Added compare_list

Author: Nicholas Holschuh

COPdem_filelist.mat

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+import xarray as xr
+import numpy as np
+
+def add_datavariable_xr(xarray_dataset,new_datavariable,varname,coordinate_names=['y','x']):
+    """
+    % (C) Nick Holschuh - Amherst College -- 2022 (Nick.Holschuh@gmail.com)
+    %
+    % This function uses xarray to construct a netcdf
+    %
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    % The inputs are:
+    %
+    %
+    %%%%%%%%%%%%%%%
+    % The outputs are:
+    %
+    %
+    %
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    """    
+    
+    coordinate_names = tuple(coordinate_names)
+    xarray_dataset = xarray_dataset.assign({varname:(coordinate_names,new_datavariable)})
+    
+    return xarray_dataset

add_datavariable_xr.py

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+import numpy as np
+
+
+def box_from_corners(xs,ys):
+    """
+    % (C) Nick Holschuh - Amherst College -- 2022 (Nick.Holschuh@gmail.com)
+    %
+    % This function prints out the minimum and maximum values of an array
+    %
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    % The inputs are:
+    %
+    %     input_array -- array of data to analyze
+    %
+    %%%%%%%%%%%%%%%
+    % The outputs are:
+    %
+    %      output -- the min and max in a 1x2 array
+    %
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    """ 
+    x_array = np.array([xs[0],xs[1],xs[1],xs[0],xs[0]])
+    y_array = np.array([ys[1],ys[1],ys[0],ys[0],ys[1]])
+    
+    box = np.stack([x_array,y_array]).T
+        
+    return box
+    

box_from_corners.py

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+import matplotlib
+
+
+def cmap_data(colormapname):
+    """
+    % (C) Nick Holschuh - Amherst College -- 2022 (Nick.Holschuh@gmail.com)
+    %
+    % This function gets the colormap object for value extraction
+    %
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    % The inputs are:
+    %
+    %     colormapname -- the string for the colormap of choice
+    %
+    %%%%%%%%%%%%%%%
+    % The outputs are:
+    %
+    %      cmap -- The colormap object, which takes values from 0-1 to generate a color.
+    %
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    """ 
+    cmap = matplotlib.cm.get_cmap(colormapname)
+    
+    return cmap
+    

cmap_data.py

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+import numpy as np
+
+def compare_list(list1,list2):
+    """
+    % (C) Nick Holschuh - Amherst College -- 2022 (Nick.Holschuh@gmail.com)
+    %
+    % This function tests the equality of every item in a list
+    %
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    % The inputs are:
+    %
+    %     list1 -- first list to compare
+    %     list2 -- second list to compare
+    %
+    %%%%%%%%%%%%%%%
+    % The outputs are:
+    %
+    %      true/false
+    %
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    """ 
+    result1 = all(map(lambda x, y: np.all(x == y), list1,list2))
+    result2 = all(map(lambda x, y: np.all(np.array(x).shape == np.array(y).shape), list1,list2))
+    
+    comb_result = np.all([result1,result2])
+    
+    return comb_result,result1,result2
+    

compare_list.py

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+import numpy as np
+
+
+def crossovers(line1,line2):
+
+    import numpy.core.umath_tests as ut
+    
+    x_down = line1[:,0]
+    y_down = line1[:,1]
+    x_up = line2[:,0]
+    y_up = line2[:,1]   
+    
+    p = np.column_stack((x_down, y_down))
+    q = np.column_stack((x_up, y_up))
+
+    (p0, p1, q0, q1) = p[:-1], p[1:], q[:-1], q[1:]
+    rhs = q0 - p0[:, np.newaxis, :]
+
+    mat = np.empty((len(p0), len(q0), 2, 2))
+    mat[..., 0] = (p1 - p0)[:, np.newaxis]
+    mat[..., 1] = q0 - q1
+    mat_inv = -mat.copy()
+    mat_inv[..., 0, 0] = mat[..., 1, 1]
+    mat_inv[..., 1, 1] = mat[..., 0, 0]
+
+    det = mat[..., 0, 0] * mat[..., 1, 1] - mat[..., 0, 1] * mat[..., 1, 0]
+    mat_inv /= det[..., np.newaxis, np.newaxis] 
+
+
+    params = ut.matrix_multiply(mat_inv, rhs[..., np.newaxis])
+    intersection = np.all((params > 0.0001) & (params < 0.9999), axis=(-1, -2))  
+    p0_s = params[intersection, 0, :] * mat[intersection, :, 0]          
+
+    xover_point = p0_s + p0[np.where(intersection)[0]]
+
+    return {'intersection_ind':[np.where(intersection)[0],np.where(intersection)[1]], 'intersection_points':xover_point}

crossovers.py

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+astropy
+hdf5storage
+ezdxf
+mat73
+imageio
+ffmpeg-python

dependencies.txt

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+import numpy as np
+import sys
+import os
+import inspect
+sys.path.append('/mnt/data01/Code/')
+
+
+def find_COPdem_fn(lat,lon):
+    """
+    This is a function that outputs the filename associated wtih the desired latitude & longitude 
+    """
+    
+    from NDH_Tools import find_nearest_xy
+    from NDH_Tools import loadmat
+    
+    curpath = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+    mf_name = curpath+'/COPdem_filelist.mat'
+    
+    file_info = loadmat(mf_name)
+    target = np.array([[float(lat)], [float(lon)]])
+    result = find_nearest_xy(file_info['latlon'],target)
+    
+    if result['distance'] > 1:
+        print('This DEM likely doesn\'t contain your point of interest.')
+        print('The DEM center is %0.2f degrees away from the target point.' % result['distance'])
+    
+    return file_info['fn'][result['index'][0]]
+    

find_COPdem_fn.py

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+
+
+def index_list(input_list,index_array):
+    """
+    % (C) Nick Holschuh - Amherst College -- 2022 (Nick.Holschuh@gmail.com)
+    %
+    % This function allows you to index a list by integer array input
+    %
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    % The inputs are:
+    %
+    %     input_list -- the list you want to sort
+    %     index_array -- array of vlues
+    %
+    %%%%%%%%%%%%%%%
+    % The outputs are:
+    %
+    %      output_list -- the sorted_list you want to sort
+    %
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    """ 
+    output_list = [input_list[i] for i in index_array]
+    
+    return output_list
+    

index_list.py

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+import xarray as xr
+import numpy as np
+
+def make_nc(input_x,input_y,input_vars,input_varnames,filename='temp.nc',description='No description provided',writefile0_or_dataset1_or_both2 = 1):
+    """
+    % (C) Nick Holschuh - Amherst College -- 2022 (Nick.Holschuh@gmail.com)
+    %
+    % This function uses xarray to construct a netcdf
+    %
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    % The inputs are:
+    %
+    %     input_x,
+    %    input_y,
+    %    input_vars,
+    %    input_varnames,
+    %    filename='temp.nc',
+    %    description='No description provided',
+    %    writefile0_or_dataset1 = 1
+    %
+    %%%%%%%%%%%%%%%
+    % The outputs are:
+    %
+    %      output -- the min and max in a 1x2 array
+    %
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    """    
+    data_dict = {}
+    if input_vars.ndim == 2:
+        data_dict[input_varnames[0]] = (['y','x'],input_vars)
+    else:
+        for ind1,i in enumerate(input_varnames):
+            data_dict[i] = (['y','x'],input_vars[:,:,ind1])
+            
+    coord_dict = {'x':(['x'],input_x),'y':(['y'],input_y)}
+    
+    ds = xr.Dataset(
+        data_vars=data_dict,
+        coords=coord_dict,
+        attrs=dict(description=description),
+    )
+    
+    if writefile0_or_dataset1_or_both2 == 0:
+        ds.to_netcdf(path=filename)
+    elif writefile0_or_dataset1_or_both2 == 2:
+        ds.to_netcdf(path=filename)
+        return ds
+    else:
+        return ds

make_nc.py

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+def remove_arrow(input_axisObject,num=1):
+    """
+    % (C) Nick Holschuh - Amherst College - 2022 (Nick.Holschuh@gmail.com)
+    % This function removes contours (but I don't remember why I needed this)
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    % The inputs are as follows:
+    %
+    %      input_axisObject - axis object to remove to remove the most recent line or lines
+    %
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    """
+    
+    for i in range(num):
+        try:
+            input_axisObject.patches.pop(-1)
+        except:
+            print('Couldn''t remove image')

remove_arrow.py

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+def remove_image(input_axisObject,num=1):
+    """
+    % (C) Nick Holschuh - Amherst College - 2022 (Nick.Holschuh@gmail.com)
+    % This function removes contours (but I don't remember why I needed this)
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    % The inputs are as follows:
+    %
+    %      input_axisObject - axis object to remove to remove the most recent line or lines
+    %
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    """
+    
+    for i in range(num):
+        try:
+            input_axisObject.images[-1].remove()
+        except:
+            print('Couldn''t remove image')

remove_image.py

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+def remove_line(input_axisObject,num=1):
+    """
+    % (C) Nick Holschuh - Amherst College - 2022 (Nick.Holschuh@gmail.com)
+    % This function removes contours (but I don't remember why I needed this)
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    % The inputs are as follows:
+    %
+    %      input_axisObject - axis object to remove to remove the most recent line or lines
+    %
+    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    """
+    
+    for i in range(num):
+        try:
+            input_axisObject.lines.pop(-1)
+        except:
+            print('Couldn''t remove image')