import ghhops_server as hs
import rhino3dm
hops = hs.Hops()
@hops.component(
"/add",
name="Add",
nickname="Add",
description="Add numbers with CPython",
category="Maths",
subcategory="CPython",
inputs=[
hs.HopsNumber("A", "A", "First number"),
hs.HopsNumber("B", "B", "Second number"),
],
outputs=[hs.HopsNumber("Sum", "S", "A + B")],
)
def add(a, b):
# testing error report
f = 12 / 0
return a + b
@hops.component(
"/pointat",
name="PointAt",
nickname="PtAt",
description="Get point along curve",
allindexes.extend(pathIndexes)
slen.append(pathLen)
g.remove_nodes_from(pathNodes)
return strips, allindexes, slen
#MST COMPONENT ---------------------
@hops.component(
"/mst",
name = "mst",
inputs=[
hs.HopsMesh("Input Mesh", "M", "Mesh"),
hs.HopsNumber("Weigth", "W", "Weight per face", hs.HopsParamAccess.LIST)
],
outputs=[
#hs.HopsPoint("list of points","P","shortest path points", hs.HopsParamAccess.LIST),
#hs.HopsInteger("list of faces indexes","F","shortest path face indexes", hs.HopsParamAccess.LIST),
hs.HopsString("list of faces indexes","F","shortest path face indexes", hs.HopsParamAccess.LIST),
hs.HopsCurve("lines", "L", "MST Lines", hs.HopsParamAccess.LIST)
]
)
def mst( mesh, weights):
mstGraph = mp.graphFromMesh(mesh) #convert the mesh to a nx graph
#plot = mu.plotGraphSave(mstGraph)
return G
def extract_types(G):
return list(nx.get_edge_attributes(G, 'type').values())
@hops.component(
"/lines_14",
name="Lines",
inputs=[
hs.HopsCurve("Edges", "E", access=hs.HopsParamAccess.LIST),
hs.HopsString("EdgesTypes", "ET", access=hs.HopsParamAccess.LIST),
hs.HopsPoint("AgentsCoordinates", "AC", access=hs.HopsParamAccess.LIST),
hs.HopsString("AgentsTypes", "AT", access=hs.HopsParamAccess.LIST),
hs.HopsNumber("Updater", "u"),
],
outputs=[
hs.HopsString("NewTypes", "NT", access=hs.HopsParamAccess.LIST)
]
)
def lines(edges: [Curve], edges_types: [str],
agents_coordinates: [Point3d], agents_types: [str],
_):
G = init_graph(edges, edges_types)
EdgeType.set_weight(G)
city_graph = CityGraph(G, geo_graph=False)
print_edges(G)
print_nodes(G)
return extract_types(G)
app = Flask(__name__)
hops: hs.HopsFlask = hs.Hops(app)
# flask app can be used for other stuff drectly
@app.route("/help")
def help():
return "Welcome to Grashopper Hops for CPython!"
#-- Point At component --#
@hops.component(
"/pointat",
name="PointAt",
description="Get a point along a curve",
inputs=[
hs.HopsCurve("Curve", "C", "Curve to evaluate"),
hs.HopsNumber("t", "t", "Parameter on Curve to evaluate.")
],
outputs=[
hs.HopsPoint("P", "P", "Point on Curve at parameter t")
]
)
def pointat(curve: rh.Curve, t: float=0):
pt = curve.PointAt(t)
return pt
#-- Profile Levels component --#
@hops.component(
"/plevels",
name="ProfileLevels",
description="Creates a series of levels from a profile",
# System and Rhino can only be loaded after rhinoinside is initialized
import System # noqa
import Rhino # noqa
hops = hs.Hops(app=rhinoinside)
@hops.component(
"/add",
name="Add",
nickname="Add",
description="Add numbers with CPython",
category="Maths",
subcategory="CPython",
inputs=[
hs.HopsNumber("A", "A", "First number"),
hs.HopsNumber("B", "B", "Second number"),
],
outputs=[hs.HopsNumber("Sum", "S", "A + B")],
)
def add(a, b):
# testing error report
f = 12 / 0
return a + b
@hops.component(
"/pointat",
name="PointAt",
nickname="PtAt",
description="Get point along curve",
from PIL import Image
from matplotlib import pyplot as plt
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
app = Flask(__name__)
hops = hs.Hops(app)
@hops.component("/runml",
name="runml",
description="Run Machine Learning prediction",
icon="examples/pointat.png",
inputs=[
hs.HopsCurve("Curve", "C", "Curve to evaluate"),
hs.HopsNumber("t", "t", "Parameter on Curve to evaluate"),
],
outputs=[
hs.HopsPoint("P", "P", "Point on curve at t"),
])
def run_ml():
new_model = tf.keras.models.load_model(
"D:\German Profile\Desktop\PREDICTION MODEL\pattern_model.h5")
new_model.summary()
print("Model OK")
#LOAD QUERY
query_flat = np.loadtxt("query.txt") #load
query = np.reshape(query_flat, (128, 128, 3))
query = query.astype(np.float32)
query_norm = np.multiply(query, 2)
