I'm running some simulation code using ubuntu and I keep running into the same error. I am trying to read data from the .dat file. But there is some error which I could not find.
This is error message:
At line 1939 of file CompoundMPIBSC20200823.f90 (unit = 11, file = 'C-340120b.dat')
Fortran runtime error: Bad value during floating point read"
And C-340120b.dat file looks like this:
C-340120b.dat file
6 10 1.531581196563372e-15
0.0014553174 0.0055615333 0.0119703978 0.0203850084 0.0305528957 0.0422600997 0.0553257997 0.0695976542 0.0849475255 0.1012676622
0.1184670631 0.1364683308 0.1552047081 0.1746171362 0.1946516651 0.2152575030 0.2363847713 0.2579826431 0.2799978445 0.3023733948
0.3250475021 0.3479528735 0.3710162901 0.3941586119 0.4172949742 0.4403351781 0.4631846022 0.4857450840 0.5079162267 0.5295967855
0.5506862041 0.5710862773 0.5907027347 0.6094469646 0.6272378474 0.6440030470 0.6596808652 0.6742212377 0.6875870601 0.6997550335
0.7107163254 0.7204769965 0.7290581527 0.7364958286 0.7428406051 0.7481569620 0.7525223874 0.7560262383 0.7587683705 0.7608575997
0.7624099924 0.7635469966 0.7643935146 0.7650758792 0.7657198055 0.7664483527 0.7673799150 0.7686263022 0.7702908977 0.7724669898
0.7752362202 0.7786672785 0.7828147402 0.7877181912 0.7934015601 0.7998727305 0.8071233638 0.8151290525 0.8238496666 0.8332299862
0.8432005800 0.8536788860 0.8645704950 0.8757706624 0.8871659719 0.8986361128 0.9100557539 0.9212965992 0.9322292652 0.9427254733
0.9526599682 0.9619125838 0.9703700425 0.9779277777 0.9844915839 0.9899791031 0.9943211058 0.9974625834 0.9993636185 1.0000000000
6 11 1.475893077189510e-15
0.0016525844 0.0062956494 0.0135059282 0.0229208176 0.0342303569 0.0471704147 0.0615165460 0.0770787515 0.0936966304 0.1112350678
0.1295802432 0.1486359799 0.1683205560 0.1885633307 0.2093018003 0.2304793480 0.2520427451 0.2739399970 0.2961185348 0.3185236648
0.3410972443 0.3637767806 0.3864948806 0.4091790904 0.4317517598 0.4541305840 0.4762291287 0.4979578467 0.5192252758 0.5399391683
0.5600082344 0.5793434896 0.5978599757 0.6154784463 0.6321271026 0.6477428598 0.6622731054 0.6756767045 0.6879251243 0.6990032865
0.7089101737 0.7176591771 0.7252781911 0.7318094543 0.7373091004 0.7418464594 0.7455031054 0.7483717148 0.7505546359 0.7521623630
0.7533118077 0.7541244615 0.7547244701 0.7552366540 0.7557845046 0.7564881945 0.7574626361 0.7588156121 0.7606460387 0.7630423235
0.7660809513 0.7698252039 0.7743241221 0.7796116798 0.7857062078 0.7926100427 0.8003094738 0.8087748795 0.8179612002 0.8278085409
0.8382431405 0.8491784407 0.8605164079 0.8721490905 0.8839601234 0.8958267320 0.9076214149 0.9192140226 0.9304737122 0.9412709632
0.9514795610 0.9609785827 0.9696542131 0.9774014787 0.9841259295 0.9897450178 0.9941894123 0.9974040480 0.9993489837 1.0000000000
...
...
...
6 30500 2.203435261320421e-18
0.5647132406 0.8435296561 0.9197993603 0.9501219587 0.9657979424 0.9751478483 0.9812026747 0.9853454006 0.9882967561 0.9904674356
0.9921063590 0.9933715119 0.9943670960 0.9951635956 0.9958099778 0.9963412928 0.9967830203 0.9971540150 0.9974684566 0.9977371653
0.9979685074 0.9981690312 0.9983439172 0.9984973073 0.9986325426 0.9987523421 0.9988589360 0.9989541675 0.9990395697 0.9991164266
0.9991858197 0.9992486651 0.9993057428 0.9993577205 0.9994051720 0.9994485928 0.9994884125 0.9995250049 0.9995586968 0.9995897744
0.9996184895 0.9996450644 0.9996696956 0.9996925575 0.9997138054 0.9997335777 0.9997519982 0.9997691778 0.9997852163 0.9998002034
0.9998142198 0.9998273389 0.9998396267 0.9998511432 0.9998619429 0.9998720754 0.9998815859 0.9998905154 0.9998989017 0.9999067791
0.9999141790 0.9999211303 0.9999276594 0.9999337906 0.9999395461 0.9999449463 0.9999500100 0.9999547546 0.9999591958 0.9999633483
0.9999672254 0.9999708395 0.9999742019 0.9999773228 0.9999802118 0.9999828775 0.9999853278 0.9999875699 0.9999896101 0.9999914544
0.9999931080 0.9999945754 0.9999958609 0.9999969680 0.9999978997 0.9999986585 0.9999992466 0.9999996655 0.9999999164 1.0000000000
The 3 dots in the above data file are just to tell you that there are many more entries in the file. These dots are not there in the original file.
And program :
file CompoundMPIBSC20200823.f90
open(11,file=fname(mel),status='old',form='formatted')
open(12,file=fname1(mel),status='old',form='formatted')
do men=1,nen !nen=75:energy intervals Energy split number cycle
!!write(iw,*) 'mel,men:',mel,men
read (11,'(i2,I7,d22.15/(10f13.10))') na,nenerg,tcrpc,(rpw(i),i=1,ith)
read (12,'(i2,I7,d22.15/(10f15.10))') na,nenerg,tcrpc,(rpw1(i),i=1,ith)
!!write(iw,*) 'mel,men,na,nenerg:',mel,men,na,nenerg,tcrpc
ftcs(men)=tcrpc ! corresponds to the total elastic scattering cross section at the energy
penergy(men)=nenerg/1000.
rpw(ith)=1.
!---------------------------
line 1939 is
read (11,'(i2,I7,d22.15/(10f13.10))') na,nenerg,tcrpc,(rpw(i),i=1,ith)
I've tried different modifications of the code but didn't get any results.
Any help would be greatly appreciated!
You appear to have spaces padding the fields in your data file, but not in your read format. The first line of your file (with column labels) is
000000000111111111122222222223333
123456789012345678901234567890123
6 10 1.531581196563372e-15
so splitting this into i2,I7,d22.15 gives
i2 | I7 | d22.15 |
00 | 0000000 | 1111111111222222222233 | 33
12 | 3456789 | 0123456789012345678901 | 23
6 | 1 | 0 1.531581196563372e- | 15
which is clearly not as intended.
There are two ways around this problem:
As Ian Bush points out, you can forego the read format entirely, and used list-directed input, as
read (11,*) na,nenerg,tcrpc,(rpw(i),i=1,ith)
This will parse your file token by token rather than relying on column widths, and is usually a much better option for parsing data files.
If you must use a read format, you need to add space padding to it, e.g.
'(i2,X,I7,X,d22.15/10(X,f13.10))', which will then split the input string as
i2 | X | I7 | X | d22.15
00 | 0 | 0000001 | 1 | 1111111122222222223333
12 | 3 | 4567890 | 1 | 2345678901234567890123
6 | | 10 | | 1.531581196563372e-15
Related
Context
After applying a line length limit of 80 characters on the pre-commit check of markdown-lint, I was experiencing some difficulties in including a markdown table that I create with more width than 80 characters.
Note
I see value in applying the linter to the README.md because I quite often forget about the line length while typing the README.md. (In essence, the trivial solution: disable the linter or disable MD013 everywhere, is considered sub-optimal).
Pre-commit of MarkdownLint
- repo: https://github.com/markdownlint/markdownlint
rev: v0.11.0
hooks:
- id: markdownlint
Markdown table example
| Algorithm | Encoding | Adaptation | Radiation | Backend |
| ------------------------------------ | -------- | ---------- | ------------ | ---------------------------- |
| Minimum Dominating Set Approximation | Sparse | Redundancy | Neuron Death | - networkx LIF<br>- Lava LIF |
| Some Algorithm Approximation | Sparse | Redundancy | Neuron Death | - networkx LIF<br>- Lava LIF |
| | | | | |
Approach I
First I tried to include a ignore MD013 (line length check) in the relevant section of the Markdown table, however, Markdown Lint does not support such an option.
Approach II
I tried to manually apply the new line breaks to the table, however, that results in additional rows in the table:
Question
How can I stay within the 80 lines whilst including a wide markdown table, (without generating new horizontal lines)?
You could try changing your hook to another similar project: igorshubovych/markdownlint-cli
repos:
- repo: https://github.com/igorshubovych/markdownlint-cli
rev: v0.32.2
hooks:
- id: markdownlint
args: ["--fix"]
You may include a .markdownlint.yaml file in the same directory as your .pre-commit-config.yaml. Set the line length rule but ignore it for tables. Like so:
# Default state for all rules
default: true
# MD013/line-length - Line length
MD013:
line_length: 80
tables: false
Check the .markdownlint.yaml schema for other configuration options.
I store json test data in excel file.
Make use of apache POI to read the json data and parse it as request body, call it from katalon.
Then I write many lines of assertion (groovy assert) to verify each line response = test data.
Example:
Assert test.responseText.fieldA == 'abc'
Assert test.responseText.fieldB == 'xyz'
And so on if I have total of 20 fields.
I'm thinking of there is better way to make use of the json data stored in data file.
To assert the response = test data. So I can save alot of time to key in each line and modify them is the test data changed.
Please advise if this can be achieved?
Here is an example: you have two excel sheets - current values and expected values (values you are testing against).
Current values:
No. | key | value
----+-----+------
1 a 100
2 b 6
3 c 13
Expected values:
No. | key | value
----+-----+------
1 a 100
2 b 6
3 c 14
You need to add those to Data Files:
The following code will compare the values in the for loop and the assertion will fail on the third run (13!=14):
def expectedData = findTestData("expected")
def currentData = findTestData("current")
for(i=1; i<=currentData.getRowNumbers(); i++){
assert currentData.getValue(2, i) == expectedData.getValue(2, i)
}
Failure message should look like this:
2020-07-02 15:16:40.471 ERROR c.k.katalon.core.main.TestCaseExecutor - ❌ Test Cases/table comparison FAILED.
Reason:
Assertion failed:
assert currentData.getValue(2, i) == expectedData.getValue(2, i)
| | | | | | |
| 14 3 | | 13 3
| | com.kms.katalon.core.testdata.reader.SheetPOI#5aabbb29
| false
com.kms.katalon.core.testdata.reader.SheetPOI#72c927f1
I am working on a project about gentrification. My teammates pulled data from the census and cleaned it to get the values we need. The issue is, the zip code values won't print 0's (i.e. "2322" when it should be "02322"). We managed to find the tact value that prints the full zip code with the tact codes("ZCTA5 02322"). I want to remove "ZCTA5" to get the zip code alone.
I've tried the below code but it only gets rid of the "ZCTA" instead of "ZCTA5" (i.e. "502322"). I'm also concerned that if I manage to remove the 5 with the characters, it will remove all 5's in the zip codes as well.
From there I will be pulling from pgeocode to access the respective lat & lng values to create the heatmap. Please help?
I've tried the .replace(), .translate(), functions. Replace still prints the zip codes with 5. Translate gets an attribute error.
Sample data
Zipcode | Name | Change_In_Value | Change_In_Income | Change_In_Degree | Change_In_Rent
2322 | ZCTA5 02322 | -0.050242 | -0.010953 | 0.528509 | -0.013263
2324 | ZCTA5 02324 | 0.012279 | -0.022949 | -0.040456 | 0.210664
2330 | ZCTA5 02330 | 0.020438 | 0.087415 | -0.095076 | -0.147382
2332 | ZCTA5 02332 | 0.035024 | 0.054745 | 0.044315 | 1.273772
2333 | ZCTA5 02333 | -0.012588 | 0.079819 | 0.182517 | 0.156093
Translate
zipcode = []
test2 = gent_df['Name'] = gent_df['Name'].astype(str).translate({ord('ZCTA5'): None}).astype(int)
zipcode.append(test2)
test2.head()
Replace
zipcode = []
test2 = gent_df['Name'] = gent_df['Name'].astype(str).replace(r'\D', '').astype(int)
zipcode.append(test2)
test2.head()
Replace
Expected:
24093
26039
34785
38944
29826
Actual:
524093
526039
534785
538944
529826
Translate
Expected:
24093
26039
34785
38944
29826
Actual:
AttributeError Traceback (most recent call last)
<ipython-input-71-0e5ff4660e45> in <module>
3 zipcode = []
4
----> 5 test2 = gent_df['Name'] = gent_df['Name'].astype(str).translate({ord('ZCTA5'): None}).astype(int)
6 # zipcode.append(test2)
7 test2.head()
~\Anaconda3\envs\MyPyEnv\lib\site-packages\pandas\core\generic.py in __getattr__(self, name)
5178 if self._info_axis._can_hold_identifiers_and_holds_name(name):
5179 return self[name]
-> 5180 return object.__getattribute__(self, name)
5181
5182 def __setattr__(self, name, value):
AttributeError: 'Series' object has no attribute 'translate'
It looks like you are using pandas so you should be able to use the .lstrip() method. I tried this on a sample df and it worked for me:
gent_df.Name = gent_df.Name.str.lstrip(to_strip='ZCTA5')
Here is a link to the library page for .strip(), .lstrip(), and .rstrip()
I hope this helps!
There are many ways to do this. I can think of 2 off the top of my head.
If you want to keep the last 5 characters of the zipcode string, regardless of whether they are digits or not:
gent_df['Name'] = gent_df['Name'].str[-5:]
If want to get the last 5 digits of the zipcode string:
gent_df['Name'] = gent_df['Name'].str.extract(r'(\d{5})$')[0]
Include some sample data for more specific answer.
I am cleaning a restaurant data set using Pandas' read_csv.
I have columns like this:
name, online_order, book_table, rate, votes
xxxx, Yes, Yes, 4.5/5, 705
I expect them to be like this:
name, online_order, book_table, rate, votes
xxxx, Yes, Yes, 4.5, 705
You basically need to split the item(dataframe["rate"]) based on / and take out what you need. .apply this on your dataframe using lambda x: getRate(x)
def getRate(x):
return str(x).split("/")[0]
To use it with column name rate, we can use:
dataframe["rate"] = dataframe["rate"].apply(lambda x: getRate(x))
You can use the python .split() function to remove specific text, given that the text is consistently going to be "/5", and there are no instances of "/5" that you want to keep in that string. You can use it like this:
num = "4.5/5"
num.split("/5")[0]
output: '4.5'
If this isn't exactly what you need, there's more regex python functions here
You can use DataFrame.apply() to make your replacement operation on the ratecolumn:
def clean(x):
if "/" not in x :
return x
else:
return x[0:x.index('/')]
df.rate = df.rate.apply(lambda x : clean(x))
print(df)
Output
+----+-------+---------------+-------------+-------+-------+
| | name | online_order | book_table | rate | votes |
+----+-------+---------------+-------------+-------+-------+
| 0 | xxxx | Yes | Yes | 4.5 | 705 |
+----+-------+---------------+-------------+-------+-------+
EDIT
Edited to handle situations in which there could be multiple / or that it could be another number than /5 (ie : /4or /1/3 ...)
Gherkin / Behave Examples
Gherkin syntax features test automation using examples:
Feature: Scenario Outline (tutorial04)
Scenario Outline: Use Blender with <thing>
Given I put "<thing>" in a blender
When I switch the blender on
Then it should transform into "<other thing>"
Examples: Amphibians
| thing | other thing |
| Red Tree Frog | mush |
| apples | apple juice |
Examples: Consumer Electronics
| thing | other thing |
| iPhone | toxic waste |
| Galaxy Nexus | toxic waste |
The test suite would run four times, once for each example, giving a result similar to:
My problem
How can I test using confidential data in the Examples section? For example, I would like to test an internal API with user ids or SSN numbers, without keeping the data hard coded in the feature file.
Is there a way to load the Examples dynamically from an external source?
Update: Opened a github issue on the behave project.
I've come up with another solution (behave-1.2.6):
I managed to dynamically create examples for a Scenario Outline by using before_feature.
Given a feature file (x.feature):
Feature: Verify squared numbers
Scenario Outline: Verify square for <number>
Then the <number> squared is <result>
Examples: Static
| number | result |
| 1 | 1 |
| 2 | 4 |
| 3 | 9 |
| 4 | 16 |
# Use the tag to mark this outline
#dynamic
Scenario Outline: Verify square for <number>
Then the <number> squared is <result>
Examples: Dynamic
| number | result |
| . | . |
And the steps file (steps/x.step):
from behave import step
#step('the {number:d} squared is {result:d}')
def step_impl(context, number, result):
assert number*number == result
The trick is to use before_feature in environment.py as it has already parsed the examples tables to the scenario outlines, but hasn't generated the scenarios from the outline yet.
import behave
import copy
def before_feature(context, feature):
features = (s for s in feature.scenarios if type(s) == behave.model.ScenarioOutline and
'dynamic' in s.tags)
for s in features:
for e in s.examples:
orig = copy.deepcopy(e.table.rows[0])
e.table.rows = []
for num in range(1,5):
n = copy.deepcopy(orig)
# This relies on knowing that the table has two rows.
n.cells = ['{}'.format(num), '{}'.format(num*num)]
e.table.rows.append(n)
This will only operate on Scenario Outlines that are tagged with #dynamic.
The result is:
behave -k --no-capture
Feature: Verify squared numbers # features/x.feature:1
Scenario Outline: Verify square for 1 -- #1.1 Static # features/x.feature:8
Then the 1 squared is 1 # features/steps/x.py:3
Scenario Outline: Verify square for 2 -- #1.2 Static # features/x.feature:9
Then the 2 squared is 4 # features/steps/x.py:3
Scenario Outline: Verify square for 3 -- #1.3 Static # features/x.feature:10
Then the 3 squared is 9 # features/steps/x.py:3
Scenario Outline: Verify square for 4 -- #1.4 Static # features/x.feature:11
Then the 4 squared is 16 # features/steps/x.py:3
#dynamic
Scenario Outline: Verify square for 1 -- #1.1 Dynamic # features/x.feature:19
Then the 1 squared is 1 # features/steps/x.py:3
#dynamic
Scenario Outline: Verify square for 2 -- #1.2 Dynamic # features/x.feature:19
Then the 2 squared is 4 # features/steps/x.py:3
#dynamic
Scenario Outline: Verify square for 3 -- #1.3 Dynamic # features/x.feature:19
Then the 3 squared is 9 # features/steps/x.py:3
#dynamic
Scenario Outline: Verify square for 4 -- #1.4 Dynamic # features/x.feature:19
Then the 4 squared is 16 # features/steps/x.py:3
1 feature passed, 0 failed, 0 skipped
8 scenarios passed, 0 failed, 0 skipped
8 steps passed, 0 failed, 0 skipped, 0 undefined
Took 0m0.005s
This relies on having an Examples table with the correct shape as the final table, in my example, with two rows. I also don't fuss with creating new behave.model.Row objects, I just copy the one from the table and update it. For extra ugliness, if you're using a file, you can put the file name in the Examples table.
Got here looking for something else, but since I've been in similar situation with Cucumber before, maybe someone will also end up at this question, looking for a possible solution. My approach to this problem is to use BDD variables that I can later handle at runtime in my step_definitions. In my python code I can check what is the value of the Gherkin variable and map it to what's needed.
For this example:
Scenario Outline: Use Blender with <thing>
Given I put "<thing>" in a blender
When I switch the blender on
Then it should transform into "<other thing>"
Examples: Amphibians
| thing | other thing |
| Red Tree Frog | mush |
| iPhone | data.iPhone.secret_key | # can use .yaml syntax here as well
Would translate to such step_def code:
#given('I put "{thing}" in a blender')
def step_then_should_transform_into(context, other_thing):
if other_thing == BddVariablesEnum.SECRET_KEY:
basic_actions.load_secrets(context, key)
So all you have to do is to have well defined DSL layer.
Regarding the issue of using SSN numbers in testing, I'd just use fake SSNs and not worry that I'm leaking people's private information.
Ok, but what about the larger issue? You want to use a scenario outline with examples that you cannot put in your feature file. Whenever I've run into this problem what I did was to give a description of the data I need and let the step implementation either create the actual data set used for testing or fetch the data set from an existing test database.
Scenario Outline: Accessing the admin interface
Given a user who <status> an admin has logged in
Then the user <ability> see the admin interface
Examples: Users
| status | ability |
| is | can |
| is not | cannot |
There's no need to show any details about the user in the feature file. The step implementation is responsible for either creating or fetching the appropriate type of user depending on the value of status.