yohanesnuwara’s gists

yohanesnuwara / modis.js

Created February 7, 2024 13:50

GEE Code to Plot NDVI EVI Over an Area

	var geometry =
	/* color: #ff0000 */
	/* displayProperties: [
	{
	"type": "rectangle"
	}
	] */
	ee.Geometry.Polygon(
	[[[98.64805742984797,2.91896461364908],
	[98.64944144969965,2.91896461364908],

yohanesnuwara / Telco_unique_values.csv

Last active January 6, 2022 03:58

Unique values of Telco preprocessed dataset

Column	Unique Values	Description
SeniorCitizen	[0, 1]	0: No older than 65; 1: Older than 65
Partner	[0, 1]	0: Single; 1: Married
Dependents	[0, 1]	0: Has no children or grandparents; 1: Has any
tenure	[1, 2, 8, 34, 45]
PhoneService	[0, 1]	0: Not subscribes to home phone service; 1: Has phone service
MultipleLines	[0, 1, 2]	0: No phone service at all; 1: Not subscribes to multiple phone lines; 2: Has multiple phone lines
OnlineSecurity	[0, 1, 2]	0: No internet service; 1: Not have additional online security; 2: Have additional online security
OnlineBackup	[0, 1, 2]	0: No internet service; Not have additional online backup; 2: Have additional online backup
DeviceProtection	[0, 1, 2]	0: No internet service; 1: Not have device protection; 2: Have device protection

yohanesnuwara / Telco_Preprocessed.csv

Last active January 5, 2022 13:37

Telecom churn data preprocessed

We can make this file beautiful and searchable if this error is corrected: It looks like row 9 should actually have 27 columns, instead of 26 in line 8.

	customerID,SeniorCitizen,Partner,Dependents,tenure,PhoneService,MultipleLines,OnlineSecurity,OnlineBackup,DeviceProtection,TechSupport,StreamingTV,StreamingMovies,Contract,PaperlessBilling,MonthlyCharges,TotalCharges,Churn,gender_Female,gender_Male,PaymentMethod_Bank transfer (automatic),PaymentMethod_Credit card (automatic),PaymentMethod_Electronic check,PaymentMethod_Mailed check,InternetService_DSL,InternetService_Fiber optic,InternetService_No
	7590-VHVEG,0,1,0,1,0,0,1,2,1,1,1,1,0,1,29.85,29.85,0,1,0,0,0,1,0,1,0,0
	5575-GNVDE,0,0,0,34,1,1,2,1,2,1,1,1,1,0,56.95,1889.5,0,0,1,0,0,0,1,1,0,0
	3668-QPYBK,0,0,0,2,1,1,2,2,1,1,1,1,0,1,53.85,108.15,1,0,1,0,0,0,1,1,0,0
	7795-CFOCW,0,0,0,45,0,0,2,1,2,2,1,1,1,0,42.3,1840.75,0,0,1,1,0,0,0,1,0,0
	9237-HQITU,0,0,0,2,1,1,1,1,1,1,1,1,0,1,70.7,151.65,1,1,0,0,0,1,0,0,1,0
	9305-CDSKC,0,0,0,8,1,2,1,1,2,1,2,2,0,1,99.65,820.5,1,1,0,0,0,1,0,0,1,0
	1452-KIOVK,0,0,1,22,1,2,1,2,1,1,2,1,0,1,89.1,1949.4,0,0,1,0,1,0,0,0,1,0
	6713-OKOMC,0,0,0,10,0,0,2,1,1,1,1,1,0,0,29.75,301.9,0,1,0,0,0,0,1,1,0

yohanesnuwara / snowflake.ipynb

Created December 24, 2021 13:16

snowflake.ipynb

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yohanesnuwara / optimization_result.csv

Created August 24, 2021 03:57

ROP optimization result

Parameter	Initial	Optimized
WOB	31236.8	73823.0
RPM	2.0	2.0
ROP	0.0027	0.0055

yohanesnuwara / optimization4.py

Created August 23, 2021 11:58

Code for optimization 4

	import pyswarm as pso

	# Define objective function. "pipe" as model
	def f(X):
	return -pipe.predict(X.reshape(1,-1)) # Minus sign to optimize

	# Lower bounds of feature variables in the order of X.columns
	lb = np.array([3480, 2e4, 1.5, 0.09, 0.1, 1, 0.001])

	# Upper bounds of feature variables in the order of X.columns

yohanesnuwara / optimization3.py

Created August 23, 2021 08:11

Code for optimization 3

	def plot_space(model, WOB, SURF_RPM, constant_inputs):
	N_matrix = np.empty((len(WOB), len(SURF_RPM)))
	for i in range(len(WOB)):
	for j in range(len(SURF_RPM)):
	# Unwrap constant variables
	Depth, PHIF, VSH, SW, KLOGH = constant_inputs.values()
	N = predict(model, Depth, PHIF, VSH, SW, KLOGH, WOB=WOB[i], SURF_RPM=SURF_RPM[j])
	N_matrix[i][j] = N

	plt.imshow(N_matrix, origin='lower', aspect='auto', cmap='inferno',

yohanesnuwara / optimization2.py

Created August 22, 2021 13:26

Code for optimization 2

	def predict(model, Depth, PHIF, VSH, SW, KLOGH, WOB, SURF_RPM):
	# Make a test input
	X_test = np.array([Depth, WOB, SURF_RPM, PHIF, VSH, SW, KLOGH])
	X_test = X_test.reshape(1,-1)

	# Predict on a test input
	y_pred = model.predict(X_test)
	return y_pred[0]

	# Predict on new inputs

yohanesnuwara / optimization1.py

Created August 22, 2021 13:14

Code for optimization 1

	# Separate feature and target
	X = df.drop(['ROP_AVG'], axis=1)
	y = df['ROP_AVG']

	# Train test split
	X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
	random_state=10)

	# Make pipeline
	steps = [('scaler', StandardScaler()),

yohanesnuwara / Data_ROP_optimization.csv

Created August 22, 2021 09:00

Data for ROP optimization

We can make this file beautiful and searchable if this error is corrected: It looks like row 10 should actually have 8 columns, instead of 3 in line 9.

	Depth,WOB,SURF_RPM,ROP_AVG,PHIF,VSH,SW,KLOGH
	3305.0,26217.863999999998,1.3147203,0.004088054,0.08671100549484449,0.07171879464621217,1.0000000001136795,0.0009999999999999998
	3310.0,83492.293,1.3286738999999999,0.005159383,0.0952084975856594,0.11654831517334588,1.0000000000000002,0.0010000000000000002
	3315.0,97087.882,1.4201163,0.005971469300000001,0.061636098298260736,0.10428323445521293,1.0,0.0009999999999999998
	3320.0,54793.206,1.5939306999999998,0.0054191089000000005,0.04349766373664589,0.11003981104314808,1.0,0.0010000000000000002
	3325.0,50301.57900000001,1.6532619999999998,0.0054352483,0.03525224077246292,0.1208077056657565,0.9999999999999998,0.0009999999999999998
	3330.0,56835.194,1.65745,0.004738274000000001,0.015965128094069806,0.08556492496762315,1.0,0.001
	3335.0,37638.028,2.1392382999999997,0.008811767199999999,0.04624312337708843,0.1098589745374595,1.0,0.0010000000000000002
	3340.0,37638.028,2.1392382999999997,0.008811767199999999,0.04123656459834146,0.0713682580625008,1.0,0.001
	3345.0,44564.583,1.97

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