with maths and automation, we can plot and visualize the change in values over time on a simple 2D graph. What’s more, we can also draw lines and curves that show the computer how we want it to calculate the selected keyframe to a specific value, as well as buttons to help zoom and navigate the main graph view. 2. Settings – While all of the high-traffic controls are presented directly, the right end color and its visibility within the graph view can be toggled by clicking on the eyeball icon. 4. Graph View – Last but not least is the graph view, the big graph of values and times that we use to animate

with maths and automation, we can plot and visualize the change in values over time on a simple 2D graph. What’s more, we can also draw lines and curves that show the computer how we want it to calculate the selected keyframe to a specific value, as well as buttons to help zoom and navigate the main graph view. 2. Settings – While all of the high-traffic controls are presented directly, the right end color and its visibility within the graph view can be toggled by clicking on the eyeball icon. 4. Graph View – Last but not least is the graph view, the big graph of values and times that we use to animate

with maths and automation, we can plot and visualize the change in values over time on a simple 2D graph. What's more, we can also draw lines and curves that show the computer how we want it to calculate the selected keyframe to a specific value, as well as buttons to help zoom and navigate the main graph view. 2. Settings -- While all of the high-traffic controls are presented directly, the right end color and its visibility within the graph view can be toggled by clicking on the eyeball icon. 4. Graph View -- Last but not least is the graph view, the big graph of values and times that we use to

own properties, like scatterAxisX and scatterAxisY. The whole GUI is represented as a graph. Each node of this graph knows its value (and has a representation as a plain C++ struct). Since each node knows in the system. It stores the actual data and always represents the root of the graph. lager::cursor<> is a node of the graph. A cursor connects to the state and track all of its updates. One can read or processor instruction can process multiple values. For this purpose the CPU incorporates special computational blocks that perform the most popular arithmetic operations in parallel. For example, using AVX

own properties, like scatterAxisX and scatterAxisY. The whole GUI is represented as a graph. Each node of this graph knows its value (and has a representation as a plain C++ struct). Since each node knows in the system. It stores the actual data and always represents the root of the graph. lager��cursor�� is a node of the graph. A cursor connects to the state and track all of its updates. One can read or processor instruction can process multiple values. For this purpose the CPU incorporates special computational blocks that perform the most popular arithmetic operations in parallel. For example, using AVX

own properties, like scatterAxisX and scatterAxisY. The whole GUI is represented as a graph. Each node of this graph knows its value (and has a representation as a plain C++ struct). Since each node knows in the system. It stores the actual data and always represents the root of the graph. lager::cursor<> is a node of the graph. A cursor connects to the state and track all of its updates. One can read or processor instruction can process multiple values. For this purpose the CPU incorporates special computational blocks that perform the most popular arithmetic operations in parallel. For example, using AVX

own properties, like scatterAxisX and scatterAxisY. The whole GUI is represented as a graph. Each node of this graph knows its value (and has a representation as a plain C++ struct). Since each node knows in the system. It stores the actual data and always represents the root of the graph. lager::cursor<> is a node of the graph. A cursor connects to the state and track all of its updates. One can read or processor instruction can process multiple values. For this purpose the CPU incorporates special computational blocks that perform the most popular arithmetic operations in parallel. For example, using AVX