Module came.Part_3_offset_distance
Functions
def calculate_distance(path_list)-
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def calculate_distance(path_list): """ This function is for getting migration axis and offset distance Args: path_list: The files in the path Returns: list11: The offset distances """ list_distance = [] for l in path_list: name1 = os.path.basename(l) if name1[:2] == 'fi': tmp = pd.read_csv(l, header=None) list_distance.append(tmp) mean_x = [] mean_y = [] for q in list_distance: mean_x.append(float(q.iloc[1, 0])) mean_y.append(float(q.iloc[1, 1])) mean_x1 = np.mean(mean_x) mean_y1 = np.mean(mean_y) print(mean_x1, mean_y1) dict1 = {} list1 = [] max_list_x = [] max_list_y = [] for r in path_list: name1 = os.path.basename(r) if name1[0:2] == 'fi': tmp = pd.read_csv(r, header=None) for l in range(1,359): x1 = float(tmp.iloc[1, 0]) y1 = float(tmp.iloc[1, 1]) x = float(tmp.iloc[l, 0]) y = float(tmp.iloc[l, 1]) distance1 = ((x - x1) ** 2 + (y - y1) ** 2) ** 0.5 dict1[distance1] = l list1.append(distance1) max_distance = max(list1) max_x_1 = float(tmp.iloc[dict1[max_distance], 0]) max_y_1 = float(tmp.iloc[dict1[max_distance], 1]) max_list_x.append(max_x_1) max_list_y.append(max_y_1) list1 = [] dict1 = {} mean_x2 = np.mean(max_list_x) mean_y2 = np.mean(max_list_y) print(mean_x2, mean_y2) line = [mean_x1, mean_y1, mean_x2, mean_y2] list11 = [] for r in path_list: list_tmp = [] name1 = os.path.basename(r) if name1[0:2] == 'fi': tmp = pd.read_csv(r, header=None) for p in range(1,359): x = float(tmp.iloc[p, 0]) y = float(tmp.iloc[p, 1]) distance2 = get_distance_point2line((x, y), line) list_tmp.append(distance2) list11.append(list_tmp) return list11This function is for getting migration axis and offset distance
Args
path_list- The files in the path
Returns
list11- The offset distances
def file_names(inputpath)-
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def file_names(inputpath): """ This function is for looping over files Args: inputpath: The folder path to be looped over Returns: namelist: The files list """ namelist = [] filePath = inputpath for i, j, k in os.walk(filePath): namelist.append([i, j, k]) return namelistThis function is for looping over files
Args
inputpath- The folder path to be looped over
Returns
namelist- The files list
def get_distance_point2line(point, line)-
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def get_distance_point2line(point, line): """ This function is for calculating the distance from points to a line Args: point: The observation points line: A line Returns: distance: The distance from points to the line """ line_point1, line_point2 = np.array(line[0:2]), np.array(line[2:]) vec1 = line_point1 - point vec2 = line_point2 - point distance = np.abs(np.cross(vec1, vec2)) / np.linalg.norm(line_point1 - line_point2) return distanceThis function is for calculating the distance from points to a line
Args
point- The observation points
line- A line
Returns
distance- The distance from points to the line
def get_file_list(path)-
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def get_file_list(path): """ This function is for getting file lists Args: path: The path for files Returns: file_list: The file list """ file_list = [] for root, dirs, files in os.walk(path): for file in files: if file != '.DS_Store': file_list.append(os.path.join(root, file)) return file_listThis function is for getting file lists
Args
path- The path for files
Returns
file_list- The file list
def off_distance(data_path)-
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def off_distance(data_path): """ This function is for saving the results in the folder Args: data_path: The data path Returns: True: Omitted """ # -------------------------------------------- excel_list = file_names(data_path) for i in range(len(excel_list)): folder_attribute = excel_list[i] # index = 1 if len(folder_attribute[2]) > 0: for fileName in folder_attribute[2]: if fileName[0:2] == 'fi': path = folder_attribute[0] create_folder(os.path.join(path, 'speed')) create_folder(os.path.join(path, 'off_distance')) # -------------------------------------------- off_distance_main(path) # q4('result') print('{}Result found'.format(path)) breakThis function is for saving the results in the folder
Args
data_path- The data path
Returns
True- Omitted
def off_distance_main(path)-
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def off_distance_main(path): """ This function is for saving and drawing the results Args: path: The file path Returns: True: Omitted """ path1 = get_file_list(path) result = calculate_distance(path1) count1 = 0 tmp = 0 index = 1 for j in result: list_averange = [] result2 = [] items = [[datas] for datas in j] savecsvs(os.path.join(path, 'off_distance', 'd{}.csv'.format(str(index))),items) index = index + 1 for b in range(len(j)): for l in result: tmp += l[b] averange = int(tmp/len(result)) tmp = 0 result2.append(averange) items = [[datas] for datas in result2] #savecsvs(os.path.join(path, 'off_distance', 'da.csv'), items) count = 1 xx = [i for i in range(43104, 43462)] for l in result: name = os.path.basename(path1[count]) #plt.xlim(0,360) plt.plot(xx,l,label='d{}'.format(count)) #plt.plot(xx, l) plt.xlabel("date") plt.ylabel("offset distance(meter)") #plt.title('%s'%l) count += 1 #plt.title('%s'%name) plt.ticklabel_format(style='scientific', axis='y', scilimits=(0, 0)) plt.legend() plt.savefig(os.path.join(path, 'off_distance', 'OffsetDistancePerDay.png')) plt.show()This function is for saving and drawing the results
Args
path- The file path
Returns
True- Omitted