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Environment EnergyTop 9 Best Solar Panel Simulation Software of 2026
Ranking roundup of Solar Panel Simulation Software for modeling performance and energy yield, with tool comparison notes on HelioScope, PV*SOL, RETScreen.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
HelioScope
API-driven study generation connects layout and equipment inputs to simulation outputs for automated design iteration.
Built for fits when engineering teams need automated, repeatable PV simulation studies with governed access and API-driven integration..
PV*SOL premium
Editor pickShading and system layout modeling within the project schema for consistent reruns across scenarios.
Built for fits when engineering teams need repeatable PV yield studies with controlled assumptions..
RETScreen
Editor pickRETScreen worksheets use a fixed structured data model for energy and financial calculations across solar scenarios.
Built for fits when analysts need repeatable solar simulations with controlled worksheets and report outputs..
Related reading
Comparison Table
This comparison table contrasts solar panel simulation tools using integration depth, data model, and automation and API surface. It also captures admin and governance controls such as provisioning, RBAC, and audit log support, alongside configuration and extensibility patterns that affect throughput. Readers can map tradeoffs between modeling schema choices, external data hookups, and how each platform handles repeatable runs in managed environments.
HelioScope
PV design workflowUtility-scale and commercial PV design simulation with model-driven layout and performance calculations that integrates into RatedPower workflows for scenario iteration.
API-driven study generation connects layout and equipment inputs to simulation outputs for automated design iteration.
HelioScope focuses on turning a PV plant description into deterministic simulation outputs that can be rerun as inputs change. The integration depth shows up in how RatedPower artifacts drive study setup for module, string, and layout assumptions, which reduces manual re-entry of design parameters. The data model connects project metadata to geometry, shading surfaces, and PV equipment selections so results can be traced back to the study configuration.
A tradeoff appears in the setup overhead required to define scene geometry and shading surfaces at enough fidelity for the desired accuracy. Teams typically use HelioScope when they need repeatable simulation throughput across many design iterations and when results must flow into downstream engineering or stakeholder reports. API and automation are most valuable when a pipeline needs consistent study generation and result extraction at scale.
- +Panel-level irradiance and shading simulation with traceable study configuration
- +RatedPower-oriented data mapping ties geometry, equipment choices, and results
- +Documented API and automation support pipeline integration and batch runs
- +Admin governance controls support project access separation
- –Scene geometry definition can add significant upfront configuration time
- –High-fidelity shading inputs increase model preparation effort
PV engineering teams
Iterate designs with consistent shading models
Faster design decision cycles
Design operations teams
Batch simulate multi-site portfolios
Higher throughput per release
Show 2 more scenarios
Platform integration engineers
Connect simulations to internal tooling
Reduced manual reporting
Integrates API endpoints and artifacts into an existing reporting or analytics pipeline.
Project administrators
Control study access and auditing
Safer collaboration and approvals
Applies RBAC-style governance and maintains auditability across shared study workspaces.
Best for: Fits when engineering teams need automated, repeatable PV simulation studies with governed access and API-driven integration.
More related reading
PV*SOL premium
engineering simulationPV and solar thermal simulation built around configurable system schematics, component models, and irradiance inputs with repeatable study configurations.
Shading and system layout modeling within the project schema for consistent reruns across scenarios.
PV*SOL premium fits teams that need repeatable PV yield studies with documented configuration inputs and consistent results across projects. The data model groups system layout, component choices, shading inputs, and performance assumptions so studies can be rerun under changed parameters. Automation and extensibility are geared toward scenario throughput, where multiple design variants share baseline assets and differ in a controlled set of parameters. Admin and governance controls focus on keeping project definitions structured, not on centralized multi-tenant policy enforcement.
A tradeoff shows up in automation depth versus custom integrations, since the primary surface centers on simulation inputs and project artifacts rather than a broad REST API for external orchestration. PV*SOL premium works best when internal teams manage study definitions and then export or hand off outputs to downstream systems. It is a strong fit for validation studies that require traceable assumptions and consistent shading and production modeling across a portfolio.
- +Project data model keeps system layout, shading, and assumptions tightly coupled
- +Scenario reruns support higher study throughput across parameter variants
- +Importable inputs reduce manual reconfiguration for repeated system studies
- +Extensibility supports integrating standardized component and dataset definitions
- –External orchestration depends more on project artifacts than fine-grained API access
- –Governance controls emphasize project structure over enterprise RBAC and audit log depth
Solar engineering teams
Run portfolio yield studies
Consistent production comparisons
Asset management analysts
Validate performance assumptions
Reduced estimation variance
Show 2 more scenarios
Technical PMOs
Standardize study configurations
Lower study rework
Enforces schema-based inputs so teams generate comparable simulations across projects.
Integration-focused teams
Automate scenario generation
Higher scenario throughput
Uses importable datasets and repeatable configurations to increase throughput for many variants.
Best for: Fits when engineering teams need repeatable PV yield studies with controlled assumptions.
RETScreen
project modelingClean-energy project modeling with solar performance and financial analysis that uses structured project inputs for repeatable energy estimates.
RETScreen worksheets use a fixed structured data model for energy and financial calculations across solar scenarios.
RETScreen’s data model maps project assumptions into consistent input fields across solar worksheets, which reduces schema drift between analysts. It also applies built-in calculation engines for energy yield, performance evaluation, and life cycle financial outputs, which helps standardize results across a portfolio. Integration depth is mostly achieved through import and export of structured inputs and model outputs rather than through programmatic provisioning or an automation API surface.
A key tradeoff is limited external extensibility compared with tools that expose full modeling objects via API and automation hooks. RETScreen fits teams that need deterministic, spreadsheet-driven simulations and repeatable reporting for feasibility studies, grid connection assessments, and performance comparisons. It is less suited to workflows that require fine-grained RBAC, audit log retention for model changes, or high-volume pipeline ingestion with schema governance.
- +Worksheet-driven solar modeling standardizes inputs across studies
- +Built-in energy and financial calculation workflow reduces manual recomputation
- +Structured inputs and exports support repeatable report production
- +Scenario comparison helps maintain consistent assumptions across variants
- –Public automation and API surface is not central to the workflow
- –Limited data-model extensibility compared with API-first simulation tools
- –Governance controls like RBAC and audit logs are not the primary focus
- –Bulk pipeline ingestion relies more on file-based practices than streaming
Energy analysts
Feasibility studies for rooftop solar
Repeatable feasibility reports
Sustainability teams
Portfolio performance and option comparisons
Aligned decision comparisons
Show 2 more scenarios
Consulting engineering firms
Client-ready modeling deliverables
Faster report turnaround
Export structured results into project documents for consistent client review packages.
Project managers
Standardized assumptions across analysts
Lower model inconsistency
Use a common worksheet schema to reduce variation across multi-person analyses.
Best for: Fits when analysts need repeatable solar simulations with controlled worksheets and report outputs.
HOMER
hybrid system simulationHybrid energy system simulation that includes PV generation modeling with dispatch and sizing across time-series profiles for scenario automation.
Scenario-based energy system simulation that links resource and component inputs to measurable performance outputs.
HOMER is solar panel simulation software that centers on system design modeling for grid-connected and off-grid energy setups. It supports scenario-based studies that connect component choices to predicted energy outcomes across operating conditions.
The modeling workflow emphasizes a structured data model for inputs like resources, loads, and component specifications. Integration depth depends on how HOMER fits existing study pipelines through file-based exchange and any available automation hooks.
- +Scenario runs tie component parameters to energy outputs for repeatable studies
- +Structured inputs for resources, loads, and system design reduce modeling ambiguity
- +Outputs support study comparison across configurations and operating assumptions
- +Automation can be achieved through repeatable data preparation and batch-like workflows
- –Limited visibility into a public API and schema-based integration surface
- –Data exchange for pipelines can rely on external file preparation steps
- –Governance controls like RBAC and audit logs are not clearly defined
- –Automation extensibility may be constrained versus API-first simulators
Best for: Fits when engineering teams need controlled, scenario-based solar system simulation with dependable input schemas and repeatable runs.
EnergyPLAN
energy system modelingEnergy system model that simulates PV-linked generation in broader energy system studies using input tables and scenario runs for comparative analysis.
Project-level scenario management that keeps PV system inputs and assumptions tied to simulation outputs.
EnergyPLAN runs solar panel simulation workflows that model PV production from inputs like site data, system configuration, and loss factors. The software supports scenario comparisons across configurations and time ranges, with outputs centered on energy yield and performance assumptions.
Integration depth is achieved through a data model that maps configuration, components, and assumptions to reusable project structures. Automation relies on repeatable configuration and exportable results formats, with an extensibility path oriented around schema-driven study inputs.
- +Scenario-based simulation for comparing PV configurations and loss assumptions
- +Structured project data model for reusing system parameters across studies
- +Exportable outputs for pipeline integration into reporting or analysis tools
- +Configuration management for consistent study replication across runs
- –Limited visibility into API and automation surface for programmatic provisioning
- –Governance controls like RBAC and audit logging are not clearly documented
- –Extensibility appears centered on input configuration rather than custom schema
- –Throughput for large batch sweeps depends on manual setup workflow
Best for: Fits when teams need repeatable PV scenario studies with controlled configuration and exportable outputs.
pvlib-python
API-first PV modelingPython library for PV modeling that provides a composable data model for PV components and atmospheric inputs with an API surface for automation and custom workflows.
Time series irradiance transposition and PV performance functions that operate directly on pandas DataFrames.
pvlib-python targets solar energy simulation and modeling with a Python-first API that maps directly to pvlib’s physical and empirical models. It covers single-diode and PV performance paths, irradiance transposition, spectral and temperature effects, and time series workflows using common weather inputs.
The library’s data model centers on pandas DataFrames for inputs and outputs, which supports automation through code-driven pipelines. Extensibility comes from model-level functions and plugin-style customization using Python composition rather than separate service components.
- +Function-level API for irradiance, transposition, and PV performance modeling
- +Pandas DataFrame inputs and outputs for consistent time series workflows
- +Deterministic Python execution for automation and reproducible simulations
- +Model composition supports custom preprocessors and calibration steps
- +Vectorized computations improve throughput for large timestamp sets
- –No built-in governance features like RBAC or audit logs
- –Admin controls rely on external tooling and repository hygiene
- –Simulation orchestration requires custom pipeline code
- –Model coverage depends on available inputs and expected schema formats
- –Large study runs need manual performance tuning and memory management
Best for: Fits when Python teams need controlled solar simulations with an API-first data model and automation in existing pipelines.
Helioscope
shading designPV design and shading simulation tool that supports iterative layout analysis with exportable results for downstream design workflows.
Scenario-based modeling that preserves configured assumptions across project runs for controlled comparisons.
Helioscope differentiates itself with a solar production simulation workflow centered on a controlled data model for assets, irradiance inputs, and modeled outputs. It supports project-level configurations, scenario comparisons, and exportable results aimed at repeatable engineering decisions.
Integration depth is strongest around file-based and workflow-driven handoffs rather than deep external system coupling. Automation and extensibility rely more on repeatable configuration and batch-style usage patterns than on a broad API and governance surface.
- +Structured project inputs that keep simulation assumptions consistent across runs
- +Scenario configuration supports repeatable comparisons for design iterations
- +Exports provide usable artifacts for downstream engineering review workflows
- +Clear separation between asset definitions and modeled outputs
- –Limited visibility into RBAC and org-wide governance controls
- –Automation and API surface appear narrow for external system orchestration
- –Schema extensibility for custom metadata is constrained
- –Throughput scaling depends on manual setup rather than provisioning automation
Best for: Fits when teams need repeatable solar simulation runs with consistent assumptions and controlled exports.
PVGIS Tooling
yield modelingSolar resource and PV yield modeling services with parameterized computations that can be used for simulation inputs in design studies.
PVGIS-aligned input parameter schema maps site and system settings to consistent simulation result fields.
PVGIS Tooling from ec.europa.eu targets solar panel simulation workflows using the PVGIS data sources behind the tool. It concentrates on a standardized modeling data model that turns site inputs into irradiance, generation, and performance outputs.
The tooling is designed for integration into automated analysis through repeatable requests and parameterized configurations. It also supports extensibility by aligning simulation inputs with PVGIS-style schema structures used across the underlying calculators.
- +Uses PVGIS-aligned schema for consistent irradiance and generation outputs
- +Repeatable parameter sets support automation and batch simulation runs
- +Directly ties site and system parameters to standardized PVGIS result fields
- +Documented, machine-friendly request patterns simplify API-based integration
- –Automation surface depends on PVGIS calculator inputs, not custom workflows
- –Limited RBAC and admin governance controls for multi-team operations
- –Extensibility is bounded by PVGIS data model constraints
- –Throughput can require client-side batching for large project portfolios
Best for: Fits when teams need PVGIS-standard simulation outputs and scriptable requests for repeatable analysis.
Radiance
irradiance physicsRadiometric and daylight simulation software used for PV lighting and irradiance modeling pipelines that feed PV performance estimates.
Radiance engine integration for parameterized scene rendering and batch simulation runs.
Radiance runs solar panel simulations using the Radiance engine and supports scene workflows used in energy modeling. It provides configurable inputs for optical and material behavior, plus batch execution suitable for high-throughput study runs.
Data exchange focuses on scene assets and render outputs rather than a proprietary analytics schema. Automation is driven through scripted runs that fit into existing pipelines.
- +Radiance scene inputs map directly to optical simulation parameters
- +Batch execution supports throughput for large study sets
- +Predictable command-line workflow fits scripted automation
- +Integration aligns with existing lighting and energy modeling pipelines
- –Governance and RBAC controls are not provided as a native management layer
- –Automation interfaces rely on external scripting rather than a service API
- –Data model is asset and output oriented instead of structured entities
- –Audit logging and change tracking depend on surrounding tooling
Best for: Fits when teams need Radiance-based solar optics simulation in an existing scripted pipeline.
How to Choose the Right Solar Panel Simulation Software
This buyer's guide covers nine solar panel simulation tools, including HelioScope, PV*SOL premium, RETScreen, HOMER, EnergyPLAN, pvlib-python, Helioscope, PVGIS Tooling, and Radiance. It focuses on how teams should evaluate integration depth, data model structure, automation and API surface, and admin and governance controls.
The guide maps these evaluation points to concrete behaviors like API-driven study generation in HelioScope, pandas DataFrame automation in pvlib-python, and scripted batch execution in Radiance.
Solar irradiance, shading, and energy modeling software for engineered PV studies
Solar panel simulation software models irradiance, shading, and energy yield from system layouts, component choices, and environment inputs. It solves repeatability problems by coupling a defined project or data schema to simulation outputs, which reduces rework across scenario variants. Tools like HelioScope and PV*SOL premium use project-centered models to keep layout, shading, and assumptions tied together across reruns.
Other tools target specific workflows, like pvlib-python for code-driven time series modeling with pandas DataFrames and PVGIS Tooling for PVGIS-aligned requests that return consistent irradiance and generation fields. The typical users include engineering teams performing layout and shading studies, analysts producing repeatable yield and reporting outputs, and developers building automation pipelines around simulation computations.
Evaluation criteria that map to integration, schema control, and governed automation
Simulation tools rarely fail because physics is missing. They fail when inputs and outputs cannot be wired into existing pipelines or when scenario reruns drift because the configuration data model is not explicit.
HelioScope and pvlib-python score higher when automation and API access exist alongside a structured study model, while tools like PV*SOL premium emphasize schema-driven project reruns with less fine-grained external orchestration. Radiance and Helioscope often fit teams that accept file-based or scripted handoffs instead of deep enterprise governance layers.
API-driven study generation from layout and equipment inputs
HelioScope connects layout and equipment inputs to simulation outputs through an API-driven study generation path. This matters for throughput when many design variants must be created and executed automatically rather than configured manually per scenario.
Project data model that keeps layout, shading, and assumptions coupled
PV*SOL premium uses a project data model that ties system layout, shading, and assumptions to scenario reruns. HelioScope also maps site geometry, surfaces, modules, and inverters into a traceable data model that supports versioned studies.
Time series computation API with a pandas DataFrame data model
pvlib-python exposes function-level APIs that operate directly on pandas DataFrames for irradiance transposition and PV performance modeling. This matters for integration because teams can run deterministic computations in existing code pipelines without a separate orchestration layer.
Scriptable batch execution and scene-based simulation for high-throughput optics
Radiance supports parameterized scene workflows and batch execution using predictable command-line operations. This matters when throughput depends on scripting and when the data model is asset and output oriented instead of entity schema based.
Structured worksheets and exports for repeatable reporting
RETScreen uses worksheet-driven solar and financial modeling with fixed structured inputs across scenarios. This matters for teams that prioritize consistent report outputs and controlled assumptions over a public API-first integration surface.
Admin and governance controls for governed access to studies and projects
HelioScope includes admin features for managing users and projects that support governance around study access and collaboration. Other tools emphasize project structure but do not clearly document deep RBAC and audit log depth, which matters for multi-team environments.
Integration-first selection workflow for PV simulation tooling
The selection process should start with how simulations must connect into an existing design pipeline. If automation must create and run many studies, the tool must expose an API or at least a scripted execution path that matches the pipeline’s control plane.
The next step should map the tool’s data model to the configuration artifacts already used for module, inverter, shading, and irradiance assumptions. HelioScope fits teams that need API-driven study generation into a RatedPower workflow, while pvlib-python fits teams that already run Python pipelines built around pandas DataFrames.
Define the automation control point and its required interface
If study creation and reruns must be generated automatically from layout and equipment inputs, choose HelioScope because it provides documented API-driven study generation that connects inputs to outputs. If the pipeline already runs Python, choose pvlib-python because its function-level APIs operate directly on pandas DataFrames for irradiance transposition and PV performance.
Validate the data model you will treat as the source of truth
If repeatability depends on keeping system layout, shading, and assumptions tightly coupled, choose PV*SOL premium because its project data model keeps those elements in one schema. If the source of truth must include geometry mapping across surfaces, modules, and inverters for versioned studies, choose HelioScope because it maps site geometry and equipment choices into a RatedPower-aligned simulation data model.
Match the integration style to the pipeline expectation
If the pipeline expects governed, project-centered artifacts and API integration, choose HelioScope because it integrates into RatedPower workflows and supports API and exportable model artifacts. If the integration needs standardized, request-based PV output fields, choose PVGIS Tooling because it uses PVGIS-aligned input parameter schema that yields consistent irradiance and generation result fields.
Plan for shading and scene preparation effort up front
If shading fidelity is a priority, model preparation effort will rise because HelioScope calls out that high-fidelity shading inputs increase setup work. If the workflow can tolerate less scene definition work, PV*SOL premium uses shading and system layout modeling within the project schema to keep reruns consistent across scenario variants.
Check governance requirements for multi-team study access
If multiple teams must collaborate under controlled access policies, choose HelioScope because it includes admin features for managing users and projects tied to study access. If governance must be enterprise-grade with RBAC and audit log depth, tools like pvlib-python and Radiance do not provide native governance layers, so an external repository or tooling layer will be required.
Select based on workflow scope, not only physics
If the work includes worksheet-based energy and financial feasibility outputs, choose RETScreen because its worksheet-driven workflow standardizes inputs across studies and supports scenario comparison. If the work includes PV generation inside broader dispatch and sizing studies, choose HOMER or EnergyPLAN because their scenario-based modeling ties component and loss assumptions to energy outcomes across time ranges.
Which teams benefit from each solar panel simulation tool
Different simulation tools optimize for different integration points. HelioScope focuses on automated design iteration with API-driven study generation and governed access through admin features. PV*SOL premium focuses on schema-driven reruns for shading and energy yield studies.
Other tools target narrower needs like Python-first modeling with pvlib-python, PVGIS-standard outputs with PVGIS Tooling, and optics scene simulation pipelines with Radiance.
Engineering teams running repeatable layout and shading studies with automation requirements
HelioScope fits when repeatable PV simulation studies require API-driven study generation that connects layout and equipment inputs to simulation outputs. Its admin features for user and project management support governance around study access and collaboration.
Engineering teams that need controlled PV yield studies driven by a project schema
PV*SOL premium fits when scenario reruns must keep system layout, shading, and assumptions tightly coupled inside a project data model. Its importable inputs reduce manual reconfiguration for repeated system studies.
Analysts and program teams producing consistent worksheet-based energy and financial reports
RETScreen fits when repeatable solar modeling depends on fixed structured worksheets and scenario comparison across variants. It standardizes inputs across studies and reduces manual recomputation for energy and financial outputs.
Developers building PV modeling inside Python code pipelines
pvlib-python fits when integration requires an API-first data model that uses pandas DataFrames for inputs and outputs. Its irradiance transposition and PV performance functions support deterministic Python execution and automation with custom calibration steps.
Teams that simulate PV-related optical and daylight effects inside scripted render pipelines
Radiance fits when solar optics modeling relies on scene assets and high-throughput batch execution using scripted command-line workflows. It integrates into lighting and energy modeling pipelines by exchanging scene assets and render outputs instead of entity schema inputs.
Pitfalls that derail PV simulation integrations and repeatable scenario execution
Common failures come from mismatched integration expectations and unclear configuration ownership. A tool that produces correct results can still fail in practice if governance and automation are not aligned with how studies get created and reviewed.
The pitfalls below map to concrete constraints seen across HelioScope, PV*SOL premium, RETScreen, pvlib-python, Radiance, and PVGIS Tooling.
Selecting a tool for modeling depth without verifying the API or automation interface
HelioScope supports documented API-driven study generation, while RETScreen and HOMER rely more on worksheet or scenario workflows than a public API-first control surface. Radiance automation depends on external scripting rather than a service-style API, so pipeline integration plans must account for that.
Assuming project configuration changes stay stable across reruns without a coupled data model
PV*SOL premium keeps shading and system layout modeling within the project schema for consistent reruns across scenarios. EnergyPLAN also ties PV system inputs and assumptions to project-level scenario management, while ad-hoc file preparation pipelines in HOMER can increase the risk of drift.
Underestimating scene or shading input preparation effort needed for higher-fidelity results
HelioScope specifically flags that high-fidelity shading inputs increase model preparation effort. Radiance also requires scene assets and optical parameters, and throughput depends on disciplined scripted scene authoring rather than clicking through a UI.
Relying on internal governance features that are not clearly provided by the simulator
HelioScope includes admin features for managing users and projects, which supports governance around study access. pvlib-python and Radiance do not provide built-in RBAC or audit log depth, so governance must be handled through external systems like repositories and CI controls.
How We Selected and Ranked These Tools
We evaluated Helioscope, PV*SOL premium, RETScreen, HOMER, EnergyPLAN, pvlib-python, Helioscope, PVGIS Tooling, and Radiance against three scored areas. Each tool received criteria-based scoring for features, ease of use, and value, and we weighted features most heavily at forty percent while ease of use and value each counted thirty percent. The scoring reflects the capabilities described in the provided tool summaries, including API and automation surface, data model structure, and governance controls, without assuming any lab benchmark beyond those described behaviors.
Helioscope separated from lower-ranked options because its API-driven study generation connects layout and equipment inputs directly to simulation outputs, and it also includes admin features for managing users and projects. That combination lifted its features score through measurable automation and its ease-of-use fit for repeatable, governed iteration.
Frequently Asked Questions About Solar Panel Simulation Software
Which solar panel simulation tools support API-first automation rather than file-based workflows?
How do users compare PV*SOL premium versus HelioScope when study governance and repeatability matter?
What integration pattern fits teams that already use Python data pipelines for time series PV modeling?
Can Radiance be used for PV simulation when the goal is optical accuracy from scene geometry?
How do HelioScope and Radiance differ when the main need is shading modeling?
Which tool is best for scenario-based feasibility reports that combine system inputs with climate and financial parameters?
What data migration or data model schema considerations should teams plan for when switching from one simulator to another?
How do admin controls and auditability show up across these tools for multi-user project work?
What causes common run-to-run inconsistencies in solar simulations, and which tools reduce that risk through configuration structure?
Which tool fits when teams need fixed schema outputs for automated analysis without custom modeling code?
Conclusion
After evaluating 9 environment energy, HelioScope stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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