Python Development Technical Note 4

Transcription

1 Python Development Technical Note 4 Peter Higgins, October 1, 2018 Introduction Programmed data analysis, and resultant presentation graphics (especially done by me) needs to be accomplished without using Matlab so as to be free from expensive licensed software that not all TAMUG researchers might have. In my case, I have only a student license which prohibits its use professionally, not to mention I m not a student. But the same analysis can be done using Python with its associated libraries such as MatPlotLib and Numpy. Python is increasingly becoming the computer language for science and engineering because it is open source and has a graphics library closely enabling all the Matlab plots of various kinds. In addition to presentation graphics, Python through its library Numpy, also implements the matrix manipulations needed for curve fitting and EOF analysis ( see eof2.eofsolve from Numpy). I am in the process now of learning Python after many year s using Matlab. This note describes my progress. Implementing Python To get Python for windows I downloaded, and installed Python37 from I selected Python released 6/27/018. The needed libraries come from including MatPlotLib, numpy and many more that support special functions. These are downloaded as.whl files that are installed using pip. When all Python related files are obtained, a GUI interface is essential for writing, and debugging Python scripts. Although Visual Studio can be used if one has already bought it; I m using Eclipse Oxygen for my programming which is freely available from Once downloaded and unwrapped, it must be configured for Python using PyDev as explained in detail here: Method To learn file data processing and data display using Python, I downloaded several Galveston Bay data files from TAMUG s TCBSatlas including those for: dissolved oxygen, salinity and temperature. Using Python with imported MatPlotLib and Numpy, the data files were opened. In some cases data was read line by line into lists and/or numpy arrays using readline(); in other cases all the data was read into one string using read(). The native storage variable for multiple values in Python is a List (of strings), but using numpy arrays, data can be stored in numeric multidimensional arrays. Python is rich with string handling functions that can filter and separate the data. This is also a function that can convert a list to a matrix (np.asarray()). Higgins, Technical Note 4 Page 1

2 Results Creating a mask to overlay contour plots A mask is needed to overlay plotted contours to show only the portion of the contour above the region of interest. Contours of DO over land are meaningless. The appropriate mask is produced by adding a point by point path in Google Earth Pro which traces the bay then boxes a surround defining the mask area. When done, save the path as a.kml file. In the program that reads and plots the data as a contour, add code to read this.kml line by line until reaching the line in this file giving coordinates of the mask (usually line 40). Read this line, strip it of extraneous characters, and split it into a list based on, as the delimiter. Then plot this polygon as a patch filled with an opaque color (black in this case) after plotting the contours. Figure 1 Google Earth mask generation Masked subplots of contours of seasonal dissolved oxygen Figure 2 is an exercise in learning to plot fiiied contours of data read from CTBSatlas files. In the plot below seasonal variation of DO is contoured as a companion to the points plots discussed next. An important feature learned is control of the colorbars and color pallets. Ordinarily the range displayed in the colorbars is automatically linked to the range of data in the z axes. This makes comparison of two figures more difficult, and can be misleading. Higgins, Technical Note 4 Page 2

3 In Python most plotting is straight-forward, however, contour plotting relies on two important steps: (1) making linspaces from the latitude and longitude sets, and (2) using griddata() to map levels to the linspace variables as in: xi = np.linspace(lgmin,lgmax, delta) yi = np.linspace(lmin,lmax, delta) zi = griddata((wxa,wya),wza,(xi[none,:], yi[:,none]), method= linear ) wherein lgmin and lgmax are limits to the longitude extent and lmin and lmax are limits to the latitude extent, delta is the number of datapoints, and Wxa, Way are the numpy arrays of longitudes and latitudes. The library function griddata maps the point array of DO levels as a function of position onto the linspace grid. In Figure 2 it can be seen that DO levels are higher in Winter for both Galveston and East Bays, and that monthly means are nearly the same accoss bays. Figure 2-Galveston and East Bay contours of seasonal DO, all years Higgins, Technical Note 4 Page 3

4 Point 3D subplots of seasonal dissolved oxygen Seasonal (in the form of June compared to December) data points of dissolved oxygen for Galveston and East bay, for all years in the CTBSatlas DO datafile, are 3D point plotted in figure 3 below. The mean of each filtered dataset is given in each plot s title. These plots confirm the result seen in figure 2 that in both bays DO levels are significantly higher in Winter. It is also noted that the monthly means of the DO datasets are similar in both bays. The coding of figures 2-3 included several features of future interest: programmed cases which coordinate title strings, axes labeling, font sizes and weights (Python also allows plot characteristics to be placed in style sheets); multi-line titles; subplots; adding variables into strings; using Python lists and Numpy float arrays, point colors and marker size and customized plot ticks. Unfilled seasonal contours of temperatures Figure 3 Galveston, and East Bay seasonal DO, all years Figures 4 and 5, shown next, are seasonal, unmasked and unfilled contours of Galveston Bay temperate in June and December. This was an exercise in specifying the number of contours, and in labeling them with chosen font size. Higgins, Technical Note 4 Page 4

5 Figure 4 Galveston Bay June temperatures, all years Figure 5 Galveston Bay December temperatures, all years It can be seen that the Winter temperatures are much lower than in the Summer, and that the Winter temperature distribution is more complex. Higgins, Technical Note 4 Page 5