def plot_axon_lines(neurites):
nRows, nCols = size2x2(neurites)
if nRows > 50:
lineWidth = 0.5
elif nRows > 25:
lineWidth = 1.0
else:
lineWidth = 2.0
for row in range(nRows):
for col in range(nCols):
if (neurites[row, col] == 1.0) | (neurites[row, col] == 3.0):
plt.plot([col + 0.1, col + 0.9], [row + 0.5, row + 0.5],
"w",
linewidth=lineWidth)
def plot_hatch_lines(matrix):
nRows, nCols = size2x2(matrix)
if nRows > 50:
lineWidth = 0.5
elif nRows > 25:
lineWidth = 1.0
else:
lineWidth = 2.0
for row in range(nRows+1):
for col in range(nCols+1):
plt.plot([col, col], [0, nRows], "k", linewidth=lineWidth)
plt.plot([0, nCols], [row, row], "k", linewidth=lineWidth)
def plot_axon_lines(neurites):
nRows, nCols = size2x2(neurites)
if nRows > 50:
lineWidth = 0.5
elif nRows > 25:
lineWidth = 1.0
else:
lineWidth = 2.0
for row in range(nRows):
for col in range(nCols):
if (neurites[row,col] == 1.0) | (neurites[row,col] == 3.0):
plt.plot([col+0.1, col+0.9], [row+0.5, row+0.5], "w", linewidth=lineWidth)
def plot_hatch_lines(matrix):
nRows, nCols = size2x2(matrix)
if nRows > 50:
lineWidth = 0.5
elif nRows > 25:
lineWidth = 1.0
else:
lineWidth = 2.0
for row in range(nRows + 1):
for col in range(nCols + 1):
plt.plot([col, col], [0, nRows], "k", linewidth=lineWidth)
plt.plot([0, nCols], [row, row], "k", linewidth=lineWidth)
from diek_functions import size2x2 from diek_functions import pause from file_functions import load_csv from plot_patterns import plot_patterns print fileName = "FP_matrix.csv" #fileName = "test.csv" fpMatrix = load_csv(fileName) nAllRows, nAllColumns = size2x2(fpMatrix) nSkipRows = 1 nSkipColumns = 6 ABCorderingColumnNo = 0 HCorderingColumnNo = 1 cellIDorderingColumnNo = 3 labelColumnNo = 5 patternLabelsRowNo = 0 nCells = nAllRows - nSkipRows # account for header row nPatterns = nAllColumns - nSkipColumns # account for header columns patternLabels = [""] * nPatterns cellLabelsSorted = [""] * nCells nPatternOccurrencesSorted = [[None for j in range(nPatterns)] for i in range(nCells)]
import numpy as np from file_functions import read_from_csv from diek_functions import size2x2 from diek_functions import pause print fileName = 'FP_matrix.csv' fileName = 'test.csv' fpDataFrame = read_from_csv(fileName) nFpRows, nFpColumns = size2x2(fpDataFrame.values) print 'nFpRows ', nFpRows print nSkipRows = 1 nSkipColumns = 6 ABCorderingColumnNo = 0 HCorderingColumnNo = 1 cellIDorderingColumnNo = 3 labelColumnNo = 5 patternLabelsRowNo = 0
import numpy as np from file_functions import read_from_csv from diek_functions import size2x2 from diek_functions import pause print fileName = 'FP_matrix.csv' fileName = 'test.csv' fpDataFrame = read_from_csv(fileName) nFpRows, nFpColumns = size2x2(fpDataFrame.values) print 'nFpRows ', nFpRows print nSkipRows = 1 nSkipColumns = 6 ABCorderingColumnNo = 0 HCorderingColumnNo = 1 cellIDorderingColumnNo = 3 labelColumnNo = 5 patternLabelsRowNo = 0
import csv from file_functions import load_csv from file_functions import write_csv from diek_functions import size2x2 from diek_functions import pause from diek_functions import time_stamp print namesFileName = "data/neuron_type_names.csv" namesMatrix = load_csv(namesFileName) nNamesRows, nNamesColumns = size2x2(namesMatrix) nNames = nNamesRows - 1 # account for names header row namesHeaderRowNo = 0 # python indexing starts at zero namesCellIdColumnNo = 3 # python indexing starts at zero nNamesHeaderRows = 1 nNamesHeaderColumns = 6 patternsFileName = "data/FP_patterns.csv" patternsMatrix = load_csv(patternsFileName)
import numpy as np from file_functions import read_from_csv from diek_functions import size2x2 from diek_functions import pause print fileName = 'FP_matrix.csv' fileName = 'test.csv' fpMatrix = read_from_csv(fileName) nFpRows, nFpColumns = size2x2(fpMatrix) nSkipRows = 1 nSkipColumns = 6 ABCorderingColumnNo = 0 HCorderingColumnNo = 1 cellIDorderingColumnNo = 3 labelColumnNo = 5 patternLabelsRowNo = 0
import numpy as np from file_functions import read_from_csv from diek_functions import size2x2 from diek_functions import pause print fileName = 'FP_matrix.csv' fileName = 'test.csv' fpMatrix = read_from_csv(fileName) nFpRows, nFpColumns = size2x2(fpMatrix) nSkipRows = 1 nSkipColumns = 6 ABCorderingColumnNo = 0 HCorderingColumnNo = 1 cellIDorderingColumnNo = 3 labelColumnNo = 5 patternLabelsRowNo = 0 #orderingColumnNo = ABCorderingColumnNo
from diek_functions import size2x2 from diek_functions import pause from file_functions import dictRead_from_csv from plot_patterns import plot_patterns print fileName = "FP_matrix.csv" fileName = "test.csv" fpDict = dictRead_from_csv(fileName) nAllRows, nAllColumns = size2x2(fpDict) nSkipRows = 1 nSkipColumns = 6 ABCorderingColumnNo = 0 HCorderingColumnNo = 1 cellIDorderingColumnNo = 3 labelColumnNo = 5 patternLabelsRowNo = 0 #orderingColumn = "ABC ordering" orderingColumn = "HC ordering" #orderingColumn = "Cell ID" nCells = nAllRows nPatterns = nAllColumns - nSkipColumns # account for header columns
from diek_functions import size2x2 from diek_functions import pause from file_functions import dictRead_from_csv from plot_patterns import plot_patterns print fileName = "FP_matrix.csv" fileName = "test.csv" fpDict = dictRead_from_csv(fileName) nAllRows, nAllColumns = size2x2(fpDict) nSkipRows = 1 nSkipColumns = 6 ABCorderingColumnNo = 0 HCorderingColumnNo = 1 cellIDorderingColumnNo = 3 labelColumnNo = 5 patternLabelsRowNo = 0 #orderingColumn = "ABC ordering" orderingColumn = "HC ordering" #orderingColumn = "Cell ID" nCells = nAllRows nPatterns = nAllColumns - nSkipColumns # account for header columns