Merge pull request #22 from bczernecki/devel

Devel

.github/workflows/R-CMD-check.yaml

@@ -31,15 +31,13 @@ jobs:
       matrix:
         config:
           - {os: macOS-latest,   r: 'release'}
-          - {os: windows-latest, r: 'release'}
           - {os: windows-latest, r: '3.6'}
           - {os: ubuntu-18.04,   r: 'release'}
           - {os: ubuntu-16.04,   r: '3.5',     rspm: "https://packagemanager.rstudio.com/cran/__linux__/xenial/latest"}
     env:
       R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
       RSPM: ${{ matrix.config.rspm }}
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
-      CODECOV_TOKEN: "b5d38865-b712-4f87-aefa-92c24cd50f78"
 
 
     steps:
@@ -110,5 +108,5 @@ jobs:
           path: check
 
       - name: Test coverage
-        run: covr::codecov(token = "{{ secrets.CODECOV_TOKEN }}")
+        run: covr::codecov()
         shell: Rscript {0}

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: thunder
 Type: Package
 Title: Computation and Visualisation of Atmospheric Convective Parameters
-Version: 0.0.12
+Version: 0.0.13
 Authors@R: c(person("Bartosz", "Czernecki", email = "nwp@amu.edu.pl", role = c("aut", "cre"),comment = c(ORCID = "0000-0001-6496-1386")),
     person("Mateusz", "Taszarek", email = "mateusz.taszarek@noaa.gov", role = c("aut"),comment = c(ORCID = "0000-0001-9578-5872")),
     person("Piotr", "Szuster", email = "retsuz@gmail.com", role = c("aut"), comment = c(ORCID = "0000-0001-7937-8046")),
@@ -10,15 +10,15 @@ Authors@R: c(person("Bartosz", "Czernecki", email = "nwp@amu.edu.pl", role = c("
     person("Liangying", "Zhang", role = c("ctb")),
     person("Tim", "Hoar", role = c("ctb")))
 Maintainer: Bartosz Czernecki <nwp@amu.edu.pl>
-Description: A freeware R package and collection of functions for rapid computation and visualisation of convective parameters commonly used in the operational prediction of severe convective storms. Core algorithm is based on a highly optimized C++ code implemented into R language via RCPP. This solution allows to derive thermodynamic and kinematic parameters from large numerical datasets such as reanalyses or operational NWP models in a reasonable amount of time. Package has been developed since 2017 by research meteorologists specializing in severe storms and is constantly updated with new features.
+Description: A freeware R package and collection of functions for rapid computation and visualisation of convective parameters commonly used in the operational prediction of severe convective storms. Core algorithm is based on a highly optimized C++ code implemented into R language via RCPP. This solution allows to derive thermodynamic and kinematic parameters from large numerical datasets such as reanalyses or operational NWP models in a reasonable amount of time. Package has been developed since 2017 by research meteorologists specializing in severe storms.
 License: GPL (>= 2)
 Imports:
     Rcpp (>= 0.12.9.4),
     climate,
     dplyr,
     aiRthermo
 Depends:
-    R (>= 3.5.0)
+    R (>= 3.1.1)
 Suggests: 
     RadioSonde,
     knitr,

README.md

@@ -4,15 +4,14 @@
 
 <!-- badges: start --> 
 [![R-CMD-check](https://github.com/bczernecki/thunder/workflows/R-CMD-check/badge.svg)](https://github.com/bczernecki/thunder/actions)
-[![Codecov test coverage](https://codecov.io/gh/bczernecki/thunder/branch/devel/graph/badge.svg?token=JGZPB7RUFI)](https://codecov.io/gh/bczernecki/thunder)
+[![Codecov test coverage](https://codecov.io/gh/bczernecki/thunder/branch/devel/graph/badge.svg)](https://codecov.io/gh/bczernecki/thunder)
 [![CRAN status](https://www.r-pkg.org/badges/version/thunder)](https://cran.r-project.org/package=thunder)
 [![CRAN RStudio mirror downloads](http://cranlogs.r-pkg.org/badges/thunder)](https://cran.r-project.org/package=thunder)
+[![](http://cranlogs.r-pkg.org/badges/grand-total/thunder?color=brightgreen)](https://cran.r-project.org/package=thunder)
 <!-- badges: end -->
 
 
 
-
-
 **`thundeR`** is a freeware R package and collection of functions for rapid computation and visualisation of convective parameters commonly used in the operational forecasting of severe convective storms. Core algorithm is based on C++ code implemented into R language via RCPP. This solution allows to compute over 100 thermodynamic and kinematic parameters in less than 0.02s per profile and process large datasets such as reanalyses or operational NWP models in a reasonable amount of time. Package has been developed since 2017 by research meteorologists specializing in severe convective storms and is constantly updated with new features.
 
 
@@ -176,21 +175,26 @@ sounding_barbs(chanhassen$pressure, chanhassen$ws, chanhassen$wd, chanhassen$alt
 
 ![](inst/figures/wind_profile.png)
 
-#### Perform sounding computations using python language:
+#### Perform sounding computations using Python with rpy2:
 
-``` py
-# install required python packages
+It is possible to launch `thunder` under Python via rpy2 library. Below you can find the minimum reproducible example:
+
+Make sure that pandas and rpy2 libraries are available for your Python environment. If not install required python packages:
+``` bash 
 pip install pandas  
 pip install rpy2  
+```
 
+Launch `thunder` under Python with `rpy2`:
+
+``` py
 # load required packages
-import pandas as pd
 from rpy2.robjects.packages import importr
 from rpy2.robjects import r,pandas2ri
 import rpy2.robjects as robjects
 pandas2ri.activate()
 
-# load thunder package (previously installed in R)
+# load thunder package (make sure that it was installed in R before)
 importr('thunder')
 
 # download North Platte sounding 
@@ -199,39 +203,23 @@ profile = robjects.r['get_sounding'](wmo_id = 72562, yy = 1999, mm = 7, dd = 3,h
 # compute convective parameters
 parameters = robjects.r['sounding_compute'](profile['pressure'], profile['altitude'], profile['temp'], profile['dpt'], profile['wd'], profile['ws'], accuracy = 2)
 
-# print a numeric vector of all variables, e.g. Surface Based CAPE (element number 14) equals 9413 J/kg
-print(parameters)
-
-#  9.41328612e+03  2.33353072e+02  1.71374349e+03  0.00000000e+00
-#  7.75000000e+02  7.75000000e+02  1.55000000e+04 -1.65508748e+01
-#  1.37209957e+02 -6.66283237e+01  2.39783133e+01  2.39783133e+01
-#  2.33647136e+01  9.41328612e+03  2.33353072e+02  1.71374349e+03
-#  0.00000000e+00  7.75000000e+02  7.75000000e+02  1.55000000e+04
-# -1.65508748e+01  1.37209957e+02 -6.66283237e+01  2.39783133e+01
-#  2.39783133e+01  2.33647136e+01  7.80512973e+03  1.15218462e+02
-#  1.51581216e+03 -4.34518196e+00  9.50000000e+02  9.50000000e+02
-#  1.50000000e+04 -1.46615255e+01  1.24941024e+02 -6.84139508e+01
-#  2.24602410e+01  2.24602410e+01  2.11684444e+01 -9.56842105e+00
-# -6.67934783e+00 -8.80368731e+00 -8.68362720e+00 -9.05783921e+00
-# -7.69846031e+00  4.25000000e+03  3.50000000e+03  0.00000000e+00
-#  2.86600000e+03  5.05734164e+01  5.29324740e+01  1.38180722e+03
-#  3.08978391e+02  2.89996928e+01  3.75856055e+01  8.70308714e+01
-#  5.79532144e-01  3.96038339e-01  4.74523874e-01  8.85009470e+00
-#  1.12138499e+01  1.38753497e+01  2.02833049e+01  2.93264862e+01
-#  6.84091499e+00  2.17033431e+01  2.83220573e+01  2.83220573e+01
-#  2.71728023e+01  1.70580481e+01  1.25343105e+01  1.25343105e+01
-#  1.17350718e+01  7.08519353e+00  6.07991292e+00  7.76810532e+00
-#  7.68968584e+00  1.98900047e+01  6.20722333e+01  1.10063007e+02
-#  1.56478369e+02  6.24627594e+00  7.77240135e+00  4.26451855e+00
-# -4.27789713e+01  2.84670169e+02  5.64779186e+00  1.97598453e+02
-#  1.41876484e+01  2.18886063e+02  7.76810532e+00  3.15000000e+01
-# -1.21377601e+01  6.04000000e+01  6.77124337e+02  4.66991353e+00
-#  6.09845434e+00  2.94595132e+01  2.94595132e+01  3.86405495e+00
-#  1.23469454e+01  2.78307139e+03  2.78307139e+03  2.53421688e+03
-#  3.88606826e+03  3.88606826e+03  3.39499774e+03
+
+# customize output and print all computed variables, e.g. Surface Based CAPE (14th element) equals 9413 J/kg
+
+print(list(map('{:.2f}'.format, parameters)))
+['9413.29', '233.35', '1713.74', '0.00', '775.00', '775.00', '15500.00', '-16.55', '137.21', '-66.63', '23.98',
+'23.98', '23.36', '9413.29', '233.35', '1713.74', '0.00', '775.00', '775.00', '15500.00', '-16.55', '137.21', 
+'-66.63', '23.98', '23.98', '23.36', '7805.13', '115.22', '1515.81', '-4.35', '950.00', '950.00', '15000.00', 
+'-14.66', '124.94', '-68.41', '22.46', '22.46', '21.17', '-9.57', '-6.68', '-8.80', '-8.68', '-9.06', '-7.70', 
+'4250.00', '3500.00', '0.00', '2866.00', '50.57', '52.93', '1381.81', '308.98', '29.00', '37.59', '87.03', '0.58', 
+'0.40', '0.47', '8.85', '11.21', '13.88', '20.28', '29.33', '6.84', '21.70', '28.32', '28.32', '27.17', '17.06', 
+'12.53', '12.53', '11.74', '7.09', '6.08', '7.77', '7.69', '19.89', '62.07', '110.06', '156.48', '6.25', '7.77', 
+'4.26', '-42.78', '284.67', '5.65', '197.60', '14.19', '218.89', '7.77', '31.50', '-12.14', '60.40', '677.12', 
+'4.67', '6.10', '29.46', '29.46', '3.86', '12.35', '2783.07', '2783.07', '2534.22', '3886.07', '3886.07', '3395.00']
 ```
 
 
+
 #### Accuracy tables for `sounding_compute()` 
 
 The interpolation algorithm used in the `sounding_compute()` function impacts accuracy of parameters such as CAPE or CIN and the performance of the script. The valid options for the `accuracy` parameter are 1, 2 or 3: