def question_c():
# generate the distribution
total_data = []
for plot_index in range(PLOTS):
current_data = []
# gaussian indexes to sample from
indexes = np.random.randint(1, 4, POINTS_PER_PLOT)
unique, number_of_samples = np.unique(indexes, return_counts=True)
# add each gaussian to data
x_data = np.array([])
y_data = np.array([])
for i in range(1, 4):
shape = int(number_of_samples[i - 1])
x_data = np.concatenate(
(np.random.normal(i, 2 * i, shape), x_data), axis=0)
y_data = np.concatenate(
(np.random.normal(i, 2 * i, shape), y_data), axis=0)
total_data.append(np.dstack((np.array([x_data]), np.array([y_data]))))
data1, data2 = total_data
# PLOT the outputs
for data in (data1, data2):
util.plot_in_R2(data.T[0], data.T[1], "Gaussian Distribution")
# save the points
util.write_list(data1, "problem3_outputs/question_c/data1.txt")
util.write_list(data2, "problem3_outputs/question_c/data2.txt")
def question_a():
# generate the distribution
x_data = np.random.uniform(-1, 1, (TOTAL_POINTS, 1))
y_data = np.random.uniform(0, 5, (TOTAL_POINTS, 1))
merged = np.concatenate((x_data, y_data), axis=1)
data1, data2 = merged[:POINTS_PER_PLOT], merged[POINTS_PER_PLOT:]
# PLOT the outputs
for data in (data1, data2):
util.plot_in_R2(data.T[0], data.T[1], "Uniform Distribution")
# save the points
util.write_list(data1, "problem3_outputs/question_a/data1.txt")
util.write_list(data2, "problem3_outputs/question_a/data2.txt")
def main():
TARGETS = [
20,
33,
36,
137,
142,
143,
144,
151,
152,
159,
166,
167,
171,
173,
183,
184,
192,
193,
194,
196,
205,
1676,
1692,
2851,
2914,
] # data within: 12-Dec-2013_12_3909_50_0.55556_25.mat #indices between 0 and 2110. the scenarios for which you want to save the damaged bridge data
TARGETS = ["173_high"] # we have retrofitted the top 25% ranked by accessibility impact
weights = get_scenario_weights(
"12-Dec-2013_12_3909_50_0.55556_25_weights.csv"
) # the annual likelihood of occurance of each of the scenarios ("targets")
y = aggregate_accessibility(TARGETS, True)
util.write_list(time.strftime("%Y%m%d") + "_accessTotACC_fromMain.txt", y)
# TODO: implement the 3 functions below so they actually do something
y_array = aggregate_accessibility_by_income(TARGETS, True)
util.write_2dlist(time.strftime("%Y%m%d") + "_accessByIncome_fromMain.txt", y_array)
y = aggregate_accessibility_by_taz(TARGETS, weights, True)
util.write_list(time.strftime("%Y%m%d") + "_accessbyTAZ_fromMain.txt", y)
y_array = aggregate_accessibility_by_taz_by_income(TARGETS, weights, True)
util.write_2dlist(time.strftime("%Y%m%d") + "_accessByTAZByIncome_fromMain.txt", y_array)
def question_d():
# In order to generate a circle, we will sample two parameters
# R and theta, and from them
obj_pnts = int(TOTAL_POINTS / 2)
plt_obj_pnts = int(POINTS_PER_PLOT / 2)
# Sample the inner circle
circle_r_data = np.random.uniform(0, 0.5, (obj_pnts, 1))
circle_t_data = np.random.uniform(0, 2 * np.pi, (obj_pnts, 1))
# Sample the outer ring
ring_r_data = np.random.uniform(1.5, 1.75, (obj_pnts, 1))
ring_t_data = np.random.uniform(0, 2 * np.pi, (obj_pnts, 1))
# concatenate each plot
r_data1 = np.concatenate(
(circle_r_data[:plt_obj_pnts], ring_r_data[:plt_obj_pnts]), axis=0)
r_data2 = np.concatenate(
(circle_r_data[plt_obj_pnts:], ring_r_data[plt_obj_pnts:]), axis=0)
t_data1 = np.concatenate(
(circle_t_data[:plt_obj_pnts], ring_t_data[:plt_obj_pnts]), axis=0)
t_data2 = np.concatenate(
(circle_t_data[plt_obj_pnts:], ring_t_data[plt_obj_pnts:]), axis=0)
polar_data1 = np.concatenate((r_data1, t_data1), axis=1)
polar_data2 = np.concatenate((r_data2, t_data2), axis=1)
# turn from polar to cartesian
total_data = []
for data in (polar_data1, polar_data2):
total_data.append(
[util.pol2cart(point[0], point[1]) for point in data])
data1, data2 = np.array(total_data[0]), np.array(total_data[1])
# PLOT the outputs
for data in (data1, data2):
util.plot_in_R2(data.T[0], data.T[1], "Circle inside a Ring")
# save the points
util.write_list(data1, "problem3_outputs/question_d/data1.txt")
util.write_list(data2, "problem3_outputs/question_d/data2.txt")
def run_simple_iteration(G, ground_motion, demand, multi, j, targets, clean_up = True):
#G is a graph (not a multigraph!), demand is a dictionary keyed by source and target of demand per weekday. multi is a boolean that is true if it is a multigraph (can have two parallel edges between nodes)
#change edge properties
newG, capacities = damage_network(G, ground_motion, multi) #also returns the number of bridges out
num_out = sum(x < 100 for x in capacities)
update_bridge_damage_dataset(capacities)
if j in targets:
affected_bridges = []
for i in range(len(capacities)):
if capacities[i] < 100:
if (i+1) not in SPECIALLY_RETROFITTED_BRIDGES:
affected_bridges.append(str(i+1))
util.write_list('20130902_modifyingCapacity/' + time.strftime("%Y%m%d")+'_modifyingCapacitytab' + str(j) + '.txt', affected_bridges)
#get max flow
start = time.time()
#node 5753 is in superdistrict 12, which is santa clara county, and node 3144 is in superdistrict 18, which is alameda county. roughly these are san jose and oakland
#node 7619 is in superdistrict 1 (7493 is also), which is sf, and node node 3144 is in superdistrict 18, which is alameda county. roughly these are san francisco and oakland
s = '3144'
t = '7493' #2702
try:
flow = nx.max_flow(newG, s, t, capacity='capacity') #not supported by multigraph
except nx.exception.NetworkXError as e:
print 'found an ERROR: ', e
flow = -1
print s in newG
print t in newG
print len(newG.nodes())
print len(newG.edges())
# sp_dict = nx.single_source_dijkstra_path_length(newG,'7493',weight='distance')
# sp = sum(sp_dict.values())/float(len(sp_dict.values()))
# sp2 = 0
# for target in demand.keys():
# sp2 += sp_dict[target]
# sp2 = sp2 / float(len(demand.keys()))
sp = 0
sp2 = 0
if clean_up == True:
damagedG= util.clean_up_graph(newG)
return (num_out, flow, sp, sp2, newG)
def main():
# Step 1: get links for projects
if len(sys.argv) > 1 and sys.argv[1] == "list":
driver = webdriver.Firefox()
projects = list_projects.get_project_links(driver)
util.write_list(projects)
driver.close()
else:
projects = util.read_list()
# Step 2: get data for every project
output = dict()
output["records"] = {"record": []}
for project_index, project_link in enumerate(projects):
output["records"]["record"].append(
get_data.get_data_from_url(project_link, project_index + 1))
print("Crawled:\t%d/%d" % (project_index + 1, len(projects)))
# Have mercy on KickStarter :)
time.sleep(MERCY_TIME)
# Write into JSON file
util.write_dict(output)
def del_persistent_link(c1, c2):
persistent_links.discard((c1.lower(), c2.lower()))
util.write_list(LINKS_FILE, list(persistent_links))
def add_persistent_link(c1, c2):
persistent_links.add((c1.lower(), c2.lower()))
util.write_list(LINKS_FILE, list(persistent_links))
def main():
TARGETS = [20, 33, 36, 137, 142, 143, 144, 151, 152, 159, 166, 167, 171, 173, 183, 184, 192, 193, 194, 196, 205, 1676, 1692, 2851, 2914]# data within: 12-Dec-2013_12_3909_50_0.55556_25.mat #indices between 0 and 2110. the scenarios for which you want to save the damaged bridge data
# TARGETS = [20]
y = aggregate_accessibility(TARGETS)
util.write_list(time.strftime("%Y%m%d")+'_accessTot_fromMain.txt', y)
def question_e():
for i in range(2):
points = plot_e_once()
util.write_list(
points, "problem3_outputs/question_e/data" + str(i + 1) + ".txt")
if (rowCount % breakpoint == 0):
input = raw_input("Press enter to continue ")
if (not emptyStringCheck(input)):
break
#Summary
print "------------------------------------------------------------------------------------------------"
cursor.close()
print "cursor closed"
database.close()
print "finished uploading"
print "------------------------------------------------------------------------------------------------"
print 'Summary'
print 'Number of total entries made: ' + str(rowCount)
print 'Number of incorrect isbns given based on length: ' + str(invalidIsbns)
write_list('List of rows with incorrect isbns lengths given: ',
invalidIsbnList)
write_list('List of rows with non numberic quantities: ',
quantityTypeFieldErrorList)
if (gBooksApiFlag == True):
print 'GOOGLE API STATS'
print '************************'
print 'Number of valid API responses: ' + str(
(rowCount - invalidApiResponses))
print 'Number of parseOtherIsbn function calls : ' + str(isbnCallCount)
print 'Number of successful parseOtherIsbn function calls: ' + str(
findsOtherIsbn)
write_list(
'List of rows with valid Apis but unsuccesful parseOtherIsbn calls: ',
unsuccesfulParseOtherIsbnList)
print '************************'
if not dbh.checkJobIfExist(job['companyId'], job['jobName']):
DataEtl.processJobData(job)
else:
jobList.remove(job)
dbh.insertJob(jobList)
if __name__ == '__main__':
dbh = DBHelper.DBHelper()
# companySt=set()
nameSt = set()
comList = []
with open('company/famous.txt', 'r', encoding='utf-8') as f:
for line in f:
line = line.strip('\r\n')
comList.append(dbh.selectCompanyByName(line))
# ori_List=dbh.selectCompany(limitNum=6000);
# print(len(ori_List))
# for com in ori_List:
# if com[1] in nameSt:
# continue
# else:
# comList.append(com)
# nameSt.add(com[1])
# random.shuffle(comList)
rtn = parseJob4CompanyList(comList)
write_list(rtn, 'jobs/jobs_1_31.txt')
# addJobOfCompanyName('建设银行')
def main():
# get and aggregate accessibility from cube using import_acc_results.py file
# TARGETS = [12, 35, 55, 71, 75, 82, 86, 87, 88, 106, 108, 115, 121, 231, 241, 247, 256, 258, 260, 261, 676, 730, 733, 1231, 1548] #first set of Cube runs
TARGETS = [
20,
33,
36,
137,
142,
143,
144,
151,
152,
159,
166,
167,
171,
173,
183,
184,
192,
193,
194,
196,
205,
1676,
1692,
2851,
2914,
] # data within: 12-Dec-2013_12_3909_50_0.55556_25.mat #indices between 0 and 2110. the scenarios for which you want to save the damaged bridge data
# TARGETS = [20]
# y = aggregate_accessibility(TARGETS)
# util.write_list(time.strftime("%Y%m%d")+'_accessTot.txt', y)
y = [
18.2339128119,
18.2338120181,
18.2338952366,
18.2338109314,
18.2270352566,
18.2177845713,
18.1998501612,
18.2177377231,
18.233770681,
18.2261430987,
18.1691203163,
18.1849249099,
18.2141010264,
18.2139231104,
18.23383158091398,
18.2253745585,
18.2155757901,
18.2012935522,
18.2138556128,
18.1758345198,
18.226103683,
18.2338211763,
18.2260523679,
18.2339486092,
18.2215360497,
]
weights = get_scenario_weights("12-Dec-2013_12_3909_50_0.55556_25_weights.csv")
# get general x values. These are the various welfare metrics.
the_filename = "/Users/mahalia/ita/20131212_bridges_flow_path_tt_vmt_bridges1eps_extensive2.txt"
new_x = freq_svm.build_x(TARGETS, the_filename)
the_filename_full = (
"/Users/mahalia/ita/20131212_bridges_flow_path_tt_vmt_bridges3eps_extensive.txt"
) # indices in the first column start at 0
x_for_predicting = freq_svm.build_x(range(1, 11728), the_filename_full)
# x_for_predicting = freq_svm.build_x(range(1, 3092), the_filename_full)
the_x = np.vstack((new_x, x_for_predicting))
the_x = preprocessing.scale(the_x)
new_x = the_x[0 : new_x.shape[0], :]
x_for_predicting = the_x[new_x.shape[0] :, :]
print "built baby x"
# pick threshold. Above this y value, the data is called a "match" and below is a "miss". For frequent itemsets, we'll be doing frequent items ONLY among the items predicted as a match so VORSICHT!
target_annual_rate = 0.002 # 1 in 475 years
threshold = freq_svm.identify_threshold(target_annual_rate, y, weights)
print "by my method I find the threshold to be: ", threshold
threshold = (
18.19933616
) # from the Matlab script called cubeAnalysiswDamagedTransit.m for 475 year return period #18.2139 #75th quantile
print "I think the threshold is: ", threshold
# label events above threshold as match and below as miss
match_label = 1
miss_label = 0 # for purposes of acesibility, low is bad so these are the true high loss cases
new_y = freq_svm.label(
y, threshold, match_label, miss_label
) # less than threshold is miss label. So, this puts high loss in accessibility as miss (lower value)
print "new_ y: ", new_y # should be mostly 1's
# ############################
# h = .02 # step size in the mesh
# # we create an instance of SVM and fit out data. We do not scale our
# # data since we want to plot the support vectors
# C = 1.0 # SVM regularization parameter
# svc = svm.SVC(kernel='linear', C=C, class_weight='auto').fit(new_x, new_y)
# rbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C, class_weight='auto').fit(new_x, new_y)
# poly_svc = svm.SVC(kernel='poly', degree=3, C=C, class_weight='auto').fit(new_x, new_y)
# lin_svc = svm.LinearSVC(C=C, class_weight='auto').fit(new_x, new_y)
# X = new_x.copy()
# # create a mesh to plot in
# x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
# y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
# xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
# np.arange(y_min, y_max, h))
# # title for the plots
# titles = ['SVC with linear kernel',
# 'SVC with RBF kernel',
# 'SVC with polynomial (degree 3) kernel',
# 'LinearSVC (linear kernel)']
# for i, clf in enumerate((svc, rbf_svc, poly_svc, lin_svc)):
# # Plot the decision boundary. For that, we will assign a color to each
# # point in the mesh [x_min, m_max]x[y_min, y_max].
# pl.subplot(2, 2, i + 1)
# Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# # Put the result into a color plot
# Z = Z.reshape(xx.shape)
# pl.contourf(xx, yy, Z, cmap=pl.cm.Paired)
# pl.axis('off')
# pl.xlabel('Percentage increase of bridges damaged (normalized)')
# pl.ylabel('Percentage incrase of travel time (normlized')
# # Plot also the training points
# pl.scatter(X[:, 0], X[:, 1], c=new_y, cmap=pl.cm.Paired)
# #plot also the prediction
# y_pred = clf.predict(x_for_predicting)
# pl.scatter(x_for_predicting[:, 0], x_for_predicting[:, 1], c= y_pred, marker='^', cmap = pl.cm.Paired)
# pl.title(titles[i])
# pl.savefig('/Users/mahalia/Dropbox/research/dailyWriting/bridges/classificationComp.png')
# ####################
# #train SVM
svm_object = freq_svm.train(new_x, new_y, "auto") # {0:1, 1:1})
######Done using Cube results. Now just use ITA results....#####
# use trained svm to predict values from large set
# print 'built x'
y_pred = freq_svm.predict(x_for_predicting, svm_object)
# y_pred = []
# for i in range(11727):
# y_pred.append(0)
util.write_list(time.strftime("%Y%m%d") + "_predictedY.txt", y_pred)
# count up annual rates for each bridge in the list when event predicted as match
miss_indices = []
for index, value in enumerate(y_pred):
if value == miss_label: # high loss means low accessibility, which means miss
miss_indices.append(index + 1) # matlab indices starting from 1
print 'we have this many "misses"=="predicted high loss cases": ', len(miss_indices)
item_indices = range(3152) # 1743 highway bridges and 1409 bart structures
with open("20131212_3eps_damagedBridges.pkl", "rb") as f:
list_of_baskets = pkl.load(
f
) # this has list of bridge indices (MATLAB INDICES that start from 1) that are damaged
# for basket in list_of_baskets:
# if '609' in basket:
# print 'found one: ', basket
lnsas, weights = travel_main_simple_simplev3.ground_motions(
3, 0.00001, "input/SF2_mtc_total_3909scenarios_1743bridgesPlusBART_3eps.txt"
)
support_list = get_support(weights, miss_indices, item_indices, list_of_baskets)
# output the sum of weights of scenarios where each bridge was damanged to plot in matlab. First column is counter stsarting at 1. second column is support
util.write_2dlist(time.strftime("%Y%m%d") + "_bridgeIndex_support.txt", support_list)
pdb.set_trace()
def main_tt():
print "chin up"
# get and aggregate travel time
# get general x values. These are the various welfare metrics.
the_filename_full = (
"/Users/mahalia/ita/20131212_bridges_flow_path_tt_vmt_bridges3eps_extensive.txt"
) # indices in the first column start at 0
x_raw = freq_svm.build_x(range(1, 11728), the_filename_full)
the_x = preprocessing.scale([[row[0]] for row in x_raw])
the_y = np.array([row[1] for row in x_raw])
break_point = 9383
new_x = np.array(the_x[0:break_point]) # 80%
x_for_predicting = the_x[break_point:] # 20%
y = np.array([row[1] for row in x_raw[0:break_point, :]]) # should be as big as the training dataset
numeps = 3 # the number of epsilons
tol = (
0.00001
) # the minimum annual rate that you care about in the original event set (the weight now is the original annual rate / number of epsilons per event)
lnsas, full_weights = travel_main_simple_simplev3.ground_motions(
numeps,
tol,
"/Users/mahalia/Documents/matlab/Research/Herbst2011/output_data/SF2_mtc_total_3909scenarios_1743bridgesPlusBART_3eps.txt",
)
weights = full_weights[0:break_point]
print "built baby x"
# pick threshold. Above this y value, the data is called a "match" and below is a "miss". For frequent itemsets, we'll be doing frequent items ONLY among the items predicted as a match so VORSICHT!
target_annual_rate = 0.002 # 1 in 475 years
threshold = freq_svm.identify_threshold(target_annual_rate, y, weights)
print "i thought: ", threshold
threshold = (
346420000
) # 18.19933616 #from the Matlab script called cubeAnalysiswDamagedTransit.m for 475 year return period #18.2139 #75th quantile
print "I think the threshold is: ", threshold
# label events above threshold as match and below as miss
match_label = 1
miss_label = 0 # for purposes of accesibility, low is bad so these are the true high loss cases
new_y = np.array(freq_svm.label(y, threshold, match_label, miss_label))
print "new_ y: ", new_y
# ############################
# h = .02 # step size in the mesh
# # we create an instance of SVM and fit out data. We do not scale our
# # data since we want to plot the support vectors
# C = 1.0 # SVM regularization parameter
# svc = svm.SVC(kernel='linear', C=C, class_weight='auto').fit(new_x, new_y)
# rbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C, class_weight='auto').fit(new_x, new_y)
# poly_svc = svm.SVC(kernel='poly', degree=3, C=C, class_weight='auto').fit(new_x, new_y)
# lin_svc = svm.LinearSVC(C=C, class_weight='auto').fit(new_x, new_y)
# X = new_x.copy()
# # create a mesh to plot in
# x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
# y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
# xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
# np.arange(y_min, y_max, h))
# # title for the plots
# titles = ['SVC with linear kernel',
# 'SVC with RBF kernel',
# 'SVC with polynomial (degree 3) kernel',
# 'LinearSVC (linear kernel)']
# for i, clf in enumerate((svc, rbf_svc, poly_svc, lin_svc)):
# # Plot the decision boundary. For that, we will assign a color to each
# # point in the mesh [x_min, m_max]x[y_min, y_max].
# pl.subplot(2, 2, i + 1)
# Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# # Put the result into a color plot
# Z = Z.reshape(xx.shape)
# pl.contourf(xx, yy, Z, cmap=pl.cm.Paired)
# pl.axis('off')
# pl.xlabel('Percentage increase of bridges damaged (normalized)')
# pl.ylabel('Percentage incrase of travel time (normlized')
# # Plot also the training points
# pl.scatter(X[:, 0], X[:, 1], c=new_y, cmap=pl.cm.Paired)
# #plot also the prediction
# y_pred = clf.predict(x_for_predicting)
# pl.scatter(x_for_predicting[:, 0], x_for_predicting[:, 1], c= y_pred, marker='^', cmap = pl.cm.Paired)
# pl.title(titles[i])
# pl.savefig('/Users/mahalia/Dropbox/research/dailyWriting/bridges/classificationComp.png')
# ####################
# #train SVM
print new_x.shape
print new_y.shape
svm_object = freq_svm.train(new_x, new_y, "auto") # {0:1, 1:1})
######Done using Cube results. Now just use ITA results....#####
# use trained svm to predict values from large set
# print 'built x'
y_pred = freq_svm.predict(x_for_predicting, svm_object)
# y_pred = []
# for i in range(11727):
# y_pred.append(0)
util.write_list(time.strftime("%Y%m%d") + "_predictedY_tt.txt", y_pred)
y_test_raw = [row[1] for row in x_raw[break_point:, :]]
y_test = freq_svm.label(y_test_raw, threshold, match_label, miss_label)
y_tot_raw = [row[1] for row in x_raw]
y_tot = freq_svm.label(y_tot_raw, threshold, match_label, miss_label)
util.write_list(time.strftime("%Y%m%d") + "_actualY_tt.txt", y_test)
print (classification_report(y_test, y_pred))
print (confusion_matrix(y_test, y_pred, labels=range(2)))
scores = cross_validation.cross_val_score(
svm_object, the_x, freq_svm.label(the_y, threshold, match_label, miss_label), cv=3
)
print ("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
# count up annual rates for each bridge in the list when event predicted as match
miss_indices = []
for index, value in enumerate(y_tot): # cheating and just using the actual data instead of predicted one
if value == miss_label:
miss_indices.append(index + 1) # matlab indices starting from 1
print 'we have this many "misses"=="predicted high loss cases": ', len(miss_indices)
item_indices = range(3152) # 1743 highway bridges and 1409 bart structures
with open("20131212_3eps_damagedBridges.pkl", "rb") as f:
list_of_baskets = pkl.load(
f
) # this has list of bridge indices (MATLAB INDICES that start from 1) that are damaged
lnsas, weights = travel_main_simple_simplev3.ground_motions(
3, 0.00001, "input/SF2_mtc_total_3909scenarios_1743bridgesPlusBART_3eps.txt"
)
support_list = get_support(weights, miss_indices, item_indices, list_of_baskets)
# output the sum of weights of scenarios where each bridge was damanged to plot in matlab. First column is counter stsarting at 1. second column is support
util.write_2dlist(time.strftime("%Y%m%d") + "_bridgeIndex_support_tt.txt", support_list)
pdb.set_trace()
for line in tempf:
sys.stdout.write(line)
tempf.close()
os.remove(tfp)
tfp = None
# if html file specified
if options.html:
if not os.path.isfile(options.html):
raise JustError("%s is not a file" % options.html)
adjusted_fl = [os.path.relpath(f, os.path.dirname(options.html)) for f in dep_list]
if options.output_mode == 'list':
util.write_list(options.html, adjusted_fl, options.section)
elif options.output_mode == 'tags':
write_tags(options.html, adjusted_fl, options.section)
elif options.output_mode == 'minified' or options.output_mode == 'one-script':
adjusted_of = os.path.relpath(options.output_file, options.html)
util.write_tags(options.html, [adjusted_of], options.section)
else:
adjusted_fl = [os.path.relpath(f, os.getcwd()) for f in dep_list]
if options.output_mode == 'list':
if options.output_file:
of = open(options.output_file, "w")
for f in adjusted_fl:
of.writeline(f)
of.close()
