def main():
num_files = 8924 #8924
max_coef_exp = 1000
max_coef_slope = 5 # +- that %pwc in one day!!
tot_num_transitions = 00 # across all users
list_diff_states = []
list_all_transitions = []
file_name_lin = "temporal_series/most_weigh_ins/all_cuts/scatter_plot_parameters_lin.dat"
file_lin = open(file_name_lin, 'wt')
file_name_exp = "temporal_series/most_weigh_ins/all_cuts/scatter_plot_parameters_exp.dat"
file_exp = open(file_name_exp, 'wt')
for index_file in range(num_files):
index_file += 1
try:
file_name = "temporal_series/most_weigh_ins/all_cuts/weigh_in_time_serie_days" + str(
index_file) + "_filters.cuts"
#file_name="temporal_series/most_weigh_ins/test_file_to_create_transition_matrix_"+str(index_file)
file = open(file_name, 'r')
list_lines_file = file.readlines()
dir = file_name.split("weigh_in_")[0]
################################
# lin: intersection + X*slope #
#exponential: A+B*exp(lambda*t)#
################################
cont_lines = 1
for line in list_lines_file:
list_one_line = line.split(" ")
trend = list_one_line[0] # i characterize the current trend:
if trend == "exp":
coef1_exp = round(float(list_one_line[1]),
2) # we dont care about the interception
coef2_exp = round(float(list_one_line[2]), 2)
coef3_exp = round(float(list_one_line[3]), 2)
if coef1_exp <= max_coef_exp and coef1_exp >= -max_coef_exp:
if coef2_exp <= max_coef_exp and coef2_exp >= -max_coef_exp:
if coef3_exp <= max_coef_exp and coef3_exp >= -max_coef_exp:
if coef2_exp < 0: # i only care about linear increase/decrease
new_coef2_exp = -1
else:
new_coef2_exp = 1
if coef3_exp < 0:
new_coef3_exp = -1
else:
new_coef3_exp = 1
length = float(list_one_line[4])
state = trend + "_" + str(
new_coef2_exp) + "_" + str(
new_coef3_exp) #+"_"+str(coef3_exp)
if state not in list_diff_states:
list_diff_states.append(state)
#print "exp",coef1_exp,coef2_exp,coef3_exp,length, state
print >> file_exp, coef1_exp, coef2_exp, coef3_exp
else:
print "bad exp_coef in file", index_file, coef1_exp, coef2_exp, coef3_exp
elif trend == "lin":
coef1_lin = round(float(list_one_line[1]),
2) # we dont care about the interception
coef2_lin = round(float(list_one_line[2]), 2)
length = float(list_one_line[3])
if coef2_lin <= max_coef_slope and coef2_lin >= -max_coef_slope:
if coef2_lin < 0: # i only care about linear increase/decrease
new_coef2_lin = -1
else:
new_coef2_lin = 1
state = trend + "_" + str(
new_coef2_lin) #+"_"+str(coef2_lin)
if state not in list_diff_states:
list_diff_states.append(state)
#print "lin",coef2_lin,length
print >> file_lin, coef1_lin, coef2_lin
else:
print "bad lin_coef in file", index_file, coef1_lin, coef2_lin
if len(
list_lines_file
) == 1: # if only one line in the file --> only one state --> diagonal term for the matrix
list = []
list.append(state)
list.append(state)
list_all_transitions.append(list)
tot_num_transitions += 1.0
else: # if more than one trend in the file
if cont_lines == 1:
old_state = state
else:
new_state = state
list = []
list.append(old_state)
list.append(new_state)
list_all_transitions.append(list)
old_state = state # i update for the next transition
tot_num_transitions += 1.0
cont_lines += 1
except IOError:
print index_file, "file doesnt exist"
list_diff_states = sorted(
list_diff_states
) # this is the order of the trends for the transition matrix
print "#num of diff states:", len(
list_diff_states) #, "namely:",list_diff_states
file_lin.close() #for the scatter plot of the parameters
file_exp.close()
########################i create the empty transition matrix
matrix = []
for i in range(len(list_diff_states)):
matrix.append(
[0.000] * len(list_diff_states)
) #ojo!!!!!!!!!!!!!!! si la creo: matrix.append(list), con list=[0.]*len(list_diff_states), al modificar unos elementos, modificare otros sin querer!!!!!!!!!!!!!!!!!!!
##################################
print "num transitions:", len(
list_all_transitions
) #," list transitions:",list_all_transitions ,"\n" #ej.: [['exp_1.0_1.0_2.0', 'lin_3.0_1.0'], ['lin_3.0_1.0', 'lin_2.0_1.0'], ['exp_2.0_1.0_3.0', 'exp_2.0_2.0_1.0'], ...]
cont_exp_to_lin = 0
cont_lin_to_exp = 0
for transition in list_all_transitions:
# print "\n\n looking for:", transition[0],transition[1]
for i in range(len(list_diff_states)):
old_state = list_diff_states[i]
for j in range(len(list_diff_states)):
new_state = list_diff_states[j]
if transition[0] == old_state and transition[1] == new_state:
matrix[i][j] += 1.000
#print " ",transition, matrix[i][j] #, list_diff_states[i], list_diff_states[j]
if old_state == 'exp_1_-1' and new_state == 'lin_1':
#print old_state, new_state
cont_exp_to_lin += 1
elif old_state == 'lin_1' and new_state == 'exp_1_-1':
#print old_state, new_state
cont_lin_to_exp += 1
print "old_state == 'exp_1_-1' and new_state== 'lin_1':", cont_exp_to_lin, "old_state == 'lin_1' and new_state== 'exp_1_-1':", cont_lin_to_exp
file_name1 = "temporal_series/most_weigh_ins/cuts/transiton_probability_matrix_test.dat"
file1 = open(file_name1, 'wt')
#print "\n\ntransition matrix:\n"
#print "\t\t\t",
# for item in list_diff_states: # print the header
# print item," ",
#print "\n"
for i in range(len(list_diff_states)):
if "exp" in list_diff_states[i]:
pass
#print list_diff_states[i],"\t",
else:
pass #print list_diff_states[i],"\t\t",
for j in range(len(list_diff_states)):
# print round(matrix[i][j]/tot_num_transitions,3)," ",
print >> file1, round(matrix[i][j] / tot_num_transitions, 3),
# print "\n"
print >> file1, "\n"
file1.close()
#### PLOTGRACE IMPLEMENTATION ###
# Draw the matrix
from PlotGrace import plot_matrix # i use a function from the module
# and just modify some of the default options
plot_matrix(
matrix,
dir + "transition_prob_matrix.agr",
xticklabels=list_diff_states,
yticklabels=list_diff_states,
xticklabelangle=90,
colorscheme='YlOrRd',
#logcolorbar=True,
reversecolorscheme=False,
mincolor=(255, 255, 255),
title="Transition Probabilities")
#!/usr/bin/env python
import sys
from PlotGrace import plot_matrix
outfilename = "matrix.agr"
colorscheme = "YlOrRd"
if len(sys.argv) >= 2:
outfilename = sys.argv[1]
if len(sys.argv) >= 3:
colorscheme = sys.argv[2]
# INPUT MATRIX
mx = []
for rowline in sys.stdin:
row = map(float, rowline.split())
mx.append(row)
# format check
num_rows = len(mx)
for row in mx:
num_cols = len(row)
if num_rows != num_cols:
print >> sys.stderr, "Warning! n_rows != n_cols"
# inform size
size = len(mx)
print >> sys.stderr, "<plot_matrix> Network size: ", size
plot_matrix(mx, outfilename, colorscheme=colorscheme, reversecolorscheme=True)
def main():
max_coef_exp = 1000
max_coef_slope = 5 # +- that %pwc in one day!!
tot_num_transitions = 0 # across all users
list_diff_states = [] # for the transition matrix
list_all_transitions = [] # for the transition matrix
database = "calorie_king_social_networking_2010"
server = "tarraco.chem-eng.northwestern.edu"
user = "******"
passwd = "n1ckuDB!"
db = Connection(server, database, user, passwd)
query = """select * from users"""
result1 = db.query(query) # is a list of dictionaries
contador_time_series = 0
contador = 0
for r1 in result1: #loop over users
contador += 1
ck_id = r1['ck_id']
list_start_end_dates_one_user = [] #without considering the gaps
list_states_one_user = [
] #without considering the gaps (just diff. behaviors)
dicc_states_one_user = {
} # the key is the initial_day, the value is the type of trend or gap
temp_dicc_states = {}
try: #only for the users with an entry in that time_series related table: one of the (filtered) time series
######################################
# for the info about different trends# (for a given, fixed user)
######################################
contador_time_series += 1
print "\n\n\n", contador, ck_id
query2 = "select * from weigh_in_cuts where (ck_id ='" + str(
ck_id) + "') order by start_day asc"
result2 = db.query(query2)
#result2: [{'fit_type': u'exp', 'stop_idx': 4L, 'ck_id': u'a48eba6e-51ad-42bc-b367-a24bb6504f0a', 'id': 68008L, 'cost': 0.5, 'param3': -0.60660499999999995, 'param2': 3.4280400000000002, 'param1': -3.1829299999999998, 'start_idx': 0L}, {'fit_type': u'lin', 'stop_idx': 16L, 'ck_id': u'a48eba6e-51ad-42bc-b367-a24bb6504f0a', 'id': 68007L, 'cost': 0.5, 'param3': None, 'param2': 0.015299699999999999, 'param1': -4.1967499999999998, 'start_idx': 5L}]
num_trends = len(result2)
for r2 in result2:
start_stop_days = [] # for that particular segment
starting_day = int(r2['start_day'])
ending_day = int(r2['stop_day'])
trend = r2['fit_type']
param1 = r2['param1']
param2 = r2['param2']
param3 = r2['param3']
start_stop_days.append(starting_day)
start_stop_days.append(ending_day)
list_start_end_dates_one_user.append(start_stop_days)
if trend == "exp":
#i dont really care about the interception
coef1_exp = param1
coef2_exp = param2
coef3_exp = param3 ################################
# lin: intersection + X*slope #
#exponential: A+B*exp(lambda*t)#
################################
if coef1_exp <= max_coef_exp and coef1_exp >= -max_coef_exp: # to avoid weird fits
if coef2_exp <= max_coef_exp and coef2_exp >= -max_coef_exp:
if coef3_exp <= max_coef_exp and coef3_exp >= -max_coef_exp:
if coef2_exp < 0 and coef3_exp > 0: # i only care about exp increase/decrease
state = "exp_down"
elif coef2_exp > 0 and coef3_exp < 0:
state = "exp_down"
elif coef2_exp > 0 and coef3_exp > 0:
state = "exp_up"
elif coef2_exp < 0 and coef3_exp < 0:
state = "exp_up"
# elif coef2_exp==0.0 or coef2_exp== -0.0:
# state="flat"
else:
print ck_id, coef2_exp, coef3_exp, "not one of the two exp types"
raw_input()
else:
print "values for the fit coef. too weird!", param1, param2, param3, ck_id
elif trend == "lin":
# i dont really care about the interception
coef2_lin = param2
if coef2_lin <= max_coef_slope and coef2_lin >= -max_coef_slope: # to avoid weird fits
if coef2_lin < 0: # i only care about linear increase/decrease
state = "lin_down"
if coef2_lin > -0.0001:
state = "flat"
elif coef2_lin == 0.0:
state = "flat"
else:
state = "lin_up"
if coef2_lin < 0.0001:
state = "flat"
list_states_one_user.append(state)
temp_dicc_states[
starting_day] = state # i save the pair starting_day, trend for that user
if state not in list_diff_states:
list_diff_states.append(state)
####################### end loop over result2 (info diff trends)
except MySQLdb.Error:
pass #for the users without an entry in that time_series related table: not one of the (filtered) time series
##########################
# for the gap info # (for the same given, fixed user ck_id)
##########################
query3 = "select * from gaps_by_frequency where (ck_id ='" + str(
ck_id) + "') order by start_day asc"
result3 = db.query(query3)
#result3: [{'file_index': 1408, 'ck_id': 8647c765-e37e-4024-92da-be838b792379, 'start_date':2009-05-07 00:00:00, 'end_date':2009-06-08 00:00:00, 'start_day': 108, 'end_day': 140, 'days_gap': 32, 'zscore_gap': 3.83125},{'file_index': 1408, 'ck_id': 8647c765-e37e-4024-92da-be838b792379, 'start_date':2009-08-04 00:00:00, 'end_date':2009-09-30 00:00:00, 'start_day': 197, 'end_day': 254, 'days_gap': 57, 'zscore_gap': 7.1496} ]
num_gaps = len(result3)
if num_gaps > 0:
for r3 in result3:
file_index = r3['file_index']
starting_gap = int(r3['start_day'])
ending_gap = int(r3['end_day'])
trend = "gap"
zscore_gap = r3[
'zscore_gap'] # threshold to consider a gap statistically sifnificant zs>=3 (imposed like that in: analyze_frequency_gaps_in_time_series_frequencies_EDIT_DB.py)
if trend not in list_diff_states:
list_diff_states.append(trend)
cont = -1 #to go over list_states_one_user
for segment in list_start_end_dates_one_user: # the list is sorted chronologically
cont += 1
start_behavior = segment[0]
end_behavior = segment[1]
if (starting_gap <= end_behavior) and (
ending_gap >= start_behavior): # if there is a gap
# in the middle of a behavior
num_trends += 2
## i cut the trend in two segments, with a gap in between
new_segment1 = [start_behavior, starting_gap]
new_segment2 = [starting_gap, ending_gap]
new_segment3 = [ending_gap, end_behavior]
temp_dicc_states[
start_behavior] = list_states_one_user[cont]
temp_dicc_states[starting_gap] = trend
temp_dicc_states[ending_gap] = list_states_one_user[
cont]
#print "gap cutting in the middle of a single behavior:",new_segment1,new_segment2,new_segment3, ck_id
for i in range(
len(list_start_end_dates_one_user)
): # i check whether there is a gap in between DIFF behaviors
try:
old_ending = list_start_end_dates_one_user[i][1]
new_beginnig = list_start_end_dates_one_user[i + 1][0]
if (starting_gap >= old_ending) and (ending_gap <=
new_beginnig):
# print "gap in the middle of two diff. behaviors:",list_states_one_user[i],trend,list_states_one_user[i+1]
temp_dicc_states[starting_gap] = trend
temp_dicc_states[
ending_gap] = list_states_one_user[i + 1]
except IndexError:
pass
#print "no room for any more gaps,",len(list_start_end_dates_one_user),i
# create a final list of all states, and then go for state in list_states: and copy code from aux, line 70 on
# end loop over result3 (gap info)
else: #if the gap info doesnt change the number of trends (NO gaps)
pass
for key in temp_dicc_states.keys(
): # i make a copy of the dicc, to be the final one
dicc_states_one_user[key] = temp_dicc_states[key]
# i account for all possible states and transsitions between states:
if len(dicc_states_one_user) > 1: # several trends for the time series
cont = 0
for key in sorted(dicc_states_one_user.keys(
)): # i print out the result of combining weigh_in cuts and gaps:
print key, dicc_states_one_user[key]
state = dicc_states_one_user[key]
if state not in list_diff_states:
list_diff_states.append(state)
list = []
if cont == 0: # for the first state
state1 = dicc_states_one_user[key]
else: # for all the rest of states in the sorted dictionary
state2 = dicc_states_one_user[key]
list.append(state1)
list.append(state2)
list_all_transitions.append(list)
state1 = state2
cont += 1
elif len(dicc_states_one_user
) == 1: # one single trend for the time series
list = []
for key in dicc_states_one_user:
list.append(dicc_states_one_user[key])
list.append(dicc_states_one_user[key])
print key, dicc_states_one_user[key]
if state not in list_diff_states:
list_diff_states.append(state)
list_all_transitions.append(list)
#print list_all_transitions
# if num_gaps>0:
# raw_input()
################################# end loop over result1 (loop over users)
# i create the empty transition matrix
list_diff_states = sorted(
list_diff_states
) # this is the order of the trends for the transition matrix
matrix = []
for i in range(len(list_diff_states)):
matrix.append(
[0.000] * len(list_diff_states)
) #ojo!!!!!!!!!!!!!!! si la creo: matrix.append(list), con list=[0.]*len(list_diff_states), al modificar unos elementos, modificare otros sin querer!!!!!!!!!!!!!!!!!!!
print "num transitions:", len(
list_all_transitions), "diff. states:", list_diff_states
for transition in list_all_transitions:
for i in range(len(list_diff_states)):
old_state = list_diff_states[i]
for j in range(len(list_diff_states)):
new_state = list_diff_states[j]
if transition[0] == old_state and transition[1] == new_state:
matrix[i][j] += 1.000
file_name1 = "temporal_series/transiton_probability_matrix_test_with_gap_info.dat"
file1 = open(file_name1, 'wt')
for i in range(len(list_diff_states)):
for j in range(len(list_diff_states)):
print >> file1, round(
matrix[i][j] / float(len(list_all_transitions)), 3),
print >> file1, "\n"
file1.close()
#### PLOTGRACE IMPLEMENTATION ###
# Draw the matrix
from PlotGrace import plot_matrix # i use a function from the module
# and just modify some of the default options
plot_matrix(
matrix,
"temporal_series/transition_prob_matrix_with_gaps.agr",
xticklabels=list_diff_states,
yticklabels=list_diff_states,
xticklabelangle=90,
colorscheme='YlOrRd',
#logcolorbar=True,
reversecolorscheme=False,
mincolor=(255, 255, 255),
title="Transition Probabilities")
def main():
min_wi = 20 #Filter1: min number of weigh ins >=
# min_timespan=0 # Filter2: min length of the serie
max_num_users_for_testing = 2000 # this is just while i test the code!!
database = "calorie_king_social_networking_2010"
server = "tarraco.chem-eng.northwestern.edu"
user = "******"
passwd = "n1ckuDB!"
db = Connection(server, database, user, passwd)
query = """select * from users"""
result1 = db.query(query) # is a list of dict.
num_users = len(result1)
dir = "../Results/"
list_diff_states = []
list_all_transitions = []
tot_num_transitions = 0.
num_useful_users = 0
cont_users = 0
for r1 in result1: #loop over users to get their number_of_weigh-ins
## r1 is a dict.: {'ck_id': u'bd84dbe2-dd6e-4125-b006-239442df2ff6', 'age': 52L, 'state': u'', 'height': 64L, 'join_date': datetime.datetime(2009, 11, 27, 10, 41, 5), 'is_staff': u'public', 'most_recent_weight': 142.0, 'initial_weight': 144.0, 'id': 1L}
# if cont_users <=max_num_users_for_testing: #COMMENT THIS LINE FOR THE FINAL RUN!!!
ck_id = r1['ck_id']
id = r1['id']
print cont_users, ck_id
cont_users += 1
query2 = "select * from weigh_in_history where (ck_id ='" + str(
ck_id) + "') order by on_day asc"
result2 = db.query(query2)
r1['num_wi'] = len(
result2
) # i add another key-value to the dict. -->> with this i ALSO modify the list of dict. result!!!
first = result2[0]['on_day']
last = result2[-1]['on_day']
time_span_days = (last - first).days + 1
if r1['num_wi'] >= min_wi: #and int(time_span_days) >= min_timespan:
dict_start_day_type_segment = {}
query3 = "select * from weigh_in_cuts where (ck_id ='" + str(
ck_id) + "') order by start_day"
result3 = db.query(query3) # is a list of dict.
for r3 in result3: # each line is a dict, each line is a segment
fit_type = str(r3['fit_type'])
state = fit_type
start_day = int(r3['start_day'])
stop_day = int(r3['stop_day'])
start_weight = float(r3['start_weight'])
stop_weight = float(r3['stop_weight'])
if fit_type != "isolated": # i get the dict states_startig days
param1 = float(r3['param1'])
try:
param2 = float(
r3['param2']
) # cos constant dont have a value for this
except:
pass
try: # cos lin and constant dont have a value for this
param3 = float(r3['param3'])
except:
pass
if fit_type == "exponent":
if param3 < 0.:
if param2 > 0.:
state = fit_type + "_desc"
dict_start_day_type_segment[start_day] = state
else:
state = fit_type + "_asc"
dict_start_day_type_segment[start_day] = state
else:
print "we shouldnt have positive taus!", ck_id
elif fit_type == "linear":
if param2 < 0.:
state = fit_type + "_desc"
if param2 > -0.001: # if very small slope, it is actually constant (this around 5% per two month)
state = "constant"
else:
state = fit_type + "_asc"
if param2 < 0.001:
state = "constant"
dict_start_day_type_segment[start_day] = state
else: # constant
dict_start_day_type_segment[start_day] = state
if len(dict_start_day_type_segment
) > 0: # user with at least one useful segment
num_useful_users += 1
query4 = "SELECT * FROM frequency_gaps where (ck_id ='" + str(
ck_id) + "') order by start_day" #gap info
result4 = db.query(query4) # is a list of dict.
for r4 in result4:
start_day = int(r4['start_day'])
stop_day = int(r4['stop_day'])
start_weight = float(r4['start_weight'])
stop_weight = float(r4['stop_weight'])
dict_start_day_type_segment[start_day] = "gap"
list_tuples_sorted_dict = sorted(
dict_start_day_type_segment.iteritems(),
key=operator.itemgetter(0)
) # the index of itermgetter is by which i order the list of tuples that was the dictionary
print ck_id,
######### once i get all gaps, i merge consecutive gaps into just one:
print " states before merging gaps:", list_tuples_sorted_dict, len(
list_tuples_sorted_dict)
flag_first_gap_index = 0
list_items_to_remove = []
for item in list_tuples_sorted_dict:
# print item
if item[1] == 'gap':
if flag_first_gap_index == 0:
flag_first_gap_index = 1
else:
list_items_to_remove.append(item)
flag_first_gap_index = 1
else:
flag_first_gap_index = 0 #i dont want to remove the first gap right after a segment
if len(
list_items_to_remove
) > 0: # IT DIDT WORK TO REMOVE THEM AS I GO OVER THE LIST IN THE PREVIOS LOOP
# print "list items to remove:",list_items_to_remove
for item in list_items_to_remove:
list_tuples_sorted_dict.remove(item)
# print "states after merging gaps:", list_tuples_sorted_dict,"\n"
# raw_input()
if len(
list_tuples_sorted_dict
) == 1: # if only one state --> diagonal term for the matrix
state = list_tuples_sorted_dict[0][1]
if state not in list_diff_states: # for the transition matrix
list_diff_states.append(state)
lista = []
lista.append(state)
lista.append(state)
list_all_transitions.append(lista)
tot_num_transitions += 1.0
else: # if more than one trend
cont = 0
for item in list_tuples_sorted_dict:
state = item[1]
if state not in list_diff_states: # for the transition matrix
list_diff_states.append(state)
if cont > 0:
new_state = state
lista = []
lista.append(old_state)
lista.append(new_state)
list_all_transitions.append(lista)
tot_num_transitions += 1.0
old_state = state # i update for the next transition
cont += 1
################# end loop over users
list_diff_states = sorted(
list_diff_states
) # this is the order of the trends for the transition matrix
print "#num of diff states:", len(
list_diff_states), "namely:", list_diff_states
print "#num useful users:", num_useful_users
print "num transitions:", len(
list_all_transitions
) #," list transitions:",list_all_transitions ,"\n" #ej.: [['exp_1.0_1.0_2.0', 'lin_3.0_1.0'], ['lin_3.0_1.0', 'lin_2.0_1.0'], ['exp_2.0_1.0_3.0', 'exp_2.0_2.0_1.0'], ...]
matrix = [] # raw count of transitions
norm_matrix = [
] # normalized count of transitios by the tot number of them
for i in range(len(list_diff_states)):
matrix.append(
[0.000] * len(list_diff_states)
) #ojo!!!!!!!!!!!!!!! si la creo: matrix.append(list), con list=[0.]*len(list_diff_states), al modificar unos elementos, modificare otros sin querer!!!!!!!!!!!!!!!!!!!
norm_matrix.append([0.000] * len(list_diff_states))
for transition in list_all_transitions:
# print "\n\n looking for:", transition[0],transition[1]
for i in range(len(list_diff_states)):
old_state = list_diff_states[i]
for j in range(len(list_diff_states)):
new_state = list_diff_states[j]
#print old_state, new_state
if transition[0] == old_state and transition[1] == new_state:
matrix[i][j] += 1.000
norm_matrix[i][j] += 1.000
for i in range(len(list_diff_states)):
for j in range(len(list_diff_states)):
norm_matrix[i][j] = norm_matrix[i][j] / tot_num_transitions
file_name1 = dir + "numerical_values_transiton_probability_matrix.dat"
file1 = open(file_name1, 'wt')
for i in range(len(list_diff_states)):
if "exp" in list_diff_states[i]:
pass
#print list_diff_states[i],"\t",
else:
pass #print list_diff_states[i],"\t\t",
for j in range(len(list_diff_states)):
# print round(matrix[i][j]/tot_num_transitions,3)," ",
print >> file1, round(
matrix[j][i] / tot_num_transitions, 3
), # so the order row/columns matches with the .agr representation
# print "\n"
print >> file1, "\n"
file1.close()
print "printed out matrix textfile:", dir + "transiton_probability_matrix_test.dat"
#### PLOTGRACE IMPLEMENTATION ###
# Draw the matrix
plot_matrix(
matrix,
dir + "transition_prob_matrix.agr",
xticklabels=list_diff_states,
yticklabels=list_diff_states,
xticklabelangle=90,
colorscheme='YlOrRd',
#logcolorbar=True,
xlabel="Initial State",
ylabel="Final State",
reversecolorscheme=False,
mincolor=(255, 255, 255),
title="Transition Probabilities"
) #########Axis for the matrix plot are X: initial state, Y: final state.
print "printed out matrix plot:", dir + "transition_prob_matrix.agr"
plot_matrix(
norm_matrix,
dir + "transition_prob_matrix_norm.agr",
xticklabels=list_diff_states,
yticklabels=list_diff_states,
xticklabelangle=90,
colorscheme='YlOrRd',
#logcolorbar=True,
xlabel="Initial State",
ylabel="Final State",
reversecolorscheme=False,
mincolor=(255, 255, 255),
title="Transition Probabilities"
) #########Axis for the matrix plot are X: initial state, Y: final state.
print "printed out matrix plot:", dir + "transition_prob_matrix_norm.agr"