Package Utils

Module Agso

Created on Sat Sep 26 20:30:41 2020

@author: KouaoLaurent alias @Daniel03

Class :

**Agso ** .

Data of Geological Welllogs

class pycsamt.utils.agso.Agso

Read Agso as pandas Series Geological conventional rocks and structurals handling.

Module Decorator

class pycsamt.utils.decorator.deprecated(reason)

Description:

used to mark functions, methods and classes deprecated, and prints warning message when it called decorators based on https://stackoverflow.com/a/40301488

Usage:

todo: write usage

Author: YingzhiGou Date: 20/06/2017

Methods

__call__(cls_or_func)

Call self as a function.

class pycsamt.utils.decorator.redirect_cls_or_func(*args, **kwargs)

Description:

used to redirected functions or classes. Deprecated functions or class can call others use functions or classes.

Usage:

Author: @Daniel03 Date: 18/10/2020

Methods

__call__(cls_or_func)

Call self as a function.

Module Func-utils

This file is part of pyCSAMT.

pyCSAMT is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

pyCSAMT is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with pyCSAMT. If not, see <https://www.gnu.org/licenses/>.

…

Created on Sun Sep 13 09:24:00 2020 @author: @Daniel03

utils

• averageData

• concat_array_from_list

• sort_array_data

• transfer_array_ (deprecated)

• interpol_scipy

• _set_depth_to_coeff

• broke_array_to_

• _OlDFUNCNOUSEsearch_fill_data (deprecated)

• _search_ToFill_Data

• straighten_out_list

• take_firstValue_offDepth

• dump_comma

• build_wellData

• compute_azimuth

• build_geochemistry_sample

• _nonelist_checker

• _order_well

• intell_index

• _nonevalue_checker

• _clean_space

*_cross_eraser * _remove_str_word * stn_check_split_type * minimum_parser_to_write_edi

pycsamt.utils.func_utils.averageData(np_array, filter_order=0, axis_average=0, astype='float32')

Parameters

* np_arraynumpy array

must be an array data

• filter_orderint

  must be the index of the column you want to sort

• axis averageint

  axis you want to see data averaged, also , it is the concatenate axis default is axis=0

• astype*: str ,

  is the ndarray dtype array . change to have an outup arry dtype , you want .

Returns

numpy array

Data averaged array

:Example :

>>> import numpy as np
>>> list8=[[4,2,0.1],[8,2,0.7],[10,1,0.18],[4,3,0.1],
...               [7,2,1.2],[10,3,0.5],[10,1,0.5],[8.2,0,1.9],
...               [10,7,0.5],[10,1,0.5],
...               [2,0,1.4],[5,4,0.5],[10,2,0.7],[7,2,1.078],
...               [10,2,3.5],[10,8,1.9]]
>>> np_test=np.array(list8)
>>> ss=averageData(np_array=np_test,filter_order=1,
...               axis_average=0, astype="int")
>>> ss

pycsamt.utils.func_utils.broke_array_to_(arrayData, keyIndex=0, broken_type='dict')

broke data array into different value with their same key

Parameters

* arrayData :np.array

data array .

• keyIndexint

  index of column to create dict key

Returns

dict

dico_brok ,dictionnary of array.

pycsamt.utils.func_utils.build_geochemistry_sample()

Build geochemistry_sample_data

Returns

np.ndarray

Sample ,Geochemistry sample Data.

Example

>>> geoch=build_geochemistry_sample()
>>> print(geoch)
... sampleData
... [['S0X4' '0' '254.0' 'PUP']
...     ['S0X4' '254' '521.0' 'mg']
...     ['S0X4' '521' '625.0' 'tut']
...     ['S0X4' '625' '984.0' 'suj']
...     ['S0X2' '0' '19.0' 'pup']
...     ['S0X2' '19' '425.0' 'hut']
...     ['S0X2' '425' '510.0' 'mgt']
...     ['S0X2' '510' '923.2' 'pyt']]

Raises

Process to build geochemistry sample data manually .

pycsamt.utils.func_utils.build_wellData(add_azimuth=False, utm_zone='49N', report_path=None, add_geochemistry_sample=False)

Parameters

* add_azimuthBool, optional

compute azimuth if add_azimut is set to True. The default is False.

• utm_zoneStr, optional

  WGS84 utm_projection. set your zone if add_azimuth is turn to True. The default is “49N”.

• report_pathstr, optional

  path to save your _well_report. The default is None. its match the current work directory

• add_geochemistry_sample: bool

  add_sample_data.Set to True if you want to add_mannually Geochimistry data. default is False.

Returns

str

name of location of well .

np.ndarray

WellData , data of build Wells .

np.ndarray

GeolData , data of build geology.

Example

>>>  import numpy as np
>>>  import os, shutil
>>>  import warnings,
>>>  form _utils.avgpylog import AvgPyLog
>>>  well=build_wellData (add_azimuth=True, utm_zone="49N")
>>>  print("nameof locations

:”,well[0])

>>>  print("CollarData

:”,well[1])

>>>  print("GeolData

:”, well[2])

… nameof locations … Shimen … CollarData … [[‘S01’ ‘477205.6935’ ‘2830978.218’ ‘987.25’ ‘-90’ ‘0.0’ ‘Shi01’ … ‘Wdanxl0’] … [‘S18’ ‘477915.4355’ ‘2830555.927’ ‘974.4’ ‘-90’ ‘2.111’ ‘Shi18’ … ‘Wdanxl0’]] … GeolData … [[‘S01’ ‘0.0’ ‘240.2’ ‘granite’] … [‘S01’ ‘240.2’ ‘256.4’ ‘ basalte’] … [‘S01’ ‘256.4’ ‘580.0’ ‘ granite’] … [‘S01’ ‘580.0’ ‘987.25’ ‘rock’] … [‘S18’ ‘0.0’ ‘110.3’ ‘sand’] … [‘S18’ ‘110.3’ ‘520.2’ ‘agrilite’] … [‘S18’ ‘520.2’ ‘631.3’ ‘ granite’] … [‘S18’ ‘631.3’ ‘974.4’ ‘ rock’]] … Shimen_wellReports_

Raises

Exception

manage the dimentionaly of ndarrays .

OSError

when report_path is not found in your O.S.

pycsamt.utils.func_utils.compute_azimuth(easting, northing, utm_zone='49N', extrapolate=False)

Parameters

* eastingnp.ndarray

Easting value of coordinates _UTM_WGS84

• northingnp.ndarray

  Northing value of coordinates._UTM_WGS84

• utm_zonestr, optional

  the utm_zone . if None try to get is through gis.get_utm_zone(latitude, longitude). latitude and longitude must be on degree decimals. The default is “49N”.

• extrapolatebool ,

  for other purpose , user can extrapolate azimuth value , in order to get the sizesize as the easting and northing size. The the value will repositionate at each point data were collected.

  Default is False as originally azimuth computation .

Returns

np.ndarray

azimuth.

Example

>>> import numpy as np
>>> import gis_tools as gis
>>>  easting=[477205.6935,477261.7258,477336.4355,477373.7903,477448.5,
...  477532.5484,477588.5806,477616.5968]
>>>   northing=[2830978.218, 2830944.879,2830900.427, 2830878.202,2830833.75,
...                  2830783.742,2830750.403,2830733.734]
>>>  test=compute_azimuth(easting=np.array(easting),
...                      northing=np.array(northing), utm_zone="49N")
>>>  print(test)

pycsamt.utils.func_utils.concat_array_from_list(list_of_array, concat_axis=0)

Small function to concatenate a list with array contents

Parameters

* list_of_arraylist

contains a list for array data. the concatenation is possible if an index array have the same size

Returns

array_like

numpy concatenated data

Example

>>> import numpy as np
>>>  np.random.seed(0)
>>> ass=np.random.randn(10)
>>> ass2=np.linspace(0,15,12)
>>>  ass=ass.reshape((ass.shape[0],1))
>>>  ass2=ass2.reshape((ass2.shape[0],1))
>>> or_list=[ass,ass2]
>>> ss_check_error=concat_array_from_list(list_of_array=or_list,
...                                          concat_axis=0)
>>>  secont test :
>>>  ass=np.linspace(0,15,14)
>>> ass2=np.random.randn(14)
>>> ass=ass.reshape((ass.shape[0],1))
>>> ass2=ass2.reshape((ass2.shape[0],1))
>>> or_list=[ass,ass2]
>>>  ss=concat_array_from_list(list_of_array=or_list, concat_axis=0)
>>> ss=concat_array_from_list(list_of_array=or_list, concat_axis=1)
>>> ss
>>> ss.shape

pycsamt.utils.func_utils.dump_comma(input_car, max_value=2, carType='mixed')

Parameters

* input_carstr,

Input character.

• max_valueint, optional

  The default is 2.

• carType: str

  Type of character , you want to entry

Returns

Tuple of input character

must be return tuple of float value, or string value

Note

carType may be as arguments parameters like [‘value’,’val’,”numeric”, “num”, “num”,”float”,”int”] or for pure character like

[“car”,”character”,”ch”,”char”,”str”, “mix”, “mixed”,”merge”,”mer”, “both”,”num&val”,”val&num&”] if not , can not possible to convert to float or integer. the defaut is mixed

Example

>>> import numpy as np
>>>  ss=dump_comma(input_car=",car,box", max_value=3,
...      carType="str")
>>>  print(ss)
... ('0', 'car', 'box')

pycsamt.utils.func_utils.intell_index(datalist, assembly_dials=False)

function to search index to differency value to string element like geological rocks and geologicals samples. It check that value are sorted in ascending order.

Parameters

* datalistlist

list of element : may contain value and rocks or sample .

• assembly_dialslist, optional

  separate on two list : values and rocks or samples. The default is False.

Returns

index: int

index of breaking up.

first_dial: list ,

first sclice of value part

secund_dial: list ,

second slice of rocks or sample part.

assemblylist

list of first_dial and second_dial

Example

>>> import numpy as np
>>> listtest =[['DH_Hole', 'Thick01', 'Thick02', 'Thick03',
...           'Thick04','Rock01', 'Rock02', 'Rock03', 'Rock04'],
...           ['S01', '0.0', '98.62776918', '204.7500461','420.0266651','520', 'GRT',
...            'ATRK', 'GRT', 'ROCK','GRANODIORITE'],
...           ['S02', '174.4293956', '313.9043882','974.8945704', 'GRT', 'ATRK', 'GRT']]
>>> listtest2=[listtest[1][1:],listtest[2][1:]]
>>> for ii in listtest2 :
>>> op=intell_index(datalist=ii)
>>> print("index:

“,op [0])

>>> print('firstDials :

‘,op [1])

>>> print('secondDials:

‘,op [2])

pycsamt.utils.func_utils.interpol_scipy(x_value, y_value, x_new, kind='linear', plot=False, fill='extrapolate')

function to interpolate data

Parameters

* x_valuenp.ndarray

value on array data : original absciss

• y_valuenp.ndarray

  value on array data : original coordinates (slope)

• x_newnp.ndarray

  new value of absciss you want to interpolate data

• kindstr

  projection kind :

  maybe : “linear”, “cubic”

• fillstr

  kind of extraolation, if None , *spi will use constraint interpolation can be “extrapolate” to fill_value.

• plotBoolean

  Set to True to see a wiewer graph

Returns

np.ndarray

y_new ,new function interplolate values .

Example

>>> import numpy as np
>>>  fill="extrapolate"
>>>  x=np.linspace(0,15,10)
>>>  y=np.random.randn(10)
>>>  x_=np.linspace(0,20,15)
>>>  ss=interpol_Scipy(x_value=x, y_value=y, x_new=x_, kind="linear")
>>>  ss

pycsamt.utils.func_utils.keepmin(array)

Keep the minimum in array and array value

pycsamt.utils.func_utils.minimum_parser_to_write_edi(edilines, parser='=')

This fonction validates edifile for writing , string with egal.we assume that dictionnary in list will be for definemeasurment E and H fied.

Parameters

• edilines (list) – list of item to parse

• parser (str) – the egal is use to parser edifile . can be changed, default is =

pycsamt.utils.func_utils.parse_wellData(filename=None, include_azimuth=False, utm_zone='49N')

Function to parse well information in*csv file

Parameters

* filenamestr, optional

full path to parser file, The default is None.

• include_azimuth: bool ,

  Way to compute azimuth automatically

• utm_zonestr,

  set coordinate _utm_WGS84. Defaut is 49N

Returns

location: str

Name of location .

WellDatanp.ndarray

Specificy the collar Data .

GeoDatanp.ndarray

specify the geology data .

SampleDataTYPE

geochemistry sample Data.

Example

>>> import numpy as np
>>> dir_=r"F:\OneDrive\Python\CodesExercices\ex_avgfiles\modules"
>>> parse_=parse_wellData(filename='Drill&GeologydataT.csv')
>>> print("NameOflocation:

“,parse_[0])

>>> print("WellData:

“,parse_[1])

>>> print("GeoData:

“,parse_[2])

>>> print("Sample:

“,parse_[3])

Raises

FileNotFoundError

if typical file deoesnt match the *csv file.

pycsamt.utils.func_utils.round_dipole_length(value, round_value=5.0)

small function to graduate dipole length 5 to 5. Goes to be reality and simple computation .

Parameters

value (float) – value of dipole length

Returns

value of dipole length rounded 5 to 5

Return type

float

pycsamt.utils.func_utils.sort_array_data(data, sort_order=0, concatenate=False, concat_axis_order=0)

Function to sort array data and concatenate numpy.ndarray

Parameters

* datanumpy.ndarray

must be in simple array , list of array and dictionary whom the value is numpy.ndarray

• sort_orderint, optional

  index of colum to sort data. The default is 0.

• concatenateBoolean , optional

  concatenate all array in the object. Must be the same dimentional if concatenate is set to True. The default is False.

• concat_axis_orderint, optional

  must the axis of concatenation . The default is axis=0.

Returns

numpy.ndarray

data , Either the simple sort data or array sorted and concatenated .

pycsamt.utils.func_utils.stn_check_split_type(data_lines)

Read data_line and check for data line the presence of split_type < ‘,’ or ‘ ‘, or any other marks.> Threshold is assume to be third of total data length.

Params data_lines

list of data to parse .

Returns

The split _type

Return type

str

Example

>>> from pycsamt.utils  import func_utils as func
>>> path =  os.path.join(os.environ["pyCSAMT"],
                  'csamtpy','data', K6.stn)
>>> with open (path, 'r', encoding='utf8') as f :
...                     data= f.readlines()
>>>  print(func.stn_check_split_type(data_lines=data))

pycsamt.utils.func_utils.straighten_out_list(main_list, list_to_straigh)

Parameters

* main_listlist

list of which the data must absolutely appear into the straighen list. in our case , it is the station list : a list of offset

• list_to_straighlist

  list contain the data (offset calculated , the depth and the resistivity (log10)),

Returns

• list

  the straighen list. some offset have been replaced by the offsets which are not in the main_list whithout change the lengh of the straighen list.

  Example

  >>>  import numpy as np
  >>>  np.random.seed(14)
  >>>  ss=np.random.randn(10)*12
  >>>  ss=ss.tolist()
  >>>  ss=[round(float(jj),4) for jj in ss]
  >>>  ss.sort()
  >>>  red=np.random.randn(7)*12
  >>>  red=red.tolist()
  >>>  test=[19, 15.012, 5.5821, 0.7234,3.1,
  ...      0.7919, 3.445, 4.7398, 5.1, 10.8, 15.51,21]
  >>>  main=[20., 0.7234, 5, 3.445, 15.51,10.7, 3,5.1]
  >>>  test.sort()
  >>>  main.sort()
  >>>  red=[round(float(ss),1) for ss in red]
  >>>  print(test)
  >>>  print(main)
  >>>  sos=straighten_out_list (main_list=main ,
  ...                         list_to_straigh=test)
  >>>  print("sos:
  

“,sos)

pycsamt.utils.func_utils.take_firstValue_offDepth(data_array, filter_order=1)

Parameters

* data_arraynp.array

array of the data .

• filter_orderint , optional

  the column you want to filter. The default is 1.

Returns

array_like

return array of the data filtered.

Example

>>>  import numpy as np
>>>  list8=[[4,2,0.1],[8,2,0.7],[10,1,0.18],[4,3,0.1],
...        [7,2,1.2],[10,3,0.5],[10,1,0.5],[8.2,0,1.9],
...        [10,7,0.5],[10,1,0.5],[2,0,1.4],[5,4,0.5],
...        [10,2,0.7],[7,2,1.078],[10,2,3.5],[10,8,1.9]]
>>>  test=np.array(list8)
>>>   print(np_test)
>>>  ss=take_firstValue_offDepth(data_array =np_test, filter_order=1)
>>>   ss=averageData(np_array=np_test,filter_order=1,
>>>                 axis_average=0, astype="int")
>>> print(ss)

pycsamt.utils.func_utils.transfer_array_(data, index_key, start_value_depth, end_value_depth, column_order_selection=0, axis=0)

Parameters

* datadict

Dictionnary of numpy ndarray .

• index_keyfloat

  key of the dictionnary . Must be a number of the first column of offset .

• start_value_depthfloat

  If the depth is not reach must add depth of the closest point. give the start value which match to the maxi depth of the data : The default is -214.

• end_value_depthfloat

  Maximum depth of the survey. The default is -904.

• column_order_selectionint,

  the index of depth column. The default is 0.

• axisint , optional

  numpy.ndarray axis . The default is 0.

Returns

numpy.ndarray

return the array data we want to top to .

Example

>>> import numpy as np
>>> sos=abs(np.random.randn(4,3)*4)
>>> sos2=abs(np.random.randn(4,3)*10.8)
>>>  print(sos2)
>>> sis1=sort_array_data(data=sos,sort_order =1,
...                    concatenate=False, concat_axis_order=0)
>>> sis2=sort_array_data(data=sos2,sort_order =1,
...                    concatenate=False, concat_axis_order=0)
>>> dico={"18.4":sis1,
...      "21.4":sis2}
>>> test=transfer_array_(data=dico, index_key=11.4,
...                      start_value_depth=-14, end_value_depth=23,
...                      column_order_selection=1)
>>> print("sis1:",sis1)
>>> print("sis2:",sis2)
>>> print("Finaltest", test)

Module Gis-tools

Module Infos

This file is part of pyCSAMT.

pyCSAMT is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

pyCSAMT is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with pyCSAMT. If not, see <https://www.gnu.org/licenses/>.

synopsis

Module contains various parameters of files handling and glossary of some technical words.

Created on Sat Dec 12 16:21:10 2020

@author:KouaoLaurent alias @Daniel03

class pycsamt.etc.infos._sensitive

Note

sensitive class . Please keep carefully the indices like it’s ranged. Better Ways to avoid redundancy in code during the program design. Core of parser of each files except files from module-geodrill. Aims are: 1. to check file . it is was the right file provided or not 2. to write file . to Zonge Avg_format or J-Format or EDI -format and else. 3. to compute value . Indice are used for computation , set and get specific value.

Methods

validate_avg(avg_data_lines)	Core function to validate avg file .
which_file([filename, deep])	Which file is class method .

static validate_avg(avg_data_lines)

Core function to validate avg file .

Parameters

avg_data_lines (list) – list of avgfile

Returns

‘yesAST’ or ‘yes’ where ‘yesAST’ is Astatic file and ‘yes’ is plainty avg file (the main file)

Return type

str

Returns

item, spliting the headAvg components strutured by file.

Return type

list

Example

>>> from csamtpy.etc.infos.Infos inmport _sensistive as SB
>>> path =  os.path.join(os.environ["pyCSAMT"],
...                                  'csamtpy','data', LCS01_2_to_1.avg)
... with open (path, 'r', encoding ='utf8') as f :
...   datalines = f.readlines()
... ss =SB._sensitive.validate_avg(avg_data_lines=datalines)

classmethod which_file(filename=None, deep=True)

Which file is class method . List of files are the typical files able to read by pyCSAMT softwares. Sensitive class method.

Parameters

**filename :str**

corresponding file to read , pathLike

deepbool ,

control reading : False for just control the extension file , not opening file . True control in deeper file and

find which file were inputted.

Returns

str

FileType could be [avg | j | edi | resp | mesh | occamdat |

stn | model | iter | logfile | startup]

List of files read by pyCSAMT :

Example

>>> files = ['K1_exp.bln','LCS01.avg' ,'LCS01_2_to_1.avg', 'K1.stn',
...            'csi000.dat','csa250.edi','LogFile.logfile',
...                 'Occam2DMesh','Occam2DModel', 'OccamDataFile.dat',
...            'S00_ss.edi', 'Startup','RESP13.resp',
...                 'ITER02.iter']
>>>  for ii in files :
>>>      path =  os.path.join(os.environ["pyCSAMT"],
...                                  'csamtpy','data', ii)
...       try :
...         print(_sensitive.which_file(path,deep=True))
...       except :pass

class pycsamt.etc.infos.notion

Singular class to explain CSAMT technical word in details. Also usefull for user to have info about any scientific context is does Know. Just call the word directly in warnings to give an overview of why error occurs. It like a short documentation using pyCSAMT software. Used everywhere in the script .

class pycsamt.etc.infos.suit

Singular class to easy manipulate word. used everywhere in the script to avoid redondancy.

Module Plot-utils

This file is part of pyCSAMT.

pyCSAMT is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

pyCSAMT is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with pyCSAMT. If not, see <https://www.gnu.org/licenses/>.

---

Created on Tue Dec 29 19:18:44 2020

@author: @Daniel03

pycsamt.utils.plot_utils.annotate_tip(layer_thickness, layer_names)

A tip to group text with the same resistivities one layer when the layer are successively the same .

Parameters

• layer_thickness (array_like |list) – thickness of layer

• layer_names (list or array_like) – names of layers , geological structures names

Returns

v , layer thickness in depth

Return type

array_like

Returns

ni , list : name of layer

Return type

array_like

Example

>>> from pycsamt.utils import plot_utils as punc
>>> rocks = ['Massive sulfide', 'Igneous rocks',
             'Igneous rocks', 'Igneous rocks',
     'Igneous rocks', 'Igneous rocks', 'Igneous rocks',
     'Igneous rocks',
     'Massive sulfide', 'Igneous rocks', 'Igneous rocks',
     'Igneous rocks']
>>> resprops = [0.0, 49.0,69.0,89.0,109.0,129.0,
                149.0,179.0,249.0,699.0,799.0,899.0]
>>> thickness, lnames = punc.annotate_tip(layer_thickness=resprops,
                                          layer_names=rocks)
>>>print(thickness)
>>> print(lnames)
>>> v, ni
...  [24.5, 149.0, 474.0, 799.0]
...  ['Massive sulfide', 'Igneous rocks', 'Massive sulfide',
      'Igneous rocks']

pycsamt.utils.plot_utils.average_rho_in_deep(dep_array, rho_array, step_descent)

function to average rho in deep according to the value provided . In fact averaged rho in shorter depth distance allow us to understand the conductive zone. in approximately. The most conductive zone is detected as the zone with lower resistivities values . But fixing values as averaged rho , can build a specific strata that could match this zone .

Parameters

* dep_arrayarray_like

the imaged depth (doi)

• rho_array: array_like

  resistivity array

• step_descentfloat

  value to step descent

Returns

array_like

rho average for each station # dep_averaged for each station

Example

>>> import numpy as np
>>> from pycsamt.utils import plot_utils as punc
>>> pseudo_depth=np.array([  0. ,  6. , 13. , 20.  ,29. , 39.,  49. ,
...                       59. , 69. , 89., 109. ,129., 149. ,179.,
...                  209. ,249. ,289., 339., 399., 459. ,529. ,609.,
...                  699., 799., 899., 999.])
>>> pseudo_depth = np.arange(0, 1220, 20)
>>> rho = np.random.randn(len(pseudo_depth))
>>> rho_aver, dep_aver= average_rho_in_deep(dep_array=pseudo_depth,
...                                        rho_array=rho,
...                                        step_descent=20.)
>>> rho_2,dep_2 =   average_rho_in_deeper (dep_array= pseudo_depth,
...                                         rho_array=rho,
...                                         step_descent=1000)
>>>  print(pseudo_depth)

pycsamt.utils.plot_utils.average_rho_in_deeper(dep_array, rho_array, step_descent)

function to average rho in deep according to the value provided . In fact averaged rho in shorter depth distance allow us to understand the conductive zone. in approximately. The most conductive zone is detected as the zone with lower resistivities values . But fixing values as averaged rho , can build a specific strata that could match this zone .

Parameters

* dep_arrayarray_like

the imaged depth (doi)

• rho_array: array_like

  resistivity array

• step_descentfloat

  value to step descent

Returns

array_like

rho average for each station # dep_averaged for each station

Example

>>> import numpy as np
>>> from pycsamt.utils import plot_utils as punc
>>> pseudo_depth=np.array([  0. ,  6. , 13. , 20.  ,29. , 39.,  49. ,
...                       59. , 69. , 89., 109. ,129., 149. ,179.,
...                  209. ,249. ,289., 339., 399., 459. ,529. ,609.,
...                  699., 799., 899., 999.])
>>> pseudo_depth = np.arange(0, 1220, 20)
>>> rho = np.random.randn(len(pseudo_depth))
>>> rho_aver, dep_aver= average_rho_in_deep(dep_array=pseudo_depth,
...                                        rho_array=rho,
...                                        step_descent=20.)
>>> rho_2,dep_2 =   average_rho_in_deeper (dep_array= pseudo_depth,
...                                         rho_array=rho,
...                                         step_descent=1000)
>>>  print(pseudo_depth)

pycsamt.utils.plot_utils.average_rho_with_locals_minmax(array)

How to compute mean value between local maxima and local minima and keep locals minima and maxima value on the final data

Parameters

array (array_like) – data to compute the local minima and local maxima

Returns

array mean with data local value averaged

Return type

array_like

Example

>>> from pycsamt.utils import plot_utils as punc
>>> mean1= punc.average_rho_with_locals_minmax(tth[0])
>>> mean2= punc.average_rho_with_locals_minmax(tth[1])
>>> print(mean1)
>>> print(mean2)

pycsamt.utils.plot_utils.build_new_station_id(station_id, new_station_name)

Fonction to build new station id including station name provided , if the length provided

doesnt match the length of station id

Parameters

• id (station) – new sites names

• mess (str) – message for debugging, Default is None

Example

>>> from pycsamt.utils import plot_utils as punc
>>> ts =[   28.,  200.,  400.,  600.,  800., 1000.,
         1200., 1400., 1600., 1800., 1807.]
>>> sto = ['S{0}'.format(i) for i in range(7)]
>>> print(sto)
>>> stn, fu =punc.build_new_station_id(station_id = sto,
                                       new_station_name =ts)
>>> print(stn, fu)

pycsamt.utils.plot_utils.build_resistivity_barplot(depth_values, res_values)

Allow to build bar plot resistivity function of investigation depth .

Parameters

* depth_valuesaray_like

model investigation depth

• res_valuesarray_like

  model_resistivities at each depth values

Returns

darray_like

resistivity barplot depth .

rarray_like

specific structure resistivities

sumdfloat

checker number that cover in fact the total depth. this numbe must absolutely match the total depth .

pycsamt.utils.plot_utils.controle_delineate_curve(res_deline=None, phase_deline=None)

fonction to controle delineate value given and return value ceilling .

Parameters

• res_deline (float|int|list) – resistivity value todelineate. unit of Res in ohm.m

• phase_deline (float|int|list) – phase value to delineate , unit of phase in degree

Returns

delineate resistivity or phase values

Return type

array_like

pycsamt.utils.plot_utils.delineate_curve(dict_loc, value, atol=0.2, replace_value=nan)

function to delineate value of rho and phase .

Parameters

• dict_loc (dict) – dictionnary composed of keys = stations id and values

• value (float |list) – value to delineate curve . for single value.

• atol (float) – tolerance parameter <=1 . Most the param is closest to 0 , most the selected data become severe.default is 0.2

• replace_value (float or else) – could be None or np.nan , Default is np.nan

Returns

dict of delineate data

Return type

dict

Example

>>> from pycsamt.utils import plot_utils as punc
>>> ts = np.array([0.1, 0.7, 2, 3, 8, 1000, 58,55, 85, 18])
>>> to =np.array([51, 78, 0.25, 188, 256, 7])
>>> tt ={'S00': ts, 'S01':to, 'S02': np.array([0, 5, 125, 789])}
>>> print(punc.delineate_curve(dict_loc = tt,
                               value=[50,70], atol = 0.1))

pycsamt.utils.plot_utils.delineate_sparseMatrix(dict_loc, delineate_dict, replace_value=nan)

Build from delineate dict a matrix according to frequency length . value doesnt exist in the delineate dict will be repalce by replacevalue . Default is nan.

Parameters

• delineate_dict (dict) – delineation value

• dict_loc (dict) – dictionnary composed of keys = stations id and values

• replace_value (float) – value to replece other value like build a sparse matrix

Returns

dit of sparse matrix

Return type

dict

pycsamt.utils.plot_utils.depth_of_investigation(doi)

Depth of investigation converter

Parameters

doi (str|float) – depth of investigation if value float is provided , it will considered as default units in meter

:returns doi:value in meter :rtype: float

pycsamt.utils.plot_utils.find_closest_station(offset_indice, model_offsets, site_offsets)

Get the indice of the closest offset

Parameters

* offset_indice: int

if the indix of the offset at the selected resistivity point.

• model_offset: array_like

  is a large band of x_nodes resistivities gnerated by mesh files

• sites_offsets: array_like

  the data set offset from Occam Data file

Returns

indexoffint

index of data offset

get_offsfloat

value of the offset at that index

pycsamt.utils.plot_utils.find_local_maxima_minima(array)

function to find minimum local and maximum local on array

Parameters

data (array_like) – value of array to find minima , maxima

Returns

tuple of index of minima and maxima local index and array of minima maxima value

Return type

array_like

Example

>>> from pycsamt.utils import func_utils as func
>>> from pycsamt.utils import plot_utils as punc
>>> ts =np.array([2, 3, 4, 7, 9, 0.25, 18, 28, 86, 10, 5])
>>> te=np.array([0.2, .3, 18, 1.6, 0.2, 0.6, 0.7, 0.8, 0.9, 1., 23.])
>>> th =func.concat_array_from_list([ts, te], concat_axis=1)
.. tth =th.T
... print(tth)
... print(punc.find_local_maxima_minima(tth[0]))

pycsamt.utils.plot_utils.find_path(path=None, ptol=0.7)

check path and return filepath , edipath or jpath .

Parameters

• path (str) – full path to edi, avag or j file or directory

• ptol (float) – tolerance that given by the program to judge if the number of typical file [EDI|J] to declare as path found is either “edipath” or “jpath” if none ,return None . less or equal to 1.

Returns

specific path

Return type

str

pycsamt.utils.plot_utils.fmt_text(data_text, fmt='~', leftspace=3, return_to_line=77)

Allow to format report with data text , fm and leftspace

Parameters

• data_text (str) – a long text

• fmt (str) – type of underline text

• leftspae (int) – How many space do you want before starting wrinting report .

• return_to_line (int) – number of character to return to line

pycsamt.utils.plot_utils.get_conductive_and_resistive_zone(data, site_names, purpose='groundwater', **kws)

function to get the probability of conductive and resistive zone . It is not absolutely True but give an overview of decison .

It is not sufficient to declare that the zone is

favorable for any drill , but just work with probability

Parameters

• data (ndarray) – resistivity datata of survey area

• site_name (list) – list of sites names

• purpose (str) – type of exploration , default is groundwater

Returns

report of exploration area

Return type

str

pycsamt.utils.plot_utils.get_frequency_id(freq_array, frequency_id)

function to get id of frequency . Frequency to plot

Parameters

• freq_array (nd.array,1) – array of frequency

• frequency_id (list or float) – frequency to plot .

Returns

new close frequency id

Return type

float|list

pycsamt.utils.plot_utils.get_station_id_input_resistivities(station_rho_value, number_of_layer=None)

Get a special station input resistivities is much benefit and more close to reality of plot . Indeeed , it take only the maximum value of resistivities below the site and the minimum , then cut out 7 Considering all the model data and choose the max resistivities and the

minim resistivities to build automatic resistivities whom COULD match the deth is less sure .Geeting a input resitivities to aplom the site , give a merly and better interpretation.

Parameters

• station_rho_value (array_like) – value of resistivities under the site thin the maximum depth

• number_of_layer (int) – number of layer to top to bottom.

pycsamt.utils.plot_utils.get_stationid(stations, station_id)

Tip to get station id from user by input either integer or station name .

Parameters

• stations (list) – list of stations known

• station_id (list, str, or int) – staion expect to plot.

Returns

constructed list for plotting

Return type

array_like

Example

>>> from pycsamt.utils import plot_utils as punc
>>> teslist = ['S{0:02}'.format(ii) for ii in range(23)]
>>> ss = punc.get_stationid (stations=teslist ,  station_id=('S04',13))
>>> print(ss)

pycsamt.utils.plot_utils.getcloser_frequency(freq_array, frequency_id)

chek whether the frequency value given is in frequency range if not found the closest value

pycsamt.utils.plot_utils.plot_errorbar(ax, x_array, y_array, y_error=None, x_error=None, color='k', marker='x', ms=2, ls=':', lw=1, e_capsize=2, e_capthick=0.5, picker=None, **kws)

convinience function to make an error bar instance @author: jpeacock-pr

pycsamt.utils.plot_utils.resetting_colorbar_bound(cbmax, cbmin, number_of_ticks=5, logscale=False)

Function to reset colorbar ticks more easy to read

Parameters

• cbmax (float) – value maximum of colorbar

• cbmin (float minimum data value) – minimum data value

• number_of_ticks (int) – number of ticks should be located on the color bar . Default is 5.

• logscale (bool) – set to True if your data are lograith data .

Returns

array of color bar ticks value.

Return type

array_like

pycsamt.utils.plot_utils.resetting_ticks(get_xyticks, number_of_ticks=None)

resetting xyticks modulo , 100

Parameters

• get_xyticks (list) – xyticks list , use to ax.get_x|yticks()

• number_of_ticks (int) – maybe the number of ticks on x or y axis

Returns

a new_list or ndarray

Return type

list or array_like

pycsamt.utils.plot_utils.share_props_for_each_plot(number_of_plot=3, **kwargs)

Function to set properties for each plot. Easy to customize line and markers. Function can add other properties which are not in kwargs.keys(). It will set according the number of subplotsplots we assume subplot are define on one columns

Parameters

number_of_plot (int) – number of subsplot you want show.

Returns

dictionarry of labels and properties.

Return type

dict

pycsamt.utils.plot_utils.slice_csamt_matrix(block_matrix, station_offsets, depth_offsets, offset_MinMax=(0, 1000), doi='2000m')

Using Wannamaker FE elements mesh to define rho matrix blocks , need after inversion to slice the model resistivity according the offset and depth we need . This function is easy tool to slice matrix and to keep the part we need . station offset , depth and model resistivity

Parameters

* block_matrixndarray(station_offsets.shape[0],

matrix of station depth Resistivity model depth_offsets.shape[0])

• depth_offsetarray_like

  depth of investigation after generating by mesh file :>z_nodes .

• station_offsetsarray_like

  station _offsets : offset generate by mesh_file :>x_nodes .

• offset_MinMaxtuple

  the interval of data to keep . eg if station location start by 0 :

  off[0] = min and off[-1]=max (min, max):–> index 0 : minimum value of station location –>index 1 :

  maximum value of station location

  default is (0,1000)

• doistr , float

  investigation depth , migth be [m|km]. If value is provided is float number , it might take value as a default unit ‘meter’. i.e : 1000=”1000m”

Returns

tuple

new sliced station offset , new sliced depth offset , new_matrix block ,

pycsamt.utils.plot_utils.slice_matrix(base_matrix, freq_array, doi=2000)

Function get a matrix and give new matrice slice from limit value

Parameters

• base_matrix (ndarray) – arrays (yaxis lenghth, station_length)

• freq_array (ndarray,1) – frequency array range

• doi – expect to be the depth of investigation from which data muts be selected in m or km

:type doi:float

Returns

matrix sliced according to doi

Return type

ndarray

pycsamt.utils.plot_utils.station_id(id_)

From id get the station name as input and return index id. Index starts at 0.

Parameters

id – name of the station or index . Be sure to hve the station name containing the letter S which mean site.

Returns

station index. If the list id_ is given will return the tuple.