# If “throw it in the ocean” is the solution, what is the problem?

2021.10.25 22:44 Educational-Ad-7072 If “throw it in the ocean” is the solution, what is the problem?

2021.10.25 22:44 jellyshins Strange question, hopefully someone can give me a little guidance.

I’m writing a paper for a class, and a topic I’d like to include is how the rock formations in Garden of the Gods might potentially impact climate or weather in COS. COS is in a rain shadow of the Rockies, but I’m trying to determine if the rock formations have a direct impact as well?
My question is a little too specific for Google, so googling the question hasn’t brought back too many helpful results for me.

2021.10.25 22:44 MAMMOTH_MkII Гордость за страну

Error in terminal:

``Requirement already satisfied: Pillow in /opt/anaconda3/lib/python3.8/site-packages (8.2.0) ``
But in visual studio code it is stating I do not have the library

2021.10.25 22:44 chiphappened Hubby…”The Terminator” Anyone else notice this image on tonight’s broadcast

2021.10.25 22:44 syorky Pixel Cups NFTs now available on Just NFTs !!!, new cups every week!

2021.10.25 22:44 ThrowAwayTheStarfish 9 FUCKING KIDS AND SHE COULDN'T GET ONE GOOD NAME WTFFFFF

2021.10.25 22:44 beckman24 Thought about taking 3k\$ from 401retirement but they want 600\$ tax fee😳

2021.10.25 22:44 pregnantghettoteeeen Looking for break dancing/hip hop studio

Hey,
I go to a break dancing studio in east bedstuy but I am located in the upper east side and it takes an hour to get to. Is there any breakdacing/hip hop studio near me?

2021.10.25 22:44 RaunchyAppleSauce I am trying to sample from a multivariate normal distribution with Matern Covariance. The code makes sense but for some reason, the result is not what I expect

Hi, y'all!
As the title says, I am trying to simulate a GP with a Matern covariance. The code makes senes and I have implemented this in R and it works as expected, but with python, it's not doing what I want it to. Any help would be appreciated

`import numpy as np` `from scipy.special import gamma, kv` `import sklearn.metrics.pairwise` `import matplotlib as mpl` `import matplotlib.pyplot as plt` `import seaborn as sn`
`class Spatial:` `"""This class is used for dealing with spatial data."""` `def __init__(self, n_points=1024, distance_metric="euclidean", variance=1,` `smoothness=0.5, spatial_range=0.95, nugget=0,` `covariance_type="matern", realizations=1):` `self.n_points = n_points` `self.distance_metric = distance_metric` `self.variance = variance` `self.smoothness = smoothness` `self.spatial_range = spatial_range` `self.nugget = nugget` `self.covariance_type = covariance_type` `self.realizations = realizations` `self.domain = self.generate_grid()` `self.distance_matrix = self.compute_distance_normalized(self.distance_metric)` `self.covariance = self.compute_covariance(self.covariance_type, self.distance_matrix)` `self.observed_data = self.observations(self.covariance, self.realizations)`
`def compute_covariance(self, covariance_type, distance_matrix):` `"""Computes the covariance matrix given the distance and covariance_type"""` `if covariance_type == 'matern':` `first_term = self.variance / (2 ** (self.smoothness - 1) * gamma(self.smoothness))` `second_term = (distance_matrix / self.spatial_range) ** self.smoothness` `third_term = kv(self.smoothness, distance_matrix / self.spatial_range)` `matern_covariance = first_term * second_term * third_term` `matern_covariance = np.nan_to_num(matern_covariance, copy=True,` `posinf=self.variance, nan=self.variance)` `if self.nugget > 0:` `matern_covariance += self.nugget * np.eye(self.n_points)` `return matern_covariance` `else:` `pass` `def generate_grid(self):` `"""Generates a grid on a unit cube"""` `x = np.random.uniform(0, 1, self.n_points)` `y = np.random.uniform(0, 1, self.n_points)` `grid = np.array([x, y]).T` `return grid`
`def compute_distance_normalized(self, distance_metric):` `"""Returns normalized distance matrix for euclidean or` `great circle distances i.e. distance_matrix / max_distance` `WARNING: MAKE SURE THAT DATA IN RADIANS BEFORE USING GREAT CIRCLE` `METRIC."""` `if distance_metric == 'euclidean':` `distance_matrix = sklearn.metrics.pairwise.euclidean_distances(self.domain)` `max_distance = np.max(distance_matrix)` `return distance_matrix / max_distance` `elif distance_metric == 'great_circle':` `distance_matrix = sklearn.metrics.pairwise.haversine_distances(self.domain)` `max_distance = np.max(distance_matrix)` `return distance_matrix / max_distance`
`def observations(self, covariance, realizations):` `if realizations == 1:` `iid_data = np.random.randn(self.n_points, 1)` `chol_decomp_lower = np.linalg.cholesky(covariance)` `observed_data = np.matmul(chol_decomp_lower, iid_data)` `observed_data = np.reshape(observed_data, (32, 32))` `plt.imshow(iid_data.reshape(32, 32))` `plt.imshow(observed_data)` `sn.heatmap(iid_data.reshape(32, 32))` `sn.heatmap(observed_data)` `return observed_data`
`# def observations(covariance):` `# iid_data = np.random.randn(1024)` `# chol_decomp_lower = np.linalg.cholesky(covariance)` `# observed_data = chol_decomp_lower @ iid_data` `# observed_data = np.reshape(observed_data, (32, 32))` `# plt.hist(iid_data)` `# plt.imshow(iid_data.reshape(32, 32))` `# plt.imshow(observed_data)` `# sn.heatmap(iid_data.reshape(32, 32))` `# sn.heatmap(observed_data)` `# return observed_data # iid_data.reshape(32, 32)` `field = Spatial()` `mean = np.zeros(1024)` `covariance = field.covariance` `data = np.random.multivariate_normal(mean=mean, cov=covariance)` `x = field.distance_matrix.reshape(-1, 1)` `y = field.covariance.reshape(-1, 1)` `plt.scatter(x=x, y=y)` `sn.heatmap(field.observed_data)` `plt.imshow(field.observed_data)` `# var = observations(field.covariance)`

2021.10.25 22:44 ieatrockswithbugsauc Got lucky as hell during 3rd period 🙃

2021.10.25 22:44 CoolAd1096 USCGC Eagle during a visit to Hampton Roads in Portsmouth a little over a month ago.

2021.10.25 22:44 JugLoverPA London broil and a variety roasted veggies

2021.10.25 22:44 timmytimmay Time to build first bow! Need help

Hello everyone. I was looking to buy a Bear Montana Longbow. I will be hunting with it. However it is not in my price range. I am wondering how much it would cost to make a 55-65# longbow at around 64 inches “axle to axle” at 29 inch draw. Also I have a lot of tools so I don’t think that is a problem. I’d love input and then eventually help on building! Thank y’all.

2021.10.25 22:44 Bloxy_Cola Right to Erasure on a Content Deleted Game

2021.10.25 22:44 Concon_of_wwI How do I get rid of mail storage on Mac?

2021.10.25 22:44 TambasInTheWater yubbn

2021.10.25 22:44 Timewalker102 Look out, here comes the Spider-Man

2021.10.25 22:44 CircuitryPercolator If you give a man a fish, he'll shoot it in to into a barrel. But, if you teach him to make lemonade like a tree, life will always give him a bigger lemon.

Combination of:

• If you give a man a fish he'll eat for a day, but if you teach a man to fish he'll eat for a lifetime
• Like shooting fish in a barrel
• Make like a tree, and leave
• When life gives you lemons, make lemonade
• There's always a bigger fish in the sea

2021.10.25 22:44 Drago451 [FREE] Celeste is here!

I've got her trapped, so just take the pipe outside the airport to go straight to her.

2021.10.25 22:44 crmsn77 Lindsay Lohan

2021.10.25 22:44 NFCAAOfficialRefBot [POST GAME THREAD] Arkansas defeats SMU, 63-56

SMU SMU @ Arkansas Arkansas
Game Start Time: 12:00 PM ET
Location: Donald W. Reynolds Razorback Stadium, Fayetteville, AR
Watch: CBS
SMU SMU

Total Passing Yards Total Rushing Yards Total Yards Interceptions Lost Fumbles Lost Field Goals Time of Possession Timeouts
314 yards 57 yards 371 yards 0 0 0/0 16:36 0
Arkansas Arkansas
Total Passing Yards Total Rushing Yards Total Yards Interceptions Lost Fumbles Lost Field Goals Time of Possession Timeouts
593 yards 0 yards 593 yards 1 1 0/0 11:18 3
Team Q1 Q2 Q3 Q4 Total
Arkansas 21 21 7 14 63
SMU 14 21 0 21 56
Plays

2021.10.25 22:44 settingswrong A nice project I found. 3 days until public mint, absolutely love the artwork! What do you guys think?

2021.10.25 22:44 thefoxygrandma *Raid Boss Idea*: The Moleman King

You nuke the Giant Excavator and the Giant digger crashes down and creates a massive sink whole leader to the kingdom of the Moleman King.
He rides on his Molerat Goliath which is a massive and I mean massive Mole Rat. He has an army of Moleman Gladiators aiding him with Missile Launchers and Bumper Swords and a Swarm of Molerat Suiciders. And in the walls you can see the moleman spectators cheering their king on.
Upon death the Molerat Goliath starts ticking as it will explode and take everyone with it and you'll lose all your loot if you don't make it out.
You start the quest by "talking" to the moleman underneath The Purveyor's Shop. He will start talking gibberish and slowly you'll be able to understand him.