From 3b001d3991cbc33bf9cc9c909ccfe47f8d3690d3 Mon Sep 17 00:00:00 2001 From: Feda Curic Date: Wed, 27 Nov 2024 15:06:01 +0100 Subject: [PATCH] Remove fmudesign Moved to semeio so newer scipy version that supports latin hypercube sampling can be used. The other tools in fmu-tools run in RMS which has its own Python environment that supports older version of scipy only. --- README.md | 2 +- pyproject.toml | 1 - src/fmu/tools/__init__.py | 17 - src/fmu/tools/sensitivities/__init__.py | 19 - src/fmu/tools/sensitivities/_designsummary.py | 127 - src/fmu/tools/sensitivities/_excel2dict.py | 712 -- .../tools/sensitivities/_tornado_onebyone.py | 288 - src/fmu/tools/sensitivities/create_design.py | 914 -- .../sensitivities/design_distributions.py | 623 -- .../tools/sensitivities/fmudesignrunner.py | 98 - tests/sensitivities/data/README.md | 4 - .../data/config/correlations.xlsx | Bin 4723 -> 0 bytes .../data/config/design_input_background.xlsx | Bin 31005 -> 0 bytes .../design_input_background_extseeds.xlsx | Bin 29812 -> 0 bytes .../design_input_background_no_seed.xlsx | Bin 29815 -> 0 bytes .../config/design_input_default_no_seed.xlsx | Bin 21883 -> 0 bytes .../data/config/design_input_example1.xlsx | Bin 16773 -> 0 bytes .../data/config/design_input_example2.xlsx | Bin 24954 -> 0 bytes .../design_input_example_velocities.xlsx | Bin 21647 -> 0 bytes .../config/design_input_mc_with_correls.xlsx | Bin 33233 -> 0 bytes .../data/config/design_input_onebyone.xlsx | Bin 22195 -> 0 bytes .../config/design_input_singlereference.xlsx | Bin 21878 -> 0 bytes ...design_input_singlereference_and_seed.xlsx | Bin 14950 -> 0 bytes tests/sensitivities/data/config/doe1.xlsx | Bin 4985 -> 0 bytes tests/sensitivities/data/config/seeds.csv | 100 - tests/sensitivities/data/config/seeds.txt | 5 - tests/sensitivities/data/config/seeds.xlsx | Bin 5208 -> 0 bytes .../data/distributions/design.csv | 111 - .../data/distributions/design.xlsx | Bin 14391 -> 0 bytes .../data/distributions/designsummary.csv | 8 - .../data/results/geovolumes_collected.csv | 5501 ---------- .../design_output_mc_with_correls.json | 9021 ----------------- tests/sensitivities/test_calc_tornado.py | 95 - tests/sensitivities/test_create_design.py | 344 - .../test_design_distributions.py | 280 - tests/sensitivities/test_designmatrix.py | 78 - tests/sensitivities/test_excel2dict.py | 231 - tests/sensitivities/test_use_cases.py | 94 - 38 files changed, 1 insertion(+), 18672 deletions(-) delete mode 100755 src/fmu/tools/sensitivities/__init__.py delete mode 100755 src/fmu/tools/sensitivities/_designsummary.py delete mode 100644 src/fmu/tools/sensitivities/_excel2dict.py delete mode 100755 src/fmu/tools/sensitivities/_tornado_onebyone.py delete mode 100644 src/fmu/tools/sensitivities/create_design.py delete mode 100644 src/fmu/tools/sensitivities/design_distributions.py delete mode 100644 src/fmu/tools/sensitivities/fmudesignrunner.py delete mode 100644 tests/sensitivities/data/README.md delete mode 100644 tests/sensitivities/data/config/correlations.xlsx delete mode 100755 tests/sensitivities/data/config/design_input_background.xlsx delete mode 100644 tests/sensitivities/data/config/design_input_background_extseeds.xlsx delete mode 100644 tests/sensitivities/data/config/design_input_background_no_seed.xlsx delete mode 100644 tests/sensitivities/data/config/design_input_default_no_seed.xlsx delete mode 100644 tests/sensitivities/data/config/design_input_example1.xlsx delete mode 100644 tests/sensitivities/data/config/design_input_example2.xlsx delete mode 100644 tests/sensitivities/data/config/design_input_example_velocities.xlsx delete mode 100755 tests/sensitivities/data/config/design_input_mc_with_correls.xlsx delete mode 100644 tests/sensitivities/data/config/design_input_onebyone.xlsx delete mode 100644 tests/sensitivities/data/config/design_input_singlereference.xlsx delete mode 100644 tests/sensitivities/data/config/design_input_singlereference_and_seed.xlsx delete mode 100644 tests/sensitivities/data/config/doe1.xlsx delete mode 100644 tests/sensitivities/data/config/seeds.csv delete mode 100644 tests/sensitivities/data/config/seeds.txt delete mode 100644 tests/sensitivities/data/config/seeds.xlsx delete mode 100644 tests/sensitivities/data/distributions/design.csv delete mode 100755 tests/sensitivities/data/distributions/design.xlsx delete mode 100644 tests/sensitivities/data/distributions/designsummary.csv delete mode 100755 tests/sensitivities/data/results/geovolumes_collected.csv delete mode 100644 tests/sensitivities/snapshots/test_create_design/test_generate_full_mc_snapshot/design_output_mc_with_correls.json delete mode 100755 tests/sensitivities/test_calc_tornado.py delete mode 100755 tests/sensitivities/test_create_design.py delete mode 100644 tests/sensitivities/test_design_distributions.py delete mode 100644 tests/sensitivities/test_designmatrix.py delete mode 100644 tests/sensitivities/test_excel2dict.py delete mode 100644 tests/sensitivities/test_use_cases.py diff --git a/README.md b/README.md index ac18be24..89f2bba4 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ **fmu-tools** is a library containing tools for pre- and post-processing in a Fast Model Update (FMU) context. These tools are used for automatic QC, -setting up and analyzing sensitivites, and visualization. +and visualization. --- diff --git a/pyproject.toml b/pyproject.toml index c4747eca..5128e657 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -76,7 +76,6 @@ Documentation = "https://equinor.github.io/fmu-tools/" [project.scripts] ensemble_well_props = "fmu.tools.ensembles.ensemble_well_props:main" -fmudesign = "fmu.tools.sensitivities.fmudesignrunner:main" rename_rms_scripts = "fmu.tools.rms.rename_rms_scripts:main" rmsvolumetrics2csv = "fmu.tools.rms.volumetrics:rmsvolumetrics2csv_main" diff --git a/src/fmu/tools/__init__.py b/src/fmu/tools/__init__.py index 01ebff36..d0fcfb88 100644 --- a/src/fmu/tools/__init__.py +++ b/src/fmu/tools/__init__.py @@ -36,23 +36,6 @@ "sample_attributes_for_sim2seis", ] -if not ROXAR: - from fmu.tools.rms import volumetrics # noqa - from fmu.tools.sensitivities import DesignMatrix # noqa - from fmu.tools.sensitivities import calc_tornadoinput # noqa - from fmu.tools.sensitivities import excel2dict_design # noqa - from fmu.tools.sensitivities import summarize_design # noqa - - __all__.extend( - [ - "volumetrics", - "DesignMatrix", - "calc_tornadoinput", - "excel2dict_design", - "summarize_design", - ] - ) - try: from fmu.tools.version import version diff --git a/src/fmu/tools/sensitivities/__init__.py b/src/fmu/tools/sensitivities/__init__.py deleted file mode 100755 index 046b7fdd..00000000 --- a/src/fmu/tools/sensitivities/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -""" -Used for pre and processing of ert sensitivities, such as -setting up design matrix to run single sensitivities with ERT and -post processing of results to create input for a tornado plot. -Output of this module can be used in custom standalone applications. -""" - -from fmu.tools.sensitivities._designsummary import summarize_design -from fmu.tools.sensitivities._excel2dict import excel2dict_design, inputdict_to_yaml -from fmu.tools.sensitivities._tornado_onebyone import calc_tornadoinput -from fmu.tools.sensitivities.create_design import DesignMatrix - -__all__ = [ - "summarize_design", - "calc_tornadoinput", - "DesignMatrix", - "excel2dict_design", - "inputdict_to_yaml", -] diff --git a/src/fmu/tools/sensitivities/_designsummary.py b/src/fmu/tools/sensitivities/_designsummary.py deleted file mode 100755 index f26caaec..00000000 --- a/src/fmu/tools/sensitivities/_designsummary.py +++ /dev/null @@ -1,127 +0,0 @@ -"""Module for summarizing design set up for one by one sensitivities""" - -import pandas as pd - - -def _get_sensitivity_type(senscase: str) -> str: - """Determine sensitivity type based on the case name""" - sensitivity_types = {"p10_p90": "mc", "ref": "ref", "skip": "skip"} - return sensitivity_types.get(senscase.lower(), "scalar") - - -def summarize_design(filename, sheetname="DesignSheet01"): - """ - Summarizes the design set up for one by one sensitivities - specified in a design matrix on standard fmu format. - - Args: - filename (str): Path to excel or csv file containting designmatrix - for one by one sensitivities on standard FMU format. - sheetname (str): Name of sheet in excel workbook which - contains the designmatrix (only for excel input). Defaults to - 'DesignSheet01'. - - Returns: - pd.DataFrame: Summary of sensitivities, - corresponding realisation numbers, - senstype('mc' or 'scalar') - and senscase (name of high and low cases). - Each row represents one sensitivity - with 1-2 cases (low/high). - Column names are ['sensno', 'sensname', - 'senstype', 'casename1', 'startreal1', 'endreal1', - 'casename2', 'startreal2', 'endreal2'] - - Example:: - >>> from fmu.tools.sensitivities import summarize_design - >>> designname = 'design_filename.xlsx' - >>> designsheet = 'DesignSheet01' - >>> designtable = summarize_design(designname, designsheet) - - """ - - # Read design matrix - if str(filename).endswith(".xlsx"): - dgn = pd.read_excel(filename, sheetname, engine="openpyxl") - # Drop empty rows or columns that have been read in - # due to having background colour/formatting - dgn.dropna(axis=0, how="all", inplace=True) - dgn = dgn.loc[:, ~dgn.columns.str.contains("^Unnamed")] - elif str(filename).endswith(".csv"): - dgn = pd.read_csv(filename) - else: - raise ValueError( - "Design matrix must be on Excel or csv format" - " and filename must end with .xlsx or .csv" - ) - - # Initialize results DataFrame with same columns - designsummary = pd.DataFrame( - columns=[ - "sensno", - "sensname", - "senstype", - "casename1", - "startreal1", - "endreal1", - "casename2", - "startreal2", - "endreal2", - ] - ) - - # Get unique sensitivity names in order of appearance - sensnames = dgn["SENSNAME"].unique() - - sensno = 0 - for sensname in sensnames: - sens_group = dgn[dgn["SENSNAME"] == sensname].copy() - # Get cases in order of appearance - cases = sens_group.drop_duplicates("SENSCASE")[["SENSCASE"]].values.flatten() - - # Skip if first case type is 'skip' - # The "skip" option was added when creating tornado plots - # with calc_tornadoinput. - # It allowed manual exclusion of sensitivities from tornado plots by changing - # SENSCASE to "skip" in the design matrix after running the experiment. - # This would exclude the sensitivity from both the - # *designsummary table and the tornado plot. - # calc_tornadoinput is deprecated so this can be removed once - # calc_tornadoinput is removed. - senstype = _get_sensitivity_type(cases[0]) - if senstype == "skip": - continue - - # First case - case1_data = sens_group[sens_group["SENSCASE"] == cases[0]] - casename1 = cases[0] - startreal1 = case1_data["REAL"].min() - endreal1 = case1_data["REAL"].max() - - # Handle second case if it exists - if len(cases) > 1: - case2_data = sens_group[sens_group["SENSCASE"] == cases[1]] - casename2 = cases[1] - startreal2 = case2_data["REAL"].min() - endreal2 = case2_data["REAL"].max() - else: - casename2 = None - startreal2 = None - endreal2 = None - - # Add row to results - designsummary.loc[sensno] = [ - sensno, - sensname, - senstype, - casename1, - startreal1, - endreal1, - casename2, - startreal2, - endreal2, - ] - - sensno += 1 - - return designsummary diff --git a/src/fmu/tools/sensitivities/_excel2dict.py b/src/fmu/tools/sensitivities/_excel2dict.py deleted file mode 100644 index 1cfdb214..00000000 --- a/src/fmu/tools/sensitivities/_excel2dict.py +++ /dev/null @@ -1,712 +0,0 @@ -"""Module for reading excel file with input for generation of -a design matrix and converting to an OrderedDict that can be -read by fmu.tools.DesignMatrix.generate -""" - -from collections import OrderedDict - -import numpy as np -import openpyxl -import pandas as pd -import yaml - - -def excel2dict_design(input_filename, sheetnames=None): - """Read excel file with input to design setup - Currently only specification of - onebyone design is implemented - - Args: - input_filename (str): Name of excel input file - sheetnames (dict): Dictionary of worksheet names to load - information from. Supported keys: general_input, defaultvalues, - and designinput. - - Returns: - OrderedDict on format for DesignMatrix.generate - """ - if not sheetnames or "general_input" not in sheetnames: - gen_input_sheet = _find_geninput_sheetname(input_filename) - else: - gen_input_sheet = sheetnames["general_input"] - - generalinput = pd.read_excel( - input_filename, gen_input_sheet, header=None, index_col=0, engine="openpyxl" - ) - generalinput.dropna(axis=0, how="all", inplace=True) - generalinput.dropna(axis=1, how="all", inplace=True) - - if str(generalinput[1]["designtype"]) == "onebyone": - returndict = _excel2dict_onebyone(input_filename, sheetnames) - elif "seeds" in generalinput[1]: - raise ValueError( - "The 'seeds' parameter has been deprecated and is no longer supported. " - "Use 'rms_seeds' instead" - ) - else: - raise ValueError( - "Generation of DesignMatrix only " - "implemented for type onebyone " - "In general_input designtype was " - "set to {}".format(str(generalinput[1]["designtype"])) - ) - return returndict - - -def inputdict_to_yaml(inputdict, filename): - """Write inputdict to yaml format - - Args: - inputdict (OrderedDict) - filename (str): path for where to write file - """ - with open(filename, "w", encoding="utf-8") as stream: - yaml.dump(inputdict, stream) - - -def _find_geninput_sheetname(input_filename): - """Finding general input sheet, allowing for name - variations.""" - xlsx = openpyxl.load_workbook(input_filename, read_only=True, keep_links=False) - sheets = xlsx.sheetnames - general_input_sheet = [] - for sheet in sheets: - if sheet in [ - "general_input", - "generalinput", - "GeneralInput", - "Generalinput", - "General_Input", - "General_input", - ]: - general_input_sheet.append(sheet) - - if len(general_input_sheet) > 1: - raise ValueError( - "More than one sheet with general input. Sheetnames are {} ".format( - general_input_sheet - ) - ) - - if not general_input_sheet: - raise ValueError( - f"No general_input sheet provided in Excel file {input_filename} " - ) - - return general_input_sheet[0] - - -def _find_onebyone_defaults_sheet(input_filename): - """Finds correct sheet name for default values to use when parsing - excel file. - - Returns: - string, name of a sheet in the excel file - """ - xlsx = openpyxl.load_workbook(input_filename, read_only=True, keep_links=False) - sheets = xlsx.sheetnames - - default_values_sheet = [] - - for sheet in sheets: - if sheet in [ - "default_values", - "defaultvalues", - "DefaultValues", - "Defaultvalues", - "Default_Values", - "Default_values", - ]: - default_values_sheet.append(sheet) - if len(default_values_sheet) > 1: - raise ValueError( - "More than one sheet with default values. Sheetnames are {} ".format( - default_values_sheet - ) - ) - - if len(default_values_sheet) == []: - raise ValueError( - f"No defaultvalues sheet provided in Excel file {input_filename} " - ) - - return default_values_sheet[0] - - -def _find_onebyone_input_sheet(input_filename): - """Finds correct sheet name for input to use when parsing excel file. - - Returns: - string, name of a sheet in the excel file - """ - xlsx = openpyxl.load_workbook(input_filename, read_only=True, keep_links=False) - sheets = xlsx.sheetnames - - design_input_sheet = [] - - for sheet in sheets: - if sheet in [ - "design_input", - "designinput", - "DesignInput", - "Designinput", - "Design_Input", - "Design_input", - ]: - design_input_sheet.append(sheet) - if len(design_input_sheet) > 1: - raise ValueError( - f"More than one sheet with design inputSheetnames are {design_input_sheet} " - ) - - if not design_input_sheet: - raise ValueError( - f"No designinput sheet provided in Excel file {input_filename} " - ) - return design_input_sheet[0] - - -def _check_designinput(dsgn_input): - """Checks for valid input in designinput sheet""" - - # Check for duplicate sensnames - sensitivity_names = [] - for row in dsgn_input.itertuples(): - if _has_value(row.sensname): - if row.sensname in sensitivity_names: - raise ValueError( - "sensname '{}' was found on more than one row in designinput " - "sheet. Two sensitivities can not share the same sensname. " - "Please correct this and rerun".format(row.sensname) - ) - sensitivity_names.append(row.sensname) - - -def _check_for_mixed_sensitivities(sens_name, sens_group): - """Checks for valid input in designinput sheet. A sensitivity cannot contain - two different sensitivity types""" - - types = sens_group.groupby("type", sort=False) - if len(types) > 1: - raise ValueError( - "The sensitivity with sensname '{}' in designinput sheet contains more " - "than one sensitivity type. For each sensname all parameters must be " - "specified using the same type (seed, scenario, dist, ref, background, " - "extern)".format(sens_name) - ) - - -def _excel2dict_onebyone(input_filename, sheetnames=None): - """Reads spesification for onebyone design - - Args: - input_filename(path): path to excel workbook - sheetnames (dict): Dictionary of worksheet names to load - information from. Supported keys: general_input, defaultvalues, - and designinput. - - Returns: - OrderedDict on format for DesignMatrix.generate - """ - - seedname = "RMS_SEED" - inputdict = OrderedDict() - - if not sheetnames or "general_input" not in sheetnames: - gen_input_sheet = _find_geninput_sheetname(input_filename) - else: - gen_input_sheet = sheetnames["general_input"] - - if not sheetnames or "designinput" not in sheetnames: - design_inp_sheet = _find_onebyone_input_sheet(input_filename) - else: - design_inp_sheet = sheetnames["designinput"] - - if not sheetnames or "defaultvalues" not in sheetnames: - default_val_sheet = _find_onebyone_defaults_sheet(input_filename) - else: - default_val_sheet = sheetnames["defaultvalues"] - - # Read general input - generalinput = pd.read_excel( - input_filename, gen_input_sheet, header=None, index_col=0, engine="openpyxl" - ) - generalinput.dropna(axis=0, how="all", inplace=True) - generalinput.dropna(axis=1, how="all", inplace=True) - - inputdict["designtype"] = generalinput[1]["designtype"] - - if "rms_seeds" in generalinput[1]: - if str(generalinput[1]["rms_seeds"]) == "None": - inputdict["seeds"] = None - else: - inputdict["seeds"] = generalinput[1]["rms_seeds"] - else: - inputdict["seeds"] = None - - if "repeats" not in generalinput[1]: - raise LookupError('"repeats" must be specified in general_input sheet') - - inputdict["repeats"] = generalinput[1]["repeats"] - - if "distribution_seed" in generalinput[1]: - if str(generalinput[1]["distribution_seed"]) == "None": - inputdict["distribution_seed"] = None - else: - inputdict["distribution_seed"] = generalinput[1]["distribution_seed"] - else: - inputdict["distribution_seed"] = None - - # Read background - if "background" in generalinput.index: - inputdict["background"] = OrderedDict() - if generalinput[1]["background"].endswith("csv") or generalinput[1][ - "background" - ].endswith("xlsx"): - inputdict["background"]["extern"] = generalinput[1]["background"] - elif str(generalinput[1]["background"]) == "None": - inputdict["background"] = None - else: - inputdict["background"] = _read_background( - input_filename, generalinput[1]["background"] - ) - else: - inputdict["background"] = None - - # Read default values - inputdict["defaultvalues"] = _read_defaultvalues(input_filename, default_val_sheet) - - # Read input for sensitivities - inputdict["sensitivities"] = OrderedDict() - designinput = pd.read_excel(input_filename, design_inp_sheet, engine="openpyxl") - designinput.dropna(axis=0, how="all", inplace=True) - designinput = designinput.loc[ - :, ~designinput.columns.astype(str).str.contains("^Unnamed") - ] - - # First column with parameter names should have spaces stripped, - # but we need to preserve NaNs: - not_nan_sensnames = ~designinput["sensname"].isnull() - designinput.loc[not_nan_sensnames, "sensname"] = ( - designinput.loc[not_nan_sensnames, "sensname"].astype(str).str.strip() - ) - - _check_designinput(designinput) - - designinput["sensname"] = designinput["sensname"].ffill() - - # Read dependencies - if "dependencies" in designinput: - inputdict["dependencies"] = OrderedDict() - for row in designinput.itertuples(): - if _has_value(row.dependencies): - inputdict["dependencies"][row.param_name] = _read_dependencies( - input_filename, row.dependencies, row.param_name - ) - - # Read decimals - if "decimals" in designinput: - inputdict["decimals"] = OrderedDict() - for row in designinput.itertuples(): - if _has_value(row.decimals) and _is_int(row.decimals): - inputdict["decimals"][row.param_name] = int(row.decimals) - - grouped = designinput.groupby("sensname", sort=False) - - # Read each sensitivity - for sensname, group in grouped: - _check_for_mixed_sensitivities(sensname, group) - - sensdict = OrderedDict() - - if group["type"].iloc[0] == "ref": - sensdict["senstype"] = "ref" - - elif group["type"].iloc[0] == "background": - sensdict["senstype"] = "background" - - elif group["type"].iloc[0] == "seed": - sensdict["seedname"] = seedname - sensdict["senstype"] = "seed" - if _has_value(group["param_name"].iloc[0]): - sensdict["parameters"] = _read_constants(group) - else: - sensdict["parameters"] = None - - elif group["type"].iloc[0] == "scenario": - sensdict = _read_scenario_sensitivity(group) - sensdict["senstype"] = "scenario" - - elif group["type"].iloc[0] == "dist": - sensdict["senstype"] = "dist" - sensdict["parameters"] = _read_dist_sensitivity(group) - sensdict["correlations"] = None - if "corr_sheet" in group: - sensdict["correlations"] = _read_correlations(group, input_filename) - - elif group["type"].iloc[0] == "extern": - sensdict["extern_file"] = str(group["extern_file"].iloc[0]) - sensdict["senstype"] = "extern" - sensdict["parameters"] = list(group["param_name"]) - - else: - raise ValueError( - f"Sensitivity {sensname} does not have a valid sensitivity type" - ) - - if "numreal" in group and _has_value(group["numreal"].iloc[0]): - # Using default number of realisations: - # 'repeats' from general_input sheet - sensdict["numreal"] = int(group["numreal"].iloc[0]) - - inputdict["sensitivities"][str(sensname)] = sensdict - - return inputdict - - -def _read_defaultvalues(filename, sheetname): - """Reads defaultvalues, also used as values for - reference/base case - - Args: - filename(path): path to excel file - sheetname (string): name of defaultsheet - - Returns: - OrderedDict with defaultvalues (parameter, value) - """ - default_dict = OrderedDict() - default_df = pd.read_excel( - filename, sheetname, header=0, index_col=0, engine="openpyxl" - ) - default_df.dropna(axis=0, how="all", inplace=True) - default_df = default_df.loc[ - :, ~default_df.columns.astype(str).str.contains("^Unnamed") - ] - - # Strip spaces before and after parameter names, if they are there - # it is probably invisible user errors in Excel. - default_df.index = [ - paramname.strip() if isinstance(paramname, str) else paramname - for paramname in default_df.index - ] - for row in default_df.itertuples(): - if str(row[0]) in default_dict: - print( - "WARNING: The default value '{}' " - "is listed twice in the sheet '{}'. " - "Only the first entry will be used in output file".format( - row[0], sheetname - ) - ) - else: - default_dict[str(row[0])] = row[1] - return default_dict - - -def _read_dependencies(filename, sheetname, from_parameter): - """Reads parameters that are set from other parameters - - Args: - filename(path): path to excel file - sheetname (string): name of dependency sheet - - Returns: - OrderedDict with design parameter, dependent parameters - and values - """ - depend_dict = OrderedDict() - depend_df = pd.read_excel( - filename, sheetname, dtype=str, na_values="", engine="openpyxl" - ) - depend_df.dropna(axis=0, how="all", inplace=True) - depend_df = depend_df.loc[ - :, ~depend_df.columns.astype(str).str.contains("^Unnamed") - ] - - if from_parameter in depend_df: - depend_dict["from_values"] = depend_df[from_parameter].tolist() - depend_dict["to_params"] = OrderedDict() - for key in depend_df: - if key != from_parameter: - depend_dict["to_params"][key] = depend_df[key].tolist() - else: - raise ValueError( - "Parameter {} specified to have derived parameters, " - "but the sheet specifying the dependencies {} does " - "not contain the input parameter. ".format(from_parameter, sheetname) - ) - return depend_dict - - -def _read_background(inp_filename, bck_sheet): - """Reads excel sheet with background parameters and distributions - - Args: - inp_filename (path): path to Excel workbook - bck_sheet (str): name of sheet with background parameters - - Returns: - OrderedDict with parameter names and distributions - """ - backdict = OrderedDict() - paramdict = OrderedDict() - bck_input = pd.read_excel(inp_filename, bck_sheet, engine="openpyxl") - bck_input.dropna(axis=0, how="all", inplace=True) - bck_input = bck_input.loc[ - :, ~bck_input.columns.astype(str).str.contains("^Unnamed") - ] - - backdict["correlations"] = None - if "corr_sheet" in bck_input: - backdict["correlations"] = _read_correlations(bck_input, inp_filename) - - if "dist_param1" not in bck_input.columns.values: - bck_input["dist_param1"] = float("NaN") - if "dist_param2" not in bck_input.columns.values: - bck_input["dist_param2"] = float("NaN") - if "dist_param3" not in bck_input.columns.values: - bck_input["dist_param3"] = float("NaN") - if "dist_param4" not in bck_input.columns.values: - bck_input["dist_param4"] = float("NaN") - - for row in bck_input.itertuples(): - if not _has_value(row.param_name): - raise ValueError( - "Background parameters specified " - "where one line has empty parameter " - "name " - ) - if not _has_value(row.dist_param1): - raise ValueError( - "Parameter {} has been input " - "in background sheet but with empty " - "first distribution parameter ".format(row.param_name) - ) - if not _has_value(row.dist_param2) and _has_value(row.dist_param3): - raise ValueError( - "Parameter {} has been input in " - "background sheet with " - 'value for "dist_param3" while ' - '"dist_param2" is empty. This is not ' - "allowed".format(row.param_name) - ) - if not _has_value(row.dist_param3) and _has_value(row.dist_param4): - raise ValueError( - "Parameter {} has been input in " - "background sheet with " - 'value for "dist_param4" while ' - '"dist_param3" is empty. This is not ' - "allowed".format(row.param_name) - ) - distparams = [ - item - for item in [ - row.dist_param1, - row.dist_param2, - row.dist_param3, - row.dist_param4, - ] - if _has_value(item) - ] - if "corr_sheet" in bck_input: - corrsheet = None if not _has_value(row.corr_sheet) else row.corr_sheet - else: - corrsheet = None - paramdict[str(row.param_name)] = [str(row.dist_name), distparams, corrsheet] - backdict["parameters"] = paramdict - - if "decimals" in bck_input: - decimals = OrderedDict() - for row in bck_input.itertuples(): - if _has_value(row.decimals) and _is_int(row.decimals): - decimals[row.param_name] = int(row.decimals) - backdict["decimals"] = decimals - - return backdict - - -def _read_scenario_sensitivity(sensgroup): - """Reads parameters and values - for scenario sensitivities - """ - sdict = OrderedDict() - sdict["cases"] = OrderedDict() - casedict1 = OrderedDict() - casedict2 = OrderedDict() - - if not _has_value(sensgroup["senscase1"].iloc[0]): - raise ValueError( - "Sensitivity {} has been input " - "as a scenario sensitivity, but " - "without a name in senscase1 column.".format(sensgroup["sensname"].iloc[0]) - ) - - for row in sensgroup.itertuples(): - if not _has_value(row.param_name): - raise ValueError( - "Scenario sensitivity {} specified " - "where one line has empty parameter " - "name ".format(row.sensname) - ) - if not _has_value(row.value1): - raise ValueError( - "Parameter {} har been input " - 'as type "scenario" but with empty ' - "value in value1 column ".format(row.param_name) - ) - casedict1[str(row.param_name)] = row.value1 - - if _has_value(sensgroup["senscase2"].iloc[0]): - for row in sensgroup.itertuples(): - if not _has_value(row.value2): - raise ValueError( - "Sensitivity {} has been input " - "with a name in senscase2 column " - "but without a value for parameter {} " - "in value2 column.".format( - sensgroup["sensname"].iloc[0], row.param_name - ) - ) - casedict2[str(row.param_name)] = row.value2 - sdict["cases"][str(sensgroup["senscase1"].iloc[0])] = casedict1 - sdict["cases"][str(sensgroup["senscase2"].iloc[0])] = casedict2 - else: - for row in sensgroup.itertuples(): - if _has_value(row.value2): - raise ValueError( - "Sensitivity {} has been input " - "with a value for parameter {} " - "in value2 column " - "but without a name for the scenario " - "in senscase2 column.".format( - sensgroup["sensname"].iloc[0], row.param_name - ) - ) - sdict["cases"][str(sensgroup["senscase1"].iloc[0])] = casedict1 - return sdict - - -def _read_constants(sensgroup): - """Reads constants to be used together with - seed sensitivity""" - if "dist_param1" not in sensgroup.columns.values: - sensgroup["dist_param1"] = float("NaN") - paramdict = OrderedDict() - for row in sensgroup.itertuples(): - if not _has_value(row.dist_param1): - raise ValueError( - "Parameter name {} has been input " - 'in a sensitivity of type "seed". \n' - "If {} was meant to be the name of " - "the seed parameter, this is " - "unfortunately not allowed. " - "The seed parameter name is standardised " - "to RMS_SEED and should not be specified.\n " - "If you instead meant to specify a constant " - "value for another parameter in the seed " - 'sensitivity, please remember "const" in ' - 'dist_name and a value in "dist_param1". '.format( - row.param_name, row.param_name - ) - ) - distparams = row.dist_param1 - paramdict[str(row.param_name)] = [str(row.dist_name), distparams] - return paramdict - - -def _read_dist_sensitivity(sensgroup): - """Reads parameters and distributions - for monte carlo sensitivities - """ - if "dist_param1" not in sensgroup.columns.values: - sensgroup["dist_param1"] = float("NaN") - if "dist_param2" not in sensgroup.columns.values: - sensgroup["dist_param2"] = float("NaN") - if "dist_param3" not in sensgroup.columns.values: - sensgroup["dist_param3"] = float("NaN") - if "dist_param4" not in sensgroup.columns.values: - sensgroup["dist_param4"] = float("NaN") - paramdict = OrderedDict() - for row in sensgroup.itertuples(): - if not _has_value(row.param_name): - raise ValueError( - "Dist sensitivity {} specified " - "where one line has empty parameter " - "name ".format(row.sensname) - ) - if not _has_value(row.dist_param1): - raise ValueError( - "Parameter {} has been input " - 'as type "dist" but with empty ' - "first distribution parameter ".format(row.param_name) - ) - if not _has_value(row.dist_param2) and _has_value(row.dist_param3): - raise ValueError( - "Parameter {} has been input with " - 'value for "dist_param3" while ' - '"dist_param2" is empty. This is not ' - "allowed".format(row.param_name) - ) - if not _has_value(row.dist_param3) and _has_value(row.dist_param4): - raise ValueError( - "Parameter {} has been input with " - 'value for "dist_param4" while ' - '"dist_param3" is empty. This is not ' - "allowed".format(row.param_name) - ) - distparams = [ - item - for item in [ - row.dist_param1, - row.dist_param2, - row.dist_param3, - row.dist_param4, - ] - if _has_value(item) - ] - if "corr_sheet" in sensgroup: - corrsheet = None if not _has_value(row.corr_sheet) else row.corr_sheet - else: - corrsheet = None - paramdict[str(row.param_name)] = [str(row.dist_name), distparams, corrsheet] - - return paramdict - - -def _read_correlations(sensgroup, inputfile): - if "corr_sheet" in sensgroup: - if not sensgroup["corr_sheet"].dropna().empty: - correlations = OrderedDict() - correlations["inputfile"] = inputfile - correlations["sheetnames"] = [] - for _index, row in sensgroup.iterrows(): - if ( - _has_value(row["corr_sheet"]) - and row["corr_sheet"] not in correlations["sheetnames"] - ): - correlations["sheetnames"].append(row["corr_sheet"]) - else: - correlations = None - else: - correlations = None - - return correlations - - -def _has_value(value): - """Returns False only if the argument is np.nan""" - try: - return not np.isnan(value) - except TypeError: - return True - - -def _is_int(teststring): - """Test if a string can be parsed as a float""" - try: - if not np.isnan(int(teststring)): - return (float(teststring) % 1) == 0 - return False # It was a "number", but it was NaN. - except ValueError: - return False diff --git a/src/fmu/tools/sensitivities/_tornado_onebyone.py b/src/fmu/tools/sensitivities/_tornado_onebyone.py deleted file mode 100755 index 4d2681e9..00000000 --- a/src/fmu/tools/sensitivities/_tornado_onebyone.py +++ /dev/null @@ -1,288 +0,0 @@ -"""Module for calculating values for a tornadoplot""" - -import pandas as pd -from deprecation import deprecated - - -def real_mask(dfr, start, end): - """Creates mask for which realisations to calc from""" - return (start <= dfr.REAL) & (end >= dfr.REAL) - - -def check_selector(resultfile, selector): - """Checks whether selector is in resultfile headers""" - if selector not in resultfile.columns: - raise ValueError("Did not find ", selector, " as column in", "resultfile") - - -def check_selection(resultfile, selector, selection): - """Checks whether selction is in resultfile values - in the column selector""" - for sel in selection: - if sel not in resultfile[selector].values: - if str(sel).strip("[]").lower() == "total": - raise ValueError("Use 'all' to sum all", selector, "not 'total'") - raise ValueError( - "Selection ", sel, " not found in column", selector, "in resultfile" - ) - resultfile[selector].values.astype(str) - - -def check_response(resultfile, response): - """Checks whether respones in in resultfile""" - if response not in resultfile.columns: - raise ValueError("Did not find ", response, " as column in", "resultfile") - - -def cut_by_ref(tornadotable, refname): - """Removes sensitivities smaller than reference sensitivity from table""" - maskref = tornadotable.sensname == refname - reflow = tornadotable[maskref].low.abs() - refhigh = tornadotable[maskref].high.abs() - refmax = max(float(reflow), float(refhigh)) - return tornadotable.loc[ - (tornadotable["sensname"] == refname) - | ( - (tornadotable["low"].abs() >= refmax) - | (tornadotable["high"].abs() >= refmax) - ) - ] - - -def sort_by_max(tornadotable): - """Sorts table based on max(abs('low', 'high'))""" - tornadotable["max"] = ( - tornadotable[["low", "high"]] - .apply(lambda x: max(x.min(), x.max(), key=abs), axis=1) - .abs() - ) - df_sorted = tornadotable.sort_values("max", ascending=True) - df_sorted.drop(["max"], axis=1, inplace=True) - return df_sorted - - -@deprecated(details="Use TornadoPlotterFMU from webviz instead") -def calc_tornadoinput( - designsummary, - resultfile, - response, - selectors, - selection, - reference="rms_seed", - scale="percentage", - cutbyref=False, - sortsens=True, -): - """ - Calculates input values for a tornadoplot for one response - and one design set up - - Args: - designsummary (pd.DataFrame): Summary of designmatrix as output - from summarize_design. - resultfile (pd.DataFrame): DataFrame with collected results - for different realizations where - 'REAL' is one of the columns. - response (str): name of response in resultfile to plot tornado for - selectors (list): List of strings. Selectors to choose/filter on - selections (list): List of lists. Values to filter on for each selector. - reference (str): Specifying what the reference is for - the tornado plots. Defaults to 'rms_seed'. - Valid choices are a sensname or a - realisation number. - scale (str): Whether to plot absolute numbers or percentage - compared to reference mean. Defaults to 'percentage'. - Valid choices are 'percentage' and 'absolute'. - cutbyref (bool): If True sensitivities smaller than ref - are excluded. Defaults to False. - sortsens (bool): If True sensitivities are sorted so that - largest comes first. Defaults to True. - - Returns: - tuple: - - * (pd.DataFrame): tornadoinput for DataFrame for - a tornado plot - * (int): Average response value for realizations in - reference sensitivity. - - Example:: - >>> import pandas as pd - >>> from fmu.tools.sensitivities import calc_tornadoplot - >>> designtable = pd.read_csv('designsummary.csv') - >>> results = pd.read_csv('resultfile.csv') - >>> response = 'STOIIP_OIL' - >>> selectors = ['ZONE', 'REGION'] - >>> selection = [['Nansen','Larsson'], ['SegmentA']] - >>> reference = 'rms_seed' - >>> scale = 'percentage' - >>> cutbyref = True - >>> sortsens = False - >>> (tornadotable, ref_value) = calc_tornadoinput( - designtable, results, response, selectors, - selection, reference, scale, cutbyref, sortsens) - - """ - # Check that chosen response exists in resultfile - check_response(resultfile, response) - - # Filter on chosen selectors(column names) and selections(values) - for itr, sel in enumerate(selection): - header = selectors[itr] - sel_stripped = str(sel).strip("[]").lower() - check_selector(resultfile, selectors[itr]) - if sel_stripped != "'all'": - resultfile[header] = resultfile[header].astype(str) - check_selection(resultfile, selectors[itr], sel) - resultfile = resultfile[(resultfile[header].isin(sel))] - - # summing over chosen selections for each realisation - headers = ["REAL"] + selectors - resultcopy = resultfile.copy() - resultcopy.set_index(headers, inplace=True) - resultcopy.sort_index(inplace=True) - dfr_summed = resultcopy.groupby(["REAL"]).sum(min_count=1) - dfr_summed.reset_index(inplace=True) - - # Creates empty dfr to store stats in, webportal tornadoplot input format - tornadoinput = pd.DataFrame( - columns=[ - "sensno", - "sensname", - "low", - "high", - "leftlabel", - "rightlabel", - "numreal1", - "numreal2", - ] - ) - - # Calculate mean for reference - if reference in designsummary["sensname"].values: - startreal = int( - designsummary[designsummary["sensname"] == reference]["startreal1"] - ) - endreal = int(designsummary[designsummary["sensname"] == reference]["endreal1"]) - mask = real_mask(dfr_summed, startreal, endreal) - ref_avg = dfr_summed[mask][response].mean() - elif reference.isdigit(): - if int(reference) >= 0 and int(reference) <= dfr_summed.REAL.max(): - startreal = int(reference) - endreal = int(reference) - mask = real_mask(dfr_summed, startreal, endreal) - ref_avg = dfr_summed[mask][response].mean() - else: - raise ValueError("Reference should be a sensname or a real number", reference) - - # for each sensitivity calculate statistics - for sensno in range(len(designsummary)): - sensname = designsummary.loc[sensno]["sensname"] - startreal = designsummary.loc[sensno]["startreal1"] - endreal = designsummary.loc[sensno]["endreal1"] - mask = real_mask(dfr_summed, startreal, endreal) - numreal1 = len(dfr_summed.REAL[mask]) - if numreal1 > 0: - avg1 = dfr_summed[mask][response].mean() - ref_avg - else: - avg1 = 0 - print( - "Warning: Number of ok realizations is 0 in" - "sensitivity {} case1".format(sensname) - ) - - if designsummary.loc[sensno]["senstype"] == "mc": - if numreal1 > 0: - p90 = dfr_summed[mask][response].quantile(0.10) - ref_avg - p10 = dfr_summed[mask][response].quantile(0.90) - ref_avg - else: - p90 = 0 - p10 = 0 - print( - f"Warning: Number of ok realizations is 0 insensitivity {sensname}" - ) - subset1name = "p90" - subset2name = "p10" - tornadoinput.loc[sensno] = [ - sensno, - sensname, - p90, - p10, - subset1name, - subset2name, - numreal1, - numreal1, - ] - elif designsummary.loc[sensno]["senstype"] == "scalar": - subset1name = designsummary.loc[sensno]["casename1"] - # if case 2 exists - if designsummary.loc[sensno]["casename2"] is not None: - startreal = designsummary.loc[sensno]["startreal2"] - endreal = designsummary.loc[sensno]["endreal2"] - mask = real_mask(dfr_summed, startreal, endreal) - numreal2 = len(dfr_summed.REAL[mask]) - if numreal2 > 0: - avg2 = dfr_summed[mask][response].mean() - ref_avg - else: - avg2 = 0 - print( - "Warning: Number of ok realizations is 0 in" - "sensitivity {} case2".format(sensname) - ) - subset2name = designsummary.loc[sensno]["casename2"] - else: - avg2 = 0 - numreal2 = 0 - subset2name = None - # If case1 has a higher value than case 2, swap values and names - if avg2 < avg1: - avg1, avg2 = avg2, avg1 - numreal1, numreal2 = numreal2, numreal1 - subset1name, subset2name = subset2name, subset1name - tornadoinput.loc[sensno] = [ - sensno, - sensname, - avg1, - avg2, - subset1name, - subset2name, - numreal1, - numreal2, - ] - elif designsummary.loc[sensno]["senstype"] == "ref": - continue - else: - raise ValueError( - "Sensitivity type should be 'mc' or 'scalar': " - + designsummary.loc[sensno]["senstype"] - + "\n" - + "Something wrong with designsummary?" - ) - - tornadoinput["true_low"] = (tornadoinput["low"] + ref_avg).round(2) - tornadoinput["true_high"] = (tornadoinput["high"] + ref_avg).round(2) - - if scale == "percentage": - if ref_avg != 0: - tornadoinput["low"] = 100 * tornadoinput["low"] / ref_avg - tornadoinput["high"] = 100 * tornadoinput["high"] / ref_avg - else: - tornadoinput["low"] = 0 - tornadoinput["high"] = 0 - - # Cut decimals for display - tornadoinput["low"] = tornadoinput["low"].round(2) - tornadoinput["high"] = tornadoinput["high"].round(2) - - # Drops sensitivities smaller than reference if specified - if cutbyref and tornadoinput["sensname"].str.contains(reference).any(): - tornadoinput = cut_by_ref(tornadoinput, reference) - - # Sorts so that largest sensitivities comes first - if sortsens: - tornadoinput = sort_by_max(tornadoinput) - - # Return results that can be used for plotting - tornadoinput = tornadoinput.drop(["sensno"], axis=1).set_index("sensname") - - return (tornadoinput, ref_avg) diff --git a/src/fmu/tools/sensitivities/create_design.py b/src/fmu/tools/sensitivities/create_design.py deleted file mode 100644 index bc93f2ec..00000000 --- a/src/fmu/tools/sensitivities/create_design.py +++ /dev/null @@ -1,914 +0,0 @@ -"""Module for generating design matrices that can be run by DESIGN2PARAMS -and DESIGN_KW in FMU/ERT. -""" - -from collections import OrderedDict -from datetime import datetime -from pathlib import Path - -import numpy as np -import pandas as pd - -import fmu.tools -from fmu.tools.sensitivities import design_distributions as design_dist - - -class DesignMatrix: - """Class for design matrix in FMU. Can contain a onebyone design - or a full montecarlo design. - - Attributes: - designvalues (pd.DataFrame): design matrix on standard fmu format - contains columns 'REAL' (realization number), and if a onebyone - design, also columns 'SENSNAME' and 'SENSCASE' - defaultvalues (OrderedDict): default values for design - backgroundvalues (pd.DataFrame): Used when background parameters are - not constant. Either a set is sampled from specified distributions - or they are read from a file. - """ - - def __init__(self): - """ - Placeholders for: - designvalues: dataframe with parameters that varies - defaultvalues: dictionary of default/base case values - backgroundvalues: dataframe with background parameters - seedvalues: list of seed values - """ - self.designvalues = pd.DataFrame(columns=["REAL"]) - self.defaultvalues = OrderedDict() - self.backgroundvalues = None - self.seedvalues = None - - def reset(self): - """Resets DesignMatrix to empty. Necessary iin case method generate - is used several times for same instance of DesignMatrix""" - self.designvalues = pd.DataFrame(columns=["REAL"]) - self.defaultvalues = OrderedDict() - self.backgroundvalues = None - self.seedvalues = None - - def generate(self, inputdict): - """Generating design matrix from input dictionary in specific - format. Adding default values and background values if existing. - Looping through sensitivities and adding them to designvalues. - - Args: - inputdict (OrderedDict): input parameters for design - """ - self.reset() # Emptying if regenerating matrix - - if "distribution_seed" in inputdict and inputdict["distribution_seed"]: - rng = np.random.RandomState(inputdict["distribution_seed"]) - else: - rng = np.random.RandomState() - - self.defaultvalues = inputdict["defaultvalues"] - - max_reals = _find_max_realisations(inputdict) - - # Reading or generating rms seed values - if "seeds" in inputdict: - self.add_seeds(inputdict["seeds"], max_reals) - - # If background values used - read or generate - if "background" in inputdict: - self.add_background(inputdict["background"], max_reals, rng) - - if inputdict["designtype"] == "onebyone": - self.designvalues["SENSNAME"] = None - self.designvalues["SENSCASE"] = None - counter = 0 - for key in inputdict["sensitivities"]: - sens = inputdict["sensitivities"][key] - numreal = sens["numreal"] if "numreal" in sens else inputdict["repeats"] - if sens["senstype"] == "ref": - sensitivity = SingleRealisationReference(key) - sensitivity.generate(range(counter, counter + numreal)) - counter += numreal - self._add_sensitivity(sensitivity) - elif sens["senstype"] == "background": - sensitivity = BackgroundSensitivity(key) - sensitivity.generate(range(counter, counter + numreal)) - counter += numreal - self._add_sensitivity(sensitivity) - elif sens["senstype"] == "seed": - if self.seedvalues is None: - raise ValueError( - "No seed values available to use for seed sensitivity" - ) - sensitivity = SeedSensitivity(key) - sensitivity.generate( - range(counter, counter + numreal), - sens["seedname"], - self.seedvalues, - sens["parameters"], - ) - counter += numreal - self._add_sensitivity(sensitivity) - elif sens["senstype"] == "scenario": - sensitivity = ScenarioSensitivity(key) - for casekey in sens["cases"]: - case = sens["cases"][casekey] - temp_case = ScenarioSensitivityCase(casekey) - temp_case.generate( - range(counter, counter + numreal), case, self.seedvalues - ) - sensitivity.add_case(temp_case) - counter += numreal - self._add_sensitivity(sensitivity) - elif sens["senstype"] == "dist": - sensitivity = MonteCarloSensitivity(key) - sensitivity.generate( - range(counter, counter + numreal), - sens["parameters"], - self.seedvalues, - sens["correlations"], - rng=rng, - ) - counter += numreal - self._add_sensitivity(sensitivity) - elif sens["senstype"] == "extern": - sensitivity = ExternSensitivity(key) - sensitivity.generate( - range(counter, counter + numreal), - sens["extern_file"], - sens["parameters"], - self.seedvalues, - ) - counter += numreal - self._add_sensitivity(sensitivity) - print("Added sensitivity :", sensitivity.sensname) - if "background" in inputdict: - self._fill_with_background_values() - self._fill_with_defaultvalues() - - if "dependencies" in inputdict: - self._fill_derived_params(inputdict["dependencies"]) - - if "decimals" in inputdict: - self._set_decimals(inputdict["decimals"]) - # Re-order columns - start_cols = ["REAL", "SENSNAME", "SENSCASE", "RMS_SEED"] - self.designvalues = self.designvalues[ - [col for col in start_cols if col in self.designvalues] - + [col for col in self.designvalues if col not in start_cols] - ] - else: - raise ValueError( - "Generation of DesignMatrix only" - "implemented for type onebyone" - "In general_input designtype is" - "set to {}".format(inputdict["designtype"]) - ) - - def to_xlsx( - self, filename, designsheet="DesignSheet01", defaultsheet="DefaultValues" - ): - """Writing design matrix to excel workfbook on standard fmu format - to be used in FMU/ERT by DESIGN2PARAMS and DESIGN_KW - - Args: - filename (str): output filename (extension .xlsx) - designsheet (str): name of excel sheet containing design matrix - (optional, defaults to 'DesignSheet01') - defaultsheet (str): name of excel sheet containing default - values (optional, defaults to 'DefaultValues') - """ - # pylint: disable=abstract-class-instantiated - basename = Path(filename).stem - extension = Path(filename).suffix - if extension != ".xlsx": - filename = basename + ".xlsx" - print( - "Warning: Output filename did not have extension .xlsx " - "but the export format is Excel .xlsx . " - "Changing outputname to {}".format(filename) - ) - - xlsxwriter = pd.ExcelWriter(filename, engine="openpyxl") - self.designvalues.to_excel( - xlsxwriter, sheet_name=designsheet, index=False, header=True - ) - # Default values from OrderdDictionay to pandas dataframe - defaults = pd.DataFrame(columns=["defaultparameters", "defaultvalue"]) - defaults["defaultparameters"] = self.defaultvalues.keys() - defaults["defaultvalue"] = self.defaultvalues.values() - defaults.to_excel( - xlsxwriter, sheet_name=defaultsheet, index=False, header=False - ) - - version_info = pd.DataFrame( - { - "Description": ["Created using fmu-tools version:", "Created on:"], - "Value": [ - fmu.tools.__version__, - datetime.now().isoformat(sep=" ", timespec="seconds"), - ], - } - ) - version_info.to_excel(xlsxwriter, sheet_name="Metadata", index=False) - - xlsxwriter.close() - print( - f"A total of {len(self.designvalues['REAL'])} realizations were generated" - ) - print(f"Designmatrix written to {filename}") - - def add_seeds(self, seeds, max_reals): - """Adding background as specified in dictionary. - Either from external file or from distributions in background - dictionary - - Args: - back_dict (OrderedDict): how to generate background values - max_values (int): number of background values to generate - """ - if seeds in [None, "None"]: - self.seedvalues = None - print("seeds is set to None in general_input") - elif seeds.lower() == "default": - self.seedvalues = [item + 1000 for item in range(max_reals)] - elif Path(seeds).is_file(): - self.seedvalues = _seeds_from_extern(seeds, max_reals) - else: - raise ValueError( - "Valid choices for seeds are None, " - '"default" or an existing filename. ' - "Neither was found in this case. seeds " - "had been specified as {} .".format(seeds) - ) - - def add_background(self, back_dict, max_values, rng: np.random.RandomState): - """Adding background as specified in dictionary. - Either from external file or from distributions in background - dictionary - - Args: - back_dict (OrderedDict): how to generate background values - max_values (int): number of background values to generate - rng (numpy.random.RandomState): Random number generator instance - """ - if back_dict is None: - self.backgroundvalues = None - elif "extern" in back_dict: - self.backgroundvalues = _parameters_from_extern(back_dict["extern"]) - elif "parameters" in back_dict: - self._add_dist_background(back_dict, max_values, rng) - - def background_to_excel(self, filename, backgroundsheet="Background"): - """Writing background values to an Excel spreadsheet - - Args: - filename (str): output filename (extension .xlsx) - backgroundsheet (str): name of excel sheet - """ - # pylint: disable=abstract-class-instantiated - xlsxwriter = pd.ExcelWriter(filename, engine="openpyxl") - self.backgroundvalues.to_excel( - xlsxwriter, sheet_name=backgroundsheet, index=False, header=True - ) - xlsxwriter.close() - print(f"Backgroundvalues written to {filename}") - - def _add_sensitivity(self, sensitivity): - """Adding a sensitivity to the design - - Args: - sensitivity of class Scenario, MonteCarlo or Extern - """ - existing_values = self.designvalues.copy() - self.designvalues = pd.concat([existing_values, sensitivity.sensvalues]) - - def _fill_with_background_values(self): - """Substituting NaNs with background values if existing. - background values not in design are added as separate colums - """ - if self.backgroundvalues is not None: - grouped = self.designvalues.groupby(["SENSNAME", "SENSCASE"], sort=False) - result_values = pd.DataFrame() - for sensname, case in grouped: - temp_df = case.reset_index() - temp_df.fillna(self.backgroundvalues, inplace=True) - temp_df.set_index("index") - for key in self.backgroundvalues: - if key not in case: - temp_df[key] = self.backgroundvalues[key] - if len(temp_df) > len(self.backgroundvalues): - raise ValueError( - "Provided number of background values " - "{} is smaller than number" - " of realisations for sensitivity {}".format( - len(self.backgroundvalues), sensname - ) - ) - else: - if len(temp_df) > len(self.backgroundvalues): - print( - "Provided number of background values " - "({}) is smaller than number" - " of realisations for sensitivity {}" - " and parameter {}. " - "Will be filled with default values.".format( - len(self.backgroundvalues), sensname, key - ) - ) - existing_values = result_values.copy() - result_values = pd.concat([existing_values, temp_df]) - - result_values = result_values.drop(["index"], axis=1) - self.designvalues = result_values - - def _fill_with_defaultvalues(self): - """Filling NaNs with default values""" - for key in self.designvalues: - if key in self.defaultvalues: - self.designvalues[key] = self.designvalues[key].fillna( - self.defaultvalues[key] - ) - elif key not in ["REAL", "SENSNAME", "SENSCASE", "RMS_SEED"]: - raise LookupError(f"No defaultvalues given for parameter {key} ") - - def _fill_derived_params(self, depend_dict): - for from_param in depend_dict: - if from_param in self.designvalues: - for param in depend_dict[from_param]["to_params"]: - self.designvalues[param] = np.nan - for index in range(len(depend_dict[from_param]["from_values"])): - fill_these = depend_dict[from_param]["from_values"][index] - fill_data = depend_dict[from_param]["to_params"][param][index] - self.designvalues[param] = self.designvalues[param].mask( - self.designvalues[from_param] == fill_these, fill_data - ) - if self.designvalues[param].isnull().any(): - raise ValueError( - "Column for derived parameter {} " - "contains NaN. Check input " - "defining dependencies. " - "Could be Wrong values or that " - "values for input variable in " - "dependencies sheet " - "should be specified as strings.".format(param) - ) - - def _add_dist_background(self, back_dict, numreal, rng: np.random.RandomState): - """Drawing background values from distributions - specified in dictionary - - Args: - back_dict (OrderedDict): parameters and distributions - numreal (int): Number of samples to generate - rng (numpy.random.RandomState): Random number generator instance - """ - assert isinstance( - numreal, int - ), f"numreal must be an integer, got {type(numreal)} with value {numreal}" - mc_background = MonteCarloSensitivity("background") - mc_background.generate( - range(numreal), - back_dict["parameters"], - "None", - back_dict["correlations"], - rng, - ) - mc_backgroundvalues = mc_background.sensvalues - - # Rounding of background values as specified - if "decimals" in back_dict: - for key in back_dict["decimals"]: - if design_dist.is_number(mc_backgroundvalues[key].iloc[0]): - mc_backgroundvalues[key] = ( - mc_backgroundvalues[key] - .astype(float) - .round(int(back_dict["decimals"][key])) - ) - else: - raise ValueError("Cannot round a string parameter") - self.backgroundvalues = mc_backgroundvalues.copy() - - def _set_decimals(self, dict_decimals): - """Rounding to specified number of decimals - - Args: - dict_decimals (dictionary): (key, value)s are (param, decimals) - """ - for key in self.designvalues: - if key in dict_decimals: - if design_dist.is_number(self.designvalues[key].iloc[0]): - self.designvalues[key] = ( - self.designvalues[key] - .astype(float) - .round(int(dict_decimals[key])) - ) - else: - raise ValueError(f"Cannot round a string parameter {key}") - - -class SeedSensitivity: - """ - A seed sensitivity is normally the reference for one by one sensitivities - It contains a list of seeds to be repeated for each sensitivity - The parameter name is hardcoded to RMS_SEED - It will be assigned the sensname 'p10_p90' which will be written to - the SENSCASE column in the output. - - Attributes: - sensname (str): name of sensitivity - sensvalues (pd.DataFrame): design values for the sensitivity - - """ - - # pylint: disable=too-few-public-methods - def __init__(self, sensname): - """Initiate method. - - Args: - sensname (str): Name of sensitivity. Defines SENSNAME in design matrix. - """ - self.sensname = sensname - self.sensvalues = None - - def generate(self, realnums, seedname, seedvalues, parameters): - """Generates parameter values for a seed sensitivity - - Args: - realnums (list): list of integers with realization numbers - seedname (str): name of seed parameter to add - seedvalues (list): list of integer seedvalues - parameters (OrderedDict): parameter names and - distributions or values. - """ - assert isinstance( - seedvalues, list - ), f"seedvalues must be a list, got {seedvalues}" - - self.sensvalues = pd.DataFrame(index=realnums) - self.sensvalues[seedname] = seedvalues[0 : len(realnums)] - - if parameters is not None: - for key in parameters: - dist_name = parameters[key][0].lower() - constant = parameters[key][1] - if dist_name != "const": - raise ValueError( - 'A sensitivity of type "seed" can only have ' - "additional parameters where dist_name is " - '"const". Check sensitivity {}"'.format(self.sensname) - ) - self.sensvalues[key] = constant - - self.sensvalues["REAL"] = realnums - self.sensvalues["SENSNAME"] = self.sensname - self.sensvalues["SENSCASE"] = "p10_p90" - - -class SingleRealisationReference: - """ - The class is used in set-ups where one wants a single realisation - containing only default values as a reference, but the realisation - itself is not included in a sensitivity. - Typically used when RMS_SEED is not a parameter. - SENSCASE will be set to 'ref' in design matrix, to flag that it should be - excluded as a sensitivity in the plot. - - Attributes: - sensname (str): name of sensitivity - sensvalues (pd.DataFrame): design values for the sensitivity - - """ - - # pylint: disable=too-few-public-methods - def __init__(self, sensname): - """Initiate. - - Args: - sensname (str): Name of sensitivity. Defines SENSNAME in design matrix. - """ - self.sensname = sensname - self.sensvalues = None - - def generate(self, realnums): - """Generates realisation number only - - Args: - realnums (list): list of intergers with realization numbers - """ - self.sensvalues = pd.DataFrame(index=realnums) - self.sensvalues["REAL"] = realnums - self.sensvalues["SENSNAME"] = self.sensname - self.sensvalues["SENSCASE"] = "ref" - - -class BackgroundSensitivity: - """ - The class is used in set-ups where one sensitivities - are run on top of varying background parameters. - Typically used when RMS_SEED is not a parameter, so the reference - for tornadoplots will be the realisations with all parameters - at their default values except the background parameters. - SENSCASE will be set to 'p10_p90' in design matrix. - - Attributes: - sensname (str): name of sensitivity - sensvalues (pd.DataFrame): design values for the sensitivity - - """ - - # pylint: disable=too-few-public-methods - def __init__(self, sensname): - """Inititate - - Args: - sensname (str): Name of sensitivity. Defines SENSNAME in design matrix. - """ - self.sensname = sensname - self.sensvalues = None - - def generate(self, realnums): - """Generates realisation number only - - Args: - realnums (list): list of intergers with realization numbers - """ - self.sensvalues = pd.DataFrame(index=realnums) - self.sensvalues["REAL"] = realnums - self.sensvalues["SENSNAME"] = self.sensname - self.sensvalues["SENSCASE"] = "p10_p90" - - -class ScenarioSensitivity: - """Each design can contain one or several single sensitivities of type - Seed, MonteCarlo or Scenario. - Each ScenarioSensitivity can contain 1-2 ScenarioSensitivityCases. - - The ScenarioSensitivity class is used for sensitivities where all - realizatons in a ScenarioSensitivityCase have identical values - but one or more parameter has a different values from the other - ScenarioSensitivityCase. - - Exception is the seed value and the special case where - varying background parameters are specified. Then these are varying - within the case. - - Attributes: - case1 (ScenarioSensitivityCase): first case, e.g. 'low case' - case2 (ScenarioSensitivityCase): second case, e.g. 'high case' - sensvalues (pd.DataFrame): design values for the sensitivity, containing - 1-2 cases - """ - - # pylint: disable=too-few-public-methods - def __init__(self, sensname): - """ - Args: - sensname (str): Name of sensitivity. - Equals SENSNAME in design matrix - """ - self.sensname = sensname - self.case1 = None - self.case2 = None - self.sensvalues = None - - def add_case(self, senscase): - """ - Adds a ScenarioSensitivityCase instance - to a ScenarioSensitivity object. - - Args: - senscase (ScenarioSensitivityCase): - Equals SENSCASE in design matrix. - """ - if self.case1 is not None: # Case 1 has been read, this is case2 - if "REAL" in senscase.casevalues and "SENSCASE" in senscase.casevalues: - self.case2 = senscase - senscase.casevalues["SENSNAME"] = self.sensname - self.sensvalues = pd.concat( - [self.sensvalues, senscase.casevalues], sort=True - ) - else: # This is the first case - if "REAL" in senscase.casevalues and "SENSCASE" in senscase.casevalues: - self.case1 = senscase - self.sensvalues = senscase.casevalues.copy() - self.sensvalues["SENSNAME"] = self.sensname - - -class ScenarioSensitivityCase: - """Each ScenarioSensitivity can contain one or - two ScenarioSensitivityCases. - - The 1-2 cases are typically 'low' and 'high' cases for one or - a set of parameters, where all realisatons in - the case have identical values except the seed value - and in special cases specified background values which may - vary within the case. - - One or two ScenarioSensitivityCase instances can be added to each - ScenarioSensitivity object. - - Attributes: - casename (str): name of the sensitivity case, - equals SENSCASE in design matrix. - casevalues (pd.DataFrame): parameters and values - for the sensitivity with realisation numbers as index. - - """ - - # pylint: disable=too-few-public-methods - def __init__(self, casename): - self.casename = casename - self.casevalues = None - - def generate(self, realnums, parameters, seedvalues): - """Generate casevalues for the ScenarioSensitivityCase - - Args: - realnums (list): list of realizaton numbers for the case - parameters (OrderedDict): - dictionary with parameter names and values - seeds (str): default or None - """ - - self.casevalues = pd.DataFrame(columns=parameters.keys(), index=realnums) - for key in parameters: - self.casevalues[key] = parameters[key] - self.casevalues["REAL"] = realnums - self.casevalues["SENSCASE"] = self.casename - - if seedvalues: - self.casevalues["RMS_SEED"] = seedvalues[: len(realnums)] - - -class MonteCarloSensitivity: - """ - For a MonteCarloSensitivity one or several parameters - are drawn from specified distributions with or without correlations. - A MonteCarloSensitivity can only contain - one case, where the name SENSCASE is automatically set to 'p10_p90' in the - design matrix to flag that p10_p90 should be calculated in TornadoPlot. - - Attributes: - sensname (str): name for the sensitivity. - Equals SENSNAME in design matrix. - sensvalues (pd.DataFrame): parameters and values for the sensitivity - with realisation numbers as index. - """ - - # pylint: disable=too-few-public-methods - def __init__(self, sensname): - self.sensname = sensname - self.sensvalues = None - - def generate(self, realnums, parameters, seedvalues, corrdict, rng): - """Generates parameter values by drawing from - defined distributions. - - Args: - realnums (list): list of integers with realization numbers - parameters (OrderedDict): - dictionary of parameters and distributions - seeds (str): default or None - corrdict(OrderedDict): correlation info - """ - self.sensvalues = pd.DataFrame(columns=parameters.keys(), index=realnums) - numreals = len(realnums) - if corrdict is None: - for key in parameters: - dist_name = parameters[key][0].lower() - dist_params = parameters[key][1] - try: - self.sensvalues[key] = design_dist.draw_values( - dist_name, dist_params, numreals, rng - ) - except ValueError as error: - raise ValueError( - "Problem with parameter {} in sensitivity " - "with sensname {}: {}.".format( - key, self.sensname, error.args[0] - ) - ) from error - else: # Some or all parameters are correlated - df_params = pd.DataFrame.from_dict( - parameters, - orient="index", - columns=["dist_name", "dist_params", "corr_sheet"], - ) - df_params["corr_sheet"] = df_params["corr_sheet"].fillna("nocorr") - df_params.reset_index(inplace=True) - df_params.rename(columns={"index": "param_name"}, inplace=True) - param_groups = df_params.groupby("corr_sheet") - - for correl, group in param_groups: - param_dict = OrderedDict() - for _, row in group.iterrows(): - param_dict[row["param_name"]] = [ - row["dist_name"], - row["dist_params"], - ] - if correl != "nocorr": - if len(group) == 1: - _printwarning(correl) - df_correlations = design_dist.read_correlations(corrdict, correl) - multivariate_parameters = df_correlations.index.values - cov_matrix = design_dist.make_covariance_matrix(df_correlations) - normalscoremeans = len(multivariate_parameters) * [0] - normalscoresamples = rng.multivariate_normal( - normalscoremeans, cov_matrix, size=numreals - ) - normalscoresamples_df = pd.DataFrame( - data=normalscoresamples, columns=multivariate_parameters - ) - for key in param_dict: - dist_name = param_dict[key][0].lower() - dist_parameters = param_dict[key][1] - if key in multivariate_parameters: - try: - self.sensvalues[key] = design_dist.draw_values( - dist_name, - dist_parameters, - numreals, - rng, - normalscoresamples_df[key], - ) - except ValueError as error: - raise ValueError( - "Problem in sensitivity " - "with sensname {} for " - "parameter {}: {}.".format( - self.sensname, key, error.args[0] - ) - ) from error - else: - raise ValueError( - "Parameter{} specified with correlation " - "matrix {} but is not listed in " - "that sheet".format(key, correl) - ) - else: # group nocorr where correlation matrix is not defined - for key in param_dict: - dist_name = param_dict[key][0].lower() - dist_parameters = param_dict[key][1] - try: - self.sensvalues[key] = design_dist.draw_values( - dist_name, dist_parameters, numreals, rng - ) - except ValueError as error: - raise ValueError( - "Problem in sensitivity " - "with sensname {} for parameter " - "{}: {}.".format(self.sensname, key, error.args[0]) - ) from error - - if self.sensname != "background": - self.sensvalues["REAL"] = realnums - self.sensvalues["SENSNAME"] = self.sensname - self.sensvalues["SENSCASE"] = "p10_p90" - if "RMS_SEED" not in self.sensvalues and seedvalues: - self.sensvalues["RMS_SEED"] = seedvalues[: len(realnums)] - - -class ExternSensitivity: - """ - Used when reading parameter values from a file - Assumed to be used with monte carlo type sensitivities and - will hence write 'p10_p90' as SENSCASE in output designmatrix - - Attributes: - sensname (str): Name of sensitivity. - Defines SENSNAME in design matrix - sensvalues (pd.DataFrame): design values for the sensitivity - - """ - - # pylint: disable=too-few-public-methods - def __init__(self, sensname): - self.sensname = sensname - self.sensvalues = None - - def generate(self, realnums, filename, parameters, seedvalues): - """Reads parameter values for a monte carlo sensitivity - from file - - Args: - realnums (list): list of intergers with realization numbers - filename (str): path where to read values from - parameters (list): list with parameter names - seeds (str): default or None - """ - self.sensvalues = pd.DataFrame(columns=parameters, index=realnums) - extern_values = _parameters_from_extern(filename) - if len(realnums) > len(extern_values): - raise ValueError( - "Number of realisations {} specified for " - "sensitivity {} is larger than rows in " - "file {}".format(len(realnums), self.sensname, filename) - ) - for param in parameters: - if param in extern_values: - self.sensvalues[param] = list(extern_values[param][: len(realnums)]) - else: - raise ValueError(f"Parameter {param} not in external file") - self.sensvalues["REAL"] = realnums - self.sensvalues["SENSNAME"] = self.sensname - self.sensvalues["SENSCASE"] = "p10_p90" - - if seedvalues: - self.sensvalues["RMS_SEED"] = seedvalues[: len(realnums)] - - -# Support functions used with several classes - - -def _parameters_from_extern(filename): - """Read parameter values or background values - from specified file. Format either Excel ('xlsx') - or csv. - - Args: - filename (str): path to file - """ - if str(filename).endswith(".xlsx"): - parameters = pd.read_excel(filename, engine="openpyxl") - parameters.dropna(axis=0, how="all", inplace=True) - parameters = parameters.loc[:, ~parameters.columns.str.contains("^Unnamed")] - elif str(filename).endswith(".csv"): - parameters = pd.read_csv(filename) - else: - raise ValueError( - "External file with parameter values should " - "be on Excel or csv format " - "and end with .xlsx or .csv" - ) - return parameters - - -def _seeds_from_extern(filename, max_reals): - """Read parameter values or background values - from specified file. Format either Excel ('xlsx') - or csv. - - Args: - filename (str): path to file - """ - if str(filename).endswith(".xlsx"): - df_seeds = pd.read_excel(filename, header=None, engine="openpyxl") - df_seeds.dropna(axis=0, how="all", inplace=True) - df_seeds.dropna(axis=1, how="all", inplace=True) - seed_numbers = df_seeds[df_seeds.columns[0]].tolist() - elif str(filename).endswith(".csv") or str(filename).endswith(".txt"): - df_seeds = pd.read_csv(filename, header=None) - seed_numbers = df_seeds[df_seeds.columns[0]].tolist() - else: - raise ValueError( - "External file with seed values should " - "be on Excel or csv format " - "and end with .xlsx .csv or .txt" - ) - - if len(seed_numbers) < max_reals: - print( - "Provided number of seed values in external file {} " - "is lower than the maximum number of realisations " - "found for the design {}, and is for those " - "sensitivities used repeatedly. " - ) - seed_numbers = [ - seed_numbers[item % len(seed_numbers)] for item in range(max_reals) - ] - return seed_numbers - - -def _find_max_realisations(inputdict): - """Finds the maximum number of realisations - in a sensitivity case""" - max_reals = inputdict["repeats"] - for key in inputdict["sensitivities"]: - sens = inputdict["sensitivities"][key] - if "numreal" in sens: - max_reals = max(sens["numreal"], max_reals) - return max_reals - - -def _printwarning(corrgroup): - print( - "#######################################################\n" - "fmudesign Warning: \n" - "Using designinput sheets where " - "corr_sheet is only specified for one parameter " - "will cause non-correlated parameters .\n" - "ONLY ONE PARAMETER WAS SPECIFIED TO USE CORR_SHEET {}\n" - "\n" - "Note change in how correlated parameters are specified \n" - "from fmudeisgn version 1.0.1 in August 2019 :\n" - "Name of correlation sheet must be specified for each " - "parameter in correlation matrix. \n" - "This to enable use of several correlation sheets. " - "This also means non-correlated parameters do not " - "have to be included in correlation matrix. \n " - "See documentation: \n" - "https://equinor.github.io/fmu-tools/" - "fmudesign.html#create-design-matrix-for-" - "one-by-one-sensitivities\n" - "\n" - "####################################################\n" - "".format(corrgroup) - ) diff --git a/src/fmu/tools/sensitivities/design_distributions.py b/src/fmu/tools/sensitivities/design_distributions.py deleted file mode 100644 index b9af4974..00000000 --- a/src/fmu/tools/sensitivities/design_distributions.py +++ /dev/null @@ -1,623 +0,0 @@ -"""Module for random sampling of parameter values from -distributions. For use in generation of design matrices -""" - -import re -from math import exp - -import numpy as np -import numpy.linalg as la -import pandas as pd -import scipy.stats - - -def _check_dist_params_normal(dist_params): - if len(dist_params) not in [2, 4]: - status = False - msg = ( - "Normal distribution must have 2 parameters" - " or 4 for a truncated normal, " - "but had " + str(len(dist_params)) + " parameters. " - ) - elif not all(is_number(param) for param in dist_params): - status = False - msg = "Parameters for normal distribution must be numbers. " - elif float(dist_params[1]) < 0: - status = False - msg = "Stddev for normal distribution must be >= 0. " - elif len(dist_params) == 4 and float(dist_params[2]) >= float(dist_params[3]): - status = False - msg = ( - "For truncated normal distribution, " - "lower bound must be less than upper bound, " - f"but got [{dist_params[2]}, {dist_params[3]}]. " - ) - else: - status = True - msg = "" - - return status, msg - - -def _check_dist_params_lognormal(dist_params): - if len(dist_params) != 2: - status = False - msg = ( - "Lognormal distribution must have 2 parameters, " - "but had " + str(len(dist_params)) + " parameters. " - ) - elif not (is_number(dist_params[0]) and is_number(dist_params[1])): - status = False - msg = "Parameters for lognormal distribution must be numbers. " - elif float(dist_params[1]) < 0: - status = False - msg = "Lognormal distribution must have stddev >= 0. " - else: - status = True - msg = "" - - return status, msg - - -def _check_dist_params_uniform(dist_params): - if len(dist_params) != 2: - status = False - msg = ( - "Uniform distribution must have 2 parameters, " - "but had " + str(len(dist_params)) + " parameters. " - ) - elif not (is_number(dist_params[0]) and is_number(dist_params[1])): - status = False - msg = "Parameters for uniform distribution must be numbers. " - elif float(dist_params[1]) < float(dist_params[0]): - status = False - msg = "Uniform distribution must have dist_param2 >= dist_param1" - else: - status = True - msg = "" - - return status, msg - - -def _check_dist_params_triang(dist_params): - if len(dist_params) != 3: - status = False - msg = ( - "Triangular distribution must have 3 parameters, " - "but had " + str(len(dist_params)) + " parameters. " - ) - elif not all(is_number(param) for param in dist_params): - status = False - msg = "Parameters for triangular distribution must be numbers. " - elif not ( - (float(dist_params[2]) >= float(dist_params[1])) - and (float(dist_params[1]) >= float(dist_params[0])) - ): - status = False - msg = "Triangular distribution must have: low <= mode <= high. " - else: - status = True - msg = "" - - return status, msg - - -def _check_dist_params_pert(dist_params): - if len(dist_params) not in [3, 4]: - status = False - msg = ( - "pert distribution must have 3 or 4 parameters, " - "but had " + str(len(dist_params)) + " parameters. " - ) - elif not all(is_number(param) for param in dist_params): - status = False - msg = "Parameters for pert distribution must be numbers. " - elif not ( - (float(dist_params[2]) >= float(dist_params[1])) - and (float(dist_params[1]) >= float(dist_params[0])) - ): - status = False - msg = "Pert distribution must have: low <= mode <= high. " - else: - status = True - msg = "" - - return status, msg - - -def _check_dist_params_logunif(dist_params): - if len(dist_params) != 2: - status = False - msg = ( - "Log uniform distribution must have 2 parameters. " - "but had " + str(len(dist_params)) + " parameters. " - ) - elif not (is_number(dist_params[0]) and is_number(dist_params[1])): - status = False - msg = "Parameters for log uniform distribution must be numbers. " - elif not ( - (float(dist_params[0]) > 0) and (float(dist_params[1]) >= float(dist_params[0])) - ): - status = False - msg = "loguniform distribution must have low > 0 and high >=low. " - else: - status = True - msg = "" - - return status, msg - - -def draw_values_normal(dist_parameters, numreals, rng, normalscoresamples=None): - """Draws values from normal or truncated normal. - Args: - dist_parameters(list): [mean, std dev, min, max], - min/max defining truncated normal - numreals(int): number of realisations to draw - rng: numpy.random.RandomState instance - normalscoresamples(list): samples for correlated parameters - Returns: - list of values - """ - status, msg = _check_dist_params_normal(dist_parameters) - - if not status: - raise ValueError(msg) - - mean = float(dist_parameters[0]) - stddev = float(dist_parameters[1]) - - if len(dist_parameters) == 2: - if normalscoresamples is not None: - return scipy.stats.norm.ppf( - scipy.stats.norm.cdf(normalscoresamples), - loc=mean, - scale=stddev, - ) - distribution = scipy.stats.norm(mean, stddev) - return distribution.rvs(size=numreals, random_state=rng) - - # Handle truncated normal case - clip1 = float(dist_parameters[2]) - clip2 = float(dist_parameters[3]) - low = (clip1 - mean) / stddev - high = (clip2 - mean) / stddev - - if normalscoresamples is not None: - return scipy.stats.truncnorm.ppf( - scipy.stats.norm.cdf(normalscoresamples), - low, - high, - loc=mean, - scale=stddev, - ) - - distribution = scipy.stats.truncnorm(low, high, loc=mean, scale=stddev) - return distribution.rvs(size=numreals, random_state=rng) - - -def draw_values_lognormal(dist_parameters, numreals, rng, normalscoresamples=None): - """Draws values from lognormal distribution. - Args: - dist_parameters(list): [mu, sigma] for the logarithm of the variable - numreals(int): number of realisations to draw - rng: numpy.random.RandomState instance - normalscoresamples(list): samples for correlated parameters - Returns: - list of values - """ - status, msg = _check_dist_params_lognormal(dist_parameters) - - if not status: - raise ValueError(msg) - - mean = float(dist_parameters[0]) - sigma = float(dist_parameters[1]) - if normalscoresamples is not None: - values = scipy.stats.lognorm.ppf( - scipy.stats.norm.cdf(normalscoresamples), - s=sigma, - loc=0, - scale=exp(mean), - ) - else: - distribution = scipy.stats.lognorm(s=sigma, scale=exp(mean)) - values = distribution.rvs(size=numreals, random_state=rng) - - return values - - -def draw_values_uniform(dist_parameters, numreals, rng, normalscoresamples=None): - """Draws values from uniform distribution. - Args: - dist_parameters(list): [minimum, maximum] - numreals(int): number of realisations to draw - rng: numpy.random.RandomState instance - normalscoresamples(list): samples for correlated parameters - Returns: - list of values - """ - if numreals < 0: - raise ValueError("numreal must be a positive integer") - status, msg = _check_dist_params_uniform(dist_parameters) - if not status: - raise ValueError(msg) - low = float(dist_parameters[0]) - high = float(dist_parameters[1]) - uscale = high - low - if normalscoresamples is not None: - return scipy.stats.uniform.ppf( - scipy.stats.norm.cdf(normalscoresamples), loc=low, scale=uscale - ) - distribution = scipy.stats.uniform(loc=low, scale=uscale) - return distribution.rvs(size=numreals, random_state=rng) - - -def draw_values_triangular(dist_parameters, numreals, rng, normalscoresamples=None): - """Draws values from triangular distribution. - Args: - dist_parameters(list): [min, mode, max] - numreals(int): number of realisations to draw - rng: numpy.random.RandomState instance - normalscoresamples(list): samples for correlated parameters - Returns: - list of values - """ - status, msg = _check_dist_params_triang(dist_parameters) - if status: - low = float(dist_parameters[0]) - mode = float(dist_parameters[1]) - high = float(dist_parameters[2]) - if normalscoresamples is not None: - if high == low: # collapsed distribution - print( - "Low and high parameters for triangular distribution" - " are equal. Using constant {}".format(low) - ) - values = scipy.stats.uniform.ppf( - scipy.stats.norm.cdf(normalscoresamples), loc=low, scale=0 - ) - else: - dist_scale = high - low - shape = (mode - low) / dist_scale - values = scipy.stats.triang.ppf( - scipy.stats.norm.cdf(normalscoresamples), - shape, - loc=low, - scale=dist_scale, - ) - else: - if high == low: # collapsed distribution - print( - "Low and high parameters for triangular distribution" - " are equal. Using constant {}".format(low) - ) - distribution = scipy.stats.uniform(loc=low, scale=0) - values = distribution.rvs(size=numreals, random_state=rng) - else: - dist_scale = high - low - shape = (mode - low) / dist_scale - distribution = scipy.stats.triang(shape, loc=low, scale=dist_scale) - values = distribution.rvs(size=numreals, random_state=rng) - else: - raise ValueError(msg) - return values - - -def draw_values_pert(dist_parameters, numreals, rng, normalscoresamples=None): - """Draws values from pert distribution. - Args: - dist_parameters(list): [min, mode, max, scale] - where scale is only specified - for a 4 parameter pert distribution - numreals(int): number of realisations to draw - rng: numpy.random.RandomState instance - normalscoresamples(list): samples for correlated parameters - Returns: - list of values - """ - status, msg = _check_dist_params_pert(dist_parameters) - if status: - low = float(dist_parameters[0]) - mode = float(dist_parameters[1]) - high = float(dist_parameters[2]) - scale = ( - float(dist_parameters[3]) - if len(dist_parameters) == 4 - else 4 # pert 3 parameter distribution - ) - if normalscoresamples is not None: - if high == low: # collapsed distribution - print( - "Low and high parameters for pert distribution" - " are equal. Using constant {}".format(low) - ) - values = scipy.stats.uniform.ppf( - scipy.stats.norm.cdf(normalscoresamples), loc=low, scale=0 - ) - else: - muval = (low + high + scale * mode) / (scale + 2) - if np.isclose(muval, mode): - alpha1 = (scale / 2) + 1 - else: - alpha1 = ((muval - low) * (2 * mode - low - high)) / ( - (mode - muval) * (high - low) - ) - alpha2 = alpha1 * (high - muval) / (muval - low) - values = scipy.stats.beta.ppf( - scipy.stats.norm.cdf(normalscoresamples), alpha1, alpha2 - ) - else: - if high == low: # collapsed distribution - print( - "Low and high parameters for pert distribution" - " are equal. Using constant {}".format(low) - ) - distribution = scipy.stats.uniform(loc=low, scale=0) - else: - muval = (low + high + scale * mode) / (scale + 2) - if np.isclose(muval, mode): - alpha1 = (scale / 2) + 1 - else: - alpha1 = ((muval - low) * (2 * mode - low - high)) / ( - (mode - muval) * (high - low) - ) - alpha2 = alpha1 * (high - muval) / (muval - low) - distribution = scipy.stats.beta(alpha1, alpha2) - values = distribution.rvs(size=numreals, random_state=rng) - else: - raise ValueError(msg) - # For pert distribution scale afterwards: - return values * (dist_parameters[2] - dist_parameters[0]) + dist_parameters[0] - - -def draw_values_loguniform(dist_parameters, numreals, rng, normalscoresamples=None): - """Draws values from loguniform distribution. - Args: - dist_parameters(list): [minimum, maximum] - numreals(int): number of realisations to draw - rng: numpy.random.RandomState instance - normalscoresamples(list): samples for correlated parameters - Returns: - list of values - """ - status, msg = _check_dist_params_logunif(dist_parameters) - if status: - low = float(dist_parameters[0]) - high = float(dist_parameters[1]) - if normalscoresamples is not None: - values = scipy.stats.reciprocal.ppf( - scipy.stats.norm.cdf(normalscoresamples), low, high - ) - else: - distribution = scipy.stats.reciprocal(low, high) - values = distribution.rvs(size=numreals, random_state=rng) - else: - raise ValueError(msg) - return values - - -def draw_values(distname, dist_parameters, numreals, rng, normalscoresamples=None): - """ - Prepare scipy distributions with parameters - Args: - distname (str): distribution name 'normal', 'lognormal', 'triang', - 'uniform', 'logunif', 'discrete', 'pert' - dist_parameters (list): list with parameters for distribution - numreals (int): number of realizations to generate - rng (numpy.random.RandomState): random number generator instance - normalscoresamples (array, optional): samples for correlated distributions - Returns: - scipy.stats distribution with parameters - """ - if distname[0:4].lower() == "norm": - values = draw_values_normal(dist_parameters, numreals, rng, normalscoresamples) - elif distname[0:4].lower() == "logn": - values = draw_values_lognormal( - dist_parameters, numreals, rng, normalscoresamples - ) - elif distname[0:4].lower() == "unif": - values = draw_values_uniform(dist_parameters, numreals, rng, normalscoresamples) - elif distname[0:6].lower() == "triang": - values = draw_values_triangular( - dist_parameters, numreals, rng, normalscoresamples - ) - elif distname[0:4].lower() == "pert": - values = draw_values_pert(dist_parameters, numreals, rng, normalscoresamples) - elif distname[0:7].lower() == "logunif": - values = draw_values_loguniform( - dist_parameters, numreals, rng, normalscoresamples - ) - elif distname[0:5].lower() == "const": - if normalscoresamples is not None: - raise ValueError( - "Parameter with const distribution " - "was defined in correlation matrix " - "but const distribution cannot " - "be used with correlation. " - ) - values = [dist_parameters[0]] * numreals - elif distname[0:4].lower() == "disc": - if normalscoresamples is not None: - raise ValueError( - "Parameter with discrete distribution " - "was defined in correlation matrix, " - "but discrete distribution cannot " - "be used with correlation. " - ) - status, result = sample_discrete(dist_parameters, numreals, rng) - if status: - values = result - else: - raise ValueError(result) - else: - raise ValueError(f"distribution name {distname} is not implemented") - return values - - -def sample_discrete(dist_params, numreals, rng): - """Sample from discrete distribution - Args: - dist_params(list): parameters for distribution - dist_params[0] is possible outcomes separated - by comma - dist_params[1] is probabilities for each outcome, - separated by comma - numreals (int): number of realisations to draw - rng: numpy.random.RandomState instance - Returns: - np.ndarray: values drawn from distribution - """ - status = True - outcomes = re.split(",", dist_params[0]) - outcomes = [item.strip() for item in outcomes] - if len(dist_params) == 2: # non uniform - weights = re.split(",", dist_params[1]) - if len(outcomes) != len(weights): - raise ValueError( - "Number of weights for discrete distribution " - "is not the same as number of values." - ) - weightnmbr = [float(weight) for weight in weights] - fractions = [weight / sum(weightnmbr) for weight in weightnmbr] - values = rng.choice(outcomes, numreals, p=fractions) - elif len(dist_params) == 1: # uniform - values = rng.choice(outcomes, numreals) - else: - status = False - values = "Wrong input for discrete distribution" - return status, values - - -def is_number(teststring): - """Test if a string can be parsed as a float""" - try: - return not np.isnan(float(teststring)) - except ValueError: - return False - - -def read_correlations(corr_dict, corrsheet): - """Reading correlation info for a - monte carlo sensitivity - - Args: - corr_dict (OrderedDict): correlation info - - Returns: - pd.DataFrame: Dataframe with correlations, parameter names - as column and index - """ - correlations = None - filename = corr_dict["inputfile"] - if corrsheet in corr_dict["sheetnames"]: - if str(filename).endswith(".xlsx"): - correlations = pd.read_excel( - filename, corrsheet, index_col=0, engine="openpyxl" - ) - correlations.dropna(axis=0, how="all", inplace=True) - correlations = correlations.loc[ - :, ~correlations.columns.str.contains("^Unnamed") - ] - else: - raise ValueError( - "Correlation matrix filename should be on " - "Excel format and end with .xlsx " - ) - else: - raise ValueError( - "Corr_sheet {} specified but cannot be " - "found in list of sheetnames".format(corrsheet) - ) - - return correlations - - -def make_covariance_matrix(df_correlations, stddevs=None): - """Read a Pandas DataFrame defining correlation coefficients for - a set of multivariate normally distributed parameters, and build - covariance matrix. - - The diagonal of the correlation coefficients matrix should be all - ones. Variances are combined with the correlation coefficients to - compute the covariance matrix. - - If the correlation matrix is not symmetric positive definite (SDP), - the matrix is projected onto the SDP manifold and returned (together - with a warning). The algorithm is according to Higham (2000) - - Args: - df_correlations (pd.DataFrame): correlation coefficients where - columns and index are both parameter names. All parameter - names in keys muvalst also exist in index and vice versa. - - Returns: - covariance matrix - """ - - corr_matrix = np.array(df_correlations.values) - - # Assume upper triangular is empty, fill it: - i_upper = np.triu_indices(len(df_correlations.columns), 1) - corr_matrix[i_upper] = corr_matrix.T[i_upper] - - # Project to nearest symmetric positive definite matrix - if not _is_positive_definite(corr_matrix): - input_corr_matrix = corr_matrix.copy() - corr_matrix = _nearest_positive_definite(corr_matrix) - print("Input correlation matrix: ") - with np.printoptions(precision=3, suppress=True): - print(input_corr_matrix) - print("Used closest positive semi-definite correlation matrix:") - with np.printoptions(precision=3, suppress=True): - print(corr_matrix) - # Previously negative eigenvalues are now close to zero, - # but might still be negative, that can be ignored - - # Support unity standard devistions - if not stddevs: - stddevs = len(corr_matrix) * [1] - - # Now generate the covariance matrix - dim = len(stddevs) - diag = np.identity(dim) - diag[range(dim), range(dim)] = stddevs - cov_matrix = np.dot(diag, corr_matrix) - return np.dot(cov_matrix, diag) - - -def _nearest_positive_definite(a_mat): - """Implementation taken from: - https://stackoverflow.com/questions/43238173/ - python-convert-matrix-to-positive-semi-definite/43244194#43244194 - """ - - b_mat = (a_mat + a_mat.T) / 2 - _, s_mat, v_mat = la.svd(b_mat) - - h_mat = np.dot(v_mat.T, np.dot(np.diag(s_mat), v_mat)) - - a2_mat = (b_mat + h_mat) / 2 - - a3_mat = (a2_mat + a2_mat.T) / 2 - - if _is_positive_definite(a3_mat): - return a3_mat - - spacing = np.spacing(la.norm(a_mat)) - identity = np.eye(a_mat.shape[0]) - kiter = 1 - while not _is_positive_definite(a3_mat): - mineig = np.min(np.real(la.eigvals(a3_mat))) - a3_mat += identity * (-mineig * kiter**2 + spacing) - kiter += 1 - - return a3_mat - - -def _is_positive_definite(b_mat): - """Returns true when input is positive-definite, via Cholesky""" - try: - _ = la.cholesky(b_mat) - return True - except la.LinAlgError: - return False diff --git a/src/fmu/tools/sensitivities/fmudesignrunner.py b/src/fmu/tools/sensitivities/fmudesignrunner.py deleted file mode 100644 index 450fa729..00000000 --- a/src/fmu/tools/sensitivities/fmudesignrunner.py +++ /dev/null @@ -1,98 +0,0 @@ -"""Script for generating a design matrix from config input""" - -import argparse -from pathlib import Path - -from fmu.tools.sensitivities import DesignMatrix, excel2dict_design - - -def get_parser(): - parser = argparse.ArgumentParser( - description="Generate design matrix to be used with ert DESIGN2PARAMS", - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - ) - - parser.add_argument( - "config", type=str, help="Input design config filename in Excel format" - ) - parser.add_argument( - "destination", - type=str, - nargs="?", - help="Destination filename for design matrix", - default="generateddesignmatrix.xlsx", - ) - parser.add_argument( - "--designinput", - type=str, - help="Alternative sheetname for the worksheet designinput", - default="designinput", - ) - parser.add_argument( - "--defaultvalues", - type=str, - help="Alternative sheetname for worksheet defaultvalues", - default="defaultvalues", - ) - parser.add_argument( - "--general_input", - type=str, - help="Alternative sheetname for the worksheet general_input", - default="general_input", - ) - - return parser - - -def main(): - """fmudesign is a command line utility for generating design matrices - - Wrapper for the the fmu.tools.sensitivities module""" - - parser = get_parser() - args = parser.parse_args() - - # Defaulted options should be reset to None, so that the other - # defaulting level inside _excel2dict can do its work. - if args.designinput == parser.get_default("designinput"): - args.designinput = None - if args.defaultvalues == parser.get_default("defaultvalues"): - args.defaultvalues = None - if args.general_input == parser.get_default("general_input"): - args.general_input = None - - sheetnames = {} - if args.designinput: - sheetnames["designinput"] = args.designinput - if args.defaultvalues: - sheetnames["defaultvalues"] = args.defaultvalues - if args.general_input: - sheetnames["general_input"] = args.general_input - - if sheetnames: - print("Worksheets changed from default:") - print(sheetnames) - - if isinstance(args.config, str): - if not Path(args.config).is_file(): - raise IOError(f"Input file {args.config} does not exist") - input_dict = excel2dict_design(args.config, sheetnames) - - if args.config == args.destination: - raise IOError( - 'Identical name "{}" have been provided for the input' - "file and the output file. " - " Exiting.....".format(args.config) - ) - - design = DesignMatrix() - - design.generate(input_dict) - - Path(args.destination).parent.mkdir(exist_ok=True, parents=True) - - design.to_xlsx(args.destination) - - -if __name__ == "__main__": - main() diff --git a/tests/sensitivities/data/README.md b/tests/sensitivities/data/README.md deleted file mode 100644 index 77167fc6..00000000 --- a/tests/sensitivities/data/README.md +++ /dev/null @@ -1,4 +0,0 @@ -Testdata for tornadoplots from one by one sensitivities (design matrix) -distributions: contains design matrix on fmu standard format in excel and .csv format -results: contains in place volumes exported from RMS in fmu standard csv format -config: contains yaml config files for add_webviz_tornado_onebyone.py \ No newline at end of file diff --git a/tests/sensitivities/data/config/correlations.xlsx b/tests/sensitivities/data/config/correlations.xlsx deleted file mode 100644 index 6504ee445b34bc61bf2caf39781c46e5ec40db19..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 4723 zcmaJ_2RNKtx7JJa7EywzK`=x_iC&@(Mhk+`iEi}XJENCq(V~}wgy;-{L=27)Wz-;g z5WU=)oO}Mbx&Jxqd!{|lxAuJNU1h)T)=-EC7wWzi%j>L3s%-R4L$K__6HE zIdZEXF*#3T6r3syxZ@Ho?)Vnv6CZEVwcF_pvwYH3W+gEns`#_l{*Woz5xt%cF!fxs z=b5qS61ar^U8KdFg>RUHkqPFt#rjcH`4PefaUQQ5c^sBeSH7tb>A7Nzp&&B4q*s{8 zb9`Tc?_nF6`iPvC&MR7At399RsV@-k{%6BK7NT5oo3$I_GCSR$yo+?rm84h;tGR-R zG~+2iuiBE;zJ0Kn=o;m)kwnu_!Ns$x2x(eChAoDHhW!0cz>p&Euy8WhaCLHq@R>S0 z^Ljbh=fc%gdI6+33$2}nVeN0D=Rg^3+l5i3*y7sXbTuvhsR%JSKH7DqpP36V8;_!;z!GzUW`LNf6pW`^R5TE{rx zSj7n@Vo|#m`qUK1p};!F7$<-+WE2FJ=$PMx;*ChsI#js3>rGU)dmIRKI8FcI5K|e> z-*kx;-KgI%0jrAVvIEvW0`f6#bpFIAsTh>MgpA$_{on8*yNQpvldB~Pp!no&APhhl zvHL_mjV@A1jk`Z;8i>(IA*;BFwRF40n0mBWC%JlNCMBYpa4Ohv>w1J*%tL7A1N&64 zP@#?pvt}HIMq71Q1*DN{ZIKeYUqI)N*E)$sn0ExZYXlIQh!Bu(2in1y@Y*%tkloqdvrn2yy%w~|!mLef?+U(!v48W82%KXKGFj85F1dXx zr0?Jdn64qT$g~2{0_BkkuPn)U|0ke6f~M6OW<*f7L$da&b7?B%$?DZOtTAr)zLJd} z0*1e!0pJsEB4U#w-+aIT5&p0%L(;=h z5Y%jen@<4u(Bbr}_*Y5hyHhMfwW0YSAy51X6Dx*Y9|9-*nQD!ItqazJ{iZDW>RQB{ z9|j5;^}SP{F=X6q|B4LdU&uggOkFK4wA@^69jzfKU>rV4-|7L7V#C`@@fwlC- z3A!oP(PhM@r5=`Nm9KBdH^b#KuKgoUSNGyVeZZ@U12S3@uM(&K#BR}V&QbrsbN{w_ zE+ctCIJE%%Y!4uotF-sFPm)XRqb6~M5gpnjWr&)H4=4Y;Ae6@cy?9}-bnS*uQ4tAN zg&IY4_`Z3ebN=4Ru$^;6=))z2eE(S*o=@x03tO!b$itCJ#!Uf4J31;#^r8IxVq^?w z$k#s)tUG^2iOM|0#?sOa!uRuo9~Ei4t|lZ~m^7f%qOk}K!yl&QfVR%25X zrQ09d2kY*rg~_p*K0f`?L=MJt9Le=nWT0;}@NYe@UYJ>j9fydBCC1#6fV^j!cMsxV zF7XqCNJ=8EToy%@De}nKg+I<(YwSdxG}-X2qzs`qhN3+mo_)T#KX#yRhK&vb1+%_x5}LKp?DTL zQ?}eV0iyjBMgHwL0CuxpGEbFsAn$fZ0429zO!J(=(~QC?CrUm9fJFSE7O0W7(?8$S&DY9Mf!f=d9)s_@v+F5v{K%6`=qPe_|**k#H>DKBp{+Kha*>2)Y zwXbEQRU=`oLimOxPwb7$jJBR4KCw=DeT0hl*NWeM&(LiL%{GS)cEeypBwulz46k&Y zD=s!Wa2Zptxqb}Nl}d9X-I)*Y$CzL{hXqQ)3CDNmHh?ZL5tT`ZM20EZxt+ zqEMfqchH@13p70??6UWs{)C*S%aE0mTx#Q65hX}PMpK9zn;RxMAYbi%cZ5zMxs*FE zV7Od@P#VakYtqFL+Bnx2cY28y$=GB`J>k7{tsc75Ygwb?$cP$aDtwq60;ag>%HCLiXkv z!LhQLRgbIZh3l?sm}^7%qud3qNk*NKc&3Fr)G^nZaPX2GKrZ-D^P-I1Z)pP)4J{N& z?*4gR5dH5|bn~{i{5cM{GmX?|0F)8C$J`I*p~WSXnPW^~x0izVu;gC~ZuhVP%@&~p z9tDe&5c*PEP}o}!YHm=ieWysk#o3hZwt_W5_bA0g1gA)MXecQQ7e$&tmipeM{n;rN zi+QvvzFsoWS)NEDR#LuSHwQtLYJi1h%idh~_JSOXowJN-RVq!pCqva%`hlQ3LncTo znRZfX4}2$LS&Ee1g?{i|<}Evo!qu^mcwL!}le`dN2GbRn3@&W$`I?$q$C1DVri2h2 z(x+L;+@J7#?*^)0mf-jC1C&lmi%hyR3&5Gfj7G`8D~FLZ>nA?5G~M zINO@#G}+Rc@%3sdRh1Y33xZ#sRdL;$B)>gxzR@fxxAsyybWyWZ)~KtQM7gx?OAEm^ zTdF`Zx6+BfNH@)5#FFk=Mc!9X91HLv0eE=7CrvjkIP_U1mUg=TEIe;k7km=t8nEWE zGh+Yfob*-f);2K39g{fh0HG~jZkt%YGNCOJH36B%qjpzR7hQXh5mJMVu(7l?+YHtF z;3AhET&D4|9c`Ki3}l-29&0QUX+WyWRV&inW?ABeIDYu$9@@MQRBtU14W@FEE)|E5p1+z%3hpD) z4V><+MHP1CF-91Q1f3$u=RX%V$*+y@ZweF-QgfOC5Vr5?;kEF+Z75U>x!(sbv>`d~ zI&*)`CiRW${)*Myvqemt2##`_g}zU{R4+5l!X?^>qmtMB{$~FqVEPnKzEp`(!YMu-e;6&s#y`rp{Ib} zjyxn`gnjg)D7>V265Kk@FI>vncUewHz*adgyVEZ{C!70v z!oYapG^6iJYO8dsgVLbnnw)U#2*%r6K9Jnh1=DiUyj}!Jkz1BK; z0>7xIE!p?BYld8L^uoK@4aIcn_0ogA>{hc}3Qb#It~ueeOqxWrTjT2tGBXR!hnU9H ze)Q-u$$-Sv!pZ6e^!0VKJYF8J{v21M1vb%HNP7G6Ur8JVGOLENPL6Jtj&3H}-p-Z~ zqo2$swOa)SpzPm0=T@#6dwsyfyzN2yWTVSFJ+rO4z=0eQ+iop;qWzW!PQ^#OA+g@73}(9G{KVD4_5}cm3WgW=Ovpr&<*#8nrcq;NC-|vjd%KZ(5qdFQL)}7X#?1EIx+MD3_Z?c z!zQc}1n+mH2aWc)*l&{oBX_OUHdvx%n_`b(=I5*a*=5&c4@1i}@m58c=h;NXUfjYP z(!eD34q-&#o+%bVMU$5W+MW64uOj!OW{7zidTo5-Y8y4u@Lq27(5^7i9YX zWX@)kFnU2G=z3VFRzk<1MEi9WMuYsT-_`W*-Zy0q zN?rak5oBfj$NO)s`Mdi~Er8NOzibG(7yh#{`hA_7Y#XH;KR8zwr!AsE*3^)5CWX-N5~>E^(kJm!HQ~kCumc20)Ta zD+#mtC7s-Dwp#?Q;)jSBroBu2*kM9EJ>k|yd#ewD%2Y|{f(feuK)J-IbQjv$#SLAh z;VVl{@v{=&5R0ER6B{o|j}??Y$2|SOjH$g`9Y7_N2St7tpVhw1;;VY0ZIEv4ya1@eLEdRJy4}ZxOq=PL}ME}&9O9kZp z_-ef2{*&vXYb83}8Qy`v$ zyg5R)=>WHiMK_m7P{s^2{VR43z1^}s zzhV3J1q2}f|KNV33O&*N_ZjJLYD0Zsc@l~0Vd*-eHGuZ$T0B<9oU{XVw7$sKh%Lh!iD zToHkY%th4XS{a=D>fj7UN%}KM)S+T$0KsMPVev6lO!61GOJ@vac}q#I)aW*m==_av zE&MdC8YTo%A$Bk-PpY5hkc`Hb;d9lGMM0&D%HW!2ww%-W>2#mvq@oiTzEBS7^SM-% z5l2I_)hf>sYl4RtOcf<_4vQMYte>2Ozx0f)d+vnOyOBOTX=F1-6bYFTpBbmchDmZB zd^8)FZ^ko!4X{Dm0bpPuP-Se! 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a/tests/sensitivities/snapshots/test_create_design/test_generate_full_mc_snapshot/design_output_mc_with_correls.json b/tests/sensitivities/snapshots/test_create_design/test_generate_full_mc_snapshot/design_output_mc_with_correls.json deleted file mode 100644 index 9fd63161..00000000 --- a/tests/sensitivities/snapshots/test_create_design/test_generate_full_mc_snapshot/design_output_mc_with_correls.json +++ /dev/null @@ -1,9021 +0,0 @@ -{ - "defaultvalues": { - "DATO": "2018-11-03", - "FAULTSEAL": 0.01, - "HUM_METHOD": "SIMPLE", - "HUM_MODE": "PREDICTION", - "INJ_UNC": 0, - "NTG1": 0.25, - "NTG2": 0.45, - "NTG6": 0.8, - "OWC1": 0.001, - "OWC2": 0.1, - "OWC3": 0.07, - "PARAM1": 0.035, - "PARAM2": 0.05, - "PARAM3": 0.02, - "RMS_SEED": 1000 - }, - "designvalues": [ - { - "DATO": "2018-11-02", - "DERIVED_PARAM1": "1", - "DERIVED_PARAM2": "a", - "FAULTSEAL": 0.114, - "NTG1": 0.85, - "NTG2": 0.61, - "OWC1": 2533.3, - "OWC2": 2459.9, - "OWC3": 2451.9, - "PARAM1": 0.031, - "PARAM2": -0.24, - 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"PARAM1": 0.036, - "PARAM2": -0.75, - "PARAM3": 0.064, - "REAL": 482, - "RMS_SEED": 1482.0, - "SENSCASE": "p10_p90", - "SENSNAME": "montecarlo" - }, - { - "DATO": "2018-11-04", - "DERIVED_PARAM1": "3", - "DERIVED_PARAM2": "c", - "FAULTSEAL": 0.265, - "NTG1": 0.92, - "NTG2": 0.6, - "OWC1": 2528.6, - "OWC2": 2490.4, - "OWC3": 2412.8, - "PARAM1": 0.033, - "PARAM2": -0.31, - "PARAM3": 0.049, - "REAL": 483, - "RMS_SEED": 1483.0, - "SENSCASE": "p10_p90", - "SENSNAME": "montecarlo" - }, - { - "DATO": "2018-11-02", - "DERIVED_PARAM1": "1", - "DERIVED_PARAM2": "a", - "FAULTSEAL": 0.002, - "NTG1": 0.85, - "NTG2": 0.56, - "OWC1": 2515.5, - "OWC2": 2465.0, - "OWC3": 2451.2, - "PARAM1": 0.036, - "PARAM2": 0.36, - "PARAM3": 0.058, - "REAL": 484, - "RMS_SEED": 1484.0, - "SENSCASE": "p10_p90", - "SENSNAME": "montecarlo" - }, - { - "DATO": "2018-11-03", - "DERIVED_PARAM1": "2", - "DERIVED_PARAM2": "b", - "FAULTSEAL": 0.739, - "NTG1": 0.78, - "NTG2": 0.63, - "OWC1": 2538.0, - "OWC2": 2487.9, - "OWC3": 2417.9, - "PARAM1": 0.029, - "PARAM2": -0.31, - "PARAM3": 1.898, - "REAL": 485, - "RMS_SEED": 1485.0, - "SENSCASE": "p10_p90", - "SENSNAME": "montecarlo" - }, - { - "DATO": "2018-11-02", - "DERIVED_PARAM1": "1", - "DERIVED_PARAM2": "a", - "FAULTSEAL": 0.171, - "NTG1": 0.94, - "NTG2": 0.58, - "OWC1": 2548.1, - "OWC2": 2467.0, - "OWC3": 2435.5, - "PARAM1": 0.031, - "PARAM2": 0.28, - "PARAM3": 0.166, - "REAL": 486, - "RMS_SEED": 1486.0, - "SENSCASE": "p10_p90", - "SENSNAME": "montecarlo" - }, - { - "DATO": "2018-11-03", - "DERIVED_PARAM1": "2", - "DERIVED_PARAM2": "b", - "FAULTSEAL": 0.002, - "NTG1": 0.92, - "NTG2": 0.61, - "OWC1": 2521.1, - "OWC2": 2470.8, - "OWC3": 2463.2, - "PARAM1": 0.041, - "PARAM2": -0.43, - "PARAM3": 0.0, - "REAL": 487, - "RMS_SEED": 1487.0, - "SENSCASE": "p10_p90", - "SENSNAME": "montecarlo" - }, - { - "DATO": "2018-11-04", - "DERIVED_PARAM1": "3", - "DERIVED_PARAM2": "c", - "FAULTSEAL": 0.001, - "NTG1": 0.84, - "NTG2": 0.61, - "OWC1": 2510.3, - "OWC2": 2456.4, - "OWC3": 2470.4, - "PARAM1": 0.036, - "PARAM2": -0.37, - "PARAM3": 6.932, - "REAL": 488, - "RMS_SEED": 1488.0, - "SENSCASE": "p10_p90", - "SENSNAME": "montecarlo" - }, - { - "DATO": "2018-11-04", - "DERIVED_PARAM1": "3", - "DERIVED_PARAM2": "c", - "FAULTSEAL": 0.003, - "NTG1": 0.82, - "NTG2": 0.55, - "OWC1": 2542.2, - "OWC2": 2474.4, - "OWC3": 2430.3, - "PARAM1": 0.031, - "PARAM2": 0.42, - "PARAM3": 126455989.192, - "REAL": 489, - "RMS_SEED": 1489.0, - "SENSCASE": "p10_p90", - "SENSNAME": "montecarlo" - }, - { - "DATO": "2018-11-02", - "DERIVED_PARAM1": "1", - "DERIVED_PARAM2": "a", - "FAULTSEAL": 0.026, - "NTG1": 0.82, - "NTG2": 0.62, - "OWC1": 2549.2, - "OWC2": 2476.4, - "OWC3": 2428.7, - "PARAM1": 0.035, - "PARAM2": 0.71, - "PARAM3": 0.116, - "REAL": 490, - "RMS_SEED": 1490.0, - "SENSCASE": "p10_p90", - "SENSNAME": "montecarlo" - }, - { - "DATO": "2018-11-03", - "DERIVED_PARAM1": "2", - "DERIVED_PARAM2": "b", - "FAULTSEAL": 0.052, - "NTG1": 0.84, - "NTG2": 0.61, - "OWC1": 2513.0, - "OWC2": 2459.8, - "OWC3": 2467.8, - "PARAM1": 0.036, - "PARAM2": 0.67, - "PARAM3": 44.882, - "REAL": 491, - "RMS_SEED": 1491.0, - "SENSCASE": "p10_p90", - "SENSNAME": "montecarlo" - }, - { - "DATO": "2018-11-02", - "DERIVED_PARAM1": "1", - "DERIVED_PARAM2": "a", - "FAULTSEAL": 0.1, - "NTG1": 0.91, - "NTG2": 0.64, - "OWC1": 2503.9, - "OWC2": 2466.9, - "OWC3": 2482.4, - "PARAM1": 0.038, - "PARAM2": 0.49, - "PARAM3": 207.649, - "REAL": 492, - "RMS_SEED": 1492.0, - "SENSCASE": "p10_p90", - "SENSNAME": "montecarlo" - }, - { - "DATO": "2018-11-04", - "DERIVED_PARAM1": "3", - "DERIVED_PARAM2": "c", - "FAULTSEAL": 0.045, - "NTG1": 0.81, - "NTG2": 0.6, - "OWC1": 2504.8, - "OWC2": 2445.6, - "OWC3": 2478.5, - "PARAM1": 0.041, - "PARAM2": -0.05, - "PARAM3": 155465.455, - "REAL": 493, - "RMS_SEED": 1493.0, - "SENSCASE": "p10_p90", - "SENSNAME": "montecarlo" - }, - { - "DATO": "2018-11-03", - "DERIVED_PARAM1": "2", - "DERIVED_PARAM2": "b", - "FAULTSEAL": 0.004, - "NTG1": 0.78, - "NTG2": 0.56, - "OWC1": 2523.1, - "OWC2": 2469.4, - "OWC3": 2453.5, - "PARAM1": 0.036, - "PARAM2": 0.78, - "PARAM3": 39771525.305, - "REAL": 494, - "RMS_SEED": 1494.0, - "SENSCASE": "p10_p90", - "SENSNAME": "montecarlo" - }, - { - "DATO": "2018-11-04", - "DERIVED_PARAM1": "3", - "DERIVED_PARAM2": "c", - "FAULTSEAL": 0.273, - "NTG1": 0.86, - "NTG2": 0.62, - "OWC1": 2543.8, - "OWC2": 2453.0, - "OWC3": 2436.9, - "PARAM1": 0.037, - "PARAM2": 0.97, - "PARAM3": 0.01, - "REAL": 495, - "RMS_SEED": 1495.0, - "SENSCASE": "p10_p90", - "SENSNAME": "montecarlo" - }, - { - "DATO": "2018-11-03", - "DERIVED_PARAM1": "2", - "DERIVED_PARAM2": "b", - "FAULTSEAL": 0.013, - "NTG1": 0.86, - "NTG2": 0.59, - "OWC1": 2512.2, - "OWC2": 2475.1, - "OWC3": 2459.1, - "PARAM1": 0.037, - "PARAM2": -0.14, - "PARAM3": 74.907, - "REAL": 496, - "RMS_SEED": 1496.0, - "SENSCASE": "p10_p90", - "SENSNAME": "montecarlo" - }, - { - "DATO": "2018-11-04", - "DERIVED_PARAM1": "3", - "DERIVED_PARAM2": "c", - "FAULTSEAL": 0.085, - "NTG1": 0.94, - "NTG2": 0.59, - "OWC1": 2532.4, - "OWC2": 2461.1, - "OWC3": 2482.6, - "PARAM1": 0.036, - "PARAM2": -0.11, - "PARAM3": 2293238.745, - "REAL": 497, - "RMS_SEED": 1497.0, - "SENSCASE": "p10_p90", - "SENSNAME": "montecarlo" - }, - { - "DATO": "2018-11-04", - "DERIVED_PARAM1": "3", - "DERIVED_PARAM2": "c", - "FAULTSEAL": 0.008, - "NTG1": 0.81, - "NTG2": 0.57, - "OWC1": 2510.8, - "OWC2": 2467.3, - "OWC3": 2453.7, - "PARAM1": 0.037, - "PARAM2": -0.5, - "PARAM3": 801.058, - "REAL": 498, - "RMS_SEED": 1498.0, - "SENSCASE": "p10_p90", - "SENSNAME": "montecarlo" - }, - { - "DATO": "2018-11-02", - "DERIVED_PARAM1": "1", - "DERIVED_PARAM2": "a", - "FAULTSEAL": 0.009, - "NTG1": 0.81, - "NTG2": 0.59, - "OWC1": 2531.6, - "OWC2": 2451.1, - "OWC3": 2479.2, - "PARAM1": 0.036, - "PARAM2": 0.76, - "PARAM3": 206.0, - "REAL": 499, - "RMS_SEED": 1499.0, - "SENSCASE": "p10_p90", - "SENSNAME": "montecarlo" - } - ] -} \ No newline at end of file diff --git a/tests/sensitivities/test_calc_tornado.py b/tests/sensitivities/test_calc_tornado.py deleted file mode 100755 index 11ea0132..00000000 --- a/tests/sensitivities/test_calc_tornado.py +++ /dev/null @@ -1,95 +0,0 @@ -from pathlib import Path - -import pandas as pd - -from fmu.tools.sensitivities import calc_tornadoinput, summarize_design - -TESTDATA = Path(__file__).parent / "data" - - -def test_designsummary(): - """Test import and summary of design matrix""" - - snorrebergdesign = summarize_design( - TESTDATA / "distributions/design.xlsx", "DesignSheet01" - ) - # checking dimensions and some values in summary of design matrix - assert snorrebergdesign.shape == (7, 9) - - assert ( - snorrebergdesign.columns - == [ - "sensno", - "sensname", - "senstype", - "casename1", - "startreal1", - "endreal1", - "casename2", - "startreal2", - "endreal2", - ] - ).all() - assert snorrebergdesign["sensname"][0] == "rms_seed" - assert snorrebergdesign["senstype"][0] == "mc" - assert snorrebergdesign["casename1"][0] == "P10_P90" - assert snorrebergdesign["startreal1"][0] == 0 - assert snorrebergdesign["endreal1"][0] == 9 - assert snorrebergdesign["casename2"][0] is None - assert snorrebergdesign["startreal2"][0] is None - assert snorrebergdesign["endreal2"][0] is None - - assert snorrebergdesign["sensname"][6] == "relp_go" - assert snorrebergdesign["senstype"][6] == "scalar" - assert snorrebergdesign["casename1"][6] == "lc" - assert snorrebergdesign["startreal1"][6] == 90 - assert snorrebergdesign["endreal1"][6] == 99 - assert snorrebergdesign["casename2"][6] == "hc" - assert snorrebergdesign["startreal2"][6] == 100 - assert snorrebergdesign["endreal2"][6] == 109 - - assert snorrebergdesign["endreal1"].sum() == 333 - - # Test same also when design matrix is in .csv format - designcsv = summarize_design(TESTDATA / "distributions/design.csv") - assert snorrebergdesign.equals(designcsv) - - -def test_calc_tornadoinput(): - """Test calculating values for tornadoplot input""" - # Read file with summary of design - summary = pd.read_csv(TESTDATA / "distributions/designsummary.csv", na_values="nan") - des_summary = summary.where(pd.notnull(summary), None) - - # Read resultfile for the test ensemble - results = pd.read_csv(TESTDATA / "results/geovolumes_collected.csv") - - # Calculate and check results of one tornado calculation - (tornadotable, ref_value) = calc_tornadoinput( - des_summary, - results, - "STOIIP_OIL", - ["ITER", "ZONE", "REGION"], - [["iter-0"], ["Nansen", "Larsson"], ["oil_zone_Nansen_Larsson"]], - "rms_seed", - "percentage", - ) - - assert int(tornadotable["low"].sum()) == -21 - assert int(tornadotable["high"].sum()) == 11 - assert int(ref_value) == 9330662 - - # Check summing over all zones and regions before calculations - (tornadotable, ref_value) = calc_tornadoinput( - des_summary, - results, - "STOIIP_OIL", - ["ITER", "ZONE", "REGION"], - [["all"], ["all"], ["all"]], - "rms_seed", - "absolute", - ) - - assert int(tornadotable["low"].sum()) == -2142167 - assert int(tornadotable["high"].sum()) == 1478053 - assert int(ref_value) == 12855200 diff --git a/tests/sensitivities/test_create_design.py b/tests/sensitivities/test_create_design.py deleted file mode 100755 index dce065e0..00000000 --- a/tests/sensitivities/test_create_design.py +++ /dev/null @@ -1,344 +0,0 @@ -"""Testing code for generation of design matrices""" - -import json -import shutil -from datetime import datetime -from pathlib import Path - -import numpy as np -import pandas as pd -import pytest -from scipy import stats - -import fmu.tools -from fmu.tools.sensitivities import DesignMatrix, excel2dict_design - -TESTDATA = Path(__file__).parent / "data" - - -def test_generate_onebyone(tmpdir): - """Test generation of onebyone design""" - - inputfile = TESTDATA / "config/design_input_example1.xlsx" - - input_dict = excel2dict_design(inputfile) - - # Note that repeats are set to 10 in general_input sheet. - # So, there are 10 rows for each senscase of type seed and scenario. - # However, there are 20 rows for multz because numreal is set to 20 in designinput. - rows_in_design_matrix = 80 - - design = DesignMatrix() - design.generate(input_dict) - # Checking dimensions of design matrix - assert design.designvalues.shape == (rows_in_design_matrix, 10) - - # Write to disk and check some validity - tmpdir.chdir() - design.to_xlsx("designmatrix.xlsx") - assert Path("designmatrix.xlsx").exists - diskdesign = pd.read_excel("designmatrix.xlsx", engine="openpyxl") - - assert ( - diskdesign.columns - == [ - "REAL", - "SENSNAME", - "SENSCASE", - "RMS_SEED", - "FAULT_POSITION", - "DC_MODEL", - "OWC1", - "OWC2", - "OWC3", - "MULTZ_ILE", - ] - ).all() - assert (diskdesign["REAL"].values == np.arange(rows_in_design_matrix)).all() - ensemble_size = 10 - sensname = ( - ["rms_seed"] * ensemble_size - + ["faults"] * 2 * ensemble_size # 2 senscases, east and west - + ["velmodel"] * ensemble_size - + ["contacts"] * 2 * ensemble_size # 2 contacts, shallow and deep - + ["multz"] * 20 - ) - assert (diskdesign["SENSNAME"] == sensname).all() - # Sensitivites of type seed like rms_seed automatically get senscase p10_p90, - # so that P10/P90 is calculated for the tornado plot. - assert ( - diskdesign[diskdesign["SENSNAME"] == "rms_seed"]["SENSCASE"] == "p10_p90" - ).all() - assert ( - diskdesign[diskdesign["SENSNAME"] == "faults"]["SENSCASE"] - == ["east"] * ensemble_size + ["west"] * ensemble_size - ).all() - assert ( - diskdesign[diskdesign["SENSNAME"] == "velmodel"]["SENSCASE"] == "alternative" - ).all() - assert ( - diskdesign[diskdesign["SENSNAME"] == "contacts"]["SENSCASE"] - == ["shallow"] * ensemble_size + ["deep"] * ensemble_size - ).all() - assert ( - diskdesign[diskdesign["SENSNAME"] == "multz"]["SENSCASE"] == ["p10_p90"] * 20 - ).all() - - # When rms_seed is set to default it means that RMS_SEED numbers - # 1000, 1001,... are used. - # Note that for most senscases, RMS_SEED goes from 1000 to 1009, - # but that it goes from 1000 to 1019 for multz because numreal - # is set to 20 in the designinput sheet. - assert ( - diskdesign["RMS_SEED"] - == list(range(1000, 1000 + ensemble_size)) * 6 + list(range(1000, 1000 + 20)) - ).all() - - diskdefaults = pd.read_excel( - "designmatrix.xlsx", sheet_name="DefaultValues", header=None, engine="openpyxl" - ) - assert (diskdefaults.columns == [0, 1]).all() - assert ( - diskdefaults.iloc[:, 0] - == [ - "RMS_SEED", - "FAULT_POSITION", - "DC_MODEL", - "OWC1", - "OWC2", - "OWC3", - "MULTZ_ILE", - "PARAM1", - "PARAM2", - "PARAM3", - "PARAM4", - ] - ).all() - - diskdefaults = diskdefaults.set_index(0) - - # FAULT_POSITION has two senscases, east with value -1 and west with value 1, - # so we expect ensemble_size number of rows with -1s and ensemble_size rows with 1s. - # We expect the remaining rows to be set to the base value - # set in the defaultvalues sheet. - fault_position_base = diskdefaults.loc["FAULT_POSITION"].to_list() - fault_position = ( - fault_position_base * ensemble_size - + [-1] * ensemble_size - + [1] * ensemble_size - + fault_position_base * (rows_in_design_matrix - 3 * ensemble_size) - ) - assert (diskdesign["FAULT_POSITION"] == fault_position).all() - - dc_model_base = diskdefaults.loc["DC_MODEL"].to_list() - dc_model = ( - dc_model_base * 3 * ensemble_size - + ["alternative"] * ensemble_size - + dc_model_base * (rows_in_design_matrix - 4 * ensemble_size) - ) - assert (diskdesign["DC_MODEL"] == dc_model).all() - - owc1_base = diskdefaults.loc["OWC1"].to_list() - owc1 = ( - owc1_base * 4 * ensemble_size - + [2600] * ensemble_size - + [2700] * ensemble_size - + owc1_base * (rows_in_design_matrix - 6 * ensemble_size) - ) - assert (diskdesign["OWC1"] == owc1).all() - - owc2_base = diskdefaults.loc["OWC2"].to_list() - owc2 = ( - owc2_base * 4 * ensemble_size - + [2700] * ensemble_size - + [2800] * ensemble_size - + owc2_base * (rows_in_design_matrix - 6 * ensemble_size) - ) - assert (diskdesign["OWC2"] == owc2).all() - - owc3_base = diskdefaults.loc["OWC3"].to_list() - owc3 = ( - owc3_base * 4 * ensemble_size - + [2800] * ensemble_size - + [2900] * ensemble_size - + owc3_base * (rows_in_design_matrix - 6 * ensemble_size) - ) - assert (diskdesign["OWC3"] == owc3).all() - - # MULTZ_ILE contains random numbers so we won't test it here. - - diskmetadata = pd.read_excel( - "designmatrix.xlsx", sheet_name="Metadata", engine="openpyxl" - ) - - assert (diskmetadata.columns == ["Description", "Value"]).all() - assert diskmetadata["Description"].iloc[0] == "Created using fmu-tools version:" - assert diskmetadata["Value"].iloc[0] == fmu.tools.__version__ - assert diskmetadata["Description"].iloc[1] == "Created on:" - - # For the timestamp, we can't check the exact value since it will differ - # each time the test runs. Instead, we can verify it's a valid datetime string - # by attempting to parse it - timestamp = diskmetadata["Value"].iloc[1] - try: - datetime.fromisoformat(timestamp) - except ValueError: - pytest.fail("Timestamp in Metadata sheet is not in expected format") - - -def test_generate_full_mc_snapshot(snapshot): - """Test that full monte carlo design matrix generation remains consistent. - - This is a snapshot test that verifies the entire output of the design matrix - generation process, including both the design values and default values. - """ - # Setup - inputfile = TESTDATA / "config/design_input_mc_with_correls.xlsx" - input_dict = excel2dict_design(inputfile) - design = DesignMatrix() - - # Generate the design matrix - design.generate(input_dict) - - # Prepare data for snapshot comparison - snapshot_dict = { - "designvalues": design.designvalues.to_dict("records"), - "defaultvalues": dict(design.defaultvalues), - } - - # Serialize to string for snapshot comparison - snapshot_str = json.dumps( - snapshot_dict, - indent=2, - sort_keys=True, - ) - - # Verify against snapshot - snapshot.assert_match(snapshot_str, "design_output_mc_with_correls.json") - - -def test_generate_full_mc(tmpdir): - """Test generation of full monte carlo""" - inputfile = TESTDATA / "config/design_input_mc_with_correls.xlsx" - input_dict = excel2dict_design(inputfile) - - design = DesignMatrix() - design.generate(input_dict) - - # Checking dimensions of design matrix - assert design.designvalues.shape == (500, 16) - - # Checking reproducibility from distribution_seed - assert design.designvalues["PARAM1"].sum() == 17.419 - - # Write to disk and check some validity - tmpdir.chdir() - design.to_xlsx("designmatrix.xlsx") - assert Path("designmatrix.xlsx").exists - diskdesign = pd.read_excel( - "designmatrix.xlsx", sheet_name="DesignSheet01", engine="openpyxl" - ) - assert "REAL" in diskdesign - assert "SENSNAME" in diskdesign - assert "SENSCASE" in diskdesign - assert not diskdesign.empty - - diskdefaults = pd.read_excel( - "designmatrix.xlsx", sheet_name="DefaultValues", engine="openpyxl" - ) - assert not diskdefaults.empty - assert len(diskdefaults.columns) == 2 - - # Make sure adding dependent discrete parameters works. - disk_depends = pd.read_excel(inputfile, sheet_name="depend1", engine="openpyxl") - df_merged = pd.merge(diskdesign, disk_depends, on="DATO", how="inner") - assert (df_merged["DERIVED_PARAM1_x"] == df_merged["DERIVED_PARAM1_y"]).all() - assert (df_merged["DERIVED_PARAM2_x"] == df_merged["DERIVED_PARAM2_y"]).all() - - # Check that variables are correlated using Pearson correlation - # Using 95% confidence intervals for correlation coefficients. - # - # The confidence interval calculation assumes: - # - Large sample size (n > 30) - # - Bivariate normal distribution of variables - # - Linear relationship between variables - # When these assumptions are violated (e.g. with skewed distributions), - # the intervals become less reliable - r_obj = stats.pearsonr(diskdesign["OWC1"], diskdesign["OWC2"]) - r_ci = r_obj.confidence_interval(confidence_level=0.95) - assert r_ci[0] <= 0.5 <= r_ci[1] - - r_obj = stats.pearsonr(diskdesign["OWC2"], diskdesign["OWC3"]) - r_ci = r_obj.confidence_interval(confidence_level=0.95) - assert r_ci[0] <= -0.7 <= r_ci[1] - - r_obj = stats.pearsonr(diskdesign["PARAM1"], diskdesign["PARAM2"]) - r_ci = r_obj.confidence_interval(confidence_level=0.95) - assert r_ci[0] <= 0 <= r_ci[1] - - # Using wide tolerance because the non-linear transformation between normal - # and target distributions can alter correlation strength. - assert np.isclose( - stats.spearmanr(diskdesign["PARAM1"], diskdesign["PARAM3"])[0], 0.2, atol=0.1 - ) - - -def test_generate_background(tmpdir): - inputfile = TESTDATA / "config/design_input_background.xlsx" - input_dict = excel2dict_design(inputfile) - source_file = TESTDATA / "config/doe1.xlsx" - dest_file = tmpdir.join("doe1.xlsx") - shutil.copy2(source_file, dest_file) - - with tmpdir.as_cwd(): - design = DesignMatrix() - design.generate(input_dict) - - # Check that background parameters have same values in different sensitivies. - background_params = ["PARAM17", "PARAM18", "PARAM19"] - - background_vals = design.designvalues.loc[ - design.designvalues["SENSNAME"] == "background", background_params - ] - velmodel_vals = design.designvalues.loc[ - design.designvalues["SENSNAME"] == "velmodel", background_params - ] - - assert (background_vals.values == velmodel_vals.values).all() - - faults_vals = design.designvalues.loc[ - design.designvalues["SENSNAME"] == "faults", background_params - ] - contacts_vals = design.designvalues.loc[ - design.designvalues["SENSNAME"] == "contacts", background_params - ] - - assert (faults_vals.values == contacts_vals.values).all() - - sens6 = design.designvalues[design.designvalues["SENSNAME"] == "sens6"] - # PARAM5 ~ TruncatedNormal(3, 1, 1, 5) - # PARAM6 ~ Uniform(0, 1) - assert np.isclose( - stats.spearmanr(sens6["PARAM5"], sens6["PARAM6"])[0], - 0.8, - atol=0.1, - ) - sens7 = design.designvalues[design.designvalues["SENSNAME"] == "sens7"] - - # PARAM9 and PARAM10 have a target correlation of 0.9 in the design config. - # The input correlation matrix is not positive semi-definite and is - # transformed to the closest positive semi-definite correlation matrix. - # The new correlation coefficient is 0.8. - # Using wide tolerance because the non-linear transformation between normal - # and target distributions can alter correlation strength. - assert np.isclose( - stats.spearmanr(sens7["PARAM9"], sens7["PARAM10"])[0], - 0.8, - atol=0.2, - ) - - assert np.isclose( - stats.spearmanr(sens7["PARAM10"], sens7["PARAM11"])[0], - 0.8, - atol=0.20, - ) diff --git a/tests/sensitivities/test_design_distributions.py b/tests/sensitivities/test_design_distributions.py deleted file mode 100644 index 1f4f85da..00000000 --- a/tests/sensitivities/test_design_distributions.py +++ /dev/null @@ -1,280 +0,0 @@ -"""Testing statistical helper functions for the design matrix generator""" - -import numbers - -import numpy as np -import pytest - -from fmu.tools.sensitivities import design_distributions as dists - -# pylint: disable=protected-access - - -def test_check_dist_params_normal(): - """Test normal dist param checker""" - # First element in returned 2-tuple is True or False: - assert not dists._check_dist_params_normal([])[0] - assert not dists._check_dist_params_normal(())[0] - - assert not dists._check_dist_params_normal([0])[0] - assert not dists._check_dist_params_normal([0, 0, 0])[0] - assert not dists._check_dist_params_normal([0, 0, 0, 0, 0])[0] - - assert not dists._check_dist_params_normal(["mean", "mu"])[0] - - assert dists._check_dist_params_normal([0, 1])[0] - assert not dists._check_dist_params_normal([0, -1])[0] - assert dists._check_dist_params_normal([0, 0])[0] # edge case - - # Truncated - assert dists._check_dist_params_normal([0, 1, 0, 1])[0] - - -def test_check_dist_params_lognormal(): - """Test lognormal dist param checker""" - assert not dists._check_dist_params_lognormal([])[0] - assert not dists._check_dist_params_lognormal(())[0] - - assert not dists._check_dist_params_lognormal([0])[0] - assert not dists._check_dist_params_lognormal([0, 0, 0])[0] - - assert not dists._check_dist_params_lognormal(["mean", "mu"])[0] - - assert dists._check_dist_params_lognormal([0, 1])[0] - assert not dists._check_dist_params_lognormal([0, -1])[0] - - assert dists._check_dist_params_lognormal([0, 0])[0] # edge case - - -def test_check_dist_params_uniform(): - """Test lognormal dist param checker""" - assert not dists._check_dist_params_uniform([])[0] - assert not dists._check_dist_params_uniform(())[0] - - assert not dists._check_dist_params_uniform([0])[0] - assert not dists._check_dist_params_uniform([0, 0, 0])[0] - - assert not dists._check_dist_params_uniform(["mean", "mu"])[0] - - assert dists._check_dist_params_uniform([0, 1])[0] - assert not dists._check_dist_params_uniform([0, -1])[0] - - assert dists._check_dist_params_uniform([0, 0])[0] # edge case - - -def test_check_dist_params_triang(): - """Test triang dist param checker""" - assert not dists._check_dist_params_triang([])[0] - assert not dists._check_dist_params_triang(())[0] - - assert not dists._check_dist_params_triang([0])[0] - assert not dists._check_dist_params_triang([0, 0])[0] - assert not dists._check_dist_params_triang([0, 0, 0, 0])[0] - - assert not dists._check_dist_params_triang(["foo", "bar", 0])[0] - - assert dists._check_dist_params_triang([0, 1, 2])[0] - assert not dists._check_dist_params_triang([0, -1, -4])[0] - assert not dists._check_dist_params_triang([0, 1000, 999.99])[0] - - assert dists._check_dist_params_triang([0, 0, 0])[0] # edge case - - -def test_check_dist_params_pert(): - """Test pert dist param checker""" - assert not dists._check_dist_params_pert([])[0] - assert not dists._check_dist_params_pert(())[0] - - assert not dists._check_dist_params_pert([0])[0] - assert not dists._check_dist_params_pert([0, 0])[0] - assert not dists._check_dist_params_pert([0, 0, 0, 0, 0])[0] - - assert not dists._check_dist_params_pert(["foo", "bar", 0])[0] - - assert dists._check_dist_params_pert([0, 1, 2])[0] - assert not dists._check_dist_params_pert([0, -1, -4])[0] - assert not dists._check_dist_params_pert([0, 1000, 999.99])[0] - assert dists._check_dist_params_pert([0, 1000, 1000, 999.99])[0] - - assert dists._check_dist_params_pert([0, 0, 0])[0] # edge case - - -def test_check_dist_params_logunif(): - """Test logunif dist param checker""" - assert not dists._check_dist_params_logunif([])[0] - assert not dists._check_dist_params_logunif(())[0] - - assert not dists._check_dist_params_logunif([0])[0] - assert not dists._check_dist_params_logunif([0, 0, 0])[0] - - assert not dists._check_dist_params_logunif(["foo", "bar"])[0] - - assert not dists._check_dist_params_logunif([0, 1])[0] - assert dists._check_dist_params_logunif([0.00001, 1])[0] - assert dists._check_dist_params_logunif([0.0000001, 0.00001])[0] - assert not dists._check_dist_params_logunif([0.00001, 0.0000001])[0] - - assert dists._check_dist_params_logunif([1, 1])[0] - - -def test_draw_values_normal(): - rng = np.random.RandomState() - values = dists.draw_values_normal([0, 1], 10, rng) - assert len(values) == 10 - assert all(isinstance(value, numbers.Number) for value in values) - - with pytest.raises( - ValueError, - match=( - "Normal distribution must have 2 parameters or 4 for a truncated normal, " - "but had 3 parameters." - ), - ): - values = dists.draw_values_normal([0, 1, 2], 10, rng) - - with pytest.raises( - ValueError, match="Parameters for normal distribution must be numbers." - ): - values = dists.draw_values_normal([0, "b"], 10, rng) - - with pytest.raises( - ValueError, match="Stddev for normal distribution must be >= 0." - ): - values = dists.draw_values_normal([0, -1], 10, rng) - - with pytest.raises( - ValueError, - match=( - "For truncated normal distribution, " - "lower bound must be less than upper bound, " - r"but got \[2, -1\]." - ), - ): - values = dists.draw_values_normal([0, 1, 2, -1], 10, rng) - - values = dists.draw_values_normal([0, 10, -1, 2], 50, rng) - assert all(-1 <= value <= 2 for value in values) - - -def test_draw_values_uniform(): - rng = np.random.RandomState() - - values = dists.draw_values_uniform([10, 100], 0, rng) - assert len(values) == 0 - - values = dists.draw_values_uniform([10, 100], 20, rng) - assert len(values) == 20 - assert all(isinstance(value, numbers.Number) for value in values) - assert all(10 <= value <= 100 for value in values) - - with pytest.raises( - ValueError, - match="Uniform distribution must have 2 parameters, but had 3 parameters.", - ): - values = dists.draw_values_uniform([10, 50, 100], 10, rng) - - with pytest.raises( - ValueError, match="Uniform distribution must have dist_param2 >= dist_param1" - ): - values = dists.draw_values_uniform([50, 10], 10, rng) - - with pytest.raises( - ValueError, match="Parameters for uniform distribution must be numbers." - ): - values = dists.draw_values_uniform(["a", 10], 10, rng) - - with pytest.raises(ValueError, match="numreal must be a positive integer"): - values = dists.draw_values_uniform([10, 50], -10, rng) - - -def test_draw_values_triangular(): - rng = np.random.RandomState() - - assert not dists.draw_values_triangular([10, 100, 1000], 0, rng).size - with pytest.raises(ValueError): - assert not dists.draw_values_triangular([10, 100, 1000], -1, rng) - - with pytest.raises(TypeError): - assert not dists.draw_values_triangular([10, 100, 1000], "somestring", rng) - - values = dists.draw_values_triangular([10, 100, 1000], 15, rng) - assert len(values) == 15 - assert all(isinstance(value, numbers.Number) for value in values) - assert all(10 <= value <= 1000 for value in values) - - -def test_draw_values_pert(): - rng = np.random.RandomState() - - assert not dists.draw_values_pert([10, 50, 100], 0, rng).size - - values = dists.draw_values_pert([10, 50, 100], 20, rng) - assert len(values) == 20 - assert all(isinstance(value, numbers.Number) for value in values) - assert all(10 <= value <= 100 for value in values) - - -def test_draw_values_loguniform(): - rng = np.random.RandomState() - - assert not dists.draw_values_uniform([10, 100], 0, rng).size - - values = dists.draw_values_uniform([10, 100], 20, rng) - assert len(values) == 20 - assert all(isinstance(value, numbers.Number) for value in values) - assert all(10 <= value <= 100 for value in values) - - -def test_sample_discrete(): - rng = np.random.RandomState() - - outcomes = ["foo", "bar.com"] - - # NB: return type from sample_discrete is different from the others, - # it returns a 3-tuple with the values in the second element. - values = dists.sample_discrete([",".join(outcomes)], 10, rng)[1] - assert all(value in outcomes for value in values) - - assert not dists.sample_discrete([",".join(outcomes)], 0, rng)[1].size - with pytest.raises(ValueError): - # pylint: disable=expression-not-assigned - dists.sample_discrete([",".join(outcomes)], -1, rng)[1] - - assert "foo" not in dists.sample_discrete([",".join(outcomes), "0,1"], 10, rng)[1] - - -def test_draw_values(): - """Test the wrapper function for drawing values""" - rng = np.random.RandomState() - - values = dists.draw_values("unif", [0, 1], 10, rng) - assert len(values) == 10 - assert all(isinstance(value, numbers.Number) for value in values) - assert all(0 <= value <= 1 for value in values) - - values = dists.draw_values("UNIF", [0, 1], 10, rng) - assert len(values) == 10 - - values = dists.draw_values("unifORM", [0, 1], 10, rng) - assert len(values) == 10 - - values = dists.draw_values("UnifORMgarbagestillworks", [0, 1], 10, rng) - assert len(values) == 10 - - with pytest.raises(ValueError): - dists.draw_values("non-existing-distribution", [0, 10], 100, rng) - - values = dists.draw_values("NORMAL", [0, 1], 10, rng) - assert len(values) == 10 - - values = dists.draw_values("LOGNORMAL", [0.1, 1], 10, rng) - assert len(values) == 10 - - values = dists.draw_values("Pert", [0.1, 1, 10], 10, rng) - assert len(values) == 10 - - values = dists.draw_values("triangular", [0.1, 1, 10], 10, rng) - assert len(values) == 10 - - values = dists.draw_values("logunif", [0.1, 1], 10, rng) - assert len(values) == 10 diff --git a/tests/sensitivities/test_designmatrix.py b/tests/sensitivities/test_designmatrix.py deleted file mode 100644 index 4ee83231..00000000 --- a/tests/sensitivities/test_designmatrix.py +++ /dev/null @@ -1,78 +0,0 @@ -"""Testing generating design matrices from dictionary input""" - -import shutil -import subprocess -from pathlib import Path - -import pandas as pd - -from fmu.tools._common import preserve_cwd -from fmu.tools.sensitivities import DesignMatrix - -TESTDATA = Path(__file__).parent / "data" - - -def valid_designmatrix(dframe): - """Performs general checks on a design matrix, that should always be valid""" - assert "REAL" in dframe - - # REAL always starts at 0 and is consecutive - assert dframe["REAL"][0] == 0 - assert dframe["REAL"].diff().dropna().unique() == 1 - - assert "SENSNAME" in dframe.columns - assert "SENSCASE" in dframe.columns - - # There should be no empty cells in the dataframe: - assert not dframe.isna().sum().sum() - - -def test_designmatrix(): - """Test the DesignMatrix class""" - - design = DesignMatrix() - - mock_dict = { - "designtype": "onebyone", - "seeds": "default", - "repeats": 10, - "defaultvalues": {}, - "sensitivities": { - "rms_seed": {"seedname": "RMS_SEED", "senstype": "seed", "parameters": None} - }, - } - - design.generate(mock_dict) - valid_designmatrix(design.designvalues) - assert len(design.designvalues) == 10 - assert isinstance(design.defaultvalues, dict) - - -@preserve_cwd -def test_endpoint(tmpdir): - """Test the installed endpoint - - Will write generated design matrices to the pytest tmpdir directory, - usually /tmp/pytest-of-/ - """ - designfile = TESTDATA / "config/design_input_onebyone.xlsx" - - # The xlsx file contains a relative path, relative to the input design sheet: - dependency = ( - pd.read_excel(designfile, header=None, engine="openpyxl") - .set_index([0])[1] - .to_dict()["background"] - ) - - tmpdir.chdir() - # Copy over input files: - shutil.copy(str(designfile), ".") - shutil.copy(Path(designfile).parent / dependency, ".") - - subprocess.run(["fmudesign", str(designfile)], check=True) - - assert Path("generateddesignmatrix.xlsx").exists # Default output file - valid_designmatrix(pd.read_excel("generateddesignmatrix.xlsx", engine="openpyxl")) - - subprocess.run(["fmudesign", str(designfile), "anotheroutput.xlsx"], check=True) - assert Path("anotheroutput.xlsx").exists diff --git a/tests/sensitivities/test_excel2dict.py b/tests/sensitivities/test_excel2dict.py deleted file mode 100644 index c759d155..00000000 --- a/tests/sensitivities/test_excel2dict.py +++ /dev/null @@ -1,231 +0,0 @@ -"""Testing excel2dict""" - -import os - -import numpy as np -import pandas as pd -import pytest - -from fmu.tools._common import preserve_cwd -from fmu.tools.sensitivities import excel2dict_design, inputdict_to_yaml -from fmu.tools.sensitivities._excel2dict import _has_value - -MOCK_GENERAL_INPUT = pd.DataFrame( - data=[ - ["designtype", "onebyone"], - ["repeats", "10"], - ["rms_seeds", "default"], - ["background", "None"], - ["distribution_seed", "None"], - ] -) - -MOCK_DESIGNINPUT = pd.DataFrame( - data=[["sensname", "numreal", "type", "param_name"], ["rms_seed", "", "seed"]] -) - - -@preserve_cwd -def test_excel2dict_design(tmpdir): - """Test that we can convert an Excelfile to a dictionary""" - tmpdir.chdir() - defaultvalues = pd.DataFrame() - # pylint: disable=abstract-class-instantiated - writer = pd.ExcelWriter("designinput.xlsx", engine="openpyxl") - MOCK_GENERAL_INPUT.to_excel( - writer, sheet_name="general_input", index=False, header=None - ) - MOCK_DESIGNINPUT.to_excel( - writer, sheet_name="designinput", index=False, header=None - ) - defaultvalues.to_excel(writer, sheet_name="defaultvalues", index=False, header=None) - writer.close() - - dict_design = excel2dict_design("designinput.xlsx") - assert isinstance(dict_design, dict) - assert dict_design["designtype"] == "onebyone" - assert dict_design["distribution_seed"] is None - assert "defaultvalues" in dict_design - - assert isinstance(dict_design["defaultvalues"], dict) - assert not dict_design["defaultvalues"] # (it is empty) - - assert isinstance(dict_design["sensitivities"], dict) - - sens = dict_design["sensitivities"] - # This now contains a key for each sensitivity to make - assert "rms_seed" in sens - assert isinstance(sens["rms_seed"], dict) - assert sens["rms_seed"]["seedname"] == "RMS_SEED" - assert sens["rms_seed"]["senstype"] == "seed" # upper-cased - - # Check that we can vary some strings - writer = pd.ExcelWriter("designinput2.xlsx", engine="openpyxl") - MOCK_GENERAL_INPUT.to_excel( - writer, sheet_name="Generalinput", index=False, header=None - ) - MOCK_DESIGNINPUT.to_excel( - writer, sheet_name="Design_input", index=False, header=None - ) - defaultvalues.to_excel(writer, sheet_name="DefaultValues", index=False, header=None) - writer.close() - - dict_design = excel2dict_design("designinput2.xlsx") - assert isinstance(dict_design, dict) - assert dict_design["sensitivities"]["rms_seed"]["senstype"] == "seed" - - # Dump to yaml: - inputdict_to_yaml(dict_design, "dictdesign.yaml") - assert os.path.exists("dictdesign.yaml") - with open("dictdesign.yaml", "r", encoding="utf-8") as inputfile: - assert "RMS_SEED" in inputfile.read() - - -@preserve_cwd -def test_duplicate_sensname_exception(tmpdir): - """Test that exceptions are raised for erroneous sensnames""" - # pylint: disable=abstract-class-instantiated - mock_erroneous_designinput = pd.DataFrame( - data=[ - ["sensname", "numreal", "type", "param_name"], - ["rms_seed", "", "seed"], - ["rms_seed", "", "seed"], - ] - ) - tmpdir.chdir() - defaultvalues = pd.DataFrame() - - writer = pd.ExcelWriter("designinput3.xlsx", engine="openpyxl") - MOCK_GENERAL_INPUT.to_excel( - writer, sheet_name="general_input", index=False, header=None - ) - mock_erroneous_designinput.to_excel( - writer, sheet_name="designinput", index=False, header=None - ) - defaultvalues.to_excel(writer, sheet_name="defaultvalues", index=False, header=None) - writer.close() - - with pytest.raises( - ValueError, match="Two sensitivities can not share the same sensname" - ): - excel2dict_design("designinput3.xlsx") - - -@preserve_cwd -def test_strip_spaces(tmpdir): - """Spaces before and after parameter names are probabaly - invisible user errors in Excel sheets. Remove them.""" - # pylint: disable=abstract-class-instantiated - mock_spacious_designinput = pd.DataFrame( - data=[ - ["sensname", "numreal", "type", "param_name"], - ["rms_seed ", "", "seed"], - ] - ) - defaultvalues_spacious = pd.DataFrame( - data=[ - ["parametername", "value"], - [" spacious_multiplier", 1.2], - ["spacious2 ", 3.3], - ] - ) - tmpdir.chdir() - writer = pd.ExcelWriter("designinput_spaces.xlsx", engine="openpyxl") - MOCK_GENERAL_INPUT.to_excel( - writer, sheet_name="general_input", index=False, header=None - ) - mock_spacious_designinput.to_excel( - writer, sheet_name="designinput", index=False, header=None - ) - defaultvalues_spacious.to_excel( - writer, sheet_name="defaultvalues", index=False, header=None - ) - writer.close() - - dict_design = excel2dict_design("designinput_spaces.xlsx") - assert list(dict_design["sensitivities"].keys())[0] == "rms_seed" - - # Check default values parameter names: - def_params = list(dict_design["defaultvalues"].keys()) - assert [par.strip() for par in def_params] == def_params - - -@preserve_cwd -def test_mixed_senstype_exception(tmpdir): - """Test that exceptions are raised for mixups in user input on types""" - # pylint: disable=abstract-class-instantiated - mock_erroneous_designinput = pd.DataFrame( - data=[ - ["sensname", "numreal", "type", "param_name"], - ["rms_seed", "", "seed"], - ["", "", "dist"], - ] - ) - tmpdir.chdir() - defaultvalues = pd.DataFrame() - - writer = pd.ExcelWriter("designinput4.xlsx", engine="openpyxl") - MOCK_GENERAL_INPUT.to_excel( - writer, sheet_name="general_input", index=False, header=None - ) - mock_erroneous_designinput.to_excel( - writer, sheet_name="designinput", index=False, header=None - ) - defaultvalues.to_excel(writer, sheet_name="defaultvalues", index=False, header=None) - writer.close() - - with pytest.raises(ValueError, match="contains more than one sensitivity type"): - excel2dict_design("designinput4.xlsx") - - -def test_has_value(): - """Test a function that is used to check if xlsx-cells are empty or not""" - assert _has_value(1) - assert not _has_value(np.nan) - - # This possibly makes no sense, but is the current implementation: - assert _has_value(None) - - -@preserve_cwd -def test_background_sheet(tmpdir): - """Test loading background values from a sheet""" - tmpdir.chdir() - general_input = pd.DataFrame( - data=[ - ["designtype", "onebyone"], - ["repeats", 3], - ["rms_seeds", "default"], - ["background", "backgroundsheet"], - ["distribution_seed", "None"], - ] - ) - defaultvalues = pd.DataFrame( - columns=["param_name", "default_value"], data=[["extraseed", "0"]] - ) - background = pd.DataFrame( - data=[ - ["param_name", "dist_name", "dist_param1"], - ["extraseed", "scenario", "30,40,50"], - ] - ) - - writer = pd.ExcelWriter("designinput.xlsx", engine="openpyxl") - general_input.to_excel(writer, sheet_name="general_input", index=False, header=None) - MOCK_DESIGNINPUT.to_excel( - writer, sheet_name="design_input", index=False, header=None - ) - defaultvalues.to_excel(writer, sheet_name="defaultvalues", index=False) - background.to_excel(writer, sheet_name="backgroundsheet", index=False, header=None) - writer.close() - - dict_design = excel2dict_design("designinput.xlsx") - - # Assert it has been interpreted correctly from input files: - assert dict_design["background"]["parameters"]["extraseed"] == [ - "scenario", - ["30,40,50"], - None, - ] - assert dict_design["repeats"] == 3 - assert dict_design["defaultvalues"]["extraseed"] == 0 diff --git a/tests/sensitivities/test_use_cases.py b/tests/sensitivities/test_use_cases.py deleted file mode 100644 index 3c8f71fa..00000000 --- a/tests/sensitivities/test_use_cases.py +++ /dev/null @@ -1,94 +0,0 @@ -"""Example use cases for fmudesign""" - -import pandas as pd - -from fmu.tools._common import preserve_cwd -from fmu.tools.sensitivities import DesignMatrix, excel2dict_design - - -@preserve_cwd -def test_prediction_rejection_sampled_ensemble(tmpdir): - """Test making a design matrix for prediction realizations based on an - ensemble made with manual history matching (rejection sampling). - - In the use-case this test is modelled on, the design matrix is used - to set up a prediction ensemble where each DATA file points to another - Eclipse run on disk which contains the history, identified by the - realization index ("HMREAL") in the history match run. - """ - tmpdir.chdir() - general_input = pd.DataFrame( - data=[ - ["designtype", "onebyone"], - ["repeats", 3], # This matches the number of HM-samples we have. - ["rms_seeds", None], # Geogrid from HM realization is used - ["background", "hmrealizations.xlsx"], - ["distribution_seed", None], - ] - ) - defaultvalues = pd.DataFrame( - columns=["param_name", "default_value"], - data=[ - # All background parameters must be mentioned in - # DefaultValues (but these defaults are not used in - # this particular test scenario) - ["HMREAL", "-1"], - ["ORAT", 6000], - ["RESTARTPATH", "FOO"], - ["HMITER", "-1"], - ], - ) - - # Background to separate file, these define some history realizations that - # all scenarios should run over: - pd.DataFrame( - columns=["RESTARTPATH", "HMREAL", "HMITER"], - data=[ - ["/scratch/foo/2020a_hm3/", 31, 3], - ["/scratch/foo/2020a_hm3/", 38, 3], - ["/scratch/foo/2020a_hm3/", 54, 3], - ], - ).to_excel("hmrealizations.xlsx") - - writer = pd.ExcelWriter("designinput.xlsx", engine="openpyxl") - general_input.to_excel(writer, sheet_name="general_input", index=False, header=None) - pd.DataFrame( - columns=[ - "sensname", - "numreal", - "type", - "param_name", - "dist_name", - "dist_param1", - "dist_param2", - ], - data=[ - ["ref", None, "background", None], - ["oil_rate", None, "dist", "ORAT", "uniform", 5000, 9000], - ], - ).to_excel(writer, sheet_name="design_input", index=False) - defaultvalues.to_excel(writer, sheet_name="defaultvalues", index=False) - writer.close() - - dict_design = excel2dict_design("designinput.xlsx") - design = DesignMatrix() - design.generate(dict_design) - - assert set(design.designvalues["RESTARTPATH"]) == {"/scratch/foo/2020a_hm3/"} - assert set(design.designvalues["HMITER"]) == {3} - assert all(design.designvalues["REAL"] == [0, 1, 2, 3, 4, 5]) - assert all( - design.designvalues["SENSNAME"] - == [ - "ref", - "ref", - "ref", - "oil_rate", - "oil_rate", - "oil_rate", - ] - ) - - # This is the most important bit in this test function, that the realization - # list in the background xlsx is repeated for each sensitivity: - assert all(design.designvalues["HMREAL"] == [31, 38, 54, 31, 38, 54])