From 66e1a10c5f4627d85da959d3bc20d1e32215b930 Mon Sep 17 00:00:00 2001 From: "michele.maris" Date: Wed, 24 May 2023 19:43:26 +0200 Subject: [PATCH] u --- docs/py_artecs.tex => CONTRIBUTING.md | 0 LICENSE | 19 + docs/content | 1 + docs/index.html | 7 - docs/py_artecs.html | 481 ------- docs/py_artecs/artecs_map.html | 900 ------------- docs/py_artecs/modelDb.html | 1765 ------------------------- docs/py_artecs/tap.html | 1187 ----------------- docs/search.js | 46 - 9 files changed, 20 insertions(+), 4386 deletions(-) rename docs/py_artecs.tex => CONTRIBUTING.md (100%) create mode 100644 LICENSE create mode 100644 docs/content delete mode 100644 docs/index.html delete mode 100644 docs/py_artecs.html delete mode 100644 docs/py_artecs/artecs_map.html delete mode 100644 docs/py_artecs/modelDb.html delete mode 100644 docs/py_artecs/tap.html delete mode 100644 docs/search.js diff --git a/docs/py_artecs.tex b/CONTRIBUTING.md similarity index 100% rename from docs/py_artecs.tex rename to CONTRIBUTING.md diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..96f1555 --- /dev/null +++ b/LICENSE @@ -0,0 +1,19 @@ +Copyright (c) 2018 The Python Packaging Authority + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/docs/content b/docs/content new file mode 100644 index 0000000..b42a9a7 --- /dev/null +++ b/docs/content @@ -0,0 +1 @@ +docs contains documentation produced by sphinx diff --git a/docs/index.html b/docs/index.html deleted file mode 100644 index a818ab7..0000000 --- a/docs/index.html +++ /dev/null @@ -1,7 +0,0 @@ - - - - - - - diff --git a/docs/py_artecs.html b/docs/py_artecs.html deleted file mode 100644 index 77e54a9..0000000 --- a/docs/py_artecs.html +++ /dev/null @@ -1,481 +0,0 @@ - - - - - - - py_artecs API documentation - - - - - - - - - -
-
-

-py_artecs

- -

A Python3 library to handle contents of ARTECS: Archive of terrestrial-type climate simulations

- -
http://wwwuser.oats.inaf.it/exobio/climates/
-
- -

through TAP and the PYVO services.

- -

Authors: Michele Maris (1), Marco Molinaro (1)

- -
(1) INAF/Trieste Astronomical Observatory
-
- -

First Issue: 2019 Nov 20

- -

Dependencies:

- -

This package needs: - numpy, scipy, pandas, pyvo

- -

Installation

- -

For the latest release:

- -
-
>>> pip3 install py_artecs
-
-
- -

for the dev version clone this GitLab repository.

- -

Files in py_artecs

- -

__init__.py : the init file

- -

tap.py : the file with the TAP library

- -

artecs_map.py : a utility class to handle a local fits file with a Temperature MAP downloaded from the archive

- -

modelDB.py : a utility class to handle a local csv file with the result of a query, this module is kept for backward compatibility, but its use is deprecated

- -

Example of session:

- -

The archive is accessed from the exop_pubblic_tap in py_artecs:

- -
-
>>> from artecs import exop_pubblic_tap
-
-
- -

Then instantiate a database object

- -
-
>>> atap=exop_pubblic_tap()
-
-
- -

all the queries to ARTECS are made through the methods in atap.

- -

Queries follows the Astronomical Data Query Language (ADQL) format

- -
-
>>> ptab=atap.search('power(SMA,1.5)>0.87');
-
-
- -

see the appendix for further references on the ADQL.

- -

To know wether the search is successfull:

- -
-
>>> atap.success()
-True
-
-
- -

The result of a query is stored in ptab as a PANDAS data_frame.

- -

In order to save the table as a csv file use the "to_csv" method of pandas data frame:

- -
-
>>> ptab.to_csv('result.csv',sep=' ')
-
-
- -

In order to get a map corresponding to the first entry in the ptab use:

- -
-
>>> TMAP=atap.get_map(ptab.URL[0])
-
-
- -

the command creates a fits file to rercover the fits filename

- -
-
>>> print(MAP.filename)
-/tmp/artecs_download_file_669d40064f5bddc8.fits
-
-
- -

note that the get_map method saved the fits inside the /tmp directory and gives it a dummy name.

- -

To specify a different name and directory use the outfile keyword in get_map

- -
-
>>> TMAP=atap.get_map(ptab.URL[0],outfile='./test.fits')
->>> print(TMAP.filename)
-./test.fits
-
-
- -

TMAP is an object of class artecs_map, so it contains the temperature MAP in the ARTECS fits file, as well as the other keywords in the original fits file.

- -

Note that the artecs_map class can be used also local copies of the ARTECS temperature maps.

- -

At last, to perform a new query reset the old one with:

- -
-
>>> atap.clear()
-
-
- -

Appendix A: ADQL Queries

- -

A query is a string - . SELECTION set of data to be selected - . HOW_MANY elements to select - . WHICH_FIELDS fields to select - . FROM which table - . WHERE condition for selection is true

- -

Examples of query strings:

- -
    -

  1. Download all the data:

    - -
    -

    SELECT * FROM exo.EXO

    -
    2. -

  2. Download only the first 10 elements:

    - -
    -

    SELECT TOP 10 * FROM exo.EXO

    -
    3. -

  3. Download only the first 10 elements with SMA in the range 0.9 to 1.1:

    - -
    -

    SELECT TOP 10 * FROM exo.EXO WHERE SMA BETWEEN 0.9 AND 1.1

    -
    - -

    alternativelly

    - -
    -

    SELECT TOP 10 * FROM exo.EXO WHERE (0.9 <= SMA) AND (SMA <= 1.1)

    -
    4. -

  4. Download only the first 10 elements with SMA in the range 0.9 to 1.1 and CONTHAB>=0.5

    - -
    -

    SELECT TOP 10 * FROM exo.EXO WHERE SMA BETWEEN 0.9 AND 1.1 AND CONTHAB>=0.5

    -
    5. -

  5. Arithmetic calculations are allowed if needed for selection so to download the first 10 elements with SMA^1.5 > 0.87

    - -
    -

    SELECT TOP 10 * FROM exo.EXO WHERE power(SMA,1.5)> 0.87

    -
    6. -

  6. returns just columns SMA and CONTHAB from previous example:

    - -
    -

    SELECT TOP 10 SMA,CONTHAB FROM exo.EXO WHERE power(SMA,1.5)> 0.87

    -
    7. -
- -

Note that the query string is not sensitive to uppercase or lowercase.

- -

For tutorials see:

- - - -

A reminder of how to form query strings can be recalled by calling:

- -
-
>>> atap.EXPLAIN()
-
-
- -

Appendix B: REFERENCES

- -

Please quote

- -

Climate bistability of Earth-like exoplanets

- -

Murante G., Provenzale A., Vladilo G.,Taffoni G., Silva L., Palazzi E.,von Hardenberg J.,Maris M., Londero E., Knapic C., Zorba C.

- -

MNRAS 492, 2638–2650 (2020)

- -

doi: 10.1093/mnras/stz3529

- -

Appendix C: Example of session with TAP

- -

It is possible to access the archive directly with TAP

- -
-
>>> import pyvo as vo
->>> tap_service = vo.dal.TAPService("http://archives.ia2.inaf.it/vo/tap/exo")
->>> tap_results = tap_service.search("SELECT top 1 exp_id, url FROM exo.EXO")
->>> print(tap_results)
->>>...
->>> len(tap_results)
->>>...
->>> tap_results.getrecord(0)
->>>...
-
-
- -

Appendix D: to install pyvo

- -
-
>>> sudo pip3 install pyvo
-
-
-
- - - - - - 
1"""
-2   .. include:: ../../README.md
-3"""
-4
-5from .modelDb import *
-6from .artecs_map import *
-7from .tap import EXOP_TAP as exop_pubblic_tap
-
- - -
-
- - \ No newline at end of file diff --git a/docs/py_artecs/artecs_map.html b/docs/py_artecs/artecs_map.html deleted file mode 100644 index b909a1e..0000000 --- a/docs/py_artecs/artecs_map.html +++ /dev/null @@ -1,900 +0,0 @@ - - - - - - - py_artecs.artecs_map API documentation - - - - - - - - - -
-
-

-py_artecs.artecs_map

- - - - - - - 
  1class artecs_map :
-  2   """
-  3A class to handle a temperature map from ARTECS.
-  4
-  5It reads the artecs_map from a fits file.
-  6
-  7   >>> AMAP=artecs_map("artecs_map.fits")
-  8
-  9Keywords in the map are returned as members of the class or using the method "parameter".
- 10
- 11Example:
- 12
- 13   >>> AMAP.temp 
- 14
- 15returns the temperature map,
- 16
- 17   >>> AMAP.parameter('temp')
- 18   
- 19returns the temperature map.
- 20
- 21
- 22   >>> AMAP.shape
- 23
- 24returns the shape of the map (rows, columns)
- 25
- 26
- 27   """
- 28   @property 
- 29   def filename(self) :
- 30      """ parameter: the filename """
- 31      return self._filename
- 32   
- 33   @property 
- 34   def p(self) :
- 35      """ the fits file pointer, normally closed.
- 36      """
- 37      return self._p
- 38   
- 39   @property 
- 40   def lst_lat(self) :
- 41      """ list of latitude bands """
- 42      return self._lst_lat
- 43   
- 44   @property 
- 45   def lst_year(self) :
- 46      """ list of time intervals """
- 47      return self._lst_year
- 48
- 49   @property 
- 50   def shape(self) :
- 51      """ the shape of the 2D maps """
- 52      return self._shape
- 53   
- 54   @property 
- 55   def TMGLOB(self) :
- 56      """ the mean surface temperature """
- 57      return self._TMGLOB
- 58   
- 59   def __init__(self,filename,closeFits=True,verbose=False) :
- 60      """ To instantiate the class pass the filename from which to load the Temperature map
- 61      
- 62      >Keywords: 
- 63      
- 64      >>`verbose`: if True verbose output
- 65      
- 66      >>`closeFits`: if True the fits file is closed after reading
- 67      """
- 68      import numpy
- 69      try :
- 70         import pyfits
- 71      except :
- 72         from astropy.io import fits as pyfits
- 73      self._filename=filename
- 74      self._p=pyfits.open(filename)
- 75      self._key=[]
- 76      self._value=[]
- 77      self._descr=[]
- 78      mkd=True
- 79      for cc in self.p[0].header.cards :
- 80         if cc[0]!='COMMENT' :
- 81            self._key.append(cc[0])
- 82            self._value.append(cc[1])
- 83            self._descr.append(cc[2])
- 84         else :   
- 85            if mkd :
- 86               self._key.append(cc[0])
- 87               self._value.append('\n'.join(self.p[0].header['COMMENT']))
- 88               self._descr.append(None)
- 89               mkd=False
- 90      
- 91      #: N of year intervals
- 92      self.N=self.parameter('N')
- 93      
- 94      #: NS number of latitude bands
- 95      self.NS=self.parameter('NS')
- 96      
- 97      #: the shape
- 98      self._shape=(self.NS,self.N)
- 99     
-100      self.year=self.p[1].data['year']
-101      self.lat=self.p[1].data['lat']
-102      self.temp=self.p[1].data['temp']      
-103      self.year.shape=self.shape
-104      self.lat.shape=self.shape
-105      self.temp.shape=self.shape
-106      
-107      #: the mean surfscr temperature
-108      self._TMGLOB=self.temp.mean()
-109
-110      self._lst_lat=self.lat[0]
-111      self._lst_year=self.year[:,0].T
-112
-113      if verbose :
-114         print('Successfully opened and readed ',filename)
-115         
-116      if closeFits :
-117         self._p.close()
-118         if verbose :
-119            print('fits file closed ',filename)
-120         
-121      
-122   def keys(self,maps=False) : 
-123      """
-124returns the list of quantities in the map
-125      
-126Keywords: 
-127
-128>`maps` (default False), if True returns just the list of of elements which are 2D maps
-129      """
-130      if maps : 
-131         return ['year','lat','temp']
-132      else :
-133         return self._key
-134   def has_key(self,key) : 
-135      """True if required `key` is in the map"""
-136      return key in self._key
-137   def parameter(self,key) :
-138      """returns a parameter from the fits file from its `key'"""
-139      return self._value[self._key.index(key)]
-140   def description(self,key) :
-141      """returns the description of a parameter in the fits file from its `key'"""
-142      return self._descr[self._key.index(key)]
-143   #def bilinear_interpolation(self,lat,year) :
-144      #"""returns bilinear interpolation of the map (not implemeted yet) """
-145      #raise Exception(
-
- - -
-
- -
- - class - artecs_map: - - - -
- - 
  2class artecs_map :
-  3   """
-  4A class to handle a temperature map from ARTECS.
-  5
-  6It reads the artecs_map from a fits file.
-  7
-  8   >>> AMAP=artecs_map("artecs_map.fits")
-  9
- 10Keywords in the map are returned as members of the class or using the method "parameter".
- 11
- 12Example:
- 13
- 14   >>> AMAP.temp 
- 15
- 16returns the temperature map,
- 17
- 18   >>> AMAP.parameter('temp')
- 19   
- 20returns the temperature map.
- 21
- 22
- 23   >>> AMAP.shape
- 24
- 25returns the shape of the map (rows, columns)
- 26
- 27
- 28   """
- 29   @property 
- 30   def filename(self) :
- 31      """ parameter: the filename """
- 32      return self._filename
- 33   
- 34   @property 
- 35   def p(self) :
- 36      """ the fits file pointer, normally closed.
- 37      """
- 38      return self._p
- 39   
- 40   @property 
- 41   def lst_lat(self) :
- 42      """ list of latitude bands """
- 43      return self._lst_lat
- 44   
- 45   @property 
- 46   def lst_year(self) :
- 47      """ list of time intervals """
- 48      return self._lst_year
- 49
- 50   @property 
- 51   def shape(self) :
- 52      """ the shape of the 2D maps """
- 53      return self._shape
- 54   
- 55   @property 
- 56   def TMGLOB(self) :
- 57      """ the mean surface temperature """
- 58      return self._TMGLOB
- 59   
- 60   def __init__(self,filename,closeFits=True,verbose=False) :
- 61      """ To instantiate the class pass the filename from which to load the Temperature map
- 62      
- 63      >Keywords: 
- 64      
- 65      >>`verbose`: if True verbose output
- 66      
- 67      >>`closeFits`: if True the fits file is closed after reading
- 68      """
- 69      import numpy
- 70      try :
- 71         import pyfits
- 72      except :
- 73         from astropy.io import fits as pyfits
- 74      self._filename=filename
- 75      self._p=pyfits.open(filename)
- 76      self._key=[]
- 77      self._value=[]
- 78      self._descr=[]
- 79      mkd=True
- 80      for cc in self.p[0].header.cards :
- 81         if cc[0]!='COMMENT' :
- 82            self._key.append(cc[0])
- 83            self._value.append(cc[1])
- 84            self._descr.append(cc[2])
- 85         else :   
- 86            if mkd :
- 87               self._key.append(cc[0])
- 88               self._value.append('\n'.join(self.p[0].header['COMMENT']))
- 89               self._descr.append(None)
- 90               mkd=False
- 91      
- 92      #: N of year intervals
- 93      self.N=self.parameter('N')
- 94      
- 95      #: NS number of latitude bands
- 96      self.NS=self.parameter('NS')
- 97      
- 98      #: the shape
- 99      self._shape=(self.NS,self.N)
-100     
-101      self.year=self.p[1].data['year']
-102      self.lat=self.p[1].data['lat']
-103      self.temp=self.p[1].data['temp']      
-104      self.year.shape=self.shape
-105      self.lat.shape=self.shape
-106      self.temp.shape=self.shape
-107      
-108      #: the mean surfscr temperature
-109      self._TMGLOB=self.temp.mean()
-110
-111      self._lst_lat=self.lat[0]
-112      self._lst_year=self.year[:,0].T
-113
-114      if verbose :
-115         print('Successfully opened and readed ',filename)
-116         
-117      if closeFits :
-118         self._p.close()
-119         if verbose :
-120            print('fits file closed ',filename)
-121         
-122      
-123   def keys(self,maps=False) : 
-124      """
-125returns the list of quantities in the map
-126      
-127Keywords: 
-128
-129>`maps` (default False), if True returns just the list of of elements which are 2D maps
-130      """
-131      if maps : 
-132         return ['year','lat','temp']
-133      else :
-134         return self._key
-135   def has_key(self,key) : 
-136      """True if required `key` is in the map"""
-137      return key in self._key
-138   def parameter(self,key) :
-139      """returns a parameter from the fits file from its `key'"""
-140      return self._value[self._key.index(key)]
-141   def description(self,key) :
-142      """returns the description of a parameter in the fits file from its `key'"""
-143      return self._descr[self._key.index(key)]
-144   #def bilinear_interpolation(self,lat,year) :
-145      #"""returns bilinear interpolation of the map (not implemeted yet) """
-146      #raise Exception(
-
- - -

A class to handle a temperature map from ARTECS.

- -

It reads the artecs_map from a fits file.

- -
-
>>> AMAP=artecs_map("artecs_map.fits")
-
-
- -

Keywords in the map are returned as members of the class or using the method "parameter".

- -

Example:

- -
-
>>> AMAP.temp 
-
-
- -

returns the temperature map,

- -
-
>>> AMAP.parameter('temp')
-
-
- -

returns the temperature map.

- -
-
>>> AMAP.shape
-
-
- -

returns the shape of the map (rows, columns)

-
- - -
- -
- - artecs_map(filename, closeFits=True, verbose=False) - - - -
- - 
 60   def __init__(self,filename,closeFits=True,verbose=False) :
- 61      """ To instantiate the class pass the filename from which to load the Temperature map
- 62      
- 63      >Keywords: 
- 64      
- 65      >>`verbose`: if True verbose output
- 66      
- 67      >>`closeFits`: if True the fits file is closed after reading
- 68      """
- 69      import numpy
- 70      try :
- 71         import pyfits
- 72      except :
- 73         from astropy.io import fits as pyfits
- 74      self._filename=filename
- 75      self._p=pyfits.open(filename)
- 76      self._key=[]
- 77      self._value=[]
- 78      self._descr=[]
- 79      mkd=True
- 80      for cc in self.p[0].header.cards :
- 81         if cc[0]!='COMMENT' :
- 82            self._key.append(cc[0])
- 83            self._value.append(cc[1])
- 84            self._descr.append(cc[2])
- 85         else :   
- 86            if mkd :
- 87               self._key.append(cc[0])
- 88               self._value.append('\n'.join(self.p[0].header['COMMENT']))
- 89               self._descr.append(None)
- 90               mkd=False
- 91      
- 92      #: N of year intervals
- 93      self.N=self.parameter('N')
- 94      
- 95      #: NS number of latitude bands
- 96      self.NS=self.parameter('NS')
- 97      
- 98      #: the shape
- 99      self._shape=(self.NS,self.N)
-100     
-101      self.year=self.p[1].data['year']
-102      self.lat=self.p[1].data['lat']
-103      self.temp=self.p[1].data['temp']      
-104      self.year.shape=self.shape
-105      self.lat.shape=self.shape
-106      self.temp.shape=self.shape
-107      
-108      #: the mean surfscr temperature
-109      self._TMGLOB=self.temp.mean()
-110
-111      self._lst_lat=self.lat[0]
-112      self._lst_year=self.year[:,0].T
-113
-114      if verbose :
-115         print('Successfully opened and readed ',filename)
-116         
-117      if closeFits :
-118         self._p.close()
-119         if verbose :
-120            print('fits file closed ',filename)
-
- - -

To instantiate the class pass the filename from which to load the Temperature map

- -
-

Keywords:

-
- -
-
-

verbose: if True verbose output

-
-
- -
-
-

closeFits: if True the fits file is closed after reading

-
-
-
- - -
-
-
- filename - - -
- - -

parameter: the filename

-
- - -
-
-
- p - - -
- - -

the fits file pointer, normally closed.

-
- - -
-
-
- lst_lat - - -
- - -

list of latitude bands

-
- - -
-
-
- lst_year - - -
- - -

list of time intervals

-
- - -
-
-
- shape - - -
- - -

the shape of the 2D maps

-
- - -
-
-
- TMGLOB - - -
- - -

the mean surface temperature

-
- - -
-
- -
- - def - keys(self, maps=False): - - - -
- - 
123   def keys(self,maps=False) : 
-124      """
-125returns the list of quantities in the map
-126      
-127Keywords: 
-128
-129>`maps` (default False), if True returns just the list of of elements which are 2D maps
-130      """
-131      if maps : 
-132         return ['year','lat','temp']
-133      else :
-134         return self._key
-
- - -

returns the list of quantities in the map

- -

Keywords:

- -
-

maps (default False), if True returns just the list of of elements which are 2D maps

-
-
- - -
-
- -
- - def - has_key(self, key): - - - -
- - 
135   def has_key(self,key) : 
-136      """True if required `key` is in the map"""
-137      return key in self._key
-
- - -

True if required key is in the map

-
- - -
-
- -
- - def - parameter(self, key): - - - -
- - 
138   def parameter(self,key) :
-139      """returns a parameter from the fits file from its `key'"""
-140      return self._value[self._key.index(key)]
-
- - -

returns a parameter from the fits file from its `key'

-
- - -
-
- -
- - def - description(self, key): - - - -
- - 
141   def description(self,key) :
-142      """returns the description of a parameter in the fits file from its `key'"""
-143      return self._descr[self._key.index(key)]
-144   #def bilinear_interpolation(self,lat,year) :
-145      #"""returns bilinear interpolation of the map (not implemeted yet) """
-146      #raise Exception(
-
- - -

returns the description of a parameter in the fits file from its `key'

-
- - -
-
-
- - \ No newline at end of file diff --git a/docs/py_artecs/modelDb.html b/docs/py_artecs/modelDb.html deleted file mode 100644 index b421a6d..0000000 --- a/docs/py_artecs/modelDb.html +++ /dev/null @@ -1,1765 +0,0 @@ - - - - - - - py_artecs.modelDb API documentation - - - - - - - - - -
-
-

-py_artecs.modelDb

- -

DEPRECATED

-
- - - - - - 
  1"""
-  2   **DEPRECATED** 
-  3"""
-  4
-  5class modelDb :
-  6   """
-  7   **DEPRECATED** 
-  8   
-  9Class to handle a local version of the ARTECS DB.
- 10
- 11It takes in input a csv file generated from a query of ARTECS and saved in csv format.
- 12
- 13See the example in the introductory page.
- 14
- 15This module is kept for compatibility with early releases of py_artecs, but it is not maintained so its use is DEPRECATED.
- 16
- 17   """
- 18   def __init__(self,project_name,project_path,csv_name,Verbose=True,csv_sep='!',csv_comment='#',csv_index_col='index',figures_path='png',filterBad=True,new=False,query=None) :
- 19      import time
- 20      from collections import OrderedDict
- 21      from matplotlib import pyplot as plt
- 22      import numpy as np
- 23      import pandas
- 24      import astropy
- 25      #
- 26      self.reset()
- 27      self.reset_plot_status()
- 28      #
- 29      self._project_name=project_name
- 30      self._project_path=project_path
- 31      self._csv_name=csv_name
- 32      self._figures_path=figures_path
- 33      #
- 34      if new : return
- 35      #
- 36      print()
- 37      print("Reading from csv file ",self._csv_name)
- 38      tic=time.time()
- 39      csv=pandas.read_csv(self._project_path+'/'+self._csv_name,sep=csv_sep,comment=csv_comment,index_col=csv_index_col)
- 40      if not 'TSTART' in csv.keys() : 
- 41         csv['TSTART']=np.zeros(len(csv))
- 42         print ("TSTART not found, replaced by a DUMMY")
- 43      csv['Natm']=csv['p_surface']/csv['GRAV']
- 44      csv['iclass']=np.array([self.classification_string2numeric(k.strip().lower()) for k in csv.CLASS])
- 45      csv['peri']=(1-csv.ECC)*csv.SMA
- 46      csv['aphe']=(1+csv.ECC)*csv.SMA
- 47      self.flagBad=np.array(csv['iclass']==self._string_class_notfound)*8+np.array(csv.iclass<0)*4+np.array(np.isnan(csv.TMGLOB))*2+np.array(csv.TSTART<0)
- 48      tic=time.time()-tic
- 49      print("%d lines in %e sec"%(len(csv),tic))
- 50      #
- 51      if not 'SOURCE' in csv.keys() :
- 52         csv['SOURCE']=np.zeros(len(csv))-1
- 53      #
- 54      if filterBad :
- 55         self.TABLE=csv[self.flagBad==0]
- 56      else :
- 57         self.TABLE=csv
- 58      #
- 59      if query != None and query!='' :
- 60         self.TABLE=self.TABLE.query(query)
- 61      #
- 62      if Verbose :
- 63         print()
- 64         print("In %s, containind %d models, the number of models with "%(csv_name,len(csv)))
- 65         print("  CLASS  == UNKNOWN                    ",(csv.iclass==-200).sum())
- 66         print("  CLASS  == SNOWBALL                   ",(csv.iclass==1).sum())
- 67         print("  CLASS  == WATERBELT                  ",(csv.iclass==2).sum())
- 68         print("  CLASS  == WARM                       ",(csv.iclass==3).sum())
- 69         print("  CLASS  == WARM_HOT                   ",(csv.iclass==4).sum())
- 70         print("  INVALID STRING IN CLASS              ",((self.flagBad>=8)).sum())
- 71         print()
- 72         for k in np.unique(csv['SOURCE']) :
- 73            print("  SOURCE == %d                          %d"%(k,(csv.SOURCE==0).sum()))
- 74         #print("  SOURCE == 1                          ",(csv.SOURCE==1).sum())
- 75         print()
- 76         print("  TMGLOB == NaN                        ",np.isnan(csv.TMGLOB).sum())
- 77         print("  TSTART<0                             ",(csv.TSTART<0).sum())
- 78         print("  TMGLOB == NaN and TSTART<0           ",((csv.TSTART<0)&np.isnan(csv.TMGLOB)).sum())
- 79         print("  TMGLOB == NaN and ZCD == NaN         ",(np.isnan(csv.TMGLOB)&np.isnan(csv.zenithal_column_density)).sum())
- 80         print("  TMGLOB == NaN and CLASS == UNDEFINED ",(np.isnan(csv.TMGLOB)&(csv.iclass==0)).sum())
- 81         print()
- 82      #
- 83      del csv 
- 84   def to_csv(self,outFile,COMMENT='') :
- 85      print("Writing csv into ",outFile)
- 86      open(outFile,'w').write('#\n#'+outFile+'\n#elaboration of '+self._csv_name+'\n#'+str(COMMENT)+'\n#!BEGIN\n')
- 87      open(outFile,'a').write(self.TABLE.to_csv(None,sep='!',index_column='index'))
- 88      open(outFile,'a').write('#!END\n')
- 89      print("written")
- 90   def reset(self) :
- 91      from collections import OrderedDict
- 92      for k in ['_project_name','_project_path','_csv_name','_figures_path','_project_name','_project_path','TABLE','flagBad','classTable'] :
- 93         self.__dict__[k]=None
- 94      self._numeric_classification=OrderedDict()
- 95      self._numeric_classification['---']=-400
- 96      self._numeric_classification['unknown']=-200
- 97      self._numeric_classification['snowball']=1
- 98      self._numeric_classification['waterbelt']=2
- 99      self._numeric_classification['warm']=3
-100      self._numeric_classification['warm_hot']=4
-101      self._string_class_notfound=-100000
-102      #
-103      self._classification_labels=self._numeric_classification.keys()
-104      self._ClassNames=['SNOWBALL','WATERBELT','WARM','WARM_HOT']
-105      self._ClassColor={'SNOWBALL':'b','WATERBELT':'g','WARM':'r','WARM_HOT':'y'}
-106      self._ClassMarker={'SNOWBALL':'o:12','WATERBELT':'v:16','WARM':'d:8','WARM_HOT':'s:6'}
-107      #
-108      self.iceTOT_threshold=None
-109   #
-110   def __len__(self) : return len(self.TABLE)
-111   #
-112   def __getitem__(self,this) : return self.TABLE[this]
-113   #
-114   def __setitem__(self,this,that) : self.TABLE[this]=that
-115   #
-116   def copy(self) :
-117      import copy
-118      out=modelDb('slice of '+self._project_name,self._project_path,None,new=True)
-119      out._project_name=self._project_name
-120      out._project_path=self._project_path
-121      out._csv_name=None
-122      out._figures_path=self._figures_path
-123      out.classTable=copy.deepcopy(self.classTable)
-124      return out
-125   #
-126   def unique_values(self) :
-127      import numpy as np
-128      from collections import OrderedDict
-129      import pandas
-130      out=OrderedDict()
-131      for k in self.keys() :
-132         out[k]=np.unique(self.TABLE[k].values)
-133      return out
-134   #
-135   def list_unique_values(self) :
-136      out=self.unique_values()
-137      for k in out.keys() :
-138         print("%s (%d) ="%(k,len(out[k])))
-139      #
-140   def classification_indexes(self,*arg,**karg) :
-141      import numpy as np
-142      from collections import OrderedDict
-143      if len(arg) == 0 : 
-144         _arg=['CLASS','OBLIQ','ECC','SMA','PRESS','SOURCE','GEO','FO_CONST','PROT','P_CO2']
-145      else :
-146         _arg=arg
-147      ipfx=karg['index_pfx'] if karg.has_key('index_pfx') else 'i_'
-148      for k in _arg : 
-149         quit=False
-150         if not k in self.keys() :
-151            print("%s not found "%k)
-152            quit=True
-153      if quit : 
-154         return
-155      classTable=OrderedDict()
-156      for k in arg :
-157         u=self.unique(k)
-158         iu=np.zeros(len(self))
-159         classTable[k]=OrderedDict()
-160         for cu,u in enumerate(self.unique(k)) :
-161            idx=np.where(np.array(self.TABLE[k]==u))[0]
-162            iu[idx]=cu
-163            classTable[k][u]=[cu,len(idx)]
-164         self.TABLE[ipfx+k]=iu
-165      self.classTable=classTable.copy()
-166      return classTable
-167   #
-168   def show_classTable(self) :
-169      for k in DB.classTable.keys() :
-170         print('%10s : %3d = '%(k,len(DB.classTable[k])),end='')
-171         for ij,j in enumerate(DB.classTable[k].keys()) : 
-172            if ij>0 : print ('|',end='')
-173            print('%3d#'%ij,end='')
-174            print('%12s'%str(j),':',end='')
-175            print("%6d "%len(DB.query('i_'+k+'=='+str(ij))),end='')
-176         print()
-177   #
-178   def unique(self,key) : 
-179      import numpy as np
-180      return np.sort(self.TABLE[key].unique())
-181   #
-182   def sort(self,sort_by) :
-183      self.TABLE=self.TABLE.sort(sort_by)
-184   #
-185   def sorted_query(self,sort_by,qstr) :
-186      out=self.copy()
-187      if type(qstr) == type('') :
-188         out.TABLE=self.TABLE.query(qstr)
-189      else :
-190         out.TABLE=self.TABLE[qstr]
-191      out.TABLE=out.TABLE.sort(sort_by)
-192      return out
-193   #
-194   def query(self,qstr) :
-195      out=self.copy()
-196      if type(qstr) == type('') :
-197         out.TABLE=self.TABLE.query(qstr)
-198      else :
-199         out.TABLE=self.TABLE[qstr]
-200      return out
-201   #
-202   def select_by_loc(self,loc_argument) :
-203      out=self.copy()
-204      out.TABLE=self.TABLE.loc[loc_argument]
-205      return out
-206   #
-207   def select_by_iloc(self,iloc_argument) :
-208      out=self.copy()
-209      out.TABLE=self.TABLE.iloc[iloc_argument]
-210      return out
-211   #
-212   def classification_string2numeric(self,strg) :
-213      try :
-214         return self._numeric_classification[strg.lower().strip()]
-215      except :
-216         print("bad strng ",strg)
-217         return self._string_class_notfound
-218   #
-219   def calc_iclass(self,string_classification_array) :
-220      return np.array([self._numeric_classification[k.strip().lower()] for k in string_classification_array])
-221   #
-222   def keys(self) : return list(self.TABLE.keys())
-223   #
-224   def has_key(self,this) : return (this in self.TABLE.keys())
-225   #
-226   def __include__(self,this) : return (this in self.TABLE.keys())
-227   #
-228   def reset_plot_status(self) :
-229      """resets the self._last_plot dictionary where handles from the last plot generated are stored"""
-230      from collections import OrderedDict
-231      self._last_plot=OrderedDict()
-232      self._last_plot['fig']=None
-233      self._last_plot['xlabel']=None
-234      self._last_plot['ylabel']=None
-235      self._last_plot['axe']=None
-236      self._last_plot['legend']=None
-237      self._last_plot['curves']=[]
-238   #
-239   def plot2(self,x,y='MolecularDepth',ylog=True,xlog=False,newfig=True,legend_loc=3,xylim=None,savefig=None,one2one_line=False) :
-240      """creates a 2 axis plot, the handles for the plot objects are stored in self._last_plot"""
-241      from collections import OrderedDict
-242      from matplotlib import pyplot as plt
-243      import numpy as np
-244      import pandas
-245      import astropy
-246      #
-247      self.reset_plot_status()
-248      #
-249      if newfig : 
-250         self._last_plot['fig']=plt.figure()
-251      else :
-252         self._last_plot['fig']=plt.gcf()
-253      #
-254      for k in self._ClassNames :  
-255         smp=self.TABLE.query('CLASS=="%s"'%k)
-256         self._last_plot['curves'].append(plt.plot(smp[x],smp[y],self._ClassColor[k]+self._ClassMarker[k][0],label=k,mew=0,markersize=int(self._ClassMarker[k][2:])))
-257      plt.gca().set_xscale('linear' if xlog==False else 'log')
-258      plt.gca().set_yscale('linear' if ylog==False else 'log')
-259      plt.title(self._project_name,fontsize=20)
-260      if xylim != None : plt.axis(xylim)
-261      #
-262      if one2one_line==True :
-263         a=plt.axis()
-264         x121=np.arange(0,1+0.01,0.01)
-265         x121=a[0]*x121+a[1]*(1-x121)
-266         self._last_plot['1:1']=plt.plot(x121,x121,'k-',lw=1)
-267         self._last_plot['1:1'][0].set_zorder(-10)
-268      #
-269      if x=='Natm' :
-270         a=plt.axis()
-271         self._last_plot['STARTNATMLINES']=[plt.plot(k*np.ones(2),[a[2],a[3]],'k-',lw=2) for k in np.unique(self.TABLE['PRESS']/self.TABLE['GRAV'])]
-272         for k in self._last_plot['STARTNATMLINES'] : k[0].set_zorder(-10)
-273      #
-274      if x=='p_surface' :
-275         a=plt.axis()
-276         self._last_plot['PRESS_LINES']=[plt.plot(k*np.ones(2),[a[2],a[3]],'k-',lw=2) for k in np.unique(self.TABLE['PRESS'])]
-277         for k in self._last_plot['PRESS_LINES'] : k[0].set_zorder(-10)
-278      #
-279      self._last_plot['axe']=plt.gca()
-280      if legend_loc > 0 :
-281         self._last_plot['legend']=plt.legend(loc=legend_loc)
-282      self._last_plot['xlabel']=plt.xlabel(x,fontsize=20)
-283      self._last_plot['ylabel']=plt.ylabel(y,fontsize=20)
-284      plt.gcf().set_figwidth(18.)
-285      plt.gcf().set_figheight(plt.gcf().get_figwidth()*3./4.)
-286      if savefig != None and savefig != False:
-287         if type(savefig)==type('') and savefig!='' :
-288            _savefig=savefig
-289         else :
-290            _savefig=self._project_name+'/'+self._figures_path+'/'+y+'_vz_'+x+'.pdf'
-291         print("Saving in ",_savefig)
-292         plt.savefig(_savefig,dpi=plt.gcf().get_dpi())
-293      plt.show()
-294   #
-295   def set_iceTOT_threshold(self,iceTOT_threshold) :
-296      """ set iceTOT_threshold : the ice coverage over which the planet is considered a snowball
-297suggested values 0.95 or 0.99
-298      """
-299      self.iceTOT_threshold=iceTOT_threshold
-300   #
-301   def flag_class_warm(self) :
-302      """returns Murante's classifications for warm calculated from scratch
-303iceTOT_threshold is the ice coverage over which the planet is considered a snowball
-304suggested values 0.95 or 0.99
-305      """
-306      import numpy as np
-307      if not self.iceTOT_threshold() : return
-308      return (self.TABLE['TMGLOB'] > self.TABLE['Tice']) * (self.TABLE['TMGLOB'] < self.TABLE['Tvap'])
-309      
-310   def flag_class_warm_hot(self) :
-311      """returns Murante's classifications for warm_hot calculated from scratch
-312iceTOT_threshold is the ice coverage over which the planet is considered a snowball
-313suggested values 0.95 or 0.99
-314      """
-315      import numpy as np
-316      if not self.iceTOT_threshold() : return
-317      return (self.TABLE['TMGLOB'] >= self.TABLE['Tvap'])
-318      
-319   def flag_class_snowball(self) :
-320      """returns Murante's classifications for warm_hot calculated from scratch
-321iceTOT_threshold is the ice coverage over which the planet is considered a snowball
-322suggested values 0.95 or 0.99
-323      """
-324      import numpy as np
-325      if not self.iceTOT_threshold() : return
-326      return (self.TABLE['ICE'] > self.iceTOT_threshold)
-327      
-328   def flag_class_waterbelt(self) :
-329      """returns Murante's classifications for warm_hot calculated from scratch
-330iceTOT_threshold is the ice coverage over which the planet is considered a snowball
-331suggested values 0.95 or 0.99
-332      """
-333      import numpy as np
-334      if not self.iceTOT_threshold() : return
-335      return (self.TABLE['TMGLOB'] <= self.TABLE['Tice'])*(self.TABLE['ICE'] <= self.iceTOT_threshold)
-336   
-337   def _testIceTot(self) :
-338      if self.iceTOT_threshold == None :
-339         raise Exception("iceTOT_threshold not set, Set iceTOT_threshold with .set_iceTOT_threshold(x) before")
-340         return False
-341      return True
-
- - -
-
- -
- - class - modelDb: - - - -
- - 
  6class modelDb :
-  7   """
-  8   **DEPRECATED** 
-  9   
- 10Class to handle a local version of the ARTECS DB.
- 11
- 12It takes in input a csv file generated from a query of ARTECS and saved in csv format.
- 13
- 14See the example in the introductory page.
- 15
- 16This module is kept for compatibility with early releases of py_artecs, but it is not maintained so its use is DEPRECATED.
- 17
- 18   """
- 19   def __init__(self,project_name,project_path,csv_name,Verbose=True,csv_sep='!',csv_comment='#',csv_index_col='index',figures_path='png',filterBad=True,new=False,query=None) :
- 20      import time
- 21      from collections import OrderedDict
- 22      from matplotlib import pyplot as plt
- 23      import numpy as np
- 24      import pandas
- 25      import astropy
- 26      #
- 27      self.reset()
- 28      self.reset_plot_status()
- 29      #
- 30      self._project_name=project_name
- 31      self._project_path=project_path
- 32      self._csv_name=csv_name
- 33      self._figures_path=figures_path
- 34      #
- 35      if new : return
- 36      #
- 37      print()
- 38      print("Reading from csv file ",self._csv_name)
- 39      tic=time.time()
- 40      csv=pandas.read_csv(self._project_path+'/'+self._csv_name,sep=csv_sep,comment=csv_comment,index_col=csv_index_col)
- 41      if not 'TSTART' in csv.keys() : 
- 42         csv['TSTART']=np.zeros(len(csv))
- 43         print ("TSTART not found, replaced by a DUMMY")
- 44      csv['Natm']=csv['p_surface']/csv['GRAV']
- 45      csv['iclass']=np.array([self.classification_string2numeric(k.strip().lower()) for k in csv.CLASS])
- 46      csv['peri']=(1-csv.ECC)*csv.SMA
- 47      csv['aphe']=(1+csv.ECC)*csv.SMA
- 48      self.flagBad=np.array(csv['iclass']==self._string_class_notfound)*8+np.array(csv.iclass<0)*4+np.array(np.isnan(csv.TMGLOB))*2+np.array(csv.TSTART<0)
- 49      tic=time.time()-tic
- 50      print("%d lines in %e sec"%(len(csv),tic))
- 51      #
- 52      if not 'SOURCE' in csv.keys() :
- 53         csv['SOURCE']=np.zeros(len(csv))-1
- 54      #
- 55      if filterBad :
- 56         self.TABLE=csv[self.flagBad==0]
- 57      else :
- 58         self.TABLE=csv
- 59      #
- 60      if query != None and query!='' :
- 61         self.TABLE=self.TABLE.query(query)
- 62      #
- 63      if Verbose :
- 64         print()
- 65         print("In %s, containind %d models, the number of models with "%(csv_name,len(csv)))
- 66         print("  CLASS  == UNKNOWN                    ",(csv.iclass==-200).sum())
- 67         print("  CLASS  == SNOWBALL                   ",(csv.iclass==1).sum())
- 68         print("  CLASS  == WATERBELT                  ",(csv.iclass==2).sum())
- 69         print("  CLASS  == WARM                       ",(csv.iclass==3).sum())
- 70         print("  CLASS  == WARM_HOT                   ",(csv.iclass==4).sum())
- 71         print("  INVALID STRING IN CLASS              ",((self.flagBad>=8)).sum())
- 72         print()
- 73         for k in np.unique(csv['SOURCE']) :
- 74            print("  SOURCE == %d                          %d"%(k,(csv.SOURCE==0).sum()))
- 75         #print("  SOURCE == 1                          ",(csv.SOURCE==1).sum())
- 76         print()
- 77         print("  TMGLOB == NaN                        ",np.isnan(csv.TMGLOB).sum())
- 78         print("  TSTART<0                             ",(csv.TSTART<0).sum())
- 79         print("  TMGLOB == NaN and TSTART<0           ",((csv.TSTART<0)&np.isnan(csv.TMGLOB)).sum())
- 80         print("  TMGLOB == NaN and ZCD == NaN         ",(np.isnan(csv.TMGLOB)&np.isnan(csv.zenithal_column_density)).sum())
- 81         print("  TMGLOB == NaN and CLASS == UNDEFINED ",(np.isnan(csv.TMGLOB)&(csv.iclass==0)).sum())
- 82         print()
- 83      #
- 84      del csv 
- 85   def to_csv(self,outFile,COMMENT='') :
- 86      print("Writing csv into ",outFile)
- 87      open(outFile,'w').write('#\n#'+outFile+'\n#elaboration of '+self._csv_name+'\n#'+str(COMMENT)+'\n#!BEGIN\n')
- 88      open(outFile,'a').write(self.TABLE.to_csv(None,sep='!',index_column='index'))
- 89      open(outFile,'a').write('#!END\n')
- 90      print("written")
- 91   def reset(self) :
- 92      from collections import OrderedDict
- 93      for k in ['_project_name','_project_path','_csv_name','_figures_path','_project_name','_project_path','TABLE','flagBad','classTable'] :
- 94         self.__dict__[k]=None
- 95      self._numeric_classification=OrderedDict()
- 96      self._numeric_classification['---']=-400
- 97      self._numeric_classification['unknown']=-200
- 98      self._numeric_classification['snowball']=1
- 99      self._numeric_classification['waterbelt']=2
-100      self._numeric_classification['warm']=3
-101      self._numeric_classification['warm_hot']=4
-102      self._string_class_notfound=-100000
-103      #
-104      self._classification_labels=self._numeric_classification.keys()
-105      self._ClassNames=['SNOWBALL','WATERBELT','WARM','WARM_HOT']
-106      self._ClassColor={'SNOWBALL':'b','WATERBELT':'g','WARM':'r','WARM_HOT':'y'}
-107      self._ClassMarker={'SNOWBALL':'o:12','WATERBELT':'v:16','WARM':'d:8','WARM_HOT':'s:6'}
-108      #
-109      self.iceTOT_threshold=None
-110   #
-111   def __len__(self) : return len(self.TABLE)
-112   #
-113   def __getitem__(self,this) : return self.TABLE[this]
-114   #
-115   def __setitem__(self,this,that) : self.TABLE[this]=that
-116   #
-117   def copy(self) :
-118      import copy
-119      out=modelDb('slice of '+self._project_name,self._project_path,None,new=True)
-120      out._project_name=self._project_name
-121      out._project_path=self._project_path
-122      out._csv_name=None
-123      out._figures_path=self._figures_path
-124      out.classTable=copy.deepcopy(self.classTable)
-125      return out
-126   #
-127   def unique_values(self) :
-128      import numpy as np
-129      from collections import OrderedDict
-130      import pandas
-131      out=OrderedDict()
-132      for k in self.keys() :
-133         out[k]=np.unique(self.TABLE[k].values)
-134      return out
-135   #
-136   def list_unique_values(self) :
-137      out=self.unique_values()
-138      for k in out.keys() :
-139         print("%s (%d) ="%(k,len(out[k])))
-140      #
-141   def classification_indexes(self,*arg,**karg) :
-142      import numpy as np
-143      from collections import OrderedDict
-144      if len(arg) == 0 : 
-145         _arg=['CLASS','OBLIQ','ECC','SMA','PRESS','SOURCE','GEO','FO_CONST','PROT','P_CO2']
-146      else :
-147         _arg=arg
-148      ipfx=karg['index_pfx'] if karg.has_key('index_pfx') else 'i_'
-149      for k in _arg : 
-150         quit=False
-151         if not k in self.keys() :
-152            print("%s not found "%k)
-153            quit=True
-154      if quit : 
-155         return
-156      classTable=OrderedDict()
-157      for k in arg :
-158         u=self.unique(k)
-159         iu=np.zeros(len(self))
-160         classTable[k]=OrderedDict()
-161         for cu,u in enumerate(self.unique(k)) :
-162            idx=np.where(np.array(self.TABLE[k]==u))[0]
-163            iu[idx]=cu
-164            classTable[k][u]=[cu,len(idx)]
-165         self.TABLE[ipfx+k]=iu
-166      self.classTable=classTable.copy()
-167      return classTable
-168   #
-169   def show_classTable(self) :
-170      for k in DB.classTable.keys() :
-171         print('%10s : %3d = '%(k,len(DB.classTable[k])),end='')
-172         for ij,j in enumerate(DB.classTable[k].keys()) : 
-173            if ij>0 : print ('|',end='')
-174            print('%3d#'%ij,end='')
-175            print('%12s'%str(j),':',end='')
-176            print("%6d "%len(DB.query('i_'+k+'=='+str(ij))),end='')
-177         print()
-178   #
-179   def unique(self,key) : 
-180      import numpy as np
-181      return np.sort(self.TABLE[key].unique())
-182   #
-183   def sort(self,sort_by) :
-184      self.TABLE=self.TABLE.sort(sort_by)
-185   #
-186   def sorted_query(self,sort_by,qstr) :
-187      out=self.copy()
-188      if type(qstr) == type('') :
-189         out.TABLE=self.TABLE.query(qstr)
-190      else :
-191         out.TABLE=self.TABLE[qstr]
-192      out.TABLE=out.TABLE.sort(sort_by)
-193      return out
-194   #
-195   def query(self,qstr) :
-196      out=self.copy()
-197      if type(qstr) == type('') :
-198         out.TABLE=self.TABLE.query(qstr)
-199      else :
-200         out.TABLE=self.TABLE[qstr]
-201      return out
-202   #
-203   def select_by_loc(self,loc_argument) :
-204      out=self.copy()
-205      out.TABLE=self.TABLE.loc[loc_argument]
-206      return out
-207   #
-208   def select_by_iloc(self,iloc_argument) :
-209      out=self.copy()
-210      out.TABLE=self.TABLE.iloc[iloc_argument]
-211      return out
-212   #
-213   def classification_string2numeric(self,strg) :
-214      try :
-215         return self._numeric_classification[strg.lower().strip()]
-216      except :
-217         print("bad strng ",strg)
-218         return self._string_class_notfound
-219   #
-220   def calc_iclass(self,string_classification_array) :
-221      return np.array([self._numeric_classification[k.strip().lower()] for k in string_classification_array])
-222   #
-223   def keys(self) : return list(self.TABLE.keys())
-224   #
-225   def has_key(self,this) : return (this in self.TABLE.keys())
-226   #
-227   def __include__(self,this) : return (this in self.TABLE.keys())
-228   #
-229   def reset_plot_status(self) :
-230      """resets the self._last_plot dictionary where handles from the last plot generated are stored"""
-231      from collections import OrderedDict
-232      self._last_plot=OrderedDict()
-233      self._last_plot['fig']=None
-234      self._last_plot['xlabel']=None
-235      self._last_plot['ylabel']=None
-236      self._last_plot['axe']=None
-237      self._last_plot['legend']=None
-238      self._last_plot['curves']=[]
-239   #
-240   def plot2(self,x,y='MolecularDepth',ylog=True,xlog=False,newfig=True,legend_loc=3,xylim=None,savefig=None,one2one_line=False) :
-241      """creates a 2 axis plot, the handles for the plot objects are stored in self._last_plot"""
-242      from collections import OrderedDict
-243      from matplotlib import pyplot as plt
-244      import numpy as np
-245      import pandas
-246      import astropy
-247      #
-248      self.reset_plot_status()
-249      #
-250      if newfig : 
-251         self._last_plot['fig']=plt.figure()
-252      else :
-253         self._last_plot['fig']=plt.gcf()
-254      #
-255      for k in self._ClassNames :  
-256         smp=self.TABLE.query('CLASS=="%s"'%k)
-257         self._last_plot['curves'].append(plt.plot(smp[x],smp[y],self._ClassColor[k]+self._ClassMarker[k][0],label=k,mew=0,markersize=int(self._ClassMarker[k][2:])))
-258      plt.gca().set_xscale('linear' if xlog==False else 'log')
-259      plt.gca().set_yscale('linear' if ylog==False else 'log')
-260      plt.title(self._project_name,fontsize=20)
-261      if xylim != None : plt.axis(xylim)
-262      #
-263      if one2one_line==True :
-264         a=plt.axis()
-265         x121=np.arange(0,1+0.01,0.01)
-266         x121=a[0]*x121+a[1]*(1-x121)
-267         self._last_plot['1:1']=plt.plot(x121,x121,'k-',lw=1)
-268         self._last_plot['1:1'][0].set_zorder(-10)
-269      #
-270      if x=='Natm' :
-271         a=plt.axis()
-272         self._last_plot['STARTNATMLINES']=[plt.plot(k*np.ones(2),[a[2],a[3]],'k-',lw=2) for k in np.unique(self.TABLE['PRESS']/self.TABLE['GRAV'])]
-273         for k in self._last_plot['STARTNATMLINES'] : k[0].set_zorder(-10)
-274      #
-275      if x=='p_surface' :
-276         a=plt.axis()
-277         self._last_plot['PRESS_LINES']=[plt.plot(k*np.ones(2),[a[2],a[3]],'k-',lw=2) for k in np.unique(self.TABLE['PRESS'])]
-278         for k in self._last_plot['PRESS_LINES'] : k[0].set_zorder(-10)
-279      #
-280      self._last_plot['axe']=plt.gca()
-281      if legend_loc > 0 :
-282         self._last_plot['legend']=plt.legend(loc=legend_loc)
-283      self._last_plot['xlabel']=plt.xlabel(x,fontsize=20)
-284      self._last_plot['ylabel']=plt.ylabel(y,fontsize=20)
-285      plt.gcf().set_figwidth(18.)
-286      plt.gcf().set_figheight(plt.gcf().get_figwidth()*3./4.)
-287      if savefig != None and savefig != False:
-288         if type(savefig)==type('') and savefig!='' :
-289            _savefig=savefig
-290         else :
-291            _savefig=self._project_name+'/'+self._figures_path+'/'+y+'_vz_'+x+'.pdf'
-292         print("Saving in ",_savefig)
-293         plt.savefig(_savefig,dpi=plt.gcf().get_dpi())
-294      plt.show()
-295   #
-296   def set_iceTOT_threshold(self,iceTOT_threshold) :
-297      """ set iceTOT_threshold : the ice coverage over which the planet is considered a snowball
-298suggested values 0.95 or 0.99
-299      """
-300      self.iceTOT_threshold=iceTOT_threshold
-301   #
-302   def flag_class_warm(self) :
-303      """returns Murante's classifications for warm calculated from scratch
-304iceTOT_threshold is the ice coverage over which the planet is considered a snowball
-305suggested values 0.95 or 0.99
-306      """
-307      import numpy as np
-308      if not self.iceTOT_threshold() : return
-309      return (self.TABLE['TMGLOB'] > self.TABLE['Tice']) * (self.TABLE['TMGLOB'] < self.TABLE['Tvap'])
-310      
-311   def flag_class_warm_hot(self) :
-312      """returns Murante's classifications for warm_hot calculated from scratch
-313iceTOT_threshold is the ice coverage over which the planet is considered a snowball
-314suggested values 0.95 or 0.99
-315      """
-316      import numpy as np
-317      if not self.iceTOT_threshold() : return
-318      return (self.TABLE['TMGLOB'] >= self.TABLE['Tvap'])
-319      
-320   def flag_class_snowball(self) :
-321      """returns Murante's classifications for warm_hot calculated from scratch
-322iceTOT_threshold is the ice coverage over which the planet is considered a snowball
-323suggested values 0.95 or 0.99
-324      """
-325      import numpy as np
-326      if not self.iceTOT_threshold() : return
-327      return (self.TABLE['ICE'] > self.iceTOT_threshold)
-328      
-329   def flag_class_waterbelt(self) :
-330      """returns Murante's classifications for warm_hot calculated from scratch
-331iceTOT_threshold is the ice coverage over which the planet is considered a snowball
-332suggested values 0.95 or 0.99
-333      """
-334      import numpy as np
-335      if not self.iceTOT_threshold() : return
-336      return (self.TABLE['TMGLOB'] <= self.TABLE['Tice'])*(self.TABLE['ICE'] <= self.iceTOT_threshold)
-337   
-338   def _testIceTot(self) :
-339      if self.iceTOT_threshold == None :
-340         raise Exception("iceTOT_threshold not set, Set iceTOT_threshold with .set_iceTOT_threshold(x) before")
-341         return False
-342      return True
-
- - -

DEPRECATED

- -

Class to handle a local version of the ARTECS DB.

- -

It takes in input a csv file generated from a query of ARTECS and saved in csv format.

- -

See the example in the introductory page.

- -

This module is kept for compatibility with early releases of py_artecs, but it is not maintained so its use is DEPRECATED.

-
- - -
- -
- - modelDb( project_name, project_path, csv_name, Verbose=True, csv_sep='!', csv_comment='#', csv_index_col='index', figures_path='png', filterBad=True, new=False, query=None) - - - -
- - 
19   def __init__(self,project_name,project_path,csv_name,Verbose=True,csv_sep='!',csv_comment='#',csv_index_col='index',figures_path='png',filterBad=True,new=False,query=None) :
-20      import time
-21      from collections import OrderedDict
-22      from matplotlib import pyplot as plt
-23      import numpy as np
-24      import pandas
-25      import astropy
-26      #
-27      self.reset()
-28      self.reset_plot_status()
-29      #
-30      self._project_name=project_name
-31      self._project_path=project_path
-32      self._csv_name=csv_name
-33      self._figures_path=figures_path
-34      #
-35      if new : return
-36      #
-37      print()
-38      print("Reading from csv file ",self._csv_name)
-39      tic=time.time()
-40      csv=pandas.read_csv(self._project_path+'/'+self._csv_name,sep=csv_sep,comment=csv_comment,index_col=csv_index_col)
-41      if not 'TSTART' in csv.keys() : 
-42         csv['TSTART']=np.zeros(len(csv))
-43         print ("TSTART not found, replaced by a DUMMY")
-44      csv['Natm']=csv['p_surface']/csv['GRAV']
-45      csv['iclass']=np.array([self.classification_string2numeric(k.strip().lower()) for k in csv.CLASS])
-46      csv['peri']=(1-csv.ECC)*csv.SMA
-47      csv['aphe']=(1+csv.ECC)*csv.SMA
-48      self.flagBad=np.array(csv['iclass']==self._string_class_notfound)*8+np.array(csv.iclass<0)*4+np.array(np.isnan(csv.TMGLOB))*2+np.array(csv.TSTART<0)
-49      tic=time.time()-tic
-50      print("%d lines in %e sec"%(len(csv),tic))
-51      #
-52      if not 'SOURCE' in csv.keys() :
-53         csv['SOURCE']=np.zeros(len(csv))-1
-54      #
-55      if filterBad :
-56         self.TABLE=csv[self.flagBad==0]
-57      else :
-58         self.TABLE=csv
-59      #
-60      if query != None and query!='' :
-61         self.TABLE=self.TABLE.query(query)
-62      #
-63      if Verbose :
-64         print()
-65         print("In %s, containind %d models, the number of models with "%(csv_name,len(csv)))
-66         print("  CLASS  == UNKNOWN                    ",(csv.iclass==-200).sum())
-67         print("  CLASS  == SNOWBALL                   ",(csv.iclass==1).sum())
-68         print("  CLASS  == WATERBELT                  ",(csv.iclass==2).sum())
-69         print("  CLASS  == WARM                       ",(csv.iclass==3).sum())
-70         print("  CLASS  == WARM_HOT                   ",(csv.iclass==4).sum())
-71         print("  INVALID STRING IN CLASS              ",((self.flagBad>=8)).sum())
-72         print()
-73         for k in np.unique(csv['SOURCE']) :
-74            print("  SOURCE == %d                          %d"%(k,(csv.SOURCE==0).sum()))
-75         #print("  SOURCE == 1                          ",(csv.SOURCE==1).sum())
-76         print()
-77         print("  TMGLOB == NaN                        ",np.isnan(csv.TMGLOB).sum())
-78         print("  TSTART<0                             ",(csv.TSTART<0).sum())
-79         print("  TMGLOB == NaN and TSTART<0           ",((csv.TSTART<0)&np.isnan(csv.TMGLOB)).sum())
-80         print("  TMGLOB == NaN and ZCD == NaN         ",(np.isnan(csv.TMGLOB)&np.isnan(csv.zenithal_column_density)).sum())
-81         print("  TMGLOB == NaN and CLASS == UNDEFINED ",(np.isnan(csv.TMGLOB)&(csv.iclass==0)).sum())
-82         print()
-83      #
-84      del csv 
-
- - - - -
-
- -
- - def - to_csv(self, outFile, COMMENT=''): - - - -
- - 
85   def to_csv(self,outFile,COMMENT='') :
-86      print("Writing csv into ",outFile)
-87      open(outFile,'w').write('#\n#'+outFile+'\n#elaboration of '+self._csv_name+'\n#'+str(COMMENT)+'\n#!BEGIN\n')
-88      open(outFile,'a').write(self.TABLE.to_csv(None,sep='!',index_column='index'))
-89      open(outFile,'a').write('#!END\n')
-90      print("written")
-
- - - - -
-
- -
- - def - reset(self): - - - -
- - 
 91   def reset(self) :
- 92      from collections import OrderedDict
- 93      for k in ['_project_name','_project_path','_csv_name','_figures_path','_project_name','_project_path','TABLE','flagBad','classTable'] :
- 94         self.__dict__[k]=None
- 95      self._numeric_classification=OrderedDict()
- 96      self._numeric_classification['---']=-400
- 97      self._numeric_classification['unknown']=-200
- 98      self._numeric_classification['snowball']=1
- 99      self._numeric_classification['waterbelt']=2
-100      self._numeric_classification['warm']=3
-101      self._numeric_classification['warm_hot']=4
-102      self._string_class_notfound=-100000
-103      #
-104      self._classification_labels=self._numeric_classification.keys()
-105      self._ClassNames=['SNOWBALL','WATERBELT','WARM','WARM_HOT']
-106      self._ClassColor={'SNOWBALL':'b','WATERBELT':'g','WARM':'r','WARM_HOT':'y'}
-107      self._ClassMarker={'SNOWBALL':'o:12','WATERBELT':'v:16','WARM':'d:8','WARM_HOT':'s:6'}
-108      #
-109      self.iceTOT_threshold=None
-
- - - - -
-
- -
- - def - copy(self): - - - -
- - 
117   def copy(self) :
-118      import copy
-119      out=modelDb('slice of '+self._project_name,self._project_path,None,new=True)
-120      out._project_name=self._project_name
-121      out._project_path=self._project_path
-122      out._csv_name=None
-123      out._figures_path=self._figures_path
-124      out.classTable=copy.deepcopy(self.classTable)
-125      return out
-
- - - - -
-
- -
- - def - unique_values(self): - - - -
- - 
127   def unique_values(self) :
-128      import numpy as np
-129      from collections import OrderedDict
-130      import pandas
-131      out=OrderedDict()
-132      for k in self.keys() :
-133         out[k]=np.unique(self.TABLE[k].values)
-134      return out
-
- - - - -
-
- -
- - def - list_unique_values(self): - - - -
- - 
136   def list_unique_values(self) :
-137      out=self.unique_values()
-138      for k in out.keys() :
-139         print("%s (%d) ="%(k,len(out[k])))
-140      #
-
- - - - -
-
- -
- - def - classification_indexes(self, *arg, **karg): - - - -
- - 
141   def classification_indexes(self,*arg,**karg) :
-142      import numpy as np
-143      from collections import OrderedDict
-144      if len(arg) == 0 : 
-145         _arg=['CLASS','OBLIQ','ECC','SMA','PRESS','SOURCE','GEO','FO_CONST','PROT','P_CO2']
-146      else :
-147         _arg=arg
-148      ipfx=karg['index_pfx'] if karg.has_key('index_pfx') else 'i_'
-149      for k in _arg : 
-150         quit=False
-151         if not k in self.keys() :
-152            print("%s not found "%k)
-153            quit=True
-154      if quit : 
-155         return
-156      classTable=OrderedDict()
-157      for k in arg :
-158         u=self.unique(k)
-159         iu=np.zeros(len(self))
-160         classTable[k]=OrderedDict()
-161         for cu,u in enumerate(self.unique(k)) :
-162            idx=np.where(np.array(self.TABLE[k]==u))[0]
-163            iu[idx]=cu
-164            classTable[k][u]=[cu,len(idx)]
-165         self.TABLE[ipfx+k]=iu
-166      self.classTable=classTable.copy()
-167      return classTable
-
- - - - -
-
- -
- - def - show_classTable(self): - - - -
- - 
169   def show_classTable(self) :
-170      for k in DB.classTable.keys() :
-171         print('%10s : %3d = '%(k,len(DB.classTable[k])),end='')
-172         for ij,j in enumerate(DB.classTable[k].keys()) : 
-173            if ij>0 : print ('|',end='')
-174            print('%3d#'%ij,end='')
-175            print('%12s'%str(j),':',end='')
-176            print("%6d "%len(DB.query('i_'+k+'=='+str(ij))),end='')
-177         print()
-
- - - - -
-
- -
- - def - unique(self, key): - - - -
- - 
179   def unique(self,key) : 
-180      import numpy as np
-181      return np.sort(self.TABLE[key].unique())
-
- - - - -
-
- -
- - def - sort(self, sort_by): - - - -
- - 
183   def sort(self,sort_by) :
-184      self.TABLE=self.TABLE.sort(sort_by)
-
- - - - -
-
- -
- - def - sorted_query(self, sort_by, qstr): - - - -
- - 
186   def sorted_query(self,sort_by,qstr) :
-187      out=self.copy()
-188      if type(qstr) == type('') :
-189         out.TABLE=self.TABLE.query(qstr)
-190      else :
-191         out.TABLE=self.TABLE[qstr]
-192      out.TABLE=out.TABLE.sort(sort_by)
-193      return out
-
- - - - -
-
- -
- - def - query(self, qstr): - - - -
- - 
195   def query(self,qstr) :
-196      out=self.copy()
-197      if type(qstr) == type('') :
-198         out.TABLE=self.TABLE.query(qstr)
-199      else :
-200         out.TABLE=self.TABLE[qstr]
-201      return out
-
- - - - -
-
- -
- - def - select_by_loc(self, loc_argument): - - - -
- - 
203   def select_by_loc(self,loc_argument) :
-204      out=self.copy()
-205      out.TABLE=self.TABLE.loc[loc_argument]
-206      return out
-
- - - - -
-
- -
- - def - select_by_iloc(self, iloc_argument): - - - -
- - 
208   def select_by_iloc(self,iloc_argument) :
-209      out=self.copy()
-210      out.TABLE=self.TABLE.iloc[iloc_argument]
-211      return out
-
- - - - -
-
- -
- - def - classification_string2numeric(self, strg): - - - -
- - 
213   def classification_string2numeric(self,strg) :
-214      try :
-215         return self._numeric_classification[strg.lower().strip()]
-216      except :
-217         print("bad strng ",strg)
-218         return self._string_class_notfound
-
- - - - -
-
- -
- - def - calc_iclass(self, string_classification_array): - - - -
- - 
220   def calc_iclass(self,string_classification_array) :
-221      return np.array([self._numeric_classification[k.strip().lower()] for k in string_classification_array])
-
- - - - -
-
- -
- - def - keys(self): - - - -
- - 
223   def keys(self) : return list(self.TABLE.keys())
-
- - - - -
-
- -
- - def - has_key(self, this): - - - -
- - 
225   def has_key(self,this) : return (this in self.TABLE.keys())
-
- - - - -
-
- -
- - def - reset_plot_status(self): - - - -
- - 
229   def reset_plot_status(self) :
-230      """resets the self._last_plot dictionary where handles from the last plot generated are stored"""
-231      from collections import OrderedDict
-232      self._last_plot=OrderedDict()
-233      self._last_plot['fig']=None
-234      self._last_plot['xlabel']=None
-235      self._last_plot['ylabel']=None
-236      self._last_plot['axe']=None
-237      self._last_plot['legend']=None
-238      self._last_plot['curves']=[]
-
- - -

resets the self._last_plot dictionary where handles from the last plot generated are stored

-
- - -
-
- -
- - def - plot2( self, x, y='MolecularDepth', ylog=True, xlog=False, newfig=True, legend_loc=3, xylim=None, savefig=None, one2one_line=False): - - - -
- - 
240   def plot2(self,x,y='MolecularDepth',ylog=True,xlog=False,newfig=True,legend_loc=3,xylim=None,savefig=None,one2one_line=False) :
-241      """creates a 2 axis plot, the handles for the plot objects are stored in self._last_plot"""
-242      from collections import OrderedDict
-243      from matplotlib import pyplot as plt
-244      import numpy as np
-245      import pandas
-246      import astropy
-247      #
-248      self.reset_plot_status()
-249      #
-250      if newfig : 
-251         self._last_plot['fig']=plt.figure()
-252      else :
-253         self._last_plot['fig']=plt.gcf()
-254      #
-255      for k in self._ClassNames :  
-256         smp=self.TABLE.query('CLASS=="%s"'%k)
-257         self._last_plot['curves'].append(plt.plot(smp[x],smp[y],self._ClassColor[k]+self._ClassMarker[k][0],label=k,mew=0,markersize=int(self._ClassMarker[k][2:])))
-258      plt.gca().set_xscale('linear' if xlog==False else 'log')
-259      plt.gca().set_yscale('linear' if ylog==False else 'log')
-260      plt.title(self._project_name,fontsize=20)
-261      if xylim != None : plt.axis(xylim)
-262      #
-263      if one2one_line==True :
-264         a=plt.axis()
-265         x121=np.arange(0,1+0.01,0.01)
-266         x121=a[0]*x121+a[1]*(1-x121)
-267         self._last_plot['1:1']=plt.plot(x121,x121,'k-',lw=1)
-268         self._last_plot['1:1'][0].set_zorder(-10)
-269      #
-270      if x=='Natm' :
-271         a=plt.axis()
-272         self._last_plot['STARTNATMLINES']=[plt.plot(k*np.ones(2),[a[2],a[3]],'k-',lw=2) for k in np.unique(self.TABLE['PRESS']/self.TABLE['GRAV'])]
-273         for k in self._last_plot['STARTNATMLINES'] : k[0].set_zorder(-10)
-274      #
-275      if x=='p_surface' :
-276         a=plt.axis()
-277         self._last_plot['PRESS_LINES']=[plt.plot(k*np.ones(2),[a[2],a[3]],'k-',lw=2) for k in np.unique(self.TABLE['PRESS'])]
-278         for k in self._last_plot['PRESS_LINES'] : k[0].set_zorder(-10)
-279      #
-280      self._last_plot['axe']=plt.gca()
-281      if legend_loc > 0 :
-282         self._last_plot['legend']=plt.legend(loc=legend_loc)
-283      self._last_plot['xlabel']=plt.xlabel(x,fontsize=20)
-284      self._last_plot['ylabel']=plt.ylabel(y,fontsize=20)
-285      plt.gcf().set_figwidth(18.)
-286      plt.gcf().set_figheight(plt.gcf().get_figwidth()*3./4.)
-287      if savefig != None and savefig != False:
-288         if type(savefig)==type('') and savefig!='' :
-289            _savefig=savefig
-290         else :
-291            _savefig=self._project_name+'/'+self._figures_path+'/'+y+'_vz_'+x+'.pdf'
-292         print("Saving in ",_savefig)
-293         plt.savefig(_savefig,dpi=plt.gcf().get_dpi())
-294      plt.show()
-
- - -

creates a 2 axis plot, the handles for the plot objects are stored in self._last_plot

-
- - -
-
- -
- - def - set_iceTOT_threshold(self, iceTOT_threshold): - - - -
- - 
296   def set_iceTOT_threshold(self,iceTOT_threshold) :
-297      """ set iceTOT_threshold : the ice coverage over which the planet is considered a snowball
-298suggested values 0.95 or 0.99
-299      """
-300      self.iceTOT_threshold=iceTOT_threshold
-
- - -

set iceTOT_threshold : the ice coverage over which the planet is considered a snowball -suggested values 0.95 or 0.99

-
- - -
-
- -
- - def - flag_class_warm(self): - - - -
- - 
302   def flag_class_warm(self) :
-303      """returns Murante's classifications for warm calculated from scratch
-304iceTOT_threshold is the ice coverage over which the planet is considered a snowball
-305suggested values 0.95 or 0.99
-306      """
-307      import numpy as np
-308      if not self.iceTOT_threshold() : return
-309      return (self.TABLE['TMGLOB'] > self.TABLE['Tice']) * (self.TABLE['TMGLOB'] < self.TABLE['Tvap'])
-
- - -

returns Murante's classifications for warm calculated from scratch -iceTOT_threshold is the ice coverage over which the planet is considered a snowball -suggested values 0.95 or 0.99

-
- - -
-
- -
- - def - flag_class_warm_hot(self): - - - -
- - 
311   def flag_class_warm_hot(self) :
-312      """returns Murante's classifications for warm_hot calculated from scratch
-313iceTOT_threshold is the ice coverage over which the planet is considered a snowball
-314suggested values 0.95 or 0.99
-315      """
-316      import numpy as np
-317      if not self.iceTOT_threshold() : return
-318      return (self.TABLE['TMGLOB'] >= self.TABLE['Tvap'])
-
- - -

returns Murante's classifications for warm_hot calculated from scratch -iceTOT_threshold is the ice coverage over which the planet is considered a snowball -suggested values 0.95 or 0.99

-
- - -
-
- -
- - def - flag_class_snowball(self): - - - -
- - 
320   def flag_class_snowball(self) :
-321      """returns Murante's classifications for warm_hot calculated from scratch
-322iceTOT_threshold is the ice coverage over which the planet is considered a snowball
-323suggested values 0.95 or 0.99
-324      """
-325      import numpy as np
-326      if not self.iceTOT_threshold() : return
-327      return (self.TABLE['ICE'] > self.iceTOT_threshold)
-
- - -

returns Murante's classifications for warm_hot calculated from scratch -iceTOT_threshold is the ice coverage over which the planet is considered a snowball -suggested values 0.95 or 0.99

-
- - -
-
- -
- - def - flag_class_waterbelt(self): - - - -
- - 
329   def flag_class_waterbelt(self) :
-330      """returns Murante's classifications for warm_hot calculated from scratch
-331iceTOT_threshold is the ice coverage over which the planet is considered a snowball
-332suggested values 0.95 or 0.99
-333      """
-334      import numpy as np
-335      if not self.iceTOT_threshold() : return
-336      return (self.TABLE['TMGLOB'] <= self.TABLE['Tice'])*(self.TABLE['ICE'] <= self.iceTOT_threshold)
-
- - -

returns Murante's classifications for warm_hot calculated from scratch -iceTOT_threshold is the ice coverage over which the planet is considered a snowball -suggested values 0.95 or 0.99

-
- - -
-
-
- - \ No newline at end of file diff --git a/docs/py_artecs/tap.html b/docs/py_artecs/tap.html deleted file mode 100644 index dfb4d84..0000000 --- a/docs/py_artecs/tap.html +++ /dev/null @@ -1,1187 +0,0 @@ - - - - - - - py_artecs.tap API documentation - - - - - - - - - -
-
-

-py_artecs.tap

- - - - - - - 
  1__DESCRIPTION__="""
-  2
-  3TAP services for ARTECS
-  4
-  5By M.Maris 1.1 - 2019 Nov 15 - 
-  6Ported to python3
-  7Needs pyvo 
-  8
-  9
- 10
- 11Example:
- 12   >>> import artecs
- 13   >>> atap=artecs.exop_pubblic_tap()
- 14   
- 15   >>> atap.EXPLAIN()
- 16   
- 17   >>> atap.keys()
- 18
- 19
- 20   >>> tab=atap.search('(0.7 <= SMA) and (SMA <=3.)')
- 21
- 22   >>> tab.FO_CONST.unique()
- 23
- 24   >>> tab.to_csv('/tmp/pippo.csv',sep=' ')
- 25
- 26   >>> MAP=atap.get_map(tab.URL[0])
- 27
- 28"""
- 29
- 30try :
- 31   import pyvo
- 32   BAD=False
- 33except :
- 34   print("PYVO not found, to install pyvo : \n\t>sudo pip install pyvo\n%s EXOP_TAP class will not work properly"%("/".join(__file__.split('/')[-2:])))
- 35   BAD=True
- 36
- 37#if BAD : 
- 38   #import sys
- 39   #sys.exit(1)
- 40#del BAD
- 41
- 42class EXOP_TAP :
- 43   def __init__(self,tap_url="http://archives.ia2.inaf.it/vo/tap/exo",table_name="exo.EXO",temporary_files_path='/tmp') :
- 44      import pyvo
- 45      #
- 46      #creates empty object
- 47      self._empty()
- 48      #
- 49      self._temporary_files_path=temporary_files_path+'/' if temporary_files_path!='' and temporary_files_path!=None else ''
- 50      #
- 51      #connects to db 
- 52      self._url=tap_url
- 53      self._table_name=table_name
- 54      #
- 55      #template for query_string, two fields: <QUERY> <FIELDS>
- 56      self._template_query="SELECT %s %s FROM "+self._table_name
- 57      #
- 58      try :
- 59         self._tap_service = pyvo.dal.TAPService(tap_url)
- 60      except :
- 61         print ("Error, unable to connect to TAP_URL '"+tap_url+"'")
- 62      #
- 63      #from db gets fields description
- 64      self._get_field_description()
- 65   #
- 66   def _empty(self) :
- 67      self._url=None
- 68      self._table_name=None
- 69      self._tap_service = None
- 70      self._elapsed_time=0.
- 71      self._search_result=None
- 72      self._field_description=None
- 73      self._field_names=None
- 74      self._search_success=None
- 75      self._template_query=None
- 76      self._download_success=None
- 77   #
- 78   def _get_field_description(self) :
- 79      self.adql_search('SELECT TOP 1 * FROM '+self._table_name)
- 80      self._field_description=self._search_result.fielddescs
- 81      self._field_names=self._search_result.fieldnames
- 82      self.clean()
- 83   #
- 84   def keys(self) :
- 85      """list of fields in the database"""
- 86      return self._field_names
- 87   #
- 88   def has_key(self,this) :
- 89      """has_key"""
- 90      return this in self._field_names
- 91   #
- 92   def __include__(self,this) :
- 93      """this in object"""
- 94      return this in self._field_names
- 95   #
- 96   def success(self) : 
- 97      """returns True if last search was successfull"""
- 98      return self._search_success==True
- 99   #
-100   def clean(self) :
-101      """cleans information from last search"""
-102      self._search_success=None
-103      self._search_result=None
-104   #
-105   def adql_search(self,adql_string) :
-106      """search on database using ADQL string """
-107      import time
-108      self.clean()
-109      self._search_success=False
-110      self._elapsed_time=time.time()
-111      self._search_result=self._tap_service.search(adql_string)
-112      self._elapsed_time=time.time()-self._elapsed_time
-113      self._search_success=True
-114   #
-115   def search_string(self,SELECTION,TOP,FIELDS,WHERE,SORT) :
-116      """creates a properly formatted adql query_string"""
-117      adql='SELECT' 
-118      adql+='' if type(SELECTION)==type(None) else ' '+SELECT
-119      adql+=' TOP %d'%TOP if type(TOP)==type(1) else '' 
-120      adql+=' *' if type(FIELDS)==type(None) else ' '+FIELDS
-121      adql+=' FROM '+self._table_name 
-122      adql+='' if WHERE=='' or WHERE==None else ' WHERE '+WHERE
-123      adql+='' if SORT=='' or SORT==None else ' SORT BY '+SORT
-124      return adql
-125   #
-126   def search(self,WHERE,SELECTION=None,FIELDS=None,TOP=None,SORT=None,as_astropy=False,as_votable=False) :
-127      """download a table from the database
-128         search('') or search(WHERE='') returns all the data in the database
-129         
-130         the table can be returned as :
-131            pandas dataframe (default)
-132            astropy table (as_astropy = True)
-133            votable (as_votable=True)
-134      """
-135      import time
-136      #
-137      adql=self.search_string(SELECTION,TOP,FIELDS,WHERE,SORT)
-138      self.adql_search(adql)
-139      #
-140      if as_astropy :
-141         return self._search_result.table
-142      elif as_votable :
-143         return self._search_result.votable
-144      else :
-145         return self._search_result.table.to_pandas()
-146   #
-147   def get_map(self,_URL,outfile=None) :
-148      """gets a map """
-149      import numpy as np
-150      import time
-151      from .artecs_map import artecs_map
-152      try :
-153         from urllib2 import urlopen
-154      except :
-155         from urllib.request import urlopen
-156      import requests
-157      import gzip
-158      import os
-159      try :
-160         from StringIO import StringIO
-161      except :
-162         from io import StringIO
-163      if type(_URL) == type("") :
-164         URL=_URL+''
-165      else :
-166         URL=_URL.decode("utf-8")
-167      #
-168      if type(outfile) != type('') :
-169         #if out file not specified creates a unique temporary name and reserves it
-170         flag = True
-171         while flag :
-172            tmp=URL+' '+time.asctime()
-173            tmp=tmp+' '+str(np.random.randint(1000000))
-174            tmp=abs(hash(tmp))
-175            tmp=hex( tmp )[2:]
-176            _o=self._temporary_files_path+'artecs_download_file_'+tmp+'.fits'
-177            flag=os.path.isfile(_o)
-178            open(_o,'w').write('a') #this is to reserve the file name
-179      else :
-180         _o=outfile
-181      #
-182      tic=time.time()
-183      request=requests.Request(URL)
-184      #response = urlopen(request)
-185      try :
-186         response = urlopen(URL)
-187      except :
-188         print("URL %s not found"%URL)
-189         if type(outfile) != type('') :
-190            os.remove(_o)
-191         self._download_success=False
-192         return
-193      #buf = StringIO(response.read())
-194      buf = response
-195      f = gzip.GzipFile(fileobj=buf)
-196      data=f.read()
-197      open(_o,'wb').write(data)
-198      tic=time.time()-tic
-199      #
-200      amap=artecs_map(_o)
-201      amap.url=URL
-202      if type(outfile) != type('') :
-203         os.remove(_o)
-204      self._download_success=True
-205      return amap
-206   #
-207   def download_map(self,URL,outfile=None,path=None) :
-208      """download the fits file corresponding at a given URL
-209      if outfile is not specified the fits file name from the URL is used
-210      if path is not specified the current file is stored in the current path
-211      """
-212      _path='' if type(path) != type('') else path+'/'
-213      if type(outfile)!=type('') :
-214         _o=URL.split('/')[-1].split('.gz')[0]
-215         _o=_path+_o
-216      else :
-217         _o=outfile
-218      self.get_map(URL,outfile=_o)
-219   # 
-220   def EXPLAIN(self) :
-221      """print a short introduction on the query language"""
-222      print ("""
-223ADQL = Astronomical Data Query Language
-224
-225A query is a string 
-226- SELECTION set of data to be selected
-227- HOW_MANY elements to select
-228- WHICH_FIELDS fields to select
-229- FROM which table
-230- WHERE condition for selection is true
-231
-232Example:
-233
-2341. Download all the data
-235   SELECT * FROM exo.EXO
-236   
-2372. Download only the first 10 elements
-238   SELECT TOP 10 * FROM exo.EXO
-239
-2403. Download only the first 10 elements with SMA in the range 0.9 to 1.1
-241   SELECT TOP 10 * FROM exo.EXO WHERE SMA BETWEEN 0.9 AND 1.1
-242   
-243   alternativelly
-244
-245   SELECT TOP 10 * FROM exo.EXO WHERE (0.9 <= SMA) AND (SMA <= 1.1)
-246
-2474. Download only the first 10 elements with SMA in the range 0.9 to 1.1 and CONTHAB>=0.5
-248   SELECT TOP 10 * FROM exo.EXO WHERE SMA BETWEEN 0.9 AND 1.1 AND CONTHAB>=0.5
-249
-2505. Arithmetic calculations are allowed if needed for selection so to download the first 10 elements with SMA^1.5 > 0.87 
-251   SELECT TOP 10 * FROM exo.EXO WHERE power(SMA,1.5)> 0.87 
-252   
-2536. returns just columns SMA and CONTHAB from previous example:
-254   SELECT TOP 10 SMA,CONTHAB FROM exo.EXO WHERE power(SMA,1.5)> 0.87 
-255   
-256Note that the query is not sensitive to uppercase or lowercase.
-257
-258Tutorials: 
-259   http://www.g-vo.org/tutorials/gaia-mock-tap.pdf
-260   http://www.ivoa.net/documents/REC/ADQL/ADQL-20081030.pdf
-261   http://www.ivoa.net/documents/
-262
-263""")
-264
-265      
-
- - -
-
- -
- - class - EXOP_TAP: - - - -
- - 
 43class EXOP_TAP :
- 44   def __init__(self,tap_url="http://archives.ia2.inaf.it/vo/tap/exo",table_name="exo.EXO",temporary_files_path='/tmp') :
- 45      import pyvo
- 46      #
- 47      #creates empty object
- 48      self._empty()
- 49      #
- 50      self._temporary_files_path=temporary_files_path+'/' if temporary_files_path!='' and temporary_files_path!=None else ''
- 51      #
- 52      #connects to db 
- 53      self._url=tap_url
- 54      self._table_name=table_name
- 55      #
- 56      #template for query_string, two fields: <QUERY> <FIELDS>
- 57      self._template_query="SELECT %s %s FROM "+self._table_name
- 58      #
- 59      try :
- 60         self._tap_service = pyvo.dal.TAPService(tap_url)
- 61      except :
- 62         print ("Error, unable to connect to TAP_URL '"+tap_url+"'")
- 63      #
- 64      #from db gets fields description
- 65      self._get_field_description()
- 66   #
- 67   def _empty(self) :
- 68      self._url=None
- 69      self._table_name=None
- 70      self._tap_service = None
- 71      self._elapsed_time=0.
- 72      self._search_result=None
- 73      self._field_description=None
- 74      self._field_names=None
- 75      self._search_success=None
- 76      self._template_query=None
- 77      self._download_success=None
- 78   #
- 79   def _get_field_description(self) :
- 80      self.adql_search('SELECT TOP 1 * FROM '+self._table_name)
- 81      self._field_description=self._search_result.fielddescs
- 82      self._field_names=self._search_result.fieldnames
- 83      self.clean()
- 84   #
- 85   def keys(self) :
- 86      """list of fields in the database"""
- 87      return self._field_names
- 88   #
- 89   def has_key(self,this) :
- 90      """has_key"""
- 91      return this in self._field_names
- 92   #
- 93   def __include__(self,this) :
- 94      """this in object"""
- 95      return this in self._field_names
- 96   #
- 97   def success(self) : 
- 98      """returns True if last search was successfull"""
- 99      return self._search_success==True
-100   #
-101   def clean(self) :
-102      """cleans information from last search"""
-103      self._search_success=None
-104      self._search_result=None
-105   #
-106   def adql_search(self,adql_string) :
-107      """search on database using ADQL string """
-108      import time
-109      self.clean()
-110      self._search_success=False
-111      self._elapsed_time=time.time()
-112      self._search_result=self._tap_service.search(adql_string)
-113      self._elapsed_time=time.time()-self._elapsed_time
-114      self._search_success=True
-115   #
-116   def search_string(self,SELECTION,TOP,FIELDS,WHERE,SORT) :
-117      """creates a properly formatted adql query_string"""
-118      adql='SELECT' 
-119      adql+='' if type(SELECTION)==type(None) else ' '+SELECT
-120      adql+=' TOP %d'%TOP if type(TOP)==type(1) else '' 
-121      adql+=' *' if type(FIELDS)==type(None) else ' '+FIELDS
-122      adql+=' FROM '+self._table_name 
-123      adql+='' if WHERE=='' or WHERE==None else ' WHERE '+WHERE
-124      adql+='' if SORT=='' or SORT==None else ' SORT BY '+SORT
-125      return adql
-126   #
-127   def search(self,WHERE,SELECTION=None,FIELDS=None,TOP=None,SORT=None,as_astropy=False,as_votable=False) :
-128      """download a table from the database
-129         search('') or search(WHERE='') returns all the data in the database
-130         
-131         the table can be returned as :
-132            pandas dataframe (default)
-133            astropy table (as_astropy = True)
-134            votable (as_votable=True)
-135      """
-136      import time
-137      #
-138      adql=self.search_string(SELECTION,TOP,FIELDS,WHERE,SORT)
-139      self.adql_search(adql)
-140      #
-141      if as_astropy :
-142         return self._search_result.table
-143      elif as_votable :
-144         return self._search_result.votable
-145      else :
-146         return self._search_result.table.to_pandas()
-147   #
-148   def get_map(self,_URL,outfile=None) :
-149      """gets a map """
-150      import numpy as np
-151      import time
-152      from .artecs_map import artecs_map
-153      try :
-154         from urllib2 import urlopen
-155      except :
-156         from urllib.request import urlopen
-157      import requests
-158      import gzip
-159      import os
-160      try :
-161         from StringIO import StringIO
-162      except :
-163         from io import StringIO
-164      if type(_URL) == type("") :
-165         URL=_URL+''
-166      else :
-167         URL=_URL.decode("utf-8")
-168      #
-169      if type(outfile) != type('') :
-170         #if out file not specified creates a unique temporary name and reserves it
-171         flag = True
-172         while flag :
-173            tmp=URL+' '+time.asctime()
-174            tmp=tmp+' '+str(np.random.randint(1000000))
-175            tmp=abs(hash(tmp))
-176            tmp=hex( tmp )[2:]
-177            _o=self._temporary_files_path+'artecs_download_file_'+tmp+'.fits'
-178            flag=os.path.isfile(_o)
-179            open(_o,'w').write('a') #this is to reserve the file name
-180      else :
-181         _o=outfile
-182      #
-183      tic=time.time()
-184      request=requests.Request(URL)
-185      #response = urlopen(request)
-186      try :
-187         response = urlopen(URL)
-188      except :
-189         print("URL %s not found"%URL)
-190         if type(outfile) != type('') :
-191            os.remove(_o)
-192         self._download_success=False
-193         return
-194      #buf = StringIO(response.read())
-195      buf = response
-196      f = gzip.GzipFile(fileobj=buf)
-197      data=f.read()
-198      open(_o,'wb').write(data)
-199      tic=time.time()-tic
-200      #
-201      amap=artecs_map(_o)
-202      amap.url=URL
-203      if type(outfile) != type('') :
-204         os.remove(_o)
-205      self._download_success=True
-206      return amap
-207   #
-208   def download_map(self,URL,outfile=None,path=None) :
-209      """download the fits file corresponding at a given URL
-210      if outfile is not specified the fits file name from the URL is used
-211      if path is not specified the current file is stored in the current path
-212      """
-213      _path='' if type(path) != type('') else path+'/'
-214      if type(outfile)!=type('') :
-215         _o=URL.split('/')[-1].split('.gz')[0]
-216         _o=_path+_o
-217      else :
-218         _o=outfile
-219      self.get_map(URL,outfile=_o)
-220   # 
-221   def EXPLAIN(self) :
-222      """print a short introduction on the query language"""
-223      print ("""
-224ADQL = Astronomical Data Query Language
-225
-226A query is a string 
-227- SELECTION set of data to be selected
-228- HOW_MANY elements to select
-229- WHICH_FIELDS fields to select
-230- FROM which table
-231- WHERE condition for selection is true
-232
-233Example:
-234
-2351. Download all the data
-236   SELECT * FROM exo.EXO
-237   
-2382. Download only the first 10 elements
-239   SELECT TOP 10 * FROM exo.EXO
-240
-2413. Download only the first 10 elements with SMA in the range 0.9 to 1.1
-242   SELECT TOP 10 * FROM exo.EXO WHERE SMA BETWEEN 0.9 AND 1.1
-243   
-244   alternativelly
-245
-246   SELECT TOP 10 * FROM exo.EXO WHERE (0.9 <= SMA) AND (SMA <= 1.1)
-247
-2484. Download only the first 10 elements with SMA in the range 0.9 to 1.1 and CONTHAB>=0.5
-249   SELECT TOP 10 * FROM exo.EXO WHERE SMA BETWEEN 0.9 AND 1.1 AND CONTHAB>=0.5
-250
-2515. Arithmetic calculations are allowed if needed for selection so to download the first 10 elements with SMA^1.5 > 0.87 
-252   SELECT TOP 10 * FROM exo.EXO WHERE power(SMA,1.5)> 0.87 
-253   
-2546. returns just columns SMA and CONTHAB from previous example:
-255   SELECT TOP 10 SMA,CONTHAB FROM exo.EXO WHERE power(SMA,1.5)> 0.87 
-256   
-257Note that the query is not sensitive to uppercase or lowercase.
-258
-259Tutorials: 
-260   http://www.g-vo.org/tutorials/gaia-mock-tap.pdf
-261   http://www.ivoa.net/documents/REC/ADQL/ADQL-20081030.pdf
-262   http://www.ivoa.net/documents/
-263
-264""")
-
- - - - -
- -
- - EXOP_TAP( tap_url='http://archives.ia2.inaf.it/vo/tap/exo', table_name='exo.EXO', temporary_files_path='/tmp') - - - -
- - 
44   def __init__(self,tap_url="http://archives.ia2.inaf.it/vo/tap/exo",table_name="exo.EXO",temporary_files_path='/tmp') :
-45      import pyvo
-46      #
-47      #creates empty object
-48      self._empty()
-49      #
-50      self._temporary_files_path=temporary_files_path+'/' if temporary_files_path!='' and temporary_files_path!=None else ''
-51      #
-52      #connects to db 
-53      self._url=tap_url
-54      self._table_name=table_name
-55      #
-56      #template for query_string, two fields: <QUERY> <FIELDS>
-57      self._template_query="SELECT %s %s FROM "+self._table_name
-58      #
-59      try :
-60         self._tap_service = pyvo.dal.TAPService(tap_url)
-61      except :
-62         print ("Error, unable to connect to TAP_URL '"+tap_url+"'")
-63      #
-64      #from db gets fields description
-65      self._get_field_description()
-
- - - - -
-
- -
- - def - keys(self): - - - -
- - 
85   def keys(self) :
-86      """list of fields in the database"""
-87      return self._field_names
-
- - -

list of fields in the database

-
- - -
-
- -
- - def - has_key(self, this): - - - -
- - 
89   def has_key(self,this) :
-90      """has_key"""
-91      return this in self._field_names
-
- - -

has_key

-
- - -
-
- -
- - def - success(self): - - - -
- - 
97   def success(self) : 
-98      """returns True if last search was successfull"""
-99      return self._search_success==True
-
- - -

returns True if last search was successfull

-
- - -
-
- -
- - def - clean(self): - - - -
- - 
101   def clean(self) :
-102      """cleans information from last search"""
-103      self._search_success=None
-104      self._search_result=None
-
- - -

cleans information from last search

-
- - -
- -
- -
- - def - search_string(self, SELECTION, TOP, FIELDS, WHERE, SORT): - - - -
- - 
116   def search_string(self,SELECTION,TOP,FIELDS,WHERE,SORT) :
-117      """creates a properly formatted adql query_string"""
-118      adql='SELECT' 
-119      adql+='' if type(SELECTION)==type(None) else ' '+SELECT
-120      adql+=' TOP %d'%TOP if type(TOP)==type(1) else '' 
-121      adql+=' *' if type(FIELDS)==type(None) else ' '+FIELDS
-122      adql+=' FROM '+self._table_name 
-123      adql+='' if WHERE=='' or WHERE==None else ' WHERE '+WHERE
-124      adql+='' if SORT=='' or SORT==None else ' SORT BY '+SORT
-125      return adql
-
- - -

creates a properly formatted adql query_string

-
- - -
-
- -
- - def - search( self, WHERE, SELECTION=None, FIELDS=None, TOP=None, SORT=None, as_astropy=False, as_votable=False): - - - -
- - 
127   def search(self,WHERE,SELECTION=None,FIELDS=None,TOP=None,SORT=None,as_astropy=False,as_votable=False) :
-128      """download a table from the database
-129         search('') or search(WHERE='') returns all the data in the database
-130         
-131         the table can be returned as :
-132            pandas dataframe (default)
-133            astropy table (as_astropy = True)
-134            votable (as_votable=True)
-135      """
-136      import time
-137      #
-138      adql=self.search_string(SELECTION,TOP,FIELDS,WHERE,SORT)
-139      self.adql_search(adql)
-140      #
-141      if as_astropy :
-142         return self._search_result.table
-143      elif as_votable :
-144         return self._search_result.votable
-145      else :
-146         return self._search_result.table.to_pandas()
-
- - -

download a table from the database -search('') or search(WHERE='') returns all the data in the database

- -

the table can be returned as : - pandas dataframe (default) - astropy table (as_astropy = True) - votable (as_votable=True)

-
- - -
-
- -
- - def - get_map(self, _URL, outfile=None): - - - -
- - 
148   def get_map(self,_URL,outfile=None) :
-149      """gets a map """
-150      import numpy as np
-151      import time
-152      from .artecs_map import artecs_map
-153      try :
-154         from urllib2 import urlopen
-155      except :
-156         from urllib.request import urlopen
-157      import requests
-158      import gzip
-159      import os
-160      try :
-161         from StringIO import StringIO
-162      except :
-163         from io import StringIO
-164      if type(_URL) == type("") :
-165         URL=_URL+''
-166      else :
-167         URL=_URL.decode("utf-8")
-168      #
-169      if type(outfile) != type('') :
-170         #if out file not specified creates a unique temporary name and reserves it
-171         flag = True
-172         while flag :
-173            tmp=URL+' '+time.asctime()
-174            tmp=tmp+' '+str(np.random.randint(1000000))
-175            tmp=abs(hash(tmp))
-176            tmp=hex( tmp )[2:]
-177            _o=self._temporary_files_path+'artecs_download_file_'+tmp+'.fits'
-178            flag=os.path.isfile(_o)
-179            open(_o,'w').write('a') #this is to reserve the file name
-180      else :
-181         _o=outfile
-182      #
-183      tic=time.time()
-184      request=requests.Request(URL)
-185      #response = urlopen(request)
-186      try :
-187         response = urlopen(URL)
-188      except :
-189         print("URL %s not found"%URL)
-190         if type(outfile) != type('') :
-191            os.remove(_o)
-192         self._download_success=False
-193         return
-194      #buf = StringIO(response.read())
-195      buf = response
-196      f = gzip.GzipFile(fileobj=buf)
-197      data=f.read()
-198      open(_o,'wb').write(data)
-199      tic=time.time()-tic
-200      #
-201      amap=artecs_map(_o)
-202      amap.url=URL
-203      if type(outfile) != type('') :
-204         os.remove(_o)
-205      self._download_success=True
-206      return amap
-
- - -

gets a map

-
- - -
-
- -
- - def - download_map(self, URL, outfile=None, path=None): - - - -
- - 
208   def download_map(self,URL,outfile=None,path=None) :
-209      """download the fits file corresponding at a given URL
-210      if outfile is not specified the fits file name from the URL is used
-211      if path is not specified the current file is stored in the current path
-212      """
-213      _path='' if type(path) != type('') else path+'/'
-214      if type(outfile)!=type('') :
-215         _o=URL.split('/')[-1].split('.gz')[0]
-216         _o=_path+_o
-217      else :
-218         _o=outfile
-219      self.get_map(URL,outfile=_o)
-
- - -

download the fits file corresponding at a given URL -if outfile is not specified the fits file name from the URL is used -if path is not specified the current file is stored in the current path

-
- - -
-
- -
- - def - EXPLAIN(self): - - - -
- - 
221   def EXPLAIN(self) :
-222      """print a short introduction on the query language"""
-223      print ("""
-224ADQL = Astronomical Data Query Language
-225
-226A query is a string 
-227- SELECTION set of data to be selected
-228- HOW_MANY elements to select
-229- WHICH_FIELDS fields to select
-230- FROM which table
-231- WHERE condition for selection is true
-232
-233Example:
-234
-2351. Download all the data
-236   SELECT * FROM exo.EXO
-237   
-2382. Download only the first 10 elements
-239   SELECT TOP 10 * FROM exo.EXO
-240
-2413. Download only the first 10 elements with SMA in the range 0.9 to 1.1
-242   SELECT TOP 10 * FROM exo.EXO WHERE SMA BETWEEN 0.9 AND 1.1
-243   
-244   alternativelly
-245
-246   SELECT TOP 10 * FROM exo.EXO WHERE (0.9 <= SMA) AND (SMA <= 1.1)
-247
-2484. Download only the first 10 elements with SMA in the range 0.9 to 1.1 and CONTHAB>=0.5
-249   SELECT TOP 10 * FROM exo.EXO WHERE SMA BETWEEN 0.9 AND 1.1 AND CONTHAB>=0.5
-250
-2515. Arithmetic calculations are allowed if needed for selection so to download the first 10 elements with SMA^1.5 > 0.87 
-252   SELECT TOP 10 * FROM exo.EXO WHERE power(SMA,1.5)> 0.87 
-253   
-2546. returns just columns SMA and CONTHAB from previous example:
-255   SELECT TOP 10 SMA,CONTHAB FROM exo.EXO WHERE power(SMA,1.5)> 0.87 
-256   
-257Note that the query is not sensitive to uppercase or lowercase.
-258
-259Tutorials: 
-260   http://www.g-vo.org/tutorials/gaia-mock-tap.pdf
-261   http://www.ivoa.net/documents/REC/ADQL/ADQL-20081030.pdf
-262   http://www.ivoa.net/documents/
-263
-264""")
-
- - -

print a short introduction on the query language

-
- - -
-
-
- - \ No newline at end of file diff --git a/docs/search.js b/docs/search.js deleted file mode 100644 index 9acf963..0000000 --- a/docs/search.js +++ /dev/null @@ -1,46 +0,0 @@ -window.pdocSearch = (function(){ -/** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oA Python3 library to handle contents of ARTECS: Archive of terrestrial-type climate simulations

\n\n
http://wwwuser.oats.inaf.it/exobio/climates/\n
\n\n

through TAP and the PYVO services.

\n\n

Authors: Michele Maris (1), Marco Molinaro (1)

\n\n
(1) INAF/Trieste Astronomical Observatory\n
\n\n

First Issue: 2019 Nov 20

\n\n

Dependencies:

\n\n

This package needs: \n numpy, scipy, pandas, pyvo

\n\n

Installation

\n\n

For the latest release:

\n\n
\n
>>> pip3 install py_artecs\n
\n
\n\n

for the dev version clone this GitLab repository.

\n\n

Files in py_artecs

\n\n

__init__.py : the init file

\n\n

tap.py : the file with the TAP library

\n\n

artecs_map.py : a utility class to handle a local fits file with a Temperature MAP downloaded from the archive

\n\n

modelDB.py : a utility class to handle a local csv file with the result of a query, this module is kept for backward compatibility, but its use is deprecated

\n\n

Example of session:

\n\n

The archive is accessed from the exop_pubblic_tap in py_artecs:

\n\n
\n
>>> from artecs import exop_pubblic_tap\n
\n
\n\n

Then instantiate a database object

\n\n
\n
>>> atap=exop_pubblic_tap()\n
\n
\n\n

all the queries to ARTECS are made through the methods in atap.

\n\n

Queries follows the Astronomical Data Query Language (ADQL) format

\n\n
\n
>>> ptab=atap.search('power(SMA,1.5)>0.87');\n
\n
\n\n

see the appendix for further references on the ADQL.

\n\n

To know wether the search is successfull:

\n\n
\n
>>> atap.success()\nTrue\n
\n
\n\n

The result of a query is stored in ptab as a PANDAS data_frame.

\n\n

In order to save the table as a csv file use the \"to_csv\" method of pandas data frame:

\n\n
\n
>>> ptab.to_csv('result.csv',sep=' ')\n
\n
\n\n

In order to get a map corresponding to the first entry in the ptab use:

\n\n
\n
>>> TMAP=atap.get_map(ptab.URL[0])\n
\n
\n\n

the command creates a fits file to rercover the fits filename

\n\n
\n
>>> print(MAP.filename)\n/tmp/artecs_download_file_669d40064f5bddc8.fits\n
\n
\n\n

note that the get_map method saved the fits inside the /tmp directory and gives it a dummy name.

\n\n

To specify a different name and directory use the outfile keyword in get_map

\n\n
\n
>>> TMAP=atap.get_map(ptab.URL[0],outfile='./test.fits')\n>>> print(TMAP.filename)\n./test.fits\n
\n
\n\n

TMAP is an object of class artecs_map, so it contains the temperature MAP in the ARTECS fits file, as well as the other keywords in the original fits file.

\n\n

Note that the artecs_map class can be used also local copies of the ARTECS temperature maps.

\n\n

At last, to perform a new query reset the old one with:

\n\n
\n
>>> atap.clear()\n
\n
\n\n

Appendix A: ADQL Queries

\n\n

A query is a string \n . SELECTION set of data to be selected\n . HOW_MANY elements to select\n . WHICH_FIELDS fields to select\n . FROM which table\n . WHERE condition for selection is true

\n\n

Examples of query strings:

\n\n
    \n

  1. Download all the data:

    \n\n
    \n

    SELECT * FROM exo.EXO

    \n
    2. \n

  2. Download only the first 10 elements:

    \n\n
    \n

    SELECT TOP 10 * FROM exo.EXO

    \n
    3. \n

  3. Download only the first 10 elements with SMA in the range 0.9 to 1.1:

    \n\n
    \n

    SELECT TOP 10 * FROM exo.EXO WHERE SMA BETWEEN 0.9 AND 1.1

    \n
    \n\n

    alternativelly

    \n\n
    \n

    SELECT TOP 10 * FROM exo.EXO WHERE (0.9 <= SMA) AND (SMA <= 1.1)

    \n
    4. \n

  4. Download only the first 10 elements with SMA in the range 0.9 to 1.1 and CONTHAB>=0.5

    \n\n
    \n

    SELECT TOP 10 * FROM exo.EXO WHERE SMA BETWEEN 0.9 AND 1.1 AND CONTHAB>=0.5

    \n
    5. \n

  5. Arithmetic calculations are allowed if needed for selection so to download the first 10 elements with SMA^1.5 > 0.87

    \n\n
    \n

    SELECT TOP 10 * FROM exo.EXO WHERE power(SMA,1.5)> 0.87

    \n
    6. \n

  6. returns just columns SMA and CONTHAB from previous example:

    \n\n
    \n

    SELECT TOP 10 SMA,CONTHAB FROM exo.EXO WHERE power(SMA,1.5)> 0.87

    \n
    7. \n
\n\n

Note that the query string is not sensitive to uppercase or lowercase.

\n\n

For tutorials see:

\n\n\n\n

A reminder of how to form query strings can be recalled by calling:

\n\n
\n
>>> atap.EXPLAIN()\n
\n
\n\n

Appendix B: REFERENCES

\n\n

Please quote

\n\n

Climate bistability of Earth-like exoplanets

\n\n

Murante G., Provenzale A., Vladilo G.,Taffoni G., Silva L., Palazzi E.,von Hardenberg J.,Maris M., Londero E., Knapic C., Zorba C.

\n\n

MNRAS 492, 2638\u20132650 (2020)

\n\n

doi: 10.1093/mnras/stz3529

\n\n

Appendix C: Example of session with TAP

\n\n

It is possible to access the archive directly with TAP

\n\n
\n
>>> import pyvo as vo\n>>> tap_service = vo.dal.TAPService("http://archives.ia2.inaf.it/vo/tap/exo")\n>>> tap_results = tap_service.search("SELECT top 1 exp_id, url FROM exo.EXO")\n>>> print(tap_results)\n>>>...\n>>> len(tap_results)\n>>>...\n>>> tap_results.getrecord(0)\n>>>...\n
\n
\n\n

Appendix D: to install pyvo

\n\n
\n
>>> sudo pip3 install pyvo\n
\n
\n"}, {"fullname": "py_artecs.artecs_map", "modulename": "py_artecs.artecs_map", "kind": "module", "doc": "

\n"}, {"fullname": "py_artecs.artecs_map.artecs_map", "modulename": "py_artecs.artecs_map", "qualname": "artecs_map", "kind": "class", "doc": "

A class to handle a temperature map from ARTECS.

\n\n

It reads the artecs_map from a fits file.

\n\n
\n
>>> AMAP=artecs_map("artecs_map.fits")\n
\n
\n\n

Keywords in the map are returned as members of the class or using the method \"parameter\".

\n\n

Example:

\n\n
\n
>>> AMAP.temp \n
\n
\n\n

returns the temperature map,

\n\n
\n
>>> AMAP.parameter('temp')\n
\n
\n\n

returns the temperature map.

\n\n
\n
>>> AMAP.shape\n
\n
\n\n

returns the shape of the map (rows, columns)

\n"}, {"fullname": "py_artecs.artecs_map.artecs_map.__init__", "modulename": "py_artecs.artecs_map", "qualname": "artecs_map.__init__", "kind": "function", "doc": "

To instantiate the class pass the filename from which to load the Temperature map

\n\n
\n

Keywords:

\n
\n\n
\n
\n

verbose: if True verbose output

\n
\n
\n\n
\n
\n

closeFits: if True the fits file is closed after reading

\n
\n
\n", "signature": "(filename, closeFits=True, verbose=False)"}, {"fullname": "py_artecs.artecs_map.artecs_map.filename", "modulename": "py_artecs.artecs_map", "qualname": "artecs_map.filename", "kind": "variable", "doc": "

parameter: the filename

\n"}, {"fullname": "py_artecs.artecs_map.artecs_map.p", "modulename": "py_artecs.artecs_map", "qualname": "artecs_map.p", "kind": "variable", "doc": "

the fits file pointer, normally closed.

\n"}, {"fullname": "py_artecs.artecs_map.artecs_map.lst_lat", "modulename": "py_artecs.artecs_map", "qualname": "artecs_map.lst_lat", "kind": "variable", "doc": "

list of latitude bands

\n"}, {"fullname": "py_artecs.artecs_map.artecs_map.lst_year", "modulename": "py_artecs.artecs_map", "qualname": "artecs_map.lst_year", "kind": "variable", "doc": "

list of time intervals

\n"}, {"fullname": "py_artecs.artecs_map.artecs_map.shape", "modulename": "py_artecs.artecs_map", "qualname": "artecs_map.shape", "kind": "variable", "doc": "

the shape of the 2D maps

\n"}, {"fullname": "py_artecs.artecs_map.artecs_map.TMGLOB", "modulename": "py_artecs.artecs_map", "qualname": "artecs_map.TMGLOB", "kind": "variable", "doc": "

the mean surface temperature

\n"}, {"fullname": "py_artecs.artecs_map.artecs_map.keys", "modulename": "py_artecs.artecs_map", "qualname": "artecs_map.keys", "kind": "function", "doc": "

returns the list of quantities in the map

\n\n

Keywords:

\n\n
\n

maps (default False), if True returns just the list of of elements which are 2D maps

\n
\n", "signature": "(self, maps=False):", "funcdef": "def"}, {"fullname": "py_artecs.artecs_map.artecs_map.has_key", "modulename": "py_artecs.artecs_map", "qualname": "artecs_map.has_key", "kind": "function", "doc": "

True if required key is in the map

\n", "signature": "(self, key):", "funcdef": "def"}, {"fullname": "py_artecs.artecs_map.artecs_map.parameter", "modulename": "py_artecs.artecs_map", "qualname": "artecs_map.parameter", "kind": "function", "doc": "

returns a parameter from the fits file from its `key'

\n", "signature": "(self, key):", "funcdef": "def"}, {"fullname": "py_artecs.artecs_map.artecs_map.description", "modulename": "py_artecs.artecs_map", "qualname": "artecs_map.description", "kind": "function", "doc": "

returns the description of a parameter in the fits file from its `key'

\n", "signature": "(self, key):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb", "modulename": "py_artecs.modelDb", "kind": "module", "doc": "

DEPRECATED

\n"}, {"fullname": "py_artecs.modelDb.modelDb", "modulename": "py_artecs.modelDb", "qualname": "modelDb", "kind": "class", "doc": "

DEPRECATED

\n\n

Class to handle a local version of the ARTECS DB.

\n\n

It takes in input a csv file generated from a query of ARTECS and saved in csv format.

\n\n

See the example in the introductory page.

\n\n

This module is kept for compatibility with early releases of py_artecs, but it is not maintained so its use is DEPRECATED.

\n"}, {"fullname": "py_artecs.modelDb.modelDb.__init__", "modulename": "py_artecs.modelDb", "qualname": "modelDb.__init__", "kind": "function", "doc": "

\n", "signature": "(\tproject_name,\tproject_path,\tcsv_name,\tVerbose=True,\tcsv_sep='!',\tcsv_comment='#',\tcsv_index_col='index',\tfigures_path='png',\tfilterBad=True,\tnew=False,\tquery=None)"}, {"fullname": "py_artecs.modelDb.modelDb.to_csv", "modulename": "py_artecs.modelDb", "qualname": "modelDb.to_csv", "kind": "function", "doc": "

\n", "signature": "(self, outFile, COMMENT=''):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.reset", "modulename": "py_artecs.modelDb", "qualname": "modelDb.reset", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.copy", "modulename": "py_artecs.modelDb", "qualname": "modelDb.copy", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.unique_values", "modulename": "py_artecs.modelDb", "qualname": "modelDb.unique_values", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.list_unique_values", "modulename": "py_artecs.modelDb", "qualname": "modelDb.list_unique_values", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.classification_indexes", "modulename": "py_artecs.modelDb", "qualname": "modelDb.classification_indexes", "kind": "function", "doc": "

\n", "signature": "(self, *arg, **karg):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.show_classTable", "modulename": "py_artecs.modelDb", "qualname": "modelDb.show_classTable", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.unique", "modulename": "py_artecs.modelDb", "qualname": "modelDb.unique", "kind": "function", "doc": "

\n", "signature": "(self, key):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.sort", "modulename": "py_artecs.modelDb", "qualname": "modelDb.sort", "kind": "function", "doc": "

\n", "signature": "(self, sort_by):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.sorted_query", "modulename": "py_artecs.modelDb", "qualname": "modelDb.sorted_query", "kind": "function", "doc": "

\n", "signature": "(self, sort_by, qstr):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.query", "modulename": "py_artecs.modelDb", "qualname": "modelDb.query", "kind": "function", "doc": "

\n", "signature": "(self, qstr):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.select_by_loc", "modulename": "py_artecs.modelDb", "qualname": "modelDb.select_by_loc", "kind": "function", "doc": "

\n", "signature": "(self, loc_argument):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.select_by_iloc", "modulename": "py_artecs.modelDb", "qualname": "modelDb.select_by_iloc", "kind": "function", "doc": "

\n", "signature": "(self, iloc_argument):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.classification_string2numeric", "modulename": "py_artecs.modelDb", "qualname": "modelDb.classification_string2numeric", "kind": "function", "doc": "

\n", "signature": "(self, strg):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.calc_iclass", "modulename": "py_artecs.modelDb", "qualname": "modelDb.calc_iclass", "kind": "function", "doc": "

\n", "signature": "(self, string_classification_array):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.keys", "modulename": "py_artecs.modelDb", "qualname": "modelDb.keys", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.has_key", "modulename": "py_artecs.modelDb", "qualname": "modelDb.has_key", "kind": "function", "doc": "

\n", "signature": "(self, this):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.reset_plot_status", "modulename": "py_artecs.modelDb", "qualname": "modelDb.reset_plot_status", "kind": "function", "doc": "

resets the self._last_plot dictionary where handles from the last plot generated are stored

\n", "signature": "(self):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.plot2", "modulename": "py_artecs.modelDb", "qualname": "modelDb.plot2", "kind": "function", "doc": "

creates a 2 axis plot, the handles for the plot objects are stored in self._last_plot

\n", "signature": "(\tself,\tx,\ty='MolecularDepth',\tylog=True,\txlog=False,\tnewfig=True,\tlegend_loc=3,\txylim=None,\tsavefig=None,\tone2one_line=False):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.set_iceTOT_threshold", "modulename": "py_artecs.modelDb", "qualname": "modelDb.set_iceTOT_threshold", "kind": "function", "doc": "

set iceTOT_threshold : the ice coverage over which the planet is considered a snowball\nsuggested values 0.95 or 0.99

\n", "signature": "(self, iceTOT_threshold):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.flag_class_warm", "modulename": "py_artecs.modelDb", "qualname": "modelDb.flag_class_warm", "kind": "function", "doc": "

returns Murante's classifications for warm calculated from scratch\niceTOT_threshold is the ice coverage over which the planet is considered a snowball\nsuggested values 0.95 or 0.99

\n", "signature": "(self):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.flag_class_warm_hot", "modulename": "py_artecs.modelDb", "qualname": "modelDb.flag_class_warm_hot", "kind": "function", "doc": "

returns Murante's classifications for warm_hot calculated from scratch\niceTOT_threshold is the ice coverage over which the planet is considered a snowball\nsuggested values 0.95 or 0.99

\n", "signature": "(self):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.flag_class_snowball", "modulename": "py_artecs.modelDb", "qualname": "modelDb.flag_class_snowball", "kind": "function", "doc": "

returns Murante's classifications for warm_hot calculated from scratch\niceTOT_threshold is the ice coverage over which the planet is considered a snowball\nsuggested values 0.95 or 0.99

\n", "signature": "(self):", "funcdef": "def"}, {"fullname": "py_artecs.modelDb.modelDb.flag_class_waterbelt", "modulename": "py_artecs.modelDb", "qualname": "modelDb.flag_class_waterbelt", "kind": "function", "doc": "

returns Murante's classifications for warm_hot calculated from scratch\niceTOT_threshold is the ice coverage over which the planet is considered a snowball\nsuggested values 0.95 or 0.99

\n", "signature": "(self):", "funcdef": "def"}, {"fullname": "py_artecs.tap", "modulename": "py_artecs.tap", "kind": "module", "doc": "

\n"}, {"fullname": "py_artecs.tap.EXOP_TAP", "modulename": "py_artecs.tap", "qualname": "EXOP_TAP", "kind": "class", "doc": "

\n"}, {"fullname": "py_artecs.tap.EXOP_TAP.__init__", "modulename": "py_artecs.tap", "qualname": "EXOP_TAP.__init__", "kind": "function", "doc": "

\n", "signature": "(\ttap_url='http://archives.ia2.inaf.it/vo/tap/exo',\ttable_name='exo.EXO',\ttemporary_files_path='/tmp')"}, {"fullname": "py_artecs.tap.EXOP_TAP.keys", "modulename": "py_artecs.tap", "qualname": "EXOP_TAP.keys", "kind": "function", "doc": "

list of fields in the database

\n", "signature": "(self):", "funcdef": "def"}, {"fullname": "py_artecs.tap.EXOP_TAP.has_key", "modulename": "py_artecs.tap", "qualname": "EXOP_TAP.has_key", "kind": "function", "doc": "

has_key

\n", "signature": "(self, this):", "funcdef": "def"}, {"fullname": "py_artecs.tap.EXOP_TAP.success", "modulename": "py_artecs.tap", "qualname": "EXOP_TAP.success", "kind": "function", "doc": "

returns True if last search was successfull

\n", "signature": "(self):", "funcdef": "def"}, {"fullname": "py_artecs.tap.EXOP_TAP.clean", "modulename": "py_artecs.tap", "qualname": "EXOP_TAP.clean", "kind": "function", "doc": "

cleans information from last search

\n", "signature": "(self):", "funcdef": "def"}, {"fullname": "py_artecs.tap.EXOP_TAP.adql_search", "modulename": "py_artecs.tap", "qualname": "EXOP_TAP.adql_search", "kind": "function", "doc": "

search on database using ADQL string

\n", "signature": "(self, adql_string):", "funcdef": "def"}, {"fullname": "py_artecs.tap.EXOP_TAP.search_string", "modulename": "py_artecs.tap", "qualname": "EXOP_TAP.search_string", "kind": "function", "doc": "

creates a properly formatted adql query_string

\n", "signature": "(self, SELECTION, TOP, FIELDS, WHERE, SORT):", "funcdef": "def"}, {"fullname": "py_artecs.tap.EXOP_TAP.search", "modulename": "py_artecs.tap", "qualname": "EXOP_TAP.search", "kind": "function", "doc": "

download a table from the database\nsearch('') or search(WHERE='') returns all the data in the database

\n\n

the table can be returned as :\n pandas dataframe (default)\n astropy table (as_astropy = True)\n votable (as_votable=True)

\n", "signature": "(\tself,\tWHERE,\tSELECTION=None,\tFIELDS=None,\tTOP=None,\tSORT=None,\tas_astropy=False,\tas_votable=False):", "funcdef": "def"}, {"fullname": "py_artecs.tap.EXOP_TAP.get_map", "modulename": "py_artecs.tap", "qualname": "EXOP_TAP.get_map", "kind": "function", "doc": "

gets a map

\n", "signature": "(self, _URL, outfile=None):", "funcdef": "def"}, {"fullname": "py_artecs.tap.EXOP_TAP.download_map", "modulename": "py_artecs.tap", "qualname": "EXOP_TAP.download_map", "kind": "function", "doc": "

download the fits file corresponding at a given URL\nif outfile is not specified the fits file name from the URL is used\nif path is not specified the current file is stored in the current path

\n", "signature": "(self, URL, outfile=None, path=None):", "funcdef": "def"}, {"fullname": "py_artecs.tap.EXOP_TAP.EXPLAIN", "modulename": "py_artecs.tap", "qualname": "EXOP_TAP.EXPLAIN", "kind": "function", "doc": "

print a short introduction on the query language

\n", "signature": "(self):", "funcdef": "def"}]; - - // mirrored in build-search-index.js (part 1) - // Also split on html tags. this is a cheap heuristic, but good enough. - elasticlunr.tokenizer.setSeperator(/[\s\-.;&_'"=,()]+|<[^>]*>/); - - let searchIndex; - if (docs._isPrebuiltIndex) { - console.info("using precompiled search index"); - searchIndex = elasticlunr.Index.load(docs); - } else { - console.time("building search index"); - // mirrored in build-search-index.js (part 2) - searchIndex = elasticlunr(function () { - this.pipeline.remove(elasticlunr.stemmer); - this.pipeline.remove(elasticlunr.stopWordFilter); - this.addField("qualname"); - this.addField("fullname"); - this.addField("annotation"); - this.addField("default_value"); - this.addField("signature"); - this.addField("bases"); - this.addField("doc"); - this.setRef("fullname"); - }); - for (let doc of docs) { - searchIndex.addDoc(doc); - } - console.timeEnd("building search index"); - } - - return (term) => searchIndex.search(term, { - fields: { - qualname: {boost: 4}, - fullname: {boost: 2}, - annotation: {boost: 2}, - default_value: {boost: 2}, - signature: {boost: 2}, - bases: {boost: 2}, - doc: {boost: 1}, - }, - expand: true - }); -})(); \ No newline at end of file -- GitLab