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pd meters

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1. How to read the prescription for measuring eye degree

1. R represents the right eye, L represents the left eye

2. S represents the sphere degree, which is used to indicate the degree of myopia or hyperopia.

3. C represents the cylinder degree, which is used to indicate the degree of scattered light

4. A represents the axis of astigmatism

5.+represents hyperopia, - represents myopia

6. VD is an abbreviation for vertical distance, measured in meters. 7. PD stands for Pupillary Distance, which is the distance between the centers of the pupils of both eyes. 8. Your left eye has a myopia of 150 degrees and astigmatism of 25 degrees, with the axis of astigmatism being 151 degrees. 9. Your right eye is nearsighted by 100 degrees, with a large astigmatism of 25 degrees and an axis of astigmatism of 7 degrees.

2. Supreme version of supply chain multi site selection - fast solution with DC capacity constraints (source code)

Supreme version of supply chain multi site selection - fast solution with DC capacity constraints (source code) Preface Previously shared the problem solving of supply chain multi site selection with DC capacity constraints. Pengzao instructed his friend to try using genetic algorithm to solve the problem, but believed that the timeliness was not high. Some friends also mentioned that the site selection result is in a mountain ditch, hoping to be closer to the city. This article provides a high-speed solution version, using the square hidden method with constraint k-means to solve, and then selecting the city. It must be noted that directly selecting DC within the city is another issue (discrete site selection), which is not within the scope of this art

pd meters
icle. This is the curved national salvation route: first select sites consecutively, and then select sites nearby. Project review: For multi site selection, simulate the allocation of 1000 customers to 8 DCs and add requirements: the number of customers for some DC services cannot be lower than a certain value (such as 40), otherwise it is not worth setting up a DC. Specify alternative cities, here as a simple demo demonstration, choose provincial capitals as alternatives. Main_cities=all_cities-d f [all_cities-d f. sort-values (by="administrative code") ["administrative code"]. type ("str"). str. ind ("0100")>0] main_cities-columns=["id", "name", "lon", "lat"]. Main_cities starts modeling and solves using k_means_straight, defining 8 clusters with a minimum capacity of 50 and a maximum capacity of 200 for each cluster. Record the time consumption and display the results. from k_means_constrained import KMeansConstrainedfrom datetime import datetimenow = datetime.now()clf = KMeansConstrained(n_clusters=8,size_min=50,size_max=200, random_state=0)X = customer_df[["lat","lon"]].valuescustomer_df["dc"]= clf.fit_predict(X)cons = datetime.now()-nowprint(f"time usage is {cons} s")customer_df["dc"].value_counts() The solution took only 1.7 seconds to complete and met the preset conditions. Choose a nearby major city to calculate the distance between 8 DCs and each provincial capital, and select the provincial capital with the smallest distance as the new DC location. def geo_distance(p1: pd.Series,p2:list): dis = haversine_distances([[radians(_) for _ in p1.values], [radians(_) for _ in p2]])[0][1]* 6371000/1000 # multiply by Earth radius to get kilometers return disfor i, dc_pos in enumerate(main_cities[["name","lat","lo

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