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Commit 72929ee8 authored by suazo2's avatar suazo2
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%% Cell type:code id:9ef9c1b3 tags:
``` python
import boto3
from botocore.config import Config
import pandas as pd
import matplotlib as mp
import matplotlib.pyplot as plt
```
%% Cell type:code id:d7385261 tags:
``` python
ACCESS_KEY = 'AKIA5II7XXI23QL6ICS5'
SECRET_KEY = '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'
my_config = Config(
region_name = 'us-east-1',
retries = {
'max_attempts': 10,
'mode': 'standard'
}
)
```
%% Cell type:code id:f1ab6dae tags:
``` python
# create IoT Analytics client
client = boto3.client('iotanalytics',
aws_access_key_id=ACCESS_KEY,
aws_secret_access_key=SECRET_KEY,
config=my_config
)
```
%% Cell type:code id:f2bcc76c tags:
``` python
print(client.list_datasets()['datasetSummaries'])
print(client.list_channels()['channelSummaries'])
```
%% Output
[{'datasetName': 'emission_ds', 'status': 'ACTIVE', 'creationTime': datetime.datetime(2023, 4, 2, 23, 11, 0, 5000, tzinfo=tzlocal()), 'lastUpdateTime': datetime.datetime(2023, 4, 3, 2, 38, 32, 191000, tzinfo=tzlocal()), 'triggers': [{'schedule': {'expression': 'cron(10/5 * * * ? *)'}}], 'actions': [{'actionName': 'action_11', 'actionType': 'QUERY'}]}]
[{'channelName': 'emissionchannel', 'channelStorage': {'serviceManagedS3': {}}, 'status': 'ACTIVE', 'creationTime': datetime.datetime(2023, 4, 2, 22, 59, 6, 629000, tzinfo=tzlocal()), 'lastUpdateTime': datetime.datetime(2023, 4, 2, 22, 59, 6, 629000, tzinfo=tzlocal()), 'lastMessageArrivalTime': datetime.datetime(2023, 4, 3, 2, 56, 23, 350000, tzinfo=tzlocal())}]
%% Cell type:code id:e31db523 tags:
``` python
# Load dataset
dataset_name = "emission_ds"
dataset = client.get_dataset_content(datasetName=dataset_name)
# get pandas data frame
df = pd.read_csv(dataset['entries'][0]['dataURI'])
```
%% Cell type:code id:e1c4a0a3 tags:
``` python
df
```
%% Output
device_id max_co2 timestep_time __dt
0 device_0 12353.41 118.0 2023-04-03 00:00:00.000
1 device_3 9638.66 120.0 2023-04-03 00:00:00.000
2 device_2 11307.68 119.0 2023-04-03 00:00:00.000
3 device_1 18960.98 119.0 2023-04-03 00:00:00.000
4 device_4 19466.87 120.0 2023-04-03 00:00:00.000
.. ... ... ... ...
793 device_4 15216.78 19.0 2023-04-03 00:00:00.000
794 device_1 9460.68 18.0 2023-04-03 00:00:00.000
795 device_0 9818.41 17.0 2023-04-03 00:00:00.000
796 device_2 11307.68 18.0 2023-04-03 00:00:00.000
797 device_0 9818.41 18.0 2023-04-03 00:00:00.000
[798 rows x 4 columns]
%% Cell type:code id:756b529a tags:
``` python
# Bar graph max emission per vehicle
max_emission = df.groupby('device_id')['max_co2'].max()
max_emission.plot.bar(x='device_id', y='max_co2', rot=0)
```
%% Output
<AxesSubplot: xlabel='device_id'>
%% Cell type:code id:2b50f3d0 tags:
``` python
vehicle_0 = df[df['device_id'] == "device_0"]
vehicle_0 = vehicle_0.sort_values(by=['timestep_time'])
vehicle_1 = df[df['device_id'] == "device_1"]
vehicle_1 = vehicle_1.sort_values(by=['timestep_time'])
vehicle_2 = df[df['device_id'] == "device_2"]
vehicle_2 = vehicle_2.sort_values(by=['timestep_time'])
vehicle_3 = df[df['device_id'] == "device_3"]
vehicle_3 = vehicle_3.sort_values(by=['timestep_time'])
vehicle_4 = df[df['device_id'] == "device_4"]
vehicle_4 = vehicle_4.sort_values(by=['timestep_time'])
fig, axes = plt.subplots(nrows=3, ncols=2)
vehicle_0.plot(x="timestep_time", y=["max_co2"], ax=axes[0,0])
vehicle_1.plot(x="timestep_time", y=["max_co2"], ax=axes[0,1])
vehicle_2.plot(x="timestep_time", y=["max_co2"], ax=axes[1,0])
vehicle_3.plot(x="timestep_time", y=["max_co2"], ax=axes[1,1])
vehicle_4.plot(x="timestep_time", y=["max_co2"], ax=axes[2,0])
```
%% Output
<AxesSubplot: xlabel='timestep_time'>
%% Cell type:code id:c5e15ebc tags:
``` python
# Average emission over time (All 5 vehicles)
df_mean = df.groupby('timestep_time')['max_co2'].mean()
df_mean.plot(x="timestep_time", y=["max_co2"])
```
%% Output
<AxesSubplot: xlabel='timestep_time'>
%% Cell type:code id:fe3e0b2b tags:
``` python
```
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