add initial commit

README.md

@@ -1 +1 @@
-# aquavis
+# This program takes a csv file and displays the data in a useful way. 

data_loaders/csv_loader.py

@@ -0,0 +1,28 @@
+from abc import ABC, abstractmethod
+import pandas as pd
+from data_loaders.data_loader import DataLoader
+
+
+class CsvLoader(DataLoader):
+	def __init__(self, name, delimiter):
+		"""
+		Reads the given csv file
+
+		@type name: str
+		@param name: the name of the csv file
+		@type delimiter: str
+		@param delimiter: the delimiter separating the column names in the csv file
+		"""
+
+		self.name1 = name
+		self.delim = delimiter
+
+	def load(self):
+		"""
+		Reads the csv file into a dataframe
+
+		@rtype: Pandas DataFrame
+		@returns: the pandas dataframe containing the read data from the csv file
+		"""
+
+		return (pd.read_csv(self.name1, self.delim))

data_loaders/data_loader.py

@@ -0,0 +1,6 @@
+from abc import ABC, abstractmethod
+
+
+class DataLoader(ABC):
+	def load(self):
+		pass

driver.py

@@ -0,0 +1,321 @@
+from abc import ABC, abstractmethod
+from processors.drop_close import DropClose
+from processors.outlier_removal import OutlierRemoval
+from processors.distance_calculator import DistanceCalculator
+from processors.step_range import StepRange
+from processors.processor import Processor
+from data_loaders.csv_loader import CsvLoader
+from data_loaders.data_loader import DataLoader  
+from models.gpr import GPR
+from models.svr import SV
+from models.model_manager import ModelManager
+from models.model import Model
+from visualizers.visualization import Visualization
+from visualizers.two_d_vis import TwoDVis
+from visualizers.three_d_vis import ThreeDVis
+import matplotlib as mpl 
+from matplotlib import pyplot as plt
+import argparse
+import json
+
+
+def set_defaults():
+	"""
+	Gives variables default values
+
+	@rtype: tuple
+	@returns: a tuple containing key variables for future methods
+	"""
+
+	delimiter = ","
+	lat = "Latitude"
+	lon = "Longitude"
+	dep = "Depth"
+	range_name = "Current Step"
+	tol = 0.0001
+	outlier_tol = 3
+	range_min = 0
+	range_max = 1
+	ans = 'n'
+	tol_mse = 0.01
+	ans1 = 1
+	length = 20
+	ans2 = 2
+	ans3 = 2
+	return (delimiter, lat, lon, dep, range_name, tol, outlier_tol, range_min, range_max, ans, tol_mse, ans1, length, ans2, ans3)
+
+def arg_parse_and_json(tup):
+	"""
+	Parses user-inputted arguments and reads possible json file
+
+	@type tup: tuple
+	@param tup: the necessary variables for the functions of this method
+	@rtype: tuple
+	@returns: a tuple containing key variables for future methods
+	"""
+
+	parser = argparse.ArgumentParser(description = "This program takes a csv file and displays the data in a useful way.")
+	parser.add_argument('--json', '-j', help = "name of json file (default = no file)")
+	parser.add_argument('--file', '-f', required = True, help = "name of csv file")
+	parser.add_argument('--extra_data', '-e', required = True, help = "name of column of user-selected data")
+	parser.add_argument('--delimiter', '-d', help = "delimiter separating the column names in the file (make this first in json file) (default = ',')")
+	parser.add_argument('--latitude', '-l', help = "name of latitude column (default = 'Latitude')")
+	parser.add_argument('--longitude', '-lo', help = "name of longitude column (default = 'Longitude')")
+	parser.add_argument('--depth', '-de', help = "name of depth column (default = 'Depth')")
+	parser.add_argument('--current_step', '-c', help = "name of current step column (default = 'Current Step')")
+	parser.add_argument('--dist_tol', '-dt', help = "tolerance for points being too close together on chart (default = 0.0001)")
+	parser.add_argument('--outlier_tol', '-o', help = "tolerance for outliers (in standard deviations) (default = 3)")
+	parser.add_argument('--first', '-fi', help = "first step in range being examined (default = 0)")
+	parser.add_argument('--last', '-la', help = "last step in range being examined (default = 1)")
+	parser.add_argument('--predictor', '-p', help = "whether or not to use a predictor model to interpolate (default = no)", action = 'store_true')
+	parser.add_argument('--mse_tol', '-t', help = "tolerance for MSE (default = 0.01)")
+	parser.add_argument('--model', '-m', help = "type of model to preferably use(1(GPR)/2(SVR)) (default = 1)")
+	parser.add_argument('--size', '-s', help = "number of points per row/column in the square/cubic grid that represents the model (default = 20)")
+	parser.add_argument('--dimension', '-di', help = "dimensions of model (default = 2)")
+	parser.add_argument('--plot', '-pl', help = "dimensions of hard data plot (default = 2)")
+	args = parser.parse_args()
+
+	file_name = args.file 
+	user = args.extra_data
+
+	delimiter = tup[0]
+	lat = tup[1] 
+	lon = tup[2]
+	dep = tup[3]
+	range_name = tup[4]
+	tol = tup[5]
+	outlier_tol = tup[6]
+	range_min = tup[7]
+	range_max = tup[8]
+	ans = tup[9]
+	tol_mse = tup[10]
+	ans1 = tup[11]
+	length = tup[12]
+	ans2 = tup[13]
+	ans3 = tup[14]
+
+	if args.json != None:
+		with open(arg.json) as f:
+	  		data = json.load(f)
+		json_file = []
+		for key, value in data.items():
+			json_file.append([key, value])
+		for i in range(len(json_file)):
+			if jason_file[i][0] == "delimiter":
+				delimiter = json_file[i][1]
+			elif jason_file[i][0] == "latitude":
+				lat = json_file[i][1]
+			elif jason_file[i][0] == "longitude":
+				lon = json_file[i][1]
+			elif jason_file[i][0] == "depth":
+				dep = json_file[i][1]
+			elif jason_file[i][0] == "current_step":
+				range_name = json_file[i][1]
+			elif jason_file[i][0] == "dist_tol":
+				tol = float(json_file[i][1])
+			elif jason_file[i][0] == "outlier_tol":
+				outlier_tol = float(json_file[i][1])
+			elif jason_file[i][0] == "first":
+				range_min = int(json_file[i][1])
+			elif jason_file[i][0] == "last":
+				range_max = int(json_file[i][1])
+			elif jason_file[i][0] == "predictor":
+				if bool(jason_file[i][1]) == True:
+					ans = 'y'
+			elif jason_file[i][0] == "mse_tol":
+				tol_mse = float(json_file[i][1])
+			elif jason_file[i][0] == "model":
+				ans1 = int(json_file[i][1])
+			elif jason_file[i][0] == "size":
+				length = int(json_file[i][1])
+			elif jason_file[i][0] == "dimension":
+				ans2 = int(json_file[i][1])
+			else:
+				ans3 = int(json_file[i][1])
+
+	if args.delimiter != None:
+		delimiter = args.delimiter
+	if args.latitude != None:
+		lat = args.latitude
+	if args.depth != None:
+		dep = args.depth
+	if args.current_step != None:
+		range_name = args.current_step
+	if args.dist_tol != None:
+		tol = float(args.dist_tol)
+	if args.outlier_tol != None:
+		outlier_tol = float(args.outlier_tol)
+	if args.first != None:
+		range_min = int(args.first)
+	if args.last != None:
+		range_max = int(args.last)
+	if bool(args.predictor) == True:
+		ans = 'y'
+	if args.mse_tol != None:
+		tol_mse = float(args.mse_tol)
+	if args.model != None:
+		ans1 = int(args.model)
+	if args.size != None:
+		length = int(args.size)
+	if args.dimension != None:
+		ans2 = int(args.dimension)
+	if args.plot != None:
+		ans3 = int(args.plot)
+
+	return (delimiter, lat, lon, dep, range_name, tol, outlier_tol, range_min, range_max, ans, tol_mse, ans1, length, ans2, ans3, user, file_name)
+
+def set_up(tup):
+	"""
+	Processes and sets up data for models/graphing
+
+	@type tup: tuple
+	@param tup: the necessary variables for the functions of this method
+	@rtype: tuple
+	@returns: a tuple containing key variables for future methods
+	"""
+
+	delimiter = tup[0]
+	lat = tup[1] 
+	lon = tup[2]
+	dep = tup[3]
+	range_name = tup[4]
+	tol = tup[5]
+	outlier_tol = tup[6]
+	range_min = tup[7]
+	range_max = tup[8]
+	ans = tup[9]
+	tol_mse = tup[10]
+	ans1 = tup[11]
+	length = tup[12]
+	ans2 = tup[13]
+	ans3 = tup[14]
+	user = tup[15]
+	file_name = tup[16]
+
+	file = CsvLoader(name = file_name, delimiter = delimiter)
+	read_file = file.load()
+
+	distance = DistanceCalculator(lat_name = lat, long_name = lon, file = read_file)
+	distance_arr = distance.process()
+
+	file_ranger = StepRange(step_name = range_name, step_min = range_min, step_max = range_max, file = read_file)
+	file_ranged = file_ranger.process()
+
+	close_dropper = DropClose(dep_name = dep, dist_arr = distance_arr, tolerance = tol, file = file_ranged)
+	close_dropped = close_dropper.process()
+
+	outlier_remover = OutlierRemoval(tolerance = outlier_tol, user_name = user, file = close_dropped)
+	outlier_removed = outlier_remover.process()
+
+	distance = DistanceCalculator(lat_name = lat, long_name = lon, file = outlier_removed)
+	distance_arr = distance.process()
+
+	return (lat, lon, dep, ans, tol_mse, ans1, length, ans2, ans3, distance_arr, outlier_removed, user)
+
+def execute_model(tup):
+	"""
+	Creates a fit model and graphs predicted interpolation data
+
+	@type tup: tuple
+	@param tup: the necessary variables for the functions of this method
+	@rtype: tuple
+	@returns: a tuple containing key variables for future methods
+	"""
+
+	lat = tup[0]
+	lon = tup[1]
+	dep = tup[2]
+	ans = tup[3]
+	tol_mse = tup[4]
+	ans1 = tup[5]
+	length = tup[6]
+	ans2 = tup[7]
+	ans3 = tup[8]
+	distance_arr = tup[9]
+	outlier_removed = tup[10]
+	user = tup[11]
+
+
+	if ans == "y":
+
+		Gauss = GPR(dist_array = distance_arr, user_name = user, dep_name = dep, length = length, file = outlier_removed, lat_n = lat, lon_n = lon, dims = ans2)
+		svr_mod = SV(dist_arr = distance_arr, user_name = user, dep_name = dep, length = length, file = outlier_removed, lat_n = lat, lon_n = lon, dims = ans2)
+		if ans1 == 1:
+			model_manager = ModelManager(primary = Gauss, secondary = svr_mod, tolerance = tol_mse, dimension = ans2)
+			x = model_manager.fit()
+		else:
+			model_manager = ModelManager(primary = svr_mod, secondary = Gauss, tolerance = tol_mse, dimension = ans2)
+			x = model_manager.fit()
+		if ans2 == 2:
+			gridpoints = model_manager.predict()
+			plt.figure()
+			cmap = mpl.cm.jet;
+			plt.xlabel("Distance (m)")
+			plt.ylabel(dep)
+			for i in range(len(gridpoints[1])):
+				gridpoints[1][i] = gridpoints[1][i] * -1
+			cb = plt.colorbar(plt.scatter(gridpoints[0], gridpoints[1], c = gridpoints[2], cmap = cmap))
+			cb.set_label(user)
+			if len(gridpoints) == 4:
+				plt.figure()
+				plt.xlabel("Distance (m)")
+				plt.ylabel(dep)
+				cb1 = plt.colorbar(plt.scatter(gridpoints[0], gridpoints[1], c = gridpoints[3], cmap = cmap))
+				cb1.set_label("Variance (" + str(user) + ")")
+			plt.show()
+		else:
+			cmap = mpl.cm.jet
+			gridpoints = model_manager.predict()
+			fig = plt.figure()
+			ax = fig.add_subplot(111, projection = '3d')
+			ax.set_xlabel(lat)
+			ax.set_ylabel(lon)
+			ax.set_zlabel(dep)
+			for i in range(len(gridpoints[2])):
+				gridpoints[2][i] = gridpoints[2][i] * -1
+			cb = plt.colorbar(ax.scatter(gridpoints[0], gridpoints[1], gridpoints[2], c = gridpoints[3], cmap = cmap))
+			cb.set_label(user)
+			if len(gridpoints) == 5:
+				fig1 = plt.figure()
+				axis = fig1.add_subplot(111, projection = '3d')
+				axis.set_xlabel(lat)
+				axis.set_ylabel(lon)
+				axis.set_zlabel(dep)
+				cb1 = plt.colorbar(axis.scatter(gridpoints[0], gridpoints[1], gridpoints[2], c = gridpoints[4], cmap = cmap))
+				cb1.set_label("Variance (" + str(user) + ")")
+			plt.show()
+		return (lat, lon, dep, ans3, distance_arr, outlier_removed, user)
+def plot_data(tup):
+	"""
+	Plots and shows the hard data
+
+	@type tup: tuple
+	@param tup: the necessary variables for the functions of this method
+	"""
+
+	lat = tup[0]
+	lon = tup[1]
+	dep = tup[2]
+	ans3 = tup[3]
+	distance_arr = tup[4]
+	outlier_removed = tup[5]
+	user = tup[6]
+	if ans3 == 2:
+		graph = TwoDVis(dep_name = dep, dist_arr = distance_arr, user_name = user, file = outlier_removed)
+		graph.plot()
+	else:
+		graph = ThreeDVis(dep_name = dep, dist_arr = distance_arr, user_name = user, lat_name = lat, lon_name = lon, file = outlier_removed)
+		graph.plot()
+
+def main():
+	"""
+	Executes all methods in the class
+	"""
+
+	a = set_defaults()
+	b = arg_parse_and_json(a)
+	c = set_up(b)
+	d = execute_model(c)
+	plot_data(d)
+
+main()