1/13/2024 0 Comments Texshop autocomplete![]() ![]() But we do have lots of other parameters like salinity, turbidity, fDOM, temperature etc. Or maybe there are other suitable ways to do so? By the way, we don't have information about the speed of the flow in and out of the bay, however, it's probably possible to get it later. Is it possible to "imagine" the narrow streams of water intake as 3 point-source of "pollution" and calculate the influence of each point on the bay? Like, for example, to understand what source is more responsible for the increased level of pollution: river, bay or sea? Could it be done with kriging in ArcGIS Pro, and then probably visualized the influence with arrows, where the length of the arrow will represent the strength of the influence (2nd picture)? We have a boat with a flow-through system of sensors, so we measure different parameters every 5 sec (60m approx.). You can see a map with explanations in the 1st picture. We need to calculate the influence of 3 different sources of water intake (river, eutrophicated bay and sea) on the bay, located in the middle of these sources. Wayfarer Asks: How to calculate the influence of 3 different sources of water intake on a bay? I am running this within JupyterLab at this time. I can't imagine I'm running into a bug, here, and I must be doing something wrong? Just in case, however, I want to note that I a have polars=0.16.7 and matplotlib=3.7.0. I'm at a complete loss as to what is going wrong. Additionally, even if I cast the dtype to a python datetime.datetime, or even cast the whole polars dataframe to either a pandas dataframe or a numpy 2d array, the behavior does not change (which I thought would rule out an interoperability issue). ![]() Alternatively, I can occasionally find vague references to a problem with matplotlib and pandas where they had differences between epochs, however I am not using pandas here. I've been trying to find an explanation or workaround, but unfortunately all of my searching is only turning up people asking how to initially parse strings into dates in Polars - something I'm past, here. If I drop the two set_major_formatter() calls, the dates are simply integers starting from 0, one per day. However, across the X axis the dates are formatted "1970-01" and so on. This prints the graph as I expect to see, with a correct title (note: dates look correct there!), Y axis ranges, etc. Start=datestamp_min.date(), end=datestamp_max.date())) Plt.title('Total Cores/RAM Allocation\n'.format( # relevant imports from earlier in the projectįig = plt.figure(figsize=(12,8), facecolor='white')Īx_cpu.set_ylabel('Cores', fontsize=14, color='purple')Īx_ram.set_ylabel('RAM (GB)', fontsize=14, color='orange')Īx_cpu.plot(binned_df_cpu_all, color='purple', alpha=0.8)Īx_ram.plot(binned_df_ram_all, color='orange', alpha=0.8)Īx_major_formatter(('%Y-%m'))Īx_major_formatter(('%Y-%m'))
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