rgerkin

#!pip install --user tabulate # Install the tabulate package
from tabulate import tabulate
import numpy as np
import pandas as pd
import IPython.display as d

# Some random data
data = np.random.rand(10,4)
# Columns A, B, C, D
columns = [chr(x) for x in range(65,69)]

rgerkin

'''
Neuron simulator export for:

Components:
    RS (Type: izhikevich2007Cell:  v0=-0.06 (SI voltage) k=7.0E-7 (SI conductance_per_voltage) vr=-0.06 (SI voltage) vt=-0.04 (SI voltage) vpeak=0.035 (SI voltage) a=30.0 (SI per_time) b=-2.0E-9 (SI conductance) c=-0.05 (SI voltage) d=1.0E-10 (SI current) C=1.0E-10 (SI capacitance))
    RS_Iext (Type: pulseGenerator:  delay=0.5 (SI time) duration=1.0 (SI time) amplitude=1.0E-10 (SI current))
    net1 (Type: network)
    sim1 (Type: Simulation:  length=1.6 (SI time) step=2.5E-6 (SI time))

rgerkin

from sklearn.cross_validation import ShuffleSplit
from sklearn.ensemble import RandomForestRegressor

n_features = [1,2,3,4,5,10,33,100,333,1000,3333,10000] # The numbers we agreed on. 
n_splits = 10 # The number of splits we agreed on.  
n_obs = int(X_all.shape[0]/2) # X_all is my matrix of all the training and leaderboard molecule features, including the leak.  
                              # It has two (consecutive) rows for each molecule, the first is the weaker concentration and the second
                              # is the stronger one.  
shuffle_split = ShuffleSplit(n_obs,n_splits,test_size=0.17,random_state=0) # This reproduces the splits I put on GitHub.  

rgerkin

rgerkin

 wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
 chmod +x miniconda.sh
 ./miniconda.sh -b
 export PATH=~/miniconda/bin:$PATH
 conda update --yes conda
 conda install --yes python=2.7 atlas numpy scipy matplotlib
 sudo apt-get update
 sudo apt-get install gcc libxml2-dev libxslt-dev python-dev lib32z1-dev
 sudo apt-get install default-jre
 pip install -U git+https://github.com/neuralensemble/libNeuroML@development

rgerkin

class RateTest(sciunit.Test):
	def __init__(self, mean, std, input_current):
		self.mean, self.std, self.input_current = mean, std, input_current
	
	required_capabilities = (
		neurounit.Capabilities.ReceivesCurrent,
		neurounit.Capabilities.ProducesFiringRate
	)
	
	def run_test(self, model):

rgerkin

class RateTest(sciunit.Test):
	def __init__(self, mean, std, input_current):
		self.mean, self.std, self.input_current = mean, std, input_current
	
	required_capabilities = (
		neurounit.Capabilities.ReceivesCurrent,
		neurounit.Capabilities.ProducesFiringRate
	)
	
	def run_test(self, model):