class RunGraph:
def __init__(self, filename='testFile.txt'):
self.filename = filename
self.extractObject = Extract(self.filename)
self.nodes = self.extractObject.nodes
def add_node(self, node):
self.extractObject.add_node(node)
self.extractObject.write_to_json()
def get_results(self, src, dest):
#This function takes a source and destination node adn calculate the path between them.
calculate_obj = Graph(self.nodes)
came_from, cost = calculate_obj.calculate_path(self.nodes[src],self.nodes[dest])
path = calculate_obj.reconstruct_path(came_from, self.nodes[src], self.nodes[dest])#reverse the path
is_path_valid = calculate_obj.validate_rules(path)#enforce validation rules
final_data = dict()
if is_path_valid:
final_data['path'] = path
final_data['cost'] = cost[path[-1]]
return final_data
else:
return is_path_valid
def __init__(self, opt, device):
super(STR, self).__init__()
self.opt = opt
# Trans
# self.Trans = Trans.TPS_SpatialTransformerNetwork(F = opt.num_fiducial,
# i_size = (opt.imgH, opt.imgW),
# i_r_size= (opt.imgH, opt.imgW),
# i_channel_num=opt.input_channel,
# device = device)
#Extract
if self.opt.extract =='RCNN':
self.Extract = self.Extract = Extract.RCNN_extractor(opt.input_channel, opt.output_channel)
elif 'efficientnet' in self.opt.extract :
self.Extract = Extract.EfficientNet(opt)
elif 'resnet' in self.opt.extract :
self.Extract = Extract.ResNet_FeatureExtractor(opt.input_channel, opt.output_channel)
else:
raise print('invalid extract model name!')
# self.Extract = Extract.RCNN_extractor(opt.input_channel, opt.output_channel)
# self.Extract = Extract.ResNet_FeatureExtractor(opt.input_channel, opt.output_channel)
self.FeatureExtraction_output = opt.output_channel # (imgH/16 -1 )* 512
self.AdaptiveAvgPool = nn.AdaptiveAvgPool2d((None,1)) # imgH/16-1 -> 1
# Sequence
self.Seq = nn.Sequential(
BidirectionalLSTM(self.FeatureExtraction_output, opt.hidden_size, opt.hidden_size),
# BidirectionalLSTM(1536, opt.hidden_size, opt.hidden_size),
BidirectionalLSTM(opt.hidden_size, opt.hidden_size, opt.hidden_size))
self.Seq_output = opt.hidden_size
#Pred
self.Pred = Pred.Attention(self.Seq_output, opt.hidden_size, opt.num_classes, device=device)
def obtain_month_states(data):
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
state = ex.obtain_state(data)
state_list = state[:,0]
fire_list = []
for st in state_list:
state_fires = []
print(st)
for i in range (1992,2016):
for k in months:
month_num = months.index(k) + 1
result = ex.obtain_monthly_state(data,st,i,k)
result = result['discovery_month'].value_counts().sort_index()
if result.empty:
result = 0
fire = [i, month_num, result]
state_fires.append(fire)
else:
X = np.array([i, month_num])
X = X.astype(int)
Y = np.array([result])
Y = Y.astype(int)
Y = Y.squeeze()
fire = np.hstack((X, Y)).tolist()
state_fires.append(fire)
fire_list.append(state_fires)
return fire_list
def __init__(self, opt, device):
super(model, self).__init__()
self.opt = opt
#Trans
self.Trans = Trans.TPS_SpatialTransformerNetwork(
F=opt.num_fiducial,
i_size=(opt.imgH, opt.imgW),
i_r_size=(opt.imgH, opt.imgW),
i_channel_num=opt.input_channel,
device=device)
#Extract
if self.opt.extract == 'RCNN':
self.Extract = self.Extract = Extract.RCNN_extractor(
opt.input_channel, opt.output_channel)
elif 'efficientnet' in self.opt.extract:
self.Extract = Extract.EfficientNet(opt)
elif 'resnet' in self.opt.extract:
self.Extract = Extract.ResNet_FeatureExtractor(
opt.input_channel, opt.output_channel)
else:
raise print('invalid extract model name!')
# self.AdaptiveAvgPool = nn.AdaptiveAvgPool2d((None,1)) # imgH/16-1 -> 1
# Position aware module
self.PAM = PositionEnhancement.PositionAwareModule(
opt.output_channel, opt.hidden_size, opt.output_channel, 2)
self.PAttnM_bot = PositionEnhancement.AttnModule(
opt, opt.hidden_size, opt.bot_n_cls, device)
self.PAttnM_mid = PositionEnhancement.AttnModule(
opt, opt.hidden_size, opt.mid_n_cls, device)
self.PAttnM_top = PositionEnhancement.AttnModule(
opt, opt.hidden_size, opt.top_n_cls, device)
# Hybrid branch
self.Hybrid_bot = Hybrid.HybridBranch(opt.output_channel,
opt.batch_max_length + 1,
opt.bot_n_cls, device)
self.Hybrid_mid = Hybrid.HybridBranch(opt.output_channel,
opt.batch_max_length + 1,
opt.mid_n_cls, device)
self.Hybrid_top = Hybrid.HybridBranch(opt.output_channel,
opt.batch_max_length + 1,
opt.top_n_cls, device)
# # Dynamically fusing module
self.Dynamic_fuser_top = PositionEnhancement.DynamicallyFusingModule(
opt.top_n_cls)
self.Dynamic_fuser_mid = PositionEnhancement.DynamicallyFusingModule(
opt.mid_n_cls)
self.Dynamic_fuser_bot = PositionEnhancement.DynamicallyFusingModule(
opt.bot_n_cls)
def save_csv(forest_path):
data = ex.load_dataset(forest_path)
data = obtain_month_states(data) # fs
state = ex.load_dataset(forest_path)
state = ex.obtain_state(state)
state_list = state[:,0]
state_list = state_list.tolist()
for i in state_list:
index = state_list.index(i)
values = data[index]
np.savetxt("state_fire/" + i + "_total" + ".csv", values, delimiter=',', fmt='%d')
return
def startExtract(conn):
if (input_db_details['dbtype'] == 'csv'):
rows = ext.processExtractCSV(input_db_details['csvloc'])
dataDFList = ['csv', rows]
elif (input_db_details['dbtype'] == 'tweets'):
rows = twt.extractTweets(input_db_details['search_words'],
input_db_details['date_since'])
dataDFList = [['tweets', rows]]
else:
dataDFList = ext.startExtractProcess(conn)
return dataDFList
def test_download_data():
"""
Evalúa la función extracción de datos
"""
temp_db = pg_temp.TempDB(databases=['testdb'])
select_query = "SELECT * FROM pg_attribute"
conn = Extract.db_connection()
df = Extract.download_data(conn, select_query)
temp_db.cleanup()
assert df.size > 0
def __init__(self, opt, device):
super(SCATTER, self).__init__()
self.opt = opt
#Trans
self.Trans = Trans.TPS_SpatialTransformerNetwork(F = opt.num_fiducial, i_size = (opt.imgH, opt.imgW),
i_r_size= (opt.imgH, opt.imgW), i_channel_num=opt.input_channel, device = device)
#Extract
if self.opt.extract =='RCNN':
self.Extract = self.Extract = Extract.RCNN_extractor(opt.input_channel, opt.output_channel)
elif 'efficientnet' in self.opt.extract :
self.Extract = Extract.EfficientNet(opt)
else:
raise print('invalid extract model name!')
# self.Extract = Extract.ResNet_FeatureExtractor(opt.input_channel, opt.output_channel)
self.FeatureExtraction_output = opt.output_channel # (imgH/16 -1 )* 512
self.AdaptiveAvgPool = nn.AdaptiveAvgPool2d((None,1)) # imgH/16-1 -> 1
# VISUAL FEATURES
self.VFR = VFR.Visual_Features_Refinement(kernel_size = (3,1), num_classes = opt.num_classes,
in_channels = self.FeatureExtraction_output, out_channels=1, stride=1)
# CTC DECODER
self.CTC = CTC.CTC_decoder(opt.output_channel, opt.output_channel, opt.num_classes, device)
# Selective Contextual Refinement Block
# self.SCR_1 = SCR.Selective_Contextual_refinement_block(input_size = self.FeatureExtraction_output,
# hidden_size = int(self.FeatureExtraction_output/2),
# output_size = self.FeatureExtraction_output,
# num_classes = opt.num_classes, decoder_fix = False, device = device,
# batch_max_length = opt.batch_max_length)
# self.SCR_2 = SCR.Selective_Contextual_refinement_block(input_size = self.FeatureExtraction_output,
# hidden_size = int(self.FeatureExtraction_output/2),
# output_size = self.FeatureExtraction_output,
# num_classes = opt.num_classes, decoder_fix = False, device = device,
# batch_max_length = opt.batch_max_length)
# self.SCR_3 = SCR.Selective_Contextual_refinement_block(input_size = self.FeatureExtraction_output,
# hidden_size = int(self.FeatureExtraction_output/2),
# output_size = self.FeatureExtraction_output,
# num_classes = opt.num_classes, decoder_fix = True, device = device,
# batch_max_length = opt.batch_max_length)
self.SCR = SCR.SCR_Blocks(input_size = self.FeatureExtraction_output,
hidden_size = int(self.FeatureExtraction_output/2),
output_size = self.FeatureExtraction_output,
num_classes = opt.num_classes, device = device,
batch_max_length = opt.batch_max_length,
n_blocks=opt.scr_n_blocks)
def activity_users():
apname_last = Extract.extractSpecific(createFiles.realFile,
'APName').replace('APName',
'').split()[-1]
ssid = Extract.extractSpecific(createFiles.realFile,
'SSID').split('SSID')[-1]
user_name = Extract.extractSpecific(createFiles.realFile,
'Username').split('Username')[-1]
mac_address = Extract.client_mac(createFiles.realFile)[-1]
ip_address = Extract.extractSpecific(createFiles.realFile,
'IPAddress').split('IPAddress')[-1]
if mac_address is not '0:0:0:0:0:0':
ExportToDB.send_to_db(mac_address, ip_address, apname_last, ssid,
user_name)
def deauth_users():
apname_last = Extract.extractSpecific(createFiles.realFile,
'APName').replace('APName',
'').split()[-1]
reason_code = Extract.extractSpecific(createFiles.realFile,
'ReasonCode').split('ReasonCode')[-1]
user_ip_address = Extract.extractSpecific(
createFiles.realFile, 'UserIpAddress').split('UserIpAddress')[-1]
user_name = Extract.extractSpecific(createFiles.realFile,
'UserName').split('UserName')[-1]
mac_address = Extract.client_mac(createFiles.realFile)[-1]
if mac_address is not '0:0:0:0:0:0':
ExportToDB.disassociate_users(mac_address, user_ip_address,
apname_last, reason_code, user_name)
def upload_file():
# check if the post request has the file part
if 'file' not in request.files:
resp = jsonify({'message': 'No file part in the request'})
resp.status_code = 400
return resp
file = request.files['file']
if file.filename == '':
resp = jsonify({'message': 'No file selected for uploading'})
resp.status_code = 400
return resp
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
file.save(file_path)
resp = jsonify({'message': 'File successfully uploaded'})
dpi = 300
documentText, fname = OCR.Convert(file_path, dpi, str(1))
data = Extract.Info(fname)
data = Align.restructure(data)
return str(data)
else:
resp = jsonify({'message': 'Allowed file type is pdf'})
resp.status_code = 400
return resp
def sky_median(self, X=2, Y=10, distance=5):
'''Generates a sky spectrum from the median of a large sky area.
Args:
X/Y: the X/Y position of the center coordinate
distance: the radius around the center coordinate
Returns:
{'wave_nm': wavelength of sky spectrum,
'spec_adu', the median sky spectrum,
'all_spec': and a matrix of spectra,
'num_spec': The number of spectra}
Side Effects:
Stores the returned values into the class'''
if self.positions[0]=='MeanX':
if X<60:
X = 1024
Y=1024
if distance<60: distance*= 60
res = Extract.sky_median(self.KT, self.SegMap, ixmap=self.OK,
pixel_shift=self.pixel_shift,X=X,Y=Y,distance=distance)
return res
def spectrum_near_position(self, X, Y, distance=2, onto=None,
sky_spec=None):
'''Interpolate spectra in a circle radius distance arround X,Y
See spectrum_near_position
Args:
X,Y: The X/Y position of the central spectrum
small, large: the small and large radius of extraciton
onto: The wavelength grid to interpolate onto, None to ignore.
sky_spec: The sky spectrum to subtract
Returns:
{'wave_nm': wavelength of sky spectrum,
'spec_adu', the median sky spectrum,
'all_spec': and a matrix of spectra,
'num_spec': The number of spectra}'''
if self.positions[0]=='MeanX':
if distance<60: distance*=60
spectra = self.spectra_near_position(X,Y, distance)
return Extract.interp_and_sum_spectra(
spectra,
onto=onto, sky_spec=sky_spec), spectra
def spectra_near_position(self, X, Y, distance=2):
'''Returns all spectra within distance of (X,Y)
Notice spectra_* methods return all spectra, spectrum_* methods
convert spectra into a single spectrum.
Args:
X,Y: The X/Y position of the spectrum to extract in as
distance: The extraction radius in arcsecond
Returns a list of (wavelength, spectra, index). E.g.:
[[array(365...1000) , array(50 .. 63), 950] ..]
Example:
import SegMap as SM
sm = SM.SegmentationMap("/path/b_ifu20130808_23_08_44.fits_SI.mat")
spec = sm.spectra_near_position(5, 5)
print len (spec)
>> 34
ll, ss = spec[0][0], spec[0][1]
plot(ll, ss) # Plots the spectrum
'''
if self.positions[0]=='MeanX':
if distance<60: distance*=60
return Extract.spectra_near_position(self.KT, self.SegMap,
X,Y, distance=distance, ixmap=self.OK,
pixel_shift=self.pixel_shift)
def TestMod(self, filepath, modname, basedir=None):
'''
Returns 0 if the test didn't complete
Returns 3 if the test found inconsistencies
else returns the a dict with containing the tp2 name(tpname) and the path to the main folder(foldpath) of the mod
:param basedir:
:return:
'''
if basedir is None:
basedir = self.dir
#regexlist = [rb'(?:[^\r\n\t\f\v /]+/)*(?:[sS][eE][tT][uU][pP]-)?(?P<tpname>[^\r\n\t\f\v /]*?)\.[Tt][Pp]2',
# rb'((?:[^\r\n\t\f\v /]+?/)*?)(%(tpname)s)(?=\r?$)(?m)']
if isinstance(modname, str):
modname = modname.encode('ascii')
regexlist = [
rb'((?:[^\r\n\t\f\v /]+?/)*?)(%s)(?:/backup\r?$)?(?=\r?$)(?mi)' %
modname
]
res = Extract.Check_Archive(filepath,
basedir=self.dir,
regex=regexlist)
logging.info('Check_Archive returned {}'.format(res))
if isinstance(res, int):
return res
else:
return res
def last_twelve(data):
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
data = ex.obtain_month_year(data)
for entry in data:
entry[1] = months.index(entry[1]) + 1
data = data.astype(int)
data = data[np.lexsort((data[:, 1], data[:, 0]))]
data = np.delete(data, 0, 0)
two_fifteen = data[-12:]
x1 = data[:, [0, 1]]
y1 = data[:, 2]
x = x1[:-12]
y = y1[:-12]
model = make_pipeline(PolynomialFeatures(5), sk.Ridge(alpha=0.001, fit_intercept=False))
model.fit(x, y)
y_poly_pred = model.predict(x)
for i in range(1,13):
values = model.predict([[2015,i]])
x = np.vstack([x,[2015,i]])
y_poly_pred = np.append(y_poly_pred,values)
y = np.append(y,values)
temp = list(range(1,y_poly_pred.size + 1))
print(two_fifteen)
print(y[-12:])
plt.scatter(temp,y)
plt.plot(temp, y_poly_pred, color='red')
plt.title("Regression of fires every month each year - prediction of last 12")
plt.show()
def test_db_connection():
"""
Evalúa la función de conexión a la base de datos
"""
connection = Extract.db_connection()
connection.close()
assert str(type(connection)) == "<class 'psycopg2.extensions.connection'>"
def month_year_pred(data):
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
data = ex.obtain_month_year(data)
for entry in data:
entry[1] = months.index(entry[1]) + 1
data = data.astype(int)
data = data[np.lexsort((data[:,1],data[:,0]))]
data = np.delete(data,0,0)
x = data[:,[0,1]]
y = data[:,2]
model = make_pipeline(PolynomialFeatures(5), sk.Ridge(alpha=0.001, fit_intercept=False))
model.fit(x, y)
y_poly_pred = model.predict(x)
for i in range(1,13):
year = 2016
month = i
value = model.predict([[year,month]])
x = np.vstack([x, [year,month]])
y_poly_pred = np.append(y_poly_pred,value)
y = np.append(y,value)
temp = np.zeros((x[:,0].size,1))
for i in range(0,x[:,0].size):
temp[i] = i + 1
plt.scatter(temp,y)
plt.plot(temp, y_poly_pred, color='red')
plt.title("Regression of fires every month each year")
plt.show()
def rogue_ssid_detected():
# ApRogueMode
rogue_detected = Extract.extractSpecific(createFiles.realFile,
'ApRogueDetected').replace(
'ApRogueDetected',
'').split()[-1]
rogue_mode = Extract.extractSpecific(createFiles.realFile,
'ApRogueMode').replace(
'ApRogueMode', '').split()[-1]
rogue_apname_last = Extract.extractSpecific(createFiles.realFile,
'APName').replace(
'APName', '').split()[-1]
rogue_ssid = Extract.extractSpecific(
createFiles.realFile, 'ApRogueApSsid').split('ApRogueApSsid')[-1]
rogue_detected_ch = Extract.extractSpecific(
createFiles.realFile,
'ApRogueDetectedChannel').split('ApRogueDetectedChannel')[-1]
rogue_mac_address = Extract.extractSpecific(
createFiles.realFile,
'ApRogueApMacAddress').split('ApRogueApMacAddress')[-1]
rogue_rssi = Extract.extractSpecific(createFiles.realFile,
'ApRSSI').split('ApRSSI')[-1]
ExportToDB.ssid_rogue_detected(rogue_mode, rogue_ssid, rogue_apname_last,
rogue_detected_ch, rogue_mac_address,
rogue_rssi)
def __init__(self, opt, device):
super(STR, self).__init__()
self.opt = opt
#Trans
self.Trans = Trans.TPS_SpatialTransformerNetwork(F = opt.num_fiducial,
i_size = (opt.imgH, opt.imgW),
i_r_size= (opt.imgH, opt.imgW),
i_channel_num=opt.input_channel,
device = device)
#Extract
if self.opt.extract =='RCNN':
self.Extract = self.Extract = Extract.RCNN_extractor(opt.input_channel, opt.output_channel)
elif 'efficientnet' in self.opt.extract :
self.Extract = Extract.EfficientNet(opt)
else:
raise print('invalid extract model name!')
# self.Extract = Extract.ResNet_FeatureExtractor(opt.input_channel, opt.output_channel)
self.FeatureExtraction_output = opt.output_channel # (imgH/16 -1 )* 512
self.AdaptiveAvgPool = nn.AdaptiveAvgPool2d((None,1)) # imgH/16-1 -> 1
# Sequence
self.Seq = nn.Sequential(
BidirectionalLSTM(self.FeatureExtraction_output, opt.hidden_size, opt.hidden_size),
BidirectionalLSTM(opt.hidden_size, opt.hidden_size, opt.hidden_size))
self.Seq_output = opt.hidden_size
#Pred
if opt.pred =='arcface':
print('using ArcFace Loss')
self.Pred_bot = Pred_jamo_arcface.Attention(self.Seq_output, opt.hidden_size, opt.bottom_n_cls, device=device)
self.Pred_mid = Pred_jamo_arcface.Attention_mid(self.Seq_output, opt.hidden_size, opt.middle_n_cls, opt.bottom_n_cls, device=device)
self.Pred_top = Pred_jamo_arcface.Attention_top(self.Seq_output, opt.hidden_size, opt.top_n_cls, opt.middle_n_cls, opt.bottom_n_cls, device=device)
else :
self.Pred_bot = Pred_jamo.Attention(self.Seq_output, opt.hidden_size, opt.bottom_n_cls, device=device)
self.Pred_mid = Pred_jamo.Attention_mid(self.Seq_output, opt.hidden_size, opt.middle_n_cls, opt.bottom_n_cls, device=device)
self.Pred_top = Pred_jamo.Attention_top(self.Seq_output, opt.hidden_size, opt.top_n_cls, opt.middle_n_cls, opt.bottom_n_cls, device=device)
def main(dir, *args):
extract = Extract.Extract()
extract.EXECUTE = E
extract.DL = 0
print DATE
extract.run('EXTRACT', DATE, dir, *args)
ex_outdirs = copy.copy(extract.OUTDIRS)
extract.OUTDIRS = []
L_key = map_tags(extract, ex_outdirs)
map_outdirs = copy.copy(extract.OUTDIRS)
extract.OUTDIRS = []
merge_hdfs(extract, map_outdirs, L_key)
def year_poly_reg(data):
data = ex.obtain_total_year(data)
data = np.delete(data,0,0)
x = data[:,0]
x = x.reshape((-1,1))
y = data[:,1]
model = make_pipeline(PolynomialFeatures(5), sk.LinearRegression())
model.fit(x,y)
pred_y = model.predict(x)
plt.scatter(x,y)
plt.plot(x,pred_y, color='red')
plt.show()
def CollideWatchStage(Track1CPA):
UTC = Track1CPA[7]
Ltm = Extract.timestamp_to_date(UTC)
Hour = float(Ltm[11:13])
if (0 <= Hour and Hour < 4) or (12 <= Hour and Hour < 16): # First officer
return 1
if (4 <= Hour and Hour < 8) or (16 <= Hour
and Hour < 20): # Second officer
return 2
if (8 <= Hour and Hour < 12) or (20 <= Hour
and Hour < 24): # Third officer
return 3
def evaluatee(url):
page = requests.get(url)
soup = BeautifulSoup(page.content, 'html.parser')
rs = soup.prettify()
r = rs.encode()
e = Extract(url, "test", 1, rs, 1, rs, rs)
e.DoEvaluate()
abc = 0
if (b'\xc2\xa9' in r):
abc = 1
else:
abc = 0
#print(e.phishScore.DomainName)
links = soup.find_all("a")
c = 0
for i in links:
if e.phishScore.DomainName in links:
c += 1
title = -1
if e.phishScore.DomainName in soup.find_all("title"):
title = 1
X = [
e.phishScore.HTTPSPresent, e.phishScore.DomainLength,
e.phishScore.NonAlphabetical,
rs.count(e.phishScore.DomainName), e.phishScore.OutsideRationInBody,
abc, title
] #e.phishScore.TitleContainDomainName]
X = np.array(X)
X = X.reshape(1, -1)
classifier = pickle.load(open("phishing_model.pkl", 'rb'))
predict = classifier.predict(X)
#print(predict)
return predict
if predict == 0:
print("Phishing Website")
else:
print("Legitimate site")
def uptime_instance():
#uptime instance
uptime = Extract.extractSpecific(createFiles.realFile,
'UpTimeInstance').replace(
'UpTimeInstance', '')
pattern = re.compile(r'(?:[0-9]:?){6}')
uptime_non_zero = re.findall(pattern, uptime) #filter 0 out
#pick last element in list
lastet_uptime = uptime_non_zero[-1]
wlc_coe = Extract.extractSpecific(
createFiles.realFile,
'UDP: 172.30.232.2:32768-172.30.232.250:162').split('UDP: ')[-1]
wlc_eng = Extract.extractSpecific(
createFiles.realFile,
'UDP: 172.30.253.2:32768-172.30.232.250:162').split('UDP: ')[-1]
if wlc_eng == '172.31.253.2:32769-172.30.232.250:162':
ExportToDB.uptime_wlc(ip_address='172.31.253.2 - EnG',
date_string=lastet_uptime)
elif wlc_coe == '172.30.232.2:32768-172.30.232.250:162':
ExportToDB.uptime_wlc(ip_address='172.31.232.2 - CoE',
date_string=lastet_uptime)
def graph_total_cause(data):
"""
Graph to show use the total causes of
:param data:
:return:
"""
data = ex.obtain_total_cause(data)
x = data[:,0]
y = data[:,1]
y = y.astype(int)
plt.xticks(rotation=90)
plt.bar(x,y)
plt.savefig('total_causes.png')
plt.show()
def draw(self):
if self.subtract_sky:
sky = self.SM[self.spx_ix].sky_median()
sky_spec = sky["wave_nm"], sky["spec_adu"]
else:
sky_spec = None
x, y, v = Extract.segmap_to_img(
self.SM[self.spx_ix].SegMap, sky_spec=sky_spec, minl=500, maxl=700, positions=self.positions
)
self.Xs = x
self.Ys = y
self.Values = v
self.draw_selection_circle()
def draw(self):
if self.subtract_sky:
sky = self.SM[self.spx_ix].sky_median()
sky_spec = sky['wave_nm'], sky['spec_adu']
else:
sky_spec = None
x, y, v = Extract.segmap_to_img(self.SM[self.spx_ix].SegMap,
sky_spec=sky_spec,
minl=500,
maxl=700,
positions=self.positions)
self.Xs = x
self.Ys = y
self.Values = v
self.draw_selection_circle()
def year_pred_poly(data):
data = ex.obtain_total_year(data)
data = np.delete(data,0,0)
x = data[:,0]
x = x.reshape((-1,1))
y = data[:,1]
model = make_pipeline(PolynomialFeatures(5), sk.Ridge(alpha=0.1, fit_intercept=False ))
model.fit(x,y)
ridge = model.named_steps['ridge']
pred_y = model.predict(x)
for i in range(1,5):
year = 2015+i
value = model.predict([[year]])
x = np.vstack([x,[year]])
pred_y = np.append(pred_y,value)
y = np.append(y,value)
plt.scatter(x,y)
plt.plot(x,pred_y, color='red')
plt.show()
def spectra_in_annulus(self, X, Y, small=4, large=6):
'''Returns all spectra in the annulus between radius small and large.
Notice spectra_* methods return all spectra, spectrum_* methods
convert spectra into a single spectrum.
Args:
X,Y: The X/Y position of the central spectrum
small,large: The small and large radius of extraction
Returns:
List of spectra, see spectra_near_position'''
if self.positions[0]=='MeanX':
if small<60: small *= 60
if large<60: large *= 60
return Extract.spectra_in_annulus(self.KT, self.SegMap,
X, Y, small=small, large=large, ixmap=self.OK,
pixel_shift=self.pixel_shift)
def draw(self, figure_number=1, minl=450, maxl=800,
subtract_sky=False, outfile=None, cmin=None, cmax=None):
'''Draw a figure showing the segmentation map cube
Args:
figure_number: Plot figure number
minl/maxl: Minimum/Maximum wavelength to sum over
outfile: The path to the output file
Returns:
Nothing
Side Effects:
Plots a new figure(figure_number) with the data cube.
'''
sky_spec = None
if subtract_sky:
if self.sky_spectrum[0] is None:
raise Exception("Request to subtract sky; however, no sky has been measured")
sky_spec = self.sky_spectrum
x,y,v = Extract.segmap_to_img(self.SegMap, minl=minl, maxl=maxl,
sky_spec=sky_spec,positions=self.positions,
signal_field=self.signal_field)
fig = pl.figure(figure_number, figsize=(9,8))
if self.positions[0]=='MeanX':
pl.xlim(-100,2200)
pl.ylim(-100,2200)
else:
pl.xlim(-12, 17)
pl.ylim(-7, 27)
c = v[:]
if cmin is not None: c[c<cmin] = cmin
if cmax is not None: c[c>cmax] = cmax
pl.scatter(x,y,c=c, s=60, picker=0.5, marker='h')
return x,y,v
def spectrum_in_annulus(self, X, Y, small=4, large=6, onto=None):
'''Returns the interpolated spectrum in an annulus arround X,Y
See spectra_in_annulus
Args:
X,Y: The X/Y position of the central spectrum
small, large: the small and large radius of extraciton
onto: The wavelength grid to interpolate onto, None to ignore.
Returns:
{'wave_nm': wavelength of sky spectrum,
'spec_adu', the median sky spectrum,
'all_spec': and a matrix of spectra,
'num_spec': The number of spectra}'''
if self.positions[0]=='MeanX':
if small<60: small *= 60
if large<60: large *= 60
spectra = self.spectra_in_annulus(X,Y, small, large)
return Extract.interp_and_sum_spectra(
spectra, onto=onto), spectra
def __init__(self, segmap=None, positions=('OnSkyX', 'OnSkyY'),
signal_field='SpexSpecFit', norm=None):
''' Loads the segmap, creates the KD Tree.
Args:
segmap: Either a filename or a segmentation map array '''
if (type(segmap) == str) or (type(segmap) == unicode):
print "Loading: %s" % segmap
mat = scipy.io.loadmat(segmap)
self.SegMap = mat['SegmentsInfo']
else:
self.SegMap = segmap
self.norm = norm
if norm is not None:
for i in xrange(len(norm)):
self.SegMap[i]['SpexSpecCenter'] /= norm[i]
self.SegMap[i]['SpexSpecFit'] /= norm[i]
self.positions=positions
self.signal_field=signal_field
self.KT, self.OK = Extract.segmap_to_kdtree(self.SegMap,
positions=positions,signal_field=signal_field)
def ExtractThis(filename,destpath): import Extract as extract return extract.allNoProgress(filename,destpath)
geog = 'gcsd000a11a_e'
# try:
# a.intersect([featureClasses[geog], featureClasses['southernontarioboundary']], 'gcs_so')
# except:
# l.write("gcs_so alrady exists... Moving on")
featureClasses = rd.featureClasses()
start = time.clock()
l.write('Started join task')
# Iterate through new tables and join to feature classes
for table in sorted(newTables):
try:
# Get list of unique columns of table from class attribute
shortName = table[0:13]
#print shortName
columns = e.fieldsFromTable(table, tableName[shortName], columnPrimary[shortName], columnSecondary[shortName])
for c in sorted(columns):
print c, columns[c]
# Join table to new fc with name geog_table
newFcName = 'gcs_so' + '_' + table[:9]
print 'New layer being created: ' + newFcName
#print columnPrimary[shortName], columnSecondary[shortName]
j.fcToTable(featureClasses['gcs_so'], tables[table], newFcName, columns, geogId[geog], columnPrimary[shortName], columnSecondary[shortName])
l.write(newFcName + ' joined successfully')
except:
pass
l.write('Error during join: ' + traceback.format_exc())
end = time.clock()
elapsed = (end - start)
l.write('Finsihed join task')
l.write('Time for task: ' + str(elapsed) + ' seconds')
