 → 100644
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import requests
import json
import gzip
from main import df
import pandas as pd
kairosdb_server = "http://192.168.0.220:8080"
kairos_url = "http://192.168.0.220:8080/api/v1/datapoints/query"
# Simple test [without compression]
# pushdata_kdb():
data = [
{
"name": "sneha",
"datapoints": df[['Datetime', 'RH']].values.tolist(),
"tags": {"project": "kairos"}
}
]
response = requests.post(kairosdb_server + "/api/v1/datapoints", json.dumps(data))
print("Simple test [without compression]: \t%d (status code)" % response.status_code)
# query my data of max temperature for yearly monthly and weekly
def maxtemp(query):
if query == "months":
payload = json.dumps({
"metrics": [
{
"tags": {
"project": [
"kairos"
]
},
"name": "sneha",
"aggregators": [
{
"name": "max",
"sampling": {
"value": "1",
"unit": "months"
}
}
]
}
],
"plugins": [],
"cache_time": 0,
"start_absolute": 1094668200000,
"end_absolute": 1112553000000
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", kairos_url, headers=headers, data=payload)
return response.json()
elif query == "weeks":
payload = json.dumps({
"metrics": [
{
"tags": {
"project": [
"kairos"
]
},
"name": "sneha",
"aggregators": [
{
"name": "max",
"sampling": {
"value": "1",
"unit": "weeks"
}
}
]
}
],
"plugins": [],
"cache_time": 0,
"start_absolute": 1094668200000,
"end_absolute": 1112553000000
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", kairos_url, headers=headers, data=payload)
return response.json()
elif query == "days":
payload = json.dumps({
"metrics": [
{
"tags": {
"project": [
"kairos"
]
},
"name": "sneha",
"aggregators": [
{
"name": "max",
"sampling": {
"value": "1",
"unit": "days"
}
}
]
}
],
"plugins": [],
"cache_time": 0,
"start_absolute": 1094668200000,
"end_absolute": 1112553000000
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", kairos_url, headers=headers, data=payload)
return response.json()
else:
return {"failed": "data out of format"}
# query my data of min temperature for yearly monthly and weekly
def mintemp(query):
if query == "months":
payload = json.dumps({
"metrics": [
{
"tags": {
"project": [
"kairos"
]
},
"name": "sneha",
"aggregators": [
{
"name": "min",
"sampling": {
"value": "1",
"unit": "months"
}
}
]
}
],
"plugins": [],
"cache_time": 0,
"start_absolute": 1094668200000,
"end_absolute": 1112553000000
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", kairos_url, headers=headers, data=payload)
return response.json()
elif query == "weeks":
payload = json.dumps({
"metrics": [
{
"tags": {
"project": [
"kairos"
]
},
"name": "sneha",
"aggregators": [
{
"name": "min",
"sampling": {
"value": "1",
"unit": "weeks"
}
}
]
}
],
"plugins": [],
"cache_time": 0,
"start_absolute": 1094668200000,
"end_absolute": 1112553000000
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", kairos_url, headers=headers, data=payload)
return response.json()
elif query == "days":
payload = json.dumps({
"metrics": [
{
"tags": {
"project": [
"kairos"
]
},
"name": "sneha",
"aggregators": [
{
"name": "min",
"sampling": {
"value": "1",
"unit": "days"
}
}
]
}
],
"plugins": [],
"cache_time": 0,
"start_absolute": 1094668200000,
"end_absolute": 1112553000000
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", kairos_url, headers=headers, data=payload)
return response.json()
else:
return {"failed": "data out of format"}
# query my data of avg temperature for yearly monthly and weekly
def avgtemp(query):
if query == "months":
payload = json.dumps({
"metrics": [
{
"tags": {
"project": [
"kairos"
]
},
"name": "sneha",
"aggregators": [
{
"name": "avg",
"sampling": {
"value": "1",
"unit": "months"
}
}
]
}
],
"plugins": [],
"cache_time": 0,
"start_absolute": 1094668200000,
"end_absolute": 1112553000000
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", kairos_url, headers=headers, data=payload)
return response.json()
elif query == "weeks":
payload = json.dumps({
"metrics": [
{
"tags": {
"project": [
"kairos"
]
},
"name": "sneha",
"aggregators": [
{
"name": "avg",
"sampling": {
"value": "1",
"unit": "weeks"
}
}
]
}
],
"plugins": [],
"cache_time": 0,
"start_absolute": 1094668200000,
"end_absolute": 1112553000000
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", kairos_url, headers=headers, data=payload)
return response.json()
elif query == "days":
payload = json.dumps({
"metrics": [
{
"tags": {
"project": [
"kairos"
]
},
"name": "sneha",
"aggregators": [
{
"name": "avg",
"sampling": {
"value": "1",
"unit": "days"
}
}
]
}
],
"plugins": [],
"cache_time": 0,
"start_absolute": 1094668200000,
"end_absolute": 1112553000000
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", kairos_url, headers=headers, data=payload)
return response.json()
else:
return {"failed": "data out of format"}
# function to collect all the values convert into df and then to excel(list of list)
def dailytempdata():
MAX_temp = maxtemp("days")['queries'][0]['results'][0]['values']
MIN_temp = mintemp("days")['queries'][0]['results'][0]['values']
AVG_temp = avgtemp("days")['queries'][0]['results'][0]['values']
df1 = pd.DataFrame(MAX_temp, columns=["Updated_Date", "max"])
df2 = pd.DataFrame(MIN_temp, columns=["Updated_Date", "min"])
df3 = pd.DataFrame(AVG_temp, columns=["Updated_Date", "avg"])
a = pd.merge(df1, df2, on="Updated_Date")
b = pd.merge(a, df3, on="Updated_Date")
b['Updated_Date'] = pd.to_datetime(a['Updated_Date'], unit='ms')
b['Updated_Date'] = b['Updated_Date'].dt.strftime('%Y-%m-%d')
b.to_excel("scripts/Data/dailyreport.xlsx")
fpath = 'scripts/Data/'
fname = 'dailyreport.xlsx'
filepath = fpath + fname
return filepath, fname
def monthlytempdata():
MAX_temp = maxtemp("months")['queries'][0]['results'][0]['values']
MIN_temp = mintemp("months")['queries'][0]['results'][0]['values']
AVG_temp = avgtemp("months")['queries'][0]['results'][0]['values']
df1 = pd.DataFrame(MAX_temp, columns=["Updated_Date", "max"])
df2 = pd.DataFrame(MIN_temp, columns=["Updated_Date", "min"])
df3 = pd.DataFrame(AVG_temp, columns=["Updated_Date", "avg"])
a = pd.merge(df1, df2, on="Updated_Date")
b = pd.merge(a, df3, on="Updated_Date")
b['Updated_Date'] = pd.to_datetime(a['Updated_Date'], unit='ms')
b['Updated_Date'] = b['Updated_Date'].dt.strftime('%Y-%m-%d')
b.to_excel("scripts/Data/monthreport.xlsx", index=False)
path = 'scripts/Data/'
fname = 'monthreport.xlsx'
filepath = path + fname
return filepath, fname
def weeklytempdata():
MAX_temp = maxtemp("weeks")['queries'][0]['results'][0]['values']
MIN_temp = mintemp("weeks")['queries'][0]['results'][0]['values']
AVG_temp = avgtemp("weeks")['queries'][0]['results'][0]['values']
df1 = pd.DataFrame(MAX_temp, columns=["Updated_Date", "max"])
df2 = pd.DataFrame(MIN_temp, columns=["Updated_Date", "min"])
df3 = pd.DataFrame(AVG_temp, columns=["Updated_Date", "avg"])
a = pd.merge(df1, df2, on="Updated_Date")
b = pd.merge(a, df3, on="Updated_Date")
b['Updated_Date'] = pd.to_datetime(a['Updated_Date'], unit='ms')
b['Updated_Date'] = b['Updated_Date'].dt.strftime('%Y-%m-%d')
b.to_excel("scripts/Data/weekreport.xlsx", index=False)
path = 'scripts/Data/'
fname = 'weekreport.xlsx'
filepath = path + fname
return filepath, fname
# def dailytempdata():
# return None
\ No newline at end of file
task2/main.py 0 → 100644
View file @ 5dd1b131
import pandas as pd
from datetime import datetime
epoch = datetime.strptime('2004-12-31 1:00:00', '%Y-%m-%d %H:%M:%S').timestamp()
print(epoch)
pd.options.display.max_rows = 99999
df = pd.read_csv("airrrr1-AirQualityUCI-_1_ - Worksheet.csv")
print(df.head(10))
print(df.Datetime)
print(df["Datetime"].values.tolist())
seconds = []
for j in df['Datetime'].values:
seconds.append(int(datetime.strptime(j, "%m/%d/%Y%H:%M:%S").timestamp()) * 1000)
print(seconds)
df['Datetime'] = seconds
print(df['Datetime'])
task2/qu.py 0 → 100644
View file @ 5dd1b131
# from os import path
#
# import pandas as pd
# import requests
# import json
# import gzip
# from main import df
#
#
# import openpyxl
#
# kairosdb_server = "http://192.168.0.220:8080"
# url = 'http://192.168.0.220:8080/api/v1/datapoints/query'
# # Simple test [without compression]
# data = [
# {
# "name": "bana",
# "datapoints": df[['Datetime', 'RH']].values.tolist(),
# "tags": {"project": "kairos"}
# }
# ]
#
# response = requests.post(kairosdb_server + "/api/v1/datapoints", json.dumps(data))
# print("Simple test [without compression]: \t %d (status code)" % response.status_code)
#
#
# def minm(query):
# if query == "months":
#
# payload = json.dumps({
# "metrics": [
# {
# "tags": {},
# "name": "bana",
# "aggregators": [
# {
# "name": "min",
# "sampling": {
# "value": "1",
# "unit": "months"
# }
# }
# ]
# }
# ],
# "plugins": [],
# "cache_time": 0,
# "start_absolute": 1096741800000,
# "end_absolute": 1112553000000
# })
# headers = {
# 'Content-Type': 'application/json'
# }
#
# response = requests.request("POST", url, headers=headers, data=payload)
#
# return response.json()
# elif query == "weeks":
# payload = json.dumps({
# "metrics": [
# {
# "tags": {},
# "name": "bana",
# "aggregators": [
# {
# "name": "min",
# "sampling": {
# "value": "1",
# "unit": "weeks"
# }
# }
# ]
# }
# ],
# "plugins": [],
# "cache_time": 0,
# "start_absolute": 1096741800000,
# "end_absolute": 1112553000000
# })
# headers = {
# 'Content-Type': 'application/json'
# }
#
# response = requests.request("POST", url, headers=headers, data=payload)
#
# return response.json
# elif query == "days":
# payload = json.dumps({
# "metrics": [
# {
# "tags": {},
# "name": "bana",
# "aggregators": [
# {
# "name": "min",
# "sampling": {
# "value": "1",
# "unit": "days"
# }
# }
# ]
# }
# ],
# "plugins": [],
# "cache_time": 0,
# "start_absolute": 1096741800000,
# "end_absolute": 1112553000000
# })
# headers = {
# 'Content-Type': 'application/json'
# }
#
# response = requests.request("POST", url, headers=headers, data=payload)
#
# return response.json
# else:
# return ("failed")
#
# def maxm(query):
# if query == "months":
#
# payload = json.dumps({
# "metrics": [
# {
# "tags": {},
# "name": "bana",
# "aggregators": [
# {
# "name": "max",
# "sampling": {
# "value": "1",
# "unit": "months"
# }
# }
# ]
# }
# ],
# "plugins": [],
# "cache_time": 0,
# "start_absolute": 1096741800000,
# "end_absolute": 1112553000000
# })
# headers = {
# 'Content-Type': 'application/json'
# }
#
# response = requests.request("POST", url, headers=headers, data=payload)
#
# return response.json()
# elif query == "weeks":
# payload = json.dumps({
# "metrics": [
# {
# "tags": {},
# "name": "bana",
# "aggregators": [
# {
# "name": "max",
# "sampling": {
# "value": "1",
# "unit": "weeks"
# }
# }
# ]
# }
# ],
# "plugins": [],
# "cache_time": 0,
# "start_absolute": 1096741800000,
# "end_absolute": 1112553000000
# })
# headers = {
# 'Content-Type': 'application/json'
# }
#
# response = requests.request("POST", url, headers=headers, data=payload)
#
# return response.json
# elif query == "days":
# payload = json.dumps({
# "metrics": [
# {
# "tags": {},
# "name": "bana",
# "aggregators": [
# {
# "name": "max",
# "sampling": {
# "value": "1",
# "unit": "days"
# }
# }
# ]
# }
# ],
# "plugins": [],
# "cache_time": 0,
# "start_absolute": 1096741800000,
# "end_absolute": 1112553000000
# })
# headers = {
# 'Content-Type': 'application/json'
# }
#
# response = requests.request("POST", url, headers=headers, data=payload)
#
# return response.json
# else:
# return ("failed")
#
# def avg(query):
# if query == "months":
#
# payload = json.dumps({
# "metrics": [
# {
# "tags": {},
# "name": "bana",
# "aggregators": [
# {
# "name": "avg",
# "sampling": {
# "value": "1",
# "unit": "months"
# }
# }
# ]
# }
# ],
# "plugins": [],
# "cache_time": 0,
# "start_absolute": 1096741800000,
# "end_absolute": 1112553000000
# })
# headers = {
# 'Content-Type': 'application/json'
# }
#
# response = requests.request("POST", url, headers=headers, data=payload)
#
# return response.json()
# elif query == "weeks":
# payload = json.dumps({
# "metrics": [
# {
# "tags": {},
# "name": "bana",
# "aggregators": [
# {
# "name": "avg",
# "sampling": {
# "value": "1",
# "unit": "weeks"
# }
# }
# ]
# }
# ],
# "plugins": [],
# "cache_time": 0,
# "start_absolute": 1096741800000,
# "end_absolute": 1112553000000
# })
# headers = {
# 'Content-Type': 'application/json'
# }
#
# response = requests.request("POST", url, headers=headers, data=payload)
#
# return response.json
# elif query == "days":
# payload = json.dumps({
# "metrics": [
# {
# "tags": {},
# "name": "bana",
# "aggregators": [
# {
# "name": "avg",
# "sampling": {
# "value": "1",
# "unit": "days"
# }
# }
# ]
# }
# ],
# "plugins": [],
# "cache_time": 0,
# "start_absolute": 1096741800000,
# "end_absolute": 1112553000000
# })
# headers = {
# 'Content-Type': 'application/json'
# }
#
# response = requests.request("POST", url, headers=headers, data=payload)
#
# return response.json
# else:
# return ("failed")
# def monthlytempdata():
# MINM = minm("months")['queries'][0]['results'][0]['values']
#
# df2 = pd.DataFrame(MINM, columns=["Updated_Date", "minm"])
# MAXM = minm("months")['queries'][0]['results'][0]['values']
#
# df3 = pd.DataFrame(MAXM, columns=["Updated_Date", "maxm"])
#
# AVG = minm("months")['queries'][0]['results'][0]['values']
# df4 = pd.DataFrame(AVG, columns=["Updated_Date", "avg"])
#
#
# a = pd.merge(df2, df3, on="Updated_Date")
# b = pd.merge(a, df4, on="Updated_Date")
# b["Updated date"] = pd.to_datetime(b["Updated Date"], unit='ms')
# b.to_excel("scripts/Data/monthreport.xlsx", index=None)
# fpath = 'scripts/Data/'
# fname = 'monthreport.xlsx'
# filepath = fpath + fname
# return filepath, fname
# def dailytempdata():
# MINM = minm("daily")['queries'][0]['results'][0]['values']
#
# df2 = pd.DataFrame(MINM, columns=["Updated_Date", "minm"])
# MAXM = maxm("daily")['queries'][0]['results'][0]['values']
#
# df3 = pd.DataFrame(MAXM, columns=["Updated_Date", "maxm"])
#
# AVG = avg("daily")['queries'][0]['results'][0]['values']
# df4 = pd.DataFrame(AVG, columns=["Updated_Date", "avg"])
#
#
# a = pd.merge(df2, df3, on="Updated_Date")
# b = pd.merge(a, df4, on="Updated_Date")
# b["Updated date"] = pd.to_datetime(b["Updated Date"], unit='ms')
# b.to_excel("scripts/Data/dailyreport.xlsx", index=None)
# fpath = 'scripts/Data/'
# fname = 'dailyreport.xlsx'
# filepath = fpath + fname
# return filepath, fname
# def weeklytempdata():
# MINM = minm("weeks")['queries'][0]['results'][0]['values']
#
# df2 = pd.DataFrame(MINM, columns=["Updated_Date", "minm"])
# MAXM = maxm("weeks")['queries'][0]['results'][0]['values']
#
# df3 = pd.DataFrame(MAXM, columns=["Updated_Date", "maxm"])
#
# AVG = avg("weeks")['queries'][0]['results'][0]['values']
# df4 = pd.DataFrame(AVG, columns=["Updated_Date", "avg"])
#
#
# a = pd.merge(df2, df3, on="Updated_Date")
# b = pd.merge(a, df4, on="Updated_Date")
# b["Updated date"] = pd.to_datetime(b["Updated Date"], unit='ms')
# b.to_excel("scripts/Data/weekreport.xlsx", index=None)
# fpath = 'scripts/Data/'
# fname = 'weekreport.xlsx'
# filepath = fpath + fname
# return filepath, fname
# # def yearlytempdata():
# # MAX_temp= maxtemp("years")['queries'][0]['results'][0]['values']
# # MIN_temp= mintemp("years")['queries'][0]['results'][0]['values']
# # AVG_temp=avgtemp("years")['queries'][0]['results'][0]['values']
# #
# # df1=pd.DataFrame(MAX_temp,columns=["Updated_Date","maxtemp"])
# # df2=pd.DataFrame(MIN_temp, columns=["Updated_Date", "mintemp"])
# # df3=pd.DataFrame(AVG_temp, columns=["Updated_Date", "avgtemp"])
#
# # a=pd.merge(df2,df3,on="Updated_Date")
# # b=pd.merge(a,df4,on="Updated_Date")
# # b["Updated date"]=pd.to_datetime(b["Updated Date"], unit='ms')
# # b.to_excel("scripts/Data/weekreport.xlsx")
# # b.to_excel("scripts/Data/dailyreport.xlsx", index=None)
# # b.to_excel("scripts/Data/weekreport.xlsx", index=None)
# # path='scripts/Data/'
# # fname='monthlyreport.xlsx'
# # filepath=fpath+fname
# # return filepath,fname
\ No newline at end of file
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