Unix Terminal Coding

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All Tags Eugene Tryfonides is the creator of this place .

twitter.com/egelor:

    amiagoodperson:

f(z) = 0.75 * (z^2 + 2z) + c, c = e^(i * (t + k))

    amiagoodperson:

    f(z) = 0.75 * (z^2 + 2z) + c, c = e^(i * (t + k))

    (via derwents)

    — 6 days ago with 69 notes

    Power Inverter 12V to 230V, 220V, 120V, NEW circuit diagram, very easy, …

    (Source: youtube.com)

    — 3 weeks ago

    #inverter 

    How to convert a UPS backup into an inverter

    (Source: youtube.com)

    — 3 weeks ago

    #inverter  #car battery 

    How to connect solar panels to battery bank/charge controller/inverter, …

    (Source: youtube.com)

    — 3 weeks ago with 1 note

    #batteries  #energy  #power  #solar 
    book-aesthete:

Cybernetics or Control and Communication in the Animal and the Machine Norbert Wiener. Paris, 1948.
First edition of the first book to discuss electronic computing (preceded only by a few technical reports). Wiener was a professor of Mathematics at MIT. “The revolutionary aspect of this work can hardly be underestimated.” (Origins of Cyberspace).

    book-aesthete:

    Cybernetics or Control and Communication in the Animal and the Machine
    Norbert Wiener. Paris, 1948.

    First edition of the first book to discuss electronic computing (preceded only by a few technical reports). Wiener was a professor of Mathematics at MIT. “The revolutionary aspect of this work can hardly be underestimated.” (Origins of Cyberspace).

    (via beholders-eye)

    — 3 weeks ago with 303 notes

    #cybernetics  #books 
    cattrebuchet:

wow look what i can do (not much) (based on Albers’ “homage to the square” series)

    cattrebuchet:

    wow look what i can do (not much) (based on Albers’ “homage to the square” series)

    (via fyprocessing)

    — 3 weeks ago with 13 notes

    #processing 

    heinzehavinga:

    image

    I asked the amazing Bees & Bombs on Twitter about keeping gif file sizes smaller the day before yesterday and he replied with some good tips. I’ve also been counting how long GIF usually last and I’ve been making them way to long. 

    I made a small gif to combine my new found knowlegde and was able to keep it < 1 mb which is nice. Source code on the sketchpad.

    (via fyprocessing)

    — 2 months ago with 47 notes

    #processing 
    kqedscience:

Tiny Helicopters Can’t Out-Hover Hummingbirds
“The spinning blades of micro-helicopters are about as efficient at hovering as the average hummingbird, which have had more than 42 million years of natural selection to hone their energetically efficient flight.
That said, hummingbird wings can still generate lift more efficiently than the best micro-helicopter blades, according to a new analysis led by David Lentink, an assistant professor of mechanical engineering at Stanford University. The findings could lead to more powerful, bird-inspired robotic vehicles.”
Learn more about this new research at Futurity.org.

    kqedscience:

    Tiny Helicopters Can’t Out-Hover Hummingbirds

    The spinning blades of micro-helicopters are about as efficient at hovering as the average hummingbird, which have had more than 42 million years of natural selection to hone their energetically efficient flight.

    That said, hummingbird wings can still generate lift more efficiently than the best micro-helicopter blades, according to a new analysis led by David Lentink, an assistant professor of mechanical engineering at Stanford University. The findings could lead to more powerful, bird-inspired robotic vehicles.”

    Learn more about this new research at Futurity.org.

    — 2 months ago with 92 notes

    fouriestseries:

Given the starting positions, velocities, and masses of three objects interacting via gravity, the classical three-body problem involves determining the motions of the three particles throughout time.
What’s cool about the three-body system is that it’s impossible to solve for the motions of the objects exactly. That is, we can’t write down an equation that describes the system. Instead of finding an exact solution, we solve the system numerically, which amounts to finding an accurate approximation.
The three-body problem is an example of a chaotic system, meaning that even a slight change in the starting conditions drastically changes the time-evolution of the system.
The GIF above shows a planar (i.e., two-dimensional) three-body system.
Mathematica code:
G = 1; time = 30; 
mA = 1; xA0 = 0; yA0 = 0; vxA0 = 0; vyA0 = 0; 
mB = 1; xB0 = 1; yB0 = 0; vxB0 = 0; vyB0 = 0; 
mC = 1; xC0 = 0; yC0 = 0.8; vxC0 = 0; vyC0 = 0; 
soln1 = NDSolve[
    {mA*Derivative[2][xA][t] == 
     -((G*mA*mB*(xA[t] - xB[t]))/((xA[t] - xB[t])^2 + 
      (yA[t] - yB[t])^2)^(3/2)) - 
      (G*mA*mC*(xA[t] - xC[t]))/((xA[t] - xC[t])^2 + 
      (yA[t] - yC[t])^2)^(3/2), 
    mA*Derivative[2][yA][t] == 
      -((G*mA*mB*(yA[t] - yB[t]))/((xA[t] - xB[t])^2 + 
      (yA[t] - yB[t])^2)^(3/2)) - 
      (G*mA*mC*(yA[t] - yC[t]))/((xA[t] - xC[t])^2 + 
      (yA[t] - yC[t])^2)^(3/2),
    mB*Derivative[2][xB][t] == 
      -((G*mB*mC*(xB[t] - xC[t]))/((xB[t] - xC[t])^2 + 
      (yB[t] - yC[t])^2)^(3/2)) - 
      (G*mB*mA*(xB[t] - xA[t]))/((xB[t] - xA[t])^2 + 
      (yB[t] - yA[t])^2)^(3/2),
    mB*Derivative[2][yB][t] == 
      -((G*mB*mC*(yB[t] - yC[t]))/((xB[t] - xC[t])^2 + 
      (yB[t] - yC[t])^2)^(3/2)) - 
      (G*mB*mA*(yB[t] - yA[t]))/((xB[t] - xA[t])^2 + 
      (yB[t] - yA[t])^2)^(3/2),
    mC*Derivative[2][xC][t] == 
      -((G*mC*mA*(xC[t] - xA[t]))/((xC[t] - xA[t])^2 + 
      (yC[t] - yA[t])^2)^(3/2)) - 
      (G*mC*mB*(xC[t] - xB[t]))/((xC[t] - xB[t])^2 + 
      (yC[t] - yB[t])^2)^(3/2),
    mC*Derivative[2][yC][t] == 
      -((G*mC*mA*(yC[t] - yA[t]))/((xC[t] - xA[t])^2 + 
      (yC[t] - yA[t])^2)^(3/2)) - 
      (G*mC*mB*(yC[t] - yB[t]))/((xC[t] - xB[t])^2 + 
      (yC[t] - yB[t])^2)^(3/2),
    xA[0] == xA0, yA[0] == yA0,
    Derivative[1][xA][0] == vxA0, Derivative[1][yA][0] == vyA0,
    xB[0] == xB0, yB[0] == yB0,
    Derivative[1][xB][0] == vxB0, Derivative[1][yB][0] == vyB0,
    xC[0] == xC0, yC[0] == yC0, 
    Derivative[1][xC][0] == vxC0, Derivative[1][yC][0] == vyC0
    },
    {xA, yA, xB, yB, xC, yC},{t, 0, time}, MaxSteps -&gt; 100000]
x1[t_] := Evaluate[xA[t] /. soln1[[1,1]]]
y1[t_] := Evaluate[yA[t] /. soln1[[1,2]]]
x2[t_] := Evaluate[xB[t] /. soln1[[1,3]]]
y2[t_] := Evaluate[yB[t] /. soln1[[1,4]]]
x3[t_] := Evaluate[xC[t] /. soln1[[1,5]]]
y3[t_] := Evaluate[yC[t] /. soln1[[1,6]]]
Manipulate[Show[
    {ParametricPlot[
      {{x1[t], y1[t]}, {x2[t], y2[t]}, {x3[t], y3[t]}},
      {t, tmax - 0.5, tmax}, 
      Axes -&gt; False, PlotRange -&gt; {{-0.55, 1.45}, 
      {-0.55, 1.08}}, PlotStyle -&gt; {Red, Green, Blue}, 
      GridLines -&gt; {Table[0.25*x + 0.07, {x, -100, 100}], 
      Table[0.25*y + 0.01, {y, -100, 100}]}, 
      GridLinesStyle -&gt; Directive[LightGray]]}, 
    {Graphics[{Opacity[0.7], EdgeForm[Directive[Black]], Red, 
      Disk[{x1[tmax], y1[tmax]}, 0.03], Green, 
      Disk[{x2[tmax], y2[tmax]}, 0.03], Blue, 
      Disk[{x3[tmax], y3[tmax]}, 0.03]}]}, ImageSize -&gt; 600], 
    {tmax, 6.05, 16.05}]

    fouriestseries:

    Given the starting positions, velocities, and masses of three objects interacting via gravity, the classical three-body problem involves determining the motions of the three particles throughout time.

    What’s cool about the three-body system is that it’s impossible to solve for the motions of the objects exactly. That is, we can’t write down an equation that describes the system. Instead of finding an exact solution, we solve the system numerically, which amounts to finding an accurate approximation.

    The three-body problem is an example of a chaotic system, meaning that even a slight change in the starting conditions drastically changes the time-evolution of the system.

    The GIF above shows a planar (i.e., two-dimensional) three-body system.

    Mathematica code:

    G = 1; time = 30; 
    mA = 1; xA0 = 0; yA0 = 0; vxA0 = 0; vyA0 = 0; 
    mB = 1; xB0 = 1; yB0 = 0; vxB0 = 0; vyB0 = 0; 
    mC = 1; xC0 = 0; yC0 = 0.8; vxC0 = 0; vyC0 = 0; 
    soln1 = NDSolve[
        {mA*Derivative[2][xA][t] == 
         -((G*mA*mB*(xA[t] - xB[t]))/((xA[t] - xB[t])^2 + 
          (yA[t] - yB[t])^2)^(3/2)) - 
          (G*mA*mC*(xA[t] - xC[t]))/((xA[t] - xC[t])^2 + 
          (yA[t] - yC[t])^2)^(3/2), 
        mA*Derivative[2][yA][t] == 
          -((G*mA*mB*(yA[t] - yB[t]))/((xA[t] - xB[t])^2 + 
          (yA[t] - yB[t])^2)^(3/2)) - 
          (G*mA*mC*(yA[t] - yC[t]))/((xA[t] - xC[t])^2 + 
          (yA[t] - yC[t])^2)^(3/2),
        mB*Derivative[2][xB][t] == 
          -((G*mB*mC*(xB[t] - xC[t]))/((xB[t] - xC[t])^2 + 
          (yB[t] - yC[t])^2)^(3/2)) - 
          (G*mB*mA*(xB[t] - xA[t]))/((xB[t] - xA[t])^2 + 
          (yB[t] - yA[t])^2)^(3/2),
        mB*Derivative[2][yB][t] == 
          -((G*mB*mC*(yB[t] - yC[t]))/((xB[t] - xC[t])^2 + 
          (yB[t] - yC[t])^2)^(3/2)) - 
          (G*mB*mA*(yB[t] - yA[t]))/((xB[t] - xA[t])^2 + 
          (yB[t] - yA[t])^2)^(3/2),
        mC*Derivative[2][xC][t] == 
          -((G*mC*mA*(xC[t] - xA[t]))/((xC[t] - xA[t])^2 + 
          (yC[t] - yA[t])^2)^(3/2)) - 
          (G*mC*mB*(xC[t] - xB[t]))/((xC[t] - xB[t])^2 + 
          (yC[t] - yB[t])^2)^(3/2),
        mC*Derivative[2][yC][t] == 
          -((G*mC*mA*(yC[t] - yA[t]))/((xC[t] - xA[t])^2 + 
          (yC[t] - yA[t])^2)^(3/2)) - 
          (G*mC*mB*(yC[t] - yB[t]))/((xC[t] - xB[t])^2 + 
          (yC[t] - yB[t])^2)^(3/2),
        xA[0] == xA0, yA[0] == yA0,
        Derivative[1][xA][0] == vxA0, Derivative[1][yA][0] == vyA0,
        xB[0] == xB0, yB[0] == yB0,
        Derivative[1][xB][0] == vxB0, Derivative[1][yB][0] == vyB0,
        xC[0] == xC0, yC[0] == yC0, 
        Derivative[1][xC][0] == vxC0, Derivative[1][yC][0] == vyC0
        },
        {xA, yA, xB, yB, xC, yC},{t, 0, time}, MaxSteps -> 100000]
    x1[t_] := Evaluate[xA[t] /. soln1[[1,1]]]
    y1[t_] := Evaluate[yA[t] /. soln1[[1,2]]]
    x2[t_] := Evaluate[xB[t] /. soln1[[1,3]]]
    y2[t_] := Evaluate[yB[t] /. soln1[[1,4]]]
    x3[t_] := Evaluate[xC[t] /. soln1[[1,5]]]
    y3[t_] := Evaluate[yC[t] /. soln1[[1,6]]]
    Manipulate[Show[
        {ParametricPlot[
          {{x1[t], y1[t]}, {x2[t], y2[t]}, {x3[t], y3[t]}},
          {t, tmax - 0.5, tmax}, 
          Axes -> False, PlotRange -> {{-0.55, 1.45}, 
          {-0.55, 1.08}}, PlotStyle -> {Red, Green, Blue}, 
          GridLines -> {Table[0.25*x + 0.07, {x, -100, 100}], 
          Table[0.25*y + 0.01, {y, -100, 100}]}, 
          GridLinesStyle -> Directive[LightGray]]}, 
        {Graphics[{Opacity[0.7], EdgeForm[Directive[Black]], Red, 
          Disk[{x1[tmax], y1[tmax]}, 0.03], Green, 
          Disk[{x2[tmax], y2[tmax]}, 0.03], Blue, 
          Disk[{x3[tmax], y3[tmax]}, 0.03]}]}, ImageSize -> 600], 
        {tmax, 6.05, 16.05}]

    (via derwents)

    — 2 months ago with 138 notes

    #mathematics  #code  #pure 
    kqedscience:

MIT Finger Device Reads to the Blind in Real Time
“Scientists at the Massachusetts Institute of Technology are developing an audio reading device to be worn on the index finger of people whose vision is impaired, giving them affordable and immediate access to printed words.
The so-called FingerReader, a prototype produced by a 3-D printer, fits like a ring on the user’s finger, equipped with a small camera that scans text. A synthesized voice reads words aloud, quickly translating books, restaurant menus and other needed materials for daily living, especially away from home or office.”
Read more from Boston.com.

    kqedscience:

    MIT Finger Device Reads to the Blind in Real Time

    Scientists at the Massachusetts Institute of Technology are developing an audio reading device to be worn on the index finger of people whose vision is impaired, giving them affordable and immediate access to printed words.

    The so-called FingerReader, a prototype produced by a 3-D printer, fits like a ring on the user’s finger, equipped with a small camera that scans text. A synthesized voice reads words aloud, quickly translating books, restaurant menus and other needed materials for daily living, especially away from home or office.”

    Read more from Boston.com.

    — 3 months ago with 4770 notes

    derwents:

sucysucyfivedolla:

sigsauer-ist:

goodbye

i thought this was a fuckin skate 3 glitch gif at first

nyoom

    derwents:

    sucysucyfivedolla:

    sigsauer-ist:

    goodbye

    i thought this was a fuckin skate 3 glitch gif at first

    nyoom

    (Source: relentless-soul)

    — 3 months ago with 177642 notes