Sanjay Mehrotra

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Dumpers status
Dumper 2221
Jajang dumper
First fuel modelling of Jajang Dumper
nlevel bowser R02
nlevel test
HM Plant model 1
Two weeks data model
HM Plant calibration table
Boli calibration table
boli dg metric
Updated till 1:40 pm
Boli DG metric
SHows hourly data where > 0 litres consumed
Boli summary
First fuel level upload for verification
painful dips
Fuel level dipping for several minutes not reaching zero
two small refills
Now we can see the section of fuel graph with low EER (high CPH).
Vehno. KA22 C 6455;
Drain detection POC
Published qtest
leakage in moving asset
Fuel Leakage cases
6 to 8 am
Peculiar spikes
This one has peculiar spikes in raw levels.
A noisy sensor example
Uncalibrated testing
Balance 42 assets were tested today for 3 dates.
tsoutlier works on reduced window
The blue dots are after applying tsclean() ... green lines are original data so here tsoutliers has worked perfectly to detect the outlier points at 02:14
tsoutlier fails
The blue and green lines are coinciding which means tsoutliers has failed to detect the outlier points at 02:14
Jobner drain on May 10
Zoom into the May 10th date to see the clear drain
dip before the refill
Just before the filling starts there is huge fall in level
7 am to 8 am consumption is very high
Is this fake refuel?
From 1500 to 1600 the consumption increased 5 times normal
Sensor seems dies at 1 AM.
all vehicles bird's eye view
drain inchara 6455
The detected drain by omnicomm is 22 lits while actually this looks to be 42 lits drain.
is this a drain test?
Sudden fall of level
APCO 5046
Inchara anamoly
Dec to March 2022
POC base reports
APCO reports
These are analytics on APCO vehicles
base reports produced from the POC
Fuel tsd facet ncol =1
Operator wise performance
Two key operator performance on same assets
classification of time series using kmeans
EB Watts, DG Watts, DCV and Ibatt are the 4 dimensions on which we use a kmeans with k = 5.
Bitcoin Price with Moving Averages
Over two window sizes: 365 days and 1458 days
Bitcoin Price
Energy Distribution
To get a feel of variations in the distributions of energy across the POC sites. ALso across dates for the same site.
Classification of Time Series Data of battery voltage into three classes using Decision Tree Learning.
Bagru2 has many ELM errors.
Dud DG performance
Kalwara DG performance analysis
Dand Analysis of LPU
Bagru1 AC watts
WIth both battery and DG on shaded
Reliability measured as count of observations received every 15 minutes across all parameters from Ganapathi plaza from ELM4100 DC EM
reliability_AC EM
Observation counts of ELM8420 in DAND for the entire day of 22nd, captured every 15 minutes.
These are number of observations received every 15 minutes for each parameter seen separately. The data shows RUT955 counts for the entire day of March 22 (midnight to midnight).
Telecom Sites CPH
Field Survey results
BNP field survey results summarised
Histogram for per capita heads
Except Roads, Drains and Footpaths all other expense heads included in the per capita expense histogram.
Histogram with two side tails highlighted
Boxed wards on both sides. X-axis is Per km spent on Roads, Drains, Footpath and Lighting.
Budget utlisation waffle charts
Sample waffle charts for Bellanduru budget utlization. Also shows relative size of budget across each year
Toilet Budget per capita for social media
Designing this for social media
Histogram Toilets
Histogram across all wards on Toilet Budgets
Histogram on Lightings - Highlight Bellanduru
Histogram of wards showing Budget per capita on Lightings - highlighting bellandur
Hisotgram across all wards Budget per capita on head: parks
Bellanduru - Drinking Water
Drinking water per capita histogram across ward
Bellanduru - Roads
Histogram of Roads, Drains and Foothpaths - Budget per capita
Leaflet testing
Expense vs Road Length (Correlation)
Scatter plot for all 198 wards, total expense vs total road length. In addition color of point denotes: Political Party. Size: ex Mayor (large size) In addition we see the fitted curve & correlation coeff.
Normalised Road expenses in last 5 years
Normalised expenses means Expenses per km or road existing in the ward.
Road Expenses (per kms of roads int he ward)
Unit expenses (Expenses per kms) on roads
Montreal Elections
Ward Budgets 2019-20
BBMP Budget for FY 2019-20, ward wise
Expenses on Roads, Drains and Footpaths
Expenses over 2015 to 2020
Budget utilisation
Utlisation of BBMP Budget from 2015-16 to 2019-20
Bangalore ward maps
Payment delays
Dot plot of projects 10L to 1 Cr in value
Projects between the value of 10L to 1 Crores and who have data stamp availability for FINISHED DATE and PAID DATE in all 4 combinations. The points are shown in jitter mode to expose the density of the projects every year.
ward 100 to 120
Expense to Approved amounts
Sample of 10 wards plotted on Net Spent year on year vs approved amounts on the same job numbers that were undertaken.
Plots of financial summary
We are using this to test our citation function
Plot - test
for checking attendance overlaps
Magma colours
Testing the publishing
Shift duty visualisation
Shift duty rosters can be published using spreadsheets but then the actual IN and OUT of an employee is the difficult thing to track - even if they are electronically checked in and out. The data is immense and difficult to visualise in relation to each other. These duty hours line charts are simple to understand and give immediate insights.
Seems a hack attempt from Macau
these mysql logs include all SELECT statements of jupiter along with APIs that start around 22:44
these logs are combined logs for 10 minutes of API firing as well as MySQL: including ejabberd and jupiter_dev
combined_log with xmpp
These logs have all UPDATE, INSERT including XMPP MySQL,. Note: We are truncating the scripts at 40 char.
API log merged with MySQL log
xmpp error log
filtered on mysql
last 100 queries
Listing of topics and subtopics in class id 22
R api logs
R API flooding
api logs - 26 july 2017
These are sample logs captured
APi flood
with color text
TIme plot of API counts
How to add more levels to a factor without changing the original levels
This doc explains when you are capturing data and you know you are going to grow a variable into many levels, how do you maintain the old factors.
just a simple histogram