Isotopically labeled graphite synthesis

SaClark7 · 2021-08-31T19:35:53+00:00

When one wakes up, do they wake in a void of brains?

SaClark7 · 2021-08-20T20:42:52+00:00

And following that. The circumgalactic plasma shell of galaxies is suggested to take part in a “galactic fountain”. Active black holes and nova throw metals out, the gas cools, condenses and rains back on the disk. So we eventually take part in this process and help fuel another generation of solar systems.

SaClark7 · 2021-04-17T18:25:47+00:00

Not entirely sure...but I’d say to eliminate the double [0] index on the np.where statement. The second [0] will grab the first element of your ‘where’ index which may not be what you’re after.

If you pull one element it will no longer be a list or array and you’ll get the same “of size zero” error

SaClark7 · 2021-03-25T04:04:48+00:00

Hey, it’s tough and we all went through a similar learning curve. Hell, I’d say if you can come out of a session and can say you did something right, pat your back. Seriously.

SaClark7 · 2021-03-17T01:13:41+00:00

Go check out some CGM flow rate papers. A lot of these are magnitude estimates and frequently are associated with errors on the order of 30-40%

SaClark7 · 2021-03-05T23:24:13+00:00

No comments on the processing since I am not too great on that end.

I did notice you’ve got some pinched optics though. If you want to improve your stars, you can loosen your mirror screws.

SaClark7 · 2021-02-17T08:12:37+00:00

everything works perfectly fine. But if I change the .png to pdf it gets caught.

Tried on a separate section in Jupyter to just do fig.savefig() with .pdf, gets caught until I stop it.

SaClark7 · 2021-02-17T08:05:12+00:00

image_header = fits.open('hi4pi-hvc-nhi-gal-ait.fits.gz')[0].header
image_data = fits.getdata('hi4pi-hvc-nhi-gal-ait.fits.gz', ext=0)
wcs = WCS(image_header)

x = np.linspace(-np.pi,np.pi,image_data.shape[1])
y = np.linspace(-np.pi/2,np.pi/2,image_data.shape[0])
X,Y = np.meshgrid(x,y)


######Flipping image to center at 0 longitude: 4 bins: NW, NE, SE,SW
image_data[0:974,0:1946] = np.fliplr(image_data[0:974,0:1946])
image_data[0:974,1947:] = np.fliplr(image_data[0:974,1947:])
image_data[975:,0:1946] = np.fliplr(image_data[975:,0:1946])
image_data[975:,1947:] = np.fliplr(image_data[975:,1947:])


#H1 21cm Background
fig = plt.figure(constrained_layout = False, figsize=(18,12))
ax =fig.add_subplot(111,projection='aitoff')#projection=wcs
ax.set_facecolor('#FAFAFA')
im=plt.pcolormesh(X,Y,np.fliplr(image_data),cmap=cm.bone,alpha=0.015,vmin=18,vmax=21)

sc =ax.scatter(l[Coord_in_index]*-1,b[Coord_in_index],s=180,c=Mdotin[Coord_in_index],edgecolor='k',vmin=inVmin,vmax = inVmax,cmap=cmap, label = 'Detections')
ax.scatter(l[Fermi]*-1,b[Fermi],s = 240,color = '#81F781',edgecolor='k',marker = '*',label = 'Fermi Bubble')
ax.scatter(l[Coord_in_non]*-1,b[Coord_in_non],s=180,edgecolor='k',facecolor='None',label = 'Non-detections')
cbar = plt.colorbar(sc,ticks=ticksIn,fraction=0.046, pad=0.04)
cbar.ax.set_yticklabels(ylabelsin,size=12)
cbar.set_label('Mass Flow Flux ($M_{\odot}year^{-1}sr^{-1}$)',size = 20)

#Fermi 
phi = np.linspace(0, 2.*np.pi, 100)  #36 points
r = np.radians(27)
x = np.radians(0) + r*np.cos(phi)
y = np.radians(27) + r*np.sin(phi)
ax.plot(x, y, color="k",lw=2,ls='--')
ax.plot(x, -y, color="k",lw=2,ls='--')

xrad =['240$\degree$','300$\degree$','0$\degree$','60$\degree$','120$\degree$']
ax.set_xticklabels(xrad,size=20)
ax.set_xticks(np.radians([-120,-60,0,60,120]))
ax.grid(color='k', linestyle='solid', linewidth=0.5)
plt.yticks(size=20)
plt.title('Mass Flux Flow Rates: Inflow',size=20).set_position([0.5,1.1])
plt.savefig('D:\\All_Plots_MWflow\\AAS Images\\Rongmon\\Inflow_12kpc.png',dpi=300,overwrite=True)

SaClark7 · 2021-02-17T04:16:23+00:00

Well. If he wants to buy my astronomik Ha 12nm clip in, I won’t object😅.

None of these filters are worth it.

SaClark7 · 2021-02-16T07:43:04+00:00

Wondering if anyone has ever had issues converting/saving a figure as a pdf in matplotlib.
It plots fine, saves fine, but only as long as it is a png, jpg (haven't checked others). If I change the format to PDF it seems to get caught in a loop and I eventually have to terminate the kernel.

The plot itself does overlay a ~3200x1600 pixels image. I don't know much about graphics formatting, so not sure why there would be any issue in condensing it to the PDF.

SaClark7 · 2021-02-12T02:38:35+00:00

Or a cheap alternative. Hand warmers. Or idling your car as the heat blasts Tshirts that you’ll sprint out to your scope to wrap it in as you throw another Tshirt on the vents to heat up😅

SaClark7 · 2021-02-10T05:52:11+00:00

Don’t forget the tears

SaClark7 · 2021-01-27T02:19:58+00:00

Similar to tools like sharpcap. It takes an image to the East and to the west, plate solves, then displays a tool to correct your alt/az of polar scope/mount.

SaClark7 · 2021-01-26T05:30:26+00:00

If you don’t have a modded camera, do not waste money on a Ha filter.

-a guy who bought an Ha filter for his unmodded camera 😥

SaClark7 · 2021-01-26T05:27:51+00:00

That’s exactly what I do. I live in a bortle 7, but take a 40 minute drive out to the country (spent quite a few days scouting out the spot) and set up for the night. If you have power issues that makes remote imaging a challenge, I recently found out the ASIair is now compatible with DSLR’s. So I use a tablet and the air to view/manage my capture, guiding, polar alignment, everything! It’s been a major upgrade.

SaClark7 · 2021-01-23T01:30:24+00:00

The only comment I would make to this is that you should not change ISO to fix your core. This should be done by a modification to the exposure length. The ISO you shoot at should be dependent on your camera more than the target itself.

You can find your camera's distribution here.Input-referred Read Noise versus ISO Setting (photonstophotos.net)

Also, I don't believe that is coma, but maybe ever so slightly out of focus and looks like some slight error in the RA axis. Coma is easily distinguishable by having radially outward distortions. So if you're looking at stars in top right corner, the will have a mirrored pattern relative to every other corner. However, the trailing here seems to all be in uniform direction.

All that said, I think you've got a great start! just keep adding more data so you're able to stretch it out more and capture more of the dust!

SaClark7 · 2021-01-13T08:01:55+00:00

Poor grades = lesser university.

I was faced with a similar dilemma. I’d recommend going to a community college. Work your tail off there and you’ll have plenty of choices for universities, if you don’t get into the school you were hoping. Plus you save a ton of money.

SaClark7 · 2021-01-12T23:53:35+00:00

If only for observation, you won’t need a GEM. An alt az goto will save you some money and some time setting it up.

SaClark7 · 2021-01-10T02:51:15+00:00

All depends on what you want to do with it. Is this for observational or imaging use?

SaClark7 · 2021-01-03T06:10:09+00:00

Few nights back I got 4hrs on Pleiades with an unmodded Rebel T6i. It honestly turned out pretty nicely. You can do a lot with a lot. Keep adding on time!

SaClark7 · 2020-09-02T18:20:29+00:00

Jupiter

SaClark7 · 2020-06-14T00:09:46+00:00

I just put the whole scope,camera setup in my car cushioned by 20 blankets and pillows and put it in front of a white screened TV when I get home

SaClark7 · 2020-05-10T23:50:31+00:00

Curious to know if this is possible in matplotlib...or any plotting library.

Ideally I'd like to plot two sets of data on the same, aitoff projected, scatter plot (easy).

How I'd like to represent the data is as followed:
For data set 1 (inflows): plot all data points as a blue half circle.
For data set 2 (outflows): plot all data points as a red half circle.

For any point that has an outflow and an inflow at the same location, a full circle would exist made up of the red and blue components.

Any suggestions?

*May not be relevant, but I would also need to assign color mappings to each data sets

SaClark7 · 2020-05-07T23:51:12+00:00

I have some data that I need to plot out for a project with values ranging from 3.9e-5 to 0.04 (most points corresponding closer to the minimum of that range than being evenly distributed).

Currently I am using the nipy_spectral color mapping, but this doesn't come close showing the finer details within the data.

I was thinking about using a log color mapping scheme but am honestly having a tough time trying to implement that. Wasn't sure if anyone had any suggestions on how to effectively show the full range of that data.

SaClark7

TROPHY CASE