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train · 15.9k rows

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6,900	<ASSISTANT_TASK:> Python Code: # DO NOT EDIT ! from pyesdoc.ipython.model_topic import NotebookOutput # DO NOT EDIT ! DOC = NotebookOutput('cmip6', 'awi', 'awi-cm-1-0-mr', 'seaice') # Set as follows: DOC.set_author("name", "email") # TODO - please enter value(s) # Set as follows: DOC.set_contributor("name", "email") # TODO - please enter value(s) # Set publication status: # 0=do not publish, 1=publish. DOC.set_publication_status(0) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.model.model_overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.model.model_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.variables.prognostic') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "Sea ice temperature" # "Sea ice concentration" # "Sea ice thickness" # "Sea ice volume per grid cell area" # "Sea ice u-velocity" # "Sea ice v-velocity" # "Sea ice enthalpy" # "Internal ice stress" # "Salinity" # "Snow temperature" # "Snow depth" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "TEOS-10" # "Constant" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.seawater_properties.ocean_freezing_point_value') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.resolution.name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.resolution.canonical_horizontal_resolution') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.resolution.number_of_horizontal_gridpoints') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.tuning_applied.description') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.tuning_applied.target') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.tuning_applied.simulations') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.tuning_applied.metrics_used') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.tuning_applied.variables') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.typical_parameters') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "Ice strength (P*) in units of N m{-2}" # "Snow conductivity (ks) in units of W m{-1} K{-1} " # "Minimum thickness of ice created in leads (h0) in units of m" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.key_parameter_values.additional_parameters') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.assumptions.description') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.assumptions.on_diagnostic_variables') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.assumptions.missing_processes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.conservation.description') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.conservation.properties') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "Energy" # "Mass" # "Salt" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.conservation.budget') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.conservation.was_flux_correction_used') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.key_properties.conservation.corrected_conserved_prognostic_variables') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Ocean grid" # "Atmosphere Grid" # "Own Grid" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.grid_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Structured grid" # "Unstructured grid" # "Adaptive grid" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Finite differences" # "Finite elements" # "Finite volumes" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.thermodynamics_time_step') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.dynamics_time_step') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.grid.discretisation.horizontal.additional_details') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.grid.discretisation.vertical.layering') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "Zero-layer" # "Two-layers" # "Multi-layers" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.grid.discretisation.vertical.number_of_layers') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.grid.discretisation.vertical.additional_details') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.grid.seaice_categories.has_mulitple_categories') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.grid.seaice_categories.number_of_categories') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.grid.seaice_categories.category_limits') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.grid.seaice_categories.ice_thickness_distribution_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.grid.seaice_categories.other') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.grid.snow_on_seaice.has_snow_on_ice') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.grid.snow_on_seaice.number_of_snow_levels') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.grid.snow_on_seaice.snow_fraction') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.grid.snow_on_seaice.additional_details') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.dynamics.horizontal_transport') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Incremental Re-mapping" # "Prather" # "Eulerian" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.dynamics.transport_in_thickness_space') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Incremental Re-mapping" # "Prather" # "Eulerian" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.dynamics.ice_strength_formulation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Hibler 1979" # "Rothrock 1975" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.dynamics.redistribution') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "Rafting" # "Ridging" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.dynamics.rheology') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Free-drift" # "Mohr-Coloumb" # "Visco-plastic" # "Elastic-visco-plastic" # "Elastic-anisotropic-plastic" # "Granular" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.energy.enthalpy_formulation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Pure ice latent heat (Semtner 0-layer)" # "Pure ice latent and sensible heat" # "Pure ice latent and sensible heat + brine heat reservoir (Semtner 3-layer)" # "Pure ice latent and sensible heat + explicit brine inclusions (Bitz and Lipscomb)" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.energy.thermal_conductivity') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Pure ice" # "Saline ice" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_diffusion') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Conduction fluxes" # "Conduction and radiation heat fluxes" # "Conduction, radiation and latent heat transport" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.energy.basal_heat_flux') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Heat Reservoir" # "Thermal Fixed Salinity" # "Thermal Varying Salinity" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.energy.fixed_salinity_value') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.energy.heat_content_of_precipitation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.energy.precipitation_effects_on_salinity') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.mass.new_ice_formation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_vertical_growth_and_melt') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_lateral_melting') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Floe-size dependent (Bitz et al 2001)" # "Virtual thin ice melting (for single-category)" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.mass.ice_surface_sublimation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.mass.frazil_ice') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.salt.has_multiple_sea_ice_salinities') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.salt.sea_ice_salinity_thermal_impacts') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.salinity_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Constant" # "Prescribed salinity profile" # "Prognostic salinity profile" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.constant_salinity_value') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.salt.mass_transport.additional_details') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.salinity_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Constant" # "Prescribed salinity profile" # "Prognostic salinity profile" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.constant_salinity_value') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.salt.thermodynamics.additional_details') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.ice_thickness_distribution.representation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Explicit" # "Virtual (enhancement of thermal conductivity, thin ice melting)" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.representation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Explicit" # "Parameterised" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.ice_floe_size_distribution.additional_details') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.are_included') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.formulation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Flocco and Feltham (2010)" # "Level-ice melt ponds" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.melt_ponds.impacts') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "Albedo" # "Freshwater" # "Heat" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_aging') # PROPERTY VALUE(S): # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_aging_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.has_snow_ice_formation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.snow_ice_formation_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.redistribution') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.thermodynamics.snow_processes.heat_diffusion') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Single-layered heat diffusion" # "Multi-layered heat diffusion" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.radiative_processes.surface_albedo') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Delta-Eddington" # "Parameterized" # "Multi-band albedo" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.seaice.radiative_processes.ice_radiation_transmission') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "Delta-Eddington" # "Exponential attenuation" # "Ice radiation transmission per category" # "Other: [Please specify]" # TODO - please enter value(s) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Document Authors Step2: Document Contributors Step3: Document Publication Step4: Document Table of Contents Step5: 1.2. Model Name Step6: 2. Key Properties --> Variables Step7: 3. Key Properties --> Seawater Properties Step8: 3.2. Ocean Freezing Point Value Step9: 4. Key Properties --> Resolution Step10: 4.2. Canonical Horizontal Resolution Step11: 4.3. Number Of Horizontal Gridpoints Step12: 5. Key Properties --> Tuning Applied Step13: 5.2. Target Step14: 5.3. Simulations Step15: 5.4. Metrics Used Step16: 5.5. Variables Step17: 6. Key Properties --> Key Parameter Values Step18: 6.2. Additional Parameters Step19: 7. Key Properties --> Assumptions Step20: 7.2. On Diagnostic Variables Step21: 7.3. Missing Processes Step22: 8. Key Properties --> Conservation Step23: 8.2. Properties Step24: 8.3. Budget Step25: 8.4. Was Flux Correction Used Step26: 8.5. Corrected Conserved Prognostic Variables Step27: 9. Grid --> Discretisation --> Horizontal Step28: 9.2. Grid Type Step29: 9.3. Scheme Step30: 9.4. Thermodynamics Time Step Step31: 9.5. Dynamics Time Step Step32: 9.6. Additional Details Step33: 10. Grid --> Discretisation --> Vertical Step34: 10.2. Number Of Layers Step35: 10.3. Additional Details Step36: 11. Grid --> Seaice Categories Step37: 11.2. Number Of Categories Step38: 11.3. Category Limits Step39: 11.4. Ice Thickness Distribution Scheme Step40: 11.5. Other Step41: 12. Grid --> Snow On Seaice Step42: 12.2. Number Of Snow Levels Step43: 12.3. Snow Fraction Step44: 12.4. Additional Details Step45: 13. Dynamics Step46: 13.2. Transport In Thickness Space Step47: 13.3. Ice Strength Formulation Step48: 13.4. Redistribution Step49: 13.5. Rheology Step50: 14. Thermodynamics --> Energy Step51: 14.2. Thermal Conductivity Step52: 14.3. Heat Diffusion Step53: 14.4. Basal Heat Flux Step54: 14.5. Fixed Salinity Value Step55: 14.6. Heat Content Of Precipitation Step56: 14.7. Precipitation Effects On Salinity Step57: 15. Thermodynamics --> Mass Step58: 15.2. Ice Vertical Growth And Melt Step59: 15.3. Ice Lateral Melting Step60: 15.4. Ice Surface Sublimation Step61: 15.5. Frazil Ice Step62: 16. Thermodynamics --> Salt Step63: 16.2. Sea Ice Salinity Thermal Impacts Step64: 17. Thermodynamics --> Salt --> Mass Transport Step65: 17.2. Constant Salinity Value Step66: 17.3. Additional Details Step67: 18. Thermodynamics --> Salt --> Thermodynamics Step68: 18.2. Constant Salinity Value Step69: 18.3. Additional Details Step70: 19. Thermodynamics --> Ice Thickness Distribution Step71: 20. Thermodynamics --> Ice Floe Size Distribution Step72: 20.2. Additional Details Step73: 21. Thermodynamics --> Melt Ponds Step74: 21.2. Formulation Step75: 21.3. Impacts Step76: 22. Thermodynamics --> Snow Processes Step77: 22.2. Snow Aging Scheme Step78: 22.3. Has Snow Ice Formation Step79: 22.4. Snow Ice Formation Scheme Step80: 22.5. Redistribution Step81: 22.6. Heat Diffusion Step82: 23. Radiative Processes Step83: 23.2. Ice Radiation Transmission
6,901	<ASSISTANT_TASK:> Python Code: import os from urllib.request import urlopen # MovieLensのサイトから、Zipファイルを取得し、ローカルに保存します. # この処理は少しだけ時間がかかるので、未ダウンロードの場合のみ、実行します. file_name = "data/ml-100k.zip" if not os.path.exists(os.path.dirname(file_name)): os.makedirs(os.path.dirname(file_name)) if not os.path.exists("data/ml-100k.zip"): url = "http://files.grouplens.org/datasets/movielens/ml-100k.zip" with urlopen(url) as res: with open("data/ml-100k.zip", "wb") as f: f.write(res.read()) # Zipファイルを解凍します. from shutil import unpack_archive unpack_archive("data/ml-100k.zip", "data/", "zip") import numpy as np import pandas as pd udata = pd.read_csv("data/ml-100k/u1.base", delimiter="\t", names=("user", "movie", "rating", "timestamp")) udata.tail() # 評価が3以上のデータを抽出. high_rate = udata.loc[udata["rating"] >= 3] # movieを行, columnsを列にした後、欠損部分（=NaN）を0埋め. raw = high_rate.pivot(index="movie", columns="user", values="rating") df = raw.fillna(0) # whereメソッドはわかりにくいですが、以下で3未満以外(つまり3以上)を1で埋めて返します df = df.where(df < 3, 1) df.head() # 評価として取り込んだデータの数 df.astype(bool).sum(axis=1).sum() item1 = np.array([1,1,0]) item2 = np.array([1,0,1]) from scipy.spatial.distance import cosine sim = 1 - cosine(item1, item2) print(sim) sim = 1 - cosine(df.iloc[0], df.iloc[1]) print(sim) # 上記の雰囲気で、総当たりで全アイテムの距離を計算する. from scipy.spatial.distance import pdist # 類似度 d = pdist(df, "cosine") d = 1 - d # 結果を行列に変換します（上記だとベクトルで見辛い！！） from scipy.spatial.distance import squareform d = squareform(d) # nan ができるので、0に補正します. d[np.isnan(d)] = 0 # ここでちょっとしたトリックで、自分自身は「-1」に補正して、類似度を最低にします. d = d - np.eye(d.shape[0]) # 表示してみる. print(d) # 映画ID=1（indexが0始まりなことに注意） movie_id = 0 # 評価の良い順に並べます. # ソート後のインデックスを収納. id = d[movie_id].argsort()[::-1] # 最初の5件を表示してみます. for i in id[:5]: print("{i:0>3d}: {v: .3f}".format(i=i, v=d[movie_id, i])) # 指定したユーザーへレコメンドするアイテムを5個出力する関数 def get_recommend_items(user_id): # 高く評価した映画のリストを取得 favorite = df.loc[:, user_id].nonzero() # 評価テーブルから、高評価の行を取り出す table = d[favorite] # 列ごとに類似度を合計 table[np.where(table < 0)] = 0 indicator = table.sum(axis=0) # 類似度の高い順にソート sorted_id = indicator.argsort()[::-1] # 評価済み映画のリストを取得 reviewed = raw[raw.loc[:, user_id].notnull()].index.tolist() # 評価済みを削除 recommend_id = [i for i in sorted_id if i not in reviewed] # 5件だけ返す return recommend_id[:5] # 試しにUser_ID=100の人 recommends = get_recommend_items(100) print(recommends) utest = pd.read_csv("data/ml-100k/u1.test", delimiter="\t", names=("user", "movie", "rating", "timestamp")) utest.head() # 好評価のみを対象とした、行列（行=映画、列=ユーザー）を作成します. high_rate_test = utest.loc[udata["rating"] >= 3] raw_test = high_rate_test.pivot(index="movie", columns="user", values="rating") df_test = raw_test.fillna(0) df_test = df_test.where(df_test < 3, 1) ### 試しに、userId=1の人でテスト. user_id = 1 # (1) レコメンド対象 recommends = set(get_recommend_items(user_id)) # (2) テストデータ内に存在する閲覧データ real = set(df_test.loc[:, user_id].nonzero()[0]) # (1) と (2) の積集合 real & recommends # テストデータに存在するユーザーの一覧を取得する. users = df_test.columns # 全ユーザー数 all = len(users) # 成功数 good = 0 # 1ユーザーごとに、成功 or not を判定する. for user_id in users: real = set(df_test.loc[:, user_id].nonzero()[0]) recommends = set(get_recommend_items(user_id)) matches = real & recommends good += 1 if matches else 0 # 結果を表示. print("全件={0}, 成功数={1}, 成功率={2}%".format(all, good, good * 100 // all)) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: 2. データの前処理 Step2: 上記の形式だとモデルの学習に用いづらいため、行が映画、列がユーザーの行列(マトリックス) に変換します。 Step3: （参考までに） Step4: 全70,000件中、66,103件は好評価のようです（94%）。今回は、評価>=3の考慮はあまり意味がないかもしれません(笑)。 Step5: 上記は、それぞれ3つの特徴を持つベクトルで、コサイン距離（=類似度）は以下のように計算します。 Step6: 上記の要領で、実際に映画ID=1と映画ID=2の類似度を計算してみると、以下のようになります。 Step7: 上記の 0.32は相対的な数値でありそれ自体に意味はありませんが、他の類似度と比較することで、より類似しているアイテムを見つけることができます。 Step8: 上記で、各映画ごとの類似度を総当たりで計算することができました。 Step9: 上記の処理では、指定した映画に類似する映画を知ることができます。 Step10: これで、レコメンド処理の実装が完了しました！！！ Step11: 無事にレコメンドができたようです（ホッとしますw）。
6,902	<ASSISTANT_TASK:> Python Code: %matplotlib inline from matplotlib import pylab import matplotlib.patches as patches import matplotlib.path as path from ipywidgets import interact import numpy import sys import random import itertools import time import scipy.special import math pylab.rcParams['figure.figsize'] = 16, 10 pylab.rcParams['image.cmap'] = 'viridis' try: sys.path.append('../..') from crocodile.synthesis import * from util.visualize import * print("Crocodile mode") except ImportError: print("Stand-alone mode") # Convolution and FFT helpers def conv(a, b): return ifft(fft(a) * fft(b)) def coordinates(N): return numpy.fft.fftshift(numpy.fft.fftfreq(N)) def fft(a): if len(a.shape) == 1: return numpy.fft.fftshift(numpy.fft.fft(numpy.fft.ifftshift(a))) elif len(a.shape) == 2: return numpy.fft.fftshift(numpy.fft.fft2(numpy.fft.ifftshift(a))) def ifft(a): if len(a.shape) == 1: return numpy.fft.fftshift(numpy.fft.ifft(numpy.fft.ifftshift(a))) elif len(a.shape) == 2: return numpy.fft.fftshift(numpy.fft.ifft2(numpy.fft.ifftshift(a))) def pad_mid(a, N): N0 = a.shape[0] assert N >= N0 return numpy.pad(a, len(a.shape) * [(N//2-N0//2, (N+1)//2-(N0+1)//2)], mode='constant', constant_values=0.0) def extract_mid(a, N): assert N <= a.shape[0] cx = a.shape[0] // 2 s = N // 2 if N % 2 == 0: return a[len(a.shape) * [slice(cx - s, cx + s)]] else: return a[len(a.shape) * [slice(cx - s, cx + s + 1)]] def anti_aliasing_function(shape, m, c): if len(numpy.array(shape).shape) == 0: mult = 2 - 1/shape/4 return scipy.special.pro_ang1(m, m, c, multcoordinates(shape))[0] return numpy.outer(anti_aliasing_function(shape[0], m, c), anti_aliasing_function(shape[1], m, c)) def coordinates2(N): N2 = N // 2 if N % 2 == 0: return numpy.mgrid[-N2:N2, -N2:N2][::-1] / N else: return numpy.mgrid[-N2:N2+1, -N2:N2+1][::-1] / N def _show(a, name, scale, axes): size = a.shape[0] if size % 2 == 0: low,high = -0.5, 0.5 (size - 2) / size else: low,high = -0.5 * (size - 1) / size, 0.5 * (size - 1) / size low = (low - 1/size/2) * scale high = (high - 1/size/2) * scale cax=axes.imshow(a, extent=(low,high,low,high)); axes.set_title(name); axes.figure.colorbar(cax,shrink=.4,pad=0.025) def show_grid(grid, name, theta, axes): return _show(grid, name, theta, axes) def show_image(img, name, theta, axes): return _show(img, name, img.shape[0] / theta, axes) def extract_oversampled(a, Qpx, N): result = numpy.empty((Qpx, N), dtype=complex) for xf in range(Qpx): # Determine start offset. mx = a.shape[0]//2 - Qpx(N//2) + xf # Extract every Qpx-th pixel result[xf] = a[mx : mx+QpxN : Qpx] return result def kernel_oversample(ff, Qpx, s=None): # Pad the far field to the required pixel size N = ff.shape[0] if s is None: s = N padff = pad_mid(ff, NQpx) # Obtain oversampled uv-grid af = fft(padff) # Extract kernels return extract_oversampled(af, Qpx, s) # Helper for marking ranges in a graph def mark_range(lbl, x0, x1, y0=None, y1=None, ax=None): if ax is None: ax = pylab.gca() if y0 is None: y0 = ax.get_ylim()[1] if y1 is None: y1 = ax.get_ylim()[0] wdt = ax.get_xlim()[1] - ax.get_xlim()[0] ax.add_patch(patches.PathPatch(patches.Path([(x0,y0), (x0,y1)]), linestyle="dashed")) ax.add_patch(patches.PathPatch(patches.Path([(x1,y0), (x1,y1)]), linestyle="dashed")) if pylab.gca().get_yscale() == 'linear': lbl_y = (y07+y1) / 8 else: # Some type of log scale lbl_y = (y0*7y1)*(1/8) ax.annotate(lbl, (x1+wdt/200, lbl_y)) def error_approx(yB, yN, xN, alpha=0, dim=1, hexagon=False): # gridding error assert yB < yN pswf = anti_aliasing_function(int(yN)2, alpha, 2numpy.piyNxN) pswf /= numpy.prod(numpy.arange(2alpha-1,0,-2, dtype=float)) # double factorial grid_error = numpy.abs(numpy.sum(pswf[::2] - pswf[1::2])) # correction error b_error = numpy.abs(pswf[int(yN) + int(yB)]) if dim >= 2 and hexagon: b_error = numpy.abs(pswf[int(yN) + int(yB/2)])(dim-1) else: b_error = dim return numpy.abs(grid_error) / (2xM) / b_error import math N = 512 yB = N / 8 xA = 1 / 8 xA_size = int(math.ceil(xA2N)) yB_size = int(math.ceil(yB2)) print("image_size=%d, xA_size=%d, yB_size=%d" % (N, xA_size, yB_size)) overhead = 2.2 alpha = 0 max_par = 100 err_best = 10000 xM_step_best = xM_best = xN_best = yN_best = None for xM_step in numpy.arange(int(numpy.ceil(1 / 2 / xA)), 1, -1): if N % xM_step != 0: continue xM = 1 / 2 / xM_step xN = (xM - xA) / 2 yN = numpy.floor(overhead xA * yB / xM) par = 2 * numpy.pi * xN * yN if xN < 1 / N: continue print("xM = 1/%d, xN = %.4f, yN = %d, par = %.1f" % (xM_step2, xN, yN, par), flush=True, end="") if yN <= yB or par > max_par: print(", par too high") break err = error_approx(yB, yN, xN, alpha=alpha) print(", err = %g" % (err)) if err < err_best: err_best = err; xM_step_best = xM_step; xM_best = xM; xN_best = xN; yN_best = yN xM_step = xM_step_best; xM = xM_best; xN = xN_best; yN = yN_best print("Chose xM=1/%d" % (xM_step2)) xM_size = N // xM_step xM_yN_size = int(numpy.ceil(xM2yN2)) yN_size = xM_yN_size xM_step print("xM_size=%d, xM_yN_size=%d, yN_size=%d" % (xM_size, xM_yN_size, yN_size)) print("xM_step=%d" % xM_step) print(yB, yN) print("(Parameters for Sze-Tan: x0=%f, R=%d)" % (yB / yN / 2, int(numpy.floor(xN2yN)))) nsubgrid = int(math.ceil(N / xA_size)) nfacet = int(math.ceil(N / yB_size)) best_maxdxdy = N; best_subgrid_off = None; best_facet_off = None; best_ny = N while best_subgrid_off is None: print("Trying %d sub-grids, %d facets (%.1f%% overhead):" % (nsubgrid, nfacet, 100 * (1 / (N / nsubgrid / xA_size * N / nfacet / yB_size) - 1))) facet_y0s = numpy.arange(nfacet) * N / nfacet subgrid_x0s = numpy.arange(nsubgrid) / nsubgrid warnx_count = 0; warny_count = 0 for Ny in xM_step * numpy.arange(1, N // xM_step): if N % Ny != 0: continue Nx = N // Ny subgrid_off = Nx * numpy.round(N * subgrid_x0s / Nx) facet_off = Ny * numpy.round(facet_y0s / Ny) maxdx = numpy.max(numpy.abs(subgrid_off - N * subgrid_x0s)) maxdy = numpy.max(numpy.abs(facet_off - facet_y0s)) warnx = warny = "" if maxdx > (xA_size - N / nsubgrid) / 2: warnx = " (> %.1f!)" % ((xA_size - N / nsubgrid) / 2) warnx_count+=1 if maxdy > (yB_size - N / nfacet) / 2: warny = " (> %.1f!)" % ((yB_size - N / nfacet) / 2) warny_count+=1 print("Nx=%d, Ny=%d, maxdx=%.1f%s, maxdy=%.1f%s" % (Nx, Ny, maxdx, warnx, maxdy, warny)) # Select if warnx == "" and warny == "" and best_ny > Ny: # best_maxdxdy > max(maxdx, maxdy): best_maxdxdy = max(maxdx, maxdy) best_subgrid_off = subgrid_off.astype(int) best_facet_off = facet_off.astype(int) best_ny = Ny # No solution found? Crudely use number of warnings as indicator what we need more of if best_subgrid_off is None: if warnx_count >= warny_count: nsubgrid += 1 else: nfacet += 1 assert best_maxdxdy != N subgrid_off = best_subgrid_off facet_off = best_facet_off Nx = N // best_ny; Ny = best_ny; print ("Chose Nx=%d, Ny=%d, Nx0s=%s, y0s=%s" % (N // Ny, Ny, subgrid_off, facet_off)) def whole(xs): return numpy.all(numpy.abs(xs - numpy.around(xs)) < 1e-13) assert whole(numpy.outer(subgrid_off, facet_off) / N) assert whole(facet_offxM_size/N) pylab.figure(figsize=(16, 1)); pylab.title("Subgrid Centre Offsets") pylab.plot(((subgrid_off+N//2)%N-N//2)/N,numpy.zeros_like(subgrid_off), "b\|", markersize=30); pylab.plot(coordinates(N//Nx), numpy.zeros(N//Nx), "g\|") pylab.xlim(-.5,.5); pylab.yticks([]) for i, x in enumerate(subgrid_x0s): mark_range("$x_{0,%d}$"%i, 0, (x + .5) % 1 - .5) pylab.figure(figsize=(16, 1)); pylab.title("Facet Centre Offsets") pylab.plot((facet_off+N//2)%N-N//2,numpy.zeros_like(facet_off), "b\|", markersize=30); pylab.plot(coordinates(N//Ny)N, numpy.zeros(N//Ny), "g\|") pylab.xlim(-N/2,N/2); pylab.yticks([]) for i, y in enumerate(facet_y0s): mark_range("$y_{0,%d}$"%i, 0, (y + N//2) % N - N//2) pylab.show(); def greatest_prime_factor(x): i = 2 while i i <= x: while i < x and x % i == 0: x //= i i += 1 return x yP_size_options = (int(numpy.ceil( int(yB+yN2) / Ny )) + numpy.arange(0,12)) Ny yP_size_primes = numpy.vectorize(greatest_prime_factor)(yP_size_options) print(", ".join(["%d: %d" % yp for yp in zip(yP_size_options, yP_size_primes)])) yP_size = yP_size_options[numpy.argmin(yP_size_primes)] print("Chose yP_size = %d (%.1f %% overhead)" % (yP_size, 100 * yP_size / (yB_size/2+yN_size) - 100)) assert whole(subgrid_offyP_size/N) xM_yP_size = int(xM2yP_size) xMxN_yP_size = xM_yP_size + 2int(numpy.ceil(xNyP_size)) # same margin both sides print("yP_size=%d, xM_yP_size=%d, xMxN_yP_size=%d" % (yP_size, xM_yP_size, xMxN_yP_size)) G = numpy.random.rand(N)-0.5 FG = fft(G) subgrid = numpy.empty((nsubgrid, xA_size), dtype=complex) subgrid_A = numpy.zeros_like(subgrid, dtype=int) subgrid_border = (subgrid_off + numpy.hstack([subgrid_off[1:],[N]])) // 2 print(subgrid_border) for i in range(nsubgrid): left = (subgrid_border[i-1] - subgrid_off[i] + xA_size//2) % N right = subgrid_border[i] - subgrid_off[i] + xA_size//2 assert left >= 0 and right <= xA_size, "xA not large enough to cover subgrids!" subgrid_A[i,left:right] = 1 subgrid[i] = subgrid_A[i] extract_mid(numpy.roll(G, -subgrid_off[i]), xA_size) facet = numpy.empty((nfacet, yB_size), dtype=complex) facet_B = numpy.zeros_like(facet, dtype=bool) facet_split = numpy.array_split(range(N), nfacet) facet_border = (facet_off + numpy.hstack([facet_off[1:],[N]])) // 2 print(facet_border) for j in range(nfacet): left = (facet_border[j-1] - facet_off[j] + yB_size//2) % N right = facet_border[j] - facet_off[j] + yB_size//2 assert left >= 0 and right <= yB_size, "yB not large enough to cover facets!" facet_B[j,left:right] = 1 facet[j] = facet_B[j] * extract_mid(numpy.roll(FG, -facet_off[j]), yB_size) pswf = anti_aliasing_function(yN_size, alpha, 2numpy.piyNxN).real pylab.semilogy(extract_mid(numpy.abs(fft(pswf)), int(numpy.floor(xN2yN2))), "s") Fb = 1/extract_mid(pswf, yB_size) Fn = pswf[(yN_size//2)%int(1/2/xM)::int(1/2/xM)] facet_m0_trunc = pswf * numpy.sinc(coordinates(yN_size)xM_size/NyN_size) facet_m0_trunc = xM_sizeyP_size/N extract_mid(ifft(pad_mid(facet_m0_trunc, yP_size)), xMxN_yP_size).real print(N / 5, yP_size / 5, yN_size/ 5) xN_yP_size = xMxN_yP_size - xM_yP_size RNjMiBjFj = numpy.empty((nsubgrid, nfacet, xM_yN_size), dtype=complex) for j in range(nfacet): BjFj = ifft(pad_mid(facet[j] * Fb, yP_size)) for i in range(nsubgrid): MiBjFj = facet_m0_trunc * extract_mid(numpy.roll(BjFj, -subgrid_off[i]yP_size//N), xMxN_yP_size) MiBjFj_sum = numpy.array(extract_mid(MiBjFj, xM_yP_size)) MiBjFj_sum[:xN_yP_size//2] += MiBjFj[-xN_yP_size//2:] MiBjFj_sum[-xN_yP_size//2:] += MiBjFj[:xN_yP_size//2:] RNjMiBjFj[i,j] = Fn extract_mid(fft(MiBjFj_sum), xM_yN_size) # - redistribution of RNjMiBjFj here - fig = pylab.figure(figsize=(16, 8)) ax1, ax2 = fig.add_subplot(211), fig.add_subplot(212) err_sum = err_sum_img = 0 for i in range(nsubgrid): approx = numpy.zeros(xM_size, dtype=complex) for j in range(nfacet): approx += numpy.roll(pad_mid(RNjMiBjFj[i,j], xM_size), facet_off[j]xM_size//N) approx = subgrid_A[i] extract_mid(ifft(approx), xA_size) ax1.semilogy(xA2coordinates(xA_size), numpy.abs( approx - subgrid[i] )) ax2.semilogy(Ncoordinates(xA_size), numpy.abs( fft(approx - subgrid[i]) )) err_sum += numpy.abs(approx - subgrid[i])2 err_sum_img += numpy.abs(fft(approx - subgrid[i]))2 mark_range("$x_A$", -xA, xA, ax=ax1); mark_range("$N/2$", -N/2, N/2, ax=ax2) print("RMSE:", numpy.sqrt(numpy.mean(err_sum)), "(image:", numpy.sqrt(numpy.mean(err_sum_img)), ")") FNjSi = numpy.empty((nsubgrid, nfacet, xM_yN_size), dtype=complex) for i in range(nsubgrid): FSi = fft(pad_mid(subgrid[i], xM_size)) for j in range(nfacet): FNjSi[i,j] = extract_mid(numpy.roll(FSi, -facet_off[j]xM_size//N), xM_yN_size) # - redistribution of FNjSi here - fig = pylab.figure(figsize=(16, 8)) ax1, ax2 = fig.add_subplot(211), fig.add_subplot(212) err_sum = err_sum_img = 0 for j in range(nfacet): approx = numpy.zeros(yB_size, dtype=complex) for i in range(nsubgrid): NjSi = numpy.zeros(xMxN_yP_size, dtype=complex) NjSi_mid = extract_mid(NjSi, xM_yP_size) NjSi_mid[:] = ifft(pad_mid(Fn * FNjSi[i,j], xM_yP_size)) # updates NjSi_tile via reference! NjSi[-xN_yP_size//2:] = NjSi_mid[:xN_yP_size//2] NjSi[:xN_yP_size//2:] = NjSi_mid[-xN_yP_size//2:] FMiNjSi = fft(numpy.roll(pad_mid(facet_m0_trunc * NjSi, yP_size), subgrid_off[i]yP_size//N)) approx += extract_mid(FMiNjSi, yB_size) approx = Fb * facet_B[j] err_sum += numpy.abs(ifft(approx - facet[j]))2 err_sum_img += numpy.abs(approx - facet[j])2 ax1.semilogy(coordinates(yB_size), numpy.abs(ifft(facet[j] - approx))) ax2.semilogy(yB_sizecoordinates(yB_size), numpy.abs(facet[j] - approx)) print("RMSE:", numpy.sqrt(numpy.mean(err_sum)), "(image:", numpy.sqrt(numpy.mean(err_sum_img)), ")") mark_range("$x_A$", -xA, xA, ax=ax1) mark_range("$x_M$", -xM, xM, ax=ax1) mark_range("$y_B$", -yB, yB, ax=ax2) mark_range("$0.5$", -.5, .5, ax=ax1) pylab.show(fig) print(nsubgrid,"x",nsubgrid,"subgrids,",nfacet,"x", nfacet,"facets") subgrid_2 = numpy.empty((nsubgrid, nsubgrid, xA_size, xA_size), dtype=complex) facet_2 = numpy.empty((nfacet, nfacet, yB_size, yB_size), dtype=complex) G_2 = numpy.exp(2jnumpy.pinumpy.random.rand(N,N))numpy.random.rand(N,N)/2 for i0,i1 in itertools.product(range(nsubgrid), range(nsubgrid)): subgrid_2[i0,i1] = extract_mid(numpy.roll(G_2, (-subgrid_off[i0], -subgrid_off[i1]), (0,1)), xA_size) subgrid_2[i0,i1] = numpy.outer(subgrid_A[i0], subgrid_A[i1]) FG_2 = fft(G_2) for j0,j1 in itertools.product(range(nfacet), range(nfacet)): facet_2[j0,j1] = extract_mid(numpy.roll(FG_2, (-facet_off[j0], -facet_off[j1]), (0,1)), yB_size) facet_2[j0,j1] = numpy.outer(facet_B[j0], facet_B[j1]) def slice_a(fill_val, axis_val, dims, axis): return tuple( [ axis_val if i == axis else fill_val for i in range(dims) ] ) def pad_mid_a(a, N, axis): N0 = a.shape[axis] if N == N0: return a pad = slice_a((0,0), (N//2-N0//2, (N+1)//2-(N0+1)//2), len(a.shape), axis) return numpy.pad(a, pad, mode='constant', constant_values=0.0) def extract_mid_a(a, N, axis): assert N <= a.shape[axis] cx = a.shape[axis] // 2 if N % 2 != 0: slc = slice(cx - N // 2, cx + N // 2 + 1) else: slc = slice(cx - N // 2, cx + N // 2) return a[slice_a(slice(None), slc, len(a.shape), axis)] def fft_a(a, axis): return numpy.fft.fftshift(numpy.fft.fft(numpy.fft.ifftshift(a, axis),axis=axis),axis) def ifft_a(a, axis): return numpy.fft.fftshift(numpy.fft.ifft(numpy.fft.ifftshift(a, axis),axis=axis),axis) def broadcast_a(a, dims, axis): return a[slice_a(numpy.newaxis, slice(None), dims, axis)] def prepare_facet(facet, axis): BF = pad_mid_a(facet * broadcast_a(Fb, len(facet.shape), axis), yP_size, axis) return ifft_a(BF, axis) def extract_subgrid(BF, i, axis): dims = len(BF.shape) BF_mid = extract_mid_a(numpy.roll(BF, -subgrid_off[i]yP_size//N, axis), xMxN_yP_size, axis) MBF = broadcast_a(facet_m0_trunc,dims,axis) BF_mid MBF_sum = numpy.array(extract_mid_a(MBF, xM_yP_size, axis)) xN_yP_size = xMxN_yP_size - xM_yP_size # [:xN_yP_size//2] / [-xN_yP_size//2:] for axis, [:] otherwise slc1 = slice_a(slice(None), slice(xN_yP_size//2), dims, axis) slc2 = slice_a(slice(None), slice(-xN_yP_size//2,None), dims, axis) MBF_sum[slc1] += MBF[slc2] MBF_sum[slc2] += MBF[slc1] return broadcast_a(Fn,len(BF.shape),axis) * \ extract_mid_a(fft_a(MBF_sum, axis), xM_yN_size, axis) t = time.time() NMBF_NMBF = numpy.empty((nsubgrid, nsubgrid, nfacet, nfacet, xM_yN_size, xM_yN_size), dtype=complex) for j0,j1 in itertools.product(range(nfacet), range(nfacet)): BF_F = prepare_facet(facet_2[j0,j1], 0) BF_BF = prepare_facet(BF_F, 1) for i0 in range(nsubgrid): NMBF_BF = extract_subgrid(BF_BF, i0, 0) for i1 in range(nsubgrid): NMBF_NMBF[i0,i1,j0,j1] = extract_subgrid(NMBF_BF, i1, 1) print(time.time() - t, "s") t = time.time() for j0,j1 in itertools.product(range(nfacet), range(nfacet)): BF_F = prepare_facet(facet_2[j0,j1], 0) for i0 in range(nsubgrid): NMBF_F = extract_subgrid(BF_F, i0, 0) NMBF_BF = prepare_facet(NMBF_F, 1) for i1 in range(nsubgrid): NMBF_NMBF[i0,i1,j0,j1] = extract_subgrid(NMBF_BF, i1, 1) print(time.time() - t, "s") t = time.time() for j0,j1 in itertools.product(range(nfacet), range(nfacet)): F_BF = prepare_facet(facet_2[j0,j1], 1) for i1 in range(nsubgrid): F_NMBF = extract_subgrid(F_BF, i1, 1) BF_NMBF = prepare_facet(F_NMBF, 0) for i0 in range(nsubgrid): NMBF_NMBF[i0,i1,j0,j1] = extract_subgrid(BF_NMBF, i0, 0) print(time.time() - t, "s") pylab.rcParams['figure.figsize'] = 16, 8 err_sum = err_sum_img = 0 for i0,i1 in itertools.product(range(nsubgrid), range(nsubgrid)): approx = numpy.zeros((xM_size, xM_size), dtype=complex) for j0,j1 in itertools.product(range(nfacet), range(nfacet)): approx += numpy.roll(pad_mid(NMBF_NMBF[i0,i1,j0,j1], xM_size), (facet_off[j0]xM_size//N, facet_off[j1]xM_size//N), (0,1)) approx = extract_mid(ifft(approx), xA_size) approx = numpy.outer(subgrid_A[i0], subgrid_A[i1]) err_sum += numpy.abs(approx - subgrid_2[i0,i1])2 / nsubgrid2 err_sum_img += numpy.abs(fft(approx - subgrid_2[i0,i1]))2 / nsubgrid2 pylab.imshow(numpy.log(numpy.sqrt(err_sum)) / numpy.log(10)); pylab.colorbar(); pylab.show() pylab.imshow(numpy.log(numpy.sqrt(err_sum_img)) / numpy.log(10)); pylab.colorbar(); pylab.show() print("RMSE:", numpy.sqrt(numpy.mean(err_sum)), "(image:", numpy.sqrt(numpy.mean(err_sum_img)), ")") pylab.rcParams['figure.figsize'] = 16, 4 gc_alpha = 0; xGp = 5/N; gc_x0 = 0.35 gc_support = int(2xGpN) print("parameter:", numpy.pigc_support/2, "x0:", gc_x0) x0_size = int(Ngc_x02) gc_pswf = anti_aliasing_function(N, gc_alpha, numpy.pigc_support/2) gc = pad_mid(extract_mid(1 / gc_pswf, x0_size), N) pylab.semilogy(x0_sizecoordinates(x0_size), numpy.abs(extract_mid(gc, x0_size))); pylab.legend(["F[n]"]); pylab.xlim((-N/1.8, N/1.8)) mark_range("$x_0N$", -gc_x0N,gc_x0N); mark_range("$N/2$", -N/2,N/2); pylab.title("Grid correction"); pylab.show(); FG_2_gc = FG_2 * numpy.outer(gc, gc) show_image(numpy.log(numpy.maximum(1e-15, numpy.abs(FG_2_gc))) / numpy.log(10), "FG_2_cropped", N) G_2_gc = ifft(FG_2_gc) crop = pad_mid(numpy.ones(x0_size), N) G_2_cropped = ifft(FG_2 * numpy.outer(crop,crop)) subgrid_2 = numpy.empty((nsubgrid, nsubgrid, xA_size, xA_size), dtype=complex) facet_2 = numpy.empty((nfacet, nfacet, yB_size, yB_size), dtype=complex) for i0,i1 in itertools.product(range(nsubgrid), range(nsubgrid)): subgrid_2[i0,i1] = extract_mid(numpy.roll(G_2_gc, (-subgrid_off[i0], -subgrid_off[i1]), (0,1)), xA_size) subgrid_2[i0,i1] = numpy.outer(subgrid_A[i0], subgrid_A[i1]) fig = pylab.figure(figsize=(32,32)) for j0,j1 in itertools.product(range(nfacet), range(nfacet)): facet_2[j0,j1] = extract_mid(numpy.roll(FG_2_gc, (-facet_off[j0], -facet_off[j1]), (0,1)), yB_size) facet_2[j0,j1] = numpy.outer(facet_B[j0], facet_B[j1]) show_image(numpy.log(numpy.maximum(1e-15, numpy.abs(facet_2[j0,j1]))) / numpy.log(10), "facet_%d%d" % (j0,j1), N/nfacet, axes=fig.add_subplot(nfacet,nfacet,j1+(nfacet-j0-1)nfacet+1), norm=(-15,8)) pylab.show(fig) NMBF_NMBF = numpy.empty((nsubgrid, nsubgrid, nfacet, nfacet, xM_yN_size, xM_yN_size), dtype=complex) for j0,j1 in itertools.product(range(nfacet), range(nfacet)): F_BF = prepare_facet(facet_2[j0,j1], 1) for i1 in range(nsubgrid): F_NMBF = extract_subgrid(F_BF, i1, 1) BF_NMBF = prepare_facet(F_NMBF, 0) for i0 in range(nsubgrid): NMBF_NMBF[i0,i1,j0,j1] = extract_subgrid(BF_NMBF, i0, 0) from pylru import lrudecorator @lrudecorator(100) def make_approx_subgrid(i0,i1): approx = numpy.zeros((xM_size, xM_size), dtype=complex) for j0,j1 in itertools.product(range(nfacet), range(nfacet)): approx += numpy.roll(pad_mid(NMBF_NMBF[i0,i1,j0,j1], xM_size), (facet_off[j0]xM_size//N, facet_off[j1]xM_size//N), (0,1)) # Extract region that is set in subgrid for comparison approx_compare = extract_mid(ifft(approx), xA_size) approx_compare = numpy.outer(subgrid_A[i0], subgrid_A[i1]) rmse = numpy.sqrt(numpy.mean(numpy.abs(approx_compare - subgrid_2[i0,i1])2 / nsubgrid2)) # Return full approximation. We degrid from it, so bounds don't matter return ifft(approx), rmse / numpy.mean(numpy.abs(approx_compare)) oversample = 2*14 print("grid support:", gc_support) print("oversampling:", oversample) kernel = kernel_oversample(gc_pswf, oversample, gc_support).real kernel /= numpy.sum(kernel[0]) r = numpy.arange(-oversample(gc_support//2), oversample((gc_support+1)//2)) / oversample pylab.semilogy(r, numpy.transpose(kernel).flatten().real); mark_range("$Nx_G$", -NxGp,NxGp); pylab.title("Gridding kernel (oversampled x%d)" % oversample); pylab.show(); @interact(iu=(0, N, 0.01),iv=(0, N, 0.01)) def test_degrid_accuracy(iu,iv, show_subgrid=False): u = (iu - N//2) / N; v = (iv - N//2) / N su = numpy.sum((iu+N//2)%N >= subgrid_border) % nsubgrid sv = numpy.sum((iv+N//2)%N >= subgrid_border) % nsubgrid siu = iu + xA_size//2-(subgrid_off[su] + N//2) % N siv = iv + xA_size//2-(subgrid_off[sv] + N//2) % N dAM = (xM_size - xA_size) // 2 deg = conv_predict(N, 1, numpy.array([(u,v,0)]), None, G_2_gc, kernel)[0] if whole(iu) and whole(iv): actual = G_2_cropped[int(iv),int(iu)] print("actual: ", actual) print("degridded: ", deg) print("degrid error: ", numpy.abs(deg-actual)) else: print("degridded: ", deg) approx_subgrid, rmse = make_approx_subgrid(sv, su) print("subgrid: (%d/%d), rmse: %g" % (su, sv, rmse)) sou = (((subgrid_off[su] + N//2) % N) - N//2) / N sov = (((subgrid_off[sv] + N//2) % N) - N//2) / N deg_ap = conv_predict(N, 2xM, numpy.array([(u-sou,v-sov,0)]), None, approx_subgrid, kernel)[0] print("recomb+degrid:", deg_ap); print("recomb error: ", numpy.abs(deg_ap-deg)) if whole(iu) and whole(iv): print("total error: ", numpy.abs(deg_ap-actual)) if show_subgrid: fig = pylab.figure() ax = fig.add_subplot(111) show_grid(numpy.abs(numpy.log(approx_subgrid) / numpy.log(10)), "subgrid_%d%d" % (su,sv), N, axes=ax) ax.add_patch(patches.Rectangle((u-sou-gc_support//2/N, v-sov-gc_support//2/N), gc_support/N, gc_support/N, fill=False)) pylab.show(fig) nvis = 15000 nvis_sg = 500 us = (N - gc_support) / N * (numpy.random.rand(nvis) - 0.5) vs = (N - gc_support) / N * (numpy.random.rand(nvis) - 0.5) sus = numpy.sum((us[:,None] * N + N) % N >= subgrid_border,axis=1) % nsubgrid svs = numpy.sum((vs[:,None] * N + N) % N >= subgrid_border,axis=1) % nsubgrid uvws = numpy.transpose([us, vs, numpy.zeros_like(us)]) deg = conv_predict(N, 1, uvws, None, G_2_gc, kernel) sel_sg = numpy.empty((nsubgrid, nsubgrid, nvis_sg), dtype=int) uvw_sg = numpy.empty((nsubgrid, nsubgrid, nvis_sg, 3), dtype=float) deg_ap_sg = numpy.empty((nsubgrid, nsubgrid, nvis_sg), dtype=complex) for sv in range(nsubgrid): for su in range(nsubgrid): # Sub-grid offsets sou = (((subgrid_off[su] + N//2) % N) - N//2) / N sov = (((subgrid_off[sv] + N//2) % N) - N//2) / N print(sov, sou) # Determine uvws sel_sg[sv,su] = numpy.array(numpy.where( (sus == su) & (svs == sv) ))[0,:nvis_sg] uvw_sg[sv,su] = numpy.transpose([us[sel_sg[sv,su]]-sou, vs[sel_sg[sv,su]]-sov, numpy.zeros_like(us[sel_sg[sv,su]])]) # Handle sub-grids overlapping the sides of the grid uvw_sg[sv,su,numpy.abs(uvw_sg[sv,su,:,0] /2/xM) >= 0.5,0] -= 1 uvw_sg[sv,su,numpy.abs(uvw_sg[sv,su,:,1] /2/xM) >= 0.5,1] -= 1 # Degrid from sub-grids approx_subgrid, rmse = make_approx_subgrid(sv, su) deg_ap_sg[sv,su] = conv_predict(N, 2xM, uvw_sg[sv,su], None, approx_subgrid, kernel) # Check accuracy diff = deg_ap_sg[sv,su] - deg[sel_sg[sv,su]] print("Facet %d/%d: %d vis, rmse %g" % (sv,su,len(sel_sg[sv,su]), numpy.sqrt(numpy.mean(numpy.abs(diff)*2)))) import os.path import h5py out_prefix = "../../data/grid/T05b_" with h5py.File(out_prefix + "in.h5",'w') as f: f['pswf'] = numpy.fft.ifftshift(pswf) f['sepkern/kern'] = kernel for j0,j1 in itertools.product(range(nfacet), range(nfacet)): f["j0=%d/j1=%d/facet" % (j0,j1)] = numpy.fft.ifftshift(facet_2[j0,j1]) for i0,i1 in itertools.product(range(nsubgrid), range(nsubgrid)): f['i0=%d/i1=%d/j0=%d/j1=%d/nmbf' % (i0,i1,j0,j1)] = \ numpy.fft.ifftshift(NMBF_NMBF[i0,i1,j0,j1]) for i0,i1 in itertools.product(range(nsubgrid), range(nsubgrid)): #f["i0=%d/i1=%d/subgrid" % (i0,i1)] = numpy.fft.ifftshift(subgrid_2[i0, i1]) f["i0=%d/i1=%d/approx" % (i0,i1)] = numpy.fft.ifftshift(make_approx_subgrid(i0, i1)[0]) for sv in range(nsubgrid): for su in range(nsubgrid): # Write f['i0=%d/i1=%d/uvw' % (sv,su)] = uvws[sel_sg[sv,su]] f['i0=%d/i1=%d/uvw_subgrid' % (sv,su)] = uvw_sg[sv,su] f['i0=%d/i1=%d/vis' % (sv,su)] = deg[sel_sg[sv,su]] #f['i0=%d/i1=%d/vis_approx' % (sv,su)] = deg_ap <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Parametrisation Step2: Choose "gridding" function extents Step3: Placing facets and subgrids Step4: Sizing the intermediate grid Step5: We need a bunch of array constants derived from the gridding function Step6: Facet $\rightarrow$ Subgrid Step7: Subgrid $\rightarrow$ facet Step8: 2D case Step9: Given that the amount of data has been squared, performance is a bit more of a concern now. Fortunately, the entire procedure is completely separable, so let us first re-define the operations to work on one array axis exclusively Step10: This allows us to define the two fundamental operations - going from $F$ to $b\ast F$ and from $b\ast F$ to $n\ast m(b\ast F)$ separately Step11: Having those operations separately means that we can shuffle things around quite a bit without affecting the result. The obvious first choice might be to do all facet-preparation up-front, as this allows us to share the computation across all subgrids Step12: However, remember that prepare_facet increases the amount of data involved, which in turn means that we need to shuffle more data through subsequent computations. Step13: Degridding Step14: From this we derive the new $\mathcal F G$ that we are going to feed to the recombination algorithm Step15: Which in turn leads to new facets. Note how the grid correction pattern is clearly larger than any individual facet. Step16: The recombination algorithm again, using the new data. Step17: In order to obtain visibilities at non-integer positions we need an oversampled gridding function, as usual Step18: Test systematically Step19: Write test data set
6,903	<ASSISTANT_TASK:> Python Code: from PyQt5.QtCore import QSettings myOrganization = 'MyOrg' # 组织名称 myApplication = 'MyApp' # 应用名称 settings = QSettings(myOrganization, myApplication) settings.setValue("editor/wrapMargin", 68) print (settings.value("editor/wrapMargin")) print (settings.value("editor/someth")) # 如果在程序中多处用到 QSettings ，那么可以一次性定义，然后统一使用 from PyQt5.QtCore import QCoreApplication QCoreApplication.setOrganizationName("MyOrg2nd") QCoreApplication.setOrganizationDomain("MyOrg2nd.com") QCoreApplication.setApplicationName("MyApp2nd") settings_2nd = QSettings() print (settings.applicationName()) print (settings_2nd.applicationName()) print (settings_2nd.organizationName()) # 测试键是否存在 settings.contains("editor") settings.contains("editor/wrapMargin") settings.remove("editor") #删除一个键，键不存在也不会报错 settings.contains("editor/wrapMargin") settings.clear() #清空所有键（只是清空自己添加的，系统固有的还在） settings.allKeys() #所有键 settings.fileName() #储存位置 settings_ini = QSettings("conf.ini", QSettings.IniFormat) #使用 ini 文件来储存 settings_ini.fileName() <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: 最简单的用法
6,904	<ASSISTANT_TASK:> Python Code: import pandas as pd import swap base_collection_path = '/nfs/slac/g/ki/ki18/cpd/swap/pickles/15.09.02/' base_directory = '/nfs/slac/g/ki/ki18/cpd/swap_catalog_diagnostics/' annotated_catalog_path = base_directory + 'annotated_catalog.csv' cut_empty = True stages = [1, 2] categories = ['ID', 'ZooID', 'location', 'mean_probability', 'category', 'kind', 'flavor', 'state', 'status', 'truth', 'stage', 'line'] annotation_categories = ['At_X', 'At_Y', 'PD', 'PL'] catalog = [] for stage in stages: print(stage) collection_path = base_collection_path + 'stage{0}'.format(stage) + '/CFHTLS_collection.pickle' collection = swap.read_pickle(collection_path, 'collection') for ID in collection.list(): subject = collection.member[ID] catalog_i = [] # for stage1 we shall skip the tests for now if (stage == 1) * (subject.category == 'test'): continue # flatten out x and y. also cut out empty entries annotationhistory = subject.annotationhistory x_unflat = annotationhistory['At_X'] x = np.array([xi for xj in x_unflat for xi in xj]) # cut out catalogs with no clicks if (len(x) < 1) and (cut_empty): continue # oh yeah there's that absolutely nutso entry with 50k clicks if len(x) > 10000: continue for category in categories: if category == 'stage': catalog_i.append(stage) elif category == 'line': catalog_i.append(line) else: catalog_i.append(subject.__dict__[category]) for category in annotation_categories: catalog_i.append(list(annotationhistory[category])) catalog.append(catalog_i) catalog = pd.DataFrame(catalog, columns=categories + annotation_categories) # save catalog catalog.to_csv(annotated_catalog_path) knownlens_dir = '/nfs/slac/g/ki/ki18/cpd/code/strongcnn/catalog/knownlens/' knownlensID = pd.read_csv(knownlens_dir + 'knownlensID', sep=' ') listfiles_d1_d11 = pd.read_csv(knownlens_dir + 'listfiles_d1_d11.txt', sep=' ') knownlenspath = knownlens_dir + 'knownlens.csv' X2 = listfiles_d1_d11[listfiles_d1_d11['CFHTID'].isin(knownlensID['CFHTID'])] # cuts down to like 212 entries. ZooID = [] for i in range(len(Y)): ZooID.append(X2['ZooID'][X2['CFHTID'] == knownlensID['CFHTID'][i]].values[0]) knownlensID['ZooID'] = ZooID knownlensID.to_csv(knownlenspath) # code to regenerate the catalogs base_directory = '/nfs/slac/g/ki/ki18/cpd/swap_catalog_diagnostics/' cluster_directory = base_directory ## uncomment this line when updating the shared catalog! # base_directory = '/nfs/slac/g/ki/ki18/cpd/swap_catalog/' # cluster_directory = base_directory + 'clusters/' field_directory = base_directory knownlens_path = base_directory + 'knownlens.csv' collection_path = base_directory + 'annotated_catalog.csv' catalog_path = cluster_directory + 'catalog.csv' # if we're rerunning this code, we should remove the old cluster pngs, # all of which have _.png from glob import glob files_to_delete = glob(cluster_directory + '_.png') from os import remove for delete_this_file in files_to_delete: remove(delete_this_file) # run create catalog code. This can take a while. from subprocess import call command = ['python', '/nfs/slac/g/ki/ki18/cpd/code/strongcnn/code/create_catalogs.py', '--collection', collection_path, '--knownlens', knownlens_path, '--clusters', cluster_directory, '--fields', field_directory, #'--augment', augmented_directory, #'--do_a_few', '100', ] call(command) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Create the knownlens catalog Step2: Convert the annotated catalog and knownlens catalog into cluster catalogs and cutouts
6,905	<ASSISTANT_TASK:> Python Code: # The interpreter can be used as a calculator, and can also echo or concatenate strings. 3 + 3 3 * 3 3 ** 3 3 / 2 # classic division - output is a floating point number # Use quotes around strings, single or double, but be consistent to the extent possible 'dogs' "dogs" "They're going to the beach" 'He said "I like mac and cheese"' # sometimes you can't escape the escape 'He said "I\'d like mac and cheese"' # + operator can be used to concatenate strings 'dogs' + "cats" print('Hello World!') a = 5 b = 10 a + b b = 38764289.1097 a + b a = 5 b = 10 while b > a: print("b="+str(b)) b = b-1 a = 5 type(a) a = int(5) type(a) c = 'dogs' print(type(c)) c = str('dogs') print(type(c)) customer = 'Carol' pizzas = 2 print(customer + ' ordered ' + pizzas + ' pizzas.') customer = 'Carol' pizzas = str(2) print(customer + ' ordered ' + pizzas + ' pizzas.') customer = 'Carol' pizzas = 2 print(customer + ' ordered ' + str(pizzas) + ' pizzas.') # Read unstructured text # One way is to open the whole file as a block file_path = "./beowulf" # We can save the path to the file as a variable file_in = open(file_path, "r") # Options are 'r', 'w', and 'a' (read, write, append) beowulf_a = file_in.read() file_in.close() print(beowulf_a) # Another way is to read the file as a list of individual lines with open(file_path, "r") as b: beowulf_b = b.readlines() print(beowulf_b) # In order to get a similar printout to the first method, we use a for loop # to print line by line - more on for loops below! for l in beowulf_b: print(l) # We now have two variables with the content of our 'beowulf' file represented using two different data structures. # Why do you think we get the different outputs from the next two statements? # Beowulf text stored as one large string print("As string:", beowulf_a[0]) # Beowulf text stored as a list of lines print("As list of lines:", beowulf_b[0]) # We can confirm our expectations by checking on the types of our two beowulf variables print(type(beowulf_a)) print(type(beowulf_b)) # Read CSV files using the Pandas read_csv method. # Note: Pandas also includes methods for reading Excel. # First we need to import the pandas library import pandas as pd # Create a variable to hold the path to the file fpath = "aaj1945_DataS1_Egg_shape_by_species_v2.csv" egg_data = pd.read_csv(fpath) # We can get all kinds of info about the dataset # info() provides an overview of the structure print(egg_data.info()) # Look at the first five rows egg_data.head() # Names of columns print(egg_data.columns.values) # Dimensions (number of rows and columns) print(egg_data.shape) # And much more! But as a final example we can perform operations on the data. # Descriptive statistics on the "Number of eggs" column print(egg_data["Number of eggs"].describe()) # Or all of the columns in whole table with numeric data types: print(egg_data.describe()) # Fun with types this = 12 that = 15 the_other = "27" my_stuff = [this,that,the_other,["a","b","c",4]] more_stuff = { "item1": this, "item2": that, "item3": the_other, "item4": my_stuff } this + that # this won't work ... # this + that + the_other # ... but this will ... this + that + int(the_other) # ...and this too str(this) + str(that) + the_other # Separate list items with commas! number_list = [1, 2, 3, 4, 5] string_list = ['apples', 'oranges', 'pears', 'grapes', 'pineapples'] combined_list = [1, 2, 'oranges', 3.14, 'peaches', 'grapes', 99.19876] # Nested lists - lists of lists - are allowed. list_of_lists = [[1, 2, 3], ['oranges', 'grapes', 8], [['small list'], ['bigger', 'list', 55], ['url_1', 'url_2'] ] ] # Create an empty list empty_list = [] # As we did above, by using square brackets around a comma-separated sequence of items new_list = [1, 2, 3] # Using the type constructor constructed_list = list('purple') # Using a list comprehension result_list = [i for i in range(1, 20)] empty_list new_list result_list constructed_list constructed_list_int = list(123) constructed_list_str = list('123') constructed_list_str ordered = [3, 2, 7, 1, 19, 0] ordered # There is a 'sort' method for sorting list items as needed: ordered.sort() ordered string_list = ['apples', 'oranges', 'pears', 'grapes', 'pineapples'] string_list[0] # We can use positions to 'slice' or select sections of a list: string_list[3:] # start at index '3' and continue to the end string_list[:3] # start at index '0' and go up to, but don't include index '3' string_list[1:4] # start at index '1' and go up to and don't include index '4' # If we don't know the position of a list item, we can use the 'index()' method to find out. # Note that in the case of duplicate list items, this only returns the position of the first one: string_list.index('pears') string_list.append('oranges') string_list string_list.index('oranges') # one more time with lists and dictionaries list_ex1 = my_stuff[0] + my_stuff[1] + int(my_stuff[2]) print(list_ex1) # we can use parentheses to split a continuous group of commands over multiple lines list_ex2 = ( str(my_stuff[0]) + str(my_stuff[1]) + my_stuff[2] + my_stuff[3][0] ) print(list_ex2) dict_ex1 = ( more_stuff['item1'] + more_stuff['item2'] + int(more_stuff['item3']) ) print(dict_ex1) dict_ex2 = ( str(more_stuff['item1']) + str(more_stuff['item2']) + more_stuff['item3'] ) print(dict_ex2) # Now try it yourself ... # print out the phrase "The answer: 42" using the following # variables and one or more of your own and the 'print()' function # (remember spaces are characters as well) start = "The" answer = 42 <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Try It Yourself Step2: Variables can be reassigned Step3: The ability to reassign variable values becomes important when iterating through groups of objects for batch processing or other purposes. In the example below, the value of b is dynamically updated every time the while loop is executed Step4: Variable data types can be inferred, so Python does not require us to declare the data type of a variable on assignment. Step5: is equivalent to Step6: There are cases when we may want to declare the data type, for example to assign a different data type from the default that will be inferred. Concatenating strings provides a good example. Step7: Above, Python has inferred the type of the variable pizza to be an integer. Since strings can only be concatenated with other strings, our print statement generates an error. There are two ways we can resolve the error Step8: Given the following variable assignments Step9: Structure Step11: Lists Step12: There are multiple ways to create a list Step13: We can inspect our lists Step14: The above output for constructed_list may seem odd. Referring to the documentation, we see that the argument to the type constructor is an iterable, which according to the documentation is "An object capable of returning its members one at a time." In our construtor statement above Step15: Lists in Python are Step16: Info on additional list methods is available at https
6,906	<ASSISTANT_TASK:> Python Code: # Import all functions from external file from download_and_process_DE_functions import * # Jupyter functions %matplotlib inline download_from = 'original_sources' #download_from = 'opsd_server' if download_from == 'original_sources': # BNetzA Power plant list url_bnetza = ('http://www.bundesnetzagentur.de/SharedDocs/Downloads/DE/' 'Sachgebiete/Energie/Unternehmen_Institutionen/Versorgungssicherheit/' 'Erzeugungskapazitaeten/Kraftwerksliste/Kraftwerksliste_CSV.csv' '?__blob=publicationFile&v=10') # UBA Power plant list url_uba = ('https://www.umweltbundesamt.de/sites/default/files/medien/' '372/dokumente/kraftwerke_de_ab_100_mw_0.xls') if download_from == 'opsd_server': # Specify direction to original_data folder on the opsd data server # BNetzA Power plant list url_bnetza = 'http://data.open-power-system-data.org/conventional_power_plants/' url_bnetza = url_bnetza + '2020-10-01' url_bnetza = url_bnetza +'/original_data/Kraftwerksliste_CSV.csv' # UBA Power plant list url_uba = 'http://data.open-power-system-data.org/conventional_power_plants/' url_uba = url_uba + '2020-10-01' url_uba = url_uba +'/original_data/kraftwerke-de-ab-100-mw_0.xls' plantlist = getbnetzalist(url_bnetza) # clean unnamed columns plantlist.drop([c for c in plantlist.columns if 'Unnamed:' in c], axis=1, inplace=True) plantlist.head() plantlist_uba = getubalist(url_uba) plantlist_uba.head() dict_columns = { 'Kraftwerksnummer Bundesnetzagentur': 'id', 'Unternehmen': 'company', 'Kraftwerksname': 'name', 'PLZ\n(Standort Kraftwerk)': 'postcode', 'Ort\n(Standort Kraftwerk)': 'city', 'Straße und Hausnummer (Standort Kraftwerk)': 'street', 'Bundesland': 'state', 'Blockname': 'block', ('Datum der Aufnahme der kommerziellen Stromeinspeisung der Erzeugungseinheit [Datum/jahr]'): 'commissioned', ('Kraftwerksstatus \n(in Betrieb/\nvorläufig stillgelegt/\nsaisonale ' 'Konservierung\nReservekraftwerk/\nSonderfall)'): 'status', ('Kraftwerksstatus \n(in Betrieb/\nvorläufig stillgelegt/\nsaisonale ' 'Konservierung\nGesetzlich an Stilllegung gehindert/\nSonderfall)'): 'status', ('Kraftwerksstatus \n(in Betrieb/\nvorläufig stillgelegt/\nsaisonale ' 'Konservierung\nNetzreserve/ Sicherheitsbereitschaft/\nSonderfall)'): 'status', 'Energieträger': 'fuel_basis', ('Spezifizierung "Mehrere Energieträger" und "Sonstige Energieträger" - ' 'Hauptbrennstoff'): 'fuel_multiple1', 'Spezifizierung "Mehrere Energieträger" - Zusatz- / Ersatzbrennstoffe': 'fuel_multiple2', ('Auswertung\nEnergieträger (Zuordnung zu einem Hauptenergieträger bei ' 'Mehreren Energieträgern)'): 'fuel', 'Förderberechtigt nach EEG\n(ja/nein)': 'eeg', 'Wärmeauskopplung (KWK)\n(ja/nein)': 'chp', 'Netto-Nennleistung (elektrische Wirkleistung) in MW': 'capacity', ('Bezeichnung Verknüpfungspunkt (Schaltanlage) mit dem Stromnetz der ' 'Allgemeinen Versorgung gemäß Netzbetreiber'): 'network_node', 'Netz- oder Umspannebene des Anschlusses': 'voltage', 'Name Stromnetzbetreiber': 'network_operator', 'Kraftwerksname / Standort': 'uba_name', 'Betreiber ': 'uba_company', 'Standort-PLZ': 'uba_postcode', 'Kraftwerksstandort': 'uba_city', 'Elektrische Bruttoleistung (MW)': 'uba_capacity', 'Fernwärme-leistung (MW)': 'uba_chp_capacity', 'Inbetriebnahme (ggf. Ertüchtigung)': 'uba_commissioned', 'Anlagenart': 'uba_technology', 'Primärenergieträger': 'uba_fuel', } plantlist.rename(columns=dict_columns, inplace=True) # Check if all columns have been translated for columnnames in plantlist.columns: # if columnnames not in dict_columns.values(): if columnnames not in dict_columns.values(): logger.error("Untranslated column: "+ columnnames) # first remove line breaks plantlist['fuel'] = plantlist['fuel'].str.replace('\n', ' ') # Delete entries without fuel and name plantlist = plantlist.dropna(subset = ['fuel','name']) dict_fuels = { 'Steinkohle': 'Hard coal', 'Erdgas': 'Natural gas', 'Braunkohle': 'Lignite', 'Kernenergie': 'Nuclear', 'Pumpspeicher': 'Hydro PSP', 'Biomasse': 'Biomass and biogas', 'Mineralölprodukte': 'Oil', 'Laufwasser': 'Hydro', 'Sonstige Energieträger (nicht erneuerbar) ': 'Other fuels', 'Abfall': 'Waste', 'Speicherwasser (ohne Pumpspeicher)': 'Hydro Reservoir', 'Unbekannter Energieträger (nicht erneuerbar)': 'Other fuels', 'Sonstige Energieträger (nicht erneuerbar)': 'Other fuels', 'Mehrere Energieträger (nicht erneuerbar)': 'Mixed fossil fuels', 'Deponiegas': 'Sewage and landfill gas', 'Windenergie (Onshore-Anlage)': 'Onshore', 'Windenergie (Onshore-Anlage)neu': 'Onshore', 'Windenergie (Offshore-Anlage)': 'Offshore', 'Solare Strahlungsenergie': 'Solar', 'Klärgas': 'Sewage and landfill gas', 'Geothermie': 'Geothermal', 'Grubengas': 'Other fossil fuels', 'Sonstige Speichertechnologien': 'Storage Technologies' } plantlist["fuel"].replace(dict_fuels, inplace=True) # Check if all fuels have been translated for fuelnames in plantlist["fuel"].unique(): if fuelnames not in dict_fuels.values(): print(dict_fuels.values(), fuelnames) logger.error("Untranslated fuel: " + fuelnames) dict_plantstatus = { 'in Betrieb': 'operating', 'In Betrieb': 'operating', 'vorläufig stillgelegt': 'shutdown_temporary', 'Vorläufig stillgelegt': 'shutdown_temporary', 'Vorläufig Stillgelegt': 'shutdown_temporary', 'Sonderfall': 'special_case', 'saisonale Konservierung': 'seasonal_conservation', 'Saisonale Konservierung': 'seasonal_conservation', 'Reservekraftwerk':'reserve', 'Endgültig Stillgelegt 2011': 'shutdown_2011', 'Endgültig Stillgelegt 2012': 'shutdown_2012', 'Endgültig Stillgelegt 2013': 'shutdown_2013', 'Endgültig Stillgelegt 2014': 'shutdown_2014', 'Endgültig Stillgelegt 2015': 'shutdown_2015', 'Endgültig stillgelegt 2015': 'shutdown_2015', 'Endgültig Stillgelegt 2016': 'shutdown_2016', 'Gesetzlich an Stilllegung gehindert': 'operating', 'Endgültig Stillgelegt 2011 (ohne StA)': 'shutdown_2011', 'Endgültig Stillgelegt 2012 (ohne StA)': 'shutdown_2012', 'Endgültig Stillgelegt 2013 (mit StA)': 'shutdown_2013', 'Endgültig Stillgelegt 2013 (ohne StA)': 'shutdown_2013', 'Endgültig Stillgelegt 2014 (mit StA)': 'shutdown_2014', 'Endgültig Stillgelegt 2014 (ohne StA)': 'shutdown_2014', 'Endgültig Stillgelegt 2015 (mit StA)': 'shutdown_2015', 'Endgültig Stillgelegt 2015 (ohne StA)': 'shutdown_2015', 'Endgültig Stillgelegt 2016 (mit StA)': 'shutdown_2016', 'Sicherheitsbereitschaft': 'reserve', 'Vorläufig Stillgelegt (mit StA)': 'shutdown_temporary', 'Vorläufig Stillgelegt (ohne StA)': 'shutdown_temporary', 'Endgültig Stillgelegt 2016 (ohne StA)': 'shutdown_2016', 'Endgültig Stillgelegt 2017 (mit StA)' : 'shutdown_2017', 'Endgültig Stillgelegt 2017 (ohne StA)': 'shutdown_2017', 'Endgültig Stillgelegt 2018 (mit StA)' : 'shutdown_2018', 'Endgültig Stillgelegt 2018 (ohne StA)': 'shutdown_2018', 'Endgültig Stillgelegt 2019 (mit StA)': 'shutdown_2019', 'Endgültig Stillgelegt 2019 (ohne StA)': 'shutdown_2019', 'gesetzlich an Stilllegung gehindert' : 'operating', 'Netzreserve' : 'reserve', 'Wegfall IWA nach DE' : 'special_case', } plantlist['status'].replace(dict_plantstatus, inplace=True) # Check if all fuels have been translated for statusnames in plantlist['status'].unique(): if statusnames not in dict_plantstatus.values(): logger.error('Untranslated plant status: '+ statusnames) dict_yesno ={ 'Nein': 'no', 'nein': 'no', 'Ja': 'yes', 'ja': 'yes', } plantlist['chp'].replace(dict_yesno, inplace=True) # Check if all fuels have been translated for chpnames in plantlist['chp'].unique(): if (chpnames not in dict_yesno.values()) & (str(chpnames) != 'nan'): logger.error('Untranslated chp capability: ' + str(chpnames)) plantlist['eeg'].replace(dict_yesno, inplace=True) # Check if all fuels have been translated for eegnames in plantlist['eeg'].unique(): if (eegnames not in dict_yesno.values()) & (str(eegnames) != 'nan'): logger.error('Untranslated EEG type: ' + str(eegnames)) dict_uba_columns = { 'Kraftwerksname / Standort': 'uba_name', 'Betreiber ': 'uba_company', 'Standort-PLZ': 'uba_postcode', 'Kraftwerksstandort': 'uba_city', 'Elektrische Bruttoleistung (MW)': 'uba_capacity', 'Fernwärme-leistung (MW)': 'uba_chp_capacity', 'Inbetriebnahme (ggf. Ertüchtigung)': 'uba_commissioned', 'Anlagenart': 'uba_technology', 'Primärenergieträger': 'uba_fuel', 'Bundesland':'uba_state', } plantlist_uba.rename(columns=dict_uba_columns, inplace=True) # Check if all columns have been translated for columnnames in plantlist_uba.columns: if columnnames not in dict_uba_columns.values(): logger.error('Untranslated column: ' + columnnames) # Prepare for matching plantlist_uba['uba_id_string'] = (plantlist_uba['uba_name'] + '_' + plantlist_uba['uba_fuel']) # Set Index of BNetzA power plant list to Kraftwerksnummer_Bundesnetzagentur plantlist['bnetza_id'] = plantlist['id'] plantlist = plantlist.set_index('id') # remove line breaks in some columns plantlist['network_node'] = plantlist['network_node'].str.replace('\n', ' ') plantlist['company'] = plantlist['company'].str.replace('\n', ' ') plantlist['name'] = plantlist['name'].str.replace('\n', ' ') plantlist['fuel'] = plantlist['fuel'].str.replace('\n', ' ') plantlist['block'] = plantlist['block'].str.replace('\n', ' ') plantlist['network_operator'] = plantlist['network_operator'].str.replace('\n', ' ') plantlist['street'] = plantlist['street'].str.replace('\n', ' ') plantlist['commissioned'] = plantlist['commissioned'].str.replace('\n', ' ') plantlist.head() plantlist.loc[plantlist['bnetza_id'] == 'BNA0834', 'fuel'] = 'Natural gas' plantlist.loc[plantlist['bnetza_id'] == 'BNA0662a', 'fuel'] = 'Hard coal' plantlist.loc[plantlist['bnetza_id'] == 'BNA0662b', 'fuel'] = 'Hard coal' # read matching list matchinglist = getmatchinglist() matchinglist.head() match1t1 = matchinglist[ (matchinglist.duplicated(subset=['uba_id_string'], keep=False) == False) & (matchinglist.duplicated(subset=['ID BNetzA'], keep=False) == False)] match1t1 = pd.merge(match1t1, plantlist_uba, left_on='uba_id_string', right_on='uba_id_string', how='left') match1t1 = match1t1.set_index('ID BNetzA') #Add comment match1t1['merge_comment'] = ('List matching type: Single UBA power plant ' 'assigned to single BNetzA power plant') match1t1.head() # Matching structure (example): # bnetza_id uba_id # 1 1 # 2 1 # 3 1 # 4 2 # 5 2 # Get relevant entries from the matchinglist and merge the corresponding # UBA Data to the list. matchnt1= matchinglist[ (matchinglist.duplicated(subset=['uba_id_string'], keep=False) == True) & (matchinglist.duplicated(subset=['ID BNetzA'], keep=False)== False)] matchnt1 = pd.merge(matchnt1, plantlist_uba, left_on='uba_id_string', right_on='uba_id_string', how='left') matchnt1 = matchnt1.set_index('ID BNetzA') # Import BNetzA Capacities and CHP criterion into matchnt1 dataframe plantlist_capacities = pd.DataFrame(plantlist[['capacity', 'chp']]).rename( columns={'capacity': 'capacity_bnetza', 'chp': 'chp_bnetza'}) matchnt1 = pd.merge(matchnt1, plantlist_capacities, left_index=True, right_index=True, how='left') # Get sum of BNetzA Capacitites for each UBA Index and merge into matchnt1 dataframe plantlist_uba_capacitysum = pd.DataFrame( matchnt1.groupby('uba_id_string').sum()['capacity_bnetza']).rename( columns={'capacity_bnetza': 'capacity_bnetza_aggregate'}) matchnt1 = pd.merge(matchnt1, plantlist_uba_capacitysum, left_on='uba_id_string', right_index=True, how='left') # Scale UBA Capacities based BNetzA Data matchnt1['uba_capacity_scaled'] = (matchnt1['uba_capacity'] * matchnt1['capacity_bnetza'] / matchnt1['capacity_bnetza_aggregate']) # determine sum of capacities with chp capability and add to matchnt1 plantlist_uba_chp_capacities = matchnt1[(matchnt1['chp_bnetza'] == 'yes')] plantlist_uba_chp_capacitysum = pd.DataFrame( plantlist_uba_chp_capacities.groupby('uba_id_string') .sum()['capacity_bnetza']) plantlist_uba_chp_capacitysum = plantlist_uba_chp_capacitysum.rename( columns={'capacity_bnetza': 'capacity_bnetza_with_chp'}) matchnt1 = pd.merge(matchnt1, plantlist_uba_chp_capacitysum, left_on='uba_id_string', right_index=True, how='left',) matchnt1['uba_chp_capacity'] = pd.to_numeric(matchnt1['uba_chp_capacity'], errors='coerce') matchnt1['uba_chp_capacity_scaled'] = (matchnt1['uba_chp_capacity'] * matchnt1['capacity_bnetza'] / matchnt1['capacity_bnetza_with_chp']) # Change column names for merge later on matchnt1['uba_chp_capacity_original'] = matchnt1['uba_chp_capacity'] matchnt1['uba_chp_capacity'] = matchnt1['uba_chp_capacity_scaled'] matchnt1['uba_capacity_original'] = matchnt1['uba_capacity'] matchnt1['uba_capacity'] = matchnt1['uba_capacity_scaled'] #Add comment matchnt1['merge_comment'] = ('List matching type: UBA capacity distributed ' 'proportionally to multiple BNetzA power plants') matchnt1.head() # The resulting DataFrame should be called "match1tn" # Matching structure: # bnetza_id uba_id # 1 1 # 1 2 # 1 3 # 2 4 # 2 5 # Get relevant entries from the matchinglist and merge the corresponding UBA Data to the list. match1tn= matchinglist[ (matchinglist.duplicated(subset=['ID BNetzA'], keep=False) == True) & (matchinglist.duplicated(subset=['uba_id_string'], keep=False)== False)] match1tn = pd.merge(match1tn, plantlist_uba, left_on='uba_id_string', right_on='uba_id_string', how='left') match1tn = match1tn.set_index('ID BNetzA') match1tn.head() # Import BNetzA Capacities and CHP criterion into match1tn dataframe plantlist_capacities = pd.DataFrame(plantlist[['capacity','chp']]).rename( columns = {'capacity': 'capacity_bnetza', 'chp': 'chp_bnetza'}) match1tn = pd.merge(match1tn, plantlist_capacities, left_index=True, right_index=True, how='left') match1tn.index.names=['ID BNetzA'] match1tn.head() # Get sum of UBA Capacitites per BNetzA Index and merge to match1tn dataframe plantlist_bnetza_capacitysum = pd.DataFrame( match1tn.groupby(match1tn.index).sum()['uba_capacity']) plantlist_bnetza_capacitysum = plantlist_bnetza_capacitysum.rename( columns={'uba_capacity':'uba_capacity_aggregate'}) match1tn = pd.merge(match1tn, plantlist_bnetza_capacitysum, left_index=True, right_index=True, how='left') match1tn['uba_chp_capacity'] = pd.to_numeric(match1tn['uba_chp_capacity'], errors='coerce') match1tn # Get sum of UBA CHP Capacities per BNetzA Index and merge to match1tn dataframe plantlist_bnetza_chp_capacitysum = pd.DataFrame( match1tn.groupby(match1tn.index).sum()['uba_chp_capacity']) plantlist_bnetza_chp_capacitysum = plantlist_bnetza_chp_capacitysum.rename( columns={'uba_chp_capacity': 'uba_chp_capacity_aggregate'}) match1tn = pd.merge(match1tn, plantlist_bnetza_chp_capacitysum, left_index=True, right_index=True, how='left') # Get UBA Technology for each BNetzA Index and merge into match1tn dataframe ## Option 1: Take all technologies and merge them #match1tn['uba_technology_aggregate'] = pd.DataFrame( # match1tn.groupby(match1tn.index) # .transform(lambda x: ', '.join(x))['uba_technology']) ## Option 2 (currently preferred): Take technology with highest occurence match1tn['uba_technology_aggregate'] = pd.DataFrame( match1tn.groupby(match1tn.index)['uba_technology'] .agg(lambda x: x.value_counts().index[0])) # Get UBA Plant name match1tn['uba_name_aggregate'] = pd.DataFrame( match1tn.groupby(match1tn.index).transform(lambda x: ', '.join(x))['uba_name']) # Get UBA company name match1tn['uba_company_aggregate'] = pd.DataFrame( match1tn.groupby(match1tn.index)['uba_company'] .agg(lambda x:x.value_counts().index[0])) # Change column names for merge later on match1tn = match1tn.rename( columns={'uba_chp_capacity': 'uba_chp_capacity_original', 'uba_capacity': 'uba_capacity_original', 'uba_chp_capacity_aggregate': 'uba_chp_capacity', 'uba_capacity_aggregate': 'uba_capacity'}) #Add comment match1tn['merge_comment'] = ('List matching type: Multiple UBA capacities ' 'aggregated to single BNetzA power plant') # Drop duplicate rows and keep first entry match1tn = match1tn.reset_index().drop_duplicates(subset='ID BNetzA',keep='first').set_index('ID BNetzA') match1tn.head() # Merge the UBA DataFrames # Merge first two dataframes plantlist_uba_for_merge = match1t1.append(matchnt1, sort=True) # Add third dataframe plantlist_uba_for_merge = plantlist_uba_for_merge.append(match1tn,sort=True) # Merge plantlist_uba_for_merge into the plantlist plantlist = pd.merge(plantlist, plantlist_uba_for_merge, left_index=True, right_index=True, how='left',sort=True) plantlist.head() # Delete solar, wind onshore, and wind offshore plantlist = plantlist[(plantlist['fuel'] != 'Solar') & (plantlist['fuel'] != 'Onshore') & (plantlist['fuel'] != 'Offshore')] # Delete aggregate values plantlist = plantlist[(plantlist['company'] != 'EEG-Anlagen < 10 MW') & (plantlist['company'] != 'Nicht-EEG-Anlagen < 10 MW')] # Add columns with empty data plantlist['shutdown'] = 'NaN' plantlist['shutdown'] = pd.to_numeric( plantlist['status'].str.extract('[\w].+(\d\d\d\d)', expand=False), errors='coerce') plantlist.loc[plantlist['shutdown'] > 0, 'status'] = 'shutdown' # Fill retrofit data column # Identify restrofit dates in UBA list plantlist['retrofit'] = pd.to_numeric( plantlist['uba_commissioned'].str.extract('[(.+](\d\d\d\d)', expand=False), errors='coerce') # Split multiple commissioning dates as listed in UBA plantlist['uba_commissioned_1'] = pd.to_numeric( plantlist['uba_commissioned'].str.extract('(\d\d\d\d)', expand=False), errors='coerce') plantlist.loc[plantlist['uba_commissioned_1'].isnull(), 'uba_commissioned_1'] = pd.to_numeric( plantlist['uba_commissioned'].str.extract('(\d\d\d\d).+[\w]', expand=False), errors='coerce').loc[plantlist['uba_commissioned_1'].isnull()] plantlist['uba_commissioned_2'] = pd.to_numeric( plantlist['uba_commissioned'].str.extract('[\w].+(\d\d\d\d).+[\w]', expand=False), errors='coerce') plantlist['uba_commissioned_3'] = pd.to_numeric( plantlist['uba_commissioned'].str.extract('[\w].+(\d\d\d\d)', expand=False), errors='coerce') plantlist.loc[plantlist['retrofit'] == plantlist['uba_commissioned_1'], 'uba_commissioned_1'] = '' plantlist.loc[plantlist['retrofit'] == plantlist['uba_commissioned_2'], 'uba_commissioned_2'] = '' plantlist.loc[plantlist['retrofit'] == plantlist['uba_commissioned_3'], 'uba_commissioned_3'] = '' # Split multiple commissioning dates as listed in BNetzA plantlist['commissioned_1'] = pd.to_numeric( plantlist['commissioned'].str.extract('(\d\d\d\d)', expand=False), errors='coerce') plantlist.loc[plantlist['commissioned_1'].isnull(), 'commissioned_1'] = pd.to_numeric( plantlist['commissioned'].str.extract('(\d\d\d\d).+[\w]', expand=False), errors='coerce').loc[plantlist['commissioned_1'].isnull()] plantlist['commissioned_2'] = pd.to_numeric( plantlist['commissioned'].str.extract('[\w].+(\d\d\d\d).+[\w]', expand=False), errors='coerce') plantlist['commissioned_3'] = pd.to_numeric( plantlist['commissioned'].str.extract('[\w].+(\d\d\d\d)', expand=False), errors='coerce') # Show plantlist plantlist[plantlist['status'] == 'shutdown'] plantlist['capacity_float'] = pd.to_numeric( plantlist['capacity'], errors='coerce') plantlist['commissioned_float'] = pd.to_numeric( plantlist[['commissioned','commissioned_1','commissioned_2','commissioned_3']].max(axis=1), errors='coerce') plantlist['retrofit_float'] = pd.to_numeric( plantlist['retrofit'], errors='coerce') plantlist.head() # Split uba_technology information into technology (GT, CC,...) and type (HKW, IKW, ...) plantlist['technology'] = plantlist['uba_technology'] plantlist['type'] = plantlist['uba_technology'] dict_technology = { 'GT': 'Gas turbine', 'GT / DT': 'Combined cycle', 'DT': 'Steam turbine', 'GuD': 'Combined cycle', 'DKW': 'Steam turbine', 'LWK': 'Run-of-river', 'PSW': 'Pumped storage', 'DWR': 'Steam turbine', #Pressurized water reactor 'G/AK': 'Gas turbine', #GT with heat recovery 'SWR': 'Steam turbine', #boiling water reactor 'SWK': 'Reservoir', #storage power plant 'SSA': 'Steam turbine', #bus bar 'HKW (DT)': 'Steam turbine', 'HKW / GuD': 'Combined cycle', 'GuD / HKW': 'Combined cycle', 'IKW / GuD': 'Combined cycle', 'IKW /GuD': 'Combined cycle', 'GuD / IKW': 'Combined cycle', 'HKW / SSA': 'Steam turbine', 'IKW / SSA': 'Steam turbine', 'SSA / IKW': 'Steam turbine', 'HKW': '', 'IKW': '', 'IKW / HKW': '', 'HKW / IKW': '', 'IKW / HKW / GuD' : 'Combined cycle', 'HKW / GuD / IKW' : 'Combined cycle', 'GuD / HKW / IKW': 'Combined cycle', } plantlist['technology'].replace(dict_technology, inplace=True) plantlist['technology'].unique() # Check if all technologies have been translated for technology in plantlist['technology'].unique(): if (technology not in dict_technology.values()) & (str(technology) != 'nan'): logger.error('Untranslated technology: ' + str(technology)) # Translate types dict_type = { 'HKW': 'CHP', #thermal power plant, 'HKW (DT)': 'CHP', 'IKW': 'IPP', #industrial power plant 'HKW / GuD': 'CHP', 'GuD / HKW': 'CHP', 'IKW / GuD': 'IPP', 'IKW /GuD': 'IPP', 'GuD / IKW': 'IPP', 'IKW / SSA': 'IPP', 'HKW / SSA': 'CHP', 'IKW / HKW': 'CHP', 'HKW / IKW': 'CHP', 'SSA / IKW': 'IPP', 'GT': '', 'GT / DT': '', 'DT': '', 'GuD': '', 'DKW': '', 'LWK': '', 'PSW': '', 'DWR': '', #Pressurized water reactor 'G/AK': 'CHP', #GT with heat recovery 'SWR': '', #boiling water reactor 'SWK': '', #storage power plant 'SSA': '', 'WEA': '', 'IKW / HKW / GuD' : 'CHP', 'HKW / GuD / IKW': 'CHP', 'GuD / HKW / IKW': 'CHP', } plantlist['type'].replace(dict_type, inplace=True) plantlist['type'].unique() # Check if all types have been translated for type in plantlist['type'].unique(): if (type not in dict_type.values()) & (str(type) != 'nan'): logger.error('Untranslated type: ' + str(type)) # Set technology based on fuels plantlist.loc[(plantlist['fuel'] == 'Nuclear') & ((plantlist['technology'] == '') \| ( plantlist['technology'].isnull())), 'technology'] = 'Steam turbine' plantlist.loc[(plantlist['fuel'] == 'Lignite') & ((plantlist['technology'] == '') \| ( plantlist['technology'].isnull())), 'technology'] = 'Steam turbine' plantlist.loc[(plantlist['fuel'] == 'Hard Coal') & ((plantlist['technology'] == '') \| ( plantlist['technology'].isnull())), 'technology'] = 'Steam turbine' plantlist.loc[(plantlist['fuel'] == 'Hard coal') & ((plantlist['technology'] == '') \| ( plantlist['technology'].isnull())), 'technology'] = 'Steam turbine' plantlist.loc[(plantlist['fuel'] == 'Hydro') & ((plantlist['technology'] == '') \| ( plantlist['technology'].isnull())), 'technology'] = 'Run-of-river' plantlist.loc[(plantlist['fuel'] == 'Hydro PSP') & ((plantlist['technology'] == '') \| (plantlist['technology'].isnull())), 'technology'] = 'Pumped storage' plantlist.loc[(plantlist['fuel'] == 'Hydro PSP'), 'fuel'] = 'Hydro' plantlist.loc[(plantlist['fuel'] == 'Hydro Reservoir') & ((plantlist['technology'] == '') \| (plantlist['technology'].isnull())), 'technology'] = 'RES' plantlist.loc[(plantlist['fuel'] == 'Hydro Reservoir'), 'fuel'] = 'Hydro' plantlist.loc[(plantlist['fuel'] == 'reservoir') & ((plantlist['technology'] == '') \| (plantlist['technology'].isnull())), 'technology'] = 'RES' # Set technology based on name and block information combined with fuels (e.g. combined-cycle, gas turbine) # Define technology CC as combination of GT and DT plantlist.loc[((plantlist['name'].str.contains("GT")) \| (plantlist['block'].str.contains("GT"))) & ((plantlist['name'].str.contains("DT")) \| (plantlist['block'].str.contains("DT"))) & ((plantlist['technology'] == '') \| (plantlist['technology'].isnull())), 'technology'] = 'Combined cycle' # Define technology CC if specified as GuD plantlist.loc[((plantlist['name'].str.contains("GuD")) \| (plantlist['block'].str.contains("GuD")) \| (plantlist['name'].str.contains("GUD")) \| (plantlist['name'].str.contains("GUD"))) & ((plantlist['technology'] == '') \| (plantlist['technology'].isnull())), 'technology'] = 'Combined cycle' # Define technology GT plantlist.loc[((plantlist['name'].str.contains("GT")) \| (plantlist['block'].str.contains("GT")) \| (plantlist['name'].str.contains("Gasturbine")) \| (plantlist['block'].str.contains("Gasturbine"))) & ((plantlist['technology'] == '') \| (plantlist['technology'].isnull())), 'technology'] = 'Gas turbine' # Define technology ST plantlist.loc[((plantlist['name'].str.contains("DT")) \| (plantlist['block'].str.contains("DT")) \| (plantlist['name'].str.contains("Dampfturbine")) \| (plantlist['block'].str.contains("Dampfturbine")) \| (plantlist['name'].str.contains("Dampfkraftwerk")) \| (plantlist['block'].str.contains("Dampfkraftwerk")) \| (plantlist['name'].str.contains("DKW")) \| (plantlist['block'].str.contains("DKW"))) & ((plantlist['technology'] == '') \| (plantlist['technology'].isnull())), 'technology'] = 'Steam turbine' # Define technology CB plantlist.loc[((plantlist['name'].str.contains("motor")) \| (plantlist['block'].str.contains("motor")) \| (plantlist['name'].str.contains("Motor")) \| (plantlist['block'].str.contains("Motor"))) & ((plantlist['technology'] == '') \| (plantlist['technology'].isnull())), 'technology'] = 'Combustion Engine' # Identify stroage technologies plantlist.loc[(plantlist['fuel'] == 'Other fuels') & ((plantlist[ 'fuel_basis'] == 'Sonstige Speichertechnologien') & (plantlist['technology'].isnull())), 'technology'] = 'Storage technologies' # Set technology ST for all technologies which could not be identified plantlist.loc[((plantlist['technology'] == '') \| (plantlist['technology'].isnull())), 'technology'] = 'Steam turbine' # Add country Code plantlist['country_code'] = plantlist['state'] dict_state_country = { 'Brandenburg': 'DE', 'Baden-Württemberg': 'DE', 'Niedersachsen': 'DE', 'Bayern': 'DE', 'Mecklenburg-Vorpommern': 'DE', 'Sachsen-Anhalt': 'DE', 'Hessen': 'DE', 'Nordrhein-Westfalen': 'DE', 'Berlin': 'DE', 'Saarland': 'DE', 'Thüringen': 'DE', 'Sachsen': 'DE', 'Bremen': 'DE', 'Schleswig-Holstein': 'DE', 'Hamburg': 'DE', 'Rheinland-Pfalz': 'DE', 'Österreich': 'AT', 'Luxemburg': 'LU', 'Schweiz': 'CH', } plantlist['country_code'].replace(dict_state_country, inplace=True) # Check if all types have been translated for plant_type in plantlist['country_code'].unique(): if (plant_type not in dict_state_country.values()) & (str(plant_type) != 'nan'): logger.error('Untranslated type: ' + str(plant_type)) # Efficiencies data_efficiencies_bnetza = pd.read_csv(os.path.join('input/data/DE', 'input_efficiency_de.csv'), sep=',', # CSV field separator, default is ',' decimal='.', # Decimal separator, default is '.') index_col='id', encoding='utf8') data_efficiencies_bnetza['efficiency_net'] = pd.to_numeric( data_efficiencies_bnetza['efficiency_net'], errors='coerce') data_efficiencies_bnetza = data_efficiencies_bnetza.dropna(subset=['efficiency_net']) plantlist = pd.merge( plantlist, data_efficiencies_bnetza, left_index=True, right_index=True, how='left') plantlist.head() plantlist.iloc[:,6:-1].head() plantlist_for_efficiency_analysis = plantlist plantlist_for_efficiency_analysis = plantlist_for_efficiency_analysis.dropna(subset=['efficiency_net']) # Plot efficiencies for lignite, coal, oil, and natural gas fuel_for_plot = ['Lignite', 'Hard coal', 'Oil', 'Natural gas'] col_dict = {'Lignite': 'brown', 'Hard coal': 'grey', 'Oil': 'k', 'Natural gas': 'orange'} fig, ax = plt.subplots(figsize=(16,8)) for fuels in fuel_for_plot: sub_df = plantlist_for_efficiency_analysis[plantlist_for_efficiency_analysis.fuel == fuels] if len(sub_df['efficiency_net']) > 10: x = np.array(sub_df['commissioned_float'].astype(int)) fit = np.polyfit(x, sub_df['efficiency_net'], deg=1) ax.plot(x, fit[0]x + fit[1], color=col_dict[fuels]) sub_df.plot(ax=ax, kind='scatter', x='commissioned_float', y='efficiency_net', c=col_dict[fuels], label=fuels) data_efficiencies_literature = pd.read_csv(os.path.join('input/data/DE','input_efficiency_literature_by_fuel_technology.csv'), sep=',', # CSV field separator, default is ',' decimal='.', # Decimal separator, default is '.') encoding='utf8') #data_efficiencies_literature['technology'] = data_efficiencies_literature['technology'].str.upper() data_efficiencies_literature = data_efficiencies_literature.set_index(['fuel','technology']) data_efficiencies_literature plantlist = plantlist.join(data_efficiencies_literature,on=['fuel','technology']) plantlist['efficiency_literature'] = plantlist['efficiency_intercept'] + plantlist['efficiency_slope']plantlist[['commissioned_float','retrofit_float']].max(axis=1) plantlist.head() data_plant_locations = pd.read_csv(os.path.join('input/data/DE','input_plant_locations_de.csv'), sep=',', # CSV field separator, default is ',' decimal='.', # Decimal separator, default is '.') encoding='utf8') data_plant_locations = data_plant_locations.set_index('id') data_plant_locations['lat'] = pd.to_numeric(data_plant_locations['lat'], errors='coerce') data_plant_locations['lon'] = pd.to_numeric(data_plant_locations['lon'], errors='coerce') plantlist = pd.merge(plantlist, data_plant_locations, left_index=True, right_index=True, how='left') plantlist.head() plantlist[plantlist.lat.isnull()] # read energy source level allocation table energy_source_level_allocator = pd.read_csv(os.path.join('input', 'energy_source_level_allocator.csv'), sep=',', # CSV field separator, default is ',' decimal='.', # Decimal separator, default is '.') index_col='fuel', encoding='utf8') plantlist = pd.merge(energy_source_level_allocator, plantlist, left_index = True, right_on='fuel', how='outer') plantlist # Merge uba_name_aggregate and uba_name plantlist.loc[plantlist['uba_name_aggregate'].isnull(), 'uba_name_aggregate'] = plantlist['uba_name'][plantlist['uba_name_aggregate'].isnull()] # Drop columns not relevant for output colsToDrop = ['bnetza_id', 'capacity', 'uba_name', 'uba_capacity_original', 'uba_chp_capacity_original', 'uba_city', 'uba_commissioned', 'uba_company', 'uba_company_aggregate', 'uba_fuel', 'uba_postcode', 'uba_state', 'uba_technology', 'uba_technology_aggregate', 'retrofit', 'uba_commissioned_1', 'uba_commissioned_2', 'uba_commissioned_3', 'commissioned_1', 'commissioned_2', 'commissioned_3', 'fuel_basis', 'fuel_multiple1', 'fuel_multiple2', 'efficiency_gross', 'efficiency_intercept', 'efficiency_slope', 'source_type', 'date' ] plantlist = plantlist.drop(colsToDrop, axis=1) # Rename columns plantlist = plantlist.rename(columns={'commissioned': 'commissioned_original', 'commissioned_float': 'commissioned', 'retrofit_float': 'retrofit', 'capacity_float': 'capacity_net_bnetza', 'uba_capacity': 'capacity_gross_uba', 'uba_chp_capacity': 'chp_capacity_uba', 'efficiency_net': 'efficiency_data', 'efficiency_literature': 'efficiency_estimate', 'uba_name_aggregate': 'name_uba', 'name': 'name_bnetza', 'block': 'block_bnetza', 'country_code': 'country', 'fuel': 'energy_source', }) # Sort columns columns_sorted = [ 'name_bnetza', 'block_bnetza', 'name_uba', 'company', 'street', 'postcode', 'city', 'state', 'country', 'capacity_net_bnetza', 'capacity_gross_uba', 'energy_source', 'technology', 'chp', 'chp_capacity_uba', 'commissioned', 'commissioned_original', 'retrofit', 'shutdown', 'status', 'type', 'lat', 'lon', 'eic_code_plant', 'eic_code_block', 'efficiency_data', 'efficiency_source', 'efficiency_estimate', 'energy_source_level_1', 'energy_source_level_2', 'energy_source_level_3', 'eeg', 'network_node', 'voltage', 'network_operator', 'merge_comment', 'comment'] plantlist = plantlist.reindex(columns=columns_sorted) plantlist.head() # Round capacity values as well as the efficiency estimate to five decimals- plantlist.capacity_net_bnetza = plantlist.capacity_net_bnetza.round(decimals=5) plantlist.capacity_gross_uba = plantlist.capacity_gross_uba.round(decimals=5) plantlist.efficiency_estimate = plantlist.efficiency_estimate.round(decimals=5) pivot_status_capacity = pd.pivot_table( plantlist, values='capacity_net_bnetza', columns='status', index='energy_source', aggfunc=np.sum ) pivot_status_capacity.sort_values(by='operating', inplace=True, ascending=0) pivot_status_capacity_plot=pivot_status_capacity.plot(kind='barh', stacked=True, legend=True, figsize=(12, 6)) pivot_status_capacity_plot.set_xlabel("MW") pivot_status_capacity_plot plantlist_filtered = plantlist pivot_age_capacity = pd.pivot_table( plantlist_filtered, values='capacity_net_bnetza', columns='energy_source', index='commissioned', aggfunc=np.sum, dropna=True ) pivot_age_capacity_plot=pivot_age_capacity.plot(kind='bar', stacked=True, legend=True, figsize=(17, 10)) pivot_age_capacity_plot.set_ylabel("MW") xaxis_labels = pivot_age_capacity.index.astype(int) pivot_age_capacity_plot.set_xticklabels(xaxis_labels) pivot_age_capacity_plot plantlist_for_plot = plantlist.copy(deep=True) plantlist_for_plot['capacity_float'] = pd.to_numeric(plantlist_for_plot['capacity_net_bnetza'], errors='coerce') plantlist_for_plot['commissioned_float'] = pd.to_numeric(plantlist_for_plot['commissioned'], errors='coerce') age_capacity_plot = plantlist_for_plot.plot(kind='scatter', x='commissioned_float', y='capacity_float', figsize=(17, 10)) age_capacity_plot.set_xlabel("commissioned") age_capacity_plot.set_ylabel("MW") age_capacity_plot plantlist[plantlist.capacity_net_bnetza == 0] #Show all Plants with commisioning dates below 1900 plantlist[plantlist['commissioned'] <= 1900] # Show all Plants with invalid commisioning dates plantlist[plantlist['commissioned'].isnull()] # TODO: improve this comparison, it creates many false positives capacitycomparison = pd.DataFrame(plantlist.capacity_net_bnetza / plantlist.capacity_gross_uba) capacitycomparison['Name'] = plantlist.name_bnetza capacitycomparison['Block'] = plantlist.block_bnetza capacitycomparison['BnetzaCapacity'] = plantlist.capacity_net_bnetza capacitycomparison['UBACapacity'] = plantlist.capacity_gross_uba capacitycomparison.dropna(inplace=True) capacitycomparison.sort_values(by=0) output_path = 'output' plantlist.to_csv( os.path.join(output_path, 'conventional_power_plants_DE.csv'), encoding='utf-8', index_label='id' ) plantlist.to_excel( os.path.join(output_path, 'conventional_power_plants_DE.xlsx'), sheet_name='plants', index_label='id' ) plantlist.to_sql( 'conventional_power_plants_DE', sqlite3.connect(os.path.join(output_path ,'conventional_power_plants.sqlite')), if_exists="replace", index_label='id' ) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: 3. Download settings Step2: 4. Define functions Step3: 5.2 Download the UBA Plant list Step4: 6. Translate contents Step5: 6.2 Fuel types Step6: 6.3 Power plant status Step7: 6.4 CHP Capability Step8: 6.5 EEG Step9: 6.6 UBA Columns Step10: 7. Process data Step11: Manual adjustments Step12: 7.2 Merge data from UBA List Step13: 7.2.1 case 1-1 Step14: 7.2.2 case n-1 Step15: 7.2.3 case 1-n Step16: 7.2.4 Merge into plantlist Step17: 7.3 Delete fuels not in focus Step18: 7.4 Add Columns for shutdown and retrofit Step19: 7.5 Convert input colums to usable data types Step20: 7.6 Identify generation technology Step21: 7.6.2 Identify generation technology based on BNetzA information Step22: 7.7 Add country code Step23: 7.8 Add efficiency data Step24: 7.8.1.2 Plot efficiencies by year of commissioning Step25: 7.8.2 Efficiencies from literature Step26: 7.8.2.2 Apply efficiency approximation from literature Step27: 7.9 Add geodata and EIC Codes Step28: 7.10 Allocate energy source levels Step29: 8. Define final output Step30: 8.1 Round values Step31: 8.2 Verification Step32: 8.2.2 Power plant age Step33: 8.2.3 Block size vs year of commissioning Step34: 8.3 Logical checks Step35: 8.3.2 Commissioning Dates Step36: 8.3.3 Compare UBA and BNetzA data Step37: 9. Result export
6,907	<ASSISTANT_TASK:> Python Code:: # create sequences of images, input sequences and output words for an image def create_sequences(tokenizer, max_length, descriptions, photos, vocab_size): X1, X2, y = list(), list(), list() # walk through each image identifier for key, desc_list in descriptions.items(): # walk through each description for the image for desc in desc_list: # encode the sequence seq = tokenizer.texts_to_sequences([desc])[0] # split one sequence into multiple X,y pairs for i in range(1, len(seq)): # split into input and output pair in_seq, out_seq = seq[:i], seq[i] # pad input sequence in_seq = pad_sequences([in_seq], maxlen=max_length)[0] # encode output sequence out_seq = to_categorical([out_seq], num_classes=vocab_size)[0] # store X1.append(photos[key][0]) X2.append(in_seq) y.append(out_seq) return array(X1), array(X2), array(y) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description:
6,908	<ASSISTANT_TASK:> Python Code: %matplotlib inline import matplotlib.pyplot as plt plt.style.use('ggplot') # le code qui suit n'est pas indispensable, il génère automatiquement un menu # dans le notebook from jyquickhelper import add_notebook_menu add_notebook_menu() url = "https://www.insee.fr/fr/statistiques/fichier/1892086/pop-totale-france.xls" url = "pop-totale-france.txt" import pandas df=pandas.read_csv(url, sep="\t", encoding="latin-1") df.head(n=5) df=pandas.read_csv(url, sep="\t", encoding="latin-1", skiprows=3) df.head(n=5) df.tail(n=5) from actuariat_python.data import population_france_year df = population_france_year() df.head(n=3) df.tail(n=3) from actuariat_python.data import table_mortalite_france_00_02 df=table_mortalite_france_00_02() df.head() df.plot(x="Age",y=["Homme", "Femme"],xlim=[0,100]) from actuariat_python.data import fecondite_france df=fecondite_france() df.head() df.plot(x="age", y=["2005","2015"]) from actuariat_python.data import table_mortalite_euro_stat table_mortalite_euro_stat() import os os.stat("mortalite.txt") import pandas df = pandas.read_csv("mortalite.txt", sep="\t", encoding="utf8", low_memory=False) df.head() df [ ((df.age=="Y60") \| (df.age=="Y61")) & (df.annee == 2000) & (df.pays=="FR") & (df.genre=="F")] <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Population française janvier 2017 Step2: La récupération de ces données est implémentée dans la fonction population_france_year Step3: D'après cette table, il y a plus de personnes âgées de 110 ans que de 109 ans. C'est dû au fait que la dernière ligne aggrège toutes les personnes âgées de plus de 110 ans. Step4: Taux de fécondité (France) Step5: Table de mortalité étendue 1960-2010
6,909	<ASSISTANT_TASK:> Python Code: def decaying_sin(params, x): amp = params['amp'] phaseshift = params['phase'] freq = params['frequency'] decay = params['decay'] return amp * np.sin(xfreq + phaseshift) np.exp(-xxdecay) x = np.linspace(0.0, 10.0, 100) default_params = {"amp" : 10.0, "decay" : 0.05, "phase" : 1.0, "frequency" : 4.0} data = decaying_sin(default_params, x) eps = np.linspace(0.0, 10.0, 100) eps.fill(0.01) a = plt.plot(x, data) def objective_function(params): model = decaying_sin(params, x) return (data - model) / eps params = lmfit.Parameters() params.add('amp', value=1) params.add('decay', value=0.1) params.add('phase', value=0.1) params.add('frequency', value=1.0) fig, ax = plt.subplots() a = ax.plot(x, data) b = ax.plot(x, decaying_sin(params, x)) fig, ax2 = plt.subplots() def plotter(params, a, b): current_data = decaying_sin(params, x) ax2.plot(x, data) ax2.plot(x, current_data) axes = plt.gca() axes.set_ylim(-10.0, 10.0) out = lmfit.minimize(objective_function, params, iter_cb=plotter) out.params.pretty_print() <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Plotting function for default parameters Step2: Defining objective function
6,910	<ASSISTANT_TASK:> Python Code: import matplotlib as mpl from matplotlib import cm import matplotlib.pyplot as plt from qutip import * from qutip.piqs import * #TLS parameters N = 6 ntls = N nds = num_dicke_states(ntls) [jx, jy, jz] = jspin(N) jp = jspin(N, "+") jm = jp.dag() w0 = 1 gE = 0.1 gD = 0.01 gP = 0.1 gCP = 0.1 gCE = 0.1 gCD = 0.1 h = w0 * jz #photonic parameters nphot = 16 wc = 1 kappa = 1 ratio_g = 2 g = ratio_g/np.sqrt(N) a = destroy(nphot) #TLS liouvillian system = Dicke(N = N) system.hamiltonian = h system.emission = 0 system.dephasing = gD system.pumping = gP system.collective_pumping = 0 system.collective_emission = 0 system.collective_dephasing = 0 liouv = system.liouvillian() system #TLS liouvillian 2 system2 = Dicke(N = N) system2.hamiltonian = h system2.emission = gE system2.dephasing = gD system2.pumping = 0 system2.collective_pumping = 0 system2.collective_emission = 0 system2.collective_dephasing = 0 liouv2 = system2.liouvillian() #TLS liouvillian 3 system3 = Dicke(N = N) system3.hamiltonian = h system3.emission = gE system3.dephasing = gD system3.pumping = 0#gP system3.collective_pumping = gCP system3.collective_emission = 0 system3.collective_dephasing = 0 liouv3 = system3.liouvillian() #TLS liouvillian 4 system4 = Dicke(N = N) system4.hamiltonian = h system4.emission = gE system4.dephasing = gD system4.pumping = 0 system4.collective_pumping = 0 system4.collective_emission = gCE system4.collective_dephasing = 0 liouv4 = system4.liouvillian() print(system) #photonic liouvilian h_phot = wc * a.dag() * a c_ops_phot = [np.sqrt(kappa) * a] liouv_phot = liouvillian(h_phot, c_ops_phot) #identity operators id_tls = to_super(qeye(nds)) id_phot = to_super(qeye(nphot)) # light-matter superoperator h_int = g * tensor(a + a.dag(), jx) liouv_int = -1j* spre(h_int) + 1j* spost(h_int) # 1 total liouvillian liouv_sum = super_tensor(liouv_phot, id_tls) + super_tensor(id_phot, liouv) liouv_tot = liouv_sum + liouv_int #2 total liouvillian liouv_sum2 = super_tensor(liouv_phot, id_tls) + super_tensor(id_phot, liouv2) liouv_tot2 = liouv_sum2 + liouv_int #3 total liouvillian liouv_sum3 = super_tensor(liouv_phot, id_tls) + super_tensor(id_phot, liouv3) liouv_tot3 = liouv_sum3 + liouv_int #4 total liouvillian liouv_sum4 = super_tensor(liouv_phot, id_tls) + super_tensor(id_phot, liouv4) liouv_tot4 = liouv_sum4 + liouv_int #total operators jz_tot = tensor(qeye(nphot), jz) jp_tot = tensor(qeye(nphot), jp) jm_tot = tensor(qeye(nphot), jm) jpjm_tot = tensor(qeye(nphot), jpjm) nphot_tot = tensor(a.dag()a, qeye(nds)) adag_tot = tensor(a.dag(), qeye(nds)) a_tot = tensor(a, qeye(nds)) # 4 calculate steady state rho_ss4 = steadystate(liouv_tot4, method="direct") nphot_ss4 = expect(nphot_tot, rho_ss4) psi4 = rho_ss4.ptrace(0) print("Ensemble 4 is ok") # takes a couple of minutes # 1 calculate steady state rho_ss = steadystate(liouv_tot, method="direct") nphot_ss = expect(nphot_tot, rho_ss) psi = rho_ss.ptrace(0) # 2 calculate steady state rho_ss2 = steadystate(liouv_tot2, method="direct") nphot_ss2 = expect(nphot_tot, rho_ss2) psi2 = rho_ss2.ptrace(0) # 3 calculate steady state rho_ss3 = steadystate(liouv_tot3, method="direct") nphot_ss3 = expect(nphot_tot, rho_ss3) psi3 = rho_ss3.ptrace(0) # 4 calculate steady state rho_ss4 = steadystate(liouv_tot4, method="direct") nphot_ss4 = expect(nphot_tot, rho_ss4) psi4 = rho_ss4.ptrace(0) # calculate Wigner function for photonic states nx = 1000 xvec = np.linspace(-6, 6, nx) W = wigner(psi, xvec, xvec) print("1 ok") W2 = wigner(psi2, xvec, xvec) print("2 ok") W3 = wigner(psi3, xvec, xvec) print("3 ok") W4 = wigner(psi4, xvec, xvec) print("4 ok") # strings for the plot title g_string = np.round(g,4) gE_string = np.round(gE,4) gD_string = np.round(gD,4) gP_string = np.round(gP,4) gCE_string = np.round(gCE,4) gCP_string = np.round(gCP,4) gCD_string = np.round(gCD,4) k_string = np.round(kappa,4) plt.rc('text', usetex = True) label_size = 25 plt.rc('xtick', labelsize=label_size) plt.rc('ytick', labelsize=label_size) # 1 Wp = np.round(W,3) wmap = wigner_cmap(Wp) # Generate Wigner colormap nrm = mpl.colors.Normalize(0, Wp.max()) # 2 Wp2 = np.round(W2,3) wmap2 = wigner_cmap(Wp2) # Generate Wigner colormap nrm2 = mpl.colors.Normalize(0, Wp2.max()) # 3 Wp3 = np.round(W3,3) wmap3 = wigner_cmap(Wp3) # Generate Wigner colormap nrm3 = mpl.colors.Normalize(0, Wp3.max()) # 4 Wp4 = np.round(W4,3) wmap4 = wigner_cmap(Wp4) # Generate Wigner colormap nrm4 = mpl.colors.Normalize(0, Wp4.max()) fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 10)) axes[0,0].contourf(xvec, xvec, Wp, 100, cmap=wmap, norm=nrm) axes[0,1].contourf(xvec, xvec, Wp2, 100, cmap=wmap2, norm=nrm2) axes[1,0].contourf(xvec, xvec, Wp3, 100, cmap=wmap3, norm=nrm3) axes[1,1].contourf(xvec, xvec, Wp4, 100, cmap=wmap4, norm=nrm4) axes[1,0].set_xlabel(r'$x$', fontsize = label_size) axes[1,1].set_xlabel(r'$x$', fontsize = label_size) #axes[0,0].set_ylabel(r'$p$', fontsize = label_size) axes[0, 0].set_title(r"$\kappa$, $\gamma_\phi$, $\gamma_\uparrow$", fontsize = label_size, position=(0.2, 0.85)) axes[0, 1].set_title(r"$\kappa$, $\gamma_\phi$, $\gamma_\downarrow$", fontsize = label_size, position=(0.8, 0.85)) axes[1, 0].set_title(r"$\kappa$, $\gamma_\phi$, $\gamma_\downarrow$, $\gamma_\Uparrow$", fontsize = label_size, position=(0.3, 0.85)) axes[1, 1].set_title(r"$\kappa$, $\gamma_\phi$, $\gamma_\downarrow$, $\gamma_\Downarrow$", fontsize = label_size, position=(0.7, 0.85)) axes[0,0].set_xticks([-5,0,5]) axes[0,0].set_yticks([-5,0,5]) axes[0,0].set_ylabel(r'$p$', fontsize = label_size) axes[0,1].set_xticks([-5,0,5]) axes[0,1].set_yticks([-5,0,5]) axes[1,0].set_ylabel(r'$p$', fontsize = label_size) axes[1,0].set_xticks([-5,0,5]) axes[1,0].set_yticks([-5,0,5]) axes[1,1].set_xticks([-5,0,5]) axes[1,1].set_yticks([-5,0,5]) plt.show() plt.close() rhoss_list = [] rhoss_list.append(rho_ss) rhoss_list.append(rho_ss2) rhoss_list.append(rho_ss3) rhoss_list.append(rho_ss4) wigner_list = [] wigner_list.append(W) wigner_list.append(W2) wigner_list.append(W3) wigner_list.append(W4) # save data save_file = False if save_file == True: file_name = str("superradiance_rhoss_Nmax{}.npz".format(N)) np.savez('{}'.format(file_name), rhoss_list) file_name = str("superradiance_wigner_list_Nmax{}.npz".format(N)) np.savez('{}'.format(file_name), wigner_list) qutip.about() <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Wigner function Visualization Step2: The Wigner function of the photonic part of the system displays the two displaced squeezed blobs typical of superradiance depending on the local and collective incoherent terms affecting the dynamics [2,3]. Step3: References
6,911	<ASSISTANT_TASK:> Python Code: import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np %matplotlib inline mpl.rcParams['figure.figsize'] = (13,9) # change default figure size cmap1 = 'Blues' x = np.arange(0, np.pi, 0.1) y = np.arange(0, 2np.pi, 0.1) xx, yy = np.meshgrid(x, y) zz = np.clip(6(np.cos(xx) * np.sin(yy) + np.random.rand(xx.shape)np.cos(yy)), 0, 5.5) fig, ax = plt.subplots(figsize=(12, 1)) cb = mpl.colorbar.ColorbarBase(ax, orientation='horizontal', cmap=cmap1) col_levels = np.arange(0,6.5,0.5) # sequence from 0 to 6 with step 0.5 tick_loc = np.arange(0,7,1) # sequence from 0 to 6 with step 1 fmt = '%1.2f' # tick labels format with two decimal places fig, ax = plt.subplots(figsize=(12, 1)) cb = mpl.colorbar.ColorbarBase(ax, cmap=cmap1, boundaries=col_levels, ticks=tick_loc, format=fmt, orientation='horizontal') cb.ax.tick_params(labelsize=20) cbkw = dict(ticks=tick_loc, format=fmt, orientation='vertical') fig, ax = plt.subplots() p = ax.contourf(x, y, zz, cmap=cmap1, levels=col_levels) cb1 = fig.colorbar(p, ax=ax, cbkw) cb1.ax.tick_params(labelsize=20) def myplot(cmap=cmap1): Test a colormap on random data with other params being the same fig, ax = plt.subplots() p = ax.contourf(x, y, zz, cmap=cmap, levels=col_levels) cb1 = fig.colorbar(p, ax=ax, cbkw) cb1.ax.tick_params(labelsize=20) cmap2 = 'gist_stern_r' myplot(cmap2) super_random_color_sequence = ['r', (0,0,1), '0.2', 'yellow', '#0044AB'] cmap3 = mpl.colors.ListedColormap(super_random_color_sequence) myplot(cmap3) c_white_trans = mpl.colors.colorConverter.to_rgba('w', alpha=0.0) cmap4 = mpl.colors.LinearSegmentedColormap.from_list('some_name_for_this_cmap', [c_white_trans, '#770000', 'red', 'yellow'], 64) fig, ax = plt.subplots() # First plot p1 = ax.contourf(x, y, xx, cmap='viridis') cb1 = fig.colorbar(p1, ax=ax, orientation='vertical', label='data1', pad=0.05) # Second plot p2 = ax.contourf(x, y, zz, cmap=cmap4, levels=col_levels) cb2 = fig.colorbar(p2, ax=ax, label='data2', **cbkw) cb2.ax.tick_params(labelsize=20) import seaborn as sns try: sns.color_palette('jet') except ValueError as e: print(e) HTML(html) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: We will start with the colormap used in the original question. Step2: In the absence of the data used in the given figure, we create a random array Step3: 1. Customizing a colorbar Step4: Thus we got a simple colorbar with smooth colour range for levels between 0 and 1. Step5: Now, to apply these settings to a data plot, we use a method colorbar() instead of the base colorbar class. We also put colour levels and colour map name in the contourf(). Other keywords are passed as before, but let's pack them into a dictinary. Step7: We will use this plot for other examples below, so let's make a function of it. Step8: 1.2 Colour map Step9: But what if you want to customize the colormap or even create your own one? There are several ways to do this. Below are some of the easiest examples. Step10: 1.2.2 LinearSegmentedColormap Step11: To demonstrate the colour transparency we overlay two plots Step12: 1.2.3 And kids, remember to avoid the rainbow or jet colormaps! Step13: Further reading
6,912	<ASSISTANT_TASK:> Python Code: import pandas as pd test_data = pd.read_csv("../data/person-video-sparse-multiple-choice.csv") test_data.head() import crowdtruth from crowdtruth.configuration import DefaultConfig class TestConfig(DefaultConfig): inputColumns = ["videolocation", "subtitles", "imagetags", "subtitletags"] outputColumns = ["selected_answer"] # processing of a closed task open_ended_task = True def processJudgments(self, judgments): # pre-process output to match the values in annotation_vector for col in self.outputColumns: # transform to lowercase judgments[col] = judgments[col].apply(lambda x: str(x).lower()) # remove square brackets from annotations judgments[col] = judgments[col].apply(lambda x: str(x).replace('[','')) judgments[col] = judgments[col].apply(lambda x: str(x).replace(']','')) # remove the quotes around the annotations judgments[col] = judgments[col].apply(lambda x: str(x).replace('"','')) return judgments data, config = crowdtruth.load( file = "../data/person-video-sparse-multiple-choice.csv", config = TestConfig() ) data['judgments'].head() results = crowdtruth.run(data, config) results["units"].head() import matplotlib.pyplot as plt %matplotlib inline plt.hist(results["units"]["uqs"]) plt.xlabel("Video Fragment Quality Score") plt.ylabel("Video Fragment") results["units"]["unit_annotation_score"].head() results["workers"].head() plt.hist(results["workers"]["wqs"]) plt.xlabel("Worker Quality Score") plt.ylabel("Workers") <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Declaring a pre-processing configuration Step2: Our test class inherits the default configuration DefaultConfig, while also declaring some additional attributes that are specific to the Person Type/Role Annotation in Video task Step3: Pre-processing the input data Step4: Computing the CrowdTruth metrics Step5: results is a dict object that contains the quality metrics for the video fragments, annotations and crowd workers. Step6: The uqs column in results["units"] contains the video fragment quality scores, capturing the overall workers agreement over each video fragment. Here we plot its histogram Step7: The unit_annotation_score column in results["units"] contains the video fragment-annotation scores, capturing the likelihood that an annotation is expressed in a video fragment. For each video fragment, we store a dictionary mapping each annotation to its video fragment-relation score. Step8: The worker metrics are stored in results["workers"] Step9: The wqs columns in results["workers"] contains the worker quality scores, capturing the overall agreement between one worker and all the other workers.
6,913	<ASSISTANT_TASK:> Python Code: info_struct=dict() info_struct['addr']=10000 info_struct['content']='' class Server(object): content='' def recv(self, info): pass def send(self, info): pass def show(self): pass class infoServer(Server): def recv(self,info): self.content=info return 'recv OK!' def send(self, info): pass def show(self): print('SHOW:%s'%self.content) class serverProxy(object): pass class infoServerProxy(serverProxy): server='' def __init__(self,server): self.server=server def recv(self,info): return self.server.recv(info) def show(self): self.server.show() class WhiteInfoServerProxy(infoServerProxy): whilte_list=[] def recv(self,info): try: assert type(info)==dict except: return 'info structure is not correct' addr = info.get('addr',0) if not addr in self.whilte_list: return 'Your address is not the white list' else: content=info.get('content','') return self.server.recv(content) def addWhite(self, addr): self.whilte_list.append(addr) def rmvWhite(self, addr): self.whilte_list.remove(addr) def clearWhite(self): self.whilte_list=[] info_struct=dict() info_struct['addr']=10010 info_struct['content']='Hello World!' info_server = infoServer() info_server_proxy = WhiteInfoServerProxy(info_server) print(info_server_proxy.recv(info_struct)) info_server_proxy.show() info_server_proxy.addWhite(10010) print(info_server_proxy.recv(info_struct)) info_server_proxy.show() <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: infoServer有接收和发送的功能，发送功能由于暂时用不到，保留。另外新加一个接口show，用来展示服务器接收的内容。接收的数据格式必须如info_struct所示，服务器仅接受info_struct的content字段。那么，如何给这个服务器设置一个白名单，使得只有白名单里的地址可以访问服务器呢？修改Server结构是个方法，但这显然不符合软件设计原则中的单一职责原则。在此基础之上，使用代理，是个不错的方法。代理配置如下：
6,914	<ASSISTANT_TASK:> Python Code: from sklearn import datasets import numpy as np from sklearn.cross_validation import train_test_split from sklearn.preprocessing import StandardScaler # Load the iris data iris = datasets.load_iris() # Create a variable for the feature data X = iris.data # Create a variable for the target data y = iris.target # Random split the data into four new datasets, training features, training outcome, test features, # and test outcome. Set the size of the test data to be 30% of the full dataset. X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) # Load the standard scaler sc = StandardScaler() # Compute the mean and standard deviation based on the training data sc.fit(X_train) # Scale the training data to be of mean 0 and of unit variance X_train_std = sc.transform(X_train) # Scale the test data to be of mean 0 and of unit variance X_test_std = sc.transform(X_test) # Feature Test Data, non-standardized X_test[0:5] # Feature Test Data, standardized. X_test_std[0:5] <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Load Data Step2: Split Data For Cross Validation Step3: Standardize Feature Data
6,915	<ASSISTANT_TASK:> Python Code: %run "../Functions/1. Google form analysis.ipynb" binarized = getAllBinarized() answersCount = len(binarized.index) totalScorePerQuestion = pd.DataFrame(data=np.dot(np.ones(answersCount),binarized),index=binarized.columns,columns=['score']) totalScorePerQuestion['perc'] = totalScorePerQuestion['score'] * 100 / answersCount display(totalScorePerQuestion.sort_values(by = 'score')) biologists = getSurveysOfBiologists(gform) nonBiologists = gform.drop(biologists.index) biologistsScores = biologists.apply(getGFormRowScore, axis=1) nonBiologistsScores = nonBiologists.apply(getGFormRowScore, axis=1) #print(len(gform), len(biologists), len(nonBiologists)) #print(len(gform), len(biologistsScores), len(nonBiologistsScores)) #print(type(biologistsScores), len(biologistsScores),\ #type(nonBiologistsScores), len(nonBiologistsScores)) ttest = ttest_ind(biologistsScores, nonBiologistsScores) ttest biologistsScores.values np.std(biologistsScores) np.std(nonBiologistsScores) gfBefores = getGFormBefores(gform) biologistsBefores = getSurveysOfBiologists(gfBefores, hardPolicy = False) nonBiologistsBefores = gfBefores.drop(biologistsBefores.index) biologistsBeforesScores = biologistsBefores.apply(getGFormRowScore, axis=1) nonBiologistsBeforesScores = nonBiologistsBefores.apply(getGFormRowScore, axis=1) #print(len(gfBefores), len(biologistsBefores), len(nonBiologistsBefores)) #print(len(gfBefores), len(biologistsBeforesScores), len(nonBiologistsBeforesScores)) #print(type(biologistsScores), len(biologistsScores),\ #type(nonBiologistsScores), len(nonBiologistsScores)) ttest = ttest_ind(biologistsBeforesScores, nonBiologistsBeforesScores) ttest np.std(biologistsBeforesScores) nonBiologistsBeforesScores np.std(nonBiologistsBeforesScores) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Sorted total answers to questions Step2: Cross-samples t-tests Step3: Conclusion
6,916	<ASSISTANT_TASK:> Python Code: #!pip install -I "phoebe>=2.3,<2.4" import phoebe from phoebe import u # units import numpy as np import matplotlib.pyplot as plt logger = phoebe.logger() b = phoebe.default_binary() b.add_dataset('orb', compute_times=phoebe.linspace(0,10,10), dataset='orb01') b.add_dataset('lc', compute_times=phoebe.linspace(0,1,101), dataset='lc01') print(b.computes) print(b.filter(context='compute')) b.set_value(qualifier='irrad_method', value='none') b.add_compute(phoebe.compute.phoebe, compute='preview', irrad_method='none') print(b.filter(compute='preview', context='compute')) b.add_compute('phoebe', compute='detailed', irrad_method='wilson') print(b.get_compute('detailed')) print(b.filter(qualifier='enabled', dataset='lc01')) b.set_value(qualifier='enabled', dataset='lc01', compute='preview', value=False) print(b.filter(qualifier='enabled', dataset='lc01')) b.set_value_all('enabled@lc01', True) print(b.filter(qualifier='enabled', dataset='lc01')) b.run_compute(compute='preview') print(b.models) b.set_value(qualifier='incl', kind='orbit', value=90) b.run_compute(compute='preview', model='run_with_incl_90') b.set_value(qualifier='incl', kind='orbit', value=85) b.run_compute(compute='preview', model='run_with_incl_85') b.set_value(qualifier='incl', kind='orbit', value=80) b.run_compute(compute='preview', model='run_with_incl_80') print(b.models) b.remove_model('latest') print(b.models) b.filter(model='run_with_incl_90') b.filter(component='primary', model='run_with_incl_90') b.get_parameter(qualifier='us', component='primary', model='run_with_incl_90') b.get_value(qualifier='us', dataset='orb01', component='primary', model='run_with_incl_90')[:10] <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Now we'll import our packages and initialize the default PHOEBE bundle. Step2: And we'll attach some dummy datasets. See the datasets tutorial for more details. Step3: Default Compute Options Step4: Adding Compute Options Step5: Editing Compute Options Step6: as you can see, there is a copy for both of our compute options ('preview' and 'detailed'). Step7: or to enable/disable a dataset for all sets of compute options, we can use the set_value_all method Step8: If the enabled parameter is missing for a set of compute options - it is likely that that particular backend does not support that dataset type. Step9: Storing/Tagging Models Step10: We will now have three new sets of synthetics which can be compared, plotted, or removed. Step11: To remove a model, call remove_model. Step12: Accessing Synthetics from Models
6,917	<ASSISTANT_TASK:> Python Code: import numpy as np import nibabel as nb import matplotlib.pyplot as plt # Let's create a short helper function to plot 3D NIfTI images def plot_slice(fname): # Load the image img = nb.load(fname) data = img.get_data() # Cut in the middle of the brain cut = int(data.shape[-1]/2) + 10 # Plot the data plt.imshow(np.rot90(data[..., cut]), cmap="gray") plt.gca().set_axis_off() %%bash ANAT_NAME=sub-01_ses-test_T1w ANAT=/data/ds000114/sub-01/ses-test/anat/${ANAT_NAME} bet ${ANAT} /output/${ANAT_NAME}_brain -m -f 0.3 fslmaths ${ANAT} -s 2 /output/${ANAT_NAME}_smooth fslmaths /output/${ANAT_NAME}_smooth -mas /output/${ANAT_NAME}_brain_mask /output/${ANAT_NAME}_smooth_mask f = plt.figure(figsize=(12, 4)) for i, img in enumerate(["T1w", "T1w_smooth", "T1w_brain_mask", "T1w_smooth_mask"]): f.add_subplot(1, 4, i + 1) if i == 0: plot_slice("/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_%s.nii.gz" % img) else: plot_slice("/output/sub-01_ses-test_%s.nii.gz" % img) plt.title(img) from nipype.interfaces import fsl # Skullstrip process skullstrip = fsl.BET( in_file="/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz", out_file="/output/sub-01_T1w_brain.nii.gz", mask=True) skullstrip.run() # Smoothing process smooth = fsl.IsotropicSmooth( in_file="/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz", out_file="/output/sub-01_T1w_smooth.nii.gz", fwhm=4) smooth.run() # Masking process mask = fsl.ApplyMask( in_file="/output/sub-01_T1w_smooth.nii.gz", out_file="/output/sub-01_T1w_smooth_mask.nii.gz", mask_file="/output/sub-01_T1w_brain_mask.nii.gz") mask.run() f = plt.figure(figsize=(12, 4)) for i, img in enumerate(["T1w", "T1w_smooth", "T1w_brain_mask", "T1w_smooth_mask"]): f.add_subplot(1, 4, i + 1) if i == 0: plot_slice("/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_%s.nii.gz" % img) else: plot_slice("/output/sub-01_%s.nii.gz" % img) plt.title(img) from nipype.interfaces import fsl # Skullstrip process skullstrip = fsl.BET( in_file="/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz", mask=True) bet_result = skullstrip.run() # skullstrip object # Smooth process smooth = fsl.IsotropicSmooth( in_file="/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz", fwhm=4) smooth_result = smooth.run() # smooth object # Mask process mask = fsl.ApplyMask(in_file=smooth_result.outputs.out_file, mask_file=bet_result.outputs.mask_file) mask_result = mask.run() f = plt.figure(figsize=(12, 4)) for i, img in enumerate([skullstrip.inputs.in_file, smooth_result.outputs.out_file, bet_result.outputs.mask_file, mask_result.outputs.out_file]): f.add_subplot(1, 4, i + 1) plot_slice(img) plt.title(img.split('/')[-1].split('.')[0].split('test_')[-1]) # Import Node and Workflow object and FSL interface from nipype import Node, Workflow from nipype.interfaces import fsl # For reasons that will later become clear, it's important to # pass filenames to Nodes as absolute paths from os.path import abspath in_file = abspath("/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz") # Skullstrip process skullstrip = Node(fsl.BET(in_file=in_file, mask=True), name="skullstrip") # Smooth process smooth = Node(fsl.IsotropicSmooth(in_file=in_file, fwhm=4), name="smooth") # Mask process mask = Node(fsl.ApplyMask(), name="mask") # Initiation of a workflow wf = Workflow(name="smoothflow", base_dir="/output/working_dir") # First the "simple", but more restricted method wf.connect(skullstrip, "mask_file", mask, "mask_file") # Now the more complicated method wf.connect([(smooth, mask, [("out_file", "in_file")])]) wf.write_graph("workflow_graph.dot") from IPython.display import Image Image(filename="/output/working_dir/smoothflow/workflow_graph.png") wf.write_graph(graph2use='flat') from IPython.display import Image Image(filename="/output/working_dir/smoothflow/graph_detailed.png") # Specify the base directory for the working directory wf.base_dir = "/output/working_dir" # Execute the workflow wf.run() f = plt.figure(figsize=(12, 4)) for i, img in enumerate(["/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz", "/output/working_dir/smoothflow/smooth/sub-01_ses-test_T1w_smooth.nii.gz", "/output/working_dir/smoothflow/skullstrip/sub-01_ses-test_T1w_brain_mask.nii.gz", "/output/working_dir/smoothflow/mask/sub-01_ses-test_T1w_smooth_masked.nii.gz"]): f.add_subplot(1, 4, i + 1) plot_slice(img) !tree /output/working_dir/smoothflow/ -I 'js\|json\|html\|pklz\|_report' from niflow.nipype1.workflows.fmri.fsl import create_susan_smooth susan = create_susan_smooth(separate_masks=False) susan.write_graph("susan_workflow.dot") from IPython.display import Image Image(filename="susan_workflow.png") print("Inputs:\n", susan.inputs.inputnode) print("Outputs:\n", susan.outputs.outputnode) susan.inputs from nipype import Function extract_func = lambda list_out: list_out[0] list_extract = Node(Function(input_names=["list_out"], output_names=["out_file"], function=extract_func), name="list_extract") # Initiate workflow with name and base directory wf2 = Workflow(name="susanflow", base_dir="/output/working_dir") # Create new skullstrip and mask nodes skullstrip2 = Node(fsl.BET(in_file=in_file, mask=True), name="skullstrip") mask2 = Node(fsl.ApplyMask(), name="mask") # Connect the nodes to each other and to the susan workflow wf2.connect([(skullstrip2, mask2, [("mask_file", "mask_file")]), (skullstrip2, susan, [("mask_file", "inputnode.mask_file")]), (susan, list_extract, [("outputnode.smoothed_files", "list_out")]), (list_extract, mask2, [("out_file", "in_file")]) ]) # Specify the remaining input variables for the susan workflow susan.inputs.inputnode.in_files = abspath( "/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz") susan.inputs.inputnode.fwhm = 4 wf2.write_graph(dotfilename='/output/working_dir/full_susanflow.dot', graph2use='colored') from IPython.display import Image Image(filename="/output/working_dir/full_susanflow.png") wf2.write_graph(dotfilename='/output/working_dir/full_susanflow_toplevel.dot', graph2use='orig') from IPython.display import Image Image(filename="/output/working_dir/full_susanflow_toplevel.png") wf2.run() f = plt.figure(figsize=(12, 4)) for i, e in enumerate([["/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz", 'input'], ["/output/working_dir//susanflow/mask/sub-01_ses-test_T1w_smooth_masked.nii.gz", 'output']]): f.add_subplot(1, 2, i + 1) plot_slice(e[0]) plt.title(e[1]) %time wf2.run() wf.inputs.smooth.fwhm = 1 wf.run() # write your solution here # importing Node and Workflow from nipype import Workflow, Node # importing all interfaces from nipype.interfaces.fsl import ExtractROI, MCFLIRT, SliceTimer # extracting all time levels but not the first four extract = Node(ExtractROI(t_min=4, t_size=-1, output_type='NIFTI'), name="extract") # using MCFLIRT for motion correction to the mean volume mcflirt = Node(MCFLIRT(mean_vol=True, output_type='NIFTI'), name="mcflirt") # correcting for slice wise acquisition (acquired with interleaved order and time repetition was 2.5) slicetimer = Node(SliceTimer(interleaved=True, output_type='NIFTI', time_repetition=2.5), name="slicetimer") # Initiation of a workflow wf_ex1 = Workflow(name="exercise1", base_dir="/output/working_dir") # connect nodes with each other wf_ex1.connect([(extract, mcflirt, [('roi_file', 'in_file')]), (mcflirt, slicetimer, [('out_file', 'in_file')])]) # providing a input file for the first extract node extract.inputs.in_file = "/data/ds000114/sub-01/ses-test/func/sub-01_ses-test_task-fingerfootlips_bold.nii.gz" # write your solution here wf_ex1.write_graph("workflow_graph.dot") from IPython.display import Image Image(filename="/output/working_dir/exercise1/workflow_graph.png") wf_ex1.write_graph(graph2use='flat') from IPython.display import Image Image(filename="/output/working_dir/exercise1/graph_detailed.png") wf_ex1.run() ! ls -lh /output/working_dir/exercise1 <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Example 1 - Command-line execution Step2: This is simple and straightforward. We can see that this does exactly what we wanted by plotting the four steps of processing. Step3: Example 2 - Interface execution Step4: This is more verbose, although it does have its advantages. There's the automated input validation we saw previously, some of the options are named more meaningfully, and you don't need to remember, for example, that fslmaths' smoothing kernel is set in sigma instead of FWHM -- Nipype does that conversion behind the scenes. Step5: Here we didn't need to name the intermediate files; Nipype did that behind the scenes, and then we passed the result object (which knows those names) onto the next step in the processing stream. This is somewhat more concise than the example above, but it's still a procedural script. And the dependency relationship between the stages of processing is not particularly obvious. To address these issues, and to provide solutions to problems we might not know we have yet, Nipype offers Workflows. Step6: This looks mostly similar to what we did above, but we've left out the two crucial inputs to the ApplyMask step. We'll set those up by defining a Workflow object and then making connections among the Nodes. Step7: The Workflow object has a method called connect that is going to do most of the work here. This routine also checks if inputs and outputs are actually provided by the nodes that are being connected. Step8: Now the workflow is complete! Step9: This representation makes the dependency structure of the workflow obvious. (By the way, the names of the nodes in this graph are the names we gave our Node objects above, so pick something meaningful for those!) Step10: Here you see very clearly, that the output mask_file of the skullstrip node is used as the input mask_file of the mask node. For more information on graph visualization, see the Graph Visualization section. Step11: The specification of base_dir is very important (and is why we needed to use absolute paths above) because otherwise all the outputs would be saved somewhere in the temporary files. Unlike interfaces, which by default spit out results to the local directly, the Workflow engine executes things off in its own directory hierarchy. Step12: Perfect! Step13: As you can see, the name of the working directory is the name we gave the workflow base_dir. And the name of the folder within is the name of the workflow object smoothflow. Each node of the workflow has its' own subfolder in the smoothflow folder. And each of those subfolders contains the output of the node as well as some additional files. Step14: Calling this function will return a pre-written Workflow object Step15: Let's display the graph to see what happens here. Step16: We see that the workflow has an inputnode and an outputnode. While not strictly necessary, this is standard practice for workflows (especially those that are intended to be used as nested workflows in the context of a longer analysis graph) and makes it more clear how to connect inputs and outputs from this workflow. Step17: Note that inputnode and outputnode are just conventions, and the Workflow object exposes connections to all of its component nodes Step18: Let's see how we would write a new workflow that uses this nested smoothing step. Step19: Now let's create a new workflow susanflow that contains the susan workflow as a sub-node. To be sure, let's also recreate the skullstrip and the mask node from the examples above. Step20: First, let's see what this new processing graph looks like. Step21: We can see how there is a nested smoothing workflow (blue) in the place of our previous smooth node. This provides a very detailed view, but what if you just wanted to give a higher-level summary of the processing steps? After all, that is the purpose of encapsulating smaller streams in a nested workflow. That, fortunately, is an option when writing out the graph Step22: That's much more manageable. Now let's execute the workflow Step23: As a final step, let's look at the input and the output. It's exactly what we wanted. Step24: So, why are workflows so great? Step25: That happened quickly! Workflows (actually this is handled by the Node code) are smart and know if their inputs have changed from the last time they are run. If they have not, they don't recompute; they just turn around and pass out the resulting files from the previous run. This is done on a node-by-node basis, also. Step26: By changing an input value of the smooth node, this node will be re-executed. This triggers a cascade such that any file depending on the smooth node (in this case, the mask node, also recompute). However, the skullstrip node hasn't changed since the first time it ran, so it just coughed up its original files. Step27: Defining all nodes Step28: Creating a workflow Step29: Exercise 2 Step30: We learnt 2 methods of plotting graphs Step31: And more detailed graph Step32: if everything works good, we're ready to run the workflow Step33: we can now check the output
6,918	<ASSISTANT_TASK:> Python Code: %matplotlib inline import numpy as np import pylab as pl import astropy.io.fits as fits import rtpipe import rtlib_cython as rtlib import astropy.units as units import astropy.coordinates as coord from astropy.time import Time # confirm version is is earlier than 1.54 if using old dm scale print(rtpipe.__version__) dmdelay_new = lambda dm, f0, f1: 4.1488e-3dm(1./f02 - 1./f12) # inconsistent with rtpipe dmdelay = lambda dm, f0, f1: 4.2e-3dm(1./f02 - 1./f12) # consistent with rtpipe, but requires scaling by 1.2% #name = 'puppi_57648_C0531+33_0048_3756.99.ar.paz' name = 'puppi_57648_C0531+33_0048_3757.00.ar.paz.pT' fits.info(name) hdu = fits.open(name) hdu0, hdu1, hdu2, hdu3, hdu4 = hdu[0], hdu[1], hdu[2], hdu[3], hdu[4] hdu0.header nch0 = 512 obsfreq = 1780. df = 1.5625 tint0 = 2.04780080448418e-05 #dm0 = 560.5 dm0 = 553.7 # equivalent to 560.5 in correct scaling dmt_ao = dmdelay(dm0, obsfreq1e-3, 1e4)/(243600) bary_ao = 75.7555109 # seconds, from presto mjd0 = int(57648) # subtract this for convenience. must be an integer mjd #mjdfrac_ao = (37750 + 0.319370962786796)/(243600) - dmt_ao # start time from header #mjdfrac_ao = (37750 + 0.336629511829426)/(243600) - dmt_ao # start time from header, second round mjdfrac_ao = 57648.43692057008 - dmt_ao - mjd0 + bary_ao/(243600) nint0 = 4096 dt_ao = tint0nint0 tbin = 8 nint1 = nint0/tbin tint0 = dt_ao/nint0 print('Orig time resolution {0}'.format(tint0)) tint1 = tint0tbin print('New time resolution {0}'.format(tint1)) data = fits.getdata(name, ext=4) spec = data[0][9] # spectrum read as (npol, nchan, nint) array specI = spec[:2].mean(axis=0) # average over first two to get stokes I confirm* specI = specI.reshape(nch0, nint1, tbin).mean(axis=2) # use array slicing to rebin # flag bad channels flagch = range(0, 16) + [99, 100, 101, 286, 287, 310, 311] + range(320, 356) + range(nch0-110, nch0) specI[flagch] = 0 specI = np.ma.masked_equal(specI, 0) fbin = 2 nch1 = nch0/fbin print('Orig freq resolution {0}'.format(df)) print('New freq resolution {0}'.format(dffbin)) specAO = specI.reshape(nch1, fbin, nint1).mean(axis=1) # use array slicing to rebin meanspec = specAO.mean(axis=1) specAO = specAO - meanspec[:,None] specAO = (specAO-np.ma.mean(specAO))/np.ma.std(specAO) # convert to snr per pixel #specAO = specAO/5 # scale by gain relative to VLA? print(specAO.shape) tbary_ao = np.linspace(mjdfrac_ao, mjdfrac_ao+(dt_ao/(243600)), nint1) freqs_ao = np.linspace(obsfreq - fbindfnch1, obsfreq, nch1) extent_ao = (tbary_ao[0], tbary_ao[-1], freqs_ao[0], freqs_ao[-1]) fig = pl.figure(figsize=(12,8)) pl.imshow(specAO, interpolation='nearest', aspect='auto', extent=extent_ao) pl.colorbar() pl.figure(figsize=(8,8)) pl.subplot(211) pl.plot(tbary_ao, specAO.mean(axis=0)) pl.ylabel('Amp (arb)') pl.xlabel('Time (s)') pl.subplot(212) pl.plot(freqs_ao, meanspec) pl.xlabel('Freq (MHz)') pl.ylabel('Amp (arb)') sp = specAO.reshape(nch1, nint1/16, 16).mean(axis=2) print(sp.mean(axis=0).max()/sp.mean(axis=0)[:20].std()) tpk_ao = tbary_ao[np.where(specAO.mean(axis=0) == specAO.mean(axis=0).max())][0] print('%.10f' % tpk_ao) sigma = 4 peakbins = np.where(specAO.mean(axis=0) >= sigmaspecAO.mean(axis=0)[:256].std()) peaktimes = tbary_ao[peakbins] window = (peaktimes.max()-peaktimes.min())243600 print('{0} sigma limit selects {1} ms of pulse ({2} bins)'.format(sigma, window1e3, len(peaktimes))) print('Window width is a bit wider than properly-dedispersed pulse, due to extra sweep') Sint_sys = specAO.mean(axis=0)[peakbins].mean() noise = specAO.mean(axis=0)[:peakbins[0][0]].std()/np.sqrt(len(peaktimes)) Sint = (3./np.sqrt(600e6window2))Sint_sys/noise print('Integrated flux density over {0} ms pulse: {1} mJy'.format(window, Sint1e3)) Sspec_sys = specAO[:, peakbins[0]].mean(axis=1) noise_spec = specAO[:, :peakbins[0][0]].std()/np.sqrt(len(peaktimes)) Sspec = (3./np.sqrt(3.125e6window2))Sspec_sys/noise_spec print('A (less good) integrated flux density over 2 ms pulse: {0} mJy'.format(int(Sspec.mean()1e3))) fig = pl.figure(figsize=(15,7)) ax = fig.add_subplot(111) pl.plot(1e-3freqs_ao[::-1], Sspec, 'k.') pl.text(0.75, 0.88, '57648, Arecibo', horizontalalignment='left', fontsize=24, verticalalignment='center', transform=ax.transAxes) pl.errorbar(1.600, 1.2Sspec.max(), yerr=(3./np.sqrt(3.125e6window2)), fmt='k.', ecolor='k') pl.ylim(-0.03, Sspec.max()1.4) pl.xlabel('Frequency (GHz)', fontsize=18) pl.ylabel('Flux density (Jy)', fontsize=18) xt = pl.setp(ax.get_xticklabels(), fontsize=18) yt = pl.setp(ax.get_yticklabels(), fontsize=18) ax.xaxis.set_tick_params(width=4, color='k') ax.yaxis.set_tick_params(width=4, color='k') fig.savefig('specAO_57648.pdf', format='pdf') calstring = 2.0520 2.89698 0.00279 2.1800 **** *** 2.3080 *** ***** 2.4360 3.53585 0.00377 2.5640 3.69554 0.00376 2.6920 3.85507 0.00423 2.8200 4.00438 0.00486 2.9480 4.11069 0.00562 3.0520 4.20375 0.00631 3.1800 4.29385 0.00662 3.3080 4.36557 0.00715 3.4360 4.43684 0.00786 3.5640 4.46937 0.00850 3.6920 4.52488 0.00860 3.8200 4.53571 0.00969 3.9480 4.54625 0.00859 # parse flux scale freq = [] flux = [] eflux = [] for line in calstring.split('\n'): if '' not in line: result = line.split() freq.append(float(result[0])) flux.append(float(result[1])) eflux.append(float(result[2])) calfreq = np.array(freq) calflux = np.array(flux) print(calfreq, calflux) def getscannum(sdmfile): sdm = rtpipe.parsesdm.getsdm(sdmfile) for scan in sdm.scans(): try: print('Scan {0} binary data file: {1}'.format(scan.idx, scan.bdf.fname)) bdfscan = int(scan.idx) except IOError: pass return bdfscan def read_cut(sdmfile, scan, segment, dm=558., dt=1, gainfile=None, kwargs): if not gainfile: gainfile = '.'.join(sdmfile.split('.')[:-1] + ['GN']) st = rtpipe.RT.set_pipeline(sdmfile, scan, dmarr=[dm], dtarr=[dt], flaglist=[('badap', 3., 0.2)], uvoversample=1.5, gainfile=gainfile, flagantsol=True, timesub='mean', logfile=False, savecands=False, savenoise=False, kwargs) data = rtpipe.RT.pipeline_reproduce(st, candloc=[segment,0,0,0,0], product='data') u, v, w = rtpipe.parsesdm.get_uvw_segment(st, segment) return st, data, u, v, w def correctdata(st, data, u, v, w, corr='ph,dm', lm = (-3.835e-04,5.406e-04)): lm gives (ra, dec) = (5 31 58.703708986 33 8 52.5067634154) as quoted in Chatterjee et al (2017) data2 = data.copy() if 'ph' in corr: l1, m1 = lm rtlib.phaseshift_threaded(data2, st, l1, m1, u, v) if 'dm' in corr: rtlib.dedisperse_par(data2, st['freq'], st['inttime'], st['dmarr'][0], [0, st['nbl']]) return data2 # get array2 for bin in array near value def find_nearest(array, array2, value): idx = (np.abs(array-value)).argmin() return array2[idx] def getscale(st): # get flux scaling at nearest frequency scale = [] for i in range(len(st['freq'])): freq = st['freq'][i] scale.append(find_nearest(calfreq, calflux, freq)) # print(i, st['freq'][i], scale) scale = np.array(scale, dtype='complex64')[None,None,:,None] return scale def correct_all(st, data, u, v, w): scale = getscale(st) dataph = correctdata(st, datascale, u, v, w, corr='ph') dataphdm = correctdata(st, dataph, u, v, w, corr='dm') return dataphdm key = '57648' read = {} sdmfile = '16A-496_sb32698778_1_02h00m_000.57648.37452900463.cut/' scannum = getscannum(sdmfile) read[key] = read_cut(sdmfile, scannum, 7, npix_max=7400, chans=range(2,256)) st, data, u, v, w = read[key] st['dmarr'] = [dm0] scale = getscale(st) dataph = correctdata(st, datascale, u, v, w, corr='ph,dm') intrange = (30, 60) dint = intrange[1] - intrange[0] specVLA = dataph[intrange[0]:intrange[1]].mean(axis=3).mean(axis=1).real specVLA = (specVLA-specVLA.mean())/specVLA.std() # in SNR units print(specAO.shape, specVLA.shape) dmt_vla = dmdelay(dm0, st['freq'][-1], 1e4)/(243600) topo_vla = 75.7533100 # seconds, from presto mjdfrac_vla = st['segmenttimes'][7][0] + intrange[0]st['inttime']/(243600) - mjd0 - dmt_vla + topo_vla/(243600) print('VLA dMJD: {0}'.format(mjdfrac_vla)) print('AO dMJD: {0}'.format(mjdfrac_ao)) print('Diff: {0} s'.format((mjdfrac_ao-mjdfrac_vla)243600)) tbary_vla = np.linspace(mjdfrac_vla, mjdfrac_vla+(dintst['inttime']/(243600)), dint) freqs_vla = 1e3st['freq'] extent_vla = (tbary_vla[0], tbary_vla[-1], freqs_vla[0], freqs_vla[-1]) # put them together fig = pl.figure(figsize=(8,8)) fig.add_subplot(211) pl.imshow(specVLA.transpose(), interpolation='nearest', origin='bottom', extent=extent_vla, aspect='auto') fig.add_subplot(212) pl.imshow(specAO, interpolation='nearest', aspect='auto', extent=extent_ao) tpk_vla = tbary_vla[np.where(specVLA.mean(axis=1) == specVLA.mean(axis=1).max())][0] print('%.10f' % tpk_vla) gap = 30 specregrid = np.zeros(shape=(nch1+st['nchan']+gap, len(tbary_ao))) for idx in range(len(tbary_ao)): specregrid[254+gap:, idx] = specAO[:, idx] idxs_vla = [np.argmin(np.abs(tbary_vla-tbary_ao[i])) for i in range(len(tbary_ao))] for idx_ao in range(len(idxs_vla)): idx_vla = idxs_vla[idx_ao] specregrid[:254, idx_ao] += specVLA[idx_vla, ::-1] # assumes fixed relative gain between VLA and AO == 3 fig = pl.figure(figsize=(12,12)) ax = fig.add_subplot(211) pl.imshow(specregrid, interpolation='nearest', aspect='equal', vmax=0.8specregrid.max(), cmap='Greys')#, vmin=-0.8) ax.fill_between(np.arange(0, len(tbary_ao)), 254np.ones(len(tbary_ao)), (254+gap)np.ones(len(tbary_ao)), facecolor='k') pl.xlabel('Time (ms)', fontsize=14) pl.ylabel('Frequency (GHz)', fontsize=14) xticks = np.arange(0, 600, 100) pl.xticks(xticks, np.array(tbin2e-2xticks, dtype='int')) ntot = nch1+len(st['freq'])+gap yticks = np.arange(0, ntot, 80) print(yticks) pl.yticks(yticks) pl.yticks(yticks, [st['freq'][-1], st['freq'][-80], st['freq'][-160], st['freq'][-240], np.round(1e-3freqs_ao[ntot-320], 3), np.round(1e-3freqs_ao[ntot-400], 3), np.round(1e-3freqs_ao[ntot-480], 3)]) pl.xlim(150, len(tbary_ao)) pl.ylim(ntot-50, 0) xt = pl.setp(ax.get_xticklabels(), fontsize=14) yt = pl.setp(ax.get_yticklabels(), fontsize=14) ax.xaxis.set_tick_params(width=3, color='k') ax.yaxis.set_tick_params(width=3, color='k') ax.text(170, 330, "Arecibo", rotation=90, fontsize=20) ax.text(170, 130, "VLA", rotation=90, fontsize=20) fig.savefig('aovla_spec.pdf', format='pdf') # some residual dm sweep? print(243600(tbary_ao[np.where(specAO[128:].mean(axis=0) == specAO[128:].mean(axis=0).max())][0] - tbary_ao[np.where(specAO[:128].mean(axis=0) == specAO[:128].mean(axis=0).max())][0])) dmt_ao_delta = dmdelay(dm0, obsfreq1e-3, st['freq'][-1]) - dmdelay(560, obsfreq1e-3, st['freq'][-1]) dmt_ao_inter = dmdelay(dm0, obsfreq1e-3, (obsfreq-500)1e-3) - dmdelay(560, obsfreq1e-3, (obsfreq-500)1e-3) print(dmt_ao_delta, dmt_ao_inter) # 4 bursts with ao, vla, effelsberg coverage # (ami-la covers all 4 vla bursts) # 57643 (AO-C, VLA-S) # 57645 (AO-L, VLA-S) # 57648 (AO-L, VLA-S, Eff-C) # 57649 (AO-L, VLA-S, Eff-C) # shows detections # limits assume 2 ms pulse width #s43d = np.array([[3.0, 4.0], [0.508, 0.00365]]) #s43l = np.array([[4.0], [0.00365]]) # useless data! s45d = np.array([[1.38, 3.0], [0.0025, (5/2.)0.064]]) s45l = np.array([[1.38], [0.0025]]) # too high? s48d = np.array([[1.4, 3.0, 4.85], [0.057, (5/2.)0.111, 0.0285]]) # fixed for 2 ms width s48l = np.array([[4.85], [0.0285]]) s49d = np.array([[1.42, 3.0, 4.9], [0.0025, (5/2.)0.167, 0.0285]]) s49l = np.array([[1.42, 4.9], [0.0025, 0.028*5]]) fig = pl.figure(figsize=(10,5)) ax = fig.add_subplot(111) # overplot upper limit symbols #ax.plot(s43l[0], s43l[1], 'cv', ms=10) ax.plot(s45l[0], s45l[1], 'kv', ms=10) ax.plot(s48l[0], s48l[1], 'kv', ms=10) ax.plot(s49l[0], s49l[1], 'kv', ms=10) # plot lines with points first #ax.plot(s43d[0], s43d[1], 'c.-', ms=10, label='12 Sep 2016') ax.plot(s45d[0], s45d[1], 'k.--', ms=10, label='57645') ax.plot(s48d[0], s48d[1], 'k.-.', ms=10, label='57648') ax.plot(s49d[0], s49d[1], 'k.-', ms=10, label='57649') ax.set_xlabel('Frequency (GHz)', fontsize=14) ax.set_ylabel('Integrated Flux density (Jy; 2 ms)', fontsize=14) ax.legend(fontsize=14) xt = pl.setp(ax.get_xticklabels(), fontsize=14) yt = pl.setp(ax.get_yticklabels(), fontsize=14) ax.xaxis.set_tick_params(width=3, color='k') ax.yaxis.set_tick_params(width=3, color='k') fig.savefig('multispec.pdf', format='pdf') def speclim(s): freqs = s[0] fluxes = s[1] for i in range(len(freqs)-1): freqi = freqs[i] freqi1 = freqs[i+1] # correct for plotting offset if freqi <= 1.5: freqi = 1.4 elif freqi1 > 4.2: freqi1 = 4.85 print(freqi, freqi1) print(fluxes[i], fluxes[i+1]) print(np.log10(fluxes[i]/fluxes[i+1])/np.log10(freqi/freqi1)) speclim(s45d) speclim(s48d) speclim(s49d) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Useful functions Step2: Read coherently dedispersed Arecibo dynamic spectrum Step3: Define python names for Arecibo header info Step4: Read dynamic spectrum and average down a bit Step5: Arecibo burst SNR Step6: crude integrated flux measurement Step7: Make nice Arecibo burst spectrum like the VLA ones Step10: VLA Step11: Read data with rtpipe and phase it to FRB 121102 Step12: Select VLA data for comparison to Arecibo Step13: Calculate dm delay and topocentric correction for VLA Step14: Regrid VLA and AO data to same fixed image grid Step15: Plot it Step16: Calculate residual sweep and compare to DM model Step17: Burst spectra with and without detections Step18: Calculate implied spectral index and limits
6,919	<ASSISTANT_TASK:> Python Code: %matplotlib inline import matplotlib.pyplot as plt from sklearn.datasets import load_breast_cancer import numpy as np from functools import reduce # Import our custom utilities from imp import reload from utils import irf_jupyter_utils from utils import irf_utils reload(irf_jupyter_utils) reload(irf_utils) load_breast_cancer = load_breast_cancer() X_train, X_test, y_train, y_test, rf = irf_jupyter_utils.generate_rf_example(n_estimators=10) print("Training feature dimensions", X_train.shape, sep = ":\n") print("\n") print("Training outcome dimensions", y_train.shape, sep = ":\n") print("\n") print("Test feature dimensions", X_test.shape, sep = ":\n") print("\n") print("Test outcome dimensions", y_test.shape, sep = ":\n") print("\n") print("first 5 rows of the training set features", X_train[:2], sep = ":\n") print("\n") print("first 5 rows of the training set outcomes", y_train[:2], sep = ":\n") all_rf_tree_data = irf_utils.get_rf_tree_data(rf=rf, X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test) #all_rf_tree_data np.random.seed(12) gen_random_leaf_paths = irf_utils.generate_rit_samples(all_rf_tree_data=all_rf_tree_data, bin_class_type=1) #for i in range(7): # print(next(gen_random_leaf_paths)) rit0 = irf_utils.build_tree(feature_paths=gen_random_leaf_paths, max_depth=3, noisy_split=False, num_splits=2) for node in rit0.traverse_depth_first(): print(node[0], node[1]._val) for idx, node in enumerate(rit0.leaf_nodes()): print(idx, node[1]._val) print("Root:\n", rit0._val) #print("Some child:\n", tree.children[0].children[1]._val) len(rit0) # If noisy split is False, this should pass #assert(len(rit0) == 1 + 5 + 52) assert(len(rit0) <= 1 + 5 + 52) M = 10 np.random.seed(12) # Create the weighted randomly sampled paths as a generator gen_random_leaf_paths = irf_utils.generate_rit_samples(all_rf_tree_data=all_rf_tree_data, bin_class_type=1) # Create the RIT object rit = irf_utils.build_tree(feature_paths=gen_random_leaf_paths, max_depth=3, noisy_split=False, num_splits=2) # Get the intersected node values rit_intersected_values = [node[1]._val for node in rit.traverse_depth_first()] # Leaf node values i.e. final intersected features rit_leaf_node_values = [node[1]._val for node in rit.leaf_nodes()] rit_leaf_node_union_value = reduce(np.union1d, rit_leaf_node_values) rit_outputs = {"rit": rit, "rit_intersected_values": rit_intersected_values, "rit_leaf_node_values": rit_leaf_node_values, "rit_leaf_node_union_value": rit_leaf_node_union_value} rit_outputs['rit_intersected_values'] def get_rit_tree_data(all_rf_tree_data, bin_class_type=1, random_state=12, #M=10, # number of trees (RIT) to build feature_paths=gen_random_leaf_paths, max_depth=3, noisy_split=False, num_splits=2): A wrapper for the Random Intersection Trees (RIT) algorithm # Set the random seed for reproducibility np.random.seed(12) # Create the weighted randomly sampled paths as a generator gen_random_leaf_paths = irf_utils.generate_rit_samples( all_rf_tree_data=all_rf_tree_data, bin_class_type=bin_class_type) # Create the RIT object rit = irf_utils.build_tree(feature_paths=gen_random_leaf_paths, max_depth=max_depth, noisy_split=noisy_split, num_splits=num_splits) # Get the intersected node values # CHECK remove this for the final value rit_intersected_values = [node[1]._val for node in rit.traverse_depth_first()] # Leaf node values i.e. final intersected features rit_leaf_node_values = [node[1]._val for node in rit.leaf_nodes()] rit_leaf_node_union_value = reduce(np.union1d, rit_leaf_node_values) rit_outputs = {"rit": rit, "rit_intersected_values": rit_intersected_values, "rit_leaf_node_values": rit_leaf_node_values, "rit_leaf_node_union_value": rit_leaf_node_union_value} return rit_outputs a = get_rit_tree_data(all_rf_tree_data=all_rf_tree_data, bin_class_type=1, random_state=12, #M=10, # number of trees (RIT) to build max_depth=3, noisy_split=False, num_splits=2) a['rit_intersected_values'] b = irf_utils.get_rit_tree_data(all_rf_tree_data=all_rf_tree_data, bin_class_type=1, random_state=12, M=1, max_depth=3, noisy_split=False, num_splits=2) b['rit0']['rit_intersected_values'] c = irf_utils.get_rit_tree_data(all_rf_tree_data=all_rf_tree_data, bin_class_type=1, random_state=12, M=10, max_depth=3, noisy_split=False, num_splits=2) c['rit1'] # Print the feature ranking print("Feature ranking:") feature_importances_rank_idx = all_rf_tree_data['feature_importances_rank_idx'] feature_importances = all_rf_tree_data['feature_importances'] for f in range(X_train.shape[1]): print("%d. feature %d (%f)" % (f + 1 , feature_importances_rank_idx[f] , feature_importances[feature_importances_rank_idx[f]])) # Plot the feature importances of the forest feature_importances_std = all_rf_tree_data['feature_importances_std'] plt.figure() plt.title("Feature importances") plt.bar(range(X_train.shape[1]) , feature_importances[feature_importances_rank_idx] , color="r" , yerr = feature_importances_std[feature_importances_rank_idx], align="center") plt.xticks(range(X_train.shape[1]), feature_importances_rank_idx) plt.xlim([-1, X_train.shape[1]]) plt.show() uniq_feature_paths = all_rf_tree_data['dtree0']['all_uniq_leaf_paths_features'] leaf_node_classes = all_rf_tree_data['dtree0']['all_leaf_node_classes'] ones_only = [i for i, j in zip(uniq_feature_paths, leaf_node_classes) if j == 1] ones_only ones_only_seven = ones_only[-7:] ones_only_seven # Construct a binary version of the RIT manually! # This should come in useful for unit tests! node0 = ones_only_seven[0] node1 = np.intersect1d(node0, ones_only_seven[1]) node2 = np.intersect1d(node1, ones_only_seven[2]) node3 = np.intersect1d(node1, ones_only_seven[3]) node4 = np.intersect1d(node0, ones_only_seven[4]) node5 = np.intersect1d(node4, ones_only_seven[5]) node6 = np.intersect1d(node4, ones_only_seven[6]) intersected_nodes_seven = [node0, node1, node2, node3, node4, node5, node6] for idx, node in enumerate(intersected_nodes_seven): print("node" + str(idx), node) rit_output = reduce(np.union1d, (node2, node3, node5, node6)) rit_output ones_only_seven_gen = (n for n in ones_only_seven) #for i in range(len(ones_only_seven)): # print(next(ones_only_seven_gen)) rit_man0 = irf_utils.build_tree( feature_paths=ones_only_seven_gen, max_depth=3, noisy_split=False, num_splits=2) print("Root:\n", rit_man0._val) print("Root:\n", rit_man0._val) print("node1:\n", rit_man0.children[0]._val) print("node4:\n", rit_man0.children[1]._val) print("node2:\n", rit_man0.children[0].children[0]._val) print("node3:\n", rit_man0.children[0].children[1]._val) print("node5:\n", rit_man0.children[1].children[0]._val) print("node6:\n", rit_man0.children[1].children[1]._val) for node in rit_man0.traverse_depth_first(): print(node[1]._val) for node in rit_man0.leaf_nodes(): print(node[1]._val) # Now plot the trees individually irf_jupyter_utils.draw_tree(decision_tree = all_rf_tree_data['rf_obj'].estimators_[0]) irf_jupyter_utils.pretty_print_dict(inp_dict = all_rf_tree_data['dtree0']) # Count the number of samples passing through the leaf nodes sum(all_rf_tree_data['dtree0']['tot_leaf_node_values']) irf_jupyter_utils.pretty_print_dict(inp_dict = all_rf_tree_data['dtree0']['all_leaf_paths_features']) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Step 1 Step2: Check out the data Step3: Step 2 Step4: STEP 3 Step5: Print out all of the intersected nodes and their ids Step7: Print out all of the leaf node values Step8: Perform Manual CHECKS on the irf_utils Step 4 Step9: Plot Ranked Feature Importances Step10: Manually construct a binary RIT Step11: Manually extract the last seven values Step12: Manually leaf node 1's paths as a generator Step13: Create a binary RIT without the randomized split option Step14: Check Output of Decision Tree Extracted Data Step15: Compare to our dict of extracted data from the tree Step16: Check output against the diagram
6,920	<ASSISTANT_TASK:> Python Code: def pretty_print_review_and_label(i): print(labels[i] + "\t:\t" + reviews[i][:80] + "...") g = open('reviews.txt','r') # What we know! reviews = list(map(lambda x:x[:-1],g.readlines())) g.close() g = open('labels.txt','r') # What we WANT to know! labels = list(map(lambda x:x[:-1].upper(),g.readlines())) g.close() len(reviews) reviews[0] labels[0] print("labels.txt \t : \t reviews.txt\n") pretty_print_review_and_label(2137) pretty_print_review_and_label(12816) pretty_print_review_and_label(6267) pretty_print_review_and_label(21934) pretty_print_review_and_label(5297) pretty_print_review_and_label(4998) from collections import Counter import numpy as np # Create three Counter objects to store positive, negative and total counts positive_counts = Counter() negative_counts = Counter() total_counts = Counter() # TODO: Loop over all the words in all the reviews and increment the counts in the appropriate counter objects print('# of reviews') print(len(reviews)) print('# of labels') print(len(labels)) for i in range(len(labels)): if labels[i] == 'POSITIVE': for word in reviews[i].split(" "): positive_counts[word] += 1 total_counts[word] += 1 else: for word in reviews[i].split(" "): negative_counts[word] += 1 total_counts[word] += 1 # Examine the counts of the most common words in positive reviews positive_counts.most_common() # Examine the counts of the most common words in negative reviews negative_counts.most_common() # Create Counter object to store positive/negative ratios pos_neg_ratios = Counter() #for term, count in (total_counts.most_common()): # print(total_counts[term]) # print(total_counts[count]) for term,cnt in list(total_counts.most_common()): if(cnt > 100): pos_neg_ratio = positive_counts[term] / float(negative_counts[term]+1) pos_neg_ratios[term] = pos_neg_ratio print("Pos-to-neg ratio for 'the' = {}".format(pos_neg_ratios["the"])) print("Pos-to-neg ratio for 'amazing' = {}".format(pos_neg_ratios["amazing"])) print("Pos-to-neg ratio for 'terrible' = {}".format(pos_neg_ratios["terrible"])) # TODO: Convert ratios to logs for word,ratio in pos_neg_ratios.most_common(): pos_neg_ratios[word] = np.log(ratio) print("Pos-to-neg ratio for 'the' = {}".format(pos_neg_ratios["the"])) print("Pos-to-neg ratio for 'amazing' = {}".format(pos_neg_ratios["amazing"])) print("Pos-to-neg ratio for 'terrible' = {}".format(pos_neg_ratios["terrible"])) # words most frequently seen in a review with a "POSITIVE" label pos_neg_ratios.most_common() # words most frequently seen in a review with a "NEGATIVE" label list(reversed(pos_neg_ratios.most_common()))[0:30] # Note: Above is the code Andrew uses in his solution video, # so we've included it here to avoid confusion. # If you explore the documentation for the Counter class, # you will see you could also find the 30 least common # words like this: pos_neg_ratios.most_common()[:-31:-1] from IPython.display import Image review = "This was a horrible, terrible movie." Image(filename='sentiment_network.png') review = "The movie was excellent" Image(filename='sentiment_network_pos.png') # TODO: Create set named "vocab" containing all of the words from all of the reviews vocab = set(total_counts.keys()) vocab_size = len(vocab) print(vocab_size) from IPython.display import Image Image(filename='sentiment_network_2.png') # TODO: Create layer_0 matrix with dimensions 1 by vocab_size, initially filled with zeros layer_0 = np.zeros((1, vocab_size)) layer_0.shape from IPython.display import Image Image(filename='sentiment_network.png') # Create a dictionary of words in the vocabulary mapped to index positions # (to be used in layer_0) word2index = {} for i,word in enumerate(vocab): word2index[word] = i # display the map of words to indices word2index def update_input_layer(review): Modify the global layer_0 to represent the vector form of review. The element at a given index of layer_0 should represent how many times the given word occurs in the review. Args: review(string) - the string of the review Returns: None global layer_0 # clear out previous state by resetting the layer to be all 0s layer_0 = 0 # TODO: count how many times each word is used in the given review and store the results in layer_0 # count how many times each word is used in the given review and store the results in layer_0 for word in review.split(" "): layer_0[0][word2index[word]] += 1 update_input_layer(reviews[0]) layer_0 def get_target_for_label(label): Convert a label to `0` or `1`. Args: label(string) - Either "POSITIVE" or "NEGATIVE". Returns: `0` or `1`. if label == 'POSITIVE': return 1 else: return 0 # TODO: Your code here labels[0] get_target_for_label(labels[0]) labels[1] get_target_for_label(labels[1]) import time import sys import numpy as np # Encapsulate our neural network in a class class SentimentNetwork: def __init__(self, reviews, labels, hidden_nodes = 10, learning_rate = 0.1): Create a SentimenNetwork with the given settings Args: reviews(list) - List of reviews used for training labels(list) - List of POSITIVE/NEGATIVE labels associated with the given reviews hidden_nodes(int) - Number of nodes to create in the hidden layer learning_rate(float) - Learning rate to use while training # Assign a seed to our random number generator to ensure we get # reproducable results during development np.random.seed(1) # process the reviews and their associated labels so that everything # is ready for training self.pre_process_data(reviews, labels) # Build the network to have the number of hidden nodes and the learning rate that # were passed into this initializer. Make the same number of input nodes as # there are vocabulary words and create a single output node. self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate) def pre_process_data(self, reviews, labels): review_vocab = set() # TODO: populate review_vocab with all of the words in the given reviews # Remember to split reviews into individual words # using "split(' ')" instead of "split()". for review in reviews: for word in review.split(" "): review_vocab.add(word) # Convert the vocabulary set to a list so we can access words via indices self.review_vocab = list(review_vocab) label_vocab = set() for label in labels: label_vocab.add(label) # TODO: populate label_vocab with all of the words in the given labels. # There is no need to split the labels because each one is a single word. # Convert the label vocabulary set to a list so we can access labels via indices self.label_vocab = list(label_vocab) # Store the sizes of the review and label vocabularies. self.review_vocab_size = len(self.review_vocab) self.label_vocab_size = len(self.label_vocab) # Create a dictionary of words in the vocabulary mapped to index positions self.word2index = {} # TODO: populate self.word2index with indices for all the words in self.review_vocab # like you saw earlier in the notebook for i,word in enumerate(self.review_vocab): self.word2index[word] = i # Create a dictionary of labels mapped to index positions self.label2index = {} # TODO: do the same thing you did for self.word2index and self.review_vocab, # but for self.label2index and self.label_vocab instead for i,word in enumerate(self.label_vocab): self.label2index[word] = i def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate): # Store the number of nodes in input, hidden, and output layers. self.input_nodes = input_nodes self.hidden_nodes = hidden_nodes self.output_nodes = output_nodes # Store the learning rate self.learning_rate = learning_rate # Initialize weights # TODO: initialize self.weights_0_1 as a matrix of zeros. These are the weights between # the input layer and the hidden layer. self.weights_0_1 = np.zeros((self.input_nodes, self.hidden_nodes)) # TODO: initialize self.weights_1_2 as a matrix of random values. # These are the weights between the hidden layer and the output layer. self.weights_1_2 = np.random.normal(0.0, self.output_nodes-0.5, (self.hidden_nodes, self.output_nodes)) # TODO: Create the input layer, a two-dimensional matrix with shape # 1 x input_nodes, with all values initialized to zero self.layer_0 = np.zeros((1,input_nodes)) def update_input_layer(self,review): # TODO: You can copy most of the code you wrote for update_input_layer # earlier in this notebook. # # However, MAKE SURE YOU CHANGE ALL VARIABLES TO REFERENCE # THE VERSIONS STORED IN THIS OBJECT, NOT THE GLOBAL OBJECTS. # For example, replace "layer_0 = 0" with "self.layer_0 = 0" for word in review.split(" "): # NOTE: This if-check was not in the version of this method created in Project 2, # and it appears in Andrew's Project 3 solution without explanation. # It simply ensures the word is actually a key in word2index before # accessing it, which is important because accessing an invalid key # with raise an exception in Python. This allows us to ignore unknown # words encountered in new reviews. if(word in self.word2index.keys()): self.layer_0[0][self.word2index[word]] += 1 def get_target_for_label(self,label): # TODO: Copy the code you wrote for get_target_for_label # earlier in this notebook. if label == 'POSITIVE': return 1 else: return 0 def sigmoid(self,x): # TODO: Return the result of calculating the sigmoid activation function # shown in the lectures return (1 + np.exp(-x)) def sigmoid_output_2_derivative(self,output): # TODO: Return the derivative of the sigmoid activation function, # where "output" is the original output from the sigmoid fucntion return output (1 - output) def train(self, training_reviews, training_labels): # make sure out we have a matching number of reviews and labels assert(len(training_reviews) == len(training_labels)) # Keep track of correct predictions to display accuracy during training correct_so_far = 0 # Remember when we started for printing time statistics start = time.time() # loop through all the given reviews and run a forward and backward pass, # updating weights for every item for i in range(len(training_reviews)): # TODO: Get the next review and its correct label # TODO: Implement the forward pass through the network. # That means use the given review to update the input layer, # then calculate values for the hidden layer, # and finally calculate the output layer. # # Do not use an activation function for the hidden layer, # but use the sigmoid activation function for the output layer. # TODO: Implement the back propagation pass here. # That means calculate the error for the forward pass's prediction # and update the weights in the network according to their # contributions toward the error, as calculated via the # gradient descent and back propagation algorithms you # learned in class. # TODO: Keep track of correct predictions. To determine if the prediction was # correct, check that the absolute value of the output error # is less than 0.5. If so, add one to the correct_so_far count. # For debug purposes, print out our prediction accuracy and speed # throughout the training process. elapsed_time = float(time.time() - start) reviews_per_second = i / elapsed_time if elapsed_time > 0 else 0 sys.stdout.write("\rProgress:" + str(100 * i/float(len(training_reviews)))[:4] \ + "% Speed(reviews/sec):" + str(reviews_per_second)[0:5] \ + " #Correct:" + str(correct_so_far) + " #Trained:" + str(i+1) \ + " Training Accuracy:" + str(correct_so_far * 100 / float(i+1))[:4] + "%") if(i % 2500 == 0): print("") def test(self, testing_reviews, testing_labels): Attempts to predict the labels for the given testing_reviews, and uses the test_labels to calculate the accuracy of those predictions. # keep track of how many correct predictions we make correct = 0 # we'll time how many predictions per second we make start = time.time() # Loop through each of the given reviews and call run to predict # its label. for i in range(len(testing_reviews)): pred = self.run(testing_reviews[i]) if(pred == testing_labels[i]): correct += 1 # For debug purposes, print out our prediction accuracy and speed # throughout the prediction process. elapsed_time = float(time.time() - start) reviews_per_second = i / elapsed_time if elapsed_time > 0 else 0 sys.stdout.write("\rProgress:" + str(100 * i/float(len(testing_reviews)))[:4] \ + "% Speed(reviews/sec):" + str(reviews_per_second)[0:5] \ + " #Correct:" + str(correct) + " #Tested:" + str(i+1) \ + " Testing Accuracy:" + str(correct * 100 / float(i+1))[:4] + "%") def run(self, review): Returns a POSITIVE or NEGATIVE prediction for the given review. # TODO: Run a forward pass through the network, like you did in the # "train" function. That means use the given review to # update the input layer, then calculate values for the hidden layer, # and finally calculate the output layer. # # Note: The review passed into this function for prediction # might come from anywhere, so you should convert it # to lower case prior to using it. # TODO: The output layer should now contain a prediction. # Return `POSITIVE` for predictions greater-than-or-equal-to `0.5`, # and `NEGATIVE` otherwise. pass mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1) mlp.test(reviews[-1000:],labels[-1000:]) mlp.train(reviews[:-1000],labels[:-1000]) mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.01) mlp.train(reviews[:-1000],labels[:-1000]) mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.001) mlp.train(reviews[:-1000],labels[:-1000]) from IPython.display import Image Image(filename='sentiment_network.png') def update_input_layer(review): global layer_0 # clear out previous state, reset the layer to be all 0s layer_0 = 0 for word in review.split(" "): layer_0[0][word2index[word]] += 1 update_input_layer(reviews[0]) layer_0 review_counter = Counter() for word in reviews[0].split(" "): review_counter[word] += 1 review_counter.most_common() # TODO: -Copy the SentimentNetwork class from Projet 3 lesson # -Modify it to reduce noise, like in the video mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1) mlp.train(reviews[:-1000],labels[:-1000]) mlp.test(reviews[-1000:],labels[-1000:]) Image(filename='sentiment_network_sparse.png') layer_0 = np.zeros(10) layer_0 layer_0[4] = 1 layer_0[9] = 1 layer_0 weights_0_1 = np.random.randn(10,5) layer_0.dot(weights_0_1) indices = [4,9] layer_1 = np.zeros(5) for index in indices: layer_1 += (1 weights_0_1[index]) layer_1 Image(filename='sentiment_network_sparse_2.png') layer_1 = np.zeros(5) for index in indices: layer_1 += (weights_0_1[index]) layer_1 # TODO: -Copy the SentimentNetwork class from Project 4 lesson # -Modify it according to the above instructions mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1) mlp.train(reviews[:-1000],labels[:-1000]) mlp.test(reviews[-1000:],labels[-1000:]) Image(filename='sentiment_network_sparse_2.png') # words most frequently seen in a review with a "POSITIVE" label pos_neg_ratios.most_common() # words most frequently seen in a review with a "NEGATIVE" label list(reversed(pos_neg_ratios.most_common()))[0:30] from bokeh.models import ColumnDataSource, LabelSet from bokeh.plotting import figure, show, output_file from bokeh.io import output_notebook output_notebook() hist, edges = np.histogram(list(map(lambda x:x[1],pos_neg_ratios.most_common())), density=True, bins=100, normed=True) p = figure(tools="pan,wheel_zoom,reset,save", toolbar_location="above", title="Word Positive/Negative Affinity Distribution") p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], line_color="#555555") show(p) frequency_frequency = Counter() for word, cnt in total_counts.most_common(): frequency_frequency[cnt] += 1 hist, edges = np.histogram(list(map(lambda x:x[1],frequency_frequency.most_common())), density=True, bins=100, normed=True) p = figure(tools="pan,wheel_zoom,reset,save", toolbar_location="above", title="The frequency distribution of the words in our corpus") p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], line_color="#555555") show(p) # TODO: -Copy the SentimentNetwork class from Project 5 lesson # -Modify it according to the above instructions mlp = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=20,polarity_cutoff=0.05,learning_rate=0.01) mlp.train(reviews[:-1000],labels[:-1000]) mlp.test(reviews[-1000:],labels[-1000:]) mlp = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=20,polarity_cutoff=0.8,learning_rate=0.01) mlp.train(reviews[:-1000],labels[:-1000]) mlp.test(reviews[-1000:],labels[-1000:]) mlp_full = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=0,polarity_cutoff=0,learning_rate=0.01) mlp_full.train(reviews[:-1000],labels[:-1000]) Image(filename='sentiment_network_sparse.png') def get_most_similar_words(focus = "horrible"): most_similar = Counter() for word in mlp_full.word2index.keys(): most_similar[word] = np.dot(mlp_full.weights_0_1[mlp_full.word2index[word]],mlp_full.weights_0_1[mlp_full.word2index[focus]]) return most_similar.most_common() get_most_similar_words("excellent") get_most_similar_words("terrible") import matplotlib.colors as colors words_to_visualize = list() for word, ratio in pos_neg_ratios.most_common(500): if(word in mlp_full.word2index.keys()): words_to_visualize.append(word) for word, ratio in list(reversed(pos_neg_ratios.most_common()))[0:500]: if(word in mlp_full.word2index.keys()): words_to_visualize.append(word) pos = 0 neg = 0 colors_list = list() vectors_list = list() for word in words_to_visualize: if word in pos_neg_ratios.keys(): vectors_list.append(mlp_full.weights_0_1[mlp_full.word2index[word]]) if(pos_neg_ratios[word] > 0): pos+=1 colors_list.append("#00ff00") else: neg+=1 colors_list.append("#000000") from sklearn.manifold import TSNE tsne = TSNE(n_components=2, random_state=0) words_top_ted_tsne = tsne.fit_transform(vectors_list) p = figure(tools="pan,wheel_zoom,reset,save", toolbar_location="above", title="vector T-SNE for most polarized words") source = ColumnDataSource(data=dict(x1=words_top_ted_tsne[:,0], x2=words_top_ted_tsne[:,1], names=words_to_visualize, color=colors_list)) p.scatter(x="x1", y="x2", size=8, source=source, fill_color="color") word_labels = LabelSet(x="x1", y="x2", text="names", y_offset=6, text_font_size="8pt", text_color="#555555", source=source, text_align='center') p.add_layout(word_labels) show(p) # green indicates positive words, black indicates negative words <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Note Step2: Lesson Step3: Project 1 Step4: We'll create three Counter objects, one for words from postive reviews, one for words from negative reviews, and one for all the words. Step5: TODO Step6: Run the following two cells to list the words used in positive reviews and negative reviews, respectively, ordered from most to least commonly used. Step7: As you can see, common words like "the" appear very often in both positive and negative reviews. Instead of finding the most common words in positive or negative reviews, what you really want are the words found in positive reviews more often than in negative reviews, and vice versa. To accomplish this, you'll need to calculate the ratios of word usage between positive and negative reviews. Step8: Examine the ratios you've calculated for a few words Step9: Looking closely at the values you just calculated, we see the following Step10: Examine the new ratios you've calculated for the same words from before Step11: If everything worked, now you should see neutral words with values close to zero. In this case, "the" is near zero but slightly positive, so it was probably used in more positive reviews than negative reviews. But look at "amazing"'s ratio - it's above 1, showing it is clearly a word with positive sentiment. And "terrible" has a similar score, but in the opposite direction, so it's below -1. It's now clear that both of these words are associated with specific, opposing sentiments. Step12: End of Project 1. Step13: Project 2 Step14: Run the following cell to check your vocabulary size. If everything worked correctly, it should print 74074 Step15: Take a look at the following image. It represents the layers of the neural network you'll be building throughout this notebook. layer_0 is the input layer, layer_1 is a hidden layer, and layer_2 is the output layer. Step16: TODO Step17: Run the following cell. It should display (1, 74074) Step18: layer_0 contains one entry for every word in the vocabulary, as shown in the above image. We need to make sure we know the index of each word, so run the following cell to create a lookup table that stores the index of every word. Step20: TODO Step21: Run the following cell to test updating the input layer with the first review. The indices assigned may not be the same as in the solution, but hopefully you'll see some non-zero values in layer_0. Step23: TODO Step24: Run the following two cells. They should print out'POSITIVE' and 1, respectively. Step25: Run the following two cells. They should print out 'NEGATIVE' and 0, respectively. Step29: End of Project 2. Step30: Run the following cell to create a SentimentNetwork that will train on all but the last 1000 reviews (we're saving those for testing). Here we use a learning rate of 0.1. Step31: Run the following cell to test the network's performance against the last 1000 reviews (the ones we held out from our training set). Step32: Run the following cell to actually train the network. During training, it will display the model's accuracy repeatedly as it trains so you can see how well it's doing. Step33: That most likely didn't train very well. Part of the reason may be because the learning rate is too high. Run the following cell to recreate the network with a smaller learning rate, 0.01, and then train the new network. Step34: That probably wasn't much different. Run the following cell to recreate the network one more time with an even smaller learning rate, 0.001, and then train the new network. Step35: With a learning rate of 0.001, the network should finall have started to improve during training. It's still not very good, but it shows that this solution has potential. We will improve it in the next lesson. Step36: Project 4 Step37: Run the following cell to recreate the network and train it. Notice we've gone back to the higher learning rate of 0.1. Step38: That should have trained much better than the earlier attempts. It's still not wonderful, but it should have improved dramatically. Run the following cell to test your model with 1000 predictions. Step39: End of Project 4. Step40: Project 5 Step41: Run the following cell to recreate the network and train it once again. Step42: That should have trained much better than the earlier attempts. Run the following cell to test your model with 1000 predictions. Step43: End of Project 5. Step44: Project 6 Step45: Run the following cell to train your network with a small polarity cutoff. Step46: And run the following cell to test it's performance. It should be Step47: Run the following cell to train your network with a much larger polarity cutoff. Step48: And run the following cell to test it's performance. Step49: End of Project 6.
6,921	<ASSISTANT_TASK:> Python Code: # DO NOT EDIT ! from pyesdoc.ipython.model_topic import NotebookOutput # DO NOT EDIT ! DOC = NotebookOutput('cmip6', 'ncar', 'sandbox-2', 'toplevel') # Set as follows: DOC.set_author("name", "email") # TODO - please enter value(s) # Set as follows: DOC.set_contributor("name", "email") # TODO - please enter value(s) # Set publication status: # 0=do not publish, 1=publish. DOC.set_publication_status(0) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.model_overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.model_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.flux_correction.details') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.genealogy.year_released') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.genealogy.CMIP3_parent') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.genealogy.CMIP5_parent') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.genealogy.previous_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.software_properties.repository') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.software_properties.code_version') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.software_properties.code_languages') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.software_properties.components_structure') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.software_properties.coupler') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "OASIS" # "OASIS3-MCT" # "ESMF" # "NUOPC" # "Bespoke" # "Unknown" # "None" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.coupling.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.coupling.atmosphere_double_flux') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.coupling.atmosphere_fluxes_calculation_grid') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Atmosphere grid" # "Ocean grid" # "Specific coupler grid" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.coupling.atmosphere_relative_winds') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.description') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.global_mean_metrics_used') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.regional_metrics_used') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.trend_metrics_used') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.energy_balance') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.fresh_water_balance') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.global') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.atmos_ocean_interface') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.atmos_land_interface') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.atmos_sea-ice_interface') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.ocean_seaice_interface') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.land_ocean_interface') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.global') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.atmos_ocean_interface') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.atmos_land_interface') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.atmos_sea-ice_interface') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.ocean_seaice_interface') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.runoff') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.iceberg_calving') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.endoreic_basins') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.snow_accumulation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.salt.ocean_seaice_interface') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.conservation.momentum.details') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CO2.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CO2.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CH4.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CH4.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.N2O.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.N2O.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.tropospheric_O3.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.tropospheric_O3.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.stratospheric_O3.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.stratospheric_O3.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CFC.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CFC.equivalence_concentration') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "Option 1" # "Option 2" # "Option 3" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CFC.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.SO4.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.SO4.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.black_carbon.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.black_carbon.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.organic_carbon.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.organic_carbon.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.nitrate.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.nitrate.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_albedo_effect.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_albedo_effect.aerosol_effect_on_ice_clouds') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_albedo_effect.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_lifetime_effect.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_lifetime_effect.aerosol_effect_on_ice_clouds') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_lifetime_effect.RFaci_from_sulfate_only') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_lifetime_effect.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.dust.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.dust.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.tropospheric_volcanic.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.tropospheric_volcanic.historical_explosive_volcanic_aerosol_implementation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Type A" # "Type B" # "Type C" # "Type D" # "Type E" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.tropospheric_volcanic.future_explosive_volcanic_aerosol_implementation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Type A" # "Type B" # "Type C" # "Type D" # "Type E" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.tropospheric_volcanic.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.stratospheric_volcanic.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.stratospheric_volcanic.historical_explosive_volcanic_aerosol_implementation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Type A" # "Type B" # "Type C" # "Type D" # "Type E" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.stratospheric_volcanic.future_explosive_volcanic_aerosol_implementation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Type A" # "Type B" # "Type C" # "Type D" # "Type E" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.stratospheric_volcanic.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.sea_salt.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.sea_salt.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.other.land_use.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "M" # "Y" # "E" # "ES" # "C" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.other.land_use.crop_change_only') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.other.land_use.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.other.solar.provision') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "N/A" # "irradiance" # "proton" # "electron" # "cosmic ray" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.radiative_forcings.other.solar.additional_information') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Document Authors Step2: Document Contributors Step3: Document Publication Step4: Document Table of Contents Step5: 1.2. Model Name Step6: 2. Key Properties --> Flux Correction Step7: 3. Key Properties --> Genealogy Step8: 3.2. CMIP3 Parent Step9: 3.3. CMIP5 Parent Step10: 3.4. Previous Name Step11: 4. Key Properties --> Software Properties Step12: 4.2. Code Version Step13: 4.3. Code Languages Step14: 4.4. Components Structure Step15: 4.5. Coupler Step16: 5. Key Properties --> Coupling Step17: 5.2. Atmosphere Double Flux Step18: 5.3. Atmosphere Fluxes Calculation Grid Step19: 5.4. Atmosphere Relative Winds Step20: 6. Key Properties --> Tuning Applied Step21: 6.2. Global Mean Metrics Used Step22: 6.3. Regional Metrics Used Step23: 6.4. Trend Metrics Used Step24: 6.5. Energy Balance Step25: 6.6. Fresh Water Balance Step26: 7. Key Properties --> Conservation --> Heat Step27: 7.2. Atmos Ocean Interface Step28: 7.3. Atmos Land Interface Step29: 7.4. Atmos Sea-ice Interface Step30: 7.5. Ocean Seaice Interface Step31: 7.6. Land Ocean Interface Step32: 8. Key Properties --> Conservation --> Fresh Water Step33: 8.2. Atmos Ocean Interface Step34: 8.3. Atmos Land Interface Step35: 8.4. Atmos Sea-ice Interface Step36: 8.5. Ocean Seaice Interface Step37: 8.6. Runoff Step38: 8.7. Iceberg Calving Step39: 8.8. Endoreic Basins Step40: 8.9. Snow Accumulation Step41: 9. Key Properties --> Conservation --> Salt Step42: 10. Key Properties --> Conservation --> Momentum Step43: 11. Radiative Forcings Step44: 12. Radiative Forcings --> Greenhouse Gases --> CO2 Step45: 12.2. Additional Information Step46: 13. Radiative Forcings --> Greenhouse Gases --> CH4 Step47: 13.2. Additional Information Step48: 14. Radiative Forcings --> Greenhouse Gases --> N2O Step49: 14.2. Additional Information Step50: 15. Radiative Forcings --> Greenhouse Gases --> Tropospheric O3 Step51: 15.2. Additional Information Step52: 16. Radiative Forcings --> Greenhouse Gases --> Stratospheric O3 Step53: 16.2. Additional Information Step54: 17. Radiative Forcings --> Greenhouse Gases --> CFC Step55: 17.2. Equivalence Concentration Step56: 17.3. Additional Information Step57: 18. Radiative Forcings --> Aerosols --> SO4 Step58: 18.2. Additional Information Step59: 19. Radiative Forcings --> Aerosols --> Black Carbon Step60: 19.2. Additional Information Step61: 20. Radiative Forcings --> Aerosols --> Organic Carbon Step62: 20.2. Additional Information Step63: 21. Radiative Forcings --> Aerosols --> Nitrate Step64: 21.2. Additional Information Step65: 22. Radiative Forcings --> Aerosols --> Cloud Albedo Effect Step66: 22.2. Aerosol Effect On Ice Clouds Step67: 22.3. Additional Information Step68: 23. Radiative Forcings --> Aerosols --> Cloud Lifetime Effect Step69: 23.2. Aerosol Effect On Ice Clouds Step70: 23.3. RFaci From Sulfate Only Step71: 23.4. Additional Information Step72: 24. Radiative Forcings --> Aerosols --> Dust Step73: 24.2. Additional Information Step74: 25. Radiative Forcings --> Aerosols --> Tropospheric Volcanic Step75: 25.2. Historical Explosive Volcanic Aerosol Implementation Step76: 25.3. Future Explosive Volcanic Aerosol Implementation Step77: 25.4. Additional Information Step78: 26. Radiative Forcings --> Aerosols --> Stratospheric Volcanic Step79: 26.2. Historical Explosive Volcanic Aerosol Implementation Step80: 26.3. Future Explosive Volcanic Aerosol Implementation Step81: 26.4. Additional Information Step82: 27. Radiative Forcings --> Aerosols --> Sea Salt Step83: 27.2. Additional Information Step84: 28. Radiative Forcings --> Other --> Land Use Step85: 28.2. Crop Change Only Step86: 28.3. Additional Information Step87: 29. Radiative Forcings --> Other --> Solar Step88: 29.2. Additional Information
6,922	<ASSISTANT_TASK:> Python Code: # Obtain sample data and set new Grass mapset import urllib from zipfile import ZipFile import os.path zip_path = "/home/jovyan/work/tmp/nc_spm_08_grass7.zip" mapset_path = "/home/jovyan/grassdata" if not os.path.exists(zip_path): urllib.urlretrieve("https://grass.osgeo.org/sampledata/north_carolina/nc_spm_08_grass7.zip", zip_path) if not os.path.exists(mapset_path): with ZipFile(zip_path, 'r') as zip_ref: zip_ref.extractall(mapset_path) # a proper directory is already set, download files import urllib urllib.urlretrieve("http://ncsu-geoforall-lab.github.io/geospatial-modeling-course/grass/data/lid_be_pts.txt", "lid_be_pts.txt") urllib.urlretrieve("http://ncsu-geoforall-lab.github.io/geospatial-modeling-course/grass/data/lid_mr_pts.txt", "lid_mr_pts.txt") urllib.urlretrieve("http://ncsu-geoforall-lab.github.io/geospatial-modeling-course/grass/data/lid_be_pts.txt", "lid_be_pts.txt") # using Python to initialize GRASS GIS import os import sys import subprocess from IPython.display import Image # create GRASS GIS runtime environment gisbase = subprocess.check_output(["grass", "--config", "path"]).strip() os.environ['GISBASE'] = gisbase sys.path.append(os.path.join(gisbase, "etc", "python")) # do GRASS GIS imports import grass.script as gs import grass.script.setup as gsetup # set GRASS GIS session data rcfile = gsetup.init(gisbase, "/home/jovyan/grassdata", "nc_spm_08_grass7", "user1") # using Python to initialize GRASS GIS # default font displays os.environ['GRASS_FONT'] = 'sans' # overwrite existing maps os.environ['GRASS_OVERWRITE'] = '1' gs.set_raise_on_error(True) gs.set_capture_stderr(True) # using Python to initialize GRASS GIS # set display modules to render into a file (named map.png by default) os.environ['GRASS_RENDER_IMMEDIATE'] = 'cairo' os.environ['GRASS_RENDER_FILE_READ'] = 'TRUE' os.environ['GRASS_LEGEND_FILE'] = 'legend.txt' !g.region rural_1m res=2 -p !r.in.xyz input=lid_be_pts.txt output=lid_be_binn2m method=n !r.in.xyz input=lid_mr_pts.txt output=lid_mr_binn2m method=n !d.erase !d.rast lid_mr_binn2m !d.legend lid_mr_binn2m at=2,20,2,5 Image(filename="map.png") !r.report lid_mr_binn2m unit=p !r.univar lid_mr_binn2m !d.rast lid_be_binn2m !d.legend lid_be_binn2m at=2,20,2,5 !r.report lid_be_binn2m unit=p !r.univar lid_be_binn2m Image(filename="map.png") !v.patch P079214,P079215,P079218,P079219 out=planimetry_rural !d.vect planimetry_rural Image(filename="map.png") !g.region rural_1m res=6 -ap !r.in.xyz input=lid_be_pts.txt out=lid_be_binn6m meth=n !d.erase !d.rast lid_be_binn6m !d.legend lid_be_binn6m at=2,20,2,5 Image(filename="map.png") !r.report lid_be_binn6m unit=p !r.univar lid_be_binn6m Image(filename="map.png") !r.in.xyz input=lid_be_pts.txt out=lid_be_binmean6m meth=mean !r.colors lid_be_binmean6m color=elevation !d.rast lid_be_binmean6m !d.legend lid_be_binmean6m at=2,40,2,5 !r.in.xyz input=lid_mr_pts.txt out=lid_mr_binmean6m meth=mean !r.colors lid_mr_binmean6m co=elevation !d.rast lid_mr_binmean6m !d.legend lid_mr_binmean6m at=2,40,2,5 Image(filename="map.png") !r.in.xyz input=lid_be_pts.txt out=lid_be_binrange6m meth=range !r.in.xyz input=lid_mr_pts.txt out=lid_mr_binrange6m meth=range !d.erase !d.rast lid_be_binrange6m !d.legend lid_be_binrange6m at=2,40,2,5 !d.rast lid_mr_binrange6m !d.legend lid_mr_binrange6m at=2,40,2,5 Image(filename="map.png") !d.vect planimetry_rural !d.vect lakes type=boundary co=violet !d.vect streams co=blue Image(filename="map.png") !g.region rural_1m -p !d.erase !d.rast ortho_2001_t792_1m !d.rast lid_be_binrange6m val=0.5-20. !d.erase !d.rast ortho_2001_t792_1m !d.rast lid_mr_binrange6m val=0.5-20. Image(filename="map.png") !g.region rural_1m -p !v.in.ascii -ztr input=lid_be_pts.txt out=elev_lid_bepts z=3 !d.erase !d.rast ortho_2001_t792_1m Image(filename="map.png") !d.vect elev_lid_bepts size=2 col=red Image(filename="map.png") !d.vect elev_lidrural_mrpts size=4 col=0:100:0 !d.vect elev_lid792_bepts size=2 col=yellow Image(filename="map.png") !g.region rural_1m -p !v.extract elev_lidrural_mrpts out=elev_lidrur_first type=point where="Return=1" !v.surf.rst input=elev_lid792_bepts elevation=elev_lidrural_1m npmin=120 segmax=25 !v.surf.rst input=elev_lidrur_first elevation=elev_lidrurfirst_1m npmin=120 segmax=25 tension=100 smooth=0.5 !d.erase !d.rast elev_lidrural_1m !d.rast elev_lidrurfirst_1m Image(filename="map.png") !g.region rural_1m -p !d.erase !d.rast ortho_2001_t792_1m Image(filename="map.png") !d.vect elev_lidrural_mrpts where=return=1 col=red size=2 !d.vect elev_lidrural_mrpts where=return=2 col=green size=3 !d.vect elev_lidrural_mrpts where=return=3 col=blue !d.vect elev_lidrural_mrpts where=return=4 col=yellow Image(filename="map.png") # end the GRASS session os.remove(rcfile) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: In startup pannel set GIS Data Directory to path to datasets, Step2: Range of coordinates at lower resolution Step3: Decrease resolution and try the above steps with Step4: Compute a raster map representing mean elevation for each 6m cell. Step5: Compute range Step6: Identify the features that are associated with large range values. Step7: Display only the high values of range (0.5-20m) overlayed over orthophoto. Step8: Do not forget to zoom/set the display to computational region Step9: Interpolation Step10: Display bare ground and multiple return points over orthophoto Step11: Display our imported points Step12: Display points available in the data set Step13: Extract first return to get points for DSM. Step14: Remove all layers except for elev_lidrural_1m and elev_lidrurfirst_1m and Step15: Condition for subset in GUI Step16: Can you guess what is in the area that does not have any 1st return points?
6,923	<ASSISTANT_TASK:> Python Code: %matplotlib inline import os import random import numpy as np import pickle import matplotlib.pyplot from matplotlib.pyplot import imshow from PIL import Image from scipy.spatial import distance from igraph import * from tqdm import tqdm images, pca_features, pca = pickle.load(open('../data/features_caltech101.p', 'rb')) for i, f in list(zip(images, pca_features))[0:5]: print("image: %s, features: %0.2f,%0.2f,%0.2f,%0.2f... "%(i, f[0], f[1], f[2], f[3])) num_images = 10000 if len(images) > num_images: sort_order = sorted(random.sample(xrange(len(images)), num_images)) images = [images[i] for i in sort_order] pca_features = [pca_features[i] for i in sort_order] kNN = 30 graph = Graph(directed=True) graph.add_vertices(len(images)) for i in tqdm(range(len(images))): distances = [ distance.cosine(pca_features[i], feat) for feat in pca_features ] idx_kNN = sorted(range(len(distances)), key=lambda k: distances[k])[1:kNN+1] for j in idx_kNN: graph.add_edge(i, j, weight=distances[j]) summary(graph) pickle.dump([images, graph], open('../data/graph_caltech101_30knn.p', 'wb')) #images, graph = pickle.load(open('../data/graph_caltech101_30knn.p', 'rb')) def get_concatenated_images(indexes, thumb_height): thumbs = [] for idx in indexes: img = Image.open(images[idx]) img = img.convert('RGB') img = img.resize((int(img.width * thumb_height / img.height), thumb_height)) thumbs.append(img) concat_image = np.concatenate([np.asarray(t) for t in thumbs], axis=1) return concat_image # pick two random indices idx1 = int(len(images) * random.random()) idx2 = int(len(images) * random.random()) # run get_shortest_paths path = graph.get_shortest_paths(idx1, to=idx2, mode=OUT, output='vpath', weights='weight')[0] # retrieve the images, concatenate into one, and display them results_image = get_concatenated_images(path, 200) matplotlib.pyplot.figure(figsize = (16,12)) imshow(results_image) # pick two random indices idx1 = int(len(images) * random.random()) idx2 = int(len(images) * random.random()) # run get_shortest_paths path = graph.get_shortest_paths(idx1, to=idx2, mode=OUT, output='vpath', weights='weight')[0] # retrieve the images, concatenate into one, and display them results_image = get_concatenated_images(path, 200) matplotlib.pyplot.figure(figsize = (16,12)) imshow(results_image) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Next, open your saved feature vectors with pickle, and ensure the images are in the correct paths. Step2: The following cell is optional. If you wish to restrict your graph to a smaller set of images (perhaps for testing purposes), this cell will take a random subsample of your image set of size num_images. Set this number however you wish or skip the cell if you intend to use all the images. Step3: Next, we are going to build our graph. The graph will contain one vertex for every image. The edges of the graph are found by taking the k nearest neighbor images to each image in the set, and adding an edge between them whose distance is given by cosine distance. Step4: Once the graph has been saved, we can save it along with the image paths to disk, so we can load them and use them later. Step5: Later, we can retrieve them in the following way (uncomment the following line). Step6: For the sake of convenience, we define a helper function which will concatenate a sequence of images into a single image so we can display the sequences in this notebook. The helper function takes a thumb_height and resizes all the images so they have that as their height. Step7: Now we can do a query. We'll just pick two random indices (idx1 and idx2) and run igraph's get_shortest_paths method, using the cosine distance ('weight' as the weights). Step8: Not too bad! Let's try again...
6,924	<ASSISTANT_TASK:> Python Code: import pandas as pd import numpy as np import os import requests import easydict import linecache import pprint import random import itertools pp = pprint.PrettyPrinter(indent=4) species = 'yeast' # species of interest to load of and save the resut for if species=='human': association_file_name = 'goa_human.gaf.gz' # human association_file_url = 'http://geneontology.org/gene-associations/goa_human.gaf.gz' expression_file = 'WholeBlood.Gene.Filter6_20.TPM10_20.normalizedLimmaVoom.txt' elif species=='yeast': association_file_name = 'sgd.gaf.gz' # yeast association_file_url = 'http://current.geneontology.org/annotations/sgd.gaf.gz' expression_file = 'EisenYeastData_Measures.txt' expression_url = 'http://www.i3s.unice.fr/~pasquier/web/userfiles/downloads/datasets/EisenYeastData_Measures.txt' args = easydict.EasyDict({ "go_dir": 'gene_ontology/raw/', # directory to the Gene Ontology 'go.obo' file "association_file_dir": 'species/{}/association_file/raw'.format(species), # directory to the human association file "gene_expression_raw_dir": 'species/{}/gene_expression/raw'.format(species), # directory to the raw gene expression data "result_gene_ontology_dir": 'species/{}/gene_expression/processed'.format(species), # directory in which the results would be saved "max_num_pairs": -1, # maximum number of pairs randomly chosen (-1 means all) "download_gene_ontology": True, # download the latest version of gene ontology into the specified directory above "download_association_file": True, # download association file of the specieis of interest into the specified directory above #"threshold": 0.8, # absolute pearson correlations below this cutoff point would be removed "seed": 2021 # seed to make sure the random negative samples are reproducable }) os.makedirs(args.result_gene_ontology_dir, exist_ok=True) # create 'result_gene_ontology_dir' folder (if it does not exist already) np.random.seed(args.seed) random.seed(args.seed) subontology_map = {"C":"CC", "P":"BP", "F":"MF"} f"{args.gene_expression_raw_dir}/{expression_file}" if species=='yeast': if os.path.exists(f"{args.gene_expression_raw_dir}/{expression_file}") is not True: os.makedirs(args.gene_expression_raw_dir, exist_ok=True) # create 'gene_expression_raw_dir' folder (if it does not exist already) r = requests.get(expression_url, allow_redirects=True) open('{}/{}'.format(args.gene_expression_raw_dir, expression_file), 'wb').write(r.content) elif species=='human': assert os.path.exists(f"{args.gene_expression_raw_dir}/{expression_file}") is True, f"\nYou need to download the expression file first using the link and guideline provided above! \nPut the {expression_file} file in '{args.gene_expression_raw_dir}/' directory." if args.download_gene_ontology: os.makedirs(args.go_dir, exist_ok=True) # create 'data_loc' folder (if it does not exist already) print("Downloading the latest version of Gene Ontology into '{}'...".format(args.go_dir)) url = 'http://current.geneontology.org/ontology/go.obo' r = requests.get(url, allow_redirects=True) open('{}/go.obo'.format(args.go_dir), 'wb').write(r.content) print("Gene Ontology {}".format(linecache.getline('{}/go.obo'.format(args.go_dir), 2))) # Now: releases/2020-10-09 Reading Gene Ontology to extract Terms and their Descriptive Names with open("{}/go.obo".format(args.go_dir)) as f: content = f.readlines() content = "".join([x for x in content]) content = content.split("[Typedef]")[0].split("[Term]") print("Information of the last GO term in the file:\n~~~~~~~~~~~~~~~~~~~~~~~~~{}".format(content[-1])) Going through every GO term and extract information needed ('id', 'alt_id', 'namespace', and 'is_obsolete') go_term_dict = {} for c in content: go_id = '' for l in c.split("\n"): # id if "id: GO:" in l[0:len("id: GO:")]: go_id = l.split("id: ")[1] go_term_dict[go_id] = {} # alt_id if "alt_id:" in l[0:len("alt_id")+1]: go_term_dict[go_id].setdefault("alt_id", []).append(l.split("alt_id: ")[1]) # namespace if "namespace:" in l[0:len("namespace")+1]: go_term_dict[go_id]["namespace"] = l.split("namespace: ")[1] # is_obsolete if "is_obsolete:" in l[0:len("is_obsolete")+1]: go_term_dict[go_id]["is_obsolete"] = l.split("is_obsolete: ")[1] printing how the key:values are organized for every GO term for i in range(15): print(list(go_term_dict)[i], end=": ") pp.pprint(go_term_dict[list(go_term_dict)[i]]) grouping GO terms based on the sub-ontologies they belong to subontology_go_term_dict = {} for go_id in go_term_dict: if not go_term_dict[go_id].get('is_obsolete', False): # or => if 'is_obsolete' not in go_term_dict[go_id]: subontology_go_term_dict.setdefault(go_term_dict[go_id]['namespace'].split('_')[1][0].upper(), []).append(go_id) including 'alt_id' into the sub-ontology's groups of GO terms for go_id in go_term_dict: if go_term_dict[go_id].get('alt_id', False): # or => if 'alt_id' in go_term_dict[go_id]: for alt_id in go_term_dict[go_id].get('alt_id'): subontology_go_term_dict[go_term_dict[go_id]['namespace'].split('_')[1][0].upper()].append(alt_id) printing how the key:values are organized for different sub-ontologies for subontology in subontology_go_term_dict: print("{} ({}):: {} <= {} GO term (with 'alt_id') => {}".format( subontology, subontology_map[subontology], " ".join(subontology_go_term_dict[subontology][:3]), len(subontology_go_term_dict[subontology]), " ".join(subontology_go_term_dict[subontology][-3:]))) if args.download_association_file: os.makedirs(args.association_file_dir, exist_ok=True) # create 'data_loc' folder (if it does not exist already) print("Downloading the latest version of association file into '{}'...".format(args.association_file_dir)) r = requests.get(association_file_url, allow_redirects=True) open('{}/{}'.format(args.association_file_dir, association_file_name), 'wb').write(r.content) print("Done!") df = pd.read_csv("{}/{}".format(args.association_file_dir, association_file_name), sep='\t', comment="!", skip_blank_lines=True, header=None, dtype=str) df = df.iloc[:,[1, 2, 3, 4, 6, 8]] if len(df[df[3].isnull()])==0: df = df[~df[3].str.contains("NOT")] df = df.dropna().reset_index(drop=True) else: df = df[df[3].isnull()] df = df.dropna().reset_index(drop=True) df = df.drop(df.columns[2], axis=1) df keeping track of the gene ids and their mappings protein_gene_id_map = {} for gene_id, protein_id in zip(df[1], df[2]): protein_gene_id_map[protein_id] = gene_id df = df[(df[6]!='ND') & (df[6]!='IEA')].reset_index(drop=True) df protein dictionary to keep track of annotations for proteins (from each sub-ontology) proteins_dict = {} for index, row in df.iterrows(): gene = row[1] go_term_id = row[4] subontology = row[8] if go_term_id in subontology_go_term_dict[subontology]: proteins_dict.setdefault(gene, dict()).setdefault(subontology, set()).add(go_term_id) printing how the key:values are organized for every gene/protein for i in range(5): print(list(proteins_dict)[i], end=": ") pp.pprint(proteins_dict[list(proteins_dict)[i]]) print("\nTotal number of genes/proteins annotated:", len(proteins_dict)) keeping track of fully annotated genes/proteins fully_annotated_proteins_wo_iea = [] for protein in proteins_dict: if len(proteins_dict[protein]) == 3: fully_annotated_proteins_wo_iea.append(protein) print("Out of {} proteins {} are (experimentally or manually) annotated by all three sub-ontologies.".format(len(proteins_dict), len(fully_annotated_proteins_wo_iea))) df_gene_expression = pd.read_csv("{}/{}".format(args.gene_expression_raw_dir, expression_file), sep='\t') df_gene_expression.iloc[:, 0] = [i.split(".")[0] for i in df_gene_expression.iloc[:,0]] # useful for human df_gene_expression if species == 'yeast': from fancyimpute import KNN#, NuclearNormMinimization, SoftImpute, BiScaler XY_incomplete = df_gene_expression.to_numpy()[:, 1:] XY_filled_knn = KNN(k=10).fit_transform(XY_incomplete) XY_filled_knn = np.round(XY_filled_knn, 2) if species == 'yeast': # replacing the incomplete dataframe with the imputed one df_gene_expression.iloc[:, 1:] = XY_filled_knn df_gene_expression if species == 'human': import urllib.parse import urllib.request GENENAME_ids = {} url = 'https://www.uniprot.org/uploadlists/' params = { 'from': 'ENSEMBL_ID', 'to': 'ACC', 'format': 'tab', 'query': " ".join([i.split(".")[0] for i in df_gene_expression.Name]) } data = urllib.parse.urlencode(params) data = data.encode('utf-8') req = urllib.request.Request(url, data) with urllib.request.urlopen(req) as f: response = f.read() #print(response.decode('utf-8')) for i, mapping in enumerate(response.decode('utf-8').strip().split("\n")): if i!=0: id1, id2 = mapping.split("\t") GENENAME_ids[id1] = id2 if species == 'human': df_gene_expression = df_gene_expression[df_gene_expression['Name'].isin(GENENAME_ids)] # for the nagation add ~ df_gene_expression = df_gene_expression.reset_index(drop=True) df_gene_expression.Name = [GENENAME_ids[i] for i in list(df_gene_expression.Name)] df_gene_expression df_gene_expression = df_gene_expression[df_gene_expression.iloc[:,0].isin(fully_annotated_proteins_wo_iea)] # for the nagation add ~ df_gene_expression = df_gene_expression.reset_index(drop=True) df_gene_expression rpearson = np.abs(np.corrcoef(df_gene_expression.iloc[:, 1:].to_numpy())) seq_list = {i:seq for i, seq in enumerate(df_gene_expression.iloc[:,0].to_numpy())} print(seq_list) list_of_pairs_full = list(itertools.combinations(list(seq_list.keys()), 2)) print("Full length of pairs:", len(list_of_pairs_full)) if args.max_num_pairs == -1: list_of_pairs = [list_of_pairs_full[i] for i in np.random.choice(len(list_of_pairs_full), len(list_of_pairs_full), replace=False)] args.max_num_pairs = len(list_of_pairs) else: list_of_pairs = [list_of_pairs_full[i] for i in np.random.choice(len(list_of_pairs_full), len(list_of_pairs_full), replace=False)] file = f'{species}_gene_expression.tsv' print(f"Saving data into the file '{file}' with the binning strategy.") with open(f"{args.result_gene_ontology_dir}/{file}", 'w') as fw: fw.write("Gene_1\tGene_2\tExpression_Value\n") #while len(list_of_pairs)<args.max_num_pairs: k = 0 for pair in list_of_pairs: r = rpearson[pair[0], pair[1]] if 0.8<=r:# and k<=args.max_num_pairs: k = k + 1 # transformation of the correlation coefficient into a Fishers’ Z-score will be done by arctanh (inverse hyperbolic tangent function) fw.write("{}\t{}\t{}\n".format(seq_list[pair[0]], seq_list[pair[1]], np.round(np.arctanh(r), 8))) # The Fisher transform equals the inverse hyperbolic tangen/arctanh #fw.write("{} {} {}\n".format(seq_list[pair[0]], seq_list[pair[1]], np.round(r, 8))) # The Fisher transform equals the inverse hyperbolic tangen/arctanh k2 = 0 for pair in list_of_pairs: r = rpearson[pair[0], pair[1]] if 0.6<=r and r<0.8 and k2<k:# and k<=args.max_num_pairs: k2 = k2 + 1 fw.write("{}\t{}\t{}\n".format(seq_list[pair[0]], seq_list[pair[1]], np.round(np.arctanh(r), 8))) # The Fisher transform equals the inverse hyperbolic tangen/arctanh k2 = 0 for pair in list_of_pairs: r = rpearson[pair[0], pair[1]] if 0.4<=r and r<0.6 and k2<k:# and k<=args.max_num_pairs: k2 = k2 + 1 fw.write("{}\t{}\t{}\n".format(seq_list[pair[0]], seq_list[pair[1]], np.round(np.arctanh(r), 8))) # The Fisher transform equals the inverse hyperbolic tangen/arctanh k2 = 0 for pair in list_of_pairs: r = rpearson[pair[0], pair[1]] if 0.2<=r and r<0.4 and k2<k:# and k<=args.max_num_pairs: k2 = k2 + 1 fw.write("{}\t{}\t{}\n".format(seq_list[pair[0]], seq_list[pair[1]], np.round(np.arctanh(r), 8))) # The Fisher transform equals the inverse hyperbolic tangen/arctanh k2 = 0 for pair in list_of_pairs: r = rpearson[pair[0], pair[1]] if 0.0<=r and r<0.2 and k2<k:# and k<=args.max_num_pairs: k2 = k2 + 1 fw.write("{}\t{}\t{}\n".format(seq_list[pair[0]], seq_list[pair[1]], np.round(np.arctanh(r), 8))) # The Fisher transform equals the inverse hyperbolic tangen/arctanh df = pd.read_csv(f"species/{species}/gene_expression/processed/{species}_gene_expression.tsv", sep="\t", dtype=str) df ge_genes = set(list(df.Gene_1) + list(df.Gene_2)) print(f"Number of {species} genes:", len(ge_genes)) with open(f'{args.result_gene_ontology_dir}/{species}_gene_expression_genes.tsv', 'w') as fw: for gene in sorted(ge_genes): fw.write(f"{gene}\n") <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Configuration<a id='2'></a> Step2: asserting raw data exist Step9: Loading Gene Ontology<a id='3'></a> Step11: Loading Genes and Annotations<a id='4'></a> Step14: removing 'ND' and 'IEA' annotations Step16: Taking into account only fully annotated genes/proteins Step17: Loading Gene Expression data<a id='5'></a> Step18: Imputing the missing values if needed (using fancyimpute package) Step19: Removing proteins without complete annotation Step20: Computing Absolute Pearson Correlation<a id='6'></a> Step21: Saving the Results<a id='7'></a> Step22: back to top<br>
6,925	<ASSISTANT_TASK:> Python Code: # We'll make the number of bins, B B = 50 plt.figure(0) plt.hist(X[:, 0], bins = B, normed = True) plt.title("Dimension 1 ($x$-axis)") plt.figure(1) plt.hist(X[:, 1], bins = B, normed = True) plt.title("Dimension 2 ($y$-axis)") rng = np.random.RandomState(74) t = rng.normal(size = (2, 2)) Xr = X @ t plt.scatter(Xr[:, 0], Xr[:, 1]) plt.figure(0) plt.hist(Xr[:, 0], bins = B, normed = True) plt.title("Dimension 1 ($x$-axis)") plt.figure(1) plt.hist(Xr[:, 1], bins = B, normed = True) plt.title("Dimension 2 ($y$-axis)") from sklearn.decomposition import RandomizedPCA pca = RandomizedPCA(n_components = 2, random_state = 12) Xt = pca.fit_transform(Xr) plt.scatter(Xt[:, 0], Xt[:, 1]) plt.figure(0) plt.hist(Xt[:, 0], bins = B, normed = True) plt.title("Dimension 1 ($x$-axis)") plt.figure(1) plt.hist(Xt[:, 1], bins = B, normed = True) plt.title("Dimension 2 ($y$-axis)") # First, define the method that, given a 1D histogram of the data, will return the # solution to the generalized eigenvalue problem as defined in Eq. 2 of the Fergus paper. import scipy.linalg as sla def solve(hist): hist : 1D histogram W = pairwise.rbf_kernel(hist.reshape(-1, 1)) P = np.diag(hist) PW = P @ W # matrix-matrix multiplication in Python 3 PWP = PW @ P Ds = np.diag(PWP.sum(axis = 0)) # sum along the COLUMNS of PWP Dh = np.diag(PW.sum(axis = 0)) # sum along the COLUMNS of PW # Solve the generalized eigenvalue problem for symmetric matrices (this is, after all, a laplacian). L = Ds - PWP b = P @ Dh v, U = sla.eigh(L, b = b) return [v, U] # Second, we'll create the 1D histograms for each dimension. h1, e1 = np.histogram(Xt[:, 0], bins = B) h2, e2 = np.histogram(Xt[:, 1], bins = B) # Sidestep: add a small amount to each histogram bin, then renormalize to a density. h1 = np.array(h1, dtype = np.float) + 0.01 h1 /= h1.sum() h2 = np.array(h2, dtype = np.float) + 0.01 h2 /= h2.sum() # Finally, we'll invoke the solver we wrote. v1, U1 = solve(h1) v2, U2 = solve(h2) # How do the eigenvalues and eigenfunctions look? plt.figure(0) plt.title("Eigenvalues for axis 1") plt.plot(v1) plt.figure(1) plt.title("Eigenvalues for axis 2") plt.plot(v2) plt.figure(0) plt.title("Axis 1") plt.plot(U1[:, 0], label = "EF 1") plt.plot(U1[:, 1], label = "EF 2") plt.plot(U1[:, 2], label = "EF 3") plt.legend(loc = 0) plt.figure(1) plt.title("Axis 2") plt.plot(U2[:, 0], label = "EF 1") plt.plot(U2[:, 1], label = "EF 2") plt.plot(U2[:, 2], label = "EF 3") plt.legend(loc = 0) # Assign the eigenfunctions EF = U2[:, 1:3] # Take the eigenfunctions, starting at index 1 (inclusive) and ending at 3 (exclusive). EV = v2[1:3] # Take the eigenvalues associated with the eigenfunctions # Create the x-axis from the histogram edges. x1 = np.array([e1[i:i + 2].mean() for i in range(e1.shape[0] - 1)]) # Finds the midpoints of the histogram bins. x2 = np.array([e2[i:i + 2].mean() for i in range(e2.shape[0] - 1)]) # Now, use the eigenfunctions (y-axis) to create the interpolators. f1 = interp1d(x1, EF[:, 0]) f2 = interp1d(x2, EF[:, 1]) # What do they look like? newdata = np.linspace(x1.min(), x1.max(), 100) plt.figure(0) plt.title("$x_1$ dimension") plt.plot(x1, EF[:, 0], 'o', label = 'Original') plt.plot(newdata, f1(newdata), '-', label = 'Interpolator') plt.legend(loc = 0) newdata = np.linspace(x2.min(), x2.max(), 100) plt.figure(1) plt.title("$x_2$ dimension") plt.plot(x2, EF[:, 1], 'o', label = 'Original') plt.plot(newdata, f2(newdata), '-', label = 'Interpolator') plt.legend(loc = 0) # We now have our eigenfunctions, at least in 1D. Let's see if we can reconstruct that heatmap from Fig 1. plt.title("Original Data") _ = plt.hexbin(Xt[:, 0], Xt[:, 1]) x = np.linspace(-2, 2.5, num = 100) # Our original (rotated) data varies from about (-2, 2.5) on the x-axis. y = np.linspace(-1, 1.0, num = 100) # ...and from about (-1, 1) on the y-axis. # Evaluate the eigenfunctions on each dimension. fx = f1(x) fy = f2(y) # Plot the heatmaps along the grid. plt.figure(0) plt.title("Dim $x_1$: $\sigma_1$ = {:.2f}".format(EV[0])) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: You see the data are easily separable along both the $x$ and $y$ axes. Put another way--if someone gives you an $x$ value of a data point and asks you what cluster the point comes from, it's easy to make that assessment. Same with just providing the $y$ value of a data point--you could easily predict what cluster it comes from. Step2: We've effectively rotated the data about 45 degrees. If we now plot the 1D histograms for the $x$ and $y$ axes again... Step3: Good luck telling me where the clusters are! Now the dimensions are highly correlated. This is why, if this was the original data we were given, we need to perform a rotation on the data--decorrelate the axes so they become separable in one dimension (i.e. histograms). Step5: The $x$ axis is still a lost cause, but now the $y$ axis has become separable again! Step6: We can see things happening with the eigenfunctions--and, clearly, the eigenfunctions associated with the smallest eigenvalues are useless, but we knew this from looking at the examples in the Fergus paper itself--but let's continue on to get a feel for things in the interpolation process.
6,926	<ASSISTANT_TASK:> Python Code: import sqlite3 import sys from glob import glob conn = sqlite3.connect('/users/mikespears/Desktop/mydb.db') #file-based db #conn = sqlite3.connect(':memory:') # in-memory db c = conn.cursor() c.execute('''DROP TABLE IF EXISTS uoftcoders''') c.execute('''CREATE TABLE uoftcoders (date, title, attendance)''') conn.commit() c.execute('''INSERT INTO uoftcoders VALUES (?, ?, ?)''', ["March 7, 2019", "Full Text Search with SQLITE", 12]) c.execute('''INSERT INTO uoftcoders VALUES (?, ?, ?)''', ["March 14, 2019", "Software defined radio", 15]) conn.commit() c.execute('''SELECT * FROM uoftcoders WHERE date = ?''', ["March 7, 2019"]) print(c.fetchall()) c.execute('''SELECT * FROM uoftcoders WHERE attendance > ?''', [10]) print(c.fetchall()) #basic pattern matching with LIKE c.execute('''SELECT * FROM uoftcoders WHERE date LIKE ?''', ["%march_%"]) print(c.fetchall()) c.execute('''DROP TABLE IF EXISTS files''') c.execute('''CREATE VIRTUAL TABLE files USING fts4(filename, text)''') #c.execute('''CREATE VIRTUAL TABLE files USING fts4(filename, text, tokenize=porter)''') #c.execute('''CREATE VIRTUAL TABLE files USING fts4(filename, text, tokenize=unicode61)''') conn.commit() # Insert all the files in a directory (that contains only plaintext files) textFileDir = "/users/mikespears/downloads/science/" allFiles = glob(textFileDir + ".") for path in allFiles: with open(path) as file: try: text = file.read() c.execute('''INSERT INTO files(filename, text) VALUES(?, ?)''', [path, text]) #print(path) except: e = sys.exc_info()[0] print("Read or insert error: %s (in %s)" % (e, path)) continue conn.commit() c.execute('''SELECT COUNT() from files''') print(c.fetchall()) #basic token search text column for row in c.execute('''SELECT filename FROM FILES WHERE text MATCH ?''', ['relativity']): print(row) #implicit 'AND' for row in c.execute('''SELECT filename FROM FILES WHERE text MATCH ?''', ['special relativity']): print(row) # OR for row in c.execute('''SELECT filename FROM FILES WHERE text MATCH ?''', ['special OR relativity']): print(row) #search for a phrase for row in c.execute('''SELECT filename FROM FILES WHERE text MATCH ?''', ['"special relativity"']): print(row) #exclude a token for row in c.execute('''SELECT filename FROM FILES WHERE text MATCH ?''', ['relativity -special']): print(row) #NEAR operator for row in c.execute('''SELECT filename FROM FILES WHERE text MATCH ?''', ["relativity NEAR special"]): print(row) #specify a column in the search term for row in c.execute('''SELECT filename FROM FILES WHERE text MATCH ?''', ['filename:faq']): print(row) for row in c.execute('''SELECT snippet(files, ">>>>", "<<<<", "..."), filename FROM FILES WHERE text MATCH ?''', ['relativity']): print ("\n\n--------%s\n\n" % row[1]) print(row[0]) #get offset for row in c.execute('''SELECT offsets(FILES) FROM FILES WHERE text MATCH ?''', ['filename:faq']): print(row) #get matchinfo for row in c.execute('''SELECT matchinfo(FILES) FROM FILES WHERE files MATCH ?''', ['filename:faq']): print(row) #direct access to full-text index c.execute('''DROP TABLE IF EXISTS files_terms''') c.execute('''CREATE VIRTUAL TABLE files_terms USING fts4aux(files)''') conn.commit() for row in c.execute('''SELECT from files_terms ORDER BY occurrences DESC'''): print(row) conn.close() <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Connect to a database file or create an in-memory database Step2: SQLite basics Step3: Full-Text-Search
6,927	<ASSISTANT_TASK:> Python Code: import seaborn as sns; sns.set_style("whitegrid") import random from matplotlib import pyplot as plt %matplotlib inline import numpy as np class MIR(object): '''Class for the MIR exam. Parameters ---------- study_level: int The level of preparation you have. Must be an integer between 0 (you answer randomly everything) and 10 (you know all the correct answers). n_questions: int The number of questions that the exam hast. The MIR has 225 questions, but this could change in principle. answer_unknowns: bool Whether we will randomly answer the questions we don't know or we will decide to not answer them. ''' def __init__(self, study_level, n_questions, answer_unknowns): self.study_level = study_level self.n_questions = n_questions self.answer_unknowns = answer_unknowns def solveExam(self): '''Answer every question in the exam. Return the mark out of 10''' score = 0 for question in range(0, self.n_questions): score = self._answerQuestion(score) return((score / (3 * self.n_questions)) * 10) def _answerQuestion(self, score): '''Answer a question. A higher study_level gives a higher chance of getting it right. If we dont know the answer, randomly guess if answer_unknowns is True. Returns the updated score''' chance = random.randint(1, 10) if chance <= self.study_level: score += 3 elif self.answer_unknowns: result = random.choice(['a', 'b', 'c', 'd']) answer = random.choice(['a', 'b', 'c', 'd']) if result == answer: score += 3 else: score -= 1 return(score) def _getScoreProgress(self): '''Answer all questions in a single exam and returns the score progress as a list.''' score_Progress = [] score = 0 for question in range(0, self.n_questions): score = self._answerQuestion(score) score_Progress.append((score / (3 * self.n_questions)) * 10) return(score_Progress) itera = 0 mir = MIR(study_level = 0, n_questions=225, answer_unknowns=True) max_iter = 10000 while itera < max_iter: itera += 1 plt.plot(mir._getScoreProgress()) plt.ylabel("Score / 10") plt.xlabel("Question number") plt.title("{} random MIR exams".format(max_iter)) plt.xlim(0, 225) plt.show() def solve_ManyExams(study_level, max_iter=1000): ''' Returns two lists: distrib1: The marks answering randomly unknown questions distrib2: The marks answering only the questions you know.''' iteration = 0 distrib1 = [] distrib2 = [] while iteration < max_iter: mir1 = MIR( study_level=study_level, n_questions=225, answer_unknowns=True) mir2 = MIR( study_level=study_level, n_questions=225, answer_unknowns=False) distrib1.append(mir1.solveExam()) distrib2.append(mir2.solveExam()) iteration += 1 return(distrib1, distrib2) distrib1, distrib2 = solve_ManyExams(study_level=5) sns.distplot(distrib1, hist=False, label='Answering unknowns') sns.distplot(distrib2, hist=False, label='Not answering unknowns') plt.xlabel("Score / 10") plt.ylabel("Density") plt.title("10000 MIRs, study level of 5/10") plt.legend() plt.show() print("Answering unknowns:\t%.2f+-%.2f\n" % (np.mean(distrib1), np.std(distrib1))) print("Not Answering unknowns:\t%.2f+-%.2f" % (np.mean(distrib2), np.std(distrib2))) mean_list = [] std_list = [] for study_level in range(0,11): distrib1, distrib2 = solve_ManyExams(study_level, max_iter=50000) # Lots of iterations to get clean distributions mean_diff = np.mean(distrib1) - np.mean(distrib2) std_diff = np.std(distrib1) - np.std(distrib2) mean_list.append(mean_diff) std_list.append(std_diff) plt.plot(list(range(0,11)),mean_list, label="Mean score gain") plt.plot(list(range(0,11)),std_list, label="Variability gain") plt.xlabel("Study level") plt.ylabel("Score difference / 10") plt.xticks([x for x in range(0, 11)]) plt.legend() plt.show() <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: What happens if we take the exam 10000 times randomly, without having studied at all? Step2: There's chances that you can luck out and get close to a 1.5/10 even if you haven't studied at all! Notice, however, that if you are unlucky, you might score a -1.5/10. Ouch! Step3: Answering the questions you don't know doesn't affect your mean score. It just increases the variability, making it possible to get slightly higher (and lower) marks.
6,928	<ASSISTANT_TASK:> Python Code: %matplotlib inline import numpy as np import scipy as sp import matplotlib as mpl import matplotlib.cm as cm import matplotlib.pyplot as plt import pandas as pd import time pd.set_option('display.width', 500) pd.set_option('display.max_columns', 100) pd.set_option('display.notebook_repr_html', True) import seaborn as sns sns.set_style("whitegrid") sns.set_context("poster") from bs4 import BeautifulSoup import requests import csv import os import random import sys import json sys.path.insert(0, '/aclImdb/') # function to get name of movie from each URL def get_movie(url): ''' Scrapes a given URL from IMDB.com. The URL's page contains many reviews for one particular movie. This function returns the name of that movie. ''' pageText = requests.get(url) # Keep asking for the page until you get it. Sleep if necessary. while (pageText==None): time.sleep(5) pageText = requests.get(url) soup = BeautifulSoup(pageText.text,"html.parser") # Some of our URL's are expired! Return None if so. if soup == None or soup.find("div",attrs={"id":"tn15title"}) == None: return None return soup.find("div",attrs={"id":"tn15title"}).find("a").get_text() # get all urls for train and test, neg and pos with open('aclImdb/train/urls_pos.txt','r') as f: train_pos_urls = f.readlines() with open('aclImdb/train/urls_neg.txt','r') as f: train_neg_urls = f.readlines() with open('aclImdb/test/urls_pos.txt','r') as f: test_pos_urls = f.readlines() with open('aclImdb/test/urls_neg.txt','r') as f: test_neg_urls = f.readlines() print len(train_pos_urls), len(train_neg_urls), len(test_pos_urls), len(test_neg_urls) def make_url_dict(url_list): ''' Input: List of URLs. Output: Dictionary of URL: movie based on scraped movie title. ''' url_dict = dict(zip(url_list, [None]*len(url_list))) index = 0 for url in url_list: if url_dict[url] == None: url_dict[url] = get_movie(url) # Every once in awhile, let us know how many URLs we have digested out of 12,500 total. if random.random() < 0.001: print index index += 1 time.sleep(0.001) %time train_pos_dict = make_url_dict(train_pos_urls) fp = open("url_movie_train_pos.json","w") json.dump(train_pos_dict, fp) fp.close() print len(train_pos_dict.keys()), len(list(set(list(train_pos_urls)))) %time train_neg_dict = make_url_dict(train_neg_urls) fp = open("url_movie_train_neg.json","w") json.dump(train_neg_dict, fp) fp.close() %time test_pos_dict = make_url_dict(test_pos_urls) fp = open("url_movie_test_pos.json","w") json.dump(test_pos_dict, fp) fp.close() %time test_neg_dict = make_url_dict(test_neg_urls) fp = open("url_movie_test_neg.json","w") json.dump(test_neg_dict, fp) fp.close() # Reload with open("url_movie_tr_pos.json", "r") as fd: train_pos_dict = json.load(fd) with open("url_movie_train_neg.json", "r") as fd: train_neg_dict = json.load(fd) with open("url_movie_test_pos.json", "r") as fd: test_pos_dict = json.load(fd) with open("url_movie_test_neg.json", "r") as fd: test_neg_dict = json.load(fd) def data_collect(directory, pos, url_dict, url_list): ''' Inputs: directory: Directory to collect reviews from. ex) 'aclImdb/train/pos/' Pos: True or False, depending on whether the reviews are labelled positive or not. url_dict: the relevant URL-Movie dictionary (created above) for the particular category url_list: the list of URLs for that particular category ''' # Column names for the data frame review_df = pd.DataFrame(columns=['movie_id', 'stars', 'positive', 'text', 'url', 'movie_name']) # Crawl over the directory, attaining relevant data for each of the .txt review files. train_pos_names = list(os.walk(directory))[0][2] for review in train_pos_names: # Andrew L. Maas's stanford group encoded the reviewID and number of stars for a review in the file's name. # For example, "0_10.txt" means reviewID 0 received 10 stars. The reviews are in the same order as the URLs, # so the reviewID is precisely the location of that movie's URL in the respective URL list. stars = int(review.split("_")[1].split(".")[0]) movieID = int(review.split("_")[0]) #everything before the underscore fp = open('%(dir)s%(review)s' % {'dir': directory, 'review': review}, 'r') text = fp.read() url = url_list[movieID] movie_name = url_dict[url] reviewDict = {'movie_id': [movieID], 'stars': [stars], 'positive': [pos], 'text': [text], 'url': [url], 'movie_name': [movie_name]} review_df = review_df.append(pd.DataFrame(reviewDict)) return review_df # First get the positive reviews for the train_df. train_df = data_collect('aclImdb/train/pos/', True, train_pos_dict, train_pos_urls) # Then append the negative reviews train_df = train_df.append(data_collect('aclImdb/train/neg/', False, train_neg_dict, train_neg_urls)) # First get the positive reviews for the train_df. test_df = data_collect('aclImdb/test/pos/', True, test_pos_dict, test_pos_urls) # Then append the negative reviews test_df = test_df.append(data_collect('aclImdb/test/neg/', False, test_neg_dict, test_neg_urls)) train_df_dict = {feature: train_df[feature].values.tolist() for feature in train_df.columns.values} test_df_dict = {feature: test_df[feature].values.tolist() for feature in test_df.columns.values} # Train fp = open("train_df_dict.json","w") json.dump(train_df_dict, fp) fp.close() # Test fp = open("test_df_dict.json","w") json.dump(test_df_dict, fp) fp.close() with open("train_df_dict.json", "r") as fd: train_df_dict = json.load(fd) with open("test_df_dict.json", "r") as fd: test_df_dict = json.load(fd) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Below we write a function to scrape an IMDB url and return a movie name. Step2: Now let's get the list of URLs for each of our data sets Step3: Let's see how long each list is. Step4: There are 12500 reviews in each sub data set. Each review has a corresponding URL. However, the URL lists have duplicates, as two reviews can be for the same movie and thus be found on the same IMDB webpage. Step5: Let's make a dictionary of stored movie names for each subdata set, saving into a JSON file so we only have to do this once. Step6: If we did this right for training positives, the length of the dictionary keys should be equal to the number of unique URLs in its URL list. Step7: Now that we have saved movie names associated with each URL, we can finally create our data table of reviews. We will define a function data_collect which iterates over our directories, making a pandas dataframe out of all the reviews in a particular category (e.g. Test Set, Positive Reviews). Step8: Data Collection Step9: Now we'll create a testing data frame. Step10: Let's create a dictionary out of each dataframe so that we can save each in JSON format. Step11: Let's reopen.
6,929	<ASSISTANT_TASK:> Python Code: import tensorflow as tf from tensorflow import keras x = tf.constant([[5, 2], [1, 3]]) print(x) x.numpy() print("dtype:", x.dtype) print("shape:", x.shape) print(tf.ones(shape=(2, 1))) print(tf.zeros(shape=(2, 1))) x = tf.random.normal(shape=(2, 2), mean=0.0, stddev=1.0) x = tf.random.uniform(shape=(2, 2), minval=0, maxval=10, dtype="int32") initial_value = tf.random.normal(shape=(2, 2)) a = tf.Variable(initial_value) print(a) new_value = tf.random.normal(shape=(2, 2)) a.assign(new_value) for i in range(2): for j in range(2): assert a[i, j] == new_value[i, j] added_value = tf.random.normal(shape=(2, 2)) a.assign_add(added_value) for i in range(2): for j in range(2): assert a[i, j] == new_value[i, j] + added_value[i, j] a = tf.random.normal(shape=(2, 2)) b = tf.random.normal(shape=(2, 2)) c = a + b d = tf.square(c) e = tf.exp(d) a = tf.random.normal(shape=(2, 2)) b = tf.random.normal(shape=(2, 2)) with tf.GradientTape() as tape: tape.watch(a) # Start recording the history of operations applied to `a` c = tf.sqrt(tf.square(a) + tf.square(b)) # Do some math using `a` # What's the gradient of `c` with respect to `a`? dc_da = tape.gradient(c, a) print(dc_da) a = tf.Variable(a) with tf.GradientTape() as tape: c = tf.sqrt(tf.square(a) + tf.square(b)) dc_da = tape.gradient(c, a) print(dc_da) with tf.GradientTape() as outer_tape: with tf.GradientTape() as tape: c = tf.sqrt(tf.square(a) + tf.square(b)) dc_da = tape.gradient(c, a) d2c_da2 = outer_tape.gradient(dc_da, a) print(d2c_da2) class Linear(keras.layers.Layer): y = w.x + b def __init__(self, units=32, input_dim=32): super(Linear, self).__init__() w_init = tf.random_normal_initializer() self.w = tf.Variable( initial_value=w_init(shape=(input_dim, units), dtype="float32"), trainable=True, ) b_init = tf.zeros_initializer() self.b = tf.Variable( initial_value=b_init(shape=(units,), dtype="float32"), trainable=True ) def call(self, inputs): return tf.matmul(inputs, self.w) + self.b # Instantiate our layer. linear_layer = Linear(units=4, input_dim=2) # The layer can be treated as a function. # Here we call it on some data. y = linear_layer(tf.ones((2, 2))) assert y.shape == (2, 4) assert linear_layer.weights == [linear_layer.w, linear_layer.b] class Linear(keras.layers.Layer): y = w.x + b def __init__(self, units=32): super(Linear, self).__init__() self.units = units def build(self, input_shape): self.w = self.add_weight( shape=(input_shape[-1], self.units), initializer="random_normal", trainable=True, ) self.b = self.add_weight( shape=(self.units,), initializer="random_normal", trainable=True ) def call(self, inputs): return tf.matmul(inputs, self.w) + self.b # Instantiate our lazy layer. linear_layer = Linear(4) # This will also call `build(input_shape)` and create the weights. y = linear_layer(tf.ones((2, 2))) # Prepare a dataset. (x_train, y_train), _ = tf.keras.datasets.mnist.load_data() dataset = tf.data.Dataset.from_tensor_slices( (x_train.reshape(60000, 784).astype("float32") / 255, y_train) ) dataset = dataset.shuffle(buffer_size=1024).batch(64) # Instantiate our linear layer (defined above) with 10 units. linear_layer = Linear(10) # Instantiate a logistic loss function that expects integer targets. loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) # Instantiate an optimizer. optimizer = tf.keras.optimizers.SGD(learning_rate=1e-3) # Iterate over the batches of the dataset. for step, (x, y) in enumerate(dataset): # Open a GradientTape. with tf.GradientTape() as tape: # Forward pass. logits = linear_layer(x) # Loss value for this batch. loss = loss_fn(y, logits) # Get gradients of the loss wrt the weights. gradients = tape.gradient(loss, linear_layer.trainable_weights) # Update the weights of our linear layer. optimizer.apply_gradients(zip(gradients, linear_layer.trainable_weights)) # Logging. if step % 100 == 0: print("Step:", step, "Loss:", float(loss)) class ComputeSum(keras.layers.Layer): Returns the sum of the inputs. def __init__(self, input_dim): super(ComputeSum, self).__init__() # Create a non-trainable weight. self.total = tf.Variable(initial_value=tf.zeros((input_dim,)), trainable=False) def call(self, inputs): self.total.assign_add(tf.reduce_sum(inputs, axis=0)) return self.total my_sum = ComputeSum(2) x = tf.ones((2, 2)) y = my_sum(x) print(y.numpy()) # [2. 2.] y = my_sum(x) print(y.numpy()) # [4. 4.] assert my_sum.weights == [my_sum.total] assert my_sum.non_trainable_weights == [my_sum.total] assert my_sum.trainable_weights == [] # Let's reuse the Linear class # with a `build` method that we defined above. class MLP(keras.layers.Layer): Simple stack of Linear layers. def __init__(self): super(MLP, self).__init__() self.linear_1 = Linear(32) self.linear_2 = Linear(32) self.linear_3 = Linear(10) def call(self, inputs): x = self.linear_1(inputs) x = tf.nn.relu(x) x = self.linear_2(x) x = tf.nn.relu(x) return self.linear_3(x) mlp = MLP() # The first call to the `mlp` object will create the weights. y = mlp(tf.ones(shape=(3, 64))) # Weights are recursively tracked. assert len(mlp.weights) == 6 mlp = keras.Sequential( [ keras.layers.Dense(32, activation=tf.nn.relu), keras.layers.Dense(32, activation=tf.nn.relu), keras.layers.Dense(10), ] ) class ActivityRegularization(keras.layers.Layer): Layer that creates an activity sparsity regularization loss. def __init__(self, rate=1e-2): super(ActivityRegularization, self).__init__() self.rate = rate def call(self, inputs): # We use `add_loss` to create a regularization loss # that depends on the inputs. self.add_loss(self.rate * tf.reduce_sum(inputs)) return inputs # Let's use the loss layer in a MLP block. class SparseMLP(keras.layers.Layer): Stack of Linear layers with a sparsity regularization loss. def __init__(self): super(SparseMLP, self).__init__() self.linear_1 = Linear(32) self.regularization = ActivityRegularization(1e-2) self.linear_3 = Linear(10) def call(self, inputs): x = self.linear_1(inputs) x = tf.nn.relu(x) x = self.regularization(x) return self.linear_3(x) mlp = SparseMLP() y = mlp(tf.ones((10, 10))) print(mlp.losses) # List containing one float32 scalar # Losses correspond to the last forward pass. mlp = SparseMLP() mlp(tf.ones((10, 10))) assert len(mlp.losses) == 1 mlp(tf.ones((10, 10))) assert len(mlp.losses) == 1 # No accumulation. # Let's demonstrate how to use these losses in a training loop. # Prepare a dataset. (x_train, y_train), _ = tf.keras.datasets.mnist.load_data() dataset = tf.data.Dataset.from_tensor_slices( (x_train.reshape(60000, 784).astype("float32") / 255, y_train) ) dataset = dataset.shuffle(buffer_size=1024).batch(64) # A new MLP. mlp = SparseMLP() # Loss and optimizer. loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) optimizer = tf.keras.optimizers.SGD(learning_rate=1e-3) for step, (x, y) in enumerate(dataset): with tf.GradientTape() as tape: # Forward pass. logits = mlp(x) # External loss value for this batch. loss = loss_fn(y, logits) # Add the losses created during the forward pass. loss += sum(mlp.losses) # Get gradients of the loss wrt the weights. gradients = tape.gradient(loss, mlp.trainable_weights) # Update the weights of our linear layer. optimizer.apply_gradients(zip(gradients, mlp.trainable_weights)) # Logging. if step % 100 == 0: print("Step:", step, "Loss:", float(loss)) # Instantiate a metric object accuracy = tf.keras.metrics.SparseCategoricalAccuracy() # Prepare our layer, loss, and optimizer. model = keras.Sequential( [ keras.layers.Dense(32, activation="relu"), keras.layers.Dense(32, activation="relu"), keras.layers.Dense(10), ] ) loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3) for epoch in range(2): # Iterate over the batches of a dataset. for step, (x, y) in enumerate(dataset): with tf.GradientTape() as tape: logits = model(x) # Compute the loss value for this batch. loss_value = loss_fn(y, logits) # Update the state of the `accuracy` metric. accuracy.update_state(y, logits) # Update the weights of the model to minimize the loss value. gradients = tape.gradient(loss_value, model.trainable_weights) optimizer.apply_gradients(zip(gradients, model.trainable_weights)) # Logging the current accuracy value so far. if step % 200 == 0: print("Epoch:", epoch, "Step:", step) print("Total running accuracy so far: %.3f" % accuracy.result()) # Reset the metric's state at the end of an epoch accuracy.reset_state() class F1Score(keras.metrics.Metric): def __init__(self, name="f1_score", dtype="float32", threshold=0.5, kwargs): super().__init__(name=name, dtype=dtype, kwargs) self.threshold = 0.5 self.true_positives = self.add_weight( name="tp", dtype=dtype, initializer="zeros" ) self.false_positives = self.add_weight( name="fp", dtype=dtype, initializer="zeros" ) self.false_negatives = self.add_weight( name="fn", dtype=dtype, initializer="zeros" ) def update_state(self, y_true, y_pred, sample_weight=None): y_pred = tf.math.greater_equal(y_pred, self.threshold) y_true = tf.cast(y_true, tf.bool) y_pred = tf.cast(y_pred, tf.bool) true_positives = tf.cast(y_true & y_pred, self.dtype) false_positives = tf.cast(~y_true & y_pred, self.dtype) false_negatives = tf.cast(y_true & ~y_pred, self.dtype) if sample_weight is not None: sample_weight = tf.cast(sample_weight, self.dtype) true_positives = sample_weight false_positives = sample_weight false_negatives = sample_weight self.true_positives.assign_add(tf.reduce_sum(true_positives)) self.false_positives.assign_add(tf.reduce_sum(false_positives)) self.false_negatives.assign_add(tf.reduce_sum(false_negatives)) def result(self): precision = self.true_positives / (self.true_positives + self.false_positives) recall = self.true_positives / (self.true_positives + self.false_negatives) return precision recall * 2.0 / (precision + recall) def reset_state(self): self.true_positives.assign(0) self.false_positives.assign(0) self.false_negatives.assign(0) m = F1Score() m.update_state([0, 1, 0, 0], [0.3, 0.5, 0.8, 0.9]) print("Intermediate result:", float(m.result())) m.update_state([1, 1, 1, 1], [0.1, 0.7, 0.6, 0.0]) print("Final result:", float(m.result())) # Prepare our layer, loss, and optimizer. model = keras.Sequential( [ keras.layers.Dense(32, activation="relu"), keras.layers.Dense(32, activation="relu"), keras.layers.Dense(10), ] ) loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3) # Create a training step function. @tf.function # Make it fast. def train_on_batch(x, y): with tf.GradientTape() as tape: logits = model(x) loss = loss_fn(y, logits) gradients = tape.gradient(loss, model.trainable_weights) optimizer.apply_gradients(zip(gradients, model.trainable_weights)) return loss # Prepare a dataset. (x_train, y_train), _ = tf.keras.datasets.mnist.load_data() dataset = tf.data.Dataset.from_tensor_slices( (x_train.reshape(60000, 784).astype("float32") / 255, y_train) ) dataset = dataset.shuffle(buffer_size=1024).batch(64) for step, (x, y) in enumerate(dataset): loss = train_on_batch(x, y) if step % 100 == 0: print("Step:", step, "Loss:", float(loss)) class Dropout(keras.layers.Layer): def __init__(self, rate): super(Dropout, self).__init__() self.rate = rate def call(self, inputs, training=None): if training: return tf.nn.dropout(inputs, rate=self.rate) return inputs class MLPWithDropout(keras.layers.Layer): def __init__(self): super(MLPWithDropout, self).__init__() self.linear_1 = Linear(32) self.dropout = Dropout(0.5) self.linear_3 = Linear(10) def call(self, inputs, training=None): x = self.linear_1(inputs) x = tf.nn.relu(x) x = self.dropout(x, training=training) return self.linear_3(x) mlp = MLPWithDropout() y_train = mlp(tf.ones((2, 2)), training=True) y_test = mlp(tf.ones((2, 2)), training=False) # We use an `Input` object to describe the shape and dtype of the inputs. # This is the deep learning equivalent of declaring a type. # The shape argument is per-sample; it does not include the batch size. # The functional API focused on defining per-sample transformations. # The model we create will automatically batch the per-sample transformations, # so that it can be called on batches of data. inputs = tf.keras.Input(shape=(16,), dtype="float32") # We call layers on these "type" objects # and they return updated types (new shapes/dtypes). x = Linear(32)(inputs) # We are reusing the Linear layer we defined earlier. x = Dropout(0.5)(x) # We are reusing the Dropout layer we defined earlier. outputs = Linear(10)(x) # A functional `Model` can be defined by specifying inputs and outputs. # A model is itself a layer like any other. model = tf.keras.Model(inputs, outputs) # A functional model already has weights, before being called on any data. # That's because we defined its input shape in advance (in `Input`). assert len(model.weights) == 4 # Let's call our model on some data, for fun. y = model(tf.ones((2, 16))) assert y.shape == (2, 10) # You can pass a `training` argument in `__call__` # (it will get passed down to the Dropout layer). y = model(tf.ones((2, 16)), training=True) inputs = tf.keras.Input(shape=(784,), dtype="float32") x = keras.layers.Dense(32, activation="relu")(inputs) x = keras.layers.Dense(32, activation="relu")(x) outputs = keras.layers.Dense(10)(x) model = tf.keras.Model(inputs, outputs) # Specify the loss, optimizer, and metrics with `compile()`. model.compile( loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True), optimizer=keras.optimizers.Adam(learning_rate=1e-3), metrics=[keras.metrics.SparseCategoricalAccuracy()], ) # Train the model with the dataset for 2 epochs. model.fit(dataset, epochs=2) model.predict(dataset) model.evaluate(dataset) class CustomModel(keras.Model): def __init__(self, args, kwargs): super().__init__(args, *kwargs) self.loss_tracker = keras.metrics.Mean(name="loss") self.accuracy = keras.metrics.SparseCategoricalAccuracy() self.loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True) self.optimizer = keras.optimizers.Adam(learning_rate=1e-3) def train_step(self, data): # Unpack the data. Its structure depends on your model and # on what you pass to `fit()`. x, y = data with tf.GradientTape() as tape: y_pred = self(x, training=True) # Forward pass loss = self.loss_fn(y, y_pred) gradients = tape.gradient(loss, self.trainable_weights) self.optimizer.apply_gradients(zip(gradients, self.trainable_weights)) # Update metrics (includes the metric that tracks the loss) self.loss_tracker.update_state(loss) self.accuracy.update_state(y, y_pred) # Return a dict mapping metric names to current value return {"loss": self.loss_tracker.result(), "accuracy": self.accuracy.result()} @property def metrics(self): # We list our `Metric` objects here so that `reset_states()` can be # called automatically at the start of each epoch. return [self.loss_tracker, self.accuracy] inputs = tf.keras.Input(shape=(784,), dtype="float32") x = keras.layers.Dense(32, activation="relu")(inputs) x = keras.layers.Dense(32, activation="relu")(x) outputs = keras.layers.Dense(10)(x) model = CustomModel(inputs, outputs) model.compile() model.fit(dataset, epochs=2) from tensorflow.keras import layers class Sampling(layers.Layer): Uses (z_mean, z_log_var) to sample z, the vector encoding a digit. def call(self, inputs): z_mean, z_log_var = inputs batch = tf.shape(z_mean)[0] dim = tf.shape(z_mean)[1] epsilon = tf.keras.backend.random_normal(shape=(batch, dim)) return z_mean + tf.exp(0.5 z_log_var) * epsilon class Encoder(layers.Layer): Maps MNIST digits to a triplet (z_mean, z_log_var, z). def __init__(self, latent_dim=32, intermediate_dim=64, kwargs): super(Encoder, self).__init__(kwargs) self.dense_proj = layers.Dense(intermediate_dim, activation=tf.nn.relu) self.dense_mean = layers.Dense(latent_dim) self.dense_log_var = layers.Dense(latent_dim) self.sampling = Sampling() def call(self, inputs): x = self.dense_proj(inputs) z_mean = self.dense_mean(x) z_log_var = self.dense_log_var(x) z = self.sampling((z_mean, z_log_var)) return z_mean, z_log_var, z class Decoder(layers.Layer): Converts z, the encoded digit vector, back into a readable digit. def __init__(self, original_dim, intermediate_dim=64, kwargs): super(Decoder, self).__init__(kwargs) self.dense_proj = layers.Dense(intermediate_dim, activation=tf.nn.relu) self.dense_output = layers.Dense(original_dim, activation=tf.nn.sigmoid) def call(self, inputs): x = self.dense_proj(inputs) return self.dense_output(x) class VariationalAutoEncoder(layers.Layer): Combines the encoder and decoder into an end-to-end model for training. def __init__(self, original_dim, intermediate_dim=64, latent_dim=32, kwargs): super(VariationalAutoEncoder, self).__init__(kwargs) self.original_dim = original_dim self.encoder = Encoder(latent_dim=latent_dim, intermediate_dim=intermediate_dim) self.decoder = Decoder(original_dim, intermediate_dim=intermediate_dim) def call(self, inputs): z_mean, z_log_var, z = self.encoder(inputs) reconstructed = self.decoder(z) # Add KL divergence regularization loss. kl_loss = -0.5 * tf.reduce_mean( z_log_var - tf.square(z_mean) - tf.exp(z_log_var) + 1 ) self.add_loss(kl_loss) return reconstructed # Our model. vae = VariationalAutoEncoder(original_dim=784, intermediate_dim=64, latent_dim=32) # Loss and optimizer. loss_fn = tf.keras.losses.MeanSquaredError() optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3) # Prepare a dataset. (x_train, _), _ = tf.keras.datasets.mnist.load_data() dataset = tf.data.Dataset.from_tensor_slices( x_train.reshape(60000, 784).astype("float32") / 255 ) dataset = dataset.shuffle(buffer_size=1024).batch(32) @tf.function def training_step(x): with tf.GradientTape() as tape: reconstructed = vae(x) # Compute input reconstruction. # Compute loss. loss = loss_fn(x, reconstructed) loss += sum(vae.losses) # Add KLD term. # Update the weights of the VAE. grads = tape.gradient(loss, vae.trainable_weights) optimizer.apply_gradients(zip(grads, vae.trainable_weights)) return loss losses = [] # Keep track of the losses over time. for step, x in enumerate(dataset): loss = training_step(x) # Logging. losses.append(float(loss)) if step % 100 == 0: print("Step:", step, "Loss:", sum(losses) / len(losses)) # Stop after 1000 steps. # Training the model to convergence is left # as an exercise to the reader. if step >= 1000: break original_dim = 784 intermediate_dim = 64 latent_dim = 32 # Define encoder model. original_inputs = tf.keras.Input(shape=(original_dim,), name="encoder_input") x = layers.Dense(intermediate_dim, activation="relu")(original_inputs) z_mean = layers.Dense(latent_dim, name="z_mean")(x) z_log_var = layers.Dense(latent_dim, name="z_log_var")(x) z = Sampling()((z_mean, z_log_var)) encoder = tf.keras.Model(inputs=original_inputs, outputs=z, name="encoder") # Define decoder model. latent_inputs = tf.keras.Input(shape=(latent_dim,), name="z_sampling") x = layers.Dense(intermediate_dim, activation="relu")(latent_inputs) outputs = layers.Dense(original_dim, activation="sigmoid")(x) decoder = tf.keras.Model(inputs=latent_inputs, outputs=outputs, name="decoder") # Define VAE model. outputs = decoder(z) vae = tf.keras.Model(inputs=original_inputs, outputs=outputs, name="vae") # Add KL divergence regularization loss. kl_loss = -0.5 * tf.reduce_mean(z_log_var - tf.square(z_mean) - tf.exp(z_log_var) + 1) vae.add_loss(kl_loss) # Loss and optimizer. loss_fn = tf.keras.losses.MeanSquaredError() optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3) # Prepare a dataset. (x_train, _), _ = tf.keras.datasets.mnist.load_data() dataset = tf.data.Dataset.from_tensor_slices( x_train.reshape(60000, 784).astype("float32") / 255 ) dataset = dataset.map(lambda x: (x, x)) # Use x_train as both inputs & targets dataset = dataset.shuffle(buffer_size=1024).batch(32) # Configure the model for training. vae.compile(optimizer, loss=loss_fn) # Actually training the model. vae.fit(dataset, epochs=1) import numpy as np input_dim = 784 classes = 10 # This is the main network we'll actually use to predict labels. main_network = keras.Sequential( [keras.layers.Dense(64, activation=tf.nn.relu), keras.layers.Dense(classes),] ) # It doesn't need to create its own weights, so let's mark its layers # as already built. That way, calling `main_network` won't create new variables. for layer in main_network.layers: layer.built = True # This is the number of weight coefficients to generate. Each layer in the # main network requires output_dim * input_dim + output_dim coefficients. num_weights_to_generate = (classes * 64 + classes) + (64 * input_dim + 64) # This is the hypernetwork that generates the weights of the `main_network` above. hypernetwork = keras.Sequential( [ keras.layers.Dense(16, activation=tf.nn.relu), keras.layers.Dense(num_weights_to_generate, activation=tf.nn.sigmoid), ] ) # Loss and optimizer. loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) optimizer = tf.keras.optimizers.Adam(learning_rate=1e-4) # Prepare a dataset. (x_train, y_train), _ = tf.keras.datasets.mnist.load_data() dataset = tf.data.Dataset.from_tensor_slices( (x_train.reshape(60000, 784).astype("float32") / 255, y_train) ) # We'll use a batch size of 1 for this experiment. dataset = dataset.shuffle(buffer_size=1024).batch(1) @tf.function def train_step(x, y): with tf.GradientTape() as tape: # Predict weights for the outer model. weights_pred = hypernetwork(x) # Reshape them to the expected shapes for w and b for the outer model. # Layer 0 kernel. start_index = 0 w0_shape = (input_dim, 64) w0_coeffs = weights_pred[:, start_index : start_index + np.prod(w0_shape)] w0 = tf.reshape(w0_coeffs, w0_shape) start_index += np.prod(w0_shape) # Layer 0 bias. b0_shape = (64,) b0_coeffs = weights_pred[:, start_index : start_index + np.prod(b0_shape)] b0 = tf.reshape(b0_coeffs, b0_shape) start_index += np.prod(b0_shape) # Layer 1 kernel. w1_shape = (64, classes) w1_coeffs = weights_pred[:, start_index : start_index + np.prod(w1_shape)] w1 = tf.reshape(w1_coeffs, w1_shape) start_index += np.prod(w1_shape) # Layer 1 bias. b1_shape = (classes,) b1_coeffs = weights_pred[:, start_index : start_index + np.prod(b1_shape)] b1 = tf.reshape(b1_coeffs, b1_shape) start_index += np.prod(b1_shape) # Set the weight predictions as the weight variables on the outer model. main_network.layers[0].kernel = w0 main_network.layers[0].bias = b0 main_network.layers[1].kernel = w1 main_network.layers[1].bias = b1 # Inference on the outer model. preds = main_network(x) loss = loss_fn(y, preds) # Train only inner model. grads = tape.gradient(loss, hypernetwork.trainable_weights) optimizer.apply_gradients(zip(grads, hypernetwork.trainable_weights)) return loss losses = [] # Keep track of the losses over time. for step, (x, y) in enumerate(dataset): loss = train_step(x, y) # Logging. losses.append(float(loss)) if step % 100 == 0: print("Step:", step, "Loss:", sum(losses) / len(losses)) # Stop after 1000 steps. # Training the model to convergence is left # as an exercise to the reader. if step >= 1000: break <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Introduction Step2: You can get its value as a NumPy array by calling .numpy() Step3: Much like a NumPy array, it features the attributes dtype and shape Step4: A common way to create constant tensors is via tf.ones and tf.zeros (just like np.ones and np.zeros) Step5: You can also create random constant tensors Step6: Variables Step7: You update the value of a Variable by using the methods .assign(value), .assign_add(increment), or .assign_sub(decrement) Step8: Doing math in TensorFlow Step9: Gradients Step10: By default, variables are watched automatically, so you don't need to manually watch them Step11: Note that you can compute higher-order derivatives by nesting tapes Step13: Keras layers Step14: You would use a Layer instance much like a Python function Step15: The weight variables (created in __init__) are automatically Step17: You have many built-in layers available, from Dense to Conv2D to LSTM to Step18: Layer gradients Step20: Trainable and non-trainable weights Step22: Layers that own layers Step23: Note that our manually-created MLP above is equivalent to the following Step25: Tracking losses created by layers Step27: Any model incorporating this layer will track this regularization loss Step28: These losses are cleared by the top-level layer at the start of each forward Step29: Keeping track of training metrics Step30: In addition to this, similarly to the self.add_loss() method, you have access Step31: Let's test-drive it Step32: Compiled functions Step33: Training mode & inference mode Step34: The Functional API for model-building Step35: The Functional API tends to be more concise than subclassing, and provides a few other Step36: You can always subclass the Model class (it works exactly like subclassing Step39: End-to-end experiment example 1 Step41: Next, we have a Decoder class, which maps the probabilistic latent space coordinates Step43: Finally, our VariationalAutoEncoder composes together an encoder and a decoder, and Step44: Now, let's write a training loop. Our training step is decorated with a @tf.function to Step45: As you can see, building and training this type of model in Keras Step46: Much more concise, right? Step47: The use of the Functional API and fit reduces our example from 65 lines to 25 lines Step48: This is our training loop. For each batch of data
6,930	<ASSISTANT_TASK:> Python Code: # Ignore numpy warnings import warnings warnings.filterwarnings('ignore') import matplotlib.pyplot as plt %matplotlib inline # Some defaults: plt.rcParams['figure.figsize'] = (12, 6) # Default plot size %reset -f import pycuda from pycuda import compiler import pycuda.driver as drv import pycuda.driver as cuda drv.init() print("%d device(s) found." % drv.Device.count()) for ordinal in range(drv.Device.count()): dev = drv.Device(ordinal) print "Device #%d: %s" % (ordinal, dev.name()) print " Compute Capability: %d.%d" % dev.compute_capability() print " Total Memory: %s KB" % (dev.total_memory()//(1024)) atts = [(str(att), value) for att, value in dev.get_attributes().iteritems()] atts.sort() for att, value in atts: print " %s: %s" % (att, value) import pycuda.autoinit import pycuda.driver as cuda (free,total)=cuda.mem_get_info() print("Global memory occupancy:%f%% free"%(free*100/total)) for devicenum in range(cuda.Device.count()): device=cuda.Device(devicenum) attrs=device.get_attributes() #Beyond this point is just pretty printing print("\n===Attributes for device %d"%devicenum) for (key,value) in attrs.iteritems(): print("%s:%s"%(str(key),str(value))) ! jupyter-nbconvert "01 PyCUDA verify CUDA 8.0.ipynb" --to slides --reveal-prefix=reveal.js --post serve --ServerPostProcessor.ip="0.0.0.0" <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: PyCUDA Imports Step2: Available CUDA Devices
6,931	<ASSISTANT_TASK:> Python Code: %pylab inline from scipy import stats Ns=np.arange(20,200,4); K=10000; ps=np.zeros((Ns.size,3)) res=np.zeros(4) cs=np.zeros((Ns.size,8)) i=0 for N in Ns: for k in range(K): x1=np.zeros(N);x1[N/2:]=1 x2=np.mod(range(N),2) y= 42+x1+x2+x1x2+np.random.randn(N)2 tot=np.square(y-y.mean()).sum() x=np.ones((N,4)) x[:,1]=x1x2 x[:,2]=x1(1-x2) x[:,3]=(1-x1)x2 res[0]=np.linalg.lstsq(x,y)[1] x=np.ones((N,2)) x[:,1]=x1 res[1]=tot-np.linalg.lstsq(x,y)[1] x[:,1]=x2 res[2]=tot-np.linalg.lstsq(x,y)[1] res[3]=tot-res[0]-res[1]-res[2] mss=res/np.float32(np.array([N-4,1,1,1])) F=mss[1:]/mss[0] p=1-stats.f.cdf(F,1,N-4) p=p<0.05 ps[i,:]+=np.int32(p) cs[i,p[0]4+p[1]*2+p[2]]+=1 i+=1 ps/=float(K) cs/=float(K) for k in range(ps.shape[1]): plt.plot(Ns/4, ps[:,k]) plt.legend(['A','B','X'],loc=2) plt.xlabel('N per cell') plt.ylabel('expected power'); plt.figure(figsize=(7,6)) for k in [0,1,2,3,6,7]: plt.plot(Ns/4, cs[:,k]) plt.legend(['nothing','X','B','BX','AB','ABX'],loc=2) plt.xlabel('N per cell') plt.ylabel('pattern frequency'); <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Now we look at the probability that the various configurations of significant and non-significant results will be obtained.
6,932	<ASSISTANT_TASK:> Python Code: %matplotlib inline from matplotlib import colors import matplotlib.pylab as plt from oedes.fvm import mesh1d from oedes import context,init_notebook,testing,models import numpy as np from oedes.functions import Aux2 init_notebook() class CustomMobility(models.MobilityModel): def mu_func(self, T, E, c): mu0 = 5e-11 mu1 = 5e-9 W = 0.04 f0 = 0.3 f = c / 0.3e27 return (mu1 - mu0) * Aux2((f0 - f) / W) + mu0 def mobility(self, parent, ctx, eq): mu_cell = self.mu_func(ctx.varsOf(eq.thermal)['T'], ctx.varsOf(eq.poisson)['Ecellm'], ctx.varsOf(eq)['c']) mu_face = eq.mesh.faceaverage(mu_cell) ctx.varsOf(eq).update(mu_face = mu_face, mu_cell = mu_cell) mesh = mesh1d(2e-6) def solve(mu_ions, mobility_model, additional_params=None, voltage=5.): model = models.BaseModel() models.std.electronic_device(model, mesh, 'pn', mobility_model = mobility_model) cation, anion, initial_salt = models.std.add_ions(model, mesh, zc=1, za=-1) model.setUp() xinit = initial_salt(0.1e27) params = {'T': 300., 'electron.energy': 0., 'electron.N0': 0.3e27, 'hole.energy': -2., 'hole.N0': 0.3e27, 'electrode0.workfunction': 2., 'electrode1.workfunction': 0., 'electrode0.voltage': voltage, 'electrode1.voltage': 0, 'cation.mu': mu_ions, 'anion.mu': mu_ions, 'npi': 0, 'epsilon_r': 3. } if additional_params is not None: params.update(additional_params) c = context(model,x=xinit) c.transient(params, 1, 1e-9) return c def transientplot(data): N0 = 5e27 n = 20 for it, t in enumerate(10*np.linspace(-5, -1, n + 1)): out = data.attime(t).output() c = 1 - (1. - it / n) ncolor = colors.rgb2hex((1,1 - c,1 - c)) pcolor = colors.rgb2hex((1 - c,1 - c,1)) plt.plot(mesh.cells['center'] 1e9,out['electron.c'] / N0,ncolor) plt.plot(mesh.cells['center'] * 1e9,out['hole.c'] / N0,pcolor) testing.store(out['electron.c'], rtol=1e-7, atol=1e-3 * N0) testing.store(out['hole.c'], rtol=1e-7, atol=1e-3 * N0) plt.yscale('log') plt.ylim([1e-5, 1.]) plt.xlabel('$x$ [nm]') plt.ylabel('$c/N_0$') mu_params = {'electron.mu':5e-11,'hole.mu':5e-11} c=solve(5e-11, models.MobilityFromParams(), mu_params) transientplot(c) c=solve(mu_ions=5e-11, mobility_model=CustomMobility()) transientplot(c) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Model and parameters Step2: Results Step3: Concentration dependent mobility
6,933	<ASSISTANT_TASK:> Python Code: import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=False) DO NOT MODIFY THIS CELL def fully_connected(prev_layer, num_units): Create a fully connectd layer with the given layer as input and the given number of neurons. :param prev_layer: Tensor The Tensor that acts as input into this layer :param num_units: int The size of the layer. That is, the number of units, nodes, or neurons. :returns Tensor A new fully connected layer layer = tf.layers.dense(prev_layer, num_units, activation=tf.nn.relu) return layer DO NOT MODIFY THIS CELL def conv_layer(prev_layer, layer_depth): Create a convolutional layer with the given layer as input. :param prev_layer: Tensor The Tensor that acts as input into this layer :param layer_depth: int We'll set the strides and number of feature maps based on the layer's depth in the network. This is not a good way to make a CNN, but it helps us create this example with very little code. :returns Tensor A new convolutional layer strides = 2 if layer_depth % 3 == 0 else 1 conv_layer = tf.layers.conv2d(prev_layer, layer_depth4, 3, strides, 'same', activation=tf.nn.relu) return conv_layer DO NOT MODIFY THIS CELL def train(num_batches, batch_size, learning_rate): # Build placeholders for the input samples and labels inputs = tf.placeholder(tf.float32, [None, 28, 28, 1]) labels = tf.placeholder(tf.float32, [None, 10]) # Feed the inputs into a series of 20 convolutional layers layer = inputs for layer_i in range(1, 20): layer = conv_layer(layer, layer_i) # Flatten the output from the convolutional layers orig_shape = layer.get_shape().as_list() layer = tf.reshape(layer, shape=[-1, orig_shape[1] orig_shape[2] * orig_shape[3]]) # Add one fully connected layer layer = fully_connected(layer, 100) # Create the output layer with 1 node for each logits = tf.layers.dense(layer, 10) # Define loss and training operations model_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels)) train_opt = tf.train.AdamOptimizer(learning_rate).minimize(model_loss) # Create operations to test accuracy correct_prediction = tf.equal(tf.argmax(logits,1), tf.argmax(labels,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # Train and test the network with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for batch_i in range(num_batches): batch_xs, batch_ys = mnist.train.next_batch(batch_size) # train this batch sess.run(train_opt, {inputs: batch_xs, labels: batch_ys}) # Periodically check the validation or training loss and accuracy if batch_i % 100 == 0: loss, acc = sess.run([model_loss, accuracy], {inputs: mnist.validation.images, labels: mnist.validation.labels}) print('Batch: {:>2}: Validation loss: {:>3.5f}, Validation accuracy: {:>3.5f}'.format(batch_i, loss, acc)) elif batch_i % 25 == 0: loss, acc = sess.run([model_loss, accuracy], {inputs: batch_xs, labels: batch_ys}) print('Batch: {:>2}: Training loss: {:>3.5f}, Training accuracy: {:>3.5f}'.format(batch_i, loss, acc)) # At the end, score the final accuracy for both the validation and test sets acc = sess.run(accuracy, {inputs: mnist.validation.images, labels: mnist.validation.labels}) print('Final validation accuracy: {:>3.5f}'.format(acc)) acc = sess.run(accuracy, {inputs: mnist.test.images, labels: mnist.test.labels}) print('Final test accuracy: {:>3.5f}'.format(acc)) # Score the first 100 test images individually. This won't work if batch normalization isn't implemented correctly. correct = 0 for i in range(100): correct += sess.run(accuracy,feed_dict={inputs: [mnist.test.images[i]], labels: [mnist.test.labels[i]]}) print("Accuracy on 100 samples:", correct/100) num_batches = 800 batch_size = 64 learning_rate = 0.002 tf.reset_default_graph() with tf.Graph().as_default(): train(num_batches, batch_size, learning_rate) def fully_connected(prev_layer, num_units, is_training): Create a fully connectd layer with the given layer as input and the given number of neurons. :param prev_layer: Tensor The Tensor that acts as input into this layer :param num_units: int The size of the layer. That is, the number of units, nodes, or neurons. :returns Tensor A new fully connected layer layer = tf.layers.dense(prev_layer, num_units, activation=None, use_bias=False) layer = tf.layers.batch_normalization(layer, training=is_training) layer = tf.nn.relu(layer) return layer def conv_layer(prev_layer, layer_depth, is_training): Create a convolutional layer with the given layer as input. :param prev_layer: Tensor The Tensor that acts as input into this layer :param layer_depth: int We'll set the strides and number of feature maps based on the layer's depth in the network. This is not a good way to make a CNN, but it helps us create this example with very little code. :returns Tensor A new convolutional layer strides = 2 if layer_depth % 3 == 0 else 1 conv_layer = tf.layers.conv2d(prev_layer, layer_depth4, 3, strides, 'same', activation=None) conv_layer = tf.layers.batch_normalization(conv_layer, training=is_training) conv_layer = tf.nn.relu(conv_layer) return conv_layer def train(num_batches, batch_size, learning_rate): # Build placeholders for the input samples and labels inputs = tf.placeholder(tf.float32, [None, 28, 28, 1]) labels = tf.placeholder(tf.float32, [None, 10]) is_training = tf.placeholder(tf.bool) # Feed the inputs into a series of 20 convolutional layers layer = inputs for layer_i in range(1, 20): layer = conv_layer(layer, layer_i, is_training) # Flatten the output from the convolutional layers orig_shape = layer.get_shape().as_list() layer = tf.reshape(layer, shape=[-1, orig_shape[1] orig_shape[2] * orig_shape[3]]) # Add one fully connected layer layer = fully_connected(layer, 100, is_training) # Create the output layer with 1 node for each logits = tf.layers.dense(layer, 10) # Define loss and training operations model_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels)) with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)): train_opt = tf.train.AdamOptimizer(learning_rate).minimize(model_loss) # Create operations to test accuracy correct_prediction = tf.equal(tf.argmax(logits,1), tf.argmax(labels,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # Train and test the network with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for batch_i in range(num_batches): batch_xs, batch_ys = mnist.train.next_batch(batch_size) # train this batch sess.run(train_opt, {inputs: batch_xs, labels: batch_ys, is_training:True}) # Periodically check the validation or training loss and accuracy if batch_i % 100 == 0: loss, acc = sess.run([model_loss, accuracy], {inputs: mnist.validation.images, labels: mnist.validation.labels, is_training: False}) print('Batch: {:>2}: Validation loss: {:>3.5f}, Validation accuracy: {:>3.5f}'.format(batch_i, loss, acc)) elif batch_i % 25 == 0: loss, acc = sess.run([model_loss, accuracy], {inputs: batch_xs, labels: batch_ys, is_training:False}) print('Batch: {:>2}: Training loss: {:>3.5f}, Training accuracy: {:>3.5f}'.format(batch_i, loss, acc)) # At the end, score the final accuracy for both the validation and test sets acc = sess.run(accuracy, {inputs: mnist.validation.images, labels: mnist.validation.labels, is_training:False}) print('Final validation accuracy: {:>3.5f}'.format(acc)) acc = sess.run(accuracy, {inputs: mnist.test.images, labels: mnist.test.labels, is_training:False}) print('Final test accuracy: {:>3.5f}'.format(acc)) # Score the first 100 test images individually. This won't work if batch normalization isn't implemented correctly. correct = 0 for i in range(100): correct += sess.run(accuracy,feed_dict={inputs: [mnist.test.images[i]], labels: [mnist.test.labels[i]], is_training:False}) print("Accuracy on 100 samples:", correct/100) num_batches = 800 batch_size = 64 learning_rate = 0.002 tf.reset_default_graph() with tf.Graph().as_default(): train(num_batches, batch_size, learning_rate) def fully_connected(prev_layer, num_units, is_training): Create a fully connectd layer with the given layer as input and the given number of neurons. :param prev_layer: Tensor The Tensor that acts as input into this layer :param num_units: int The size of the layer. That is, the number of units, nodes, or neurons. :returns Tensor A new fully connected layer layer = tf.layers.dense(prev_layer, num_units, activation=None, use_bias=False) gamma = tf.Variable(tf.ones([num_units])) beta = tf.Variable(tf.zeros([num_units])) pop_mean = tf.Variable(tf.zeros([num_units]), trainable=False) pop_variance = tf.Variable(tf.ones([num_units]), trainable=False) epsilon = 1e-3 def b_training(): batch_mean, batch_variance = tf.nn.moments(layer, [0]) decay = 0.99 train_mean = tf.assign(pop_mean, pop_mean * decay + batch_mean * (1 - decay)) train_variance = tf.assign(pop_variance, pop_variance * decay + batch_variance * (1 - decay)) with tf.control_dependencies([train_mean, train_variance]): return tf.nn.batch_normalization(layer, batch_mean, batch_variance, beta, gamma, epsilon) def b_infering(): return tf.nn.batch_normalization(layer, pop_mean, pop_variance, beta, gamma, epsilon) bnorm_layer = tf.cond(is_training, b_training, b_infering) return tf.nn.relu(bnorm_layer) def conv_layer(prev_layer, layer_depth, is_training): Create a convolutional layer with the given layer as input. :param prev_layer: Tensor The Tensor that acts as input into this layer :param layer_depth: int We'll set the strides and number of feature maps based on the layer's depth in the network. This is not a good way to make a CNN, but it helps us create this example with very little code. :returns Tensor A new convolutional layer strides = 2 if layer_depth % 3 == 0 else 1 in_channels = prev_layer.get_shape().as_list()[3] out_channels = layer_depth4 weights = tf.Variable( tf.truncated_normal([3, 3, in_channels, out_channels], stddev=0.05)) layer = tf.nn.conv2d(prev_layer, weights, strides=[1,strides, strides, 1], padding='SAME') gamma = tf.Variable(tf.ones([out_channels])) beta = tf.Variable(tf.zeros([out_channels])) pop_mean = tf.Variable(tf.zeros([out_channels]), trainable=False) pop_variance = tf.Variable(tf.ones([out_channels]), trainable=False) epsilon = 1e-3 def b_training(): batch_mean, batch_variance = tf.nn.moments(layer, [0,1,2], keep_dims=False) decay = 0.99 train_mean = tf.assign(pop_mean, pop_mean decay + batch_mean * (1 - decay)) train_variance = tf.assign(pop_variance, pop_variance * decay + batch_variance * (1 - decay)) with tf.control_dependencies([train_mean, train_variance]): return tf.nn.batch_normalization(layer, batch_mean, batch_variance, beta, gamma, epsilon) def b_infering(): return tf.nn.batch_normalization(layer, pop_mean, pop_variance, beta, gamma, epsilon) bnorm_layer = tf.cond(is_training, b_training, b_infering) return tf.nn.relu(bnorm_layer) def train(num_batches, batch_size, learning_rate): # Build placeholders for the input samples and labels inputs = tf.placeholder(tf.float32, [None, 28, 28, 1]) labels = tf.placeholder(tf.float32, [None, 10]) is_training = tf.placeholder(tf.bool) # Feed the inputs into a series of 20 convolutional layers layer = inputs for layer_i in range(1, 20): layer = conv_layer(layer, layer_i, is_training) # Flatten the output from the convolutional layers orig_shape = layer.get_shape().as_list() layer = tf.reshape(layer, shape=[-1, orig_shape[1] * orig_shape[2] * orig_shape[3]]) # Add one fully connected layer layer = fully_connected(layer, 100, is_training) # Create the output layer with 1 node for each logits = tf.layers.dense(layer, 10) # Define loss and training operations model_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels)) train_opt = tf.train.AdamOptimizer(learning_rate).minimize(model_loss) # Create operations to test accuracy correct_prediction = tf.equal(tf.argmax(logits,1), tf.argmax(labels,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # Train and test the network with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for batch_i in range(num_batches): batch_xs, batch_ys = mnist.train.next_batch(batch_size) # train this batch sess.run(train_opt, {inputs: batch_xs, labels: batch_ys, is_training:True}) # Periodically check the validation or training loss and accuracy if batch_i % 100 == 0: loss, acc = sess.run([model_loss, accuracy], {inputs: mnist.validation.images, labels: mnist.validation.labels, is_training:False}) print('Batch: {:>2}: Validation loss: {:>3.5f}, Validation accuracy: {:>3.5f}'.format(batch_i, loss, acc)) elif batch_i % 25 == 0: loss, acc = sess.run([model_loss, accuracy], {inputs: batch_xs, labels: batch_ys, is_training:False}) print('Batch: {:>2}: Training loss: {:>3.5f}, Training accuracy: {:>3.5f}'.format(batch_i, loss, acc)) # At the end, score the final accuracy for both the validation and test sets acc = sess.run(accuracy, {inputs: mnist.validation.images, labels: mnist.validation.labels, is_training:False}) print('Final validation accuracy: {:>3.5f}'.format(acc)) acc = sess.run(accuracy, {inputs: mnist.test.images, labels: mnist.test.labels, is_training:False}) print('Final test accuracy: {:>3.5f}'.format(acc)) # Score the first 100 test images individually. This won't work if batch normalization isn't implemented correctly. correct = 0 for i in range(100): correct += sess.run(accuracy,feed_dict={inputs: [mnist.test.images[i]], labels: [mnist.test.labels[i]], is_training:False}) print("Accuracy on 100 samples:", correct/100) num_batches = 800 batch_size = 64 learning_rate = 0.002 tf.reset_default_graph() with tf.Graph().as_default(): train(num_batches, batch_size, learning_rate) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step3: Batch Normalization using tf.layers.batch_normalization<a id="example_1"></a> Step6: We'll use the following function to create convolutional layers in our network. They are very basic Step8: Run the following cell, along with the earlier cells (to load the dataset and define the necessary functions). Step10: With this many layers, it's going to take a lot of iterations for this network to learn. By the time you're done training these 800 batches, your final test and validation accuracies probably won't be much better than 10%. (It will be different each time, but will most likely be less than 15%.) Step12: TODO Step13: TODO Step15: With batch normalization, you should now get an accuracy over 90%. Notice also the last line of the output Step17: TODO Step18: TODO
6,934	<ASSISTANT_TASK:> Python Code: from jyquickhelper import add_notebook_menu add_notebook_menu() %matplotlib inline import numpy.random as rnd import numpy N = 2000 bruit1 = rnd.normal(size=(N,)) temps = numpy.arange(N) bruit1[:5], temps[:5] import random bruit2 = numpy.zeros((N,)) for i in range(0, 10): h = random.randint(0, N-1) bruit2[h] = rnd.normal() + 10 serie = [] y = 10 for i in range(N//2+100): serie.append(y + bruit1[i] + 0.0004 * temps[i] + bruit2[i]) if i > 30: y = 0.9 * serie[-2] Y = numpy.array(serie[-1000:]) Y[:5] import matplotlib.pyplot as plt fig, ax = plt.subplots(1, 2, figsize=(12, 4)) ax[0].plot(numpy.arange(len(Y)), Y) ax[1].plot(numpy.arange(800, 900), Y[800:900]) ax[0].set_title("Série temporelle simulée") ax[1].set_title("Même série temporelle simulée"); from pandas import DataFrame df = DataFrame(dict(Y=Y)) df.head() from statsmodels.tsa.tsatools import detrend df["notrend"] = detrend(df.Y) df.head() ax = df.plot() ax.set_title("Sans tendance"); from statsmodels.tsa.stattools import acf cor = acf(df.notrend) fig, ax = plt.subplots(1, 1) ax.plot(cor) ax.set_title("Autocorrélogramme"); from statsmodels.tsa.stattools import pacf pcor = pacf(df.notrend) fig, ax = plt.subplots(1, 1) ax.plot(pcor[1:]) ax.set_title("Autocorrélogramme partiel"); def lagged_ts(serie, lag): dim = serie.shape[0] res = numpy.zeros((dim - lag + 1, lag)) for i in range(lag): res[:, i] = serie[i:dim-lag+i+1] return res lagged_ts(Y, 3) lag = lagged_ts(Y, 60) lag.shape from numpy.linalg import svd # u @ numpy.diag(s) @ vh u, s, vh = svd(lag) u.shape, s.shape, vh.shape d = numpy.zeros((941, 60)) d[:60,:60] = numpy.diag(s) (u @ d @ vh).shape fig, ax = plt.subplots(1,2, figsize=(12,4)) DataFrame(dict(valeur=s)).plot(kind="bar", ax=ax[0]) DataFrame(dict(valeur=s[1:15])).plot(kind="bar", ax=ax[1]) ax[0].set_title("Valeurs propres") ax[1].set_title("Valeurs propres sans la première"); np = 12 fig, ax = plt.subplots(np, 3, figsize=(14, np2)) for n in range(np): i = n if n < 5 else n 5 - 15 d = numpy.zeros((941, 60)) d[i, i] = s[i] X2 = u @ d @ vh pos = 0 #X2.shape[1] - 1 # série reconstruites avec un axe ax[n, 0].plot(X2[:,pos]) ax[n, 1].set_title("i=%d" % i) # série reconstruites avec un axe ax[n, 1].plot(X2[800:850,pos]) ax[n, 1].set_title("i=%d" % i) d = numpy.zeros((941, 60)) d[:i+1, :i+1] = numpy.diag(s[:i+1]) X2 = u @ d @ vh ax[n, 2].plot(X2[800:850,pos]) ax[n, 2].plot(Y[800:850]) ax[n, 2].set_title("-->i=%d + 1" % i) fig, ax = plt.subplots(1, 1, figsize=(5,5)) for i in range(0, 8): ax.plot([0, 5], [i, i], 'k-') if i < 6: ax.plot([i, i], [0, 7], 'k-') if i < 4: ax.text(i + 0.1, 1.5, "Y(t-%d)" % (4-i)) ax.text(i + 0.1, 0.5, "Y(t-%d)" % (3-i)) ax.text(4.1, 1.5, "Y(t)") ax.text(4.05, 0.5, "Y(t+1)=?") plt.axis('off'); d = numpy.zeros((941, 60)) for i in range(0, 30): d[i, i] = s[i] X2 = u @ d @ vh from sklearn.covariance import EllipticEnvelope env = EllipticEnvelope(support_fraction=0.9) env.fit(X2[:,:30]) out = env.predict(X2[:,:30]) fig, ax = plt.subplots(1, 1, figsize=(12,2)) ax.plot((1 - out)/2, "-") ax.set_title("Points aberrants d'une série temporelles."); fig, ax = plt.subplots(1, 1, figsize=(14,2)) ax.plot(X2[:,0], label="serie") ax.plot((1 - out)5, "-", label="outlier") ax.set_title("Points aberrants sur la série reconstruite") ax.legend(); fig, ax = plt.subplots(1, 1, figsize=(14,2)) ax.plot(Y, label="serie") ax.plot((1 - out)5, "-", label="outlier") ax.set_title("Points aberrants sur la série brute") ax.legend(); fig, ax = plt.subplots(1, 1, figsize=(14,2)) outp = env.decision_function(X2[:,:30]) ax.plot(Y, label="serie") ax.plot(outp, "-", label="Proba not outlier") ax.set_title("Points aberrants sur la série brute") ax.legend(); <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Une série articielle Step2: On crée un bruit aberrant. Step3: Autocorrélations Step4: L'autocorrélogramme à proprement parler. Step5: Etant donnée que la série $Y_t$ dépend de $Y_{t-2}$, on observe un pic pour $cor(Y_t, Y_{t-2})d$ et pour tous les $d$ pairs. $cor(Y_t, Y{t-4}) \sim cor(Y_t, Y_{t-2})^2$. On enlève ces effets récursifs en calculant l'autocorrélogramme partiel qui correspond à l'estimation des coefficients d'un modèle autorégressif infini. Step6: SSA Step7: Je me représente la méthode SVD comme une façon de projeter des vecteurs sur l'espace vectoriel constitué des premiers vecteurs propres, à chaque dimension supplémentaire, c'est comme une pièce du puzzle qui s'assemble jusqu'à recomposer l'ensemble. Ce qu'on peut voir aussi comme ceci Step8: La prédiction Step9: Les points aberrants Step10: L'idéal serait d'utiliser une méthode basée sur une ACP. Le plus proche reste le modèle gaussien avec EllipticEnvelope. Step11: On observe des plages contiguës. Cela signifie que d'une valeur aberrante contamine des vecteurs décalées consécutifs de la série $Y$. Il ne reste plus qu'à repérer la valeur incriminée. Step12: Ce qui a l'air de correspondre à la fin des grandes plages. On recommence avec la probabilité d'être un outlier.
6,935	<ASSISTANT_TASK:> Python Code: %matplotlib inline import numpy as np import matplotlib.pyplot as plt from scipy import linalg plt.style.use('ggplot') plt.rc('axes', grid=False) # turn off the background grid for images my_matrix = np.array([[1,2],[1,1]]) print(my_matrix.shape) print(my_matrix) my_matrix_transposed = np.transpose(my_matrix) print(my_matrix_transposed) my_matrix_inverse = linalg.inv(my_matrix) print(my_matrix_inverse) my_matrix_inverse.dot(my_matrix) my_matrix_inverse * my_matrix_inverse A = np.array([[1,2],[1,1]]) print(A) b = np.array([[4],[3]]) print(b) # Solve by inverting A and then mulitply by b linalg.inv(A).dot(b) # Cleaner looking linalg.solve(A,b) A = np.array([[1,3,5],[2,5,1],[2,3,8]]) b = np.array([[10],[8],[3]]) print(linalg.inv(A)) print(linalg.solve(A,b)) import sympy as sym AA = sym.Matrix([[1,3,5],[2,5,1],[2,3,8]]) bb = sym.Matrix([[10],[8],[3]]) print(AA-1) print(AA-1 * bb) %timeit AA*-1 bb %timeit linalg.solve(A,b) print(A) plt.imshow(A, interpolation='nearest', cmap=plt.cm.Blues); I = np.load("test_data.npy") # load in a saved numpy array I.ndim, I.shape, I.dtype print("The minimum value of the array I is {0:.2f}".format(I.min())) print("The maximum value of the array I is {0:.2f}".format(I.max())) print("The mean value of the array I is {0:.2f}".format(I.mean())) print("The standard deviation of the array I is {0:.2f}".format(I.std())) #flatten() collapses n-dimentional data into 1-d plt.hist(I.flatten(),bins=30); II = I + 8 print("The minimum value of the array II is {0:.2f}".format(II.min())) print("The maximum value of the array II is {0:.2f}".format(II.max())) print("The mean value of the array II is {0:.2f}".format(II.mean())) print("The standard deviation of the array II is {0:.2f}".format(II.std())) plt.imshow(I, cmap=plt.cm.gray) plt.colorbar(); fig, ax = plt.subplots(1,5,sharey=True) fig.set_size_inches(12,6) fig.tight_layout() ax[0].imshow(I, cmap=plt.cm.viridis) ax[0].set_xlabel('viridis') ax[1].imshow(I, cmap=plt.cm.hot) ax[1].set_xlabel('hot') ax[2].imshow(I, cmap=plt.cm.magma) ax[2].set_xlabel('magma') ax[3].imshow(I, cmap=plt.cm.spectral) ax[3].set_xlabel('spectral') ax[4].imshow(I, cmap=plt.cm.gray) ax[4].set_xlabel('gray') plt.imsave('Splash.png', I, cmap=plt.cm.gray) # Write the array I to a PNG file Ipng = plt.imread('Splash.png') # Read in the PNG file print("The original data has a min = {0:.2f} and a max = {1:.2f}".format(I.min(), I.max())) print("The PNG file has a min = {0:.2f} and a max = {1:.2f}".format(Ipng.min(), Ipng.max())) X = np.linspace(-5, 5, 500) Y = np.linspace(-5, 5, 500) X, Y = np.meshgrid(X, Y) # turns two 1-d arrays (X, Y) into one 2-d grid Z = np.sqrt(X2+Y2)+np.sin(X2+Y2) Z.min(), Z.max(), Z.mean() from matplotlib.colors import LightSource ls = LightSource(azdeg=0,altdeg=40) shadedfig = ls.shade(Z,plt.cm.copper) fig, ax = plt.subplots(1,3) fig.set_size_inches(12,6) fig.tight_layout() ax[0].imshow(shadedfig) contlevels = [1,2,Z.mean()] ax[1].axis('equal') ax[1].contour(Z,contlevels) ax[2].imshow(shadedfig) ax[2].contour(Z,contlevels); I2 = plt.imread('doctor5.png') print("The image I2 has a shape [height,width] of {0}".format(I2.shape)) print("The image I2 is made up of data of type {0}".format(I2.dtype)) print("The image I2 has a maximum value of {0}".format(I2.max())) print("The image I2 has a minimum value of {0}".format(I2.min())) plt.imshow(I2,cmap=plt.cm.gray); fig, ax = plt.subplots(1,4) fig.set_size_inches(12,6) fig.tight_layout() # You can show just slices of the image - Rememeber: The origin is the upper left corner ax[0].imshow(I2, cmap=plt.cm.gray) ax[0].set_xlabel('Original') ax[1].imshow(I2[0:300,0:100], cmap=plt.cm.gray) ax[1].set_xlabel('[0:300,0:100]') # 300 rows, 100 columns ax[2].imshow(I2[:,0:100], cmap=plt.cm.gray) # ":" = whole range ax[2].set_xlabel('[:,0:100]') # all rows, 100 columns ax[3].imshow(I2[:,::-1], cmap=plt.cm.gray); ax[3].set_xlabel('[:,::-1]') # reverse the columns fig, ax = plt.subplots(1,2) fig.set_size_inches(12,6) fig.tight_layout() CutLine = 300 ax[0].imshow(I2, cmap=plt.cm.gray) ax[0].hlines(CutLine, 0, 194, color='b', linewidth=3) ax[1].plot(I2[CutLine,:], color='b', linewidth=3) ax[1].set_xlabel("X Value") ax[1].set_ylabel("Pixel Value") from scipy import ndimage fig, ax = plt.subplots(1,5) fig.set_size_inches(14,6) fig.tight_layout() ax[0].imshow(I2, cmap=plt.cm.gray) I3 = ndimage.rotate(I2,45,cval=0.75) # cval is the value to set pixels outside of image ax[1].imshow(I3, cmap=plt.cm.gray) # Rotate and reshape I4 = ndimage.rotate(I2,45,reshape=False,cval=0.75) # Rotate and do not reshape ax[2].imshow(I4, cmap=plt.cm.gray) I5 = ndimage.shift(I2,(10,30),cval=0.75) # Shift image ax[3].imshow(I5, cmap=plt.cm.gray) I6 = ndimage.gaussian_filter(I2,5) # Blur image ax[4].imshow(I6, cmap=plt.cm.gray); import astropy.io.fits as fits x = fits.open('bsg01.fits') x.info() x[0].header xd = x[0].data print("The image x has a shape [height,width] of {0}".format(xd.shape)) print("The image x is made up of data of type {0}".format(xd.dtype)) print("The image x has a maximum value of {0}".format(xd.max())) print("The image x has a minimum value of {0}".format(xd.min())) fig, ax = plt.subplots(1,2) fig.set_size_inches(12,6) fig.tight_layout() ax[0].imshow(xd,cmap=plt.cm.gray) ax[1].hist(xd.flatten(),bins=20); CopyData = np.copy(xd) CutOff = 40 mask = np.where(CopyData > CutOff) CopyData[mask] = 50 # You can not just throw data away, you have to set it to something. fig, ax = plt.subplots(1,2) fig.set_size_inches(12,6) fig.tight_layout() ax[0].imshow(CopyData,cmap=plt.cm.gray) ax[1].hist(CopyData.flatten(),bins=20); fig, ax = plt.subplots(1,2) fig.set_size_inches(12,6) fig.tight_layout() ax[0].imshow(xd, cmap=plt.cm.gray) # Open another file 'bsg02.fits' y = fits.open('bsg02.fits') yd = y[0].data ax[1].imshow(yd, cmap=plt.cm.gray); fig, ax = plt.subplots(1,3) fig.set_size_inches(12,6) fig.tight_layout() ax[0].imshow(xd, cmap=plt.cm.gray) ax[1].imshow(yd, cmap=plt.cm.gray) z = xd - yd # Subtract the images pixel by pixel ax[2].imshow(z, cmap=plt.cm.gray); S = fits.open('SolarSpectra.fits') S.info() Data = S[0].data Head = S[0].header Head # The FITS header has the information to make an array of wavelengths Start = Head['CRVAL1'] Number = Head['NAXIS1'] Delta = Head['CDELT1'] End = Start + (Number * Delta) Wavelength = np.arange(Start,End,Delta) fig, ax = plt.subplots(2,1) fig.set_size_inches(11,8.5) fig.tight_layout() # Full spectra ax[0].plot(Wavelength, Data, color='b') ax[0].set_ylabel("Flux") ax[0].set_xlabel("Wavelength [angstroms]") # Just the visible range with the hydrogen Balmer lines ax[1].set_xlim(4000,7000) ax[1].set_ylim(0.6,1.2) ax[1].plot(Wavelength, Data, color='b') ax[1].set_ylabel("Flux") ax[1].set_xlabel("Wavelength [angstroms]") H_Balmer = [6563,4861,4341,4102,3970,3889,3835,3646] ax[1].vlines(H_Balmer,0,2, color='r', linewidth=3, alpha = 0.25) redfilter = plt.imread('sphereR.jpg') redfilter.shape,redfilter.dtype redfilter = plt.imread('sphereR.jpg')[:,:,0] redfilter.shape,redfilter.dtype plt.imshow(redfilter,cmap=plt.cm.gray); greenfilter = plt.imread('sphereG.jpg')[:,:,0] bluefilter = plt.imread('sphereB.jpg')[:,:,0] fig, ax = plt.subplots(1,3) fig.set_size_inches(12,3) fig.tight_layout() ax[0].set_title("Red Filter") ax[1].set_title("Green Filter") ax[2].set_title("Blue Filter") ax[0].imshow(redfilter,cmap=plt.cm.gray) ax[1].imshow(greenfilter,cmap=plt.cm.gray) ax[2].imshow(bluefilter,cmap=plt.cm.gray); rgb = np.zeros((480,640,3),dtype='uint8') print(rgb.shape, rgb.dtype) plt.imshow(rgb,cmap=plt.cm.gray); rgb[:,:,0] = redfilter rgb[:,:,1] = greenfilter rgb[:,:,2] = bluefilter fig, ax = plt.subplots(1,4) fig.set_size_inches(14,3) fig.tight_layout() ax[0].set_title("Red Filter") ax[1].set_title("Green Filter") ax[2].set_title("Blue Filter") ax[3].set_title("All Filters Stacked") ax[0].imshow(redfilter,cmap=plt.cm.gray) ax[1].imshow(greenfilter,cmap=plt.cm.gray) ax[2].imshow(bluefilter,cmap=plt.cm.gray) ax[3].imshow(rgb,cmap=plt.cm.gray); print("The image rgb has a shape [height,width] of {0}".format(rgb.shape)) print("The image rgb is made up of data of type {0}".format(rgb.dtype)) print("The image rgb has a maximum value of {0}".format(rgb.max())) print("The image rgb has a minimum value of {0}".format(rgb.min())) rgb[:,:,0] = redfilter * 1.5 plt.imshow(rgb) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Let us work with the matrix Step2: numpy matrix multiply uses the dot() function Step3: Caution the * will just multiply the matricies on an element-by-element basis Step4: Solving system of linear equations Step5: System of 3 equations example (Numpy) Step6: System of 3 equations example (SymPy) - Python's Symbolic Math Package Step7: SymPy is slower than NumPy Step8: Images are just 2-d arrays - imshow will display 2-d arrays as images Step9: Read in some data Step10: Math on images applies to every value (pixel) Step11: Show the image represenation of I with a colorbar Step12: Colormap reference Step13: WARNING! Common image formats DO NOT preserve dynamic range of original data!! Step14: Creating images from math Step15: Fancy Image Display Step16: Reading in images (imread) - Common Formats Step17: Images are just arrays that can be sliced. Step18: Simple image manipulation Step19: ndimage can do much more Step20: You can use masks on images Step21: You can add and subtract images Step22: The two images above may look the same but they are not! Subtracting the two images reveals the truth. Step23: FITS Tables - An astronomical example Step24: Pseudocolor - All color astronomy images are fake. Step25: We just want to read in one of the three channels Step26: Need to create a blank 3-d array to hold all of the images Step27: Fill the array with the filtered images
6,936	<ASSISTANT_TASK:> Python Code: import pandas as pd # data package import matplotlib.pyplot as plt # graphics import sys # system module, used to get Python version import os # operating system tools (check files) import datetime as dt # date tools, used to note current date import numpy as np # these are new import requests, io # internet and input tools import numpy as np %matplotlib inline print('\nPython version: ', sys.version) print('Pandas version: ', pd.__version__) print('Requests version: ', requests.__version__) print("Today's date:", dt.date.today()) #import scorecard data from csv file on the web url = 'https://collegescorecard.ed.gov/downloads/Most-Recent-Cohorts-Scorecard-Elements.csv' r = requests.get(url) #read csv data, create a dataframe with the csv data scorecard = pd.read_csv(url, na_values=['PrivacySuppressed']) scorecard1 = pd.DataFrame(scorecard) #create a new data frame with only the relevant columns newscore = scorecard1[['INSTNM', 'CITY', 'STABBR', 'PREDDEG', 'CONTROL', 'LOCALE', 'SAT_AVG', 'UGDS', 'UGDS_BLACK', 'UGDS_ASIAN', 'UGDS_HISP', 'UGDS_WHITE', 'md_earn_wne_p10', 'PCTFLOAN', 'gt_25k_p6', 'GRAD_DEBT_MDN_SUPP', 'GRAD_DEBT_MDN10YR_SUPP']] #rename the columns so we can read them easily newscore2 = newscore.rename(columns={'INSTNM': 'UNIVERSITY', 'STABBR': 'STATE', 'PREDDEG': 'DEGTYPE', 'md_earn_wne_p10': 'MEDIAN EARNINGS, 10 YEARS OUT', 'gt_25k_p6': 'PERCENT EARNING 25k+, 6 YRS OUT', 'GRAD_DEBT_MDN_SUPP': 'MEDIAN DEBT', 'GRAD_DEBT_MDN10YR_SUPP': 'MEDIAN 10-YR MONTHLY PAYMENTS'}) topmedian = newscore2.sort('MEDIAN EARNINGS, 10 YEARS OUT', ascending=False).head(20) topmedian2 = topmedian.set_index('UNIVERSITY') topmedian3 = topmedian2.sort('MEDIAN EARNINGS, 10 YEARS OUT', ascending=True) topmedianalt = newscore2.sort('MEDIAN EARNINGS, 10 YEARS OUT', ascending=True).head(20) topmedianalt2 = topmedianalt.set_index('UNIVERSITY') topmedianalt3 = topmedianalt2.sort('MEDIAN EARNINGS, 10 YEARS OUT', ascending=False) plt.style.use('fivethirtyeight') fig, ax = plt.subplots() fig.set_size_inches(14, 8) ax.spines['left'].set_position(('outward', 10)) ax.spines['bottom'].set_position(('outward', 10)) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.yaxis.set_ticks_position('left') ax.xaxis.set_ticks_position('bottom') ax.set_xlim(0,90000) ax.set_ylim(0,50000) x = newscore2['MEDIAN EARNINGS, 10 YEARS OUT'] y = newscore2['MEDIAN DEBT'] colors = 'CONTROL'== 1 == 'red', 2 == 'blue', 3 =='green' Public = plt.scatter(newscore2[newscore["CONTROL"] == 1]["MEDIAN EARNINGS, 10 YEARS OUT"], newscore2[newscore["CONTROL"] == 1]["MEDIAN DEBT"], s=newscore2['UGDS']/10, c="blue", alpha=0.9) Private_nonprofit = plt.scatter(newscore2[newscore["CONTROL"] == 2]["MEDIAN EARNINGS, 10 YEARS OUT"], newscore2[newscore["CONTROL"] == 2]["MEDIAN DEBT"], s=newscore2['UGDS']/10, c="lightblue", alpha=0.9) Private_forprofit = plt.scatter(newscore2[newscore["CONTROL"] == 3]["MEDIAN EARNINGS, 10 YEARS OUT"], newscore2[newscore["CONTROL"] == 3]["MEDIAN DEBT"], s=newscore2['UGDS']/10, c="darkblue", alpha=0.9) plt.legend((Public, Private_nonprofit, Private_forprofit), ('Public', 'Private nonprofit', 'Private forprofit'), scatterpoints=1, markerscale=.1, loc='upper left', ncol=1, fontsize=14) plt.xlabel('Median earnings', size=14) plt.ylabel('Median debt', size=14) plt.title('Debt to Earnings by University', fontsize=24) plt.show() plt.style.use('fivethirtyeight') fig, ax = plt.subplots() fig.set_size_inches(14, 6) ax.spines['left'].set_position(('outward', 10)) ax.spines['bottom'].set_position(('outward', 10)) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.yaxis.set_ticks_position('left') ax.xaxis.set_ticks_position('bottom') ax.set_xlim(0,100000) ax.set_ylim(0,600) x = newscore2['MEDIAN EARNINGS, 10 YEARS OUT'] y = newscore2['MEDIAN DEBT'] Urban = plt.scatter(newscore2[newscore["LOCALE"] == 12]["MEDIAN EARNINGS, 10 YEARS OUT"], newscore2[newscore["LOCALE"] == 12]["MEDIAN 10-YR MONTHLY PAYMENTS"], s=newscore2['UGDS']/15, c="maroon", alpha=0.9) plt.scatter(newscore2[newscore["LOCALE"] == 13]["MEDIAN EARNINGS, 10 YEARS OUT"], newscore2[newscore["LOCALE"] == 13]["MEDIAN 10-YR MONTHLY PAYMENTS"], s=newscore2['UGDS']/15, c="maroon", alpha=0.9) plt.scatter(newscore2[newscore["LOCALE"] == 21]["MEDIAN EARNINGS, 10 YEARS OUT"], newscore2[newscore["LOCALE"] == 21]["MEDIAN 10-YR MONTHLY PAYMENTS"], s=newscore2['UGDS']/15, c="orange", alpha=0.9) Suburban = plt.scatter(newscore2[newscore["LOCALE"] == 22]["MEDIAN EARNINGS, 10 YEARS OUT"], newscore2[newscore["LOCALE"] == 22]["MEDIAN 10-YR MONTHLY PAYMENTS"], s=newscore2['UGDS']/15, c="orange", alpha=0.9) plt.scatter(newscore2[newscore["LOCALE"] == 23]["MEDIAN EARNINGS, 10 YEARS OUT"], newscore2[newscore["LOCALE"] == 23]["MEDIAN 10-YR MONTHLY PAYMENTS"], s=newscore2['UGDS']/15, c="orange", alpha=0.9) Rural = plt.scatter(newscore2[newscore["LOCALE"] == 31]["MEDIAN EARNINGS, 10 YEARS OUT"], newscore2[newscore["LOCALE"] == 31]["MEDIAN 10-YR MONTHLY PAYMENTS"], s=newscore2['UGDS']/15, c="red", alpha=0.9) plt.scatter(newscore2[newscore["LOCALE"] == 32]["MEDIAN EARNINGS, 10 YEARS OUT"], newscore2[newscore["LOCALE"] == 32]["MEDIAN 10-YR MONTHLY PAYMENTS"], s=newscore2['UGDS']/15, c="red", alpha=0.9) plt.scatter(newscore2[newscore["LOCALE"] == 33]["MEDIAN EARNINGS, 10 YEARS OUT"], newscore2[newscore["LOCALE"] == 33]["MEDIAN 10-YR MONTHLY PAYMENTS"], s=newscore2['UGDS']/15, c="red", alpha=0.9) plt.scatter(newscore2[newscore["LOCALE"] == 41]["MEDIAN EARNINGS, 10 YEARS OUT"], newscore2[newscore["LOCALE"] == 41]["MEDIAN 10-YR MONTHLY PAYMENTS"], s=newscore2['UGDS']/15, c="red", alpha=0.9) plt.scatter(newscore2[newscore["LOCALE"] == 42]["MEDIAN EARNINGS, 10 YEARS OUT"], newscore2[newscore["LOCALE"] == 42]["MEDIAN 10-YR MONTHLY PAYMENTS"], s=newscore2['UGDS']/15, c="red", alpha=0.9) plt.scatter(newscore2[newscore["LOCALE"] == 43]["MEDIAN EARNINGS, 10 YEARS OUT"], newscore2[newscore["LOCALE"] == 43]["MEDIAN 10-YR MONTHLY PAYMENTS"], s=newscore2['UGDS']/15, c="red", alpha=0.9) plt.legend((Urban, Suburban, Rural), ('Urban', 'Suburban', 'Rural'), scatterpoints=1, markerscale=.1, loc='upper left', ncol=1, fontsize=14) plt.xlabel('Median earnings', size=14) plt.ylabel('Median 10-yr monthly payments - debt', size=14) plt.title('Debt to Earnings by Locale (Urban, Suburban, Rural)', fontsize=16) plt.show() plt.style.use('fivethirtyeight') fig, ax = plt.subplots() topmedian3['MEDIAN EARNINGS, 10 YEARS OUT'].plot(kind='barh', ax=ax, color="lightgreen") fig.set_size_inches(14, 10) ax.set_xlabel('Median Earnings') ax.set_title('Top 20 Universities with Highest Median Earnings (10 years post graduation)') ax.spines['left'].set_position(('outward', 10)) ax.spines['bottom'].set_position(('outward', 10)) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.yaxis.set_ticks_position('left') ax.xaxis.set_ticks_position('bottom') plt.style.use('fivethirtyeight') fig, ax = plt.subplots() topmedianalt3['MEDIAN EARNINGS, 10 YEARS OUT'].plot(kind='barh', ax=ax, color="orange") fig.set_size_inches(14, 10) ax.set_xlabel('Median Earnings') ax.set_title('20 Universities with the Lowest Median Earnings (10 years post graduation)') ax.spines['left'].set_position(('outward', 10)) ax.spines['bottom'].set_position(('outward', 10)) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.yaxis.set_ticks_position('left') ax.xaxis.set_ticks_position('bottom') inst_summary = pd.pivot_table(newscore2, index=['CONTROL'], values=['MEDIAN EARNINGS, 10 YEARS OUT', 'MEDIAN DEBT'], aggfunc=np.sum) inst_summary = inst_summary.reset_index() plt.style.use('fivethirtyeight') fig, ax = plt.subplots() fig.set_size_inches(12, 6) n_groups = 3 index = np.arange(n_groups) bar_width = 0.5 opacity = 1.0 ax.bar(inst_summary['CONTROL'], inst_summary['MEDIAN EARNINGS, 10 YEARS OUT']/inst_summary['MEDIAN DEBT'], bar_width, alpha=opacity, align='center', color='lightblue') ax.set_title('Median Earnings to Debt Ratio by Institution Type', fontsize=16) ax.set_xlabel('Institution Type', fontsize=14) ax.set_ylabel('Median Earnings to Debt Ratio', fontsize=14) plt.tick_params(axis='both', which='major', labelsize=12) # Tufte-like axes ax.spines['left'].set_position(('outward', 10)) ax.spines['bottom'].set_position(('outward', 10)) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) ax.yaxis.set_ticks_position('left') ax.xaxis.set_ticks_position('bottom') ax.set_xticks(index + bar_width*2) ax.set_xticklabels(('Public', 'Private Non Profit', 'Private For Profit')) ax.xaxis.grid(False) plt.show() <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Debt to Earnings by University Step2: Debt to Earnings by Locale Step3: Top 20 Universities by Median Earnings Step4: 20 Universities with the Lowest Median Earnings
6,937	<ASSISTANT_TASK:> Python Code: import numpy as np import scipy.integrate as integrate from scipy.stats import norm import matplotlib.pyplot as plt def f_YgivenX(y,x,sigman): return np.exp(-((y-x)*2)/(2sigman*2))/np.sqrt(2np.pi)/sigman def f_Y(y,sigman): return 0.5(f_YgivenX(y,+1,sigman)+f_YgivenX(y,-1,sigman)) def integrand(y, sigman): value = f_Y(y,sigman) if value < 1e-20: return_value = 0 else: return_value = value np.log2(value) return return_value def C_BIAWGN(sigman): # numerical integration of the h(Y) part integral = integrate.quad(integrand, -np.inf, np.inf, args=(sigman))[0] # take into account h(Y\|X) return -integral - 0.5np.log2(2np.pinp.exp(1)sigman*2) def V_integrand(z, C, sigman): sigmanq = np.square(sigman) m1 = np.square(1 - np.log2(1 + np.exp(-2/sigmanq + 2np.sqrt(2)z/sigman)) - C) m2 = np.exp(-np.square(z)) if np.isinf(m1) or np.isinf(m2): value = 0 else: value = m1m2 return value # compute the dispersion using numerical integration def V_BIAWGN(C, sigman): integral = integrate.quad(V_integrand, -np.inf, np.inf, args=(C,sigman))[0] return integral/np.sqrt(np.pi) # Alternative implementation using Gauss-Hermite Quadrature x_GH, w_GH = np.polynomial.hermite.hermgauss(40) def V_BIAWGN_GH(C, sigman): integral = sum(w_GH * [np.square(1-np.log2(1 + np.exp(-2/np.square(sigman) + 2np.sqrt(2)xi/sigman)) - C) for xi in x_GH]) return integral / np.sqrt(np.pi) def get_Pe_finite_length(n, r, sigman): # compute capacity C = C_BIAWGN(sigman) # compute dispersion V = V_BIAWGN_GH(C, sigman) # Q-function is "norm.sf" (survival function) return norm.sf((n(C-r) + 0.5np.log2(n))/np.sqrt(nV)) esno_dB_range = np.linspace(-4,3,100) esno_lin_range = [10(esno_db/10) for esno_db in esno_dB_range] # compute sigma_n sigman_range = [np.sqrt(1/2/esno_lin) for esno_lin in esno_lin_range] capacity_BIAWGN = [C_BIAWGN(sigman) for sigman in sigman_range] Pe_BIAWGN_r12_n100 = [get_Pe_finite_length(100, 0.5, sigman) for sigman in sigman_range] Pe_BIAWGN_r12_n500 = [get_Pe_finite_length(500, 0.5, sigman) for sigman in sigman_range] Pe_BIAWGN_r12_n1000 = [get_Pe_finite_length(1000, 0.5, sigman) for sigman in sigman_range] Pe_BIAWGN_r12_n5000 = [get_Pe_finite_length(5000, 0.5, sigman) for sigman in sigman_range] fig = plt.figure(1,figsize=(10,7)) plt.semilogy(esno_dB_range, Pe_BIAWGN_r12_n100) plt.semilogy(esno_dB_range, Pe_BIAWGN_r12_n500) plt.semilogy(esno_dB_range, Pe_BIAWGN_r12_n1000) plt.semilogy(esno_dB_range, Pe_BIAWGN_r12_n5000) plt.axvspan(-4, -2.83, alpha=0.5, color='gray') plt.axvline(x=-2.83, color='k') plt.ylim((1e-8,1)) plt.xlim((-4,2)) plt.xlabel('$E_s/N_0$ (dB)', fontsize=16) plt.ylabel('$P_e$', fontsize=16) plt.legend(['$n = 100$', '$n=500$','$n=1000$', '$n=5000$'], fontsize=16) plt.text(-3.2, 1e-4, 'Capacity limit', {'color': 'k', 'fontsize': 20, 'rotation': -90}) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.grid(True) #plt.savefig('BI_AWGN_Pe_R12.pdf',bbox_inches='tight') #specify esno esno = -2.83 n_range = np.linspace(10,2000,100) sigman = np.sqrt(0.510*(-esno/10)) C = C_BIAWGN(sigman) V = V_BIAWGN_GH(C, sigman) r_Pe_1em3 = [C - np.sqrt(V/n)norm.isf(1e-3) + 0.5np.log2(n)/n for n in n_range] r_Pe_1em6 = [C - np.sqrt(V/n)norm.isf(1e-6) + 0.5np.log2(n)/n for n in n_range] r_Pe_1em9 = [C - np.sqrt(V/n)norm.isf(1e-9) + 0.5*np.log2(n)/n for n in n_range] fig = plt.figure(1,figsize=(10,7)) plt.plot(n_range, r_Pe_1em3) plt.plot(n_range, r_Pe_1em6) plt.plot(n_range, r_Pe_1em9) plt.axhline(y=C, color='k') plt.ylim((0,0.55)) plt.xlim((0,2000)) plt.xlabel('Length $n$', fontsize=16) plt.ylabel('Rate $r$ (bit/channel use)', fontsize=16) plt.legend(['$P_e = 10^{-3}$', '$P_e = 10^{-6}$','$P_e = 10^{-9}$', '$C$'], fontsize=16) plt.xticks(fontsize=14) plt.yticks(fontsize=14) plt.grid(True) #plt.savefig('BI_AWGN_r_esno_m283.pdf',bbox_inches='tight') <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Conditional pdf $f_{Y\|X}(y\|x)$ for a channel with noise variance (per dimension) $\sigma_n^2$. This is merely the Gaussian pdf with mean $x$ and variance $\sigma_n^2$ Step2: Output pdf $f_Y(y) = \frac12[f_{Y\|X}(y\|X=+1)+f_{Y\|X}(y\|X=-1)]$ Step3: This is the function we like to integrate, $f_Y(y)\cdot\log_2(f_Y(y))$. We need to take special care of the case when the input is 0, as we defined $0\cdot\log_2(0)=0$, which is usually treated as "nan" Step4: Compute the capacity using numerical integration. We have Step5: Compute the dispersion of the BI-AWGN channel, which is given by (see, e.g., [1]). This is a Step6: The finite-length capacity for the BI-AWGN channel is given by Step7: Show finite length capacity estimates for some codes of different lengths $n$ Step8: Different representation, for a given channel (and here, we pick $E_s/N_0 = -2.83$ dB), show the rate the code should at most have to allow for decoding with an error rate $P_e$ (here we specify different $P_e$) if a certain length $n$ is available.
6,938	<ASSISTANT_TASK:> Python Code: def sum_p(X): y = 0 for x_i in range(int(X)): y += x_i return y from numba import jit @jit def sum_j(X): y = 0 for x_i in range(int(X)): y += x_i return y import os import time import pandas as pd import matplotlib %matplotlib inline # Different platforms require different functions to properly measure current timestamp: if os.name == 'nt': now = time.clock else: now = time.time def run_benchmarks(functions, call_parameters, num_times, logy=False, logx=False): # Executes one function several times and measure performances: def _apply_function(function, num_times): for j in range(num_times): t_0 = now() y = function(*call_parameters) duration = (now() - t_0) yield float(duration) def _name(function): return '${' + function.__name__ + '(x)}$' # Execute all functions the requested number of times and collects durations: def _apply_functions(functions, num_times): for function in functions: yield pd.Series(_apply_function(function, num_times), name=_name(function)) # Collects and plots the results: df = pd.concat(_apply_functions(functions, num_times), axis=1) ax = df.plot(figsize=(10,5), logy=logy, logx=logx, title='$T[f(x)]$ in seconds', style='o-') run_benchmarks(functions=[sum_p, sum_j], call_parameters=(10000000,), num_times=5, logy=True) # Logarithmic scale run_benchmarks(functions=[sum_j], call_parameters=(1000000000000000.,), num_times=5, logy=True) # Logarithmic scale from numba import jit @jit def sum_j(x): y = 0. x_i = 0. while x_i < x: y += x_i x_i += 1. return y %load_ext Cython %%cython def sum_c(double x): cdef double y = 0. cdef double x_i = 0. while x_i < x: y += x_i x_i += 1. return y run_benchmarks(functions=[sum_j, sum_c], call_parameters=(1000000000.,), num_times=10) %%cython --annotate def sum_c(double x): cdef double y = 0. cdef double x_i = 0. while x_i < x: y += x_i x_i += 1. return y <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Then we define $sum_j(x)$ that is identical but just with decorator @jit in the definition. Step2: Lets benchmark them! Step3: Benchmark results Step4: Numba caching Step5: Numba JIT functionality works in the following way Step6: About Cython Step7: The numba jitted function is comparable with the cythonized one, lets check what was the C code cython used, just to give us an idea of the efficiency of the code generated.
6,939	<ASSISTANT_TASK:> Python Code: data_in_shape = (3, 5) L = ZeroPadding1D(padding=1) layer_0 = Input(shape=data_in_shape) layer_1 = L(layer_0) model = Model(inputs=layer_0, outputs=layer_1) # set weights to random (use seed for reproducibility) np.random.seed(240) data_in = 2 * np.random.random(data_in_shape) - 1 result = model.predict(np.array([data_in])) data_out_shape = result[0].shape data_in_formatted = format_decimal(data_in.ravel().tolist()) data_out_formatted = format_decimal(result[0].ravel().tolist()) print('') print('in shape:', data_in_shape) print('in:', data_in_formatted) print('out shape:', data_out_shape) print('out:', data_out_formatted) DATA['convolutional.ZeroPadding1D.0'] = { 'input': {'data': data_in_formatted, 'shape': data_in_shape}, 'expected': {'data': data_out_formatted, 'shape': data_out_shape} } data_in_shape = (4, 4) L = ZeroPadding1D(padding=3) layer_0 = Input(shape=data_in_shape) layer_1 = L(layer_0) model = Model(inputs=layer_0, outputs=layer_1) # set weights to random (use seed for reproducibility) np.random.seed(241) data_in = 2 * np.random.random(data_in_shape) - 1 result = model.predict(np.array([data_in])) data_out_shape = result[0].shape data_in_formatted = format_decimal(data_in.ravel().tolist()) data_out_formatted = format_decimal(result[0].ravel().tolist()) print('') print('in shape:', data_in_shape) print('in:', data_in_formatted) print('out shape:', data_out_shape) print('out:', data_out_formatted) DATA['convolutional.ZeroPadding1D.1'] = { 'input': {'data': data_in_formatted, 'shape': data_in_shape}, 'expected': {'data': data_out_formatted, 'shape': data_out_shape} } data_in_shape = (4, 4) L = ZeroPadding1D(padding=(3,2)) layer_0 = Input(shape=data_in_shape) layer_1 = L(layer_0) model = Model(inputs=layer_0, outputs=layer_1) # set weights to random (use seed for reproducibility) np.random.seed(242) data_in = 2 * np.random.random(data_in_shape) - 1 result = model.predict(np.array([data_in])) data_out_shape = result[0].shape data_in_formatted = format_decimal(data_in.ravel().tolist()) data_out_formatted = format_decimal(result[0].ravel().tolist()) print('') print('in shape:', data_in_shape) print('in:', data_in_formatted) print('out shape:', data_out_shape) print('out:', data_out_formatted) DATA['convolutional.ZeroPadding1D.2'] = { 'input': {'data': data_in_formatted, 'shape': data_in_shape}, 'expected': {'data': data_out_formatted, 'shape': data_out_shape} } import os filename = '../../../test/data/layers/convolutional/ZeroPadding1D.json' if not os.path.exists(os.path.dirname(filename)): os.makedirs(os.path.dirname(filename)) with open(filename, 'w') as f: json.dump(DATA, f) print(json.dumps(DATA)) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: [convolutional.ZeroPadding1D.1] padding 3 on 4x4 input Step2: [convolutional.ZeroPadding1D.2] padding (3,2) on 4x4 input Step3: export for Keras.js tests
6,940	<ASSISTANT_TASK:> Python Code: #Begin spark session from pyspark.sql import SparkSession spark = SparkSession.builder.getOrCreate() #Create pysplice context. Allows you to create a Spark dataframe using our Native Spark DataSource from splicemachine.spark import PySpliceContext splice = PySpliceContext(spark) #Initialize our Feature Store API from splicemachine.features import FeatureStore from splicemachine.features.constants import FeatureType fs = FeatureStore(splice) #Initialize MLFlow from splicemachine.mlflow_support import * mlflow.register_feature_store(fs) mlflow.register_splice_context(splice) %%sql SELECT ltv.CUSTOMERID, ((w.WEEK_END_DATE - ltv.CUSTOMER_START_DATE)/ 7) CUSTOMERWEEK, CAST(w.WEEK_END_DATE as TIMESTAMP) CUSTOMER_TS, ltv.CUSTOMER_LIFETIME_VALUE as CUSTOMER_LTV FROM retail_rfm.weeks w --splice-properties useSpark=True INNER JOIN twimlcon_fs.customer_lifetime ltv ON w.WEEK_END_DATE >= ltv.CUSTOMER_START_DATE AND w.WEEK_END_DATE <= ltv.CUSTOMER_START_DATE + 28 --only first 4 weeks ORDER BY 1,2 {limit 8} ; sql = SELECT ltv.CUSTOMERID, ((w.WEEK_END_DATE - ltv.CUSTOMER_START_DATE)/ 7) CUSTOMERWEEK, CAST(w.WEEK_END_DATE as TIMESTAMP) CUSTOMER_TS, ltv.CUSTOMER_LIFETIME_VALUE as CUSTOMER_LTV FROM retail_rfm.weeks w --splice-properties useSpark=True INNER JOIN twimlcon_fs.customer_lifetime ltv ON w.WEEK_END_DATE > ltv.CUSTOMER_START_DATE AND w.WEEK_END_DATE <= ltv.CUSTOMER_START_DATE + 28 --only first 4 weeks pks = ['CUSTOMERID','CUSTOMERWEEK'] # Each unique training row is identified by the customer and their week of spending activity join_keys = ['CUSTOMERID'] # This is the primary key of the Feature Sets that we want to join to fs.create_training_view( 'twimlcon_customer_lifetime_value', sql=sql, primary_keys=pks, join_keys=join_keys, ts_col = 'CUSTOMER_TS', # How we join each unique row with our eventual Features label_col='CUSTOMER_LTV', # The thing we want to predict desc = 'The current (as of queried) lifetime value of each customer per week of being a customer' ) #Spark Dataframe all_features = fs.get_training_set_from_view('twimlcon_customer_lifetime_value') all_features.limit(8).toPandas() #SQL used to generate the Dataframe sql = fs.get_training_set_from_view('twimlcon_customer_lifetime_value',return_sql=True) print(sql) import re # get training set as a SQL statement feats = fs.get_training_view_features('twimlcon_customer_lifetime_value') # Grab only up to 4 weeks of RFM values desired_features = ['CUSTOMER_LIFETIME_DAYS'] + [f.name for f in feats if re.search('_[0-4]W',f.name)] all_features = fs.get_training_set_from_view('twimlcon_customer_lifetime_value', features = desired_features).dropna() top_features, feature_importances = fs.run_feature_elimination( all_features, features=desired_features, label = 'CUSTOMER_LTV', n = 10, verbose=2, step=30, model_type='regression', log_mlflow=True, mlflow_run_name='Feature_Elimination_LTV', return_importances=True ) model_training_df = fs.get_training_set_from_view('twimlcon_customer_lifetime_value', features = top_features).dropna() from splicemachine.notebook import get_mlflow_ui get_mlflow_ui() ############### # SparkML Model ############### from pyspark.ml.regression import LinearRegression, RandomForestRegressor from pyspark.ml.feature import VectorAssembler,StandardScaler from pyspark.ml import Pipeline from pyspark.ml.evaluation import RegressionEvaluator mlflow.set_experiment('Predict Lifetime Value from Initial Customer Activity') run_tags={'project': 'TWIMLcon Demo', 'team': 'INSERT YOUR NAME HERE' } features_list = [f.name for f in top_features] features_str = ','.join(features_list) va = VectorAssembler(inputCols=features_list, outputCol='features_raw') scaler = StandardScaler(inputCol="features_raw", outputCol="features") with mlflow.start_run(run_name = f"Regression LTV", tags = run_tags): lr = LinearRegression(featuresCol = 'features', labelCol = 'CUSTOMER_LTV', maxIter=10, regParam=0.3, elasticNetParam=0.8) #lr = RandomForestRegressor(featuresCol = 'features', labelCol = 'CUSTOMER_LTV') pipeline = Pipeline( stages=[va, scaler, lr]) # log everything mlflow.log_feature_transformations(pipeline) mlflow.log_pipeline_stages(pipeline) #train train,test = model_training_df.randomSplit([0.80,0.20]) model = pipeline.fit(train) predictions = model.transform(test) lr_model = model.stages[-1] print("Coefficients: " + str(lr_model.coefficients)) print("Intercept: " + str(lr_model.intercept)) # log metric pred_evaluator = RegressionEvaluator(predictionCol="prediction", labelCol="CUSTOMER_LTV",metricName="r2") r2 = pred_evaluator.evaluate(predictions) print("R Squared (R2) on test data = %g" % r2) mlflow.log_metric('r2',r2) mlflow.log_model(model) run_id = mlflow.current_run_id() from splicemachine.notebook import get_mlflow_ui get_mlflow_ui() %store features_list %store features_str spark.stop() <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Write any SQL to get your label. The label doesn't have to be apart of the Feature Store Step3: Create a Training View Step4: Easily extract all features Step5: Automatic Feature Selection Step6: Train a Machine Learning Model Step7: Store most important features for use in the next jupyter notebook
6,941	<ASSISTANT_TASK:> Python Code: import os import ml_metadata import tensorflow_data_validation as tfdv import tensorflow_model_analysis as tfma from ml_metadata.metadata_store import metadata_store from ml_metadata.proto import metadata_store_pb2 from tfx.orchestration import metadata from tfx.types import standard_artifacts !python -c "import tfx; print('TFX version: {}'.format(tfx.__version__))" !python -c "import kfp; print('KFP version: {}'.format(kfp.__version__))" import yaml # Set `PATH` to include the directory containing TFX CLI. PATH=%env PATH %env PATH=/home/jupyter/.local/bin:{PATH} %cd pipeline !gsutil ls #TODO: Set your environment resource settings here for GCP_REGION, ARTIFACT_STORE_URI, ENDPOINT, and CUSTOM_SERVICE_ACCOUNT. GCP_REGION = 'us-central1' ARTIFACT_STORE_URI = 'gs://dougkelly-sandbox-kubeflowpipelines-default' #Change ENDPOINT = '60ff837483ecde05-dot-us-central2.pipelines.googleusercontent.com' #Change CUSTOM_SERVICE_ACCOUNT = 'tfx-tuner-caip-service-account@dougkelly-sandbox.iam.gserviceaccount.com' #Change PROJECT_ID = !(gcloud config get-value core/project) PROJECT_ID = PROJECT_ID[0] # Set your resource settings as environment variables. These override the default values in pipeline/config.py. %env GCP_REGION={GCP_REGION} %env ARTIFACT_STORE_URI={ARTIFACT_STORE_URI} %env CUSTOM_SERVICE_ACCOUNT={CUSTOM_SERVICE_ACCOUNT} %env PROJECT_ID={PROJECT_ID} PIPELINE_NAME = 'tfx_covertype_lab_04' MODEL_NAME = 'tfx_covertype_classifier' DATA_ROOT_URI = 'gs://workshop-datasets/covertype/small' CUSTOM_TFX_IMAGE = 'gcr.io/{}/{}'.format(PROJECT_ID, PIPELINE_NAME) RUNTIME_VERSION = '2.3' PYTHON_VERSION = '3.7' USE_KFP_SA=False ENABLE_TUNING=False %env PIPELINE_NAME={PIPELINE_NAME} %env MODEL_NAME={MODEL_NAME} %env DATA_ROOT_URI={DATA_ROOT_URI} %env KUBEFLOW_TFX_IMAGE={CUSTOM_TFX_IMAGE} %env RUNTIME_VERSION={RUNTIME_VERSION} %env PYTHON_VERIONS={PYTHON_VERSION} %env USE_KFP_SA={USE_KFP_SA} %env ENABLE_TUNING={ENABLE_TUNING} !tfx pipeline compile --engine kubeflow --pipeline_path runner.py !tfx pipeline create \ --pipeline_path=runner.py \ --endpoint={ENDPOINT} \ --build_target_image={CUSTOM_TFX_IMAGE} !tfx pipeline update --pipeline_path runner.py --endpoint {ENDPOINT} !tfx run create --pipeline_name={PIPELINE_NAME} --endpoint={ENDPOINT} grpc_host = 'localhost' grpc_port = 7000 connection_config = metadata_store_pb2.MetadataStoreClientConfig() connection_config.host = grpc_host connection_config.port = grpc_port store = metadata_store.MetadataStore(connection_config) for artifact_type in store.get_artifact_types(): print(artifact_type.name) for execution_type in store.get_execution_types(): print(execution_type.name) for context_type in store.get_context_types(): print(context_type.name) with metadata.Metadata(connection_config) as store: schema_artifacts = store.get_artifacts_by_type(standard_artifacts.Schema.TYPE_NAME) stats_artifacts = store.get_artifacts_by_type(standard_artifacts.ExampleStatistics.TYPE_NAME) anomalies_artifacts = store.get_artifacts_by_type(standard_artifacts.ExampleAnomalies.TYPE_NAME) schema_file = os.path.join(schema_artifacts[-1].uri, 'schema.pbtxt') print("Generated schame file:{}".format(schema_file)) stats_path = stats_artifacts[-1].uri train_stats_file = os.path.join(stats_path, 'train', 'stats_tfrecord') eval_stats_file = os.path.join(stats_path, 'eval', 'stats_tfrecord') print("Train stats file:{}, Eval stats file:{}".format( train_stats_file, eval_stats_file)) anomalies_path = anomalies_artifacts[-1].uri train_anomalies_file = os.path.join(anomalies_path, 'train', 'anomalies.pbtxt') eval_anomalies_file = os.path.join(anomalies_path, 'eval', 'anomalies.pbtxt') print("Train anomalies file:{}, Eval anomalies file:{}".format( train_anomalies_file, eval_anomalies_file)) schema = tfdv.load_schema_text(schema_file) tfdv.display_schema(schema=schema) train_stats = tfdv.load_statistics(train_stats_file) eval_stats = tfdv.load_statistics(eval_stats_file) tfdv.visualize_statistics(lhs_statistics=eval_stats, rhs_statistics=train_stats, lhs_name='EVAL_DATASET', rhs_name='TRAIN_DATASET') train_anomalies = tfdv.load_anomalies_text(train_anomalies_file) tfdv.display_anomalies(train_anomalies) eval_anomalies = tfdv.load_anomalies_text(eval_anomalies_file) tfdv.display_anomalies(eval_anomalies) with metadata.Metadata(connection_config) as store: model_eval_artifacts = store.get_artifacts_by_type(standard_artifacts.ModelEvaluation.TYPE_NAME) hyperparam_artifacts = store.get_artifacts_by_type(standard_artifacts.HyperParameters.TYPE_NAME) model_eval_path = model_eval_artifacts[-1].uri print("Generated model evaluation result:{}".format(model_eval_path)) best_hparams_path = os.path.join(hyperparam_artifacts[-1].uri, 'best_hyperparameters.txt') print("Generated model best hyperparameters result:{}".format(best_hparams_path)) # Latest pipeline run Tuner search space. json.loads(file_io.read_file_to_string(best_hparams_path))['space'] # Latest pipeline run Tuner searched best_hyperparameters artifacts. json.loads(file_io.read_file_to_string(best_hparams_path))['values'] eval_result = tfma.load_eval_result(model_eval_path) tfma.view.render_slicing_metrics( eval_result, slicing_column='Wilderness_Area') <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Option 1 Step2: The pipeline source can be found in the pipeline folder. Switch to the pipeline folder and compile the pipeline. Step3: 2.1 Create AI Platform Pipelines cluster Step4: CUSTOM_SERVICE_ACCOUNT - In the gcp console Click on the Navigation Menu. Navigate to IAM & Admin, then to Service Accounts and use the service account starting with prifix - 'tfx-tuner-caip-service-account'. This enables CloudTuner and the Google Cloud AI Platform extensions Tuner component to work together and allows for distributed and parallel tuning backed by AI Platform Vizier's hyperparameter search algorithm. Please see the lab setup README for setup instructions. Step5: 2.3 Compile pipeline Step6: 2.4 Deploy pipeline to AI Platform Step7: (optional) If you make local changes to the pipeline, you can update the deployed package on AI Platform with the following command Step8: 2.5 Create and monitor pipeline run Step9: 2.6 Configure Kubernetes port forwarding Step10: Connect to ML Metadata service Step11: Important Step12: Display the registered execution types. Step13: List the registered context types. Step14: Visualizing TFX artifacts Step15: Visualize schema Step16: Visualize statistics Step17: Visualize anomalies Step18: Retrieve model artifacts Step19: Return best hyperparameters Step20: Visualize model evaluations
6,942	<ASSISTANT_TASK:> Python Code: # Work in a temporary directory import tempfile import os os.chdir(tempfile.mkdtemp()) # Since this is running from an IPython notebook, # we prefix all our commands with "!" # When running on the command line, omit the leading "!" ! msmb -h ! msmb FsPeptide --data_home ./ ! tree # Remember '\' is the line-continuation marker # You can enter this command on one line ! msmb DihedralFeaturizer \ --out featurizer.pkl \ --transformed diheds \ --top fs_peptide/fs-peptide.pdb \ --trjs "fs_peptide/.xtc" \ --stride 10 ! msmb RobustScaler \ -i diheds \ --transformed scaled_diheds.h5 ! msmb tICA -i scaled_diheds.h5 \ --out tica_model.pkl \ --transformed tica_trajs.h5 \ --n_components 4 \ --lag_time 2 from msmbuilder.dataset import dataset ds = dataset('tica_trajs.h5') %matplotlib inline import msmexplorer as msme import numpy as np txx = np.concatenate(ds) _ = msme.plot_histogram(txx) ! msmb MiniBatchKMeans -i tica_trajs.h5 \ --transformed labeled_trajs.h5 \ --out clusterer.pkl \ --n_clusters 100 \ --random_state 42 ! msmb MarkovStateModel -i labeled_trajs.h5 \ --out msm.pkl \ --lag_time 2 from msmbuilder.utils import load msm = load('msm.pkl') clusterer = load('clusterer.pkl') assignments = clusterer.partial_transform(txx) assignments = msm.partial_transform(assignments) from matplotlib import pyplot as plt msme.plot_free_energy(txx, obs=(0, 1), n_samples=10000, pi=msm.populations_[assignments], xlabel='tIC 1', ylabel='tIC 2') plt.scatter(clusterer.cluster_centers_[msm.state_labels_, 0], clusterer.cluster_centers_[msm.state_labels_, 1], s=1e4 msm.populations_, # size by population c=msm.left_eigenvectors_[:, 1], # color by eigenvector cmap="coolwarm", zorder=3 ) plt.colorbar(label='First dynamical eigenvector') plt.tight_layout() <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Get example data Step2: Featurization Step3: Preprocessing Step4: Intermediate kinetic model Step5: tICA Histogram Step6: Clustering Step7: MSM Step8: Plot Free Energy Landscape
6,943	<ASSISTANT_TASK:> Python Code: from jyquickhelper import add_notebook_menu add_notebook_menu() def pas_de_voyelle(mot): s = "" for c in mot : if c not in "aeiouy" : s += c return s pas_de_voyelle("bonjour"), pas_de_voyelle("au revoir") mat = [[0,1,0],[0,0,1]] mat_dict = { } for i,line in enumerate(mat) : for j,c in enumerate(line) : if c != 0 : mat_dict[i,j] = c mat_dict sum ( 1/i for i in range(1,11) ) from math import log s = 0 N = 100 while N > 1 : for i in range(1, N): s += log(i) N //= 2 print(s) def calcul(N): s = 0 c = 0 while N > 1 : for i in range(1, N): s += log(i) c += 1 N //= 2 return c for i in range(10000,100000, 10000) : print( i, calcul(i), i * 2 ) def strange(mot): s = "" for i in range(len(mot)//2) : s += mot[i] + mot[-i-1] if len(mot)%2 == 1 : s += mot[len(mot)//2] return s strange("abcdef"), strange("kayak") dictionnaire_depart = { "cle1":"valeur1", "cle2":"valeur2" } dictionnaire_retourne = { } for k,v in dictionnaire_depart.items(): dictionnaire_retourne[v] = k dictionnaire_retourne dictionnaire_depart = { "cle1":"valeur1", "cle2":"valeur2" } print ( dictionnaire_depart.items() ) print ( list ( dictionnaire_depart.items() ) ) dictionnaire_depart.items() [0] def ensemble(a,b): res = [ ] while a < b : res.append ( a ) a += 1 return res def iterateur(a,b): while a < b : yield a a += 1 print( iterateur(0,10) ) print( ensemble(0,10) ) iterateur(0,10) [0] for x in iterateur(0,10): print(x) from math import exp 1/1000 * sum ( exp ( i / 1000 ) for i in range(1,1001) ) from math import log s = 0 ii = 1 N = 7 for i in range(1,N): ii = 2 for k in range(1,ii): s += log(k) print(s) from math import log def calcul(N): s = 0 ii = 1 c = 0 for i in range(1,N): ii = 2 for k in range(1,ii): s += log(k) c += 1 return c for N in range(10,20): print(calcul(N), 2**N) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Enoncé 1 Step2: Cette réponse n'est qu'une réponse parmi d'autres. Certains utilisaient la méthode replace, d'autres un test c == "a" or c == "e" .... Step3: Pour cette question, le code écrit fonction doit fonctionner pour n'importe quelle matrice. Step4: Q4 Step5: La première boucle s'exécute pour les valeurs $N$, $N/2$, $N/4$, ... jusqu'à ce que $N \leqslant 1$. La boucle imbriquée fait la somme des $log$ de 1 à $N$. Le nombre des opérations est en $O(N + N/2 + N/4 + ...)$, soit quelque chose comme $N \sum_{i=1}^{\ln_2 N} \frac{1}{2^i} \leqslant N \sum_{i=1}^{\infty} \frac{1}{2^i} \leqslant 2N$ (c'est une somme géométrique). On vérifie avec le code suivant qui compte le nombre de fois où on ajoute un logarithme. Step6: Enoncé 2 Step7: Q2 Step8: La méthode items retourne un itérateur et non une liste. Un itéreur n'est pas un ensemble mais une façon de parcourir tous les éléments d'un ensemble. Step9: Le python est un langage paresseux car très lent. Il faut lui demander de façon explicite de construire un ensemble ou de copier un ensemble. Par défaut, il ne copie jamais un dictionnaire ou une liste et il préfère retourner un itérateur plutôt qu'une copie d'un ensemble. La plupart du temps, on ne s'en aperçoit pas à moins de vouloir accéder à un élément précis de l'ensemble Step10: La fonction ensemble suivante retourne une liste d'éléments, la fonction iterateur retourne une façon de parcourir un ensemble. On appelle ce type ce fonction un générateur. Step11: On ne peut accéder aux éléments d'un générateur car cela n'a pas de sens Step12: Mais on peut parcourir les éléments générés Step13: Q3 Step14: Q4 Step15: A chaque itération $i$, on calcule $2^i$ logarithmes. On fait $N$ itérations soit $1 + 2 + 4 + ... + 2^N$ calculs, c'est-à-dire environ $O(1 + 2^1 + 2^2 + 2^3 + ... + 2^N) = O(2^{N+1}) = O(2^N)$ (c'est une somme géométrique).
6,944	<ASSISTANT_TASK:> Python Code: import os import matplotlib.pyplot as plt import torch import pyro import pyro.contrib.gp as gp import pyro.distributions as dist smoke_test = ('CI' in os.environ) # ignore; used to check code integrity in the Pyro repo assert pyro.__version__.startswith('1.7.0') pyro.set_rng_seed(0) # note that this helper function does three different things: # (i) plots the observed data; # (ii) plots the predictions from the learned GP after conditioning on data; # (iii) plots samples from the GP prior (with no conditioning on observed data) def plot(plot_observed_data=False, plot_predictions=False, n_prior_samples=0, model=None, kernel=None, n_test=500): plt.figure(figsize=(12, 6)) if plot_observed_data: plt.plot(X.numpy(), y.numpy(), 'kx') if plot_predictions: Xtest = torch.linspace(-0.5, 5.5, n_test) # test inputs # compute predictive mean and variance with torch.no_grad(): if type(model) == gp.models.VariationalSparseGP: mean, cov = model(Xtest, full_cov=True) else: mean, cov = model(Xtest, full_cov=True, noiseless=False) sd = cov.diag().sqrt() # standard deviation at each input point x plt.plot(Xtest.numpy(), mean.numpy(), 'r', lw=2) # plot the mean plt.fill_between(Xtest.numpy(), # plot the two-sigma uncertainty about the mean (mean - 2.0 * sd).numpy(), (mean + 2.0 * sd).numpy(), color='C0', alpha=0.3) if n_prior_samples > 0: # plot samples from the GP prior Xtest = torch.linspace(-0.5, 5.5, n_test) # test inputs noise = (model.noise if type(model) != gp.models.VariationalSparseGP else model.likelihood.variance) cov = kernel.forward(Xtest) + noise.expand(n_test).diag() samples = dist.MultivariateNormal(torch.zeros(n_test), covariance_matrix=cov)\ .sample(sample_shape=(n_prior_samples,)) plt.plot(Xtest.numpy(), samples.numpy().T, lw=2, alpha=0.4) plt.xlim(-0.5, 5.5) N = 20 X = dist.Uniform(0.0, 5.0).sample(sample_shape=(N,)) y = 0.5 * torch.sin(3X) + dist.Normal(0.0, 0.2).sample(sample_shape=(N,)) plot(plot_observed_data=True) # let's plot the observed data kernel = gp.kernels.RBF(input_dim=1, variance=torch.tensor(5.), lengthscale=torch.tensor(10.)) gpr = gp.models.GPRegression(X, y, kernel, noise=torch.tensor(1.)) plot(model=gpr, kernel=kernel, n_prior_samples=2) kernel2 = gp.kernels.RBF(input_dim=1, variance=torch.tensor(0.1), lengthscale=torch.tensor(10.)) gpr2 = gp.models.GPRegression(X, y, kernel2, noise=torch.tensor(0.1)) plot(model=gpr2, kernel=kernel2, n_prior_samples=2) optimizer = torch.optim.Adam(gpr.parameters(), lr=0.005) loss_fn = pyro.infer.Trace_ELBO().differentiable_loss losses = [] num_steps = 2500 if not smoke_test else 2 for i in range(num_steps): optimizer.zero_grad() loss = loss_fn(gpr.model, gpr.guide) loss.backward() optimizer.step() losses.append(loss.item()) # let's plot the loss curve after 2500 steps of training plt.plot(losses); plot(model=gpr, plot_observed_data=True, plot_predictions=True) gpr.kernel.variance.item() gpr.kernel.lengthscale.item() gpr.noise.item() # Define the same model as before. pyro.clear_param_store() kernel = gp.kernels.RBF(input_dim=1, variance=torch.tensor(5.), lengthscale=torch.tensor(10.)) gpr = gp.models.GPRegression(X, y, kernel, noise=torch.tensor(1.)) # note that our priors have support on the positive reals gpr.kernel.lengthscale = pyro.nn.PyroSample(dist.LogNormal(0.0, 1.0)) gpr.kernel.variance = pyro.nn.PyroSample(dist.LogNormal(0.0, 1.0)) optimizer = torch.optim.Adam(gpr.parameters(), lr=0.005) loss_fn = pyro.infer.Trace_ELBO().differentiable_loss losses = [] num_steps = 2500 if not smoke_test else 2 for i in range(num_steps): optimizer.zero_grad() loss = loss_fn(gpr.model, gpr.guide) loss.backward() optimizer.step() losses.append(loss.item()) plt.plot(losses); plot(model=gpr, plot_observed_data=True, plot_predictions=True) # tell gpr that we want to get samples from guides gpr.set_mode('guide') print('variance = {}'.format(gpr.kernel.variance)) print('lengthscale = {}'.format(gpr.kernel.lengthscale)) print('noise = {}'.format(gpr.noise)) N = 1000 X = dist.Uniform(0.0, 5.0).sample(sample_shape=(N,)) y = 0.5 torch.sin(3*X) + dist.Normal(0.0, 0.2).sample(sample_shape=(N,)) plot(plot_observed_data=True) # initialize the inducing inputs Xu = torch.arange(20.) / 4.0 # initialize the kernel and model pyro.clear_param_store() kernel = gp.kernels.RBF(input_dim=1) # we increase the jitter for better numerical stability sgpr = gp.models.SparseGPRegression(X, y, kernel, Xu=Xu, jitter=1.0e-5) # the way we setup inference is similar to above optimizer = torch.optim.Adam(sgpr.parameters(), lr=0.005) loss_fn = pyro.infer.Trace_ELBO().differentiable_loss losses = [] num_steps = 2500 if not smoke_test else 2 for i in range(num_steps): optimizer.zero_grad() loss = loss_fn(sgpr.model, sgpr.guide) loss.backward() optimizer.step() losses.append(loss.item()) plt.plot(losses); # let's look at the inducing points we've learned print("inducing points:\n{}".format(sgpr.Xu.data.numpy())) # and plot the predictions from the sparse GP plot(model=sgpr, plot_observed_data=True, plot_predictions=True) # initialize the inducing inputs Xu = torch.arange(10.) / 2.0 # initialize the kernel, likelihood, and model pyro.clear_param_store() kernel = gp.kernels.RBF(input_dim=1) likelihood = gp.likelihoods.Gaussian() # turn on "whiten" flag for more stable optimization vsgp = gp.models.VariationalSparseGP(X, y, kernel, Xu=Xu, likelihood=likelihood, whiten=True) # instead of defining our own training loop, we will # use the built-in support provided by the GP module num_steps = 1500 if not smoke_test else 2 losses = gp.util.train(vsgp, num_steps=num_steps) plt.plot(losses); plot(model=vsgp, plot_observed_data=True, plot_predictions=True) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Throughout the tutorial we'll want to visualize GPs. So we define a helper function for plotting Step2: Data Step3: Define model Step4: Let's see what samples from this GP function prior look like. Note that this is before we've conditioned on the data. The shape these functions take—their smoothness, their vertical scale, etc.—is controlled by the GP kernel. Step5: For example, if we make variance and noise smaller we will see function samples with smaller vertical amplitude Step6: Inference Step7: Let's see if we're learned anything reasonable Step8: Here the thick red curve is the mean prediction and the blue band represents the 2-sigma uncertainty around the mean. It seems we learned reasonable kernel hyperparameters, as both the mean and uncertainty give a reasonable fit to the data. (Note that learning could have easily gone wrong if we e.g. chose too large of a learning rate or chose bad initital hyperparameters.) Step9: The period of the sinusoid that generated the data is $T = 2\pi/3 \approx 2.09$ so learning a lengthscale that's approximiately equal to a quarter period makes sense. Step10: Let's inspect the hyperparameters we've learned Step11: Note that the MAP values are different from the MLE values due to the prior. Step12: Using the sparse GP is very similar to using the basic GP used above. We just need to add an extra parameter $X_u$ (the inducing points). Step13: We can see that the model learns a reasonable fit to the data. There are three different sparse approximations that are currently implemented in Pyro
6,945	<ASSISTANT_TASK:> Python Code: %config InlineBackend.figure_format = 'retina' %matplotlib inline import numpy as np import scipy as sp import matplotlib.pyplot as plt import pandas as pd import seaborn as sns sns.set_style("white") filename="burrito_current.csv" df = pd.read_csv(filename) N = df.shape[0] m_best = ['Volume','Cost','Tortilla','Temp','Meat','Fillings','Meat:filling', 'Uniformity','Salsa','Synergy','Wrap','overall','Google','Yelp'] for m in m_best: print m print 'High,', df.Location[df[m].idxmax()], df[m][df[m].idxmax()] print 'Low,', df.Location[df[m].idxmin()], df[m][df[m].idxmin()] <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Load data Step2: Find the best location for each dimension
6,946	<ASSISTANT_TASK:> Python Code: for i in ['a','b','c']: try: result = i2 except TypeError: print("Type error attempting to run on {i}".format(i=i)) else: print result x = 5 y = 0 try: z = x/y except ZeroDivisionError: print("Cannot divide by zero") finally: print 'all done' def ask(): while True: try: input = int(raw_input("Enter an integer: ")) except: print 'Could not make conversion. Try again' else: print input2 ask() <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Problem 2 Step2: Problem 3
6,947	<ASSISTANT_TASK:> Python Code: from yargy import Parser, rule, and_ from yargy.predicates import gram, is_capitalized, dictionary GEO = rule( and_( gram('ADJF'), # так помечается прилагательное, остальные пометки описаны в # http://pymorphy2.readthedocs.io/en/latest/user/grammemes.html is_capitalized() ), gram('ADJF').optional().repeatable(), dictionary({ 'федерация', 'республика' }) ) parser = Parser(GEO) text = ''' В Чеченской республике на день рождения ... Донецкая народная республика провозгласила ... Башня Федерация — одна из самых высоких ... ''' for match in parser.findall(text): print([_.value for _ in match.tokens]) from yargy import Parser from yargy.predicates import gram from yargy.pipelines import morph_pipeline from yargy.interpretation import fact from IPython.display import display Person = fact( 'Person', ['position', 'name'] ) Name = fact( 'Name', ['first', 'last'] ) POSITION = morph_pipeline([ 'премьер министр', 'президент' ]) NAME = rule( gram('Name').interpretation( Name.first.inflected() ), gram('Surn').interpretation( Name.last.inflected() ) ).interpretation( Name ) PERSON = rule( POSITION.interpretation( Person.position.inflected() ), NAME.interpretation( Person.name ) ).interpretation( Person ) parser = Parser(PERSON) text = ''' 12 марта по приказу президента Владимира Путина ... ''' for match in parser.findall(text): display(match.fact) from yargy.tokenizer import MorphTokenizer tokenizer = MorphTokenizer() text = '''Ростов-на-Дону Длительностью 18ч. 10мин. Яндекс.Такси π ≈ 3.1415 1 500 000$ http://vk.com ''' for line in text.splitlines(): print([_.value for _ in tokenizer(line)]) tokenizer = MorphTokenizer() list(tokenizer('марки стали')) from yargy import and_, not_ from yargy.tokenizer import MorphTokenizer from yargy.predicates import is_capitalized, eq tokenizer = MorphTokenizer() token = next(tokenizer('Стали')) predicate = is_capitalized() assert predicate(token) == True predicate = and_( is_capitalized(), not_(eq('марки')) ) assert predicate(token) == True from pymorphy2.shapes import is_roman_number from yargy.parser import Context from yargy.tokenizer import Tokenizer from yargy.predicates import custom tokenizer = Tokenizer() token = next(tokenizer('XL')) predicate = custom(is_roman_number, types='LATIN') predicate = predicate.activate(Context(tokenizer)) # проверяется, что tokenizer поддерживает тип 'LATIN' assert predicate(token) == True token = next(tokenizer('XS')) assert predicate(token) == False from yargy import or_, rule from yargy.predicates import normalized RULE = or_( rule(normalized('dvd'), '-', normalized('диск')), rule(normalized('видео'), normalized('файл')) ) from yargy import Parser from yargy.pipelines import morph_pipeline RULE = morph_pipeline([ 'dvd-диск', 'видео файл', 'видеофильм', 'газета', 'электронный дневник', 'эссе', ]) parser = Parser(RULE) text = 'Видео файл на dvd-диске' for match in parser.findall(text): print([_.value for _ in match.tokens]) from yargy import rule, or_ KEY = or_( rule('р', '.'), rule('размер') ).named('KEY') VALUE = or_( rule('S'), rule('M'), rule('L'), ).named('VALUE') SIZE = rule( KEY, VALUE ).named('SIZE') SIZE.normalized.as_bnf from yargy.predicates import in_ VALUE = rule( in_('SML') ).named('VALUE') SIZE = rule( KEY, VALUE ).named('SIZE') SIZE.normalized.as_bnf from yargy import forward EXPR = forward() EXPR.define(or_( rule('a'), rule('(', EXPR, '+', EXPR, ')') ).named('EXPR')) EXPR.normalized.as_bnf from yargy import not_ from yargy.predicates import eq WORD = not_(eq('»')) TEXT = forward() TEXT.define(or_( rule(WORD), rule(WORD, TEXT) )) TITLE = rule( '«', TEXT, '»' ).named('TITLE') TITLE.normalized.as_bnf TITLE = rule( '«', not_(eq('»')).repeatable(), '»' ).named('TITLE') TITLE.normalized.as_bnf parser = Parser( or_( PERSON, TITLE ) ) text = 'Президент Владимир Путин в фильме «Интервью с Путиным» ..' for match in parser.findall(text): print([_.value for _ in match.tokens]) match = parser.match('Президент Владимир Путин') print([_.value for _ in match.tokens]) match = parser.match('Президент Владимир Путин 25 мая') print(match) from IPython.display import display from yargy.predicates import ( lte, gte, dictionary ) MONTHS = { 'январь', 'февраль', 'март', 'апрель', 'мая', 'июнь', 'июль', 'август', 'сентябрь', 'октябрь', 'ноябрь', 'декабрь' } MONTH_NAME = dictionary(MONTHS) MONTH = and_( gte(1), lte(12) ) DAY = and_( gte(1), lte(31) ) YEAR = and_( gte(1900), lte(2100) ) DATE = or_( rule(DAY, MONTH_NAME, YEAR), rule(YEAR, '-', MONTH, '-', DAY), rule(YEAR, 'г', '.') ).named('DATE') parser = Parser(DATE) text = '''2015г. 18 июля 2016 2016-01-02 ''' for line in text.splitlines(): match = parser.match(line) display(match.tree.as_dot) from yargy.interpretation import fact Date = fact( 'Date', ['year', 'month', 'day'] ) DATE = or_( rule( DAY.interpretation( Date.day ), MONTH_NAME.interpretation( Date.month ), YEAR.interpretation( Date.year ) ), rule( YEAR.interpretation( Date.year ), '-', MONTH.interpretation( Date.month ), '-', DAY.interpretation( Date.day ) ), rule( YEAR.interpretation( Date.year ), 'г', '.' ) ).interpretation( Date ).named('DATE') parser = Parser(DATE) for line in text.splitlines(): match = parser.match(line) display(match.tree.as_dot) for line in text.splitlines(): match = parser.match(line) display(match.fact) MONTHS = { 'январь': 1, 'февраль': 2, 'март': 3, 'апрель': 4, 'мая': 5, 'июнь': 6, 'июль': 7, 'август': 8, 'сентябрь': 9, 'октябрь': 10, 'ноябрь': 11, 'декабрь': 12 } DATE = rule( DAY.interpretation( Date.day.custom(int) ), MONTH_NAME.interpretation( Date.month.normalized().custom(MONTHS.get) ), YEAR.interpretation( Date.year.custom(int) ) ).interpretation( Date ) parser = Parser(DATE) match = parser.match('18 июня 2016') match.fact NAME = rule( gram('Name').interpretation( Name.first.inflected() ), gram('Surn').interpretation( Name.last.inflected() ) ).interpretation( Name ) parser = Parser(NAME) for match in parser.findall('Илье Ивановым, Павлом Семенов'): print([_.value for _ in match.tokens]) parser = Parser(NAME) for match in parser.findall('Сашу Иванову, Саше Иванову'): display(match.fact) from yargy.relations import gnc_relation gnc = gnc_relation() NAME = rule( gram('Name').interpretation( Name.first.inflected() ).match(gnc), gram('Surn').interpretation( Name.last.inflected() ).match(gnc) ).interpretation( Name ) parser = Parser(NAME) for match in parser.findall('Илье Ивановым, Павлом Семенов, Саша Быков'): print([_.value for _ in match.tokens]) parser = Parser(NAME) for match in parser.findall('Сашу Иванову, Саше Иванову'): display(match.fact) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Настоящие сложные грамматики для топонимов собраны в репозитории <a href="https Step2: Грамматики для имён собраны в репозитории Natasha Step3: Для каждого токена Pymorph2 возвращает набор граммем. Например, "NOUN, sing, femn" — "существительное в единственном числе женского рода". Полный список в <a href="https Step4: Токенизатор работает на правилах. В <a href="ref.ipynb#Токенизатор">справочнике</a> показано, как менять стандартные правила и добавлять новые. Step5: <a href="ref.ipynb#predicates.custom">custom</a> создаёт предикат из произвольной функции. Например, предикат для римских цифр Step6: Газеттир Step7: удобно использовать morph_pipeline Step8: Список газеттиров в <a href="ref.ipynb#Газеттир">справочнике</a>. Step9: В Yargy терминал грамматики — предикат. Используем встроенный предикат in_, сократим запись VALUE Step10: Как быть, когда правая часть правила ссылается на левую? Например Step11: Рекурсивные правила описывают последовательности токенов произвольной длины. Грамматика для текста в кавычках Step12: Для удобства в Yargy есть метод repeatable с ним запись короче. Библиотека автоматически добавит forward Step13: Парсер Step14: match — пытается разобрать весь текст целиком Step15: Интерпретация Step16: Интерпретация — процесс преобразования дерева разбора в объект с набором полей. Для даты, например, нужно получить структуры вида Date(year=2016, month=1, day=2). Пользователь размечает дерево на вершины-атрибуты и вершины-конструкторы методом interpretation Step17: Из размеченного дерева библиотека собирает объект Step18: Подробнее об интерпретации в <a href="#ref.ipynb#Интерпретация">справочнике</a>. Step19: Подробнее в <a href="ref.ipynb#Нормализация">справочнике</a>. Step20: У неё есть две проблемы. Она срабатывает на словосочетаниях, где имя и фамилия в разных падежах Step21: Имя и фамилия приводятся к нормальной форме независимо, получается женщина "Иванов" Step22: В Yargy связь между словами и словосочетаниями устанавливается методом match. Для согласования по числу в match передаём number_relation, для согласования по падежу, роду и числу — gnc_relation
6,948	<ASSISTANT_TASK:> Python Code: data_in_shape = (5, 5, 2) conv = SeparableConv2D(4, (3,3), strides=(1,1), padding='valid', data_format='channels_last', depth_multiplier=1, activation='linear', use_bias=True) layer_0 = Input(shape=data_in_shape) layer_1 = conv(layer_0) model = Model(inputs=layer_0, outputs=layer_1) # set weights to random (use seed for reproducibility) weights = [] for w in model.get_weights(): np.random.seed(160) weights.append(2 * np.random.random(w.shape) - 1) model.set_weights(weights) print('depthwise_kernel shape:', weights[0].shape) print('depthwise_kernel:', format_decimal(weights[0].ravel().tolist())) print('pointwise_kernel shape:', weights[1].shape) print('pointwise_kernel:', format_decimal(weights[1].ravel().tolist())) print('b shape:', weights[2].shape) print('b:', format_decimal(weights[2].ravel().tolist())) data_in = 2 * np.random.random(data_in_shape) - 1 result = model.predict(np.array([data_in])) data_out_shape = result[0].shape data_in_formatted = format_decimal(data_in.ravel().tolist()) data_out_formatted = format_decimal(result[0].ravel().tolist()) print('') print('in shape:', data_in_shape) print('in:', data_in_formatted) print('out shape:', data_out_shape) print('out:', data_out_formatted) DATA['convolutional.SeparableConv2D.0'] = { 'input': {'data': data_in_formatted, 'shape': data_in_shape}, 'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights], 'expected': {'data': data_out_formatted, 'shape': data_out_shape} } data_in_shape = (5, 5, 2) conv = SeparableConv2D(4, (3,3), strides=(1,1), padding='valid', data_format='channels_last', depth_multiplier=2, activation='relu', use_bias=True) layer_0 = Input(shape=data_in_shape) layer_1 = conv(layer_0) model = Model(inputs=layer_0, outputs=layer_1) # set weights to random (use seed for reproducibility) weights = [] for w in model.get_weights(): np.random.seed(161) weights.append(2 * np.random.random(w.shape) - 1) model.set_weights(weights) print('depthwise_kernel shape:', weights[0].shape) print('depthwise_kernel:', format_decimal(weights[0].ravel().tolist())) print('pointwise_kernel shape:', weights[1].shape) print('pointwise_kernel:', format_decimal(weights[1].ravel().tolist())) print('b shape:', weights[2].shape) print('b:', format_decimal(weights[2].ravel().tolist())) data_in = 2 * np.random.random(data_in_shape) - 1 result = model.predict(np.array([data_in])) data_out_shape = result[0].shape data_in_formatted = format_decimal(data_in.ravel().tolist()) data_out_formatted = format_decimal(result[0].ravel().tolist()) print('') print('in shape:', data_in_shape) print('in:', data_in_formatted) print('out shape:', data_out_shape) print('out:', data_out_formatted) DATA['convolutional.SeparableConv2D.1'] = { 'input': {'data': data_in_formatted, 'shape': data_in_shape}, 'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights], 'expected': {'data': data_out_formatted, 'shape': data_out_shape} } data_in_shape = (5, 5, 4) conv = SeparableConv2D(16, (3,3), strides=(1,1), padding='valid', data_format='channels_last', depth_multiplier=3, activation='relu', use_bias=True) layer_0 = Input(shape=data_in_shape) layer_1 = conv(layer_0) model = Model(inputs=layer_0, outputs=layer_1) # set weights to random (use seed for reproducibility) weights = [] for w in model.get_weights(): np.random.seed(162) weights.append(2 * np.random.random(w.shape) - 1) model.set_weights(weights) print('depthwise_kernel shape:', weights[0].shape) print('depthwise_kernel:', format_decimal(weights[0].ravel().tolist())) print('pointwise_kernel shape:', weights[1].shape) print('pointwise_kernel:', format_decimal(weights[1].ravel().tolist())) print('b shape:', weights[2].shape) print('b:', format_decimal(weights[2].ravel().tolist())) data_in = 2 * np.random.random(data_in_shape) - 1 result = model.predict(np.array([data_in])) data_out_shape = result[0].shape data_in_formatted = format_decimal(data_in.ravel().tolist()) data_out_formatted = format_decimal(result[0].ravel().tolist()) print('') print('in shape:', data_in_shape) print('in:', data_in_formatted) print('out shape:', data_out_shape) print('out:', data_out_formatted) DATA['convolutional.SeparableConv2D.2'] = { 'input': {'data': data_in_formatted, 'shape': data_in_shape}, 'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights], 'expected': {'data': data_out_formatted, 'shape': data_out_shape} } data_in_shape = (5, 5, 2) conv = SeparableConv2D(4, (3,3), strides=(2,2), padding='valid', data_format='channels_last', depth_multiplier=1, activation='relu', use_bias=True) layer_0 = Input(shape=data_in_shape) layer_1 = conv(layer_0) model = Model(inputs=layer_0, outputs=layer_1) # set weights to random (use seed for reproducibility) weights = [] for w in model.get_weights(): np.random.seed(163) weights.append(2 * np.random.random(w.shape) - 1) model.set_weights(weights) print('depthwise_kernel shape:', weights[0].shape) print('depthwise_kernel:', format_decimal(weights[0].ravel().tolist())) print('pointwise_kernel shape:', weights[1].shape) print('pointwise_kernel:', format_decimal(weights[1].ravel().tolist())) print('b shape:', weights[2].shape) print('b:', format_decimal(weights[2].ravel().tolist())) data_in = 2 * np.random.random(data_in_shape) - 1 result = model.predict(np.array([data_in])) data_out_shape = result[0].shape data_in_formatted = format_decimal(data_in.ravel().tolist()) data_out_formatted = format_decimal(result[0].ravel().tolist()) print('') print('in shape:', data_in_shape) print('in:', data_in_formatted) print('out shape:', data_out_shape) print('out:', data_out_formatted) DATA['convolutional.SeparableConv2D.3'] = { 'input': {'data': data_in_formatted, 'shape': data_in_shape}, 'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights], 'expected': {'data': data_out_formatted, 'shape': data_out_shape} } data_in_shape = (5, 5, 2) conv = SeparableConv2D(4, (3,3), strides=(1,1), padding='same', data_format='channels_last', depth_multiplier=1, activation='relu', use_bias=True) layer_0 = Input(shape=data_in_shape) layer_1 = conv(layer_0) model = Model(inputs=layer_0, outputs=layer_1) # set weights to random (use seed for reproducibility) weights = [] for w in model.get_weights(): np.random.seed(164) weights.append(2 * np.random.random(w.shape) - 1) model.set_weights(weights) print('depthwise_kernel shape:', weights[0].shape) print('depthwise_kernel:', format_decimal(weights[0].ravel().tolist())) print('pointwise_kernel shape:', weights[1].shape) print('pointwise_kernel:', format_decimal(weights[1].ravel().tolist())) print('b shape:', weights[2].shape) print('b:', format_decimal(weights[2].ravel().tolist())) data_in = 2 * np.random.random(data_in_shape) - 1 result = model.predict(np.array([data_in])) data_out_shape = result[0].shape data_in_formatted = format_decimal(data_in.ravel().tolist()) data_out_formatted = format_decimal(result[0].ravel().tolist()) print('') print('in shape:', data_in_shape) print('in:', data_in_formatted) print('out shape:', data_out_shape) print('out:', data_out_formatted) DATA['convolutional.SeparableConv2D.4'] = { 'input': {'data': data_in_formatted, 'shape': data_in_shape}, 'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights], 'expected': {'data': data_out_formatted, 'shape': data_out_shape} } data_in_shape = (5, 5, 2) conv = SeparableConv2D(4, (3,3), strides=(1,1), padding='same', data_format='channels_last', depth_multiplier=2, activation='relu', use_bias=False) layer_0 = Input(shape=data_in_shape) layer_1 = conv(layer_0) model = Model(inputs=layer_0, outputs=layer_1) # set weights to random (use seed for reproducibility) weights = [] for w in model.get_weights(): np.random.seed(165) weights.append(2 * np.random.random(w.shape) - 1) model.set_weights(weights) print('depthwise_kernel shape:', weights[0].shape) print('depthwise_kernel:', format_decimal(weights[0].ravel().tolist())) print('pointwise_kernel shape:', weights[1].shape) print('pointwise_kernel:', format_decimal(weights[1].ravel().tolist())) # print('b shape:', weights[2].shape) # print('b:', format_decimal(weights[2].ravel().tolist())) data_in = 2 * np.random.random(data_in_shape) - 1 result = model.predict(np.array([data_in])) data_out_shape = result[0].shape data_in_formatted = format_decimal(data_in.ravel().tolist()) data_out_formatted = format_decimal(result[0].ravel().tolist()) print('') print('in shape:', data_in_shape) print('in:', data_in_formatted) print('out shape:', data_out_shape) print('out:', data_out_formatted) DATA['convolutional.SeparableConv2D.5'] = { 'input': {'data': data_in_formatted, 'shape': data_in_shape}, 'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights], 'expected': {'data': data_out_formatted, 'shape': data_out_shape} } data_in_shape = (5, 5, 2) conv = SeparableConv2D(4, (3,3), strides=(2,2), padding='same', data_format='channels_last', depth_multiplier=2, activation='relu', use_bias=True) layer_0 = Input(shape=data_in_shape) layer_1 = conv(layer_0) model = Model(inputs=layer_0, outputs=layer_1) # set weights to random (use seed for reproducibility) weights = [] for w in model.get_weights(): np.random.seed(166) weights.append(2 * np.random.random(w.shape) - 1) model.set_weights(weights) print('depthwise_kernel shape:', weights[0].shape) print('depthwise_kernel:', format_decimal(weights[0].ravel().tolist())) print('pointwise_kernel shape:', weights[1].shape) print('pointwise_kernel:', format_decimal(weights[1].ravel().tolist())) print('b shape:', weights[2].shape) print('b:', format_decimal(weights[2].ravel().tolist())) data_in = 2 * np.random.random(data_in_shape) - 1 result = model.predict(np.array([data_in])) data_out_shape = result[0].shape data_in_formatted = format_decimal(data_in.ravel().tolist()) data_out_formatted = format_decimal(result[0].ravel().tolist()) print('') print('in shape:', data_in_shape) print('in:', data_in_formatted) print('out shape:', data_out_shape) print('out:', data_out_formatted) DATA['convolutional.SeparableConv2D.6'] = { 'input': {'data': data_in_formatted, 'shape': data_in_shape}, 'weights': [{'data': format_decimal(w.ravel().tolist()), 'shape': w.shape} for w in weights], 'expected': {'data': data_out_formatted, 'shape': data_out_shape} } import os filename = '../../../test/data/layers/convolutional/SeparableConv2D.json' if not os.path.exists(os.path.dirname(filename)): os.makedirs(os.path.dirname(filename)) with open(filename, 'w') as f: json.dump(DATA, f) print(json.dumps(DATA)) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: [convolutional.SeparableConv2D.1] 4 3x3 filters on 5x5x2 input, strides=(1,1), padding='valid', data_format='channels_last', depth_multiplier=2, activation='relu', use_bias=True Step2: [convolutional.SeparableConv2D.2] 16 3x3 filters on 5x5x4 input, strides=(1,1), padding='valid', data_format='channels_last', depth_multiplier=3, activation='relu', use_bias=True Step3: [convolutional.SeparableConv2D.3] 4 3x3 filters on 5x5x2 input, strides=(2,2), padding='valid', data_format='channels_last', depth_multiplier=1, activation='relu', use_bias=True Step4: [convolutional.SeparableConv2D.4] 4 3x3 filters on 5x5x2 input, strides=(1,1), padding='same', data_format='channels_last', depth_multiplier=1, activation='relu', use_bias=True Step5: [convolutional.SeparableConv2D.5] 4 3x3 filters on 5x5x2 input, strides=(1,1), padding='same', data_format='channels_last', depth_multiplier=2, activation='relu', use_bias=False Step6: [convolutional.SeparableConv2D.6] 4 3x3 filters on 5x5x2 input, strides=(2,2), padding='same', data_format='channels_last', depth_multiplier=2, activation='relu', use_bias=True Step7: export for Keras.js tests
6,949	<ASSISTANT_TASK:> Python Code: %reload_ext rpy2.ipython import pandas as pd %%R # help() # help(function) # help(package='package-name) %%R # install # install.packages('package-name') # already installed with conda #install.packages("foreign") #install.packages("Rcmdr", dependencies = TRUE) # new installs install.packages("nortest", repos="http://cran.rstudio.com/") install.packages("sas7bdat", repos="http://cran.rstudio.com/") install.packages("Hmisc", repos="http://cran.rstudio.com/") install.packages("pastecs", repos="http://cran.rstudio.com/") %%R # import # library('package-name') library(foreign) library(nortest) library(sas7bdat) library(Hmisc) library(pastecs) # jupyter "magic : integration with shell %pwd %%R # import excel : via txt tab separated fichierTexte <- read.table("data/labo-2/SR_Data.txt", header = TRUE) head(fichierTexte) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Aide Step2: Autres ressources Step3: Espace de travail Step4: Import fichier externe
6,950	<ASSISTANT_TASK:> Python Code: from dolfin import * from rbnics import * from problems import * from reduction_methods import * @OnlineStabilization() class AdvectionDominated(EllipticCoerciveProblem): # Default initialization of members def __init__(self, V, kwargs): # Call the standard initialization EllipticCoerciveProblem.__init__(self, V, kwargs) # ... and also store FEniCS data structures for assembly assert "subdomains" in kwargs assert "boundaries" in kwargs self.subdomains, self.boundaries = kwargs["subdomains"], kwargs["boundaries"] self.u = TrialFunction(V) self.v = TestFunction(V) self.dx = Measure("dx")(subdomain_data=subdomains) self.ds = Measure("ds")(subdomain_data=boundaries) # Store advection and forcing expressions self.beta = Constant((1.0, 1.0)) self.f = Constant(1.0) # Store terms related to stabilization self.delta = 0.5 self.h = CellDiameter(V.mesh()) # Return custom problem name def name(self): return "AdvectionDominated1POD" # Return theta multiplicative terms of the affine expansion of the problem. def compute_theta(self, term): mu = self.mu if term == "a": theta_a0 = 10.0*(- mu[0]) theta_a1 = 1.0 if self.stabilized: delta = self.delta theta_a2 = - delta 10.0*(- mu[0]) theta_a3 = delta else: theta_a2 = 0.0 theta_a3 = 0.0 return (theta_a0, theta_a1, theta_a2, theta_a3) elif term == "f": theta_f0 = 1.0 if self.stabilized: delta = self.delta theta_f1 = delta else: theta_f1 = 0.0 return (theta_f0, theta_f1) else: raise ValueError("Invalid term for compute_theta().") # Return forms resulting from the discretization of the affine expansion of the problem operators. def assemble_operator(self, term): v = self.v dx = self.dx if term == "a": u = self.u beta = self.beta h = self.h a0 = inner(grad(u), grad(v)) dx a1 = inner(beta, grad(u)) * v * dx a2 = inner(div(grad(u)), h * inner(beta, grad(v))) * dx a3 = inner(inner(beta, grad(u)), h * inner(beta, grad(v))) * dx return (a0, a1, a2, a3) elif term == "f": f = self.f beta = self.beta h = self.h f0 = f * v * dx f1 = inner(f, h * inner(beta, grad(v))) * dx return (f0, f1) elif term == "k": u = self.u k0 = inner(grad(u), grad(v)) * dx return (k0,) elif term == "m": u = self.u m0 = inner(u, v) * dx return (m0,) elif term == "dirichlet_bc": bc0 = [DirichletBC(self.V, Constant(0.0), self.boundaries, 1), DirichletBC(self.V, Constant(0.0), self.boundaries, 2)] return (bc0,) elif term == "inner_product": u = self.u x0 = inner(grad(u), grad(v)) * dx return (x0,) else: raise ValueError("Invalid term for assemble_operator().") mesh = Mesh("data/square.xml") subdomains = MeshFunction("size_t", mesh, "data/square_physical_region.xml") boundaries = MeshFunction("size_t", mesh, "data/square_facet_region.xml") V = FunctionSpace(mesh, "Lagrange", 2) problem = AdvectionDominated(V, subdomains=subdomains, boundaries=boundaries) mu_range = [(0.0, 6.0)] problem.set_mu_range(mu_range) reduction_method = PODGalerkin(problem) reduction_method.set_Nmax(15) reduction_method.initialize_training_set(100) reduced_problem = reduction_method.offline() online_mu = (6.0, ) reduced_problem.set_mu(online_mu) reduced_problem.solve(online_stabilization=True) reduced_problem.export_solution(filename="online_solution_with_stabilization") reduced_problem.export_error(filename="online_error_with_stabilization") reduced_problem.solve(online_stabilization=False) reduced_problem.export_solution(filename="online_solution_without_stabilization") reduced_problem.export_error(filename="online_error_without_stabilization") reduction_method.initialize_testing_set(100) reduction_method.error_analysis(online_stabilization=True, filename="error_analysis_with_stabilization") reduction_method.error_analysis(online_stabilization=False, filename="error_analysis_without_stabilization") reduction_method.speedup_analysis(online_stabilization=True, filename="speedup_analysis_with_stabilization") reduction_method.speedup_analysis(online_stabilization=False, filename="speedup_analysis_without_stabilization") <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: 3. Affine decomposition Step2: 4. Main program Step3: 4.2. Create Finite Element space (Lagrange P2) Step4: 4.3. Allocate an object of the AdvectionDominated class Step5: 4.4. Prepare reduction with a POD-Galerkin method Step6: 4.5. Perform the offline phase Step7: 4.6. Perform an online solve Step8: 4.7. Perform an error analysis Step9: 4.8. Perform a speedup analysis
6,951	<ASSISTANT_TASK:> Python Code: import numpy as np import h5py from sklearn import svm, cross_validation from sklearn.naive_bayes import MultinomialNB import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline # First we load the file file_location = '../results_database/text_wall_street_big.hdf5' f = h5py.File(file_location, 'r') # Now we need to get the letters and align them text_directory = '../data/wall_street_letters.npy' letters_sequence = np.load(text_directory) Nletters = len(letters_sequence) symbols = set(letters_sequence) # Load the particular example Nspatial_clusters = 8 Ntime_clusters = 40 Nembedding = 3 parameters_string = '/' + str(Nspatial_clusters) parameters_string += '-' + str(Ntime_clusters) parameters_string += '-' + str(Nembedding) # Set the parameters for the simulation maximal_lags = np.arange(8, 21, 3) # Run the delay analysis N = 50000 delays = np.arange(0, 25, 1) accuracy_matrix = np.zeros((maximal_lags.size, delays.size)) for maximal_lag_index, maximal_lag in enumerate(maximal_lags): # Extract the appropriate database run_name = '/low-resolution' + str(maximal_lag) nexa = f[run_name + parameters_string] # Now we load the time and the code vectors time = nexa['time'] code_vectors = nexa['code-vectors'] code_vectors_distance = nexa['code-vectors-distance'] code_vectors_softmax = nexa['code-vectors-softmax'] code_vectors_winner = nexa['code-vectors-winner'] for delay_index, delay in enumerate(delays): X = code_vectors_softmax[:(N - delay)] y = letters_sequence[delay:N] X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.10) clf = svm.SVC(C=1.0, cache_size=200, kernel='linear') clf.fit(X_train, y_train) score = clf.score(X_test, y_test) * 100.0 accuracy_matrix[maximal_lag_index, delay_index] = score print('delay_index', delay_index) print('maximal_lag_index', maximal_lag_index) print('maximal_lag', maximal_lag) print('delay', delay) print('score', score) print('-------------') fig = plt.figure(figsize=(16, 12)) ax = fig.add_subplot(111) for maximal_lag_index in range(maximal_lags.size): ax.plot(delays, accuracy_matrix[maximal_lag_index, :], 'o-', lw=2, markersize=10, label=str(maximal_lags[maximal_lag_index])) ax.set_xlabel('Delays') ax.set_ylabel('Accuracy') ax.set_ylim([0, 105]) ax.set_title('Latency analysis for different lags') ax.legend() <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Load all the data Step2: Latency analysis Step3: Plot it
6,952	<ASSISTANT_TASK:> Python Code: # Package imports import numpy as np import matplotlib.pyplot as plt from testCases import * import sklearn import sklearn.datasets import sklearn.linear_model from planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets %matplotlib inline np.random.seed(1) # set a seed so that the results are consistent X, Y = load_planar_dataset() # Visualize the data: plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral); ### START CODE HERE ### (≈ 3 lines of code) shape_X = X.shape shape_Y = Y.shape m = Y.flatten().shape # training set size ### END CODE HERE ### print ('The shape of X is: ' + str(shape_X)) print ('The shape of Y is: ' + str(shape_Y)) print ('I have m = %d training examples!' % (m)) # Train the logistic regression classifier clf = sklearn.linear_model.LogisticRegressionCV(); clf.fit(X.T, Y.T); # Plot the decision boundary for logistic regression plot_decision_boundary(lambda x: clf.predict(x), X, Y) plt.title("Logistic Regression") # Print accuracy LR_predictions = clf.predict(X.T) print ('Accuracy of logistic regression: %d ' % float((np.dot(Y,LR_predictions) + np.dot(1-Y,1-LR_predictions))/float(Y.size)100) + '% ' + "(percentage of correctly labelled datapoints)") # GRADED FUNCTION: layer_sizes def layer_sizes(X, Y): Arguments: X -- input dataset of shape (input size, number of examples) Y -- labels of shape (output size, number of examples) Returns: n_x -- the size of the input layer n_h -- the size of the hidden layer n_y -- the size of the output layer ### START CODE HERE ### (≈ 3 lines of code) n_x = X.shape[0] # size of input layer n_h = 4 n_y = Y.shape[0] # size of output layer ### END CODE HERE ### return (n_x, n_h, n_y) X_assess, Y_assess = layer_sizes_test_case() (n_x, n_h, n_y) = layer_sizes(X_assess, Y_assess) print("The size of the input layer is: n_x = " + str(n_x)) print("The size of the hidden layer is: n_h = " + str(n_h)) print("The size of the output layer is: n_y = " + str(n_y)) # GRADED FUNCTION: initialize_parameters def initialize_parameters(n_x, n_h, n_y): Argument: n_x -- size of the input layer n_h -- size of the hidden layer n_y -- size of the output layer Returns: params -- python dictionary containing your parameters: W1 -- weight matrix of shape (n_h, n_x) b1 -- bias vector of shape (n_h, 1) W2 -- weight matrix of shape (n_y, n_h) b2 -- bias vector of shape (n_y, 1) np.random.seed(2) # we set up a seed so that your output matches ours although the initialization is random. ### START CODE HERE ### (≈ 4 lines of code) W1 = np.random.randn(n_h, n_x) 0.01 b1 = np.zeros((n_h, 1)) W2 = np.random.randn(n_y, n_h) * 0.01 b2 = np.zeros((n_y, 1)) ### END CODE HERE ### assert (W1.shape == (n_h, n_x)) assert (b1.shape == (n_h, 1)) assert (W2.shape == (n_y, n_h)) assert (b2.shape == (n_y, 1)) parameters = {"W1": W1, "b1": b1, "W2": W2, "b2": b2} return parameters n_x, n_h, n_y = initialize_parameters_test_case() parameters = initialize_parameters(n_x, n_h, n_y) print("W1 = " + str(parameters["W1"])) print("b1 = " + str(parameters["b1"])) print("W2 = " + str(parameters["W2"])) print("b2 = " + str(parameters["b2"])) # GRADED FUNCTION: forward_propagation def forward_propagation(X, parameters): Argument: X -- input data of size (n_x, m) parameters -- python dictionary containing your parameters (output of initialization function) Returns: A2 -- The sigmoid output of the second activation cache -- a dictionary containing "Z1", "A1", "Z2" and "A2" # Retrieve each parameter from the dictionary "parameters" ### START CODE HERE ### (≈ 4 lines of code) W1 = parameters["W1"] b1 = parameters["b1"] W2 = parameters["W2"] b2 = parameters["b2"] ### END CODE HERE ### # Implement Forward Propagation to calculate A2 (probabilities) ### START CODE HERE ### (≈ 4 lines of code) Z1 = np.dot(W1, X) + b1 A1 = np.tanh(Z1) Z2 = np.dot(W2, A1) + b2 A2 = sigmoid(Z2) ### END CODE HERE ### assert(A2.shape == (1, X.shape[1])) cache = {"Z1": Z1, "A1": A1, "Z2": Z2, "A2": A2} return A2, cache X_assess, parameters = forward_propagation_test_case() A2, cache = forward_propagation(X_assess, parameters) # Note: we use the mean here just to make sure that your output matches ours. print(np.mean(cache['Z1']) ,np.mean(cache['A1']),np.mean(cache['Z2']),np.mean(cache['A2'])) # GRADED FUNCTION: compute_cost def compute_cost(A2, Y, parameters): Computes the cross-entropy cost given in equation (13) Arguments: A2 -- The sigmoid output of the second activation, of shape (1, number of examples) Y -- "true" labels vector of shape (1, number of examples) parameters -- python dictionary containing your parameters W1, b1, W2 and b2 Returns: cost -- cross-entropy cost given equation (13) m = Y.shape[1] # number of example # Retrieve W1 and W2 from parameters ### START CODE HERE ### (≈ 2 lines of code) W1 = parameters["W1"] W2 = parameters["W2"] ### END CODE HERE ### # Compute the cross-entropy cost ### START CODE HERE ### (≈ 2 lines of code) logprobs = np.multiply(Y, np.log(A2)) + np.multiply(np.log(1 - A2), 1 - Y) cost = - 1 / m * np.sum(logprobs) ### END CODE HERE ### cost = np.squeeze(cost) # makes sure cost is the dimension we expect. # E.g., turns [[17]] into 17 assert(isinstance(cost, float)) return cost A2, Y_assess, parameters = compute_cost_test_case() print("cost = " + str(compute_cost(A2, Y_assess, parameters))) # GRADED FUNCTION: backward_propagation def backward_propagation(parameters, cache, X, Y): Implement the backward propagation using the instructions above. Arguments: parameters -- python dictionary containing our parameters cache -- a dictionary containing "Z1", "A1", "Z2" and "A2". X -- input data of shape (2, number of examples) Y -- "true" labels vector of shape (1, number of examples) Returns: grads -- python dictionary containing your gradients with respect to different parameters m = X.shape[1] # First, retrieve W1 and W2 from the dictionary "parameters". ### START CODE HERE ### (≈ 2 lines of code) W1 = parameters["W1"] W2 = parameters["W2"] ### END CODE HERE ### # Retrieve also A1 and A2 from dictionary "cache". ### START CODE HERE ### (≈ 2 lines of code) A1 = cache["A1"] A2 = cache["A2"] ### END CODE HERE ### # Backward propagation: calculate dW1, db1, dW2, db2. ### START CODE HERE ### (≈ 6 lines of code, corresponding to 6 equations on slide above) dZ2 = A2 - Y dW2 = 1 / m * np.dot(dZ2, A1.T) db2 = 1 / m * np.sum(dZ2, axis=1, keepdims=True) dZ1 = np.dot(W2.T, dZ2) * (1 - np.power(A1, 2)) dW1 = 1 / m * np.dot(dZ1, X.T) db1 = 1 / m * np.sum(dZ1, axis=1, keepdims=True) ### END CODE HERE ### grads = {"dW1": dW1, "db1": db1, "dW2": dW2, "db2": db2} return grads parameters, cache, X_assess, Y_assess = backward_propagation_test_case() grads = backward_propagation(parameters, cache, X_assess, Y_assess) print ("dW1 = "+ str(grads["dW1"])) print ("db1 = "+ str(grads["db1"])) print ("dW2 = "+ str(grads["dW2"])) print ("db2 = "+ str(grads["db2"])) # GRADED FUNCTION: update_parameters def update_parameters(parameters, grads, learning_rate = 1.2): Updates parameters using the gradient descent update rule given above Arguments: parameters -- python dictionary containing your parameters grads -- python dictionary containing your gradients Returns: parameters -- python dictionary containing your updated parameters # Retrieve each parameter from the dictionary "parameters" ### START CODE HERE ### (≈ 4 lines of code) W1 = parameters["W1"] b1 = parameters["b1"] W2 = parameters["W2"] b2 = parameters["b2"] ### END CODE HERE ### # Retrieve each gradient from the dictionary "grads" ### START CODE HERE ### (≈ 4 lines of code) dW1 = grads["dW1"] db1 = grads["db1"] dW2 = grads["dW2"] db2 = grads["db2"] ## END CODE HERE ### # Update rule for each parameter ### START CODE HERE ### (≈ 4 lines of code) W1 = W1 - learning_rate * dW1 b1 = b1 - learning_rate * db1 W2 = W2 - learning_rate * dW2 b2 = b2 - learning_rate * db2 ### END CODE HERE ### parameters = {"W1": W1, "b1": b1, "W2": W2, "b2": b2} return parameters parameters, grads = update_parameters_test_case() parameters = update_parameters(parameters, grads) print("W1 = " + str(parameters["W1"])) print("b1 = " + str(parameters["b1"])) print("W2 = " + str(parameters["W2"])) print("b2 = " + str(parameters["b2"])) # GRADED FUNCTION: nn_model def nn_model(X, Y, n_h, num_iterations = 10000, print_cost=False): Arguments: X -- dataset of shape (2, number of examples) Y -- labels of shape (1, number of examples) n_h -- size of the hidden layer num_iterations -- Number of iterations in gradient descent loop print_cost -- if True, print the cost every 1000 iterations Returns: parameters -- parameters learnt by the model. They can then be used to predict. np.random.seed(3) n_x = layer_sizes(X, Y)[0] n_y = layer_sizes(X, Y)[2] # Initialize parameters, then retrieve W1, b1, W2, b2. Inputs: "n_x, n_h, n_y". Outputs = "W1, b1, W2, b2, parameters". ### START CODE HERE ### (≈ 5 lines of code) parameters = initialize_parameters(n_x, n_h, n_y) W1 = parameters["W1"] b1 = parameters["b1"] W2 = parameters["W2"] b2 = parameters["b2"] ### END CODE HERE ### # Loop (gradient descent) for i in range(0, num_iterations): ### START CODE HERE ### (≈ 4 lines of code) # Forward propagation. Inputs: "X, parameters". Outputs: "A2, cache". A2, cache = forward_propagation(X, parameters) # Cost function. Inputs: "A2, Y, parameters". Outputs: "cost". cost = compute_cost(A2, Y, parameters) # Backpropagation. Inputs: "parameters, cache, X, Y". Outputs: "grads". grads = backward_propagation(parameters, cache, X, Y) # Gradient descent parameter update. Inputs: "parameters, grads". Outputs: "parameters". parameters = update_parameters(parameters, grads) ### END CODE HERE ### # Print the cost every 1000 iterations if print_cost and i % 1000 == 0: print ("Cost after iteration %i: %f" %(i, cost)) return parameters X_assess, Y_assess = nn_model_test_case() parameters = nn_model(X_assess, Y_assess, 4, num_iterations=10000, print_cost=False) print("W1 = " + str(parameters["W1"])) print("b1 = " + str(parameters["b1"])) print("W2 = " + str(parameters["W2"])) print("b2 = " + str(parameters["b2"])) # GRADED FUNCTION: predict def predict(parameters, X): Using the learned parameters, predicts a class for each example in X Arguments: parameters -- python dictionary containing your parameters X -- input data of size (n_x, m) Returns predictions -- vector of predictions of our model (red: 0 / blue: 1) # Computes probabilities using forward propagation, and classifies to 0/1 using 0.5 as the threshold. ### START CODE HERE ### (≈ 2 lines of code) A2, cache = forward_propagation(X, parameters) predictions = (A2 > 0.5) ### END CODE HERE ### return predictions parameters, X_assess = predict_test_case() predictions = predict(parameters, X_assess) print("predictions mean = " + str(np.mean(predictions))) # Build a model with a n_h-dimensional hidden layer parameters = nn_model(X, Y, n_h = 4, num_iterations = 10000, print_cost=True) # Plot the decision boundary plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y) plt.title("Decision Boundary for hidden layer size " + str(4)) # Print accuracy predictions = predict(parameters, X) print ('Accuracy: %d' % float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)100) + '%') # This may take about 2 minutes to run plt.figure(figsize=(16, 32)) hidden_layer_sizes = [1, 2, 3, 4, 5, 20, 50] for i, n_h in enumerate(hidden_layer_sizes): plt.subplot(5, 2, i+1) plt.title('Hidden Layer of size %d' % n_h) parameters = nn_model(X, Y, n_h, num_iterations = 5000) plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y) predictions = predict(parameters, X) accuracy = float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)100) print ("Accuracy for {} hidden units: {} %".format(n_h, accuracy)) # Datasets noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure = load_extra_datasets() datasets = {"noisy_circles": noisy_circles, "noisy_moons": noisy_moons, "blobs": blobs, "gaussian_quantiles": gaussian_quantiles} ### START CODE HERE ### (choose your dataset) dataset = "gaussian_quantiles" ### END CODE HERE ### X, Y = datasets[dataset] X, Y = X.T, Y.reshape(1, Y.shape[0]) # make blobs binary if dataset == "blobs": Y = Y%2 # Visualize the data plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral); <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: 2 - Dataset Step2: Visualize the dataset using matplotlib. The data looks like a "flower" with some red (label y=0) and some blue (y=1) points. Your goal is to build a model to fit this data. Step3: You have Step4: Expected Output Step5: You can now plot the decision boundary of these models. Run the code below. Step7: Expected Output Step9: Expected Output (these are not the sizes you will use for your network, they are just used to assess the function you've just coded). Step11: Expected Output Step13: Expected Output Step15: Expected Output Step17: Expected output Step19: Expected Output Step21: Expected Output Step22: Expected Output Step23: Expected Output Step24: Expected Output Step25: Interpretation
6,953	<ASSISTANT_TASK:> Python Code: # Standard import pandas as pd import numpy as np %matplotlib inline import matplotlib.pyplot as plt # Dimensionality reduction and Clustering from sklearn.decomposition import PCA from sklearn.cluster import KMeans from sklearn.cluster import MeanShift, estimate_bandwidth from sklearn import manifold, datasets from sklearn import preprocessing from itertools import cycle # Plotting tools and classifiers from matplotlib.colors import ListedColormap from sklearn.preprocessing import StandardScaler from sklearn.datasets import make_moons, make_circles, make_classification from sklearn.svm import SVC from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import BaggingClassifier from sklearn.ensemble import GradientBoostingClassifier from sklearn.ensemble import VotingClassifier from sklearn.naive_bayes import GaussianNB from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA from sklearn import cross_validation from sklearn.cross_validation import LeaveOneOut # Let's read the data in and clean it def get_NaNs(df): columns = list(df.columns.get_values()) row_metrics = df.isnull().sum(axis=1) rows_with_na = [] for i, x in enumerate(row_metrics): if x > 0: rows_with_na.append(i) return rows_with_na def remove_NaNs(df): rows_with_na = get_NaNs(df) cleansed_df = df.drop(df.index[rows_with_na], inplace=False) return cleansed_df initial_data = pd.DataFrame.from_csv('Data_Adults_1_reduced_2.csv') cleansed_df = remove_NaNs(initial_data) # Let's also get rid of nominal data numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64'] X = cleansed_df.select_dtypes(include=numerics) print X.shape # Let's extract ADHd and Bipolar patients (mutually exclusive) ADHD = X.loc[X['ADHD'] == 1] ADHD = ADHD.loc[ADHD['Bipolar'] == 0] BP = X.loc[X['Bipolar'] == 1] BP = BP.loc[BP['ADHD'] == 0] print ADHD.shape print BP.shape # Keeping a backup of the data frame object because numpy arrays don't play well with certain scikit functions ADHD = pd.DataFrame(ADHD.drop(['Patient_ID', 'Age', 'ADHD', 'Bipolar'], axis = 1, inplace = False)) BP = pd.DataFrame(BP.drop(['Patient_ID', 'Age', 'ADHD', 'Bipolar'], axis = 1, inplace = False)) print ADHD.shape print BP.shape ADHD_clust = pd.DataFrame(ADHD) BP_clust = pd.DataFrame(BP) # This is a consequence of how we dropped columns, I apologize for the hacky code data = pd.concat([ADHD_clust, BP_clust]) kmeans = KMeans(n_clusters=2) kmeans.fit(data.get_values()) labels = kmeans.labels_ cluster_centers = kmeans.cluster_centers_ print('Estimated number of clusters: %d' % len(cluster_centers)) print data.shape for label in [0, 1]: ds = data.get_values()[np.where(labels == label)] plt.plot(ds[:,0], ds[:,1], '.') lines = plt.plot(cluster_centers[label,0], cluster_centers[label,1], 'o') ADHD_iso = pd.DataFrame(ADHD_clust) BP_iso = pd.DataFrame(BP_clust) BP_iso['ADHD-Bipolar'] = 0 ADHD_iso['ADHD-Bipolar'] = 1 print BP_iso.columns data = pd.DataFrame(pd.concat([ADHD_iso, BP_iso])) class_labels = data['ADHD-Bipolar'] data = data.drop(['ADHD-Bipolar'], axis = 1, inplace = False) print data.shape data = data.get_values() # Leave one Out cross validation def leave_one_out(classifier, values, labels): leave_one_out_validator = LeaveOneOut(len(values)) classifier_metrics = cross_validation.cross_val_score(classifier, values, labels, cv=leave_one_out_validator) accuracy = classifier_metrics.mean() deviation = classifier_metrics.std() return accuracy, deviation svc = SVC(gamma = 2, C = 1) bc = BaggingClassifier(n_estimators = 22) gb = GradientBoostingClassifier() dt = DecisionTreeClassifier(max_depth = 22) qda = QDA() gnb = GaussianNB() vc = VotingClassifier(estimators=[('gb', gb), ('bc', bc), ('gnb', gnb)],voting='hard') classifier_accuracy_list = [] classifiers = [(gnb, "Gaussian NB"), (qda, "QDA"), (svc, "SVM"), (bc, "Bagging Classifier"), (vc, "Voting Classifier"), (dt, "Decision Trees")] for classifier, name in classifiers: accuracy, deviation = leave_one_out(classifier, data, class_labels) print '%s accuracy is %0.4f (+/- %0.3f)' % (name, accuracy, deviation) classifier_accuracy_list.append((name, accuracy)) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: we've now dropped the last of the discrete numerical inexplicable data, and removed children from the mix Step2: Clustering and other grouping experiments Step3: K-Means clustering Step4: Classification Experiments
6,954	<ASSISTANT_TASK:> Python Code: # DO NOT EDIT ! from pyesdoc.ipython.model_topic import NotebookOutput # DO NOT EDIT ! DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-lm', 'atmos') # Set as follows: DOC.set_author("name", "email") # TODO - please enter value(s) # Set as follows: DOC.set_contributor("name", "email") # TODO - please enter value(s) # Set publication status: # 0=do not publish, 1=publish. DOC.set_publication_status(0) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.overview.model_overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.overview.model_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.overview.model_family') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "AGCM" # "ARCM" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.overview.basic_approximations') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "primitive equations" # "non-hydrostatic" # "anelastic" # "Boussinesq" # "hydrostatic" # "quasi-hydrostatic" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.resolution.horizontal_resolution_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.resolution.canonical_horizontal_resolution') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.resolution.range_horizontal_resolution') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.resolution.number_of_vertical_levels') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.resolution.high_top') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_dynamics') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_shortwave_radiative_transfer') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_longwave_radiative_transfer') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.orography.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "present day" # "modified" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.orography.changes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "related to ice sheets" # "related to tectonics" # "modified mean" # "modified variance if taken into account in model (cf gravity waves)" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "spectral" # "fixed grid" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "finite elements" # "finite volumes" # "finite difference" # "centered finite difference" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_order') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "second" # "third" # "fourth" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.horizontal_pole') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "filter" # "pole rotation" # "artificial island" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.grid_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Gaussian" # "Latitude-Longitude" # "Cubed-Sphere" # "Icosahedral" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.vertical.coordinate_type') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "isobaric" # "sigma" # "hybrid sigma-pressure" # "hybrid pressure" # "vertically lagrangian" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.timestepping_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Adams-Bashforth" # "explicit" # "implicit" # "semi-implicit" # "leap frog" # "multi-step" # "Runge Kutta fifth order" # "Runge Kutta second order" # "Runge Kutta third order" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.prognostic_variables') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "surface pressure" # "wind components" # "divergence/curl" # "temperature" # "potential temperature" # "total water" # "water vapour" # "water liquid" # "water ice" # "total water moments" # "clouds" # "radiation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_boundary_condition') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "sponge layer" # "radiation boundary condition" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_heat') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_wind') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.lateral_boundary.condition') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "sponge layer" # "radiation boundary condition" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.diffusion_horizontal.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.diffusion_horizontal.scheme_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "iterated Laplacian" # "bi-harmonic" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Heun" # "Roe and VanLeer" # "Roe and Superbee" # "Prather" # "UTOPIA" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.scheme_characteristics') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "Eulerian" # "modified Euler" # "Lagrangian" # "semi-Lagrangian" # "cubic semi-Lagrangian" # "quintic semi-Lagrangian" # "mass-conserving" # "finite volume" # "flux-corrected" # "linear" # "quadratic" # "quartic" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.conserved_quantities') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "dry mass" # "tracer mass" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.conservation_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "conservation fixer" # "Priestley algorithm" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "VanLeer" # "Janjic" # "SUPG (Streamline Upwind Petrov-Galerkin)" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_characteristics') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "2nd order" # "4th order" # "cell-centred" # "staggered grid" # "semi-staggered grid" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_staggering_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Arakawa B-grid" # "Arakawa C-grid" # "Arakawa D-grid" # "Arakawa E-grid" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.conserved_quantities') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "Angular momentum" # "Horizontal momentum" # "Enstrophy" # "Mass" # "Total energy" # "Vorticity" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.conservation_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "conservation fixer" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.aerosols') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "sulphate" # "nitrate" # "sea salt" # "dust" # "ice" # "organic" # "BC (black carbon / soot)" # "SOA (secondary organic aerosols)" # "POM (particulate organic matter)" # "polar stratospheric ice" # "NAT (nitric acid trihydrate)" # "NAD (nitric acid dihydrate)" # "STS (supercooled ternary solution aerosol particle)" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.spectral_integration') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "wide-band model" # "correlated-k" # "exponential sum fitting" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.transport_calculation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "two-stream" # "layer interaction" # "bulk" # "adaptive" # "multi-stream" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.spectral_intervals') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.greenhouse_gas_complexity') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "CO2" # "CH4" # "N2O" # "CFC-11 eq" # "CFC-12 eq" # "HFC-134a eq" # "Explicit ODSs" # "Explicit other fluorinated gases" # "O3" # "H2O" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.ODS') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "CFC-12" # "CFC-11" # "CFC-113" # "CFC-114" # "CFC-115" # "HCFC-22" # "HCFC-141b" # "HCFC-142b" # "Halon-1211" # "Halon-1301" # "Halon-2402" # "methyl chloroform" # "carbon tetrachloride" # "methyl chloride" # "methylene chloride" # "chloroform" # "methyl bromide" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.other_flourinated_gases') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "HFC-134a" # "HFC-23" # "HFC-32" # "HFC-125" # "HFC-143a" # "HFC-152a" # "HFC-227ea" # "HFC-236fa" # "HFC-245fa" # "HFC-365mfc" # "HFC-43-10mee" # "CF4" # "C2F6" # "C3F8" # "C4F10" # "C5F12" # "C6F14" # "C7F16" # "C8F18" # "c-C4F8" # "NF3" # "SF6" # "SO2F2" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.physical_representation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "bi-modal size distribution" # "ensemble of ice crystals" # "mean projected area" # "ice water path" # "crystal asymmetry" # "crystal aspect ratio" # "effective crystal radius" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.optical_methods') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "T-matrix" # "geometric optics" # "finite difference time domain (FDTD)" # "Mie theory" # "anomalous diffraction approximation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.physical_representation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "cloud droplet number concentration" # "effective cloud droplet radii" # "droplet size distribution" # "liquid water path" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.optical_methods') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "geometric optics" # "Mie theory" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_inhomogeneity.cloud_inhomogeneity') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Monte Carlo Independent Column Approximation" # "Triplecloud" # "analytic" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.physical_representation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "number concentration" # "effective radii" # "size distribution" # "asymmetry" # "aspect ratio" # "mixing state" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.optical_methods') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "T-matrix" # "geometric optics" # "finite difference time domain (FDTD)" # "Mie theory" # "anomalous diffraction approximation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_gases.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_radiation.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_radiation.name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_radiation.spectral_integration') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "wide-band model" # "correlated-k" # "exponential sum fitting" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_radiation.transport_calculation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "two-stream" # "layer interaction" # "bulk" # "adaptive" # "multi-stream" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_radiation.spectral_intervals') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_GHG.greenhouse_gas_complexity') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "CO2" # "CH4" # "N2O" # "CFC-11 eq" # "CFC-12 eq" # "HFC-134a eq" # "Explicit ODSs" # "Explicit other fluorinated gases" # "O3" # "H2O" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_GHG.ODS') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "CFC-12" # "CFC-11" # "CFC-113" # "CFC-114" # "CFC-115" # "HCFC-22" # "HCFC-141b" # "HCFC-142b" # "Halon-1211" # "Halon-1301" # "Halon-2402" # "methyl chloroform" # "carbon tetrachloride" # "methyl chloride" # "methylene chloride" # "chloroform" # "methyl bromide" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_GHG.other_flourinated_gases') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "HFC-134a" # "HFC-23" # "HFC-32" # "HFC-125" # "HFC-143a" # "HFC-152a" # "HFC-227ea" # "HFC-236fa" # "HFC-245fa" # "HFC-365mfc" # "HFC-43-10mee" # "CF4" # "C2F6" # "C3F8" # "C4F10" # "C5F12" # "C6F14" # "C7F16" # "C8F18" # "c-C4F8" # "NF3" # "SF6" # "SO2F2" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.physical_reprenstation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "bi-modal size distribution" # "ensemble of ice crystals" # "mean projected area" # "ice water path" # "crystal asymmetry" # "crystal aspect ratio" # "effective crystal radius" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.optical_methods') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "T-matrix" # "geometric optics" # "finite difference time domain (FDTD)" # "Mie theory" # "anomalous diffraction approximation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.physical_representation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "cloud droplet number concentration" # "effective cloud droplet radii" # "droplet size distribution" # "liquid water path" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.optical_methods') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "geometric optics" # "Mie theory" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_inhomogeneity.cloud_inhomogeneity') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Monte Carlo Independent Column Approximation" # "Triplecloud" # "analytic" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.physical_representation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "number concentration" # "effective radii" # "size distribution" # "asymmetry" # "aspect ratio" # "mixing state" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.optical_methods') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "T-matrix" # "geometric optics" # "finite difference time domain (FDTD)" # "Mie theory" # "anomalous diffraction approximation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_gases.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Mellor-Yamada" # "Holtslag-Boville" # "EDMF" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.scheme_type') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "TKE prognostic" # "TKE diagnostic" # "TKE coupled with water" # "vertical profile of Kz" # "non-local diffusion" # "Monin-Obukhov similarity" # "Coastal Buddy Scheme" # "Coupled with convection" # "Coupled with gravity waves" # "Depth capped at cloud base" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.closure_order') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.counter_gradient') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_type') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "mass-flux" # "adjustment" # "plume ensemble" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_method') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "CAPE" # "bulk" # "ensemble" # "CAPE/WFN based" # "TKE/CIN based" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.processes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "vertical momentum transport" # "convective momentum transport" # "entrainment" # "detrainment" # "penetrative convection" # "updrafts" # "downdrafts" # "radiative effect of anvils" # "re-evaporation of convective precipitation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.microphysics') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "tuning parameter based" # "single moment" # "two moment" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_type') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "mass-flux" # "cumulus-capped boundary layer" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "same as deep (unified)" # "included in boundary layer turbulence" # "separate diagnosis" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.processes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "convective momentum transport" # "entrainment" # "detrainment" # "penetrative convection" # "re-evaporation of convective precipitation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.microphysics') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "tuning parameter based" # "single moment" # "two moment" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.microphysics_precipitation.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_precipitation.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_precipitation.hydrometeors') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "liquid rain" # "snow" # "hail" # "graupel" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_cloud_microphysics.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_cloud_microphysics.processes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "mixed phase" # "cloud droplets" # "cloud ice" # "ice nucleation" # "water vapour deposition" # "effect of raindrops" # "effect of snow" # "effect of graupel" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.atmos_coupling') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "atmosphere_radiation" # "atmosphere_microphysics_precipitation" # "atmosphere_turbulence_convection" # "atmosphere_gravity_waves" # "atmosphere_solar" # "atmosphere_volcano" # "atmosphere_cloud_simulator" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.uses_separate_treatment') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.processes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "entrainment" # "detrainment" # "bulk cloud" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.prognostic_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.diagnostic_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.prognostic_variables') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "cloud amount" # "liquid" # "ice" # "rain" # "snow" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.optical_cloud_properties.cloud_overlap_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "random" # "maximum" # "maximum-random" # "exponential" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.optical_cloud_properties.cloud_inhomogeneity') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "prognostic" # "diagnostic" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.function_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.function_order') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.convection_coupling') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "coupled with deep" # "coupled with shallow" # "not coupled with convection" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "prognostic" # "diagnostic" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.function_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.function_order') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.convection_coupling') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "coupled with deep" # "coupled with shallow" # "not coupled with convection" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.isscp_attributes.top_height_estimation_method') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "no adjustment" # "IR brightness" # "visible optical depth" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.isscp_attributes.top_height_direction') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "lowest altitude level" # "highest altitude level" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.run_configuration') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Inline" # "Offline" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_grid_points') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_sub_columns') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_levels') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.frequency') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "surface" # "space borne" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.gas_absorption') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.effective_radius') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.lidar_inputs.ice_types') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "ice spheres" # "ice non-spherical" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.lidar_inputs.overlap') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "max" # "random" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.sponge_layer') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Rayleigh friction" # "Diffusive sponge layer" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.background') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "continuous spectrum" # "discrete spectrum" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.subgrid_scale_orography') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "effect on drag" # "effect on lifting" # "enhanced topography" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.source_mechanisms') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "linear mountain waves" # "hydraulic jump" # "envelope orography" # "low level flow blocking" # "statistical sub-grid scale variance" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.calculation_method') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "non-linear calculation" # "more than two cardinal directions" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.propagation_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "linear theory" # "non-linear theory" # "includes boundary layer ducting" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.dissipation_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "total wave" # "single wave" # "spectral" # "linear" # "wave saturation vs Richardson number" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.source_mechanisms') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "convection" # "precipitation" # "background spectrum" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.calculation_method') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "spatially dependent" # "temporally dependent" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.propagation_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "linear theory" # "non-linear theory" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.dissipation_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "total wave" # "single wave" # "spectral" # "linear" # "wave saturation vs Richardson number" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.solar_pathways.pathways') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "SW radiation" # "precipitating energetic particles" # "cosmic rays" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.solar_constant.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "fixed" # "transient" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.solar_constant.fixed_value') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.solar_constant.transient_characteristics') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.orbital_parameters.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "fixed" # "transient" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.orbital_parameters.fixed_reference_date') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.orbital_parameters.transient_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.orbital_parameters.computation_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Berger 1978" # "Laskar 2004" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.insolation_ozone.solar_ozone_impact') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.volcanos.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.volcanos.volcanoes_treatment.volcanoes_implementation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "high frequency solar constant anomaly" # "stratospheric aerosols optical thickness" # "Other: [Please specify]" # TODO - please enter value(s) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Document Authors Step2: Document Contributors Step3: Document Publication Step4: Document Table of Contents Step5: 1.2. Model Name Step6: 1.3. Model Family Step7: 1.4. Basic Approximations Step8: 2. Key Properties --> Resolution Step9: 2.2. Canonical Horizontal Resolution Step10: 2.3. Range Horizontal Resolution Step11: 2.4. Number Of Vertical Levels Step12: 2.5. High Top Step13: 3. Key Properties --> Timestepping Step14: 3.2. Timestep Shortwave Radiative Transfer Step15: 3.3. Timestep Longwave Radiative Transfer Step16: 4. Key Properties --> Orography Step17: 4.2. Changes Step18: 5. Grid --> Discretisation Step19: 6. Grid --> Discretisation --> Horizontal Step20: 6.2. Scheme Method Step21: 6.3. Scheme Order Step22: 6.4. Horizontal Pole Step23: 6.5. Grid Type Step24: 7. Grid --> Discretisation --> Vertical Step25: 8. Dynamical Core Step26: 8.2. Name Step27: 8.3. Timestepping Type Step28: 8.4. Prognostic Variables Step29: 9. Dynamical Core --> Top Boundary Step30: 9.2. Top Heat Step31: 9.3. Top Wind Step32: 10. Dynamical Core --> Lateral Boundary Step33: 11. Dynamical Core --> Diffusion Horizontal Step34: 11.2. Scheme Method Step35: 12. Dynamical Core --> Advection Tracers Step36: 12.2. Scheme Characteristics Step37: 12.3. Conserved Quantities Step38: 12.4. Conservation Method Step39: 13. Dynamical Core --> Advection Momentum Step40: 13.2. Scheme Characteristics Step41: 13.3. Scheme Staggering Type Step42: 13.4. Conserved Quantities Step43: 13.5. Conservation Method Step44: 14. Radiation Step45: 15. Radiation --> Shortwave Radiation Step46: 15.2. Name Step47: 15.3. Spectral Integration Step48: 15.4. Transport Calculation Step49: 15.5. Spectral Intervals Step50: 16. Radiation --> Shortwave GHG Step51: 16.2. ODS Step52: 16.3. Other Flourinated Gases Step53: 17. Radiation --> Shortwave Cloud Ice Step54: 17.2. Physical Representation Step55: 17.3. Optical Methods Step56: 18. Radiation --> Shortwave Cloud Liquid Step57: 18.2. Physical Representation Step58: 18.3. Optical Methods Step59: 19. Radiation --> Shortwave Cloud Inhomogeneity Step60: 20. Radiation --> Shortwave Aerosols Step61: 20.2. Physical Representation Step62: 20.3. Optical Methods Step63: 21. Radiation --> Shortwave Gases Step64: 22. Radiation --> Longwave Radiation Step65: 22.2. Name Step66: 22.3. Spectral Integration Step67: 22.4. Transport Calculation Step68: 22.5. Spectral Intervals Step69: 23. Radiation --> Longwave GHG Step70: 23.2. ODS Step71: 23.3. Other Flourinated Gases Step72: 24. Radiation --> Longwave Cloud Ice Step73: 24.2. Physical Reprenstation Step74: 24.3. Optical Methods Step75: 25. Radiation --> Longwave Cloud Liquid Step76: 25.2. Physical Representation Step77: 25.3. Optical Methods Step78: 26. Radiation --> Longwave Cloud Inhomogeneity Step79: 27. Radiation --> Longwave Aerosols Step80: 27.2. Physical Representation Step81: 27.3. Optical Methods Step82: 28. Radiation --> Longwave Gases Step83: 29. Turbulence Convection Step84: 30. Turbulence Convection --> Boundary Layer Turbulence Step85: 30.2. Scheme Type Step86: 30.3. Closure Order Step87: 30.4. Counter Gradient Step88: 31. Turbulence Convection --> Deep Convection Step89: 31.2. Scheme Type Step90: 31.3. Scheme Method Step91: 31.4. Processes Step92: 31.5. Microphysics Step93: 32. Turbulence Convection --> Shallow Convection Step94: 32.2. Scheme Type Step95: 32.3. Scheme Method Step96: 32.4. Processes Step97: 32.5. Microphysics Step98: 33. Microphysics Precipitation Step99: 34. Microphysics Precipitation --> Large Scale Precipitation Step100: 34.2. Hydrometeors Step101: 35. Microphysics Precipitation --> Large Scale Cloud Microphysics Step102: 35.2. Processes Step103: 36. Cloud Scheme Step104: 36.2. Name Step105: 36.3. Atmos Coupling Step106: 36.4. Uses Separate Treatment Step107: 36.5. Processes Step108: 36.6. Prognostic Scheme Step109: 36.7. Diagnostic Scheme Step110: 36.8. Prognostic Variables Step111: 37. Cloud Scheme --> Optical Cloud Properties Step112: 37.2. Cloud Inhomogeneity Step113: 38. Cloud Scheme --> Sub Grid Scale Water Distribution Step114: 38.2. Function Name Step115: 38.3. Function Order Step116: 38.4. Convection Coupling Step117: 39. Cloud Scheme --> Sub Grid Scale Ice Distribution Step118: 39.2. Function Name Step119: 39.3. Function Order Step120: 39.4. Convection Coupling Step121: 40. Observation Simulation Step122: 41. Observation Simulation --> Isscp Attributes Step123: 41.2. Top Height Direction Step124: 42. Observation Simulation --> Cosp Attributes Step125: 42.2. Number Of Grid Points Step126: 42.3. Number Of Sub Columns Step127: 42.4. Number Of Levels Step128: 43. Observation Simulation --> Radar Inputs Step129: 43.2. Type Step130: 43.3. Gas Absorption Step131: 43.4. Effective Radius Step132: 44. Observation Simulation --> Lidar Inputs Step133: 44.2. Overlap Step134: 45. Gravity Waves Step135: 45.2. Sponge Layer Step136: 45.3. Background Step137: 45.4. Subgrid Scale Orography Step138: 46. Gravity Waves --> Orographic Gravity Waves Step139: 46.2. Source Mechanisms Step140: 46.3. Calculation Method Step141: 46.4. Propagation Scheme Step142: 46.5. Dissipation Scheme Step143: 47. Gravity Waves --> Non Orographic Gravity Waves Step144: 47.2. Source Mechanisms Step145: 47.3. Calculation Method Step146: 47.4. Propagation Scheme Step147: 47.5. Dissipation Scheme Step148: 48. Solar Step149: 49. Solar --> Solar Pathways Step150: 50. Solar --> Solar Constant Step151: 50.2. Fixed Value Step152: 50.3. Transient Characteristics Step153: 51. Solar --> Orbital Parameters Step154: 51.2. Fixed Reference Date Step155: 51.3. Transient Method Step156: 51.4. Computation Method Step157: 52. Solar --> Insolation Ozone Step158: 53. Volcanos Step159: 54. Volcanos --> Volcanoes Treatment
6,955	<ASSISTANT_TASK:> Python Code: energy = 6 applicator = 10 ssd = 100 x = [0.99, -0.14, -1.0, -1.73, -2.56, -3.17, -3.49, -3.57, -3.17, -2.52, -1.76, -1.04, -0.17, 0.77, 1.63, 2.36, 2.79, 2.91, 3.04, 3.22, 3.34, 3.37, 3.08, 2.54, 1.88, 1.02, 0.99] y = [5.05, 4.98, 4.42, 3.24, 1.68, 0.6, -0.64, -1.48, -2.38, -3.77, -4.81, -5.26, -5.51, -5.58, -5.23, -4.64, -3.77, -2.77, -1.68, -0.29, 1.23, 2.68, 3.8, 4.6, 5.01, 5.08, 5.05] width, length, poi = parameterise_single_insert(x, y) print("Width = {0:0.2f} cm\nLength = {1:0.2f} cm".format(width, length)) display_parameterisation(x, y, width, length, poi) data_filelist = glob('*_data.csv') assert len(data_filelist) <= 1, "There appears to be more than 1 data.csv file, please delete the old version(s)" assert len(data_filelist) == 1, "I need at least one data file" data_filename = data_filelist[0] data = pd.read_csv(data_filename) reference = ( (data['Energy (MeV)'] == energy) & (data['Applicator (cm)'] == applicator) & (data['SSD (cm)'] == ssd) ) input_dataframe = data[reference] label = np.array(input_dataframe['Label']).astype(str) width_data = np.array( input_dataframe['Width (cm @ 100SSD)']).astype(float) length_data = np.array( input_dataframe['Length (cm @ 100SSD)']).astype(float) factor_data = np.array( input_dataframe['Insert factor (dose insert / dose open)']).astype(float) input_dataframe number_of_data = len(input_dataframe) if number_of_data >= 8: ratio_perim_area_data = convert2_ratio_perim_area(width_data, length_data) ratio_perim_area = convert2_ratio_perim_area(width, length) factor = float(spline_model_with_deformability(width, ratio_perim_area, width_data, ratio_perim_area_data, factor_data)) else: factor = np.nan print("Width = {0:0.2f} cm\nLength = {1:0.2f} cm\nFactor = {2:0.4f}".format( width, length, factor)) if number_of_data >= 8: ratio_perim_area_data = convert2_ratio_perim_area(width_data, length_data) figure = interactive( width_data, length_data, ratio_perim_area_data, factor_data, label) else: figure = fallback_scatter(width_data, length_data, factor_data, label) show(figure) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Load model data Step2: Only use the data for the specified energy, applicator, and ssd Step3: Calculate the factor Step4: Display the model
6,956	<ASSISTANT_TASK:> Python Code: # DO NOT EDIT ! from pyesdoc.ipython.model_topic import NotebookOutput # DO NOT EDIT ! DOC = NotebookOutput('cmip6', 'snu', 'sandbox-2', 'landice') # Set as follows: DOC.set_author("name", "email") # TODO - please enter value(s) # Set as follows: DOC.set_contributor("name", "email") # TODO - please enter value(s) # Set publication status: # 0=do not publish, 1=publish. DOC.set_publication_status(0) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.key_properties.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.key_properties.model_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.key_properties.ice_albedo') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "prescribed" # "function of ice age" # "function of ice density" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.key_properties.atmospheric_coupling_variables') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.key_properties.oceanic_coupling_variables') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.key_properties.prognostic_variables') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "ice velocity" # "ice thickness" # "ice temperature" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.key_properties.software_properties.repository') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.key_properties.software_properties.code_version') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.key_properties.software_properties.code_languages') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.grid.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.grid.adaptive_grid') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.grid.base_resolution') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.grid.resolution_limit') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.grid.projection') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.glaciers.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.glaciers.description') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.glaciers.dynamic_areal_extent') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.ice.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.ice.grounding_line_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "grounding line prescribed" # "flux prescribed (Schoof)" # "fixed grid size" # "moving grid" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.ice.ice_sheet') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.ice.ice_shelf') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.ice.mass_balance.surface_mass_balance') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.ice.mass_balance.basal.bedrock') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.ice.mass_balance.basal.ocean') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.ice.mass_balance.frontal.calving') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.ice.mass_balance.frontal.melting') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.ice.dynamics.description') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.ice.dynamics.approximation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "SIA" # "SAA" # "full stokes" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.ice.dynamics.adaptive_timestep') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.landice.ice.dynamics.timestep') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Document Authors Step2: Document Contributors Step3: Document Publication Step4: Document Table of Contents Step5: 1.2. Model Name Step6: 1.3. Ice Albedo Step7: 1.4. Atmospheric Coupling Variables Step8: 1.5. Oceanic Coupling Variables Step9: 1.6. Prognostic Variables Step10: 2. Key Properties --> Software Properties Step11: 2.2. Code Version Step12: 2.3. Code Languages Step13: 3. Grid Step14: 3.2. Adaptive Grid Step15: 3.3. Base Resolution Step16: 3.4. Resolution Limit Step17: 3.5. Projection Step18: 4. Glaciers Step19: 4.2. Description Step20: 4.3. Dynamic Areal Extent Step21: 5. Ice Step22: 5.2. Grounding Line Method Step23: 5.3. Ice Sheet Step24: 5.4. Ice Shelf Step25: 6. Ice --> Mass Balance Step26: 7. Ice --> Mass Balance --> Basal Step27: 7.2. Ocean Step28: 8. Ice --> Mass Balance --> Frontal Step29: 8.2. Melting Step30: 9. Ice --> Dynamics Step31: 9.2. Approximation Step32: 9.3. Adaptive Timestep Step33: 9.4. Timestep
6,957	<ASSISTANT_TASK:> Python Code: import numpy as np import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers import tensorflow_addons as tfa num_classes = 100 input_shape = (32, 32, 3) (x_train, y_train), (x_test, y_test) = keras.datasets.cifar100.load_data() print(f"x_train shape: {x_train.shape} - y_train shape: {y_train.shape}") print(f"x_test shape: {x_test.shape} - y_test shape: {y_test.shape}") weight_decay = 0.0001 batch_size = 128 num_epochs = 50 dropout_rate = 0.2 image_size = 64 # We'll resize input images to this size. patch_size = 8 # Size of the patches to be extracted from the input images. num_patches = (image_size // patch_size) 2 # Size of the data array. embedding_dim = 256 # Number of hidden units. num_blocks = 4 # Number of blocks. print(f"Image size: {image_size} X {image_size} = {image_size 2}") print(f"Patch size: {patch_size} X {patch_size} = {patch_size 2} ") print(f"Patches per image: {num_patches}") print(f"Elements per patch (3 channels): {(patch_size 2) * 3}") def build_classifier(blocks, positional_encoding=False): inputs = layers.Input(shape=input_shape) # Augment data. augmented = data_augmentation(inputs) # Create patches. patches = Patches(patch_size, num_patches)(augmented) # Encode patches to generate a [batch_size, num_patches, embedding_dim] tensor. x = layers.Dense(units=embedding_dim)(patches) if positional_encoding: positions = tf.range(start=0, limit=num_patches, delta=1) position_embedding = layers.Embedding( input_dim=num_patches, output_dim=embedding_dim )(positions) x = x + position_embedding # Process x using the module blocks. x = blocks(x) # Apply global average pooling to generate a [batch_size, embedding_dim] representation tensor. representation = layers.GlobalAveragePooling1D()(x) # Apply dropout. representation = layers.Dropout(rate=dropout_rate)(representation) # Compute logits outputs. logits = layers.Dense(num_classes)(representation) # Create the Keras model. return keras.Model(inputs=inputs, outputs=logits) def run_experiment(model): # Create Adam optimizer with weight decay. optimizer = tfa.optimizers.AdamW( learning_rate=learning_rate, weight_decay=weight_decay, ) # Compile the model. model.compile( optimizer=optimizer, loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=[ keras.metrics.SparseCategoricalAccuracy(name="acc"), keras.metrics.SparseTopKCategoricalAccuracy(5, name="top5-acc"), ], ) # Create a learning rate scheduler callback. reduce_lr = keras.callbacks.ReduceLROnPlateau( monitor="val_loss", factor=0.5, patience=5 ) # Create an early stopping callback. early_stopping = tf.keras.callbacks.EarlyStopping( monitor="val_loss", patience=10, restore_best_weights=True ) # Fit the model. history = model.fit( x=x_train, y=y_train, batch_size=batch_size, epochs=num_epochs, validation_split=0.1, callbacks=[early_stopping, reduce_lr], ) _, accuracy, top_5_accuracy = model.evaluate(x_test, y_test) print(f"Test accuracy: {round(accuracy * 100, 2)}%") print(f"Test top 5 accuracy: {round(top_5_accuracy * 100, 2)}%") # Return history to plot learning curves. return history data_augmentation = keras.Sequential( [ layers.Normalization(), layers.Resizing(image_size, image_size), layers.RandomFlip("horizontal"), layers.RandomZoom( height_factor=0.2, width_factor=0.2 ), ], name="data_augmentation", ) # Compute the mean and the variance of the training data for normalization. data_augmentation.layers[0].adapt(x_train) class Patches(layers.Layer): def __init__(self, patch_size, num_patches): super(Patches, self).__init__() self.patch_size = patch_size self.num_patches = num_patches def call(self, images): batch_size = tf.shape(images)[0] patches = tf.image.extract_patches( images=images, sizes=[1, self.patch_size, self.patch_size, 1], strides=[1, self.patch_size, self.patch_size, 1], rates=[1, 1, 1, 1], padding="VALID", ) patch_dims = patches.shape[-1] patches = tf.reshape(patches, [batch_size, self.num_patches, patch_dims]) return patches class MLPMixerLayer(layers.Layer): def __init__(self, num_patches, hidden_units, dropout_rate, args, kwargs): super(MLPMixerLayer, self).__init__(args, *kwargs) self.mlp1 = keras.Sequential( [ layers.Dense(units=num_patches), tfa.layers.GELU(), layers.Dense(units=num_patches), layers.Dropout(rate=dropout_rate), ] ) self.mlp2 = keras.Sequential( [ layers.Dense(units=num_patches), tfa.layers.GELU(), layers.Dense(units=embedding_dim), layers.Dropout(rate=dropout_rate), ] ) self.normalize = layers.LayerNormalization(epsilon=1e-6) def call(self, inputs): # Apply layer normalization. x = self.normalize(inputs) # Transpose inputs from [num_batches, num_patches, hidden_units] to [num_batches, hidden_units, num_patches]. x_channels = tf.linalg.matrix_transpose(x) # Apply mlp1 on each channel independently. mlp1_outputs = self.mlp1(x_channels) # Transpose mlp1_outputs from [num_batches, hidden_dim, num_patches] to [num_batches, num_patches, hidden_units]. mlp1_outputs = tf.linalg.matrix_transpose(mlp1_outputs) # Add skip connection. x = mlp1_outputs + inputs # Apply layer normalization. x_patches = self.normalize(x) # Apply mlp2 on each patch independtenly. mlp2_outputs = self.mlp2(x_patches) # Add skip connection. x = x + mlp2_outputs return x mlpmixer_blocks = keras.Sequential( [MLPMixerLayer(num_patches, embedding_dim, dropout_rate) for _ in range(num_blocks)] ) learning_rate = 0.005 mlpmixer_classifier = build_classifier(mlpmixer_blocks) history = run_experiment(mlpmixer_classifier) class FNetLayer(layers.Layer): def __init__(self, num_patches, embedding_dim, dropout_rate, args, *kwargs): super(FNetLayer, self).__init__(args, *kwargs) self.ffn = keras.Sequential( [ layers.Dense(units=embedding_dim), tfa.layers.GELU(), layers.Dropout(rate=dropout_rate), layers.Dense(units=embedding_dim), ] ) self.normalize1 = layers.LayerNormalization(epsilon=1e-6) self.normalize2 = layers.LayerNormalization(epsilon=1e-6) def call(self, inputs): # Apply fourier transformations. x = tf.cast( tf.signal.fft2d(tf.cast(inputs, dtype=tf.dtypes.complex64)), dtype=tf.dtypes.float32, ) # Add skip connection. x = x + inputs # Apply layer normalization. x = self.normalize1(x) # Apply Feedfowrad network. x_ffn = self.ffn(x) # Add skip connection. x = x + x_ffn # Apply layer normalization. return self.normalize2(x) fnet_blocks = keras.Sequential( [FNetLayer(num_patches, embedding_dim, dropout_rate) for _ in range(num_blocks)] ) learning_rate = 0.001 fnet_classifier = build_classifier(fnet_blocks, positional_encoding=True) history = run_experiment(fnet_classifier) class gMLPLayer(layers.Layer): def __init__(self, num_patches, embedding_dim, dropout_rate, args, *kwargs): super(gMLPLayer, self).__init__(args, *kwargs) self.channel_projection1 = keras.Sequential( [ layers.Dense(units=embedding_dim 2), tfa.layers.GELU(), layers.Dropout(rate=dropout_rate), ] ) self.channel_projection2 = layers.Dense(units=embedding_dim) self.spatial_projection = layers.Dense( units=num_patches, bias_initializer="Ones" ) self.normalize1 = layers.LayerNormalization(epsilon=1e-6) self.normalize2 = layers.LayerNormalization(epsilon=1e-6) def spatial_gating_unit(self, x): # Split x along the channel dimensions. # Tensors u and v will in th shape of [batch_size, num_patchs, embedding_dim]. u, v = tf.split(x, num_or_size_splits=2, axis=2) # Apply layer normalization. v = self.normalize2(v) # Apply spatial projection. v_channels = tf.linalg.matrix_transpose(v) v_projected = self.spatial_projection(v_channels) v_projected = tf.linalg.matrix_transpose(v_projected) # Apply element-wise multiplication. return u * v_projected def call(self, inputs): # Apply layer normalization. x = self.normalize1(inputs) # Apply the first channel projection. x_projected shape: [batch_size, num_patches, embedding_dim * 2]. x_projected = self.channel_projection1(x) # Apply the spatial gating unit. x_spatial shape: [batch_size, num_patches, embedding_dim]. x_spatial = self.spatial_gating_unit(x_projected) # Apply the second channel projection. x_projected shape: [batch_size, num_patches, embedding_dim]. x_projected = self.channel_projection2(x_spatial) # Add skip connection. return x + x_projected gmlp_blocks = keras.Sequential( [gMLPLayer(num_patches, embedding_dim, dropout_rate) for _ in range(num_blocks)] ) learning_rate = 0.003 gmlp_classifier = build_classifier(gmlp_blocks) history = run_experiment(gmlp_classifier) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Prepare the data Step2: Configure the hyperparameters Step3: Build a classification model Step4: Define an experiment Step5: Use data augmentation Step6: Implement patch extraction as a layer Step7: The MLP-Mixer model Step8: Build, train, and evaluate the MLP-Mixer model Step9: The MLP-Mixer model tends to have much less number of parameters compared Step10: Build, train, and evaluate the FNet model Step11: As shown in the FNet paper, Step12: Build, train, and evaluate the gMLP model
6,958	<ASSISTANT_TASK:> Python Code: import warnings import scipy as sp import numpy as np import porespy as ps import openpnm as op import matplotlib.pyplot as plt ws = op.Workspace() ws.settings["loglevel"] = 40 warnings.filterwarnings('ignore') %matplotlib inline np.random.seed(10) # NBVAL_IGNORE_OUTPUT im = ps.generators.overlapping_spheres(shape=[200, 200, 200], r=10, porosity=0.5, maxiter=0) plt.imshow(im[:, :, 50]); eps = ps.metrics.porosity(im) print(f"Porosity: {eps100:.1f}%") # NBVAL_IGNORE_OUTPUT im_3d = ps.visualization.show_3D(im) plt.imshow(im_3d, cmap=plt.cm.magma); # NBVAL_IGNORE_OUTPUT snow = ps.networks.snow2(im, boundary_width=[[0, 3], 0, 0]) # NBVAL_IGNORE_OUTPUT proj = op.io.PoreSpy.import_data(snow.network) print(proj) # NBVAL_IGNORE_OUTPUT net = proj[0] geo = proj[1] print(net) # NBVAL_IGNORE_OUTPUT print(geo) # NBVAL_IGNORE_OUTPUT fig, ax = plt.subplots(1, 1, figsize=[8, 8]) ax = op.topotools.plot_connections(network=net, alpha=0.8, color='grey', ax=ax) ax = op.topotools.plot_coordinates(network=net, ax=ax, color='b', markersize=50) ax = op.topotools.plot_coordinates(network=net, pores=net.pores('xmax'), ax=ax, color='r', markersize=50) m = np.meshgrid(range(50, 195, 10), range(50, 195, 10)) m = np.vstack([-10np.ones_like(m[0].flatten()), m[0].flatten(), m[1].flatten()]).T op.topotools.find_surface_pores(network=net, markers=m, label='left') op.topotools.clone_pores(network=net, pores=net.pores('left'), labels='left_boundary') net['pore.coords'][net.pores('left_boundary')] *= [0, 1, 1] # NBVAL_IGNORE_OUTPUT Ps = net.pores('xmax') net.set_label('right_boundary', pores=Ps) Ts = net.find_neighbor_throats(pores=net.pores('right_boundary'), mode='or') net.set_label('right_boundary', throats=Ts) fig, ax = plt.subplots(1, 1, figsize=[5, 5]) ax = op.topotools.plot_coordinates(network=net, color='g', alpha=0.2, ax=ax) ax = op.topotools.plot_connections(network=net, throats=net.throats('right_boundary'), color='r', ax=ax) ax = op.topotools.plot_connections(network=net, throats=net.throats('left_boundary'), color='b', ax=ax) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Let's check out the porosity of the generated image! Step2: Let's visualize the image using porespy's 3D visualizer Step3: OpenPNM has an IO class specifically for importing the output from PoreSpy. The import_data method can either accept a handle to a dictionary (as output from the snow algorithm above), or it can accept a filename to a saved dctionary (saved using Python's pickle library). All IO methods in OpenPNM return a project which is a list, in this case containing a network and a geometry object. Step4: We can unpack the network and geometry objects from the project using the indices in the list as follows Step5: It is important to note that the net object only has topological information and labels. The geo object was created by the openpnm.io.PoreSpy import class to extract all geometric information from the supplied snow dict and put in on a geometry object. We can print geo to confirm Step6: Now let's plot things to see what we have Step7: This looks pretty good, but it only has boundary pores on the right face, indicated by the red dots. When we ran the snow algorithm we specifically told it to only put boundary pores the "right". We could have added them to all faces during the extraction, but for the sake of demonstration we can add them after the fact, although the result is slightly different, as you'll see. Step8: Now we pass these points in as markers to the find_surface_pores function Step9: Lastly we want to "clone" these pores and translate them to domain edge Step10: Now let's inspect the result using the quick plotting tools in the topotools module. First we'll add a new label called 'right_boundary' to match the 'left_boundary' we added above, then we'll plot the throats that connect to ther 'right_boundary' or 'left_boundary'
6,959	<ASSISTANT_TASK:> Python Code: # Import the MetPy unit registry from metpy.units import units length = 10.4 * units.inches width = 20 * units.meters print(length, width) area = length * width print(area) area.to('m^2') # Your code goes here # %load solutions/distance.py 10 * units.degC - 5 * units.degC 25 * units.degC + 5 * units.delta_degF 273 * units.kelvin + 10 * units.kelvin 273 * units.kelvin - 10 * units.kelvin # 12 UTC temperature temp_initial = 20 * units.degC temp_initial # New 18 UTC temperature temp_new = temp_initial + 5 * units.delta_degC temp_new # Your code goes here # %load solutions/temperature_change.py import metpy.constants as mpconst mpconst.earth_avg_radius mpconst.dry_air_molecular_weight mpconst.Re mpconst.Md import metpy.calc as mpcalc import numpy as np # Make some fake data for us to work with np.random.seed(19990503) # So we all have the same data u = np.random.randint(0, 15, 10) * units('m/s') v = np.random.randint(0, 15, 10) * units('m/s') print(u) print(v) direction = mpcalc.wind_direction(u, v) print(direction) # Your code goes here # %load solutions/wind_speed.py mpcalc.dewpoint_rh(25 * units.degC, 75 * units.percent) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Don't forget that you can use tab completion to see what units are available! Just about every imaginable quantity is there, but if you find one that isn't, we're happy to talk about adding it. Step2: That's great, now we have an area, but it is not in a very useful unit still. Units can be converted using the .to() method. While you won't see m$^2$ in the units list, we can parse complex/compound units as strings Step3: Exercise Step4: Solution Step5: Temperature Step6: We can add a delta to an offset unit as well Step7: Absolute temperature scales like Kelvin and Rankine do not have an offset and therefore can be used in addition/subtraction without the need for a delta verion of the unit. Step8: Example Step9: Maybe the surface temperature increased by 5 degrees Celsius so far today - is this a temperature of 5 degC, or a temperature change of 5 degC? We subconsciously know that its a delta of 5 degC, but often write it as just adding two temperatures together, when it really is Step10: Exercise Step11: Solution Step12: <a href="#top">Top</a> Step13: You may also notice in the table that most constants have a short name as well that can be used Step14: <a href="#top">Top</a> Step15: Let's use the wind_direction function from MetPy to calculate wind direction from these values. Remember you can look at the docstring or the website for help. Step16: Exercise Step17: Solution Step18: As one final demonstration, we will calculation the dewpoint given the temperature and relative humidity
6,960	<ASSISTANT_TASK:> Python Code: %matplotlib inline %config InlineBackend.figure_format = 'svg' import matplotlib as mpl mpl.rcParams['font.size'] = 8 figsize =(8,4) mpl.rcParams['figure.figsize'] = figsize import numpy as np from scipy.optimize import fsolve import matplotlib.pyplot as plt from utils import riemann_tools from ipywidgets import interact from ipywidgets import widgets from clawpack import riemann from exact_solvers import nonlinear_elasticity # %load exact_solvers/nonlinear_elasticity.py def dsigma(eps, K1, K2): "Derivative of stress w.r.t. strain." return K1 + 2K2eps def lambda1(q, xi, aux): eps = q[0] rho, K1, K2 = aux return -np.sqrt(dsigma(eps, K1, K2)/rho) def lambda2(q, xi, aux): return -lambda1(q,xi,aux) def make_plot_function(q_l, q_r, aux_l, aux_r): states, speeds, reval, wave_types = \ nonlinear_elasticity.exact_riemann_solution(q_l,q_r,aux_l,aux_r) def plot_function(t,which_char): ax = riemann_tools.plot_riemann(states,speeds,reval,wave_types, t=t,t_pointer=0, extra_axes=True, variable_names=['Strain','Momentum']) if which_char == 1: riemann_tools.plot_characteristics(reval,lambda1,(aux_l,aux_r),ax[0]) elif which_char == 2: riemann_tools.plot_characteristics(reval,lambda2,(aux_l,aux_r),ax[0]) nonlinear_elasticity.phase_plane_plot(q_l, q_r, aux_l, aux_r, ax[3]) plt.show() return plot_function def plot_riemann_nonlinear_elasticity(rho_l,rho_r,v_l,v_r): plot_function = make_plot_function(rho_l,rho_r,v_l,v_r) interact(plot_function, t=widgets.FloatSlider(value=0.,min=0,max=1.,step=0.1), which_char=widgets.Dropdown(options=[None,1,2], description='Show characteristics')); aux_l = np.array((1., 5., 1.)) aux_r = np.array((1., 2., 1.)) q_l = np.array([2.1, 0.]) q_r = np.array([0.0, 0.]) plot_riemann_nonlinear_elasticity(q_l, q_r, aux_l, aux_r) solver = riemann.nonlinear_elasticity_1D_py.nonlinear_elasticity_1D problem_data = {'stress_relation' : 'quadratic'} fw_states, fw_speeds, fw_reval = \ riemann_tools.riemann_solution(solver,q_l,q_r,aux_l,aux_r, problem_data=problem_data, verbose=False, stationary_wave=True, fwave=True) plot_function = \ riemann_tools.make_plot_function(fw_states,fw_speeds, fw_reval, layout='vertical', variable_names=('Strain','Momentum')) interact(plot_function, t=widgets.FloatSlider(value=0.4,min=0,max=.9,step=.1)); ex_states, ex_speeds, ex_reval, wave_types = \ nonlinear_elasticity.exact_riemann_solution(q_l,q_r,aux_l,aux_r) varnames = nonlinear_elasticity.conserved_variables plot_function = riemann_tools.make_plot_function([ex_states,fw_states], [ex_speeds,fw_speeds], [ex_reval,fw_reval], [wave_types,['contact']*3], ['Exact','$f$-wave'], layout='vertical', variable_names=varnames) interact(plot_function, t=widgets.FloatSlider(value=0.4,min=0, max=0.9, step=0.1)); <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: In this chapter we investigate a nonlinear model of elastic strain in heterogeneous materials. This system is equivalent to the $p$-system of gas dynamics, although the stress-strain relation we will use here is very different from the pressure-density relation typically used in gas dynamics. The equations we consider are Step2: Approximate solution of the Riemann problem using $f$-waves Step3: Comparison of exact and approximate solutions
6,961	<ASSISTANT_TASK:> Python Code: import datetime as dt import graphlab as gl sf = gl.SFrame.read_csv('raw_data/global_earthquakes.csv', verbose=False) sf.show() useful_columns = ['time', 'latitude', 'longitude', 'mag', 'type', 'location'] sf = sf[useful_columns] mask = sf['type'] == 'nuclear explosion' sf[mask] mask = sf['type'] == 'earthquake' sf = sf[mask] sf = sf.remove_column('type') print "Number of earthquake events:", sf.num_rows() sf['time'] = sf['time'].str_to_datetime(str_format='%Y-%m-%dT%H:%M:%s%ZP') sf['time'] = sf['time'].apply(lambda x: x.replace(tzinfo=None)) quakes = gl.TimeSeries(sf, index='time') quakes.print_rows(3) quakes[4:7].print_rows() quakes[['latitude', 'longitude']].print_rows(3) start = dt.datetime(2014, 5, 1) end = dt.datetime(2014, 5, 2) quakes.slice(start, end).print_rows(3) print "Earliest timestamp:", quakes.min_time print "Latest timestamp:", quakes.max_time print "Timestamp range:", quakes.range print "Index column:", quakes.index_col_name print "Value columns:", quakes.value_col_names print quakes.index[:3] big_one = quakes.argmax('mag') quakes[big_one] sf2 = quakes.to_sframe() print type(sf2) sf_recent = gl.SFrame.read_csv('raw_data/global_earthquakes_recent.csv', verbose=False) # Trim away the columns we're not interested in. sf_recent = sf_recent[useful_columns] # Remove any non-earthquake events. mask = sf_recent['type'] == 'earthquake' sf_recent = sf_recent[mask] sf_recent = sf_recent.remove_column('type') # Convert the timestamp to a `datetime` type. sf_recent['time'] = sf_recent['time'].str_to_datetime(str_format='%Y-%m-%dT%H:%M:%s%ZP') sf_recent['time'] = sf_recent['time'].apply(lambda x: x.replace(tzinfo=None)) # Convert to a `TimeSeries` object. recent_quakes = gl.TimeSeries(sf_recent, index='time') recent_quakes.print_rows(3) all_quakes = quakes.union(recent_quakes) print all_quakes.min_time print all_quakes.max_time grp = quakes.group('location') print grp grp.group_info().topk('group_size', k=8) oklahoma_quakes = grp.get_group('Oklahoma') oklahoma_quakes.print_rows(3) grp = quakes.group(quakes.date_part.HOUR) hour_counts = grp.group_info() hour_counts.print_rows(5) import matplotlib.pyplot as plt %matplotlib notebook plt.style.use('ggplot') fig, ax = plt.subplots() ax.bar(hour_counts['time.hour'], hour_counts['group_size'], color='dodgerblue') ax.set_xlabel('Hour of the day') ax.set_ylabel('Number of earthquakes') fig.show() import graphlab.aggregate as agg daily_stats = quakes.resample(period=dt.timedelta(days=1), upsample_method='none', downsample_method={'count': agg.COUNT('latitude'), 'avg_mag': agg.MEAN('mag'), 'max_mag': agg.MAX('mag')}) daily_stats['count'] = daily_stats['count'].fillna(0) daily_stats.print_rows(5) def compute_daily_stats(data): daily = data.resample(period=dt.timedelta(days=1), upsample_method='none', downsample_method={'count': agg.COUNT('latitude'), 'avg_mag': agg.MEAN('mag'), 'max_mag': agg.MAX('mag')}) daily['count'] = daily['count'].fillna(0) return daily # Save the daily counts and recent daily counts. daily_stats.save('working_data/global_daily_stats.ts') compute_daily_stats(recent_quakes).save('working_data/global_daily_update.ts') # Filter just the Oklahoma data from the recent events. grp = recent_quakes.group('location') recent_oklahoma_quakes = grp.get_group('Oklahoma') # Compute daily stats for the Oklahoma quake events. compute_daily_stats(oklahoma_quakes).save('working_data/ok_daily_stats.ts') compute_daily_stats(recent_oklahoma_quakes).save('working_data/ok_daily_update.ts') <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Inspect the data visually Step2: A small bit of data cleaning Step3: 2. Convert to a TimeSeries object Step4: Convert from SFrame to TimeSeries Step5: 3. Basic TimeSeries operations Step6: Some operations are little different Step7: Some operations are unique to TimeSeries Step8: Working with the time index Step9: We can always go back to an SFrame Step10: 4. Appending more data Step11: Get the union of the two datasets Step12: 5. Grouping observations by value Step13: The group_info SFrame tells us what the group names are and how many observations are in each group. Step14: The get_group method lets us isolate just the observations for any group. Step15: 6. Grouping observations by time component Step16: 7. Resampling Step17: 8. Setting up the next notebooks
6,962	<ASSISTANT_TASK:> Python Code: traj = md.load('ala2.h5') atoms, bonds = traj.topology.to_dataframe() atoms psi_indices, phi_indices = [6, 8, 14, 16], [4, 6, 8, 14] angles = md.compute_dihedrals(traj, [phi_indices, psi_indices]) from pylab import * from math import pi figure() title('Dihedral Map: Alanine dipeptide') scatter(angles[:, 0], angles[:, 1], marker='x', c=traj.time) cbar = colorbar() cbar.set_label('Time [ps]') xlabel(r'$\Phi$ Angle [radians]') xlim(-pi, pi) ylabel(r'$\Psi$ Angle [radians]') ylim(-pi, pi) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Because alanine dipeptide is a little nonstandard in the sense that it's basically dominated by the ACE and NME capping residues, we need to find the indicies of the atoms involved in the phi and psi angles somewhat manually. For standard cases, see compute_phi() and compute_psi() for easier solutions that don't require you to manually find the indices of each dihedral angle. Step2: Lets plot our dihedral angles in a scatter plot using matplotlib. What conformational states of Alanine dipeptide did we sample?
6,963	<ASSISTANT_TASK:> Python Code: # Importar Librerías import numpy as np import matplotlib.pyplot as plt from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation from keras.optimizers import SGD # Guardar semilla para numeros aleatorios seed = 21 np.random.seed(seed) def generate_data(n): # Lista para guardar datos etiquetados output = list() # Generación de n tuplas aleatorias input = 2 * np.random.random_sample((n,2)) - 1 # Asignación datos dependiendo del cuadrante for i in input: # Cuadrante 1 if i[0] > 0 and i[1] > 0: output.append(0) # Cuadrante 2 elif i[0] < 0 and i[1] > 0: output.append(1) # Cuadrante 3 elif i[0] < 0 and i[1] < 0: output.append(0) # Cuadrante 4 elif i[0] > 0 and i[1] < 0: output.append(1) return input, output (x_training, y_training) = generate_data(1000) (x_test, y_test) = generate_data(1000) # Plot de datos de entrenamiento %matplotlib inline plt.figure(figsize=(7,7)) set1 = x_training[np.logical_and(x_training[:,0] < 0, x_training[:,1] < 0)] set2 = x_training[np.logical_and(x_training[:,0] < 0, x_training[:,1] > 0)] set3 = x_training[np.logical_and(x_training[:,0] > 0, x_training[:,1] > 0)] set4 = x_training[np.logical_and(x_training[:,0] > 0, x_training[:,1] < 0)] set1 = np.concatenate((set1, set3), axis=0) set2 = np.concatenate((set2, set4), axis=0) plt.plot(set1[:,0], set1[:,1], 'r.') plt.plot(set2[:,0], set2[:,1], 'bx') plt.show() # Creación de una neurona model = Sequential() # Dimensión input = 1, Dimensión output = 2, función de activación es Relu model.add(Dense(output_dim=1, input_dim=2, init="normal")) model.add(Activation("sigmoid")) model.compile(loss='mean_squared_error', optimizer='sgd', metrics=['accuracy']) print "Neurona inicializada" # Entrenar a la neurona model.fit(x_training, y_training, nb_epoch=1000,verbose=0) print "Neurona entrenada" # Evaluar la neurona loss_and_metrics = model.evaluate(x_test, y_test, batch_size=1000) print "Loss: " print loss_and_metrics[0] print "Accuracy: " print loss_and_metrics[1]100 print round(model.predict(np.array([-1,-1]).reshape(1,2))[0][0],4) print round(model.predict(np.array([1,1]).reshape(1,2))[0][0],4) print round(model.predict(np.array([-1,1]).reshape(1,2))[0][0],4) print round(model.predict(np.array([1,-1]).reshape(1,2))[0][0],4) from keras.models import Sequential from keras.layers.core import Dense, Activation xor = Sequential() # Dimensión input = 2, Dimensión output = 1, función de activación es Relu xor.add(Dense(8, input_dim = 2, activation = "relu")) xor.add(Dense(1, activation = "sigmoid")) xor.compile(loss='mean_squared_error', optimizer='sgd', metrics=['accuracy']) print "Red inicializada" # Entrenar a la red xor.fit(x_training, y_training, nb_epoch=1000, verbose=0) print "Red entrenada" # Evaluar la red evaluacion = xor.evaluate(x_test, y_test, batch_size=1000) print "Loss: " print evaluacion[0] print "Accuracy: " print evaluacion[1]100 print xor.predict_classes(np.array([-1,-1]).reshape(1,2))[0][0] print xor.predict_classes(np.array([1,1]).reshape(1,2))[0][0] print xor.predict_classes(np.array([-1,1]).reshape(1,2))[0][0] print xor.predict_classes(np.array([1,-1]).reshape(1,2))[0][0] import pandas as pd url = 'http://mldata.org/repository/data/download/csv/regression-datasets-housing/' df = pd.read_csv(url, sep=',',header=None, names=['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE','DIS','RAD','TAX','PTRATIO','B','LSTAT','MEDV']) from sklearn.cross_validation import train_test_split df_train, df_test = train_test_split(df,test_size=0.25, random_state=0) df.shape df.info() df.describe() from sklearn.preprocessing import StandardScaler scaler = StandardScaler().fit(df_train) X_train_scaled = pd.DataFrame(scaler.transform(df_train),columns=df_train.columns) X_test_scaled = pd.DataFrame(scaler.transform(df_test),columns=df_test.columns) y_train_scaled = X_train_scaled.pop('MEDV') y_test_scaled = X_test_scaled.pop('MEDV') def generate_model(optimizer, activation): model = Sequential() model.add(Dense(200, input_dim=X_train_scaled.shape[1], init='uniform')) model.add(Activation(activation)) model.add(Dense(1, init='uniform')) model.add(Activation('linear')) model.compile(optimizer=optimizer,loss='mean_squared_error') return model sgd = SGD(lr=0.02) model_d = generate_model(sgd,"sigmoid") hist = model_d.fit(X_train_scaled.as_matrix(), y_train_scaled.as_matrix(), nb_epoch=300, verbose=0, validation_data=(X_test_scaled.as_matrix(), y_test_scaled.as_matrix())) %matplotlib inline epochs = np.arange(300) plt.figure(figsize=(6,6)) plt.plot(epochs, hist.history['loss'], 'b-') plt.title(u"Función de activación sigmoidal") plt.xlabel("# epochs") plt.ylabel("MSE") plt.show() sgd = SGD(lr=0.02) model2 = generate_model(sgd, "relu") hist2 = model2.fit(X_train_scaled.as_matrix(), y_train_scaled.as_matrix(), nb_epoch=300, verbose=0, validation_data=(X_test_scaled.as_matrix(), y_test_scaled.as_matrix())) %matplotlib inline epochs = np.arange(300) plt.plot(epochs, hist2.history['loss'], 'r-') plt.title(u"Función de activación ReLu") plt.xlabel("# epochs") plt.ylabel("MSE") plt.show() n_lr = 20 lear_rate = np.linspace(0,0.02,n_lr) %matplotlib inline epochs = np.arange(300) plt.figure(figsize=(15,7)) for n, i in enumerate(lear_rate): sgd = SGD(lr=i) model = generate_model(sgd, "sigmoid") hist = model.fit(X_train_scaled.as_matrix(), y_train_scaled.as_matrix(), nb_epoch=300, verbose=0, validation_data=(X_test_scaled.as_matrix(), y_test_scaled.as_matrix())) plt.plot(epochs, hist.history['loss'], label="Lear_rate:"+str(i)) plt.title(u"Variación de learning rate con activación Sigmoid") plt.xlabel("# epochs") plt.ylabel("MSE") plt.ylim([0, 2]) plt.legend() plt.show() #Disminución de la cantidad de learning rates a probar con objetivo de una mejor visualización n_lr = 5 lear_rate = np.linspace(0,0.02,n_lr) %matplotlib inline epochs = np.arange(300) plt.figure(figsize=(15,7)) for n, i in enumerate(lear_rate): sgd = SGD(lr=i) model = generate_model(sgd, "sigmoid") hist = model.fit(X_train_scaled.as_matrix(), y_train_scaled.as_matrix(), nb_epoch=300, verbose=0, validation_data=(X_test_scaled.as_matrix(), y_test_scaled.as_matrix())) plt.plot(epochs, hist.history['loss'], label="Lear_rate:"+str(i)) plt.title(u"Variación de learning rate con activación Sigmoid") plt.xlabel("# epochs") plt.ylabel("MSE") plt.ylim([0, 2]) plt.legend() plt.show() from sklearn import cross_validation Xm = X_train_scaled.as_matrix() ym = y_train_scaled.as_matrix() mse_cvs1 = [] mse_cvs2 = [] for nfold in [5, 10]: kfold = cross_validation.KFold(len(Xm), nfold) cvscores1 = [] cvscores2 = [] for i, (train, val) in enumerate(kfold): # create models sgd = SGD(lr=0.02) model1 = generate_model(sgd, 'sigmoid') model2 = generate_model(sgd, 'relu') # Fit the models model1.fit(Xm[train], ym[train], nb_epoch=300, verbose=0) model2.fit(Xm[train], ym[train], nb_epoch=300, verbose=0) # evaluate the models # sigmoid score scores1 = model1.evaluate(Xm[val], ym[val]) # relu score scores2 = model2.evaluate(Xm[val], ym[val]) # Store values cvscores1.append(scores1) cvscores2.append(scores2) mse_cvs1.append(np.mean(cvscores1)) mse_cvs2.append(np.mean(cvscores2)) print "Results for sigmoid and CV" print mse_cvs1 print "Results for relu and CV" print mse_cvs2 model1 = generate_model(SGD(lr=0.02), "sigmoid") model2 = generate_model(SGD(lr=0.02), "relu") model1.fit(X_train_scaled.as_matrix(), y_train_scaled.as_matrix(), nb_epoch=300, verbose=0) model2.fit(X_train_scaled.as_matrix(), y_train_scaled.as_matrix(), nb_epoch=300, verbose=0) score_sigmoid = model1.evaluate(X_test_scaled.as_matrix(), y_test_scaled.as_matrix()) score_relu = model2.evaluate(X_test_scaled.as_matrix(), y_test_scaled.as_matrix()) print "Result for sigmoid in testing set" print score_sigmoid print "Result for relu in testing set" print score_relu n_decay = 5 lear_decay = np.logspace(-6,0,n_decay) %matplotlib inline epochs = np.arange(300) plt.figure(figsize=(15,7)) for i in lear_decay: sgd = SGD(lr=0.02, decay=i) model = generate_model(sgd, "sigmoid") hist = model.fit(X_train_scaled.as_matrix(), y_train_scaled.as_matrix(), nb_epoch=300, verbose=0, validation_data=(X_test_scaled.as_matrix(), y_test_scaled.as_matrix())) plt.plot(epochs, hist.history['loss'], label="decay = "+str(i)) plt.title(u"Variación de learning decay") plt.xlabel("# epochs") plt.ylabel("MSE") plt.legend() plt.ylim([0,2]) plt.show() n_decay = 21 momentum = np.linspace(0,1,n_decay) %matplotlib inline epochs = np.arange(300) plt.figure(figsize=(15,7)) for i in momentum: sgd = SGD(lr=0.02,momentum=i) model = generate_model(sgd, "sigmoid") hist = model.fit(X_train_scaled.as_matrix(), y_train_scaled.as_matrix(), nb_epoch=300, verbose=0, validation_data=(X_test_scaled.as_matrix(), y_test_scaled.as_matrix())) plt.plot(epochs, hist.history['loss'],label="Momentum:"+str(i)) plt.title(u"Variación de momentum") plt.xlabel("# epochs") plt.ylabel("MSE") plt.legend() plt.ylim([0,2]) plt.show() n_batches = 21 batch_sizes = np.round(np.linspace(1,X_train_scaled.shape[0],n_batches)) %matplotlib inline epochs = np.arange(300) for i in batch_sizes: sgd = SGD(lr=0.02) model = generate_model(sgd, "sigmoid") hist = model.fit(X_train_scaled.as_matrix(), y_train_scaled.as_matrix(), batch_size=i, nb_epoch=300, verbose=0, validation_data=(X_test_scaled.as_matrix(), y_test_scaled.as_matrix())) if i == 1: plt.plot(epochs, hist.history['loss'], 'r-', label="SGD batch=1") elif i == X_train_scaled.shape[0]: plt.plot(epochs, hist.history['loss'], 'b-', label="Batch") else: plt.plot(epochs, hist.history['loss'], 'g-') plt.title(u"Variación de batch sizes en d)") plt.xlabel("# epochs") plt.ylabel("MSE") plt.legend() plt.show() %matplotlib inline epochs = np.arange(300) for i in batch_sizes: sgd = SGD(lr=0.02) model = generate_model(sgd, "relu") hist = model.fit(X_train_scaled.as_matrix(), y_train_scaled.as_matrix(), batch_size=i, nb_epoch=300, verbose=0, validation_data=(X_test_scaled.as_matrix(), y_test_scaled.as_matrix())) # SGD if i == 1: plt.plot(epochs, hist.history['loss'], 'r-', label="SGD batch=1") # BATCH elif i == X_train_scaled.shape[0]: plt.plot(epochs, hist.history['loss'], 'b-', label="Batch") # MINIBATCH else: plt.plot(epochs, hist.history['loss'], 'g-') plt.title(u"Variación de batch sizes en e)") plt.xlabel("# epochs") plt.ylabel("MSE") plt.legend() plt.show() import cPickle as pickle import os from scipy.misc import imread # Inicializar semilla aleatoria np.random.seed(20) # Carga de un archivo de CIFAR def load_CIFAR_one(filename): with open(filename, 'rb') as f: datadict = pickle.load(f) X = datadict['data'] Y = datadict['labels'] Y = np.array(Y) return X, Y # Carga todos los archivos CIFAR y generar Training set, Testing set y Validation set def load_CIFAR10(PATH, n_files=6): xs = [] ys = [] # Juntar toda la data de entrenamiento for b in range(1, n_files): f = os.path.join(PATH, 'data_batch_%d' % (b, )) X, Y = load_CIFAR_one(f) xs.append(X) ys.append(Y) Xtr = np.concatenate(xs) Ytr = np.concatenate(ys) del X, Y # Obtener subconjunto para validacion a partir de data de entrenamiento v_size = np.random.randint(1000, 5000) indices = np.random.choice(np.arange(Xtr.shape[0]), v_size) mask_tr = np.ones(Xtr.shape[0], dtype=bool) mask_tr[indices] = False mask_v = np.invert(mask_tr) # Obtener conjunto de validacion Xv = Xtr[mask_v] Yv = Ytr[mask_v] # Obtener conjunto de prueba Xtr = Xtr[mask_tr] Ytr = Ytr[mask_tr] # Obtener data de prueba Xte, Yte = load_CIFAR_one(os.path.join(PATH, 'test_batch')) return Xtr, Ytr, Xte, Yte, Xv, Yv # Cargar desde carpeta local data Xtr, Ytr, Xte, Yte, Xv, Yv = load_CIFAR10("data") label_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] from sklearn.preprocessing import StandardScaler # Centrar dataset y escalar segun preferencia def preprocess(X, with_mean=True, with_std=True): scaler = StandardScaler(with_mean, with_std).fit(X) return scaler.transform(X) # Data solo centrada #Xtr_c = preprocess(Xtr, with_mean=True, with_std=False) # Data solo escalada #Xtr_s = preprocess(Xtr, with_mean=False, with_std=True) # Data centrada y escalada # Centrar y escalar datos de entrenamiento y validación Xtr_cs = preprocess(Xtr) Xv_cs = preprocess(Xv) Xte_cs = preprocess(Xte) # Dimension de ejemplos, vectores de 3072 features (32x32x3 pixeles) input_dim = Xtr.shape[1] from keras.utils.np_utils import to_categorical # Convertir etiquetas a una representación amigable Ytr_conv = to_categorical(Ytr,10) Yv_conv = to_categorical(Yv,10) Yte_conv = to_categorical(Yte,10) # Modelo 1: Red simple de 1 capa MLPmodel = Sequential() MLPmodel.add(Dense(50, input_dim=input_dim, init='uniform')) MLPmodel.add(Activation('relu')) MLPmodel.add(Dense(10)) MLPmodel.add(Activation('softmax')) sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=False) MLPmodel.compile(loss='mse', optimizer=sgd, metrics=['accuracy']) MLPmodel.fit(Xtr_cs, Ytr_conv, nb_epoch=20, batch_size=16, verbose=0) score = MLPmodel.evaluate(Xv_cs, Yv_conv,verbose=0, batch_size=16) print "Loss:",score[0], "Accuracy:",score[1] # Modelo 2 # Combinacion de capas con activaciones tangente hiperbolica, momentum utilizando nesterov model2 = Sequential() model2.add(Dense(50, input_dim=input_dim, init='uniform')) model2.add(Activation('tanh')) model2.add(Dense(50, init='uniform')) model2.add(Activation('tanh')) model2.add(Dense(10, init='uniform')) model2.add(Activation('softmax')) sgd = SGD(lr=0.1, decay=1e-6, momentum=0.8, nesterov=True) model2.compile(loss='mse', optimizer=sgd, metrics=['accuracy']) model2.fit(Xtr_cs, Ytr_conv, nb_epoch=20, batch_size=32, verbose=0) score = model2.evaluate(Xv_cs, Yv_conv, batch_size=32, verbose=0) print "Loss:",score[0], "Accuracy:",score[1] # Modelo 3 # Combinación anterior cambiando funciones tanh por sigmoidales y perdida entropia cruzada model3 = Sequential() model3.add(Dense(256, input_dim=input_dim, init='uniform')) model3.add(Activation('sigmoid')) model3.add(Dense(256, init='normal')) model3.add(Activation('sigmoid')) model3.add(Dense(10, init='normal')) model3.add(Activation('softmax')) sgd = SGD(lr=0.1, decay=1e-6, momentum=0.8, nesterov=True) model3.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) model3.fit(Xtr_cs, Ytr_conv, nb_epoch=20, batch_size=32, verbose=0) score = model3.evaluate(Xv_cs, Yv_conv, batch_size=32, verbose=0) print "Loss:",score[0], "Accuracy:",score[1] from top_level_features import hog_features from top_level_features import color_histogram_hsv from top_level_features import extract_features Xtr, Ytr, Xte, Yte, Xv, Yv = load_CIFAR10("data") #features = extract_features(Xtr,[hog_features]) #extrae hog features #features = extract_features(Xtr,[color_histogram_hsv]) #extrae histogramas de color # Extraer features hog (cambios en gradiente) y histograma en espacio de color hsv features = extract_features(Xtr,[hog_features, color_histogram_hsv]) features_v = extract_features(Xv,[hog_features, color_histogram_hsv]) features_te = extract_features(Xte,[hog_features, color_histogram_hsv]) print Xtr.shape # Obtuvimos en vez de 3072 features o variables, 154 variables # Se reduce la dimensionalidad print features.shape print features_v.shape print features_te.shape # El mejor modelo fue usado para probar las nuevas features # Probando con la extracción de ambos tipos de features model3 = Sequential() model3.add(Dense(256, input_dim=features.shape[1], init='uniform')) model3.add(Activation('sigmoid')) model3.add(Dense(256, init='normal')) model3.add(Activation('sigmoid')) model3.add(Dense(10, init='normal')) model3.add(Activation('softmax')) sgd = SGD(lr=0.1, decay=1e-6, momentum=0.8, nesterov=True) model3.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) Ytr_conv = to_categorical(Ytr, 10) Yv_conv = to_categorical(Yv, 10) Yte_conv = to_categorical(Yte, 10) model3.fit(features, Ytr_conv, nb_epoch=20, batch_size=32, verbose=0) print model3.evaluate(features_v, Yv_conv, batch_size=32, verbose=0) print model3.evaluate(features_te, Yte_conv, batch_size=32, verbose=0) #Probando con la extracción solo de hog features features = extract_features(Xtr,[hog_features]) #extrae hog features features_te = extract_features(Xte,[hog_features]) #extrae hog features model3 = Sequential() model3.add(Dense(256, input_dim=features.shape[1], init='uniform')) model3.add(Activation('sigmoid')) model3.add(Dense(256, init='normal')) model3.add(Activation('sigmoid')) model3.add(Dense(10, init='normal')) model3.add(Activation('softmax')) sgd = SGD(lr=0.1, decay=1e-6, momentum=0.8, nesterov=True) model3.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) model3.fit(features, Ytr_conv, nb_epoch=20, batch_size=32, verbose=0) model3.evaluate(features_te, Yte_conv, batch_size=32, verbose=0) print features.shape print features_te.shape #Probando con la extracción solo de hsv features features = extract_features(Xtr,[color_histogram_hsv]) #extrae hog features features_te = extract_features(Xte,[color_histogram_hsv]) #extrae hog features model3 = Sequential() model3.add(Dense(256, input_dim=features.shape[1], init='uniform')) model3.add(Activation('sigmoid')) model3.add(Dense(256, init='normal')) model3.add(Activation('sigmoid')) model3.add(Dense(10, init='normal')) model3.add(Activation('softmax')) sgd = SGD(lr=0.1, decay=1e-6, momentum=0.8, nesterov=True) model3.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) model3.fit(features, Ytr_conv, nb_epoch=20, batch_size=32, verbose=0) model3.evaluate(features_te, Yte_conv, batch_size=32, verbose=0) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: El problema se considera XOR, o or exclusivo debido a que [(-),(-)] y [(+),(+)] son etiquetados con con circulos y [(-),(+)] y [(+),(-)] son etiquetados con cruces, podemos compararlo con el or exclusivo que obtiene 0 para [1,1] y [0,0] y obtiene 1 para [1,0] y [0,1] Step2: Como se puede apreciar, la neurona a pesar de la cantidad de datos de entrenamiento utilizados, no es capaz de aprender la función XOR arrojando resultados inconsistentes, siendo incapaz de clasificar de forma determinante los ejemplos de testing en alguna clase. Esto es debido a que las clases del set no son linealmente separable. Step3: Se puede observar que con un perceptrón multicapa se obtiene un accuracy del 93% pudiendo clasificar correctamente los datos. Step4: En las lineas 5 a 7 se puede observar que este set se divide para generar el training set y el testing set. Del total de datos, el 25% se deja aparte para pruebas y el restante 75% permanece para entrenamiento. Step5: c) Normalización de datos. Este procedimiento es necesario para evitar cualquier clase de problemas con la convergencia de nuestra función de optimización, pues es posible que debido a los diversos rangos de datos la convergencia favorezca a ciertos valores, deteniéndose el algoritmo en un punto donde se ha aprendido mal sobre el training set y no se tenga capacidad de generalización. Step6: d) Gráfico de MSE versus número de epochs utilizados para entrenar para red FF de 3 capas, 200 unidades ocultas y activación sigmoidal entrenada con SGD con parámetros $\eta$=0.2 y 300 epochs de entrenamiento. Step7: El error obtenido en un comienzo es muy alto pero al aumentar el número de epochs éste disminuye considerablemente convergiendo a un número muy bajo pero a su vez la convergencia posee comportamiento oscilatorio. Step8: Podemos observar que con la función de activación ReLu se llega a un menor error cuadrático que con Sigmoid, además la convergencia es más estable. Step9: Se puede observar que a medida se aumenta el learning rate la convergencia va cambiando, para lear_rate = 0 no aprende, pero mientras va aumentando se acerca de a poco a un error más pequeño debido a que aprende de forma lenta pero estable, a medida aumenta la el learning rate pasa a ser más oscilatoria, como se observa en lear_rate = 0.02 donde el resultado es muy oscilatorio. Step10: Los resultados para el MSE utilizando función de activación sigmoid muestran errores en cross validation de 0.36 para 5 folds y 0.28 para 10 folds, mientras que los errores utilizando función relu bajan a aproximadamente 0.13 en ambos casos. La estimación con función de activación relu es más confiable y predijo de buena forma el comportamiento del testing set. Step11: Se observa que un decay igual a 1.0 no tiene un efecto en la disminución del MSE. A medida que el factor de decaimiento disminuye el MSE tiende a descender de forma más rápida y converger a valores más bajos. Valores extremadamente bajos de decay poseen un comportamiento demasiado inestable, el MSE puede llegar a oscilar entre el mínimo encontrado (cualitativamente en MSE=0.25) y MSE = 1.0 Step12: j) Entrenar el modelo en d) y e) cambiando el tamaño del batch. Comparar sgd, batch y mini-batch Step13: Podemos observar en los gráficos anteriores que al variar el tamaño del batch desde 1 hasta el tamaño total, tenemos 3 resultados distinguibles en cada gráfico Step14: b) Escalamiento y centrado de datos. Aparte de las ventajas mencionadas anteriormente, los resultados experimentales con datos no centrados y no escalados ofrecen resultados peores que si la data se normaliza. Step15: c) Creación de red neuronal para problema CIFAR. En primer lugar se adaptan las etiquetas a una representación manejable por la red. Step16: (d) Extracción de features y experimentación.
6,964	<ASSISTANT_TASK:> Python Code: from sympy import * init_printing() pi.evalf(10) alpha, beta, gamma, x, y = symbols('alpha beta gamma x y') alpha, beta f= Function('f') diff(sin(x+1)cos(y), x, y) test = diff(f(x)+1,x) test Md = Function('M_d')(x) Md q1, q2, q3 = symbols('q_1 q_2 q_3') q = Matrix([q1, q2, q3]) q acol = Matrix([q12+sin(q2), exp(q3), q2-q1]) J = acol.jacobian(q) J n, m = symbols('n m', integer=True) M = MatrixSymbol('M', n, m) b = MatrixSymbol('b', m, 1) Mb (Mb).T (Mb).T Transpose(Mb).doit() A = IndexedBase('A') i = Idx('i') A[2], A[i] x = symbols('x') expr = abs(sin(x*2)) expr ccode(expr) tanh(x).rewrite(exp) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Code printing
6,965	<ASSISTANT_TASK:> Python Code: print("Exemplo 9.1") import numpy as np Vm = 12 phi = 10 omega = 50 T = 2np.pi/omega f = 1/T print("Amplitude:",Vm,"V") print("Fase:",phi,"º") print("Frequência angular:",omega,"rad/s") print("Período:",T,"s") print("Frequência:",f,"Hz") print("Problema Prático 9.1") Vm = 30 #30sin(4pit - 75º) = 30cos(4pit + 165º) phi = -75 omega = 4np.pi T = 2np.pi/omega f = 1/T print("Amplitude:",Vm,"V") print("Fase:",phi,"º") print("Frequência angular:",omega,"rad/s") print("Período:",T,"s") print("Frequência:",f,"Hz") print("Exemplo 9.2") #v1 = -10cos(wt + 50º) = 10cos(wt + 50 - 180) = 10cos(wt - 130º) #v2 = 12sen(wt - 10º) = 12cos(wt - 100º) #-130 - (-100) = -30 phi = 30 print("v2 esta avancada em {}º em relação a v1".format(phi)) print("Problema Prático 9.2") #i1 = -4sen(377t + 55) = 4sen(377t + 55 + 180) = 4sen(377t + 235) = 4cos(377t + 145) #i2 = 5cos(377t - 65) phi = 145 - (-65) print("i1 esta avancada em {}º em relação a i2".format(phi)) print("Exemplo 9.4") #6cos(50t - 40) #r = 6 #phi = -40 #-4sen(30t + 50) = 4sen(30t + 50 + 180) = 4cos(30t + 140) #r = 4 #phi = 140 print("I: 6[-40º]") print("V: 4[140º]") print("Problema Prático 9.4") #7cos(2t + 40) #r = 7 #phi = 40 #-4sen(10t + 10) = 4sen(10t + 10 + 180) = 4cos(10t + 100) #r = 4 #phi = 100 print("V: 7[40º]") print("I: 4[100º]") print("Exemplo 9.5") import numpy as np r = np.sqrt((-3)2 + 42) phi = np.arctan(4/(-3))180/np.pi + 180 print("I: {}[{}º]".format(r,phi)) #j = 1[90º] #V = 8e^(-j20) = 8[-20º] #jV = 18 [90 -20] = 8[70º] print("V: 8[70º]") print("Problema Prático 9.5") print("v(t) = 25cos(wt + 220)") #j(12 - j5) = 5 + 12j r = np.sqrt(52 + 122) phi = np.arctan(12/5)180/np.pi print("I: {}[{}º]".format(r,phi)) print("Exemplo 9.6") #4cos(wt + 30) = 4[30] #5sen(wt + 20) = 5cos(wt + 70) = 5[-110] x = 4np.cos(30np.pi/180) + 5np.cos(-110np.pi/180) y = 4np.sin(30np.pi/180) + 5np.sin(-110np.pi/180) print("i1 + i2: {} + j{}".format(x,y)) r = np.sqrt(x2 + y2) phi = np.arctan(y/x)180/np.pi print("I: {}[{}]".format(r,phi)) print("i(t): {}cos(wt + {})".format(r,phi)) print("Problema Prático 9.6") #-10sen(wt - 30) = 10sen(wt + 150) = 10sen(wt + 60) = 10[60] #20cos(wt + 45) = 20[45] x = 10np.cos(60np.pi/180) + 20np.cos(45np.pi/180) y = 10np.sin(60np.pi/180) + 20np.sin(45np.pi/180) print("v1 + v2: {} + j{}".format(x,y)) r = np.sqrt(x2 + y2) phi = np.arctan(y/x)180/np.pi print("V: {}[{}]".format(r,phi)) print("v(t): {}cos(wt + {})".format(r,phi)) print("Exemplo 9.7") #4I + 8I/jw - 3jwI = 50[75] #4I -4jI - 6jI = 50[75] #I = 50[75] / (4 - j10) r = np.sqrt(42 + (-10)2) phi = np.arctan((-10)/4)180/np.pi R = 50/r Phi = 75 - phi print("Fasor I: {}[{}]".format(R,Phi)) print("i(t) = {}cos(wt + {}º)".format(R,Phi)) print("Problema Prático 9.7") #2Vjw + 5V + 10v/jw = 50[-30] #5V -2jV + 10jV = 50[-30] #V = 50[-30] / (5 + j8) r = np.sqrt(52 + 82) phi = np.arctan(8/5)180/np.pi R = 50/r Phi = -30 - phi print("Fasor I: {}[{}]".format(R,Phi)) print("i(t) = {}cos(wt + {}º)".format(R,Phi)) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Problema Prático 9.1 Step2: Exemplo 9.2 Step3: Problema Prático 9.2 Step4: Fasores Step5: Problema Prático 9.4 Step6: Exemplo 9.5 Step7: Problema Prático 9.5 Step8: Exemplo 9.6 Step9: Problema Prático 9.6 Step10: Exemplo 9.7 Step11: Problema prático 9.7
6,966	<ASSISTANT_TASK:> Python Code: import datetime import json import os import time import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt %matplotlib inline import pandas as pd import scipy.sparse import seaborn as sns sns.set(context="paper", font_scale=1.5, rc={"lines.linewidth": 2}, font='DejaVu Serif') DATA_DIR = '/hdd2/dawen/data/ml-20m/' def timestamp_to_date(timestamp): return datetime.datetime.fromtimestamp(timestamp).strftime('%Y-%m-%d %H:%M:%S') raw_data = pd.read_csv(os.path.join(DATA_DIR, 'ratings.csv'), header=0) # binarize the data (only keep ratings >= 4) raw_data = raw_data[raw_data['rating'] > 3.5] # sort the raw data accorindg to timestamp raw_data = raw_data.sort_index(by=['timestamp']) raw_data tstamp = np.array(raw_data['timestamp']) print("Time span of the dataset: From %s to %s" % (timestamp_to_date(np.min(tstamp)), timestamp_to_date(np.max(tstamp)))) # apparently the timestamps are ordered, check to make sure for i in xrange(tstamp.size - 1): if tstamp[i] > tstamp[i + 1]: print("not ordered") plt.hist(tstamp, bins=50) xticks = np.linspace(tstamp[0], tstamp[-1], 10) plt.xticks(xticks, map(lambda x: timestamp_to_date(x)[:7], xticks), rotation=90) pass start_t = time.mktime(datetime.datetime.strptime("1995-01-01", "%Y-%m-%d").timetuple()) raw_data = raw_data[raw_data['timestamp'] >= start_t] tr_vd_raw_data = raw_data[:int(0.8 * raw_data.shape[0])] def get_count(tp, id): playcount_groupbyid = tp[[id]].groupby(id, as_index=False) count = playcount_groupbyid.size() return count def filter_triplets(tp, min_uc=5, min_sc=0): # Only keep the triplets for songs which were listened to by at least min_sc users. if min_sc > 0: songcount = get_count(tp, 'movieId') tp = tp[tp['movieId'].isin(songcount.index[songcount >= min_sc])] # Only keep the triplets for users who listened to at least min_uc songs # After doing this, some of the songs will have less than min_uc users, but should only be a small proportion if min_uc > 0: usercount = get_count(tp, 'userId') tp = tp[tp['userId'].isin(usercount.index[usercount >= min_uc])] # Update both usercount and songcount after filtering usercount, songcount = get_count(tp, 'userId'), get_count(tp, 'movieId') return tp, usercount, songcount tr_vd_raw_data, user_activity, item_popularity = filter_triplets(tr_vd_raw_data) sparsity = 1. * tr_vd_raw_data.shape[0] / (user_activity.shape[0] * item_popularity.shape[0]) print("After filtering, there are %d watching events from %d users and %d movies (sparsity: %.3f%%)" % (tr_vd_raw_data.shape[0], user_activity.shape[0], item_popularity.shape[0], sparsity * 100)) unique_uid = user_activity.index unique_sid = item_popularity.index song2id = dict((sid, i) for (i, sid) in enumerate(unique_sid)) user2id = dict((uid, i) for (i, uid) in enumerate(unique_uid)) with open(os.path.join(DATA_DIR, 'pro', 'unique_uid.txt'), 'w') as f: for uid in unique_uid: f.write('%s\n' % uid) with open(os.path.join(DATA_DIR, 'pro', 'unique_sid.txt'), 'w') as f: for sid in unique_sid: f.write('%s\n' % sid) np.random.seed(13579) n_ratings = tr_vd_raw_data.shape[0] vad = np.random.choice(n_ratings, size=int(0.125 * n_ratings), replace=False) vad_idx = np.zeros(n_ratings, dtype=bool) vad_idx[vad] = True vad_raw_data = tr_vd_raw_data[vad_idx] train_raw_data = tr_vd_raw_data[~vad_idx] print "There are total of %d unique users in the training set and %d unique users in the entire dataset" % \ (len(pd.unique(train_raw_data['userId'])), len(unique_uid)) print "There are total of %d unique items in the training set and %d unique items in the entire dataset" % \ (len(pd.unique(train_raw_data['movieId'])), len(unique_sid)) train_sid = set(pd.unique(train_raw_data['movieId'])) left_sid = list() for i, sid in enumerate(unique_sid): if sid not in train_sid: left_sid.append(sid) move_idx = vad_raw_data['movieId'].isin(left_sid) train_raw_data = train_raw_data.append(vad_raw_data[move_idx]) vad_raw_data = vad_raw_data[~move_idx] print "There are total of %d unique items in the training set and %d unique items in the entire dataset" % \ (len(pd.unique(train_raw_data['movieId'])), len(unique_sid)) test_raw_data = raw_data[int(0.8 * len(raw_data)):] test_raw_data = test_raw_data[test_raw_data['movieId'].isin(unique_sid)] test_raw_data = test_raw_data[test_raw_data['userId'].isin(unique_uid)] print len(train_raw_data), len(vad_raw_data), len(test_raw_data) train_timestamp = np.asarray(tr_vd_raw_data['timestamp']) print("train: from %s to %s" % (timestamp_to_date(train_timestamp[0]), timestamp_to_date(train_timestamp[-1]))) test_timestamp = np.asarray(test_raw_data['timestamp']) print("test: from %s to %s" % (timestamp_to_date(test_timestamp[0]), timestamp_to_date(test_timestamp[-1]))) def numerize(tp): uid = map(lambda x: user2id[x], tp['userId']) sid = map(lambda x: song2id[x], tp['movieId']) tp['uid'] = uid tp['sid'] = sid return tp[['timestamp', 'uid', 'sid']] train_data = numerize(train_raw_data) train_data.to_csv(os.path.join(DATA_DIR, 'pro', 'train.csv'), index=False) vad_data = numerize(vad_raw_data) vad_data.to_csv(os.path.join(DATA_DIR, 'pro', 'validation.csv'), index=False) test_data = numerize(test_raw_data) test_data.to_csv(os.path.join(DATA_DIR, 'pro', 'test.csv'), index=False) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Confirmed the timestamps are ordered Step2: Now we select the data from 1995-01-01 to the last day as the dataset (i.e., all the dataset) Step3: Take the first 80% of the data as train and validation set Step4: Split 12.5% (10% of the total ratings) as validation set Step5: Make sure there is no empty users/items Step6: For test data, only keep the users and items that appear in the training/validation sets Step7: Basic data information Step8: Numerize the data into (timestamp, user_index, item_index) format
6,967	<ASSISTANT_TASK:> Python Code: def pretty_print_review_and_label(i): print(labels[i] + "\t:\t" + reviews[i][:80] + "...") g = open('reviews.txt','r') # What we know! reviews = list(map(lambda x:x[:-1],g.readlines())) g.close() g = open('labels.txt','r') # What we WANT to know! labels = list(map(lambda x:x[:-1].upper(),g.readlines())) g.close() len(reviews) reviews[0] labels[0] print("labels.txt \t : \t reviews.txt\n") pretty_print_review_and_label(2137) pretty_print_review_and_label(12816) pretty_print_review_and_label(6267) pretty_print_review_and_label(21934) pretty_print_review_and_label(5297) pretty_print_review_and_label(4998) from collections import Counter import numpy as np positive_counts = Counter() negative_counts = Counter() total_counts = Counter() for i in range(len(reviews)): if(labels[i] == 'POSITIVE'): for word in reviews[i].split(" "): positive_counts[word] += 1 total_counts[word] += 1 else: for word in reviews[i].split(" "): negative_counts[word] += 1 total_counts[word] += 1 positive_counts.most_common() pos_neg_ratios = Counter() for term,cnt in list(total_counts.most_common()): if(cnt > 100): pos_neg_ratio = positive_counts[term] / float(negative_counts[term]+1) pos_neg_ratios[term] = pos_neg_ratio for word,ratio in pos_neg_ratios.most_common(): if(ratio > 1): pos_neg_ratios[word] = np.log(ratio) else: pos_neg_ratios[word] = -np.log((1 / (ratio+0.01))) # words most frequently seen in a review with a "POSITIVE" label pos_neg_ratios.most_common() # words most frequently seen in a review with a "NEGATIVE" label list(reversed(pos_neg_ratios.most_common()))[0:30] from IPython.display import Image review = "This was a horrible, terrible movie." Image(filename='sentiment_network.png') review = "The movie was excellent" Image(filename='sentiment_network_pos.png') vocab = set(total_counts.keys()) vocab_size = len(vocab) print(vocab_size) list(vocab) import numpy as np layer_0 = np.zeros((1,vocab_size)) layer_0 from IPython.display import Image Image(filename='sentiment_network.png') word2index = {} for i,word in enumerate(vocab): word2index[word] = i word2index def update_input_layer(review): global layer_0 # clear out previous state, reset the layer to be all 0s layer_0 = 0 for word in review.split(" "): layer_0[0][word2index[word]] += 1 update_input_layer(reviews[0]) layer_0 def get_target_for_label(label): if(label == 'POSITIVE'): return 1 else: return 0 labels[0] get_target_for_label(labels[0]) labels[1] get_target_for_label(labels[1]) import time import sys import numpy as np # Let's tweak our network from before to model these phenomena class SentimentNetwork: def __init__(self, reviews,labels,hidden_nodes = 10, learning_rate = 0.1): # set our random number generator np.random.seed(1) self.pre_process_data(reviews, labels) self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate) def pre_process_data(self, reviews, labels): review_vocab = set() for review in reviews: for word in review.split(" "): review_vocab.add(word) self.review_vocab = list(review_vocab) label_vocab = set() for label in labels: label_vocab.add(label) self.label_vocab = list(label_vocab) self.review_vocab_size = len(self.review_vocab) self.label_vocab_size = len(self.label_vocab) self.word2index = {} for i, word in enumerate(self.review_vocab): self.word2index[word] = i self.label2index = {} for i, label in enumerate(self.label_vocab): self.label2index[label] = i def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate): # Set number of nodes in input, hidden and output layers. self.input_nodes = input_nodes self.hidden_nodes = hidden_nodes self.output_nodes = output_nodes # Initialize weights self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes)) self.weights_1_2 = np.random.normal(0.0, self.output_nodes-0.5, (self.hidden_nodes, self.output_nodes)) self.learning_rate = learning_rate self.layer_0 = np.zeros((1,input_nodes)) def update_input_layer(self,review): # clear out previous state, reset the layer to be all 0s self.layer_0 = 0 for word in review.split(" "): if(word in self.word2index.keys()): self.layer_0[0][self.word2index[word]] += 1 def get_target_for_label(self,label): if(label == 'POSITIVE'): return 1 else: return 0 def sigmoid(self,x): return 1 / (1 + np.exp(-x)) def sigmoid_output_2_derivative(self,output): return output * (1 - output) def train(self, training_reviews, training_labels): assert(len(training_reviews) == len(training_labels)) correct_so_far = 0 start = time.time() for i in range(len(training_reviews)): review = training_reviews[i] label = training_labels[i] #### Implement the forward pass here #### ### Forward pass ### # Input Layer self.update_input_layer(review) # Hidden layer layer_1 = self.layer_0.dot(self.weights_0_1) # Output layer layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2)) #### Implement the backward pass here #### ### Backward pass ### # TODO: Output error layer_2_error = layer_2 - self.get_target_for_label(label) # Output layer error is the difference between desired target and actual output. layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2) # TODO: Backpropagated error layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated to the hidden layer layer_1_delta = layer_1_error # hidden layer gradients - no nonlinearity so it's the same as the error # TODO: Update the weights self.weights_1_2 -= layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step self.weights_0_1 -= self.layer_0.T.dot(layer_1_delta) * self.learning_rate # update input-to-hidden weights with gradient descent step if(np.abs(layer_2_error) < 0.5): correct_so_far += 1 reviews_per_second = i / float(time.time() - start) sys.stdout.write("\rProgress:" + str(100 * i/float(len(training_reviews)))[:4] + "% Speed(reviews/sec):" + str(reviews_per_second)[0:5] + " #Correct:" + str(correct_so_far) + " #Trained:" + str(i+1) + " Training Accuracy:" + str(correct_so_far * 100 / float(i+1))[:4] + "%") if(i % 2500 == 0): print("") def test(self, testing_reviews, testing_labels): correct = 0 start = time.time() for i in range(len(testing_reviews)): pred = self.run(testing_reviews[i]) if(pred == testing_labels[i]): correct += 1 reviews_per_second = i / float(time.time() - start) sys.stdout.write("\rProgress:" + str(100 * i/float(len(testing_reviews)))[:4] \ + "% Speed(reviews/sec):" + str(reviews_per_second)[0:5] \ + "% #Correct:" + str(correct) + " #Tested:" + str(i+1) + " Testing Accuracy:" + str(correct * 100 / float(i+1))[:4] + "%") def run(self, review): # Input Layer self.update_input_layer(review.lower()) # Hidden layer layer_1 = self.layer_0.dot(self.weights_0_1) # Output layer layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2)) if(layer_2[0] > 0.5): return "POSITIVE" else: return "NEGATIVE" mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1) # evaluate our model before training (just to show how horrible it is) mlp.test(reviews[-1000:],labels[-1000:]) # train the network mlp.train(reviews[:-1000],labels[:-1000]) mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.01) # train the network mlp.train(reviews[:-1000],labels[:-1000]) mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.001) # train the network mlp.train(reviews[:-1000],labels[:-1000]) from IPython.display import Image Image(filename='sentiment_network.png') def update_input_layer(review): global layer_0 # clear out previous state, reset the layer to be all 0s layer_0 *= 0 for word in review.split(" "): layer_0[0][word2index[word]] += 1 update_input_layer(reviews[0]) layer_0 review_counter = Counter() for word in reviews[0].split(" "): review_counter[word] += 1 review_counter.most_common() <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Lesson Step2: Project 1 Step3: Transforming Text into Numbers Step4: Project 2 Step5: Project 3 Step6: Understanding Neural Noise
6,968	<ASSISTANT_TASK:> Python Code: import pandas as pd import glob import os import numpy as np from time import time import logging import gensim import bz2 import re from stop_words import get_stop_words def getTopicForQuery (question,stoplist,dictionary,lda): Returns the topic probability distribution for a given input question, filtering with the stoplist and finding the matches in the dictionary of words we have from our topic modelling algorithm. @param question : The string from which we want to extract the topic @param stoplist : The list of common words for the language, that we want to exclude @param dictionary : The dictionary of all the words we find for a given lda model (associated to lda) @param lda : the model of lda (Latent Dirichlet Allocation) that we want to model the topics from. @return the topic probability distribution for the given question # 1. Question -> Lower case -> Split -> Exclude common words temp = question.lower() words = re.findall(r'\w+', temp, flags = re.UNICODE \| re.LOCALE) important_words = [] important_words = filter(lambda x: x not in stoplist, words) # 2. Find matches in the dictionary of words and get the topics ques_vec = [] ques_vec = dictionary.doc2bow(important_words) return ldamodel.get_document_topics(ques_vec,minimum_probability=0) stop_words_de = get_stop_words('de') with open ("../datas/stop_dictionaries/French_stop_words_changed.txt", "r") as myfile: stop_words=myfile.read() stop_words = stop_words.split(',') stop_words = stop_words_de+stop_words ldamodel = gensim.models.LdaModel.load('../datas/lda/ldamodelFR.model', mmap='r') dataset_tmp = [] path = '../datas/scrap/Voting' allFiles = glob.glob(os.path.join(path, 'Session*.csv')) for file_ in allFiles: print(file_) data_tmp = pd.read_csv(file_) dataset_tmp += [data_tmp] data_frame = pd.concat(dataset_tmp) parl = data_frame.ParlGroupCode.unique().tolist() #for group in parl : # data_frame.loc[data_frame.ParlGroupCode==group,'ParlGroupCode']= parl.index(group) data_frame.head() votation_frame = data_frame#[['BillTitle','BusinessTitle','FirstName','LastName','Decision','ParlGroupCode','VoteEnd']] votation_frame = votation_frame.fillna(value='') votation_frame['text'] = votation_frame['BillTitle']+' '+votation_frame['BusinessTitle'] text_dict = {'text': votation_frame.text.unique()} topic_learning_frame = pd.DataFrame(text_dict) topic_learning_frame.head() def insert_topic(data_frame) : dict_ = {} dict_['text'] =data_frame['text'].values with open ("../datas/lda/topics.txt", "r") as myfile: s=myfile.read() topics = s.split('\n') topics_dic = {} for topic in topics : if len(topic)>1 : name = topic.split(':') topics_dic[name[0]] = name[1] dictionary = gensim.corpora.Dictionary.load('../datas/lda/ldaDictionaryFR.dict') for index, text in zip(data_frame.index,data_frame['text'].values) : if index%1000 == 0 : print(index) for topic in getTopicForQuery(text,stop_words,dictionary,ldamodel) : if (topics_dic[str(topic[0])]) in dict_ : dict_[topics_dic[str(topic[0])]] +=[topic[1]] else : dict_[topics_dic[str(topic[0])]] =[topic[1]] return dict_ if not os.path.exists("../datas/nlp_results"): os.makedirs("../datas/nlp_results") from yandex_translate import YandexTranslate translate = YandexTranslate('trnsl.1.1.20161208T132730Z.fe490b34d7db4e4f.0a4c7781a0273d520073a1550b6a6624c1c3fd0a') text_eng = [] for text in topic_learning_frame.text: s = translate.translate(text, 'fr-en') text_eng += [s['text'][0]] topics_dict = insert_topic(topic_learning_frame) topics_frame = pd.DataFrame(topics_dict) #topics_frame['text_eng'] = text_eng topics_frame.head(5) #topics_frame.to_csv('../datas/nlp_results/voting_with_topics_unique.csv',index=False) (pd.merge(votation_frame,topics_frame)).to_csv('../datas/nlp_results/voting_with_topics.csv',index=False) def insert_topic_unique(data_frame) : dict_ = {} dict_['text'] =data_frame['text'].values dict_['Topic'] = [] with open ("../datas/lda/topics.txt", "r") as myfile: s=myfile.read() topics = s.split('\n') topics_dic = {} for topic in topics : if len(topic)>1 : name = topic.split(':') topics_dic[name[0]] = name[1] dictionary = gensim.corpora.Dictionary.load('../datas/lda/ldaDictionary') for index, text in zip(data_frame.index,data_frame['text'].values) : if index%1000 == 0 : print(index) max_ = 0 for topic in getTopicForQuery(text,stop_words,dictionary,ldamodel) : max_old = max_ max_ = max(max_,topic[1]) if max_old != max_ : topic_id = topic[0] dict_['Topic'] += [topics_dic[str(topic_id)]] return dict_ topics_s = insert_topic_unique(topic_learning_frame) topics_ss = pd.DataFrame(topics_s) topics_ss.head() topics_ss.to_csv("../datas/nlp_results/voting_single_topic.csv") data_complete = (pd.merge(votation_frame,topics_ss)) data_complete.head(2) data_complete.VoteEnd = [x[:10] for x in data_complete.VoteEnd] data_complete.head(2) not_used = ['Unnamed: 0','BusinessNumber','BusinessShortNumber','CantonID','ID','IdLegislativePeriod', 'IdSession','IdVote','PersonNumber','RegistrationNumber','BillTitle','BusinessTitle','DecisionText', 'Language','MeaningNo','MeaningYes','ParlGroupColour','ParlGroupCode','ParlGroupNameAbbreviation', 'Subject','text'] data_used = data_complete.drop(not_used ,axis=1) #data_used.head() data_mean = data_used.set_index(['VoteEnd','Topic','FirstName']) data_mean.head(1000) data_used.to_csv("../datas/nlp_results/voting_single_topic_not_unique.csv") for year in data_mean.index.get_level_values('VoteEnd').unique() : for Topic in data_mean.loc[year].index.get_level_values('Topic').unique() : print("the {0} the subject : {1} had a result of {2}".format( year,Topic,data_mean.loc[year,Topic].Decision.mean())) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step2: 1. initialisation of function for topic determination Step3: Now we load the lda model we use along with the stop words, in order to have them available for the time we will use them, avoiding to reload them every time we call getTopicForQuery. We also load our lda model for once. Step4: 2. Creation of the Voting DataFrame Step5: 2.1 slection of interesting fields in data_frame Step6: We create a smaller DataFrame which contains only the subjects that are voted, we do not repeat the text each time for each person who votes. Hence we will perform the NLP once on each unique entry. Step7: 3 Topic Clustering Step8: We now create a frame using the topics found using insert_topic Step9: Finally merging the topics with the original frame containing the name and decision of parlementeer ?
6,969	<ASSISTANT_TASK:> Python Code: def zero_args(): # code goes here pass def one_arg(a): # code goes here pass def two_args(a, b): # code goes here pass def optional_arg(a, b=0): # <--- please note, optional arguments are listed LAST # code goes here pass def two_options(a=True, b=False): # code goes here pass # To refreash your memory, calling these functions look something like: zero_args() one_arg(10) two_args(10, 20) optional_arg(10) optional_arg(10, b=20) two_options() two_options(a=True) from math import sqrt # <-- importing the square root function from the math module. def pythagoras (a, b): c2 = a 2 + b 2 return sqrt(c2) # note, this line would be math.sqrt(c2) if we had written 'import math' instead of 'from math...' # Lets call it! round(pythagoras(10, 23), 3) def is_prime(num): Returns True if number is prime, False otherwise if num <= 1: return False # numbers <= 1 are not prime # check for factors for i in range(2,num): # for loop that iterates 2-to-num. Each number in the iteration is called "i" if (num % i) == 0: # modular arithmetic; this asks if num is divisible by i (with no remainder). return False return True a = 10 def change_a(): a = 20 change_a() print(a) a = 10 print(id(a)) def change_a(): a = 20 print(id(a)) change_a() def variables(): x = 10 y = 10 print(x + y) # <--- note the error message; NameError def variables(): x = 10 y = 10 print(x + y) return x, y a, b = variables() # this line maps x,y to a,b . print(a + b) a = [i for i in range(10)] b = [i for i in range(20)] c = [i for i in "abcde"] print(a, b, c, sep="\n") print(i) # YOUR CODE HERE! <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Simple stuff right? Okay lets move on and look at the body and return statement. I’m going to create a function that calculates the hypotenuse of a triangle when given sides 'a' and 'b' (i.e. the Pythagorean Theorem). Step3: So our pythagoras function takes the arguments 'a' and 'b'. The second line calculates 'c2' and once we have that we return the square root of 'c2'. Step4: To give you a quick run down this code works out if a number, 'n', is prime by seeing if there is a 'q' that is a divisor of n. where Step5: We print A, and number we get is 10. Why isn't it 20? We called the 'change a' function and the function quite clearly assigned A to 20. what gives? Step6: Basically, the "A" inside the function is separate from the "A" outside of it. Here's another example Step7: So Python is telling us X is not defined and that’s not a bug, X and Y are defined within the scope of the function, but not defined within the main program (e.g the place where we are calling it). There are a few fixes; we could move the print statement into the function by indenting it. Or, we could 'save' the function variables for use in the main program. For example Step8: Why does Python do this? Step9: In short, self-contained blocks of code (e.g. functions) have their own 'namespace', and that means if a variable is defined inside that block then that variable is said to be defined locally.
6,970	<ASSISTANT_TASK:> Python Code: # egrep.py import sys, re # sys.argv is the list of command-line arguments # sys.argv[0] is the name of the program itself # sys.argv[1] will be the regex specified at the command line regex = sys.argv[1] # for every line passed into the script for line in sys.stdin: # if it matches the regex, write it to stdout if re.search(regex, line): sys.stdout.write(line) # line_count.py import sys count = 0 for line in sys.stdin: count += 1 # print goes to sys.stdout print(count) !type SomeFile.txt \| python egrep.py "[0-9]" \| python line_count.py # 'r' means read-only file_for_reading = open('reading_file.txt', 'r') # 'w' is write—will destroy the file if it already exists! file_for_writing = open('writing_file.txt', 'w') # 'a' is append—for adding to the end of the file file_for_appending = open('appending_file.txt', 'a') # don't forget to close your files when you're done file_for_writing.close() with open('SomeFile.txt', 'r') as f: for line in f: print(line.strip()) # After with block, file is closed import csv with open('stocks.csv', 'r') as f: reader = csv.reader(f, delimiter=',') for row in reader: date = row[0] symbol = row[1] closing_price = float(row[2]) print(date, symbol, closing_price) with open('stocks-headers.csv', 'r') as f: reader = csv.DictReader(f, delimiter=':') for row in reader: date = row['date'] symbol = row['symbol'] closing_price = float(row['closing_price']) print(date, symbol, closing_price) some_html = <html> <head> <title>A web page</title> </head> <body> <p id="author">Joel Grus</p> <p id="subject" class="important">Data Science</p> </body> </html> from bs4 import BeautifulSoup import requests html = requests.get('http://www.example.com').text html = some_html soup = BeautifulSoup(html, 'html5lib') first_paragraph = soup.find('p') first_paragraph soup.p.text, soup.p.text.split() soup.p['id'] soup.p.get('id') soup.find_all('p') [p for p in soup('p') if p.get('id')] soup('p', {'class' : 'important'}) soup('p', 'important') [p for p in soup('p') if 'important' in p.get('class', [])] import json json_string = { "title" : "Data Science Book", "author" : "Joel Grus", "publicationYear" : 2014, "topics" : [ "data", "science", "data science"] } # parse the JSON into a Python Dictionary dict = json.loads(json_string) if 'data science' in dict['topics']: print(dict) endpoint = 'https://api.github.com/users/joelgrus/repos' repos = json.loads(requests.get(endpoint).text) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Reading Files Step2: Use a with block to ensure that files are closed Step3: Delimited Files Step5: HTML And The Parsing Thereof Step7: Using APIs Step8: Using An Unauthenticated API
6,971	<ASSISTANT_TASK:> Python Code: !ls import sha # Our first commit data1 = 'This is the start of my paper2.' meta1 = 'date: 1/1/12' hash1 = sha.sha(data1 + meta1).hexdigest() print('Hash:', hash1) # Our second commit, linked to the first data2 = 'Some more text in my paper...' meta2 = 'date: 1/2/12' # Note we add the parent hash here! hash2 = sha.sha(data2 + meta2 + hash1).hexdigest() print('Hash:', hash2) %%bash git config --global user.name "John Doe" git config --global user.email "johndoe@uw.edu" %%bash # Put here your preferred editor. If this is not set, git will honor # the $EDITOR environment variable git config --global core.editor /usr/bin/nano # my preferred editor # On Windows Notepad will do in a pinch, # I recommend Notepad++ as a free alternative # On the mac, you can set nano or emacs as a basic option %%bash # And while we're at it, we also turn on the use of color, which is very useful git config --global color.ui "auto" %%bash git config --global credential.helper cache # Set the cache to timeout after 2 hours (setting is in seconds) git config --global credential.helper 'cache --timeout=7200' !cat ~/.gitconfig !git %%bash rm -rf test git init test %%bash cd test ls %%bash cd test ls -la %%bash cd test ls -l .git %%bash cd test echo "My first bit of text" > file1.txt %%bash cd test ls -al %%bash cd test git add file1.txt %%bash cd test git status %%bash cd test git commit -a -m "This is our first commit" %%bash cd test git log %%bash cd test echo "And now some more text..." >> file1.txt %%bash cd test git diff %%bash cd test git commit -a -m "I have made great progress on this critical matter." %%bash cd test git log %%bash cd test git log --oneline --topo-order --graph %%bash cd test # We create our alias (this saves it in git's permanent configuration file): git config --global alias.slog "log --oneline --topo-order --graph" # And now we can use it git slog %%bash cd test git mv file1.txt file-newname.txt git status %%bash cd test git commit -a -m"I like this new name better" echo "Let's look at the log again:" git slog %%bash cd test git status ls %%bash cd test git branch experiment git checkout experiment %%bash cd test echo "Some crazy idea" > experiment.txt git add experiment.txt git commit -a -m"Trying something new" git slog %%bash cd test git checkout master git slog %%bash cd test echo "All the while, more work goes on in master..." >> file-newname.txt git commit -a -m"The mainline keeps moving" git slog %%bash cd test ls %%bash cd test git merge experiment git slog %%bash cd test ls echo "Let's see if we have any remote repositories here:" git remote -v %%bash cd test git remote add origin https://github.com/jakevdp/test.git %%bash cd test git remote -v %%bash cd test git push origin master %%bash # Here I clone my 'test' repo but with a different name, test2, to simulate a 2nd computer git clone https://github.com/jakevdp/test.git test2 cd test2 pwd git remote -v %%bash cd test2 # working on computer #2 echo "More new content on my experiment" >> experiment.txt git commit -a -m"More work, on machine #2" %%bash cd test2 git push origin master %%bash cd test git pull origin master %%bash cd test git branch trouble git checkout trouble echo "This is going to be a problem..." >> experiment.txt git commit -a -m"Changes in the trouble branch" %%bash cd test git checkout master echo "More work on the master branch..." >> experiment.txt git commit -a -m"Mainline work" %%bash cd test git merge trouble %%bash cd test cat experiment.txt %%bash cd test cat experiment.txt %%bash cd test git commit -a -m"Completed merge of trouble, fixing conflicts along the way" git slog <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: A repository Step2: And this is pretty much the essence of Git! Step3: Other settings Step4: Password memory Step5: Github offers in its help pages instructions on how to configure the credentials helper for Mac OSX and Windows. Step6: Stage 1 Step7: git init Step8: Note Step9: Now let's edit our first file in the test directory with a text editor... I'm doing it programatically here for automation purposes, but you'd normally be editing by hand Step10: git add Step11: We can now ask git about what happened with status Step12: git commit Step13: In the commit above, we used the -m flag to specify a message at the command line. Step14: git diff Step15: And now we can ask git what is different Step16: The cycle of git virtue Step17: git log revisited Step18: Sometimes it's handy to see a very summarized version of the log Step19: Defining an alias Step20: git mv and rm Step21: Note that these changes must be committed too, to become permanent! In git's world, until something hasn't been committed, it isn't permanently recorded anywhere. Step22: And git rm works in a similar fashion. Step23: We are now going to try two different routes of development Step24: 3. Using remotes as a single user Step25: Since the above cell didn't produce any output after the git remote -v call, it means we have no remote repositories configured. Step26: Let's see the remote situation again Step27: Pushing changes to a remote repository Step28: We can now see this repository publicly on github. Step29: Let's now make some changes in one 'computer' and synchronize them on the second. Step30: Now we put this new work up on the github server so it's available from the internet Step31: Now let's fetch that work from machine #1 Step32: An important aside Step33: And now we go back to the master branch, where we change the same file Step34: The conflict... Step35: Let's see what git has put into our file Step36: At this point, we go into the file with a text editor, decide which changes to keep, and make a new commit that records our decision. I've now made the edits, in this case I decided that both pieces of text were useful, but integrated them with some changes Step37: Let's then make our new commit
6,972	<ASSISTANT_TASK:> Python Code: # Load regex package import re # Create a variable containing a text string text = 'The quick brown fox jumped over the lazy brown bear.' # Find any of fox, snake, or bear re.findall(r'\b(fox\|snake\|bear)\b', text) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Create some text Step2: Apply regex
6,973	<ASSISTANT_TASK:> Python Code: import pandas as pd import numpy as np df = pd.read_csv('output/embedded_1k_reviews.csv') df['text-similarity-babbage-001'] = df.babbage_similarity.apply(eval).apply(np.array) matrix = np.vstack(df.babbage_similarity.values) matrix.shape from sklearn.cluster import KMeans n_clusters = 4 kmeans = KMeans(n_clusters = n_clusters,init='k-means++',random_state=42) kmeans.fit(matrix) labels = kmeans.labels_ df['Cluster'] = labels df.groupby('Cluster').Score.mean().sort_values() from sklearn.manifold import TSNE import matplotlib import matplotlib.pyplot as plt tsne = TSNE(n_components=2, perplexity=15, random_state=42, init='random', learning_rate=200) vis_dims2 = tsne.fit_transform(matrix) x = [x for x,y in vis_dims2] y = [y for x,y in vis_dims2] for category, color in enumerate(['purple', 'green', 'red', 'blue']): xs = np.array(x)[df.Cluster==category] ys = np.array(y)[df.Cluster==category] plt.scatter(xs, ys, color=color, alpha=0.3) avg_x = xs.mean() avg_y = ys.mean() plt.scatter(avg_x, avg_y, marker='x', color=color, s=100) plt.title("Clusters identified visualized in language 2d using t-SNE") import openai # Reading a review which belong to each group. rev_per_cluster = 3 for i in range(n_clusters): print(f"Cluster {i} Theme:", end=" ") reviews = "\n".join(df[df.Cluster == i].combined.str.replace("Title: ", "").str.replace("\n\nContent: ", ": ").sample(rev_per_cluster, random_state=42).values) response = openai.Completion.create( engine="davinci-instruct-beta-v3", prompt=f"What do the following customer reviews have in common?\n\nCustomer reviews:\n\"\"\"\n{reviews}\n\"\"\"\n\nTheme:", temperature=0, max_tokens=64, top_p=1, frequency_penalty=0, presence_penalty=0 ) print(response["choices"][0]["text"].replace('\n','')) sample_cluster_rows = df[df.Cluster == i].sample(rev_per_cluster, random_state=42) for j in range(rev_per_cluster): print(sample_cluster_rows.Score.values[j], end=", ") print(sample_cluster_rows.Summary.values[j], end=": ") print(sample_cluster_rows.Text.str[:70].values[j]) print("-" * 100) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: 1. Find the clusters using K-means Step2: It looks like cluster 2 focused on negative reviews, while cluster 0 and 1 focused on positive reviews. Step3: Visualization of clusters in a 2d projection. The red cluster clearly represents negative reviews. The blue cluster seems quite different from the others. Let's see a few samples from each cluster.
6,974	<ASSISTANT_TASK:> Python Code: WORKING_DIR = u"/path/to/folder/to/music" FILENAME_PREFIX = u"filename_without_ext" FILENAME_EXTENSION = u"wav" OUTPUT_PATTERN = u"/path/to/your/music/<%(prefix)s >%(album)s< (%(suffix)s)>/<<%(discnumber)s->%(tracknumber)s >%(title)s.flac" PICTURE = u"Folder.jpg" ANSI_ENCODING = "gbk" FILES_TO_COPY = ["Artworks.tar"] DELETE_TARGET_DIR = False # If clean the target folder at first INPUT_EXTRAINFO = u"%s.ini" % FILENAME_PREFIX INPUT_CUE = u"%s.cue" % FILENAME_PREFIX INPUT_AUDIO = u"%s.%s" % (FILENAME_PREFIX, FILENAME_EXTENSION) import sys sys.path.append(u"/path/to/your/GatesMusicPet/") from music_pet.meta import * from music_pet.utils import * from music_pet.audio import FLAC, init_flacs import subprocess import os, sys cd $WORKING_DIR global_report = [] NOT_PARSED = 1 NO_TRACK = 2 albumList = parse_cue(INPUT_CUE, encoding="U8") extraMetas = parse_ini(INPUT_EXTRAINFO) for album in albumList.values(): for extraMeta in extraMetas: album.update_all_tracks(extraMeta) albumList.fix_album_names() flacs = [] for album in albumList.values(): flacs = init_flacs(album, OUTPUT_PATTERN) for flac in flacs: flac.set_input_file(u"%s/%s" % ( WORKING_DIR, filename_safe(flac.get_tag(u"original_file")))) flac.set_next_start_time_from_album(album) flac.cover_picture = PICTURE for l in album.detail(): print(l) commands = [] tmpified_files = {} for flac in flacs: b_is_wav = flac.get_tag(u"@input_fullpath").endswith(u".wav") b_tempified = flac.get_tag(u"@input_fullpath") in tmpified_files if not b_is_wav and not b_tempified: commands.append(flac.command_build_tempwav(memoize=tmpified_files)) commands.append(flac.command()) commands.append(command_copy_to([PICTURE] + FILES_TO_COPY, parent_folder(flac.get_tag(u"@output_fullpath")))) if not b_is_wav and not b_tempified: commands.append(flac.command_clear_tempwav()) flac.create_target_dir() for cmd in commands: print(cmd) print(u"") cd $WORKING_DIR for cmd in commands: print(u"Executing:\n%s\n\n" % cmd) try: p = subprocess.check_output(cmd, shell=True, ) except subprocess.CalledProcessError as ex: p = u"Process received an error! code=%s, output=%s" % (ex.returncode, ex.output) global_report.append((3, u"Process Error, code=%s" % ex.returncode, cmd)) print(p) print(u"\n\n") for error in global_report: print(u"%s\n%s\n\n" % (error[1], error[2])) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Filename Step2: Output Prefix Step3: Others Step4: Parse CUE Step5: Covert Files
6,975	<ASSISTANT_TASK:> Python Code: import numpy N = 30 # number of points along each axis X = numpy.linspace(-2, 2, N) # computes a 1D-array for x Y = numpy.linspace(-2, 2, N) # computes a 1D-array for y x, y = numpy.meshgrid(X, Y) # generates a mesh grid from matplotlib import pyplot %matplotlib inline pyplot.scatter(x, y) pyplot.xlabel('x') pyplot.ylabel('y') # velocity component functions def get_u( x, y, S, gamma ): return gamma/(2numpy.pi)(numpy.arctan((x-S)/y)-numpy.arctan((x+S)/y)) def get_v( x, y, S, gamma ): return gamma/(4numpy.pi)(numpy.log(((x+S)2+y2)/((x-S)2+y2))) def plot_uv(u,v): pyplot.figure(figsize=(8,11)) # set size pyplot.xlabel('x', fontsize=16) # label x pyplot.ylabel('y', fontsize=16) # label y m = numpy.sqrt(u2+v2) # compute velocity magnitude velocity = pyplot.contourf(x, y, m, vmin=0) # plot magnitude contours cbar = pyplot.colorbar(velocity, orientation='horizontal') cbar.set_label('Velocity magnitude', fontsize=16); pyplot.quiver(x, y, u, v) # plot vector field # pyplot.streamplot(x, y, u, v) # plots streamlines - this is slow! # compute the velocity gamma = -4 # sheet strength S = 1 # sheet extents u = get_u(x,y,S,gamma) v = get_v(x,y,S,gamma) # plot it plot_uv(u,v) pyplot.plot([-min(S,2),min(S,2)],[0,0],'k-',lw=2) # draw the vortex sheet alpha = numpy.pi/10 # free-stream angle U_inf = numpy.cos(alpha) # free-stream in x V_inf = numpy.sin(alpha) # free-stream in y # superimpose to get velocity gamma = -4 # sheet strength S = 0.5 # sheet extents u = U_inf+get_u(x,y,S,gamma) v = V_inf+get_v(x,y,S,gamma) # plot it plot_uv(u,v) pyplot.plot([-min(S,2),min(S,2)],[0,0],'k-',lw=2) # draw the vortex sheet # vortex panel class class Panel: # save the inputs and pre-compute factors for the coordinate tranform def __init__( self, x0, y0, x1, y1, gamma ): self.x,self.y,self.gamma = [x0,x1],[y0,y1],gamma self.xc = 0.5(x0+x1) # panel x-center self.yc = 0.5(y0+y1) # panel y-center self.S = numpy.sqrt( # ... (x1-self.xc)2+(y1-self.yc)2) # panel width self.sx = (x1-self.xc)/self.S # unit vector in x self.sy = (y1-self.yc)/self.S # unit vector in y # get the velocity! def velocity( self, x, y ): gamma = self.gamma xp,yp = self.transform_xy( x, y ) # transform up = get_u( xp, yp, self.S, gamma ) # get u prime vp = get_v( xp, yp, self.S, gamma ) # get v prime return self.rotate_uv( up, vp ) # rotate back # plot the panel def plot(self): return pyplot.plot(self.x,self.y,'k-',lw=2) # transform from global to panel coordinates def transform_xy( self, x, y ): xt = x-self.xc # shift x yt = y-self.yc # shift y xp = xtself.sx+ytself.sy # rotate x yp = ytself.sx-xtself.sy # rotate y return [ xp, yp ] # rotate velocity back to global coordinates def rotate_uv( self, up, vp): u = upself.sx-vpself.sy # reverse rotate u prime v = vpself.sx+upself.sy # reverse rotate v prime return [ u, v ] # define panel my_panel = Panel(x0=-0.7,y0=0.5,x1=0.5,y1=-0.4,gamma=-2) # compute velocity on grid u,v = my_panel.velocity(x,y) # plot it plot_uv(u,v) # plot the flow on the grid my_panel.plot() # plot the panel # your code here from IPython.core.display import HTML def css_styling(): styles = open('../styles/custom.css', 'r').read() return HTML(styles) css_styling() <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Lets visualize the grid to see what we made. We need to import pyplot which has a large set of plotting functions similar to matlab, such as a scatter plot. Step2: As expected, a grid of equally space points. Step3: Not the prettiest equations, but nothing numpy can't handle. Step4: Now we can compute the velocity on the grid and plot it Step5: Quiz 1 Step6: The dark blue circle is a stagnation point, ie the fluid has stopped, ie $u=v=0$. Step7: Now we can define a general panel and compute its velocity. Step8: Quiz 5 Step9: Ignore the line below - it just loads the style sheet.
6,976	<ASSISTANT_TASK:> Python Code: # Author: Olaf Hauk <olaf.hauk@mrc-cbu.cam.ac.uk> # # License: BSD (3-clause) import mne from mne.datasets import sample from mne.minimum_norm.resolution_matrix import make_inverse_resolution_matrix from mne.minimum_norm.spatial_resolution import resolution_metrics print(__doc__) data_path = sample.data_path() subjects_dir = data_path + '/subjects/' fname_fwd_emeg = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif' fname_cov = data_path + '/MEG/sample/sample_audvis-cov.fif' fname_evo = data_path + '/MEG/sample/sample_audvis-ave.fif' # read forward solution with EEG and MEG forward_emeg = mne.read_forward_solution(fname_fwd_emeg) # forward operator with fixed source orientations forward_emeg = mne.convert_forward_solution(forward_emeg, surf_ori=True, force_fixed=True) # create a forward solution with MEG only forward_meg = mne.pick_types_forward(forward_emeg, meg=True, eeg=False) # noise covariance matrix noise_cov = mne.read_cov(fname_cov) # evoked data for info evoked = mne.read_evokeds(fname_evo, 0) # make inverse operator from forward solution for MEG and EEGMEG inv_emeg = mne.minimum_norm.make_inverse_operator( info=evoked.info, forward=forward_emeg, noise_cov=noise_cov, loose=0., depth=None) inv_meg = mne.minimum_norm.make_inverse_operator( info=evoked.info, forward=forward_meg, noise_cov=noise_cov, loose=0., depth=None) # regularisation parameter snr = 3.0 lambda2 = 1.0 / snr ** 2 rm_emeg = make_inverse_resolution_matrix(forward_emeg, inv_emeg, method='MNE', lambda2=lambda2) ple_psf_emeg = resolution_metrics(rm_emeg, inv_emeg['src'], function='psf', metric='peak_err') sd_psf_emeg = resolution_metrics(rm_emeg, inv_emeg['src'], function='psf', metric='sd_ext') del rm_emeg rm_meg = make_inverse_resolution_matrix(forward_meg, inv_meg, method='MNE', lambda2=lambda2) ple_psf_meg = resolution_metrics(rm_meg, inv_meg['src'], function='psf', metric='peak_err') sd_psf_meg = resolution_metrics(rm_meg, inv_meg['src'], function='psf', metric='sd_ext') del rm_meg brain_ple_emeg = ple_psf_emeg.plot('sample', 'inflated', 'lh', subjects_dir=subjects_dir, figure=1, clim=dict(kind='value', lims=(0, 2, 4))) brain_ple_emeg.add_text(0.1, 0.9, 'PLE PSF EMEG', 'title', font_size=16) brain_ple_meg = ple_psf_meg.plot('sample', 'inflated', 'lh', subjects_dir=subjects_dir, figure=2, clim=dict(kind='value', lims=(0, 2, 4))) brain_ple_meg.add_text(0.1, 0.9, 'PLE PSF MEG', 'title', font_size=16) # Subtract the two distributions and plot this difference diff_ple = ple_psf_emeg - ple_psf_meg brain_ple_diff = diff_ple.plot('sample', 'inflated', 'lh', subjects_dir=subjects_dir, figure=3, clim=dict(kind='value', pos_lims=(0., .5, 1.)), smoothing_steps=20) brain_ple_diff.add_text(0.1, 0.9, 'PLE EMEG-MEG', 'title', font_size=16) brain_sd_emeg = sd_psf_emeg.plot('sample', 'inflated', 'lh', subjects_dir=subjects_dir, figure=4, clim=dict(kind='value', lims=(0, 2, 4))) brain_sd_emeg.add_text(0.1, 0.9, 'SD PSF EMEG', 'title', font_size=16) brain_sd_meg = sd_psf_meg.plot('sample', 'inflated', 'lh', subjects_dir=subjects_dir, figure=5, clim=dict(kind='value', lims=(0, 2, 4))) brain_sd_meg.add_text(0.1, 0.9, 'SD PSF MEG', 'title', font_size=16) # Subtract the two distributions and plot this difference diff_sd = sd_psf_emeg - sd_psf_meg brain_sd_diff = diff_sd.plot('sample', 'inflated', 'lh', subjects_dir=subjects_dir, figure=6, clim=dict(kind='value', pos_lims=(0., .5, 1.)), smoothing_steps=20) brain_sd_diff.add_text(0.1, 0.9, 'SD EMEG-MEG', 'title', font_size=16) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: EEGMEG Step2: MEG Step3: Visualization Step4: These plots show that with respect to peak localization error, adding EEG to
6,977	<ASSISTANT_TASK:> Python Code: from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf mnist = input_data.read_data_sets('MNIST_data', one_hot = True) ################## build a softmax regression model # input data x = tf.placeholder(tf.float32, shape = [None, 784]) # real label y_ = tf.placeholder(tf.float32, shape=[None, 10]) def weight_variable(shape): initial = tf.truncated_normal(shape, stddev = 0.1) return tf.Variable(initial) def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding = 'SAME') def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) x_image = tf.reshape(x, [-1, 28, 28, 1]) h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) W_conv2 = weight_variable([5, 5, 32, 64]) b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) h_pool2 = max_pool_2x2(h_conv2) W_fc1 = weight_variable([7 * 7 * 64, 1024]) b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pool2, [-1, 7764]) h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) keep_prob = tf.placeholder(tf.float32) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) W_fc2 = weight_variable([1024, 10]) b_fc2 = bias_variable([10]) y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)) #train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) train_step = tf.train.AdamOptimizer(1e-3).minimize(cross_entropy) correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for i in range(20000): batch = mnist.train.next_batch(50) if i%100 == 0: train_accuracy = accuracy.eval(feed_dict={x:batch[0], y_: batch[1], keep_prob: 1.0}) print("step %d, training accuracy %g"%(i, train_accuracy)) train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) print("test accuracy %g"% accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Build a Multilayer Convolutional Network Step2: Convolution and Pooling Step3: First Convolutional Layer Step4: To apply the layer, we first reshape x to a 4d tensor, with the second and third dimensions corresponding to Step5: We then convolve x_image with the weight tensor, add the bias, apply the ReLU function, and finally max pool. The max_pool_2x2 method will reduce the image size to 14x14. Step6: Second Convolutional Layer Step7: Densely Connected Layer Step8: Dropout Step9: Readout Layer Step10: Train and Evaluate the Model
6,978	<ASSISTANT_TASK:> Python Code: # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr> # Joan Massich <mailsik@gmail.com> # # License: BSD Style. import os.path as op import mne from mne.datasets import eegbci from mne.datasets import fetch_fsaverage # Download fsaverage files fs_dir = fetch_fsaverage(verbose=True) subjects_dir = op.dirname(fs_dir) # The files live in: subject = 'fsaverage' trans = 'fsaverage' # MNE has a built-in fsaverage transformation src = op.join(fs_dir, 'bem', 'fsaverage-ico-5-src.fif') bem = op.join(fs_dir, 'bem', 'fsaverage-5120-5120-5120-bem-sol.fif') raw_fname, = eegbci.load_data(subject=1, runs=[6]) raw = mne.io.read_raw_edf(raw_fname, preload=True) # Clean channel names to be able to use a standard 1005 montage new_names = dict( (ch_name, ch_name.rstrip('.').upper().replace('Z', 'z').replace('FP', 'Fp')) for ch_name in raw.ch_names) raw.rename_channels(new_names) # Read and set the EEG electrode locations montage = mne.channels.make_standard_montage('standard_1005') raw.set_montage(montage) raw.set_eeg_reference(projection=True) # needed for inverse modeling # Check that the locations of EEG electrodes is correct with respect to MRI mne.viz.plot_alignment( raw.info, src=src, eeg=['original', 'projected'], trans=trans, show_axes=True, mri_fiducials=True, dig='fiducials') fwd = mne.make_forward_solution(raw.info, trans=trans, src=src, bem=bem, eeg=True, mindist=5.0, n_jobs=1) print(fwd) # Use fwd to compute the sensitivity map for illustration purposes eeg_map = mne.sensitivity_map(fwd, ch_type='eeg', mode='fixed') brain = eeg_map.plot(time_label='EEG sensitivity', subjects_dir=subjects_dir, clim=dict(lims=[5, 50, 100])) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Load the data Step2: Setup source space and compute forward
6,979	<ASSISTANT_TASK:> Python Code: from libtools import * training = pd.read_csv('data-test.csv') training.head() training.describe() training = training.fillna(-99999) blind = pd.read_csv('blind.csv') blind.head() blind.describe() training_SH = divisao_sh(training) training_LM = divisao_lm(training) blind_SH = divisao_sh(blind) blind_LM = divisao_lm(blind) training_SH.head() training_LM.head() blind_SH.head() blind_LM.head() X_SH = training_SH.drop(['Facies'],axis=1) y_SH = training_SH['Facies'] X_LM = training_LM.drop(['Facies'],axis=1) y_LM = training_LM['Facies'] X_SH_blind = blind_SH.drop(['Facies'],axis=1) y_SH_blind = blind_SH['Facies'] X_LM_blind = blind_LM.drop(['Facies'],axis=1) y_LM_blind = blind_LM['Facies'] from sklearn.model_selection import train_test_split X_train_SH, X_test_SH, y_train_SH, y_test_SH = train_test_split(X_SH, y_SH, test_size=0.1) X_train_LM, X_test_LM, y_train_LM, y_test_LM = train_test_split(X_LM, y_LM, test_size=0.1) from sklearn.ensemble import ExtraTreesClassifier from sklearn.metrics import classification_report,confusion_matrix ETC_SH = ExtraTreesClassifier(n_estimators=500, bootstrap=True) ETC_LM = ExtraTreesClassifier(n_estimators=500) ETC_SH.fit(X_train_SH, y_train_SH) ETC_LM.fit(X_train_LM, y_train_LM) pred_SH = ETC_SH.predict(X_test_SH) print(confusion_matrix(y_test_SH,pred_SH)) print(classification_report(y_test_SH,pred_SH)) pred_LM = ETC_LM.predict(X_test_LM) print(confusion_matrix(y_test_LM,pred_LM)) print(classification_report(y_test_LM,pred_LM)) blind_pred_SH = ETC_SH.predict(X_SH_blind) print(confusion_matrix(y_SH_blind, blind_pred_SH)) print(classification_report(y_SH_blind, blind_pred_SH)) blind_pred_LM = ETC_LM.predict(X_LM_blind) print(confusion_matrix(y_LM_blind, blind_pred_LM)) print(classification_report(y_LM_blind, blind_pred_LM)) blind_pred_SH = pd.DataFrame(blind_pred_SH, index=X_SH_blind.index) blind_pred_LM = pd.DataFrame(blind_pred_LM, index=X_LM_blind.index) pred_blind = pd.concat([blind_pred_SH,blind_pred_LM]) pred_blind = pred_blind.sort_index() y_blind = blind['Facies'] print(confusion_matrix(y_blind, pred_blind)) print(classification_report(y_blind, pred_blind)) training_data = pd.read_csv('training.csv') training_data.head() training_data.describe() training_data_SH = divisao_sh(training_data) training_data_LM = divisao_lm(training_data) training_data_SH.describe() training_data_LM.describe() X_SH = training_data_SH.drop(['Facies'],axis=1) y_SH = training_data_SH['Facies'] X_LM = training_data_LM.drop(['Facies'],axis=1) y_LM = training_data_LM['Facies'] X_SH.describe() X_LM.describe() from sklearn.model_selection import train_test_split X_train_SH, X_test_SH, y_train_SH, y_test_SH = train_test_split(X_SH, y_SH, test_size=0.1) X_train_LM, X_test_LM, y_train_LM, y_test_LM = train_test_split(X_LM, y_LM, test_size=0.1) ETC_SH = ExtraTreesClassifier(n_estimators=500, bootstrap=True) ETC_LM = ExtraTreesClassifier(n_estimators=500) ETC_SH.fit(X_train_SH, y_train_SH) ETC_LM.fit(X_train_LM, y_train_LM) pred_SH = ETC_SH.predict(X_test_SH) print(confusion_matrix(y_test_SH,pred_SH)) print(classification_report(y_test_SH,pred_SH)) pred_LM = ETC_LM.predict(X_test_LM) print(confusion_matrix(y_test_LM,pred_LM)) print(classification_report(y_test_LM,pred_LM)) validation = pd.read_csv('validation_data_nofacies.csv') validation.head() validation.describe() validation['Label_Form_SH_LM'] = validation.Formation.apply((label_two_groups_formation)) validation.head() validation_SH = divisao_sh(validation) validation_LM = divisao_lm(validation) validation_SH.head() validation_LM.head() X_val_SH = validation_SH.drop(['Formation','Well Name','Depth','NM_M'], axis=1) X_val_LM = validation_LM.drop(['Formation','Well Name','Depth','NM_M'], axis=1) X_val_SH.head() X_val_LM.head() pred_val_SH = ETC_SH.predict(X_val_SH) pred_val_LM =ETC_LM.predict(X_val_LM) pred_val_SH = pd.DataFrame(pred_val_SH, index=X_val_SH.index) pred_val_LM = pd.DataFrame(pred_val_LM, index=X_val_LM.index) pred_val = pd.concat([pred_val_SH,pred_val_LM]) pred_val = pred_val.sort_index() pred_val.describe() validation['Facies Pred'] = pred_val validation=validation.drop(['Label_Form_SH_LM'],axis=1) validation.head() validation.to_csv('Prediction.csv') <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Loading the data training data without Shankle well Step2: Loading the SHANKLE well Step3: Using the complete training data Step4: Applying ExtraTreeClassifier Step5: Making the division between SH and LM Step6: Removing the colums
6,980	<ASSISTANT_TASK:> Python Code: # Authors: Eric Larson <larson.eric.d@gmail.com> # # License: BSD (3-clause) from os import path as op import mne from mne.preprocessing import maxwell_filter print(__doc__) data_path = op.join(mne.datasets.misc.data_path(verbose=True), 'movement') pos = mne.chpi.read_head_pos(op.join(data_path, 'simulated_quats.pos')) raw = mne.io.read_raw_fif(op.join(data_path, 'simulated_movement_raw.fif')) raw_stat = mne.io.read_raw_fif(op.join(data_path, 'simulated_stationary_raw.fif')) mne.viz.plot_head_positions(pos, mode='traces') # extract our resulting events events = mne.find_events(raw, stim_channel='STI 014') events[:, 2] = 1 raw.plot(events=events) topo_kwargs = dict(times=[0, 0.1, 0.2], ch_type='mag', vmin=-500, vmax=500) # 0. Take average of stationary data (bilateral auditory patterns) evoked_stat = mne.Epochs(raw_stat, events, 1, -0.2, 0.8).average() evoked_stat.plot_topomap(title='Stationary', topo_kwargs) # 1. Take a naive average (smears activity) evoked = mne.Epochs(raw, events, 1, -0.2, 0.8).average() evoked.plot_topomap(title='Moving: naive average', topo_kwargs) # 2. Use raw movement compensation (restores pattern) raw_sss = maxwell_filter(raw, head_pos=pos) evoked_raw_mc = mne.Epochs(raw_sss, events, 1, -0.2, 0.8).average() evoked_raw_mc.plot_topomap(title='Moving: movement compensated', **topo_kwargs) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Visualize the "subject" head movements (traces) Step2: Process our simulated raw data (taking into account head movements)
6,981	<ASSISTANT_TASK:> Python Code: # install Pint if necessary try: import pint except ImportError: !pip install pint # download modsim.py if necessary from os.path import basename, exists def download(url): filename = basename(url) if not exists(filename): from urllib.request import urlretrieve local, _ = urlretrieve(url, filename) print('Downloaded ' + local) download('https://raw.githubusercontent.com/AllenDowney/' + 'ModSim/main/modsim.py') # import functions from modsim from modsim import * download('https://raw.githubusercontent.com/AllenDowney/' + 'ModSim/main/data/glucose_insulin.csv') data = pd.read_csv('glucose_insulin.csv', index_col='time'); I0 = 360 k = 0.25 gamma = 0.004 G_T = 80 params = I0, k, gamma, G_T # Solution def make_system(params, data): I0, k, gamma, G_T = params init = State(I=I0) t_0 = data.index[0] t_end = data.index[-1] G=interpolate(data.glucose) system = System(I0=I0, k=k, gamma=gamma, G_T=G_T, G=G, init=init, t_0=t_0, t_end=t_end, dt=1) return system # Solution system = make_system(params, data) # Solution def slope_func(t, state, system): I = state k, gamma = system.k, system.gamma G, G_T = system.G, system.G_T dIdt = -k * I + gamma * (G(t) - G_T) * t return dIdt # Solution slope_func(system.t_0, system.init, system) # Solution results, details = run_solve_ivp(system, slope_func, t_eval=data.index) details.message # Solution results.tail() # Solution data.insulin.plot(style='o', color='C2', alpha=0.5, label='insulin data') results.I.plot(style='-', color='C2', label='simulation') decorate(xlabel='Time (min)', ylabel='Concentration ($\mu$U/mL)') # Solution def error_func(params, data): Computes an array of errors to be minimized. params: sequence of parameters actual: array of values to be matched returns: array of errors print(params) # make a System with the given parameters system = make_system(params, data) # solve the ODE results, details = run_solve_ivp(system, slope_func, t_eval=data.index) # compute the difference between the model # results and actual data errors = results.I - data.insulin return errors.iloc[2:] # Solution error_func(params, data) # Solution best_params, details = leastsq(error_func, params, data) # Solution details.mesg # Solution system2 = make_system(best_params, data) # Solution results2, details = run_solve_ivp(system2, slope_func, t_eval=data.index) details.message # Solution data.insulin.plot(style='o', color='C2', alpha=0.5, label='insulin data') results2.I.plot(style='-', color='C2', label='simulation') decorate(xlabel='Time (min)', ylabel='Concentration ($\mu$U/mL)') # Solution I0, k, gamma, G_T = best_params # Solution I_max = data.insulin.max() Ib = data.insulin[0] I_max, Ib # Solution # The value of G0 is the best estimate from the glucose model G0 = 272 Gb = data.glucose[0] G0, Gb # Solution phi_1 = (I_max - Ib) / k / (G0 - Gb) phi_1 # Solution phi_2 = gamma * 1e4 phi_2 phi_1_interval = 2, 4 phi_1_interval phi_2_interval = 20, 35 phi_2_interval <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: The following cells download and read the data. Step2: In Chapter 17 I present the glucose minimal model; in Chapter 18 we implemented it using run_simulation and run_solve_ivp. Step3: Exercise Step4: Exercise Step6: Exercise Step7: Exercise Step8: Exercise Step9: According to Pacini and Bergman, here are the normal ranges for these quantities.
6,982	<ASSISTANT_TASK:> Python Code: assert duplicates((1, 1, 2, 3, 4, 5, 6, 8, 2, 4, -7, 12, -7)) == (1, 2, 4, -7) assert duplicates([1, 1, 2, 3, 4, 5, "asd", 8, "asd", 4, -7, 12, -7]) == (1, 2, 4, "asd", -7) assert square_collection([1, 2, 3, 4, 5, 6]) == [1, 4, 9, 16, 25, 36] a = [12, 1, 2, 3, 4, 7, 8, 10] b = [1, 12, 33, 4, 7, 9, 10] assert intersection(a, b) == [12, 1, 4, 7, 10] <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Napisz generator liczb pseudolosowych z czestotliwosciami 0,25 dla zakresu 1-50 i 0,75 dla zakresu 51-100. Step2: Zaimplementuj linked liste w pythonie wykorzystujac namedtuples
6,983	<ASSISTANT_TASK:> Python Code: from __future__ import print_function %matplotlib inline import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.axes_grid1 import make_axes_locatable import matplotlib as mpl from ase.io import read from pyqstem.util import atoms_plot from pyqstem import PyQSTEM from ase.build import mx2 mpl.rc('font',*{'size' : 13}) atoms=mx2(formula='MoS2', kind='2H', a=3.18, thickness=3.19, size=(2, 2, 1), vacuum=2) cell=atoms.get_cell() cell[1,0]=0 atoms.set_cell(cell) atoms.wrap() # wrap atoms outside the unit cell atoms.center() # center the atoms in the unit cell atoms=(3,3,1) scan_range=[[cell[0,0],2cell[0,0],30], [cell[1,1],2cell[1,1],30]] fig,ax=plt.subplots(figsize=(7,5)) atoms_plot(atoms,scan_range=scan_range,ax=ax,legend=True) qstem = PyQSTEM('STEM') qstem.set_atoms(atoms) resolution = (0.02,0.02) # resolution in x and y-direction [Angstrom] samples = (300,300) # samples in x and y-direction defocus = -50 # defocus [Angstrom] v0 = 300 # acceleration voltage [keV] alpha = 20 # convergence angle [mrad] astigmatism = 40 # astigmatism magnitude [Angstrom] astigmatism_angle = 100 # astigmatism angle [deg.] aberrations = {'a33': 3000, 'phi33': 120} # higher order aberrations [Angstrom] or [deg.] qstem.build_probe(v0,alpha,(300,300),resolution=(0.02,0.02),defocus=defocus,astig_mag=astigmatism, astig_angle=astigmatism_angle,aberrations=aberrations) wave=qstem.get_wave() wave.view(cmap='inferno') qstem.build_potential(5,scan_range=scan_range) potential=qstem.get_potential_or_transfunc() fig,(ax1,ax2)=plt.subplots(1,2,figsize=(10,6)) qstem.view(ax=ax1) potential.view(ax=ax2,cmap='inferno',method='real') detector1_radii=(70,200) # inner and outer radius of detector 1 detector2_radii=(0,70) # inner and outer radius of detector 2 qstem.add_detector('detector1',detector1_radii) qstem.add_detector('detector2',detector2_radii) qstem.run() img1=np.array(qstem.read_detector('detector1')) img2=np.array(qstem.read_detector('detector2')) img1=np.tile(img1,(2,2)) img2=np.tile(img2,(2,2)) extent=[0,scan_range[0][1]3-scan_range[0][0],0,scan_range[1][1]3-scan_range[1][0]] fig,(ax1,ax2)=plt.subplots(1,2,figsize=(10,6)) ims1=ax1.imshow(img1.T,extent=extent,interpolation='nearest',cmap='gray') divider = make_axes_locatable(ax1) cax1 = divider.append_axes("right", size="5%", pad=0.05) plt.colorbar(ims1, cax=cax1) ax1.set_xlabel('x [Angstrom]') ax1.set_ylabel('y [Angstrom]') ims2=ax2.imshow(img2.T,extent=extent,interpolation='nearest',cmap='gray') divider = make_axes_locatable(ax2) cax2 = divider.append_axes("right", size="5%", pad=0.05) plt.colorbar(ims2, cax=cax2) ax2.set_xlabel('x [Angstrom]') ax2.set_ylabel('y [Angstrom]') plt.tight_layout() plt.show() <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: We create an orthorhombic unit cell of MoS2. The unit cell is repeated 3x3 times, in order to accomodate the size of the probe at all scan positions. We set a scan range that covers the central unit cell. Step2: We create a QSTEM object in STEM mode and set the atomic object. Step3: We build a (very bad) probe. Building the probe will also determine the resolution of the potential, when we build it. Step4: The potential is build and imported to python. Step5: We can view the extent of the potential using the .view() method of the PyQSTEM object. When the potential is build in this way, it is made to cover exactly the maximum probe extent. Step6: We add a couple of detectors and run qstem. Step7: After running we can extract the results from the detectors.
6,984	<ASSISTANT_TASK:> Python Code: %matplotlib inline import numpy as np import matplotlib.pyplot as plt from scipy.integrate import odeint from mpl_toolkits.mplot3d import Axes3D from numpy.linalg import eigvals def Lorenz(state,t,sigma,r,b): ''' Returns the RHS of the Lorenz equations ''' # unpack the state vector x = state[0] y = state[1] z = state[2] # compute state derivatives xd = sigma * (y-x) yd = (r-z)x - y zd = xy - bz # return the state derivatives return [xd, yd, zd] def SolveLorenz(state0,t,sigma=10.,r=28.,b=8./3.0): ''' use ODEINT to integrate the lorenz equations from initial condition state0 at t=0 for the range of times given in the numpy array t ''' Lorenz_p = lambda state,t: Lorenz(state,t,sigma,r,b) state = odeint(Lorenz_p, state0, t) return state def PlotLorenzXvT(state,t,sigma,r,b): ''' make time series plots of solutions of the Lorenz equations X(t),Y(t),Z(t) ''' plt.figure() ax = plt.subplot(111) X = state[:,0] Y = state[:,1] Z = state[:,2] ax.plot(t,X,'r',label='X') ax.hold(True) ax.plot(t,Y,'g',label='Y') ax.plot(t,Z,'b',label='Z') ax.set_xlabel('time t') plt.title('Lorenz Equations: $\sigma=${}, $r=${}, $b=${}'.format(sigma,r,b)) # Shrink current axis's height by 10% on the bottom box = ax.get_position() ax.set_position([box.x0, box.y0 + box.height 0.1, box.width, box.height * 0.9]) # Put a legend below current axis ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),ncol=3) plt.show() def PlotLorenz3D(state,sigma,r,b): ''' Show 3-D Phase portrait using mplot3D ''' # do some fancy 3D plotting fig = plt.figure() ax = fig.gca(projection='3d') ax.plot(state[:,0],state[:,1],state[:,2]) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') plt.title('Lorenz Equations: $\sigma=${}, $r=${}, $b=${}'.format(sigma,r,b)) plt.show() # Set the parameters sigma= 10. b = 8./3 # set the initial condition X0 = [2.0, 3.0, 4.0] # set the time for integration t = np.arange(0.0, 30.0, 0.01) # set the Rayleigh number r = 0.5 # solve the Equations state = SolveLorenz(X0,t,sigma,r,b) # and Visualize as a time series PlotLorenzXvT(state,t,sigma,r,b) # and as a 3-D phase portrait PlotLorenz3D(state,sigma,r,b) # set the Rayleigh number r = 10.0 X0 = [2.,3.,4.] state = SolveLorenz(X0,t,sigma,r,b) PlotLorenzXvT(state,t,sigma,r,b) PlotLorenz3D(state,sigma,r,b) # now change the initial condition so X=-2 X0 = [-2.0, -3.0, 4.0] state = SolveLorenz(X0,t,sigma,r,b) PlotLorenzXvT(state,t,sigma,r,b) PlotLorenz3D(state,sigma,r,b) # set the Rayleigh number r = 28.0 X0 = [2.,3.,4.] state = SolveLorenz(X0,t,sigma,r,b) PlotLorenzXvT(state,t,sigma,r,b) PlotLorenz3D(state,sigma,r,b) # set the Rayleigh number r = 350 X0 = [2.,3.,4.] t = np.arange(0,8.,.0001) state = SolveLorenz(X0,t,sigma,r,b) PlotLorenzXvT(state,t,sigma,r,b) PlotLorenz3D(state,sigma,r,b) sigma = 10 b = 8./3. r_H = sigma(sigma+b+3)/(sigma-b -1.) # critical value of r at Hopf bifurcation r_max = 28. ra = np.linspace(1,28.,20) xstar = lambda r: np.sqrt(b(r-1)) J = lambda r: np.array([[-sigma,sigma,0],[1,-1,-xstar(r)],[xstar(r),xstar(r),-b]]) # plot out the eigenvalues import matplotlib.cm as cm cmap = cm.get_cmap('coolwarm') fig = plt.figure() for r in ra: L = eigvals(J(r)) plt.plot(np.real(L),np.imag(L),'o',color=cmap((r-min(ra))/(max(ra)-min(ra)))) plt.hold(True) # plot out eigenvalues at the Hopf Bifurcation L = eigvals(J(r_H)) plt.plot(np.real(L),np.imag(L),'sy') plt.xlabel('Re$(\lambda)$') plt.ylabel('Im$(\lambda)$') plt.title('Eigenvalues of $C^+$ for $r\in[1,{}]$, $r_H={}$'.format(max(ra),r_H)) plt.grid() plt.show() # start by running the Lorenz system long enough to get on the attractor r = 28.0 X0 = [1.,0.,0.] t = np.arange(0,20,.01) state = SolveLorenz(X0,t,sigma,r,b) # extract the final state and perturb it by a small amount epsilon X0 = state[-1] epsilon=1.e-6 X1 = X0 + epsilonnp.random.rand(3) delta_0 = np.sqrt(np.sum((X1-X0)2)) # Now run both initial conditions t=np.arange(0.,50.,.0001) state0 = SolveLorenz(X0,t,sigma,r,b) state1 = SolveLorenz(X1,t,sigma,r,b) # Compare the two trajectories as time-series X plt.figure() ax = plt.subplot(111) ax.plot(t,state0[:,0],'r',t,state1[:,0],'b') plt.xlabel('t') plt.ylabel('X(t)') plt.show() # and in the phase space fig = plt.figure() ax = fig.gca(projection='3d') ax.plot(state0[:,0],state0[:,1],state0[:,2],'r-') plt.hold(True) ax.plot(state1[:,0],state1[:,1],state1[:,2],'b-') ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') plt.title('Lorenz Equations: $\sigma=${}, $r=${}, $b=${}'.format(sigma,r,b)) plt.show() # calculate the distance between the two trajectories delta = state1-state0 delta = np.sqrt(np.sum(delta2,1)) # and plot them plt.figure() plt.semilogy(t,delta) plt.xlabel('t') plt.ylabel('$\|\|\delta(t)\|\|$') plt.grid() # now fit the first part with a straight line to determine the slope # we'll pick the line between tmin and tmax to avoid initial transients and later saturation tmin = 1. tmax = 12. imin = int(np.argwhere(t<tmin)[-1]) imax = int(np.argwhere(t>tmax)[0]) tfit = t[imin:imax] p= np.polyfit(tfit,np.log(delta[imin:imax]),1) plt.hold(True) plt.semilogy(tfit,np.exp(p[1]+p[0]tfit),'r') plt.title('Liapunov Exponent Estimate $\lambda={}$'.format(p[0])) plt.show() Y = state0[:,1] Z = state0[:,2] plt.figure() plt.subplot(2,1,1) ihalf = int(len(Y)/2.) print ihalf,len(Y) plt.plot(Y[:ihalf],Z[:ihalf]) plt.xlabel('Y') plt.ylabel('Z') plt.title('Lorenz system, $Y,Z$ plane: $r={}$, $\sigma={}$,$b={}$'.format(r,sigma,b)) plt.grid() plt.subplot(2,1,2) plt.plot(t,Z) plt.xlabel('$t$') plt.ylabel('$Z(t)$') plt.title('$Z$ time series') plt.show() # first let's estimate the centered derivatve of Z to isolate the extrema dZ = np.zeros(Z.shape) dZ[1:-2] = Z[2:-1] - Z[0:-3] dZ.shape plt.figure() plt.plot(t,dZ,t,np.zeros(t.shape),'k:') plt.ylabel('$dZ$') plt.xlabel('t') # now let's find all all intervals that contain zero crossings icross = np.nonzero(dZ[:-2]*dZ[1:-1] <= 0) Zextreme = Z[icross] # and pick out all Extremes greater than mean(Z) meanZ = np.mean(Z) Zn = Zextreme[Zextreme > meanZ] # now plot the Lorenz map Z_{n+1} vs Z{n} plt.figure() plt.plot(Zn[:-2],Zn[1:-1],'bo') xlim = plt.gca().get_xlim() plt.hold(True) plt.plot(xlim,xlim,'k') plt.xlabel('$Z_n$') plt.ylabel('$Z_{n+1}$') plt.title('Lorenz map: $r={}$, $\sigma={}$, $b={}$'.format(r,sigma,b)) plt.show() <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Exploring the Lorenz Equations Step2: Subcritical behavior $r<1$ Step3: Damped Oscillation $r=10$ Step4: Chaos and the strange attractor $r=28$ Step5: Limit Cycle at large Rayleigh number Step6: Stability of Fixed Points Step7: Liapunov Exponents Step8: Estimating the Liapunov exponent Step9: Calculating the Lorenz Map Step10: The Lorenz Map
6,985	<ASSISTANT_TASK:> Python Code: import numpy as np import matplotlib.pyplot as plt import xarray as xr # personal packages from xlearn.cluster import KMeans from pyingrid import Ingrid import geoxarray %matplotlib inline ig = Ingrid('http://iridl.ldeo.columbia.edu', 'SOURCES/.NOAA/.NCEP-NCAR/.CDAS-1/.MONTHLY', '.Intrinsic/.PressureLevel/.phi')\ .do('P(500)VALUES') \ .do('T(Dec 1979)(Mar 2010)RANGE')\ .do('yearly-anomalies') \ .do('T(Dec)(Jan)(Feb)(Mar)VALUES')\ .do('X -90 2 60 GRID Y 80 -2 20 GRID') print(ig) print('\n[Final data URL]:\n', ig.to_url()) ds = ig.to_dataset() print(ds) da = ds.sel(P=500).phi.load() print(da.name, da.dims) print(da.coords) m = KMeans(n_clusters=4, random_state=0, n_jobs=-1).fit(da) print(m) fig, axes = plt.subplots(2,2, figsize=(8,8)) regimes = ['NAO$^-$', 'NAO$^+$', 'Blocking', 'Atlantic Ridge'] tags = list('abcd') for i in range(m.n_clusters): m.plot_cluster_centers(label=i, proj='ortho', plot_type='contourf+', levels=np.arange(-110, 111, 20), units='m', ax=axes.flat[i]) title = '{}, {}'.format(regimes[i], axes.flat[i].title.get_text()) plt.title(title) plt.text(0, 1, tags[i], transform=axes.flat[i].transAxes, va='bottom', fontsize=plt.rcParams['font.size']*2, fontweight='bold') <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: The data is from the Columbia University IRI data library Step2: Convert the data from the library into an xarray Dataset Step3: Get the DataArray and load it into memory Step4: Perform the k-means clustering Step5: Show the results
6,986	<ASSISTANT_TASK:> Python Code: import pods, GPy, itertools %matplotlib inline from matplotlib import pyplot as plt s = pods.datasets.singlecell() Ydf = s['Y'] Y = Ydf.values labels = s['labels'] marker = '<>^vsd' Ydf.describe() import numpy as np # obtain a centred version of data. centredY = Y - Y.mean() # compute inner product matrix C = np.dot(centredY,centredY.T) # perform eigendecomposition V, U = np.linalg.eig(C) # sort eigenvalues and vectors according to size ind = V.argsort() ev = V[ind[::-1]] U = U[:, ind[::-1]] def legend_to_the_right(ax): box = ax.get_position() ax.set_position([box.x0, box.y0, box.width, box.height]) _ = ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), numpoints=1) def plot_latent(ax, x, y, marker, labels): GPy.plotting.Tango.reset() # make sure labels are in order of input: ulabels = [] for lab in labels: if not lab in ulabels: ulabels.append(lab) for i, [label, m] in enumerate(zip(ulabels, itertools.cycle(marker))): symbol = marker[i % len(marker)] ind = labels == label ax.plot(x[ind], y[ind], marker=symbol, markerfacecolor=GPy.plotting.Tango.nextMedium(), linestyle='', label=label, mew=.2, alpha=1) fig, [ax1, ax2] = plt.subplots(1,2,figsize=(13,4)) fracs = (ev.real/ev.real.sum())[:30] # Take only the first 30 eigenvalues ax1.bar(np.arange(len(fracs)), fracs) plot_latent(ax2, U[:,0], U[:,1], marker, labels) legend_to_the_right(ax2) m = GPy.models.GPLVM(Y, 2, kernel=GPy.kern.RBF(2, ARD=1)+GPy.kern.Bias(2)) m.optimize(messages=1) fig, [ax1, ax2] = plt.subplots(1,2,figsize=(9,4)) m.kern.plot_ARD(ax=ax1) m.plot_latent(labels=labels, marker=marker, legend=False, ax=ax2) _ = legend_to_the_right(ax2) fig, [ax1, ax2] = plt.subplots(1,2,figsize=(9,4)) m.plot_latent(labels=labels, marker=marker, legend=False, ax=ax1) m.plot_magnification(labels=labels, marker=marker, legend=False, ax=ax2) _ = legend_to_the_right(ax2) m = GPy.models.BayesianGPLVM(Y, 5, num_inducing=15, kernel=GPy.kern.RBF(5,ARD=1)+GPy.kern.Bias(5)) m.optimize('bfgs', max_iters=5e3, messages=1) fig, [ax1, ax2] = plt.subplots(1,2,figsize=(9,4)) m.kern.plot_ARD(ax=ax1) m.plot_latent(labels=s['labels'], marker='<>^vsd', legend=False, ax=ax2) legend_to_the_right(ax2) n_neighbors = 40 import sklearn.manifold m = sklearn.manifold.Isomap(n_neighbors=n_neighbors, n_components=2) X = m.fit_transform(Ydf) fig, ax = plt.subplots(1) plot_latent(ax, X[:, 0], X[:, 1], marker, labels) _ = legend_to_the_right(ax) n_neighbors = 30 m = sklearn.manifold.LocallyLinearEmbedding(n_neighbors=n_neighbors, n_components=2) X = m.fit_transform(Y) fig, ax = plt.subplots(1) plot_latent(ax, X[:, 0], X[:, 1], marker, labels) _ = legend_to_the_right(ax) m = sklearn.manifold.TSNE(n_components=2) X = m.fit_transform(Y) fig, ax = plt.subplots(1) plot_latent(ax, X[:, 0], X[:, 1], marker, labels) _ = legend_to_the_right(ax) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Next we load in the data. We've provided a convenience function for loading in the data with GPy. It is loaded in as a pandas DataFrame. This allows us to summarize it with the describe attribute. Step2: Principal Component Analysis Step3: Here we will define a little routine, which puts the legend on the right side of the plot, so that the legend does not overwright the data plot Step4: PCA Result Step5: GP-LVM on the Data Step6: Bayesian GP-LVM Step7: This gives a really nice result. Broadly speaking two latent dimensions dominate the representation. When we visualize using these two dimensions we can see the entire cell phylogeny laid out nicely in the two dimensions. Additionally we can see the missclassification of the some cells, using the 'standard' approach of repeated k-means clustering and PCA on sub clustered (This was used to get the sample colors of the 64 cellstage). Step8: Locally Linear Embedding Step9: t-SNE (t-distributed Stochastic Neighbour Embedding)
6,987	<ASSISTANT_TASK:> Python Code: EUCLIDEAN = 'euclidean' MANHATTAN = 'manhattan' PEARSON = 'pearson' def read_ratings_df(): date_parser = lambda time_in_secs: datetime.utcfromtimestamp(float(time_in_secs)) return pd.read_csv('ml-latest-small/ratings.csv', parse_dates=['timestamp'], date_parser=date_parser) class MovieData(object): def __init__(self): self.ratings_df = read_ratings_df() self.ratings = defaultdict(dict) self.init_ratings() def init_ratings(self): for _, row in self.ratings_df.iterrows(): self.ratings[row['userId']][row['movieId']] = row def get_movies(self, user_id): return set(self.ratings[user_id].keys()) def get_unique_user_ids(self): return self.ratings_df['userId'].unique() def get_shared_ratings(self, user1_id, user2_id): movies1 = self.get_movies(user1_id) movies2 = self.get_movies(user2_id) shared_movies = movies1 & movies2 ratings = {} for movie_id in shared_movies: ratings[movie_id] = ( self.ratings[user1_id][movie_id]['rating'], self.ratings[user2_id][movie_id]['rating'], ) return ratings @staticmethod def shared_ratings_to_np_arrays(shared_ratings): return np.array(shared_ratings.values()).T def get_euclidean_distance(self, user1_id, user2_id): shared_ratings = self.get_shared_ratings(user1_id, user2_id) if len(shared_ratings) == 0: return 0 ratings = self.shared_ratings_to_np_arrays(shared_ratings) ratings1 = ratings[0] ratings2 = ratings[1] sum_of_squares = np.power(ratings1 - ratings2, 2).sum() return 1 / (1 + sqrt(sum_of_squares)) def get_manhattan_distance(self, user1_id, user2_id): shared_ratings = self.get_shared_ratings(user1_id, user2_id) if len(shared_ratings) == 0: return 0 ratings = self.shared_ratings_to_np_arrays(shared_ratings) ratings1 = ratings[0] ratings2 = ratings[1] manhattan_sum = np.abs(ratings1 - ratings2).sum() return 1 / (1 + manhattan_sum) def get_pearson_correlation(self, user1_id, user2_id): shared_ratings = self.get_shared_ratings(user1_id, user2_id) num_ratings = len(shared_ratings) if num_ratings == 0: return 0 ratings = self.shared_ratings_to_np_arrays(shared_ratings) ratings1 = ratings[0] ratings2 = ratings[1] mean1 = ratings1.mean() mean2 = ratings2.mean() std1 = ratings1.std() std2 = ratings2.std() if std1 == 0 or std2 == 0: return 0 std_scores_1 = (ratings1 - mean1) / std1 std_scores_2 = (ratings2 - mean2) / std2 # numerically stable calculation of the Pearson correlation coefficient return abs((std_scores_1 * std_scores_2).sum() / (num_ratings - 1)) def get_similar_users(self, user_id, metric=EUCLIDEAN): metrics = { EUCLIDEAN: self.get_euclidean_distance, MANHATTAN: self.get_manhattan_distance, PEARSON: self.get_pearson_correlation, } distance_f = metrics[metric] similar_users = {} for similar_user_id in self.ratings: if similar_user_id == user_id: continue distance = distance_f(user_id, similar_user_id) if distance > 0: similar_users[similar_user_id] = distance return similar_users def predict_score(self, user_id, movie_id): similar_users = self.get_similar_users(user_id) total_rating_sum = 0 similarity_sum = 0 for similar_user_id, similarity in similar_users.items(): user_ratings = self.ratings[similar_user_id] if movie_id in user_ratings: total_rating_sum += similarity * user_ratings[movie_id]['rating'] similarity_sum += similarity if similarity_sum == 0: return 0 return total_rating_sum / similarity_sum movie_data = MovieData() def explore_shared_ratings(movie_data): unique_user_ids = movie_data.get_unique_user_ids() n_pairs = 30 samples = np.random.choice(unique_user_ids, size=(n_pairs, 2)) for index, sample in enumerate(samples): user1_id = sample[0] user2_id = sample[1] num_movies_1 = len(movie_data.get_movies(user1_id)) num_movies_2 = len(movie_data.get_movies(user2_id)) num_shared_ratings = len(movie_data.get_shared_ratings(user1_id, user2_id)) print 'pair %2d, user1 movies: %4d, user2 movies: %4d, shared movies: %3d' % ( index + 1, num_movies_1, num_movies_2, num_shared_ratings) explore_shared_ratings(movie_data) def explore_distances(movie_data): unique_user_ids = movie_data.get_unique_user_ids() n_pairs = 30 samples = np.random.choice(unique_user_ids, size=(n_pairs, 2)) for index, sample in enumerate(samples): user1_id = sample[0] user2_id = sample[1] num_shared_ratings = len(movie_data.get_shared_ratings(user1_id, user2_id)) euclidean_distance = movie_data.get_euclidean_distance(user1_id, user2_id) manhattan_distance = movie_data.get_manhattan_distance(user1_id, user2_id) pearson_correlation = movie_data.get_pearson_correlation(user1_id, user2_id) print 'pair %2d, shared movies: %3d, euclidean: %.3f, manhattan: %.3f, pearson: %.3f' % ( index + 1, num_shared_ratings, euclidean_distance, manhattan_distance, pearson_correlation) explore_distances(movie_data) def explore_similar_users(movie_data): unique_user_ids = movie_data.get_unique_user_ids() n_users = 30 user_ids = np.random.choice(unique_user_ids, size=n_users, replace=False) for index, user_id in enumerate(user_ids): similar_users = movie_data.get_similar_users(user_id) distances = similar_users.values() print 'user %3d, similar users: %d, max similarity: %.3f, mean: %.3f, std: %.3f' % ( index + 1, len(similar_users), np.max(distances), np.mean(distances), np.std(distances)) explore_similar_users(movie_data) def explore_predict_score(movie_data): ratings_df = movie_data.ratings_df rating_indices = ratings_df.index n_ratings = 30 sample = np.random.choice(rating_indices, size=n_ratings, replace=False) for index, rating_index in enumerate(sample): row = ratings_df.ix[rating_index] user_id = row['userId'] movie_id = row['movieId'] rating = row['rating'] score = movie_data.predict_score(user_id, movie_id) print 'rating %2d, rating: %.1f, predicted: %.3f' % (index + 1, rating, score) explore_predict_score(movie_data) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Explore shared ratings Step2: We are looking at 30 random user pairs. We can notice how small on average is the intersection of the movies they rated (compared to the their total number of ratings). Step3: Various distances (euclidean, manhattan, pearson correlation). Step4: Max similarity of 1.0 in most cases is probably an intersection of one movie.
6,988	<ASSISTANT_TASK:> Python Code: import numpy as np from scipy.spatial import distance shape = (6, 6) xs, ys = np.indices(shape) xs = xs.reshape(shape[0] * shape[1], 1) ys = ys.reshape(shape[0] * shape[1], 1) X = np.hstack((xs, ys)) mid_x, mid_y = (shape[0]-1)/2.0, (shape[1]-1)/2.0 result = distance.cdist(X, np.atleast_2d([mid_x, mid_y]), 'minkowski', p=1).reshape(shape) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description:
6,989	<ASSISTANT_TASK:> Python Code: x = 1 y = 2 x + y x def add_numbers(x, y): return x + y add_numbers(x, y) def add_numbers(x,y,z=None): if (z==None): return x+y else: return x+y+z print(add_numbers(1, 2)) print(add_numbers(1, 2, 3)) def add_numbers(x, y, z=None, flag=False): if (flag): print('Flag is true!') if (z==None): return x + y else: return x + y + z print(add_numbers(1, 2, flag=True)) def add_numbers(x,y): return x+y a = add_numbers a(1,2) type('This is a string') type(None) type(1) type(1.0) type(add_numbers) x = (1, 'a', 2, 'b') type(x) x = [1, 'a', 2, 'b'] type(x) x.append(3.3) print(x) for item in x: print(item) i=0 while( i != len(x) ): print(x[i]) i = i + 1 [1,2] + [3,4] [1]3 1 in [1, 2, 3] x = 'This is a string' print(x[0]) #first character print(x[0:1]) #first character, but we have explicitly set the end character print(x[0:2]) #first two characters print(x[::-1]) x[-1] x[-4:-2] x[:3] x[3:] firstname = 'Christopher' lastname = 'Brooks' print(firstname + ' ' + lastname) print(firstname3) print('Chris' in firstname) firstname = 'Christopher Arthur Hansen Brooks'.split(' ')[0] # [0] selects the first element of the list lastname = 'Christopher Arthur Hansen Brooks'.split(' ')[-1] # [-1] selects the last element of the list print(firstname) print(lastname) 'Chris' + 2 'Chris' + str(2) x = {'Christopher Brooks': 'brooksch@umich.edu', 'Bill Gates': 'billg@microsoft.com'} x['Christopher Brooks'] # Retrieve a value by using the indexing operator x['Kevyn Collins-Thompson'] = "Test Test" x['Kevyn Collins-Thompson'] for name in x: print(x[name]) for email in x.values(): print(email) for name, email in x.items(): print(name) print(email) x = ('Christopher', 'Brooks', 'brooksch@umich.edu') fname, lname, email = x fname lname x = ('Christopher', 'Brooks', 'brooksch@umich.edu', 'Ann Arbor') fname, lname, email, location = x print("Chris" + 2) print('Chris' + str(2)) sales_record = { 'price': 3.24, 'num_items': 4, 'person': 'Chris'} sales_statement = '{} bought {} item(s) at a price of {} each for a total of {}' print(sales_statement.format(sales_record['person'], sales_record['num_items'], sales_record['price'], sales_record['num_items']sales_record['price'])) import csv import pandas as pd # Nice, sets decimple point %precision 2 with open('mpg.csv') as csvfile: mpg = list(csv.DictReader(csvfile)) df = pd.read_csv('mpg.csv') mpg[:3] # The first three dictionaries in our list. df len(mpg) mpg[0].keys() sum(float(d['cty']) for d in mpg) / len(mpg) sum(float(d['hwy']) for d in mpg) / len(mpg) # set returns unique values cylinders = set(d['cyl'] for d in mpg) cylinders CtyMpgByCyl = [] for c in cylinders: # iterate over all the cylinder levels summpg = 0 cyltypecount = 0 for d in mpg: # iterate over all dictionaries if d['cyl'] == c: # if the cylinder level type matches, summpg += float(d['cty']) # add the cty mpg cyltypecount += 1 # increment the count CtyMpgByCyl.append((c, summpg / cyltypecount)) # append the tuple ('cylinder', 'avg mpg') CtyMpgByCyl.sort(key=lambda x: x[0]) CtyMpgByCyl vehicleclass = set(d['class'] for d in mpg) # what are the class types vehicleclass HwyMpgByClass = [] for t in vehicleclass: # iterate over all the vehicle classes summpg = 0 vclasscount = 0 for d in mpg: # iterate over all dictionaries if d['class'] == t: # if the cylinder amount type matches, summpg += float(d['hwy']) # add the hwy mpg vclasscount += 1 # increment the count HwyMpgByClass.append((t, summpg / vclasscount)) # append the tuple ('class', 'avg mpg') HwyMpgByClass.sort(key=lambda x: x[1]) HwyMpgByClass import datetime as dt import time as tm tm.time() dtnow = dt.datetime.fromtimestamp(tm.time()) dtnow dtnow.year, dtnow.month, dtnow.day, dtnow.hour, dtnow.minute, dtnow.second # get year, month, day, etc.from a datetime delta = dt.timedelta(days = 100) # create a timedelta of 100 days delta dt.date.today() today = dt.date.today() today - delta # the date 100 days ago today > today-delta # compare dates class Person: department = 'School of Information' #a class variable def set_name(self, new_name): #a method self.name = new_name def set_location(self, new_location): self.location = new_location person = Person() person.set_name('Christopher Brooks') person.set_location('Ann Arbor, MI, USA') print('{} live in {} and works in the department {}'.format(person.name, person.location, person.department)) store1 = [10.00, 11.00, 12.34, 2.34] store2 = [9.00, 11.10, 12.34, 2.01] cheapest = map(min, store1, store2) cheapest for item in cheapest: print (item) people = ['Dr. Christopher Brooks', 'Dr. Kevyn Collins-Thompson', 'Dr. VG Vinod Vydiswaran', 'Dr. Daniel Romero'] def split_title_and_name(person): title = person.split(' ')[0] lname = person.split(' ')[-1] return title +" "+ lname list(map(split_title_and_name, people)) # Single function only my_function = lambda a, b, c : a + b + c my_function(1, 2, 3) people = ['Dr. Christopher Brooks', 'Dr. Kevyn Collins-Thompson', 'Dr. VG Vinod Vydiswaran', 'Dr. Daniel Romero'] def split_title_and_name(person): return person.split()[0] + ' ' + person.split()[-1] #option 1 for person in people: print(split_title_and_name(person) == (lambda x: x.split()[0] + ' ' + x.split()[-1])(person)) #option 2 list(map(split_title_and_name, people)) == list(map(lambda person: person.split()[0] + ' ' + person.split()[-1], people)) my_list = [] for number in range(0, 1000): if number % 2 == 0: my_list.append(number) my_list my_list = [number for number in range(0,1000) if number % 2 == 0] my_list def times_tables(): lst = [] for i in range(10): for j in range (10): lst.append(ij) return lst times_tables() == [ji for i in range(10) for j in range(10)] lowercase = 'abcdefghijklmnopqrstuvwxyz' digits = '0123456789' correct_answer = [a+b+c+d for a in lowercase for b in lowercase for c in digits for d in digits] correct_answer[0:100] import numpy as np mylist = [1, 2, 3] x = np.array(mylist) x y = np.array([4, 5, 6]) y m = np.array([[7, 8, 9], [10, 11, 12]]) m m.shape n = np.arange(0, 30, 2) # start at 0 count up by 2, stop before 30 n n = n.reshape(3, 5) # reshape array to be 3x5 n o = np.linspace(0, 4, 9) # return 9 evenly spaced values from 0 to 4 o o.resize(3, 3) o np.ones((3, 2)) np.zeros((2, 3)) np.eye(3) np.diag(y) np.array([1, 2, 3] 3) np.repeat([1, 2, 3], 3) p = np.ones([2, 3], int) p np.vstack([p, 2p]) np.hstack([p, 2p]) print(x + y) # elementwise addition [1 2 3] + [4 5 6] = [5 7 9] print(x - y) # elementwise subtraction [1 2 3] - [4 5 6] = [-3 -3 -3] print(x * y) # elementwise multiplication [1 2 3] * [4 5 6] = [4 10 18] print(x / y) # elementwise divison [1 2 3] / [4 5 6] = [0.25 0.4 0.5] print(x*2) # elementwise power [1 2 3] ^2 = [1 4 9] x.dot(y) # dot product 14 + 25 + 36 z = np.array([y, y2]) print(len(z)) # number of rows of array z = np.array([y, y2]) z z.shape z.T z.T.shape z.dtype z = z.astype('f') z.dtype a = np.array([-4, -2, 1, 3, 5]) a.sum() a.max() a.min() a.mean() a.std() a.argmax() a.argmin() s = np.arange(13)2 s s[0], s[4], s[-1] s[1:5] s[-4:] s[-5::-2] r = np.arange(36) r.resize((6, 6)) r r[2, 2] r[3, 3:6] r[:2, :-1] r[-1, ::2] r[r > 30] r[r > 30] = 30 r r2 = r[:3,:3] r2 r2[:] = 0 r2 r r_copy = r.copy() r_copy r_copy[:] = 10 print(r_copy, '\n') print(r) test = np.random.randint(0, 10, (4,3)) test for row in test: print(row) for i in range(len(test)): print(test[i]) for i, row in enumerate(test): print('row', i, 'is', row) test2 = test2 test2 for i, j in zip(test, test2): print(i,'+',j,'=',i+j) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: <br> Step2: <br> Step3: <br> Step4: <br> Step5: <br> Step6: <br> Step7: <br> Step8: <br> Step9: <br> Step10: <br> Step11: <br> Step12: <br> Step13: <br> Step14: <br> Step15: <br> Step16: <br> Step17: <br> Step18: <br> Step19: <br> Step20: <br> Step21: <br> Step22: <br> Step23: <br> Step24: <br> Step25: <br> Step26: <br> Step27: <br> Step28: <br> Step29: <br> Step30: <br> Step31: <br> Step32: <br> Step33: <br> Step34: <br> Step35: <br> Step36: <br> Step37: <br> Step38: <br> Step39: <br> Step40: <br> Step41: <br> Step42: <br> Step43: <br> Step44: <br> Step45: <br> Step46: <br> Step47: <br> Step48: <br> Step49: <br> Step50: <br> Step51: <br> Step52: <br> Step53: <br> Step54: <br> Step55: <br> Step56: <br> Step57: <br> Step58: <br> Step59: <br> Step60: <br> Step61: <br> Step62: <br> Step63: <br> Step64: <br> Step65: <br> Step66: <br> Step67: <br> Step68: <br> Step69: <br> Step70: <br> Step71: <br> Step72: <br> Step73: <br> Step74: <br> Step75: <br> Step76: <br> Step77: <br> Step78: <br> Step79: <br> Step80: <br> Step81: <br> Step82: <br> Step83: <br> Step84: <br> Step85: <br> Step86: <br> Step87: <br> Step88: <br> Step89: <br> Step90: <br> Step91: <br> Step92: <br> Step93: <br> Step94: <br> Step95: <br> Step96: <br> Step97: <br> Step98: <br> Step99: <br>
6,990	<ASSISTANT_TASK:> Python Code: # Set up the exercise import math from learntools.core import binder binder.bind(globals()) from learntools.intro_to_programming.ex2 import * print('Setup complete.') # TODO: Complete the function def get_expected_cost(beds, baths): value = ____ return value # Check your answer q1.check() #%%RM_IF(PROD)%% # Complete the function def get_expected_cost(beds, baths): value = 80000 + 30000 * beds + 10000 * baths return value q1.assert_check_passed() # Uncomment to see a hint #_COMMENT_IF(PROD)_ q1.hint() # Uncomment to view the solution #_COMMENT_IF(PROD)_ q1.solution() # TODO: Use the get_expected_cost function to fill in each value option_one = ____ option_two = ____ option_three = ____ option_four = ____ print(option_one) print(option_two) print(option_three) print(option_four) # Check your answer q2.check() #%%RM_IF(PROD)%% # Use the get_expected_cost function to fill in each value option_one = get_expected_cost(2, 3) option_two = get_expected_cost(3, 2) option_three = get_expected_cost(3, 3) option_four = get_expected_cost(3, 4) print(option_one) print(option_two) print(option_three) print(option_four) q2.assert_check_passed() # Uncomment to see a hint #_COMMENT_IF(PROD)_ q2.hint() # Uncomment to view the solution #_COMMENT_IF(PROD)_ q2.solution() # TODO: Finish defining the function def get_cost(sqft_walls, sqft_ceiling, sqft_per_gallon, cost_per_gallon): cost = ____ return cost # Check your answer q3.check() #%%RM_IF(PROD)%% def get_cost(sqft_walls, sqft_ceiling, sqft_per_gallon, cost_per_gallon): total_sqft = sqft_walls + sqft_ceiling gallons_needed = total_sqft / sqft_per_gallon cost = cost_per_gallon * gallons_needed return cost q3.assert_check_passed() # Uncomment to see a hint #_COMMENT_IF(PROD)_ q3.hint() # Uncomment to view the solution #_COMMENT_IF(PROD)_ q3.solution() # TODO: Set the project_cost variable to the cost of the project project_cost = ____ # Check your answer q4.check() #%%RM_IF(PROD)%% # Set the project_cost variable to the cost of the project project_cost = get_cost(432, 144, 400, 15) q4.assert_check_passed() # Uncomment to see a hint #_COMMENT_IF(PROD)_ q4.hint() # Uncomment to view the solution #_COMMENT_IF(PROD)_ q4.solution() test_value = 2.17 rounded_value = math.ceil(test_value) print(rounded_value) def get_actual_cost(sqft_walls, sqft_ceiling, sqft_per_gallon, cost_per_gallon): cost = ____ return cost # Check your answer q5.check() #%%RM_IF(PROD)%% def get_actual_cost(sqft_walls, sqft_ceiling, sqft_per_gallon, cost_per_gallon): total_sqft = sqft_walls + sqft_ceiling gallons_needed = total_sqft / sqft_per_gallon gallons_to_buy = math.ceil(gallons_needed) cost = cost_per_gallon * gallons_to_buy return cost q5.assert_check_passed() # Uncomment to see a hint #_COMMENT_IF(PROD)_ q5.hint() # Uncomment to view the solution #_COMMENT_IF(PROD)_ q5.solution() get_actual_cost(432, 144, 400, 15) get_actual_cost(594, 288, 400, 15) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Question 1 Step2: Question 2 Step3: Question 3 Step4: Question 4 Step5: 🌶️ Question 5 Step6: Use the next code cell to define the function get_actual_cost(). You'll need to use the math.ceil() function to do this. Step7: Once your function is verified as correct, run the next code cell to calculate the updated cost of your project. Step8: Say you're working with a slightly larger room. Run the next code cell to calculate the cost of the project.
6,991	<ASSISTANT_TASK:> Python Code: import pandas as pd import matplotlib.pyplot as plt import seaborn as sns sns.set_style("white") #Note the new use of the dtype option here. We can directly tell pandas to use the Speed column as a category in one step. speeddf = pd.read_csv("../Class04/Class04_speed_data.csv",dtype={'Speed':'category'}) #We'll use a different tool to plot the data now that we know how to group the data by a category. This will help us make better combined plots later on. groups = speeddf.groupby('Speed') # Plot trainfig, ax = plt.subplots() ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling # The next step is to cycle through the groups (based on our categories) and plot each one on the same axis. for name, group in groups: ax.plot(group['Grade'], group['Bumpiness'], marker='o', linestyle='', ms=8, label=name) ax.set_aspect(1) ax.legend(bbox_to_anchor=(1.2,0.5)) ax.set_xlabel('Grade') ax.set_ylabel('Bumpiness') import numpy as np from sklearn.cross_validation import train_test_split from sklearn.tree import DecisionTreeClassifier # Create our decision boundary mesh # point in the mesh x_min = 0.0; x_max = 1.0 # Mesh x size y_min = 0.0; y_max = 1.0 # Mesh y size h = .01 # step size in the mesh xx, yy = np.meshgrid(np.arange(x_min, x_max+h, h), np.arange(y_min, y_max+h, h)) # Split the data into training and testing sets and prepare the features and labels train, test = train_test_split(speeddf, test_size=0.2, random_state=23) features_train = train[['Grade','Bumpiness']].values labels_train = train['Speed'].values features_test = test[['Grade','Bumpiness']].values labels_test = test['Speed'].values class_labels = ["slow", "fast"] # Load the model and fit the data dtmodel = DecisionTreeClassifier(random_state=32) dtmodel.fit(features_train,labels_train) y_pred = dtmodel.predict(features_test) # Predict the boundary Z = pd.Series(dtmodel.predict(np.c_[xx.ravel(), yy.ravel()]), dtype='category').cat.codes.values.reshape(xx.shape) # First plot our points testfig1, ax = plt.subplots() plt.pcolormesh(xx, yy, Z, cmap= plt.cm.cool, alpha=0.1,axes=ax) ax.set_aspect(1) # Plot test points groups = test.groupby('Speed') # The next step is to cycle through the groups (based on our categories) and plot each one on the same axis. for name, group in groups: ax.plot(group['Grade'], group['Bumpiness'], marker='o', linestyle='', ms=8, label=name) ax.legend(bbox_to_anchor=(1.2,0.5)) ax.set_xlabel('Grade') ax.set_ylabel('Bumpiness') import sklearn.metrics as metrics recall_score = metrics.recall_score(labels_test, y_pred,labels=class_labels,average=None) prec_score = metrics.precision_score(labels_test, y_pred,labels=class_labels,average=None) f1_score = metrics.f1_score(labels_test, y_pred,labels=class_labels,average=None) acc_score = metrics.accuracy_score(labels_test, y_pred) matt_score = metrics.matthews_corrcoef(labels_test, y_pred) print("Class-dependent Metrics") print("Sensitivity/Recall Score: {}".format(recall_score)) print("Precision Score: {}".format(prec_score)) print("F1 Score: {}".format(f1_score)) print("\nClass-independent Metrics") print("Accuracy Score: {}".format(acc_score)) print("Matthews Correlation Coefficient (MCC): {}".format(matt_score)) # Load the model and fit the data dtmodel = DecisionTreeClassifier(min_samples_leaf=10,random_state=32) dtmodel.fit(features_train,labels_train) y_pred = dtmodel.predict(features_test) # Predict the boundary Z = pd.Series(dtmodel.predict(np.c_[xx.ravel(), yy.ravel()]), dtype='category').cat.codes.values.reshape(xx.shape) # First plot our points testfig1, ax = plt.subplots() plt.pcolormesh(xx, yy, Z, cmap= plt.cm.cool, alpha=0.1,axes=ax) ax.set_aspect(1) # Plot test points groups = test.groupby('Speed') # The next step is to cycle through the groups (based on our categories) and plot each one on the same axis. for name, group in groups: ax.plot(group['Grade'], group['Bumpiness'], marker='o', linestyle='', ms=8, label=name) ax.legend(bbox_to_anchor=(1.2,0.5)) ax.set_xlabel('Grade') ax.set_ylabel('Bumpiness') matt_score = metrics.matthews_corrcoef(labels_test, y_pred) print("Matthews Correlation Coefficient (MCC): {}".format(matt_score)) # Load the model and fit the data dtmodel = DecisionTreeClassifier(min_samples_leaf=5,random_state=32) dtmodel.fit(features_train,labels_train) y_pred = dtmodel.predict(features_test) # Predict the boundary Z = pd.Series(dtmodel.predict(np.c_[xx.ravel(), yy.ravel()]), dtype='category').cat.codes.values.reshape(xx.shape) # First plot our points testfig1, ax = plt.subplots() plt.pcolormesh(xx, yy, Z, cmap= plt.cm.cool, alpha=0.1,axes=ax) ax.set_aspect(1) # Plot test points groups = test.groupby('Speed') # The next step is to cycle through the groups (based on our categories) and plot each one on the same axis. for name, group in groups: ax.plot(group['Grade'], group['Bumpiness'], marker='o', linestyle='', ms=8, label=name) ax.legend(bbox_to_anchor=(1.2,0.5)) ax.set_xlabel('Grade') ax.set_ylabel('Bumpiness') matt_score = metrics.matthews_corrcoef(labels_test, y_pred) print("Matthews Correlation Coefficient (MCC): {}".format(matt_score)) # Load the model and fit the data from sklearn.ensemble import RandomForestClassifier rfmodel = RandomForestClassifier(n_estimators=100,random_state=32) rfmodel.fit(features_train,labels_train) y_pred = rfmodel.predict(features_test) # Predict the boundary Z = pd.Series(rfmodel.predict(np.c_[xx.ravel(), yy.ravel()]), dtype='category').cat.codes.values.reshape(xx.shape) # First plot our points testfig1, ax = plt.subplots() plt.pcolormesh(xx, yy, Z, cmap= plt.cm.cool, alpha=0.1,axes=ax) ax.set_aspect(1) # Plot test points groups = test.groupby('Speed') # The next step is to cycle through the groups (based on our categories) and plot each one on the same axis. for name, group in groups: ax.plot(group['Grade'], group['Bumpiness'], marker='o', linestyle='', ms=8, label=name) ax.legend(bbox_to_anchor=(1.2,0.5)) ax.set_xlabel('Grade') ax.set_ylabel('Bumpiness') matt_score = metrics.matthews_corrcoef(labels_test, y_pred) print("Matthews Correlation Coefficient (MCC): {}".format(matt_score)) rfmodel.feature_importances_ # Load the model and fit the data from sklearn.ensemble import AdaBoostClassifier abcmodel = AdaBoostClassifier(n_estimators=100,random_state=32) abcmodel.fit(features_train,labels_train) y_pred = abcmodel.predict(features_test) # Predict the boundary Z = pd.Series(abcmodel.predict(np.c_[xx.ravel(), yy.ravel()]), dtype='category').cat.codes.values.reshape(xx.shape) # First plot our points testfig1, ax = plt.subplots() plt.pcolormesh(xx, yy, Z, cmap= plt.cm.cool, alpha=0.1,axes=ax) ax.set_aspect(1) # Plot test points groups = test.groupby('Speed') # The next step is to cycle through the groups (based on our categories) and plot each one on the same axis. for name, group in groups: ax.plot(group['Grade'], group['Bumpiness'], marker='o', linestyle='', ms=8, label=name) ax.legend(bbox_to_anchor=(1.2,0.5)) ax.set_xlabel('Grade') ax.set_ylabel('Bumpiness') matt_score = metrics.matthews_corrcoef(labels_test, y_pred) print("Matthews Correlation Coefficient (MCC): {}".format(matt_score)) import xgboost xgbmodel = xgboost.XGBClassifier(n_estimators=100, seed=32) xgbmodel.fit(features_train,labels_train) y_pred = xgbmodel.predict(features_test) # Predict the boundary Z = pd.Series(xgbmodel.predict(np.c_[xx.ravel(), yy.ravel()]), dtype='category').cat.codes.values.reshape(xx.shape) # First plot our points testfig1, ax = plt.subplots() plt.pcolormesh(xx, yy, Z, cmap= plt.cm.cool, alpha=0.1,axes=ax) ax.set_aspect(1) # Plot test points groups = test.groupby('Speed') # The next step is to cycle through the groups (based on our categories) and plot each one on the same axis. for name, group in groups: ax.plot(group['Grade'], group['Bumpiness'], marker='o', linestyle='', ms=8, label=name) ax.legend(bbox_to_anchor=(1.2,0.5)) ax.set_xlabel('Grade') ax.set_ylabel('Bumpiness') matt_score = metrics.matthews_corrcoef(labels_test, y_pred) print("Matthews Correlation Coefficient (MCC): {}".format(matt_score)) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: We'll import the DecisionTreeClassifier and use all of the default values except for the random_state. We'll provide that so that the output is consistent run-to-run. The decision tree classifier uses the random number generator to make decisions about branching, so if we don't set this, we'll get different results every time we run the algorithm. Step2: Take a look at the decision boundary for this classifier Step3: So our decision boundary is cleaned up significantly and we got a bump in the test performance of the model. Let's check one more value to see if we can do any better. Step4: We got an MCC of 0.894 with a fairly simple decision boundary. That's good! There are, perhaps, a few too many wiggles in the boundary, but overall it is looking pretty good. Note that all of the boundaries are straight lines- that is because the decision tree is choosing cutoff values of "Grade" and "Bumpiness" to split the dataset along those lines. Overall this isn't too bad. Step5: We see that the ensemble does a reasonable job- perhaps not better, in this case, than the decision tree by itself. However, there is something else that we get out of using the ensemble Step6: Both features (Grade and Bumpiness) have just about the same importance in our model (about 50% each). That isn't too surprising since we faked the data to begin with... Step7: XGBoost
6,992	<ASSISTANT_TASK:> Python Code: from keras.applications import inception_v3 from keras import backend as K # We will not be training our model, # so we use this command to disable all training-specific operations K.set_learning_phase(0) # Build the InceptionV3 network. # The model will be loaded with pre-trained ImageNet weights. model = inception_v3.InceptionV3(weights='imagenet', include_top=False) # Dict mapping layer names to a coefficient # quantifying how much the layer's activation # will contribute to the loss we will seek to maximize. # Note that these are layer names as they appear # in the built-in InceptionV3 application. # You can list all layer names using `model.summary()`. layer_contributions = { 'mixed2': 0.2, 'mixed3': 3., 'mixed4': 2., 'mixed5': 1.5, } # Get the symbolic outputs of each "key" layer (we gave them unique names). layer_dict = dict([(layer.name, layer) for layer in model.layers]) # Define the loss. loss = K.variable(0.) for layer_name in layer_contributions: # Add the L2 norm of the features of a layer to the loss. coeff = layer_contributions[layer_name] activation = layer_dict[layer_name].output # We avoid border artifacts by only involving non-border pixels in the loss. scaling = K.prod(K.cast(K.shape(activation), 'float32')) loss += coeff * K.sum(K.square(activation[:, 2: -2, 2: -2, :])) / scaling # This holds our generated image dream = model.input # Compute the gradients of the dream with regard to the loss. grads = K.gradients(loss, dream)[0] # Normalize gradients. grads /= K.maximum(K.mean(K.abs(grads)), 1e-7) # Set up function to retrieve the value # of the loss and gradients given an input image. outputs = [loss, grads] fetch_loss_and_grads = K.function([dream], outputs) def eval_loss_and_grads(x): outs = fetch_loss_and_grads([x]) loss_value = outs[0] grad_values = outs[1] return loss_value, grad_values def gradient_ascent(x, iterations, step, max_loss=None): for i in range(iterations): loss_value, grad_values = eval_loss_and_grads(x) if max_loss is not None and loss_value > max_loss: break print('...Loss value at', i, ':', loss_value) x += step * grad_values return x import scipy from keras.preprocessing import image def resize_img(img, size): img = np.copy(img) factors = (1, float(size[0]) / img.shape[1], float(size[1]) / img.shape[2], 1) return scipy.ndimage.zoom(img, factors, order=1) def save_img(img, fname): pil_img = deprocess_image(np.copy(img)) scipy.misc.imsave(fname, pil_img) def preprocess_image(image_path): # Util function to open, resize and format pictures # into appropriate tensors. img = image.load_img(image_path) img = image.img_to_array(img) img = np.expand_dims(img, axis=0) img = inception_v3.preprocess_input(img) return img def deprocess_image(x): # Util function to convert a tensor into a valid image. if K.image_data_format() == 'channels_first': x = x.reshape((3, x.shape[2], x.shape[3])) x = x.transpose((1, 2, 0)) else: x = x.reshape((x.shape[1], x.shape[2], 3)) x /= 2. x += 0.5 x = 255. x = np.clip(x, 0, 255).astype('uint8') return x import numpy as np # Playing with these hyperparameters will also allow you to achieve new effects step = 0.01 # Gradient ascent step size num_octave = 3 # Number of scales at which to run gradient ascent octave_scale = 1.4 # Size ratio between scales iterations = 20 # Number of ascent steps per scale # If our loss gets larger than 10, # we will interrupt the gradient ascent process, to avoid ugly artifacts max_loss = 10. # Fill this to the path to the image you want to use base_image_path = '/home/ubuntu/data/original_photo_deep_dream.jpg' # Load the image into a Numpy array img = preprocess_image(base_image_path) # We prepare a list of shape tuples # defining the different scales at which we will run gradient ascent original_shape = img.shape[1:3] successive_shapes = [original_shape] for i in range(1, num_octave): shape = tuple([int(dim / (octave_scale * i)) for dim in original_shape]) successive_shapes.append(shape) # Reverse list of shapes, so that they are in increasing order successive_shapes = successive_shapes[::-1] # Resize the Numpy array of the image to our smallest scale original_img = np.copy(img) shrunk_original_img = resize_img(img, successive_shapes[0]) for shape in successive_shapes: print('Processing image shape', shape) img = resize_img(img, shape) img = gradient_ascent(img, iterations=iterations, step=step, max_loss=max_loss) upscaled_shrunk_original_img = resize_img(shrunk_original_img, shape) same_size_original = resize_img(original_img, shape) lost_detail = same_size_original - upscaled_shrunk_original_img img += lost_detail shrunk_original_img = resize_img(original_img, shape) save_img(img, fname='dream_at_scale_' + str(shape) + '.png') save_img(img, fname='final_dream.png') from matplotlib import pyplot as plt plt.imshow(deprocess_image(np.copy(img))) plt.show() <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Next, we compute the "loss", the quantity that we will seek to maximize during the gradient ascent process. In Chapter 5, for filter Step2: Now let's define a tensor that contains our loss, i.e. the weighted sum of the L2 norm of the activations of the layers listed above. Step3: Now we can set up the gradient ascent process Step4: Finally, here is the actual Deep Dream algorithm.
6,993	<ASSISTANT_TASK:> Python Code: # access yelp.csv using a relative path import pandas as pd import seaborn as sns yelp = pd.read_csv('C:/Users/Joshuaw/Documents/GA_Data_Science/data/yelp.csv') yelp.head() # read the data from yelp.json into a list of rows # each row is decoded into a dictionary using using json.loads() import json import pandas as pd import seaborn as sns with open('C:/Users/Joshuaw/Documents/GA_Data_Science/data/yelp.json', 'rU') as f: data = [json.loads(row) for row in f] # show the first review data[0] # convert the list of dictionaries to a DataFrame #note that the cool, funny, and useful vote types were in a nested dictionary: u'votes': {u'cool': 2, u'funny': 0, u'useful': 5} yelp = pd.DataFrame(data) yelp.head(2) # add DataFrame columns for cool, useful, and funny, do this by creating a new column for each column #use list comprehension to pass the nested structure, remember it's as rows, e.g. row['votes]['cool] yelp['cool'] = [row['votes']['cool'] for row in data] yelp['useful'] = [row['votes']['useful'] for row in data] yelp['funny'] = [row['votes']['funny'] for row in data] # drop the votes column #since votes was a nested data type votes:cool, votes:funny, votes:useful, the votes column has all types of vote data #making the data not so useful so drop it, remember axis=1 indicates that we want to drop a particular column, we would use #axis=0 to drop a specific row yelp.drop('votes', axis=1, inplace=True) yelp.head(1) # treat stars as a categorical variable and look for differences between groups yelp.groupby('stars').mean() # correlation matrix %matplotlib inline import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt sns.set(style="white") #create correlation matrix corr = yelp.corr() # Generate a mask for the upper triangle mask = np.zeros_like(corr, dtype=np.bool) mask[np.triu_indices_from(mask)] = True # Set up the matplotlib figure f, ax = plt.subplots(figsize=(11, 8)) # Generate a custom diverging colormap cmap = sns.diverging_palette(220, 10, as_cmap=True) # Draw the heatmap with the mask and correct aspect ratio sns.heatmap(corr, cmap=cmap, vmax=.3, square=True, linewidths=.5, cbar_kws={"shrink": .5}) #sns.heatmap(yelp.corr()) # multiple scatter plots sns.set(style="white") sns.pairplot(yelp, x_vars=['cool', 'useful', 'funny'], y_vars='stars', size=6, aspect=0.7, kind='reg', markers = '+') feature_cols = ['cool', 'useful', 'funny'] x = yelp[feature_cols] y = yelp.stars #feature_cols = ['cool', 'useful', 'funny'] #X = yelp[feature_cols] #y = yelp.stars #instantiate the linear regression model from sklearn.linear_model import LinearRegression #store the regression model function as a variable called 'linreg' #we specify that we want to fit a model that includes an intercept, because we don't assume the data has been centered linreg = LinearRegression(fit_intercept=True) #fit a linear regression model linreg.fit(x, y) #find coeffecient of determination aka R2 and store it as a variable #result = 0.044 r2 = linreg.score(x, y, sample_weight=None) #create a list of lists that shows the independent variables and their coeffecients #note: need to see whether this is a b weight or a beta weight and how to look at significance of the coeffecients coef = zip(feature_cols, linreg.coef_) print(r2) print(coef) print(linreg.intercept_) #overall votes is a poor predictor of stars, sentiment is likely to be a better predictor from sklearn.cross_validation import train_test_split from sklearn import metrics import numpy as np # define a function that accepts a list of features and returns testing RMSE def train_test_rmse(feature_cols): X = yelp[feature_cols] y = yelp.stars X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1) linreg = LinearRegression() linreg.fit(X_train, y_train) y_pred = linreg.predict(X_test) return np.sqrt(metrics.mean_squared_error(y_test, y_pred)) # calculate RMSE with all three features train_test_rmse(['cool', 'useful', 'funny']) print train_test_rmse(['cool', 'useful']) print train_test_rmse(['cool', 'funny']) print train_test_rmse(['useful', 'funny']) # new feature: review length (number of characters) yelp['length'] = yelp.text.apply(len) # new features: whether or not the review contains 'love' or 'hate' yelp['love'] = yelp.text.str.contains('love', case=False).astype(int) yelp['hate'] = yelp.text.str.contains('hate', case=False).astype(int) # add new features to the model and calculate RMSE train_test_rmse(['cool', 'useful', 'funny', 'length', 'love', 'hate']) # split the data (outside of the function) x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=1) # create a NumPy array with the same shape as y_test y_null = np.zeros_like(y_test, dtype=float) # fill the array with the mean of y_test y_null.fill(y_test.mean()) # calculate null RMSE import numpy as np print np.sqrt(metrics.mean_squared_error(y_test, y_null)) # import and instantiate KNN from sklearn.neighbors import KNeighborsClassifier knn = KNeighborsClassifier(n_neighbors=50) # classification models will automatically treat the response value (1/2/3/4/5) as unordered categories knn.fit(X_train, y_train) y_pred_class = knn.predict(X_test) print metrics.accuracy_score(y_test, y_pred_class) # use linear regression to make continuous predictions linreg = LinearRegression() linreg.fit(x_train, y_train) y_pred = linreg.predict(x_test) # round its predictions to the nearest integer y_pred_class = y_pred.round() # calculate classification accuracy of the rounded predictions print metrics.accuracy_score(y_test, y_pred_class) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Task 1 (Bonus) Step2: Task 2 Step3: Task 3 Step4: Task 4 Step5: Task 5 Step6: Task 6 Step7: Task 7 (Bonus) Step8: Task 8 (Bonus) Step9: Task 9 (Bonus) Step10: Task 10 (Bonus)
6,994	<ASSISTANT_TASK:> Python Code: full_survey = ds.cadence_plot(fieldID=1427, mjd_center=61404, mjd_range=[-1825, 1825], observedOnly=False, colorbar=True); plt.close() full_survey[0] half_survey = ds.cadence_plot(fieldID=1427, mjd_center=61404, mjd_range=[-1825, 1], observedOnly=False, colorbar=True); second_year = ds.cadence_plot(fieldID=1427, mjd_center=60200, mjd_range=[-150, 150], observedOnly=False, colorbar=True); secondYearObs = ds.cadence_plot(fieldID=1427, mjd_center=60300, mjd_range=[-0, 30], observedOnly=False) plt.close() secondYearObs[0] df['obsID'] = df.index.values uniqueObs = df.groupby(['night', 'filter']) aa = uniqueObs['airmass'].agg({'myInds': lambda x: x.idxmin()}).myInds.astype(int).values ourOpSim = df.ix[aa] axs = df.hist(by='filter', column='airmass', histtype='step', lw=2, alpha=1, color='k', normed=True); axs = df.ix[aa].hist(by='filter', column='airmass', histtype='step', lw=2, alpha=1, color='r', ax=axs, normed=True) df.obsID.unique().size, df.obsID.size ourOpSim.head() ourOpSim.obsID.values ourOpSim.obsID.to_csv('FirstSet_obsHistIDs.csv') ourOpSim.to_csv('SelectedKrakenVisits.csv') xx = ourOpSim.groupby(['night', 'filter']).aggregate('count') assert(all(xx.max() == 1)) dff = uniqueObs['airmass'].agg({'myInds': lambda x: x.idxmin()}) aa = dff.myInds.astype(int).values aa.sort() l = [] for key in keys: l.append(uniqueObs.get_group(key).airmass.idxmin()) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: List of obsHistIDs with unique nights Step2: How much does it help our airmass distribution by choosing the lowest airmass of the available ones Step3: Our culled opsim that we shall try out first is now 'ourOpSim' . We can write this our to a csv file, or a database. We can also view the list of obsHistIDs Step4: Scratch
6,995	<ASSISTANT_TASK:> Python Code: %matplotlib inline import pickle as pkl import numpy as np import tensorflow as tf import matplotlib.pyplot as plt from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('MNIST_data') def model_inputs(real_dim, z_dim): inputs_real = tf.placeholder(tf.float32, (None, real_dim), name='input_real') inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z') return inputs_real, inputs_z def generator(z, out_dim, n_units=128, reuse=False, alpha=0.01): ''' Build the generator network. Arguments --------- z : Input tensor for the generator out_dim : Shape of the generator output n_units : Number of units in hidden layer reuse : Reuse the variables with tf.variable_scope alpha : leak parameter for leaky ReLU Returns ------- out, logits: ''' with tf.variable_scope('generator', reuse=reuse): # Hidden layer h1 = tf.layers.dense(z, n_units, activation=None) # Leaky ReLU h1 = tf.maximum(alpha * h1, h1) # Logits and tanh output logits = tf.layers.dense(h1, out_dim, activation=None) out = tf.tanh(logits) return out def discriminator(x, n_units=128, reuse=False, alpha=0.01): ''' Build the discriminator network. Arguments --------- x : Input tensor for the discriminator n_units: Number of units in hidden layer reuse : Reuse the variables with tf.variable_scope alpha : leak parameter for leaky ReLU Returns ------- out, logits: ''' with tf.variable_scope('discriminator', reuse=reuse): # Hidden layer h1 = tf.layers.dense(x, n_units, activation=None) # Leaky ReLU h1 = tf.maximum(alpha * h1, h1) logits = tf.layers.dense(h1, 1, activation=None) out = tf.sigmoid(logits) return out, logits # Size of input image to discriminator input_size = 784 # 28x28 MNIST images flattened # Size of latent vector to generator z_size = 100 # Sizes of hidden layers in generator and discriminator g_hidden_size = 128 d_hidden_size = 128 # Leak factor for leaky ReLU alpha = 0.01 # Label smoothing smooth = 0.1 tf.reset_default_graph() # Create our input placeholders input_real, input_z = model_inputs(input_size, z_size) # Build the model g_model = generator(input_z, input_size) # g_model is the generator output d_model_real, d_logits_real = discriminator(input_real) d_model_fake, d_logits_fake = discriminator(g_model, reuse=True) # Calculate losses d_loss_real = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_logits_real) * (1 - smooth))) d_loss_fake = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_logits_real))) d_loss = d_loss_real + d_loss_fake g_loss = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake))) # Optimizers learning_rate = 0.002 # Get the trainable_variables, split into G and D parts t_vars = tf.trainable_variables() g_vars = [var for var in t_vars if var.name.startswith('generator')] d_vars = [var for var in t_vars if var.name.startswith('discriminator')] d_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(d_loss, var_list=d_vars) g_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(g_loss, var_list=g_vars) batch_size = 100 epochs = 100 samples = [] losses = [] saver = tf.train.Saver(var_list = g_vars) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for e in range(epochs): for ii in range(mnist.train.num_examples//batch_size): batch = mnist.train.next_batch(batch_size) # Get images, reshape and rescale to pass to D batch_images = batch[0].reshape((batch_size, 784)) batch_images = batch_images*2 - 1 # Sample random noise for G batch_z = np.random.uniform(-1, 1, size=(batch_size, z_size)) # Run optimizers _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z}) _ = sess.run(g_train_opt, feed_dict={input_z: batch_z}) # At the end of each epoch, get the losses and print them out train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_images}) train_loss_g = g_loss.eval({input_z: batch_z}) print("Epoch {}/{}...".format(e+1, epochs), "Discriminator Loss: {:.4f}...".format(train_loss_d), "Generator Loss: {:.4f}".format(train_loss_g)) # Save losses to view after training losses.append((train_loss_d, train_loss_g)) # Sample from generator as we're training for viewing afterwards sample_z = np.random.uniform(-1, 1, size=(16, z_size)) gen_samples = sess.run( generator(input_z, input_size, reuse=True), feed_dict={input_z: sample_z}) samples.append(gen_samples) saver.save(sess, './checkpoints/generator.ckpt') # Save training generator samples with open('train_samples.pkl', 'wb') as f: pkl.dump(samples, f) %matplotlib inline import matplotlib.pyplot as plt fig, ax = plt.subplots() losses = np.array(losses) plt.plot(losses.T[0], label='Discriminator') plt.plot(losses.T[1], label='Generator') plt.title("Training Losses") plt.legend() def view_samples(epoch, samples): fig, axes = plt.subplots(figsize=(7,7), nrows=4, ncols=4, sharey=True, sharex=True) for ax, img in zip(axes.flatten(), samples[epoch]): ax.xaxis.set_visible(False) ax.yaxis.set_visible(False) im = ax.imshow(img.reshape((28,28)), cmap='Greys_r') return fig, axes # Load samples from generator taken while training with open('train_samples.pkl', 'rb') as f: samples = pkl.load(f) _ = view_samples(-1, samples) rows, cols = 10, 6 fig, axes = plt.subplots(figsize=(7,12), nrows=rows, ncols=cols, sharex=True, sharey=True) for sample, ax_row in zip(samples[::int(len(samples)/rows)], axes): for img, ax in zip(sample[::int(len(sample)/cols)], ax_row): ax.imshow(img.reshape((28,28)), cmap='Greys_r') ax.xaxis.set_visible(False) ax.yaxis.set_visible(False) saver = tf.train.Saver(var_list=g_vars) with tf.Session() as sess: saver.restore(sess, tf.train.latest_checkpoint('checkpoints')) sample_z = np.random.uniform(-1, 1, size=(16, z_size)) gen_samples = sess.run( generator(input_z, input_size, reuse=True), feed_dict={input_z: sample_z}) view_samples(0, [gen_samples]) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Model Inputs Step2: Generator network Step3: Discriminator Step4: Hyperparameters Step5: Build network Step6: Discriminator and Generator Losses Step7: Optimizers Step8: Training Step9: Training loss Step10: Generator samples from training Step11: These are samples from the final training epoch. You can see the generator is able to reproduce numbers like 5, 7, 3, 0, 9. Since this is just a sample, it isn't representative of the full range of images this generator can make. Step12: Below I'm showing the generated images as the network was training, every 10 epochs. With bonus optical illusion! Step13: It starts out as all noise. Then it learns to make only the center white and the rest black. You can start to see some number like structures appear out of the noise. Looks like 1, 9, and 8 show up first. Then, it learns 5 and 3.
6,996	<ASSISTANT_TASK:> Python Code: import pandas as pd import numpy as np import matplotlib.pyplot as plt import scipy.optimize as spo def parab(X): ## X = 2 is the min Y = (X - 2)*2 + 1.5 return Y initial_guess = 3 opt_methods_no_Jacobian = ['Nelder-Mead','Powell','CG','BFGS','L-BFGS-B','TNC','COBYLA','SLSQP'] mins = [spo.minimize(parab,initial_guess,method=om,) for om in opt_methods_no_Jacobian] min_df = pd.DataFrame({'Method' : opt_methods_no_Jacobian, 'min' : [m.x for m in mins] }).set_index('Method') min_df def plot_mins(f,min_df,xmin=-5,xmax=5,ticks=1000,title='',xguess=3,legend_pos='upper left'): x = np.linspace(xmin, xmax, ticks) y = f(x) plt.plot(x, y ,color='b') plt.plot(min_df, f(min_df),'g',linewidth=13,marker="+",label='solutions') plt.plot(xguess, f(xguess),'black',linewidth=13,marker="",label='initial guess') plt.axis([xmin-1, xmax+1, y.min()-1, y.max()+1]) plt.title(title) plt.grid(True) plt.legend(loc=legend_pos) plt.show() plot_mins(f=parab,min_df=min_df,xmin=-3,xmax=8,title='Parabola') def non_convex_1(X): if type(X) is float: if X <=0 or X >=4: return 5.5 else: return (X - 2)2 + 1.5 else: Y = (X - 2)2 + 1.5 Y[np.logical_or(X <= 0 , X >=4)] = 5.5 return Y initial_guess = 4.5 mins = [spo.minimize(non_convex_1,initial_guess,method=om,) for om in opt_methods_no_Jacobian] min_df = pd.DataFrame({'Method' : opt_methods_no_Jacobian, 'min' : [m.x for m in mins] }).set_index('Method') min_df plot_mins(f=non_convex_1,min_df=min_df,xmin=-2,xmax=9,title='Non-Convex example N.1 - initial guess = 4.5', xguess=4.5,legend_pos='lower right') initial_guess = 3.9 mins = [spo.minimize(non_convex_1,initial_guess,method=om,) for om in opt_methods_no_Jacobian] min_df = pd.DataFrame({'Method' : opt_methods_no_Jacobian, 'min' : [m.x for m in mins] }).set_index('Method') min_df plot_mins(f=non_convex_1,min_df=min_df,xmin=-2,xmax=9,title='Non-Convex example N.1 - initial guess = 3.9', xguess=3.9,legend_pos='lower right') from scipy.optimize import basinhopping res = basinhopping(non_convex_1, 4.5, minimizer_kwargs={"method": "BFGS"},niter=200) res.x from scipy.optimize import minimize, rosen, rosen_der mins = [spo.minimize(rosen,[1.3, 0.7, 0.8, 1.9, 1.2],method=om) for om in opt_methods_no_Jacobian] min_df = pd.DataFrame({'Method' : opt_methods_no_Jacobian, 'min' : [m.x for m in mins], 'min_value': [rosen(m.x) for m in mins] }).set_index('Method') min_df.sort_values(by = 'min_value' , inplace=True) min_df res = basinhopping(rosen, [1.3, 0.7, 0.8, 1.9, 1.2], minimizer_kwargs={"method": "BFGS"},niter=200) print(">>> basinhopping min: "+str(res.x)+" - min_value:"+str(rosen(res.x))) min_df = pd.DataFrame({'Method' : opt_methods_no_Jacobian+['Basin Hopping'], 'min' : [m.x for m in mins]+[res.x], 'min_value': [rosen(m.x) for m in mins]+[rosen(res.x)] }).set_index('Method') min_df.sort_values(by = 'min_value' , inplace=True) min_df def my_rosen(x,out_value=10e10): ## no negative values if np.any(x<0.0): return out_value return rosen(x) mins = [spo.minimize(my_rosen,[1.3, 0.7, 0.8, 1.9, 1.2],method=om) for om in opt_methods_no_Jacobian] res = basinhopping(my_rosen, [1.3, 0.7, 0.8, 1.9, 1.2], minimizer_kwargs={"method": "BFGS"},niter=200) min_df = pd.DataFrame({'Method' : opt_methods_no_Jacobian+['Basin Hopping'], 'min' : [m.x for m in mins]+[res.x], 'min_value': [rosen(m.x) for m in mins]+[rosen(res.x)] }).set_index('Method') min_df.sort_values(by = 'min_value' , inplace=True) min_df def my_rosen(x,out_value=10e10,approx = 0.1): ## no negative values if np.any(x<0.0): return out_value ## x1 = x2 = x3 = x4 if type(x) is list: x = np.array(x) if np.absolute(x[0] - x[1])<approx and np.absolute(x[0] - x[2])<approx and np.absolute(x[0] - x[3])<approx and np.absolute(x[1] - x[2])<approx and np.absolute(x[1] - x[3])<approx and np.absolute(x[2] - x[3])<approx: rosen(x) else: return out_value mins = [spo.minimize(my_rosen,[1.3, 0.7, 0.8, 1.9, 1.2],method=om) for om in opt_methods_no_Jacobian] res = basinhopping(my_rosen, [1.3, 0.7, 0.8, 1.9, 1.2], minimizer_kwargs={"method": "BFGS"},niter=200) min_df = pd.DataFrame({'Method' : opt_methods_no_Jacobian+['Basin Hopping'], 'min' : [m.x for m in mins]+[res.x], 'min_value': [rosen(m.x) for m in mins]+[rosen(res.x)] }).set_index('Method') min_df.sort_values(by = 'min_value' , inplace=True) min_df opt_methods_no_Jacobian_bounds = ['SLSQP','L-BFGS-B','TNC'] ## handling bounds for SLSQP, L-BFGS-B, TNC bnds = ((0,1),(0,1),(0,1),(0,1),(0,1)) mins = [spo.minimize(rosen,[1.3, 0.7, 0.8, 1.9, 1.2],method=om,bounds=bnds) for om in opt_methods_no_Jacobian_bounds] min_df = pd.DataFrame({'Method' : opt_methods_no_Jacobian_bounds, 'min' : [m.x for m in mins], 'min_value': [rosen(m.x) for m in mins] }).set_index('Method') min_df.sort_values(by = 'min_value' , inplace=True) min_df ## handling constraints for SLSQP (the only that seems to support constraints) cons = ({'type': 'eq','fun' : lambda x: np.array([x[0]- x[1]]),'jac' : lambda x: np.array([1,-1,0,0,0])}, {'type': 'eq','fun' : lambda x: np.array([x[0]- x[2]]),'jac' : lambda x: np.array([1,0,-1,0,0])}, {'type': 'eq','fun' : lambda x: np.array([x[0]- x[3]]),'jac' : lambda x: np.array([1,0,0,-1,0])}, {'type': 'eq','fun' : lambda x: np.array([x[0]- x[4]]),'jac' : lambda x: np.array([1,0,0,0,-1])}, {'type': 'eq','fun' : lambda x: np.array([x[1]- x[2]]),'jac' : lambda x: np.array([0,1,-1,0,0])}, {'type': 'eq','fun' : lambda x: np.array([x[1]- x[3]]),'jac' : lambda x: np.array([0,1,0,-1,0])}, {'type': 'eq','fun' : lambda x: np.array([x[1]- x[4]]),'jac' : lambda x: np.array([0,1,0,0,-1])}, {'type': 'eq','fun' : lambda x: np.array([x[2]- x[3]]),'jac' : lambda x: np.array([0,0,1,-1,0])}, {'type': 'eq','fun' : lambda x: np.array([x[2]- x[4]]),'jac' : lambda x: np.array([0,0,1,0,-1])}, {'type': 'eq','fun' : lambda x: np.array([x[3]- x[4]]),'jac' : lambda x: np.array([0,0,0,1,-1])}) bnds = ((0,1),(0,1),(0,1),(0,1),(0,1)) mins = spo.minimize(rosen,[0.5, 0.7, 0.8, 0.7, 1.2],method='SLSQP',bounds=bnds,constraints=cons) min_df = pd.DataFrame({'Method' : 'SLSQP', 'min' : [mins.x] , 'min_value': rosen(mins.x) }).set_index('Method') min_df cons = ({'type': 'ineq','fun' : lambda x: np.array(4-np.sum(np.array(x))),'jac' : lambda x: np.array([-1,-1,-1,-1,-1])}) bnds = ((0,1),(0,1),(0,1),(0,1),(0,1)) mins = spo.minimize(rosen,[0.5, 0.7, 0.8, 0.7, 1.2],method='SLSQP',bounds=bnds,constraints=cons) min_df = pd.DataFrame({'Method' : 'SLSQP', 'min' : [mins.x] , 'min_value': rosen(mins.x) }).set_index('Method') min_df def my_rosen(x,out_value=10e10,approx = 0.1): ## no negative values if np.any(x<0.0): return out_value if type(x) is list: x = np.array(x) ## x1 = x2 = x3 = x4 if np.absolute(np.sum(x)-4)<approx: rosen(x) else: return out_value mins = [spo.minimize(my_rosen,[1.3, 0.7, 0.8, 1.9, 1.2],method=om) for om in opt_methods_no_Jacobian] res = basinhopping(my_rosen, [1.3, 0.7, 0.8, 1.9, 1.2], minimizer_kwargs={"method": "BFGS"},niter=200) min_df = pd.DataFrame({'Method' : opt_methods_no_Jacobian+['Basin Hopping'], 'min' : [m.x for m in mins]+[res.x], 'min_value': [rosen(m.x) for m in mins]+[rosen(res.x)] }).set_index('Method') min_df.sort_values(by = 'min_value' , inplace=True) min_df class MyBounds(object): def __init__(self, xmax=[1.0,1.0,1.0,1.0,1.0], xmin=[0,0,0,0,0] ): self.xmax = np.array(xmax) self.xmin = np.array(xmin) def __call__(self, *kwargs): x = kwargs["x_new"] tmax = bool(np.all(x <= self.xmax)) tmin = bool(np.all(x >= self.xmin)) cons = bool(np.sum(x) <= 4) return tmax and tmin and cons mybounds = MyBounds() res = basinhopping(rosen, [1.3, 0.7, 0.8, 1.9, 1.2], minimizer_kwargs={"method": "BFGS"}, niter=200,accept_test=mybounds) print(">>> basinhopping min: "+str(res.x)+" - min_value:"+str(rosen(res.x))) import matplotlib.pyplot as plt import pandas.io.data as web def get_data(symbols, add_ref=True, data_source='yahoo', price='Adj Close', start='1/21/2010', end='4/15/2016'): Read stock data (adjusted close) for given symbols from. if add_ref and 'SPY' not in symbols: # add SPY for reference, if absent symbols.insert(0, 'SPY') df = web.DataReader(symbols, data_source=data_source, start=start, end=end) return df[price,:,:] def compute_daily_returns(df): Compute and return the daily return values. # Note: Returned DataFrame must have the same number of rows daily_returns = (df / df.shift(1)) - 1 daily_returns.ix[0,:] = 0 return daily_returns def fill_missing_values(df_data): Fill missing values in data frame, in place. df_data.fillna(method='ffill',inplace=True) df_data.fillna(method='backfill',inplace=True) return df_data def cumulative_returns(df): return df/df.ix[0,:] - 1 df = fill_missing_values(get_data(symbols=['GOOG','SPY','IBM','GLD'], start='4/21/2015', end='7/15/2016')) dr = compute_daily_returns(df) def cumulative_returns_obj(alloc,df=df): if type(alloc) is list: alloc = np.array(alloc) cr = cumulative_returns(df).ix[-1,:] return -1 np.dot(cr , alloc) cons = ({'type': 'ineq','fun' : lambda x: np.array(1-np.sum(np.array(x))),'jac' : lambda x: np.array([-1,-1,-1,-1])}) bnds = ((0,1),(0,1),(0,1),(0,1)) mins = spo.minimize(cumulative_returns_obj,[0.25, 0.25, 0.25, 0.25],method='SLSQP',bounds=bnds,constraints=cons) min_df = pd.DataFrame({'Asset' : df.columns, 'allocation' : mins.x , 'portfolio cumul. ret': -1 * cumulative_returns_obj(mins.x) }).set_index('Asset') min_df cumulative_returns(df).ix[-1,:] class MyBoundsP(object): def __init__(self, xmax=[1,1,1,1], xmin=[0,0,0,0] ): self.xmax = np.array(xmax) self.xmin = np.array(xmin) def __call__(self, *kwargs): x = kwargs["x_new"] tmax = bool(np.all(np.array(x) <= self.xmax)) tmin = bool(np.all(np.array(x) >= self.xmin)) conss = bool( np.sum(np.array(x)) <= 1) return tmax and tmin and conss myboundsp = MyBoundsP() def print_fun(x, f, accepted): print("at minimum %.4f accepted %d" % (f, int(accepted))) res = basinhopping(cumulative_returns_obj, [.25,0.25,0.25,0.25], minimizer_kwargs={"method": "BFGS"}, niter=10,accept_test=myboundsp,callback=print_fun , T=-1.0, stepsize=-500000) print(">>> basinhopping max: "+str(res.x)+" - max_value:"+str(-1 cumulative_returns_obj(res.x))) res def average_daily_return_obj(alloc,dr=dr): if type(alloc) is list: alloc = np.array(alloc) return -100 * np.dot(dr.mean(),alloc) cons = ({'type': 'ineq','fun' : lambda x: np.array(1-np.sum(np.array(x))),'jac' : lambda x: np.array([-1,-1,-1,-1])}) bnds = ((0,1),(0,1),(0,1),(0,1)) mins = spo.minimize(average_daily_return_obj,[0.25, 0.25, 0.25, 0.25],method='SLSQP',bounds=bnds,constraints=cons) min_df = pd.DataFrame({'Asset' : df.columns, 'allocation' : mins.x , 'portfolio avg daily ret': -1 * average_daily_return_obj(mins.x)/100 }).set_index('Asset') min_df dr.mean() def std_daily_return_obj(alloc,dr=dr): if type(alloc) is list: alloc = np.array(alloc) return 10000000 * np.dot(dr.std(),alloc) cons = ({'type': 'eq','fun' : lambda x: np.array(1-np.sum(np.array(x))),'jac' : lambda x: np.array([-1,-1,-1,-1])}) bnds = ((0,1),(0,1),(0,1),(0,1)) mins = spo.minimize(std_daily_return_obj,[0.25, 0.25, 0.25, 0.25],method='SLSQP',bounds=bnds,constraints=cons) min_df = pd.DataFrame({'Asset' : df.columns, 'allocation' : mins.x , 'portfolio risk': std_daily_return_obj(mins.x) / 10000000 }).set_index('Asset') min_df dr.std() def sharpe_ratio_obj(alloc,dr=dr,sample_freq='d',risk_free_rate=0.0): if type(alloc) is list: alloc = np.array(alloc) sr = ( np.sum(alloc * dr,axis=1) - risk_free_rate).mean() / np.sum(alloc * dr,axis=1).std() if sample_freq == 'd': sr = sr * np.sqrt(252) elif sample_freq == 'w': sr = sr * np.sqrt(52) elif sample_freq == 'm': sr = sr * np.sqrt(12) else: raise Exception('unkown sample frequency :'+str(sample_freq)) return -1sr cons = ({'type': 'ineq','fun' : lambda x: np.array(1-np.sum(np.array(x))),'jac' : lambda x: np.array([-1,-1,-1,-1])}) bnds = ((0,1),(0,1),(0,1),(0,1)) mins = spo.minimize(sharpe_ratio_obj,[0.25, 0.25, 0.25, 0.25],method='SLSQP',bounds=bnds,constraints=cons) min_df = pd.DataFrame({'Asset' : df.columns, 'max_point' : mins.x, 'max_value': -1 average_daily_return_obj(mins.x) }).set_index('Asset') min_df <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Non-Convex example Step2: The problem with such function is the fact it is not convex. Hence, starting from initial guess Step3: We can see that Basin Hopping is able to find the global minimum instead of previous methods. Step4: We can see that all optimizers provide close minima although Powell makes a better job this time. Step5: Pretty close the other ones. Step6: Bounds and contraints Step7: We know that the global minima is (1,1,1,1) so we should not have sub-optimal solution requiring the constraints that Step8: We can see that no one handled this constraint properly. Step9: This is a very interesting and disastrous results. The constraints leads us to a sub-optimal solution even Step10: Better ... even if not the optimal solution (1,1,1,1,1). This is pretty important for the use case of Step11: Finally, let's see how Basin Hopping behaves best working with bonds and constraints. Step15: Optimizing a portfolio Step16: 1. Cumulative return Step17: According to this allocation all the money should be put on GOOG as it is expected as GOOG has the Step18: As excercise, let's check if Basin Hopping gets the same result. Step19: This behaviour is very disappointing. It turns out that each iteration of basinhopping rejected the Step20: According to this allocation all the money should be put on GOOG as it is expected as Step21: 3. Rsk (Standard deviation of daily return) Step22: In order to make the optmizer get the rigth solution we had to multiply Step23: 4. Sharpe ratio
6,997	<ASSISTANT_TASK:> Python Code: # Start by importing torch import torch # Construct a bunch of ones some_ones = torch.ones(2, 2) print(some_ones) # Construct a bunch of zeros some_zeros = torch.zeros(2, 2) print(some_zeros) # Construct some normally distributed values some_normals = torch.randn(2, 2) print(some_normals) torch_tensor = torch.randn(5, 5) numpy_ndarray = torch_tensor.numpy() back_to_torch = torch.from_numpy(numpy_ndarray) # Create two tensors a = torch.randn(5, 5) b = torch.randn(5, 5) print(a) print(b) # Indexing by i,j another_tensor = a[2, 2] print(another_tensor) # The above returns a tensor type! To get the python value: python_value = a[2, 2].item() print(python_value) # Getting a whole row or column or range first_row = a[0, :] first_column = a[:, 0] combo = a[2:4, 2:4] print(combo) # Addition c = a + b # Elementwise multiplication: c_ij = a_ij * b_ij c = a * b # Matrix multiplication: c_ik = a_ij * b_jk c = a.mm(b) # Matrix vector multiplication c = a.matmul(b[:, 0]) a = torch.randn(5, 5) print(a.size()) vec = a[:, 0] print(vec.size()) # Matrix multiple 5x5 * 5x5 --> 5x5 aa = a.mm(a) # matrix vector 5x5 * 5 --> 5 v1 = a.matmul(vec) print(v1) print(1, vec) vec_as_matrix = vec.view(5, 1) print(2, vec_as_matrix) v2 = a.mm(vec_as_matrix) print(v2) # Add one to all elements a.add_(1) # Divide all elements by 2 a.div_(2) # Set all elements to 0 a.zero_() # Add a dummy dimension, e.g. (n, m) --> (n, m, 1) a = torch.randn(10, 10) # At the end print(a.unsqueeze(-1).size()) # At the beginning print(a.unsqueeze(0).size()) # In the middle print(a.unsqueeze(1).size()) # What you give you can take away print(a.unsqueeze(0).squeeze(0).size()) # View things differently, i.e. flat print(a.view(100, 1).size()) # Or not flat print(a.view(50, 2).size()) # Copy data across a new dummy dimension! a = torch.randn(2) a = a.unsqueeze(-1) print(a) print(a.expand(2, 3)) # Check if you have it do_i_have_cuda = torch.cuda.is_available() if do_i_have_cuda: print('Using fancy GPUs') # One way a = a.cuda() a = a.cpu() # Another way device = torch.device('cuda') a = a.to(device) device = torch.device('cpu') a = a.to(device) else: print('CPU it is!') # Batched matrix multiply a = torch.randn(10, 5, 5) b = torch.randn(10, 5, 5) # The same as for i in 1 ... 10, c_i = a[i].mm(b[i]) c = a.bmm(b) print(c.size()) # A tensor that will remember gradients x = torch.randn(1, requires_grad=True) print(x) print(x.grad) y = x.exp() print(y.grad) y.backward() print(y.grad) print(x.grad) import math print(x.grad, y, math.exp(x[0])) # Compute another thingy with x. z = x * 2 z.backward() print(x.grad) # Should be 2! But it will be 2 + e^x. x_a = torch.randn(1, requires_grad=True) x_b = torch.randn(1, requires_grad=True) x = x_a * x_b x1 = x 2 x2 = 1 / x1 x3 = x2.exp() x4 = 1 + x3 x5 = x4.log() x6 = x5 (1/3) x6.backward() print(x_a.grad) print(x_b.grad) x = torch.randn(1, requires_grad=True) y = torch.tanh(x) y.backward() print(x.grad) # Set our random seeds import random import numpy as np def set_seed(seed): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) if torch.cuda.is_available(): torch.cuda.manual_seed(seed) # Get ourselves a simple dataset from sklearn.datasets import make_classification set_seed(7) X, Y = make_classification(n_features=2, n_redundant=0, n_informative=1, n_clusters_per_class=1) print('Number of examples: %d' % X.shape[0]) print('Number of features: %d' % X.shape[1]) # Take a peak %matplotlib inline import matplotlib import matplotlib.pyplot as plt plt.scatter(X[:, 0], X[:, 1], marker='o', c=Y, s=25, edgecolor='k') plt.show() # Convert data to PyTorch X, Y = torch.from_numpy(X), torch.from_numpy(Y) # Gotcha: "Expected object of scalar type Float but got scalar type Double" # If you see this it's because numpy defaults to Doubles whereas pytorch has floats. X, Y = X.float(), Y.float() # Define dimensions num_feats = 2 hidden_size = 100 num_outputs = 1 # Learning rate eta = 0.1 num_steps = 1000 # Input to hidden weights W1 = torch.randn(hidden_size, num_feats, requires_grad=True) b1 = torch.zeros(hidden_size, requires_grad=True) # Hidden to output W2 = torch.randn(num_outputs, hidden_size, requires_grad=True) b2 = torch.zeros(num_outputs, requires_grad=True) # Group parameters parameters = [W1, b1, W2, b2] # Get random order indices = torch.randperm(X.size(0)) print(indices) # Keep running average losses for a learning curve? avg_loss = [] # Run! for step in range(num_steps): # Get example i = indices[step % indices.size(0)] x_i, y_i = X[i], Y[i] # Run example hidden = torch.relu(W1.matmul(x_i) + b1) y_hat = torch.sigmoid(W2.matmul(hidden) + b2) # Compute loss binary cross entropy: -(y_i * log(y_hat) + (1 - y_i) * log(1 - y_hat)) # Epsilon for numerical stability eps = 1e-6 loss = -(y_i * (y_hat + eps).log() + (1 - y_i) * (1 - y_hat + eps).log()) # Add to our running average learning curve. Don't forget .item()! if step == 0: avg_loss.append(loss.item()) else: old_avg = avg_loss[-1] new_avg = (loss.item() + old_avg * len(avg_loss)) / (len(avg_loss) + 1) avg_loss.append(new_avg) # Zero out all previous gradients for param in parameters: # It might start out as None if param.grad is not None: # In place param.grad.zero_() # Backward pass loss.backward() # Update parameters for param in parameters: # In place! param.data = param.data - eta * param.grad plt.plot(range(num_steps), avg_loss) plt.ylabel('Loss') plt.xlabel('Step') plt.show() import torch.nn as nn # Linear layer: in_features, out_features linear = nn.Linear(10, 10) print(linear) # Convolution layer: in_channels, out_channels, kernel_size, stride conv = nn.Conv2d(1, 20, 5, 1) print(conv) # RNN: num_inputs, num_hidden, num_layers rnn = nn.RNN(10, 10, 1) print(rnn) print(linear.weight) print([k for k,v in conv.named_parameters()]) # Make our own model! class Net(nn.Module): def __init__(self): super(Net, self).__init__() # 1 input channel to 20 feature maps of 5x5 kernel. Stride 1. self.conv1 = nn.Conv2d(1, 20, 5, 1) # 20 input channels to 50 feature maps of 5x5 kernel. Stride 1. self.conv2 = nn.Conv2d(20, 50, 5, 1) # Full connected of final 4x4 image to 500 features self.fc1 = nn.Linear(4450, 500) # From 500 to 10 classes self.fc2 = nn.Linear(500, 10) def forward(self, x): x = F.relu(self.conv1(x)) x = F.max_pool2d(x, 2, 2) x = F.relu(self.conv2(x)) x = F.max_pool2d(x, 2, 2) x = x.view(-1, 4450) x = F.relu(self.fc1(x)) x = self.fc2(x) return F.log_softmax(x, dim=1) # Initialize it model = Net() import torch.optim as optim # Initialize with model parameters optimizer = optim.SGD(model.parameters(), lr=0.01) import tqdm import torch.nn.functional as F def train(model, train_loader, optimizer, epoch): # For things like dropout model.train() # Avg loss total_loss = 0 # Iterate through dataset for data, target in tqdm.tqdm(train_loader): # Zero grad optimizer.zero_grad() # Forward pass output = model(data) # Negative log likelihood loss function loss = F.nll_loss(output, target) # Backward pass loss.backward() total_loss += loss.item() # Update optimizer.step() # Print average loss print("Train Epoch: {}\t Loss: {:.6f}".format(epoch, total_loss / len(train_loader))) def test(model, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: output = model(data) test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) from torchvision import datasets, transforms # See the torch DataLoader for more details. train_loader = torch.utils.data.DataLoader( datasets.MNIST('../data', train=True, download=True, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=32, shuffle=True) test_loader = torch.utils.data.DataLoader( datasets.MNIST('../data', train=False, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=32, shuffle=True) for epoch in range(1, 10 + 1): train(model, train_loader, optimizer, epoch) test(model, test_loader) <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Tensors Step2: PyTorch tensors and NumPy ndarrays even share the same memory handles, so you can switch between the two types essentially for free Step3: Like NumPy, there are a zillion different operations you can do with tensors. Best thing to do is to go to https Step4: In-place operations exist to, generally denoted by a trailing '' (e.g. my_tensor.my_inplace_function). Step5: Manipulate dimensions... Step6: If you have a GPU... Step7: And many more! Step8: Autograd Step9: At first the 'grad' parameter is None Step10: Let's do an operation. Take y = e^x. Step11: To run the gradient computing magic, call '.backward()' on a variable. Step12: For all dependent variables {x_1, ..., x_n} that were used to compute y, dy/x_i is computed and stored in the x_i.grad field. Step13: Important! Remember to zero gradients before subsequent calls to backwards. Step14: Also important! Under the hood PyTorch stores all the stuff required to compute gradients (call stack, cached values, etc). If you want to save a variable just to keep it around (say for logging or plotting) remember to call .item() to get the python value and free the PyTorch machinery memory. Step15: We'll train a one layer neural net to classify this dataset. Let's define the parameter sizes Step16: And now run a few steps of SGD! Step17: torch.nn Step18: A note on convolution sizes Step19: Updating is now as easy as Step20: Testing loops are similar. Step21: MNIST
6,998	<ASSISTANT_TASK:> Python Code: import json from pyspark.streaming import StreamingContext from pyspark.streaming.kafka import KafkaUtils from pyspark import SparkContext from pyspark.sql import SQLContext from pyspark.sql.functions import explode from pyspark.ml.feature import VectorAssembler from pyspark.mllib.tree import RandomForest, RandomForestModel #custom modules import MySQLConnection IMPORTANT: MUST use class paths when using spark-submit $SPARK_HOME/bin/spark-submit --packages org.apache.spark:spark-streaming-kafka_2.10:1.6.2,mysql:mysql-connector-java:5.1.28 ProcessSensorReadings.py def writeLumbarReadings(time, rdd): try: # Convert RDDs of the words DStream to DataFrame and run SQL query connectionProperties = MySQLConnection.getDBConnectionProps('/home/erik/mysql_credentials.txt') sqlContext = SQLContext(rdd.context) if rdd.isEmpty() == False: lumbarReadings = sqlContext.jsonRDD(rdd) lumbarReadingsIntermediate = lumbarReadings.selectExpr("readingID","readingTime","deviceID","metricTypeID","uomID","actual.y AS actualYaw","actual.p AS actualPitch","actual.r AS actualRoll","setPoints.y AS setPointYaw","setPoints.p AS setPointPitch","setPoints.r AS setPointRoll") assembler = VectorAssembler( inputCols=["actualPitch"], # Must be in same order as what was used to train the model. Testing using only pitch since model has limited dataset. outputCol="features") lumbarReadingsIntermediate = assembler.transform(lumbarReadingsIntermediate) predictions = loadedModel.predict(lumbarReadingsIntermediate.map(lambda x: x.features)) predictionsDF = lumbarReadingsIntermediate.map(lambda x: x.readingID).zip(predictions).toDF(["readingID","positionID"]) combinedDF = lumbarReadingsIntermediate.join(predictionsDF, lumbarReadingsIntermediate.readingID == predictionsDF.readingID).drop(predictionsDF.readingID) combinedDF = combinedDF.drop("features") combinedDF.show() combinedDF.write.jdbc("jdbc:mysql://localhost/biosensor", "SensorReadings", properties=connectionProperties) except: pass def writeLumbarTrainingReadings(time, rddTraining): try: # Convert RDDs of the words DStream to DataFrame and run SQL query connectionProperties = MySQLConnection.getDBConnectionProps('/home/erik/mysql_credentials.txt') sqlContext = SQLContext(rddTraining.context) if rddTraining.isEmpty() == False: lumbarTrainingReading = sqlContext.jsonRDD(rddTraining) lumbarTrainingReadingFinal = lumbarTrainingReading.selectExpr("deviceID","metricTypeID","uomID","positionID","actual.y AS actualYaw","actual.p AS actualPitch","actual.r AS actualRoll","setPoints.y AS setPointYaw","setPoints.p AS setPointPitch","setPoints.r AS setPointRoll") lumbarTrainingReadingFinal.write.jdbc("jdbc:mysql://localhost/biosensor", "SensorTrainingReadings", properties=connectionProperties) except: pass if __name__ == "__main__": sc = SparkContext(appName="Process Lumbar Sensor Readings") ssc = StreamingContext(sc, 2) # 2 second batches loadedModel = RandomForestModel.load(sc, "../machine_learning/models/IoTBackBraceRandomForest.model") #Process Readings streamLumbarSensor = KafkaUtils.createDirectStream(ssc, ["LumbarSensorReadings"], {"metadata.broker.list": "localhost:9092"}) lineSensorReading = streamLumbarSensor.map(lambda x: x[1]) lineSensorReading.foreachRDD(writeLumbarReadings) #Process Training Readings streamLumbarSensorTraining = KafkaUtils.createDirectStream(ssc, ["LumbarSensorTrainingReadings"], {"metadata.broker.list": "localhost:9092"}) lineSensorTrainingReading = streamLumbarSensorTraining.map(lambda x: x[1]) lineSensorTrainingReading.foreachRDD(writeLumbarTrainingReadings) # Run and then wait for termination signal ssc.start() ssc.awaitTermination() <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Script to Process the Sensor Readings - ProcessSensorReadings.py Step2: The "writeLumbarReadings" method takes the rdd received from Spark Streaming as an input. It then extracts the JSON data and converts to a SQLContext dataframe. Step3: The "writeLumbarTrainingReadings" method also accepts an RDD from Spark Streaming but does not need to do any machine learning processing since we already know the posture from the JSON data. Step4: In the main part of the script the machine learning model is loaded and then two Spark StreamingContexts are created to listen for either actual device readings or training readings. The appropriate methods are then called upon receipt.
6,999	<ASSISTANT_TASK:> Python Code: %matplotlib inline import numpy as np import scipy as sp import matplotlib as mpl import matplotlib.cm as cm import matplotlib.pyplot as plt import pandas as pd pd.set_option('display.width', 500) pd.set_option('display.max_columns', 100) pd.set_option('display.notebook_repr_html', True) import seaborn as sns #sets up styles and gives us more plotting options URL = "tmrw.co" # User-entered website # Time period 17th Jan - 16th April (arbitrary ) # API credentials # Email address 705762800217-compute@developer.gserviceaccount.com # Key IDs 948ee8e2a420ef14a5d5a29bd35104fe2f1e6ed4 # open file. It is requested via API explorer using request parameters: #Account: TMRW Tech Hub #Property: TMRW #View: All Web Site Data #ids: ga:123303369 #start-date: 2017-01-15 #end-date: yesterday #metrics #ga:sessions #ga:percentNewSessions #ga:bounceRate #ga:pageviewsPerSession #ga:avgSessionDuration #ga:goal1ConversionRate #ga:goal1Completions #dimensions #ga:city #ga:userAgeBracket #sort #ga:goal1ConversionRate # Open file TMRW_users= pd.read_csv("files/TMRW_geo_loc_API.csv") #TMRW_users[TMRW_users.Age=='55-64'] TMRW_users # rename columns TMRW_users.columns=['city','age', 'new_sessions','sessions','bounce_rate','asd','goal1','goal1CR','PPS'] TMRW_users=TMRW_users.sort_values('goal1CR') TMRW_users_filter = TMRW_users[TMRW_users.sessions > 80] TMRW_users_filter TMRW_users_filter.describe() # will need to convert dnumbers into tim #TMRW_users_filter_new =pd.to_datetime(TMRW_users_filter['asd'], format='%H:%M') # Check if the number of sessions is enough for analysis if TMRW_users.sessions.sum() < 80: print("Error") #if sessions are too small remove #if one conversion - remove # 1. Too small sessions TMRW_users.describe().loc['mean', 'sessions'] #algo for bucketing into varios secsions: #buckets by goal1CR TMRW_users_goal1CR = TMRW_users_filter.nlargest(3, 'goal1CR') TMRW_users_goal1CR_gCity = TMRW_users_goal1CR.groupby(['city']).mean() #this is not too correct- average of proportion, but ok for now TMRW_users_goal1CR_gAge = TMRW_users_goal1CR.groupby(['age']).mean() TMRW_users_goal1CR_gCity TMRW_users_goal1CR.loc[11,'age'] TMRW_users_goal1CR_gAge.loc[: , 'goal1'] TMRW_users_goal1CR.loc[] 'age'.max # The most converting audience print(+" is most converting Demographic category") #x = TMRW_users_goal1CR_gAge.index y = TMRW_users_goal1CR.[: , 'goal1CR'] plt.hist(y) plt.title("Top converting buckets") plt.xlabel("Conv rate") plt.ylabel("Frequency") plt.show() TMRW_users_bounce_rate = TMRW_users_filter.nlargest(3, 'bounce_rate') TMRW_users_bounce_rate #if the_largest traffic source = ('not_set') then output error '' TMRW_users_PPS = TMRW_users_filter.nlargest(3, 'PPS') TMRW_users_PPS TMRW_users_agcities=TMRW_users.groupby(["city"]).sum() TMRW_users_agcities selected=TMRW_users_agcities.loc[:,'Goal 1 Completions'] selected labels = selected.index sizes = selected # colours are taken from http://tools.medialab.sciences-po.fr/iwanthue/ colors = ['#1f394d','#2a7585', '#163c45', '#004a6e'] explode = (0, 0, 0, 0) plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', shadow=False, startangle=90) plt.axis('equal') plt.title('Cities by conversion') plt.show() # Conversion traffic # Generate text print (" segment converts best") import pandas as pd import numpy as np import matplotlib.pyplot as plt input_mob = pd.read_csv('files/TMRW_mob.csv') input_mob.columns=['device','sessions','%news', 'new_users','bounce_rate','PPS', 'ASD', 'goal1CR','goal1'] input_mob = input_mob.set_index('device') def p2f(x): return float(x.strip('%'))/100 input_mob conv_increase = float(input_mob.loc['mobile','sessions']) * p2f(input_mob.loc['desktop','goal1CR']) - float(input_mob.loc['mobile','goal1']) conv_increase=int(conv_increase) # Check if data is valid is_valid = True output_chart_data = input_mob.loc['mobile',['sessions','goal1']] # convert percentages into float mob_CR = p2f(input_mob.loc['desktop','goal1CR']) desk_CR = p2f(input_mob.loc['mobile','goal1CR']) def funnel_cart(): return # calculate difference mob_dif = mob_CR / desk_CR # if mobile goal1CR is less by more than 20% then reult is failed if mob_dif > 1.5: #funnel_chart() output_chart_data else: is_valid = False # if not then build chart # http://stackoverflow.com/questions/21397549/stack-bar-plot-in-matplotlib-and-add-label-to-each-section-and-suggestions # http://pandas.pydata.org/pandas-docs/stable/visualization.html#bar-plots #mob_chart = output_chart_data x = {1} y = {100,10} #ou = pd.DataFrame([100,10], columns=['sessions', 'goal1']) output_chart_data.plot.barh(stacked=True) #plt.barh(x,y,'stacked') mob_conv = input_mob.loc['mobile','goal1CR'] plt.xlabel('Visits') plt.title('Only %s of all mobile visitors end up completing a conversion' % mob_conv) #need to specify analytics time perdiod print("Have %s more conversions per month by optmiising mobile UX" % conv_increase) plt.show() <END_TASK>	<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Settings Step2: Skipped step Step3: Mobile analytics

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