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carracing action. Search the latest version for your platform.The present invention relates to a switching matrix, and more particularly to a switching matrix for receiving and switching input data signals.
In a data switching matrix which receives and switches input data signals, data signal input terminals and data signal output terminals are provided in a rectangular arrangement, and the matrix is switched by sequentially selecting corresponding ones of the data signal input terminals and the data signal output terminals for receiving and switching input data signals.
In such a switching matrix, when the data signal input terminals are connected to corresponding ones of the data signal output terminals, it is difficult to make connections between the data signal input terminals and the data signal output terminals in the rectangular arrangement for the reasons described below. This difficulty increases with an increase in the number of the data signal input terminals.
1. For example, assume that the data signal input terminals are arranged in an L shape with an axis L.sub.1 and data signal output terminals are provided only in an U shape with an axis U.sub.1. In this case, the data signal input terminals L.sub.1 are connected
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How to do Continuous looping with x-axis autoscale?
First of all, I have a DataFrame, the X data are minute-columns of time, the Y data is a series of counts of events. I want to autoscale the X axis to show a graph that plots the last 60 hours of data on the X axis. So I want 60 hours of data.
This is what I want:
This is what I have now:
def plot_plot(df, dayDf):
xticks_display = [0]
for tick in df[dayDf.index].values:
xticks_display.append(tick)
fig, ax = plt.subplots(figsize=(15,15))
ax.xaxis.set_major_formatter(plt.AutoDateFormatter(ax.xaxis, use_minimal=True))
ax.set_xticks(xticks_display)
ax.set_ylabel(«Count»)
ax.set_title(«Count»)
ax.set_xlabel(«Second»)
fig.autofmt_xdate()
plt.grid(True, which=’both’)
plt.tight_layout()
plt.show()
df = prepare_data()
plot_plot(df, dayDf)
I don’t know how to continue. I’ve looked at:
Continuous looping with pandas
