Maier_Christopher_Lab11_LATE.ipynb Shih_ChienTung_Lab11.ipynb: 0026 print "" Wang_Xinao_Lab11.ipynb: 0201 rxID = 127 crawford_melissa_macrawf2_lab11_LATE.ipynb: 0076 plt.grid(True) dandala_Kris_Lab11_LATE.ipynb: 0087 # add a 'best fit' line dandala_Kris_Lab11_LATE.ipynb: 0192 solutions = [] dandala_Kris_Lab11_LATE.ipynb: 0196 # the histogram of the data Vankayala_Anuraag_Lab11.ipynb: 0087 # add a 'best fit' line Vankayala_Anuraag_Lab11.ipynb: 0196 # the histogram of the data Mao_Chengfeng_lab11.ipynb: 0087 # add a 'best fit' line Mao_Chengfeng_lab11.ipynb: 0196 # the histogram of the data Shih_ChienTung_Lab11.ipynb Maier_Christopher_Lab11_LATE.ipynb: 0150 print "" Tang_Yuhan_Lab11.ipynb: 0012 A = np.matrix(A) Tang_Yuhan_Lab11.ipynb: 0043 rxID = 175 Tang_Yuhan_Lab11.ipynb: 0044 rxID = 172 Tang_Yuhan_Lab11.ipynb: 0079 test = [] Wu_Yi-Luen_Lab11.ipynb: 0019 rxID = [] Wu_Yi-Luen_Lab11.ipynb: 0043 rxID = 175 Wu_Yi-Luen_Lab11.ipynb: 0051 for i in range(len(rxID)): Wu_Yi-Luen_Lab11.ipynb: 0084 t,deast,dnorth,dup = get_fluctuations(rxID[i], tstart, tstop) Wu_Yi-Luen_Lab11.ipynb: 0122 rxdata = GEONET.get_rx_data(rxID[i], tstart, tstop) Hsu_HengWei_Lab11_FA2014.ipynb: 0009 for j in range(len(testd)): Hsu_HengWei_Lab11_FA2014.ipynb: 0012 A = np.matrix(A) Hsu_HengWei_Lab11_FA2014.ipynb: 0014 A[i].append(testd[i][a]) Hsu_HengWei_Lab11_FA2014.ipynb: 0017 i = 30 Hsu_HengWei_Lab11_FA2014.ipynb: 0019 rxID = [] Hsu_HengWei_Lab11_FA2014.ipynb: 0021 if y != None: Hsu_HengWei_Lab11_FA2014.ipynb: 0025 lat[i] *=111.0e3 Hsu_HengWei_Lab11_FA2014.ipynb: 0036 for i in range(22650): Hsu_HengWei_Lab11_FA2014.ipynb: 0038 i += 0.1 Hsu_HengWei_Lab11_FA2014.ipynb: 0040 arr = numpy.array(test[i]).reshape(-1,).tolist() Hsu_HengWei_Lab11_FA2014.ipynb: 0043 rxID = 175 Hsu_HengWei_Lab11_FA2014.ipynb: 0044 rxID = 172 Hsu_HengWei_Lab11_FA2014.ipynb: 0048 for a in range(len(testd[0])): Hsu_HengWei_Lab11_FA2014.ipynb: 0049 lon.append(rxdata['lon'][1:10]) Hsu_HengWei_Lab11_FA2014.ipynb: 0051 for i in range(len(rxID)): Hsu_HengWei_Lab11_FA2014.ipynb: 0053 A[j].append(1) Hsu_HengWei_Lab11_FA2014.ipynb: 0054 rxid1vec = data_before['rxID'] Hsu_HengWei_Lab11_FA2014.ipynb: 0060 return y Hsu_HengWei_Lab11_FA2014.ipynb: 0067 test.append(np.dot(((((A.T)*A)).I) * A.T,t)) Hsu_HengWei_Lab11_FA2014.ipynb: 0070 lat.append(rxdata['lat'][1:10]) Hsu_HengWei_Lab11_FA2014.ipynb: 0072 temp_t = [] Hsu_HengWei_Lab11_FA2014.ipynb: 0074 v = [] Hsu_HengWei_Lab11_FA2014.ipynb: 0078 A[i].append(1) Hsu_HengWei_Lab11_FA2014.ipynb: 0079 test = [] Hsu_HengWei_Lab11_FA2014.ipynb: 0089 jlon = [130,145] # [min lon, max lon] for the map Hsu_HengWei_Lab11_FA2014.ipynb: 0099 while i < 45: Hsu_HengWei_Lab11_FA2014.ipynb: 0103 j += 0.1 Hsu_HengWei_Lab11_FA2014.ipynb: 0109 for i in rxid1vec: Hsu_HengWei_Lab11_FA2014.ipynb: 0122 rxdata = GEONET.get_rx_data(rxID[i], tstart, tstop) Hsu_HengWei_Lab11_FA2014.ipynb: 0127 for j in rxid2vec: Hsu_HengWei_Lab11_FA2014.ipynb: 0133 testd[a].append(((lat[a]-i)**2+(lon[a]-j)**2)**0.5) Hsu_HengWei_Lab11_FA2014.ipynb: 0138 y = x.index(i) Hsu_HengWei_Lab11_FA2014.ipynb: 0139 for i in x: Hsu_HengWei_Lab11_FA2014.ipynb: 0140 for a in range(len(rxID)): Hsu_HengWei_Lab11_FA2014.ipynb: 0149 jlat = [30, 45] # [min lat, max lat] for the map Hsu_HengWei_Lab11_FA2014.ipynb: 0153 v.append(arr[1]) Hsu_HengWei_Lab11_FA2014.ipynb: 0157 lon[i] *=111.0e3 * cos(lat_ref) Hsu_HengWei_Lab11_FA2014.ipynb: 0158 for i in deast: Hsu_HengWei_Lab11_FA2014.ipynb: 0161 s = np.random.normal(mu, sigma, 1000) Hsu_HengWei_Lab11_FA2014.ipynb: 0165 rxid2vec = data_after ['rxID'] Hsu_HengWei_Lab11_FA2014.ipynb: 0167 for i in range(len(testd)): Hsu_HengWei_Lab11_FA2014.ipynb: 0171 while j < 145: Hsu_HengWei_Lab11_FA2014.ipynb: 0178 A=[ [] for i in range(len(rxID))] Hsu_HengWei_Lab11_FA2014.ipynb: 0179 A[j].append(testd[j][a]) Hsu_HengWei_Lab11_FA2014.ipynb: 0180 A=[ [] for j in range(len(rxID))] Hsu_HengWei_Lab11_FA2014.ipynb: 0182 j = 130 Hsu_HengWei_Lab11_FA2014.ipynb: 0187 lon = [] Hsu_HengWei_Lab11_FA2014.ipynb: 0191 rxID.pop(i) Hsu_HengWei_Lab11_FA2014.ipynb: 0196 temp_t.append(sum(np.dot(A,arr))) Hsu_HengWei_Lab11_FA2014.ipynb: 0198 rxID.append(i) Hsu_HengWei_Lab11_FA2014.ipynb: 0204 count, bins, ignored = plt.hist(s, 30, normed=True) Hsu_HengWei_Lab11_FA2014.ipynb: 0205 lat = [] Vashistha_siddhant_lab11_LATE.ipynb: 0044 rxID = 172 Li_Boyu_Lab11_LATELATE.ipynb: 0043 rxID = 175 dandala_Kris_Lab11_LATE.ipynb: 0012 A = np.matrix(A) Mao_Chengfeng_lab11.ipynb: 0054 rxid1vec = data_before['rxID'] snyder_bradley_lab11.ipynb: 0060 return y Tang_Yuhan_Lab11.ipynb Shih_ChienTung_Lab11.ipynb: 0021 A = np.matrix(A) Shih_ChienTung_Lab11.ipynb: 0071 rxID = 175 Shih_ChienTung_Lab11.ipynb: 0072 rxID = 172 Shih_ChienTung_Lab11.ipynb: 0134 test = [] Wu_Yi-Luen_Lab11.ipynb: 0071 rxID = 175 Wu_Yi-Luen_Lab11.ipynb: 0250 ylabel('Filtered Upward displacement [m]') Hsu_HengWei_Lab11_FA2014.ipynb: 0021 A = np.matrix(A) Hsu_HengWei_Lab11_FA2014.ipynb: 0071 rxID = 175 Hsu_HengWei_Lab11_FA2014.ipynb: 0072 rxID = 172 Hsu_HengWei_Lab11_FA2014.ipynb: 0134 test = [] TsungHan_Lei_Lab11.ipynb: 0250 ylabel('Filtered Upward displacement [m]') crawford_melissa_macrawf2_lab11_LATE.ipynb: 0177 pi0 = 1.0-pi1 crawford_melissa_macrawf2_lab11_LATE.ipynb: 0228 fig = plt.figure() Deng_Fei_Lab11.ipynb: 0007 x.append(east[i] - np.mean(east[:i-1])) Deng_Fei_Lab11.ipynb: 0011 w = 60 # window size for running average removal (rudimentary HPF) Deng_Fei_Lab11.ipynb: 0032 x.append(east[i] - east[0]) Deng_Fei_Lab11.ipynb: 0048 z.append(up[i] - np.mean(up[i-w:i-1])) Deng_Fei_Lab11.ipynb: 0052 elif i < w: Deng_Fei_Lab11.ipynb: 0080 x.append(east[i] - np.mean(east[i-w:i-1])) Deng_Fei_Lab11.ipynb: 0087 y.append(north[i] - north[0]) Deng_Fei_Lab11.ipynb: 0095 z.append(up[i] - up[0]) Deng_Fei_Lab11.ipynb: 0126 y.append(north[i] - np.mean(north[i-w:i-1])) Deng_Fei_Lab11.ipynb: 0136 t.append(time[i]) Deng_Fei_Lab11.ipynb: 0175 if i == 0 or i == 1: Deng_Fei_Lab11.ipynb: 0221 z.append(up[i] - np.mean(up[:i-1])) Deng_Fei_Lab11.ipynb: 0285 y.append(north[i] - np.mean(north[:i-1])) Vashistha_siddhant_lab11_LATE.ipynb: 0072 rxID = 172 Li_Boyu_Lab11_LATELATE.ipynb: 0071 rxID = 175 Li_Boyu_Lab11_LATELATE.ipynb: 0073 figure(figsize=(20,7)) Li_Boyu_Lab11_LATELATE.ipynb: 0103 threshold = MAP_thres(sigma,pi1) Li_Boyu_Lab11_LATELATE.ipynb: 0139 xlabel('Data Collection') Li_Boyu_Lab11_LATELATE.ipynb: 0165 pi1 = 0.00001 Li_Boyu_Lab11_LATELATE.ipynb: 0168 counter = 0 Li_Boyu_Lab11_LATELATE.ipynb: 0181 sigma = np.std(sorted(dataset)) Li_Boyu_Lab11_LATELATE.ipynb: 0228 fig = plt.figure() Li_Boyu_Lab11_LATELATE.ipynb: 0249 dataset = deast Li_Boyu_Lab11_LATELATE.ipynb: 0264 title('Distribution for Filtered %s Displacement'%name) Li_Boyu_Lab11_LATELATE.ipynb: 0271 return threshold Li_Boyu_Lab11_LATELATE.ipynb: 0274 def MAP_thres(sigma,pi1): Li_Boyu_Lab11_LATELATE.ipynb: 0304 for i in range(0,len(east)): Li_Boyu_Lab11_LATELATE.ipynb: 0318 plt.legend([data,line],['data','Threshold'],loc = 'best') Li_Boyu_Lab11_LATELATE.ipynb: 0319 ylabel('%s Displacement'%name) Bai_Hongyang.ipynb: 0136 t.append(time[i]) Bai_Hongyang.ipynb: 0250 ylabel('Filtered Upward displacement [m]') dandala_Kris_Lab11_LATE.ipynb: 0021 A = np.matrix(A) dandala_Kris_Lab11_LATE.ipynb: 0108 for rxID in rxID_list: dandala_Kris_Lab11_LATE.ipynb: 0191 return cls devasia_manuel_lab11.ipynb: 0148 pi1 = 0.01 devasia_manuel_lab11.ipynb: 0165 pi1 = 0.00001 devasia_manuel_lab11.ipynb: 0168 counter = 0 devasia_manuel_lab11.ipynb: 0210 time_list = [] devasia_manuel_lab11.ipynb: 0234 plt.figure() Mao_Chengfeng_lab11.ipynb: 0204 plot(t,deast,'k.-') Wang_Hongru_Lab11_LATE.ipynb: 0052 elif i < w: Wang_Hongru_Lab11_LATE.ipynb: 0175 if i == 0 or i == 1: Wang_Hongru_Lab11_LATE.ipynb: 0191 return cls Wang_Hongru_Lab11_LATE.ipynb: 0234 plt.figure() Wang_Hongru_Lab11_LATE.ipynb: 0250 ylabel('Filtered Upward displacement [m]') feddersen_lab11.ipynb: 0148 pi1 = 0.01 Ling_Wang-Jung_Lab11.ipynb survila2_lab11.ipynb: 0191 t = time McLaughlin_SeanLab11_LATE.ipynb: 0191 t = time Antonacci_Lab11.ipynb: 0191 t = time TsungHan_Lei_Lab11.ipynb: 0136 for i in range(len(t)): crawford_melissa_macrawf2_lab11_LATE.ipynb: 0075 #print y chuma_kabaghe_lab11_LATELATE.ipynb: 0186 #print len(t) Bai_Hongyang.ipynb: 0136 for i in range(len(t)): Wang_Hongru_Lab11_LATE.ipynb: 0191 t = time feddersen_lab11.ipynb: 0191 t = time Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0191 t = time survila2_lab11.ipynb Ling_Wang-Jung_Lab11.ipynb: 0175 t = time McLaughlin_SeanLab11_LATE.ipynb: 0175 t = time Antonacci_Lab11.ipynb: 0175 t = time chuma_kabaghe_lab11_LATELATE.ipynb: 0122 ylabel('Eastward Displacement [m]') Wang_Hongru_Lab11_LATE.ipynb: 0175 t = time feddersen_lab11.ipynb: 0175 t = time Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0175 t = time McLaughlin_SeanLab11_LATE.ipynb Ling_Wang-Jung_Lab11.ipynb: 0256 t = time survila2_lab11.ipynb: 0256 t = time Antonacci_Lab11.ipynb: 0005 theta1 = long1*degrees_to_radians Antonacci_Lab11.ipynb: 0016 # (1, theta, phi) and (1, theta, phi) Antonacci_Lab11.ipynb: 0023 cos = (math.sin(phi1)*math.sin(phi2)*math.cos(theta1 - theta2) + Antonacci_Lab11.ipynb: 0027 # Convert latitude and longitude to Antonacci_Lab11.ipynb: 0031 # spherical coordinates in radians. Antonacci_Lab11.ipynb: 0039 # theta = longitude Antonacci_Lab11.ipynb: 0051 phi2 = (90.0 - lat2)*degrees_to_radians Antonacci_Lab11.ipynb: 0077 # Compute spherical distance from spherical coordinates. Antonacci_Lab11.ipynb: 0091 for i in xrange(len(east)): Antonacci_Lab11.ipynb: 0099 # sin phi sin phi' cos(theta-theta') + cos phi cos phi' Antonacci_Lab11.ipynb: 0128 # cosine( arc length ) = Antonacci_Lab11.ipynb: 0145 degrees_to_radians = math.pi/180.0 Antonacci_Lab11.ipynb: 0156 phi1 = (90.0 - lat1)*degrees_to_radians Antonacci_Lab11.ipynb: 0165 rxID = 911 Antonacci_Lab11.ipynb: 0182 theta2 = long2*degrees_to_radians Antonacci_Lab11.ipynb: 0202 # phi = 90 - latitude Antonacci_Lab11.ipynb: 0206 # For two locations in spherical coordinates Antonacci_Lab11.ipynb: 0245 arc = math.acos( cos ) Antonacci_Lab11.ipynb: 0248 # distance = rho * arc length Antonacci_Lab11.ipynb: 0256 t = time Antonacci_Lab11.ipynb: 0259 math.cos(phi1)*math.cos(phi2)) Deng_Fei_Lab11.ipynb: 0091 for i in xrange(len(east)): chuma_kabaghe_lab11_LATELATE.ipynb: 0061 continue devasia_manuel_lab11.ipynb: 0237 %matplotlib inline Mao_Chengfeng_lab11.ipynb: 0045 N = 20 snyder_bradley_lab11.ipynb: 0110 z.append(up[i]) snyder_bradley_lab11.ipynb: 0224 y.append(north[i]) snyder_bradley_lab11.ipynb: 0226 x.append(east[i]) snyder_bradley_lab11.ipynb: 0237 %matplotlib inline Wang_Hongru_Lab11_LATE.ipynb: 0061 continue Wang_Hongru_Lab11_LATE.ipynb: 0256 t = time feddersen_lab11.ipynb: 0045 N = 20 feddersen_lab11.ipynb: 0256 t = time Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0045 N = 20 Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0061 continue Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0237 %matplotlib inline Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0256 t = time Antonacci_Lab11.ipynb Ling_Wang-Jung_Lab11.ipynb: 0263 t = time survila2_lab11.ipynb: 0263 t = time McLaughlin_SeanLab11_LATE.ipynb: 0016 phi2 = (90.0 - lat2)*degrees_to_radians McLaughlin_SeanLab11_LATE.ipynb: 0018 theta2 = long2*degrees_to_radians McLaughlin_SeanLab11_LATE.ipynb: 0019 cos = (math.sin(phi1)*math.sin(phi2)*math.cos(theta1 - theta2) + McLaughlin_SeanLab11_LATE.ipynb: 0023 math.cos(phi1)*math.cos(phi2)) McLaughlin_SeanLab11_LATE.ipynb: 0080 # Compute spherical distance from spherical coordinates. McLaughlin_SeanLab11_LATE.ipynb: 0088 theta1 = long1*degrees_to_radians McLaughlin_SeanLab11_LATE.ipynb: 0091 for i in xrange(len(east)): McLaughlin_SeanLab11_LATE.ipynb: 0098 # sin phi sin phi' cos(theta-theta') + cos phi cos phi' McLaughlin_SeanLab11_LATE.ipynb: 0115 # For two locations in spherical coordinates McLaughlin_SeanLab11_LATE.ipynb: 0122 # theta = longitude McLaughlin_SeanLab11_LATE.ipynb: 0136 # (1, theta, phi) and (1, theta, phi) McLaughlin_SeanLab11_LATE.ipynb: 0143 degrees_to_radians = math.pi/180.0 McLaughlin_SeanLab11_LATE.ipynb: 0154 phi1 = (90.0 - lat1)*degrees_to_radians McLaughlin_SeanLab11_LATE.ipynb: 0179 arc = math.acos( cos ) McLaughlin_SeanLab11_LATE.ipynb: 0187 # Convert latitude and longitude to McLaughlin_SeanLab11_LATE.ipynb: 0193 # spherical coordinates in radians. McLaughlin_SeanLab11_LATE.ipynb: 0204 # phi = 90 - latitude McLaughlin_SeanLab11_LATE.ipynb: 0225 # cosine( arc length ) = McLaughlin_SeanLab11_LATE.ipynb: 0250 rxID = 911 McLaughlin_SeanLab11_LATE.ipynb: 0253 # distance = rho * arc length McLaughlin_SeanLab11_LATE.ipynb: 0263 t = time Wu_Yi-Luen_Lab11.ipynb: 0147 d = [] Wu_Yi-Luen_Lab11.ipynb: 0168 w = 20 # window size for running average removal (rudimentary HPF) crawford_melissa_macrawf2_lab11_LATE.ipynb: 0033 ax = fig.add_subplot(111) Deng_Fei_Lab11.ipynb: 0091 for i in xrange(len(east)): Bai_Hongyang.ipynb: 0168 w = 20 # window size for running average removal (rudimentary HPF) devasia_manuel_lab11.ipynb: 0108 data = GEONET.get_geonet_data(tstart, tstop) devasia_manuel_lab11.ipynb: 0171 std_dev = np.std(data) Mao_Chengfeng_lab11.ipynb: 0117 plt.title(title) Wang_Hongru_Lab11_LATE.ipynb: 0168 w = 20 # window size for running average removal (rudimentary HPF) Wang_Hongru_Lab11_LATE.ipynb: 0263 t = time feddersen_lab11.ipynb: 0147 d = [] feddersen_lab11.ipynb: 0263 t = time Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0263 t = time Wu_Yi-Luen_Lab11.ipynb Shih_ChienTung_Lab11.ipynb: 0018 rxID = [] Shih_ChienTung_Lab11.ipynb: 0042 rxID = 175 Shih_ChienTung_Lab11.ipynb: 0048 for i in range(len(rxID)): Shih_ChienTung_Lab11.ipynb: 0064 rxdata = GEONET.get_rx_data(rxID[i], tstart, tstop) Shih_ChienTung_Lab11.ipynb: 0078 t,deast,dnorth,dup = get_fluctuations(rxID[i], tstart, tstop) Tang_Yuhan_Lab11.ipynb: 0042 rxID = 175 Tang_Yuhan_Lab11.ipynb: 0148 ylabel('Filtered Upward displacement [m]') Antonacci_Lab11.ipynb: 0106 d = [] Antonacci_Lab11.ipynb: 0120 w = 20 # window size for running average removal (rudimentary HPF) Hsu_HengWei_Lab11_FA2014.ipynb: 0018 rxID = [] Hsu_HengWei_Lab11_FA2014.ipynb: 0042 rxID = 175 Hsu_HengWei_Lab11_FA2014.ipynb: 0048 for i in range(len(rxID)): Hsu_HengWei_Lab11_FA2014.ipynb: 0064 rxdata = GEONET.get_rx_data(rxID[i], tstart, tstop) TsungHan_Lei_Lab11.ipynb: 0055 plot(t,dup,'k.-') # This should result in a plot of the east displacements for rx 550 TsungHan_Lei_Lab11.ipynb: 0148 ylabel('Filtered Upward displacement [m]') crawford_melissa_macrawf2_lab11_LATE.ipynb: 0038 pi1 = 0.001 crawford_melissa_macrawf2_lab11_LATE.ipynb: 0043 print std crawford_melissa_macrawf2_lab11_LATE.ipynb: 0122 std = np.std(deast) crawford_melissa_macrawf2_lab11_LATE.ipynb: 0149 std = [] Li_Boyu_Lab11_LATELATE.ipynb: 0042 rxID = 175 Li_Boyu_Lab11_LATELATE.ipynb: 0142 pi1 = 0.1 Bai_Hongyang.ipynb: 0055 plot(t,dup,'k.-') # This should result in a plot of the east displacements for rx 550 Bai_Hongyang.ipynb: 0120 w = 20 # window size for running average removal (rudimentary HPF) Bai_Hongyang.ipynb: 0148 ylabel('Filtered Upward displacement [m]') dandala_Kris_Lab11_LATE.ipynb: 0007 for i in range(len(deast)): devasia_manuel_lab11.ipynb: 0038 pi1 = 0.001 Wang_Hongru_Lab11_LATE.ipynb: 0055 plot(t,dup,'k.-') # This should result in a plot of the east displacements for rx 550 Wang_Hongru_Lab11_LATE.ipynb: 0120 w = 20 # window size for running average removal (rudimentary HPF) Wang_Hongru_Lab11_LATE.ipynb: 0148 ylabel('Filtered Upward displacement [m]') de Paula Vasconcelos_Thiago_Lab11.ipynb: 0149 std = [] feddersen_lab11.ipynb: 0106 d = [] feddersen_lab11.ipynb: 0142 pi1 = 0.1 feddersen_lab11.ipynb: 0145 pi1 = 0.5 Hsu_HengWei_Lab11_FA2014.ipynb Shih_ChienTung_Lab11.ipynb: 0001 lon[i] *=111.0e3 * cos(lat_ref) Shih_ChienTung_Lab11.ipynb: 0013 A = np.matrix(A) Shih_ChienTung_Lab11.ipynb: 0020 i = 30 Shih_ChienTung_Lab11.ipynb: 0021 rxID = [] Shih_ChienTung_Lab11.ipynb: 0022 i += 0.1 Shih_ChienTung_Lab11.ipynb: 0024 if y != None: Shih_ChienTung_Lab11.ipynb: 0028 lat[i] *=111.0e3 Shih_ChienTung_Lab11.ipynb: 0029 v.append(arr[1]) Shih_ChienTung_Lab11.ipynb: 0038 for i in range(22650): Shih_ChienTung_Lab11.ipynb: 0039 jlon = [130,145] # [min lon, max lon] for the map Shih_ChienTung_Lab11.ipynb: 0042 arr = numpy.array(test[i]).reshape(-1,).tolist() Shih_ChienTung_Lab11.ipynb: 0045 rxID = 175 Shih_ChienTung_Lab11.ipynb: 0046 rxID = 172 Shih_ChienTung_Lab11.ipynb: 0049 for a in range(len(testd[0])): Shih_ChienTung_Lab11.ipynb: 0050 lon.append(rxdata['lon'][1:10]) Shih_ChienTung_Lab11.ipynb: 0053 for i in range(len(rxID)): Shih_ChienTung_Lab11.ipynb: 0055 for j in range(len(testd)): Shih_ChienTung_Lab11.ipynb: 0058 rxid1vec = data_before['rxID'] Shih_ChienTung_Lab11.ipynb: 0062 for i in x: Shih_ChienTung_Lab11.ipynb: 0063 test = [] Shih_ChienTung_Lab11.ipynb: 0064 lon = [] Shih_ChienTung_Lab11.ipynb: 0065 return y Shih_ChienTung_Lab11.ipynb: 0067 testd[a].append(((lat[a]-i)**2+(lon[a]-j)**2)**0.5) Shih_ChienTung_Lab11.ipynb: 0075 lat.append(rxdata['lat'][1:10]) Shih_ChienTung_Lab11.ipynb: 0077 temp_t = [] Shih_ChienTung_Lab11.ipynb: 0079 v = [] Shih_ChienTung_Lab11.ipynb: 0083 A[i].append(1) Shih_ChienTung_Lab11.ipynb: 0092 A[i].append(testd[i][a]) Shih_ChienTung_Lab11.ipynb: 0098 temp_t.append(sum(np.dot(A,arr))) Shih_ChienTung_Lab11.ipynb: 0099 while i < 45: Shih_ChienTung_Lab11.ipynb: 0101 rxID.pop(i) Shih_ChienTung_Lab11.ipynb: 0102 j += 0.1 Shih_ChienTung_Lab11.ipynb: 0107 for i in rxid1vec: Shih_ChienTung_Lab11.ipynb: 0112 A[j].append(1) Shih_ChienTung_Lab11.ipynb: 0119 test.append(np.dot(((((A.T)*A)).I) * A.T,t)) Shih_ChienTung_Lab11.ipynb: 0123 rxdata = GEONET.get_rx_data(rxID[i], tstart, tstop) Shih_ChienTung_Lab11.ipynb: 0126 for j in rxid2vec: Shih_ChienTung_Lab11.ipynb: 0135 y = x.index(i) Shih_ChienTung_Lab11.ipynb: 0137 for a in range(len(rxID)): Shih_ChienTung_Lab11.ipynb: 0149 jlat = [30, 45] # [min lat, max lat] for the map Shih_ChienTung_Lab11.ipynb: 0152 rxID.append(i) Shih_ChienTung_Lab11.ipynb: 0156 for i in deast: Shih_ChienTung_Lab11.ipynb: 0159 s = np.random.normal(mu, sigma, 1000) Shih_ChienTung_Lab11.ipynb: 0164 rxid2vec = data_after ['rxID'] Shih_ChienTung_Lab11.ipynb: 0166 for i in range(len(testd)): Shih_ChienTung_Lab11.ipynb: 0170 while j < 145: Shih_ChienTung_Lab11.ipynb: 0174 A=[ [] for i in range(len(rxID))] Shih_ChienTung_Lab11.ipynb: 0177 A[j].append(testd[j][a]) Shih_ChienTung_Lab11.ipynb: 0178 A=[ [] for j in range(len(rxID))] Shih_ChienTung_Lab11.ipynb: 0180 j = 130 Shih_ChienTung_Lab11.ipynb: 0201 count, bins, ignored = plt.hist(s, 30, normed=True) Shih_ChienTung_Lab11.ipynb: 0202 lat = [] Tang_Yuhan_Lab11.ipynb: 0013 A = np.matrix(A) Tang_Yuhan_Lab11.ipynb: 0045 rxID = 175 Tang_Yuhan_Lab11.ipynb: 0046 rxID = 172 Tang_Yuhan_Lab11.ipynb: 0063 test = [] Wu_Yi-Luen_Lab11.ipynb: 0021 rxID = [] Wu_Yi-Luen_Lab11.ipynb: 0045 rxID = 175 Wu_Yi-Luen_Lab11.ipynb: 0053 for i in range(len(rxID)): Wu_Yi-Luen_Lab11.ipynb: 0123 rxdata = GEONET.get_rx_data(rxID[i], tstart, tstop) Wang_Xinao_Lab11.ipynb: 0120 print sigma Deng_Fei_Lab11.ipynb: 0133 count = 0 Vashistha_siddhant_lab11_LATE.ipynb: 0046 rxID = 172 Li_Boyu_Lab11_LATELATE.ipynb: 0045 rxID = 175 dandala_Kris_Lab11_LATE.ipynb: 0013 A = np.matrix(A) Mao_Chengfeng_lab11.ipynb: 0040 data_before = GEONET.get_geonet_data_at_time(tbefore) Mao_Chengfeng_lab11.ipynb: 0058 rxid1vec = data_before['rxID'] snyder_bradley_lab11.ipynb: 0065 return y snyder_bradley_lab11.ipynb: 0133 count = 0 Wang_Xinao_Lab11.ipynb Maier_Christopher_Lab11_LATE.ipynb: 0123 rxID = 127 Hsu_HengWei_Lab11_FA2014.ipynb: 0081 print sigma TsungHan_Lei_Lab11.ipynb Tang_Yuhan_Lab11.ipynb: 0178 ylabel('Filtered Upward displacement [m]') Ling_Wang-Jung_Lab11.ipynb: 0195 for i in range(len(t)): Wu_Yi-Luen_Lab11.ipynb: 0066 plot(t,dup,'k.-') # This should result in a plot of the east displacements for rx 550 Wu_Yi-Luen_Lab11.ipynb: 0178 ylabel('Filtered Upward displacement [m]') Vashistha_siddhant_lab11_LATE.ipynb: 0062 t,deast,dnorth,dup,lat,lon = get_fluctuations(rxID, tstart, tstop) Bai_Hongyang.ipynb: 0010 return result Bai_Hongyang.ipynb: 0066 plot(t,dup,'k.-') # This should result in a plot of the east displacements for rx 550 Bai_Hongyang.ipynb: 0178 ylabel('Filtered Upward displacement [m]') Bai_Hongyang.ipynb: 0195 for i in range(len(t)): Vankayala_Anuraag_Lab11.ipynb: 0072 lon_ref = np.mean(lon[1:10]) Vankayala_Anuraag_Lab11.ipynb: 0117 lat_ref = np.mean(lat[1:10]) Vankayala_Anuraag_Lab11.ipynb: 0134 alt_ref = np.mean(alt[1:10]) Mao_Chengfeng_lab11.ipynb: 0010 return result Mao_Chengfeng_lab11.ipynb: 0029 lon_list.append(lon) Mao_Chengfeng_lab11.ipynb: 0098 lat_list.append(lat) Wang_Hongru_Lab11_LATE.ipynb: 0066 plot(t,dup,'k.-') # This should result in a plot of the east displacements for rx 550 Wang_Hongru_Lab11_LATE.ipynb: 0079 rxID = 167 Wang_Hongru_Lab11_LATE.ipynb: 0178 ylabel('Filtered Upward displacement [m]') Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0072 lon_ref = np.mean(lon[1:10]) Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0117 lat_ref = np.mean(lat[1:10]) Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0134 alt_ref = np.mean(alt[1:10]) crawford_melissa_macrawf2_lab11_LATE.ipynb Maier_Christopher_Lab11_LATE.ipynb: 0238 plt.grid(True) Tang_Yuhan_Lab11.ipynb: 0056 pi0 = 1.0-pi1 Tang_Yuhan_Lab11.ipynb: 0359 fig = plt.figure() Ling_Wang-Jung_Lab11.ipynb: 0241 #print y Antonacci_Lab11.ipynb: 0013 ax = fig.add_subplot(111) Wu_Yi-Luen_Lab11.ipynb: 0080 std = [] Wu_Yi-Luen_Lab11.ipynb: 0223 print std Wu_Yi-Luen_Lab11.ipynb: 0264 std = np.std(deast) Wu_Yi-Luen_Lab11.ipynb: 0283 pi1 = 0.001 Li_Boyu_Lab11_LATELATE.ipynb: 0359 fig = plt.figure() chuma_kabaghe_lab11_LATELATE.ipynb: 0240 #print x devasia_manuel_lab11.ipynb: 0283 pi1 = 0.001 Vankayala_Anuraag_Lab11.ipynb: 0374 print x Mao_Chengfeng_lab11.ipynb: 0383 rxID = 213 de Paula Vasconcelos_Thiago_Lab11.ipynb: 0080 std = [] feddersen_lab11.ipynb: 0067 pi0 = 1-pi1 Deng_Fei_Lab11.ipynb Tang_Yuhan_Lab11.ipynb: 0003 x.append(east[i] - np.mean(east[:i-1])) Tang_Yuhan_Lab11.ipynb: 0006 w = 60 # window size for running average removal (rudimentary HPF) Tang_Yuhan_Lab11.ipynb: 0025 x.append(east[i] - east[0]) Tang_Yuhan_Lab11.ipynb: 0039 z.append(up[i] - np.mean(up[i-w:i-1])) Tang_Yuhan_Lab11.ipynb: 0042 elif i < w: Tang_Yuhan_Lab11.ipynb: 0064 x.append(east[i] - np.mean(east[i-w:i-1])) Tang_Yuhan_Lab11.ipynb: 0069 y.append(north[i] - north[0]) Tang_Yuhan_Lab11.ipynb: 0074 z.append(up[i] - up[0]) Tang_Yuhan_Lab11.ipynb: 0098 y.append(north[i] - np.mean(north[i-w:i-1])) Tang_Yuhan_Lab11.ipynb: 0106 t.append(time[i]) Tang_Yuhan_Lab11.ipynb: 0134 if i == 0 or i == 1: Tang_Yuhan_Lab11.ipynb: 0175 z.append(up[i] - np.mean(up[:i-1])) Tang_Yuhan_Lab11.ipynb: 0225 y.append(north[i] - np.mean(north[:i-1])) McLaughlin_SeanLab11_LATE.ipynb: 0097 for i in xrange(len(east)): Antonacci_Lab11.ipynb: 0097 for i in xrange(len(east)): Hsu_HengWei_Lab11_FA2014.ipynb: 0195 count = 0 Li_Boyu_Lab11_LATELATE.ipynb: 0029 training_z.append(z) Li_Boyu_Lab11_LATELATE.ipynb: 0045 for rxID in receiver_set: Li_Boyu_Lab11_LATELATE.ipynb: 0048 for rxID in rxID_List: Li_Boyu_Lab11_LATELATE.ipynb: 0049 training_t = [] Li_Boyu_Lab11_LATELATE.ipynb: 0057 training_z = [] Li_Boyu_Lab11_LATELATE.ipynb: 0081 plot(t, dir_data, 'k.-') # This should result in a plot of the east displacements for rx 550 Li_Boyu_Lab11_LATELATE.ipynb: 0089 plot_all(rxID=217, tstart=tstart, tstop=tstop) Li_Boyu_Lab11_LATELATE.ipynb: 0091 training_x.append(x) Li_Boyu_Lab11_LATELATE.ipynb: 0160 rxID_List = rxdata['rxID'] Li_Boyu_Lab11_LATELATE.ipynb: 0162 t, x, y, z = get_fluctuations(rxID, training_tstart, training_tstop) Li_Boyu_Lab11_LATELATE.ipynb: 0165 def plot_all(rxID, tstart, tstop): Li_Boyu_Lab11_LATELATE.ipynb: 0168 plot_all(rxID=1162, tstart=tstart, tstop=tstop) Li_Boyu_Lab11_LATELATE.ipynb: 0169 receiver_set = [] Li_Boyu_Lab11_LATELATE.ipynb: 0184 training_y = [] Li_Boyu_Lab11_LATELATE.ipynb: 0187 training_t.append(t) Li_Boyu_Lab11_LATELATE.ipynb: 0220 training_x = [] Li_Boyu_Lab11_LATELATE.ipynb: 0232 training_y.append(y) Li_Boyu_Lab11_LATELATE.ipynb: 0246 receiver_set.append(int(rxID)) Li_Boyu_Lab11_LATELATE.ipynb: 0251 plot_all(rxID=911, tstart=tstart, tstop=tstop) Bai_Hongyang.ipynb: 0106 t.append(time[i]) devasia_manuel_lab11.ipynb: 0202 plt.legend(loc='best') Mao_Chengfeng_lab11.ipynb: 0202 plt.legend(loc='best') snyder_bradley_lab11.ipynb: 0041 count += 1 snyder_bradley_lab11.ipynb: 0195 count = 0 Wang_Hongru_Lab11_LATE.ipynb: 0005 posix_time = rxdata['UT'][0] Wang_Hongru_Lab11_LATE.ipynb: 0042 elif i < w: Wang_Hongru_Lab11_LATE.ipynb: 0134 if i == 0 or i == 1: Labelle_Julia_lab11_LATELATE.ipynb: 0052 t.append(time[count]) Vashistha_siddhant_lab11_LATE.ipynb Shih_ChienTung_Lab11.ipynb: 0050 rxID = 172 Tang_Yuhan_Lab11.ipynb: 0050 rxID = 172 Hsu_HengWei_Lab11_FA2014.ipynb: 0050 rxID = 172 TsungHan_Lei_Lab11.ipynb: 0058 t,deast,dnorth,dup,lat,lon = get_fluctuations(rxID, tstart, tstop) chuma_kabaghe_lab11_LATELATE.ipynb: 0053 #print threshold chuma_kabaghe_lab11_LATELATE.ipynb: 0079 data = GEONET.get_geonet_data_at_time(tstart) Labelle_Julia_lab11_LATELATE.ipynb: 0187 for x in deast: Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0066 ylabel('Frequency') Li_Boyu_Lab11_LATELATE.ipynb Shih_ChienTung_Lab11.ipynb: 0041 rxID = 175 Tang_Yuhan_Lab11.ipynb: 0041 rxID = 175 Tang_Yuhan_Lab11.ipynb: 0062 xlabel('Data Collection') Tang_Yuhan_Lab11.ipynb: 0080 pi1 = 0.00001 Tang_Yuhan_Lab11.ipynb: 0083 counter = 0 Tang_Yuhan_Lab11.ipynb: 0089 sigma = np.std(sorted(dataset)) Tang_Yuhan_Lab11.ipynb: 0116 fig = plt.figure() Tang_Yuhan_Lab11.ipynb: 0119 threshold = MAP_thres(sigma,pi1) Tang_Yuhan_Lab11.ipynb: 0130 dataset = deast Tang_Yuhan_Lab11.ipynb: 0138 return threshold Tang_Yuhan_Lab11.ipynb: 0139 def MAP_thres(sigma,pi1): Tang_Yuhan_Lab11.ipynb: 0148 figure(figsize=(20,7)) Tang_Yuhan_Lab11.ipynb: 0155 for i in range(0,len(east)): Tang_Yuhan_Lab11.ipynb: 0160 title('Distribution for Filtered %s Displacement'%name) Tang_Yuhan_Lab11.ipynb: 0164 plt.legend([data,line],['data','Threshold'],loc = 'best') Tang_Yuhan_Lab11.ipynb: 0165 ylabel('%s Displacement'%name) Wu_Yi-Luen_Lab11.ipynb: 0041 rxID = 175 Wu_Yi-Luen_Lab11.ipynb: 0124 pi1 = 0.1 Hsu_HengWei_Lab11_FA2014.ipynb: 0041 rxID = 175 crawford_melissa_macrawf2_lab11_LATE.ipynb: 0116 fig = plt.figure() Deng_Fei_Lab11.ipynb: 0015 training_x.append(x) Deng_Fei_Lab11.ipynb: 0016 training_z.append(z) Deng_Fei_Lab11.ipynb: 0028 for rxID in receiver_set: Deng_Fei_Lab11.ipynb: 0032 for rxID in rxID_List: Deng_Fei_Lab11.ipynb: 0033 training_t = [] Deng_Fei_Lab11.ipynb: 0038 training_z = [] Deng_Fei_Lab11.ipynb: 0050 plot(t, dir_data, 'k.-') # This should result in a plot of the east displacements for rx 550 Deng_Fei_Lab11.ipynb: 0055 plot_all(rxID=217, tstart=tstart, tstop=tstop) Deng_Fei_Lab11.ipynb: 0076 plot_all(rxID=911, tstart=tstart, tstop=tstop) Deng_Fei_Lab11.ipynb: 0104 rxID_List = rxdata['rxID'] Deng_Fei_Lab11.ipynb: 0105 t, x, y, z = get_fluctuations(rxID, training_tstart, training_tstop) Deng_Fei_Lab11.ipynb: 0106 def plot_all(rxID, tstart, tstop): Deng_Fei_Lab11.ipynb: 0109 plot_all(rxID=1162, tstart=tstart, tstop=tstop) Deng_Fei_Lab11.ipynb: 0111 receiver_set = [] Deng_Fei_Lab11.ipynb: 0118 training_y = [] Deng_Fei_Lab11.ipynb: 0122 training_t.append(t) Deng_Fei_Lab11.ipynb: 0142 training_x = [] Deng_Fei_Lab11.ipynb: 0151 training_y.append(y) Deng_Fei_Lab11.ipynb: 0168 receiver_set.append(int(rxID)) chuma_kabaghe_lab11_LATELATE.ipynb: 0149 print rxID devasia_manuel_lab11.ipynb: 0080 pi1 = 0.00001 devasia_manuel_lab11.ipynb: 0083 counter = 0 devasia_manuel_lab11.ipynb: 0094 plt.figure(figsize=(4,2)) Wang_Hongru_Lab11_LATE.ipynb: 0007 plt.figure(figsize=(10,5)) feddersen_lab11.ipynb: 0124 pi1 = 0.1 Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0149 print rxID chuma_kabaghe_lab11_LATELATE.ipynb Ling_Wang-Jung_Lab11.ipynb: 0243 #print len(t) survila2_lab11.ipynb: 0170 ylabel('Eastward Displacement [m]') McLaughlin_SeanLab11_LATE.ipynb: 0056 continue crawford_melissa_macrawf2_lab11_LATE.ipynb: 0093 #print x Vashistha_siddhant_lab11_LATE.ipynb: 0057 #print threshold Vashistha_siddhant_lab11_LATE.ipynb: 0090 data = GEONET.get_geonet_data_at_time(tstart) Li_Boyu_Lab11_LATELATE.ipynb: 0244 print rxID Bai_Hongyang.ipynb: 0108 figure(figsize=(12,2)) devasia_manuel_lab11.ipynb: 0053 rxID = 170 Vankayala_Anuraag_Lab11.ipynb: 0067 #print pi1 Wang_Hongru_Lab11_LATE.ipynb: 0056 continue Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0056 continue Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0244 print rxID Bai_Hongyang.ipynb Tang_Yuhan_Lab11.ipynb: 0215 ylabel('Filtered Upward displacement [m]') Tang_Yuhan_Lab11.ipynb: 0230 t.append(time[i]) Ling_Wang-Jung_Lab11.ipynb: 0007 for i in range(len(t)): Antonacci_Lab11.ipynb: 0170 w = 20 # window size for running average removal (rudimentary HPF) Wu_Yi-Luen_Lab11.ipynb: 0076 plot(t,dup,'k.-') # This should result in a plot of the east displacements for rx 550 Wu_Yi-Luen_Lab11.ipynb: 0170 w = 20 # window size for running average removal (rudimentary HPF) Wu_Yi-Luen_Lab11.ipynb: 0215 ylabel('Filtered Upward displacement [m]') TsungHan_Lei_Lab11.ipynb: 0007 for i in range(len(t)): TsungHan_Lei_Lab11.ipynb: 0011 return result TsungHan_Lei_Lab11.ipynb: 0076 plot(t,dup,'k.-') # This should result in a plot of the east displacements for rx 550 TsungHan_Lei_Lab11.ipynb: 0215 ylabel('Filtered Upward displacement [m]') Deng_Fei_Lab11.ipynb: 0230 t.append(time[i]) chuma_kabaghe_lab11_LATELATE.ipynb: 0112 figure(figsize=(12,2)) dandala_Kris_Lab11_LATE.ipynb: 0272 D = [] Mao_Chengfeng_lab11.ipynb: 0011 return result Mao_Chengfeng_lab11.ipynb: 0205 pi_1 = 0.01 Wang_Hongru_Lab11_LATE.ipynb: 0076 plot(t,dup,'k.-') # This should result in a plot of the east displacements for rx 550 Wang_Hongru_Lab11_LATE.ipynb: 0170 w = 20 # window size for running average removal (rudimentary HPF) Wang_Hongru_Lab11_LATE.ipynb: 0215 ylabel('Filtered Upward displacement [m]') dandala_Kris_Lab11_LATE.ipynb Maier_Christopher_Lab11_LATE.ipynb: 0089 # add a 'best fit' line Maier_Christopher_Lab11_LATE.ipynb: 0196 solutions = [] Maier_Christopher_Lab11_LATE.ipynb: 0199 # the histogram of the data Shih_ChienTung_Lab11.ipynb: 0073 A = np.matrix(A) Tang_Yuhan_Lab11.ipynb: 0068 for rxID in rxID_list: Tang_Yuhan_Lab11.ipynb: 0073 A = np.matrix(A) Tang_Yuhan_Lab11.ipynb: 0133 return cls Wu_Yi-Luen_Lab11.ipynb: 0063 for i in range(len(deast)): Hsu_HengWei_Lab11_FA2014.ipynb: 0073 A = np.matrix(A) Bai_Hongyang.ipynb: 0225 D = [] Vankayala_Anuraag_Lab11.ipynb: 0001 num_bins = 50 Vankayala_Anuraag_Lab11.ipynb: 0023 plt.xlabel('X') Vankayala_Anuraag_Lab11.ipynb: 0049 x = deast Vankayala_Anuraag_Lab11.ipynb: 0055 plt.plot(bins, y, 'r--') Vankayala_Anuraag_Lab11.ipynb: 0070 y = mlab.normpdf(bins, mu, sigma) Vankayala_Anuraag_Lab11.ipynb: 0089 # add a 'best fit' line Vankayala_Anuraag_Lab11.ipynb: 0199 # the histogram of the data Mao_Chengfeng_lab11.ipynb: 0070 y = mlab.normpdf(bins, mu, sigma) Mao_Chengfeng_lab11.ipynb: 0084 i += 1 Mao_Chengfeng_lab11.ipynb: 0089 # add a 'best fit' line Mao_Chengfeng_lab11.ipynb: 0199 # the histogram of the data Wang_Hongru_Lab11_LATE.ipynb: 0084 i += 1 Wang_Hongru_Lab11_LATE.ipynb: 0133 return cls Wang_Hongru_Lab11_LATE.ipynb: 0140 lat = [30, 45] # [min lat, max lat] for the map Wang_Hongru_Lab11_LATE.ipynb: 0180 lon = [130,145] # [min lon, max lon] for the map devasia_manuel_lab11.ipynb Tang_Yuhan_Lab11.ipynb: 0029 time_list = [] Tang_Yuhan_Lab11.ipynb: 0117 pi1 = 0.00001 Tang_Yuhan_Lab11.ipynb: 0120 counter = 0 Tang_Yuhan_Lab11.ipynb: 0153 pi1 = 0.01 Tang_Yuhan_Lab11.ipynb: 0179 plt.figure() McLaughlin_SeanLab11_LATE.ipynb: 0075 %matplotlib inline Antonacci_Lab11.ipynb: 0056 data = GEONET.get_geonet_data(tstart, tstop) Antonacci_Lab11.ipynb: 0152 std_dev = np.std(data) Wu_Yi-Luen_Lab11.ipynb: 0209 pi1 = 0.001 crawford_melissa_macrawf2_lab11_LATE.ipynb: 0209 pi1 = 0.001 Deng_Fei_Lab11.ipynb: 0196 plt.legend(loc='best') Li_Boyu_Lab11_LATELATE.ipynb: 0117 pi1 = 0.00001 Li_Boyu_Lab11_LATELATE.ipynb: 0120 counter = 0 Li_Boyu_Lab11_LATELATE.ipynb: 0137 plt.figure(figsize=(4,2)) chuma_kabaghe_lab11_LATELATE.ipynb: 0052 rxID = 170 Mao_Chengfeng_lab11.ipynb: 0013 dlat = lat2 - lat1 Mao_Chengfeng_lab11.ipynb: 0093 a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2 Mao_Chengfeng_lab11.ipynb: 0101 c = 2 * asin(sqrt(a)) Mao_Chengfeng_lab11.ipynb: 0111 lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2]) Mao_Chengfeng_lab11.ipynb: 0135 dlon = lon2 - lon1 Mao_Chengfeng_lab11.ipynb: 0155 # 6367 km is the radius of the Earth Mao_Chengfeng_lab11.ipynb: 0196 plt.legend(loc='best') snyder_bradley_lab11.ipynb: 0075 %matplotlib inline Wang_Hongru_Lab11_LATE.ipynb: 0179 plt.figure() feddersen_lab11.ipynb: 0069 rxID = 1000 feddersen_lab11.ipynb: 0153 pi1 = 0.01 feddersen_lab11.ipynb: 0178 pi1 = 0.0001 Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0075 %matplotlib inline Vankayala_Anuraag_Lab11.ipynb Maier_Christopher_Lab11_LATE.ipynb: 0088 # add a 'best fit' line Maier_Christopher_Lab11_LATE.ipynb: 0095 # the histogram of the data TsungHan_Lei_Lab11.ipynb: 0071 lon_ref = np.mean(lon[1:10]) TsungHan_Lei_Lab11.ipynb: 0118 lat_ref = np.mean(lat[1:10]) TsungHan_Lei_Lab11.ipynb: 0136 alt_ref = np.mean(alt[1:10]) crawford_melissa_macrawf2_lab11_LATE.ipynb: 0202 print x chuma_kabaghe_lab11_LATELATE.ipynb: 0062 #print pi1 dandala_Kris_Lab11_LATE.ipynb: 0019 x = deast dandala_Kris_Lab11_LATE.ipynb: 0025 plt.xlabel('X') dandala_Kris_Lab11_LATE.ipynb: 0053 plt.plot(bins, y, 'r--') dandala_Kris_Lab11_LATE.ipynb: 0072 y = mlab.normpdf(bins, mu, sigma) dandala_Kris_Lab11_LATE.ipynb: 0088 # add a 'best fit' line dandala_Kris_Lab11_LATE.ipynb: 0095 # the histogram of the data dandala_Kris_Lab11_LATE.ipynb: 0222 num_bins = 50 Mao_Chengfeng_lab11.ipynb: 0072 y = mlab.normpdf(bins, mu, sigma) Mao_Chengfeng_lab11.ipynb: 0088 # add a 'best fit' line Mao_Chengfeng_lab11.ipynb: 0095 # the histogram of the data Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0071 lon_ref = np.mean(lon[1:10]) Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0118 lat_ref = np.mean(lat[1:10]) Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0136 alt_ref = np.mean(alt[1:10]) Mao_Chengfeng_lab11.ipynb Maier_Christopher_Lab11_LATE.ipynb: 0073 # add a 'best fit' line Maier_Christopher_Lab11_LATE.ipynb: 0161 # the histogram of the data Shih_ChienTung_Lab11.ipynb: 0050 rxid1vec = data_before['rxID'] Tang_Yuhan_Lab11.ipynb: 0121 plot(t,deast,'k.-') McLaughlin_SeanLab11_LATE.ipynb: 0030 N = 20 Antonacci_Lab11.ipynb: 0082 plt.title(title) Hsu_HengWei_Lab11_FA2014.ipynb: 0038 data_before = GEONET.get_geonet_data_at_time(tbefore) Hsu_HengWei_Lab11_FA2014.ipynb: 0050 rxid1vec = data_before['rxID'] TsungHan_Lei_Lab11.ipynb: 0010 return result TsungHan_Lei_Lab11.ipynb: 0136 lon_list.append(lon) TsungHan_Lei_Lab11.ipynb: 0141 lat_list.append(lat) crawford_melissa_macrawf2_lab11_LATE.ipynb: 0183 rxID = 213 Deng_Fei_Lab11.ipynb: 0155 plt.legend(loc='best') Bai_Hongyang.ipynb: 0010 return result Bai_Hongyang.ipynb: 0143 pi_1 = 0.01 dandala_Kris_Lab11_LATE.ipynb: 0058 y = mlab.normpdf(bins, mu, sigma) dandala_Kris_Lab11_LATE.ipynb: 0066 i += 1 dandala_Kris_Lab11_LATE.ipynb: 0073 # add a 'best fit' line dandala_Kris_Lab11_LATE.ipynb: 0161 # the histogram of the data devasia_manuel_lab11.ipynb: 0079 c = 2 * asin(sqrt(a)) devasia_manuel_lab11.ipynb: 0087 lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2]) devasia_manuel_lab11.ipynb: 0107 dlon = lon2 - lon1 devasia_manuel_lab11.ipynb: 0126 # 6367 km is the radius of the Earth devasia_manuel_lab11.ipynb: 0155 plt.legend(loc='best') devasia_manuel_lab11.ipynb: 0169 dlat = lat2 - lat1 devasia_manuel_lab11.ipynb: 0182 a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2 Vankayala_Anuraag_Lab11.ipynb: 0058 y = mlab.normpdf(bins, mu, sigma) Vankayala_Anuraag_Lab11.ipynb: 0073 # add a 'best fit' line Vankayala_Anuraag_Lab11.ipynb: 0161 # the histogram of the data Wang_Hongru_Lab11_LATE.ipynb: 0066 i += 1 feddersen_lab11.ipynb: 0024 result = [] feddersen_lab11.ipynb: 0030 N = 20 Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0030 N = 20 snyder_bradley_lab11.ipynb Shih_ChienTung_Lab11.ipynb: 0012 return y McLaughlin_SeanLab11_LATE.ipynb: 0081 z.append(up[i]) McLaughlin_SeanLab11_LATE.ipynb: 0157 y.append(north[i]) McLaughlin_SeanLab11_LATE.ipynb: 0158 x.append(east[i]) McLaughlin_SeanLab11_LATE.ipynb: 0167 %matplotlib inline Hsu_HengWei_Lab11_FA2014.ipynb: 0012 return y Hsu_HengWei_Lab11_FA2014.ipynb: 0147 count = 0 Deng_Fei_Lab11.ipynb: 0089 count += 1 Deng_Fei_Lab11.ipynb: 0147 count = 0 devasia_manuel_lab11.ipynb: 0167 %matplotlib inline Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0167 %matplotlib inline Lab11_CheatDetect.ipynb Wang_Hongru_Lab11_LATE.ipynb Tang_Yuhan_Lab11.ipynb: 0033 elif i < w: Tang_Yuhan_Lab11.ipynb: 0113 if i == 0 or i == 1: Tang_Yuhan_Lab11.ipynb: 0126 return cls Tang_Yuhan_Lab11.ipynb: 0161 plt.figure() Tang_Yuhan_Lab11.ipynb: 0174 ylabel('Filtered Upward displacement [m]') Ling_Wang-Jung_Lab11.ipynb: 0212 t = time survila2_lab11.ipynb: 0212 t = time McLaughlin_SeanLab11_LATE.ipynb: 0048 continue McLaughlin_SeanLab11_LATE.ipynb: 0212 t = time Antonacci_Lab11.ipynb: 0139 w = 20 # window size for running average removal (rudimentary HPF) Antonacci_Lab11.ipynb: 0212 t = time Wu_Yi-Luen_Lab11.ipynb: 0058 plot(t,dup,'k.-') # This should result in a plot of the east displacements for rx 550 Wu_Yi-Luen_Lab11.ipynb: 0139 w = 20 # window size for running average removal (rudimentary HPF) Wu_Yi-Luen_Lab11.ipynb: 0174 ylabel('Filtered Upward displacement [m]') TsungHan_Lei_Lab11.ipynb: 0058 plot(t,dup,'k.-') # This should result in a plot of the east displacements for rx 550 TsungHan_Lei_Lab11.ipynb: 0071 rxID = 167 TsungHan_Lei_Lab11.ipynb: 0174 ylabel('Filtered Upward displacement [m]') Deng_Fei_Lab11.ipynb: 0005 posix_time = rxdata['UT'][0] Deng_Fei_Lab11.ipynb: 0033 elif i < w: Deng_Fei_Lab11.ipynb: 0113 if i == 0 or i == 1: Li_Boyu_Lab11_LATELATE.ipynb: 0008 plt.figure(figsize=(10,5)) chuma_kabaghe_lab11_LATELATE.ipynb: 0048 continue Bai_Hongyang.ipynb: 0058 plot(t,dup,'k.-') # This should result in a plot of the east displacements for rx 550 Bai_Hongyang.ipynb: 0139 w = 20 # window size for running average removal (rudimentary HPF) Bai_Hongyang.ipynb: 0174 ylabel('Filtered Upward displacement [m]') dandala_Kris_Lab11_LATE.ipynb: 0020 i += 1 dandala_Kris_Lab11_LATE.ipynb: 0126 return cls dandala_Kris_Lab11_LATE.ipynb: 0134 lat = [30, 45] # [min lat, max lat] for the map dandala_Kris_Lab11_LATE.ipynb: 0180 lon = [130,145] # [min lon, max lon] for the map devasia_manuel_lab11.ipynb: 0161 plt.figure() Mao_Chengfeng_lab11.ipynb: 0020 i += 1 feddersen_lab11.ipynb: 0212 t = time Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0048 continue Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0212 t = time de Paula Vasconcelos_Thiago_Lab11.ipynb Wu_Yi-Luen_Lab11.ipynb: 0179 std = [] crawford_melissa_macrawf2_lab11_LATE.ipynb: 0179 std = [] feddersen_lab11.ipynb Tang_Yuhan_Lab11.ipynb: 0102 pi1 = 0.01 Ling_Wang-Jung_Lab11.ipynb: 0213 t = time survila2_lab11.ipynb: 0213 t = time McLaughlin_SeanLab11_LATE.ipynb: 0040 N = 20 McLaughlin_SeanLab11_LATE.ipynb: 0213 t = time Antonacci_Lab11.ipynb: 0123 d = [] Antonacci_Lab11.ipynb: 0213 t = time Wu_Yi-Luen_Lab11.ipynb: 0123 d = [] Wu_Yi-Luen_Lab11.ipynb: 0167 pi1 = 0.1 Wu_Yi-Luen_Lab11.ipynb: 0169 pi1 = 0.5 crawford_melissa_macrawf2_lab11_LATE.ipynb: 0067 pi0 = 1-pi1 Li_Boyu_Lab11_LATELATE.ipynb: 0167 pi1 = 0.1 devasia_manuel_lab11.ipynb: 0073 rxID = 1000 devasia_manuel_lab11.ipynb: 0102 pi1 = 0.01 devasia_manuel_lab11.ipynb: 0160 pi1 = 0.0001 Mao_Chengfeng_lab11.ipynb: 0040 N = 20 Mao_Chengfeng_lab11.ipynb: 0171 result = [] Wang_Hongru_Lab11_LATE.ipynb: 0213 t = time Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0040 N = 20 Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb: 0213 t = time Labelle_Julia_lab11_LATELATE.ipynb Deng_Fei_Lab11.ipynb: 0039 t.append(time[count]) Vashistha_siddhant_lab11_LATE.ipynb: 0189 for x in deast: Gleason_Cole_Lab11_EXTRA_FILE_IN_EMAIL.ipynb Ling_Wang-Jung_Lab11.ipynb: 0207 t = time survila2_lab11.ipynb: 0207 t = time McLaughlin_SeanLab11_LATE.ipynb: 0033 N = 20 McLaughlin_SeanLab11_LATE.ipynb: 0052 continue McLaughlin_SeanLab11_LATE.ipynb: 0190 %matplotlib inline McLaughlin_SeanLab11_LATE.ipynb: 0207 t = time Antonacci_Lab11.ipynb: 0207 t = time TsungHan_Lei_Lab11.ipynb: 0068 lon_ref = np.mean(lon[1:10]) TsungHan_Lei_Lab11.ipynb: 0110 lat_ref = np.mean(lat[1:10]) TsungHan_Lei_Lab11.ipynb: 0122 alt_ref = np.mean(alt[1:10]) Vashistha_siddhant_lab11_LATE.ipynb: 0060 ylabel('Frequency') Li_Boyu_Lab11_LATELATE.ipynb: 0194 print rxID chuma_kabaghe_lab11_LATELATE.ipynb: 0052 continue chuma_kabaghe_lab11_LATELATE.ipynb: 0194 print rxID devasia_manuel_lab11.ipynb: 0190 %matplotlib inline Vankayala_Anuraag_Lab11.ipynb: 0068 lon_ref = np.mean(lon[1:10]) Vankayala_Anuraag_Lab11.ipynb: 0110 lat_ref = np.mean(lat[1:10]) Vankayala_Anuraag_Lab11.ipynb: 0122 alt_ref = np.mean(alt[1:10]) Mao_Chengfeng_lab11.ipynb: 0033 N = 20 snyder_bradley_lab11.ipynb: 0190 %matplotlib inline Wang_Hongru_Lab11_LATE.ipynb: 0052 continue Wang_Hongru_Lab11_LATE.ipynb: 0207 t = time feddersen_lab11.ipynb: 0033 N = 20 feddersen_lab11.ipynb: 0207 t = time