I've been using Orocommerce for a while now, and I'm facing a problem that could be very problematic in the long term.
I extensively use the product attributes system in Orocommerce for almost all of my products (for things like color, material type, etc). And for internal purposes, they are all enum types.
Since I handle a lot of different attributes and attribute values, I use Orocommerce's import system to import CSV files containing my attributes and their values (with the enum.enum_options.X.label field, where X is replaced with a number).
Through testing, I discovered that this system is rather destructive: The CSV files absolutely need to contain all the enum values that already exist on Orocommerce (plus the ones that I need to add) before importing this file, or else any attribute value that already exists on Orocommerce, but isn't present on the imported file will get permanently deleted, resetting all products that were using these attribute values back to "N/A".
For example, if I have a "Color" attribute, and already have Green, Blue, and White, but want to add the color Red, I will have to import a CSV that contains Green, Blue, White and Red. If I forget to add White to the file and import it, the attribute will get removed from Orocommerce, no questions asked.
This is much, much too destructive because it is possible, through importing a single attributes CSV, to destroy all products on the platform. There are absolutely no guardrails that makes the import fail if products already use this attribute value.
Is there a setting that makes it so that the import system only adds new attribute values, and doesn't delete any, or make it so that the import fails if attribute values that are already used on products is about to get deleted?
Any help on this would be appreciated.
You can always override the default import strategy or a whole import job to match your requirements. For more details, see the import architecture guide.
Related
I am a relatively new user of Tabulator so please forgive me if I am asking anything that, perhaps, should be obvious.
I have a Tabulator report that I am able to print and create as a PDF, but the report's formatting (as shown on the screen) is not used in either output.
For printing I have used printAsHtml and printStyled=true, but this doesn't produce a printout that matches what is on the screen. I have formatted number fields (with comma separators) and these are showing correctly, but the number columns should be right-aligned but all of the columns appear as left-aligned.
I am also using Tree View where the tree rows are coloured differently to the main table, but when I print the report with a tree open it colours the whole table with the tree colours and not just the tree.
For the PDF none of the Tabulator formatting is being used. I've looked for anything similar to the printStyled option, but I can't see anything. I've also looked at the autoTable option, but I am struggling to find what to use.
I want to format the print and PDF outputs so that they look as close to the screen representation as possible.
Is there anywhere I could look that would provide examples of how to achieve the above? The Tabulator documentation is very good, but the provided examples don't appear to explain what I am trying to do.
Perhaps there are there CSS classes that I am missing or even mis-using? I have tried including .tabulator-print-table in my CSS, but I am probably not using it correctly. I also couldn't find anything equivalent for producing PDFs. Some examples would help immensely.
Thank you in advance for any advice or assistance.
Formatting is deliberately not included in these, below i will outline why:
Downloaders
Downloaded files do not contain formatted data, only the raw data, this is because a lot of the formatters create visual elements (progress bar, star formatter etc) that cannot be replicated sensibly in downloaded files.
If you want to change the format of data in the download you will need to use an accessor, the accessorDownload option is the one you want to use in this case. The accessors transform the data as it is leaving the table.
For instance we could create an accessor that prepended "Mr " to the front of every name in a column:
var mrAccessor= function(value, data, type, params, column, row){
return "Mr " + value;
}
Assign it to a columns definition:
{title:"Name", field:"name", accessorDownload:mrAccessor}
Printing
Printing also does not include the formatters, this is because when you print a Tabulator table, the whole table is actually rebuilt as a standard HTML table, which allows the printer to work out how to layout everything across multiple pages with column headers etc. The downside of this is that it is only loosely styled like a Tabulator and so formatted contents generated inside Tabulator cells will likely break when added to a normal td element.
For this reason there is also a accessorPrint option that works in the same way as the download accessor but for printing.
If you want to use the same accessor for both occasions, you can assign the function once to the accessor option and it will be applied in both instances.
Checkout the Accessor Documentation for full details.
I'm interested in using the Visions library to automate the process of identifying certain types of security (stock) identifiers. The documentation mentions that it could be used in such a way for ISBN codes but I'm looking for a more concrete example of how to do it. I think the process would be pretty much identical for the fields I'm thinking of as they all have check digits (ISIN, SEDOL, CUSIP).
My general idea is that I would create custom types for the different identifier types and could use those types to
Take a dataframe where the types are unknown and identify columns matching the types (even if it's not a 100% match)
Validate the types on a dataframe where the intended type is known
Great question and use-case! Unfortunately, the documentation on making new types probably needs a little love right now as there were API breaking changes with the 0.7.0 release. Both the previous link and this post from August, 2020 should cover the conceptual idea of type creation in greater detail. If any of those examples break then mea culpa and our apologies, we switched to a dispatch based implementation to support different backends (pandas, numpy, dask, spark, etc...) for each type. You shouldn't have to worry about that for now but if you're interested you can find the default type definitions here with their backends here.
Building an ISBN Type
We need to make two basic decisions when defining a type:
What defines the type
What other types are our new type related to?
For the ISBN use-case O'Reilly provides a validation regex to match ISBN-10 and ISBN-13 codes. So,
What defines a type?
We want every element in the sequence to be a string which matches a corresponding ISBN-10 or ISBN-13 regex
What other types are our new type related to?
Since ISBN's are themselves strings we can use the default String type provided by visions.
Type Definition
from typing import Sequence
import pandas as pd
from visions.relations import IdentityRelation, TypeRelation
from visions.types.string import String
from visions.types.type import VisionsBaseType
isbn_regex = "^(?:ISBN(?:-1[03])?:?●)?(?=[0-9X]{10}$|(?=(?:[0-9]+[-●]){3})[-●0-9X]{13}$|97[89][0-9]{10}$|(?=(?:[0-9]+[-●]){4})[-●0-9]{17}$)(?:97[89][-●]?)?[0-9]{1,5}[-●]?[0-9]+[-●]?[0-9]+[-●]?[0-9X]$"
class ISBN(VisionsBaseType):
#staticmethod
def get_relations() -> Sequence[TypeRelation]:
relations = [
IdentityRelation(String),
]
return relations
#staticmethod
def contains_op(series: pd.Series, state: dict) -> bool:
return series.str.contains(isbn_regex).all()
Looking at this closely there are three things to take note of.
The new type inherits from VisionsBaseType
We had to define a get_relations method which is how we relate a new type to others we might want to use in a typeset. In this case, I've used an IdentityRelation to String which means ISBNs are subsets of String. We can also use InferenceRelation's when we want to support relations which change the underlying data (say converting the string '4.2' to the float 4.2).
A contains_op this is our definition of the type. In this case, we are applying a regex string to every element in the input and verifying it matched the regex provided by O'Reilly.
Extensions
In theory ISBNs can be encoded in what looks like a 10 or 13 digit integer as well - to work with those you might want to create an InferenceRelation between Integer and ISBN. A simple implementation would involve coercing Integers to string and applying the above regex.
I've created a model with Uppaal in which several integer variables change over the course of time. Now I would like to save the values of the variables during the modelling process somewhere (best in xml or a text file). In the Uppaal documentation (https://www.it.uu.se/research/group/darts/uppaal/documentation.shtml) I found the method in point 13 (How do I export and interpret the traces from Uppaal?) and tried the Java API way already, in the hope that it can output the variables as well as the traces. Unfortunately this method seems to be limited to traces. Does anyone know a method to save the variable values from Uppaal?
Hopeful greetings,
Josi
Solution from the comments.
to export the variable value tractory over time, one may use SMC query in the verifier.
For example:
Typeset the following query: simulate 1 [<=300] { Gate.len }
Click Check
Right-click on the query, and from the popup menu choose Simulations (1)
Observe a new window popup with a plot
Right-click on the plot and choose Export Comma Separated Values
Follow the save file dialog and observe the resulting file to contain time and value sequence.
Note that SMC assumes that all channels are broadcast and there are no deadlocks.
Sometimes it is useful to assign a 'tag', which can be a simple string, to a matplotlib artist in order to later find it easily.
If we imagine a scenario where say plt.Line2D had a property called tag which can be retrieved using plt.Line2D.get_tag() it would be very easy to find it later in a complicated plot.
The only thing I can find that looks remotely similar is the group ID: for example line.set_gid() and line.get_gid(). I haven't found any good documentation on this. The only reference is this. Is this meant for such use as described above? Is it reserved for other operations in matplotlib?
This would be very useful for grouping different artists and then performing operations on them later, for example:
for line in ax.get_lines():
if line.get_tag() == 'group A'
line.set_color('red')
# or whatever other operation
Does such a thing exist?
You can use the gid for such purposes. The only side-effect is that those names will appear in a saved svg file as the gid tag.
Alternatively you can assign any attribute to a python object.
line, = plt.plot(...)
line.myid = "group A"
just make sure not to use any existing attribute in such case.
I am wondering, is there an official way to specify a different widget editor for the same column in a DataGrid (different rows)?
I found dojox.grid.cells._MultipleEditor, but it is quite complicated and not officially supported.
This is for creating things like a property sheet with DataGrid.
EDIT: People seem to suggest using dgrid. However, I am not sure if dgrid has this feature. Also, unfortunately, ... drum roll... horror music... I must support IE6.
Well, there seems to be a way to do it. Doesn't seem to show much negative side effects (so far)...
Create one column for each value type, one after the other.
Tag each column with a CSS class to indicate its value type (via classes). For example: classes="multivalue int"
For each column, tag it with the correct editor widget and the appropriate constraint & options.
Put styles on each row (with onStyleRow) that correspond to each type. For example, add a type-int class to the row that has an int type.
Put in a CSS style that initially hides all the multi-valued cells:
.dojoxGridCell.multivalue { display:none; }
Un-hide all the cells with the correct type:
.dojoxGridRow.type-int .dojoxGridCell.multivalue.int
{
display:table-cell;
*display:block; /* For IE6/7 */
}
For this to work, obviously, each row must match exactly one column.
Obviously, you must set all these fields to the same property name. DataGrid allows you to do that.
Put display:none (via CSS etc.) on all the header cells of multi-valued columns except the first one. Otherwise, you'll end up with too many header cells.