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stringlengths 8
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stringlengths 4
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values | file_size
int64 0
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1.6M
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⌀ | language
stringclasses 14
values |
|---|---|---|---|---|---|---|---|
2D
|
shigemorita/2Dpy
|
contour.py
|
.py
| 2,436
| 70
|
import numpy
import pandas
from matplotlib import pyplot
from matplotlib import ticker
data_file = "contour.csv"
num_contour = 16
pyplot.rcParams["axes.linewidth"] = 1.5
pyplot.rcParams["figure.dpi"] = 100
pyplot.rcParams["figure.figsize"] = (4, 4)
pyplot.rcParams["font.family"] = "serif"
pyplot.rcParams["font.size"] = 16
pyplot.rcParams["xtick.major.width"] = 1.5
pyplot.rcParams["ytick.major.width"] = 1.5
pyplot.rcParams["xtick.minor.width"] = 1.5
pyplot.rcParams["ytick.minor.width"] = 1.5
pyplot.rcParams["xtick.major.size"] = 6
pyplot.rcParams["ytick.major.size"] = 6
pyplot.rcParams["xtick.minor.size"] = 3
pyplot.rcParams["ytick.minor.size"] = 3
pyplot.rcParams["axes.labelpad"] = 15
pyplot.rcParams["xtick.major.pad"] = 18
pyplot.rcParams["ytick.major.pad"] = 18
pyplot.rcParams["xtick.top"] = True
pyplot.rcParams["ytick.right"] = True
pyplot.rcParams["xtick.direction"] = "in"
pyplot.rcParams["ytick.direction"] = "in"
data = pandas.read_csv(data_file, header=0, index_col=0)
x = data.columns[0:].astype(float)
y = data.index[0:].astype(float)
z = data.values
zmax = numpy.absolute(z).max()
fig = pyplot.figure()
ax = fig.add_subplot(1, 1, 1)
ax.contour(x, y, z, num_contour, cmap="bwr", vmin=-1 * zmax, vmax=zmax)
ax.set_xlim(1200, 1000)
ax.set_xticks(numpy.arange(1200, 1000 - 1, -50))
ax.xaxis.set_minor_locator(ticker.MultipleLocator(10))
ax.set_ylim(1200, 1000)
ax.set_yticks(numpy.arange(1200, 1000 - 1, -50))
ax.yaxis.set_minor_locator(ticker.MultipleLocator(10))
ax.set_title("Synchronous")
ax.set_xlabel(r"$\nu$${_{1}}$ / cm${^{-1}}$")
ax.set_ylabel(r"$\nu$${_{2}}$ / cm${^{-1}}$")
pyplot.savefig("contour.png", dpi=200, bbox_inches="tight")
fig = pyplot.figure()
ax = fig.add_subplot(1, 1, 1)
ax.contour(x, y, z, num_contour, cmap="bwr", vmin=-1 * zmax, vmax=zmax)
fig = pyplot.figure()
ax = fig.add_subplot(1, 1, 1)
ax.pcolormesh(x, y, z, cmap="bwr", vmin=-1 * zmax, vmax=zmax)
fig = pyplot.figure()
ax = fig.add_subplot(1, 1, 1)
ax.contour(x, y, z, num_contour, colors="black", linewidths=0.5, linestyles="solid", vmin=-1 * zmax, vmax=zmax)
ax.pcolormesh(x, y, z, cmap="jet", vmin=-1 * zmax, vmax=zmax)
fig = pyplot.figure()
ax = fig.add_subplot(1, 1, 1)
ax.contour(x, y, z, num_contour, colors="black", linewidths=0.5, vmin=-1 * zmax, vmax=zmax)
ax.contourf(x, y, z, levels=0, colors=["gray", "white"], vmin=-1 * zmax, vmax=zmax)
|
Python
|
2D
|
shigemorita/2Dpy
|
2Dpy.py
|
.py
| 2,013
| 68
|
import math
import numpy
import pandas
from matplotlib import pyplot
hetero = False
inputfile1 = "spec.csv"
# hetero=True
# inputfile1="spec1.csv"
# inputfile2="spec2.csv"
left_large = True
dynamic = True
num_contour = 16
# file read
spec1 = pandas.read_csv(inputfile1, header=0, index_col=0).T
if hetero == False: inputfile2 = inputfile1
spec2 = pandas.read_csv(inputfile2, header=0, index_col=0).T
if len(spec1) != len(spec2): raise Exception("data mismatching")
spec1.T.plot(legend=None)
pyplot.show()
if left_large: pyplot.xlim(max(spec1.columns), min(spec1.columns))
if hetero:
spec2.T.plot(legend=None)
if left_large:
pyplot.xlim(max(spec2.columns), min(spec2.columns))
pyplot.show()
if dynamic:
spec1 = spec1 - spec1.mean()
spec2 = spec2 - spec2.mean()
def contourplot(spec):
x = spec.columns[0:].astype(float)
y = spec.index[0:].astype(float)
z = spec.values
zmax = numpy.absolute(z).max()
pyplot.figure(figsize=(6, 6))
pyplot.contour(x, y, z, num_contour, cmap="bwr", vmin=-1 * zmax, vmax=zmax)
# pyplot.pcolormesh(x,y,z,cmap='jet',vmin=-1*zmax,vmax=zmax)
if left_large:
pyplot.xlim(max(x), min(x))
pyplot.ylim(max(y), min(y))
pyplot.show()
# synchronous correlation
sync = pandas.DataFrame(spec1.values.T @ spec2.values / (len(spec1) - 1))
sync.index = spec1.columns
sync.columns = spec2.columns
sync = sync.T
contourplot(sync)
sync.to_csv(inputfile1[: len(inputfile1) - 4] + "_sync.csv")
# Hilbert-Noda transformation matrix
noda = numpy.zeros((len(spec1), len(spec1)))
for i in range(len(spec1)):
for j in range(len(spec1)):
if i != j: noda[i, j] = 1 / math.pi / (j - i)
# asynchronouse correlation
asyn = pandas.DataFrame(spec1.values.T @ noda @ spec2.values / (len(spec1) - 1))
asyn.index = spec1.columns
asyn.columns = spec2.columns
asyn = asyn.T
contourplot(asyn)
asyn.to_csv(inputfile1[: len(inputfile1) - 4] + "_async.csv")
|
Python
|
2D
|
shigemorita/2Dpy
|
contour.ipynb
|
.ipynb
| 4,357
| 152
|
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy\n",
"import pandas\n",
"from matplotlib import pyplot\n",
"from matplotlib import ticker"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_file = \"contour.csv\"\n",
"num_contour = 16\n",
"pyplot.rcParams[\"axes.linewidth\"] = 1.5\n",
"pyplot.rcParams[\"figure.dpi\"] = 100\n",
"pyplot.rcParams[\"figure.figsize\"] = (4, 4)\n",
"pyplot.rcParams[\"font.family\"] = \"serif\"\n",
"pyplot.rcParams[\"font.size\"] = 16\n",
"pyplot.rcParams[\"xtick.major.width\"] = 1.5\n",
"pyplot.rcParams[\"ytick.major.width\"] = 1.5\n",
"pyplot.rcParams[\"xtick.minor.width\"] = 1.5\n",
"pyplot.rcParams[\"ytick.minor.width\"] = 1.5\n",
"pyplot.rcParams[\"xtick.major.size\"] = 6\n",
"pyplot.rcParams[\"ytick.major.size\"] = 6\n",
"pyplot.rcParams[\"xtick.minor.size\"] = 3\n",
"pyplot.rcParams[\"ytick.minor.size\"] = 3\n",
"pyplot.rcParams[\"axes.labelpad\"] = 8\n",
"pyplot.rcParams[\"xtick.major.pad\"] = 12\n",
"pyplot.rcParams[\"ytick.major.pad\"] = 12\n",
"pyplot.rcParams[\"xtick.top\"] = True\n",
"pyplot.rcParams[\"ytick.right\"] = True\n",
"pyplot.rcParams[\"xtick.direction\"] = \"in\"\n",
"pyplot.rcParams[\"ytick.direction\"] = \"in\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = pandas.read_csv(data_file, header=0, index_col=0)\n",
"x = data.columns[0:].astype(float)\n",
"y = data.index[0:].astype(float)\n",
"z = data.values\n",
"zmax = numpy.absolute(z).max()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = pyplot.figure()\n",
"ax = fig.add_subplot(1, 1, 1)\n",
"ax.contour(x, y, z, num_contour, cmap=\"bwr\", vmin=-1 * zmax, vmax=zmax)\n",
"\n",
"ax.set_xlim(1200, 1000)\n",
"ax.set_xticks(numpy.arange(1200, 1000 - 1, -50))\n",
"ax.xaxis.set_minor_locator(ticker.MultipleLocator(10))\n",
"\n",
"ax.set_ylim(1200, 1000)\n",
"ax.set_yticks(numpy.arange(1200, 1000 - 1, -50))\n",
"ax.yaxis.set_minor_locator(ticker.MultipleLocator(10))\n",
"\n",
"ax.set_title(\"Synchronous\")\n",
"ax.set_xlabel(r\"$\\nu$${_{1}}$ / cm${^{-1}}$\")\n",
"ax.set_ylabel(r\"$\\nu$${_{2}}$ / cm${^{-1}}$\")\n",
"\n",
"pyplot.savefig(\"contour.png\", dpi=200, bbox_inches=\"tight\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = pyplot.figure()\n",
"ax = fig.add_subplot(1, 1, 1)\n",
"ax.contour(x, y, z, num_contour, cmap=\"bwr\", vmin=-1 * zmax, vmax=zmax)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = pyplot.figure()\n",
"ax = fig.add_subplot(1, 1, 1)\n",
"ax.pcolormesh(x, y, z, cmap=\"bwr\", vmin=-1 * zmax, vmax=zmax)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = pyplot.figure()\n",
"ax = fig.add_subplot(1, 1, 1)\n",
"ax.contour(x, y, z, num_contour, colors=\"black\", linewidths=0.5, linestyles=\"solid\", vmin=-1 * zmax, vmax=zmax)\n",
"ax.pcolormesh(x, y, z, cmap=\"jet\", vmin=-1 * zmax, vmax=zmax)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = pyplot.figure()\n",
"ax = fig.add_subplot(1, 1, 1)\n",
"ax.contour(x, y, z, num_contour, colors=\"black\", linewidths=0.5, vmin=-1 * zmax, vmax=zmax)\n",
"ax.contourf(x, y, z, levels=0, colors=[\"gray\", \"white\"], vmin=-1 * zmax, vmax=zmax)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
Unknown
|
2D
|
shigemorita/2Dpy
|
2Dpy.ipynb
|
.ipynb
| 3,678
| 141
|
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"import numpy\n",
"import pandas\n",
"from matplotlib import pyplot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"hetero = False\n",
"inputfile1 = \"spec.csv\"\n",
"\n",
"# hetero=True\n",
"# inputfile1=\"spec1.csv\"\n",
"# inputfile2=\"spec2.csv\"\n",
"\n",
"left_large = True\n",
"dynamic = True\n",
"num_contour = 16"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# file read\n",
"spec1 = pandas.read_csv(inputfile1, header=0, index_col=0).T\n",
"if hetero == False:\n",
" inputfile2 = inputfile1\n",
"spec2 = pandas.read_csv(inputfile2, header=0, index_col=0).T\n",
"if len(spec1) != len(spec2):\n",
" raise Exception(\"data mismatching\")\n",
"spec1.T.plot(legend=None)\n",
"if left_large: pyplot.xlim(max(spec1.columns), min(spec1.columns))\n",
"if hetero:\n",
" spec2.T.plot(legend=None)\n",
" if left_large: pyplot.xlim(max(spec2.columns), min(spec2.columns))\n",
"if dynamic:\n",
" spec1 = spec1 - spec1.mean()\n",
" spec2 = spec2 - spec2.mean()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def contourplot(spec):\n",
" x = spec.columns[0:].astype(float)\n",
" y = spec.index[0:].astype(float)\n",
" z = spec.values\n",
" zmax = numpy.absolute(z).max()\n",
" pyplot.figure(figsize=(6, 6))\n",
" pyplot.contour(x, y, z, num_contour, cmap=\"bwr\", vmin=-1 * zmax, vmax=zmax)\n",
" # pyplot.pcolormesh(x,y,z,cmap='jet',vmin=-1*zmax,vmax=zmax)\n",
" if left_large:\n",
" pyplot.xlim(max(x), min(x))\n",
" pyplot.ylim(max(y), min(y))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# synchronous correlation\n",
"sync = pandas.DataFrame(spec1.values.T @ spec2.values / (len(spec1) - 1))\n",
"sync.index = spec1.columns\n",
"sync.columns = spec2.columns\n",
"sync = sync.T\n",
"contourplot(sync)\n",
"sync.to_csv(inputfile1[: len(inputfile1) - 4] + \"_sync.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Hilbert-Noda transformation matrix\n",
"noda = numpy.zeros((len(spec1), len(spec1)))\n",
"for i in range(len(spec1)):\n",
" for j in range(len(spec1)):\n",
" if i != j: noda[i, j] = 1 / math.pi / (j - i)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# asynchronouse correlation\n",
"asyn = pandas.DataFrame(spec1.values.T @ noda @ spec2.values / (len(spec1) - 1))\n",
"asyn.index = spec1.columns\n",
"asyn.columns = spec2.columns\n",
"asyn = asyn.T\n",
"contourplot(asyn)\n",
"asyn.to_csv(inputfile1[: len(inputfile1) - 4] + \"_async.csv\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
Unknown
|
2D
|
GiggleLiu/QuantumMPS
|
resolve_env.jl
|
.jl
| 386
| 20
|
using Pkg
deps = ["Yao", "DelimitedFiles", "FileIO", "Fire", "JLD2", "KrylovKit", "StatsBase"]
extras = ["CUDAnative", "CuArrays"]
USE_CUDA = !("nocuda" in ARGS)
if USE_CUDA
deps = vcat(deps, extras)
end
for x in deps
println("Installing $x ...")
Pkg.add(x)
end
if USE_CUDA
println("Installing CuYao ...")
Pkg.clone("https://github.com/QuantumBFS/CuYao.jl")
end
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
j1j2.jl
|
.jl
| 2,133
| 61
|
include("applications.jl")
using Fire
@main function sample_cluster()
m = model(Val(:cluster); nbit=3, B=10)
@show gensample(m, X)
end
"""
julia j1j2.jl train [--symmetry <su2|u1|general>] [--depth <Int>]
Train a 4 x 4 frustrated Heisenberg model with J2 = 0.5.
Available ansatz symmetries includes `general`, `u1` and `su2`.
"""
@main function train(;symmetry::Symbol=:su2, depth::Int=5)
USE_CUDA || @warn "You are not using GPU (35 x speed up), this training may take life long. Turn on the switch in file `applications.jl` if you have a GPU that supports CUDA!"
model = simple_model_j1j2(4, 4)
ansatz = simple_ansatz(16, symmetry, depth; load_params=false)
run_train(ansatz, model; SAVE_ID=Symbol(symmetry,:_d,depth), niter=500, start_point=0)
end
"""
julia j1j2.jl measure <energy|fidelity|szsz> [--symmetry <su2|u1|general>] [--depth <Int>]
Load a pre-trained ansatz for 4 x 4 frustrated Heisenberg model with J2 = 0.5, tasks includes
* energy, sample the energy.
* szsz, calculate the <sz(i)*sz(j)> correlation matrix.
Also, we can obtain some exact quantities in simulation for analysis
* fidelity, calculated the exact fidelity
* energy_exact, calculated the exact energy
Pre-trained ansatz includes
* --symmetry su2, --depth <1-5>
* --symmetry u1, --depth 5
* --symmetry general, --depth 5
"""
@main function measure(task::String; symmetry::Symbol=:su2, depth::Int=5)
model = simple_model_j1j2(4, 4)
ansatz = simple_ansatz(16, symmetry, depth; load_params=true)
nbit = nbit_simulated(ansatz)
if task == "energy"
eng_sample = energy(ansatz, model)/nbit
println("Sampled value of energy/site = $eng_sample")
elseif task == "energy_exact"
eng = energy_exact(ansatz, model)/nbit
println("Exact value of energy/site = $eng")
elseif task == "fidelity"
gs = ground_state(model)
fid = fidelity_exact(ansatz, gs)[]
println("Exact value of fidelity = $fid")
elseif task == "szsz"
correlation_matrix(ansatz; SAVE_ID=symmetry)
else
throw(ArgumentError("$task is not defined!"))
end
end
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
applications.jl
|
.jl
| 4,101
| 126
|
push!(LOAD_PATH, abspath("src"))
using Yao
using QMPS
using DelimitedFiles, JLD2, FileIO, Pkg
# CUDA switch
const USE_CUDA = haskey(Pkg.installed(), "CuYao")
USE_CUDA && println("Hint: Using CUDA since `CuYao` is detected. Edit file `applications.jl` to modify CUDA settings, like switching computing devices.")
USE_CUDA && include("CuChem.jl")
USE_CUDA && device!(CuDevice(0))
"""Heisenberg model without frustration, with open boundary condition."""
simple_model_heis(size...) = Heisenberg(size...; periodic=false)
"""Heisenberg model with frustration strenth J2 = 0.5, with open boundary condition."""
simple_model_j1j2(size...) = J1J2(size...; periodic=false, J2=0.5)
"""
simple_ansatz(nbit::Int, symmetry::Symbol, depth::Int; load_params::Bool=false)
Load a predefined MPS inspired ansatz with 4 virtual qubits, batch size 4096.
If `load_params` is `true`, load parameters in training step 500.
"""
function simple_ansatz(nbit::Int, symmetry::Symbol, depth::Int; load_params::Bool=false)
V = 4 # number of virtual qubits
batch_size = 4096
load_step = 500
ansatz = model(Val(symmetry); nbit=nbit, nlayer=depth, V=V, B=batch_size, pairs=pair_ring(V+1))
USE_CUDA && (ansatz = ansatz |> cu)
if load_params
filename = "data/chem_$(symmetry)_d$(depth)_N$(nbit)_V$(V)_S$(load_step).jld2"
params = load_training(filename)[3]
println("loading file $filename")
println("Number of parameters is ", params |> length)
dispatch!(ansatz.circuit, params)
end
ansatz
end
"""
save_training(filename, qopt::Adam, loss, params, fidelity)
Save training status.
"""
function save_training(filename, qopt::Adam, loss, params, fidelity)
save(filename, "qopt", qopt, "loss", loss, "params", params, "fidelity", fidelity)
end
"""
load_training(filename) -> Tuple
Load training status (qopt, loss, params, fidelity).
"""
function load_training(filename)
res = load(filename)
res["qopt"], res["loss"], res["params"], res["fidelity"]
end
"""
run_train(ansatz, model; SAVE_ID, niter=500, start_point=0, save_step=0)
Run a variational quantum eigensolver to solve a `model` Hamiltonian with MPS inspired `ansatz`
* SAVE_ID, token used for saving data,
* niter, number of training steps,
* start_point, load a save point, default is 0 (random parameters).
* save_step, save the training every `save_step` steps.
"""
function run_train(ansatz, model; SAVE_ID, niter::Int=500, start_point::Int=0, save_step::Int=10)
nbit = nbit_simulated(ansatz)
V = ansatz.nbit_virtual
filename(k::Int) = "data/chem_$(SAVE_ID)_N$(nbit)_V$(V)_S$(k).jld2"
if start_point==0
optimizer = Adam(lr=0.1)
history = Float64[]
fidelities = Float64[]
else
optimizer, history, _params, fidelities = load_training(filename(start_point))
dispatch!(ansatz.circuit, _params)
end
qo = QMPSOptimizer(ansatz, model, optimizer)
gs = ground_state(model)
for (k_, p) in enumerate(qo)
k = k_ + start_point
curr_loss = energy(ansatz, model)/nbit
fid = fidelity_exact(ansatz, gs)[]
push!(history, curr_loss)
push!(fidelities, fid)
println("step = $k, energy/site = $curr_loss, fidelity = $(fid)")
flush(stdout)
if k%save_step == 0 || k == niter
save_training(filename(k), qo.optimizer, history, parameters(ansatz.circuit), fidelities)
k >= niter && break
end
end
end
"""
correlation_matrix(ansatz, op; SAVE_ID)
Calculate and save correlation matrix <Z_i Z_j>.
"""
function correlation_matrix(ansatz; SAVE_ID)
nbit = nbit_simulated(ansatz)
V = ansatz.nbit_virtual
om = zeros(Float64, nbit, nbit)
for i =1:nbit, j=1:nbit
if i!=j
om[i,j] = measure_corr(ansatz, i=>Z, j=>Z) |> real
println("<σz($i)σz($j)> = $(om[i,j])")
end
end
for (token, var) in [
("om", om),
]
writedlm("data/chem_$(SAVE_ID)_$(token)$(tag)_N$(nbit)_V$V.dat", var)
end
end
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
CuChem.jl
|
.jl
| 292
| 10
|
using CUDAnative, CuArrays
using CUDAnative: device!, devices, CuDevice
using CuYao
import CuYao: cu
CuArrays.allowscalar(false)
function cu(chem::QuantumMPS)
QuantumMPS(chem.nbit_measure, chem.nbit_virtual, chem.nbit_ancilla, chem.circuit, chem.initial_reg |> cu, chem.input_state)
end
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
TFI_onefile.jl
|
.jl
| 3,911
| 130
|
using Yao
using Statistics: mean
using LinearAlgebra
rotor(noleading::Bool=false, notrailing::Bool=false) = noleading ? (notrailing ? Rx(0) : chain(Rx(0), Rz(0))) : (notrailing ? chain(Rz(0), Rx(0)) : chain(Rz(0), Rx(0), Rz(0)))
function twoqubit_circuit(nlayer::Int, nrepeat::Int)
nbit_measure = nbit_virtual = 1
nbit_used = nbit_measure + nbit_virtual
circuit = chain(nbit_used)
for i=1:nrepeat
unit = chain(nbit_used)
for j=1:nlayer
push!(unit, put(nbit_used, 1=>rotor(true, false)))
#push!(unit, put(nbit_used, 2=>rotor(true, false)))
push!(unit, control(nbit_used, 1, 2=>(j%2==1 ? X : Z)))
j == nlayer && push!(unit, put(nbit_used, 1=>rotor(false, true)))
#j == nlayer && push!(unit, put(nbit_used, 2=>rotor(false, true)))
end
push!(unit, Measure{nbit_used, 1, AbstractBlock}(Z, (1,), 0, false))
if i==nrepeat
for k=2:nbit_used
push!(unit, Measure{nbit_used, 1, AbstractBlock}(Z, (k,), 0, false))
end
end
push!(circuit, unit)
end
dispatch!(circuit, :random)
end
circuit = twoqubit_circuit(2, 2)
function gensample(circuit, basis; nbatch=1024)
mblocks = collect_blocks(Measure, circuit)
for m in mblocks
m.operator = basis
end
reg = zero_state(nqubits(circuit); nbatch=nbatch)
reg |> circuit
mblocks
end
function energy(circuit, model::TFIChain; nbatch=1024)
# measuring Z basis
mblocks = gensample(circuit, Z; nbatch=nbatch)
local eng = 0.0
for (a, b, v) in ((i, i+1, 1.0) for i=1:model.length-1)
eng += v*mean(mblocks[a].results .* mblocks[b].results)
end
eng/=4
# measuring X basis
mblocks = gensample(circuit, X; nbatch=nbatch)
engx = sum(mean.([m.results for m in mblocks]))
eng + model.h*engx/2
end
struct TFIChain
length::Int
h::Float64
periodic::Bool
TFIChain(length::Int; h::Real, periodic::Bool) = new(length, Float64(h), periodic)
end
function hamiltonian(model::TFIChain)
model.periodic && throw()
nbit = model.length
sum(repeat(nbit, Z, (i,i+1)) for i=1:nbit-1)*0.25 +
sum(put(nbit, i=>X) for i=1:nbit)*0.5model.h
end
using Test, Random
nbit_simulated(qmps) = length(collect_blocks(Measure, qmps))
function expand_circuit(circuit)
nbit = nbit_simulated(circuit)
nm = 1
nv = 1
c = chain(nbit)
for (i, blk) in enumerate(circuit)
blk = chain([b for b in blk if !(b isa Measure)]...)
push!(c, concentrate(nbit, blk, [(i-1)*nm+1:i*nm..., nbit-nv+1:nbit...]))
end
c
end
function train(circuit, model; maxiter=200, α=0.1, nbatch=1024)
rots = collect(RotationGate, circuit)
for i in 1:maxiter
for r in rots
r.theta += π/2
E₊ = energy(circuit, model; nbatch=nbatch)
r.theta -= π
E₋ = energy(circuit, model; nbatch=nbatch)
r.theta += π/2
g = 0.5(E₊ - E₋)
r.theta -= g*α
end
println("Iter $i, E/N = $(energy(circuit, model, nbatch=nbatch)/model.length)")
end
circuit
end
nspin = 4
model = TFIChain(nspin; h=0.5, periodic=false)
h = hamiltonian(model)
EG = eigen(mat(h) |> Matrix).values[1]
@show EG/nspin
circuit = twoqubit_circuit(2, nspin-1)
train(circuit, model; α=0.5)
@testset "energy-goodqn tfi" begin
Random.seed!(4)
hei = TFIChain(4; h=0., periodic=false)
nbit = hei.length
circuit = twoqubit_circuit(2, nbit-1)
println("Number of parameters is ", circuit|> nparameters)
bigc = expand_circuit(circuit)
eng = energy(circuit, hei; nbatch=10000)
hami = hamiltonian(hei)
@show bigc
@show parameters(circuit)
@show parameters(bigc)
eng_exact = expect(hami, zero_state(nbit) |> bigc) |> real
@show eng, eng_exact
@test isapprox(eng, eng_exact, rtol=0.2)
end
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
chainmodel.jl
|
.jl
| 1,080
| 32
|
include("applications.jl")
function train(nsite;depth::Int=2)
symmetry = :twoqubit
model = TFI(nsite; h=0.5, periodic=false)
ansatz = simple_ansatz(nsite, symmetry, depth; load_params=false)
run_train(ansatz, model; SAVE_ID=Symbol(symmetry,:_d,depth), niter=500, start_point=0)
end
function measure(task::String; symmetry::Symbol=:su2, depth::Int=5)
model = simple_model_heis(6)
ansatz = simple_ansatz(6, symmetry, depth; load_params=true)
nbit = nbit_simulated(ansatz)
if task == "energy"
eng_sample = energy(ansatz, model)/nbit
println("Sampled value of energy/site = $eng_sample")
elseif task == "energy_exact"
eng = energy_exact(ansatz, model)/nbit
println("Exact value of energy/site = $eng")
elseif task == "fidelity"
gs = ground_state(model)
fid = fidelity_exact(ansatz, gs)[]
println("Exact value of fidelity = $fid")
elseif task == "szsz"
correlation_matrix(ansatz; SAVE_ID=symmetry)
else
throw(ArgumentError("$task is not defined!"))
end
end
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
src/TFI.jl
|
.jl
| 1,078
| 44
|
export TFI
struct TFI{D} <: AbstractModel{D}
size::NTuple{D, Int}
h::Float64
periodic::Bool
TFI(size::Int...; h::Real, periodic::Bool) = new{length(size)}(size, Float64(h), periodic)
end
function get_bonds(model::TFI{1})
nbit, = model.size
[(i, i%nbit+1, 1.0) for i in 1:(model.periodic ? nbit : nbit-1)]
end
Base.size(model::TFI) = model.size
function hamiltonian(model::TFI{1})
model.periodic && throw()
nbit = nspin(model)
sum(repeat(nbit, Z, (i,i+1)) for i=1:nbit-1)*0.25 +
sum(put(nbit, i=>X) for i=1:nbit)*0.5model.h
end
function energy(chem::QuantumMPS, model::TFI)
energy(chem, Z, model) +
energy(chem, X, model)
end
function energy(chem::QuantumMPS, pauli::ZGate, model::TFI)
res = gensample(chem, pauli)
local eng = 0.0
for bond in ((i, i+1, 1.0) for i=1:nspin(model)-1)
eng += bond[3]*mean(res[:,bond[1]].*res[:,bond[2]])
end
eng/4
end
function energy(chem::QuantumMPS, pauli::XGate, model::TFI)
res = gensample(chem, pauli)
eng = mean(sum(res, dims=2))
model.h*eng/2
end
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
src/Adam.jl
|
.jl
| 972
| 41
|
export Adam, update!
mutable struct Adam
lr::AbstractFloat
gclip::AbstractFloat
beta1::AbstractFloat
beta2::AbstractFloat
eps::AbstractFloat
t::Int
fstm
scndm
end
Adam(; lr=0.001, gclip=0, beta1=0.9, beta2=0.999, eps=1e-8)=Adam(lr, gclip, beta1, beta2, eps, 0, nothing, nothing)
function update!(w, g, p::Adam)
gclip!(g, p.gclip)
if p.fstm===nothing; p.fstm=zero(w); p.scndm=zero(w); end
p.t += 1
lmul!(p.beta1, p.fstm)
BLAS.axpy!(1-p.beta1, g, p.fstm)
lmul!(p.beta2, p.scndm)
BLAS.axpy!(1-p.beta2, g .* g, p.scndm)
fstm_corrected = p.fstm / (1 - p.beta1 ^ p.t)
scndm_corrected = p.scndm / (1 - p.beta2 ^ p.t)
BLAS.axpy!(-p.lr, @.(fstm_corrected / (sqrt(scndm_corrected) + p.eps)), w)
end
function gclip!(g, gclip)
if gclip == 0
g
else
gnorm = vecnorm(g)
if gnorm <= gclip
g
else
BLAS.scale!(gclip/gnorm, g)
end
end
end
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
src/gradient.jl
|
.jl
| 1,173
| 39
|
export QMPSOptimizer, gradients_exact
struct QMPSOptimizer
chem::QuantumMPS
model::AbstractModel
optimizer
diff_blocks
params::Vector
QMPSOptimizer(chem::QuantumMPS, model::AbstractModel, optimizer) = new(chem, model, optimizer, collect_blocks(AbstractDiff, chem.circuit), parameters(chem.circuit))
end
# TODO: setiparameters! throw number of parameters mismatch error!
function gradient(chem::QuantumMPS, db::AbstractDiff, model::AbstractModel)
db.block.theta += π/2
epos = energy(chem, model)
db.block.theta -= π
eneg = energy(chem, model)
db.block.theta += π/2
real(epos-eneg)/2
end
import Base: iterate
function iterate(qo::QMPSOptimizer, state::Int=1)
# initialize the parameters
grad = gradient.(Ref(qo.chem), qo.diff_blocks, Ref(qo.model))
update!(qo.params, grad, qo.optimizer)
dispatch!(qo.chem.circuit, qo.params)
(grad, state+1)
end
function gradients_exact(chem, hami; dbs=nothing)
nbit = nbit_simulated(chem)
circuit = expand_circuit(chem)
if dbs == nothing
dbs = collect_blocks(AbstractDiff, circuit)
end
opdiff.(()->state_exact(chem), dbs, Ref(hami))
end
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
src/correlation.jl
|
.jl
| 1,928
| 60
|
export measure_corr
"""
measure correlator.
e.g. measure_corr(chem, 1=>X, 3=>X) will measure <σₓ¹σₓ³> from a quantum MPS.
"""
function measure_corr(chem::QuantumMPS, si::Pair{Int, <:PauliGate}, sj::Pair{Int, <:PauliGate})
si.first > sj.first && return measure_corr(chem, sj, si)
si.first == sj.first && throw(ArgumentError("Qubit address conflict error!"))
T = datatype(chem.initial_reg)
input_state = chem.input_state
reg = chem.initial_reg |> copy
nv = chem.nbit_virtual + chem.nbit_ancilla
nrep = nrepeat(chem)
for i = nrep+1:nrep+nv
input_state[i] == 1 && apply!(reg, put(nv+1, (i-nrep+1)=>XGate{T}()))
end
local res = nothing
input_state[1] == 1 && apply!(reg, put(nv+1, 1=>XGate{T}()))
for i=1:nrep
reg |> getblock(chem, i)
if i!=nrep
res_i = _measure!(reg, i, [si, sj], input_state, true)
if res_i != nothing
if res != nothing
return mean(res.*res_i)
else
res = res_i
end
end
end
end
for i=1:nv+1-chem.nbit_ancilla
res_i = _measure!(reg, i+nrep-1, [si, sj], input_state, false)
if res_i != nothing
if res != nothing
return mean(res.*res_i)
else
res = res_i
end
end
end
throw()
end
function _measure!(reg::AbstractRegister{B, T}, i, sl, input_state, reset) where {B, T}
for si in sl
if i==si.first
op_i = eigen!(si.second |> mat |>Matrix)
reg |> put(nqubits(reg), 1=>matblock(T.(op_i.vectors' |> Matrix)))
return @inbounds 1 .- 2 .* (reset ? measure_collapseto!(reg, 1; config=input_state[i+1]) : measure_remove!(reg, 1))
end
end
reset ? measure_collapseto!(reg, 1; config=input_state[i+1]) : measure_remove!(reg, 1)
nothing
end
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
src/AbstractModel.jl
|
.jl
| 1,050
| 41
|
export AbstractModel, Heisenberg
export heisenberg_ij, hamiltonian, heisenberg_term, ground_state, energy, energy_exact, get_bonds, energy, heisenberg_2d, nspin
abstract type AbstractModel{D} end
abstract type AbstractHeisenberg{D} <: AbstractModel{D} end
nspin(model::AbstractModel) = prod(size(model))
"""
energy_exact(tc::QuantumMPS, model::AbstractModel) -> Float64
Exact ground state energy.
"""
function energy_exact(tc::QuantumMPS, model::AbstractModel)
nbit = nbit_simulated(tc)
expect(hamiltonian(model), state_exact(tc)) |> real
end
"""
ground_state(model::AbstractModel) -> DefaultRegister
Get the exact ground state of a model.
"""
function ground_state(model::AbstractModel)
# get the ground state
hami = hamiltonian(model)
E, v = eigsolve(mat(hami), 1, :SR)
register(v[1])
end
"""
get_bonds(model::AbstractHeisenberg) -> Vector
Get the weighted bonds of a Heisenberg model in the form Tuple(i,j,w_ij).
"""
function get_bonds end
include("Heisenberg.jl")
include("J1J2.jl")
include("TFI.jl")
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
src/Core.jl
|
.jl
| 2,936
| 90
|
export getblock, nbit_used, nbit_simulated, nrepeat, expand_circuit, QuantumMPS
export state_exact, fidelity_exact
export gensample
"""
QuantumMPS{RT}
Members:
`nbit_measure`, number of qubits measured in a single iteration, or physical qubits.
`nbit_virtual`, number of virtual qubits to represent the virtual bond dimension in quantum MPS.
`circuit`, the circuit structure (measurements are not included).
`initial_reg`, the initial state (GPUReg or a regular one), always prepaired in |0>.
`nbit_ancilla`, number of ancilla qubits.
"""
struct QuantumMPS{RT}
nbit_measure::Int
nbit_virtual::Int
nbit_ancilla::Int
circuit::AbstractBlock
initial_reg::RT
input_state::Vector{Int}
end
getblock(chem::QuantumMPS, i::Int) = chem.circuit[i]
nrepeat(chem::QuantumMPS) = length(chem.circuit)
nbit_used(chem::QuantumMPS) = nqubits(chem.circuit[1])
nbit_simulated(chem::QuantumMPS) = chem.nbit_measure*nrepeat(chem) + chem.nbit_virtual
"""convert a chem circuit to a circuit with no reuse"""
function expand_circuit(tnchem)
nbit = nbit_simulated(tnchem) + tnchem.nbit_ancilla
nm = tnchem.nbit_measure
nv = tnchem.nbit_virtual + tnchem.nbit_ancilla
c = chain(nbit)
for (i, blk) in enumerate(tnchem.circuit)
push!(c, concentrate(nbit, blk, [(i-1)*nm+1:i*nm..., nbit-nv+1:nbit...]))
end
c
end
function state_exact(chem::QuantumMPS)
circuit = expand_circuit(chem)
if chem.nbit_ancilla == 0
return product_state(nqubits(circuit), chem.input_state|>packbits) |> circuit
else
nbit = nqubits(circuit)
product_state(nbit, chem.input_state|>packbits) |> circuit |> focus!((1:nbit-chem.nbit_ancilla)...) |> remove_env!
end
end
function remove_env!(reg::DefaultRegister)
reg.state = dropdims(sum(reg |> rank3, dims=2), dims=2)
reg
end
function fidelity_exact(chem::QuantumMPS, ground_state::AbstractRegister)
fidelity(ground_state, state_exact(chem))
end
function gensample(chem::QuantumMPS, pauli::PauliGate)
input_state = chem.input_state
reg = chem.initial_reg |> copy
nv = chem.nbit_virtual + chem.nbit_ancilla
nrep = nrepeat(chem)
T = datatype(chem.initial_reg)
op = eigen!(pauli |> mat |>Matrix)
G = matblock(T.(op.vectors' |> Matrix))
rotor = put(nv+1, 1=>G)
local res = similar(reg |> state, Int, nbatch(reg), nbit_simulated(chem))
for i = nrep+1:nrep+nv
input_state[i] == 1 && apply!(reg, put(nv+1, (i-nrep+1)=>X))
end
input_state[1] == 1 && apply!(reg, put(nv+1, 1=>X))
for i=1:nrep
reg |> getblock(chem, i)
if i!=nrep
reg |> rotor
@inbounds res[:,i] = 2 .* measure_collapseto!(reg, 1; config=input_state[i+1]) .- 1
end
end
for i=1:nv+1-chem.nbit_ancilla
reg |> put(nqubits(reg), 1=>G)
@inbounds res[:,i+nrep-1] = 2 .* measure_remove!(reg, 1) .- 1
end
res
end
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
src/J1J2.jl
|
.jl
| 1,535
| 55
|
export J1J2
"""
J1J2{D} <: AbstractHeisenberg{D}
frustrated Heisenberg model.
"""
struct J1J2{D} <: AbstractHeisenberg{D}
size::NTuple{D, Int}
periodic::Bool
J2::Float64
J1J2(size::Int...; J2::Real, periodic::Bool) = new{length(size)}(size, periodic, Float64(J2))
end
Base.size(model::J1J2) = model.size
function hamiltonian(model::J1J2)
nbit = nspin(model)
sum(x->x[3]*heisenberg_ij(nbit, x[1], x[2]), get_bonds(model))*0.25
end
@inline function get_site(ij, mn, pbc::Val{true})
Tuple(mod(i-1,m)+1 for (i,m) in zip(ij, mn))
end
@inline function get_site(ij, mn, pbc::Val{false})
Tuple(i<=m ? i : 0 for (i,m) in zip(ij, mn))
end
function get_bonds(model::J1J2{2})
m, n = model.size
cis = LinearIndices(model.size)
bonds = Tuple{Int, Int, Float64}[]
for i=1:m, j=1:n
for (_i, _j) in [(i+1, j), (i, j+1)]
sites = get_site((_i, _j), (m, n), Val(model.periodic))
if all(sites .> 0)
push!(bonds, (cis[i,j], cis[sites...], 1.0))
end
end
for (_i, _j) in [(i-1, j-1), (i-1, j+1)]
sites = get_site((_i, _j), (m, n), Val(model.periodic))
if all(sites .> 0)
push!(bonds, (cis[i,j], cis[sites...], model.J2))
end
end
end
bonds
end
function get_bonds(model::J1J2{1})
nbit, = model.size
vcat([(i, i%nbit+1, 1.0) for i in 1:(model.periodic ? nbit : nbit-1)], [(i, (i+1)%nbit+1, model.J2) for i in 1:(model.periodic ? nbit : nbit-2)])
end
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
src/Heisenberg.jl
|
.jl
| 1,635
| 51
|
struct Heisenberg{D} <: AbstractHeisenberg{D}
size::NTuple{D, Int}
periodic::Bool
Heisenberg(size::Int...; periodic::Bool) = new{length(size)}(size, periodic)
end
Base.size(model::Heisenberg) = model.size
heisenberg_ij(nbit::Int, i::Int, j::Int=i+1) = put(nbit, i=>X)*put(nbit, j=>X) + put(nbit, i=>Y)*put(nbit, j=>Y) + put(nbit, i=>Z)*put(nbit, j=>Z)
const heisenberg_term = repeat(2, X, 1:2) + repeat(2, Y, 1:2) + repeat(2, Z, 1:2)
function hamiltonian(model::Heisenberg)
nbit = nspin(model)
sum(x->heisenberg_ij(nbit, x[1], x[2]), get_bonds(model))*0.25
end
function get_bonds(model::Heisenberg{2})
m, n = model.size
cis = LinearIndices(model.size)
bonds = Tuple{Int, Int, Float64}[]
for i=1:m, j=1:n
(i!=m || model.periodic) && push!(bonds, (cis[i,j], cis[i%m+1,j], 1.0))
(j!=n || model.periodic) && push!(bonds, (cis[i,j], cis[i,j%n+1], 1.0))
end
bonds
end
function get_bonds(model::AbstractModel{1})
nbit, = model.size
[(i, i%nbit+1, 1.0) for i in 1:(model.periodic ? nbit : nbit-1)]
end
"""
energy(chem::QuantumMPS, model::AbstractHeisenberg) -> Float64
Ground state energy by sampling Quantum MPS.
The hamiltonian is limited to Heisenberg and J1J2 Type.
"""
function energy(chem::QuantumMPS, model::AbstractHeisenberg)
energy(chem, X, model) + energy(chem, Y, model) + energy(chem, Z, model)
end
function energy(chem::QuantumMPS, pauli::PauliGate, model::AbstractHeisenberg)
res = gensample(chem, pauli)
local eng = 0.0
for bond in get_bonds(model)
eng += bond[3]*mean(res[:,bond[1]].*res[:,bond[2]])
end
eng/4
end
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
src/QMPS.jl
|
.jl
| 370
| 22
|
module QMPS
using Yao
using Yao.ConstGate: SWAP
using BitBasis: packbits
using StatsBase
using StatsBase: mean
using LinearAlgebra
using KrylovKit
using QuAlgorithmZoo
PauliGate{T} = Union{XGate{T}, YGate{T}, ZGate{T}}
include("Adam.jl")
include("Core.jl")
include("AbstractModel.jl")
include("gradient.jl")
include("correlation.jl")
include("ansatz/ansatz.jl")
end
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
src/ansatz/su2_circuit.jl
|
.jl
| 1,700
| 51
|
function su2_unit(nbit::Int, i::Int, j::Int)
put(nbit, (i,j)=>rot(SWAP, 0.0))
end
"""
su2_circuit(nbit_virtual::Int, nlayer::Int, nrepeat::Int, pairs::Vector) -> Sequence
SU(2) symmetry quantum circuit ansatz for evolving states in S^2 = 0 good quantum number block. It requires `2+nbit_virtual` qubits, `pairs` is the geometry of entanglements.
"""
function su2_circuit(nbit_virtual::Int, nlayer::Int, nrepeat::Int, pairs::Vector)
nbit_used = 2 + nbit_virtual
circuit = chain(nbit_used)
for i=1:nrepeat
unit = chain(nbit_used)
if i==1
for j=2-(nrepeat%2):2:nbit_virtual
push!(unit, singlet_block(nbit_used, j, j+1))
end
end
if i%2 != nrepeat%2
push!(unit, singlet_block(nbit_used, 1, nbit_used))
else
if i!=1
push!(unit, swap(nbit_used, 1, nbit_used)) # fix swap parameter order!
end
end
for j=1:nlayer
nring = nbit_virtual+1
nring <= 1 && continue
ops = [su2_unit(nbit_used, i, j) for (i,j) in pairs]
push!(unit, chain(nbit_used, ops))
end
push!(circuit, unit)
end
dispatch!(circuit, :random)
end
"""construct a circuit for generating singlets."""
function singlet_block(nbit::Int, i::Int, j::Int)
unit = chain(nbit)
push!(unit, put(nbit, i=>chain(X, H)))
push!(unit, control(nbit, -i, j=>X))
end
function model(::Val{:su2}; nbit, V, B=4096, nlayer=5, pairs)
nrepeat = nbit - V
c = su2_circuit(V, nlayer, nrepeat, pairs) |> autodiff(:QC)
chem = QuantumMPS(1, V, 1, c, zero_state(V+2, nbatch=B), zeros(Int, nbit+1))
chem
end
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
src/ansatz/general_circuit.jl
|
.jl
| 2,153
| 62
|
using Yao
export random_circuit, pair_ring
"""
pair_ring(n::Int) -> Vector
Pair ring.
"""
pair_ring(n::Int) = [i=>mod(i, n)+1 for i=1:n]
"""
cnot_entangler(n::Int, pairs::Vector{Pair}) = ChainBlock
Arbitrary entangler unit, support lazy construction.
"""
cnot_entangler(n::Int, pairs) = chain(n, control(n, [ctrl], target=>X) for (ctrl, target) in pairs)
"""
rotor(noleading::Bool=false, notrailing::Bool=false) -> MatrixBlock
`Rz(η)⋅Rx(θ)⋅Rz(ξ)`, remove the first Rz gate if `noleading == true`, remove the last Rz gate if `notrailing == true`.
"""
rotor(noleading::Bool=false, notrailing::Bool=false) = noleading ? (notrailing ? Rx(0) : chain(Rx(0), Rz(0))) : (notrailing ? chain(Rz(0), Rx(0)) : chain(Rz(0), Rx(0), Rz(0)))
"""
rotorset(noleading::Bool=false, notrailing::Bool=false) -> ChainBlock
A sequence of rotors applied on all sites.
"""
rotorset(nbit::Int, noleading::Bool=false, notrailing::Bool=false) = chain(nbit, [put(nbit, j=>rotor(noleading, notrailing)) for j=1:nbit])
"""
A kind of widely used differentiable quantum circuit, angles in the circuit are randomely initialized.
ref:
1. Kandala, A., Mezzacapo, A., Temme, K., Takita, M., Chow, J. M., & Gambetta, J. M. (2017).
Hardware-efficient Quantum Optimizer for Small Molecules and Quantum Magnets. Nature Publishing Group, 549(7671), 242–246.
https://doi.org/10.1038/nature23879.
"""
function random_circuit(nbit_measure::Int, nbit_virtual::Int, nlayer::Int, nrepeat::Int, entangler_pairs)
nbit_used = nbit_measure + nbit_virtual
circuit = chain(nbit_used)
entangler = cnot_entangler(nbit_used, entangler_pairs)
for i=1:nrepeat
unit = chain(nbit_used)
for j=1:nlayer
push!(unit, rotorset(nbit_used, false, false))
push!(unit, entangler)
end
push!(circuit, unit)
end
dispatch!(circuit, :random)
end
function model(::Val{:general}; nbit::Int, V::Int, B::Int=4096, nlayer::Int=5, pairs)
c = random_circuit(1, V, nlayer, nbit-V, pairs) |> autodiff(:QC)
chem = QuantumMPS(1, V, 0, c, zero_state(V+1, nbatch=B), zeros(Int, nbit))
chem
end
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
src/ansatz/ansatz.jl
|
.jl
| 531
| 19
|
export model
"""
model(which::Symbol; nbit::Int, V::Int, B::Int=4096, nlayer::Int=5)
predefined models, `which` should be one of :random, :u1, :su2.
* `nbit` is the system size (length of MPS),
* `V` is the number of virtual qubits,
* `B` is the batch size.
* `nlayer` is the number of layers in a block.
"""
model(which::Symbol, args...; kwargs...) = model(Val(which), args...; kwargs...)
include("general_circuit.jl")
include("u1_circuit.jl")
include("su2_circuit.jl")
include("twoqubit_circuit.jl")
include("cluster.jl")
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
src/ansatz/twoqubit_circuit.jl
|
.jl
| 946
| 28
|
export twoqubit_circuit
function twoqubit_circuit(nlayer::Int, nrepeat::Int)
nbit_measure = nbit_virtual = 1
nbit_used = nbit_measure + nbit_virtual
circuit = chain(nbit_used)
for i=1:nrepeat
unit = chain(nbit_used)
for j=1:nlayer
push!(unit, put(nbit_used, 1=>rotor(true, false)))
#push!(unit, put(nbit_used, 2=>rotor(true, false)))
push!(unit, control(nbit_used, 1, 2=>(j%2==1 ? X : Z)))
j == nlayer && push!(unit, put(nbit_used, 1=>rotor(false, true)))
#j == nlayer && push!(unit, put(nbit_used, 2=>rotor(false, true)))
end
push!(circuit, unit)
end
dispatch!(circuit, :random)
end
function model(::Val{:twoqubit}; nbit::Int, B::Int=4096, nlayer::Int=5, kwargs...)
V = 1
c = twoqubit_circuit(nlayer, nbit-V) |> autodiff(:QC)
chem = QuantumMPS(1, V, 0, c, zero_state(V+1, nbatch=B), zeros(Int, nbit))
chem
end
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
src/ansatz/cluster.jl
|
.jl
| 479
| 14
|
cluster_block(isfirst::Val{true}) = chain(2, [repeat(2, H, 1:2), control(2, 1, 2=>Z)])
cluster_block(isfirst::Val{false}) = chain(2, [swap(2, 1, 2), put(2, 2=>H), control(2, 1, 2=>Z)])
function cluster_circuit(nrepeat::Int)
sequence([cluster_block(Val(i==1)) for i=1:nrepeat])
end
function model(::Val{:cluster}; nbit, B=4096)
nrepeat = nbit - 1
c = cluster_circuit(nrepeat)
chem = QuantumMPS(1, 1, 0, c, zero_state(2, nbatch=B), zeros(Int, nbit))
chem
end
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
src/ansatz/u1_circuit.jl
|
.jl
| 1,115
| 36
|
function u1_unit(nbit::Int, i::Int, j::Int)
chain(nbit, put(nbit, i=>Rz(0)),
put(nbit, j=>Rz(0)),
put(nbit, (i,j)=>rot(SWAP, 0))
)
end
"""
u1_circuit(nbit_measure::Int, nbit_virtual::Int, nlayer::Int, nrepeat::Int, entangler_pairs) -> Sequence
U(1) symmetric quantum circuit ansatz.
"""
function u1_circuit(nbit_measure::Int, nbit_virtual::Int, nlayer::Int, nrepeat::Int, entangler_pairs)
nbit_used = nbit_measure + nbit_virtual
circuit = chain(nbit_used)
for i=1:nrepeat
unit = chain(nbit_used)
for j=1:nlayer
push!(unit, chain(nbit_used, u1_unit(nbit_used, i, j) for (i,j) in entangler_pairs))
for k=1:(i==nrepeat ? nbit_used : nbit_measure)
put(nbit_used, k=>Rz(0))
end
end
push!(circuit, unit)
end
dispatch!(circuit, :random)
end
function model(::Val{:u1}; nbit = 20, V=4, B=4096, nlayer=5, pairs)
nrepeat = (nbit - V)
c = u1_circuit(1, V, nlayer, nrepeat, pairs) |> autodiff(:QC)
chem = QuantumMPS(1, V, 0, c, zero_state(V+1, nbatch=B), [i%2 for i=1:nbit])
chem
end
|
Julia
|
2D
|
GiggleLiu/QuantumMPS
|
test/runtests.jl
|
.jl
| 3,587
| 82
|
push!(LOAD_PATH, abspath("src"))
using Yao
using LinearAlgebra, Statistics
using BitBasis: packbits
using QMPS
using Test, Random
# make it cluster state
@testset "convert wave function check" begin
chem = model(:su2; nbit=9, nlayer=2, B=10, V=5, pairs=pair_ring(5))
c = random_circuit(1, 4, 2, 5, pair_ring(5))
circuit = expand_circuit(chem)
@test zero_state(nqubits(circuit)) |> circuit |> statevec |> length == 2^10
end
@testset "measure check" begin
Random.seed!(5)
chem = model(:su2; nbit=9, nlayer=2, B=10000, V=5, pairs=pair_ring(5))
circuit = expand_circuit(chem)
for (i, j) in [(3,5), (5,3), (3,7), (7,3), (6,8), (8,6)]
@show (i,j)
mean35 = expect(heisenberg_ij(nqubits(circuit), i, j), zero_state(nqubits(circuit)) |> circuit) |> real
eng = sum(g->measure_corr(chem, i=>g, j=>g), [X, Y, Z])
@test isapprox(mean35, eng, rtol=0.4)
end
end
@testset "j1j2" begin
j1j2 = J1J2(4; periodic=false, J2=0.5)
@test get_bonds(j1j2) == [(1, 2, 1.0),(2, 3, 1.0), (3, 4, 1.0), (1,3, 0.5), (2,4, 0.5)]
j1j2 = J1J2(4; periodic=true, J2=0.5)
@test get_bonds(j1j2) == [(1, 2, 1.0), (2, 3, 1.0), (3, 4, 1.0), (4, 1, 1.0), (1,3, 0.5), (2,4, 0.5), (3,1, 0.5), (4,2, 0.5)]
j1j2 = J1J2(3, 3; periodic=false, J2=0.5)
prs = [1=>2, 1=>4, 2=>3, 2=>5, 2=>4, 3=>6, 3=>5, 4=>5, 4=>7, 5=>6, 5=>8, 5=>1, 5=>7, 6=>9, 6=>2, 6=>8, 7=>8, 8=>9, 8=>4, 9=>5]
vs = [1.0, 1, 1, 1, 0.5, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0, 0.5, 0.5, 1.0, 0.5, 0.5, 1.0, 1.0, 0.5, 0.5]
tps = [(i,j,v) for ((i,j), v) in zip(prs, vs)]
@test sort(get_bonds(j1j2)) == sort(tps)
j1j2 = J1J2(3, 3; periodic=true, J2=0.5)
@test sort(get_bonds(j1j2)) == sort([(1,2,1.0), (1,4,1.0), (1,9,0.5), (1,6,0.5), (2,3,1.0), (2,5,1.0), (2,7,0.5), (2,4,0.5), (3,1,1.0), (3,6,1.0), (3,8,0.5), (3,5,0.5),
(4,5,1.0), (4,7,1.0), (4,3,0.5), (4,9,0.5), (5,6,1.0), (5,8,1.0), (5,1,0.5), (5,7,0.5), (6,4,1.0), (6,9,1.0), (6,2,0.5), (6,8,0.5), (7,8,1.0), (7,1,1.0), (7,6,0.5), (7,3,0.5),
(8,9,1.0), (8,2,1.0), (8,4,0.5), (8,1,0.5), (9,7,1.0), (9,3,1.0), (9,5,0.5), (9,2,0.5)])
end
@testset "energy-goodqn" begin
Random.seed!(2)
for hei in [Heisenberg(10; periodic=false), Heisenberg(3, 3; periodic=false), J1J2(3,3; J2=0.5, periodic=false)]
nbit = nspin(hei)
for xmodel in [:u1, :su2]
@show xmodel
pairs = pair_ring(xmodel==:su2 ? 4 : 5)
chem = model(:general; nbit=nbit, B=10000, V=4, pairs=pairs)
println("Number of parameters is ", chem.circuit |> nparameters)
circuit = expand_circuit(chem)
eng = energy(chem, hei)
hami = hamiltonian(hei)
eng_exact = expect(hami, product_state(nbit, chem.input_state |> packbits) |> circuit) |> real
@test isapprox(eng, eng_exact, rtol=0.3)
end
end
end
@testset "energy-goodqn tfi" begin
Random.seed!(11)
for hei in [TFI(2; h=0.5, periodic=false)]
nbit = nspin(hei)
for xmodel in [:twoqubit]
@show xmodel
chem = model(xmodel; nbit=nbit, B=10000)
println("Number of parameters is ", chem.circuit |> nparameters)
circuit = expand_circuit(chem)
eng = energy(chem, hei)
hami = hamiltonian(hei)
@show circuit
eng_exact = expect(hami, product_state(nbit, chem.input_state |> packbits) |> circuit) |> real
@show eng, eng_exact
@test isapprox(eng, eng_exact, rtol=0.3)
end
end
end
|
Julia
|
2D
|
daihui/QuantumWalkSimulation
|
classicalRW.py
|
.py
| 1,583
| 58
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'levitan'
import math
from numpy import *
import numpy as np
import random
from matplotlib import pyplot as plt
def classicalRanNum():
coinX = int(random.choice(['1', '-1']))
coinY = int(random.choice(['1', '-1']))
return coinX, coinY
# print classicalRanNum()
def classicalWalkerPosition():
positionX = 0
positionY = 0
return positionX, positionY
def classicalWalk(walkNum):
walkerPositionX, walkerPositionY = classicalWalkerPosition()
for i in range(0, walkNum):
coinX, coinY = classicalRanNum()
walkerPositionX += coinX
walkerPositionY += coinY
return walkerPositionX, walkerPositionY
def classicalRWDistr(walkNum, matrixNum,satNum):
# positionList = []
walkerCount = zeros([2 *matrixNum + 1, 2 * matrixNum + 1])
for i in range(0, satNum):
walker = classicalWalk(walkNum)
#print walker,walker[0],walker[1]
# positionList.append(walker)
walkerCount[walker[0]+matrixNum][walker[1]+matrixNum] += float(1.0 / satNum)
return walkerCount
def Plot2D(classcialWalker,steps):
plt.figure(1)
ax1=plt.subplot(111)
plt.sca(ax1)
plt.title('2D distribution of %s steps Classical Random Walk' %steps)
plt.xlabel('X Position(started in center)')
plt.ylabel('Y Position(started in center)')
plt.imshow(classcialWalker)
plt.savefig('Fig/CRW_' + str(steps) + '.png')
plt.close()
for i in range(1,11):
classicalWalker=classicalRWDistr(i*10,100,100000)
Plot2D(classicalWalker,i*10)
|
Python
|
2D
|
daihui/QuantumWalkSimulation
|
AnimatedScatter.py
|
.py
| 4,512
| 125
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'levitan'
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from numpy import *
import QWithDiffShift as QDS
import time
class AnimatedScatterQW(object):
"""An animated scatter plot using matplotlib.animations.FuncAnimation."""
def __init__(self, X0, X1, steps, shiftGateNum, node):
self.X0 = X0
self.X1 = X1
self.steps = steps
self.shiftGateNum = shiftGateNum
self.node = node
self.step = 1
self.linedata = zeros([self.node, 1])
self.distri = self.distribution()
# Setup the figure and axes...
self.fig = plt.figure()
self.ax1 = plt.subplot(211)
plt.title('Quantum Walk in Cycle Graph,view in cycle')
self.step_text1 = self.ax1.text(-1.8, 1.2, '')
self.step_text1.set_text('')
self.ax2 = plt.subplot(212)
# self.ax2_axis=self.ax2.axis([1, self.node, 0, 1])
# self.ax2_axis.set_axis()
plt.title('Quantum Walk in Cycle Graph,position 0 probability')
# plt.title('Quantum Walk in Cycle Graph,position probability by step')
# Then setup FuncAnimation.
self.ani = animation.FuncAnimation(self.fig, self.update, frames=200, interval=500,
init_func=self.setup_plot, blit=True)
def setup_plot(self):
"""Initial drawing of the scatter plot."""
data = next(self.distri)
# temp=array([data[:,2]]).T
# print temp
# self.linedata=column_stack((self.linedata,temp))
# print self.linedata
x = data[:, 0]
y = data[:, 1]
d = data[:, 2]
self.ax1.axis([-4.5, 4.5, -1.5, 1.5])
self.scat = self.ax1.scatter(x, y, s=d, c='red', marker='o', animated=True)
self.ax2.axis([1, 5000, 0, 1])
self.line, = self.ax2.plot([], [])
# For FuncAnimation's sake, we need to return the artist we'll be using
# Note that it expects a sequence of artists, thus the trailing comma.
return self.scat, self.line,
def distribution(self):
"""Generate a quantum walk distribution in Cycle Graph . """
step = 1
radius = 1
node = self.node
data = zeros([node, 3], float)
Filename = 'Data/' + time.strftime('%Y%m%d-%H-%M-%S') + '.txt'
distrFlie = open(Filename, 'w+')
for j in range(node):
data[j, 0] = sin(j * 2 * math.pi / node) * radius
data[j, 1] = cos(j * 2 * math.pi / node) * radius
while True:
distribution = QDS.QWCicleDistribution(self.X0, self.X1, step, self.shiftGateNum, node)
# data[:,2]= QDS.ciclePosiTrans(distribution, step, node)
data[:, 2] = distribution
# write distribution to txt file
distrFlie.write(str(step) + '\t')
for x in range(shape(distribution)[0]):
distrFlie.write(str(distribution[x]) + '\t')
distrFlie.write('\n')
self.step = step
print ('step: %s') % step
step += 1
yield data
distrFlie.close()
def update(self, i):
"""Update the scatter plot."""
data = next(self.distri)
# Set x and y data...
temp = array([data[:, 2]]).T
# print temp
self.linedata = column_stack((self.linedata, temp))
m, n = shape(self.linedata)
# print self.linedata
self.scat.set_offsets(data[:, :2])
# self.line.set_offsets(data[:,:2])
# Set sizes...
self.scat._sizes = data[:, 2] * 200
# self.line._sizes = data[:,2]*500
lineResize = self.linedata.copy()
lineResize.resize(1, self.step)
# print lineResize[0]
x = arange(1, self.step + 1)
y = lineResize
# print x,y
self.line.set_data(x, y)
self.step_text1.set_text('Setp:%s' % self.step)
# self.ax2.axis([1, self.step, 0, 1])
# self.ax2_axis.set_axis([[1, self.step, 0, 1]])
# self.scat.set_title(self.Title)
# Set colors..
# self.scat.set_array(data[3])
# We need to return the updated artist for FuncAnimation to draw..
# Note that it expects a sequence of artists, thus the trailing comma.
return self.scat, self.line, self.step_text1,
def show(self):
plt.show()
if __name__ == '__main__':
a = AnimatedScatterQW(1, 0, 100, 3, 8)
a.show()
|
Python
|
2D
|
daihui/QuantumWalkSimulation
|
quantumRWTest.py
|
.py
| 582
| 23
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'levitan'
import quantumRW as QW
from numpy import *
from matplotlib import pyplot as plt
import quantumRWMat as QWM
totalSteps=100
plotSteps=10
# steps=50
# qwalker= QW.distriQW(1/sqrt(2),1j/sqrt(2),1/sqrt(2),1j/sqrt(2),steps,1)
#QW.Plot2D(qwalker)
#QW.PlotX(qwalker,steps)
#QW.writeQW(qwalker,'QW_'+str(steps)+'.txt')
#qwalker=QW.quantumWalker(1/sqrt(2),1j/sqrt(2),1/sqrt(2),1j/sqrt(2),totalSteps,plotSteps)
distribution=QWM.QWDistribution(1/sqrt(2),1j/sqrt(2),1/sqrt(2),1j/sqrt(2),100)
QWM.Plot2D(distribution,100)
|
Python
|
2D
|
daihui/QuantumWalkSimulation
|
quantumRWMat.py
|
.py
| 5,568
| 143
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'levitan'
"""
This is matrix release for 2D quantum walk simulation.
量子游走的基本过程：有一个初始量子态，coin是一个幺正变换，一次游走即coin对量子态变换一次
然后根据量子态walk一步，得到一个位置概率分布，如此反复。
"""
from numpy import *
from matplotlib import pyplot as plt
# 初始化量子态，是一个对角矩阵
def initQuanStat(X0, X1, Y0, Y1):
initQuanStat = zeros([4, 4], complex)
initQuanStat[0][0] = X0
initQuanStat[1][1] = X1
initQuanStat[2][2] = Y0
initQuanStat[3][3] = Y1
return initQuanStat
# 定义2D hadamard coin，实际是2个1D Coin的直积态
def hadamardCoin():
hadamardCoin = array(([1 / sqrt(2), 1 / sqrt(2), 0, 0], [1 / sqrt(2), -1 / sqrt(2), 0, 0]
, [0, 0, 1 / sqrt(2), 1 / sqrt(2)], [0, 0, 1 / sqrt(2), -1 / sqrt(2)]), complex)
return hadamardCoin
# 根据走了的步数定义空位置矩阵
def initPositionMap(steps):
positionMap = zeros([2 * steps + 1, 2 * steps + 1, 4, 4], complex)
return positionMap
# 对量子态经行coin变换
def coinOperator(positionMap, coin):
dimension = shape(positionMap)[0]
for i in range(dimension):
for j in range(dimension):
positionMap[i][j] = dot(positionMap[i][j], coin)
return positionMap
# 根据量子态进行位置变换，相当于一次walk
def shiftOperator(coinMap, step):
newPositionMap = initPositionMap(step)
for i in range(2 * step - 1):
for j in range(2 * step - 1):
newPositionMap[i][j][0][0] += coinMap[i][j][0][0]
newPositionMap[i][j][1][0] += coinMap[i][j][1][0]
newPositionMap[i][j][2][2] += coinMap[i][j][2][2]
newPositionMap[i][j][3][2] += coinMap[i][j][3][2]
newPositionMap[i][j + 2][0][1] += coinMap[i][j][0][1]
newPositionMap[i][j + 2][1][1] += coinMap[i][j][1][1]
newPositionMap[i][j + 2][2][2] += coinMap[i][j][2][2]
newPositionMap[i][j + 2][3][2] += coinMap[i][j][3][2]
newPositionMap[i + 2][j][0][0] += coinMap[i][j][0][0]
newPositionMap[i + 2][j][1][0] += coinMap[i][j][1][0]
newPositionMap[i + 2][j][2][3] += coinMap[i][j][2][3]
newPositionMap[i + 2][j][3][3] += coinMap[i][j][3][3]
newPositionMap[i + 2][j + 2][0][1] += coinMap[i][j][0][1]
newPositionMap[i + 2][j + 2][1][1] += coinMap[i][j][1][1]
newPositionMap[i + 2][j + 2][2][3] += coinMap[i][j][2][3]
newPositionMap[i + 2][j + 2][3][3] += coinMap[i][j][3][3]
return newPositionMap
# quantum walk 的整体封装，返回位置分布的量子态
def quantumWalk(X0, X1, Y0, Y1, steps):
initPositionMap = zeros([1, 1, 4, 4], complex)
initPositionMap[0][0] = initQuanStat(X0, X1, Y0, Y1)
coin = hadamardCoin()
for step in range(1, steps + 1):
positionMap = initPositionMap
coinMap = coinOperator(positionMap, coin)
initPositionMap = shiftOperator(coinMap, step)
return initPositionMap
# 计算QW的位置分布
def QWDistribution(X0, X1, Y0, Y1, steps):
positionMap = quantumWalk(X0, X1, Y0, Y1, steps)
dimension = shape(positionMap)[0]
distribution = zeros([dimension, dimension], dtype=float)
sum = 0.0
for i in range(dimension):
for j in range(dimension):
distribution[i][j] = float((positionMap[i][j][0][0].real ** 2 + positionMap[i][j][0][0].imag ** 2 + \
positionMap[i][j][1][0].real ** 2 + positionMap[i][j][1][0].imag ** 2 + \
positionMap[i][j][0][1].real ** 2 + positionMap[i][j][0][1].imag ** 2 + \
positionMap[i][j][1][1].real ** 2 + positionMap[i][j][1][1].imag ** 2 + \
positionMap[i][j][2][2].real ** 2 + positionMap[i][j][2][2].imag ** 2 + \
positionMap[i][j][3][2].real ** 2 + positionMap[i][j][3][2].imag ** 2 + \
positionMap[i][j][2][3].real ** 2 + positionMap[i][j][2][3].imag ** 2 + \
positionMap[i][j][3][3].real ** 2 + positionMap[i][j][3][3].imag ** 2))
sum += distribution[i][j]
return distribution / sum
def writeQWtoArray(distribution, filename):
distrFlie = open(filename, 'w+')
for x in range(shape(distribution)[0]):
for y in range(shape(distribution)[1]):
distrFlie.write(str(distribution[x][y]) + '\t')
distrFlie.write('\n')
distrFlie.close()
def writeQWtoList(distribution, filename):
distrFlie = open('Data/' + filename, 'w+')
for x in range(shape(distribution)[0]):
for y in range(shape(distribution)[1]):
distrFlie.write(str(x) + '\t' + str(y) + '\t' + str(distribution[x][y]) + '\n')
distrFlie.close()
def Plot2D(qw, steps):
plt.figure(1)
ax1 = plt.subplot(111)
plt.sca(ax1)
plt.title('2D distribution of %s steps Quantum Walk with hadamard coin' % steps)
plt.xlabel('X Position (started in center)')
plt.ylabel('Y Position (started in center)')
plt.imshow(qw)
plt.savefig('Fig/QWM_' + str(steps) + '.png')
plt.close()
def PlotX(qw, Y):
qwX = qw[:][Y]
plt.plot(qwX)
plt.title('a Slice of 2D distribution of Quantum Walk')
plt.xlabel('Position(started in %s)' % Y)
plt.ylabel('Probability')
plt.show()
|
Python
|
2D
|
daihui/QuantumWalkSimulation
|
QWithDiffShiftTest.py
|
.py
| 3,092
| 86
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'levitan'
from numpy import *
from matplotlib import pyplot as plt
import QWithDiffShift as QDS
# distribution=QDS.QWDistribution(1/sqrt(2),1j/sqrt(2),700,1)
# QDS.PlotX(distribution)
def QDSPlot(X0, X1, steps, shiftGateNum):
for step in range(1, steps + 1):
distributionQDS = QDS.QWDistribution(X0, X1, step, shiftGateNum)
distributionQW = QDS.QWDistribution(X0, X1, 7 * step, 1)
QDS.PlotComp(distributionQDS, distributionQW, step, 'QDS-VS-QW-H')
def QDSCPlot(X0, X1, steps, shiftGateNum, node):
for step in range(1, steps + 1):
distribution = QDS.QWCicleDistribution(X0, X1, step, shiftGateNum, node)
distributionTrans = QDS.ciclePosiTrans(distribution, step, shiftGateNum)
QDS.PlotX(distributionTrans, step, 'QDS_CT')
# QDSPlot(1/sqrt(2),1j/sqrt(2),15,3)
# QDSCPlot(1/sqrt(2),1j/sqrt(2),15,3,50)
def QDSCiclePlot(X0, X1, steps, shiftGateNum, node):
for step in range(1, steps + 1):
distribution = QDS.QWCicleDistribution(X0, X1, step, shiftGateNum, node)
distributionTrans = QDS.ciclePosiTrans(distribution, step, shiftGateNum)
QDS.PlotCicle(distributionTrans, step, shiftGateNum, 'QDSCicle_K4_H_')
def QDSCicleContourfPlot(Filename, walkSteps, PiSteps, node, point):
# x,y=ogrid[0:50:PiSteps,1:200:walkSteps]
x = arange(0, 2 * pi, 2 * pi / PiSteps)
y = arange(0, walkSteps, 1)
v = linspace(0, 1, 50, endpoint=True)
# print x,shape(x)
file = open(Filename, 'r')
dataList = zeros([walkSteps * PiSteps, node])
i = 0
for line in file.readlines():
for j in range(node):
dataList[i][j] = line.split('\t')[2 + j]
# print dataList[i]
i += 1
# print dataList[2]
z = zeros([walkSteps, PiSteps], float)
for yi in range(walkSteps):
for xi in range(PiSteps):
z[yi][xi] = float(dataList[xi * walkSteps + yi][point])
file.close()
plt.figure()
plt.title('Distribution of %s steps Quantum Walk in Cicle with different phase' % walkSteps)
plt.xlabel('Phase')
plt.ylabel('Step')
plt.contourf(x, y, z, v, cmap=plt.cm.jet)
x = plt.colorbar(ticks=v)
print x
plt.show()
# QDSCiclePlot(1, 0, 10, 2, 4)
# QDSPlot(1 , 0, 10, 3)
# QDS.QWCicleDistrWrite(1, 0, 2000, 3, 8)
# QDS.QWCicleWithPhaseDistrWrite(1/sqrt(2), 1/sqrt(2), 400, 2, 5, 0, 2)
# QDS.QWCicleWithPhaseLoopDistrWrite(1/sqrt(2), 1j/sqrt(2), 200, 2, 4, pi, 2)
# QDS.QWCicleWithPhaseLoopSearch(1/sqrt(2), 1/sqrt(2), 200, 3, 8, pi, 3)
# for i in range(51):
# QDS.QWCicleWithPhaseLoopSearch(1, 0, 200, 3, 8, 2*pi*i/50, 3)
# for i in range(51):
# QDS.QWCicleWithPhaseLoopDistrWritePosi(1/sqrt(2), 1/sqrt(2), 200, 2, 4, 2*pi*i/50, 2)
# for i in range(51):
# QDS.QWCicleWithPhaseDistrWrite(1/sqrt(2), 1/sqrt(2), 200, 2, 5, 2*pi*i/50, 2)
# for i in range(51):
# QDS.QWCicleWithPhaseSearch(1/sqrt(2), 1/sqrt(2), 200, 2, 4, 2*pi*i/50, 2)
QDSCicleContourfPlot('Data/Search mark_0 X00.707106781187_X10.707106781187_2-4_Steps200Aver.txt', 200, 51,4,2)
|
Python
|
2D
|
daihui/QuantumWalkSimulation
|
classicalRWMat.py
|
.py
| 1,888
| 54
|
#!/usr/bin/env python
#-*- coding: utf-8 -*-
__author__ = 'levitan'
"""
This is a matrix release for 2D classic random walk simulation.
经典随机游走的基本过程：在一个初始位置，投一次coin（随机选择），根据coin的结果，
选择向某一个方向走一步，然后再投一次coin，周而复始。
classicalRanMun():产生一个随机coin
classcalWalkerPosition():walker的初始位置，设为(0,0)
classicalWalk():根据参数walkNum随机游走walkNum步，返回最后位置walkerPosition
classicalRWDistr():重复satNum次随机游走，得到walkerPosition的统计分布
Plot2D():画图函数，画出2D的经典随机游走位置概率分布
"""
from numpy import *
import random
from matplotlib import pyplot as plt
def classicalRanNum():
return mat([[random.choice([1,-1])],[random.choice([1,-1])]],int)
def classicalWalkerPosition():
return mat([[0],[0]],int)
def classicalWalk(walkNum):
walkerPosition = classicalWalkerPosition()
for i in range(0, walkNum):
coin= classicalRanNum()
walkerPosition+=coin
return walkerPosition
def classicalRWDistr(walkNum, matrixNum,satNum):
walkerCount = zeros([2 *matrixNum + 1, 2 * matrixNum + 1])
for i in range(0, satNum):
walker = classicalWalk(walkNum)
walkerCount[int(walker[0])+matrixNum][int(walker[1])+matrixNum] += float(1.0 / satNum)
return walkerCount
def Plot2D(classcialWalker,steps):
plt.figure(1)
ax1=plt.subplot(111)
plt.sca(ax1)
plt.title('2D distribution of %s steps Classical Random Walk' %steps)
plt.xlabel('X Position(started in center)')
plt.ylabel('Y Position(started in center)')
plt.imshow(classcialWalker)
plt.savefig('Fig/CRW_' + str(steps) + '.png')
plt.close()
#for i in range(1,2):
# classicalWalker=classicalRWDistr(i*10,100,100000)
# Plot2D(classicalWalker,i*10)
|
Python
|
2D
|
daihui/QuantumWalkSimulation
|
QWithDiffShift.py
|
.py
| 25,076
| 598
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'levitan'
"""
This is matrix release for 1D quantum walk with different shift simulation(graph).
基本过程：有一个初始量子态，coin是一个幺正变换，一次游走即coin对量子态变换一次
然后根据量子态walk一步，或几步（根据shift而定）得到一个位置概率分布，如此反复。
"""
from numpy import *
from matplotlib import pyplot as plt
import time
import pylab as py
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import animation
# -------------------------------------------
# Quantum walk in line with different shift
# -------------------------------------------
# 初始化量子态，是一个2*1维矩阵
def initQuanStat(X0, X1):
initQuanStat = zeros([2, 1], complex)
initQuanStat[0][0] = X0
initQuanStat[1][0] = X1
return initQuanStat
# 定义1D hadamard coin
def hadamardCoin():
hadamardCoin = array([[1 / sqrt(2), 1 / sqrt(2)], [1 / sqrt(2), -1 / sqrt(2)]], complex)
return hadamardCoin
# 定义1D phase coin
def phaseCoin(Psi):
phaseCoin = array([[1, 0], [0, exp(1j * Psi)]], complex)
return phaseCoin
# 根据shift的最大值和走了的步数定义空位置矩阵
def initPositionMap(steps, shiftNum, shiftGateNum):
stepNum = 0
for j in range(0, shiftGateNum):
stepNum += power(2, j)
dimension = stepNum * (steps - 1) + 1
for i in range(0, shiftNum):
dimension += power(2, i)
positionMap = zeros([dimension, 2, 1], complex)
return positionMap
# 对量子态经行coin变换
def coinOperator(coin, positionMap):
dimension = shape(positionMap)[0]
for i in range(dimension):
positionMap[i] = dot(coin, positionMap[i])
return positionMap
# 根据量子态进行位置变换，相当于一次walk
def shiftOperator(positionMap, step, shiftGateNum):
for shiftNum in range(1, shiftGateNum + 1):
newPositionMap = initPositionMap(step, shiftNum, shiftGateNum)
coin = hadamardCoin()
coinMap = coinOperator(coin, positionMap)
for i in range(shape(coinMap)[0]):
newPositionMap[i][0][0] += coinMap[i][0][0]
newPositionMap[i + power(2, shiftNum - 1)][1][0] += coinMap[i][1][0]
positionMap = newPositionMap
return newPositionMap
# quantum walk 的整体封装，返回位置分布的量子态
def quantumWalk(X0, X1, steps, shiftGateNum):
initPositionMap = zeros([1, 2, 1], complex)
initPositionMap[0] = initQuanStat(X0, X1)
for step in range(1, steps + 1):
positionMap = initPositionMap
initPositionMap = shiftOperator(positionMap, step, shiftGateNum)
return initPositionMap
# 计算QW的位置分布
def QWDistribution(X0, X1, steps, shiftGateNum):
positionMap = quantumWalk(X0, X1, steps, shiftGateNum)
dimension = shape(positionMap)[0]
distribution = zeros([dimension], dtype=float)
sum = 0.0
for i in range(dimension):
distribution[i] = float(positionMap[i][0][0].real ** 2 + positionMap[i][0][0].imag ** 2 + \
positionMap[i][1][0].real ** 2 + positionMap[i][1][0].imag ** 2)
sum += distribution[i]
print('sum: %s') % sum
return distribution
# 将结果写到文本文档
def writeQWtoArray(distribution):
Filename = 'Data/' + time.strftime('%Y%m%d-%H-%M-%S') + '.txt'
distrFlie = open(Filename, 'w+')
for x in range(shape(distribution)[0]):
distrFlie.write(str(distribution[x]) + '\n')
distrFlie.close()
# 画出位置概率分布图
def PlotX(distribution, step, figName):
plt.plot(distribution)
plt.title('The Distribution of %s Quantum Walk with Different Shift') % step
plt.xlabel('Started from the 0')
plt.ylabel('Probability')
# plt.show()
plt.savefig('Fig/' + str(figName) + str(step) + '.png')
plt.close()
# 画出QDS与QW的概率分布对比图
def PlotComp(distributionQDS, distributionQW, step, figName):
QDS, = plt.plot(distributionQDS, 'r')
QW, = plt.plot(distributionQW, 'b')
plt.axis([0, 7 * step, 0, 0.27])
plt.legend([QDS, QW, ], ['QDS', 'QW'])
plt.title('The Compare between QDS and QW')
plt.xlabel('X Position (started from the center)')
plt.ylabel('Probability')
# plt.show()
plt.savefig('Fig/' + str(figName) + str(step) + '.png')
plt.close()
# -------------------------------------------
# Quantum walk in cicle with different shift
# -------------------------------------------
def initQuanStatAverPosiCicle(X0, X1, Node):
initQuanStat = zeros([Node, 2, 1], complex)
for node in range(Node):
initQuanStat[node][0][0] = X0 * 1 / sqrt(Node)
initQuanStat[node][1][0] = X1 * 1 / sqrt(Node)
return initQuanStat
def positionCicleMap(node):
positionCicleMap = zeros([node, 2, 1], complex)
return positionCicleMap
# 根据量子态进行位置变换，相当于一次walk
def shiftCicleOperator(positionMap, step, shiftGateNum, node):
for shiftNum in range(1, shiftGateNum + 1):
newPositionMap = positionCicleMap(node)
coin = hadamardCoin()
coinMap = coinOperator(coin, positionMap)
for i in range(node):
newPositionMap[i][0][0] += coinMap[i][0][0]
if (i + power(2, shiftNum - 1) >= node):
newPositionMap[i + power(2, shiftNum - 1) - node][1][0] += coinMap[i][1][0]
# print('loop')
else:
newPositionMap[i + power(2, shiftNum - 1)][1][0] += coinMap[i][1][0]
positionMap = newPositionMap
return newPositionMap
# 加入phase coin 版本, 修正移位操作，实现complete graph
def shiftCicleWithPhaseOperator(positionMap, step, shiftGateNum, node, Psi, phaseGate):
# 先整体走一步
moveOne = positionMap.copy()
for i in range(1, node):
positionMap[i] = moveOne[i - 1]
positionMap[0] = moveOne[node - 1]
# 根据延时门再各自走对应步数
for shiftNum in range(1, shiftGateNum + 1):
newPositionMap = positionCicleMap(node)
HadamardCoin = hadamardCoin()
PhaseCoin = phaseCoin(Psi)
coinMap = coinOperator(HadamardCoin, positionMap)
if shiftNum == phaseGate:
newMap = coinMap.copy()
coinMap = coinOperator(PhaseCoin, newMap)
print 'gate:%s phase coin' % shiftNum
for i in range(node):
newPositionMap[i][0][0] += coinMap[i][0][0]
if (i + power(2, shiftNum - 1) >= node):
newPositionMap[i + power(2, shiftNum - 1) - node][1][0] += coinMap[i][1][0]
# print('loop')
else:
newPositionMap[i + power(2, shiftNum - 1)][1][0] += coinMap[i][1][0]
positionMap = newPositionMap
return newPositionMap
# phase coin ，complete graph with loop
def shiftCicleWithPhaseLoopOperator(positionMap, step, shiftGateNum, node, Psi, phaseGate):
# 根据延时门各自走对应步数
for shiftNum in range(1, shiftGateNum + 1):
newPositionMap = positionCicleMap(node)
HadamardCoin = hadamardCoin()
PhaseCoin = phaseCoin(Psi)
coinMap = coinOperator(HadamardCoin, positionMap)
if shiftNum == phaseGate:
newMap = coinMap.copy()
coinMap = coinOperator(PhaseCoin, newMap)
print 'gate:%s phase coin' % shiftNum
for i in range(node):
newPositionMap[i][0][0] += coinMap[i][0][0]
if (i + power(2, shiftNum - 1) >= node):
newPositionMap[i + power(2, shiftNum - 1) - node][1][0] += coinMap[i][1][0]
# print('loop')
else:
newPositionMap[i + power(2, shiftNum - 1)][1][0] += coinMap[i][1][0]
positionMap = newPositionMap
return newPositionMap
# search position with marked coin ，complete graph with loop for 2 gate,4 node
def shiftCicleWithPhaseLoopSearchOperator(positionMap, step, shiftGateNum, node, Psi, phaseGate):
# 根据延时门各自走对应步数
for shiftNum in range(1, shiftGateNum + 1):
newPositionMap = positionCicleMap(node)
HadamardCoin = hadamardCoin()
# PhaseCoin = phaseCoin(Psi)
coinMap = coinOperator(HadamardCoin, positionMap)
if shiftNum == phaseGate:
# print 'gate:%s phase coin' % shiftNum
newPositionMap[0][0][0] += coinMap[0][0][0]
newPositionMap[4][1][0] += coinMap[0][1][0] * exp(1j * Psi)
newPositionMap[4][0][0] += coinMap[4][0][0]
newPositionMap[0][1][0] += coinMap[4][1][0] * exp(1j * Psi)
for i in range(1, node):
if i == 4:
print i
else:
newPositionMap[i][0][0] += coinMap[i][0][0]
if (i + power(2, shiftNum - 1) >= node):
newPositionMap[i + power(2, shiftNum - 1) - node][1][0] += coinMap[i][1][0]
# print('loop')
else:
newPositionMap[i + power(2, shiftNum - 1)][1][0] += coinMap[i][1][0]
else:
for i in range(node):
newPositionMap[i][0][0] += coinMap[i][0][0]
if (i + power(2, shiftNum - 1) >= node):
newPositionMap[i + power(2, shiftNum - 1) - node][1][0] += coinMap[i][1][0]
# print('loop')
else:
newPositionMap[i + power(2, shiftNum - 1)][1][0] += coinMap[i][1][0]
positionMap = newPositionMap
return newPositionMap
# search position with marked point 0，实现complete graph
def shiftCicleWithPhaseSearchOperator(positionMap, step, shiftGateNum, node, Psi, phaseGate):
# 先整体走一步
moveOne = positionMap.copy()
for i in range(1, node):
positionMap[i] = moveOne[i - 1]
positionMap[0] = moveOne[node - 1]
# 根据延时门再各自走对应步数
for shiftNum in range(1, shiftGateNum + 1):
newPositionMap = positionCicleMap(node)
HadamardCoin = hadamardCoin()
# PhaseCoin = phaseCoin(Psi)
coinMap = coinOperator(HadamardCoin, positionMap)
for i in range(node):
newPositionMap[i][0][0] += coinMap[i][0][0]
if (i + power(2, shiftNum - 1) >= node):
newPositionMap[i + power(2, shiftNum - 1) - node][1][0] += coinMap[i][1][0]
# print('loop')
else:
newPositionMap[i + power(2, shiftNum - 1)][1][0] += coinMap[i][1][0]
positionMap = newPositionMap
# 标记点0，附加一个Psi的相位
newPositionMap[0] = newPositionMap[0] * exp(1j * Psi)
return newPositionMap
# quantum walk 的整体封装，返回位置分布的量子态
def quantumWalkCicle(X0, X1, steps, shiftGateNum, node):
print 'begin'
initPositionMap = zeros([node, 2, 1], complex)
initPositionMap[0] = initQuanStat(X0, X1)
for step in range(1, steps + 1):
positionMap = initPositionMap
initPositionMap = shiftCicleOperator(positionMap, step, shiftGateNum, node)
return initPositionMap
'''
Release 1, for animate plotting
'''
# 计算概率分布
def QWCicleDistribution(X0, X1, steps, shiftGateNum, node):
positionMap = quantumWalkCicle(X0, X1, steps, shiftGateNum, node)
dimension = shape(positionMap)[0]
distribution = zeros([dimension], dtype=float)
sum = 0.0
for i in range(dimension):
distribution[i] = float(positionMap[i][0][0].real ** 2 + positionMap[i][0][0].imag ** 2 + \
positionMap[i][1][0].real ** 2 + positionMap[i][1][0].imag ** 2)
sum += distribution[i]
print('sum: %s') % sum
return distribution
'''
Release 2 : for write data to txt file
'''
# quantum walk in cycle graph release for write data to txt file(直接将每一步的态与分布写到文件保存)
def QWCicleDistrWrite(X0, X1, steps, shiftGateNum, node):
print 'begin'
initPositionMap = zeros([node, 2, 1], complex)
initPositionMap[0] = initQuanStat(X0, X1)
DsitriFilename = 'Data/' + time.strftime('%Y%m%d-%H-%M-%S_') + 'Position' + str(shiftGateNum) + \
str(node) + str(steps) + '.txt'
StateFilename = 'Data/' + time.strftime('%Y%m%d-%H-%M-%S_') + 'State' + str(shiftGateNum) + \
str(node) + str(steps) + '.txt'
distrFile = open(DsitriFilename, 'w+')
stateFile = open(StateFilename, 'w+')
for step in range(1, steps + 1):
print 'step: %s' % step
positionMap = initPositionMap
initPositionMap = shiftCicleOperator(positionMap, step, shiftGateNum, node)
dimension = shape(initPositionMap)[0]
distribution = zeros([dimension], dtype=float)
sum = 0.0
for i in range(dimension):
distribution[i] = float(initPositionMap[i][0][0].real ** 2 + initPositionMap[i][0][0].imag ** 2 + \
initPositionMap[i][1][0].real ** 2 + initPositionMap[i][1][0].imag ** 2)
sum += distribution[i]
stateFile.write(str(initPositionMap[i][0][0]) + str(initPositionMap[i][1][0]) + '\t')
distrFile.write(str(distribution[i]) + '\t')
print('sum: %s') % sum
distrFile.write('\n')
stateFile.write('\n')
stateFile.close()
distrFile.close()
print 'finish'
'''
Release 3 : add phase coin and modified for complete graph
'''
# quantum walk in cycle graph release for phase coin (添加phase coin，对shift operator进行了修正)
def QWCicleWithPhaseDistrWrite(X0, X1, steps, shiftGateNum, node, Psi, gate):
print 'begin'
initPositionMap = zeros([node, 2, 1], complex)
initPositionMap[0] = initQuanStat(X0, X1)
# initPositionMap = initQuanStatAverPosiCicle(X0, X1,node)
PositionPsiFilename = 'Data/K5_X0' + str(X0) + '_X1' + str(X1) + '_' + str(shiftGateNum) + '-' + str(
node) + '_Steps' + str(steps) + '.txt'
PosiFile = open(PositionPsiFilename, 'a')
# DsitriFilename = 'Data/' + time.strftime('%Y%m%d-%H-%M-%S') + '_Position_' + str(shiftGateNum) + \
# str(node) + str(steps) + '.txt'
# StateFilename = 'Data/' + time.strftime('%Y%m%d-%H-%M-%S') + '_State_' + str(shiftGateNum) + \
# str(node) + str(steps) + '.txt'
# distrFile = open(DsitriFilename, 'w+')
# stateFile = open(StateFilename, 'w+')
# infoS = 'State: X0 %s X1 %s, steps: %s, shiftGateNum: %s, node: %s, Psi: %s, gate: %s\n' % (
# X0, X1, steps, shiftGateNum, node, Psi, gate)
# infoD = 'State: X0 %s X1 %s, steps: %s, shiftGateNum: %s, node: %s, Psi: %s, gate: %s \n' % (
# X0, X1, steps, shiftGateNum, node, Psi, gate)
# stateFile.write(infoS)
# distrFile.write(infoD)
for step in range(1, steps + 1):
print 'step: %s' % step
positionMap = initPositionMap
initPositionMap = shiftCicleWithPhaseOperator(positionMap, step, shiftGateNum, node, Psi, gate)
dimension = shape(initPositionMap)[0]
distribution = zeros([dimension], dtype=float)
sum = 0.0
PosiFile.write(str(Psi) + '\t')
PosiFile.write(str(step) + '\t')
for i in range(dimension):
distribution[i] = float(initPositionMap[i][0][0].real ** 2 + initPositionMap[i][0][0].imag ** 2 + \
initPositionMap[i][1][0].real ** 2 + initPositionMap[i][1][0].imag ** 2)
sum += distribution[i]
PosiFile.write(str(distribution[i]) + '\t')
# stateFile.write(str(initPositionMap[i][0][0]) + str(initPositionMap[i][1][0]) + '\t')
# distrFile.write(str(distribution[i]) + '\t')
PosiFile.write('\n')
print('sum: %s') % sum
# distrFile.write('\n')
# stateFile.write('\n')
# stateFile.close()
# distrFile.close()
PosiFile.close()
print 'finish'
'''
Release 4 : add phase coin and modified for complete graph with loop
'''
# quantum walk in cycle graph with loop release for phase coin
def QWCicleWithPhaseLoopDistrWrite(X0, X1, steps, shiftGateNum, node, Psi, gate):
print 'begin'
initPositionMap = zeros([node, 2, 1], complex)
initPositionMap[0] = initQuanStat(X0, X1)
#initPositionMap=initQuanStatAverPosiCicle(X0,X1,node)
DsitriFilename = 'Data/' + time.strftime('%Y%m%d-%H-%M-%S') + '_Position_' + str(shiftGateNum) + \
str(node) + str(steps) + '.txt'
StateFilename = 'Data/' + time.strftime('%Y%m%d-%H-%M-%S') + '_State_' + str(shiftGateNum) + \
str(node) + str(steps) + '.txt'
distrFile = open(DsitriFilename, 'w+')
stateFile = open(StateFilename, 'w+')
infoS = 'State: X0 %s X1 %s, steps: %s, shiftGateNum: %s, node: %s, Psi: %s, gate: %s\n' % (
X0, X1, steps, shiftGateNum, node, Psi, gate)
infoD = 'State: X0 %s X1 %s, steps: %s, shiftGateNum: %s, node: %s, Psi: %s, gate: %s \n' % (
X0, X1, steps, shiftGateNum, node, Psi, gate)
stateFile.write(infoS)
distrFile.write(infoD)
for step in range(1, steps + 1):
print 'step: %s' % step
positionMap = initPositionMap
initPositionMap = shiftCicleWithPhaseLoopOperator(positionMap, step, shiftGateNum, node, Psi, gate)
dimension = shape(initPositionMap)[0]
distribution = zeros([dimension], dtype=float)
sum = 0.0
for i in range(dimension):
distribution[i] = float(initPositionMap[i][0][0].real ** 2 + initPositionMap[i][0][0].imag ** 2 + \
initPositionMap[i][1][0].real ** 2 + initPositionMap[i][1][0].imag ** 2)
sum += distribution[i]
stateFile.write(str(initPositionMap[i][0][0]) + str(initPositionMap[i][1][0]) + '\t')
distrFile.write(str(distribution[i]) + '\t')
print('sum: %s') % sum
distrFile.write('\n')
stateFile.write('\n')
stateFile.close()
distrFile.close()
print 'finish'
'''
Release 5 : add phase coin and modified for complete graph with loop, write position
probability file with Psi
'''
# quantum walk in cycle graph with loop release for phase coin
def QWCicleWithPhaseLoopDistrWritePosi(X0, X1, steps, shiftGateNum, node, Psi, gate):
print 'begin'
initPositionMap = zeros([node, 2, 1], complex)
# initPositionMap[0] = initQuanStat(X0, X1)
initPositionMap = initQuanStatAverPosiCicle(X0, X1, node)
PositionPsiFilename = 'Data/Loop_X0' + str(X0) + '_X1' + str(X1) + '_' + str(shiftGateNum) + '-' + str(
node) + '_Steps' + str(steps) + 'Aver.txt'
PosiFile = open(PositionPsiFilename, 'a')
for step in range(1, steps + 1):
print 'step: %s' % step
positionMap = initPositionMap
initPositionMap = shiftCicleWithPhaseLoopOperator(positionMap, step, shiftGateNum, node, Psi, gate)
dimension = shape(initPositionMap)[0]
distribution = zeros([dimension], dtype=float)
sum = 0.0
for i in range(dimension):
distribution[i] = float(initPositionMap[i][0][0].real ** 2 + initPositionMap[i][0][0].imag ** 2 + \
initPositionMap[i][1][0].real ** 2 + initPositionMap[i][1][0].imag ** 2)
sum += distribution[i]
if i == 0:
PosiFile.write(str(Psi) + '\t')
PosiFile.write(str(step) + '\t')
PosiFile.write(str(distribution[i]) + '\n')
print('sum: %s') % sum
PosiFile.close()
print 'finish'
'''
Release 6 : Search one position by marked in complete graph with loop
'''
# quantum walk search in cycle graph with loop
def QWCicleWithPhaseLoopSearch(X0, X1, steps, shiftGateNum, node, Psi, gate):
print 'begin'
initPositionMap = zeros([node, 2, 1], complex)
initPositionMap[0] = initQuanStat(X0, X1)
# initPositionMap=initQuanStatAverPosiCicle(X0,X1,node)
# DsitriFilename = 'Data/' + time.strftime('%Y%m%d-%H-%M-%S') + '_Search_Position_' + str(shiftGateNum) + \
# str(node) + str(steps) + '.txt'
# StateFilename = 'Data/' + time.strftime('%Y%m%d-%H-%M-%S') + '_Search_State_' + str(shiftGateNum) + \
# str(node) + str(steps) + '.txt'
# distrFile = open(DsitriFilename, 'w+')
# stateFile = open(StateFilename, 'w+')
# infoS = 'Search State: X0 %s X1 %s, steps: %s, shiftGateNum: %s, node: %s, Psi: %s, gate: %s\n' % (
# X0, X1, steps, shiftGateNum, node, Psi, gate)
# infoD = 'Search State: X0 %s X1 %s, steps: %s, shiftGateNum: %s, node: %s, Psi: %s, gate: %s \n' % (
# X0, X1, steps, shiftGateNum, node, Psi, gate)
# stateFile.write(infoS)
# distrFile.write(infoD)
PositionPsiFilename = 'Data/Loop_Search_X0' + str(X0) + '_X1' + str(X1) + '_' + str(shiftGateNum) + '-' + str(
node) + '_Steps' + str(steps) + '.txt'
PosiFile = open(PositionPsiFilename, 'a')
for step in range(1, steps + 1):
print 'step: %s' % step
positionMap = initPositionMap
initPositionMap = shiftCicleWithPhaseLoopSearchOperator(positionMap, step, shiftGateNum, node, Psi, gate)
dimension = shape(initPositionMap)[0]
distribution = zeros([dimension], dtype=float)
sum = 0.0
for i in range(dimension):
distribution[i] = float(initPositionMap[i][0][0].real ** 2 + initPositionMap[i][0][0].imag ** 2 + \
initPositionMap[i][1][0].real ** 2 + initPositionMap[i][1][0].imag ** 2)
sum += distribution[i]
if i == 1:
PosiFile.write(str(Psi) + '\t')
PosiFile.write(str(step) + '\t')
PosiFile.write(str(distribution[i]) + '\n')
# stateFile.write(str(initPositionMap[i][0][0]) + str(initPositionMap[i][1][0]) + '\t')
# distrFile.write(str(distribution[i]) + '\t')
print('sum: %s') % sum
# distrFile.write('\n')
# stateFile.write('\n')
# stateFile.close()
# distrFile.close()
PosiFile.close()
print 'finish'
'''
Release 7 : Search one position by marked in complete graph
'''
# quantum walk in cycle graph release for phase coin (添加phase coin，对shift operator进行了修正)
def QWCicleWithPhaseSearch(X0, X1, steps, shiftGateNum, node, Psi, gate):
print 'begin'
initPositionMap = zeros([node, 2, 1], complex)
# initPositionMap[0] = initQuanStat(X0, X1)
initPositionMap = initQuanStatAverPosiCicle(X0, X1, node)
PositionPsiFilename = 'Data/Search mark_0 X0' + str(X0) + '_X1' + str(X1) + '_' + str(shiftGateNum) + '-' + str(
node) + '_Steps' + str(steps) + 'Aver.txt'
PosiFile = open(PositionPsiFilename, 'a')
for step in range(1, steps + 1):
print 'step: %s' % step
positionMap = initPositionMap
initPositionMap = shiftCicleWithPhaseSearchOperator(positionMap, step, shiftGateNum, node, Psi, gate)
dimension = shape(initPositionMap)[0]
distribution = zeros([dimension], dtype=float)
sum = 0.0
PosiFile.write(str(Psi) + '\t')
PosiFile.write(str(step) + '\t')
for i in range(dimension):
distribution[i] = float(initPositionMap[i][0][0].real ** 2 + initPositionMap[i][0][0].imag ** 2 + \
initPositionMap[i][1][0].real ** 2 + initPositionMap[i][1][0].imag ** 2)
sum += distribution[i]
PosiFile.write(str(distribution[i]) + '\t')
print('sum: %s') % sum
PosiFile.write('\n')
PosiFile.close()
print 'finish'
# 对位置列表进行位移，使原点保持不变
#TODO 位置变换有点问题，暂时不用
def ciclePosiTrans(positionMap, steps, shiftGateNum):
node = shape(positionMap)[0]
positionMapTrans = zeros([node], float)
initNode = (power(2, shiftGateNum) - 1) * steps % node
for i in range(node):
positionMapTrans[i] = positionMap[(initNode + i) % node]
return positionMapTrans
# 画出2D位置分布图
def PlotCicle(distribution, steps, shiftGateNum, figName):
node = shape(distribution)[0]
radius = 1
x = zeros([node], float)
y = zeros([node], float)
for i in range(node):
x[i] = sin(i * 2 * math.pi / node) * radius
y[i] = cos(i * 2 * math.pi / node) * radius
plt.figure(1, figsize=(20, 9))
ax1 = plt.subplot(121)
plt.sca(ax1)
plt.title('Distribution of %s steps Quantum Walk in Cicle' % steps)
plt.xlabel('X Position')
plt.ylabel('Y Position')
# plt.imshow(positionMap)
# plt.axis([0, 2 * int(radius) + 3, 0, 2 * int(radius) + 3])
# plt.grid(True)
plt.scatter(x, y, distribution * 2000, c='r', marker='o')
plt.axis([-1.5, 1.5, -1.5, 1.5])
plt.subplot(122)
plt.plot(distribution)
plt.axis([0, node - 1, 0, 1])
plt.title('Distribution of %s steps QW with Different Shift' % steps)
plt.xlabel('Started from 0')
plt.ylabel('Probability')
# plt.show()
plt.savefig('Fig/' + str(figName) + str(steps) + '_' + str(shiftGateNum) + '.png')
plt.close()
|
Python
|
2D
|
daihui/QuantumWalkSimulation
|
quantumRW.py
|
.py
| 7,551
| 163
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'levitan'
from numpy import *
from matplotlib import pyplot as plt
import copy
dimension = 1
def initQuantumStateList(X0, X1, Y0, Y1, totalSteps):
initquantumStateList = zeros([2 * totalSteps + 1, 2 * totalSteps + 1, 2, 2], dtype=complex)
initquantumStateList[totalSteps][totalSteps][0][0] = complex(X0)
initquantumStateList[totalSteps][totalSteps][0][1] = complex(X1)
initquantumStateList[totalSteps][totalSteps][1][0] = complex(Y0)
initquantumStateList[totalSteps][totalSteps][1][1] = complex(Y1)
return initquantumStateList
# print quantumState(1,1,0,0,1,1,0,0)
def hadamardCoin(quantumStateList, totalSteps):
global dimension
for x in range(dimension):
for y in range(dimension):
tempState = zeros([2, 2], dtype=complex)
tempState = copy.deepcopy(
quantumStateList[totalSteps - (dimension - 1) / 2 + x][totalSteps - (dimension - 1) / 2 + y])
quantumStateList[totalSteps - (dimension - 1) / 2 + x][totalSteps - (dimension - 1) / 2 + y][0][
0] = complex((tempState[0][0] + tempState[0][1]) / sqrt(2))
quantumStateList[totalSteps - (dimension - 1) / 2 + x][totalSteps - (dimension - 1) / 2 + y][0][
1] = complex((tempState[0][0] - tempState[0][1]) / sqrt(2))
quantumStateList[totalSteps - (dimension - 1) / 2 + x][totalSteps - (dimension - 1) / 2 + y][1][
0] = complex((tempState[1][0] + tempState[1][1]) / sqrt(2))
quantumStateList[totalSteps - (dimension - 1) / 2 + x][totalSteps - (dimension - 1) / 2 + y][1][
1] = complex((tempState[1][0] - tempState[1][1]) / sqrt(2))
# print quantumStateList
return quantumStateList
# qs=quantumState(0,1,0,0,0,1,0,0)
# print qs
# print hadamardCoin(qs)
def shiftOperator(quantumStateList, totalSteps):
global dimension
newQuanStatList = zeros([2 * totalSteps + 1, 2 * totalSteps + 1, 2, 2], dtype=complex)
for x in range(dimension):
for y in range(dimension):
newQuanStat1 = zeros([2, 2], dtype=complex)
newQuanStat1[0][0] = \
quantumStateList[totalSteps - (dimension - 1) / 2 + x][totalSteps - (dimension - 1) / 2 + y][0][0]
newQuanStat1[0][1] = 0.0
newQuanStat1[1][0] = \
quantumStateList[totalSteps - (dimension - 1) / 2 + x][totalSteps - (dimension - 1) / 2 + y][1][0]
newQuanStat1[1][1] = 0.0
newQuanStatList[totalSteps - (dimension - 1) / 2 + x - 1][
totalSteps - (dimension - 1) / 2 + y - 1] += (newQuanStat1 / sqrt(2))
newQuanStat2 = zeros([2, 2], dtype=complex)
newQuanStat2[0][0] = \
quantumStateList[totalSteps - (dimension - 1) / 2 + x][totalSteps - (dimension - 1) / 2 + y][0][0]
newQuanStat2[0][1] = 0.0
newQuanStat2[1][0] = 0.0
newQuanStat2[1][1] = \
quantumStateList[totalSteps - (dimension - 1) / 2 + x][totalSteps - (dimension - 1) / 2 + y][1][1]
newQuanStatList[totalSteps - (dimension - 1) / 2 + x - 1][
totalSteps - (dimension - 1) / 2 + y + 1] += (newQuanStat2 / sqrt(2))
newQuanStat3 = zeros([2, 2], dtype=complex)
newQuanStat3[0][0] = 0.0
newQuanStat3[0][1] = \
quantumStateList[totalSteps - (dimension - 1) / 2 + x][totalSteps - (dimension - 1) / 2 + y][0][1]
newQuanStat3[1][0] = 0.0
newQuanStat3[1][1] = \
quantumStateList[totalSteps - (dimension - 1) / 2 + x][totalSteps - (dimension - 1) / 2 + y][1][1]
newQuanStatList[totalSteps - (dimension - 1) / 2 + x + 1][
totalSteps - (dimension - 1) / 2 + y + 1] += (newQuanStat3 / sqrt(2))
newQuanStat4 = zeros([2, 2], dtype=complex)
newQuanStat4[0][0] = 0.0
newQuanStat4[0][1] = \
quantumStateList[totalSteps - (dimension - 1) / 2 + x][totalSteps - (dimension - 1) / 2 + y][0][1]
newQuanStat4[1][0] = \
quantumStateList[totalSteps - (dimension - 1) / 2 + x][totalSteps - (dimension - 1) / 2 + y][1][0]
newQuanStat4[1][1] = 0.0
newQuanStatList[totalSteps - (dimension - 1) / 2 + x + 1][
totalSteps - (dimension - 1) / 2 + y - 1] += (newQuanStat4 / sqrt(2))
dimension += 2
return newQuanStatList
def quantumWalker(X0, X1, Y0, Y1, totalSteps,plotSteps):
global dimension
initStateList = initQuantumStateList(X0, X1, Y0, Y1, totalSteps)
newQuanStatList = zeros([2 * totalSteps + 1, 2 * totalSteps + 1, 2, 2], dtype=complex)
shiftQuanStatList = initStateList
for i in range(1,totalSteps+1):
newQuanStatList = hadamardCoin(shiftQuanStatList, totalSteps)
shiftQuanStatList = shiftOperator(newQuanStatList, totalSteps)
if (i!=0)&(i%plotSteps == 0) :
print dimension
print i
plotQW=distriQW(shiftQuanStatList,dimension,totalSteps)
Plot2D(plotQW,i)
dimension = 1
return shiftQuanStatList
def distriQW(quantumWalkerList, dim,totalSteps):
distribution = zeros([2 * totalSteps + 1, 2 * totalSteps + 1], dtype=float)
sum = 0.0
# print quantumWalkerList
for x in range(dim):
for y in range(dim):
distribution[totalSteps-(dim-1)/2+x][totalSteps-(dim-1)/2 +y] += float(
abs((quantumWalkerList[totalSteps-(dim-1)/2+x][totalSteps-(dim-1)/2 +y][0][0].real ** 2 + quantumWalkerList[totalSteps-(dim-1)/2+x][totalSteps-(dim-1)/2 +y][0][0].imag ** 2))
+ abs((quantumWalkerList[totalSteps-(dim-1)/2+x][totalSteps-(dim-1)/2 +y][0][1].real ** 2 + quantumWalkerList[totalSteps-(dim-1)/2+x][totalSteps-(dim-1)/2 +y][0][1].imag ** 2))
+ abs((quantumWalkerList[totalSteps-(dim-1)/2+x][totalSteps-(dim-1)/2 +y][1][0].real ** 2 + quantumWalkerList[totalSteps-(dim-1)/2+x][totalSteps-(dim-1)/2 +y][1][0].imag ** 2))
+ abs((quantumWalkerList[totalSteps-(dim-1)/2+x][totalSteps-(dim-1)/2 +y][1][1].real ** 2 + quantumWalkerList[totalSteps-(dim-1)/2+x][totalSteps-(dim-1)/2 +y][1][1].imag ** 2)))
sum += distribution[totalSteps-dim +x][totalSteps-dim +y]
print('Sum= %s' % sum)
return distribution / sum
def writeQWtoArray(distribution, filename):
distrFlie = open(filename, 'w+')
for x in range(shape(distribution)[0]):
for y in range(shape(distribution)[1]):
distrFlie.write(str(distribution[x][y]) + '\t')
distrFlie.write('\n')
distrFlie.close()
def writeQWtoList(distribution, filename):
distrFlie = open('Data/' + filename, 'w+')
for x in range(shape(distribution)[0]):
for y in range(shape(distribution)[1]):
distrFlie.write(str(x) + '\t' + str(y) + '\t' + str(distribution[x][y]) + '\n')
distrFlie.close()
def Plot2D(qw,steps):
plt.figure(1)
ax1 = plt.subplot(111)
plt.sca(ax1)
plt.title('2D distribution of %s steps Quantum Walk with hadamard coin' %steps)
plt.xlabel('X Position (started in center)')
plt.ylabel('Y Position (started in center)')
plt.imshow(qw)
plt.savefig('Fig/QW_' + str(steps) + '.png')
plt.close()
def PlotX(qw, Y):
qwX = qw[:][Y]
plt.plot(qwX)
plt.title('a Slice of 2D distribution of Quantum Walk')
plt.xlabel('Position(started in %s)' % Y)
plt.ylabel('Probability')
plt.show()
|
Python
|
2D
|
heartcored98/Standalone-DeepLearning-Chemistry
|
Lec04/Lec04_lipo_smiles_cnn_prediction.ipynb
|
.ipynb
| 704,913
| 550
|
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[](https://colab.research.google.com/github/heartcored98/Standalone-DeepLearning-Chemistry/blob/master/Lec04/Lec04_lipo_smiles_cnn_prediction.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Install miniconda and rdkit"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"!wget https://raw.githubusercontent.com/heartcored98/Standalone-DeepLearning-Chemistry/master/Lec04/utils.py -O utils.py\n",
"!mkdir results\n",
"!mkdir images\n",
"\n",
"!wget -c https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
"!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
"!time bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
"!time conda install -q -y -c conda-forge rdkit\n",
"\n",
"import sys\n",
"import os\n",
"sys.path.append('/usr/local/lib/python3.7/site-packages/')\n",
"\"\"\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Predict Lipophilicity Property Using CNN\n",
"=====================\n",
"\n",
"So far, we predicted lipophilicity with simple molecular representation (fingerprint). This time, we would employ CNN architecture with SMILES representation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Dataset과 DataLoader 준비하기 \n",
"\n",
"[MoleculeNet](http://moleculenet.ai/datasets-1)에서 원본 데이터셋을 확인할 수 있습니다. 혹은 직접 데이터셋 링크를 통해서 csv 파일을 다운받을 수도 있습니다. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Vocab Size : total 40 characters\n"
]
}
],
"source": [
"!wget -q \"http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/Lipophilicity.csv\" -O Lipophilicity.csv\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"\n",
"def get_splitted_lipo_dataset_with_vocab(ratios=[0.8, 0.1, 0.1], seed=123):\n",
"\n",
" raw_data = pd.read_csv('Lipophilicity.csv') # Open original dataset\n",
" smiles = raw_data['smiles']\n",
" \n",
" # Construct char-level vocabulary\n",
" vocab = set()\n",
" for mol in smiles:\n",
" vocab.update(list(mol))\n",
" vocab = sorted(vocab)\n",
" char_to_ix = {char: i for i, char in enumerate(vocab)}\n",
" \n",
" train_val, test = train_test_split(raw_data, test_size=ratios[2], random_state=seed)\n",
" train, val = train_test_split(train_val, test_size=ratios[1]/(ratios[0]+ratios[1]), random_state=seed)\n",
" \n",
" return [train, val, test], vocab\n",
"\n",
"datasets, vocab = get_splitted_lipo_dataset_with_vocab()\n",
"print(\"Vocab Size : total {} characters\".format(len(vocab)))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from torch.utils.data import Dataset, DataLoader\n",
"\n",
"class cnnDataset(Dataset):\n",
" def __init__(self, df, vocab, max_len=120):\n",
" self.smiles = df[\"smiles\"]\n",
" self.exp = df[\"exp\"].values\n",
" self.vocab = vocab \n",
" \n",
" self.X = np.zeros((len(self.smiles), max_len))\n",
" \n",
" char_to_ix = {char: i for i, char in enumerate(self.vocab)}\n",
"\n",
" for i, smiles in enumerate(self.smiles):\n",
" for j, char in enumerate(smiles[:max_len]):\n",
" self.X[i][j] = char_to_ix[char] + 1 # zero-index for empty position\n",
" \n",
" def __len__(self):\n",
" return len(self.smiles)\n",
" \n",
" def __getitem__(self, index):\n",
" return self.X[index], self.exp[index]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Model Construction\n",
"\n",
"\n",
"CNN 기반의 Lipophilicity 예측 아키텍쳐를 구현하여 봅시다. 이를 위해 크게 두가지의 Module을 구현하고 사용합니다. \n",
"\n",
"- **ResBlock** 2D input을 받아서 Convolution하는 module입니다. \n",
"- **CNNNet** mol 입력을 받아서 embedding으로 변환한 뒤 ResBlock등을 통과시키고 Molecular vector를 바탕으로 최종 Y값을 예측하는 module입니다. \n",
"\n",
"위 모듈들을 사용하여 CNNNet을 구현해봅시다."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"\n",
"\n",
"class ResBlock(nn.Module):\n",
" def __init__(self, in_filter, out_filter, stride, use_bn, dp_rate, block_type):\n",
" super(ResBlock, self).__init__() \n",
" self.use_bn = use_bn\n",
" self.block_type = block_type\n",
" self.conv1 = nn.Conv2d(in_filter, out_filter, kernel_size=3, stride=stride, padding=1, bias=False)\n",
" self.conv2 = nn.Conv2d(out_filter, out_filter, kernel_size=3, stride=1, padding=1, bias=False)\n",
" self.relu = nn.ReLU()\n",
" self.bn1 = nn.BatchNorm2d(out_filter)\n",
" self.bn2 = nn.BatchNorm2d(out_filter)\n",
" self.dropout = nn.Dropout2d(dp_rate)\n",
" self.shortcut = nn.Sequential()\n",
" if in_filter != out_filter:\n",
" self.shortcut.add_module(\n",
" 'conv', nn.Conv2d(in_filter, out_filter,\n",
" kernel_size=1, stride=stride, \n",
" padding=0, bias=False)\n",
" )\n",
" \n",
" def forward(self, _x):\n",
" if self.block_type == 'a': #original residual block\n",
" x = self.relu(self.bn1(self.conv1(_x))) if self.use_bn else self.relu(self.conv1(_x))\n",
" x = self.bn2(self.conv2(x)) if self.use_bn else self.conv2(x)\n",
" x = x + self.shortcut(_x)\n",
" return self.dropout(self.relu(x))\n",
" \n",
" elif self.block_type == 'b': # BN after addition\n",
" x = self.relu(self.bn1(self.conv1(_x))) if self.use_bn else self.relu(self.conv1(_x))\n",
" x = self.conv2(x) + self.shortcut(_x)\n",
" return self.dropout(self.relu(self.bn2(x)) if self.use_bn else self.relu(x))\n",
" \n",
" elif self.block_type == 'c': # ReLU before addition\n",
" x = self.relu(self.bn1(self.conv1(_x))) if self.use_bn else self.relu(self.conv1(_x))\n",
" x = self.relu(self.bn2(self.conv2(x))) if self.use_bn else self.relu(self.conv2(x))\n",
" return self.dropout(x + self.shortcut(_x))\n",
" \n",
" elif self.block_type == 'd': # ReLU-only pre-activation\n",
" x = self.bn1(self.conv1(self.relu(_x))) if self.use_bn else self.conv1(self.relu(_x))\n",
" x = self.bn2(self.conv2(self.relu(x))) if self.use_bn else self.conv2(self.relu(x))\n",
" return self.dropout(x + self.shortcut(_x))\n",
" \n",
" elif self.block_type == 'e': # full pre-activation\n",
" x = self.conv1(self.relu(self.bn1(_x))) if self.use_bn else self.conv1(self.relu(_x))\n",
" x = self.conv2(self.relu(self.bn2(x))) if self.use_bn else self.conv2(self.relu(x))\n",
" return self.dropout(x + self.shortcut(_x))\n",
" \n",
"\n",
"class Net(nn.Module):\n",
" def __init__(self, args):\n",
" super(Net, self).__init__() \n",
" \n",
" # Create Atom Element embedding layer\n",
" self.embedding = self.create_emb_layer(args.vocab_size, args.emb_train)\n",
" \n",
" # Create Residual Convolution layer\n",
" list_res_blocks = list()\n",
" n_channel = 1\n",
" for i in range(args.n_stage):\n",
" if i==0:\n",
" list_res_blocks.append(ResBlock(n_channel, n_channel*args.start_channel, args.stride, args.use_bn, args.dp_rate, args.block_type))\n",
" n_channel *= args.start_channel\n",
" else:\n",
" list_res_blocks.append(ResBlock(n_channel, n_channel*2, args.stride, args.use_bn, args.dp_rate, args.block_type))\n",
" n_channel *= 2\n",
" for j in range(args.n_layer-1):\n",
" list_res_blocks.append(ResBlock(n_channel, n_channel, 1, args.use_bn, args.dp_rate, args.block_type))\n",
" self.res_blocks = nn.Sequential(*list_res_blocks)\n",
" \n",
" # Create MLP layers\n",
" fc_shape = self._estimate_fc_shape((1, args.max_len,))\n",
" self.fc1 = nn.Linear(fc_shape[-1], 100)\n",
" self.fc2 = nn.Linear(100, 50)\n",
" self.fc3 = nn.Linear(50, 1)\n",
" self.relu = nn.ReLU()\n",
"\n",
" def create_emb_layer(self, vocab_size, emb_train):\n",
" emb_layer = nn.Embedding(vocab_size, vocab_size)\n",
" weight_matrix = torch.zeros((vocab_size, vocab_size))\n",
" for i in range(vocab_size):\n",
" weight_matrix[i][i] = 1\n",
" emb_layer.load_state_dict({'weight': weight_matrix})\n",
"\n",
" if not emb_train:\n",
" emb_layer.weight.requires_grad = False\n",
" return emb_layer\n",
"\n",
" def _estimate_fc_shape(self, input_shape):\n",
" dummy_input = torch.zeros(input_shape).long()\n",
" dummy_output = self._conv_forward(dummy_input)\n",
" fc_shape = dummy_output.view(dummy_output.shape[0], -1).shape\n",
" return fc_shape \n",
" \n",
" def _conv_forward(self, x):\n",
" embeds = self.embedding(x)\n",
" embeds = embeds.view(embeds.shape[0], 1, embeds.shape[1], embeds.shape[2])\n",
" x = self.res_blocks(embeds)\n",
" return x\n",
" \n",
" def forward(self, x):\n",
" x = self._conv_forward(x) \n",
" x = x.view(x.shape[0], -1)\n",
" x = self.relu(self.fc1(x))\n",
" x = self.relu(self.fc2(x))\n",
" x = self.fc3(x)\n",
" return torch.squeeze(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Train, Validation, Test\n",
"\n",
"Data, Model, Loss, Optimization을 모두 같이 사용하여 봅시다. Epoch 별로 train과 validation, test가 이루어질 수 있게 함수를 나누었습니다.\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def train(model, dataloader, optimizer, criterion, args, **kwargs):\n",
" \n",
" epoch_train_loss = 0\n",
" list_train_loss = list()\n",
" cnt_iter = 0\n",
" for batch_idx, batch in enumerate(dataloader):\n",
" X, y = batch[0].long(), batch[1].float()\n",
" X, y = X.to(args.device), y.to(args.device)\n",
" \n",
" model.train()\n",
" optimizer.zero_grad()\n",
"\n",
" pred_y = model(X) \n",
" train_loss = criterion(pred_y, y)\n",
" epoch_train_loss += train_loss.item()\n",
" list_train_loss.append({'epoch':batch_idx/len(dataloader)+kwargs['epoch'], 'train_loss':train_loss.item()})\n",
" train_loss.backward()\n",
" optimizer.step()\n",
" \n",
" cnt_iter += 1\n",
" return model, list_train_loss\n",
"\n",
"\n",
"def validate(model, dataloader, criterion, args):\n",
" \n",
" epoch_val_loss = 0\n",
" cnt_iter = 0\n",
" for batch_idx, batch in enumerate(dataloader):\n",
" X, y = batch[0].long(), batch[1].float()\n",
" X, y = X.to(args.device), y.to(args.device)\n",
" \n",
" model.eval()\n",
" pred_y = model(X)\n",
" val_loss = criterion(pred_y, y)\n",
" epoch_val_loss += val_loss.item()\n",
" cnt_iter += 1\n",
"\n",
" return epoch_val_loss/cnt_iter\n",
"\n",
"def test(model, dataloader, args, **kwargs):\n",
"\n",
" list_y, list_pred_y = list(), list()\n",
" for batch_idx, batch in enumerate(dataloader):\n",
" X, y = batch[0].long(), batch[1].float()\n",
" X, y = X.to(args.device), y.to(args.device)\n",
" \n",
" model.eval()\n",
" pred_y = model(X)\n",
" list_y += y.cpu().detach().numpy().tolist()\n",
" list_pred_y += pred_y.cpu().detach().numpy().tolist()\n",
"\n",
" mae = mean_absolute_error(list_y, list_pred_y)\n",
" std = np.std(np.array(list_y)-np.array(list_pred_y))\n",
" return mae, std, list_y, list_pred_y\n",
"\n",
"\n",
"def experiment(partition, args):\n",
" ts = time.time()\n",
" args.input_shape = (args.max_len, args.vocab_size)\n",
" \n",
" model = Net(args)\n",
" \n",
" model.to(args.device)\n",
" criterion = nn.MSELoss()\n",
" \n",
" # Initialize Optimizer\n",
" trainable_parameters = filter(lambda p: p.requires_grad, model.parameters())\n",
" if args.optim == 'ADAM':\n",
" optimizer = optim.Adam(trainable_parameters, lr=args.lr, weight_decay=args.l2_coef)\n",
" elif args.optim == 'RMSProp':\n",
" optimizer = optim.RMSprop(trainable_parameters, lr=args.lr, weight_decay=args.l2_coef)\n",
" elif args.optim == 'SGD':\n",
" optimizer = optim.SGD(trainable_parameters, lr=args.lr, weight_decay=args.l2_coef)\n",
" else:\n",
" assert False, \"Undefined Optimizer Type\"\n",
" \n",
" # Train, Validate, Evaluate\n",
" list_train_loss = list()\n",
" list_val_loss = list()\n",
" list_mae = list()\n",
" list_std = list()\n",
" \n",
" args.best_mae = 10000\n",
" for epoch in range(args.epoch):\n",
" model, train_losses = train(model, partition['train'], optimizer, criterion, args, **{'epoch':epoch})\n",
" val_loss = validate(model, partition['val'], criterion, args)\n",
" mae, std, true_y, pred_y = test(model, partition['test'], args, **{'epoch':epoch})\n",
" \n",
" list_train_loss += train_losses\n",
" list_val_loss.append({'epoch':epoch, 'val_loss':val_loss})\n",
" list_mae.append({'epoch':epoch, 'mae':mae})\n",
" list_std.append({'epoch':epoch, 'std':std})\n",
" \n",
" if args.best_mae > mae or epoch==0:\n",
" args.best_epoch = epoch\n",
" args.best_mae = mae\n",
" args.best_std = std\n",
" args.best_true_y = true_y\n",
" args.best_pred_y = pred_y\n",
" \n",
"\n",
" # End of experiments\n",
" te = time.time()\n",
" args.elapsed = te-ts\n",
" args.train_losses = list_train_loss\n",
" args.val_losses = list_val_loss\n",
" args.maes = list_mae\n",
" args.stds = list_std\n",
"\n",
" return model, args "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Experiment\n",
"\n",
"실험을 진행해봅시다. 이때 Embedding, Model Architecture, Optimizer, Training Configuration을 설정할 필요가 있습니다. \n",
"첫번째 실험으로 Learning Rate와 N Stage를 바꿔가면서 실험해보도록 하겠습니다. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import argparse\n",
"import time \n",
"from sklearn.metrics import mean_absolute_error\n",
"from utils import *\n",
"\n",
"seed = 123\n",
"np.random.seed(seed)\n",
"torch.manual_seed(seed)\n",
"\n",
"parser = argparse.ArgumentParser()\n",
"args = parser.parse_args(\"\")\n",
"\n",
"args.vocab_size = len(vocab) + 1\n",
"args.max_len = 70\n",
"\n",
"args.n_layer = 1\n",
"args.n_stage = 3\n",
"\n",
"args.lr = 0.00005\n",
"args.l2_coef = 0.0001\n",
"args.optim = 'ADAM'\n",
"args.epoch = 100\n",
"args.test_batch_size= 128\n",
"args.emb_train = False\n",
"args.start_channel = 64\n",
"args.stride = 2\n",
"args.use_bn = True\n",
"args.dp_rate = 0.3\n",
"args.block_type = 'a'\n",
"args.shuffle = True\n",
"args.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
"\n",
"args.batch_size = 256\n",
"args.exp_name = 'exp1_lr_stage'\n",
"\n",
"\n",
"writer = Writer(prior_keyword=['n_layer', 'n_stage','block_type', 'use_bn', 'lr', 'dp_rate', 'emb_train', 'epoch', 'batch_size'])\n",
"#writer.clear()\n",
"\n",
"# Define Hyperparameter Search Space\n",
"list_lr = [0.001, 0.005, 0.025]\n",
"list_n_stage = [1,2,3,4,5]\n",
"\n",
"\n",
"train_dataloader = DataLoader(cnnDataset(datasets[0], vocab, args.max_len), batch_size=args.batch_size, shuffle=True)\n",
"val_dataloader = DataLoader(cnnDataset(datasets[1], vocab, args.max_len), batch_size=args.batch_size, shuffle=False)\n",
"test_dataloader = DataLoader(cnnDataset(datasets[2], vocab, args.max_len), batch_size=args.batch_size, shuffle=False)\n",
"partition = {'train': train_dataloader, 'val': val_dataloader, 'test': test_dataloader}\n",
"\n",
"cnt_exp = 0\n",
"for lr in list_lr:\n",
" for n_stage in list_n_stage:\n",
" args.lr = lr\n",
" args.n_stage = n_stage\n",
"\n",
" model, result = experiment(partition, args)\n",
" writer.write(result)\n",
" \n",
" cnt_exp += 1 \n",
" print('[Exp {:2}] got mae: {:2.3f}, std: {:2.3f} at epoch {:2}'.format(cnt_exp, result.best_mae, result.best_std, result.best_epoch))\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x432 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x648 with 15 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1139.38x648 with 60 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"results = writer.read(exp_name='exp1_lr_stage')\n",
"variable1 = 'n_stage'\n",
"variable2 = 'lr'\n",
"\n",
"\n",
"plot_performance(results, variable1, variable2, args,\n",
" 'Performance depends on {} vs {}'.format(variable1, variable2),\n",
" 'exp1_Performance {} vs {}'.format(variable1, variable2))\n",
"\n",
"plot_distribution(results, variable1, variable2, 'true_y', 'pred_y', \n",
" 'Prediction results depends on {} vs {}'.format(variable1, variable2),\n",
" 'exp1_Prediction {} vs {}'.format(variable1, variable2))\n",
"\n",
"plot_loss(results, variable1, variable2, 'epoch', 'loss', \n",
" 'Loss depends on {} vs {}'.format(variable1, variable2),\n",
" 'exp1_Loss {} vs {}'.format(variable1, variable2))\n",
"\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "build_central"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
Unknown
|
2D
|
heartcored98/Standalone-DeepLearning-Chemistry
|
Lec04/utils.py
|
.py
| 7,950
| 213
|
import os
import numpy as np
from rdkit import Chem, DataStructs
from rdkit.Chem import AllChem
from rdkit.Chem.Crippen import MolLogP
from rdkit.Chem.rdMolDescriptors import CalcTPSA
from torch.utils.data import Dataset
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split, KFold
from decimal import Decimal
import json
from os import listdir
from os.path import isfile, join
import pandas as pd
import hashlib
class Writer():
def __init__(self, prior_keyword=[], dir='./results'):
self.prior_keyword = prior_keyword
self.dir = dir
def generate_hash(self, args):
str_as_bytes = str.encode(str(args))
hashed = hashlib.sha256(str_as_bytes).hexdigest()[:24]
return hashed
def write(self, args, prior_keyword=None):
dict_args = vars(args)
if 'bar' in dict_args:
#del dict_args['bar']
pass
if prior_keyword:
self.prior_keyword = prior_keyword
filename = 'exp_{}'.format(args.exp_name)
for keyword in self.prior_keyword:
value = str(dict_args[keyword])
if value.isdigit():
filename += keyword + ':{:.2E}_'.format(Decimal(dict_args[keyword]))
else:
filename += keyword + ':{}_'.format(value)
# hashcode = self.generate_hash(args)
# filename += hashcode
filename += '.json'
with open(self.dir+'/'+filename, 'w') as outfile:
json.dump(dict_args, outfile)
def read(self, exp_name=''):
list_result = list()
filenames = [f for f in listdir(self.dir) if isfile(join(self.dir, f))]
for filename in filenames:
with open(join(self.dir, filename), 'r') as infile:
result = json.load(infile)
if len(exp_name) > 0:
if result['exp_name'] == exp_name:
list_result.append(result)
else:
list_result.append(result)
return pd.DataFrame(list_result)
def clear(self, exp_name=''):
filenames = [f for f in listdir(self.dir) if isfile(join(self.dir, f))]
for filename in filenames:
if len(exp_name) > 0:
result = json.load(open(join(self.dir, filename), 'r'))
if result['exp_name'] == exp_name:
os.remove(join(self.dir, filename))
else:
os.remove(join(self.dir, filename))
import matplotlib.pyplot as plt
from matplotlib import gridspec
from matplotlib.font_manager import FontProperties
import seaborn as sns
def generate_setting(args, var1, var2):
dict_args = vars(args)
output = '{:92}'.format('[Exp Settings]') + '\n'
output += '-'*91 + '\n'
num_var = 3
cnt_var = 0
for keyword, value in dict_args.items():
if keyword != var1 and keyword != var2 and type(value) != list and not 'best' in keyword and keyword != 'elapsed':
str_value = str(value)
if str_value.isdigit():
if type(value) == float:
temp = '| {}={:.2E}'.format(keyword, Decimal(dict_args[keyword]))
if type(value) == int:
temp = '| {}={}'.format(keyword, str_value[:15])
else:
temp = '| {}={}'.format(keyword, str_value[:15])
output += '{:<30}'.format(temp[:30])
cnt_var += 1
if cnt_var % num_var == 0:
cnt_var = 0
output += '|\n'
output += '-'*91 + '\n'
return output
def plot_performance(results, variable1, variable2, args, title='', filename=''):
fig, ax = plt.subplots(1, 2)
fig.set_size_inches(15, 6)
sns.set_style("darkgrid", {"axes.facecolor": ".9"})
sns.barplot(x=variable1, y='best_mae', hue=variable2, data=results, ax=ax[0])
sns.barplot(x=variable1, y='best_std', hue=variable2, data=results, ax=ax[1])
font = FontProperties()
font.set_family('monospace')
font.set_size('large')
alignment = {'horizontalalignment': 'center', 'verticalalignment': 'baseline'}
fig.text(0.5, -0.6, generate_setting(args, variable1, variable2), fontproperties=font, **alignment)
fig.suptitle(title)
filename = filename if len(filename) > 0 else title
plt.savefig('./images/{}.png'.format(filename))
def plot_distribution(results, variable1, variable2, x='true_y', y='pred_y', title='', filename='', **kwargs):
list_v1 = results[variable1].unique()
list_v2 = results[variable2].unique()
list_data = list()
for value1 in list_v1:
for value2 in list_v2:
row = results.loc[results[variable1]==value1]
row = row.loc[results[variable2]==value2]
best_true_y = list(row.best_true_y)[0]
best_pred_y = list(row.best_pred_y)[0]
for i in range(len(best_true_y)):
list_data.append({x:best_true_y[i], y:best_pred_y[i], variable1:value1, variable2:value2})
df = pd.DataFrame(list_data)
g = sns.FacetGrid(df, row=variable2, col=variable1, margin_titles=True)
g.map(plt.scatter, x, y, alpha=0.3)
def identity(**kwargs):
plt.plot(np.linspace(-1.5,4,50), np.linspace(-1.5,4,50),'k',linestyle='dashed')
g.map(identity)
g.set_axis_labels(x, y)
g.fig.suptitle(title) # can also get the figure from plt.gcf()
plt.subplots_adjust(top=kwargs.get('top',0.93))
filename = filename if len(filename) > 0 else title
plt.savefig('./images/{}.png'.format(filename))
def plot_loss(results, variable1, variable2, x='true_y', y='pred_y', title='', filename='', **kwargs):
list_v1 = results[variable1].unique()
list_v2 = results[variable2].unique()
list_data = list()
for value1 in list_v1:
for value2 in list_v2:
row = results.loc[results[variable1]==value1]
row = row.loc[results[variable2]==value2]
train_losses = list(row.train_losses)[0]
val_losses = list(row.val_losses)[0]
maes = list(row.maes)[0]
for item in train_losses:
item.update({'type':'train', 'loss':item['train_loss'], variable1:value1, variable2:value2})
for item in val_losses:
item.update({'type':'val', 'loss':item['val_loss'], variable1:value1, variable2:value2})
for item in maes:
item.update({'type':'mae', variable1:value1, variable2:value2})
list_data += train_losses + val_losses + maes
df = pd.DataFrame(list_data)
temp_mae = df.loc[df['mae'] < df['mae'].quantile(0.98)]
ymax = temp_mae['mae'].max()
ymin = temp_mae['mae'].min()
temp_loss = df.loc[df['loss'] < df['loss'].quantile(0.98)]
lossmax = temp_loss['loss'].max()
lossmin = temp_loss['loss'].min()
g = sns.FacetGrid(df, row=variable2, col=variable1, hue='type', margin_titles=False)
axes = g.axes
for i in range(len(axes)):
for j in range(len(axes[0])):
if i==0:
g.axes[i][j].yaxis.set_label_coords(1.1,0.9)
def mae_line(x, y, **kwargs):
ax2 = plt.gca().twinx()
ax2.plot(x, y,'g--')
ax2.set_ylim(kwargs['ymax']*1.05, kwargs['ymin']*0.95)
ax2.grid(False)
g.map(plt.plot, x, y)
g.map(mae_line, 'epoch', 'mae', ymin=ymin, ymax=ymax)
g.set_axis_labels(x, y)
g.fig.suptitle(title) # can also get the figure from plt.gcf()
g.add_legend()
for ax in g.axes.flatten():
ax.set_ylim(lossmin, lossmax)
plt.subplots_adjust(top=kwargs.get('top', 0.93))
filename = filename if len(filename) > 0 else title
plt.savefig('./images/{}.png'.format(filename))
|
Python
|
2D
|
heartcored98/Standalone-DeepLearning-Chemistry
|
Lec06/Lec06_hyperparameter_tuning_with_tensorboard.ipynb
|
.ipynb
| 12,043
| 347
|
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[](https://colab.research.google.com/github/heartcored98/Standalone-DeepLearning-Chemistry/blob/master/Lec06/Lec06_hyperparameter_tuning_with_tensorboard.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Experiments Management with Tensorboard\n",
"=====================\n",
"\n",
"실제 딥러닝 연구나 개발을 하게 되면 꼭 마주치게 되는 것이 바로 수십개의 실험 결과를 관리하고 하이퍼파라미터에 따른 성능변화를 모니터링하고, 해당 실험 결과를 다시 재현하는 것입니다. \n",
"\n",
"본 실습에서는 Tensorboard와 Hparam 기능을 활용해서 Cifar10 데이터셋에 대한 다수의 실험 결과를 관리하고 성능을 모니터링하는지 알아봅니다. \n",
"\n",
"Cifar 10 튜토리얼 코드는 [이곳](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html)을 참조하였습니다. \n",
"Pytorch Tensorboard 튜토리얼 코드는 [이곳](https://pytorch.org/docs/stable/tensorboard.html)을 참조하였습니다."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Dataset과 DataLoader 준비하기 \n",
"\n",
"Cifar 10 데이터셋을 준비합니다. 빠른 실험을 위해 10%의 데이터셋만 활용해봅니다."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Files already downloaded and verified\n",
"Files already downloaded and verified\n"
]
}
],
"source": [
"import torch\n",
"import torchvision\n",
"import torchvision.transforms as transforms\n",
"\n",
"transform = transforms.Compose(\n",
" [transforms.ToTensor(),\n",
" transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
"\n",
"trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
" download=True, transform=transform)\n",
"\n",
"trainset, _ = torch.utils.data.random_split(trainset, [5000, 45000])\n",
"trainset, valset = torch.utils.data.random_split(trainset, [4000, 1000])\n",
"\n",
"testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
" download=True, transform=transform)\n",
"testset, _ = torch.utils.data.random_split(testset, [2000, 8000])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Model Construction\n",
"\n",
"간단한 CNN 아키텍쳐를 구현해봅니다. \n",
"두개의 `Conv` 모듈과 `MaxPool` 모듈을 통과한 후 `FC` 레이어를 통해 10개의 클래스를 분류합니다. 이 때 `Dropout` 모듈의 활성화 확률을 외부 변수로 입력 받습니다."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"from torch.utils.tensorboard import SummaryWriter\n",
"\n",
"\n",
"class Net(nn.Module):\n",
" def __init__(self, args):\n",
" super(Net, self).__init__()\n",
" self.conv1 = nn.Conv2d(3, 6, 5)\n",
" self.pool = nn.MaxPool2d(2, 2)\n",
" self.conv2 = nn.Conv2d(6, 16, 5)\n",
" self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
" self.fc2 = nn.Linear(120, 84)\n",
" self.fc3 = nn.Linear(84, 10)\n",
" self.dropout = nn.Dropout(p=args.dp_rate)\n",
" self.relu = nn.ReLU()\n",
"\n",
"\n",
" def forward(self, x):\n",
" x = self.pool(self.relu(self.conv1(x)))\n",
" x = self.pool(self.relu(self.conv2(x)))\n",
" x = x.view(-1, 16 * 5 * 5)\n",
" x = self.dropout(self.relu(self.fc1(x)))\n",
" x = self.dropout(self.relu(self.fc2(x)))\n",
" x = self.fc3(x)\n",
" return x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Train, Validation, Test\n",
"\n",
"Data, Model, Loss, Optimization을 모두 같이 사용하여 봅시다. Epoch 별로 train과 validation, test가 이루어질 수 있게 함수를 나누었습니다. 이 때 train_loss, val_loss, accuracy가 기록되도록 합니다."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"def train(model, dataloader, optimizer, criterion):\n",
" \n",
" epoch_train_loss = 0\n",
" cnt_iter = 0\n",
" for batch_idx, (images, y) in enumerate(dataloader):\n",
" images, y = images.to(args.device), y.to(args.device)\n",
"\n",
" model.train()\n",
" optimizer.zero_grad()\n",
" pred_y = model(images)\n",
" train_loss = criterion(pred_y, y)\n",
" epoch_train_loss += train_loss.item()\n",
" \n",
" train_loss.backward()\n",
" optimizer.step()\n",
" \n",
" epoch_train_loss /= len(dataloader)\n",
" return model, epoch_train_loss\n",
"\n",
"\n",
"def validate(model, dataloader, criterion):\n",
" \n",
" epoch_val_loss = 0\n",
" with torch.no_grad():\n",
" for batch_idx, (images, y) in enumerate(dataloader):\n",
" images, y = images.to(args.device), y.to(args.device)\n",
"\n",
" model.eval()\n",
" pred_y = model(images)\n",
" val_loss = criterion(pred_y, y)\n",
" epoch_val_loss += val_loss.item()\n",
"\n",
" epoch_val_loss /= len(dataloader)\n",
" return epoch_val_loss\n",
"\n",
"\n",
"def test(model, dataloader):\n",
" \n",
" correct = 0\n",
" total = 0\n",
" with torch.no_grad():\n",
" for batch_idx, (images, labels) in enumerate(dataloader):\n",
" images, labels = images.to(args.device), labels.to(args.device)\n",
" outputs = model(images)\n",
" _, predicted = torch.max(outputs.data, 1)\n",
" total += labels.size(0)\n",
" correct += (predicted == labels).sum().item()\n",
" \n",
" accuracy = 100 * correct / total\n",
" return accuracy\n",
"\n",
"\n",
"def experiment(partition, args):\n",
" \n",
" seed = 123\n",
" np.random.seed(seed)\n",
" torch.manual_seed(seed)\n",
" \n",
" writer = SummaryWriter()\n",
"\n",
" \n",
" model = Net(args) \n",
" model.to(args.device)\n",
" criterion = nn.CrossEntropyLoss()\n",
" \n",
" # Initialize Optimizer\n",
" trainable_parameters = filter(lambda p: p.requires_grad, model.parameters())\n",
" if args.optim == 'ADAM':\n",
" optimizer = optim.Adam(trainable_parameters, lr=args.lr, weight_decay=args.l2_coef)\n",
" elif args.optim == 'RMSProp':\n",
" optimizer = optim.RMSprop(trainable_parameters, lr=args.lr, weight_decay=args.l2_coef)\n",
" elif args.optim == 'SGD':\n",
" optimizer = optim.SGD(trainable_parameters, lr=args.lr, weight_decay=args.l2_coef)\n",
" else:\n",
" assert False, \"Undefined Optimizer Type\"\n",
" \n",
" args.best_acc = 0\n",
" for epoch in range(args.epoch):\n",
" model, train_loss = train(model, partition['train'], optimizer, criterion)\n",
" val_loss = validate(model, partition['val'], criterion)\n",
" accuracy = test(model, partition['test'])\n",
" \n",
" if accuracy > args.best_acc:\n",
" args.best_acc = accuracy\n",
" args.best_epoch = epoch\n",
" \n",
" writer.add_scalar('Loss/train', train_loss, epoch)\n",
" writer.add_scalar('Loss/val', val_loss, epoch)\n",
" writer.add_scalar('Metric/acc', accuracy, epoch)\n",
" \n",
" writer.add_hparams(\n",
" hparam_dict=vars(args),\n",
" metric_dict={'best_acc':args.best_acc, 'best_epoch':args.best_epoch}\n",
" )\n",
" \n",
" return model, args "
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Exp 1] got acc: 44.200, at epoch 18\n",
"[Exp 2] got acc: 44.450, at epoch 19\n",
"[Exp 3] got acc: 43.050, at epoch 18\n",
"[Exp 4] got acc: 37.850, at epoch 19\n",
"[Exp 5] got acc: 26.000, at epoch 19\n",
"[Exp 6] got acc: 45.450, at epoch 14\n",
"[Exp 7] got acc: 46.950, at epoch 17\n",
"[Exp 8] got acc: 46.700, at epoch 19\n",
"[Exp 9] got acc: 40.550, at epoch 17\n",
"[Exp 10] got acc: 28.250, at epoch 19\n",
"[Exp 11] got acc: 43.100, at epoch 14\n",
"[Exp 12] got acc: 42.700, at epoch 17\n",
"[Exp 13] got acc: 39.600, at epoch 15\n",
"[Exp 14] got acc: 34.750, at epoch 19\n",
"[Exp 15] got acc: 10.400, at epoch 0\n"
]
}
],
"source": [
"import argparse\n",
"import time \n",
"import numpy as np\n",
"\n",
"\n",
"parser = argparse.ArgumentParser()\n",
"args = parser.parse_args(\"\")\n",
"\n",
"\n",
"# ==== Model Architecture Config ==== #\n",
"args.dp_rate = 0.3\n",
"\n",
"\n",
"# ==== Optimizer Config ==== #\n",
"args.lr = 0.00005\n",
"args.l2_coef = 0.0001\n",
"args.optim = 'ADAM'\n",
"\n",
"\n",
"# ==== Training Config ==== #\n",
"args.epoch = 20\n",
"args.batch_size = 256\n",
"args.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
"args.exp_name = 'exp1_lr_stage'\n",
"\n",
"\n",
"# ==== DataLoader Preparation ==== #\n",
"trainloader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size,\n",
" shuffle=True)\n",
"valloader = torch.utils.data.DataLoader(valset, batch_size=args.batch_size,\n",
" shuffle=False)\n",
"testloader = torch.utils.data.DataLoader(testset, batch_size=args.batch_size*2,\n",
" shuffle=False)\n",
"partition = {'train': trainloader, 'val': valloader, 'test': testloader}\n",
"\n",
"\n",
"# ==== Experiment ==== #\n",
"list_dp_rate = [0, 0.2, 0.4, 0.6, 0.8]\n",
"list_lr = [0.001, 0.005, 0.01]\n",
"\n",
"cnt_exp = 0\n",
"for lr in list_lr:\n",
" for dp_rate in list_dp_rate:\n",
" args.lr = lr\n",
" args.dp_rate = dp_rate\n",
"\n",
" model, result = experiment(partition, args)\n",
"\n",
" cnt_exp += 1\n",
" print('[Exp {:2}] got acc: {:2.3f}, at epoch {:2}'.format(cnt_exp, result.best_acc, result.best_epoch))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"%load_ext tensorboard"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%tensorboard --logdir runs/"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "build_central"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
Unknown
|
2D
|
heartcored98/Standalone-DeepLearning-Chemistry
|
Lec03/Lec03_MLP_with_molecular_fingerprint.ipynb
|
.ipynb
| 84,942
| 1,075
|
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[](https://colab.research.google.com/github/heartcored98/Standalone-DeepLearning-Chemistry/blob/master/Lec03/Lec03_MLP_with_molecular_fingerprint.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2019-12-29 14:44:01-- https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
"Resolving repo.continuum.io (repo.continuum.io)... 104.18.200.79, 104.18.201.79, 2606:4700::6812:c94f, ...\n",
"Connecting to repo.continuum.io (repo.continuum.io)|104.18.200.79|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 71785000 (68M) [application/x-sh]\n",
"Saving to: ‘Miniconda3-latest-Linux-x86_64.sh’\n",
"\n",
"Miniconda3-latest-L 100%[===================>] 68.46M 4.01MB/s in 17s \n",
"\n",
"2019-12-29 14:44:19 (3.97 MB/s) - ‘Miniconda3-latest-Linux-x86_64.sh’ saved [71785000/71785000]\n",
"\n",
"/bin/sh: 1: time: not found\n",
"/bin/sh: 1: time: not found\n"
]
}
],
"source": [
"!wget -c https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
"!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
"!time bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
"!time conda install -q -y -c conda-forge rdkit"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"sys.path.append('/usr/local/lib/python3.7/site-packages/')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MLP with Molecular Fingerprint\n",
"\n",
"앞선 강의에서 neural network의 가장 기본적인 형태인 Multilayer Perceptron(MLP)의 구조에 대해서 배워보았습니다. 이번 시간에는 분자를 표현하는 방법 중 하나인 molecular fingerprint를 MLP의 input으로 사용하여 분자의 lipophilicity를 예측하는 코드를 작성해봅니다."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Dataset과 DataLoader 준비하기\n",
"\n",
"`torch.utils.data.Dataset`과 `torch.utils.data.DataLoader` 클래스는 pytorch에서 데이터 파일을 관리하고 training을 위한 batch 형태로 넘겨주는 역할을합니다. 지난 튜토리얼에서 보았던 CIFAR-10 dataset과 dataloader는 torchvision이라는 pytorch의 이미지 데이터 및 프로세싱을 관리하는 패키지에 이미 포함되어있었습니다. 그러나 분자 데이터는 pytorch에서 제공하지 않기에, lipophilicity 데이터 csv 파일을 이용하여 custom dataset과 dataloader를 만들어 사용해야합니다.\n",
"\n",
"\n",
"### 1.1 데이터의 형태 알기\n",
"먼저 wget 명령어로 lipophilicity csv 데이터 파일을 받도록합시다. IPython내에서 리눅스 명령어를 실행시키기 위해서는 맨 앞에 `!`를 붙여주시면 됩니다."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"!wget -q \"http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/Lipophilicity.csv\" -O Lipophilicity.csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"데이터 관리를 위한 `pandas` 패키지를 이용하여 이 데이터가 어떠한 형태를 가지고 있는지 알아봅시다.\n",
"\n",
"`pandas`는 `dataframe`의 형태로 데이터를 관리합니다. 다운로드 된 csv파일로부터 만든 `dataframe`은 분자의 smiles representation과 lipophilicity 값을 행으로써 가지고 있습니다."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CMPD_CHEMBLID</th>\n",
" <th>exp</th>\n",
" <th>smiles</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>CHEMBL596271</td>\n",
" <td>3.54</td>\n",
" <td>Cn1c(CN2CCN(CC2)c3ccc(Cl)cc3)nc4ccccc14</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>CHEMBL1951080</td>\n",
" <td>-1.18</td>\n",
" <td>COc1cc(OC)c(cc1NC(=O)CSCC(=O)O)S(=O)(=O)N2C(C)...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>CHEMBL1771</td>\n",
" <td>3.69</td>\n",
" <td>COC(=O)[C@@H](N1CCc2sccc2C1)c3ccccc3Cl</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>CHEMBL234951</td>\n",
" <td>3.37</td>\n",
" <td>OC[C@H](O)CN1C(=O)C(Cc2ccccc12)NC(=O)c3cc4cc(C...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>CHEMBL565079</td>\n",
" <td>3.10</td>\n",
" <td>Cc1cccc(C[C@H](NC(=O)c2cc(nn2C)C(C)(C)C)C(=O)N...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4195</td>\n",
" <td>CHEMBL496929</td>\n",
" <td>3.85</td>\n",
" <td>OCCc1ccc(NC(=O)c2cc3cc(Cl)ccc3[nH]2)cc1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4196</td>\n",
" <td>CHEMBL199147</td>\n",
" <td>3.21</td>\n",
" <td>CCN(C1CCN(CCC(c2ccc(F)cc2)c3ccc(F)cc3)CC1)C(=O...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4197</td>\n",
" <td>CHEMBL15932</td>\n",
" <td>2.10</td>\n",
" <td>COc1cccc2[nH]ncc12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4198</td>\n",
" <td>CHEMBL558748</td>\n",
" <td>2.65</td>\n",
" <td>Clc1ccc2ncccc2c1C(=O)NCC3CCCCC3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4199</td>\n",
" <td>CHEMBL237889</td>\n",
" <td>2.70</td>\n",
" <td>CN1C(=O)C=C(CCc2ccc3ccccc3c2)N=C1N</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4200 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" CMPD_CHEMBLID exp smiles\n",
"0 CHEMBL596271 3.54 Cn1c(CN2CCN(CC2)c3ccc(Cl)cc3)nc4ccccc14\n",
"1 CHEMBL1951080 -1.18 COc1cc(OC)c(cc1NC(=O)CSCC(=O)O)S(=O)(=O)N2C(C)...\n",
"2 CHEMBL1771 3.69 COC(=O)[C@@H](N1CCc2sccc2C1)c3ccccc3Cl\n",
"3 CHEMBL234951 3.37 OC[C@H](O)CN1C(=O)C(Cc2ccccc12)NC(=O)c3cc4cc(C...\n",
"4 CHEMBL565079 3.10 Cc1cccc(C[C@H](NC(=O)c2cc(nn2C)C(C)(C)C)C(=O)N...\n",
"... ... ... ...\n",
"4195 CHEMBL496929 3.85 OCCc1ccc(NC(=O)c2cc3cc(Cl)ccc3[nH]2)cc1\n",
"4196 CHEMBL199147 3.21 CCN(C1CCN(CCC(c2ccc(F)cc2)c3ccc(F)cc3)CC1)C(=O...\n",
"4197 CHEMBL15932 2.10 COc1cccc2[nH]ncc12\n",
"4198 CHEMBL558748 2.65 Clc1ccc2ncccc2c1C(=O)NCC3CCCCC3\n",
"4199 CHEMBL237889 2.70 CN1C(=O)C=C(CCc2ccc3ccccc3c2)N=C1N\n",
"\n",
"[4200 rows x 3 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv('Lipophilicity.csv')\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2 smiles로부터 molecular fingerprint 만들기\n",
"\n",
"smiles string들로부터 molecular fingerprint를 만들어봅시다. 이를 위해서는 화학 관련 패키지 `rdkit`을 사용합니다.\n",
"\n",
"우리의 데이터프레임에 `fp`행이 추가되었고, 이 행은 각 분자들의 2048 길이의 molecular fingerprint vector를 가지고 있습니다."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CMPD_CHEMBLID</th>\n",
" <th>exp</th>\n",
" <th>smiles</th>\n",
" <th>fp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>CHEMBL596271</td>\n",
" <td>3.54</td>\n",
" <td>Cn1c(CN2CCN(CC2)c3ccc(Cl)cc3)nc4ccccc14</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>CHEMBL1951080</td>\n",
" <td>-1.18</td>\n",
" <td>COc1cc(OC)c(cc1NC(=O)CSCC(=O)O)S(=O)(=O)N2C(C)...</td>\n",
" <td>[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>CHEMBL1771</td>\n",
" <td>3.69</td>\n",
" <td>COC(=O)[C@@H](N1CCc2sccc2C1)c3ccccc3Cl</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>CHEMBL234951</td>\n",
" <td>3.37</td>\n",
" <td>OC[C@H](O)CN1C(=O)C(Cc2ccccc12)NC(=O)c3cc4cc(C...</td>\n",
" <td>[0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>CHEMBL565079</td>\n",
" <td>3.10</td>\n",
" <td>Cc1cccc(C[C@H](NC(=O)c2cc(nn2C)C(C)(C)C)C(=O)N...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4195</td>\n",
" <td>CHEMBL496929</td>\n",
" <td>3.85</td>\n",
" <td>OCCc1ccc(NC(=O)c2cc3cc(Cl)ccc3[nH]2)cc1</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4196</td>\n",
" <td>CHEMBL199147</td>\n",
" <td>3.21</td>\n",
" <td>CCN(C1CCN(CCC(c2ccc(F)cc2)c3ccc(F)cc3)CC1)C(=O...</td>\n",
" <td>[0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4197</td>\n",
" <td>CHEMBL15932</td>\n",
" <td>2.10</td>\n",
" <td>COc1cccc2[nH]ncc12</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4198</td>\n",
" <td>CHEMBL558748</td>\n",
" <td>2.65</td>\n",
" <td>Clc1ccc2ncccc2c1C(=O)NCC3CCCCC3</td>\n",
" <td>[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4199</td>\n",
" <td>CHEMBL237889</td>\n",
" <td>2.70</td>\n",
" <td>CN1C(=O)C=C(CCc2ccc3ccccc3c2)N=C1N</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4200 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" CMPD_CHEMBLID exp smiles \\\n",
"0 CHEMBL596271 3.54 Cn1c(CN2CCN(CC2)c3ccc(Cl)cc3)nc4ccccc14 \n",
"1 CHEMBL1951080 -1.18 COc1cc(OC)c(cc1NC(=O)CSCC(=O)O)S(=O)(=O)N2C(C)... \n",
"2 CHEMBL1771 3.69 COC(=O)[C@@H](N1CCc2sccc2C1)c3ccccc3Cl \n",
"3 CHEMBL234951 3.37 OC[C@H](O)CN1C(=O)C(Cc2ccccc12)NC(=O)c3cc4cc(C... \n",
"4 CHEMBL565079 3.10 Cc1cccc(C[C@H](NC(=O)c2cc(nn2C)C(C)(C)C)C(=O)N... \n",
"... ... ... ... \n",
"4195 CHEMBL496929 3.85 OCCc1ccc(NC(=O)c2cc3cc(Cl)ccc3[nH]2)cc1 \n",
"4196 CHEMBL199147 3.21 CCN(C1CCN(CCC(c2ccc(F)cc2)c3ccc(F)cc3)CC1)C(=O... \n",
"4197 CHEMBL15932 2.10 COc1cccc2[nH]ncc12 \n",
"4198 CHEMBL558748 2.65 Clc1ccc2ncccc2c1C(=O)NCC3CCCCC3 \n",
"4199 CHEMBL237889 2.70 CN1C(=O)C=C(CCc2ccc3ccccc3c2)N=C1N \n",
"\n",
" fp \n",
"0 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"1 [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"2 [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"3 [0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... \n",
"4 [0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"... ... \n",
"4195 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"4196 [0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"4197 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"4198 [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"4199 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
"\n",
"[4200 rows x 4 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from rdkit import Chem, DataStructs\n",
"from rdkit.Chem import AllChem\n",
"\n",
"fps = []\n",
"for i, smiles in enumerate(df[\"smiles\"]):\n",
" mol = Chem.MolFromSmiles(smiles)\n",
" arr = np.zeros((1,))\n",
" \n",
" fp = AllChem.GetMorganFingerprintAsBitVect(mol, 2048)\n",
" DataStructs.ConvertToNumpyArray(fp, arr)\n",
" \n",
" fps.append(arr)\n",
" \n",
"df[\"fp\"] = fps\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.3 Train, Validation, Test set으로 나누기\n",
"\n",
"역시 데이터 관리를 위한 패키지인 `sklearn`의 `train_test_split` 함수를 이용하여 우리의 전체 `dataframe`을 train, validation, test `dataframe`으로 나누어봅시다. 물론 `train_test_split` 함수를 사용하지 않고도 `dataframe`을 나누는 것이 가능합니다.\n",
"\n",
"전체 데이터를 8:1:1의 비율로 각각 train, validation, test `dataframe`으로 나누었습니다. 그 결과 train에는 3359개, validation과 test에는 421, 420개의 분자가 랜덤하게 배정되었습니다."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'train': CMPD_CHEMBLID exp smiles \\\n",
" 1369 CHEMBL199237 2.70 O=C(NCc1ccccn1)c2ccc(Oc3ccccc3)cc2 \n",
" 3084 CHEMBL277863 2.05 COC(=O)N1CCN([C@H](CN2CCCC2)C1)C(=O)Cc3ccc(Cl)... \n",
" 2141 CHEMBL1824036 -0.51 CS(=O)(=O)c1ccc2OCC(=O)N(CCN3CCC(CC3)NCc4ccc5O... \n",
" 3741 CHEMBL87266 1.35 Cc1cc(N)c2cc(NC(=O)CC(=O)Nc3ccc4nc(C)cc(N)c4c3... \n",
" 2192 CHEMBL513370 2.36 COc1ccc(N(C(C(=O)NC[C@@H](C)O)c2ccccc2F)C(=O)c... \n",
" ... ... ... ... \n",
" 3180 CHEMBL578061 4.06 N(c1ccccc1)c2ccnc(Nc3ccccc3)n2 \n",
" 266 CHEMBL97 2.16 COc1ccc2nccc([C@H](O)C3CC4CCN3CC4C=C)c2c1 \n",
" 2398 CHEMBL138649 3.60 Oc1ccc2OC(=CC(=O)c2c1)c3ccccc3 \n",
" 2073 CHEMBL272705 2.21 C[C@H](CO)Nc1nc(SCc2occc2)nc3NC(=O)Sc13 \n",
" 2480 CHEMBL177611 0.60 Clc1ccc(cc1)C(=O)N[C@H]2CN3CCC2CC3 \n",
" \n",
" fp \n",
" 1369 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" 3084 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" 2141 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" 3741 [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" 2192 [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" ... ... \n",
" 3180 [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... \n",
" 266 [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" 2398 [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" 2073 [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" 2480 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" \n",
" [3359 rows x 4 columns],\n",
" 'val': CMPD_CHEMBLID exp smiles \\\n",
" 1992 CHEMBL896 3.08 OCCOCCN1CCN(CC1)C(c2ccccc2)c3ccc(Cl)cc3 \n",
" 845 CHEMBL505521 1.85 CNC1=Nc2ncccc2C(=NC1c3cccs3)c4occc4 \n",
" 4006 CHEMBL2059859 3.24 Cc1ccc(cc1NC(=O)c2ccc(OCc3ccccn3)cc2)c4nc5cccc... \n",
" 193 CHEMBL174107 -0.84 CSc1ccc(cc1)N2C(=NC(=NC2(C)C)N)N \n",
" 963 CHEMBL1807872 -0.03 O[C@@H](CNCCCOCCNCCc1cccc(Cl)c1)c2ccc(O)c3NC(=... \n",
" ... ... ... ... \n",
" 3761 CHEMBL1526668 3.20 O=C(N1CCCCC1)c2csc3CCCCc23 \n",
" 4041 CHEMBL1008 3.66 CC(C)COCC(CN(Cc1ccccc1)c2ccccc2)N3CCCC3 \n",
" 779 CHEMBL1779045 2.24 Cc1cccc(c1)N(Cc2cc(F)c(F)cc2F)C(=O)O[C@H]3CN4C... \n",
" 1800 CHEMBL1604278 1.25 Cc1cc(N)n(n1)c2ccccc2 \n",
" 1604 CHEMBL481596 2.63 CN1CCN(CC1)c2cc3c(Nc4ccc(F)cc4F)c(cnc3cc2F)C(=O)N \n",
" \n",
" fp \n",
" 1992 [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" 845 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" 4006 [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" 193 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" 963 [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" ... ... \n",
" 3761 [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" 4041 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, ... \n",
" 779 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" 1800 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" 1604 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" \n",
" [421 rows x 4 columns],\n",
" 'test': CMPD_CHEMBLID exp smiles \\\n",
" 2030 CHEMBL1950846 0.90 CN[C@@H](C)C(=O)N[C@H](C(=O)N[C@H]1CCCN(C1)S(=... \n",
" 3043 CHEMBL1411716 3.49 CSc1ccc(cc1)C(=O)C(C)(C)N2CCOCC2 \n",
" 3552 CHEMBL1428115 2.10 CC(CC(=O)NCc1ccccc1)C2CCCO2 \n",
" 380 CHEMBL1807870 0.99 O[C@@H](CNCCCSCCNCCc1ccccc1Cl)c2ccc(O)c3NC(=O)... \n",
" 730 CHEMBL354788 1.20 NC1=NC(Nc2c(F)ccc(F)c12)c3occc3 \n",
" ... ... ... ... \n",
" 2454 CHEMBL502 2.41 COc1cc2CC(CC3CCN(Cc4ccccc4)CC3)C(=O)c2cc1OC \n",
" 4159 CHEMBL193233 -1.01 COc1ccc(cc1N2CC(=O)NS2(=O)=O)c3ccccc3 \n",
" 2406 CHEMBL1800656 1.92 CCN(CCNCCc1ccc(O)c2NC(=O)Sc12)C(=O)CCOCCc3ccccc3 \n",
" 167 CHEMBL256576 2.82 C[C@@H](Oc1cccc2ncnc(Nc3ccc4c(cnn4Cc5ccccn5)c3... \n",
" 2493 CHEMBL1822878 4.30 Oc1ccc(Br)c2ccccc12 \n",
" \n",
" fp \n",
" 2030 [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" 3043 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" 3552 [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" 380 [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" 730 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" ... ... \n",
" 2454 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" 4159 [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... \n",
" 2406 [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... \n",
" 167 [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" 2493 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
" \n",
" [420 rows x 4 columns]}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"train_val, test = train_test_split(df, test_size=0.1, random_state=123)\n",
"train, val = train_test_split(train_val, test_size=0.1/0.9, random_state=123)\n",
"\n",
"dataframes = {\n",
" \"train\": train,\n",
" \"val\": val,\n",
" \"test\": test\n",
"}\n",
"\n",
"dataframes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.4 Custom Dataset 만들기\n",
"\n",
"이제 `dataframe`들을 감싸는 custom 데이터셋을 만들어봅시다. 모든 custom 데이터셋은 `torch.utils.data.Dataset`을 상속하고, 데이터셋의 길이를 반환하는 `__len__` 메서드와 `index`번째 아이템을 요청하였을때 무엇이 반환되는지 결정하는 `__getitem__` 메서드를 작성해야합니다.\n",
"\n",
"우리의 데이터셋은 각자 `pandas dataframe`을 가지고 `index`번째 아이템을 요청시 데이터프레임의 `index`번째 분자의 molecular fingerprint와 lipophilicity 값을 반환합니다."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'train': <__main__.FPDataset at 0x7ff2a3f1ab10>,\n",
" 'val': <__main__.FPDataset at 0x7ff2a3f1add0>,\n",
" 'test': <__main__.FPDataset at 0x7ff2574dcdd0>}"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from torch.utils.data import Dataset\n",
"\n",
"class FPDataset(Dataset):\n",
" \n",
" def __init__(self, df):\n",
" self.fps = list(df[\"fp\"])\n",
" self.exp = list(df[\"exp\"].values)\n",
" \n",
" def __len__(self):\n",
" return len(self.fps)\n",
" \n",
" def __getitem__(self, index):\n",
" return self.fps[index], self.exp[index]\n",
" \n",
"datasets = {\n",
" \"train\": FPDataset(dataframes[\"train\"]),\n",
" \"val\": FPDataset(dataframes[\"val\"]),\n",
" \"test\": FPDataset(dataframes[\"test\"]),\n",
"}\n",
"\n",
"datasets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"우리의 데이터셋이 molecular fingerprint와 lipophilicity 값을 반환하는 것을 다음과 같이 확인할 수 있습니다."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([0., 0., 0., ..., 0., 0., 0.]), 2.7)"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datasets[\"train\"][0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. MLP Model 만들기\n",
"\n",
"### 2.1 `nn.Module`을 상속하여 custom Model 만들기\n",
"\n",
"`pytoch`를 통해 MLP model을 만들어봅시다. 모든 custom model 또는 sub-model은 `torch.nn.Module`을 상속하고 `forward` 함수를 작성해야합니다. `forward` 함수는 input 데이터가 어떠한 레이어들과 activation function들을 통과할지에 대한 정보를 담고 있습니다. 거의 모든 기본 레이어들과 activation function들은 `torch.nn` 모듈에 구현되어있습니다. 우리의 model의 `__init__` 함수에서는 `forward` 함수에서 사용할 레이어들과 activation function들을 미리 만들어 저장해둡니다.\n",
"\n",
"`nn.Linear` object는 일반적인 fully-connected layer를 말합니다. 이는 두가지 argument를 받는데 각각 input과 output의 dimension입니다. 우리의 input molecular fingerprint는 2048 dimension이니 첫 layer의 input dimension은 반드시 2048이 되어야합니다. 또한 우리의 output은 lipophilicity 값이기 때문에 마지막 layer의 output dimension은 1이 되어야합니다.\n",
"\n",
"`nn.ReLU`는 ReLU activation function입니다. `forward` 함수를 보면 하나의 레이어를 통과한 뒤 ReLU activation function을 거치는 것을 볼 수 있습니다. 그러나 맨 마지막 레이어 이후에는 이를 적용하면 안되는데, 그 이유는 output이 0보다 큰 값으로 고정되어 버리기 때문입니다."
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"MLP(\n",
" (linear1): Linear(in_features=2048, out_features=1024, bias=True)\n",
" (linear2): Linear(in_features=1024, out_features=256, bias=True)\n",
" (linear3): Linear(in_features=256, out_features=1, bias=True)\n",
" (dropout1): Dropout(p=0.5, inplace=False)\n",
" (dropout2): Dropout(p=0.5, inplace=False)\n",
" (relu): ReLU()\n",
")"
]
},
"execution_count": 180,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import torch.nn as nn\n",
"\n",
"class MLP(nn.Module):\n",
" \n",
" def __init__(self):\n",
" super(MLP, self).__init__()\n",
" \n",
" self.linear1 = nn.Linear(2048, 1024)\n",
" self.linear2 = nn.Linear(1024, 256)\n",
" self.linear3 = nn.Linear(256, 1)\n",
" self.dropout1 = nn.Dropout(0.5)\n",
" self.dropout2 = nn.Dropout(0.5)\n",
" self.relu = nn.ReLU()\n",
" \n",
" def forward(self, x):\n",
" out = self.linear1(x)\n",
" out = self.dropout1(out)\n",
" out = self.relu(out)\n",
" out = self.linear2(out)\n",
" out = self.dropout2(out)\n",
" out = self.relu(out)\n",
" out = self.linear3(out)\n",
" return out\n",
" \n",
"model = MLP()\n",
"model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2 Model을 GPU에 올리기\n",
"\n",
"지난 pytorch tutorial에서 임의의 tensor 또는 model을 GPU 상에 올리는 방법을 배웠습니다. 이를 적용하여 우리의 모델을 GPU에 올려봅시다."
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"device(type='cuda', index=0)"
]
},
"execution_count": 181,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import torch\n",
"\n",
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"device"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"MLP(\n",
" (linear1): Linear(in_features=2048, out_features=1024, bias=True)\n",
" (linear2): Linear(in_features=1024, out_features=256, bias=True)\n",
" (linear3): Linear(in_features=256, out_features=1, bias=True)\n",
" (dropout1): Dropout(p=0.5, inplace=False)\n",
" (dropout2): Dropout(p=0.5, inplace=False)\n",
" (relu): ReLU()\n",
")"
]
},
"execution_count": 182,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.to(device)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Model을 Training하기\n",
"\n",
"### 3.1 Loss와 Optimizer 생성하기\n",
"\n",
"훈련을 위한 model이 준비되었습니다. 이제 이 model을 training 시키기 위해서는 사용할 loss function과 optimizer를 결정해야합니다. 역시 `torch.nn`과 `torch.optim` 모듈에 기본적인 loss function들과 optimizer들이 구현되어있습니다.\n",
"\n",
"`nn.MSELoss`와 `optim.Adam`을 사용해봅시다.\n",
"\n",
"`nn.MSELoss`는 mean squared error의 줄임말입니다. 예측값과 실제값의 차이의 제곱의 평균을 loss로서 사용합니다.\n",
"\n",
"모든 optimizer는 SGD를 기반으로하고 다양한 기능이 추가되어있습니다. 일반적으로 잘 작동한다고 알려진 `optim.Adam`을 사용해봅시다. 모든 optimizer를 생성하기 위해서는 optimizer가 update할 parameter들과 learning rate, l2 regularization coefficient가 필요합니다. 뒤의 두 값을 적절히 설정해주고, `model.parameters()`를 통해 model의 모든 parameter들을 optimizer가 update 할 수 있도록 해줍시다."
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {},
"outputs": [],
"source": [
"import torch.optim as optim\n",
"\n",
"criterion = nn.MSELoss()\n",
"optimizer = optim.Adam(model.parameters(), lr=0.01, weight_decay=0.0001)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.2 DataLoader 만들기\n",
"\n",
"`Pytorch`에서 `Dataset`과 `DataLoader`는 그 역할이 구분되어있습니다. `Dataset`의 경우 일반적으로 파일로부터 데이터를 읽어오고, 하나의 데이터 엔트리를 제공해주는 역할을 하며, `DataLoader`의 경우 기본적으로 `Dataset`이 제공하는 데이터 엔트리들을 모아 `batch`의 형태로 제공하고, 데이터를 랜덤하게 섞어서 제공하거나, 라벨별로 데이터의 비율을 조절하여 제공하는 등, training에 필요한 다양한 기능을 구현하여 제공합니다.\n",
"\n",
"위에서 train, validation, test를 위한 `dataset`을 만들었으니, 각각에 해당하는 `dataloader`를 만들어줍시다."
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'train': <torch.utils.data.dataloader.DataLoader at 0x7ff20e12bb50>,\n",
" 'val': <torch.utils.data.dataloader.DataLoader at 0x7ff20e12bd90>,\n",
" 'test': <torch.utils.data.dataloader.DataLoader at 0x7ff20e12bad0>}"
]
},
"execution_count": 184,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from torch.utils.data import DataLoader\n",
"\n",
"dataloaders = {\n",
" \"train\": DataLoader(datasets[\"train\"], batch_size=256, shuffle=True),\n",
" \"val\": DataLoader(datasets[\"val\"], batch_size=256, shuffle=False),\n",
" \"test\": DataLoader(datasets[\"test\"], batch_size=256, shuffle=False)\n",
"}\n",
"\n",
"dataloaders"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`DataLoader`는 iterable 한 object로 for문을 이용하여 하나의 `batch`를 얻을 수 있습니다.\n",
"\n",
"하나의 `batch`의 크기가 `batch_size`라면, `DataLoaer`가 반환하는 `tensor`들의 `shape`은 `(batch_size, ...)`입니다."
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([256, 2048]) torch.Size([256])\n",
"torch.Size([256, 2048]) torch.Size([256])\n",
"torch.Size([256, 2048]) torch.Size([256])\n",
"torch.Size([256, 2048]) torch.Size([256])\n",
"torch.Size([256, 2048]) torch.Size([256])\n",
"torch.Size([256, 2048]) torch.Size([256])\n",
"torch.Size([256, 2048]) torch.Size([256])\n",
"torch.Size([256, 2048]) torch.Size([256])\n",
"torch.Size([256, 2048]) torch.Size([256])\n",
"torch.Size([256, 2048]) torch.Size([256])\n",
"torch.Size([256, 2048]) torch.Size([256])\n",
"torch.Size([256, 2048]) torch.Size([256])\n",
"torch.Size([256, 2048]) torch.Size([256])\n",
"torch.Size([31, 2048]) torch.Size([31])\n"
]
}
],
"source": [
"for data in dataloaders[\"train\"]:\n",
" fp, exp = data\n",
" print(fp.shape, exp.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.3 Model Training\n",
"\n",
"Model training을 위한 모든것이 준비되었으니, training을 시작해봅시다.\n",
"\n",
"- `epoch`은 `model`이 전체 데이터셋을 모두 학습하는 것을 가리키는 단위입니다. 아래에 `epoch`이 61로 설정되어있으니 우리의 `model`은 전체 데이터셋을 총 61번 학습하게 됩니다.\n",
"- `optimizer.zero_grad()`는 `optimizer`의 gradient값들을 모두 0으로 초기화시켜 이전 gradient 계산값들이 더 이상 영향을 끼치지 못하게합니다.\n",
"- `model.train()`, `model.eval()` 함수는 `model`을 각각 `train`, `eval` 모드로 전환합니다. 특정 레이어들은 모델이 어느 모드에 있는지에 따라 다르게 행동하합니다. (ex. `nn.Dropout`) 그러한 레이어들이 정상적으로 작동하게 하기 위해서는 두 함수들을 적절한 위치에서 실행시켜야합니다.\n",
"- `outputs = model(fps)` 라인에서 `fps`가 `model`의 `forward` 함수를 통과하므로 이 과정을 forward, `train_loss.backward()` 라인에서 각 `tensor`들의 gradient가 계산되므로 이 과정을 backward라고 부릅니다.\n",
"- `optimizer.step()` 라인에서 계산된 gradient값과 learning rate에 따라 `model parameter` 값이 실제로 바뀝니다. \n",
"- `train_losses`, `val_losses` 리스트에 각 `epoch`의 `train loss`와 `validation loss`가 저장됩니다."
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Epoch 0] Train Loss: 24.117 Validation Loss: 1.557\n",
"[Epoch 10] Train Loss: 0.634 Validation Loss: 0.942\n",
"[Epoch 20] Train Loss: 0.456 Validation Loss: 1.177\n",
"[Epoch 30] Train Loss: 0.432 Validation Loss: 1.016\n",
"[Epoch 40] Train Loss: 0.359 Validation Loss: 0.890\n",
"[Epoch 50] Train Loss: 0.326 Validation Loss: 0.876\n",
"[Epoch 60] Train Loss: 0.327 Validation Loss: 0.948\n"
]
}
],
"source": [
"train_losses = []\n",
"val_losses = []\n",
"\n",
"for epoch in range(61):\n",
" model.train()\n",
" epoch_train_loss = 0.0\n",
" for i, data in enumerate(dataloaders[\"train\"]):\n",
" fps, labels = data[0].to(device, torch.float), data[1].to(device)\n",
"\n",
" optimizer.zero_grad()\n",
"\n",
" outputs = model(fps)\n",
" train_loss = criterion(outputs, labels.view(-1, 1))\n",
" train_loss.backward()\n",
" optimizer.step()\n",
" \n",
" epoch_train_loss += train_loss.item()\n",
" \n",
" model.eval()\n",
" epoch_val_loss = 0.0\n",
" with torch.no_grad():\n",
" for i, data in enumerate(dataloaders[\"val\"]):\n",
" fps, labels = data[0].to(device, torch.float), data[1].to(device)\n",
" \n",
" outputs = model(fps)\n",
" val_loss = criterion(outputs, labels.view(-1, 1))\n",
" epoch_val_loss += val_loss.item()\n",
" \n",
" epoch_train_loss /= len(dataloaders[\"train\"])\n",
" epoch_val_loss /= len(dataloaders[\"val\"])\n",
" train_losses.append(epoch_train_loss)\n",
" val_losses.append(epoch_val_loss)\n",
" \n",
" if epoch%10 == 0:\n",
" print(\"[Epoch %d] Train Loss: %.3f Validation Loss: %.3f\" %\n",
" (epoch, epoch_train_loss, epoch_val_loss))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. 결과 분석하기\n",
"\n",
"### 4.1 Loss Graph 그리기\n",
"\n",
"`epoch`에 따른 `train loss`와 `validation loss`를 `list`에 저장했으므로 이를 그래프로 그려봅시다. 그래프 관련 패키지 `matplotlib`와 `seaborn`을 사용합니다.\n",
"\n",
"일정 구간 이후로 `validation loss`가 `train loss`에 비해 값이 큰 것을 볼 수 있습니다. `Overfitting`이 발생했다는 것을 알 수 있습니다. 이를 해결하기 위하여 `l2 regularization coefficient`를 조절하거나 `dropout` 레이어의 `dropout rate`를 조절하는 등 다양한 방향으로 `hyperparameter tuning`을 진행할 수 있으나, 이번 강의에서는 넘어가도록 하겠습니다."
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"epochs = range(61)\n",
"plt.plot(epochs, train_losses, 'r')\n",
"plt.plot(epochs, val_losses, 'b')\n",
"plt.legend([\"Train Loss\", \"Validation Loss\"])\n",
"plt.xlabel(\"Epoch\")\n",
"plt.ylabel(\"Loss\")\n",
"plt.ylim(top=3)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.2 Test Set Inference\n",
"\n",
"Test set 데이터들의 lipophilicity 값을 훈련시킨 모델을 통해 예측하고, 실제값과 비교하여 mean absolute error (MAE) 값을 알아봅시다. MAE 값 계산을 위해 `sklearn` 패키지의 `mean_absolute_error` 함수를 사용합니다. "
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.7743894614990184"
]
},
"execution_count": 188,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import mean_absolute_error\n",
"\n",
"model.eval()\n",
"mae = 0.0\n",
"predictions = []\n",
"truths = []\n",
"with torch.no_grad():\n",
" for data in dataloaders[\"test\"]:\n",
" fps, labels = data[0].to(device, torch.float), data[1].to(device)\n",
" \n",
" outputs = model(fps)\n",
" \n",
" labels = labels.cpu().detach().numpy().tolist()\n",
" outputs = [output[0] for output in outputs.cpu().detach().numpy().tolist()]\n",
" \n",
" truths += labels\n",
" predictions += outputs\n",
" \n",
" mae += mean_absolute_error(labels, outputs)\n",
" \n",
" mae /= len(dataloaders[\"test\"])\n",
"\n",
"mae"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.3 Visualization\n",
"\n",
"MAE 값만으로는 우리의 모델이 얼마나 잘 작동하고 있는지 알기 어렵습니다. x축을 실제값, y축을 예측값으로하는 `scatter plot` 그래프를 통해 모델의 정확도를 눈으로 확인해봅시다.\n",
"\n",
"그래프 중앙의 검은 실선은 `y=x` 그래프입니다. `scatter plot`이 이 그래프에 가깝게 밀집되어 있을 수록 모델의 정확도가 높다는 것을 알 수 있습니다."
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df_prediction = pd.DataFrame(list(zip(predictions, truths)), columns=[\"Prediction\", \"Truth\"])\n",
"\n",
"g = sns.FacetGrid(df_prediction, height=5)\n",
"g = g.map(plt.scatter, \"Truth\", \"Prediction\")\n",
"plt.plot(np.linspace(-1.5,4,50), np.linspace(-1.5,4,50),'k',linestyle='dashed')\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "build_central"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
Unknown
|
2D
|
heartcored98/Standalone-DeepLearning-Chemistry
|
Lec03/utils.py
|
.py
| 7,950
| 213
|
import os
import numpy as np
from rdkit import Chem, DataStructs
from rdkit.Chem import AllChem
from rdkit.Chem.Crippen import MolLogP
from rdkit.Chem.rdMolDescriptors import CalcTPSA
from torch.utils.data import Dataset
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split, KFold
from decimal import Decimal
import json
from os import listdir
from os.path import isfile, join
import pandas as pd
import hashlib
class Writer():
def __init__(self, prior_keyword=[], dir='./results'):
self.prior_keyword = prior_keyword
self.dir = dir
def generate_hash(self, args):
str_as_bytes = str.encode(str(args))
hashed = hashlib.sha256(str_as_bytes).hexdigest()[:24]
return hashed
def write(self, args, prior_keyword=None):
dict_args = vars(args)
if 'bar' in dict_args:
#del dict_args['bar']
pass
if prior_keyword:
self.prior_keyword = prior_keyword
filename = 'exp_{}'.format(args.exp_name)
for keyword in self.prior_keyword:
value = str(dict_args[keyword])
if value.isdigit():
filename += keyword + ':{:.2E}_'.format(Decimal(dict_args[keyword]))
else:
filename += keyword + ':{}_'.format(value)
# hashcode = self.generate_hash(args)
# filename += hashcode
filename += '.json'
with open(self.dir+'/'+filename, 'w') as outfile:
json.dump(dict_args, outfile)
def read(self, exp_name=''):
list_result = list()
filenames = [f for f in listdir(self.dir) if isfile(join(self.dir, f))]
for filename in filenames:
with open(join(self.dir, filename), 'r') as infile:
result = json.load(infile)
if len(exp_name) > 0:
if result['exp_name'] == exp_name:
list_result.append(result)
else:
list_result.append(result)
return pd.DataFrame(list_result)
def clear(self, exp_name=''):
filenames = [f for f in listdir(self.dir) if isfile(join(self.dir, f))]
for filename in filenames:
if len(exp_name) > 0:
result = json.load(open(join(self.dir, filename), 'r'))
if result['exp_name'] == exp_name:
os.remove(join(self.dir, filename))
else:
os.remove(join(self.dir, filename))
import matplotlib.pyplot as plt
from matplotlib import gridspec
from matplotlib.font_manager import FontProperties
import seaborn as sns
def generate_setting(args, var1, var2):
dict_args = vars(args)
output = '{:92}'.format('[Exp Settings]') + '\n'
output += '-'*91 + '\n'
num_var = 3
cnt_var = 0
for keyword, value in dict_args.items():
if keyword != var1 and keyword != var2 and type(value) != list and not 'best' in keyword and keyword != 'elapsed':
str_value = str(value)
if str_value.isdigit():
if type(value) == float:
temp = '| {}={:.2E}'.format(keyword, Decimal(dict_args[keyword]))
if type(value) == int:
temp = '| {}={}'.format(keyword, str_value[:15])
else:
temp = '| {}={}'.format(keyword, str_value[:15])
output += '{:<30}'.format(temp[:30])
cnt_var += 1
if cnt_var % num_var == 0:
cnt_var = 0
output += '|\n'
output += '-'*91 + '\n'
return output
def plot_performance(results, variable1, variable2, args, title='', filename=''):
fig, ax = plt.subplots(1, 2)
fig.set_size_inches(15, 6)
sns.set_style("darkgrid", {"axes.facecolor": ".9"})
sns.barplot(x=variable1, y='best_mae', hue=variable2, data=results, ax=ax[0])
sns.barplot(x=variable1, y='best_std', hue=variable2, data=results, ax=ax[1])
font = FontProperties()
font.set_family('monospace')
font.set_size('large')
alignment = {'horizontalalignment': 'center', 'verticalalignment': 'baseline'}
fig.text(0.5, -0.6, generate_setting(args, variable1, variable2), fontproperties=font, **alignment)
fig.suptitle(title)
filename = filename if len(filename) > 0 else title
plt.savefig('./images/{}.png'.format(filename))
def plot_distribution(results, variable1, variable2, x='true_y', y='pred_y', title='', filename='', **kwargs):
list_v1 = results[variable1].unique()
list_v2 = results[variable2].unique()
list_data = list()
for value1 in list_v1:
for value2 in list_v2:
row = results.loc[results[variable1]==value1]
row = row.loc[results[variable2]==value2]
best_true_y = list(row.best_true_y)[0]
best_pred_y = list(row.best_pred_y)[0]
for i in range(len(best_true_y)):
list_data.append({x:best_true_y[i], y:best_pred_y[i], variable1:value1, variable2:value2})
df = pd.DataFrame(list_data)
g = sns.FacetGrid(df, row=variable2, col=variable1, margin_titles=True)
g.map(plt.scatter, x, y, alpha=0.3)
def identity(**kwargs):
plt.plot(np.linspace(-1.5,4,50), np.linspace(-1.5,4,50),'k',linestyle='dashed')
g.map(identity)
g.set_axis_labels(x, y)
g.fig.suptitle(title) # can also get the figure from plt.gcf()
plt.subplots_adjust(top=kwargs.get('top',0.93))
filename = filename if len(filename) > 0 else title
plt.savefig('./images/{}.png'.format(filename))
def plot_loss(results, variable1, variable2, x='true_y', y='pred_y', title='', filename='', **kwargs):
list_v1 = results[variable1].unique()
list_v2 = results[variable2].unique()
list_data = list()
for value1 in list_v1:
for value2 in list_v2:
row = results.loc[results[variable1]==value1]
row = row.loc[results[variable2]==value2]
train_losses = list(row.train_losses)[0]
val_losses = list(row.val_losses)[0]
maes = list(row.maes)[0]
for item in train_losses:
item.update({'type':'train', 'loss':item['train_loss'], variable1:value1, variable2:value2})
for item in val_losses:
item.update({'type':'val', 'loss':item['val_loss'], variable1:value1, variable2:value2})
for item in maes:
item.update({'type':'mae', variable1:value1, variable2:value2})
list_data += train_losses + val_losses + maes
df = pd.DataFrame(list_data)
temp_mae = df.loc[df['mae'] < df['mae'].quantile(0.98)]
ymax = temp_mae['mae'].max()
ymin = temp_mae['mae'].min()
temp_loss = df.loc[df['loss'] < df['loss'].quantile(0.98)]
lossmax = temp_loss['loss'].max()
lossmin = temp_loss['loss'].min()
g = sns.FacetGrid(df, row=variable2, col=variable1, hue='type', margin_titles=False)
axes = g.axes
for i in range(len(axes)):
for j in range(len(axes[0])):
if i==0:
g.axes[i][j].yaxis.set_label_coords(1.1,0.9)
def mae_line(x, y, **kwargs):
ax2 = plt.gca().twinx()
ax2.plot(x, y,'g--')
ax2.set_ylim(kwargs['ymax']*1.05, kwargs['ymin']*0.95)
ax2.grid(False)
g.map(plt.plot, x, y)
g.map(mae_line, 'epoch', 'mae', ymin=ymin, ymax=ymax)
g.set_axis_labels(x, y)
g.fig.suptitle(title) # can also get the figure from plt.gcf()
g.add_legend()
for ax in g.axes.flatten():
ax.set_ylim(lossmin, lossmax)
plt.subplots_adjust(top=kwargs.get('top', 0.93))
filename = filename if len(filename) > 0 else title
plt.savefig('./images/{}.png'.format(filename))
|
Python
|
2D
|
heartcored98/Standalone-DeepLearning-Chemistry
|
Lec02/4_cifar10_tutorial.ipynb
|
.ipynb
| 41,974
| 689
|
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[](https://colab.research.google.com/github/heartcored98/Standalone-DeepLearning-Chemistry/blob/master/Lec02/4_cifar10_tutorial.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Training a Classifier\n",
"=====================\n",
"\n",
"This is it. You have seen how to define neural networks, compute loss and make\n",
"updates to the weights of the network.\n",
"\n",
"Now you might be thinking,\n",
"\n",
"What about data?\n",
"----------------\n",
"\n",
"Generally, when you have to deal with image, text, audio or video data,\n",
"you can use standard python packages that load data into a numpy array.\n",
"Then you can convert this array into a ``torch.*Tensor``.\n",
"\n",
"- For images, packages such as Pillow, OpenCV are useful\n",
"- For audio, packages such as scipy and librosa\n",
"- For text, either raw Python or Cython based loading, or NLTK and\n",
" SpaCy are useful\n",
"\n",
"Specifically for vision, we have created a package called\n",
"``torchvision``, that has data loaders for common datasets such as\n",
"Imagenet, CIFAR10, MNIST, etc. and data transformers for images, viz.,\n",
"``torchvision.datasets`` and ``torch.utils.data.DataLoader``.\n",
"\n",
"This provides a huge convenience and avoids writing boilerplate code.\n",
"\n",
"For this tutorial, we will use the CIFAR10 dataset.\n",
"It has the classes: ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’,\n",
"‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’. The images in CIFAR-10 are of\n",
"size 3x32x32, i.e. 3-channel color images of 32x32 pixels in size.\n",
"\n",
".. figure:: /_static/img/cifar10.png\n",
" :alt: cifar10\n",
"\n",
" cifar10\n",
"\n",
"\n",
"Training an image classifier\n",
"----------------------------\n",
"\n",
"We will do the following steps in order:\n",
"\n",
"1. Load and normalizing the CIFAR10 training and test datasets using\n",
" ``torchvision``\n",
"2. Define a Convolutional Neural Network\n",
"3. Define a loss function\n",
"4. Train the network on the training data\n",
"5. Test the network on the test data\n",
"\n",
"1. Loading and normalizing CIFAR10\n",
"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
"\n",
"Using ``torchvision``, it’s extremely easy to load CIFAR10.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torchvision\n",
"import torchvision.transforms as transforms"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The output of torchvision datasets are PILImage images of range [0, 1].\n",
"We transform them to Tensors of normalized range [-1, 1].\n",
"<div class=\"alert alert-info\"><h4>Note</h4><p>If running on Windows and you get a BrokenPipeError, try setting\n",
" the num_worker of torch.utils.data.DataLoader() to 0.</p></div>\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Files already downloaded and verified\n",
"Files already downloaded and verified\n"
]
}
],
"source": [
"transform = transforms.Compose(\n",
" [transforms.ToTensor(),\n",
" transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
"\n",
"trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
" download=True, transform=transform)\n",
"trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,\n",
" shuffle=True, num_workers=2)\n",
"\n",
"testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
" download=True, transform=transform)\n",
"testloader = torch.utils.data.DataLoader(testset, batch_size=4,\n",
" shuffle=False, num_workers=2)\n",
"\n",
"classes = ('plane', 'car', 'bird', 'cat',\n",
" 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us show some of the training images, for fun.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" deer ship plane plane\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"# functions to show an image\n",
"\n",
"\n",
"def imshow(img):\n",
" img = img / 2 + 0.5 # unnormalize\n",
" npimg = img.numpy()\n",
" plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
" plt.show()\n",
"\n",
"\n",
"# get some random training images\n",
"dataiter = iter(trainloader)\n",
"images, labels = dataiter.next()\n",
"\n",
"# show images\n",
"imshow(torchvision.utils.make_grid(images))\n",
"# print labels\n",
"print(' '.join('%5s' % classes[labels[j]] for j in range(4)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. Define a Convolutional Neural Network\n",
"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
"Copy the neural network from the Neural Networks section before and modify it to\n",
"take 3-channel images (instead of 1-channel images as it was defined).\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"\n",
"\n",
"class Net(nn.Module):\n",
" def __init__(self):\n",
" super(Net, self).__init__()\n",
" self.conv1 = nn.Conv2d(3, 6, 5)\n",
" self.pool = nn.MaxPool2d(2, 2)\n",
" self.conv2 = nn.Conv2d(6, 16, 5)\n",
" self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
" self.fc2 = nn.Linear(120, 84)\n",
" self.fc3 = nn.Linear(84, 10)\n",
"\n",
" def forward(self, x):\n",
" x = self.pool(F.relu(self.conv1(x)))\n",
" x = self.pool(F.relu(self.conv2(x)))\n",
" x = x.view(-1, 16 * 5 * 5)\n",
" x = F.relu(self.fc1(x))\n",
" x = F.relu(self.fc2(x))\n",
" x = self.fc3(x)\n",
" return x\n",
"\n",
"device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
"net = Net().to(device)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3. Define a Loss function and optimizer\n",
"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
"Let's use a Classification Cross-Entropy loss and SGD with momentum.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import torch.optim as optim\n",
"\n",
"criterion = nn.CrossEntropyLoss()\n",
"optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4. Train the network\n",
"^^^^^^^^^^^^^^^^^^^^\n",
"\n",
"This is when things start to get interesting.\n",
"We simply have to loop over our data iterator, and feed the inputs to the\n",
"network and optimize.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 2000] loss: 2.249\n",
"[1, 4000] loss: 1.910\n",
"[1, 6000] loss: 1.692\n",
"[1, 8000] loss: 1.598\n",
"[1, 10000] loss: 1.523\n",
"[1, 12000] loss: 1.468\n",
"[2, 2000] loss: 1.407\n",
"[2, 4000] loss: 1.372\n",
"[2, 6000] loss: 1.342\n",
"[2, 8000] loss: 1.326\n",
"[2, 10000] loss: 1.320\n",
"[2, 12000] loss: 1.274\n",
"Finished Training\n"
]
}
],
"source": [
"for epoch in range(2): # loop over the dataset multiple times\n",
"\n",
" running_loss = 0.0\n",
" for i, data in enumerate(trainloader, 0):\n",
" # get the inputs; data is a list of [inputs, labels]\n",
" inputs, labels = data\n",
" inputs = inputs.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" # zero the parameter gradients\n",
" optimizer.zero_grad()\n",
"\n",
" # forward + backward + optimize\n",
" outputs = net(inputs)\n",
" loss = criterion(outputs, labels)\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" # print statistics\n",
" running_loss += loss.item()\n",
" if i % 2000 == 1999: # print every 2000 mini-batches\n",
" print('[%d, %5d] loss: %.3f' %\n",
" (epoch + 1, i + 1, running_loss / 2000))\n",
" running_loss = 0.0\n",
"\n",
"print('Finished Training')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's quickly save our trained model:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"PATH = './cifar_net.pth'\n",
"torch.save(net.state_dict(), PATH)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"See `here <https://pytorch.org/docs/stable/notes/serialization.html>`_\n",
"for more details on saving PyTorch models.\n",
"\n",
"5. Test the network on the test data\n",
"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
"\n",
"We have trained the network for 2 passes over the training dataset.\n",
"But we need to check if the network has learnt anything at all.\n",
"\n",
"We will check this by predicting the class label that the neural network\n",
"outputs, and checking it against the ground-truth. If the prediction is\n",
"correct, we add the sample to the list of correct predictions.\n",
"\n",
"Okay, first step. Let us display an image from the test set to get familiar.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"GroundTruth: cat ship ship plane\n"
]
}
],
"source": [
"dataiter = iter(testloader)\n",
"images, labels = dataiter.next()\n",
"\n",
"# print images\n",
"imshow(torchvision.utils.make_grid(images))\n",
"print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, let's load back in our saved model (note: saving and re-loading the model\n",
"wasn't necessary here, we only did it to illustrate how to do so):\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"net = Net()\n",
"net.load_state_dict(torch.load(PATH))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay, now let us see what the neural network thinks these examples above are:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"outputs = net(images)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The outputs are energies for the 10 classes.\n",
"The higher the energy for a class, the more the network\n",
"thinks that the image is of the particular class.\n",
"So, let's get the index of the highest energy:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: dog ship ship ship\n"
]
}
],
"source": [
"_, predicted = torch.max(outputs, 1)\n",
"\n",
"print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]\n",
" for j in range(4)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The results seem pretty good.\n",
"\n",
"Let us look at how the network performs on the whole dataset.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy of the network on the 10000 test images: 52 %\n"
]
}
],
"source": [
"correct = 0\n",
"total = 0\n",
"with torch.no_grad():\n",
" for data in testloader:\n",
" images, labels = data\n",
" outputs = net(images)\n",
" _, predicted = torch.max(outputs.data, 1)\n",
" total += labels.size(0)\n",
" correct += (predicted == labels).sum().item()\n",
"\n",
"print('Accuracy of the network on the 10000 test images: %d %%' % (\n",
" 100 * correct / total))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That looks way better than chance, which is 10% accuracy (randomly picking\n",
"a class out of 10 classes).\n",
"Seems like the network learnt something.\n",
"\n",
"Hmmm, what are the classes that performed well, and the classes that did\n",
"not perform well:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy of plane : 46 %\n",
"Accuracy of car : 63 %\n",
"Accuracy of bird : 55 %\n",
"Accuracy of cat : 11 %\n",
"Accuracy of deer : 32 %\n",
"Accuracy of dog : 53 %\n",
"Accuracy of frog : 64 %\n",
"Accuracy of horse : 50 %\n",
"Accuracy of ship : 88 %\n",
"Accuracy of truck : 54 %\n"
]
}
],
"source": [
"class_correct = list(0. for i in range(10))\n",
"class_total = list(0. for i in range(10))\n",
"with torch.no_grad():\n",
" for data in testloader:\n",
" images, labels = data\n",
" outputs = net(images)\n",
" _, predicted = torch.max(outputs, 1)\n",
" c = (predicted == labels).squeeze()\n",
" for i in range(4):\n",
" label = labels[i]\n",
" class_correct[label] += c[i].item()\n",
" class_total[label] += 1\n",
"\n",
"\n",
"for i in range(10):\n",
" print('Accuracy of %5s : %2d %%' % (\n",
" classes[i], 100 * class_correct[i] / class_total[i]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay, so what next?\n",
"\n",
"How do we run these neural networks on the GPU?\n",
"\n",
"Training on GPU\n",
"----------------\n",
"Just like how you transfer a Tensor onto the GPU, you transfer the neural\n",
"net onto the GPU.\n",
"\n",
"Let's first define our device as the first visible cuda device if we have\n",
"CUDA available:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cuda:0\n"
]
}
],
"source": [
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"# Assuming that we are on a CUDA machine, this should print a CUDA device:\n",
"\n",
"print(device)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The rest of this section assumes that ``device`` is a CUDA device.\n",
"\n",
"Then these methods will recursively go over all modules and convert their\n",
"parameters and buffers to CUDA tensors:\n",
"\n",
".. code:: python\n",
"\n",
" net.to(device)\n",
"\n",
"\n",
"Remember that you will have to send the inputs and targets at every step\n",
"to the GPU too:\n",
"\n",
".. code:: python\n",
"\n",
" inputs, labels = data[0].to(device), data[1].to(device)\n",
"\n",
"Why dont I notice MASSIVE speedup compared to CPU? Because your network\n",
"is really small.\n",
"\n",
"**Exercise:** Try increasing the width of your network (argument 2 of\n",
"the first ``nn.Conv2d``, and argument 1 of the second ``nn.Conv2d`` –\n",
"they need to be the same number), see what kind of speedup you get.\n",
"\n",
"**Goals achieved**:\n",
"\n",
"- Understanding PyTorch's Tensor library and neural networks at a high level.\n",
"- Train a small neural network to classify images\n",
"\n",
"Training on multiple GPUs\n",
"-------------------------\n",
"If you want to see even more MASSIVE speedup using all of your GPUs,\n",
"please check out :doc:`data_parallel_tutorial`.\n",
"\n",
"Where do I go next?\n",
"-------------------\n",
"\n",
"- :doc:`Train neural nets to play video games </intermediate/reinforcement_q_learning>`\n",
"- `Train a state-of-the-art ResNet network on imagenet`_\n",
"- `Train a face generator using Generative Adversarial Networks`_\n",
"- `Train a word-level language model using Recurrent LSTM networks`_\n",
"- `More examples`_\n",
"- `More tutorials`_\n",
"- `Discuss PyTorch on the Forums`_\n",
"- `Chat with other users on Slack`_\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "build_central"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
Unknown
|
2D
|
heartcored98/Standalone-DeepLearning-Chemistry
|
Lec02/1_tensor_tutorial.ipynb
|
.ipynb
| 14,739
| 661
|
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[](https://colab.research.google.com/github/heartcored98/Standalone-DeepLearning-Chemistry/blob/master/Lec02/1_tensor_tutorial.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"What is PyTorch?\n",
"================\n",
"\n",
"It’s a Python-based scientific computing package targeted at two sets of\n",
"audiences:\n",
"\n",
"- A replacement for NumPy to use the power of GPUs\n",
"- a deep learning research platform that provides maximum flexibility\n",
" and speed\n",
"\n",
"Getting Started\n",
"---------------\n",
"\n",
"Tensors\n",
"^^^^^^^\n",
"\n",
"Tensors are similar to NumPy’s ndarrays, with the addition being that\n",
"Tensors can also be used on a GPU to accelerate computing.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"import torch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-info\"><h4>Note</h4><p>An uninitialized matrix is declared,\n",
" but does not contain definite known\n",
" values before it is used. When an\n",
" uninitialized matrix is created,\n",
" whatever values were in the allocated\n",
" memory at the time will appear as the initial values.</p></div>\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Construct a 5x3 matrix, uninitialized:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[5.6719e-11, 7.3471e+28, 2.6383e+23],\n",
" [2.7376e+20, 1.8040e+28, 1.8750e-19],\n",
" [7.3909e+22, 2.4176e-12, 2.6209e+20],\n",
" [4.1641e+12, 8.9625e-01, 7.9309e+34],\n",
" [7.9439e+08, 3.2604e-12, 7.3113e+34]])\n"
]
}
],
"source": [
"x = torch.empty(5, 3)\n",
"print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Construct a randomly initialized matrix:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[0.5083, 0.1435, 0.1073],\n",
" [0.3408, 0.7163, 0.5453],\n",
" [0.3454, 0.5327, 0.3057],\n",
" [0.4452, 0.4718, 0.9694],\n",
" [0.5347, 0.3872, 0.0346]])\n"
]
}
],
"source": [
"x = torch.rand(5, 3)\n",
"print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Construct a matrix filled zeros and of dtype long:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[0, 0, 0],\n",
" [0, 0, 0],\n",
" [0, 0, 0],\n",
" [0, 0, 0],\n",
" [0, 0, 0]])\n"
]
}
],
"source": [
"x = torch.zeros(5, 3, dtype=torch.long)\n",
"print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Construct a tensor directly from data:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([5.5000, 3.0000])\n"
]
}
],
"source": [
"x = torch.tensor([5.5, 3])\n",
"print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"or create a tensor based on an existing tensor. These methods\n",
"will reuse properties of the input tensor, e.g. dtype, unless\n",
"new values are provided by user\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[1., 1., 1.],\n",
" [1., 1., 1.],\n",
" [1., 1., 1.],\n",
" [1., 1., 1.],\n",
" [1., 1., 1.]], dtype=torch.float64)\n",
"tensor([[ 0.6871, -0.3288, 1.9868],\n",
" [ 0.6195, -0.7003, -0.1749],\n",
" [-0.9775, -0.5506, -1.5817],\n",
" [ 0.1792, -0.6192, -0.3876],\n",
" [-0.0242, -1.0104, 0.0879]])\n"
]
}
],
"source": [
"x = x.new_ones(5, 3, dtype=torch.double) # new_* methods take in sizes\n",
"print(x)\n",
"\n",
"x = torch.randn_like(x, dtype=torch.float) # override dtype!\n",
"print(x) # result has the same size"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get its size:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([5, 3])\n"
]
}
],
"source": [
"print(x.size())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-info\"><h4>Note</h4><p>``torch.Size`` is in fact a tuple, so it supports all tuple operations.</p></div>\n",
"\n",
"Operations\n",
"\n",
"There are multiple syntaxes for operations. In the following\n",
"example, we will take a look at the addition operation.\n",
"\n",
"Addition: syntax 1\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[ 0.9464, -0.1668, 2.8770],\n",
" [ 1.4936, 0.1152, 0.0520],\n",
" [-0.2967, -0.4471, -0.5918],\n",
" [ 0.2529, 0.2058, 0.1976],\n",
" [ 0.4931, -0.1540, 0.5656]])\n"
]
}
],
"source": [
"y = torch.rand(5, 3)\n",
"print(x + y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Addition: syntax 2\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[ 0.9464, -0.1668, 2.8770],\n",
" [ 1.4936, 0.1152, 0.0520],\n",
" [-0.2967, -0.4471, -0.5918],\n",
" [ 0.2529, 0.2058, 0.1976],\n",
" [ 0.4931, -0.1540, 0.5656]])\n"
]
}
],
"source": [
"print(torch.add(x, y))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Addition: providing an output tensor as argument\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[ 0.9464, -0.1668, 2.8770],\n",
" [ 1.4936, 0.1152, 0.0520],\n",
" [-0.2967, -0.4471, -0.5918],\n",
" [ 0.2529, 0.2058, 0.1976],\n",
" [ 0.4931, -0.1540, 0.5656]])\n"
]
}
],
"source": [
"result = torch.empty(5, 3)\n",
"torch.add(x, y, out=result)\n",
"print(result)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Addition: in-place\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[ 0.9464, -0.1668, 2.8770],\n",
" [ 1.4936, 0.1152, 0.0520],\n",
" [-0.2967, -0.4471, -0.5918],\n",
" [ 0.2529, 0.2058, 0.1976],\n",
" [ 0.4931, -0.1540, 0.5656]])\n"
]
}
],
"source": [
"# adds x to y\n",
"y.add_(x)\n",
"print(y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-info\"><h4>Note</h4><p>Any operation that mutates a tensor in-place is post-fixed with an ``_``.\n",
" For example: ``x.copy_(y)``, ``x.t_()``, will change ``x``.</p></div>\n",
"\n",
"You can use standard NumPy-like indexing with all bells and whistles!\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[ 0.6871, -0.3288, 1.9868],\n",
" [ 0.6195, -0.7003, -0.1749],\n",
" [-0.9775, -0.5506, -1.5817],\n",
" [ 0.1792, -0.6192, -0.3876],\n",
" [-0.0242, -1.0104, 0.0879]])\n",
"tensor([-0.3288, -0.7003, -0.5506, -0.6192, -1.0104])\n"
]
}
],
"source": [
"print(x)\n",
"print(x[:, 1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Resizing: If you want to resize/reshape tensor, you can use ``torch.view``:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([4, 4]) torch.Size([16]) torch.Size([2, 8])\n"
]
}
],
"source": [
"x = torch.randn(4, 4)\n",
"y = x.view(16)\n",
"z = x.view(-1, 8) # the size -1 is inferred from other dimensions\n",
"print(x.size(), y.size(), z.size())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you have a one element tensor, use ``.item()`` to get the value as a\n",
"Python number\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([0.0317])\n",
"0.031710706651210785\n"
]
}
],
"source": [
"x = torch.randn(1)\n",
"print(x)\n",
"print(x.item())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Read later:**\n",
"\n",
"\n",
" 100+ Tensor operations, including transposing, indexing, slicing,\n",
" mathematical operations, linear algebra, random numbers, etc.,\n",
" are described\n",
" `here <https://pytorch.org/docs/torch>`_.\n",
"\n",
"NumPy Bridge\n",
"------------\n",
"\n",
"Converting a Torch Tensor to a NumPy array and vice versa is a breeze.\n",
"\n",
"The Torch Tensor and NumPy array will share their underlying memory\n",
"locations (if the Torch Tensor is on CPU), and changing one will change\n",
"the other.\n",
"\n",
"Converting a Torch Tensor to a NumPy Array\n",
"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([1., 1., 1., 1., 1.])\n"
]
}
],
"source": [
"a = torch.ones(5)\n",
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1. 1. 1. 1. 1.]\n"
]
}
],
"source": [
"b = a.numpy()\n",
"print(b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"See how the numpy array changed in value.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([2., 2., 2., 2., 2.])\n",
"[2. 2. 2. 2. 2.]\n"
]
}
],
"source": [
"a.add_(1)\n",
"print(a)\n",
"print(b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Converting NumPy Array to Torch Tensor\n",
"^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
"See how changing the np array changed the Torch Tensor automatically\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2. 2. 2. 2. 2.]\n",
"tensor([2., 2., 2., 2., 2.], dtype=torch.float64)\n"
]
}
],
"source": [
"import numpy as np\n",
"a = np.ones(5)\n",
"b = torch.from_numpy(a)\n",
"np.add(a, 1, out=a)\n",
"print(a)\n",
"print(b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All the Tensors on the CPU except a CharTensor support converting to\n",
"NumPy and back.\n",
"\n",
"CUDA Tensors\n",
"------------\n",
"\n",
"Tensors can be moved onto any device using the ``.to`` method.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([1.0317], device='cuda:0')\n",
"tensor([1.0317], dtype=torch.float64)\n"
]
}
],
"source": [
"# let us run this cell only if CUDA is available\n",
"# We will use ``torch.device`` objects to move tensors in and out of GPU\n",
"if torch.cuda.is_available():\n",
" device = torch.device(\"cuda\") # a CUDA device object\n",
" y = torch.ones_like(x, device=device) # directly create a tensor on GPU\n",
" x = x.to(device) # or just use strings ``.to(\"cuda\")``\n",
" z = x + y\n",
" print(z)\n",
" print(z.to(\"cpu\", torch.double)) # ``.to`` can also change dtype together!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "build_central"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
Unknown
|
2D
|
heartcored98/Standalone-DeepLearning-Chemistry
|
Lec02/3_neural_networks_tutorial.ipynb
|
.ipynb
| 14,030
| 463
|
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[](https://colab.research.google.com/github/heartcored98/Standalone-DeepLearning-Chemistry/blob/master/Lec02/3_neural_networks_tutorial.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Neural Networks\n",
"===============\n",
"\n",
"Neural networks can be constructed using the ``torch.nn`` package.\n",
"\n",
"Now that you had a glimpse of ``autograd``, ``nn`` depends on\n",
"``autograd`` to define models and differentiate them.\n",
"An ``nn.Module`` contains layers, and a method ``forward(input)``\\ that\n",
"returns the ``output``.\n",
"\n",
"For example, look at this network that classifies digit images:\n",
"\n",
".. figure:: /_static/img/mnist.png\n",
" :alt: convnet\n",
"\n",
" convnet\n",
"\n",
"It is a simple feed-forward network. It takes the input, feeds it\n",
"through several layers one after the other, and then finally gives the\n",
"output.\n",
"\n",
"A typical training procedure for a neural network is as follows:\n",
"\n",
"- Define the neural network that has some learnable parameters (or\n",
" weights)\n",
"- Iterate over a dataset of inputs\n",
"- Process input through the network\n",
"- Compute the loss (how far is the output from being correct)\n",
"- Propagate gradients back into the network’s parameters\n",
"- Update the weights of the network, typically using a simple update rule:\n",
" ``weight = weight - learning_rate * gradient``\n",
"\n",
"Define the network\n",
"------------------\n",
"\n",
"Let’s define this network:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Net(\n",
" (conv1): Conv2d(1, 6, kernel_size=(3, 3), stride=(1, 1))\n",
" (conv2): Conv2d(6, 16, kernel_size=(3, 3), stride=(1, 1))\n",
" (fc1): Linear(in_features=576, out_features=120, bias=True)\n",
" (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
" (fc3): Linear(in_features=84, out_features=10, bias=True)\n",
")\n"
]
}
],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"\n",
"\n",
"class Net(nn.Module):\n",
"\n",
" def __init__(self):\n",
" super(Net, self).__init__()\n",
" # 1 input image channel, 6 output channels, 3x3 square convolution\n",
" # kernel\n",
" self.conv1 = nn.Conv2d(1, 6, 3)\n",
" self.conv2 = nn.Conv2d(6, 16, 3)\n",
" # an affine operation: y = Wx + b\n",
" self.fc1 = nn.Linear(16 * 6 * 6, 120) # 6*6 from image dimension \n",
" self.fc2 = nn.Linear(120, 84)\n",
" self.fc3 = nn.Linear(84, 10)\n",
"\n",
" def forward(self, x):\n",
" # Max pooling over a (2, 2) window\n",
" x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n",
" # If the size is a square you can only specify a single number\n",
" x = F.max_pool2d(F.relu(self.conv2(x)), 2)\n",
" x = x.view(-1, self.num_flat_features(x))\n",
" x = F.relu(self.fc1(x))\n",
" x = F.relu(self.fc2(x))\n",
" x = self.fc3(x)\n",
" return x\n",
"\n",
" def num_flat_features(self, x):\n",
" size = x.size()[1:] # all dimensions except the batch dimension\n",
" num_features = 1\n",
" for s in size:\n",
" num_features *= s\n",
" return num_features\n",
"\n",
"\n",
"net = Net()\n",
"print(net)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You just have to define the ``forward`` function, and the ``backward``\n",
"function (where gradients are computed) is automatically defined for you\n",
"using ``autograd``.\n",
"You can use any of the Tensor operations in the ``forward`` function.\n",
"\n",
"The learnable parameters of a model are returned by ``net.parameters()``\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n",
"torch.Size([6, 1, 3, 3])\n"
]
}
],
"source": [
"params = list(net.parameters())\n",
"print(len(params))\n",
"print(params[0].size()) # conv1's .weight"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's try a random 32x32 input.\n",
"Note: expected input size of this net (LeNet) is 32x32. To use this net on\n",
"the MNIST dataset, please resize the images from the dataset to 32x32.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[-0.0993, -0.0403, -0.1058, -0.0521, 0.0638, -0.0672, 0.0500, -0.0254,\n",
" 0.0138, -0.0132]], grad_fn=<AddmmBackward>)\n"
]
}
],
"source": [
"input = torch.randn(1, 1, 32, 32)\n",
"out = net(input)\n",
"print(out)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zero the gradient buffers of all parameters and backprops with random\n",
"gradients:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"net.zero_grad()\n",
"out.backward(torch.randn(1, 10))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-info\"><h4>Note</h4><p>``torch.nn`` only supports mini-batches. The entire ``torch.nn``\n",
" package only supports inputs that are a mini-batch of samples, and not\n",
" a single sample.\n",
"\n",
" For example, ``nn.Conv2d`` will take in a 4D Tensor of\n",
" ``nSamples x nChannels x Height x Width``.\n",
"\n",
" If you have a single sample, just use ``input.unsqueeze(0)`` to add\n",
" a fake batch dimension.</p></div>\n",
"\n",
"Before proceeding further, let's recap all the classes you’ve seen so far.\n",
"\n",
"**Recap:**\n",
" - ``torch.Tensor`` - A *multi-dimensional array* with support for autograd\n",
" operations like ``backward()``. Also *holds the gradient* w.r.t. the\n",
" tensor.\n",
" - ``nn.Module`` - Neural network module. *Convenient way of\n",
" encapsulating parameters*, with helpers for moving them to GPU,\n",
" exporting, loading, etc.\n",
" - ``nn.Parameter`` - A kind of Tensor, that is *automatically\n",
" registered as a parameter when assigned as an attribute to a*\n",
" ``Module``.\n",
" - ``autograd.Function`` - Implements *forward and backward definitions\n",
" of an autograd operation*. Every ``Tensor`` operation creates at\n",
" least a single ``Function`` node that connects to functions that\n",
" created a ``Tensor`` and *encodes its history*.\n",
"\n",
"**At this point, we covered:**\n",
" - Defining a neural network\n",
" - Processing inputs and calling backward\n",
"\n",
"**Still Left:**\n",
" - Computing the loss\n",
" - Updating the weights of the network\n",
"\n",
"Loss Function\n",
"-------------\n",
"A loss function takes the (output, target) pair of inputs, and computes a\n",
"value that estimates how far away the output is from the target.\n",
"\n",
"There are several different\n",
"`loss functions <https://pytorch.org/docs/nn.html#loss-functions>`_ under the\n",
"nn package .\n",
"A simple loss is: ``nn.MSELoss`` which computes the mean-squared error\n",
"between the input and the target.\n",
"\n",
"For example:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor(0.7564, grad_fn=<MseLossBackward>)\n"
]
}
],
"source": [
"output = net(input)\n",
"target = torch.randn(10) # a dummy target, for example\n",
"target = target.view(1, -1) # make it the same shape as output\n",
"criterion = nn.MSELoss()\n",
"\n",
"loss = criterion(output, target)\n",
"print(loss)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, if you follow ``loss`` in the backward direction, using its\n",
"``.grad_fn`` attribute, you will see a graph of computations that looks\n",
"like this:\n",
"\n",
"::\n",
"\n",
" input -> conv2d -> relu -> maxpool2d -> conv2d -> relu -> maxpool2d\n",
" -> view -> linear -> relu -> linear -> relu -> linear\n",
" -> MSELoss\n",
" -> loss\n",
"\n",
"So, when we call ``loss.backward()``, the whole graph is differentiated\n",
"w.r.t. the loss, and all Tensors in the graph that has ``requires_grad=True``\n",
"will have their ``.grad`` Tensor accumulated with the gradient.\n",
"\n",
"For illustration, let us follow a few steps backward:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<MseLossBackward object at 0x7f6fa1288d50>\n",
"<AddmmBackward object at 0x7f6fa1288e90>\n",
"<AccumulateGrad object at 0x7f6fa1288d50>\n"
]
}
],
"source": [
"print(loss.grad_fn) # MSELoss\n",
"print(loss.grad_fn.next_functions[0][0]) # Linear\n",
"print(loss.grad_fn.next_functions[0][0].next_functions[0][0]) # ReLU"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Backprop\n",
"--------\n",
"To backpropagate the error all we have to do is to ``loss.backward()``.\n",
"You need to clear the existing gradients though, else gradients will be\n",
"accumulated to existing gradients.\n",
"\n",
"\n",
"Now we shall call ``loss.backward()``, and have a look at conv1's bias\n",
"gradients before and after the backward.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"conv1.bias.grad before backward\n",
"tensor([0., 0., 0., 0., 0., 0.])\n",
"conv1.bias.grad after backward\n",
"tensor([ 0.0044, 0.0183, -0.0169, 0.0036, -0.0023, 0.0275])\n"
]
}
],
"source": [
"net.zero_grad() # zeroes the gradient buffers of all parameters\n",
"\n",
"print('conv1.bias.grad before backward')\n",
"print(net.conv1.bias.grad)\n",
"\n",
"loss.backward()\n",
"\n",
"print('conv1.bias.grad after backward')\n",
"print(net.conv1.bias.grad)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we have seen how to use loss functions.\n",
"\n",
"**Read Later:**\n",
"\n",
" The neural network package contains various modules and loss functions\n",
" that form the building blocks of deep neural networks. A full list with\n",
" documentation is `here <https://pytorch.org/docs/nn>`_.\n",
"\n",
"**The only thing left to learn is:**\n",
"\n",
" - Updating the weights of the network\n",
"\n",
"Update the weights\n",
"------------------\n",
"The simplest update rule used in practice is the Stochastic Gradient\n",
"Descent (SGD):\n",
"\n",
" ``weight = weight - learning_rate * gradient``\n",
"\n",
"We can implement this using simple Python code:\n",
"\n",
".. code:: python\n",
"\n",
" learning_rate = 0.01\n",
" for f in net.parameters():\n",
" f.data.sub_(f.grad.data * learning_rate)\n",
"\n",
"However, as you use neural networks, you want to use various different\n",
"update rules such as SGD, Nesterov-SGD, Adam, RMSProp, etc.\n",
"To enable this, we built a small package: ``torch.optim`` that\n",
"implements all these methods. Using it is very simple:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import torch.optim as optim\n",
"\n",
"# create your optimizer\n",
"optimizer = optim.SGD(net.parameters(), lr=0.01)\n",
"\n",
"# in your training loop:\n",
"optimizer.zero_grad() # zero the gradient buffers\n",
"output = net(input)\n",
"loss = criterion(output, target)\n",
"loss.backward()\n",
"optimizer.step() # Does the update"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
".. Note::\n",
"\n",
" Observe how gradient buffers had to be manually set to zero using\n",
" ``optimizer.zero_grad()``. This is because gradients are accumulated\n",
" as explained in the `Backprop`_ section.\n",
"\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "build_central"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
Unknown
|
2D
|
heartcored98/Standalone-DeepLearning-Chemistry
|
Lec02/2_autograd_tutorial.ipynb
|
.ipynb
| 12,383
| 466
|
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[](https://colab.research.google.com/github/heartcored98/Standalone-DeepLearning-Chemistry/blob/master/Lec02/2_autograd_tutorial.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Autograd: Automatic Differentiation\n",
"===================================\n",
"\n",
"Central to all neural networks in PyTorch is the ``autograd`` package.\n",
"Let’s first briefly visit this, and we will then go to training our\n",
"first neural network.\n",
"\n",
"\n",
"The ``autograd`` package provides automatic differentiation for all operations\n",
"on Tensors. It is a define-by-run framework, which means that your backprop is\n",
"defined by how your code is run, and that every single iteration can be\n",
"different.\n",
"\n",
"Let us see this in more simple terms with some examples.\n",
"\n",
"Tensor\n",
"--------\n",
"\n",
"``torch.Tensor`` is the central class of the package. If you set its attribute\n",
"``.requires_grad`` as ``True``, it starts to track all operations on it. When\n",
"you finish your computation you can call ``.backward()`` and have all the\n",
"gradients computed automatically. The gradient for this tensor will be\n",
"accumulated into ``.grad`` attribute.\n",
"\n",
"To stop a tensor from tracking history, you can call ``.detach()`` to detach\n",
"it from the computation history, and to prevent future computation from being\n",
"tracked.\n",
"\n",
"To prevent tracking history (and using memory), you can also wrap the code block\n",
"in ``with torch.no_grad():``. This can be particularly helpful when evaluating a\n",
"model because the model may have trainable parameters with\n",
"``requires_grad=True``, but for which we don't need the gradients.\n",
"\n",
"There’s one more class which is very important for autograd\n",
"implementation - a ``Function``.\n",
"\n",
"``Tensor`` and ``Function`` are interconnected and build up an acyclic\n",
"graph, that encodes a complete history of computation. Each tensor has\n",
"a ``.grad_fn`` attribute that references a ``Function`` that has created\n",
"the ``Tensor`` (except for Tensors created by the user - their\n",
"``grad_fn is None``).\n",
"\n",
"If you want to compute the derivatives, you can call ``.backward()`` on\n",
"a ``Tensor``. If ``Tensor`` is a scalar (i.e. it holds a one element\n",
"data), you don’t need to specify any arguments to ``backward()``,\n",
"however if it has more elements, you need to specify a ``gradient``\n",
"argument that is a tensor of matching shape.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"import torch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a tensor and set ``requires_grad=True`` to track computation with it\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[1., 1.],\n",
" [1., 1.]], requires_grad=True)\n"
]
}
],
"source": [
"x = torch.ones(2, 2, requires_grad=True)\n",
"print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Do a tensor operation:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[3., 3.],\n",
" [3., 3.]], grad_fn=<AddBackward0>)\n"
]
}
],
"source": [
"y = x + 2\n",
"print(y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"``y`` was created as a result of an operation, so it has a ``grad_fn``.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<AddBackward0 object at 0x7fb054cb53d0>\n"
]
}
],
"source": [
"print(y.grad_fn)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Do more operations on ``y``\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[27., 27.],\n",
" [27., 27.]], grad_fn=<MulBackward0>) tensor(27., grad_fn=<MeanBackward0>)\n"
]
}
],
"source": [
"z = y * y * 3\n",
"out = z.mean()\n",
"\n",
"print(z, out)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"``.requires_grad_( ... )`` changes an existing Tensor's ``requires_grad``\n",
"flag in-place. The input flag defaults to ``False`` if not given.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n",
"True\n",
"<SumBackward0 object at 0x7fb054cb59d0>\n"
]
}
],
"source": [
"a = torch.randn(2, 2)\n",
"a = ((a * 3) / (a - 1))\n",
"print(a.requires_grad)\n",
"a.requires_grad_(True)\n",
"print(a.requires_grad)\n",
"b = (a * a).sum()\n",
"print(b.grad_fn)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Gradients\n",
"---------\n",
"Let's backprop now.\n",
"Because ``out`` contains a single scalar, ``out.backward()`` is\n",
"equivalent to ``out.backward(torch.tensor(1.))``.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"out.backward()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print gradients d(out)/dx\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[4.5000, 4.5000],\n",
" [4.5000, 4.5000]])\n"
]
}
],
"source": [
"print(x.grad)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You should have got a matrix of ``4.5``. Let’s call the ``out``\n",
"*Tensor* “$o$”.\n",
"We have that $o = \\frac{1}{4}\\sum_i z_i$,\n",
"$z_i = 3(x_i+2)^2$ and $z_i\\bigr\\rvert_{x_i=1} = 27$.\n",
"Therefore,\n",
"$\\frac{\\partial o}{\\partial x_i} = \\frac{3}{2}(x_i+2)$, hence\n",
"$\\frac{\\partial o}{\\partial x_i}\\bigr\\rvert_{x_i=1} = \\frac{9}{2} = 4.5$.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mathematically, if you have a vector valued function $\\vec{y}=f(\\vec{x})$,\n",
"then the gradient of $\\vec{y}$ with respect to $\\vec{x}$\n",
"is a Jacobian matrix:\n",
"\n",
"\\begin{align}J=\\left(\\begin{array}{ccc}\n",
" \\frac{\\partial y_{1}}{\\partial x_{1}} & \\cdots & \\frac{\\partial y_{1}}{\\partial x_{n}}\\\\\n",
" \\vdots & \\ddots & \\vdots\\\\\n",
" \\frac{\\partial y_{m}}{\\partial x_{1}} & \\cdots & \\frac{\\partial y_{m}}{\\partial x_{n}}\n",
" \\end{array}\\right)\\end{align}\n",
"\n",
"Generally speaking, ``torch.autograd`` is an engine for computing\n",
"vector-Jacobian product. That is, given any vector\n",
"$v=\\left(\\begin{array}{cccc} v_{1} & v_{2} & \\cdots & v_{m}\\end{array}\\right)^{T}$,\n",
"compute the product $v^{T}\\cdot J$. If $v$ happens to be\n",
"the gradient of a scalar function $l=g\\left(\\vec{y}\\right)$,\n",
"that is,\n",
"$v=\\left(\\begin{array}{ccc}\\frac{\\partial l}{\\partial y_{1}} & \\cdots & \\frac{\\partial l}{\\partial y_{m}}\\end{array}\\right)^{T}$,\n",
"then by the chain rule, the vector-Jacobian product would be the\n",
"gradient of $l$ with respect to $\\vec{x}$:\n",
"\n",
"\\begin{align}J^{T}\\cdot v=\\left(\\begin{array}{ccc}\n",
" \\frac{\\partial y_{1}}{\\partial x_{1}} & \\cdots & \\frac{\\partial y_{m}}{\\partial x_{1}}\\\\\n",
" \\vdots & \\ddots & \\vdots\\\\\n",
" \\frac{\\partial y_{1}}{\\partial x_{n}} & \\cdots & \\frac{\\partial y_{m}}{\\partial x_{n}}\n",
" \\end{array}\\right)\\left(\\begin{array}{c}\n",
" \\frac{\\partial l}{\\partial y_{1}}\\\\\n",
" \\vdots\\\\\n",
" \\frac{\\partial l}{\\partial y_{m}}\n",
" \\end{array}\\right)=\\left(\\begin{array}{c}\n",
" \\frac{\\partial l}{\\partial x_{1}}\\\\\n",
" \\vdots\\\\\n",
" \\frac{\\partial l}{\\partial x_{n}}\n",
" \\end{array}\\right)\\end{align}\n",
"\n",
"(Note that $v^{T}\\cdot J$ gives a row vector which can be\n",
"treated as a column vector by taking $J^{T}\\cdot v$.)\n",
"\n",
"This characteristic of vector-Jacobian product makes it very\n",
"convenient to feed external gradients into a model that has\n",
"non-scalar output.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's take a look at an example of vector-Jacobian product:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([ 509.6293, -29.4507, -1803.5209], grad_fn=<MulBackward0>)\n"
]
}
],
"source": [
"x = torch.randn(3, requires_grad=True)\n",
"\n",
"y = x * 2\n",
"while y.data.norm() < 1000:\n",
" y = y * 2\n",
"\n",
"print(y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now in this case ``y`` is no longer a scalar. ``torch.autograd``\n",
"could not compute the full Jacobian directly, but if we just\n",
"want the vector-Jacobian product, simply pass the vector to\n",
"``backward`` as argument:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"v = torch.tensor([0.1, 1.0, 0.0001], dtype=torch.float)\n",
"y.backward(v)\n",
"\n",
"print(x.grad)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also stop autograd from tracking history on Tensors\n",
"with ``.requires_grad=True`` either by wrapping the code block in\n",
"``with torch.no_grad():``\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(x.requires_grad)\n",
"print((x ** 2).requires_grad)\n",
"\n",
"with torch.no_grad():\n",
"\tprint((x ** 2).requires_grad)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or by using ``.detach()`` to get a new Tensor with the same\n",
"content but that does not require gradients:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(x.requires_grad)\n",
"y = x.detach()\n",
"print(y.requires_grad)\n",
"print(x.eq(y).all())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Read Later:**\n",
"\n",
"Document about ``autograd.Function`` is at\n",
"https://pytorch.org/docs/stable/autograd.html#function\n",
"\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "build_central"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
Unknown
|
2D
|
heartcored98/Standalone-DeepLearning-Chemistry
|
Lec05/Lec05_lipo_graph_gcn_prediction.ipynb
|
.ipynb
| 608,543
| 698
|
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[](https://colab.research.google.com/github/heartcored98/Standalone-DeepLearning-Chemistry/blob/master/Lec05/Lec05_lipo_graph_gcn_prediction.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Install miniconda and rdkit"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"!wget https://raw.githubusercontent.com/heartcored98/Standalone-DeepLearning-Chemistry/master/Lec04/utils.py -O utils.py\n",
"!mkdir results\n",
"!mkdir images\n",
"\n",
"!wget -c https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
"!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
"!time bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
"!time conda install -q -y -c conda-forge rdkit\n",
"\n",
"import sys\n",
"import os\n",
"sys.path.append('/usr/local/lib/python3.7/site-packages/')\n",
"\"\"\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Predict Lipophilicity Property Using GCN\n",
"=====================\n",
"\n",
"앞선 실습에서는 SMILES와 CNN 아키텍쳐를 활용한 lipophilicity 예측을 연습해보았습니다. \n",
"이번 시간에는 분자를 Graph로 표현하여 각 원자의 벡터와 원자 간의 연결 상태를 input으로 다룰 수 있는 GCN을 구현해 봅시다."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Dataset과 DataLoader 준비하기 \n",
"\n",
"Graph 형태로 데이터를 준비하기 위해서는 임의 분자의 `SMILES` 문자열이 들어왔을 때 이를 `Rdkit` 분자 오브젝트로 바꾸고 각 원자들의 node feature vector와 adjacency matrix를 만들 필요가 있습니다. 이후에는 custom dataset을 만들어 노드 벡터들이 담긴 X, 연결 상태가 담긴 A, Lipophilicity 값 y를 반환해주면 됩니다. \n",
"\n",
"\n",
"### 1.1 데이터 준비하기\n",
"앞선 예제에서 사용했던 것처럼 `Lipophilicity.csv`를 다운 받은 후 train, val, test로 분할해줍니다. \n",
"GCN의 경우 문자열의 vocabulary set을 만드는 대신 사용할 원자들의 집합을 직접 정하는 방식으로 대체합니다. "
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {},
"outputs": [],
"source": [
"!wget -q \"http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/Lipophilicity.csv\" -O Lipophilicity.csv\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.model_selection import train_test_split\n",
"from rdkit import Chem\n",
"\n",
"\n",
"def get_splitted_lipo_dataset(ratios=[0.8, 0.1, 0.1], seed=123):\n",
"\n",
" raw_data = pd.read_csv('Lipophilicity.csv') # Open original dataset\n",
" smiles = raw_data['smiles']\n",
" \n",
" train_val, test = train_test_split(raw_data, test_size=ratios[2], random_state=seed)\n",
" train, val = train_test_split(train_val, test_size=ratios[1]/(ratios[0]+ratios[1]), random_state=seed)\n",
" \n",
" return train, val, test"
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" CMPD_CHEMBLID exp smiles\n",
"1369 CHEMBL199237 2.70 O=C(NCc1ccccn1)c2ccc(Oc3ccccc3)cc2\n",
"3084 CHEMBL277863 2.05 COC(=O)N1CCN([C@H](CN2CCCC2)C1)C(=O)Cc3ccc(Cl)...\n",
"2141 CHEMBL1824036 -0.51 CS(=O)(=O)c1ccc2OCC(=O)N(CCN3CCC(CC3)NCc4ccc5O...\n",
"3741 CHEMBL87266 1.35 Cc1cc(N)c2cc(NC(=O)CC(=O)Nc3ccc4nc(C)cc(N)c4c3...\n",
"2192 CHEMBL513370 2.36 COc1ccc(N(C(C(=O)NC[C@@H](C)O)c2ccccc2F)C(=O)c...\n",
"... ... ... ...\n",
"3180 CHEMBL578061 4.06 N(c1ccccc1)c2ccnc(Nc3ccccc3)n2\n",
"266 CHEMBL97 2.16 COc1ccc2nccc([C@H](O)C3CC4CCN3CC4C=C)c2c1\n",
"2398 CHEMBL138649 3.60 Oc1ccc2OC(=CC(=O)c2c1)c3ccccc3\n",
"2073 CHEMBL272705 2.21 C[C@H](CO)Nc1nc(SCc2occc2)nc3NC(=O)Sc13\n",
"2480 CHEMBL177611 0.60 Clc1ccc(cc1)C(=O)N[C@H]2CN3CCC2CC3\n",
"\n",
"[3359 rows x 3 columns]\n"
]
}
],
"source": [
"datasets = get_splitted_lipo_dataset()\n",
"smiles = datasets[0]\n",
"print(smiles)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2 Mol2Graph 구현하기 \n",
"\n",
"임의의 `SMILES`가 입력되었을 때 이를 분자로 바꿔봅시다. \n",
"\n",
"- 노드 행렬 X를 만들 때 원자의 종류뿐만 아니라 Degree, 붙어 있는 수소 원자의 수, Valence, 아로마틱 여부 등을 함께 넣어줌으로써 추가적인 정보를 제공합니다. \n",
"\n",
"- 인접 행렬 A를 만들 때는 `Rdkit`에서 기본적으로 계산해주는 행렬에서 대각 성분에 1을 더함으로써 이후 GCN이 이루어질 때 본인의 노드 정보도 함께 추가될 수 있도록 합니다. \n",
"\n",
"- 추가적으로 각 분자들은 임의의 원자 개수를 가지고 있으므로 미리 최대 원자 수를 설정해놓고(본 예시에서는 70개) X, A 행렬을 만들어줍니다."
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"LIST_SYMBOLS = ['C', 'N', 'O', 'S', 'F', 'H', 'Si', 'P', 'Cl', 'Br',\n",
" 'Li', 'Na', 'K', 'Mg', 'Ca', 'Fe', 'As', 'Al', 'I', 'B',\n",
" 'V', 'Tl', 'Sb', 'Sn', 'Ag', 'Pd', 'Co', 'Se', 'Ti', 'Zn',\n",
" 'Ge', 'Cu', 'Au', 'Ni', 'Cd', 'Mn', 'Cr', 'Pt', 'Hg', 'Pb']\n",
"\n",
"\n",
"def atom_feature(atom):\n",
" return np.array(char_to_ix(atom.GetSymbol(), LIST_SYMBOLS) +\n",
" char_to_ix(atom.GetDegree(), [0, 1, 2, 3, 4, 5]) +\n",
" char_to_ix(atom.GetTotalNumHs(), [0, 1, 2, 3, 4]) +\n",
" char_to_ix(atom.GetImplicitValence(), [0, 1, 2, 3, 4, 5]) +\n",
" char_to_ix(int(atom.GetIsAromatic()), [0, 1])) # (40, 6, 5, 6, 2)\n",
"\n",
"\n",
"def char_to_ix(x, allowable_set):\n",
" if x not in allowable_set:\n",
" return [0] # Unknown Atom Token\n",
" return [allowable_set.index(x)+1]\n",
"\n",
"\n",
"def mol2graph(smi, MAX_LEN):\n",
" mol = Chem.MolFromSmiles(smi)\n",
"\n",
" X = np.zeros((MAX_LEN, 5), dtype=np.uint8)\n",
" A = np.zeros((MAX_LEN, MAX_LEN), dtype=np.uint8)\n",
"\n",
" temp_A = Chem.rdmolops.GetAdjacencyMatrix(mol).astype(np.uint8, copy=False)[:MAX_LEN, :MAX_LEN]\n",
" num_atom = temp_A.shape[0]\n",
" A[:num_atom, :num_atom] = temp_A + np.eye(temp_A.shape[0], dtype=np.uint8)\n",
" \n",
" for i, atom in enumerate(mol.GetAtoms()):\n",
" feature = atom_feature(atom)\n",
" X[i, :] = feature\n",
" if i + 1 >= num_atom: break\n",
" \n",
" return X, A\n",
"\n",
"smiles = \"O=C(NCc1ccccn1)c2ccc(Oc3ccccc3)cc2\"\n",
"X, A = mol2graph(smiles, 70)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"from torch.utils.data import Dataset, DataLoader\n",
"\n",
"class gcnDataset(Dataset):\n",
" def __init__(self, df, max_len=120):\n",
" self.smiles = df[\"smiles\"]\n",
" self.exp = df[\"exp\"].values\n",
" \n",
" list_X = list()\n",
" list_A = list()\n",
" for i, smiles in enumerate(self.smiles):\n",
" X, A = mol2graph(smiles, max_len)\n",
" list_X.append(X)\n",
" list_A.append(A)\n",
" \n",
" self.X = np.array(list_X, dtype=np.uint8)\n",
" self.A = np.array(list_A, dtype=np.uint8)\n",
" \n",
" def __len__(self):\n",
" return len(self.X)\n",
" \n",
" def __getitem__(self, index):\n",
" return self.X[index], self.A[index], self.exp[index]\n",
" \n",
"sample_dataset = gcnDataset(datasets[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Model Construction\n",
"\n",
"Vanila GCN 기반의 Lipophilicity 예측 아키텍쳐를 구현하여 봅시다. 이를 위해 크게 4가지의 Module을 구현하고 사용합니다. \n",
"\n",
"- **BN1d** X 행렬에 대해서 각 노드 피처 벡터들을 Batch Normalization할 수 있는 module입니다. \n",
"- **GConv** X, A를 입력 받아서 인접한 노드들의 정보를 바탕으로 각 노드 벡터를 업데이트하는 module입니다. \n",
"- **Readout** GConv를 거친 노드 벡터들로부터 invariant한 molecular vector representation을 만들기 위한 pooling module입니다. \n",
"- **GCNNet** 노드 행렬 X를 embedding matrix로 변환한 후 `GConv` 모듈을 통과시키고 `Readout` 모듈로 molvec을 만든 후 이로부터 lipophilicity를 예측하는 module입니다. "
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"\n",
"\n",
"class BN1d(nn.Module):\n",
" def __init__(self, out_dim, use_bn):\n",
" super(BN1d, self).__init__()\n",
" self.use_bn = use_bn\n",
" self.bn = nn.BatchNorm1d(out_dim)\n",
" \n",
" def forward(self, x):\n",
" if not self.use_bn:\n",
" return x\n",
" origin_shape = x.shape\n",
" x = x.view(-1, origin_shape[-1])\n",
" x = self.bn(x)\n",
" x = x.view(origin_shape)\n",
" return x\n",
"\n",
" \n",
"class GConv(nn.Module):\n",
" def __init__(self, input_dim, output_dim, use_bn):\n",
" super(GConv, self).__init__()\n",
" self.fc = nn.Linear(input_dim, output_dim)\n",
" self.bn = BN1d(output_dim, use_bn)\n",
" self.relu = nn.ReLU()\n",
" \n",
" def forward(self, X, A):\n",
" x = self.fc(X)\n",
" x = torch.matmul(A, x)\n",
" x = self.relu(self.bn(x))\n",
" return x, A\n",
" \n",
" \n",
"class Readout(nn.Module):\n",
" def __init__(self, out_dim, molvec_dim):\n",
" super(Readout, self).__init__()\n",
" self.readout_fc = nn.Linear(out_dim, molvec_dim)\n",
" nn.init.xavier_normal_(self.readout_fc.weight.data)\n",
"\n",
" def forward(self, output_H):\n",
" molvec = self.readout_fc(output_H)\n",
" molvec = torch.mean(molvec, dim=1)\n",
" return molvec\n",
" \n",
"\n",
"class GCNNet(nn.Module):\n",
" \n",
" def __init__(self, args):\n",
" super(GCNNet, self).__init__()\n",
" \n",
" # Create Atom Element embedding layer\n",
" self.embedding = self.create_emb_layer([args.vocab_size, args.degree_size,\n",
" args.numH_size, args.valence_size,\n",
" args.isarom_size], args.emb_train) \n",
" \n",
" self.gcn_layers = nn.ModuleList()\n",
" for i in range(args.n_layer):\n",
" self.gcn_layers.append(GConv(args.in_dim if i==0 else args.out_dim, args.out_dim, args.use_bn))\n",
" \n",
" self.readout = Readout(args.out_dim, args.molvec_dim)\n",
" \n",
" self.fc1 = nn.Linear(args.molvec_dim, args.molvec_dim//2)\n",
" self.fc2 = nn.Linear(args.molvec_dim//2, args.molvec_dim//2)\n",
" self.fc3 = nn.Linear(args.molvec_dim//2, 1)\n",
" self.relu = nn.ReLU()\n",
" \n",
" def create_emb_layer(self, list_vocab_size, emb_train=False):\n",
" list_emb_layer = nn.ModuleList()\n",
" for i, vocab_size in enumerate(list_vocab_size):\n",
" vocab_size += 1\n",
" emb_layer = nn.Embedding(vocab_size, vocab_size)\n",
" weight_matrix = torch.zeros((vocab_size, vocab_size))\n",
" for i in range(vocab_size):\n",
" weight_matrix[i][i] = 1\n",
" emb_layer.load_state_dict({'weight': weight_matrix})\n",
" emb_layer.weight.requires_grad = emb_train\n",
" list_emb_layer.append(emb_layer)\n",
" return list_emb_layer\n",
"\n",
" def _embed(self, x):\n",
" list_embed = list()\n",
" for i in range(5):\n",
" list_embed.append(self.embedding[i](x[:, :, i]))\n",
" x = torch.cat(list_embed, 2)\n",
" return x\n",
" \n",
" def forward(self, x, A):\n",
" A = A.float()\n",
" x = self._embed(x) \n",
" \n",
" for i, module in enumerate(self.gcn_layers):\n",
" x, A = module(x, A)\n",
" x = self.readout(x)\n",
" \n",
" x = self.relu(self.fc1(x))\n",
" x = self.relu(self.fc2(x))\n",
" x = self.fc3(x)\n",
" return torch.squeeze(x)\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Train, Validation, Test\n",
"\n",
"Data, Model, Loss, Optimization을 모두 같이 사용하여 봅시다. Epoch 별로 train과 validation, test가 이루어질 수 있게 함수를 나누었습니다. 이 때 `DataLoader`로부터 X, A, y를 받은 후 `model(X,A)`를 수행하여 `pred_y`를 구한다는 점이 CNN 실습과 다릅니다.\n"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {},
"outputs": [],
"source": [
"def train(model, dataloader, optimizer, criterion, args, **kwargs):\n",
" \n",
" epoch_train_loss = 0\n",
" list_train_loss = list()\n",
" cnt_iter = 0\n",
" for batch_idx, batch in enumerate(dataloader):\n",
" X, A, y = batch[0].long(), batch[1].long(), batch[2].float()\n",
" X, A, y = X.to(args.device), A.to(args.device), y.to(args.device)\n",
" \n",
" model.train()\n",
" optimizer.zero_grad()\n",
"\n",
" pred_y = model(X, A)\n",
" \n",
" train_loss = criterion(pred_y, y)\n",
" epoch_train_loss += train_loss.item()\n",
" list_train_loss.append({'epoch':batch_idx/len(dataloader)+kwargs['epoch'], 'train_loss':train_loss.item()})\n",
" train_loss.backward()\n",
" optimizer.step()\n",
" \n",
" cnt_iter += 1\n",
" return model, list_train_loss\n",
"\n",
"\n",
"def validate(model, dataloader, criterion, args):\n",
" \n",
" epoch_val_loss = 0\n",
" cnt_iter = 0\n",
" for batch_idx, batch in enumerate(dataloader):\n",
" X, A, y = batch[0].long(), batch[1].long(), batch[2].float()\n",
" X, A, y = X.to(args.device), A.to(args.device), y.to(args.device)\n",
" \n",
" model.eval()\n",
" pred_y = model(X, A)\n",
" val_loss = criterion(pred_y, y)\n",
" epoch_val_loss += val_loss.item()\n",
" cnt_iter += 1\n",
"\n",
" return epoch_val_loss/cnt_iter\n",
"\n",
"def test(model, dataloader, args, **kwargs):\n",
"\n",
" list_y, list_pred_y = list(), list()\n",
" for batch_idx, batch in enumerate(dataloader):\n",
" X, A, y = batch[0].long(), batch[1].long(), batch[2].float()\n",
" X, A, y = X.to(args.device), A.to(args.device), y.to(args.device)\n",
" \n",
" model.eval()\n",
" pred_y = model(X, A)\n",
" list_y += y.cpu().detach().numpy().tolist()\n",
" list_pred_y += pred_y.cpu().detach().numpy().tolist()\n",
"\n",
" mae = mean_absolute_error(list_y, list_pred_y)\n",
" std = np.std(np.array(list_y)-np.array(list_pred_y))\n",
" return mae, std, list_y, list_pred_y\n",
"\n",
"\n",
"def experiment(partition, args):\n",
" ts = time.time()\n",
" \n",
" model = GCNNet(args) \n",
" model.to(args.device)\n",
" criterion = nn.MSELoss()\n",
" \n",
" # Initialize Optimizer\n",
" trainable_parameters = filter(lambda p: p.requires_grad, model.parameters())\n",
" if args.optim == 'ADAM':\n",
" optimizer = optim.Adam(trainable_parameters, lr=args.lr, weight_decay=args.l2_coef)\n",
" elif args.optim == 'RMSProp':\n",
" optimizer = optim.RMSprop(trainable_parameters, lr=args.lr, weight_decay=args.l2_coef)\n",
" elif args.optim == 'SGD':\n",
" optimizer = optim.SGD(trainable_parameters, lr=args.lr, weight_decay=args.l2_coef)\n",
" else:\n",
" assert False, \"Undefined Optimizer Type\"\n",
" \n",
" # Train, Validate, Evaluate\n",
" list_train_loss = list()\n",
" list_val_loss = list()\n",
" list_mae = list()\n",
" list_std = list()\n",
" \n",
" args.best_mae = 10000\n",
" for epoch in range(args.epoch):\n",
" model, train_losses = train(model, partition['train'], optimizer, criterion, args, **{'epoch':epoch})\n",
" val_loss = validate(model, partition['val'], criterion, args)\n",
" mae, std, true_y, pred_y = test(model, partition['test'], args, **{'epoch':epoch})\n",
" \n",
" list_train_loss += train_losses\n",
" list_val_loss.append({'epoch':epoch, 'val_loss':val_loss})\n",
" list_mae.append({'epoch':epoch, 'mae':mae})\n",
" list_std.append({'epoch':epoch, 'std':std})\n",
" \n",
" if args.best_mae > mae or epoch==0:\n",
" args.best_epoch = epoch\n",
" args.best_mae = mae\n",
" args.best_std = std\n",
" args.best_true_y = true_y\n",
" args.best_pred_y = pred_y\n",
" \n",
"\n",
" # End of experiments\n",
" te = time.time()\n",
" args.elapsed = te-ts\n",
" args.train_losses = list_train_loss\n",
" args.val_losses = list_val_loss\n",
" args.maes = list_mae\n",
" args.stds = list_std\n",
"\n",
" return model, args "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Experiment\n",
"\n",
"실험을 진행해봅시다. 이때 Embedding, Model Architecture, Optimizer, Training Configuration을 설정할 필요가 있습니다. \n",
"첫번째 실험으로 Learning Rate와 N Layer를 바꿔가면서 실험해보도록 하겠습니다. "
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Exp 1] got mae: 0.485, std: 0.691 at epoch 270 took 117.41767930984497 sec\n",
"[Exp 2] got mae: 0.468, std: 0.680 at epoch 258 took 146.95619344711304 sec\n",
"[Exp 3] got mae: 0.455, std: 0.656 at epoch 296 took 169.77326846122742 sec\n",
"[Exp 4] got mae: 0.436, std: 0.642 at epoch 282 took 196.60933375358582 sec\n",
"[Exp 5] got mae: 0.508, std: 0.712 at epoch 278 took 116.0598316192627 sec\n",
"[Exp 6] got mae: 0.469, std: 0.689 at epoch 213 took 144.84185910224915 sec\n",
"[Exp 7] got mae: 0.469, std: 0.680 at epoch 281 took 169.2616229057312 sec\n",
"[Exp 8] got mae: 0.546, std: 0.773 at epoch 298 took 199.6068012714386 sec\n"
]
}
],
"source": [
"import argparse\n",
"import time \n",
"from sklearn.metrics import mean_absolute_error\n",
"from utils import *\n",
"\n",
"\n",
"seed = 123\n",
"np.random.seed(seed)\n",
"torch.manual_seed(seed)\n",
"\n",
"parser = argparse.ArgumentParser()\n",
"args = parser.parse_args(\"\")\n",
"\n",
"# ==== Embedding Config ==== #\n",
"args.max_len = 70\n",
"args.vocab_size = 40\n",
"args.degree_size = 6\n",
"args.numH_size = 5\n",
"args.valence_size = 6\n",
"args.isarom_size = 2\n",
"args.emb_train = True\n",
"\n",
"\n",
"# ==== Model Architecture Config ==== #\n",
"args.in_dim = 64\n",
"args.out_dim = 256\n",
"args.molvec_dim = 512\n",
"args.n_layer = 1\n",
"args.use_bn = True\n",
"args.act = 'relu'\n",
"args.dp_rate = 0.3\n",
"\n",
"\n",
"# ==== Optimizer Config ==== #\n",
"args.lr = 0.00005\n",
"args.l2_coef = 0.0001\n",
"args.optim = 'ADAM'\n",
"\n",
"\n",
"# ==== Training Config ==== #\n",
"args.epoch = 300\n",
"args.batch_size = 256\n",
"args.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
"args.exp_name = 'exp1_lr_stage'\n",
"\n",
"\n",
"writer = Writer(prior_keyword=['n_layer', 'use_bn', 'lr', 'dp_rate', 'emb_train', 'epoch', 'batch_size'])\n",
"writer.clear()\n",
"\n",
"# Define Hyperparameter Search Space\n",
"#list_n_layer = [1]\n",
"list_lr = [0.001, 0.005]\n",
"list_n_layer = [2,3,4,5]\n",
"\n",
"\n",
"train_dataloader = DataLoader(gcnDataset(datasets[0], args.max_len), batch_size=args.batch_size, shuffle=True)\n",
"val_dataloader = DataLoader(gcnDataset(datasets[1], args.max_len), batch_size=args.batch_size, shuffle=False)\n",
"test_dataloader = DataLoader(gcnDataset(datasets[2], args.max_len), batch_size=args.batch_size, shuffle=False)\n",
"partition = {'train': train_dataloader, 'val': val_dataloader, 'test': test_dataloader}\n",
"\n",
"cnt_exp = 0\n",
"for lr in list_lr:\n",
" for n_layer in list_n_layer:\n",
" args.lr = lr\n",
" args.n_layer = n_layer\n",
"\n",
" model, result = experiment(partition, args)\n",
" writer.write(result)\n",
" \n",
" cnt_exp += 1\n",
" print('[Exp {:2}] got mae: {:2.3f}, std: {:2.3f} at epoch {:2} took {:3.1f} sec'.format(cnt_exp, result.best_mae, result.best_std, result.best_epoch, result.elapsed))"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x432 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x432 with 8 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 923.375x432 with 32 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"from matplotlib import gridspec\n",
"from matplotlib.font_manager import FontProperties\n",
"import seaborn as sns\n",
"\n",
"results = writer.read(exp_name='exp1_lr_stage')\n",
"variable1 = 'n_layer'\n",
"variable2 = 'lr'\n",
"\n",
"\n",
"plot_performance(results, variable1, variable2, args,\n",
" 'Performance depends on {} vs {}'.format(variable1, variable2),\n",
" 'exp1_Performance {} vs {}'.format(variable1, variable2))\n",
"\n",
"plot_distribution(results, variable1, variable2, 'true_y', 'pred_y', \n",
" 'Prediction results depends on {} vs {}'.format(variable1, variable2),\n",
" 'exp1_Prediction {} vs {}'.format(variable1, variable2))\n",
"\n",
"plot_loss(results, variable1, variable2, 'epoch', 'loss', \n",
" 'Loss depends on {} vs {}'.format(variable1, variable2),\n",
" 'exp1_Loss {} vs {}'.format(variable1, variable2))\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.1 Visualizing Embedding Matrix \n",
"\n",
"`args.emb_train=True`로 하게 되면 one-hot-encoding된 임베딩 weight의 값들도 Loss를 최소화하는 방향으로 학습될 수 있습니다. 실제로 학습된 모델의 embedding matrix를 시각화하여 값이 변화했는지 확인해봅시다."
]
},
{
"cell_type": "code",
"execution_count": 224,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x1080 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"fig, ax = plt.subplots(figsize=(15, 15))\n",
"ax.imshow(model.embedding[0].weight.detach().cpu().numpy(), interpolation='nearest', cmap=plt.cm.Reds)\n",
"ax.set_yticks(np.arange(len(LIST_SYMBOLS)+1))\n",
"ax.set_xticks(np.arange(len(LIST_SYMBOLS)+1))\n",
"\n",
"ax.set_yticklabels(['PAD'] + LIST_SYMBOLS)\n",
"ax.set_title(\"Embedding Weight Visualization\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "build_central"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
Unknown
|
2D
|
heartcored98/Standalone-DeepLearning-Chemistry
|
Lec05/utils.py
|
.py
| 7,950
| 213
|
import os
import numpy as np
from rdkit import Chem, DataStructs
from rdkit.Chem import AllChem
from rdkit.Chem.Crippen import MolLogP
from rdkit.Chem.rdMolDescriptors import CalcTPSA
from torch.utils.data import Dataset
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split, KFold
from decimal import Decimal
import json
from os import listdir
from os.path import isfile, join
import pandas as pd
import hashlib
class Writer():
def __init__(self, prior_keyword=[], dir='./results'):
self.prior_keyword = prior_keyword
self.dir = dir
def generate_hash(self, args):
str_as_bytes = str.encode(str(args))
hashed = hashlib.sha256(str_as_bytes).hexdigest()[:24]
return hashed
def write(self, args, prior_keyword=None):
dict_args = vars(args)
if 'bar' in dict_args:
#del dict_args['bar']
pass
if prior_keyword:
self.prior_keyword = prior_keyword
filename = 'exp_{}'.format(args.exp_name)
for keyword in self.prior_keyword:
value = str(dict_args[keyword])
if value.isdigit():
filename += keyword + ':{:.2E}_'.format(Decimal(dict_args[keyword]))
else:
filename += keyword + ':{}_'.format(value)
# hashcode = self.generate_hash(args)
# filename += hashcode
filename += '.json'
with open(self.dir+'/'+filename, 'w') as outfile:
json.dump(dict_args, outfile)
def read(self, exp_name=''):
list_result = list()
filenames = [f for f in listdir(self.dir) if isfile(join(self.dir, f))]
for filename in filenames:
with open(join(self.dir, filename), 'r') as infile:
result = json.load(infile)
if len(exp_name) > 0:
if result['exp_name'] == exp_name:
list_result.append(result)
else:
list_result.append(result)
return pd.DataFrame(list_result)
def clear(self, exp_name=''):
filenames = [f for f in listdir(self.dir) if isfile(join(self.dir, f))]
for filename in filenames:
if len(exp_name) > 0:
result = json.load(open(join(self.dir, filename), 'r'))
if result['exp_name'] == exp_name:
os.remove(join(self.dir, filename))
else:
os.remove(join(self.dir, filename))
import matplotlib.pyplot as plt
from matplotlib import gridspec
from matplotlib.font_manager import FontProperties
import seaborn as sns
def generate_setting(args, var1, var2):
dict_args = vars(args)
output = '{:92}'.format('[Exp Settings]') + '\n'
output += '-'*91 + '\n'
num_var = 3
cnt_var = 0
for keyword, value in dict_args.items():
if keyword != var1 and keyword != var2 and type(value) != list and not 'best' in keyword and keyword != 'elapsed':
str_value = str(value)
if str_value.isdigit():
if type(value) == float:
temp = '| {}={:.2E}'.format(keyword, Decimal(dict_args[keyword]))
if type(value) == int:
temp = '| {}={}'.format(keyword, str_value[:15])
else:
temp = '| {}={}'.format(keyword, str_value[:15])
output += '{:<30}'.format(temp[:30])
cnt_var += 1
if cnt_var % num_var == 0:
cnt_var = 0
output += '|\n'
output += '-'*91 + '\n'
return output
def plot_performance(results, variable1, variable2, args, title='', filename=''):
fig, ax = plt.subplots(1, 2)
fig.set_size_inches(15, 6)
sns.set_style("darkgrid", {"axes.facecolor": ".9"})
sns.barplot(x=variable1, y='best_mae', hue=variable2, data=results, ax=ax[0])
sns.barplot(x=variable1, y='best_std', hue=variable2, data=results, ax=ax[1])
font = FontProperties()
font.set_family('monospace')
font.set_size('large')
alignment = {'horizontalalignment': 'center', 'verticalalignment': 'baseline'}
fig.text(0.5, -0.6, generate_setting(args, variable1, variable2), fontproperties=font, **alignment)
fig.suptitle(title)
filename = filename if len(filename) > 0 else title
plt.savefig('./images/{}.png'.format(filename))
def plot_distribution(results, variable1, variable2, x='true_y', y='pred_y', title='', filename='', **kwargs):
list_v1 = results[variable1].unique()
list_v2 = results[variable2].unique()
list_data = list()
for value1 in list_v1:
for value2 in list_v2:
row = results.loc[results[variable1]==value1]
row = row.loc[results[variable2]==value2]
best_true_y = list(row.best_true_y)[0]
best_pred_y = list(row.best_pred_y)[0]
for i in range(len(best_true_y)):
list_data.append({x:best_true_y[i], y:best_pred_y[i], variable1:value1, variable2:value2})
df = pd.DataFrame(list_data)
g = sns.FacetGrid(df, row=variable2, col=variable1, margin_titles=True)
g.map(plt.scatter, x, y, alpha=0.3)
def identity(**kwargs):
plt.plot(np.linspace(-1.5,4,50), np.linspace(-1.5,4,50),'k',linestyle='dashed')
g.map(identity)
g.set_axis_labels(x, y)
g.fig.suptitle(title) # can also get the figure from plt.gcf()
plt.subplots_adjust(top=kwargs.get('top',0.93))
filename = filename if len(filename) > 0 else title
plt.savefig('./images/{}.png'.format(filename))
def plot_loss(results, variable1, variable2, x='true_y', y='pred_y', title='', filename='', **kwargs):
list_v1 = results[variable1].unique()
list_v2 = results[variable2].unique()
list_data = list()
for value1 in list_v1:
for value2 in list_v2:
row = results.loc[results[variable1]==value1]
row = row.loc[results[variable2]==value2]
train_losses = list(row.train_losses)[0]
val_losses = list(row.val_losses)[0]
maes = list(row.maes)[0]
for item in train_losses:
item.update({'type':'train', 'loss':item['train_loss'], variable1:value1, variable2:value2})
for item in val_losses:
item.update({'type':'val', 'loss':item['val_loss'], variable1:value1, variable2:value2})
for item in maes:
item.update({'type':'mae', variable1:value1, variable2:value2})
list_data += train_losses + val_losses + maes
df = pd.DataFrame(list_data)
temp_mae = df.loc[df['mae'] < df['mae'].quantile(0.98)]
ymax = temp_mae['mae'].max()
ymin = temp_mae['mae'].min()
temp_loss = df.loc[df['loss'] < df['loss'].quantile(0.98)]
lossmax = temp_loss['loss'].max()
lossmin = temp_loss['loss'].min()
g = sns.FacetGrid(df, row=variable2, col=variable1, hue='type', margin_titles=False)
axes = g.axes
for i in range(len(axes)):
for j in range(len(axes[0])):
if i==0:
g.axes[i][j].yaxis.set_label_coords(1.1,0.9)
def mae_line(x, y, **kwargs):
ax2 = plt.gca().twinx()
ax2.plot(x, y,'g--')
ax2.set_ylim(kwargs['ymax']*1.05, kwargs['ymin']*0.95)
ax2.grid(False)
g.map(plt.plot, x, y)
g.map(mae_line, 'epoch', 'mae', ymin=ymin, ymax=ymax)
g.set_axis_labels(x, y)
g.fig.suptitle(title) # can also get the figure from plt.gcf()
g.add_legend()
for ax in g.axes.flatten():
ax.set_ylim(lossmin, lossmax)
plt.subplots_adjust(top=kwargs.get('top', 0.93))
filename = filename if len(filename) > 0 else title
plt.savefig('./images/{}.png'.format(filename))
|
Python
|
2D
|
JChemPaint/jchempaint
|
Native/OSX/setclipboard_swift.h
|
.h
| 333
| 8
|
void setClipboard(const unsigned char *pdfData,
const unsigned int pdfDataSize,
const unsigned char *svgData,
const unsigned int svgDataSize,
const unsigned char *pngData,
const unsigned int pngDataSize,
const char* smi);
|
Unknown
|
2D
|
JChemPaint/jchempaint
|
Native/OSX/setclipboard.c
|
.c
| 1,240
| 37
|
#include <stdio.h>
#include <stdlib.h>
#include <jni.h>
#include "setclipboard.h"
#include "setclipboard_swift.h"
JNIEXPORT void JNICALL Java_org_openscience_jchempaint_OsxClipboard_setClipboard(
JNIEnv *env, jclass cls, jbyteArray pdfData, jbyteArray svgData, jbyteArray pngData, jstring smi) {
int pdfDataLen = (*env)->GetArrayLength(env, pdfData);
unsigned char* pdfDataPtr = (unsigned char*)malloc(pdfDataLen);
(*env)->GetByteArrayRegion(env, pdfData, 0, pdfDataLen, (jbyte*)pdfDataPtr);
int svgDataLen = (*env)->GetArrayLength(env, svgData);
unsigned char* svgDataPtr = (unsigned char*)malloc(svgDataLen);
(*env)->GetByteArrayRegion(env, svgData, 0, svgDataLen, (jbyte*)svgDataPtr);
int pngDataLen = (*env)->GetArrayLength(env, pngData);
unsigned char* pngDataPtr = (unsigned char*)malloc(pngDataLen);
(*env)->GetByteArrayRegion(env, pngData, 0, pngDataLen, (jbyte*)pngDataPtr);
const char *nativeSmi = (*env)->GetStringUTFChars(env, smi, 0);
setClipboard(pdfDataPtr, pdfDataLen,
svgDataPtr, svgDataLen,
pngDataPtr, pngDataLen,
nativeSmi);
(*env)->ReleaseStringUTFChars(env, smi, nativeSmi);
free(pdfDataPtr);
free(svgDataPtr);
free(pngDataPtr);
}
|
C
|
2D
|
JChemPaint/jchempaint
|
Native/OSX/setclipboard.h
|
.h
| 600
| 21
|
/* DO NOT EDIT THIS FILE - it is machine generated */
#include <jni.h>
/* Header for class org_openscience_jchempaint_OsxClipboard */
#ifndef _Included_org_openscience_jchempaint_OsxClipboard
#define _Included_org_openscience_jchempaint_OsxClipboard
#ifdef __cplusplus
extern "C" {
#endif
/*
* Class: org_openscience_jchempaint_OsxClipboard
* Method: setClipboard
* Signature: ([BILjava/lang/String;)V
*/
JNIEXPORT void JNICALL Java_org_openscience_jchempaint_OsxClipboard_setClipboard
(JNIEnv *, jclass, jbyteArray, jbyteArray, jbyteArray, jstring);
#ifdef __cplusplus
}
#endif
#endif
|
Unknown
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/AtomContainerRenderer.java
|
.java
| 17,882
| 499
|
/* Copyright (C) 2008-2009 Gilleain Torrance <gilleain.torrance@gmail.com>
* 2008-2009 Arvid Berg <goglepox@users.sf.net>
* 2009 Egon Willighagen <egonw@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer;
import java.awt.Rectangle;
import java.awt.geom.AffineTransform;
import java.awt.geom.NoninvertibleTransformException;
import java.awt.geom.Rectangle2D;
import java.util.ArrayList;
import java.util.List;
import javax.vecmath.Point2d;
import org.openscience.cdk.geometry.GeometryUtil;
import org.openscience.cdk.interfaces.IAtom;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.renderer.font.IFontManager;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.jchempaint.renderer.elements.TextGroupElement;
import org.openscience.cdk.renderer.generators.IGenerator;
import org.openscience.jchempaint.renderer.visitor.IDrawVisitor;
/**
* A general renderer for {@link IAtomContainer}s. The chem object
* is converted into a 'diagram' made up of {@link IRenderingElement}s. It takes
* an {@link IDrawVisitor} to do the drawing of the generated diagram. Various
* display properties can be set using the {@link JChemPaintRendererModel}.<p>
*
* This class has several usage patterns. For just painting fit-to-screen do:
* <pre>
* renderer.paintMolecule(molecule, visitor, drawArea)
* </pre>
* for painting at a scale determined by the bond length in the RendererModel:
* <pre>
* if (moleculeIsNew) {
* renderer.setup(molecule, drawArea);
* }
* Rectangle diagramSize = renderer.paintMolecule(molecule, visitor);
* // ...update scroll bars here
* </pre>
* to paint at full screen size, but not resize with each change:
* <pre>
* if (moleculeIsNew) {
* renderer.setScale(molecule);
* Rectangle diagramBounds = renderer.calculateDiagramBounds(molecule);
* renderer.setZoomToFit(diagramBounds, drawArea);
* renderer.paintMolecule(molecule, visitor);
* } else {
* Rectangle diagramSize = renderer.paintMolecule(molecule, visitor);
* // ...update scroll bars here
* }
* </pre>
* finally, if you are scrolling, and have not changed the diagram:
* <pre>
* renderer.repaint(visitor)
* </pre>
* will just repaint the previously generated diagram, at the same scale.<p>
*
* There are two sets of methods for painting IChemObjects - those that take
* a Rectangle that represents the desired draw area, and those that return a
* Rectangle that represents the actual draw area. The first are intended for
* drawing molecules fitted to the screen (where 'screen' means any drawing
* area) while the second type of method are for drawing bonds at the length
* defined by the {@link JChemPaintRendererModel} parameter bondLength.<p>
*
* There are two numbers used to transform the model so that it fits on screen.
* The first is <tt>scale</tt>, which is used to map model coordinates to
* screen coordinates. The second is <tt>zoom</tt> which is used to, well,
* zoom the on screen coordinates. If the diagram is fit-to-screen, then the
* ratio of the bounds when drawn using bondLength and the bounds of
* the screen is used as the zoom.<p>
*
* So, if the bond length on screen is set to 40, and the average bond length
* of the model is 2 (unitless, but roughly Ångstrom scale) then the
* scale will be 20. If the model is 10 units wide, then the diagram drawn at
* 100% zoom will be 10 * 20 = 200 in width on screen. If the screen is 400
* pixels wide, then fitting it to the screen will make the zoom 200%. Since the
* zoom is just a floating point number, 100% = 1 and 200% = 2.
*
* @author maclean
* @cdk.module renderbasic
*/
public class AtomContainerRenderer implements IRenderer {
/**
* The default scale is used when the model is empty.
*/
public static final double DEFAULT_SCALE = 30.0;
protected IFontManager fontManager;
/**
* The renderer model is final as it is not intended to be replaced.
*/
protected final JChemPaintRendererModel rendererModel;
protected List<IGenerator<IAtomContainer>> generators;
protected AffineTransform transform;
protected Point2d modelCenter = new Point2d(0, 0); // model
protected Point2d drawCenter = new Point2d(150, 200); //diagram on screen
protected double scale = DEFAULT_SCALE;
protected double zoom = 1.0;
protected IRenderingElement cachedDiagram;
/**
* A renderer that generates diagrams using the specified
* generators and manages fonts with the supplied font manager.
*
* @param generators
* a list of classes that implement the IGenerator interface
* @param fontManager
* a class that manages mappings between zoom and font sizes
* @param useUserSettings Should user setting (in $HOME/.jchempaint/properties) be used or not?
*/
public AtomContainerRenderer(List<IGenerator<IAtomContainer>> generators,
IFontManager fontManager) {
rendererModel = new JChemPaintRendererModel();
this.generators = generators;
this.fontManager = fontManager;
for (IGenerator<?> generator : generators) {
if (generator.getParameters() != null)
rendererModel.registerParameters(generator);
}
}
/**
* Setup the transformations necessary to draw this Atom Container.
*
* @param atomContainer
* @param screen
*/
public void setup(IAtomContainer atomContainer, Rectangle screen) {
this.setScale(atomContainer);
Rectangle2D bounds = calculateBounds(atomContainer);
this.modelCenter = new Point2d(bounds.getCenterX(), bounds.getCenterY());
this.drawCenter = new Point2d(screen.getCenterX(), screen.getCenterY());
this.setup();
}
public void reset() {
modelCenter = new Point2d(0, 0);
drawCenter = new Point2d(200, 200);
zoom = 1.0;
setup();
}
/**
* Set the scale for an IAtomContainer. It calculates the average bond
* length of the model and calculates the multiplication factor to transform
* this to the bond length that is set in the RendererModel.
* @param atomContainer
*/
public void setScale(IAtomContainer atomContainer) {
double bondLength = GeometryUtil.getBondLengthAverage(atomContainer);
this.scale = this.calculateScaleForBondLength(bondLength);
// store the scale so that other components can access it
this.rendererModel.setScale(scale);
}
/*
public Rectangle paint(
IAtomContainer atomContainer,IDrawVisitor drawVisitor) {
// the bounds of the model
Rectangle2D modelBounds = calculateBounds(atomContainer);
// setup and draw
this.setupTransformNatural(modelBounds);
IRenderingElement diagram = this.generateDiagram(atomContainer);
this.paint(drawVisitor, diagram);
return this.convertToDiagramBounds(modelBounds);
}
*/
/**
* Paint a molecule (an IAtomContainer).
*
* @param atomContainer the molecule to paint
* @param drawVisitor the visitor that does the drawing
* @param bounds the bounds on the screen
* @param resetCenter
* if true, set the draw center to be the center of bounds
*/
public void paintMolecule(IAtomContainer atomContainer,
IDrawVisitor drawVisitor, Rectangle2D bounds, boolean resetCenter) {
// the bounds of the model
Rectangle2D modelBounds = calculateBounds(atomContainer);
this.setupTransformToFit(bounds, modelBounds,
GeometryUtil.getBondLengthAverage(atomContainer), resetCenter);
// the diagram to draw
IRenderingElement diagram = this.generateDiagram(atomContainer);
this.paint(drawVisitor, diagram);
}
/**
* Repaint using the cached diagram
*
* @param drawVisitor the wrapper for the graphics object that draws
*/
public void repaint(IDrawVisitor drawVisitor) {
this.paint(drawVisitor, cachedDiagram);
}
public Rectangle calculateDiagramBounds(IAtomContainer atomContainer) {
return this.calculateScreenBounds(
calculateBounds(atomContainer));
}
public Rectangle calculateScreenBounds(Rectangle2D modelBounds) {
double margin = this.rendererModel.getMargin();
Point2d modelScreenCenter
= this.toScreenCoordinates(modelBounds.getCenterX(),
modelBounds.getCenterY());
double w = (scale * zoom * modelBounds.getWidth()) + (2 * margin);
double h = (scale * zoom * modelBounds.getHeight()) + (2 * margin);
return new Rectangle((int) (modelScreenCenter.x - w / 2),
(int) (modelScreenCenter.y - h / 2),
(int) w,
(int) h);
}
public static Rectangle2D calculateBounds(IAtomContainer ac) {
// this is essential, otherwise a rectangle
// of (+INF, -INF, +INF, -INF) is returned!
if (ac.getAtomCount() == 0) {
return new Rectangle2D.Double();
}
else if (ac.getAtomCount() == 1) {
Point2d p = ac.getAtom(0).getPoint2d();
return new Rectangle2D.Double(p.x, p.y, 0, 0);
}
double xmin = Double.POSITIVE_INFINITY;
double xmax = Double.NEGATIVE_INFINITY;
double ymin = Double.POSITIVE_INFINITY;
double ymax = Double.NEGATIVE_INFINITY;
for (IAtom atom : ac.atoms()) {
Point2d p = atom.getPoint2d();
xmin = Math.min(xmin, p.x);
xmax = Math.max(xmax, p.x);
ymin = Math.min(ymin, p.y);
ymax = Math.max(ymax, p.y);
}
double w = xmax - xmin;
double h = ymax - ymin;
return new Rectangle2D.Double(xmin, ymin, w, h);
}
public JChemPaintRendererModel getRenderer2DModel() {
return this.rendererModel;
}
public Point2d toModelCoordinates(double screenX, double screenY) {
try {
double[] dest = new double[2];
double[] src = new double[] { screenX, screenY };
transform.inverseTransform(src, 0, dest, 0, 1);
return new Point2d(dest[0], dest[1]);
} catch (NoninvertibleTransformException n) {
return new Point2d(0,0);
}
}
public Point2d toScreenCoordinates(double modelX, double modelY) {
double[] dest = new double[2];
transform.transform(new double[] { modelX, modelY }, 0, dest, 0, 1);
return new Point2d(dest[0], dest[1]);
}
public void setModelCenter(double x, double y) {
this.modelCenter = new Point2d(x, y);
setup();
}
public void setDrawCenter(double x, double y) {
this.drawCenter = new Point2d(x, y);
setup();
}
public void setZoom(double z) {
getRenderer2DModel().setZoomFactor( z );
zoom = z;
setup();
}
/**
* Move the draw center by dx and dy.
*
* @param dx
* the x shift
* @param dy
* the y shift
*/
public void shiftDrawCenter(double dx, double dy) {
this.drawCenter.set(this.drawCenter.x + dx, this.drawCenter.y + dy);
setup();
}
public Point2d getDrawCenter() {
return drawCenter;
}
public Point2d getModelCenter() {
return modelCenter;
}
/**
* Calculate and set the zoom factor needed to completely fit the diagram
* onto the screen bounds.
*
* @param diagramBounds
* @param drawBounds
*/
public void setZoomToFit(double drawWidth,
double drawHeight,
double diagramWidth,
double diagramHeight) {
double m = this.rendererModel.getMargin();
// determine the zoom needed to fit the diagram to the screen
double widthRatio = drawWidth / (diagramWidth + (2 * m));
double heightRatio = drawHeight / (diagramHeight + (2 * m));
this.zoom = Math.min(widthRatio, heightRatio);
this.fontManager.setFontForZoom(zoom);
// record the zoom in the model, so that generators can use it
this.rendererModel.setZoomFactor(zoom);
}
/**
* The target method for paintChemModel, paintReaction, and paintMolecule.
*
* @param drawVisitor
* the visitor to draw with
* @param diagram
* the IRenderingElement tree to render
*/
private void paint(IDrawVisitor drawVisitor,
IRenderingElement diagram) {
if (diagram == null) return;
// cache the diagram for quick-redraw
this.cachedDiagram = diagram;
this.fontManager.setFontName(this.rendererModel.getFontName());
this.fontManager.setFontStyle(this.rendererModel.getFontStyle());
drawVisitor.setFontManager(this.fontManager);
drawVisitor.setTransform(this.transform);
drawVisitor.setRendererModel(this.rendererModel);
diagram.accept(drawVisitor);
}
/**
* Sets the transformation needed to draw the model on the canvas when
* the diagram needs to fit the screen.
*
* @param screenBounds
* the bounding box of the draw area
* @param modelBounds
* the bounding box of the model
* @param bondLength
* the average bond length of the model
* @param reset
* if true, model center will be set to the modelBounds center
* and the scale will be re-calculated
*/
private void setupTransformToFit(Rectangle2D screenBounds,
Rectangle2D modelBounds,
double bondLength,
boolean reset) {
if (screenBounds == null) return;
this.setDrawCenter(
screenBounds.getCenterX(), screenBounds.getCenterY());
this.scale = this.calculateScaleForBondLength(bondLength);
double drawWidth = screenBounds.getWidth();
double drawHeight = screenBounds.getHeight();
double diagramWidth = modelBounds.getWidth() * scale;
double diagramHeight = modelBounds.getHeight() * scale;
this.setZoomToFit(drawWidth, drawHeight, diagramWidth, diagramHeight);
// this controls whether editing a molecule causes it to re-center
// with each change or not
if (reset || rendererModel.isFitToScreen()) {
this.setModelCenter(
modelBounds.getCenterX(), modelBounds.getCenterY());
}
// set the scale in the renderer model for the generators
if (reset) {
this.rendererModel.setScale(scale);
}
this.setup();
}
/**
* Given a bond length for a model, calculate the scale that will transform
* this length to the on screen bond length in RendererModel.
*
* @param modelBondLength
* @param reset
* @return
*/
private double calculateScaleForBondLength(double modelBondLength) {
if (Double.isNaN(modelBondLength) || modelBondLength == 0) {
return DEFAULT_SCALE;
} else {
return this.rendererModel.getBondLength() / modelBondLength;
}
}
private void setup() {
// set the transform
try {
this.transform = new AffineTransform();
this.transform.translate(this.drawCenter.x, this.drawCenter.y);
this.transform.scale(1,-1); // Converts between CDK Y-up & Java2D Y-down coordinate-systems
this.transform.scale(this.scale, this.scale);
this.transform.scale(this.zoom, this.zoom);
this.transform.translate(-this.modelCenter.x, -this.modelCenter.y);
} catch (NullPointerException npe) {
System.err.println(String.format(
"null pointer when setting transform: " +
"drawCenter=%s scale=%s zoom=%s modelCenter=%s",
this.drawCenter,
this.scale,
this.zoom,
this.modelCenter));
}
}
protected IRenderingElement generateDiagram(IAtomContainer ac) {
ElementGroup diagram = new ElementGroup();
for (IGenerator<IAtomContainer> generator : this.generators) {
IRenderingElement element = generator.generate(ac, this.rendererModel);
if(!(element instanceof TextGroupElement) || ((TextGroupElement)element).children.size()>0)
diagram.add(element);
}
//Rgroup stuff (not contained in the ac)
return diagram;
}
public List<IGenerator<IAtomContainer>> getGenerators(){
return new ArrayList<IGenerator<IAtomContainer>>(generators);
}
/**
* Gets the currently used FontManager.
*
* @return The currently used FontManager.
*/
public IFontManager getFontManager() {
return fontManager;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/JChemPaintRendererModel.java
|
.java
| 31,772
| 954
|
/* Copyright (C) 2008-2009 Gilleain Torrance <gilleain@users.sf.net>
* 2008-2009 Arvid Berg <goglepox@users.sf.net>
* 2009 Stefan Kuhn <shk3@users.sf.net>
*
* Contact: cdk-devel@lists.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
* All we ask is that proper credit is given for our work, which includes
* - but is not limited to - adding the above copyright notice to the beginning
* of your source code files, and to any copyright notice that you may distribute
* with programs based on this work.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
* */
package org.openscience.jchempaint.renderer;
import org.openscience.cdk.CDKConstants;
import org.openscience.cdk.interfaces.IAtom;
import org.openscience.cdk.interfaces.IBond;
import org.openscience.cdk.interfaces.IChemObject;
import org.openscience.cdk.renderer.RendererModel;
import org.openscience.cdk.renderer.color.IAtomColorer;
import org.openscience.cdk.renderer.elements.Bounds;
import org.openscience.cdk.renderer.elements.RectangleElement;
import org.openscience.cdk.renderer.font.IFontManager;
import org.openscience.cdk.renderer.generators.BasicAtomGenerator;
import org.openscience.cdk.renderer.generators.BasicBondGenerator;
import org.openscience.cdk.renderer.generators.BasicSceneGenerator;
import org.openscience.cdk.renderer.generators.RingGenerator;
import org.openscience.cdk.renderer.generators.standard.StandardGenerator;
import org.openscience.cdk.renderer.selection.IChemObjectSelection;
import javax.vecmath.Point2d;
import java.awt.Color;
import java.awt.event.KeyEvent;
import java.io.Serializable;
import java.util.Collection;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Hashtable;
import java.util.Map;
/**
* Model for {@link Renderer} that contains settings for drawing objects.
*
* @cdk.module render
* @cdk.svnrev $Revision$
*/
public class JChemPaintRendererModel extends RendererModel implements Serializable, Cloneable {
private static final long serialVersionUID = -4420308906715213445L;
private RenderingParameters parameters;
private boolean rotating = false;
private Map<Integer, Boolean> flags = new HashMap<Integer, Boolean>();
/**
* The color hash is used to color substructures.
*
* @see #getColorHash()
*/
private Map<IChemObject, Color> colorHash =
new Hashtable<IChemObject, Color>();
/**
* Constructor for the RendererModel.
*/
public JChemPaintRendererModel() {
this.parameters = new RenderingParameters();
}
/**
* Constructor for the RendererModel.
*
* @param parameters
* @param useUserSettings Should user setting (in $HOME/.jchempaint/properties) be used or not?
*/
public JChemPaintRendererModel(RenderingParameters parameters, boolean useUserSettings) {
this.parameters = parameters;
}
public Point2d getSelectionRotateControl() {
return getSelection() != null && getSelection().isFilled()
? determineRotateControlPoint(getSelectionBounds())
: null;
}
public RectangleElement getSelectionBounds() {
return determineSelectionBounds(getSelection());
}
private Point2d determineRotateControlPoint(RectangleElement bounds) {
double centerX = bounds.xCoord + (bounds.width/2);
return new Point2d(centerX, bounds.yCoord + 3*(getHighlightDistance()/getScale()/getZoomFactor()));
}
private RectangleElement determineSelectionBounds(IChemObjectSelection selection) {
if (selection == null) return null;
Collection<IAtom> atoms = new HashSet<>(selection.elements(IAtom.class));
for (IBond bond : selection.elements(IBond.class)) {
atoms.add(bond.getBegin());
atoms.add(bond.getEnd());
}
Bounds bounds = new Bounds();
for (IAtom atom : atoms) {
// check if we have the actual bounds
Bounds atomBounds = atom.getProperty(CDKConstants.RENDER_BOUNDS);
if (atomBounds != null) {
bounds.add(atomBounds);
} else {
Point2d p = atom.getPoint2d();
bounds.add(p.x, p.y);
}
}
// if the selection is small, pad some width/height
if (bounds.width() < 0.01 || bounds.height() < 0.01) {
double pad = (getHighlightDistance()/getScale()/getZoomFactor());
for (IAtom a : atoms) {
bounds.add(a.getPoint2d().x - pad, a.getPoint2d().y - pad);
bounds.add(a.getPoint2d().x - pad, a.getPoint2d().y + pad);
bounds.add(a.getPoint2d().x + pad, a.getPoint2d().y - pad);
bounds.add(a.getPoint2d().x + pad, a.getPoint2d().y + pad);
}
}
return new RectangleElement(bounds.minX, bounds.maxY, bounds.width(), -bounds.height(),
false, getSelectedPartColor());
}
@Override
public void setSelection(IChemObjectSelection selection) {
super.setSelection(selection);
}
public int getArrowHeadWidth() {
if (hasParameter(BasicSceneGenerator.ArrowHeadWidth.class))
return get(BasicSceneGenerator.ArrowHeadWidth.class).intValue();
return new BasicSceneGenerator.ArrowHeadWidth().getDefault().intValue();
}
public void setArrowHeadWidth(int arrowHeadWidth) {
if (hasParameter(BasicSceneGenerator.ArrowHeadWidth.class))
set(BasicSceneGenerator.ArrowHeadWidth.class, (double) arrowHeadWidth);
}
public boolean getHighlightShapeFilled() {
return this.parameters.isHighlightShapeFilled();
}
public void setHighlightShapeFilled(boolean highlightShapeFilled) {
this.parameters.setHighlightShapeFilled(highlightShapeFilled);
}
public RenderingParameters.AtomShape getHighlightBondShape() {
return this.parameters.getHighlightBondShape();
}
public double getWedgeWidth() {
if (hasParameter(StandardGenerator.WedgeRatio.class))
return get(StandardGenerator.WedgeRatio.class);
return new StandardGenerator.WedgeRatio().getDefault();
}
public void setWedgeWidth(double wedgeWidth) {
if (hasParameter(StandardGenerator.WedgeRatio.class))
set(StandardGenerator.WedgeRatio.class, wedgeWidth);
}
public double getHashSpacing() {
if (hasParameter(StandardGenerator.HashSpacing.class))
return get(StandardGenerator.HashSpacing.class);
return new StandardGenerator.HashSpacing().getDefault();
}
public void setHashSpacing(double wedgeWidth) {
if (hasParameter(StandardGenerator.HashSpacing.class))
set(StandardGenerator.HashSpacing.class, wedgeWidth);
}
public double getRingProportion() {
if (hasParameter(BasicBondGenerator.TowardsRingCenterProportion.class))
return get(BasicBondGenerator.TowardsRingCenterProportion.class);
return new BasicBondGenerator.TowardsRingCenterProportion().getDefault();
}
public void setRingProportion(double ringProportion) {
if (hasParameter(BasicBondGenerator.TowardsRingCenterProportion.class))
set(BasicBondGenerator.TowardsRingCenterProportion.class, ringProportion);
}
public BasicAtomGenerator.Shape getCompactShape() {
if (hasParameter(BasicAtomGenerator.CompactShape.class))
return get(BasicAtomGenerator.CompactShape.class);
return new BasicAtomGenerator.CompactShape().getDefault();
}
public void setCompactShape(BasicAtomGenerator.Shape compactShape) {
if (hasParameter(BasicAtomGenerator.CompactShape.class))
set(BasicAtomGenerator.CompactShape.class, compactShape);
}
public double getScale() {
if (hasParameter(BasicSceneGenerator.Scale.class))
return super.get(BasicSceneGenerator.Scale.class);
return new BasicSceneGenerator.Scale().getDefault();
}
public void setScale(double scale) {
if (hasParameter(BasicSceneGenerator.Scale.class))
super.set(BasicSceneGenerator.Scale.class, scale);
}
public RenderingParameters.AtomShape getSelectionShape() {
return this.parameters.getSelectionShape();
}
public void setSelectionShape(RenderingParameters.AtomShape selectionShape) {
this.parameters.setSelectionShape(selectionShape);
}
/**
* Get the name of the font family (Arial, etc).
*
* @return the name of the font family as a String.
*/
public String getFontName() {
if (hasParameter(BasicSceneGenerator.FontName.class))
return get(BasicSceneGenerator.FontName.class);
return new BasicSceneGenerator.FontName().getDefault();
}
/**
* Set the name of the font family (Arial, etc).
*/
public void setFontName(String fontName) {
if (hasParameter(BasicSceneGenerator.FontName.class))
set(BasicSceneGenerator.FontName.class, fontName);
}
/**
* Get the style of the font (Normal, Bold).
*
* @return the style of the font as a member of the IFontManager.FontStyle
* enum
*/
public IFontManager.FontStyle getFontStyle() {
if (hasParameter(BasicSceneGenerator.UsedFontStyle.class))
return get(BasicSceneGenerator.UsedFontStyle.class);
return new BasicSceneGenerator.UsedFontStyle().getDefault();
}
/**
* Set the style of font to use (Normal, Bold).
*
* @param fontStyle a member of the enum in {@link IFontManager}
*/
public void setFontManager(IFontManager.FontStyle fontStyle) {
if (hasParameter(BasicSceneGenerator.UsedFontStyle.class))
set(BasicSceneGenerator.UsedFontStyle.class, fontStyle);
}
public boolean getIsCompact() {
if (hasParameter(BasicAtomGenerator.CompactAtom.class))
return get(BasicAtomGenerator.CompactAtom.class);
return new BasicAtomGenerator.CompactAtom().getDefault();
}
public void setIsCompact(boolean compact) {
if (hasParameter(BasicAtomGenerator.CompactAtom.class))
set(BasicAtomGenerator.CompactAtom.class, compact);
}
public boolean getUseAntiAliasing() {
if (hasParameter(BasicSceneGenerator.UseAntiAliasing.class))
return get(BasicSceneGenerator.UseAntiAliasing.class);
return new BasicSceneGenerator.UseAntiAliasing().getDefault();
}
public void setUseAntiAliasing(boolean bool) {
if (hasParameter(BasicSceneGenerator.UseAntiAliasing.class))
set(BasicSceneGenerator.UseAntiAliasing.class, bool);
}
public boolean getShowReactionBoxes() {
return this.parameters.isShowReactionBoxes();
}
public void setShowReactionBoxes(boolean bool) {
this.parameters.setShowReactionBoxes(bool);
fireChange();
}
public boolean getShowMoleculeTitle() {
if (hasParameter(BasicSceneGenerator.ShowMoleculeTitle.class))
return get(BasicSceneGenerator.ShowMoleculeTitle.class);
return new BasicSceneGenerator.ShowMoleculeTitle().getDefault();
}
public void setShowMoleculeTitle(boolean bool) {
if (hasParameter(BasicSceneGenerator.ShowMoleculeTitle.class))
set(BasicSceneGenerator.ShowMoleculeTitle.class, bool);
}
/**
* The length on the screen of a typical bond.
*
* @return the user-selected length of a bond, or the default length.
*/
public double getBondLength() {
if (hasParameter(BasicSceneGenerator.BondLength.class))
return get(BasicSceneGenerator.BondLength.class);
return new BasicSceneGenerator.BondLength().getDefault();
}
/**
* Set the length on the screen of a typical bond.
*
* @param length the length in pixels of a typical bond.
*
*/
public void setBondLength(double length) {
if (hasParameter(BasicSceneGenerator.BondLength.class))
set(BasicSceneGenerator.BondLength.class, length);
}
/**
* Returns the distance between two lines in a double or triple bond
*
* @return the distance between two lines in a double or triple bond
*/
public double getBondDistance() {
if (hasParameter(BasicBondGenerator.BondDistance.class))
return get(BasicBondGenerator.BondDistance.class);
return new BasicBondGenerator.BondDistance().getDefault();
}
/**
* Sets the distance between two lines in a double or triple bond
*
* @param bondDistance
* the distance between two lines in a double or triple bond
*/
public void setBondDistance(double bondDistance) {
if (hasParameter(BasicBondGenerator.BondDistance.class))
set(BasicBondGenerator.BondDistance.class, bondDistance);
}
/**
* Returns the thickness of a bond line.
*
* @return the thickness of a bond line
*/
public double getBondWidth() {
if (hasParameter(StandardGenerator.StrokeRatio.class))
return get(StandardGenerator.StrokeRatio.class);
return new StandardGenerator.StrokeRatio().getDefault();
}
/**
* Sets the thickness of a bond line.
*
* @param bondWidth
* the thickness of a bond line
*/
public void setBondWidth(double bondWidth) {
if (hasParameter(StandardGenerator.StrokeRatio.class))
set(StandardGenerator.StrokeRatio.class, bondWidth);
}
/**
* Returns the thickness of a bond line.
*
* @return the thickness of a bond line
*/
public double getBondSeparation() {
if (hasParameter(StandardGenerator.BondSeparation.class))
return get(StandardGenerator.BondSeparation.class);
return new StandardGenerator.BondSeparation().getDefault();
}
/**
* Sets the thickness of a bond line.
*
* @param bondSeparation
* the thickness of a bond line
*/
public void setBondSeparation(double bondSeparation) {
if (hasParameter(StandardGenerator.BondSeparation.class))
set(StandardGenerator.BondSeparation.class, bondSeparation);
}
/**
* Returns the thickness of an atom atom mapping line.
*
* @return the thickness of an atom atom mapping line
*/
public double getMappingLineWidth() {
return this.parameters.getMappingLineWidth();
}
/**
* Sets the thickness of an atom atom mapping line.
*
* @param mappingLineWidth
* the thickness of an atom atom mapping line
*/
public void setMappingLineWidth(double mappingLineWidth) {
this.parameters.setMappingLineWidth(mappingLineWidth);
fireChange();
}
/**
* A zoom factor for the drawing.
*
* @return a zoom factor for the drawing
*/
public double getZoomFactor() {
if (hasParameter(BasicSceneGenerator.ZoomFactor.class))
return get(BasicSceneGenerator.ZoomFactor.class);
return new BasicSceneGenerator.ZoomFactor().getDefault();
}
/**
* Returns the zoom factor for the drawing.
*
* @param zoomFactor
* the zoom factor for the drawing
*/
public void setZoomFactor(double zoomFactor) {
if (hasParameter(BasicSceneGenerator.ZoomFactor.class))
set(BasicSceneGenerator.ZoomFactor.class, zoomFactor);
}
public boolean isFitToScreen() {
if (hasParameter(BasicSceneGenerator.FitToScreen.class))
return get(BasicSceneGenerator.FitToScreen.class);
return new BasicSceneGenerator.FitToScreen().getDefault();
}
public void setFitToScreen(boolean value) {
if (hasParameter(BasicSceneGenerator.FitToScreen.class))
set(BasicSceneGenerator.FitToScreen.class, value);
}
/**
* Returns the foreground color for the drawing.
*
* @return the foreground color for the drawing
*/
public Color getForeColor() {
if (hasParameter(BasicSceneGenerator.ForegroundColor.class))
return get(BasicSceneGenerator.ForegroundColor.class);
return new BasicSceneGenerator.ForegroundColor().getDefault();
}
/**
* Sets the foreground color with which bonds and atoms are drawn
*
* @param foreColor
* the foreground color with which bonds and atoms are drawn
*/
public void setForeColor(Color foreColor) {
if (hasParameter(BasicSceneGenerator.ForegroundColor.class))
set(BasicSceneGenerator.ForegroundColor.class, foreColor);
}
/**
* Returns the background color
*
* @return the background color
*/
public Color getBackColor() {
if (hasParameter(BasicSceneGenerator.BackgroundColor.class))
return get(BasicSceneGenerator.BackgroundColor.class);
return new BasicSceneGenerator.BackgroundColor().getDefault();
}
/**
* Sets the background color
*
* @param backColor
* the background color
*/
public void setBackColor(Color backColor) {
if (hasParameter(BasicSceneGenerator.BackgroundColor.class))
set(BasicSceneGenerator.BackgroundColor.class, backColor);
}
/**
* Returns the atom-atom mapping line color
*
* @return the atom-atom mapping line color
*/
public Color getAtomAtomMappingLineColor() {
return this.parameters.getMappingColor();
}
/**
* Sets the atom-atom mapping line color
*
* @param mappingColor
* the atom-atom mapping line color
*/
public void setAtomAtomMappingLineColor(Color mappingColor) {
this.parameters.setMappingColor(mappingColor);
fireChange();
}
/**
* Returns if the drawing of atom numbers is switched on for this model
*
* @return true if the drawing of atom numbers is switched on for this model
*/
public boolean drawNumbers() {
return this.parameters.isWillDrawNumbers();
}
public boolean getKekuleStructure() {
if (hasParameter(BasicAtomGenerator.KekuleStructure.class))
return get(BasicAtomGenerator.KekuleStructure.class);
return new BasicAtomGenerator.KekuleStructure().getDefault();
}
public void setKekuleStructure(boolean kekule) {
if (hasParameter(BasicAtomGenerator.KekuleStructure.class))
set(BasicAtomGenerator.KekuleStructure.class, kekule);
}
public boolean getColorAtomsByType() {
if (hasParameter(BasicAtomGenerator.ColorByType.class))
return get(BasicAtomGenerator.ColorByType.class);
return new BasicAtomGenerator.ColorByType().getDefault();
}
public void setColorAtomsByType(boolean bool) {
if (hasParameter(BasicAtomGenerator.ColorByType.class))
set(BasicAtomGenerator.ColorByType.class, bool);
}
public boolean getShowEndCarbons() {
return this.parameters.isShowEndCarbons();
}
public void setShowEndCarbons(boolean showThem) {
this.parameters.setShowEndCarbons(showThem);
fireChange();
}
public boolean getShowImplicitHydrogens() {
return this.parameters.isShowImplicitHydrogens();
}
public void setShowImplicitHydrogens(boolean showThem) {
this.parameters.setShowImplicitHydrogens(showThem);
fireChange();
}
public boolean getShowExplicitHydrogens() {
return this.parameters.isShowExplicitHydrogens();
}
public void setShowExplicitHydrogens(boolean showThem) {
this.parameters.setShowExplicitHydrogens(showThem);
fireChange();
}
public boolean getShowAromaticity() {
if (hasParameter(RingGenerator.ShowAromaticity.class))
return get(RingGenerator.ShowAromaticity.class);
return new RingGenerator.ShowAromaticity().getDefault();
}
public void setShowAromaticity(boolean showIt) {
if (hasParameter(RingGenerator.ShowAromaticity.class))
set(RingGenerator.ShowAromaticity.class, showIt);
}
/**
* Sets if the drawing of atom numbers is switched on for this model.
*
* @param drawNumbers
* true if the drawing of atom numbers is to be switched on for
* this model
*/
public void setDrawNumbers(boolean drawNumbers) {
this.parameters.setWillDrawNumbers(drawNumbers);
fireChange();
}
/**
* Returns true if atom numbers are drawn.
*/
public boolean getDrawNumbers() {
return this.parameters.isWillDrawNumbers();
}
public Color getDefaultBondColor() {
if (hasParameter(BasicBondGenerator.DefaultBondColor.class))
return get(BasicBondGenerator.DefaultBondColor.class);
return new BasicBondGenerator.DefaultBondColor().getDefault();
}
public void setDefaultBondColor(Color defaultBondColor) {
if (hasParameter(BasicBondGenerator.DefaultBondColor.class))
set(BasicBondGenerator.DefaultBondColor.class, defaultBondColor);
}
/**
* Returns the radius around an atoms, for which the atom is marked
* highlighted if a pointer device is placed within this radius.
*
* @return The highlight distance for all atoms (in screen space)
*/
public double getHighlightDistance() {
return this.parameters.getHighlightDistance();
}
/**
* Sets the radius around an atoms, for which the atom is marked highlighted
* if a pointer device is placed within this radius.
*
* @param highlightDistance
* the highlight radius of all atoms (in screen space)
*/
public void setHighlightDistance(double highlightDistance) {
this.parameters.setHighlightDistance(highlightDistance);
fireChange();
}
/**
* Returns whether Atom-Atom mapping must be shown.
*/
public boolean getShowAtomAtomMapping() {
return this.parameters.isShowAtomAtomMapping();
}
/**
* Sets whether Atom-Atom mapping must be shown.
*/
public void setShowAtomAtomMapping(boolean value) {
this.parameters.setShowAtomAtomMapping(value);
fireChange();
}
/**
* This is used for the size of the compact atom element.
*/
public double getAtomRadius() {
if (hasParameter(BasicAtomGenerator.AtomRadius.class))
return get(BasicAtomGenerator.AtomRadius.class);
return new BasicAtomGenerator.AtomRadius().getDefault();
}
/**
* Set the radius of the compact atom representation.
*
* @param atomRadius the size of the compact atom symbol.
*
*/
public void setAtomRadius(double atomRadius) {
if (hasParameter(BasicAtomGenerator.AtomRadius.class))
set(BasicAtomGenerator.AtomRadius.class, atomRadius);
}
/**
* Returns the {@link Map} used for coloring substructures.
*
* @return the {@link Map} used for coloring substructures
*/
public Map<IChemObject, Color> getColorHash() {
return this.colorHash;
}
/**
* Returns the background color of the given atom.
*/
public Color getAtomBackgroundColor(IAtom atom) {
// logger.debug("Getting atom back color for " + atom.toString());
Color atomColor = getBackColor();
// logger.debug(" BackColor: " + atomColor.toString());
Color hashColor = (Color) this.getColorHash().get(atom);
if (hashColor != null) {
// logger.debug(
// "Background color atom according to hashing (substructure)");
atomColor = hashColor;
}
// logger.debug("Color: " + atomColor.toString());
return atomColor;
}
/**
* Returns the current atom colorer.
*
* @return The AtomColorer.
*/
public IAtomColorer getAtomColorer() {
if (hasParameter(BasicAtomGenerator.AtomColorer.class))
return get(BasicAtomGenerator.AtomColorer.class);
return new BasicAtomGenerator.AtomColorer().getDefault();
}
/**
* Sets the atom colorer.
*
* @param atomColorer
* the new colorer.
*/
public void setAtomColorer(final IAtomColorer atomColorer) {
if (hasParameter(BasicAtomGenerator.AtomColorer.class))
set(BasicAtomGenerator.AtomColorer.class, atomColorer);
}
/**
* Sets the {@link Map} used for coloring substructures
*
* @param colorHash
* the {@link Map} used for coloring substructures
*/
public void setColorHash(Map<IChemObject, Color> colorHash) {
this.colorHash = colorHash;
fireChange();
}
/**
* Sets the showTooltip attribute.
*
* @param showTooltip
* The new value.
*/
public void setShowTooltip(boolean showTooltip) {
if (hasParameter(BasicSceneGenerator.ShowTooltip.class))
set(BasicSceneGenerator.ShowTooltip.class, showTooltip);
}
/**
* Gets showTooltip attribute.
*
* @return The showTooltip value.
*/
public boolean getShowTooltip() {
if (hasParameter(BasicSceneGenerator.ShowTooltip.class))
return get(BasicSceneGenerator.ShowTooltip.class);
return new BasicSceneGenerator.ShowTooltip().getDefault();
}
/**
* Gets the color used for drawing the part which was selected externally
*/
public Color getExternalHighlightColor() {
return this.parameters.getExternalHighlightColor();
}
/**
* Sets the color used for drawing the part which was selected externally
*
* @param externalHighlightColor
* The color
*/
public void setExternalHighlightColor(Color externalHighlightColor) {
this.parameters.setExternalHighlightColor(externalHighlightColor);
}
/**
* Gets the color used for drawing the part we are hovering over.
*/
public Color getHoverOverColor() {
return this.parameters.getHoverOverColor();
}
/**
* Sets the color used for drawing the part we are hovering over.
*
* @param hoverOverColor
* The color
*/
public void setHoverOverColor(Color hoverOverColor) {
this.parameters.setHoverOverColor(hoverOverColor);
}
/**
* Gets the color used for drawing the internally selected part.
*/
public Color getSelectedPartColor() {
return this.parameters.getSelectedPartColor();
}
/**
* Sets the color used for drawing the internally selected part.
*
* @param selectedPartColor
* The color
*/
public void setSelectedPartColor(Color selectedPartColor) {
this.parameters.setSelectedPartColor(selectedPartColor);
}
public boolean showAtomTypeNames() {
return this.parameters.isShowAtomTypeNames();
}
public void setShowAtomTypeNames(boolean showAtomTypeNames) {
this.parameters.setShowAtomTypeNames(showAtomTypeNames);
}
public double getMargin() {
if (hasParameter(BasicSceneGenerator.Margin.class))
return get(BasicSceneGenerator.Margin.class);
return new BasicSceneGenerator.Margin().getDefault();
}
public void setMargin(double margin) {
if (hasParameter(BasicSceneGenerator.Margin.class))
set(BasicSceneGenerator.Margin.class, margin);
}
public Color getBoundsColor() {
return this.parameters.getBoundsColor();
}
public void setBoundsColor(Color color) {
this.parameters.setBoundsColor(color);
}
/**
* @return the on screen radius of the selection element
*/
public double getSelectionRadius() {
return this.parameters.getSelectionRadius();
}
public void setSelectionRadius(double selectionRadius) {
this.parameters.setSelectionRadius(selectionRadius);
}
/**
* @return The color used for underlining not typeable atoms.
*/
public Color getNotTypeableUnderlineColor(){
return this.parameters.getNotTypeableUnderlineColor();
}
/**
*
* @param identifier
* @return
*/
public boolean getFlag(int identifier) {
return flags.get(identifier);
}
/**
*
* @return
*/
public Map<Integer, Boolean> getFlags() {
return flags;
}
/**
*
* @param identifier
* @param flag
*/
public void setFlag(int identifier, boolean flag) {
flags.remove(identifier);
flags.put(identifier, flag);
}
/**
* Get the highlighted atom or bond.
* @return the highlighted atom or bond.
*/
IChemObject getHighlight() {
return getHighlightedAtom() != null ? getHighlightedAtom()
: getHighlightedBond();
}
/**
* Set the highlighted atom or bond.
* @return the highlighted atom or bond.
*/
void setHighlight(IChemObject chemObject) {
if (chemObject instanceof IAtom) {
setHighlightedAtom((IAtom) chemObject);
} else if (chemObject instanceof IBond) {
setHighlightedBond((IBond) chemObject);
}
}
/**
* Change the highlighted atom/bond with arrow keys.
*/
public void moveHighlight(int key) {
IChemObject hotspot = getHighlight();
if (hotspot instanceof IAtom) {
Point2d p = new Point2d(((IAtom) hotspot).getPoint2d());
switch (key) {
case KeyEvent.VK_UP: p.y++; break;
case KeyEvent.VK_DOWN: p.y--; break;
case KeyEvent.VK_LEFT: p.x--; break;
case KeyEvent.VK_RIGHT: p.x++; break;
}
IBond best = null;
double bestDist = Double.NaN;
for (IBond bond : ((IAtom) hotspot).bonds()) {
double dist = bond.get2DCenter().distanceSquared(p);
if (best == null || dist <= bestDist) {
best = bond;
bestDist = dist;
}
}
if (best != null)
setHighlight(best);
}
else if (hotspot instanceof IBond) {
IBond bond = (IBond) hotspot;
Point2d p = new Point2d(bond.get2DCenter());
switch (key) {
case KeyEvent.VK_UP: p.y++; break;
case KeyEvent.VK_DOWN: p.y--; break;
case KeyEvent.VK_LEFT: p.x--; break;
case KeyEvent.VK_RIGHT: p.x++; break;
}
if (bond.getBegin().getPoint2d().distanceSquared(p) <
bond.getEnd().getPoint2d().distanceSquared(p))
setHighlight(bond.getBegin());
else
setHighlight(bond.getEnd());
}
}
public void setRotating(boolean b) {
rotating = b;
}
public boolean isRotating() {
return rotating;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/BoundsCalculator.java
|
.java
| 4,817
| 126
|
/* Copyright (C) 2008-2009 Gilleain Torrance <gilleain@users.sf.net>
*
* Contact: cdk-devel@lists.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
* All I ask is that proper credit is given for my work, which includes
* - but is not limited to - adding the above copyright notice to the beginning
* of your source code files, and to any copyright notice that you may distribute
* with programs based on this work.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer;
import java.awt.geom.Rectangle2D;
import javax.vecmath.Point2d;
import org.openscience.cdk.interfaces.IAtom;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.interfaces.IChemModel;
import org.openscience.cdk.interfaces.IAtomContainerSet;
import org.openscience.cdk.interfaces.IReaction;
import org.openscience.cdk.interfaces.IReactionSet;
/**
* @cdk.module renderbasic
*/
public class BoundsCalculator {
public static Rectangle2D calculateBounds(IChemModel chemModel) {
IAtomContainerSet moleculeSet = chemModel.getMoleculeSet();
IReactionSet reactionSet = chemModel.getReactionSet();
Rectangle2D totalBounds = null;
if (moleculeSet != null) {
totalBounds = calculateBounds(moleculeSet);
}
if (reactionSet != null) {
if (totalBounds == null) {
totalBounds = calculateBounds(reactionSet);
} else {
totalBounds = totalBounds.createUnion(
calculateBounds(reactionSet));
}
}
return totalBounds;
}
public static Rectangle2D calculateBounds(IReactionSet reactionSet) {
Rectangle2D totalBounds = new Rectangle2D.Double();
for (IReaction reaction : reactionSet.reactions()) {
Rectangle2D reactionBounds = calculateBounds(reaction);
if (totalBounds.isEmpty()) {
totalBounds = reactionBounds;
} else {
Rectangle2D.union(totalBounds, reactionBounds, totalBounds);
}
}
return totalBounds;
}
public static Rectangle2D calculateBounds(IReaction reaction) {
// get the participants in the reaction
IAtomContainerSet reactants = reaction.getReactants();
IAtomContainerSet products = reaction.getProducts();
if (reactants == null || products == null) return null;
// determine the bounds of everything in the reaction
Rectangle2D reactantsBounds = calculateBounds(reactants);
return reactantsBounds.createUnion(calculateBounds(products));
}
public static Rectangle2D calculateBounds(IAtomContainerSet moleculeSet) {
Rectangle2D totalBounds = new Rectangle2D.Double();
for (int i = 0; i < moleculeSet.getAtomContainerCount(); i++) {
IAtomContainer ac = moleculeSet.getAtomContainer(i);
Rectangle2D acBounds = calculateBounds(ac);
if (totalBounds.isEmpty()) {
totalBounds = acBounds;
} else {
Rectangle2D.union(totalBounds, acBounds, totalBounds);
}
}
return totalBounds;
}
public static Rectangle2D calculateBounds(IAtomContainer ac) {
// this is essential, otherwise a rectangle
// of (+INF, -INF, +INF, -INF) is returned!
if (ac==null || ac.getAtomCount() == 0) {
return new Rectangle2D.Double();
} else if (ac.getAtomCount() == 1) {
Point2d p = ac.getAtom(0).getPoint2d();
return new Rectangle2D.Double(p.x, p.y, 0, 0);
}
double xmin = Double.POSITIVE_INFINITY;
double xmax = Double.NEGATIVE_INFINITY;
double ymin = Double.POSITIVE_INFINITY;
double ymax = Double.NEGATIVE_INFINITY;
for (IAtom atom : ac.atoms()) {
Point2d p = atom.getPoint2d();
xmin = Math.min(xmin, p.x);
xmax = Math.max(xmax, p.x);
ymin = Math.min(ymin, p.y);
ymax = Math.max(ymax, p.y);
}
double w = xmax - xmin;
double h = ymax - ymin;
return new Rectangle2D.Double(xmin, ymin, w, h);
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/RenderingParameters.java
|
.java
| 7,025
| 250
|
/* Copyright (C) 2009 Gilleain Torrance <gilleain@users.sf.net>
* 2009 Arvid Berg <goglepox@users.sf.net>
* 2009 Egon Willighagen <egonw@users.sf.net>
* 2009 Stefan Kuhn <shk3@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer;
import java.awt.Color;
/**
* @cdk.module render
*/
public class RenderingParameters {
/**
* When atoms are selected or in compact mode, they will
* be covered by a shape determined by this enumeration
*/
public enum AtomShape { OVAL, SQUARE, NONE};
/**
* The color used for underlining not typeable atoms.
*/
private Color notTypeableUnderlineColor = Color.red;
/**
* The width on screen of an atom-atom mapping line
*/
private double mappingLineWidth = 1.0;
/**
* The color of the box drawn at the bounds of a
* molecule, molecule set, or reaction
*/
private Color boundsColor = Color.LIGHT_GRAY;
private Color externalHighlightColor = Color.RED;
private Color hoverOverColor = new Color(112, 181, 235); // light blue
/**
* The maximum distance on the screen the mouse pointer has to be to
* highlight an element.
*/
private double highlightDistance;
private boolean highlightShapeFilled = true;
private Color mappingColor = Color.gray;
private Color selectedPartColor = new Color(112, 181, 235); // light blue
/**
* The shape to display over selected atoms
*/
private AtomShape selectionShape = AtomShape.NONE;
/**
* The radius on screen of the selection shape
*/
private double selectionRadius = 3;
private boolean showAtomAtomMapping = true;
private boolean showAtomTypeNames = false;
/**
* Determines whether methyl carbons' symbols should be drawn explicit for
* methyl carbons. Example C/\C instead of /\.
*/
private boolean showEndCarbons;
/** Determines whether explicit hydrogens should be drawn. */
private boolean showExplicitHydrogens;
/** Determines whether implicit hydrogens should be drawn. */
private boolean showImplicitHydrogens;
private boolean showReactionBoxes = false;
private boolean willDrawNumbers;
private AtomShape highlightBondShape = AtomShape.SQUARE;
public boolean isHighlightShapeFilled() {
return highlightShapeFilled;
}
public void setHighlightShapeFilled(boolean highlightShapeFilled) {
this.highlightShapeFilled = highlightShapeFilled;
}
public double getHighlightDistance() {
return highlightDistance;
}
public void setHighlightDistance(double highlightDistance) {
this.highlightDistance = highlightDistance;
}
public AtomShape getHighlightBondShape() {
return highlightBondShape;
}
public AtomShape getSelectionShape() {
return this.selectionShape;
}
public void setSelectionShape(AtomShape selectionShape) {
this.selectionShape = selectionShape;
}
public double getMappingLineWidth() {
return mappingLineWidth;
}
public Color getExternalHighlightColor() {
return externalHighlightColor;
}
public Color getHoverOverColor() {
return hoverOverColor;
}
public Color getMappingColor() {
return mappingColor;
}
public Color getSelectedPartColor() {
return selectedPartColor;
}
public boolean isShowAtomAtomMapping() {
return showAtomAtomMapping;
}
public boolean isShowAtomTypeNames() {
return showAtomTypeNames;
}
public boolean isShowEndCarbons() {
return showEndCarbons;
}
public boolean isShowExplicitHydrogens() {
return showExplicitHydrogens;
}
public boolean isShowImplicitHydrogens() {
return showImplicitHydrogens;
}
public boolean isShowReactionBoxes() {
return showReactionBoxes;
}
public boolean isWillDrawNumbers() {
return willDrawNumbers;
}
public void setMappingLineWidth(double mappingLineWidth) {
this.mappingLineWidth = mappingLineWidth;
}
public void setExternalHighlightColor(Color externalHighlightColor) {
this.externalHighlightColor = externalHighlightColor;
}
public void setHoverOverColor(Color hoverOverColor) {
this.hoverOverColor = hoverOverColor;
}
public void setMappingColor(Color mappingColor) {
this.mappingColor = mappingColor;
}
public void setSelectedPartColor(Color selectedPartColor) {
this.selectedPartColor = selectedPartColor;
}
public void setShowAtomAtomMapping(boolean showAtomAtomMapping) {
this.showAtomAtomMapping = showAtomAtomMapping;
}
public void setShowAtomTypeNames(boolean showAtomTypeNames) {
this.showAtomTypeNames = showAtomTypeNames;
}
public void setShowEndCarbons(boolean showEndCarbons) {
this.showEndCarbons = showEndCarbons;
}
public void setShowExplicitHydrogens(boolean showExplicitHydrogens) {
this.showExplicitHydrogens = showExplicitHydrogens;
}
public void setShowImplicitHydrogens(boolean showImplicitHydrogens) {
this.showImplicitHydrogens = showImplicitHydrogens;
}
public void setShowReactionBoxes(boolean showReactionBoxes) {
this.showReactionBoxes = showReactionBoxes;
}
public void setWillDrawNumbers(boolean willDrawNumbers) {
this.willDrawNumbers = willDrawNumbers;
}
public Color getBoundsColor() {
return this.boundsColor;
}
public void setBoundsColor(Color color) {
this.boundsColor = color;
}
public double getSelectionRadius() {
return this.selectionRadius;
}
public void setSelectionRadius(double selectionRadius) {
this.selectionRadius = selectionRadius;
}
public Color getNotTypeableUnderlineColor() {
return notTypeableUnderlineColor;
}
public void setNotTypeableUnderlineColor(Color notTypeableUnderlineColor) {
this.notTypeableUnderlineColor = notTypeableUnderlineColor;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/IRenderer.java
|
.java
| 2,738
| 84
|
/* Copyright (C) 2009 Egon Willighagen <egonw@users.lists.sf>
*
* Contact: cdk-devel@lists.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
* All I ask is that proper credit is given for my work, which includes
* - but is not limited to - adding the above copyright notice to the beginning
* of your source code files, and to any copyright notice that you may distribute
* with programs based on this work.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer;
import java.util.List;
import javax.vecmath.Point2d;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.renderer.generators.IGenerator;
/**
* Interface that all 2D renderers implement.
*
* @cdk.module render
*/
public interface IRenderer {
/**
* Returns the drawing model, giving access to drawing parameters.
*
* @return the rendering model
*/
public JChemPaintRendererModel getRenderer2DModel();
/**
* Converts screen coordinates into model (or world) coordinates.
*
* @param screenXTo the screen's x coordinate
* @param screenYTo the screen's y coordinate
* @return the matching model coordinates
*
* @see #toScreenCoordinates(double, double)
*/
public Point2d toModelCoordinates(double screenXTo, double screenYTo);
/**
* Converts model (or world) coordinates into screen coordinates.
*
* @param screenXTo the model's x coordinate
* @param screenYTo the model's y coordinate
* @return the matching screen coordinates
*
* @see #toModelCoordinates(double, double)
*/
public Point2d toScreenCoordinates(double screenXTo, double screenYTo);
/**
* Set a new zoom factor.
*
* @param zoomFactor the new zoom factor
*/
public void setZoom(double zoomFactor);
/**
* Set a new drawing center in screen coordinates.
*
* @param zoomFactor the new new drawing center
*/
public void shiftDrawCenter(double screenX, double screenY);
public List<IGenerator<IAtomContainer>> getGenerators();
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/Renderer.java
|
.java
| 36,342
| 995
|
/* Copyright (C) 2008-2009 Gilleain Torrance <gilleain.torrance@gmail.com>
* 2008-2009 Arvid Berg <goglepox@users.sf.net>
* 2009 Stefan Kuhn <shk3@users.sf.net>
* 2009 Egon Willighagen <egonw@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer;
import java.awt.Rectangle;
import java.awt.geom.AffineTransform;
import java.awt.geom.Rectangle2D;
import java.util.ArrayList;
import java.util.List;
import javax.vecmath.Point2d;
import org.openscience.cdk.geometry.GeometryUtil;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.interfaces.IChemModel;
import org.openscience.cdk.interfaces.IAtomContainerSet;
import org.openscience.cdk.interfaces.IReaction;
import org.openscience.cdk.interfaces.IReactionSet;
import org.openscience.cdk.renderer.font.IFontManager;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.generators.IGenerator;
import org.openscience.jchempaint.renderer.generators.IReactionGenerator;
import org.openscience.jchempaint.renderer.generators.IReactionSetGenerator;
import org.openscience.jchempaint.renderer.visitor.IDrawVisitor;
/**
* A general renderer for {@link IChemModel}s, {@link IReaction}s, and
* {@link IAtomContainer}s. The chem object
* is converted into a 'diagram' made up of {@link IRenderingElement}s. It takes
* an {@link IDrawVisitor} to do the drawing of the generated diagram. Various
* display properties can be set using the {@link JChemPaintRendererModel}.<p>
*
* This class has several usage patterns. For just painting fit-to-screen do:
* <pre>
* renderer.paintMolecule(molecule, visitor, drawArea)
* </pre>
* for painting at a scale determined by the bond length in the RendererModel:
* <pre>
* if (moleculeIsNew) {
* renderer.setup(molecule, drawArea);
* }
* Rectangle diagramSize = renderer.paintMolecule(molecule, visitor);
* // ...update scroll bars here
* </pre>
* to paint at full screen size, but not resize with each change:
* <pre>
* if (moleculeIsNew) {
* renderer.setScale(molecule);
* Rectangle diagramBounds = renderer.calculateDiagramBounds(molecule);
* renderer.setZoomToFit(diagramBounds, drawArea);
* renderer.paintMolecule(molecule, visitor);
* } else {
* Rectangle diagramSize = renderer.paintMolecule(molecule, visitor);
* // ...update scroll bars here
* }
* </pre>
* finally, if you are scrolling, and have not changed the diagram:
* <pre>
* renderer.repaint(visitor)
* </pre>
* will just repaint the previously generated diagram, at the same scale.<p>
*
* There are two sets of methods for painting IChemObjects - those that take
* a Rectangle that represents the desired draw area, and those that return a
* Rectangle that represents the actual draw area. The first are intended for
* drawing molecules fitted to the screen (where 'screen' means any drawing
* area) while the second type of method are for drawing bonds at the length
* defined by the {@link JChemPaintRendererModel} parameter bondLength.<p>
*
* There are two numbers used to transform the model so that it fits on screen.
* The first is <tt>scale</tt>, which is used to map model coordinates to
* screen coordinates. The second is <tt>zoom</tt> which is used to, well,
* zoom the on screen coordinates. If the diagram is fit-to-screen, then the
* ratio of the bounds when drawn using bondLength and the bounds of
* the screen is used as the zoom.<p>
*
* So, if the bond length on screen is set to 40, and the average bond length
* of the model is 2 (unitless, but roughly Ångstrom scale) then the
* scale will be 20. If the model is 10 units wide, then the diagram drawn at
* 100% zoom will be 10 * 20 = 200 in width on screen. If the screen is 400
* pixels wide, then fitting it to the screen will make the zoom 200%. Since the
* zoom is just a floating point number, 100% = 1 and 200% = 2.
*
* @author maclean
* @cdk.module renderextra
*/
public class Renderer extends AtomContainerRenderer implements IRenderer {
private static final double DEFAULT_BOND_LENGTH = 1.5;
/**
* Generators specific to reactions
*/
private List<IReactionGenerator> reactionGenerators;
private List<IReactionSetGenerator> reactionSetGenerators;
/**
* A renderer that generates diagrams using the specified
* generators and manages fonts with the supplied font manager.
*
* @param generators
* a list of classes that implement the IGenerator interface
* @param fontManager
* a class that manages mappings between zoom and font sizes
* @param useUserSettings Should user setting (in $HOME/.jchempaint/properties) be used or not?
*/
public Renderer(List<IGenerator<IAtomContainer>> generators, IFontManager fontManager) {
super(generators, fontManager);
}
public Renderer(List<IGenerator<IAtomContainer>> generators,
List<IReactionGenerator> reactionGenerators,
IFontManager fontManager) {
this(generators, fontManager);
this.reactionGenerators = reactionGenerators;
this.reactionSetGenerators = new ArrayList<IReactionSetGenerator>();
this.setup();
}
/**
* Setup the transformations necessary to draw this Chem Model.
*
* @param chemModel
* @param screen
*/
public void setup(IChemModel chemModel, Rectangle screen) {
this.setScale(chemModel);
Rectangle2D bounds = Renderer.calculateBounds(chemModel);
this.modelCenter = new Point2d(bounds.getCenterX(), bounds.getCenterY());
this.drawCenter = new Point2d(screen.getCenterX(), screen.getCenterY());
this.setup();
}
/**
* Setup the transformations necessary to draw this Reaction Set.
*
* @param reactionSet
* @param screen
*/
public void setup(IReactionSet reactionSet, Rectangle screen) {
this.setScale(reactionSet);
Rectangle2D bounds = Renderer.calculateBounds(reactionSet);
this.modelCenter = new Point2d(bounds.getCenterX(), bounds.getCenterY());
this.drawCenter = new Point2d(screen.getCenterX(), screen.getCenterY());
this.setup();
}
/**
* Setup the transformations necessary to draw this Reaction.
*
* @param reaction
* @param screen
*/
public void setup(IReaction reaction, Rectangle screen) {
this.setScale(reaction);
Rectangle2D bounds = Renderer.calculateBounds(reaction);
this.modelCenter = new Point2d(bounds.getCenterX(), bounds.getCenterY());
this.drawCenter = new Point2d(screen.getCenterX(), screen.getCenterY());
this.setup();
}
public void reset() {
modelCenter = new Point2d(0, 0);
drawCenter = new Point2d(200, 200);
zoom = 1.0;
setup();
}
/**
* Set the scale for an IChemModel. It calculates the average bond length of
* the model and calculates the multiplication factor to transform this
* to the bond length that is set in the RendererModel.
*
* @param chemModel
*/
public void setScale(IChemModel chemModel) {
double bondLength = Renderer.calculateBondLength(chemModel);
this.scale = this.calculateScaleForBondLength(bondLength);
// store the scale so that other components can access it
this.rendererModel.setScale(scale);
}
/**
* Set the scale for an IReactionSet. It calculates the average bond length
* of the model and calculates the multiplication factor to transform this
* to the bond length that is set in the RendererModel.
*
* @param reactionSet
*/
public void setScale(IReactionSet reactionSet) {
double bondLength = Renderer.calculateBondLength(reactionSet);
this.scale = this.calculateScaleForBondLength(bondLength);
// store the scale so that other components can access it
this.rendererModel.setScale(scale);
}
/**
* Set the scale for an IReaction. It calculates the average bond length
* of the model and calculates the multiplication factor to transform this
* to the bond length that is set in the RendererModel.
* @param reaction
*/
public void setScale(IReaction reaction) {
double bondLength = Renderer.calculateBondLength(reaction);
this.scale = this.calculateScaleForBondLength(bondLength);
// store the scale so that other components can access it
this.rendererModel.setScale(scale);
}
/**
* Set the scale for an IAtomContainerSet. It calculates the average bond length
* of the model and calculates the multiplication factor to transform this
* to the bond length that is set in the RendererModel.
* @param moleculeSet
*/
public void setScale(IAtomContainerSet moleculeSet) {
double bondLength = Renderer.calculateBondLength(moleculeSet);
this.scale = this.calculateScaleForBondLength(bondLength);
// store the scale so that other components can access it
this.rendererModel.setScale(scale);
}
/**
* Paint an IChemModel using the IDrawVisitor at a scale determined by the
* bond length in RendererModel.
*
* @param chemModel
* the chem model to draw
* @param drawVisitor
* the visitor used to draw with
* @return the rectangular area that the diagram will occupy on screen
*/
public Rectangle paintChemModel(
IChemModel chemModel, IDrawVisitor drawVisitor) {
IAtomContainerSet moleculeSet = chemModel.getMoleculeSet();
IReactionSet reactionSet = chemModel.getReactionSet();
if (moleculeSet == null && reactionSet != null) {
return paintReactionSet(reactionSet, drawVisitor);
}
if (moleculeSet != null && reactionSet == null) {
return paintMoleculeSet(moleculeSet, drawVisitor);
}
if (moleculeSet != null && moleculeSet.getAtomContainerCount() > 0 && reactionSet != null) {
Rectangle2D reactionSetBounds = Renderer.calculateBounds(reactionSet);
Rectangle2D moleculeSetBounds = Renderer.calculateBounds(moleculeSet);
Rectangle2D totalBounds = new Rectangle2D.Double();
if (reactionSetBounds != null && moleculeSetBounds != null) {
Rectangle.union(reactionSetBounds, moleculeSetBounds, totalBounds);
} else if (reactionSetBounds != null) {
totalBounds = reactionSetBounds;
} else if (moleculeSetBounds != null) {
totalBounds = moleculeSetBounds;
}
this.setupTransformNatural(totalBounds);
ElementGroup diagram = new ElementGroup();
for (IReaction reaction : reactionSet.reactions()) {
diagram.add(this.generateDiagram(reaction));
}
diagram.add(this.generateDiagram(moleculeSet));
diagram.add(this.generateDiagram(reactionSet));
this.paint(drawVisitor, diagram);
// the size of the painted diagram is returned
return this.convertToDiagramBounds(totalBounds);
}
return new Rectangle(0, 0, 0, 0);
}
public Rectangle paintReactionSet(
IReactionSet reactionSet, IDrawVisitor drawVisitor) {
// total up the bounding boxes
Rectangle2D totalBounds = new Rectangle2D.Double();
for (IReaction reaction : reactionSet.reactions()) {
Rectangle2D modelBounds = Renderer.calculateBounds(reaction);
if (totalBounds == null) {
totalBounds = modelBounds;
} else {
totalBounds = totalBounds.createUnion(modelBounds);
}
}
// setup and draw
this.setupTransformNatural(totalBounds);
ElementGroup diagram = new ElementGroup();
for (IReaction reaction : reactionSet.reactions()) {
diagram.add(this.generateDiagram(reaction));
}
diagram.add(this.generateDiagram(reactionSet));
this.paint(drawVisitor, diagram);
// the size of the painted diagram is returned
return this.convertToDiagramBounds(totalBounds);
}
public Rectangle paintReaction(
IReaction reaction, IDrawVisitor drawVisitor) {
// calculate the bounds
Rectangle2D modelBounds = Renderer.calculateBounds(reaction);
// setup and draw
this.setupTransformNatural(modelBounds);
IRenderingElement diagram = this.generateDiagram(reaction);
this.paint(drawVisitor, diagram);
return this.convertToDiagramBounds(modelBounds);
}
public Rectangle paintMoleculeSet(
IAtomContainerSet moleculeSet, IDrawVisitor drawVisitor) {
// total up the bounding boxes
Rectangle2D totalBounds = new Rectangle2D.Double();
for (IAtomContainer molecule : moleculeSet.atomContainers()) {
Rectangle2D modelBounds = Renderer.calculateBounds(molecule);
if (totalBounds == null) {
totalBounds = modelBounds;
} else {
totalBounds = totalBounds.createUnion(modelBounds);
}
}
// setup and draw
this.setupTransformNatural(totalBounds);
ElementGroup diagram = new ElementGroup();
for (IAtomContainer molecule : moleculeSet.atomContainers()) {
diagram.add(this.generateDiagram(molecule));
}
this.paint(drawVisitor, diagram);
return this.convertToDiagramBounds(totalBounds);
}
/**
* Paint a ChemModel.
*
* @param chemModel
* @param drawVisitor the visitor that does the drawing
* @param bounds the bounds of the area to paint on.
* @param resetCenter
* if true, set the modelCenter to the center of the ChemModel's bounds.
*/
public void paintChemModel(IChemModel chemModel,
IDrawVisitor drawVisitor, Rectangle2D bounds, boolean resetCenter) {
// check for an empty model
IAtomContainerSet moleculeSet = chemModel.getMoleculeSet();
IReactionSet reactionSet = chemModel.getReactionSet();
// nasty, but it seems that reactions can be read in as ChemModels
// with BOTH a ReactionSet AND a MoleculeSet...
if (moleculeSet == null || reactionSet != null) {
if (reactionSet != null) {
paintReactionSet(reactionSet, drawVisitor, bounds, resetCenter);
}
return;
}
// calculate the total bounding box
Rectangle2D modelBounds = Renderer.calculateBounds(moleculeSet);
this.setupTransformToFit(bounds, modelBounds,
Renderer.calculateBondLength(chemModel), resetCenter);
// generate the elements
IRenderingElement diagram = this.generateDiagram(moleculeSet);
// paint it
this.paint(drawVisitor, diagram);
}
/**
* Paint a set of reactions.
*
* @param reaction the reaction to paint
* @param drawVisitor the visitor that does the drawing
* @param bounds the bounds on the screen
* @param resetCenter
* if true, set the draw center to be the center of bounds
*/
public void paintReactionSet(IReactionSet reactionSet,
IDrawVisitor drawVisitor, Rectangle2D bounds, boolean resetCenter) {
// total up the bounding boxes
Rectangle2D totalBounds = null;
for (IReaction reaction : reactionSet.reactions()) {
Rectangle2D modelBounds = Renderer.calculateBounds(reaction);
if (totalBounds == null) {
totalBounds = modelBounds;
} else {
totalBounds = totalBounds.createUnion(modelBounds);
}
}
this.setupTransformToFit(bounds, totalBounds,
Renderer.calculateBondLength(reactionSet), resetCenter);
ElementGroup diagram = new ElementGroup();
for (IReaction reaction : reactionSet.reactions()) {
diagram.add(this.generateDiagram(reaction));
}
diagram.add(this.generateDiagram(reactionSet));
// paint them all
this.paint(drawVisitor, diagram);
}
/**
* Paint a reaction.
*
* @param reaction the reaction to paint
* @param drawVisitor the visitor that does the drawing
* @param bounds the bounds on the screen
* @param resetCenter
* if true, set the draw center to be the center of bounds
*/
public void paintReaction(IReaction reaction, IDrawVisitor drawVisitor,
Rectangle2D bounds, boolean resetCenter) {
// calculate the bounds
Rectangle2D modelBounds = Renderer.calculateBounds(reaction);
this.setupTransformToFit(bounds, modelBounds,
Renderer.calculateBondLength(reaction), resetCenter);
// generate the elements
IRenderingElement diagram = this.generateDiagram(reaction);
// paint it
this.paint(drawVisitor, diagram);
}
/**
* Paint a set of molecules.
*
* @param reaction the reaction to paint
* @param drawVisitor the visitor that does the drawing
* @param bounds the bounds on the screen
* @param resetCenter
* if true, set the draw center to be the center of bounds
*/
public void paintMoleculeSet(IAtomContainerSet molecules,
IDrawVisitor drawVisitor, Rectangle2D bounds, boolean resetCenter) {
// total up the bounding boxes
Rectangle2D totalBounds = null;
for (IAtomContainer molecule : molecules.atomContainers()) {
Rectangle2D modelBounds = Renderer.calculateBounds(molecule);
if (totalBounds == null) {
totalBounds = modelBounds;
} else {
totalBounds = totalBounds.createUnion(modelBounds);
}
}
this.setupTransformToFit(bounds, totalBounds,
Renderer.calculateBondLength(molecules), resetCenter);
ElementGroup diagram = new ElementGroup();
for (IAtomContainer molecule : molecules.atomContainers()) {
diagram.add(this.generateDiagram(molecule));
}
this.paint(drawVisitor, diagram);
}
/**
* Repaint using the cached diagram
*
* @param drawVisitor the wrapper for the graphics object that draws
*/
public void repaint(IDrawVisitor drawVisitor) {
this.paint(drawVisitor, cachedDiagram);
}
/**
* Given a chem model, calculates the bounding rectangle in screen space.
*
* @param model the model to draw.
* @return a rectangle in screen space.
*/
public Rectangle calculateDiagramBounds(IChemModel model) {
IAtomContainerSet moleculeSet = model.getMoleculeSet();
IReactionSet reactionSet = model.getReactionSet();
if ((moleculeSet == null && reactionSet == null)) {
return new Rectangle();
}
Rectangle2D moleculeBounds = null;
Rectangle2D reactionBounds = null;
if (moleculeSet != null) {
moleculeBounds = Renderer.calculateBounds(moleculeSet);
}
if (reactionSet != null) {
reactionBounds = Renderer.calculateBounds(reactionSet);
}
if (moleculeBounds == null && reactionBounds == null)
return new Rectangle();
if (moleculeBounds == null) {
return this.calculateScreenBounds(reactionBounds);
} else if (reactionBounds == null) {
return this.calculateScreenBounds(moleculeBounds);
} else {
Rectangle2D allbounds = new Rectangle2D.Double();
Rectangle2D.union(moleculeBounds, reactionBounds, allbounds);
return this.calculateScreenBounds(allbounds);
}
}
public Rectangle calculateDiagramBounds(IReactionSet reactionSet) {
return this.calculateScreenBounds(
Renderer.calculateBounds(reactionSet));
}
public Rectangle calculateDiagramBounds(IReaction reaction) {
return this.calculateScreenBounds(
Renderer.calculateBounds(reaction));
}
public Rectangle calculateDiagramBounds(IAtomContainerSet moleculeSet) {
return this.calculateScreenBounds(
Renderer.calculateBounds(moleculeSet));
}
public static Rectangle2D calculateBounds(IChemModel chemModel) {
IAtomContainerSet moleculeSet = chemModel.getMoleculeSet();
IReactionSet reactionSet = chemModel.getReactionSet();
Rectangle2D totalBounds = null;
if (moleculeSet != null) {
totalBounds = Renderer.calculateBounds(moleculeSet);
}
if (reactionSet != null) {
if (totalBounds == null) {
totalBounds = Renderer.calculateBounds(reactionSet);
} else {
totalBounds = totalBounds.createUnion(
Renderer.calculateBounds(reactionSet));
}
}
return totalBounds;
}
public static Rectangle2D calculateBounds(IReactionSet reactionSet) {
Rectangle2D totalBounds = new Rectangle2D.Double();
for (IReaction reaction : reactionSet.reactions()) {
Rectangle2D reactionBounds = Renderer.calculateBounds(reaction);
if (reactionBounds == null)
continue;
if (totalBounds.isEmpty()) {
totalBounds = reactionBounds;
} else {
Rectangle2D.union(totalBounds, reactionBounds, totalBounds);
}
}
return totalBounds;
}
public static Rectangle2D calculateBounds(IReaction reaction) {
// get the participants in the reaction
IAtomContainerSet reactants = reaction.getReactants();
IAtomContainerSet products = reaction.getProducts();
if (reactants == null || products == null) {
return null;
}
// determine the bounds of everything in the reaction
if (reaction.getProducts().getAtomContainerCount() > 0) {
Rectangle2D reactantsBounds = Renderer.calculateBounds(products);
if (reaction.getReactantCount() > 0) {
return reactantsBounds.createUnion(Renderer.calculateBounds(reactants));
} else {
return reactantsBounds;
}
} else if (reaction.getReactantCount() > 0) {
return Renderer.calculateBounds(reactants);
} else {
return null;
}
}
public static Rectangle2D calculateBounds(IAtomContainerSet moleculeSet) {
Rectangle2D totalBounds = null;
for (int i = 0; i < moleculeSet.getAtomContainerCount(); i++) {
IAtomContainer ac = moleculeSet.getAtomContainer(i);
Rectangle2D acBounds = Renderer.calculateBounds(ac);
if (totalBounds == null) {
totalBounds = acBounds;
} else {
Rectangle2D.union(totalBounds, acBounds, totalBounds);
}
}
return totalBounds;
}
/**
*
*
* @param model the model for which to calculate the average bond length
*/
public static double calculateBondLength(IChemModel model) {
// empty models have to have a scale
IAtomContainerSet moleculeSet = model.getMoleculeSet();
if (moleculeSet == null) {
IReactionSet reactionSet = model.getReactionSet();
if (reactionSet != null) {
return Renderer.calculateBondLength(reactionSet);
}
return 1.5;
}
return Renderer.calculateBondLength(moleculeSet);
}
public static double calculateBondLength(IReactionSet reactionSet) {
double averageBondModelLength = 0.0;
for (IReaction reaction : reactionSet.reactions()) {
averageBondModelLength += Renderer.calculateBondLength(reaction);
}
if (reactionSet.getReactionCount() == 0)
return 1.5;
return averageBondModelLength / reactionSet.getReactionCount();
}
public static double calculateBondLength(IReaction reaction) {
double avg = 0.0;
int count = 0;
for (IAtomContainer container : reaction.getReactants()) {
avg += calculateBondLength(container);
count++;
}
for (IAtomContainer container : reaction.getProducts()) {
avg += calculateBondLength(container);
count++;
}
for (IAtomContainer container : reaction.getAgents()) {
avg += calculateBondLength(container);
count++;
}
if (count == 0) return DEFAULT_BOND_LENGTH;
return avg / count;
}
/**
* Calculates average of the median bond length for a set of containers.
* Returns a default value when nothing has been drawn yet.
*
* @param containerSet the container set
* @return the bond length
*/
public static double calculateBondLength(IAtomContainerSet containerSet) {
if (containerSet.isEmpty())
return DEFAULT_BOND_LENGTH;
double avg = 0.0;
for (IAtomContainer container : containerSet) {
avg += calculateBondLength(container);
}
return avg / containerSet.getAtomContainerCount();
}
public static double calculateBondLength(IAtomContainer container) {
if (container.getBondCount() > 0)
return GeometryUtil.getBondLengthMedian(container);
else
return DEFAULT_BOND_LENGTH;
}
public JChemPaintRendererModel getRenderer2DModel() {
return this.rendererModel;
}
public void setModelCenter(double x, double y) {
this.modelCenter = new Point2d(x, y);
setup();
}
public void setDrawCenter(double x, double y) {
this.drawCenter = new Point2d(x, y);
setup();
}
public void setZoom(double z) {
getRenderer2DModel().setZoomFactor(z);
zoom = z;
setup();
}
/**
* Move the draw center by dx and dy.
*
* @param dx
* the x shift
* @param dy
* the y shift
*/
public void shiftDrawCenter(double dx, double dy) {
this.drawCenter.set(this.drawCenter.x + dx, this.drawCenter.y + dy);
setup();
}
public Point2d getDrawCenter() {
return drawCenter;
}
public Point2d getModelCenter() {
return modelCenter;
}
/**
* Calculate and set the zoom factor needed to completely fit the diagram
* onto the screen bounds.
*
* @param diagramBounds
* @param drawBounds
*/
public void setZoomToFit(double drawWidth,
double drawHeight,
double diagramWidth,
double diagramHeight) {
double m = this.rendererModel.getMargin();
// determine the zoom needed to fit the diagram to the screen
double widthRatio = drawWidth / (diagramWidth + (2 * m));
double heightRatio = drawHeight / (diagramHeight + (2 * m));
this.zoom = Math.min(widthRatio, heightRatio);
this.fontManager.setFontForZoom(zoom);
// record the zoom in the model, so that generators can use it
this.rendererModel.setZoomFactor(zoom);
}
/**
* The target method for paintChemModel, paintReaction, and paintMolecule.
*
* @param drawVisitor
* the visitor to draw with
* @param diagram
* the IRenderingElement tree to render
*/
private void paint(IDrawVisitor drawVisitor,
IRenderingElement diagram) {
if (diagram == null) {
return;
}
// cache the diagram for quick-redraw
this.cachedDiagram = diagram;
this.fontManager.setFontName(this.rendererModel.getFontName());
this.fontManager.setFontStyle(this.rendererModel.getFontStyle());
drawVisitor.setFontManager(this.fontManager);
drawVisitor.setTransform(this.transform);
drawVisitor.setRendererModel(this.rendererModel);
diagram.accept(drawVisitor);
}
/**
* Set the transform for a non-fit to screen paint.
*
* @param modelBounds
* the bounding box of the model
*/
private void setupTransformNatural(Rectangle2D modelBounds) {
this.zoom = this.rendererModel.getZoomFactor();
this.fontManager.setFontForZoom(zoom);
this.setup();
}
/**
* Sets the transformation needed to draw the model on the canvas when
* the diagram needs to fit the screen.
*
* @param screenBounds
* the bounding box of the draw area
* @param modelBounds
* the bounding box of the model
* @param bondLength
* the average bond length of the model
* @param reset
* if true, model center will be set to the modelBounds center
* and the scale will be re-calculated
*/
private void setupTransformToFit(Rectangle2D screenBounds,
Rectangle2D modelBounds,
double bondLength,
boolean reset) {
if (screenBounds == null) {
return;
}
this.setDrawCenter(
screenBounds.getCenterX(), screenBounds.getCenterY());
this.scale = this.calculateScaleForBondLength(bondLength);
double drawWidth = screenBounds.getWidth();
double drawHeight = screenBounds.getHeight();
double diagramWidth = modelBounds.getWidth() * scale;
double diagramHeight = modelBounds.getHeight() * scale;
this.setZoomToFit(drawWidth, drawHeight, diagramWidth, diagramHeight);
// this controls whether editing a molecule causes it to re-center
// with each change or not
if (reset || rendererModel.isFitToScreen()) {
this.setModelCenter(
modelBounds.getCenterX(), modelBounds.getCenterY());
}
// set the scale in the renderer model for the generators
if (reset) {
this.rendererModel.setScale(scale);
}
this.setup();
}
/**
* Given a bond length for a model, calculate the scale that will transform
* this length to the on screen bond length in RendererModel.
*
* @param modelBondLength
* @param reset
* @return
*/
private double calculateScaleForBondLength(double modelBondLength) {
if (Double.isNaN(modelBondLength) || modelBondLength == 0) {
return Renderer.DEFAULT_SCALE;
} else {
return this.rendererModel.getBondLength() / modelBondLength;
}
}
/**
* Calculate the bounds of the diagram on screen, given the current scale,
* zoom, and margin.
*
* @param modelBounds
* the bounds in model space of the chem object
* @return the bounds in screen space of the drawn diagram
*/
private Rectangle convertToDiagramBounds(Rectangle2D modelBounds) {
double cx = modelBounds.getCenterX();
double cy = modelBounds.getCenterY();
double mw = modelBounds.getWidth();
double mh = modelBounds.getHeight();
Point2d mc = this.toScreenCoordinates(cx, cy);
// special case for 0 or 1 atoms
if (mw == 0 && mh == 0) {
return new Rectangle((int) mc.x, (int) mc.y, 0, 0);
}
double margin = this.rendererModel.getMargin();
int w = (int) ((scale * zoom * mw) + (2 * margin));
int h = (int) ((scale * zoom * mh) + (2 * margin));
int x = (int) (mc.x - w / 2);
int y = (int) (mc.y - h / 2);
return new Rectangle(x, y, w, h);
}
private void setup() {
// set the transform
try {
this.transform = new AffineTransform();
// JWM 2024: we now zoom relative to one corner of the canvas if not
// FitToScreen - this mirrors other sketchers and
// make more sense IMO. The zoom relative to the center
// is fine for normal CDK molecule rendering
if (rendererModel.isFitToScreen())
this.transform.translate(this.drawCenter.x, this.drawCenter.y);
this.transform.scale(1, -1); // Converts between CDK Y-up & Java2D Y-down coordinate-systems
this.transform.scale(this.scale, this.scale);
this.transform.scale(this.zoom, this.zoom);
if (rendererModel.isFitToScreen())
this.transform.translate(-this.modelCenter.x, -this.modelCenter.y); // JWM 2024
} catch (NullPointerException npe) {
// one of the drawCenter or modelCenter points have not been set!
System.err.println(String.format(
"null pointer when setting transform: "
+ "drawCenter=%s scale=%s zoom=%s modelCenter=%s",
this.drawCenter,
this.scale,
this.zoom,
this.modelCenter));
}
}
private IRenderingElement generateDiagram(IReactionSet reactionSet) {
ElementGroup diagram = new ElementGroup();
for (IReactionSetGenerator generator : this.reactionSetGenerators) {
diagram.add(generator.generate(reactionSet, rendererModel));
}
return diagram;
}
private IRenderingElement generateDiagram(IReaction reaction) {
ElementGroup diagram = new ElementGroup();
for (IReactionGenerator generator : this.reactionGenerators) {
diagram.add(generator.generate(reaction, rendererModel));
}
diagram.add(generateDiagram(reaction.getReactants()));
diagram.add(generateDiagram(reaction.getProducts()));
return diagram;
}
private IRenderingElement generateDiagram(IAtomContainerSet moleculeSet) {
ElementGroup diagram = new ElementGroup();
for (int i = 0; i < moleculeSet.getAtomContainerCount(); i++) {
IAtomContainer ac = moleculeSet.getAtomContainer(i);
for (IGenerator<IAtomContainer> generator : this.generators) {
diagram.add(generator.generate(ac, this.rendererModel));
}
}
return diagram;
}
/**
* Return the list of generators for the Renderer
* @return
*/
public List<IGenerator<IAtomContainer>> getGenerators() {
return new ArrayList<IGenerator<IAtomContainer>>(generators);
}
/**
*
* @param reactionGenerator
*/
public void addReactionGenerator(IReactionGenerator reactionGenerator) {
reactionGenerators.add(reactionGenerator);
}
/**
*
* @param generator
*/
public void addGenerator(IGenerator<IAtomContainer> generator) {
generators.add(generator);
}
/**
*
* @param reactionSetGenerator
*/
public void addReactionSetGenerator(IReactionSetGenerator reactionSetGenerator) {
reactionSetGenerators.add(reactionSetGenerator);
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/AtomContainerTitleGenerator.java
|
.java
| 3,057
| 75
|
/* $Revision: $ $Author: $ $Date$
*
* Copyright (C) 2008 Arvid Berg <goglepox@users.sf.net>
*
* Contact: cdk-devel@lists.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
* All I ask is that proper credit is given for my work, which includes
* - but is not limited to - adding the above copyright notice to the beginning
* of your source code files, and to any copyright notice that you may distribute
* with programs based on this work.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import java.awt.Color;
import java.awt.geom.Rectangle2D;
import java.util.List;
import org.openscience.cdk.CDKConstants;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.renderer.RendererModel;
import org.openscience.cdk.renderer.generators.IGenerator;
import org.openscience.cdk.renderer.generators.IGeneratorParameter;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.jchempaint.renderer.Renderer;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.jchempaint.renderer.elements.TextElement;
/**
* @cdk.module rendercontrol
*/
public class AtomContainerTitleGenerator implements IGenerator<IAtomContainer> {
public AtomContainerTitleGenerator() {}
public IRenderingElement generate(IAtomContainer ac, RendererModel model) {
JChemPaintRendererModel jcpModel = (JChemPaintRendererModel) model;
if(ac==null ||ac.getProperty(CDKConstants.TITLE)==null|| (ac.getAtomCount()==0 && ac.getBondCount()==0))
return null;
double d = jcpModel.getBondLength() / jcpModel.getScale()/2;
Rectangle2D totalBounds = Renderer.calculateBounds(ac);
ElementGroup diagram = new ElementGroup();
double minX = totalBounds.getMinX();
double minY = totalBounds.getMinY();
double maxX = totalBounds.getMaxX();
double maxY = totalBounds.getMaxY();
Color c = Color.GRAY;
diagram.add(new TextElement(
(minX+maxX)/2, minY-d, (String)ac.getProperty(CDKConstants.TITLE), c,0.8));
return diagram;
}
public List<IGeneratorParameter<?>> getParameters() {
// TODO Auto-generated method stub
return null;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/RadicalGenerator.java
|
.java
| 3,686
| 95
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2009 Gilleain Torrance <gilleain.torrance@gmail.com>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import java.awt.Color;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import javax.vecmath.Point2d;
import org.openscience.cdk.geometry.GeometryUtil;
import org.openscience.cdk.interfaces.IAtom;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.interfaces.ISingleElectron;
import org.openscience.cdk.renderer.RendererModel;
import org.openscience.cdk.renderer.generators.IGenerator;
import org.openscience.cdk.renderer.generators.IGeneratorParameter;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.elements.OvalElement;
/**
* Generate the symbols for radicals.
*
* @author maclean
* @cdk.module renderextra
*
*/
public class RadicalGenerator implements IGenerator<IAtomContainer> {
public RadicalGenerator() {}
public IRenderingElement generate(IAtomContainer ac, RendererModel model) {
JChemPaintRendererModel jcpModel = (JChemPaintRendererModel) model;
ElementGroup group = new ElementGroup();
// TODO : put into RendererModel
final double SCREEN_RADIUS = 2.0;
final Color RADICAL_COLOR = Color.BLACK;
// XXX : is this the best option?
final double ATOM_RADIUS = jcpModel.getAtomRadius() / jcpModel.getScale();
double modelRadius = SCREEN_RADIUS / jcpModel.getScale();
Map<IAtom,Integer> singleElectronsPerAtom = new HashMap<IAtom, Integer>();
for (ISingleElectron e : ac.singleElectrons()) {
IAtom atom = e.getAtom();
if(singleElectronsPerAtom.get(atom)==null)
singleElectronsPerAtom.put(atom,0);
Point2d p = atom.getPoint2d();
int align = GeometryUtil.getBestAlignmentForLabelXY(ac, atom);
double rx = p.x;
double ry = p.y;
if (align == 1) {
rx += ATOM_RADIUS+singleElectronsPerAtom.get(atom)*ATOM_RADIUS;
} else if (align == -1) {
rx -= ATOM_RADIUS+singleElectronsPerAtom.get(atom)*ATOM_RADIUS;
} else if (align == 2) {
ry -= ATOM_RADIUS;
} else if (align == -2) {
ry += ATOM_RADIUS;
}
singleElectronsPerAtom.put(atom, singleElectronsPerAtom.get(atom)+1);
group.add(
new OvalElement(rx, ry, modelRadius, true, RADICAL_COLOR));
}
return group;
}
public List<IGeneratorParameter<?>> getParameters() {
// TODO Auto-generated method stub
return null;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/ExternalHighlightAtomGenerator.java
|
.java
| 2,129
| 50
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2010 Stefan Kuhn <shhk3@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import java.awt.Color;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.renderer.RendererModel;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.jchempaint.renderer.selection.AtomContainerSelection;
import org.openscience.cdk.renderer.selection.IChemObjectSelection;
/**
* A generator to generate the externally selected atoms in ExternalHighlightColor.
*/
public class ExternalHighlightAtomGenerator extends SelectAtomGenerator {
public ExternalHighlightAtomGenerator() {}
@Override
public IRenderingElement generate(IAtomContainer ac, RendererModel model) {
JChemPaintRendererModel jcpModel = (JChemPaintRendererModel) model;
if(model.getExternalSelectedPart()==null)
return new ElementGroup();
Color selectionColor = jcpModel.getExternalHighlightColor();
IChemObjectSelection selection = new AtomContainerSelection(model.getExternalSelectedPart());
return generate(selection, selectionColor, jcpModel);
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/IReactionSetGenerator.java
|
.java
| 385
| 13
|
package org.openscience.jchempaint.renderer.generators;
import org.openscience.cdk.interfaces.IReactionSet;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.cdk.renderer.elements.IRenderingElement;
public interface IReactionSetGenerator {
public IRenderingElement generate(IReactionSet reactionSet, JChemPaintRendererModel model);
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/SelectControlGenerator.java
|
.java
| 5,161
| 110
|
/*
* Copyright (C) 2025 John Mayfield
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import org.openscience.cdk.CDKConstants;
import org.openscience.cdk.interfaces.IAtom;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.renderer.RendererModel;
import org.openscience.cdk.renderer.elements.Bounds;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.elements.LineElement;
import org.openscience.cdk.renderer.elements.OvalElement;
import org.openscience.cdk.renderer.elements.RectangleElement;
import org.openscience.cdk.renderer.generators.IGenerator;
import org.openscience.cdk.renderer.generators.IGeneratorParameter;
import org.openscience.cdk.renderer.selection.IChemObjectSelection;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import javax.vecmath.Point2d;
import java.awt.Color;
import java.util.List;
import static org.openscience.cdk.renderer.elements.RectangleElement.createSquare;
/**
* Renders the selection control points. These allow rotating, scaling of
* the selected atoms/bonds.
*
* @cdk.module rendercontrol
*/
public class SelectControlGenerator implements IGenerator<IAtomContainer> {
private boolean autoUpdateSelection = true;
public SelectControlGenerator() {}
public IRenderingElement generate(IAtomContainer ac, RendererModel model) {
JChemPaintRendererModel jcpModel = (JChemPaintRendererModel) model;
Color selectionColor = jcpModel.getSelectedPartColor();
IChemObjectSelection selection = jcpModel.getSelection();
return generate(selection, selectionColor, jcpModel);
}
protected IRenderingElement generate(IChemObjectSelection selection, Color color, JChemPaintRendererModel model){
ElementGroup selectionControls = new ElementGroup();
if(selection==null)
return selectionControls;
if (this.autoUpdateSelection || selection.isFilled()) {
// we also divide by the zoom so the control points stay the same size
double r = (model.getSelectionRadius() / model.getScale()) / model.getZoomFactor();
if (!model.isRotating()) {
RectangleElement box = model.getSelectionBounds();
selectionControls.add(box);
if (box.width > 0.001 && box.height < -0.001 &&
selection.elements(IAtom.class).size() != 1) {
// rotation handle
Point2d p = model.getSelectionRotateControl();
if (p != null) {
selectionControls.add(new OvalElement(p.x, p.y, r, false, color));
selectionControls.add(new LineElement(p.x, p.y - r,
box.xCoord + (box.width / 2),
box.yCoord,
1 / model.getScale(),
color));
}
// box on the four corners
selectionControls.add(createSquare(box.xCoord, box.yCoord, r, true, color));
selectionControls.add(createSquare(box.xCoord + box.width, box.yCoord, r, true, color));
selectionControls.add(createSquare(box.xCoord, box.yCoord + box.height, r, true, color));
selectionControls.add(createSquare(box.xCoord + box.width, box.yCoord + box.height, r, true, color));
// box on four sides
double cx = box.xCoord + (box.width/2);
double cy = box.yCoord + (box.height/2);
selectionControls.add(createSquare(cx, box.yCoord, r, true, color));
selectionControls.add(createSquare(cx, box.yCoord + box.height, r, true, color));
selectionControls.add(createSquare(box.xCoord, cy, r, true, color));
selectionControls.add(createSquare(box.xCoord + box.width, cy, r, true, color));
}
}
}
return selectionControls;
}
public List<IGeneratorParameter<?>> getParameters() {
return null;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/AtomNumberGenerator.java
|
.java
| 2,573
| 71
|
/* Copyright (C) 2009 Gilleain Torrance <gilleain@users.sf.net>
*
* Contact: cdk-devel@lists.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
* All we ask is that proper credit is given for our work, which includes
* - but is not limited to - adding the above copyright notice to the beginning
* of your source code files, and to any copyright notice that you may distribute
* with programs based on this work.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import java.awt.Color;
import java.util.List;
import javax.vecmath.Point2d;
import org.openscience.cdk.interfaces.IAtom;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.renderer.RendererModel;
import org.openscience.cdk.renderer.generators.IGenerator;
import org.openscience.cdk.renderer.generators.IGeneratorParameter;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.jchempaint.renderer.elements.TextElement;
/**
* @author maclean
* @cdk.module renderextra
*/
public class AtomNumberGenerator implements IGenerator<IAtomContainer> {
public AtomNumberGenerator() {}
public IRenderingElement generate(IAtomContainer ac, RendererModel model) {
JChemPaintRendererModel jcpModel = (JChemPaintRendererModel) model;
ElementGroup numbers = new ElementGroup();
if (!jcpModel.drawNumbers()) return numbers;
int number = 1;
for (IAtom atom : ac.atoms()) {
Point2d p = atom.getPoint2d();
numbers.add(
new TextElement(
p.x, p.y, String.valueOf(number), Color.BLACK));
number++;
}
return numbers;
}
public List<IGeneratorParameter<?>> getParameters() {
// TODO Auto-generated method stub
return null;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/ReactionPlusGenerator.java
|
.java
| 3,346
| 82
|
/* Copyright (C) 2009 Stefan Kuhn <shk3@users.sf.net>
* 2009 Gilleain Torrance <gilleain@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import java.awt.Color;
import java.awt.geom.Rectangle2D;
import org.openscience.cdk.interfaces.IAtomContainerSet;
import org.openscience.cdk.interfaces.IReaction;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.jchempaint.renderer.Renderer;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.jchempaint.renderer.elements.TextElement;
/**
* Generate the arrow for a reaction.
*
* @author maclean
* @cdk.module renderextra
*
*/
public class ReactionPlusGenerator implements IReactionGenerator {
public IRenderingElement generate(IReaction reaction, JChemPaintRendererModel model) {
ElementGroup diagram = new ElementGroup();
Color color = model.getForeColor();
IAtomContainerSet reactants = reaction.getReactants();
if(reactants.getAtomContainerCount()>0){
Rectangle2D totalBoundsReactants = Renderer.calculateBounds(reactants);
Rectangle2D bounds1 =
Renderer.calculateBounds(reactants.getAtomContainer(0));
double axis = totalBoundsReactants.getCenterY();
for (int i = 1; i < reaction.getReactantCount(); i++) {
Rectangle2D bounds2 =
Renderer.calculateBounds(reactants.getAtomContainer(i));
diagram.add(makePlus(bounds1, bounds2, axis, color));
bounds1 = bounds2;
}
}
IAtomContainerSet products = reaction.getProducts();
if(products.getAtomContainerCount()>0){
Rectangle2D totalBoundsProducts = Renderer.calculateBounds(products);
double axis = totalBoundsProducts.getCenterY();
Rectangle2D bounds1 = Renderer.calculateBounds(products.getAtomContainer(0));
for (int i = 1; i < reaction.getProductCount(); i++) {
Rectangle2D bounds2 =
Renderer.calculateBounds(products.getAtomContainer(i));
diagram.add(makePlus(bounds1, bounds2, axis, color));
bounds1 = bounds2;
}
}
return diagram;
}
public TextElement makePlus(
Rectangle2D a, Rectangle2D b, double axis, Color color) {
double x = (a.getCenterX() + b.getCenterX()) / 2;
return new TextElement(x, axis, "+", color);
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/AtomMassGenerator.java
|
.java
| 2,589
| 77
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2008 Arvid Berg <goglepox@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import java.io.IOException;
import org.openscience.cdk.config.Isotopes;
import org.openscience.cdk.interfaces.IAtom;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.renderer.generators.BasicAtomGenerator;
import org.openscience.cdk.tools.ILoggingTool;
import org.openscience.cdk.tools.LoggingToolFactory;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.cdk.renderer.elements.AtomMassSymbolElement;
import org.openscience.cdk.renderer.elements.IRenderingElement;
/**
* @cdk.module renderextra
*/
public class AtomMassGenerator extends BasicAtomGenerator {
private static ILoggingTool logger =
LoggingToolFactory.createLoggingTool(AtomMassGenerator.class);
public AtomMassGenerator() {}
public IRenderingElement generateElements(
IAtom atom, int alignment, JChemPaintRendererModel model) {
return new AtomMassSymbolElement(
atom.getPoint2d().x,
atom.getPoint2d().y,
atom.getSymbol(),
atom.getFormalCharge(),
atom.getImplicitHydrogenCount(),
alignment,
atom.getMassNumber(),
super.getAtomColor(atom, model));
}
public boolean showCarbon(
IAtom atom, IAtomContainer ac, JChemPaintRendererModel model) {
Integer massNumber = atom.getMassNumber();
if (massNumber != null) {
try {
Integer expectedMassNumber
= Isotopes.getInstance()
.getMajorIsotope(atom.getSymbol())
.getMassNumber();
if (massNumber != expectedMassNumber)
return true;
} catch (IOException e) {
logger.warn(e);
}
}
return super.showCarbon(atom, ac, model);
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/ExtendedAtomGenerator.java
|
.java
| 9,631
| 252
|
/* Copyright (C) 2009 Gilleain Torrance <gilleain@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import org.openscience.cdk.CDKConstants;
import org.openscience.cdk.config.IsotopeFactory;
import org.openscience.cdk.config.Isotopes;
import org.openscience.cdk.interfaces.IAtom;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.interfaces.IIsotope;
import org.openscience.cdk.interfaces.IPseudoAtom;
import org.openscience.cdk.renderer.RendererModel;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.generators.BasicAtomGenerator;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.jchempaint.renderer.elements.TextGroupElement;
import org.openscience.jchempaint.renderer.elements.TextGroupElement.Position;
import javax.vecmath.Point2d;
import java.awt.Color;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import java.util.Stack;
/**
* A generator for atoms with mass, charge, etc.
*
* @author maclean
* @cdk.module renderextra
*
*/
public class ExtendedAtomGenerator extends BasicAtomGenerator {
@Override
public IRenderingElement generate(IAtomContainer ac, IAtom atom, RendererModel model) {
return generate(ac, atom, (JChemPaintRendererModel) model);
}
public IRenderingElement generate(IAtomContainer ac, IAtom atom, JChemPaintRendererModel model) {
Integer majorIsotopeNumber = null;
if(atom.getMassNumber()!=null){
try {
IIsotope isotope = Isotopes.getInstance().getMajorIsotope(atom.getSymbol());
if(isotope!=null)
majorIsotopeNumber = isotope.getMassNumber();
} catch (IOException e) {
}
}
if ((!hasCoordinates(atom)
|| invisibleHydrogen(atom, model)
|| (invisibleCarbon(atom, ac, model) && !model.getDrawNumbers()))
&& (atom.getMassNumber()==null
|| atom.getMassNumber()==majorIsotopeNumber)
&& !atom.getFlag(CDKConstants.IS_TYPEABLE)) {
return null;
} else if (model.getIsCompact()) {
return this.generateCompactElement(atom, model);
} else {
String text;
if (atom instanceof IPseudoAtom) {
text = ((IPseudoAtom) atom).getLabel();
} else if (invisibleCarbon(atom, ac, model) && model.drawNumbers()) {
text = String.valueOf(ac.indexOf(atom) + 1);
} else {
text = atom.getSymbol();
}
Point2d p = atom.getPoint2d();
Color c = getAtomColor(atom, model);
TextGroupElement textGroup = new TextGroupElement(p.x, p.y, text, c);
if(atom.getFlag(CDKConstants.IS_TYPEABLE)){
textGroup.isNotTypeableUnderlined = true;
textGroup.notTypeableUnderlineColor = model.getNotTypeableUnderlineColor();
}
decorate(textGroup, ac, atom, model);
return textGroup;
}
}
public boolean hideAtomSymbol(IAtom atom, JChemPaintRendererModel model) {
return atom.getSymbol().equals("C") && !model.getKekuleStructure();
}
public void decorate(TextGroupElement textGroup,
IAtomContainer ac,
IAtom atom,
JChemPaintRendererModel model) {
Stack<Position> unused = getUnusedPositions(ac, atom);
if (!invisibleCarbon(atom, ac, model) && model.getDrawNumbers()) {
Position position = getNextPosition(unused);
String number = String.valueOf(ac.indexOf(atom) + 1);
textGroup.addChild(number, position);
}
if (
(model.getShowImplicitHydrogens() && !invisibleCarbon(atom, ac, model))
||
// Always show implicit hydrogens on end carbons
(atom.getSymbol().equals("C") && model.getShowEndCarbons() && ac.getConnectedBondsList(atom).size() == 1)
) {
if(atom.getImplicitHydrogenCount()!=null){
int nH = atom.getImplicitHydrogenCount();
if (nH > 0) {
Position position = getNextPosition(unused);
if (nH == 1) {
textGroup.addChild("H", position);
} else {
textGroup.addChild("H", String.valueOf(nH), position);
}
}
}
}
Integer massNumber = atom.getMassNumber();
if (massNumber != null) {
try {
IsotopeFactory factory = Isotopes.getInstance();
if(factory.getMajorIsotope(atom.getSymbol())!=null){
int majorMass =
factory.getMajorIsotope(atom.getSymbol()).getMassNumber();
if (massNumber != majorMass) {
Position position = getNextPosition(unused);
textGroup.addChild(String.valueOf(massNumber), position);
}
}
} catch (IOException io) {
}
}
if(atom.getFormalCharge()!=0){
String chargeString="";
if (atom.getFormalCharge() == 1) {
chargeString = "+";
} else if (atom.getFormalCharge() > 1) {
chargeString = atom.getFormalCharge() + "+";
} else if (atom.getFormalCharge() == -1) {
chargeString = "-";
} else if (atom.getFormalCharge() < -1) {
int absCharge = Math.abs(atom.getFormalCharge());
chargeString = absCharge + "-";
}
Position position = getNextPosition(unused);
textGroup.addChild(chargeString, position);
}
if(atom.getProperty(CDKConstants.COMMENT)!=null){
//Position position = getNextPosition(unused);
textGroup.addChild((String)atom.getProperty(CDKConstants.COMMENT),Position.S,true);
}
}
private Position getNextPosition(Stack<Position> unused) {
if (unused.size() > 0) {
return unused.pop();
} else {
return Position.N;
}
}
public Stack<Position> getUnusedPositions(IAtomContainer ac, IAtom atom) {
Stack<Position> unused = new Stack<Position>();
for (Position p : Position.values()) {
unused.add(p);
}
for (IAtom connectedAtom : ac.getConnectedAtomsList(atom)) {
List<Position> used = getPosition(atom, connectedAtom);
for(int i=0;i<used.size();i++){
unused.remove(used.get(i));
}
}
return unused;
}
public List<Position> getPosition(IAtom atom, IAtom connectedAtom) {
Point2d pA = atom.getPoint2d();
Point2d pB = connectedAtom.getPoint2d();
double dx = pB.x - pA.x;
double dy = pB.y - pA.y;
List<Position> used=new ArrayList<Position>();
final double DELTA = 0.2;
if (dx < -DELTA) { // generally west
if (dy < -DELTA) {
used.add(Position.N);
used.add(Position.NW);
used.add(Position.W);
} else if (dy > -DELTA && dy < DELTA) {
used.add(Position.NW);
used.add(Position.W);
used.add(Position.SW);
} else {
used.add(Position.W);
used.add(Position.SW);
used.add(Position.S);
}
} else if (dx > -DELTA && dx < DELTA) { // north or south
if (dy < -DELTA) {
used.add(Position.NW);
used.add(Position.N);
used.add(Position.NE);
} else if (dy > -DELTA && dy < DELTA) { // right on top of the atom!
used.add(Position.NW);
used.add(Position.N);
used.add(Position.NE);
} else {
used.add(Position.SW);
used.add(Position.S);
used.add(Position.SE);
}
} else { // generally east
if (dy < -DELTA) {
used.add(Position.N);
used.add(Position.NE);
used.add(Position.E);
} else if (dy > -DELTA && dy < DELTA) {
used.add(Position.NE);
used.add(Position.E);
used.add(Position.SE);
} else {
used.add(Position.E);
used.add(Position.SE);
used.add(Position.S);
}
}
return used;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/ControllerFeedbackGenerator.java
|
.java
| 1,963
| 55
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2008 Arvid Berg <goglepox@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import org.openscience.cdk.interfaces.IAtom;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.cdk.renderer.elements.IRenderingElement;
/**
* @cdk.module rendercontrol
*/
public class ControllerFeedbackGenerator {
private JChemPaintRendererModel model;
public ControllerFeedbackGenerator(IAtomContainer ac, JChemPaintRendererModel r2dm) {
this.model = r2dm;
}
public IRenderingElement generate(IAtomContainer ac, IAtom atom) {
if (this.model.getHighlightedAtom() == atom) {
return generateHighlightElement(atom);
}
return null;
}
private IRenderingElement generateHighlightElement(IAtom atom) {
// create highlight base on symbol
// would be nice to attach it to a AtomSymbolElement and use it's
// data to calculate the surrounding highlight or change the text's
// appearance
return null;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/AtomContainerBoundsGenerator.java
|
.java
| 1,931
| 52
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2008 Arvid Berg <goglepox@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import org.openscience.cdk.geometry.GeometryUtil;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.renderer.RendererModel;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.elements.RectangleElement;
import org.openscience.cdk.renderer.generators.IGenerator;
import org.openscience.cdk.renderer.generators.IGeneratorParameter;
import java.awt.Color;
import java.util.List;
/**
* @cdk.module renderextra
*/
public class AtomContainerBoundsGenerator implements IGenerator<IAtomContainer> {
public IRenderingElement generate( IAtomContainer ac, RendererModel model) {
double[] minMax = GeometryUtil.getMinMax(ac);
return new RectangleElement(minMax[0], minMax[3], minMax[2], minMax[1],
new Color(.7f, .7f, 1.0f));
}
public List<IGeneratorParameter<?>> getParameters() {
// TODO Auto-generated method stub
return null;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/IReactionGenerator.java
|
.java
| 1,333
| 37
|
/* Copyright (C) 2009 Gilleain Torrance <gilleain@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import org.openscience.cdk.interfaces.IReaction;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.cdk.renderer.elements.IRenderingElement;
/**
* A Generator specifically for Reactions.
*
* @author maclean
*
* @cdk.module renderextra
*/
public interface IReactionGenerator {
public IRenderingElement generate(IReaction reaction, JChemPaintRendererModel model);
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/HighlightBondGenerator.java
|
.java
| 3,979
| 95
|
/* Copyright (C) 2009 Gilleain Torrance <gilleain@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.interfaces.IBond;
import org.openscience.cdk.renderer.RendererModel;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.GeneralPath;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.elements.OvalElement;
import org.openscience.cdk.renderer.elements.path.Close;
import org.openscience.cdk.renderer.elements.path.LineTo;
import org.openscience.cdk.renderer.elements.path.MoveTo;
import org.openscience.cdk.renderer.generators.BasicBondGenerator;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.jchempaint.renderer.RenderingParameters;
import javax.vecmath.Point2d;
import javax.vecmath.Vector2d;
import java.awt.Color;
import java.util.Arrays;
/**
* @cdk.module rendercontrol
*/
public class HighlightBondGenerator extends BasicBondGenerator {
public HighlightBondGenerator() {}
private boolean shouldHighlight(IBond bond, JChemPaintRendererModel model) {
return !super.bindsHydrogen(bond) || model.getShowExplicitHydrogens();
}
@Override
public IRenderingElement generate(IAtomContainer ac, RendererModel model) {
JChemPaintRendererModel jcpModel = (JChemPaintRendererModel) model;
IBond bond = model.getHighlightedBond();
if (bond != null && shouldHighlight(bond, jcpModel)) {
super.ringSet = super.getRingSet(ac);
double r = jcpModel.getHighlightDistance() / jcpModel.getScale();
r /= 1.5;
Color hColor = jcpModel.getHoverOverColor();
Point2d p = bond.get2DCenter();
boolean filled = jcpModel.getHighlightShapeFilled();
if (jcpModel.getHighlightBondShape() == RenderingParameters.AtomShape.OVAL) {
return new OvalElement(p.x, p.y, r, filled, hColor);
} else if (jcpModel.getHighlightBondShape() == RenderingParameters.AtomShape.SQUARE) {
Point2d p1 = bond.getBegin().getPoint2d();
Point2d p2 = bond.getEnd().getPoint2d();
Vector2d v = new Vector2d(p1.x - p2.x, p1.y - p2.y);
v.normalize();
v.scale(r);
p1 = new Point2d(p.x - v.x, p.y - v.y);
p2 = new Point2d(p.x + v.x, p.y + v.y);
Vector2d o = new Vector2d(-v.y, v.x);
GeneralPath rectangle = new GeneralPath(Arrays.asList(
new MoveTo(p1.x - o.x, p1.y - o.y),
new LineTo(p1.x + o.x, p1.y + o.y),
new LineTo(p2.x + o.x, p2.y + o.y),
new LineTo(p2.x - o.x, p2.y - o.y),
new Close()), hColor);
if (filled)
return rectangle;
else
return rectangle.outline(1 / ((JChemPaintRendererModel) model).getScale());
}
}
return new ElementGroup();
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/SelectBondGenerator.java
|
.java
| 3,140
| 80
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2008 Gilleain Torrance <gilleain.torrance@gmail.com>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import java.awt.Color;
import java.util.Collection;
import org.openscience.cdk.interfaces.IAtom;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.interfaces.IBond;
import org.openscience.cdk.renderer.RendererModel;
import org.openscience.cdk.renderer.generators.BasicBondGenerator;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.selection.IChemObjectSelection;
import org.openscience.jchempaint.renderer.selection.IncrementalSelection;
/**
* @cdk.module rendercontrol
*/
public class SelectBondGenerator extends BasicBondGenerator {
private boolean autoUpdateSelection = true;
public SelectBondGenerator() {}
@Override
public IRenderingElement generate(IAtomContainer ac, RendererModel model) {
JChemPaintRendererModel jcpModel = (JChemPaintRendererModel) model;
Color selectionColor = jcpModel.getSelectedPartColor();
IChemObjectSelection selection = model.getSelection();
return generate(selection, selectionColor, jcpModel);
}
protected IRenderingElement generate(IChemObjectSelection selection, Color selectionColor, JChemPaintRendererModel model){
ElementGroup selectionElements = new ElementGroup();
if(selection==null)
return selectionElements;
if (this.autoUpdateSelection || selection.isFilled()) {
Collection<IBond> bonds = selection.elements(IBond.class);
if (!bonds.isEmpty()) {
super.setOverrideColor(selectionColor);
super.setOverrideBondWidth(model.getSelectionRadius());
for (IBond bond : bonds) {
selectionElements.add(super.generateBond(bond, model));
}
}
}
if (selection instanceof IncrementalSelection) {
IncrementalSelection sel = (IncrementalSelection) selection;
if (!sel.isFinished())
selectionElements.add(sel.generate(selectionColor));
}
return selectionElements;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/ProductsBoxGenerator.java
|
.java
| 2,915
| 73
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2009 Stefan Kuhn
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import java.awt.geom.Rectangle2D;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.interfaces.IReaction;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.jchempaint.renderer.Renderer;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.elements.RectangleElement;
import org.openscience.jchempaint.renderer.elements.TextElement;
/**
* Generate the symbols for radicals.
*
* @author maclean
* @cdk.module renderextra
*
*/
public class ProductsBoxGenerator implements IReactionGenerator {
private static double DISTANCE;
public IRenderingElement generate(IReaction reaction, JChemPaintRendererModel model) {
if(!model.getShowReactionBoxes())
return null;
if (reaction.getProductCount() == 0)
return new ElementGroup();
DISTANCE = model.getBondLength() / model.getScale() / 2;
Rectangle2D totalBounds = null;
for (IAtomContainer molecule : reaction.getProducts().atomContainers()) {
Rectangle2D bounds = Renderer.calculateBounds(molecule);
if (totalBounds == null) {
totalBounds = bounds;
} else {
totalBounds = totalBounds.createUnion(bounds);
}
}
if (totalBounds == null) return null;
ElementGroup diagram = new ElementGroup();
diagram.add(new RectangleElement(totalBounds.getMinX()-DISTANCE,
totalBounds.getMaxY()+DISTANCE,
totalBounds.getMaxX()+DISTANCE,
totalBounds.getMinY()-DISTANCE,
model.getForeColor()));
diagram.add(new TextElement((totalBounds.getMinX()+totalBounds.getMaxX())/2, totalBounds.getMinY()-DISTANCE, "Products", model.getForeColor()));
return diagram;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/MergeAtomsGenerator.java
|
.java
| 3,244
| 96
|
/* Copyright (C) 2009 Stefan Kuhn <shk3@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import javax.vecmath.Point2d;
import javax.vecmath.Vector2d;
import org.openscience.cdk.geometry.GeometryUtil;
import org.openscience.cdk.interfaces.IAtom;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.renderer.RendererModel;
import org.openscience.cdk.renderer.generators.BasicAtomGenerator;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.elements.path.PathBuilder;
/**
* @cdk.module rendercontrol
*/
public class MergeAtomsGenerator extends BasicAtomGenerator {
public MergeAtomsGenerator() {}
@Override
public IRenderingElement generate(IAtomContainer ac, RendererModel model) {
JChemPaintRendererModel jcpModel = (JChemPaintRendererModel) model;
ElementGroup selectionElements = new ElementGroup();
double radius = jcpModel.getHighlightDistance() / jcpModel.getScale();
radius /= 2.0;
for(IAtom atom : model.getMerge().keySet()){
Point2d p1 = atom.getPoint2d();
Point2d p2 = model.getMerge().get( atom ).getPoint2d();
// the element size has to be scaled to model space
// so that it can be scaled back to screen space...
PathBuilder pb = new PathBuilder();
pb.color( jcpModel.getHoverOverColor() );
Vector2d vec = new Vector2d();
vec.sub( p2, p1 );
Vector2d per = GeometryUtil.calculatePerpendicularUnitVector( p1, p2 );
per.scale( radius );
Vector2d per2 = new Vector2d();
per2.scale( -1 ,per);
Vector2d v1= new Vector2d(vec);
Vector2d v2= new Vector2d();
v1.normalize();
v1.scale( radius );
v2.scale( -1, v1 );
Point2d f1 = new Point2d();
Point2d f2 = new Point2d();
Point2d f3 = new Point2d();
Point2d s1 = new Point2d();
Point2d s2 = new Point2d();
Point2d s3 = new Point2d();
f1.add( p1, per );
f2.add( p1 , v2 );
f3.add( p1, per2 );
s1.add(p2, per2);
s2.add(p2, v1);
s3.add( p2, per );
pb.moveTo(f1).quadTo( f2,f3 ).lineTo( s1 ).quadTo( s2, s3 ).close();
selectionElements.add(pb.createPath());
}
return selectionElements;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/MappingGenerator.java
|
.java
| 2,812
| 72
|
/* Copyright (C) 2009 Gilleain Torrance <gilleain@users.sf.net>
* 2009 Stefan Kuhn <sh3@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import java.awt.Color;
import javax.vecmath.Point2d;
import org.openscience.cdk.interfaces.IAtom;
import org.openscience.cdk.interfaces.IMapping;
import org.openscience.cdk.interfaces.IReaction;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.elements.LineElement;
/**
* @cdk.module renderextra
*/
public class MappingGenerator implements IReactionGenerator {
public MappingGenerator() {}
public IRenderingElement generate(IReaction reaction, JChemPaintRendererModel model) {
if(!model.getShowAtomAtomMapping())
return null;
ElementGroup elementGroup = new ElementGroup();
Color mappingColor = model.getAtomAtomMappingLineColor();
for (IMapping mapping : reaction.mappings()) {
// XXX assume that there are only 2 endpoints!
// XXX assume that the ChemObjects are actually IAtoms...
IAtom endPointA = (IAtom) mapping.getChemObject(0);
IAtom endPointB = (IAtom) mapping.getChemObject(1);
Point2d pA = endPointA.getPoint2d();
Point2d pB = endPointB.getPoint2d();
elementGroup.add(
new LineElement(pA.x, pA.y, pB.x, pB.y, getWidthForMappingLine(model), mappingColor));
}
return elementGroup;
}
/**
* Determine the width of an atom atom mapping, returning the width defined
* in the model. Note that this will be scaled
* to the space of the model.
*
* @param model the renderer model
* @return a double in chem-model space
*/
public double getWidthForMappingLine(JChemPaintRendererModel model) {
double scale = model.getScale();
return model.getMappingLineWidth() / scale;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/ReactionArrowGenerator.java
|
.java
| 2,241
| 55
|
/* Copyright (C) 2009 Stefan Kuhn <shk3@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import java.awt.geom.Rectangle2D;
import org.openscience.cdk.interfaces.IReaction;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.jchempaint.renderer.Renderer;
import org.openscience.cdk.renderer.elements.ArrowElement;
import org.openscience.cdk.renderer.elements.IRenderingElement;
/**
* Generate the arrow for a reaction.
*
* @author maclean
* @cdk.module renderextra
*
*/
public class ReactionArrowGenerator implements IReactionGenerator {
public IRenderingElement generate(IReaction reaction, JChemPaintRendererModel model) {
Rectangle2D totalBoundsReactants =
Renderer.calculateBounds(reaction.getReactants());
Rectangle2D totalBoundsProducts =
Renderer.calculateBounds(reaction.getProducts());
if (totalBoundsReactants == null || totalBoundsProducts == null)
return null;
double d = model.getBondLength() / model.getScale();
return new ArrowElement(totalBoundsReactants.getMaxX() + d,
totalBoundsReactants.getCenterY(),
totalBoundsProducts.getMinX() - d,
totalBoundsProducts.getCenterY(),
1 / model.getScale(),true,model.getForeColor());
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/SelectionToolGenerator.java
|
.java
| 2,616
| 67
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2008 Gilleain Torrance <gilleain.torrance@gmail.com>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.renderer.RendererModel;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.generators.IGenerator;
import org.openscience.cdk.renderer.generators.IGeneratorParameter;
import org.openscience.cdk.renderer.selection.IChemObjectSelection;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.jchempaint.renderer.selection.IncrementalSelection;
import java.awt.Color;
import java.util.List;
/**
* Generate the outline of the selection tool (e.g. rectangle or lasso).
*/
public class SelectionToolGenerator implements IGenerator<IAtomContainer> {
public SelectionToolGenerator() {
}
public IRenderingElement generate(IAtomContainer ac, RendererModel model) {
return generate(model.getSelection(), ((JChemPaintRendererModel) model).getSelectedPartColor());
}
protected IRenderingElement generate(IChemObjectSelection selection, Color selectionColor) {
ElementGroup selectionElements = new ElementGroup();
if (selection == null)
return selectionElements;
if (selection instanceof IncrementalSelection) {
IncrementalSelection sel = (IncrementalSelection) selection;
if (!sel.isFinished())
selectionElements.add(sel.generate(selectionColor));
}
return selectionElements;
}
public List<IGeneratorParameter<?>> getParameters() {
// TODO Auto-generated method stub
return null;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/SelectAtomGenerator.java
|
.java
| 3,698
| 89
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2008 Gilleain Torrance <gilleain.torrance@gmail.com>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import java.awt.Color;
import java.util.List;
import javax.vecmath.Point2d;
import org.openscience.cdk.interfaces.IAtom;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.renderer.RendererModel;
import org.openscience.cdk.renderer.elements.LineElement;
import org.openscience.cdk.renderer.elements.RectangleElement;
import org.openscience.cdk.renderer.generators.IGenerator;
import org.openscience.cdk.renderer.generators.IGeneratorParameter;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.elements.OvalElement;
import org.openscience.cdk.renderer.selection.IChemObjectSelection;
import org.openscience.jchempaint.renderer.RenderingParameters;
/**
* @cdk.module rendercontrol
*/
public class SelectAtomGenerator implements IGenerator<IAtomContainer> {
private boolean autoUpdateSelection = true;
public SelectAtomGenerator() {}
public IRenderingElement generate(IAtomContainer ac, RendererModel model) {
JChemPaintRendererModel jcpModel = (JChemPaintRendererModel) model;
Color selectionColor = jcpModel.getSelectedPartColor();
IChemObjectSelection selection = jcpModel.getSelection();
return generate(selection, selectionColor, jcpModel);
}
protected IRenderingElement generate(IChemObjectSelection selection, Color selectionColor, JChemPaintRendererModel model){
ElementGroup result = new ElementGroup();
if(selection==null)
return result;
if (this.autoUpdateSelection || selection.isFilled()) {
RenderingParameters.AtomShape shape = model.getSelectionShape();
double r = model.getSelectionRadius() / model.getScale();
if (shape != RenderingParameters.AtomShape.NONE) {
double d = 4 * r;
for (IAtom atom : selection.elements(IAtom.class)) {
Point2d p = atom.getPoint2d();
IRenderingElement element;
switch (shape) {
case OVAL:
result.add(new OvalElement(p.x, p.y, d, false, selectionColor));
break;
case SQUARE:
result.add(new RectangleElement(p.x - d, p.y + d, 2 * d, -2 * d, false, selectionColor));
break;
}
}
}
}
return result;
}
public List<IGeneratorParameter<?>> getParameters() {
// TODO Auto-generated method stub
return null;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/LonePairGenerator.java
|
.java
| 3,893
| 104
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2009 Gilleain Torrance <gilleain.torrance@gmail.com>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import java.awt.Color;
import java.util.List;
import javax.vecmath.Point2d;
import org.openscience.cdk.geometry.GeometryUtil;
import org.openscience.cdk.interfaces.IAtom;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.interfaces.ILonePair;
import org.openscience.cdk.renderer.RendererModel;
import org.openscience.cdk.renderer.generators.IGenerator;
import org.openscience.cdk.renderer.generators.IGeneratorParameter;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.elements.OvalElement;
/**
* Generate the symbols for lone pairs.
*
* @author maclean
* @cdk.module renderextra
*
*/
public class LonePairGenerator implements IGenerator<IAtomContainer> {
public LonePairGenerator() {}
public IRenderingElement generate(IAtomContainer ac, RendererModel model) {
JChemPaintRendererModel jcpModel = (JChemPaintRendererModel) model;
ElementGroup group = new ElementGroup();
// TODO : put into RendererModel
final double SCREEN_RADIUS = 1.0;
// separation between centers
final double SCREEN_SEPARATION = 2.5;
final Color RADICAL_COLOR = Color.BLACK;
// XXX : is this the best option?
final double ATOM_RADIUS = jcpModel.getAtomRadius();
double scale = jcpModel.getScale();
double modelAtomRadius = ATOM_RADIUS / scale;
double modelPointRadius = SCREEN_RADIUS / scale;
double modelSeparation = SCREEN_SEPARATION / scale;
for (ILonePair lp : ac.lonePairs()) {
IAtom atom = lp.getAtom();
Point2d p = atom.getPoint2d();
int align = GeometryUtil.getBestAlignmentForLabelXY(ac, atom);
double rx = p.x;
double ry = p.y;
double dx = 0;
double dy = 0;
if (align == 1) {
rx += modelAtomRadius;
dy += modelSeparation;
} else if (align == -1) {
rx -= modelAtomRadius;
dy += modelSeparation;
} else if (align == 2) {
ry -= modelAtomRadius;
dx += modelSeparation;
} else if (align == -2) {
ry += modelAtomRadius;
dx += modelSeparation;
}
group.add(
new OvalElement(rx + dx, ry + dy,
modelPointRadius, true, RADICAL_COLOR));
group.add(
new OvalElement(rx - dx, ry - dy,
modelPointRadius, true, RADICAL_COLOR));
}
return group;
}
public List<IGeneratorParameter<?>> getParameters() {
// TODO Auto-generated method stub
return null;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/HighlightAtomGenerator.java
|
.java
| 2,592
| 64
|
/* Copyright (C) 2009 Gilleain Torrance <gilleain@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import java.awt.Color;
import javax.vecmath.Point2d;
import org.openscience.cdk.interfaces.IAtom;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.renderer.RendererModel;
import org.openscience.cdk.renderer.generators.BasicAtomGenerator;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.elements.OvalElement;
/**
* @cdk.module rendercontrol
*/
public class HighlightAtomGenerator extends BasicAtomGenerator {
public HighlightAtomGenerator() {}
private boolean shouldHighlight(IAtom atom, JChemPaintRendererModel model) {
return !super.isHydrogen(atom) || model.getShowExplicitHydrogens();
}
@Override
public IRenderingElement generate(IAtomContainer ac, RendererModel model) {
JChemPaintRendererModel jcpModel = (JChemPaintRendererModel) model;
IAtom atom = model.getHighlightedAtom();
if (atom != null && shouldHighlight(atom, jcpModel)) {
Point2d p = atom.getPoint2d();
// the element size has to be scaled to model space
// so that it can be scaled back to screen space...
double radius = jcpModel.getHighlightDistance() / jcpModel.getScale();
radius /= 1.5;
boolean filled = jcpModel.getHighlightShapeFilled();
Color highlightColor = jcpModel.getHoverOverColor();
return new OvalElement(p.x, p.y, radius, filled, highlightColor);
}
return new ElementGroup();
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/BoundsGenerator.java
|
.java
| 3,101
| 81
|
/* Copyright (C) 2009 Gilleain Torrance <gilleain@users.sf.net>
* 2009 Stefan Kuhn <shk3@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import java.awt.geom.Rectangle2D;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.interfaces.IAtomContainerSet;
import org.openscience.cdk.interfaces.IReaction;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.jchempaint.renderer.Renderer;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.elements.RectangleElement;
/**
* Produce a bounding rectangle for various chem objects.
*
* @author maclean
* @cdk.module renderextra
*/
public class BoundsGenerator implements IReactionGenerator {
public BoundsGenerator() {}
public IRenderingElement generate(IReaction reaction, JChemPaintRendererModel model) {
ElementGroup elementGroup = new ElementGroup();
IAtomContainerSet reactants = reaction.getReactants();
if (reactants != null) {
elementGroup.add(this.generate(reactants, model));
}
IAtomContainerSet products = reaction.getProducts();
if (products != null) {
elementGroup.add(this.generate(products, model));
}
return elementGroup;
}
public IRenderingElement generate(IAtomContainer molecule, JChemPaintRendererModel model) {
Rectangle2D bounds = Renderer.calculateBounds(molecule);
return new RectangleElement(bounds.getMinX(),
bounds.getMaxY(),
bounds.getMaxX(),
bounds.getMinY(),
model.getBoundsColor());
}
public IRenderingElement generate(
IAtomContainerSet moleculeSet, JChemPaintRendererModel model) {
Rectangle2D totalBounds = Renderer.calculateBounds(moleculeSet);
return new RectangleElement(totalBounds.getMinX(),
totalBounds.getMaxY(),
totalBounds.getMaxX(),
totalBounds.getMinY(),
model.getBoundsColor());
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/ReactantsBoxGenerator.java
|
.java
| 2,449
| 64
|
/* Copyright (C) 2009 Stefan Kuhn <shk3@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import java.awt.Color;
import java.awt.geom.Rectangle2D;
import org.openscience.cdk.interfaces.IReaction;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.jchempaint.renderer.Renderer;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.elements.RectangleElement;
import org.openscience.jchempaint.renderer.elements.TextElement;
/**
* Generate the symbols for radicals.
*
* @author maclean
* @cdk.module renderextra
*
*/
public class ReactantsBoxGenerator implements IReactionGenerator {
public IRenderingElement generate(IReaction reaction, JChemPaintRendererModel model) {
if (!model.getShowReactionBoxes())
return null;
if (reaction.getReactantCount() == 0)
return new ElementGroup();
double d = model.getBondLength() / model.getScale()/2;
Rectangle2D totalBounds = Renderer.calculateBounds(reaction.getReactants());
ElementGroup diagram = new ElementGroup();
double minX = totalBounds.getMinX();
double minY = totalBounds.getMinY();
double maxX = totalBounds.getMaxX();
double maxY = totalBounds.getMaxY();
Color c = model.getForeColor();
diagram.add(new RectangleElement(
minX - d, maxY + d, maxX + d, minY - d, c));
diagram.add(new TextElement(
(minX+maxX)/2, minY-d, "Reactants", c));
return diagram;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/ReactionBoxGenerator.java
|
.java
| 2,599
| 65
|
/* Copyright (C) 2009 Stefan Kuhn <shk3@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import java.awt.geom.Rectangle2D;
import org.openscience.cdk.interfaces.IReaction;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.jchempaint.renderer.Renderer;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.elements.RectangleElement;
import org.openscience.jchempaint.renderer.elements.TextElement;
/**
* Generate the symbols for radicals.
*
* @author maclean
* @cdk.module renderextra
*
*/
public class ReactionBoxGenerator implements IReactionGenerator {
public IRenderingElement generate(IReaction reaction, JChemPaintRendererModel model) {
if (!model.getShowReactionBoxes())
return null;
double d = model.getBondLength() / model.getScale();
Rectangle2D totalBounds = Renderer.calculateBounds(reaction);
if (totalBounds == null) return null;
ElementGroup diagram = new ElementGroup();
diagram.add(new RectangleElement(totalBounds.getMinX()-d,
totalBounds.getMaxY()+d,
totalBounds.getMaxX()+d,
totalBounds.getMinY()-d,
model.getForeColor()));
if (reaction.getID() != null) {
diagram.add(new TextElement((totalBounds.getMinX()
+totalBounds.getMaxX())/2,
totalBounds.getMinY()-d,
reaction.getID(),
model.getForeColor()));
}
return diagram;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/ExternalHighlightBondGenerator.java
|
.java
| 2,128
| 50
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2008 Stefan Kuhn <shk3@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import java.awt.Color;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.renderer.RendererModel;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.jchempaint.renderer.selection.AtomContainerSelection;
import org.openscience.cdk.renderer.selection.IChemObjectSelection;
/**
* A generator to generate the externally selected bonds in ExternalHighlightColor.
*/
public class ExternalHighlightBondGenerator extends SelectBondGenerator {
public ExternalHighlightBondGenerator() {}
@Override
public IRenderingElement generate(IAtomContainer ac, RendererModel model) {
JChemPaintRendererModel jcpModel = (JChemPaintRendererModel) model;
if(model.getExternalSelectedPart()==null)
return new ElementGroup();
Color selectionColor = jcpModel.getExternalHighlightColor();
IChemObjectSelection selection = new AtomContainerSelection(model.getExternalSelectedPart());
return generate(selection, selectionColor, jcpModel);
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/generators/TooltipGenerator.java
|
.java
| 2,691
| 66
|
/* Copyright (C) 2009 Stefan Kuhn <shk3@users.sf.net>
*
* Contact: cdk-jchempaint@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.generators;
import java.awt.Color;
import javax.vecmath.Point2d;
import org.openscience.cdk.interfaces.IAtom;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.renderer.RendererModel;
import org.openscience.cdk.renderer.generators.BasicAtomGenerator;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.jchempaint.renderer.elements.TextGroupElement;
/**
* A generator for tooltips shown when hovering over an atom
*
*/
public class TooltipGenerator extends BasicAtomGenerator {
@Override
public IRenderingElement generate(
IAtomContainer ac, IAtom atom, RendererModel model) {
JChemPaintRendererModel jcpModel = (JChemPaintRendererModel) model;
if (jcpModel.getShowTooltip() && (atom == model.getHighlightedAtom() ||( model.getExternalSelectedPart()!=null && model.getExternalSelectedPart().contains(atom))) && model.getToolTipText(atom) != null)
{
String text = model.getToolTipText(atom);
String[] result = text.split("\\n");
Point2d p = atom.getPoint2d();
Color c = Color.black;
TextGroupElement textGroup;
if(result.length>1){
textGroup = new TextGroupElement(p.x, p.y, result[1], c, Color.yellow);
textGroup.addChild(result[0], TextGroupElement.Position.N);
}else{
textGroup = new TextGroupElement(p.x, p.y, result[0], c, Color.yellow);
}
if(result.length>2)
textGroup.addChild(result[0], TextGroupElement.Position.S);
return textGroup;
}else{
return null;
}
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/elements/WigglyLineElement.java
|
.java
| 2,253
| 68
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2009 Stefan Kuhn <stefan.kuhn@ebi.ac.uk>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.elements;
import org.openscience.cdk.renderer.elements.IRenderingVisitor;
import org.openscience.cdk.renderer.elements.LineElement;
import java.awt.Color;
/**
* An element representing a wiggly line. Note that according to IUPAC
* recommendations {@cdk.cite IUPAC2006} this is the only recommended type
* of undefined stereo bond. The criss cross wedge is discouraged.
*
* @cdk.module renderbasic
* @author shk3
*/
public class WigglyLineElement extends LineElement {
/**
* @param x1 X coordinate of start point.
* @param y1 Y coordinate of start point.
* @param x2 X coordinate of end point.
* @param y2 Y coordinate of end point.
* @param width The disired width
* @param color The desired color.
*/
public WigglyLineElement(double x1, double y1, double x2, double y2,
double width, Color color) {
super(x1, y1, x2, y2, width, color);
}
/**
* Constructor for the WigglyLineElement
*
* @param element The LineElement this WigglyLine is based on.
* @param color The desired color.
*/
public WigglyLineElement(LineElement element,
Color color) {
this(element.firstPointX, element.firstPointY, element.secondPointX, element.secondPointY,
element.width, color);
}
@Override
public void accept(IRenderingVisitor v) {
v.visit(this);
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/elements/TextGroupElement.java
|
.java
| 3,098
| 102
|
/* Copyright (C) 2009 Gilleain Torrance <gilleain@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.elements;
import org.openscience.cdk.renderer.elements.*;
import java.awt.Color;
import java.util.ArrayList;
import java.util.List;
/**
* @cdk.module renderbasic
*/
public class TextGroupElement extends TextElement {
public enum Position { NW, SW, SE, NE, S, N, W, E };
public class Child {
public final String text;
public final String subscript;
public final Position position;
public final Boolean isComment;
public Child(String text, Position position) {
this.text = text;
this.position = position;
this.subscript = null;
this.isComment = false;
}
public Child(String text, String subscript, Position position) {
this.text = text;
this.position = position;
this.subscript = subscript;
this.isComment = false;
}
public Child(String text, Position position, Boolean isComment) {
this.text = text;
this.position = position;
this.subscript = null;
this.isComment = isComment;
}
}
public Color notTypeableUnderlineColor;
public boolean isNotTypeableUnderlined;
public final List<Child> children;
public TextGroupElement(double x, double y, String text, Color color, Color backColor) {
super(x, y, text, color, backColor);
this.children = new ArrayList<Child>();
}
public TextGroupElement(double x, double y, String text, Color color) {
super(x, y, text, color);
this.children = new ArrayList<Child>();
}
public void addChild(String text, Position position) {
this.children.add(new Child(text, position));
}
public void addChild(String text, String subscript, Position position) {
this.children.add(new Child(text, subscript, position));
}
public void addChild(String text, Position position, Boolean isComment) {
this.children.add(new Child(text, position, isComment));
}
public void accept(IRenderingVisitor v) {
v.visit(this);
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/elements/TextElement.java
|
.java
| 2,101
| 72
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2008 Arvid Berg <goglepox@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.elements;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.elements.IRenderingVisitor;
import java.awt.Color;
/**
* @cdk.module render
*/
public class TextElement implements IRenderingElement {
public final double x;
public final double y;
public final String text;
public final Color color;
public final Color backColor;
public final Double extraZoom;
public TextElement(double x, double y, String text, Color color, Color backColor) {
this.backColor = backColor;
this.x = x;
this.y = y;
this.text = text;
this.color = color;
this.extraZoom=null;
}
public TextElement(double x, double y, String text, Color color) {
this.x = x;
this.y = y;
this.text = text;
this.color = color;
this.backColor = null;
this.extraZoom=null;
}
public TextElement(double x, double y, String text, Color color, double extraZoom) {
this.x = x;
this.y = y;
this.text = text;
this.color = color;
this.backColor = null;
this.extraZoom=extraZoom;
}
public void accept(IRenderingVisitor v) {
v.visit(this);
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/font/FreeSansBoldGM.java
|
.java
| 25,201
| 129
|
/* FreeSansBoldGM.java: Autogenerated by ttf2svgpath from FreeSansBold.otf */
package org.openscience.jchempaint.renderer.font;
import java.util.HashMap;
import java.util.Map;
import org.openscience.jchempaint.renderer.font.GlyphMetrics;
public class FreeSansBoldGM {
public Map<Integer, GlyphMetrics> map;
public void init() {
map = new HashMap<Integer, GlyphMetrics>();
map.put (0x25, new GlyphMetrics(22, 863, -20, 709, 889, "M206 701c103 0 184 -81 184 -184c0 -98 -84 -180 -184 -180c-101 0 -184 82 -184 182s83 182 184 182zM206 602c-47 0 -85 -38 -85 -83c0 -46 38 -84 85 -84c46 0 85 38 85 83c0 47 -38 84 -85 84zM606 709h76l-395 -729h-77zM679 352c103 0 184 -81 184 -185c0 -98 -84 -180 -184 -180c-101 0 -184 82 -184 183c0 100 83 182 184 182zM679 253c-47 0 -85 -38 -85 -84s38 -84 85 -84c46 0 85 38 85 83c0 47 -38 85 -85 85z"));
map.put (0x28, new GlyphMetrics(40, 303, -200, 729, 333, "M203 729h100c-106 -204 -140 -318 -140 -464c0 -147 34 -259 140 -465h-100c-115 173 -163 311 -163 465c0 153 48 291 163 464z"));
map.put (0x29, new GlyphMetrics(22, 285, -200, 729, 333, "M122 -200h-100c106 204 140 318 140 464c0 147 -34 259 -140 465h100c115 -173 163 -311 163 -465c0 -153 -48 -291 -163 -464z"));
map.put (0x2a, new GlyphMetrics(23, 357, 407, 729, 389, "M132 544l-109 36l22 69l109 -36v116h72v-116l109 36l22 -70l-109 -35l67 -94l-58 -43l-67 94l-67 -94l-58 43z"));
map.put (0x2b, new GlyphMetrics(50, 533, -10, 473, 584, "M533 291v-119h-182v-182h-119v182h-182v119h182v182h119v-182h182z"));
map.put (0x2c, new GlyphMetrics(50, 200, -174, 146, 250, "M50 146h150v-137c0 -78 -14 -166 -150 -183v56c60 12 76 48 76 118h-76v146z"));
map.put (0x2d, new GlyphMetrics(26, 298, 207, 342, 333, "M298 342v-135h-272v135h272z"));
map.put (0x2e, new GlyphMetrics(50, 200, 0, 146, 250, "M200 146v-146h-150v146h150z"));
map.put (0x2f, new GlyphMetrics(2, 275, -14, 714, 278, "M208 714h67l-206 -728h-67z"));
map.put (0x30, new GlyphMetrics(29, 517, -23, 724, 556, "M273 -23c-195 0 -244 167 -244 373c0 224 55 374 244 374c191 0 244 -153 244 -378c0 -199 -47 -369 -244 -369zM377 349c0 203 -40 262 -104 262c-72 0 -104 -59 -104 -260c0 -211 39 -254 104 -254c63 0 104 41 104 252z"));
map.put (0x31, new GlyphMetrics(68, 378, 0, 709, 556, "M238 489h-170v93c122 0 195 43 217 127h93v-709h-140v489z"));
map.put (0x32, new GlyphMetrics(30, 515, 0, 724, 556, "M515 499c0 -212 -243 -247 -303 -374h300v-125h-482c7 131 45 193 164 276c157 110 181 142 181 220c0 71 -38 114 -100 114c-64 0 -101 -46 -101 -125v-23h-134c-1 11 -1 19 -1 25c0 150 86 237 233 237c149 0 243 -88 243 -225z"));
map.put (0x33, new GlyphMetrics(29, 516, -23, 724, 556, "M38 498c0 192 139 226 230 226c137 0 225 -75 225 -193c0 -65 -27 -110 -93 -151c80 -39 116 -93 116 -176c0 -138 -97 -227 -248 -227c-145 0 -238 89 -239 231h136c4 -73 40 -111 106 -111c61 0 105 45 105 107c0 52 -30 93 -77 106c-24 6 -33 7 -82 7v94h12c83 0 124 36 124 107c0 59 -32 93 -88 93c-79 0 -95 -49 -97 -125h-130v12z"));
map.put (0x34, new GlyphMetrics(24, 522, 0, 709, 556, "M522 273v-116h-74v-157h-140v157h-284v118l259 434h165v-436h74zM308 273v303l-185 -303h185z"));
map.put (0x35, new GlyphMetrics(27, 517, -23, 709, 556, "M489 709v-125h-293l-23 -148c41 31 76 43 123 43c131 0 221 -100 221 -245c0 -153 -104 -257 -256 -257c-138 0 -232 84 -234 208h138c3 -57 37 -88 98 -88c70 0 114 51 114 134c0 86 -43 137 -114 137c-43 0 -75 -19 -90 -54h-126l63 395h379z"));
map.put (0x36, new GlyphMetrics(32, 519, -23, 724, 556, "M32 337c0 218 62 387 262 387c43 0 187 -8 213 -176h-130c-18 45 -42 63 -87 63c-98 0 -113 -89 -118 -207c44 46 83 63 141 63c122 0 206 -94 206 -230c0 -154 -96 -260 -237 -260c-131 0 -250 69 -250 360zM279 357c-65 0 -109 -54 -109 -132c0 -75 44 -128 108 -128c62 0 108 55 108 131c0 78 -42 129 -107 129z"));
map.put (0x37, new GlyphMetrics(29, 528, 0, 709, 556, "M528 709v-110c-168 -203 -241 -375 -254 -599h-141c32 258 102 403 249 584h-353v125h499z"));
map.put (0x38, new GlyphMetrics(22, 525, -23, 724, 556, "M501 532c0 -82 -43 -120 -92 -146c82 -44 116 -98 116 -182c0 -134 -103 -227 -251 -227c-149 0 -252 93 -252 227c0 84 34 138 116 182c-64 34 -92 77 -92 145c0 112 96 193 228 193c131 0 227 -81 227 -192zM275 611c-63 0 -106 -38 -106 -92c0 -55 44 -94 106 -94c61 0 105 39 105 93c0 55 -43 93 -105 93zM273 330c-67 0 -111 -46 -111 -118c0 -70 43 -115 111 -115s112 45 112 113c0 74 -43 120 -112 120z"));
map.put (0x39, new GlyphMetrics(28, 516, -24, 724, 556, "M516 370c0 -335 -149 -394 -261 -394c-122 0 -214 80 -217 189h135c3 -42 37 -69 86 -69c79 0 117 67 117 202c-17 -19 -49 -70 -136 -70c-126 0 -212 100 -212 246c0 148 97 250 239 250c100 0 249 -53 249 -354zM263 610c-62 0 -102 -52 -102 -134c0 -81 40 -132 104 -132s108 52 108 130c0 83 -42 136 -110 136z"));
map.put (0x3c, new GlyphMetrics(40, 529, -10, 474, 584, "M529 474v-109l-382 -133l382 -131v-111l-489 182v121z"));
map.put (0x3d, new GlyphMetrics(50, 534, 52, 411, 584, "M534 411v-119h-484v119h484zM534 171v-119h-484v119h484z"));
map.put (0x3e, new GlyphMetrics(40, 529, -10, 474, 584, "M40 -10v109l382 133l-382 131v111l489 -182v-121z"));
map.put (0x3f, new GlyphMetrics(64, 556, 0, 744, 611, "M64 481c3 226 146 263 243 263c147 0 249 -94 249 -230c0 -84 -27 -128 -120 -194c-60 -42 -67 -53 -67 -119h-124v14c0 180 170 176 170 296c0 65 -45 115 -105 115c-63 0 -110 -57 -110 -134v-9v-2h-136zM385 146v-146h-150v146h150z"));
map.put (0x5b, new GlyphMetrics(66, 308, -200, 729, 333, "M308 729v-102h-112v-725h112v-102h-242v929h242z"));
map.put (0x5d, new GlyphMetrics(18, 260, -200, 729, 333, "M18 -200v102h112v725h-112v102h242v-929h-242z"));
map.put (0x41, new GlyphMetrics(10, 687, 0, 729, 697, "M485 147h-273l-49 -147h-153l259 729h166l252 -729h-154zM444 272l-95 285l-95 -285h190z"));
map.put (0x42, new GlyphMetrics(80, 664, 0, 729, 704, "M664 210c0 -87 -51 -210 -256 -210h-328v729h325c192 0 238 -125 238 -192c0 -62 -28 -104 -100 -150c82 -48 121 -105 121 -177zM230 604v-165h163c69 0 106 29 106 82c0 54 -37 83 -106 83h-163zM230 314v-189h179c73 0 111 33 111 94c0 62 -38 95 -111 95h-179z"));
map.put (0x43, new GlyphMetrics(40, 681, -23, 741, 721, "M535 482c-8 37 -31 131 -156 131c-118 0 -189 -96 -189 -257c0 -159 68 -253 184 -253c93 0 152 53 161 146h146c-9 -167 -130 -272 -311 -272c-203 0 -330 147 -330 381c0 236 128 383 334 383c170 0 291 -95 304 -259h-143z"));
map.put (0x44, new GlyphMetrics(80, 684, 0, 729, 724, "M80 0v729h285c112 0 181 -25 230 -84c58 -69 89 -168 89 -280c0 -113 -31 -212 -89 -280c-49 -59 -119 -85 -230 -85h-285zM230 125h135c113 0 169 79 169 239c0 161 -56 240 -169 240h-135v-479z"));
map.put (0x45, new GlyphMetrics(80, 625, 0, 729, 665, "M230 314v-189h395v-125h-545v729h527v-125h-377v-165h349v-125h-349z"));
map.put (0x46, new GlyphMetrics(80, 592, 0, 729, 632, "M230 314v-314h-150v729h512v-125h-362v-165h319v-125h-319z"));
map.put (0x47, new GlyphMetrics(40, 709, -21, 743, 769, "M568 498c-21 62 -70 117 -174 117c-127 0 -204 -95 -204 -252c0 -150 87 -256 208 -256c37 0 172 24 185 162h-166v125h292v-394h-90l-18 96c-56 -82 -122 -117 -220 -117c-197 0 -341 161 -341 382c0 227 143 382 353 382c176 0 298 -95 316 -245h-141z"));
map.put (0x48, new GlyphMetrics(80, 669, 0, 729, 749, "M519 331h-289v-331h-150v729h150v-273h288v273h151v-729h-150v331z"));
map.put (0x49, new GlyphMetrics(80, 230, 0, 729, 310, "M230 729v-369h-150v369h150zM308 -23c-100 0 -228 44 -228 221v72h150v-70c0 -67 24 -95 81 -95c52 0 81 30 81 85v539h150v-539c0 -136 -85 -213 -234 -213z"));
map.put (0x4a, new GlyphMetrics(30, 492, -23, 729, 572, "M258 -23c-100 0 -228 44 -228 221v72h150v-70c0 -67 24 -95 81 -95c52 0 81 30 81 85v539h150v-539c0 -136 -85 -213 -234 -213z"));
map.put (0x4b, new GlyphMetrics(80, 723, 0, 729, 728, "M230 244v-244h-150v729h150v-320l285 320h177l-291 -314l322 -415h-179l-239 322z"));
map.put (0x4c, new GlyphMetrics(80, 579, 0, 729, 619, "M446 442h124v-123h-124v123zM230 729v-604h349v-125h-499v729h150z"));
map.put (0x4d, new GlyphMetrics(80, 790, 0, 729, 870, "M230 568v-568h-150v729h224l132 -580l128 580h226v-729h-150v568l-129 -568h-150z"));
map.put (0x4e, new GlyphMetrics(80, 673, 0, 729, 753, "M523 0l-293 504v-504h-150v729h154l289 -496v496h150v-729h-150z"));
map.put (0x4f, new GlyphMetrics(40, 742, -23, 741, 782, "M40 359c0 222 138 382 350 382c211 0 352 -155 352 -387c0 -209 -132 -377 -351 -377c-214 0 -351 160 -351 382zM391 613c-121 0 -201 -101 -201 -254s80 -254 201 -254c120 0 201 101 201 250c0 158 -78 258 -201 258z"));
map.put (0x50, new GlyphMetrics(80, 637, 0, 729, 677, "M230 260v-260h-150v729h322c152 0 235 -80 235 -226c0 -148 -86 -243 -220 -243h-187zM230 385h140c80 0 117 35 117 109c0 75 -37 110 -117 110h-140v-219z"));
map.put (0x51, new GlyphMetrics(40, 742, -54, 741, 782, "M391 741c217 0 351 -166 351 -380c0 -98 -31 -198 -80 -258l80 -76l-76 -81l-86 81c-58 -35 -114 -50 -189 -50c-214 0 -351 162 -351 382s137 382 351 382zM477 278l82 -78c22 44 33 98 33 158c0 155 -79 255 -201 255c-121 0 -201 -101 -201 -254s80 -254 200 -254c32 0 65 7 88 19l-77 73z"));
map.put (0x52, new GlyphMetrics(80, 677, 0, 729, 722, "M493 125c0 54 2 46 2 77c0 61 -28 87 -93 87h-172v-289h-150v729h391c157 0 196 -116 196 -197c0 -89 -41 -148 -123 -180c93 -40 97 -50 101 -265c0 -32 9 -47 32 -60v-27h-161c-19 35 -23 57 -23 125zM517 511c0 81 -43 93 -106 93h-181v-190h181c62 0 106 12 106 97z"));
map.put (0x53, new GlyphMetrics(40, 641, -23, 741, 681, "M615 507h-140c-5 74 -58 114 -153 114c-77 0 -126 -35 -126 -90c0 -53 31 -74 138 -95l114 -22c135 -26 193 -86 193 -201c0 -148 -111 -236 -299 -236c-186 0 -293 85 -302 241h146c5 -78 62 -121 164 -121c91 0 147 37 147 98c0 59 -37 87 -137 106l-102 20c-148 28 -205 83 -205 196c0 142 101 224 276 224c113 0 286 -35 286 -234z"));
map.put (0x54, new GlyphMetrics(30, 614, 0, 729, 644, "M401 604v-604h-150v604h-221v125h584v-125h-213z"));
map.put (0x55, new GlyphMetrics(80, 658, -23, 729, 738, "M369 -23c-123 0 -289 52 -289 258v494h150v-494c0 -88 45 -130 139 -130s139 42 139 130v494h150v-494c0 -206 -166 -258 -289 -258z"));
map.put (0x56, new GlyphMetrics(20, 643, 0, 729, 663, "M393 0h-127l-246 729h151l162 -549l159 549h151z"));
map.put (0x57, new GlyphMetrics(20, 939, 0, 729, 959, "M737 0h-135l-122 569l-119 -569h-135l-206 729h159l113 -546l113 546h148l118 -547l109 547h159z"));
map.put (0x58, new GlyphMetrics(22, 653, 0, 729, 675, "M419 372l234 -372h-178l-140 253l-139 -253h-174l230 367l-222 362h178l128 -240l134 240h174z"));
map.put (0x59, new GlyphMetrics(10, 633, 0, 729, 643, "M402 270v-270h-150v270l-242 459h167l149 -322l139 322h168z"));
map.put (0x5a, new GlyphMetrics(30, 578, 0, 729, 608, "M578 729v-125l-372 -479h372v-125h-548v125l373 479h-373v125h548z"));
map.put (0x61, new GlyphMetrics(40, 536, -23, 549, 566, "M526 373h113v-16c-2 -76 -28 -157 -68 -203l123 -154h-161l-46 56c-81 -61 -127 -79 -195 -79c-142 0 -237 86 -237 216c0 94 40 147 157 208c-6 8 -10 13 -12 15l-28 34c-28 33 -42 69 -42 106c0 94 82 167 189 167s178 -62 178 -155c0 -72 -28 -115 -117 -176l116 -144c22 42 30 72 30 115v10zM278 319l-47 -29c-27 -18 -43 -47 -43 -80c0 -60 52 -117 106 -117c31 0 73 19 120 55zM317 470c40 22 60 51 60 86c0 37 -21 58 -57 58c-34 0 -58 -20 -58 -48c0 -25 5 -33 55 -96z"));
map.put (0x62, new GlyphMetrics(65, 581, -23, 729, 621, "M308 729v-102h-112v-725h112v-102h-242v929h242z"));
map.put (0x63, new GlyphMetrics(40, 528, -23, 549, 558, "M395 743v-122h-110v122h110zM252 743v-122h-110v122h110zM533 743v-122h-110v122h110zM110 743v-122h-110v122h110zM485 540v-540h-140v540h140zM200 540v-540h-140v540h140z"));
map.put (0x64, new GlyphMetrics(40, 556, -23, 729, 621, "M613 743h130v-74c0 -35 -11 -103 -132 -116v47c63 12 66 48 66 63h-64v80zM267 -23c-136 0 -227 127 -227 285c0 163 98 287 227 287c67 0 114 -25 149 -79v259h140v-729h-140v55c-35 -53 -82 -78 -149 -78zM298 432c-70 0 -118 -70 -118 -170c0 -99 48 -168 118 -168s118 68 118 166c0 103 -47 172 -118 172z"));
map.put (0x65, new GlyphMetrics(40, 543, -23, 549, 583, "M262 726v-210l-42 -309h-67l-41 309v210h150zM262 146v-146h-150v146h150z"));
map.put (0x66, new GlyphMetrics(14, 313, 0, 729, 343, "M587 529v-93h-83v-436h-140v436h-134v-436h-140v436h-76v93h76v65c0 91 45 135 138 135c19 0 54 -1 80 -3v-105c-12 2 -28 3 -40 3c-26 0 -38 -14 -38 -42v-53h134v65c0 91 45 135 138 135c19 0 54 -1 80 -3v-105c-12 2 -28 3 -40 3c-26 0 -38 -14 -38 -42v-53h83z"));
map.put (0x67, new GlyphMetrics(40, 547, -218, 549, 612, "M40 -10v109l382 133l-382 131v111l489 -182v-121z"));
map.put (0x68, new GlyphMetrics(65, 539, 0, 729, 604, "M298 342v-135h-272v135h272z"));
map.put (0x69, new GlyphMetrics(70, 210, 0, 729, 280, "M478 540v-634c0 -88 -36 -124 -123 -124c-20 0 -55 2 -83 5v112c10 -3 16 -4 25 -4c30 0 41 11 41 42v603h140zM478 729v-125h-140v125h140zM210 540v-540h-140v540h140zM210 729v-125h-140v125h140z"));
map.put (0x6a, new GlyphMetrics(4, 210, -218, 729, 280, "M210 540v-634c0 -88 -36 -124 -123 -124c-20 0 -55 2 -83 5v112c10 -3 16 -4 25 -4c30 0 41 11 41 42v603h140zM210 729v-125h-140v125h140z"));
map.put (0x6b, new GlyphMetrics(65, 554, 0, 729, 564, "M205 330l177 210h159l-184 -204l197 -336h-164l-129 238l-56 -61v-177h-140v729h140v-399z"));
map.put (0x6c, new GlyphMetrics(65, 205, 0, 729, 270, "M205 729v-729h-140v729h140z"));
map.put (0x6d, new GlyphMetrics(65, 829, 0, 549, 894, "M65 540h139v-67c46 55 86 76 148 76c67 0 121 -29 147 -80c42 56 90 80 158 80c108 0 172 -62 172 -167v-382h-140v360c0 43 -29 70 -76 70c-60 0 -96 -40 -96 -106v-324h-140v360c0 43 -29 70 -76 70c-60 0 -96 -40 -96 -106v-324h-140v540z"));
map.put (0x6e, new GlyphMetrics(65, 548, 0, 549, 613, "M146 -32h-106l40 204h-77v99h97l30 153h-99v99h119l34 174h105l-34 -174h103l34 174h105l-34 -174h90v-99h-109l-31 -153h100v-99h-119l-40 -204h-105l40 204h-103zM205 271h103l30 153h-103z"));
map.put (0x6f, new GlyphMetrics(40, 574, -23, 549, 614, "M238 489h-170v93c122 0 195 43 217 127h93v-709h-140v489z"));
map.put (0x70, new GlyphMetrics(65, 581, -218, 549, 621, "M206 701c103 0 184 -81 184 -184c0 -98 -84 -180 -184 -180c-101 0 -184 82 -184 182s83 182 184 182zM206 602c-47 0 -85 -38 -85 -83c0 -46 38 -84 85 -84c46 0 85 38 85 83c0 47 -38 84 -85 84zM606 709h76l-395 -729h-77zM679 352c103 0 184 -81 184 -185c0 -98 -84 -180 -184 -180c-101 0 -184 82 -184 183c0 100 83 182 184 182zM679 253c-47 0 -85 -38 -85 -84s38 -84 85 -84c46 0 85 38 85 83c0 47 -38 85 -85 85z"));
map.put (0x71, new GlyphMetrics(40, 556, -218, 549, 621, "M188 729v-130l-42 -129h-56l-40 129v130h138zM424 729v-130l-42 -129h-56l-40 129v130h138z"));
map.put (0x72, new GlyphMetrics(65, 372, 0, 549, 392, "M65 540h140v-106c30 73 86 115 150 115c4 0 9 0 17 -1v-142c-19 3 -29 4 -44 4c-82 0 -123 -41 -123 -123v-287h-140v540z"));
map.put (0x73, new GlyphMetrics(30, 521, -23, 549, 556, "M262 726v-210l-42 -309h-67l-41 309v210h150zM262 146v-146h-150v146h150z"));
map.put (0x74, new GlyphMetrics(21, 308, -23, 674, 338, "M329 743h130v-74c0 -35 -11 -103 -132 -116v47c63 12 66 48 66 63h-64v80zM308 529v-93h-78v-294c0 -48 9 -59 46 -59c10 0 18 1 32 3v-98c-25 -8 -50 -11 -81 -11c-92 0 -137 42 -137 127v332h-69v93h69v145h140v-145h78z"));
map.put (0x75, new GlyphMetrics(65, 548, -23, 540, 613, "M578 -120v-80h-600v80h600z"));
map.put (0x76, new GlyphMetrics(10, 532, 0, 540, 542, "M346 0h-147l-189 540h148l117 -395l109 395h148z"));
map.put (0x77, new GlyphMetrics(10, 771, 0, 540, 781, "M618 0h-145l-81 381l-86 -381h-144l-152 540h145l86 -378l82 378h140l81 -378l82 378h145z"));
map.put (0x78, new GlyphMetrics(15, 534, 0, 540, 549, "M354 272l180 -272h-168l-91 168l-92 -168h-168l180 272l-176 268h168l88 -163l87 163h168z"));
map.put (0x79, new GlyphMetrics(10, 539, -219, 540, 549, "M211 -26v4l-201 562h154l119 -393l112 393h144l-222 -639c-13 -37 -27 -120 -180 -120c-17 0 -29 1 -50 4v105c18 -5 26 -6 37 -6c49 0 87 39 87 90z"));
map.put (0x7a, new GlyphMetrics(30, 477, 0, 540, 507, "M273 -23c-195 0 -244 167 -244 373c0 224 55 374 244 374c191 0 244 -153 244 -378c0 -199 -47 -369 -244 -369zM377 349c0 203 -40 262 -104 262c-72 0 -104 -59 -104 -260c0 -211 39 -254 104 -254c63 0 104 41 104 252z"));
map.put (0x393, new GlyphMetrics(80, 614, 0, 729, 634, "M614 595h-388v-595h-146v729h534v-134z"));
map.put (0x394, new GlyphMetrics(10, 734, 0, 729, 744, "M10 0l281 729h145l298 -729h-724zM527 125l-162 424l-152 -424h314z"));
map.put (0x398, new GlyphMetrics(40, 756, 3, 726, 796, "M398 726c215 0 358 -141 358 -362c0 -223 -158 -361 -358 -361c-138 0 -358 71 -358 353c0 229 147 370 358 370zM183 355c0 -138 81 -231 213 -231c92 0 216 48 216 240c0 193 -128 242 -214 242c-151 0 -215 -108 -215 -251zM584 314h-376v122h376v-122z"));
map.put (0x39b, new GlyphMetrics(10, 703, 0, 729, 713, "M349 539l-195 -539h-144l275 729h126l292 -729h-149z"));
map.put (0x39e, new GlyphMetrics(50, 634, 0, 729, 684, "M618 602h-568v127h568v-127zM562 310h-450v127h450v-127zM634 0h-584v127h584v-127z"));
map.put (0x3a3, new GlyphMetrics(30, 646, 0, 729, 676, "M440 390l-223 -262h429v-128h-616v117l229 273l-195 226v113h562v-128h-368c1 -1 182 -211 182 -211z"));
map.put (0x3a6, new GlyphMetrics(40, 749, -13, 723, 789, "M40 348c0 248 229 292 289 296v79h131v-79c171 -11 289 -137 289 -296c0 -245 -228 -286 -288 -290v-71h-133v71c-169 11 -288 128 -288 290zM608 353c0 93 -65 152 -147 170v-348c86 19 147 82 147 178zM328 175v348c-83 -18 -147 -76 -147 -170c0 -107 73 -162 147 -178z"));
map.put (0x3a8, new GlyphMetrics(70, 762, -10, 732, 832, "M762 369c0 -201 -131 -237 -274 -237v-142h-144v142c-136 0 -274 18 -274 237v363l140 -4v-347c0 -80 22 -121 138 -121v469h136v-469c89 0 138 19 138 121v347l140 4v-363z"));
map.put (0x3a9, new GlyphMetrics(40, 759, 0, 749, 799, "M196 711l-126 -150h-70l70 150h126zM678 381c0 136 -84 246 -222 246c-127 0 -211 -109 -211 -249c0 -143 80 -221 174 -260v-118h-317v122h123c-71 59 -118 142 -118 257c0 193 132 370 347 370c229 0 367 -164 367 -366c0 -120 -48 -204 -122 -261h120v-122h-328v117c152 61 187 165 187 264z"));
map.put (0x3b1, new GlyphMetrics(40, 632, -15, 542, 632, "M451 763l-126 -150h-70l70 150h126zM409 267c0 69 -23 157 -118 157c-91 0 -120 -70 -120 -161c0 -89 28 -157 118 -157c80 0 120 54 120 161zM40 267c0 220 144 275 251 275c69 -2 109 -17 147 -50v41h123v-425l34 22l37 -114c-46 -21 -83 -31 -112 -31c-35 0 -70 22 -83 42c-40 -27 -88 -41 -146 -41c-179 0 -251 106 -251 281z"));
map.put (0x3b2, new GlyphMetrics(60, 560, -190, 721, 600, "M60 473c0 137 43 248 223 248c142 0 223 -76 223 -197c-4 -83 -43 -121 -65 -138c73 -29 119 -89 119 -182c0 -129 -90 -222 -229 -222c-59 0 -103 11 -136 33v-205h-135v663zM296 608c-83 0 -101 -42 -101 -136l1 -317c13 -34 47 -58 117 -58c47 0 108 22 108 109c0 57 -27 103 -136 103h-74v115h66c92 0 108 62 108 102c0 50 -37 82 -89 82z"));
map.put (0x3b3, new GlyphMetrics(10, 563, -199, 548, 573, "M278 -130c22 0 44 28 44 60c0 17 -28 63 -42 92c-17 -31 -46 -77 -46 -93c0 -31 21 -59 44 -59zM113 -65c0 47 48 134 79 205l-182 400h155l113 -245l114 245h154l-184 -400c37 -69 78 -140 78 -200c0 -93 -69 -159 -163 -159c-89 0 -164 66 -164 154z"));
map.put (0x3b4, new GlyphMetrics(40, 582, -3, 727, 622, "M303 120c120 0 141 88 141 149c0 56 -20 145 -135 145c-77 0 -129 -64 -129 -146c0 -99 41 -148 123 -148zM40 260c0 207 148 250 195 259l-159 105v103h448v-114h-217l113 -76c49 -33 162 -98 162 -269c0 -217 -125 -271 -279 -271c-99 0 -263 39 -263 263z"));
map.put (0x3b5, new GlyphMetrics(40, 531, -10, 547, 571, "M449 757l-126 -150h-70l70 150h126zM531 174c-15 -95 -50 -184 -252 -184c-159 0 -239 54 -239 161c0 62 25 92 65 117c-42 26 -65 60 -65 118c0 121 97 159 239 161h9c194 0 229 -93 243 -185h-127c-5 36 -26 72 -128 72c-68 0 -104 -18 -104 -54c0 -49 38 -61 103 -61h71v-102h-71c-66 0 -103 -11 -103 -61c0 -35 36 -53 104 -53c102 0 123 36 128 71h127z"));
map.put (0x3b6, new GlyphMetrics(40, 492, -214, 736, 512, "M182 239c0 -221 310 -46 310 -272c0 -41 -16 -100 -49 -181l-124 45c29 60 43 97 43 114c0 31 -6 34 -41 34c-58 0 -281 35 -281 244c0 157 75 298 189 393h-173v120h406v-98c-132 -74 -280 -215 -280 -399z"));
map.put (0x3b7, new GlyphMetrics(10, 538, -200, 548, 598, "M489 757l-126 -150h-70l70 150h126zM43 414l-23 114c36 13 66 20 90 20c55 0 79 -43 86 -60c28 41 97 59 149 59c152 0 203 -79 203 -228v-519l-138 37v478c0 65 -11 109 -83 109c-75 0 -113 -46 -113 -140v-300h-138v426c0 7 -5 9 -11 9c-8 0 -17 -3 -22 -5z"));
map.put (0x3b8, new GlyphMetrics(40, 555, -16, 729, 595, "M298 729c109 0 257 -56 257 -372c0 -220 -52 -373 -257 -373c-155 0 -258 84 -258 373c0 316 148 372 258 372zM415 420c-5 142 -47 194 -117 194c-94 0 -114 -105 -117 -194h234zM181 305c5 -156 47 -204 117 -204c101 0 113 103 117 204h-234z"));
map.put (0x3b9, new GlyphMetrics(60, 331, -17, 540, 341, "M231 784l-66 -180h-70l22 180h114zM65 702v-98h-88v98h88zM309 702v-98h-88v98h88zM257 160c0 -17 0 -44 21 -44c9 0 32 9 63 25l48 -127c-50 -20 -91 -31 -122 -31c-67 0 -149 32 -149 161v396h139v-380z"));
map.put (0x3ba, new GlyphMetrics(60, 557, -8, 543, 567, "M557 -8h-168l-161 244l-31 -30v-214h-137v551h137v-169l170 169h184l-225 -214z"));
map.put (0x3bb, new GlyphMetrics(10, 579, -13, 739, 589, "M283 334l-122 -336h-144l178 464l-145 -91v89l153 95c-20 50 -39 55 -65 55c-31 0 -33 -7 -79 -11l15 122c33 12 57 18 76 18c86 0 127 -54 157 -120l138 86v-88l-110 -69l6 -16l146 -389c10 -27 13 -30 18 -30c13 0 43 18 48 21l33 -112c-46 -24 -81 -35 -107 -35c-64 0 -82 40 -100 89c0 0 -92 247 -96 258z"));
map.put (0x3bc, new GlyphMetrics(60, 603, -202, 543, 613, "M306 103c40 0 100 17 100 115v325h136v-379c0 -62 22 -55 25 -60l36 -68c-53 -35 -67 -36 -82 -36h-3c-40 0 -73 17 -88 46c-27 -31 -75 -55 -127 -55c-42 0 -70 8 -105 31v-224h-138v745h138v-325c0 -64 42 -115 108 -115z"));
map.put (0x3bd, new GlyphMetrics(10, 556, -25, 543, 566, "M146 -32h-106l40 204h-77v99h97l30 153h-99v99h119l34 174h105l-34 -174h103l34 174h105l-34 -174h90v-99h-109l-31 -153h100v-99h-119l-40 -204h-105l40 204h-103zM205 271h103l30 153h-103z"));
map.put (0x3be, new GlyphMetrics(40, 519, -209, 731, 539, "M213 511c0 -36 21 -84 141 -84h108v-111h-136c-89 0 -151 -50 -151 -114c0 -59 62 -100 138 -100c117 0 206 -9 206 -129c0 -43 -17 -103 -50 -182l-120 44c12 32 41 108 41 131c0 17 -4 20 -69 20c-156 0 -281 42 -281 208c6 75 19 128 119 183c-25 13 -73 54 -73 134c0 43 13 73 37 104h-61v116h426v-116h-193c-49 0 -82 -63 -82 -104z"));
map.put (0x3bf, new GlyphMetrics(40, 574, -23, 549, 614, "M306 549c169 0 268 -107 268 -290c0 -173 -103 -282 -267 -282c-166 0 -267 108 -267 286c0 177 101 286 266 286zM307 436c-76 0 -127 -70 -127 -173s51 -173 127 -173c75 0 127 70 127 171c0 106 -50 175 -127 175z"));
map.put (0x3c0, new GlyphMetrics(30, 643, -18, 545, 658, "M565 95c4 0 42 29 47 32l31 -117c-26 -10 -69 -28 -103 -28c-88 0 -114 48 -114 140v305h-184v-440h-142v440h-69l-1 118h596v-118h-63v-303c0 -16 0 -26 2 -29z"));
map.put (0x3c1, new GlyphMetrics(60, 594, -203, 555, 634, "M594 277c0 -228 -161 -285 -269 -285c-53 0 -91 13 -127 36v-231h-138v480c0 222 153 278 265 278c108 0 269 -56 269 -278zM198 270c0 -126 70 -158 127 -158c53 0 129 32 129 160c0 132 -75 165 -129 165c-51 0 -127 -25 -127 -167z"));
map.put (0x3c2, new GlyphMetrics(40, 553, -205, 554, 593, "M182 273c0 -261 370 -55 370 -296c0 -38 -14 -75 -56 -182l-136 48c19 40 35 84 50 129c0 3 -6 4 -18 4c-34 4 -352 12 -352 299c0 168 109 279 273 279c105 0 240 -51 240 -256h-136c0 81 -23 131 -112 131c-52 0 -123 -31 -123 -156z"));
map.put (0x3c3, new GlyphMetrics(40, 652, -12, 551, 662, "M182 273c0 -261 370 -55 370 -296c0 -38 -14 -75 -56 -182l-136 48c19 40 35 84 50 129c0 3 -6 4 -18 4c-34 4 -352 12 -352 299c0 168 109 279 273 279c105 0 240 -51 240 -256h-136c0 81 -23 131 -112 131c-52 0 -123 -31 -123 -156z"));
map.put (0x3c4, new GlyphMetrics(20, 537, -12, 542, 557, "M341 -12c-132 0 -152 70 -152 168v272h-169v114h517v-114h-206v-265c0 -44 3 -45 26 -45c6 0 28 4 54 10l31 -125c-21 -4 -65 -15 -101 -15z"));
map.put (0x3c5, new GlyphMetrics(60, 548, -16, 540, 608, "M612 418c46 -50 76 -115 76 -176c0 -63 -66 -265 -324 -265c-267 0 -324 201 -324 265c0 61 30 126 76 176h-74v123h282v-103c-13 -7 -142 -71 -142 -179c0 -28 35 -159 182 -159c155 0 182 131 182 159c0 108 -129 172 -142 179v103h282v-123h-74z"));
map.put (0x3c6, new GlyphMetrics(40, 748, -196, 545, 788, "M468 112c87 0 145 61 145 148c0 83 -56 151 -145 151v-299zM40 260c0 160 115 240 161 267l81 -100c-61 -41 -107 -94 -107 -163c0 -80 65 -152 160 -152v433h109c186 0 304 -126 304 -286c0 -139 -87 -273 -279 -273v-182h-139v182c-185 0 -290 120 -290 274z"));
map.put (0x3c7, new GlyphMetrics(0, 611, -199, 561, 631, "M611 -180c-33 -15 -55 -19 -81 -19c-57 0 -103 42 -133 97l-76 150l-162 -236h-159l243 367l-137 234c-14 21 -14 24 -32 24c-11 -3 -28 -4 -51 -4v119c47 7 70 9 79 9c73 0 103 -48 115 -69l107 -194l134 244h148l-203 -364l122 -227c14 -22 17 -22 29 -22c9 0 39 11 57 17v-126z"));
map.put (0x3c8, new GlyphMetrics(50, 678, -205, 540, 728, "M432 108c62 6 106 28 106 140v292h140v-319c0 -147 -97 -227 -246 -233v-193h-136v193c-150 6 -246 85 -246 233v319h140v-292c0 -102 32 -133 106 -140v415h136v-415z"));
map.put (0x3c9, new GlyphMetrics(40, 758, -15, 545, 798, "M540 101c71 0 84 87 84 140c0 122 -52 186 -127 263l129 41c102 -82 132 -233 132 -298c0 -155 -81 -262 -215 -262c-65 0 -113 26 -144 65c-31 -39 -80 -65 -144 -65c-133 0 -215 105 -215 262c0 57 31 224 133 297l129 -39c-69 -71 -128 -139 -128 -264c0 -61 16 -140 83 -140c63 3 75 37 75 62v236h134v-236c0 -23 12 -59 74 -62z"));
};
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/font/GlyphMetrics.java
|
.java
| 369
| 14
|
/* FreeSansBoldGM.java: Autogenerated by ttf2svgpath from FreeSansBold.otf */
package org.openscience.jchempaint.renderer.font;
public class GlyphMetrics {
public int xMin, xMax, yMin, yMax, adv;
public String outline;
public GlyphMetrics (int a, int b, int c, int d, int e, String s) {
xMin=a; xMax=b; yMin=c; yMax=d; adv=e; outline=s;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/selection/RectangleSelection.java
|
.java
| 2,600
| 80
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2008 Gilleain Torrance <gilleain.torrance@gmail.com>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.selection;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.elements.RectangleElement;
import javax.vecmath.Point2d;
import java.awt.Color;
/**
* Rectangle selection is defined by two points. Adding extra points will update
* the second coordinate but the first point is fixed.
*
* @cdk.module rendercontrol
*/
public class RectangleSelection extends ShapeSelection {
private Point2d first;
private Point2d second;
public RectangleSelection() {
}
public IRenderingElement generate(Color color) {
if (first == null || second == null)
return null;
double minX = Math.min(first.x, second.x);
double maxY = Math.max(first.y, second.y);
double maxX = Math.max(first.x, second.x);
double minY = Math.min(first.y, second.y);
return new RectangleElement(minX, maxY, maxX, minY, color);
}
public boolean contains(Point2d p) {
if (first == null || second == null)
return false;
double minX = Math.min(first.x, second.x);
double maxY = Math.max(first.y, second.y);
double maxX = Math.max(first.x, second.x);
double minY = Math.min(first.y, second.y);
return p.x >= minX && p.x <= maxX && p.y >= minY && p.y <= maxY;
}
public void addPoint(Point2d p) {
if (first == null)
first = p;
else
second = p;
}
public boolean isEmpty() {
return first == null || second == null;
}
public void reset() {
this.finished = true;
first = null;
second = null;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/selection/ShapeSelection.java
|
.java
| 5,420
| 183
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2008 Gilleain Torrance <gilleain.torrance@gmail.com>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.selection;
import org.openscience.cdk.AtomContainer;
import org.openscience.cdk.interfaces.IAtom;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.interfaces.IBond;
import org.openscience.cdk.interfaces.IChemModel;
import org.openscience.cdk.interfaces.IChemObject;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.selection.IChemObjectSelection;
import org.openscience.cdk.tools.manipulator.ChemModelManipulator;
import javax.vecmath.Point2d;
import java.awt.Color;
import java.util.Collection;
import java.util.Collections;
import java.util.HashSet;
import java.util.Set;
/**
* @cdk.module rendercontrol
*/
public abstract class ShapeSelection implements IncrementalSelection {
public final Set<IAtom> atoms = new HashSet<>();
public final Set<IBond> bonds = new HashSet<>();
protected boolean finished = false;
public abstract boolean contains(Point2d p);
public abstract void addPoint(Point2d p);
public abstract boolean isEmpty();
public void addAtom(IAtom atom) {
atoms.add(atom);
}
public void addBond(IBond bond) {
bonds.add(bond);
}
/**
* Call this after the drawing has finished
*/
public abstract void reset();
public abstract IRenderingElement generate(Color color);
public boolean isFinished() {
return this.finished;
}
public boolean contains(IChemObject obj) {
if (obj instanceof IAtom) {
return atoms.contains(obj);
}
if (obj instanceof IBond) {
return bonds.contains(obj);
}
return false;
}
/**
* Call this before starting a new selection.
*/
public void clear() {
this.atoms.clear();
this.bonds.clear();
this.finished = false;
}
public boolean isFilled() {
return !atoms.isEmpty() || !bonds.isEmpty();
}
/*
* Get an IAtomContainer where all the bonds have atoms in
* the AtomContainer (no dangling bonds).
*
* (non-Javadoc)
* @see org.openscience.cdk.renderer.ISelection#getConnectedAtomContainer()
*/
public IAtomContainer getConnectedAtomContainer() {
IAtomContainer ac = null;
for (IAtom atom : atoms) {
if (ac == null) ac = atom.getBuilder().newAtomContainer();
ac.addAtom(atom);
}
if (ac == null)
return new AtomContainer();
for (IBond bond : bonds) {
if (ac.contains(bond.getBegin()) && ac.contains(bond.getEnd()))
ac.addBond(bond);
}
return ac;
}
private void select(IAtomContainer atomContainer) {
for (IAtom atom : atomContainer.atoms()) {
if (contains(atom.getPoint2d())) {
atoms.add(atom);
}
}
for (IBond bond : atomContainer.bonds()) {
if (contains(bond.getAtom(0).getPoint2d())
&& contains(bond.getAtom(1).getPoint2d())) {
bonds.add(bond);
}
}
}
public void select(IChemModel chemModel) {
clear();
for (IAtomContainer atomContainer :
ChemModelManipulator.getAllAtomContainers(chemModel)) {
select(atomContainer);
}
}
public void difference(IChemObjectSelection selection) {
for (IAtom atom : selection.elements(IAtom.class)) {
if (atoms.contains(atom))
atoms.remove(atom);
else
atoms.add(atom);
}
for (IBond bond : selection.elements(IBond.class)) {
if (bonds.contains(bond))
bonds.remove(bond);
else
bonds.add(bond);
}
}
@SuppressWarnings("unchecked")
public <E extends IChemObject> Collection<E> elements(Class<E> clazz) {
Set<E> set = new HashSet<E>();
if (IAtom.class.isAssignableFrom(clazz)) {
set.addAll((Collection<? extends E>) atoms);
return set;
}
if (IBond.class.isAssignableFrom(clazz)) {
set.addAll((Collection<? extends E>) bonds);
return set;
}
if (IChemObject.class.isAssignableFrom(clazz)) {
set.addAll((Collection<? extends E>) atoms);
set.addAll((Collection<? extends E>) bonds);
return set;
}
return Collections.emptySet();
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/selection/SingleSelection.java
|
.java
| 2,750
| 94
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2009 Arvid Berg <goglepox@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.selection;
import java.util.Collection;
import java.util.Collections;
import java.util.HashSet;
import java.util.Set;
import org.openscience.cdk.Atom;
import org.openscience.cdk.Bond;
import org.openscience.cdk.interfaces.IAtom;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.interfaces.IBond;
import org.openscience.cdk.interfaces.IChemObject;
import org.openscience.cdk.renderer.selection.AbstractSelection;
import org.openscience.cdk.renderer.selection.IChemObjectSelection;
/**
* Represents a single <code>ChemObject</code>
*
* @author Arvid
* @cdk.module rendercontrol
*/
public class SingleSelection<T extends IChemObject> extends AbstractSelection
implements IChemObjectSelection {
T selection;
public SingleSelection(T item) {
selection = item;
}
public IAtomContainer getConnectedAtomContainer() {
IAtomContainer ac = selection.getBuilder().newInstance(IAtomContainer.class);
addToAtomContainer(ac, selection);
return ac;
}
public boolean isFilled() {
return selection != null;
}
public boolean contains(IChemObject obj) {
return selection == obj;
}
@SuppressWarnings("unchecked")
public <E extends IChemObject> Collection<E> elements(Class<E> clazz) {
if (selection == null)
return Collections.emptySet();
Set<E> set = new HashSet<E>();
if (IAtom.class.isAssignableFrom(clazz)) {
if (selection instanceof IAtom) {
set.add((E) selection);
return set;
} else
return Collections.emptySet();
}
if (IBond.class.isAssignableFrom(clazz)) {
if (selection instanceof IBond) {
set.add((E) selection);
return set;
} else
return Collections.emptySet();
}
if (IChemObject.class.isAssignableFrom(clazz)) {
set.add((E) selection);
return set;
}
return Collections.emptySet();
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/selection/LogicalSelection.java
|
.java
| 4,881
| 156
|
/* Copyright (C) 2009 Gilleain Torrance <gilleain@users.sf.net>
* 2009 Arvid Berg <goglepox@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.selection;
import java.awt.Color;
import java.util.ArrayList;
import java.util.Collection;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Set;
import org.openscience.cdk.AtomContainer;
import org.openscience.cdk.interfaces.IAtom;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.interfaces.IBond;
import org.openscience.cdk.interfaces.IChemModel;
import org.openscience.cdk.interfaces.IChemObject;
import org.openscience.cdk.interfaces.IAtomContainerSet;
import org.openscience.cdk.renderer.selection.IChemObjectSelection;
import org.openscience.cdk.tools.manipulator.ChemModelManipulator;
import org.openscience.cdk.renderer.elements.IRenderingElement;
/**
*
* @author maclean
* @cdk.module rendercontrol
*/
public class LogicalSelection implements IChemObjectSelection {
public enum Type { ALL, NONE };
private Type type;
private Set<IChemObject> selected = new HashSet<>();
public LogicalSelection(LogicalSelection.Type type) {
this.type = type;
}
public LogicalSelection(IChemObjectSelection sel) {
this.type = Type.ALL;
select(sel.elements(IChemObject.class));
}
public void clear() {
this.type = Type.NONE;
this.selected.clear();
}
public Type getType() {
return type;
}
public IRenderingElement generate(Color color) {
return null;
}
public IAtomContainer getConnectedAtomContainer() {
IAtomContainer result = null;
for (IAtom atom : elements(IAtom.class)) {
if (result == null) result = atom.getBuilder().newAtomContainer();
result.addAtom(atom);
}
if (result != null) {
for (IBond bond : elements(IBond.class)) {
if (result.contains(bond.getBegin()) &&
result.contains(bond.getEnd()))
result.addBond(bond);
}
}
return result;
}
public boolean isFilled() {
return !this.selected.isEmpty();
}
public boolean isFinished() {
return true;
}
public void select(IChemModel chemModel) {
if (this.type == Type.ALL) {
for (IAtomContainer mol : ChemModelManipulator.getAllAtomContainers(chemModel)) {
for (IAtom atom : mol.atoms())
this.selected.add(atom);
for (IBond bond : mol.bonds())
this.selected.add(bond);
}
}
}
public void select(Collection<IChemObject> chemObjectSet) {
this.selected.addAll(chemObjectSet);
}
public void select(IChemObject chemObject) {
this.selected.add(chemObject);
}
public void select(IAtomContainer container) {
for (IAtom atom : container.atoms())
this.selected.add(atom);
for (IBond bond : container.bonds())
this.selected.add(bond);
}
public boolean contains( IChemObject obj ) {
if(type == Type.NONE)
return false;
return selected.contains(obj);
}
@SuppressWarnings("unchecked")
public <E extends IChemObject> Collection<E> elements(Class<E> clazz) {
Collection<E> result = new ArrayList<>();
if (IAtom.class.isAssignableFrom(clazz)) {
for (IChemObject chemObj : selected) {
if (chemObj instanceof IAtom)
result.add((E)chemObj);
}
}
else if (IBond.class.isAssignableFrom(clazz)) {
for (IChemObject chemObj : selected) {
if (chemObj instanceof IBond)
result.add((E)chemObj);
}
}
else if (IChemObject.class.isAssignableFrom(clazz)) {
for (IChemObject chemObj : selected) {
result.add((E)chemObj);
}
}
return result;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/selection/IncrementalSelection.java
|
.java
| 1,848
| 53
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2009 Arvid Berg <goglepox@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.selection;
import java.awt.Color;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.selection.IChemObjectSelection;
/**
* @author Arvid
* @cdk.module rendercontrol
*/
public interface IncrementalSelection extends IChemObjectSelection {
/**
* Use this to check if the selection process has finished.
* Some implementing classes may just choose to return 'true'
* if their selection is a simple one-step process.
*
* @return true if the selection process is complete
*/
public boolean isFinished();
/**
* Generate a display element that represents this selection.
* This will be used while isFilled() && !isFinished().
*
* @param color the color of the element to generate.
* @return a rendering element for display purposes.
*/
public IRenderingElement generate(Color color);
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/selection/MultiSelection.java
|
.java
| 2,574
| 91
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2009 Arvid Berg <goglepox@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.selection;
import java.util.Collection;
import java.util.HashSet;
import java.util.Set;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.interfaces.IChemObject;
import org.openscience.cdk.renderer.selection.AbstractSelection;
/**
* @author Arvid
* @cdk.module rendercontrol
*/
public class MultiSelection<T extends IChemObject> extends AbstractSelection {
Collection<T> selection;
public MultiSelection(Collection<T> selection) {
this.selection = selection;
}
public IAtomContainer getConnectedAtomContainer() {
IAtomContainer atomContainer = null;
if (!selection.isEmpty()) {
for (T value : selection) {
if (atomContainer == null)
atomContainer = value.getBuilder().newInstance(IAtomContainer.class);
addToAtomContainer(atomContainer, value);
}
}
return atomContainer;
}
public boolean isFilled() {
return !selection.isEmpty();
}
public boolean contains(IChemObject obj) {
if (obj == null)
return false;
for (T selected : selection) {
if (selected == obj)
return true;
}
return false;
}
/**
* Return elements in selection of type clazz
*
* @param clazz
* @return A collection containing clazz objects;
*/
@SuppressWarnings("unchecked")
public <E extends IChemObject> Collection<E> elements(Class<E> clazz) {
Set<E> set = new HashSet<E>();
if (IChemObject.class.isAssignableFrom(clazz)) {
set.addAll((Collection<E>) selection);
return set;
}
for (IChemObject obj : selection) {
// Check if obj is assignable to E
if (clazz.isAssignableFrom(obj.getClass()))
set.add((E) obj);
}
return set;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/selection/LassoSelection.java
|
.java
| 2,322
| 75
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2008 Gilleain Torrance <gilleain.torrance@gmail.com>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.selection;
import java.awt.Color;
import java.awt.geom.GeneralPath;
import java.util.ArrayList;
import javax.vecmath.Point2d;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.elements.PathElement;
/**
* @cdk.module rendercontrol
*/
public class LassoSelection extends ShapeSelection {
private final ArrayList<Point2d> points;
private GeneralPath path;
public LassoSelection() {
this.points = new ArrayList<Point2d>();
this.path = null;
}
public IRenderingElement generate(Color color) {
return new PathElement(this.points, color);
}
public boolean contains(Point2d p) {
if (this.points.size() < 3) return false;
this.path = new GeneralPath();
Point2d p0 = this.points.get(0);
this.path.moveTo((float)p0.x, (float)p0.y);
for (Point2d point : this.points) {
this.path.lineTo((float)point.x, (float)point.y);
}
this.path.closePath();
return this.path.contains(p.x, p.y);
}
public void addPoint(Point2d p) {
this.points.add(new Point2d(p.x, p.y));
}
public boolean isEmpty() {
return this.points.isEmpty();
}
public void reset() {
this.finished = true;
this.points.clear();
this.path = null;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/selection/AtomContainerSelection.java
|
.java
| 3,694
| 111
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2010 Stefan Kuhn <shk3@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.selection;
import java.util.ArrayList;
import java.util.Collection;
import org.openscience.cdk.interfaces.IAtom;
import org.openscience.cdk.interfaces.IAtomContainer;
import org.openscience.cdk.interfaces.IBond;
import org.openscience.cdk.interfaces.IChemObject;
import org.openscience.cdk.renderer.selection.AbstractSelection;
/**
* This is a selection which is built from an AtomContainer and cannot be
* changed later.
*
*/
public class AtomContainerSelection extends AbstractSelection {
IAtomContainer content;
/**
* Constructor for AtomContainerSelection.
*
* @param ac The selection.
*/
public AtomContainerSelection(IAtomContainer ac){
content=ac;
}
/* (non-Javadoc)
* @see org.openscience.jchempaint.renderer.selection.IChemObjectSelection#contains(org.openscience.cdk.interfaces.IChemObject)
*/
public boolean contains(IChemObject obj) {
if(obj instanceof IAtom) {
if(content.contains((IAtom)obj)){
return true;
}
}
if(obj instanceof IBond) {
if(content.contains((IBond)obj)){
return true;
}
}
return false;
}
/* (non-Javadoc)
* @see org.openscience.jchempaint.renderer.selection.IChemObjectSelection#getConnectedAtomContainer()
*/
public IAtomContainer getConnectedAtomContainer() {
return content;
}
/* (non-Javadoc)
* @see org.openscience.jchempaint.renderer.selection.IChemObjectSelection#isFilled()
*/
public boolean isFilled() {
if(content!=null && (content.getAtomCount()>0 || content.getBondCount()>0))
return true;
else
return false;
}
/* (non-Javadoc)
* @see org.openscience.jchempaint.renderer.selection.IChemObjectSelection#elements(java.lang.Class)
*/
@SuppressWarnings("unchecked")
public <E extends IChemObject> Collection<E> elements(Class<E> clazz) {
Collection<E> result = new ArrayList<>();
if (IAtom.class.isAssignableFrom(clazz)) {
for (IAtom atom : getConnectedAtomContainer().atoms()) {
result.add((E)atom);
}
}
else if (IBond.class.isAssignableFrom(clazz)) {
for (IBond bond : getConnectedAtomContainer().bonds()) {
result.add((E)bond);
}
}
else if (IChemObject.class.isAssignableFrom(clazz)) {
for (IAtom atom : getConnectedAtomContainer().atoms()) {
result.add((E)atom);
}
for (IBond bond : getConnectedAtomContainer().bonds()) {
result.add((E)bond);
}
}
return result;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/visitor/SVGGenerator.java
|
.java
| 26,223
| 759
|
/**
* Copyright (C) 2001-2007 The Chemistry Development Kit (CDK) Project
*
* Contact: cdk-devel@lists.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
* All we ask is that proper credit is given for our work, which includes
* - but is not limited to - adding the above copyright notice to the beginning
* of your source code files, and to any copyright notice that you may distribute
* with programs based on this work.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*
*/
package org.openscience.jchempaint.renderer.visitor;
import java.awt.Color;
import java.awt.geom.AffineTransform;
import java.awt.geom.NoninvertibleTransformException;
import java.awt.geom.Rectangle2D;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import javax.vecmath.Point2d;
import javax.vecmath.Vector2d;
import org.openscience.cdk.renderer.font.IFontManager;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.cdk.renderer.elements.ArrowElement;
import org.openscience.cdk.renderer.elements.AtomSymbolElement;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.elements.LineElement;
import org.openscience.cdk.renderer.elements.OvalElement;
import org.openscience.cdk.renderer.elements.PathElement;
import org.openscience.cdk.renderer.elements.RectangleElement;
import org.openscience.jchempaint.renderer.elements.TextElement;
import org.openscience.jchempaint.renderer.elements.TextGroupElement;
import org.openscience.cdk.renderer.elements.WedgeLineElement;
import org.openscience.jchempaint.renderer.elements.WigglyLineElement;
import org.openscience.jchempaint.renderer.font.FreeSansBoldGM;
import org.openscience.jchempaint.renderer.font.GlyphMetrics;
/**
* We can only guarantee the same quality of SVG output everywhere
* by drawing paths and not using fonts. This is an indirect
* consequence of font commercialisation which has successfully
* prevented SVG fonts from becoming usable on all browsers. See
* https://github.com/JChemPaint/jchempaint/wiki/The-svg-font-problem-and-its-solution
*
* So, we convert an open font to SVG paths and use these.
* To resolve the problem of placement, we use bbox, advance and
* (maybe later) kerning values from the same font.
* To make sure bonds don't cross text, we use two passes where
* text is drawn first and the bonds second.
*
* Two-pass implementation (c) 2012 by
* @author Ralf Stephan <ralf@ark.in-berlin.de>
* @jcp.issue #2
*
* First code layer (c) 2007 by
* @author maclean
* @cdk.module rendersvg
* @cdk.bug 2403250
*/
public class SVGGenerator implements IDrawVisitor {
/**
* The renderer model cannot be set by the constructor as it needs to
* be managed by the Renderer.
*/
private JChemPaintRendererModel rendererModel;
public static final String HEADER = "<?xml version=\"1.0\"?>\n" +
"<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n" +
"\"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n" +
"<svg xmlns=\"http://www.w3.org/2000/svg\" " +
"xmlns:xlink=\"http://www.w3.org/1999/xlink\" " +
"viewBox=\"0 0 1234567890\">\n" +
"<g transform=\"translate(12345,67890)\">";
private final StringBuffer svg = new StringBuffer();
private FreeSansBoldGM the_fm;
private AffineTransform transform;
private List<IRenderingElement> elList;
private List<String> tcList;
private HashMap<String, Point2d> tgMap;
private HashMap<Integer, Point2d> ptMap;
private List<Rectangle2D> bbList;
private double trscale, subscale, subshift, tgpadding, vbpadding;
private Rectangle2D bbox;
//------------------------------------------------------------------
public SVGGenerator() {
the_fm = new FreeSansBoldGM();
the_fm.init();
elList = new ArrayList<IRenderingElement>();
tcList = new ArrayList<String>();
tgMap = new HashMap<String, Point2d>();
ptMap = new HashMap<Integer, Point2d>();
bbList = new ArrayList<Rectangle2D>();
bbox = null;
svg.append(SVGGenerator.HEADER);
newline();
svg.append("<defs>");
tgpadding = 4;
vbpadding = 40;
trscale = 0.03;
subscale = trscale * 0.7;
subshift = 0.5;
}
private void newline() {
svg.append("\n");
}
public double[] transformPoint(double x, double y) {
double[] src = new double[]{x, y};
double[] dest = new double[2];
this.transform.transform(src, 0, dest, 0, 1);
return dest;
}
public double[] invTransformPoint(double x, double y) {
double[] src = new double[]{x, y};
double[] dest = new double[2];
try {
this.transform.createInverse().transform(src, 0, dest, 0, 1);
} catch (NoninvertibleTransformException e) {
System.err.println("Cannot invert transform!\n");
}
return dest;
}
/**
* Fills two lists: tcList contains all characters used in
* atoms, tgList has all strings. We also write the character
* paths immediately as DEFS into the SVG, for reference.
*
* @param e
*/
private void writeDEFS(TextGroupElement e) {
if (e.text.length() > 1 && !tgMap.containsKey(e.text))
tgMap.put(e.text, new Point2d(0, 0));
for (char c : e.text.toCharArray()) {
String idstr = "Atom-" + c;
GlyphMetrics m = the_fm.map.get((int) c);
if (!ptMap.containsKey((int) c))
ptMap.put((int) c, new Point2d(m.xMax, m.yMax - m.yMin));
if (!tcList.contains(idstr)) {
tcList.add(idstr);
newline();
svg.append(String.format(
" <path id=\"%s\" transform=\"scale(%1.3f,%1.3f)\" d=\"%s\" />",
idstr, trscale, -trscale, m.outline));
}
}
// Set hyd and hPos according to entry
int hyd = 0, hPos = 0;
for (TextGroupElement.Child ch : e.children) {
if (ch.text.equals("H")) {
if (ch.subscript == null) hyd = 1;
else if (ch.subscript.equals("2")) hyd = 2;
else hyd = 3;
if (ch.position == TextGroupElement.Position.E) hPos = 1;
else if (ch.position == TextGroupElement.Position.W) hPos = -1;
}
}
if (hyd > 0) {
if (!tcList.contains("Atom-H")) {
tcList.add("Atom-H");
GlyphMetrics m = the_fm.map.get((int) "H".charAt(0));
svg.append(String.format(
" <path id=\"Atom-H\" transform=\"scale(%1.3f,%1.3f)\" d=\"%s\" />",
trscale, -trscale, m.outline));
}
if (hyd >= 2) {
char c = '2';
if (hyd == 3) c = '3';
String idstr = "Atom-" + c;
GlyphMetrics m = the_fm.map.get((int) c);
if (!tcList.contains(idstr)) {
tcList.add(idstr);
newline();
svg.append(String.format(
" <path id=\"%s\" transform=\"scale(%1.4f,%1.4f)\" d=\"%s\" />",
idstr, subscale, -subscale, m.outline));
}
}
}
}
/**
* In this first pass, visiting elements are copied to
* a list, and DEFS/PATH elements are written for all TextGroups.
*/
public void visit(IRenderingElement element) {
elList.add(element);
if (element instanceof ElementGroup)
((ElementGroup) element).visitChildren(this);
else if (element instanceof TextGroupElement)
writeDEFS ((TextGroupElement) element);
}
public void draw (OvalElement oval) {
newline();
double[] p1 = transformPoint(oval.xCoord - oval.radius, oval.yCoord - oval.radius);
double[] p2 = transformPoint(oval.xCoord + oval.radius, oval.yCoord + oval.radius);
double x, y, w, h;
x = Math.min(p1[0], p2[0]);
y = Math.min(p1[1], p2[1]);
w = Math.abs(p2[0] - p1[0]);
h = Math.abs(p2[1] - p1[1]);
Rectangle2D rect = new Rectangle2D.Double(x, y, w, h);
if (bbox==null) bbox=rect;
else bbox = bbox.createUnion(rect);
double r = w / 2;
svg.append(String.format(
"<ellipse cx=\"%4.2f\" cy=\"%4.2f\" rx=\"%4.2f\" ry=\"%4.2f\" " +
"style=\"stroke:black; stroke-width:1px; fill:none;\" />",
x + r, y + r, r, r));
}
public void draw (AtomSymbolElement atomSymbol) {
newline();
double[] p = transformPoint(atomSymbol.xCoord, atomSymbol.yCoord);
svg.append(String.format(
"<text x=\"%s\" y=\"%s\" style=\"fill:%s\"" +
">%s</text>",
p[0],
p[1],
toColorString(atomSymbol.color),
atomSymbol.text
));
}
// this is a stupid method, but no idea how else to do it...
private String toColorString(Color color) {
if (color == Color.RED) {
return "red";
} else if (color == Color.BLUE) {
return "blue";
} else {
return "black";
}
}
public void draw (TextElement textElement) {
newline();
double[] p = transformPoint(textElement.x, textElement.y);
svg.append(String.format(
"<text x=\"%s\" y=\"%s\">%s</text>",
p[0],
p[1],
textElement.text
));
}
/**
* At the time of this call, all that we need is in place:
* the SVG character macros are written and the bboxes computed.
* The textgroup text is now placed with its center at the
* position of the atom. Implicit hydrogens are added.
*
* @param e
*/
public void draw (TextGroupElement e) {
newline();
double[] pos = transformPoint(e.x, e.y);
// Determine the bbox of the Atom symbol text
Point2d bb;
if (e.text.length() == 1)
bb = ptMap.get((int)e.text.charAt(0));
else
bb = tgMap.get(e.text);
// Set hyd and hPos according to entry
int hyd=0, hPos=0;
for (TextGroupElement.Child c : e.children) {
if (c.text.equals ("H")) {
if (c.subscript == null) hyd=1;
else if (c.subscript.equals("2")) hyd=2;
else hyd=3;
if (c.position==TextGroupElement.Position.E) hPos=1;
else if (c.position==TextGroupElement.Position.W) hPos=-1;
}
}
// Set v to the bbox of the whole TextGroup, add it to
// the list of such bboxes and enlarge the viewport bbox
double x, y, w, h;
x = pos[0]-trscale*bb.x/2;
y = pos[1]-trscale*bb.y/2-tgpadding;
w = trscale*bb.x+tgpadding;
h = trscale*bb.y+2*tgpadding;
Rectangle2D v = new Rectangle2D.Double(x, y, w, h);
bbList.add (v);
if (bbox==null) bbox = v;
else bbox = bbox.createUnion (v);
// Output use command(s)
x = pos[0] - trscale*bb.x/2;
y = pos[1] + trscale*bb.y/2;
svg.append(String.format(
"<use xlink:href=\"#Atom-%s\" x=\"%4.2f\" y=\"%4.2f\"/>",
e.text, x, y));
if (hyd != 0) {
GlyphMetrics m = the_fm.map.get(50+hyd);
if (hPos>0)
x += trscale*bb.x;
else {
x -= trscale* the_fm.map.get((int) "H".charAt(0)).adv;
if (hyd>=2)
x -= subscale*m.adv;
}
svg.append(String.format(
"<use xlink:href=\"#Atom-H\" x=\"%4.2f\" y=\"%4.2f\"/>",
x, y));
if (hyd>=2) {
char c = '2';
if (hyd==3) c='3';
String idstr = "Atom-" + c;
x += trscale* the_fm.map.get((int) "H".charAt(0)).adv;
y += subshift*subscale*(m.yMax-m.yMin);
svg.append(String.format(
"<use xlink:href=\"#%s\" x=\"%4.2f\" y=\"%4.2f\"/>",
idstr, x, y));
}
}
}
/**
* In this second pass, everything except bonds (and arrows)
* is placed, the textgroups referring to the DEFS ids. For
* intermediate caching, we first add strings to the DEFS block.
*/
public void drawNoBonds() {
newline();
svg.append("</defs>");
if (!tgMap.isEmpty()) { newline(); svg.append("<defs>"); }
for (String s : tgMap.keySet()) {
newline();
svg.append(String.format("<g id=\"Atom-%s\">", s));
boolean first = true;
int advance = 0;
int xMin = 9999, xMax = 0, yMin = 9999, yMax = 0;
for (char c : s.toCharArray()) {
svg.append(String.format("<use xlink:href=\"#Atom-%c\" ", c));
if (first) {
first=false;
}
else {
svg.append(String.format("transform=\"translate(%4.2f,0)\"", advance*trscale));
}
GlyphMetrics m = the_fm.map.get((int)c);
if (m.xMin + advance < xMin) xMin = m.xMin + advance;
if (m.xMax + advance > xMax) xMax = m.xMax + advance;
if (m.yMin < yMin) yMin = m.yMin;
if (m.yMax > yMax) yMax = m.yMax;
advance += m.adv;
svg.append("/>");
}
svg.append("</g>");
Point2d p = tgMap.get(s);
p.x = xMax - xMin;
p.y = yMax - yMin;
}
if (!tgMap.isEmpty()) { newline(); svg.append("</defs>"); }
for (IRenderingElement element : elList) {
if (element instanceof OvalElement)
draw((OvalElement) element);
else if (element instanceof TextGroupElement)
draw((TextGroupElement) element);
else if (element instanceof AtomSymbolElement)
draw((AtomSymbolElement) element);
else if (element instanceof TextElement)
draw((TextElement) element);
else if (element instanceof RectangleElement)
draw((RectangleElement) element);
else if (element instanceof PathElement)
draw((PathElement) element);
}
}
/**
* In the third pass, bonds (and arrows) are drawn,
* taking care to leave a small distance to atoms with
* text.
*/
public void drawBonds() {
for (IRenderingElement element : elList) {
if (element instanceof WedgeLineElement)
draw((WedgeLineElement) element);
else if (element instanceof LineElement)
draw((LineElement) element);
else if (element instanceof ArrowElement)
draw((ArrowElement) element);
else if (element instanceof WigglyLineElement)
draw((WigglyLineElement) element);
}
}
/**
* This is where most of the work is done by calling
* the 2nd and 3rd passes, and finally computing
* width and height of the document which is set at last.
* @return the SVG document as String
*/
public String getResult() {
drawNoBonds();
drawBonds();
newline();
svg.append("</g>\n</svg>\n");
int i = svg.indexOf ("0 0 1234567890");
if (bbox == null)
bbox = new Rectangle2D.Double(0, 0, 0, 0);
svg.replace(i, i+14, String.format("0 0 %4.0f %4.0f",
bbox.getWidth()+2*vbpadding,
bbox.getHeight()+2*vbpadding));
i = svg.indexOf ("12345,67890");
svg.replace(i, i+11, String.format ("%4.0f,%4.0f",
-bbox.getMinX()+vbpadding,
-bbox.getMinY()+vbpadding));
return svg.toString();
}
/**
* Applies all collected bboxes to the two points and, if one is
* inside a bbox, places it at the bbox's edge, same direction.
* Intended to be cumulative, i.e., both points may be moved more
* than once, or never. Returns true if any segment is left.
* @param p1
* @param p2
*/
private boolean shorten_line(double[] p1, double[] p2)
{
for (Rectangle2D v : bbList) { // shorten line acc. to bboxes
boolean inside1 = v.contains(p1[0], p1[1]);
boolean inside2 = v.contains(p2[0], p2[1]);
if (!inside1 && !inside2) continue;
if (inside1 && inside2) return false;
double px, py, qx, qy, cx=0.0, cy=0.0;
if (inside1) {
px = p1[0]; py = p1[1];
qx = p2[0]; qy = p2[1];
} else {
px = p2[0]; py = p2[1];
qx = p1[0]; qy = p1[1];
}
if (qx<v.getX() && v.getX()<px) cx = v.getX();
if (px<v.getMaxX() && v.getMaxX()<qx) cx = v.getMaxX();
if (qy<v.getY() && v.getY()<py) cy = v.getY();
if (py<v.getMaxY() && v.getMaxY()<qy) cy = v.getMaxY();
double rx, ry;
if (qy==py) { rx=cx; ry=py; }
else if (cx == 0.0) { ry = cy; rx = px + (cy-py)*(qx-px)/(qy-py); }
else if (qx==px) { ry=cy; rx=px; }
else if (cy == 0.0) { rx = cx; ry = py + (cx-px)*(qy-py)/(qx-px); }
else { // cx, cy, qx-px, qy-py all nonzero
if (Math.abs((cx-px)/(cy-py)) > Math.abs((qx-px)/(qy-py)))
{ ry = cy; rx = px + (cy-py)*(qx-px)/(qy-py); }
else
{ rx = cx; ry = py + (cx-px)*(qy-py)/(qx-px); }
}
if (inside1) {
p1[0] = (int)rx; p1[1] = (int)ry;
} else {
p2[0] = (int)rx; p2[1] = (int)ry;
}
}
return true;
}
public void draw (WedgeLineElement wedge) {
double[] p1 = transformPoint(wedge.firstPointX, wedge.firstPointY);
double[] p2 = transformPoint(wedge.secondPointX, wedge.secondPointY);
if (bbox==null) bbox = new Rectangle2D.Double(
Math.min(p1[0], p2[0]), Math.min(p1[1], p2[1]),
Math.abs(p2[0] - p1[0]), Math.abs(p2[1] - p1[1]));
else bbox = bbox.createUnion(new Rectangle2D.Double(
Math.min(p1[0], p2[0]), Math.min(p1[1], p2[1]),
Math.abs(p2[0] - p1[0]), Math.abs(p2[1] - p1[1])));
if (!shorten_line (p1, p2)) return;
double w1[] = invTransformPoint (p1[0], p1[1]);
double w2[] = invTransformPoint (p2[0], p2[1]);
// make the vector normal to the wedge axis
Vector2d normal =
new Vector2d(w1[1] - w2[1], w2[0] - w1[0]);
normal.normalize();
normal.scale(rendererModel.getWedgeWidth() / rendererModel.getScale());
// make the triangle corners
Point2d vertexA = new Point2d(w1[0], w1[1]);
Point2d vertexB = new Point2d(w2[0], w2[1]);
Point2d vertexC = new Point2d(vertexB);
vertexB.add(normal);
vertexC.sub(normal);
if (wedge.type == WedgeLineElement.TYPE.DASHED) {
this.drawDashedWedge(vertexA, vertexB, vertexC);
} else if (wedge.type == WedgeLineElement.TYPE.WEDGED) {
this.drawFilledWedge(vertexA, vertexB, vertexC);
} else {
this.drawCrissCrossWedge(vertexA, vertexB, vertexC);
}
}
public void draw (WigglyLineElement wedge) {
//TODO add code. see http://www.w3.org/TR/SVG/paths.html#PathDataCurveCommands
}
private void drawCrissCrossWedge(Point2d vertexA, Point2d vertexB,
Point2d vertexC) {
// calculate the distances between lines
double distance = vertexB.distance(vertexA);
double gapFactor = 0.1;
double gap = distance * gapFactor;
double numberOfDashes = distance / gap;
double d = 0;
double[] old=null;
// draw by interpolating along the edges of the triangle
for (int i = 0; i < numberOfDashes; i++) {
double d2 = d-gapFactor;
Point2d p1 = new Point2d();
p1.interpolate(vertexA, vertexB, d);
Point2d p2 = new Point2d();
p2.interpolate(vertexA, vertexC, d2);
double[] p1T = this.transformPoint(p1.x, p1.y);
double[] p2T = this.transformPoint(p2.x, p2.y);
svg.append(String.format(
"<line x1=\"%4.2f\" y1=\"%4.2f\" x2=\"%4.2f\" y2=\"%4.2f\" " +
"style=\"stroke:black; stroke-width:1px;\" />",
p1T[0],
p1T[1],
p2T[0],
p2T[1]
));
if(old==null)
old = p2T;
svg.append(String.format(
"<line x1=\"%4.2f\" y1=\"%4.2f\" x2=\"%4.2f\" y2=\"%4.2f\" " +
"style=\"stroke:black; stroke-width:1px;\" />",
old[0],
old[1],
p2T[0],
p2T[1]
));
old = p1T;
if (distance * (d + gapFactor) >= distance) {
break;
} else {
d += gapFactor*2;
}
}
}
private void drawFilledWedge(
Point2d vertexA, Point2d vertexB, Point2d vertexC) {
double[] pB = this.transformPoint(vertexB.x, vertexB.y);
double[] pC = this.transformPoint(vertexC.x, vertexC.y);
double[] pA = this.transformPoint(vertexA.x, vertexA.y);
svg.append(String.format(
"<polygon points=\"%4.2f,%4.2f %4.2f,%4.2f %4.2f,%4.2f\" "+
"style=\"fill:black;"+
"stroke:black;stroke-width:1\"/>",
pB[0],pB[1],
pC[0],pC[1],
pA[0],pA[1]
));
}
public void draw (PathElement path) {
}
public void draw (LineElement line) {
newline();
double[] p1 = transformPoint(line.firstPointX, line.firstPointY);
double[] p2 = transformPoint(line.secondPointX, line.secondPointY);
if (!shorten_line (p1, p2)) return;
if (bbox == null) {
bbox = new Rectangle2D.Double(
Math.min(p1[0], p2[0]), Math.min(p1[1], p2[1]),
Math.abs(p2[0] - p1[0]), Math.abs(p2[1] - p1[1]));
} else {
bbox = bbox.createUnion(new Rectangle2D.Double(
Math.min(p1[0], p2[0]), Math.min(p1[1], p2[1]),
Math.abs(p2[0] - p1[0]), Math.abs(p2[1] - p1[1])));
}
svg.append(String.format(
"<line x1=\"%4.2f\" y1=\"%4.2f\" x2=\"%4.2f\" y2=\"%4.2f\" " +
"style=\"stroke:black; stroke-width:3px;\" />",
p1[0],
p1[1],
p2[0],
p2[1]
));
}
private void drawDashedWedge(
Point2d vertexA, Point2d vertexB, Point2d vertexC) {
// calculate the distances between lines
double distance = vertexB.distance(vertexA);
double gapFactor = 0.1;
double gap = distance * gapFactor;
double numberOfDashes = distance / gap;
double d = 0;
// draw by interpolating along the edges of the triangle
for (int i = 0; i < numberOfDashes; i++) {
Point2d p1 = new Point2d();
p1.interpolate(vertexA, vertexB, d);
Point2d p2 = new Point2d();
p2.interpolate(vertexA, vertexC, d);
double[] p1T = this.transformPoint(p1.x, p1.y);
double[] p2T = this.transformPoint(p2.x, p2.y);
svg.append(String.format(
"<line x1=\"%4.2f\" y1=\"%4.2f\" x2=\"%4.2f\" y2=\"%4.2f\" " +
"style=\"stroke:black; stroke-width:1px;\" />",
p1T[0],
p1T[1],
p2T[0],
p2T[1]
));
if (distance * (d + gapFactor) >= distance) {
break;
} else {
d += gapFactor;
}
}
}
public void draw (ArrowElement line) {
int w = (int) (line.width * this.rendererModel.getScale());
double[] a = this.transformPoint(line.startX, line.startY);
double[] b = this.transformPoint(line.startX, line.startY);
newline();
svg.append(String.format(
"<line x1=\"%4.2f\" y1=\"%4.2f\" x2=\"%4.2f\" y2=\"%4.2f\" " +
"style=\"stroke:black; stroke-width:"+w+"px;\" />",
a[0],
a[1],
b[0],
b[1]
));
double aW = rendererModel.getArrowHeadWidth() / rendererModel.getScale();
if(line.direction){
double[] c = this.transformPoint(line.startX-aW, line.startY-aW);
double[] d = this.transformPoint(line.startX-aW, line.startY+aW);
newline();
svg.append(String.format(
"<line x1=\"%s\" y1=\"%s\" x2=\"%s\" y2=\"%s\" " +
"style=\"stroke:black; stroke-width:"+w+"px;\" />",
a[0],
a[1],
c[0],
c[1]
));
newline();
svg.append(String.format(
"<line x1=\"%s\" y1=\"%s\" x2=\"%s\" y2=\"%s\" " +
"style=\"stroke:black; stroke-width:"+w+"px;\" />",
a[0],
a[1],
d[0],
d[1]
));
}else{
double[] c = this.transformPoint(line.endX+aW, line.endY-aW);
double[] d = this.transformPoint(line.endX+aW, line.endY+aW);
newline();
svg.append(String.format(
"<line x1=\"%s\" y1=\"%s\" x2=\"%s\" y2=\"%s\" " +
"style=\"stroke:black; stroke-width:"+w+"px;\" />",
a[0],
a[1],
c[0],
c[1]
));
newline();
svg.append(String.format(
"<line x1=\"%s\" y1=\"%s\" x2=\"%s\" y2=\"%s\" " +
"style=\"stroke:black; stroke-width:"+w+"px;\" />",
a[0],
a[1],
d[0],
d[1]
));
}
}
public void draw (RectangleElement rectangleElement) {
double[] pA = this.transformPoint(rectangleElement.xCoord, rectangleElement.yCoord);
double[] pB = this.transformPoint(rectangleElement.xCoord+rectangleElement.width, rectangleElement.yCoord);
double[] pC = this.transformPoint(rectangleElement.xCoord, rectangleElement.yCoord+rectangleElement.height);
double[] pD = this.transformPoint(rectangleElement.xCoord+rectangleElement.width, rectangleElement.yCoord+rectangleElement.height);
newline();
svg.append(String.format(
"<polyline points=\"%4.2f,%4.2f %4.2f,%4.2f %4.2f,%4.2f %4.2f,%4.2f %4.2f,%4.2f\" "+
"style=\"fill:none;"+
"stroke:black;stroke-width:1\"/>",
pA[0],pA[1],
pB[0],pB[1],
pD[0],pD[1],
pC[0],pC[1],
pA[0],pA[1]
));
}
public void setTransform(AffineTransform transform) {
this.transform = transform;
this.transform.setToScale(30, -30);
// System.err.println(transform.toString());
// System.err.println(String.format("scale=%f zoom=%f\n", transform.getScaleX(), transform.getScaleY()));
}
public void setFontManager(IFontManager fontManager) {
}
public void setRendererModel(JChemPaintRendererModel rendererModel) {
this.rendererModel = rendererModel;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/visitor/PrintVisitor.java
|
.java
| 3,221
| 104
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2008 The Chemistry Development Kit (CDK) project
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.visitor;
import java.awt.geom.AffineTransform;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.elements.IRenderingVisitor;
import org.openscience.cdk.renderer.elements.LineElement;
import org.openscience.cdk.renderer.elements.OvalElement;
import org.openscience.jchempaint.renderer.elements.TextElement;
import org.openscience.cdk.renderer.elements.WedgeLineElement;
/**
* @cdk.module renderextra
*/
public class PrintVisitor implements IRenderingVisitor {
private int depth;
public PrintVisitor() {
this.depth = 0;
}
public void visitElementGroup(ElementGroup elementGroup) {
this.depth += 1;
System.out.println("Group at depth " + depth);
elementGroup.visitChildren(this);
this.depth -= 1;
}
public void visitLine(LineElement lineElement) {
System.out.println("Line ["
+ lineElement.firstPointX
+ " "
+ lineElement.firstPointY
+ "]-["
+ lineElement.firstPointX
+ " "
+ lineElement.firstPointY
+"]");
}
public void visitOval(OvalElement ovalElement) {
System.out.println("Oval ["
+ ovalElement.xCoord
+ ","
+ ovalElement.yCoord
+ " "
+ ovalElement.radius
+ "]");
}
public void visitText(TextElement textElement) {
System.out.println("Text " + textElement.text);
}
public void visitWedge(WedgeLineElement wedgeElement) {
System.out.println("Wedge");
}
public void visit(IRenderingElement element) {
if (element instanceof ElementGroup) {
visitElementGroup((ElementGroup) element);
} else if (element instanceof LineElement) {
visitLine((LineElement) element);
} else if (element instanceof OvalElement) {
visitOval((OvalElement) element);
} else if (element instanceof TextElement) {
visitText((TextElement) element);
} else {
System.err.println("Visitor method for "
+ element.getClass().getName() + " is not implemented");
}
}
public void setTransform( AffineTransform transform ) {
// TODO Auto-generated method stub
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/visitor/DistanceSearchVisitor.java
|
.java
| 3,504
| 106
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2008 Gilleain Torrance <gilleain.torrance@gmail.com>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.visitor;
import java.awt.geom.AffineTransform;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.elements.IRenderingVisitor;
import org.openscience.cdk.renderer.elements.LineElement;
import org.openscience.cdk.renderer.elements.OvalElement;
import org.openscience.jchempaint.renderer.elements.TextElement;
import org.openscience.cdk.renderer.elements.WedgeLineElement;
/**
* @cdk.module renderextra
*/
public class DistanceSearchVisitor implements IRenderingVisitor {
private int x;
private int y;
private double searchRadiusSQ;
private double closestDistanceSQ;
public IRenderingElement bestHit;
public DistanceSearchVisitor(int x, int y, double searchRadius) {
this.x = x;
this.y = y;
this.searchRadiusSQ = searchRadius * searchRadius;
this.bestHit = null;
this.closestDistanceSQ = -1;
}
private void check(IRenderingElement element, double xx, double yy) {
double dSQ = (this.x - xx) * (this.x - xx) + (this.y - yy) * (this.y - yy);
if (dSQ < this.searchRadiusSQ &&
(this.closestDistanceSQ == -1 || dSQ < this.closestDistanceSQ)) {
this.bestHit = element;
this.closestDistanceSQ = dSQ;
}
}
public void visitElementGroup(ElementGroup elementGroup) {
elementGroup.visitChildren(this);
}
public void visitLine(LineElement lineElement) {
// FIXME
int xx = (int)(0.5 * (lineElement.firstPointX - lineElement.secondPointX));
int yy = (int)(0.5 * (lineElement.firstPointY - lineElement.secondPointY));
this.check(lineElement, xx, yy);
}
public void visitOval(OvalElement ovalElement) {
this.check(ovalElement, ovalElement.xCoord, ovalElement.yCoord);
}
public void visitText(TextElement textElement) {
this.check(textElement, textElement.x, textElement.y);
}
public void visitWedge(WedgeLineElement wedgeElement) {
// TODO
}
public void visit( IRenderingElement element ) {
if(element instanceof ElementGroup)
visit((ElementGroup) element);
else if(element instanceof LineElement)
visit((LineElement) element);
else if(element instanceof OvalElement)
visit((OvalElement) element);
else if(element instanceof TextElement)
visit((TextElement) element);
else
System.err.println( "Visitor method for "+element.getClass().getName()
+ " is not implemented");
}
public void setTransform( AffineTransform transform ) {
// TODO Auto-generated method stub
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/visitor/AWTDrawVisitor.java
|
.java
| 26,882
| 662
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2008 Gilleain Torrance <gilleain.torrance@gmail.com>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.visitor;
import java.awt.BasicStroke;
import java.awt.Color;
import java.awt.Font;
import java.awt.Graphics2D;
import java.awt.Point;
import java.awt.RenderingHints;
import java.awt.Stroke;
import java.awt.font.TextAttribute;
import java.awt.geom.AffineTransform;
import java.awt.geom.Line2D;
import java.awt.geom.Path2D;
import java.awt.geom.PathIterator;
import java.awt.geom.Rectangle2D;
import java.util.HashMap;
import java.util.Hashtable;
import java.util.Map;
import javax.vecmath.Point2d;
import javax.vecmath.Vector2d;
import org.openscience.cdk.geometry.BondTools;
import org.openscience.cdk.renderer.elements.Bounds;
import org.openscience.cdk.renderer.elements.MarkedElement;
import org.openscience.cdk.renderer.font.AWTFontManager;
import org.openscience.cdk.renderer.font.IFontManager;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.cdk.renderer.elements.ArrowElement;
import org.openscience.cdk.renderer.elements.AtomSymbolElement;
import org.openscience.cdk.renderer.elements.ElementGroup;
import org.openscience.cdk.renderer.elements.GeneralPath;
import org.openscience.cdk.renderer.elements.IRenderingElement;
import org.openscience.cdk.renderer.elements.LineElement;
import org.openscience.cdk.renderer.elements.OvalElement;
import org.openscience.cdk.renderer.elements.PathElement;
import org.openscience.cdk.renderer.elements.RectangleElement;
import org.openscience.jchempaint.renderer.elements.TextElement;
import org.openscience.jchempaint.renderer.elements.TextGroupElement;
import org.openscience.cdk.renderer.elements.WedgeLineElement;
import org.openscience.jchempaint.renderer.elements.WigglyLineElement;
import org.openscience.cdk.renderer.elements.path.Type;
/**
* @cdk.module renderawt
*/
public class AWTDrawVisitor extends AbstractAWTDrawVisitor {
/**
* The font manager cannot be set by the constructor as it needs to
* be managed by the Renderer.
*/
private AWTFontManager fontManager;
/**
* The renderer model cannot be set by the constructor as it needs to
* be managed by the Renderer.
*/
private JChemPaintRendererModel rendererModel;
private final Map<Integer, BasicStroke> strokeMap =
new HashMap<Integer, BasicStroke>();
private final Map<TextAttribute, Object> map =
new Hashtable<TextAttribute, Object>();
private final Graphics2D g;
public AWTDrawVisitor(Graphics2D g) {
this.g = g;
this.fontManager = null;
this.rendererModel = null;
this.fm = g.getFontMetrics();
map.put(TextAttribute.SUPERSCRIPT, TextAttribute.SUPERSCRIPT_SUB);
}
public void visitElementGroup(ElementGroup elementGroup) {
elementGroup.visitChildren(this);
}
public void visit(ElementGroup elementGroup) {
elementGroup.visitChildren(this);
}
public void visit(ArrowElement line) {
Stroke savedStroke = this.g.getStroke();
int w = (int) (line.width * this.transform.getScaleX());
if (strokeMap.containsKey(w)) {
this.g.setStroke(strokeMap.get(w));
}
else {
BasicStroke stroke = new BasicStroke(w);
this.g.setStroke(stroke);
strokeMap.put(w, stroke);
}
this.g.setColor(line.color);
int[] a = this.transformPoint(line.startX, line.startY);
int[] b = this.transformPoint(line.endX, line.endY);
this.g.drawLine(a[0], a[1], b[0], b[1]);
double arrowWidth = rendererModel.getArrowHeadWidth() / rendererModel.getScale();
double lenghtOfArrow = Math.sqrt(Math.pow(Math.abs(line.startX - line.endX), 2) + Math.pow(Math.abs(line.startY - line.endY), 2));
double fractionOfHead = arrowWidth / lenghtOfArrow;
//headpoint is a line on the arrow arrowWidth away from end
Point2d headPoint = new Point2d();
if (line.startX < line.endX)
headPoint.x = line.startX + (line.endX - line.startX) * (fractionOfHead);
else
headPoint.x = line.endX + (line.startX - line.endX) * (1 - fractionOfHead);
if (line.startY < line.endY)
headPoint.y = line.startY + (line.endY - line.startY) * (fractionOfHead);
else
headPoint.y = line.endY + (line.startY - line.endY) * (1 - fractionOfHead);
//rotate headpoint in both directions to get end points of arrow
double relativex = headPoint.x - line.startX;
double relativey = headPoint.y - line.startY;
double angle = Math.PI / 6;
double costheta = Math.cos(angle);
double sintheta = Math.sin(angle);
Point2d firstArrowPoint = new Point2d();
firstArrowPoint.x = relativex * costheta - relativey * sintheta + line.startX;
firstArrowPoint.y = relativex * sintheta + relativey * costheta + line.startY;
int[] firstArrowPointCoords = this.transformPoint(firstArrowPoint.x, firstArrowPoint.y);
this.g.drawLine(a[0], a[1], firstArrowPointCoords[0], firstArrowPointCoords[1]);
angle = -Math.PI / 6;
costheta = Math.cos(angle);
sintheta = Math.sin(angle);
Point2d secondArrowPoint = new Point2d();
secondArrowPoint.x = relativex * costheta - relativey * sintheta + line.startX;
secondArrowPoint.y = relativex * sintheta + relativey * costheta + line.startY;
int[] secondArrowPointCoords = this.transformPoint(secondArrowPoint.x, secondArrowPoint.y);
this.g.drawLine(a[0], a[1], secondArrowPointCoords[0], secondArrowPointCoords[1]);
this.g.setStroke(savedStroke);
}
public void visit(LineElement line) {
Stroke savedStroke = this.g.getStroke();
double w = line.width * this.transform.getScaleX();
// if (w <= 1f) w = 1; // mine stoke of 1
int key = (int)(10*w);
if (strokeMap.containsKey(key)) {
this.g.setStroke(strokeMap.get(key));
} else {
BasicStroke stroke = new BasicStroke((float)w, BasicStroke.CAP_ROUND, BasicStroke.JOIN_ROUND);
this.g.setStroke(stroke);
strokeMap.put(key, stroke);
}
this.g.setColor(line.color);
double[] a = this.transformPointD(line.firstPointX, line.firstPointY);
double[] b = this.transformPointD(line.secondPointX, line.secondPointY);
this.g.draw(new Line2D.Double(a[0], a[1], b[0], b[1]));
this.g.setStroke(savedStroke);
}
public void visit(OvalElement oval) {
this.g.setColor(oval.color);
int[] min =
this.transformPoint(oval.xCoord - oval.radius, oval.yCoord - oval.radius);
int[] max =
this.transformPoint(oval.xCoord + oval.radius, oval.yCoord + oval.radius);
int w = max[0] - min[0];
int h = min[1] - max[1];
if (oval.fill) {
this.g.fillOval(min[0], max[1], w, h);
} else {
this.g.drawOval(min[0], max[1], w, h);
}
}
public void visit(TextElement textElement) {
this.g.setFont(this.fontManager.getFont());
Point p = this.getTextBasePoint(
textElement.text, textElement.x, textElement.y, g);
Rectangle2D textBounds =
this.getTextBounds(
textElement.text, textElement.x, textElement.y, g,
rendererModel.getZoomFactor());
this.g.setColor(textElement.backColor!=null ? textElement.backColor : this.rendererModel.getBackColor());
this.g.fill(textBounds);
this.g.setColor(textElement.color);
if (textElement.extraZoom!=null)
this.g.setFont(new Font (this.g.getFont().getName(), 1, (int)(this.g.getFont().getSize()*textElement.extraZoom))) ;
this.g.drawString(textElement.text, p.x, p.y);
}
public void visit(WedgeLineElement wedge) {
// make the vector normal to the wedge axis
Vector2d normal =
new Vector2d(wedge.firstPointY - wedge.secondPointY, wedge.secondPointX - wedge.firstPointX);
normal.normalize();
normal.scale(rendererModel.getWedgeWidth() / rendererModel.getScale());
// make the triangle corners
Point2d vertexA = new Point2d(wedge.firstPointX, wedge.firstPointY);
Point2d vertexB = new Point2d(wedge.secondPointX, wedge.secondPointY);
Point2d vertexC = new Point2d(vertexB);
vertexB.add(normal);
vertexC.sub(normal);
this.g.setColor(wedge.color);
if (wedge.type == WedgeLineElement.TYPE.DASHED) {
this.drawDashedWedge(vertexA, vertexB, vertexC);
} else if(wedge.type == WedgeLineElement.TYPE.WEDGED){
this.drawFilledWedge(vertexA, vertexB, vertexC);
} else {
this.drawCrissCrossWedge(vertexA, vertexB, vertexC);
}
}
public void visit(WigglyLineElement wedge) {
// make the endpoints
Point2d vertexA = new Point2d(wedge.firstPointX, wedge.firstPointY);
Point2d vertexB = new Point2d(wedge.secondPointX, wedge.secondPointY);
this.g.setColor(wedge.color);
// store the current stroke
Stroke storedStroke = this.g.getStroke();
this.g.setStroke(new BasicStroke(1));
// calculate the distances between circles
double distance = vertexB.distance(vertexA);
double gapFactor = 0.1;
double gap = distance * gapFactor;
double numberOfCircles = distance*5;
int diameter = (int)(rendererModel.getBondLength()*rendererModel.getZoomFactor()*1.2*gapFactor)+2;
double d = 0;
double rad=BondTools.giveAngleBothMethods(new Point2d(wedge.firstPointX,wedge.firstPointY), new Point2d(wedge.firstPointX+100,wedge.firstPointY), new Point2d(wedge.secondPointX,wedge.secondPointY), true);
int degrees=(int)(360*(rad/(2*Math.PI)));
// draw by interpolating along the imaginary straight line
for (int i = 0; i < numberOfCircles; i++) {
Point2d p1 = new Point2d();
p1.interpolate(vertexA, vertexB, d);
Point2d p2 = new Point2d();
p2.interpolate(vertexA, vertexB, d+1/numberOfCircles);
int[] p1T = this.transformPoint(p1.x, p1.y);
int[] p2T = this.transformPoint(p2.x, p2.y);
int wh = (int) (new Point2d(p1T[0],p1T[1]).distance(new Point2d(p2T[0],p2T[1])));
this.g.drawArc(p1T[0]-diameter, p1T[1]-diameter/2, wh,wh, i % 2 == 0 ? degrees : (degrees+180)%360, 180);
if (distance * (d + gapFactor) >= distance) {
break;
} else {
d += 1/numberOfCircles;
}
}
this.g.setStroke(storedStroke);
}
private void drawCrissCrossWedge(Point2d vertexA, Point2d vertexB,
Point2d vertexC) {
// store the current stroke
Stroke storedStroke = this.g.getStroke();
this.g.setStroke(new BasicStroke(1));
// calculate the distances between lines
double distance = vertexB.distance(vertexA);
double gapFactor = 0.1;
double gap = distance * gapFactor;
double numberOfDashes = distance / gap;
double d = gapFactor;
int[] old=null;
// draw by interpolating along the edges of the triangle
for (int i = 0; i < numberOfDashes; i++) {
double d2 = d-gapFactor;
Point2d p1 = new Point2d();
p1.interpolate(vertexA, vertexB, d);
Point2d p2 = new Point2d();
p2.interpolate(vertexA, vertexC, d2);
int[] p1T = this.transformPoint(p1.x, p1.y);
int[] p2T = this.transformPoint(p2.x, p2.y);
this.g.drawLine(p1T[0], p1T[1], p2T[0], p2T[1]);
if(old==null)
old = p2T;
this.g.drawLine(old[0], old[1], p2T[0], p2T[1]);
old = p1T;
if (distance * (d + gapFactor) >= distance) {
break;
} else {
d += gapFactor*2;
}
}
this.g.setStroke(storedStroke);
}
private void drawFilledWedge(
Point2d vertexA, Point2d vertexB, Point2d vertexC) {
int[] pB = this.transformPoint(vertexB.x, vertexB.y);
int[] pC = this.transformPoint(vertexC.x, vertexC.y);
int[] pA = this.transformPoint(vertexA.x, vertexA.y);
int[] xs = new int[] { pB[0], pC[0], pA[0] };
int[] ys = new int[] { pB[1], pC[1], pA[1] };
this.g.fillPolygon(xs, ys, 3);
}
private void drawDashedWedge(
Point2d vertexA, Point2d vertexB, Point2d vertexC) {
// store the current stroke
Stroke storedStroke = this.g.getStroke();
this.g.setStroke(new BasicStroke(1));
// calculate the distances between lines
double distance = vertexB.distance(vertexA);
double gapFactor = 0.1;
double gap = distance * gapFactor;
double numberOfDashes = distance / gap;
double d = 0;
// draw by interpolating along the edges of the triangle
for (int i = 0; i < numberOfDashes; i++) {
Point2d p1 = new Point2d();
p1.interpolate(vertexA, vertexB, d);
Point2d p2 = new Point2d();
p2.interpolate(vertexA, vertexC, d);
int[] p1T = this.transformPoint(p1.x, p1.y);
int[] p2T = this.transformPoint(p2.x, p2.y);
this.g.drawLine(p1T[0], p1T[1], p2T[0], p2T[1]);
if (distance * (d + gapFactor) >= distance) {
break;
} else {
d += gapFactor;
}
}
this.g.setStroke(storedStroke);
}
public void visit(AtomSymbolElement atomSymbol) {
this.g.setFont(this.fontManager.getFont());
Point p =
super.getTextBasePoint(
atomSymbol.text, atomSymbol.xCoord, atomSymbol.yCoord, g);
Rectangle2D textBounds =
this.getTextBounds(atomSymbol.text, atomSymbol.xCoord, atomSymbol.yCoord, g,
rendererModel.getZoomFactor());
this.g.setColor(this.rendererModel.getBackColor());
this.g.fill(textBounds);
this.g.setColor(atomSymbol.color);
this.g.drawString(atomSymbol.text, p.x, p.y);
int offset = 10; // XXX
String chargeString;
if (atomSymbol.formalCharge == 0) {
return;
} else if (atomSymbol.formalCharge == 1) {
chargeString = "+";
} else if (atomSymbol.formalCharge > 1) {
chargeString = atomSymbol.formalCharge + "+";
} else if (atomSymbol.formalCharge == -1) {
chargeString = "-";
} else if (atomSymbol.formalCharge < -1) {
int absCharge = Math.abs(atomSymbol.formalCharge);
chargeString = absCharge + "-";
} else {
return;
}
int x = (int) textBounds.getCenterX();
int y = (int) textBounds.getCenterY();
if (atomSymbol.alignment == 1) { // RIGHT
this.g.drawString(
chargeString, x + offset, (int)textBounds.getMinY());
} else if (atomSymbol.alignment == -1) { // LEFT
this.g.drawString(
chargeString, x - offset, (int)textBounds.getMinY());
} else if (atomSymbol.alignment == 2) { // TOP
this.g.drawString(
chargeString, x, y - offset);
} else if (atomSymbol.alignment == -2) { // BOT
this.g.drawString(
chargeString, x, y + offset);
}
}
public void visit(RectangleElement rectangle) {
int[] p1 = this.transformPoint(rectangle.xCoord, rectangle.yCoord);
int[] p2 = this.transformPoint(rectangle.xCoord + rectangle.width, rectangle.yCoord + rectangle.height);
this.g.setColor(rectangle.color);
if (rectangle.filled) {
this.g.fill(new Rectangle2D.Double(p1[0], p1[1], p2[0] - p1[0], p2[1] - p1[1]));
} else {
this.g.draw(new Rectangle2D.Double(p1[0], p1[1], p2[0] - p1[0], p2[1] - p1[1]));
}
}
public void visit(PathElement path) {
this.g.setColor(path.color);
for (int i = 1; i < path.points.size(); i++) {
Point2d point1 = path.points.get(i - 1);
Point2d point2 = path.points.get(i);
int[] p1 = this.transformPoint(point1.x, point1.y);
int[] p2 = this.transformPoint(point2.x, point2.y);
this.g.drawLine(p1[0], p1[1], p2[0], p2[1]);
}
}
public void visit(GeneralPath path) {
this.g.setColor(path.color);
Path2D cpy = new Path2D.Double();
cpy.append(getPathIterator(path, transform), false);
if (path.fill) {
this.g.fill(cpy);
} else {
Stroke stroke = this.g.getStroke();
this.g.setStroke(new BasicStroke((float) (path.stroke * transform.getScaleX()), BasicStroke.CAP_ROUND, BasicStroke.JOIN_ROUND));
this.g.draw(cpy);
this.g.setStroke(stroke);
}
}
private static PathIterator getPathIterator(final GeneralPath path,
final AffineTransform transform) {
return new PathIterator() {
int index;
private int type(Type type) {
switch ( type ) {
case MoveTo: return SEG_MOVETO;
case LineTo: return SEG_LINETO;
case QuadTo: return SEG_QUADTO;
case CubicTo: return SEG_CUBICTO;
case Close: return SEG_CLOSE;
default: return SEG_CLOSE;
}
}
public void next() {
index++;
}
public boolean isDone() {
return index>= path.elements.size();
}
public int getWindingRule() {
return path.winding;
}
public int currentSegment(double[] coords) {
path.elements.get(index).points(coords);
transform.transform(coords, 0, coords, 0, 3);
return type(path.elements.get(index).type);
}
public int currentSegment( float[] coords ) {
double[] doubleCoords = new double[6];
int type = currentSegment(doubleCoords);
for (int i = 0; i < 5; i++)
coords[i] = (float) doubleCoords[i];
return type;
}
};
}
public void visit(TextGroupElement textGroup) {
this.g.setFont(this.fontManager.getFont());
Point p =
super.getTextBasePoint(
textGroup.text, textGroup.x, textGroup.y, g);
Rectangle2D textBounds =
this.getTextBounds(textGroup.text, textGroup.x, textGroup.y, g,
rendererModel.getZoomFactor());
this.g.setColor(textGroup.backColor!=null ? textGroup.backColor : this.rendererModel.getBackColor());
this.g.fill(textBounds);
this.g.setColor(textGroup.color);
this.g.drawString(textGroup.text, p.x, p.y);
int x = (int) textBounds.getCenterX();
int y = (int) textBounds.getCenterY();
int x1 = (int) textBounds.getMinX();
int y1 = (int) textBounds.getMinY();
int x2 = p.x + (int)textBounds.getWidth();
int y2 = (int) textBounds.getMaxY();
for (TextGroupElement.Child child : textGroup.children) {
//First we calculate the child bounds just to find width and height
Rectangle2D childBounds = getTextBounds(child.text, 0, 0, g,
rendererModel.getZoomFactor());
int oW = (int)childBounds.getWidth();
int oH = (int)childBounds.getHeight();
//use that to actually calculate the position
int cx;
int cy;
switch (child.position) {
case NE:
cx = x2;
cy = y1;
break;
case N:
cx = x1;
cy = y1;
break;
case NW:
cx = x1 - oW;
cy = y1;
break;
case W:
cx = x1 - oW;
cy = p.y;
break;
case SW:
cx = x1 - oW;
cy = y1 + oH;
break;
case S:
cx = x1;
cy = y2 + oH;
break;
case SE:
cx = x2;
cy = y2 + oH;
break;
case E:
cx = x2;
cy = p.y;
break;
default:
cx = x;
cy = y;
break;
}
if (child.isComment) {
this.g.setColor(Color.BLACK);
Font f = this.g.getFont();
Font commentFont = f.deriveFont(f.getStyle(), f.getSize()/2);
this.g.setFont(commentFont);
cx = x1;
cy = y2 + oH/2;
this.g.drawString(child.text, cx, cy);
}
else {
//for deleting background of child
//we need the bounds at the actual positions
childBounds = getTextBounds(child.text, cx, cy, g
, rendererModel.getZoomFactor());
this.g.setColor(textGroup.backColor!=null ? textGroup.backColor : this.rendererModel.getBackColor());
Rectangle2D childBackground = new Rectangle2D.Double(cx,
cy - childBounds.getHeight(), childBounds.getWidth(),
childBounds.getHeight());
this.g.fill(childBackground);
this.g.setColor(textGroup.color);
//write child
this.g.drawString(child.text, cx, cy);
if (child.subscript != null) {
int scx = (int)(cx + (childBounds.getWidth() * 0.75));
int scy = (int)(cy + (childBounds.getHeight() / 3));
Font f = this.g.getFont(); // TODO : move to font manager
Font subscriptFont = f.deriveFont(f.getStyle(), f.getSize() - 2);
this.g.setFont(subscriptFont);
this.g.setColor(textGroup.color);
//write subscript
this.g.drawString(child.subscript, scx, scy);
}
}
}
if(textGroup.isNotTypeableUnderlined){
this.g.setColor(Color.RED);
this.g.drawLine(x1,y2,x2,y2);
}
}
public void visit(IRenderingElement element) {
Color savedColor = this.g.getColor();
if (element instanceof ElementGroup)
visit((ElementGroup) element);
else if (element instanceof WedgeLineElement)
visit((WedgeLineElement) element);
else if (element instanceof WigglyLineElement)
visit((WigglyLineElement) element);
else if (element instanceof LineElement)
visit((LineElement) element);
else if (element instanceof ArrowElement)
visit((ArrowElement) element);
else if (element instanceof OvalElement)
visit((OvalElement) element);
else if (element instanceof TextGroupElement)
visit((TextGroupElement) element);
else if (element instanceof AtomSymbolElement)
visit((AtomSymbolElement) element);
else if (element instanceof TextElement)
visit((TextElement) element);
else if (element instanceof RectangleElement)
visit((RectangleElement) element);
else if (element instanceof PathElement)
visit((PathElement) element);
else if (element instanceof GeneralPath)
visit((GeneralPath)element);
else if (element instanceof Bounds) {
// ignore
} else if (element instanceof MarkedElement) {
visit(((MarkedElement) element).element());
} else
System.err.println("Visitor method for "
+ element.getClass().getName() + " is not implemented");
this.g.setColor(savedColor);
}
/**
* The font manager must be set by any renderer that uses this class!
*/
public void setFontManager(IFontManager fontManager) {
this.fontManager = (AWTFontManager) fontManager;
}
public void setRendererModel(JChemPaintRendererModel rendererModel) {
this.rendererModel = rendererModel;
if (rendererModel.getUseAntiAliasing()) {
g.setRenderingHint(RenderingHints.KEY_ANTIALIASING, RenderingHints.VALUE_ANTIALIAS_ON);
g.setRenderingHint(RenderingHints.KEY_TEXT_ANTIALIASING, RenderingHints.VALUE_TEXT_ANTIALIAS_ON);
// g.setStroke(new BasicStroke((int)rendererModel.getBondWidth()));
}
g.setRenderingHint(RenderingHints.KEY_RENDERING,
RenderingHints.VALUE_RENDER_QUALITY);
g.setRenderingHint(RenderingHints.KEY_ALPHA_INTERPOLATION,
RenderingHints.VALUE_ALPHA_INTERPOLATION_QUALITY);
g.setRenderingHint(RenderingHints.KEY_INTERPOLATION,
RenderingHints.VALUE_INTERPOLATION_NEAREST_NEIGHBOR);
g.setRenderingHint(RenderingHints.KEY_STROKE_CONTROL,
RenderingHints.VALUE_STROKE_NORMALIZE);
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/visitor/AbstractAWTDrawVisitor.java
|
.java
| 3,194
| 89
|
/* $Revision$ $Author$ $Date$
*
* Copyright (C) 2008 Gilleain Torrance <gilleain.torrance@gmail.com>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.visitor;
import java.awt.FontMetrics;
import java.awt.Graphics2D;
import java.awt.Point;
import java.awt.geom.AffineTransform;
import java.awt.geom.Rectangle2D;
/**
* @cdk.module renderawt
*/
public abstract class AbstractAWTDrawVisitor implements IDrawVisitor {
/**
* This is initially null, and must be set in the setTransform method!
*/
protected AffineTransform transform = null;
protected FontMetrics fm;
public int[] transformPoint(double x, double y) {
double[] src = new double[] {x, y};
double[] dest = new double[2];
this.transform.transform(src, 0, dest, 0, 1);
return new int[] { (int) dest[0], (int) dest[1] };
}
public double[] transformPointD(double x, double y) {
double[] src = new double[] {x, y};
double[] dest = new double[2];
this.transform.transform(src, 0, dest, 0, 1);
return new double[] { dest[0], dest[1] };
}
protected Rectangle2D getTextBounds(String text, double x, double y,
Graphics2D g, double zoomfactor) {
//it seems the font metrics do not take font size into account
//the zoom for text is limited (see AWTFontManager)
if(zoomfactor<0.5)
zoomfactor=.5;
if(zoomfactor>2.5)
zoomfactor=2.5;
Rectangle2D bounds = fm.getStringBounds(text, g);
double widthPad = 3;
double heightPad = 1;
double w = bounds.getWidth()*zoomfactor + widthPad;
double h = bounds.getHeight()*zoomfactor + heightPad;
int[] p = this.transformPoint(x, y);
return new Rectangle2D.Double(p[0] - w / 2, p[1] - h / 2, w, h);
}
protected Point getTextBasePoint(String text, double x, double y,
Graphics2D g) {
Rectangle2D stringBounds = fm.getStringBounds(text, g);
int[] p = this.transformPoint(x, y);
int baseX = (int) (p[0] - (stringBounds.getWidth() / 2));
// correct the baseline by the ascent
int baseY = (int) (p[1] +
(fm.getAscent() - stringBounds.getHeight() / 2));
return new Point(baseX, baseY);
}
public void setTransform(AffineTransform transform) {
this.transform = transform;
}
}
|
Java
|
2D
|
JChemPaint/jchempaint
|
render/src/main/java/org/openscience/jchempaint/renderer/visitor/IDrawVisitor.java
|
.java
| 1,328
| 35
|
/* Copyright (C) 2009 Gilleain Torrance <gilleain@users.sf.net>
*
* Contact: cdk-devel@list.sourceforge.net
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public License
* as published by the Free Software Foundation; either version 2.1
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*/
package org.openscience.jchempaint.renderer.visitor;
import org.openscience.cdk.renderer.font.IFontManager;
import org.openscience.jchempaint.renderer.JChemPaintRendererModel;
import org.openscience.cdk.renderer.elements.IRenderingVisitor;
/**
* @cdk.module render
*/
public interface IDrawVisitor extends IRenderingVisitor {
public void setFontManager(IFontManager fontManager);
public void setRendererModel(JChemPaintRendererModel rendererModel);
}
|
Java
|
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