def generate_graphs_for_spatial_statistics(main, sub, y_unit):
''' Takes the name of the main header of data and the subheader,
as well as value that goes on the y-axis, and plots.
main
sub
---------------------
units
Number of fields
Coverage
maps
Sparsity
Coverage
Representation
fields
Area
'''
runs = 32
modes = None
# Load data from network1 network
network1 = []
for room_len in [1,2,3,4,5]:
network1.append(load(runs,modes,500,room_len,main,sub))
# Load data from network2 network
network2 = []
for room_len in [1,2,3]:
network2.append(load(runs,modes,1500,room_len,main,sub))
graph_shit(network1, network2, main.capitalize()+': '+sub, y_unit)
# If looking at sparsity, plot expected exponential curve
if sub == 'Sparsity':
graph_expected_sparsity()
plt.legend(loc='upper right')
if main == 'units' and sub == 'Coverage':
linereggsion(network1,network2, main,sub)
if sub == 'Number of fields':
linereggsion(network1,network2, main,sub)
if sub == 'Area':
linereggsion(network1,network2, main,sub)
def regr(with_bounds):
""" 'load' returns data of the form {side_len1: [num_flds,...],
side_len2: [num_flds,...],
...
}
Note that lambduh_bounds are the errors relative to lambduh. For example,
if lambduh_bounds = [[1,...], [2,...]] and lambduhs[0] = 7, then the real value
lies in (7-1, 7+2).
"""
global plot_bounds
plot_bounds = with_bounds
# Bring in the data in a nice form.
num_flds_1 = load(side_lens=[1, 2, 2.5], subfolder='Group 1', plc_cells=500,
grd_cells=1000, modules=None)
num_flds_2 = load(side_lens=[1, 2, 2.5, 3], subfolder='Group 2', plc_cells=500,
grd_cells=1000, modules=None)
num_flds_3 = load(side_lens=[1, 2, 2.5], subfolder='Group 3', plc_cells=500,
grd_cells=1000, modules=None)
num_flds_31 = load(side_lens=[1, 2, 3], subfolder='Group 3 no cutoff', plc_cells=500,
grd_cells=1000, modules=None)
num_flds_4 = load(side_lens=[1, 2, 2.5], subfolder='Group 4', plc_cells=500,
grd_cells=1000, modules=0)
num_flds_5 = load(side_lens=[1, 2, 2.5, 3], subfolder='Group 5', plc_cells=500,
grd_cells=1000, modules=None)
# Calculate the lambdas from the place field data.
lambdus_1, bounds_1, areas_1 = calc_lambduhs(num_flds_1)
lambdus_2, bounds_2, areas_2 = calc_lambduhs(num_flds_2)
lambdus_3, bounds_3, areas_3 = calc_lambduhs(num_flds_3)
lambdus_31, bounds_31, areas_31 = calc_lambduhs(num_flds_31)
lambdus_4, bounds_4, areas_4 = calc_lambduhs(num_flds_4)
lambdus_5, bounds_5, areas_5 = calc_lambduhs(num_flds_5)
# Graph the junks
plt.figure('Poisson Parameter Regression')
_graph_poisson_parameter_regression('Group 1', lambdus_1, areas_1, bounds_1, 'b')
_graph_poisson_parameter_regression('Group 2', lambdus_2, areas_2, bounds_2, 'g')
_graph_poisson_parameter_regression('Group 3', lambdus_3, areas_3, bounds_3, 'r')
_graph_poisson_parameter_regression('Group 3 no cutoff', lambdus_31, areas_31, bounds_31, 'y')
_graph_poisson_parameter_regression('Group 4', lambdus_4, areas_4, bounds_4, 'k')
_graph_poisson_parameter_regression('Group 5', lambdus_5, areas_5, bounds_5, 'c')
_graph_init()
plt.show()
def graph_spatial_statistics():
subfolder = 'Group 1'
modes = None
place_cells = 500
grid_cells= 1000
side_lens = [1, 2, 2.5, 3]
real_curves = load(subfolder, place_cells, grid_cells, modules, side_lens)
from figures.analysis.spatialStatistics.graphCoverage import graph_coverage
graph_coverage()
from figures.analysis.spatialStatistics.graphFieldArea import graph_field_area
graph_field_area()
from figures.analysis.spatialStatistics.graphfFieldCoverage import graph_field_coverage
graph_field_coverage()
from figures.analysis.spatialStatistics.graphMapCoverage import graph_map_coverage
graph_map_coverage()
from figures.analysis.spatialStatistics.graphNumberOfFields import graph_number_of_fields
graph_number_of_fields()
from figures.analysis.spatialStatistics.graphRepresentation import graph_representation
graph_representation()
from figures.analysis.spatialStatistics.graphRepresentation import graph_sparsity
graph_sparsity()
for (main, sub, y_unit) in [('units', 'Number of fields', 'Fields'),
('units','Coverage', 'Proportion'),
('maps', 'Sparsity', 'Proportion'),
('maps', 'Coverage', 'Proportion'),
('maps', 'Representation', 'Fields'),
('fields','Area','Field Area (m$^2$)')]:
generate_graphs_for_spatial_statistics(main, sub, y_unit)
def graph_poisson_fit(calc_overall_best_fit):
real_curves = load(subfolder, place_cells, grid_cells, modes, side_lens)
theoretical_curves = best_poisson_fit(real_curves, calc_overall_best_fit)
plot_poisson_curves(real_curves, theoretical_curves)
