Npy Puns

This study, and other, provide FURTHER evidence that PNES is a physical illness, not a psychological one. Moreover, it explains that the "psychological" aspect is actually a SYMPTOM, and NOT the Cause.

https://www.sciencedaily.com/releases/2017/11/171103105821.htm

And also: (near the bottom it comments on treatments, one of which is NPY nasal delivery)

https://www.nature.com/articles/mp2012101

I’ve been trying to figure out the mechanisms of appetite suppression and how to do so, particularly to combat THC-induced CB1 partial agonism (weed munchies). So far I’ve compiled the following (+ means more of product/activation increases appetite; - means decreases:

1. Ghrelin +
2. Leptin -
3. Neuropeptide Y (NPY) +
4. 5-HT2C -
5. CB1 +
6. Peptide YY (PYY) -
7. GLP-1/glycogen (break down glucose; contradicts 10 and 13 but well-established) -
8. Cortisol +
9. Cholecystokinin (CCK) -
10. Glucose -
11. Orexin +
12. H1 -
13. Insulin (-> lower glucose) +
14. Sugars +
15. β1/2/3 adrenergic -
16. Benzos/lithium/antipsychotics + (variety of mechanisms from GABA, glutamate, 5-HT2C, H1, and α1)

Interactions between CB1 and 5-HT2C ligands and net effect (+/ag = agonism, -/ant=antagonism, effect of A to elicit change in appetite > or < B):

CB1 vs 5HT2C modulators:

CB1+5HT2C=-NPY=- (5HT2C ag to lower > CB1 ag to raise)
-CB1-5HT2C=-NPY=- (CB1 ant to lower > 5HT2C ant to raise)
CB1-5HT2C=+NPY=+ (either possible to be greater, additive)
-CB1+5HT2C=-NPY=- (same as above)

CB1 (THC) vs MCR4 modulators:
-CB1=-
MCR4=-
-CB1+MCR4= both - synergy
CB1+MCR4=+ (CB1 ag to raise > MCR4 ag to lower)
-CB1-MCR4=- (CB1 ant to lower > MCR4 ant to raise)

What are the most powerful anorectic substances, RC and not, that you’ve found the regulate appetite, either using/correcting/adding on to these or other mechanisms? Unfortunately, CB1 antagonists and 5HT2C agonists aren’t used (the latter was withdrawn from market not too long ago), so RCs may be needed here. For me, metformin, through lowering NPY among other things, effectively lowers appetite n=1 but NOT in the presence of THC. This is also the case for NDRIs/NDRAs (bupropion, amphetamine, methylphenidate, etc.). Options I can think of remaining involve RC/non-RC stimulants (from 2-FMA/4F-MPH types to purely adrenergic ones like clenbuterol/salbutamol/ephedrine), RC/non-RC metabolic drugs (DNP, liraglutide, orlistat, etc.), other on/off label rx (naltrexone, sibutramine, topiramate, phentermine, liraglutide, orlistat), RC/non-RC CB1 antagonists (not sure of names but these are useless since it defeats purpose of THC presence), and RC/non-RC 5-HT2C agonists (lorcaserin, not sure of RC names, etc.). I might be missing some. Thoughts?

(Also, I have lots (20+) of miscellaneous pathways and interactions involving interactions between ghrelin, NPY, leptin, metformin, AMPK PO4(3-), al

I'm an aspiring Data Scientist and through the last few months working with data in Pandas using the standard .csv format I found out about .npy files.

It's really not that much different but it's a LOT faster with regard to loading and handling in general, which is why I made this: https://medium.com/@peter.nistrup/what-is-npy-files-and-why-you-should-use-them-603373c78883

TL:DR; Loading .npy files is ~70x faster than .csv files. This actually adds up to a lot if you - like me - find yourself restarting your kernel often when you've changed some code in another package / directory and need to process / load your data again!

Obviously there's some limitations like the use of header / column names, but this is entirely possible to save and load using a .npy file, it's just a little more cumbersome compared to .csv formats.

I hope you find it useful!

Edit: I'm sorry about the clickbaity nature of the title. I'm in complete agreement that this isn't applicable to every scenario. As I said I'm just starting out as a Data Scientist myself so my experience is limited and as such I obviously shouldn't make assumptions like "Always" and "Never".. My apologies!

Hi I’m turning 25(F) soon. 5 4, 115 lb, white, existing medical issues are several trauma and stress related disorders including C-PTSD and PNES (they are seizure like episodes related to the PTSD. Science is iffy so far on it. The NYP is related to a few studies on my support pages) Anxiety, and depression. Physically the biggest problems are arthritis I’ve had for 7 years, and having very low endurance. I’ve also got a few suspected things like ADHD. Oh I have dyslexia so I apologize if anything is spelled wrong.

I drink maybe every 2 months with my husband. I use Marijuana sometimes to help with my PTSD. I find it makes me less paranoid, and seems to help hold off my episodes more than without. I didn’t start till after all other symptoms started..

Onto what I’m actually wanting to know. Neuropeptide Y.. I’ve seen it described as both a hormone and a transmitter in the brain. It seems to have a negative correlation to lipids. Anyways it’s been linked to PTSD, PNES, alcoholism, diminished appetite, low blood pressure, low tolerance to pain, poor stress responses and other issues if too low.

I have every one of those excluding alcohol. I’ve purposely stayed away from it even though I am almost called by alcohol once a week or so. My father and grandfather were both alcoholics so I know better...

Anyways I suspect I’m low in it, and I want to get tested. I’m still working on insurance or I’d ask a Dr I’m already seeing.. is it something a Dr can order outside of a study? Also is there any known treatment for someone with a low NPY?

Here’s hoping one of you actually know what any of this actually is.. I’m not sure how much of this is taught outside of specialists..

GitHub - nianticlabs/monodepth2: [ICCV 2019] Monocular depth estimation from a single image

So i came across this cool depth prediction model and want to use it in my project. The problem i have no idea how to load .npy weights file. I also did some research but found nothing. Most of the articles/tutorials i found are for loading training data in numpy format. I also came across this model ( GitHub - iro-cp/FCRN-DepthPrediction: Deeper Depth Prediction with Fully Convolutional Residual Networks (FCRN)). This project has the model available in 2 format .npy and .ckpt. I tried the .ckpt one but it has multiple file .data-00000-of-00001 .meta .index

Thank you for all of your answer ? Sorry if this question is a bit dumb

I am using np.load(file_name) to open the files. The files contain numpy arrays of strings, integers (1's and 0's), and dictionaries. It works for every file up until file 22. When I try to load the file, I get the error:

UnicodeDecodeError: 'utf-8' codec can't decode byte 0xbf in position 817733: invalid start byte

Then I get a bunch of gibberish

```UnicodeError: Unpickling a python object failed: UnicodeDecodeError('utf-8', b'\xc2\xa9uF~iwfU_L>EJ<C=4H&'W\x1c#^\x1c#\x1d#\x1f#!#$$_,%^Z1\\xc2\x89_F\xc2\x86Xr8%\+$L%$E$$A#$="#:"#\59U/3Q/2U6:U7;N27I.3R;?P;?A,0:&+8&:).?/4A27?16>16=15<16<26@6:KCGF>B;37814804815\xc2\x90u\xc2\x8cJ<F604503503725?;>IEG7354/23/23/12.01.0*%'$!"&#$# !%"#301@;=C?@FCD,*+/,-967=9;>:<>;<@<=D?@JCCQII]QRk]^tfgbXXZNO\xc2\x82qq\xc2\x9c\xc2\x87\xc2\x87\xc2\x92\x7f\x7fk^^F@A6773668::ADD<??59:6:;ACDKNNBEF9<=@CC<?@4883778::gbyojWOLNHESLJpif\xc2\xa7\xc2\x91\xc2\x8b\xc2\xac\xc2\x8f\xc2\x87\xc2\x9a~uOA@<248.19/2=15J=A]NR\xc2\x84tx\xc2\x80jp\xc2\x82em\xc2\x8ejt\xc2\xaf\xc2\x82\xc2\x8d\xc3\xa7\xc2\xab\xc2\xbb\xc3\xbf\xc3\x8b\xc3\xa9\xc3\xbf\xc3\xb9\xc3\xbf\xc3\xbf\xc3\xbf\xc3\xbf\xc3\xbf\xc3\xaa\xc3\xbf\xc3\xb0\xc2\xa7\xc3\x89\xc3\x85\xc2\x8b\xc2\x9d\xc3\x89\xc2\x9a\xc2\xa2\xc2\xbd\xc2\x8f\xc2\x91}[dY=GX>GQ6?I2:M8?C/5>+14*-/+--*,-+-645+*+)))&&&\'(\'*+*KDCzrn\xc2\xa1\xc2\x9a\xc2\x93\xc2\xba\xc2\xb6\xc2\xb4\xc2\x83\xc2\x81~\xc2\x84\xc2\x83\xc2\x82\xc2\x81\x7f}\xc3\x91\xc3\x8f\xc3\x8e\xc2\x9b\xc2\x99\xc2\x97\xc2\xa9\xc2\xa7\xc2\xa5}yw\xc2\xb4\xc2\xae\xc2\xad\xc2\xab\xc2\xa0\xc2\x9e\xc2\x88rr[LNFEE688355344>-0:.07),:,.<-0E69E>?888354HJJade\xc2\x83\xc2\x85\xc2\x88qtu576.0/,-,121-/.+-,+,,354-.-+++333/()F68oKQ833///555211A46FJwSZ\xc2\x98dj\xc2\xbaz}\xc3\x89\xc2\x86\xc2\x8a\xc2\xbd}\xc2\x80\xc2\xb0vu\xc2\xb6ue\xc2\xbetV\xc3\x80uU\xc2\xbfuV\xc2\xbfsQ\xc2\xbesQ\xc2\xbdsQ\xc2\xbdsQ\xc2\xberO\xc2\xbdrQ\xc2\xbeqN\xc2\xbdqO\xc2\xbdqN\xc2\xbdqN\xc2\xbdqN\xc2\xbdqP\xc2\xbcrP\xc2\xbcqO\xc2\xbdpN\xc2\xbcpM\xc2\xbcpM\xc2\xbcpM\xc2\xbbqP\xc2\xbbqO\xc2\xbcpM\xc2\xbcpM\xc2\xbbpM\xc2\xbboM\xc2\xbapO\xc2\xbbpN\xc2\xbapO\xc2\xbboM\xc2\xbboM\xc2\xbaoN\xc2\xbapN\xc2\xbapN\xc2\xbaoN\xc2\xbaoM\xc2\xbaoM\xc2\xb9oN\xc2\xb9oM\xc2\xbbnL\xc2\xbfoM\xc3\x85rO\xc3\x90uO\xc3\x9ea6\xc3\xa9j<\xc3\xafqE\xc3\xb2tE\xc3\xb2tF\xc3\xafsE\xc3\xa9rF\xc3\xa5pE\xc3\xa0oG\xc3\x9cmF\xc3\x9alE\xc3\x98kF\xc3\x96lG\xc3\x96lH\xc3\x94lG\xc3\x95kH\xc3\x95lH\xc3\x97kG\xc3\x9alF\xc3\xa0kC\xc3\xa6

Hi guys,

I'm trying to build an image classifier in R based on Google's Quick Draw dataset. Unfortunatly, the source data are in .npy format. Is there any way to load those data into R and store them as data frame / lists / matrices ?

I've already tried RcppCNPy package, but it crashes R Studio while loading data. I've also tried Reticulate package and it manages to load the data, but it is saved in numpy.ndarray and I can't convert it to dataframe.

I would be grateful for any help.

Have a nice one

Pedal board picture here: https://imgur.com/7lTwPby

So after ~8 months of experimentation, I've finally "finished" my board (read: I haven't bought a new pedal in 2 months). When I was a broke student, I used a smaller board made from mostly Boss pedals, most of which I've since sold. Now that I have a disposable income, I was a able to make some significant upgrades and make my first professional-grade board. Here's the signal chain followed by a wall of text describing my thoughts on each pedal and how I use it. You can find a picture of the underside of the board here (excuse my messy cable management) and a picture of my full rig here. I use this board with my Vox AC30CH and my Egnater Tweaker 40 in stereo. The guitars I use are a PRS 305 and a PRS Custom 24 equipped with Suhr Thornbucker pickups.

#Signal Chain

Guitar ->
Digitech Freqout ->
Digitech Whammy Ricochet ->
EHX POG2 ->
Sonuus Wahoo ->
Boss NS2 Input ->
Boss NS2 Send ->
&nbsp;&nbsp;&nbsp;&nbsp;Xotic SP Compressor ->
&nbsp;&nbsp;&nbsp;&nbsp;TC Electronic Polytune 2 Noir ->
&nbsp;&nbsp;&nbsp;&nbsp;Mojo Hand FX Crosstown Fuzz ->
&nbsp;&nbsp;&nbsp;&nbsp;EHX Glove OD ->
&nbsp;&nbsp;&nbsp;&nbsp;Vox Ice 9 Overdrive ->
&nbsp;&nbsp;&nbsp;&nbsp;Wampler Tumnus Deluxe ->
&nbsp;&nbsp;&nbsp;&nbsp;Empress ParaEQ ->
&nbsp;&nbsp;&nbsp;&nbsp;Amp Input ->
&nbsp;&nbsp;&nbsp;&nbsp;Amp Send ->
Boss NS2 Return -> Boss NS2 Output ->
DOD Rubberneck Analog Delay ->
Empress Echosystem ->
Boss MD-500 ->
Meris Polymoon ->
Strymon BigSky -> Amp Return

##Occasional Add-Ons

• Digitech Trio+
• EHX SuperEgo+
• Vox Volume Pedal

#Pedal Ramblings

##Digitech Freqout

This pedal actually turned out to be super useful for me. Because I'm usually playing in my apartment at low volumes or at church where I play through my amp head into a cab sim into the PA, I'm almost never in a place where I'll be able to get natural guitar feedback. While it may appear gimmicky at first, I've found the Freqout the be particularly useful in these contexts, especially during the beginnings or endings of songs. I also found it works really well in latch mode with the attack set as close to immediate as possible for creating ambient ebow

I have a few Numpy binary files created by LIDAR readings containing 3D point clouds. I want to be able to plot a top-down (orthogonal) view for every point cloud by reading them from a file. I looked up various 3D point cloud libraries such as Open3d, pyntcloud, etc but none of them work with NPY files (supported file formats are ply, pcd, etc). How can I plot them?

I am looking for a possible direction in which I can proceed because I have not found a way to plot point clouds by reading them from NPY files.

For some reasons I need to save pixels[] array as a numpy file, I know there's no way to get numpy going in processing. Is there any way I can at least save the pixels[] array to a file?

edit: for clarification - I've got an assigment to draw some fractals and the program has to return an RGB table as a numpy array. I decided to go with L-system because I already have it working, Python because we work mainly in Python and it's the language I use the most, and Processing because I use this enviroment a lot and got an L-system working. In case I won't find a way to get this to work I'll just learn the turtle library from python.

Interesting study:

Highlights

• Chronic sucralose diet triggers increased food intake

• Dietary sucralose creates a sweet/energy imbalance

• Sweet/energy imbalance activates a conserved neuronal starvation response

• Sucralose effect on feeding is conserved from flies to mammals

Summary Non-nutritive sweeteners like sucralose are consumed by billions of people. While animal and human studies have demonstrated a link between synthetic sweetener consumption and metabolic dysregulation, the mechanisms responsible remain unknown. Here we use a diet supplemented with sucralose to investigate the long-term effects of sweet/energy imbalance. In flies, chronic sweet/energy imbalance promoted hyperactivity, insomnia, glucose intolerance, enhanced sweet taste perception, and a sustained increase in food and calories consumed, effects that are reversed upon sucralose removal. Mechanistically, this response was mapped to the ancient insulin, catecholamine, and NPF/NPY systems and the energy sensor AMPK, which together comprise a novel neuronal starvation response pathway. Interestingly, chronic sweet/energy imbalance promoted increased food intake in mammals as well, and this also occurs through an NPY-dependent mechanism. Together, our data show that chronic consumption of a sweet/energy imbalanced diet triggers a conserved neuronal fasting response and increases the motivation to eat.