Migrating from Cubeplan

Even though the way of creating and sharing apps is the same, Cubeplan models can not be run on Pyplan. Neither dashboards generated with Cubeplan can be exported and used in Pyplan. The origin of that lack of compatibility is that they run different languages underneath to calculate computations. It is an excellent exercise for learning Pyplan translating an old Cubeplan model.

Similarities

Pyplan organizes Python code through influence diagrams the same as Cubeplan. Nodes have a Title and an Identifier to call them from other nodes. You can also point and click a node to incorporate its Id when creating a formula. Navigating through the model works exactly the same, following inputs or outputs or by exploring the influence diagram. Nodes also include a documentation as in Cubeplan. Pyplan also includes “intelligent” matrix operations by adapting the usage of the Xarray python library

Differences

One of the most important conceptual difference of Pyplan is that a node can contain different type of objects (i.e. Pandas, Numpy, Xarray) This fact is one of the most important advantage of embracing Python language since you will be able to use specific purposes libraries that use different type of data structures and objects. Pyplan natively interprets the Pandas, Numpy and Xarray objects visualising its data as table or graphs with a single click. Libraries can be installed and used with any model. The model file registers libraries dependency to require instalment when shared with other users.

Functions equivalence list

Check the examples available in Pyplan Library folder to find how to do with Pyplan basic Cubeplan operations .

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The following table contains Cubeplan basic function list and its equivalent in Pyplan.

Model Title in Cubeplan Pyplan Library Node with example PPL,XA or Pandas
Selecting Data from Array change index change_index change_index_ex /change_index_by_pos_ex PPL
Selecting Data from Array subscript subscript subscript_ex PPL
Selecting Data from Array subscript looking up lookup lookup_ex PPL
Selecting Data from Array subset subset subset_ex PPL
Selecting Data from Array slice slice_dataarray slice_dataarray_ex PPL
Basic Math and Missing Values size len() len_size_ex XA
Basic Math and Missing Values size dataarray.size len_size_ex XA
Basic Math and Missing Values undef_filter fill_all fillall_ex PPL
Basic Math and Missing Values nvx_is_null/nvx_is_nan dataarray.fillna() missing_values_methodsfillna XA
Basic Math and Missing Values undef_filter fill_inf fillinf_ex PPL
Basic Math and Missing Values min dataarray.min(teams.name) min_along_indexes XA
Basic Math and Missing Values max dataarray.max(teams.name) max_along_indexes XA
Basic Math and Missing Values if then else xr.where xr_where_exa XA
Basic Math and Missing Values min_ xr.ufuncs.minimum mini_maxi_among_2_dataarrays XA
Basic Math and Missing Values max_ xr.ufuncs.maximum pp_maximum XA
Aggregation and Rolling window operations sum dataarray.sum(teams.name) sum_ex XA
Aggregation and Rolling window operations cumulate dataarray.cumsum(teams.name) cumsum_ex XA
Aggregation and Rolling window operations cumproduct dataarray.cumprod(teams.name) cumprod_ex XA
Aggregation and Rolling window operations backward looking sum dataarray.rolling back_look_sum_example XA
Aggregation and Rolling window operations filtered aggregate aggregate aggregate_ex PPL
Working with Indexes concat concat_index concatindex_ex PPL
Working with Indexes new add_periods_ex add_periods_ex PPL
Working with Indexes new apply_fn apply_fn_ex PPL
Working with Indexes find in text find find_ex PPL
Working with Indexes splittext split_text splittext_ex PPL
Dynamic NPV IRR and Linear Depreciation npv npv npv_ex PPL
Dynamic NPV IRR and Linear Depreciation irr irr irr_ex PPL
Dynamic NPV IRR and Linear Depreciation dynamic dynamic dynamic_ex PPL
Dynamic NPV IRR and Linear Depreciation new create_time time PPL
Dynamic NPV IRR and Linear Depreciation linear depreciation linear_depreciation linear_depreciation_ex PPL
Dynamic NPV IRR and Linear Depreciation or ¦ NA XA
Reading Data from Excel nvx_da_source_cone excel_connection excel_connection_ex PPL
Reading Data from Excel nvx_read_index index_from_excel reading_example_index PPL
Reading Data from Excel nvx_read_table dataarray_from_excel dataarray_from_excel_ex PPL
Reading Data from Excel nvx_read_table pandas_from_excel pandas_from_excel_ex PPL
Interacting with Pandas new pd.read_csv pd_read_csv Pandas
Interacting with Pandas new index_from_pandas orders_date PPL
Interacting with Pandas new reading from pandas see example sales_data_by_orders_date XA

Financial Planning Library for Cubeplan users

If you are used to create financial planning models in Cubeplan then these other functions will be useful for you.

Contact your former Cubeplan distributor to ask for the module with these specific functions.

Module Title in Cubeplan Pyplan Library
Financial Planning sea_to_time sea_to_time
Financial Planning annualise annualise
Financial Planning month month
Financial Planning evatime evatime
Financial Planning new days_to_time
Financial Planning new time_to_days
Financial Planning evayears evayears
Financial Planning years to time years_to_time
Financial Planning cascade_volume cascade_volume
Financial Planning band_allocation band_allocation
Financial Planning new dispatch
Financial Planning new nvx_read_multi_sheets
Financial Planning new nvx_smart_pandas
Financial Planning new nvx_round
Financial Planning new nvx_safe_int_div

Use these functions as you used them in Cubeplan.