intersection

问题 I am looking for acknowledgement on my perception of a method regarding determining whether a point is located inside a triangle or not in 3D. Given a ray in the form R(t) = e + td and a set of three points T = {V0, V1, V2} that forms a triangle in three dimensions, I know how to find the parametic equation for the plane that the three points form and how to determine if the ray intersects this plane or not. Lastly, if it intersects, I want to know if the intersection point actually is within

问题 Hope you could help me. I am new to python and pandas, so please bear with me. I am trying to find the common word between three data frames and I am using Jupiter Notebook. Just for example: df1= A dog cat cow duck snake df2= A pig snail bird dog df3= A eagle dog snail monkey There is only one column in all data frames that is A. I would like to find the common word among all columns the words that are unique to their own columns and not in common. Example: duck is unique to df1, snail is

问题 I'm writing a basic 2D shape library in Python (primarily for manipulating SVG drawings), and I'm at a loss for how to efficiently calculate the intersection points of two ellipses. Each ellipse is defined by the following variables (all floats): c: center point (x, y) hradius: "horizontal" radius vradius: "vertical" radius phi: rotation from coordinate system's x-axis to ellipse's horizontal axis Ignoring when the ellipses are identical, there could be 0 through 4 intersection points (no

问题 I'm writing a basic 2D shape library in Python (primarily for manipulating SVG drawings), and I'm at a loss for how to efficiently calculate the intersection points of two ellipses. Each ellipse is defined by the following variables (all floats): c: center point (x, y) hradius: "horizontal" radius vradius: "vertical" radius phi: rotation from coordinate system's x-axis to ellipse's horizontal axis Ignoring when the ellipses are identical, there could be 0 through 4 intersection points (no

问题 I have two dataframes: df_small = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), columns=['a', 'b', 'c']) and df_large = pd.DataFrame(np.array([[22, 1, 2, 3, 99], [31, 4, 5, 6, 75], [73, 7, 8, 9, 23], [16, 2, 1, 2, 13], [17, 1, 4, 3, 25], [93, 3, 2, 8, 18]]), columns=['k', 'a', 'b', 'c', 'd']) Now what I want is to intersect the two and only take the rows in df_large that that do not contain the rows from df_small , hence the result should be: df_result = pd.DataFrame(np.array([[16

问题 I have two dataframes: df_small = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), columns=['a', 'b', 'c']) and df_large = pd.DataFrame(np.array([[22, 1, 2, 3, 99], [31, 4, 5, 6, 75], [73, 7, 8, 9, 23], [16, 2, 1, 2, 13], [17, 1, 4, 3, 25], [93, 3, 2, 8, 18]]), columns=['k', 'a', 'b', 'c', 'd']) Now what I want is to intersect the two and only take the rows in df_large that that do not contain the rows from df_small , hence the result should be: df_result = pd.DataFrame(np.array([[16

问题 I have array similar to this [ [0, 10]**, [1, 3]**, [5, 11]**, [14, 20]**, [10, 11]** ] ** Denotes an object containing the start and end indexes shown in the array Now, the intersections are [1, 3], [5,10], [10,11] What is the best way to write a method that returns the objects containing of the intersecting sets? (Could just store them in an array of conflicted stuff as we go along) The biggest issue I'm having is how do I do this such that each object is compared with eachother object?

问题 There is a script Here that can process the data from OSM to detect intersections in a given bounding box. It works by getting all the ways in a given bounding box and then finding other ways that share common nodes with these roads. Here is the query that does that, way ["highway"] ["highway"!~"footway|cycleway|path|service|track|proposed"] (, , , ) ->.relevant_ways; foreach.relevant_ways->.this_way{ node(w.this_way)->.this_ways_nodes; way(bn.this_ways_nodes)->.linked_ways; way.linked_ways [

问题 I have created a convex hull using scipy.spatial.ConvexHull. I need to compute the intersection point between the convex hull and a ray, starting at 0 and in the direction of some other defined point. The convex hull is known to contain 0 so the intersection should be guaranteed. The dimension of the problem can vary between 2 and 5. I have tried some google searching but haven't found an answer. I am hoping this is a common problem with known solutions in computational geometry. Thank you.

问题 This question already has an answer here : How to group by common element in array? (1 answer) Closed 7 months ago . I need to group PySpark dataframe by intersection of arrays in column. For example from dataframe like this: v1 | [1, 2, 3] v2 | [4, 5] v3 | [1, 7] result should be: [v1, v3] | [1, 2, 3, 7] [v2] | [4, 5] Because rows 1st and 3rd have value 1 in common. Is there a method like group by when intersection? Thank you in advance for ideas and suggestions how to solve this. 回答1: from