Here is the table file: 
% cat mag_diam.parlab 
mag      USNO Red Magnitude 
rad      Radius in pixel units 
lograd   log10(Radius in pixel units) 
name     Line Number Name 

% cat mag_diam.table 
# data
 17.37   7.00    0.845098    11
 12.82   20.41   1.309843    13
 16.72   7.14    0.853698    19
 14.60   12.03   1.080266    23
 14.57   12.13   1.083861    24
 18.13   3.90    0.591065    27
 14.18   13.37   1.126131    30
 19.43   6.03    0.780317    31
 16.81   6.62    0.820858    40
 15.86   11.81   1.072250    5
 16.87   8.23    0.915400    6
 19.91   5.82    0.764923    7
 19.47   5.90    0.770852    43
 16.60   10.42   1.017868    48

# Set up a generic set of plot parametes in a local file = xyplotter_auto.pars 
% Generic_Points N                             
Mfont color=green># Sjow some useful plot package info  
% mpl                           
 
# make the initial plot of the data  
% xyplotter_auto mag_diam mag lograd 1 N   

I get two primary files: 
% cat List.1 
mag_diam.table 1 3 0 0   point  b o 5  Pixels
% cat Axes.1 
Magnitude-Diameter data fron an acm i image
  12.82000 19.91000 USNO Red Magnitude 
  0.59106 1.30984 log10(Radius in pixel units) 

# fit the data (I use  curve type = "line")   
% curve_runner 1 N 

Here is the file set I get: 
% cat List.1 
mag_diam.table 1 3 0 0   point  b o 5  Pixels
line.fitcurve.1 1 2 0 0   line  r - 10  Iter1
line.fitcurve.2 1 2 0 0   line  c - 10  Iter2
% cat Axes.1 
Magnitude-Diameter data fron an acm i image
  12.82000 19.91000 USNO Red Magnitude 
  0.59106 1.30984 log10(Radius in pixel units) 

% cat List.1 
mag_diam.table   1 3 0 0   point  b o 50  acm i
line.fitcurve.1  1 2 0 0   line   r - 10  Fit1
line.fitcurve.2  1 2 0 0   line   c - 10  Fit2 
% cat Axes.1 
Magnitude-Diameter data acm i image
  12.00  21.00     USNO Red Magnitude 
  0.40   1.50      log10(Radius in pixel units) 

% xyplotter List.1 Axes.1 N 

The xyplotter_auto and curver_runner routines were used to fit a line to 14 points of magnitude,diameter data in a tbale file. The red line (Fit1) is the fit to all of the data points. After rejecting a single point in a 2.5 sigma rejection cycle, we see the final fits (Fit2) ias the cyan line.

A "tabla file" is a file of rows and colums, with an accompanying file that explains what is in each column. In many cases, the table file is accopmanied by a "parameter names" file which gives a short name for each column in the table (as opposed to a long comment line). The data column can be composed of numeric or string data. Below I show the typical examples of a table file with basename "BulgeDisk":

% cat BulgeDisk.parlab
ring      profile point number        
r         Radius                      
r25       Radius**1/4 
I_B       Intensity of Bulge Component 
I_D       Intensity of Disk Component 
I_BD      Intensity of Bulge+Disk 
logI_B    log(Intensity Bulge) 
logI_D    log(Intensity Disk) 
logI_BD   log(Intensity Bulge+Disk) 

% cat BulgeDisk.table
# data
   1       0.00    0.00000  2141410.5000     100.0000 2141510.5000     6.3307    2.0000    6.3307
   2       0.20    0.67042    43053.1055      98.0001   43151.1055     4.6340    1.9912    4.6350
   3       0.40    0.79727    20557.8496      96.0401   20653.8906     4.3130    1.9825    4.3150
   4       0.61    0.88233    12523.4248      94.1194   12617.5439     4.0977    1.9737    4.1010
   5       0.81    0.94812     8535.1309      92.2371    8627.3682     3.9312    1.9649    3.9359

A cdfp file (CoorDinate Floating Point) is a specialized file I use for coordinate-related jobs. The first line is always a single string of header information. The columns of data are all numeric, and the first two columns are always the RA in hours and the DEC in units of degrees (both in floating point format).

I used a variety of matplotlib methods in my early python days.I have collected some of source code from these python plotting examples. Examples of plotting text label or shading points are included there.

This document was heavily revised in Oct2018. The nitty-gritty of my earliest plotting tools are archived in some Pre-2018 notes.

Here I collect some hopefully useful explanations and examples.

1. File type terminology
2. Compute column statistics.
3. Colors, marker and line types

Basic types of plots: I presently plot data using 4 basic types. I use longer, mor explanatory names for the pxy_SM_plot.py code, but short name designations for the xyplotter_auto code. The xyplotter_auto ultimately uses pxy_SM_plot.py to build the plot, but this is a wrapper code built for speed and concenience.

   pxy_SM_plot.py         xyplotter_auto 
   --------------         --------------
       point                    P
       pointopen                OP 
       errorbar                 E
       line                     L 

table file: This is a file that contains multiple columns and rows of data. The data maybe text-based or numerical. The will be some marker (i.e. "# data" that indicates when the data portion of the file begins. Everything before that will usually be free-format header information.

% head -15 hetAZindo_dec01.dat
Col01 = STRUCTAZ, structure azimuth from header                                   
Col02 = AZfromDEC, azimuth based on declination                                   
Col03 = HETQfromAZ, parallactic angle from structure azimuth                      
Col04 = HETQfromDEC, parallactic angle from structure azimuth based on DEC        
Col05 = DECDEG, declination in degree uni ts                                      
Col06 = STRUCTAZ - AZfromDEC                                                      
Col07 = HETQfromAZ - HETQfromDEC                                                  
Col09 = side of sky relative to meridian                                          
STRUCTAZ, AZfromDEC, HETQfromAZ, HETQfromDEC, DECDEG, AZdif, HETQdif, direction   
# data 
        180.00 177.75   180.000 178.060   -4.306543 002.25 001.94  E     20161006T001036.7_acm_sci 
        180.00 177.75   180.000 178.060   -4.306543 002.25 001.94  E     20161006T001021.3_acm_sci 
        180.00 177.75   180.000 178.060   -4.306543 002.25 001.94  E     20161006T001032.8_acm_sci 
        180.00 177.75   180.000 178.060   -4.306543 002.25 001.94  E     20161006T001029.0_acm_sci 
        180.00 177.75   180.000 178.060   -4.306543 002.25 001.94  E     20161006T001025.2_acm_sci 

style file: This is a file usually used with plooting tools (like pxy_SM_plot.py) to specifiy the labels that fo on the plot axes. I also specifies the names of the plot data files that will supply the(X,Y) values that will be plotted. Here is an example (the style file we used for the first example in this doc):

% cat style.hetq-tz 
20160617 ACAM Data
HETQ (HET Parallactic Angle)
tz
UT20160617-hetq-tz.file
UT20160617-hetq-tz.file_1
UT20160617-hetq-tz.file_2

data point file: These files contain the X,Y data we are going to plot. The first line of a data point file is describes how the points will be plotted. Below we see that we'll plot a red (r) line, with a dashed format (:), and line thickness of 30. Were we using a point, then th last argument would specify the point size to used. The second line contains a descriptive label for the data set. This is the string that will be painted in the plot legend placed in the upper-right corner of the plot. You want this descriptive title to be short. All of the remaining lines are the X,Y data line. Note that the format of the data lines can change depending on the type of data being plotted. I show a second example below of the file we used to plot points with error bars in our first sample plot of this doc. The type of thing being plotted is "errorbar", and hence we need four numbers per point: X, the error of X, Y, the error of Y.

% head UT20160617-hetq-tz.file_1 
line r : 30
Line Model 
9.864   88.510
243.402 88.205
254.989 88.307
266.616 88.254
279.311 88.628
293.520 88.437
315.167 88.507
340.091 88.742

% head UT20160617-hetq-tz.file_2 
errorbar b o 100
With Errors
50.0  5.0    88.0 1.3
180.0 10.0   88.4 0.8
120.0 7.0   87.4  1.0

Often we'll have information in the column of a table file that we wish to summarize with some simple statistics.

% calstats.py Az1  
223.24486 90.64843 0.00000 351.27000 236.495000 3536 1.524634

% calstats.py -v Az1  
223.24486 90.64843 0.00000 351.27000 236.495000 3536 1.524634
(mean,std,min,max,median,Npnts,m.e.)

Simple stats for numbers in: Az1
Mean                     = 223.24486
Median                   = 236.49500
Standard deviation       = 90.64843
Minimum                  = 0.00000
Maximum                  = 351.27000
Number of values         = 3536
Mean error of then mean  = 1.52463
 

I wrote a script named mpl that provides a brief summary of point type a color properties. I wondered about how to change the symbol types. I googled "symbol attributes in matplotlib scatter plot" and found lots of things, the second of which was very useful!. For the sake of completeness in my offline notes, I show a small part of a graphic from that webdoc below:

Examples of marker types.

 

These will work:
blue . 5      Blue point of size five.
red  o 10     Red circle of size ten.
g d 12        Green thin-diamond of size twelve 

These will fail (in python 2.7):
blue point 5      
red  circle 10    
g thin_diamond 12       in-diamond of size twelve 
 

Commands which take color arguments can use several formats to specify the colors. For the basic built-in colors, you should use a single letter:

 

        b: blue
        g: green
        r: red
        c: cyan
        m: magenta
        y: yellow
        k: black
        w: white

Gray shades can be given as a string encoding a float in the 0-1 range, e.g.:
  color = '0.75'

For a greater range of colors, you have two options. You can specify the 
color using an html hex string, as in:
color = '#eeefff'

Line types were a little harder to find out about. Python has a jillion options, but nobody ever lists or expalins more than a few that work. Here are four that work with the type "line" in my codes:

 

These will work: 
  - 
  -- 
  : -
  .
 

Examples of line style in matplotlib that seem to work in pxy_SM_plot.py (as of Mar2017).