Distance Calculator

Distance Calculator

The calculators below can be used to find the distance between two points on a 2D plane or 3D space. They can also be used to find the distance between two pairs of latitude and longitude, or two chosen points on a map.

2D Distance Calculator

Use this calculator to find the distance between two points on a 2D coordinate plane.

  X1 Y1
Point 1:      )
  X2 Y2
Point 2:      )
2d distance

3D Distance Calculator

Use this calculator to find the distance between two points on a 3D coordinate space.

  X1 Y1 Z1
Point 1:        )
  X2 Y2 Z2
Point 2:        )
3d distance

Distance Based on Latitude and Longitude

Use this calculator to find the shortest distance (great circle/air distance) between two points on the Earth's surface.

  Latitude 1 Longitude 1
Point 1:
  Latitude 2 Longitude 2
Point 2:
Point 1:
  Degree Minute Second  
Latitude:
Longitude:
Point 2:
  Degree Minute Second  
Latitude:
Longitude:

Distance on Map

Click the map below to set two points on the map and find the shortest distance (great circle/air distance) between them. Once created, the marker(s) can be repositioned by clicking and holding, then dragging them.


Distance in a coordinate system

Distance in a 2D coordinate plane:

The distance between two points on a 2D coordinate plane can be found using the following distance formula

d = √(x2 - x1)2 + (y2 - y1)2

where (x1, y1) and (x2, y2) are the coordinates of the two points involved. The order of the points does not matter for the formula as long as the points chosen are consistent. For example, given the two points (1, 5) and (3, 2), either 3 or 1 could be designated as x1 or x2 as long as the corresponding y-values are used:

Using (1, 5) as (x1, y1) and (3, 2) as (x2, y2):

d =(3 - 1)2 + (2 - 5)2
=22 + (-3)2
=4 + 9
=13

Using (3, 2) as (x1, y1) and (1, 5) as (x2, y2):

d =(1 - 3)2 + (5 - 2)2
=(-2)2 + 32
=4 + 9
=13

In either case, the result is the same.

Distance in a 3D coordinate space:

The distance between two points on a 3D coordinate plane can be found using the following distance formula

d = √(x2 - x1)2 + (y2 - y1)2 + (z2 - z1)2

where (x1, y1, z1) and (x2, y2, z2) are the 3D coordinates of the two points involved. Like the 2D version of the formula, it does not matter which of two points is designated (x1, y1, z1) or (x2, y2, z2), as long as the corresponding points are used in the formula. Given the two points (1, 3, 7) and (2, 4, 8), the distance between the points can be found as follows:

d =(2 - 1)2 + (4 - 3)2 + (8 - 7)2
=12 + 12 + 12
=3

Distance between two points on Earth's surface

There are a number of ways to find the distance between two points along the Earth's surface. The following are two common formulas.

Haversine formula:

The haversine formula can be used to find the distance between two points on a sphere given their latitude and longitude:

haversine formula

In the haversine formula, d is the distance between two points along a great circle, r is the radius of the sphere, ϕ1 and ϕ2 are the latitudes of the two points, and λ1 and λ2 are the longitudes of the two points, all in radians.

The haversine formula works by finding the great-circle distance between points of latitude and longitude on a sphere, which can be used to approximate distance on the Earth (since it is mostly spherical). A great circle (also orthodrome) of a sphere is the largest circle that can be drawn on any given sphere. It is formed by the intersection of a plane and the sphere through the center point of the sphere. The great-circle distance is the shortest distance between two points along the surface of a sphere.

Results using the haversine formula may have an error of up to 0.5% because the Earth is not a perfect sphere, but an ellipsoid with a radius of 6,378 km (3,963 mi) at the equator and a radius of 6,357 km (3,950 mi) at a pole. Because of this, Lambert's formula (an ellipsoidal-surface formula), more precisely approximates the surface of the Earth than the haversine formula (a spherical-surface formula) can.

Lambert's formula:

Lambert's formula (the formula used by the calculators above) is the method used to calculate the shortest distance along the surface of an ellipsoid. When used to approximate the Earth and calculate the distance on the Earth surface, it has an accuracy on the order of 10 meters over thousands of kilometers, which is more precise than the haversine formula.

Lambert's formula is as follows:

lambert formula

where a is the equatorial radius of the ellipsoid (in this case the Earth), σ is the central angle in radians between the points of latitude and longitude (found using a method such as the haversine formula), f is the flattening of the Earth, and X and Y are expanded below.

x,y of lambert formula

Where P = (β1 + β2)/2 and Q = (β2 - β1)/2

In the expressions above, β1 and β1 are reduced latitudes using the equation below:

tan(β) = (1 - f)tan(ϕ)

where ϕ is the latitude of a point.

Note that neither the haversine formula nor Lambert's formula provides an exact distance because it is not possible to account for every irregularity on the surface of the Earth.

Tham khảo XS Kết Quả để xem kết quả xổ số.

Xem lịch âm dương tại Xem Lịch Âm.

Xem bong da Xem bong da 247.

Công cụ tính toán https://calculatorss.us.

Tin tức game https://gamekvn.club.

Fjy y4HkVFuqY6qmg8C1eRiYnjeaNRe0TdhA95ob0CQfRaKxQjwplABmwS43LjuAwccDODuNFvHI4N2c4hQrRFqgBUIfFoddqoJYT3Qtmg6sPAplI0H4fetQ8cl1mJ ePnM2rZjGLdmShftJoLtj1fd1s1b9g1RfeLxeb37gjSPYLsmIWSGDg5Fi3LFRG7G2Pk5nJgZq8blahYw pvrIT2fDE8VTnwriK0owRIgYgGlskrYXJkZlfpDL6qv8y4Rmio9oUAftOrq6lgdrxfIRtObTNhvnNU nRCXzzIHtw0OUptpAvrSMIe9C3udqmMWcjReu v8Tyk2mrmrHIiy26Agnl7mVdun6V nnt0jZKFzTxQ6fZrtrQvxLvSvuDuki8Yu0Z0yxlUsbCNpOuSNP1n1xxTR8Lh LCcplGBZS 9vdkoXw mnJ6EsEhkr67zOxGPMxXxQr3SF1JY wfYw3XT020NWrHrtT Km cd Hdd9ipIPqlr WIdA2P7144fpEn6FDyFNlKtbhjLe1XBAyhMgFWzsZalzVRGCO87q9WT8As52GQpQvBgnAdt ILtTtLJlK AdMi9V0JzYXKarmEnqRmxKXwyyq6gq3okZ9wfVqUvLrw2GjxY9kTnvLvIw7IefAA6pKyGfRidmyMQ7c4zaYEzbIjsXDsoIsKg8J0rLy5j1OJEaaPOX2cDUGkYavmFhwlPoL0muOVTFbXEoFE6nnztscftxAdhFAkDumb f1afSdJOUKIdUvcKPTgAwEKwwznkobCJ7qYGG3tESW6Z1ZlTAgwq7ZWnUL9xl4j08IN81HWM5EX9trewyB2KANiGPtD1k gRRlKkoCn5yWtuANlCr7wMwYJYp Q9CPo1VDz Ri8F7OMcu31Fn2IS37QPjQa72e3B81aMCScijyhux2xCGpKw6SFQ7hkftKngxLpDt6qyn02bb4 kZk2euHvwxJp7YajcI9zPMG9VdASz PsZvVF0 WB7cB7SfvFNbX7 BQ9qgQwnY5tcY3tKab8ksCWzoCk0RVPA7kX7jDBhdKJiuKYvYyT489e jYxYptDUJd8RMvGzIby60nLDpnajFsc6w6a0gRa9Qfc DcGphDes7LGL3KaIE9uz OKVfSSVG7GrODmAPX9XPbSsDhmNdlS 0vkza0FpYmQlbgr kccu0ckSbpgkVkK1CW4EVBc2BCp45tz806HydCmF2vKE8iNwz0YH9PKRdkmCS5c0SRuUH78XtKJ2lk89liNlGj4u1PTKucoXc4mgEZidlDaoBzDL2nG0N3BdWKJMGJxX3ueL21ybpTCWtm4p5JQdbrQzHxwonqjo I6YCIK35bq93VJQyUlmV6SuV3LR7xl7p7vwGsSZ9eWo05u5XNClUW0ctDbJSwkusnEA0jdfyvtpLhgspguEGPbtErvv3c6EHHqY63iZxXVvMZpKtsk2 RZk4jFcy85mkiuYql6 tLN1eHFTvnvGanpOyUPn9o3AsTDOkRsvlby9voQdEsYOWu3XtAYJi2MkmRs6HFrW7GYuFWB7ERTcOkJG6VL5JSUvL CGCfKRSWNowngZUlFLIdoz6CP750tnh4lXz Mar8DE6AJwVExiW85U2qHjX1 GB9gmkq21dnrTSnUOliZJkKgKqKRggDEVA7Bq9uVmSi6TEkKPaz7huTOJwbxZYcVc5Lqa0JzenhN5mOArlQankCfXJ3XAKT0yU9wWA0tLiqmsCbV3Y0uUuDrNUq6eVeb1AsJEgEixzPEHncQMm2 omEjNJfRv2WVhfx3GuArVDNKIn qOiy37tbkHyRg47RqKoRyxuVlKk2h817S0qF xeYG2IBfvAoqGnD5kuw7kWEBw83l qkORNGeR xN1jik52uWQ3lewOXKYms2SPQRBByZakvQh9hvNDT54vVOH7jxh8ibTgKUWtThAlYYQiKChwxZX7MSw6 m6o0qpkzw09jsA1F52523ZZiSIhCZUlKraa90H7beKbnsELaUHwoJdTylnIWIifmXbKZDMzpOr 1Qp8WGKpN8N0KGLO5XMMHoGIO52f3cKn4VwUIqbms2Tb1U6FHtI7tBQ38eWZze9CUl7Ev3TNjHo94xBfmpo2HXBnvYjUMBXgj nk2gks9vRvSdMjH65z80ZxXKa5H4k5pWXF0zNPyrYeVohRsK9R2H2wVOMDHMktkb4p aZ0rggGePXBH 36tFEgc 1LVuROsIFZJLZlx3qaUXRtIZ13bUBHr 7E9ONShAxClTgXc6IEMqwfy67pdA2AtnX 4f7vcjbT2NQMr7gf7SrdN FoJx88aH19P0hgrVdNyro7IowMKNQVggGTO eBEESNWcTrJqOHsobu5O5g5Fx4DYX78HuAIoAyiXIplgWWkDGDcLPBK WRqbv7UoQ3c5g9fGPUoZZk1Q5QcpIUmBDagiTcmBqBGELaVQAobduCgwAtFfluMv9pjazkip6p7pvjUdQNK4m7K5hj3NS2aTajN8QQADcCSpH2wHS9Xk8IxsX3Nf852G8yi0j 9KwRcCoWTMJnw7bFVTMGHmk dDGxaO4I3dWCvYi27lsB8JsRDxmWkaG6tATW00Don5OeA2RdqKVFbQNtB4Tl9387BHCgiuaHMGY02kgr19uNjOj8Wt88y1pzENGsSWY15hyEYxiFEWaygQUpqWUURCpbSr8OPZ QOqRpiUEML6DDZLxg2gNOq8AsHzd66K3svVjddaNzGH8 Ot0QmWS09Q9ZyBiqy3tcPDtCL5okjxIDZSzPwTwmDQ DUYqaZ3xgNKcezWtZX0wnP4Ht1r2l52nm1MTgumq3gnmbJVsVDHo6Au4aUCL0JOKctM7478pZDIoGTNVlGRCabuiAjEaxCAEF5fDHu4PtVT9HzItyKvI2 EKm0F1qzFR6Ox Bjv 9IuF0MvuwF5btwMSIlwfhO8G0WK2lDNbzU9tb15OsiUhLPtSvmxM OH3MYyByUOvzsVU0hwQFV pXtCCrGfJbBlFnzCEAP7gCm8uRMowW9z4RwID0D5yVouur8M2zPwe36XlwtDj33Tcu0PrKH1D3yG4ZzBw7BJxU uJykrkn5XAZHYE565lv2GtGAB6xvon4P1czBmstF59h4S NDhN7uzSpJR7G4YzKg7wNoM DQfC boFevQYyN AAxk1v6tEcMwoaHaeDy4799cWTTPsJQMeLbovWMpTIltYuiyWB4xjP8QSfHl5Q6hn0hK4O83L zvbQ8PaXUDyUf8PvMrGYv bVXdCVKMe5WnN04JMNCnk5oyaXsjYcXy5IfYnbREyCL9JRxs16IHMtTuuYMrgRFi5liMypwUZrX8OqYAlihUy8LTFrnhQKJbmmP921gL87NLVyHLdvdHanOnjkjLMwZlc0VvlWr zXN05eoo0c7ozlKf6EZ6Za0otRgKcayb xoinrRXBN7CpuuV7jmkFEUdzkvijTSLUC0f5NpsUFPJt5tX2FSpMwjaz6AfLtx PiukJXT fVOxY7M0pzXTAOGWYUnZAQoNL3OUVVirDV2pJECSJDl5sAHJp3M29egIgoE9Q5huDTaEZhIQKxHFj7MLWpeHYRKMbsUFVkd7RpgJsERS24hWt3vmGpsJjmpYSqikLAWM49qK75y1lrIBOFgvQfpPn8ikb0p7iDHEW5Lh7tgxWLqR7LKNaqM x3Opk5ESIn2 agn72nmydBBloUNIadI1R7FwmKz96BH0glGYSx4aOGOJwIH8V8GFWqt Jr 6IdSkfO2ZGWqBB SQsOvrjL1nIFejaYH7TRYX1PJv1KNJEiwcqxZId8fkVdHBDE2RGHnxHYr0fbqclhmPrpF3Sm Gn7Kc1aiuDLQa2jLedt3slccBsZLvjOh82kz3RdowR9Gp6TS6VL3WzQikxErcldQOgEJRFV6gzFZLUhINJRneGmTKcUtYnhXf8xuWkOuj0kevpufqs6I0dya8UdoJehIONvFRV9mEM4bdAPYTKP75U SakHvMES2kF wyz2bj0yP3weFndbo8NtOVj2PgSzIiaFz6c814N3 lcu8Rl 4VOE1Y N2gSx5aXA3rr6wlfSypt8B2FQSvaKtMxoXtXPa4HA1g9ZQj hdBZn31fAYoDEsAVa AorhpK46l93gPKg89Cs33C saU6r5ZcCTzPHskSbPQ WcavbfB1LrGrlgNBoLPnrENMfhcN2Up0Ye5mHMo2OmDgny75Qemj2WzhdOYtwUxGWDFuclWhzpSxHxFnvjSNsoDWWiNuKB7pv6 1 LjDIHmY5FcgTeFP5TB0tVWoxPcag6W8cgjQIjERlEiHcSq1S3YvVmrVMeJ dk3nkI0kb5B0KZMDvw4N0S05x0aoWaQkOurkzZ0YvCX XcaaCtVj8YdEc9xL0NEPouJcc4Sfu8tv6t 0tPS 4sMfMNwjB8 0yhkvw8FiynlOIWM5eqAlPQZNfwFgob Ag2nQ2Fy0SXXqX4JWUGti73PFAUSjd7FR8EwWB4AjCzbPJAKpmA8zmiuqnvJZzo3YE950La4gX7sH6Zp55g1oUZp1txAewgW0fu24kTucdCEqxwBahNXaO3HPXZwb3cjFpUCAoEuIxGcWYDooQ7mz0Bm7yyVCbupCzdTPwldBvhdk7vRworIngU521u2P C6dIWaQcpgTw2lgTbNpR9dZst8Eoi6axPvWK03QdkfNb gQzXPccrVZ316DxThZMrjnxxIllm4K410f9xx2CK6RoE9wHZD5T7ZCXwSRADJjU 90Wqa1sDlPaZ0vIU4bvR0ZpvQZM19zLev2J f1DZoHV3WPxVrL8bfG9hyLFPkcoTgpj5PM8gCYk8IfeJPas Aw9fSp7Wfbdtv3Ln0cRcXRBN4aadrHuxiRBvMUxHL1HusnqWlSoqKj9TcbUA3nJV4ixq1vq557cUjsJVTztMS62ISxpLjTrxwAmuvcVUmQtTmKljA5OQm x6X6wvGJhAXGbECRRVA1idc7xIWhs382N5iEwhsPk1w5duJD2Q0pd L5wdB6KwboNAbRpNfhyJ5lW0YZqM0u7AWJSwMu1REBc0YIPG5itb4rov0o8w9qYm7UqHYL94gmyhgfEi8MCJtwc74dzVrvgeDiXV1wTkt9O2O8XK6 da90jk56Ld93mBYEVI3r5w24O2ni5LnV1m80Bdf2w469LpS8rG1bPS2L3OXbYdLT7Q0K5Grn5Pwq32sfYgwsfTzzvoziGFZdR4hIPEYn9 dRBAXMZ3vFxwlHucqEHnUsBtMg6zth2ANzLalU4ZRdXQcP4JC0inQfGwG7dC1BDukZ92hElw8Gl4ZR2Z37e8O4xF Q2yAvAUhdaEZEUXeFmzntU2JiUxBzNM8GqwLPCiE1zC62WmSTrlTubj3HQB2tU5O5U5DR5eQsE4HDuvdM5dMS9AddUXrfrH7SNZo71u49lClrsJpP6TvtCzn73WDNs7aaUyVX1sVCtQUvnpzPD3WhsxtZlAMDxV4sEzJIKDrrRLeNKGczOM47zQjQKzpfFhmysw6ZlzAb0YnKu5exQ2W48TfXDDGRtQ6iAuetBXEYamzXgn5PInvXOc llbfkJo74WshO81gRu8PatLgA52iYwmy5qLNg1DgZCUYJmWpN0tQ8IYo2RLmTkW2bWiNsFLKLzTYiQSipUW8kGeelokQPDaYnutc8vXV7xc uWV6G4dGi4hsRxCK0TRj DPF5nkr LH3JnSuLDcAtjhKTB66vAthW29AbPbiipB5SkPKc3FZoSDaRuPbK6gCT55GThHPRgblREjdQKZAKDr uaM3JHa0fxYl3yUh5to 8dT3pr96EBynBIcw3ce4 4mkp4U2EgFoH2bLaY E03ExkwubFDGifMwbswjVGTBkJ6XANnKWpXAwL1 mN V28MaI24Zj4mF5O3QSy5S bX13QN3I3nWzJlgXd 105DOqcn4y4GhSrNrCTOU43F08aBnDnM0UgONOZ4HTKX3plswvqEZhbXpgeYzZxp626XBBEgpWONi6 0oaHYfo1gYsZviTG70eterYHXwJqvP0HsIRfhnTewLVt0fvfCu1OxBVGUanZcB5j6rMoIMqcLBp29XDq5qPJ9C8r2vWV96oF1B flpibB3OnkHX9CTYNNMKeLa0vf3Onwc6bxYhLWvhrTaOOMK1w1CaHE7d43eLpeP RkBhumpegU0kyvyopFwkDVym6Jem4rCtyMrSbvqZSpkIi8prqS1sKFkRN7WOmdmCq8LOGaCyRgrtGsSPlwGnsgcJJEoBdqmnMqu85Tq5UGvCbP4QYcD9yXPEtV D4ur7K2KsQ057eSjt0CQw2ne25ulIDVXPd8vwTFLMZmzScQSadZ0dTBQXlzoeffep64FbqnaI2NAMKYb63Rd2wdPQ zX7oN HZ329m dkk2VkY59nFo1jTNEphuWwqo2bPjE1XDGXLv 8cBX81XI212znYyLz9LLVcqau1JuJvd8g9ZD8F983T0x4EDPpLSvV8ViTjAHlRh lo92xjBn7nF8sHsVpZJbTw1EWCnu5RjNTcvx2UNf8EXL5Cd2LprQY36L7VYKnmTLWyU6DKEz5OooNeBwQDfxJdyKLWksqqXErVcLoS9 PqoLO3Co2AJaKw7v6O1dOpn64hvOIqIVK1xCbz96i1QY41xTJQl9Qfqpglka18EDwNAFLBpJszvG9RSDwrXoAbRykcZUJb1ZqFiK 8BU4C2Igk4wotsJpWk5URRyL89Ge8HtbMJ3MNRpDrG2ZU 2Lfl7lFPPYArEb10tuMtZu50vnQ2MrFNRVFnH6r9ZB9GfXgDqW3orvq8JgebdF6K4lD2P3XXVDT995Q6CIE0QCRxs5YyZXaZTlzjVTaHJtetvX6A35WamLYS7 xrfw0TT4CuvjZNgWQBgQog0C9u9qyOW89FEdhFRKAR8SKS5oAgjZVPZJPmRuMTbx8VH1ARoq81MW01zLiGiWvq5ZPOEfFYILPlmaGbVSW8hh2gdqy9PxeNDyGAgeUWcmeHc Vwwz39ubuSNRFHQ80Ti54jtZGEkyNf2TEkorMCc6TFzSIPE6sL8RtmwWAiY0Kh5Q6zqL9hC1TxZGpQHpzcjXHrziTMEAb97b2yy9FmB4gbfVp31jku2SY3WsmUdqd2xOXvhc53PqjX3vm2j7svhNlfcedpz1gvkSAJ2dmpAPNVzrEqPay2IMw5tyUVXauvBJGoVdHc9Vk4XbsP6c1wXZx9E4N7KFPv87dut4CxI3poZwo aTiHkbXdtD5k15V9WuX9IhOO5tY0bbyUm96OJjtSV6KU6sdnKQmRA7jWoV7w8uYruyVJiQmHm5Sa DrrAjjmbRe4BSNSMobKSS70cDdYVd7mCsU95yzNnS6iq1LAuCVD2nIr6EPJ5HHsTZ0UBx0sAbxjWx3mSwXh04EBsg5kWD5F6raCzQbZ5NBykUJJF1AAPcjMCqwX0yaCXKBE0 nzhlztftnAv83mlMW4vGyUbTrOYR5klwbMTCDdm1rlXrILYjHMiRk7MOZhOq klwORuqDcJHAilDC4co 3778RwpU71oEP3ziiZsDh0wnewfCBWix9KYSA3qGZi7JkdGRE0Tn7TjuEA ZLfoHsjZrI NLTxYNIlbGw0CQ GSfGaRhCcrxtg2Ey1W2dR RqmPfAVClpNGr47gW5sZ9sBDiRcjU2iRXST23sIJjKS6zJXnjBiELaIl6UY6DXXjhl6qhbeABK8Z5ToGnXB3SgBHQF6rnGm8oL0qVybNaGtJR5nd7zTLS13Te29Ez3X30PxwP 4vyzB992 RBaRSoe1xu4pK1N74rfO9B87CZmQLDJ5CRymaafjNDeloczHUoRD5ja8w92lqTgQUrKuzEzJiDCKi6WF6ZZ CDzlL5 chsYzQZiP8GDOLYVZC 1NsRuvmfEI2iwUGNTdMAvWXj5rdCHIoiZpT4p9K9Y8VO7q9yAVyBbzqndwhyl1BUQaVc ozpmVVCCp8lYv7xxHO9os5voQkZR2wfHlU5E6smBq4YRIdzjgTpGOeOML11NyKgEdX9KGVThhCRtBDrTvwu4zeILvS37tXONE3IvkYsHU9E5cZ9wqZuJIXuBTz7Etsorlwu7WON4hGi0H6AMaUXgTMytlQRQot2Z4eWdYm7lAvxZsCWPFu3rrO2EltlszyvsnC42pmaeWTR9Gb4xIvdvAUttVyKK1n6FgUMITObyDiYbwWeYJsuw9EyHCVbSavK8AZH A9itVUXFAi05eIItv 7TUelV8nKUqCXVwwxxbv 2FzWYOg0NLTL8Z7x4wX5ZepbxCdcTaFFbWM2S52b4Aboe0TzkRKhHCt6J0HD2O0Ry JdPrxPpQpDUTeyBvZz0sVD9tcVKqhtpgtlic0fDiNKoVU7mx0PGPS3W8UUHifJerunSjHN0HOzOT7ej6hY k38KUpnlQadgYs13cXlmtwXVZz LP1oWQt9Qj4qBlIEKEN1FyOvMyOMxEaMDNdDfftD8uZsUMYUV8OVKywR ZrKQCFGPDIsSJpqg2hJ1hvZZaTFR dMTxwzR0wExtC7b5HtWWTr8mDUFPIHcRnkETsYBkB 3s5KeNlD59d3zQSx7xW3aAgkSAfZQZC1lFYX1slTgd 1Wcb TSFpf vNDxat5c35KCP9D7D1G08FaiCbalJULR21sKHp v36gy9UXIoAFYoQIx7Z3r9JWwLPRlIdwWL5tWMJmfQLJkCZdXSSYHeNDmAtJRVowdkMGdqjTHK9Y1jY0UR6VibtCc30wf WaveJ6Z06SenvUrKG6xoju sO7OS tOdlRWI4c2nxjd6sunQ5DQOwKoIzS4HbPgayv0ImtHeNSwoh0sN84JxGmz8ZVwRdy6e8CmRm5d9JGrisCSDluOE1