code stringlengths 3 6.57k |
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humidity_chart() |
ChartHelper("Humidity") |
ch.get_array() |
render_template('humidity_chart.html', title="Humidity Chart", data=arr) |
app.route("/pressure_chart") |
pressure_chart() |
ChartHelper("Pressure") |
ch.get_array() |
render_template('pressure_chart.html', title="Pressure Chart", data=arr) |
app.route("/co2_chart") |
co2_chart() |
ChartHelper("CO2") |
ch.get_array() |
render_template('co2_chart.html', title="CO2 Chart", data=arr) |
app.run(host='0.0.0.0', port=5000, debug=True) |
Copyright (c) |
CapturedException(Exception) |
__init__(self, msg=None) |
sys.exc_info() |
isinstance(value, CapturedException) |
str(value) |
traceback.format_exc() |
isinstance(msg, str) |
super() |
__init__(msg) |
CaptureSuccess(Exception) |
__init__(self, out) |
super() |
__init__() |
_sinc(x) |
abs() |
torch.sin(y) |
y.clamp(1e-30, float('inf') |
torch.where(y < 1e-30, torch.ones_like(x) |
_lanczos_window(x, a) |
x.abs() |
torch.where(x < 1, _sinc(x) |
torch.zeros_like(x) |
_construct_affine_bandlimit_filter(mat, a=3, amax=16, aflt=64, up=4, cutoff_in=1, cutoff_out=1) |
torch.as_tensor(mat) |
to(torch.float32) |
torch.arange(aflt * up * 2 - 1, device=mat.device) |
roll(1 - aflt * up) |
torch.meshgrid(taps, taps) |
torch.stack([xi, yi], dim=2) |
t() |
unbind(2) |
_sinc(xi * cutoff_in) |
_sinc(yi * cutoff_in) |
_sinc(xo * cutoff_out) |
_sinc(yo * cutoff_out) |
torch.fft.ifftn(torch.fft.fftn(fi) |
torch.fft.fftn(fo) |
_lanczos_window(xi, a) |
_lanczos_window(yi, a) |
_lanczos_window(xo, a) |
_lanczos_window(yo, a) |
torch.fft.ifftn(torch.fft.fftn(wi) |
torch.fft.fftn(wo) |
f.roll([aflt * up - 1] * 2, dims=[0,1]) |
torch.nn.functional.pad(f, [0, 1, 0, 1]) |
reshape(amax * 2, up, amax * 2, up) |
f.sum([0,2], keepdim=True) |
f.reshape(amax * 2 * up, amax * 2 * up) |
_apply_affine_transformation(x, mat, up=4, **filter_kwargs) |
torch.as_tensor(mat) |
to(dtype=torch.float32, device=x.device) |
_construct_affine_bandlimit_filter(mat, up=up, **filter_kwargs) |
mat.inverse() |
unsqueeze(0) |
repeat([x.shape[0], 1, 1]) |
torch.nn.functional.affine_grid(theta, x.shape, align_corners=False) |
upfirdn2d.upsample2d(x=x, f=f, up=up, padding=p) |
torch.nn.functional.grid_sample(y, g, mode='bilinear', padding_mode='zeros', align_corners=False) |
torch.zeros_like(y) |
torch.nn.functional.grid_sample(m, g, mode='nearest', padding_mode='zeros', align_corners=False) |
__init__(self) |
torch.device('cuda') |
dict() |
dict() |
dict() |
dict() |
torch.cuda.Event(enable_timing=True) |
torch.cuda.Event(enable_timing=True) |
dict() |
render(self, **args) |
self._start_event.record(torch.cuda.current_stream(self._device) |
dnnlib.EasyDict() |
self._render_impl(res, **args) |
CapturedException() |
self._end_event.record(torch.cuda.current_stream(self._device) |
self.to_cpu(res.image) |
numpy() |
self.to_cpu(res.stats) |
numpy() |
str(res.error) |
self._end_event.synchronize() |
self._start_event.elapsed_time(self._end_event) |
get_network(self, pkl, key, **tweak_kwargs) |
self._pkl_data.get(pkl, None) |
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